THE SPATIAL DYNAMICS OF FERTILITY
1976 TO 1996
Deborah Faulkner
Thesis submitted for the degree of Doctor of Philosophy
Department of Geographical and Environmental Studies University of Adelaide
July 2005 Table of Contents ii
Table of Contents
List of Tables ix List of Figures xvi Abstract xxi Candidate Declaration xxii Acknowledgments xxiii Glossary xxiv
Chapter One: Introduction 1
1.1 Introduction 1 1.2 Study Objectives 5 1.3 Context: South Australia as a Case Study 6 1.4 Outline of Study 9
Chapter Two: Differential Fertility—Theoretical Background 12
2.1 Introduction 12 2.2 Classical Demographic Transition Theory 13 2.3 Economic Theories of Fertility Behaviour 22 2.3.1 Home Economics Approach 23 2.3.2 Socio-Economic Approaches 25 2.4 Ideational Theory 29 2.4.1 Diffusion Theory 30 2.4.2 Second Demographic Theory 31 2.4.3 Gender Equity Models 35 2.5 Synthesis 36
Chapter Three The Spatial, Socio-Economic and Cultural 39 Dynamics of Fertility in Australia
3.1 Introduction 39 Table of Contents iii
3.2 Spatial Analyses 44 3.2.1 Introduction 44 3.2.2 Urban-Rural Residence 44 3.2.3 State Variations 52 3.2.4 Regional Spatial Patterns 54 3.2.5 Synthesis 60 3.3 Socio-Economic Differentials 61 3.3.1 Demographic Characteristics 63 3.3.1.1 Age 63 3.3.1.2 Marital Status 63 3.3.2 Socio-Economic Factors 65 3.3.2.1 Educational Attainment and Fertility 65 3.3.2.2 Employment and Fertility 70 3.3.2.3 Income and Fertility 80 3.4 Values and Norms 85 3.4.1 Religion and Fertility 88 3.4.2 Birthplace and Fertility 93 3.4.3 Indigenous Population and Fertility 107 3.5 Migration 115 3.6 Macro Social, Economic and Political Conditions 115 3.7 Attitudinal Changes and Preferences 118 3.8 Synthesis 121
Chapter Four: Data and Research Methods 128
4.1 Introduction 128 4.2 Data 128 4.2.1 Fertility Data 130 4.3 Method of Analysis 133 4.3.1 Choice of Measurement 133 4.3.2 Design of Geographic Areas 134 4.3.2.1 Metropolitan Adelaide 135 Table of Contents iv
4.3.2.2 Non-Metropolitan South Australia 136 4.3.3 Mapping 140 4.3.4 Convergence, Divergence and Spatial Persistence Over Time 142 4.4 Explanation of Fertility Patterns 145 4.4.1 Introduction 145 4.4.2 Multivariate Analyses 146 4.4.3 Independent Variables 148 4.5 Conclusion 152
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 153 to 1996 Statistical Sub Divisions
5.1 Introduction 153 5.2 Geographical Patterns of Fertility by Statistical Sub-Division (SSDs) 154 1976 to 1996 5.2.1 Women Aged 45–49 Years 155 5.2.1.1 Description of Patterns 156 5.2.1.2 Change Over Time 157 5.2.1.3 Convergence/Divergence 164 5.2.1.4 Summary 170 5.2.2 Women Aged 15–44 Years 170 5.2.2.1 Description of Patterns 171 5.2.2.2 Change Over Time 177 5.2.2.3 Convergence/Divergence 178 5.2.2.4 Summary 182 5.3 Synthesis 182
Chapter Six: Spatial Analysis of Fertility in South Australia 1976 to 185 1996 Non-Metropolitan South Australia
6.1 Introduction 185 6.2 Women Aged 45–49 Years 185 6.2.1 Description of Patterns 1976–1981 185 Table of Contents v
6.2.2 Change Over Time 1976–1981 190 6.2.3 Convergence/Divergence 1976–1981 192 6.2.4 Description of Patterns 1986 193 6.2.5 Change Over Time 1981–1986 195 6.2.6 Convergence/Divergence 1981–1986 197 6.2.7 Description of Patterns 1996 197 6.2.8 Change Over Time 1986–1996 201 6.2.9 Convergence/Divergence 1986–1996 203 6.2.10 Summary 203 6.3 Women Aged 15–44 Years 205 6.3.1 Description of Patterns 1976–1981 205 6.3.2 Change Over Time 1976–1981 210 6.3.3 Convergence/Divergence 1976–1981 212 6.3.4 Description of Patterns 1986 212 6.3.5 Change Over Time 1981–1986 215 6.3.6 Convergence/Divergence 1981–1986 215 6.3.7 Description of Patterns 1996 217 6.3.8 Change Over Time 1986–1996 220 6.3.9 Convergence/Divergence 1986–1996 222 6.3.10 Summary 223 6.4 Synthesis 225
Chapter Seven: Spatial Analysis of Fertility in South Australia 1976 227 to 1996 Metropolitan South Australia
7.1 Introduction 227 7.2 Women Aged 45–49 Years 227 7.2.1 Description of Patterns 1976 and 1981 229 7.2.2 Change Over Time 1976–1981 231 7.2.3 Convergence/Divergence 1976–1981 233 7.2.4 Description of Patterns 1986 235 7.2.5 Change Over Time 1981–1986 237 Table of Contents vi
7.2.6 Convergence/Divergence 1981–1986 239 7.2.7 Description of Patterns 1996 239 7.2.8 Change Over Time 1986–1996 240 7.2.9 Convergence/Divergence 1986–1996 243 7.2.10 Summary 244 7.3 Women Aged 15–44 Years 245 7.3.1 Description of Patterns 1976 and 1981 245 7.3.2 Change Over Time 1976–1981 249 7.3.3 Convergence/Divergence 1976–1981 249 7.3.4 Description of Patterns 1986 251 7.3.5 Change Over Time 1981–1986 253 7.3.6 Convergence/Divergence 1981–1986 253 7.3.7 Description of Patterns 1996 255 7.3.8 Change Over Time 1986–1996 257 7.3.9 Convergence/Divergence 1986–1996 259 7.3.10 Summary 259 7.4 Synthesis 269
Chapter Eight: Explaining Spatial Variations in Fertility 271
8.1 Introduction 271 8.2 Independent Variables and Hypothesised Association with Fertility 272 8.2.1 Introduction 272 8.2.2 Hypothesised Relationship 273 8.2.2.1 Demographic Factors 273 8.2.2.2 Socio-Economic Factors 274 8.2.2.3 Specific Non-Metropolitan Factors 277 8.3 Non-Metropolitan South Australia 278 8.3.1 Introduction 278 8.3.2 The Role of Socio-Economic Factors—Women Aged 45-49 Years 285 8.3.2.1 Summary 288 8.3.3 The Role of Socio-Economic Factors—Women Aged 15–44 Years 290 Table of Contents vii
8.3.3.1 Summary 296 8.3.4 Summary 297 8.4 Metropolitan Adelaide 297 8.4.1 Introduction 297 8.4.2 The Role of Socio-Economic Factors—Women Aged 45–49 Years 298 8.4.2.1 Summary 305 8.4.3 The Role of Socio-Economic Factors—Women Aged 15–44 Years 306 8.4.3.1 Summary 312 8.5 Conclusion 313
Chapter Nine: Conclusion 316
9.1 Introduction 316 9.2 Review of Theory and Empirical Research 317 9.3 Research Findings 319 9.4 Implications 324
Appendices 330 4.1 Boundary Changes Affecting Comparability of Areas Over Time 330 4.2 Metropolitan Adelaide: Customised Areas and Statistical Local Areas 334 4.3 Suburbs Included in Customised Areas Metropolitan Adelaide 336 4.4 Non-Metropolitan South Australia, Statistical Sub Divisions 344 4.5 Non-Metropolitan South Australia, Membership of Section of State 345 Categories by Statistical Sub Division 4.6 Information on Census Variables Included in Correlation/Regression 349 Analyses 6.1 Non-Metropolitan South Australia: Measures of Social Change Over 354 Time (Components of Social Change) in the Average Number of Children Ever Born for Women Aged 45–49 Years for Section of State by SSD, 1986–1996(a) (Rates per 100 Women)
Table of Contents viii
6.2 Non-Metropolitan South Australia: Measures of Social Change Over 355 Time (Components of Social Change) in the Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years for Section of State by SSD, 1986–1996(a) (Rates per 100 Women) 7.1 Metropolitan South Australia: Average Number of Children Ever 356 Born to Women Aged 45–49 Years, 1976, 1981, 1986, 1996 Censuses (Rates per 100 Women) 7.2 Metropolitan South Australia: Measures of Social Change Over Time 360 (Components of Social Change) in the Average Number of Children Ever Born for Women Aged 45–49 Years 1976–1981 (Rates per 100 Women) 7.3 Metropolitan South Australia: Measures of Social Change Over Time 364 (Components of Social Change) in the Average Number of Children Ever Born for Women Aged 45–49 Years 1981–1986 (Rates per 100 Women) 7.4 Metropolitan South Australia: Measures of Social Change Over Time 368 (Components of Social Change) in the Average Number of Children Ever Born for Women Aged 45–49 Years 1986–1996 (Rates per 100 Women) 7.5 Metropolitan South Australia: Age Standardised Average Number of 372 Children Ever Born to Women Aged 15–44 Years, 1976, 1981, 1986, 1996 Censuses (Rates per 100 Women) 7.6 Metropolitan South Australia: Measures of Social Change Over Time 378 (Components of Social Change) in the Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years 1976– 1981 (Rates per 100 Women) 7.7 Metropolitan South Australia: Measures of Social Change Over Time 382 (Components of Social Change) in the Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years 1981– 1986 (Rates per 100 Women) 7.8 Metropolitan South Australia: Measures of Social Change Over Time 386 (Components of Social Change) in the Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years 1986– 1996 (Rates per 100 Women) 7.9 Metropolitan South Australia: Age Standardised Average Number of 390 Children Ever Born to Women Aged 15–24 Years in 1981, 20–29 Years in 1986 and 30–39 Years in 1996 (Rates per 100 Women)
References 394
List of Tables ix
List of Tables
Table 2.1 Phases of the Classical Demographic Transition Theory and 15 Assumed Fertility Differentials
Table 3.1 Australia: Average Issue of Women Aged 45–49 Years by 47 Urban Rural Residence, Selected Censuses
Table 3.2 Australia: Average Issue of Women Aged 15 Years and Over 47 By Urban-Rural Residence, 1986
Table 3.3 Total Fertility Rates by State and Territory, 1992 to 1995 48
Table 3.4 Australia, Total Fertility Rate By the Accessibility/Remoteness 48 Index of Australia (ARIA), 1992 to 1995 Table 3.5 Index of Total Fertility Rates, States and Territories, 1947 to 53 2001 (Australia=100)
Table 3.6 Regional Reproductive Syndromes for Metropolitan Sydney, 60 1986–87
Table 3.7 Australia: Women Aged 35–49 Years By Marital Status and 64 Children Ever Born, 1996
Table 3.8 Australia: Average Issue of Wives by Education and Year of 67 Birth
Table 3.9 Australia: Average Number of Children Ever Born to Women: 68 Qualification Attained and Age Left School by Current Age, 1986
Table 3.10 Australia: Average Issue of Wives Aged 35–39 Years by 75 Occupation of Wife 1966
Table 3.11 Australia: Percentages of Women with No Children and Three 76 or More Children, and Mean Children Ever Born, Age Group 25–29 and 35–39 by Labour Force Status of the Women, 1986 and 1996.
Table 3.12 Australia: Average Issue of Wives by Religion, 1911 and 1966 90 Censuses
Table 3.13 Australia: Average Number of Children Ever Born to Women: 92 Religious Affiliation by Current Age 1986 Census
Table 3.14 Australia, Selected Countries of Birth,(a) Total Fertility Rates, 106 1990 to 2000
List of Tables x
Table 3.15 South Australia: Indicative Estimates of Total Fertility Rates of 111 Aboriginal Women
Table 3.16 South Australia: Age Specific Fertility Rates for Indigenous 112 Women Compared to All Women, 2003
Table 4.1 South Australia: Not Stated Issue by Age, 1981, 1986 and 1996 132 Censuses
Table 4.2 Population Growth, Statistical Divisions of South Australia, 138 1976 to 1996
Table 4.3 Non-Metropolitan South Australia: Population Change By 139 Urban Centre Size and Section of State, 1976 to 1996
Table 4.4 Key Characteristics of the Population Used in the Multivariate 151 Analyses 1981–1996
Table 5.1 South Australia: Average Number of Children Ever Born to 156 Women Aged 45–49 Years for Statistical Sub-Divisions, 1976, 1981, 1986 and 1996 Censuses (per 100 women)
Table 5.2 South Australia: Measures of Change Over Time (Components 163 of Social Change) in Average Number of Children Ever Born to Women Aged 45–49 Years for Sub-Statistical Divisions, 1976–1981, 1981–1986 and 1986–1996 (per 100 Women)
Table 5.3 South Australia: Summary Statistics and Components of Social 165 Change of the Average Number of Children Ever Born to Women Aged 45–49 Years for Statistical Sub-Divisions, 1976, 1981, 1986 and 1996 Censuses (per 100 Women)
Table 5.4 South Australia: Standardised and Observed Average Number 172 of Children Ever Born for Women Aged 15–44 Years for Statistical Sub Divisions, 1976, 1981, 1986 and 1996 Censuses (per 100 Women)
Table 5.5 South Australia: Ranking of SSDs from Lowest to Highest Age 177 Standardised Average Number of Children Ever Born Per Woman at Ages 25–29 and 15–44, 1981 and 1996 Censuses
Table 5.6 South Australia: Measures of Change Over Time (Components 179 of Social Change) in Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years for Statistical Sub-Divisions, 1976–1981, 1981–1986 and 1986– 1996
Table 5.7 South Australia: Summary Statistics and Components of Social 180 Change for Women Aged 15–44 Years for Statistical Sub- Divisions, 1976, 1981, 1986 and 1996 Censuses List of Tables xi
Table 6.1 Non-Metropolitan South Australia: Average Number of 186 Children Ever Born to Ever Married Women Aged 45–49 Years By Section of State by SSD at the 1976 Census (per 100 Women)
Table 6.2 Non Metropolitan South Australia: Average Number of 188 Children Ever Born to Women Aged 45–49 Years By Section of State by SSD at the 1981 Census (per 100 Women)
Table 6.3 Measures of Social Change Over Time (Components of Social 191 Change) in the Average Number of Children Ever Born to Women Aged 45–49 Years for Section of State by SSDs, 1976–1981 (per 100 Women)
Table 6.4 Non-Metropolitan South Australia: Summary Statistics and 192 Components of Social Change For Women Aged 45–49 Years for Section of State by SSD, 1976–1981
Table 6.5 Non-Metropolitan South Australia: Average Number of 193 Children Ever Born to Women Aged 45–49 Years By Section of State by SSD at the 1986 Census (per 100 Women)
Table 6.6 Measures of Social Change Over Time (Components of Social 196 Change) in the Average Number of Children Ever Born to Women Aged 45–49 Years for Section of State by SSD, 1981– 1986 (per 100 Women)
Table 6.7 Non-Metropolitan South Australia: Summary Statistics and 198 Components of Social Change For Women Aged 45–49 Years for Section of State1 by SSD, 1981–1986
Table 6.8 Non-Metropolitan South Australia: Average Number of 199 Children Ever Born to Women Aged 45–49 Years by Section of State by SSD at the 1996 Census (per 100 women)
Table 6.9 Measures of Social Change Over Time (Components of Social 202 Change) in the Average Number of Children Ever Born to Women Aged 45–49 Years for Section of State by SSD 1986– 1996 (per 100 Women)
Table 6.10 Non-Metropolitan South Australia: Summary Statistics and 203 Components of Social Change For Women Aged 45–49 Years for Section of State1 by SSD South Australia, 1986–1996
Table 6.11 Non-Metropolitan South Australia: Average Number of 205 Children Ever Born to Women Aged 45–49 Years By Section of State Categories, 1976, 1981, 1986, 1996 (per 100 Women)
List of Tables xii
Table 6.12 Non Metropolitan South Australia: Age Standardised Average 207 Number of Children Ever Born to Ever Married Women Aged 15–44 Years by Section of State by SSD at the 1976 Census (per 100 Women)
Table 6.13 Non Metropolitan South Australia: Average Number of 208 Children Ever Born to Women Aged 15–44 Years by Section of State by SSD at the 1981 Census (per 100 Women)
Table 6.14 Measures of Social Change Over Time (Components of Social 211 Change) in the Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years for Section of State by SSD, 1976–1981
Table 6.15 Non-Metropolitan South Australia: Summary Statistics and 212 Components of Social Change For Women Aged 15–44 Years for Section of State by SSD South Australia, 1976–1981
Table 6.16 Non Metropolitan South Australia: Average Number of 213 Children Ever Born to Women aged 15–44 Years by Section of State by SSD at the 1986 Census (per 100 Women)
Table 6.17 Measures of Social Change Over Time (Components of Social 216 Change) in the Age Standardised Average Number of Children Ever Born to Women Aged 15-44 Years for Section of State by SSD, 1981–1986
Table 6.18 Non-Metropolitan South Australia: Summary Statistics and 217 Components of Social Change For Women Aged 15–44 Years for Section of State by SSD South Australia, 1981–1986
Table 6.19 Non Metropolitan South Australia: Average Number of 218 Children Ever Born to Women Aged 15–44 Years by Section of State by SSD at the 1996 Census (per 100 Women)
Table 6.20 Measures of Social Change Over Time (Components of Social 221 Change) in the Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years for Section of State by SSD, 1986–1996 (Rates per 100 Women)
Table 6.21 Non-Metropolitan South Australia: Summary Statistics and 223 Components of Social Change For Women Aged 15–44 Years for Section of State by SSD South Australia, 1986–1996
Table 6.22 Non-Metropolitan South Australia: Age Standardised Average 225 Number of Children Ever Born to Women Aged 15–44 Years By Section of State Categories, 1976, 1981, 1986, 1996 (per 100 Women)
List of Tables xiii
Table 7.1 Metropolitan South Australia: Summary Statistics and 235 Components of Social Change For Women Aged 45–49 Years, 1976–1981
Table 7.2 Metropolitan South Australia: Summary Statistics and 239 Components of Social Change For Women Aged 45–49 Years, 1981–1986
Table 7.3 Metropolitan South Australia: Summary Statistics and 243 Components of Social Change For Women Aged 45–49 Years, 1986–1996
Table 7.4 Metropolitan South Australia: Summary Statistics and 251 Components of Social Change For Women Aged 15–44 Years, 1976–1981
Table 7.5 Metropolitan South Australia: Summary Statistics and 255 Components of Social Change For Women Aged 15–44 Years, 1981-1986
Table 7.6 Metropolitan South Australia: Summary Statistics and 259 Components of Social Change For Women Aged 15–44 Years, 1986–1996
Table 7.7 Metropolitan South Australia: Number of Children Ever Born 262 to Women Aged 15–44 Years for Each Mapping Category, 1996 Census
Table 7.8 Metropolitan South Australia: Selected Summary Statistics and 267 Components of Social Change For Women Aged 15–24, 20–29 and 30–39 Years, 1981–1996
Table 7.9 Metropolitan South Australia: Selected Summary Statistics and 268 Components of Social Change For Women of Various Ages, 1981–1996
Table 8.1 Selected Characteristics of the Population by Section of State, 281 Lower South East SSD, 1986 Census
Table 8.2 Selected Characteristics of the Female Population and 284 Households of the Urban Centres of Coober Pedy and Woomera 1981, 1986 and 1996 Censuses
Table 8.3 Non-metropolitan South Australia: Correlation Coefficients 286 Between Fertility of Women Aged 45–49 Years and Selected Variables 1981, 1986, 1996 Censuses
Table 8.4 Non-Metropolitan South Australia: Regression Analyses of 287 Fertility Patterns for Women Aged 45–49 Years, 1981 Census
List of Tables xiv
Table 8.5 Non-Metropolitan South Australia: Regression Analyses of 289 Fertility Patterns for Women Aged 45–49 Years, 1986 Census
Table 8.6 Non-Metropolitan South Australia: Regression Analyses of 290 Fertility Patterns for Women Aged 45–49 Years, 1996 Census
Table 8.7 Non-Metropolitan South Australia: Correlation Coefficients 291 Between Fertility of Women Aged 15–44 Years and Selected Variables 1981, 1986, 1996 Censuses
Table 8.8 Non-Metropolitan South Australia: Regression Analyses of 293 Fertility Patterns for Women Aged 15–44 Years 1981 Census
Table 8.9 Non-Metropolitan South Australia: Regression Analyses of 294 Fertility Patterns for Women Aged 15–44 Years, 1986 Census
Table 8.10 Non-Metropolitan South Australia: Regression Analyses of 295 Fertility Patterns for Women Aged 15–44 Years, 1996 Census
Table 8.11 Non-Metropolitan South Australia: Correlation Coefficients 296 Between Fertility and Selected Characteristics of Women Aged 15–44 Years, 1996 Census
Table 8.12 Metropolitan South Australia: Correlation Coefficients 299 Between Fertility of Women Aged 45–49 Years and Selected Variables 1981, 1986, 1996 Censuses
Table 8.13 Metropolitan South Australia: Regression Analyses of Fertility 301 Patterns for Women Aged 45–49 Years, 1981 Census
Table 8.14 Metropolitan South Australia: Regression Analyses of Fertility 302 Patterns for Women Aged 45–49 Years, 1986 Census
Table 8.15 Metropolitan South Australia: Regression Analyses of Fertility 304 Patterns for Women Aged 45–49 Years, 1996 Census
Table 8.16 Metropolitan South Australia: Correlation Coefficients 307 Between Fertility of Women Aged 15–44 Years and Selected Variables 1981, 1986, 1996 Censuses
Table 8.17 Metropolitan South Australia: Regression Analyses of Fertility 308 Patterns for Women Aged 15–44 Years, 1981 Census
Table 8.18 Metropolitan South Australia: Regression Analyses of Fertility 309 Patterns for Women Aged 15–44 Years, 1986 Census
Table 8.19 Metropolitan South Australia: Regression Analyses of Fertility 311 Patterns for Women Aged 15–44 Years, 1996 Census
List of Tables xv
Table 8.20 Metropolitan South Australia: Correlation Coefficients 312 Between Fertility and Selected Characteristics of Women Aged 15–44 Years, 1996 Census
List of Figures xvi
List of Figures
Figure 3.1 O’Connell’s Model of Developmental (D) and Cyclical (C) 43 Components of Fertility Change
Figure 3.2 Australia: Total Fertility Rate by Remoteness of the Area 49
Figure 3.3 Australia: Age Specific Fertility Rates by Remoteness Area 50 2000
Figure 3.4 Metropolitan Adelaide: People Who Left School at Age 15 71 Years or Less, Or Who Did Not Go to School, 1996 Census (Standardised Ratio – Number of People in Each Statistical Local Area Compared With Number Expected)
Figure 3.5 Metropolitan Adelaide: Female Labour Force Participation of 78 Women Aged 20–54, 1996 Census (As a Percentage of All Females Aged 20–54 Years in Each Statistical Local Area)
Figure 3.6 South Australia, Index of Relative Socio-Economic 85 Disadvantage, 1986 to 2001
Figure 3.7 Metropolitan Adelaide, Index of Relative Socio-Economic 86 Disadvantage, 1996 Census (IRSD Index Number for Each Statistical Local Area)
Figure 3.8 Non-Metropolitan South Australia: Index of Relative Socio- 87 Economic Disadvantage, 1996 Census (IRSD Index Number for Each Statistical Local Area
Figure 3.9 Distribution of Buddhist Adherents in Adelaide, 1991 Census 94
Figure 3.10 Distribution of Islam Adherents in Adelaide, 1991 Census 95
Figure 3.11 Population Distribution of the Netherlands-born in Adelaide, 108 1991 Census
Figure 3.12 Population Distribution of the Malta-born in Adelaide, 1991 109 Census
Figure 3.13 Population Distribution of the Viet Nam-born in Adelaide, 110 1991 Census
Figure 3.14 Metropolitan Adelaide Population Distribution of the 113 Indigenous Population 1996 Census (As a Percentage of the Total Population in Each Statistical Local Area)
List of Figures xvii
Figure 3.15 Non-Metropolitan South Australia Population Distribution of 114 the Indigenous Population 1996 Census (As a Percentage of the Total Population in Each Statistical Local Area)
Figure 3.16 Pressures on Family Formation 120
Figure 5.1 Period-Cohort Space of Study for Women Aged 15–49 Years, 155 1976 to 1996
Figure 5.2 South Australia: Average Number of Children Ever Born to 158 Ever Married Women Aged 45–49 Years for Statistical Sub- Divisions, 1976 Census
Figure 5.3 South Australia: Average Number of Children Ever Born to 159 Women Aged 45–49 Years for Statistical Sub-Divisions, 1981 Census
Figure 5.4 South Australia: Average Number of Children Ever Born to 160 Women Aged 45–49 Years for Statistical Sub-Divisions, 1986 Census
Figure 5.5 South Australia: Average Number of Children Ever Born to 161 Women Aged 45–49 Years for Statistical Sub-Divisions, 1996 Census
Figure 5.6 Percentage Distribution of Women Aged 45–49 Years by 167 Parity, Metropolitan Adelaide and Non-Metropolitan Regions, 1976 to 1996
Figure 5.7 South Australia: Cumulative Number of Children Ever-Born 168 by Successive Age Group by Statistical Sub-Division, 1976 to 1996
Figure 5.8 South Australia: Per Cent of Women with Three or More 169 Children by Statistical Sub-Division by Successive Age Group, 1976 to 1996
Figure 5.9 South Australia: Per Cent of Women Childless by Statistical 169 Sub-Division by Successive Age Group, 1976 to 1996
Figure 5.10 South Australia: Age Standardised Average Number of 173 Children Ever Born to Ever Married Women Aged 15–44 Years for Statistical Sub-Divisions, 1976 Census
Figure 5.11 South Australia: Age Standardised Average Number of 174 Children Ever Born to Women Aged 15–44 Years for Statistical Sub-Divisions, 1981 Census
List of Figures xviii
Figure 5.12 South Australia: Age Standardised Average Number of 175 Children Ever Born to Women Aged 15–44 Years for Statistical Sub-Divisions, 1986 Census
Figure 5.13 South Australia: Age Standardised Average Number of 176 Children Ever Born to Women Aged 15–44 Years for Statistical Sub-Divisions, 1996 Census
Figure 5.14 Percentage Distribution of Women Aged 15–44 Years by 182 Parity, Adelaide and Non-Metropolitan Regions, 1976 to 1996
Figure 6.1 Non-Metropolitan South Australia: Average Number of 187 Children Ever Born to Ever Married Women Aged 45–49 Years by Section of State by SSD, 1976 Census
Figure 6.2 Non-Metropolitan South Australia: Average Number of 189 Children Ever Born to Women Aged 45–49 Years by Section of State by SSD, 1981 Census
Figure 6.3 Non-Metropolitan South Australia: Average Number of 194 Children Ever Born to Women Aged 45–49 Years by Section of State by SSD, 1986 Census
Figure 6.4 Non-Metropolitan South Australia: Average Number of 200 Children Ever Born to Women Aged 45–49 Years by Section of State by SSD, 1996 Census
Figure 6.5 Non-Metropolitan South Australia: Age Standardised Average 206 Number of Children Ever Born to Ever Married Women Aged 15–44 Years by Section of State by SSD, 1976 Census
Figure 6.6 Non-Metropolitan South Australia: Age Standardised Average 209 Number of Children Ever Born to Women Aged 15–44 Years by Section of State by SSD, 1981 Census
Figure 6.7 Non-Metropolitan South Australia: Age Standardised Average 214 Number of Children Ever Born to Women Aged 15–44 Years by Section of State by SSD, 1986 Census
Figure 6.8 Non-Metropolitan South Australia: Age Standardised Average 219 Number of Children Ever Born to Women Aged 15–44 Years by Section of State by SSD, 1996 Census
Figure 7.1 Adelaide Urban Development 1836–1994 228
Figure 7.2 Metropolitan South Australia: Average Number of Children 230 Ever Born to Ever Married Women Aged 45–49 Years, 1976 Census
List of Figures xix
Figure 7.3 Metropolitan South Australia: Average Number of Children 232 Ever Born to Women Aged 45–49 Years, 1981 Census
Figure 7.4 Metropolitan South Australia: Positional Change in the 234 Average Number of Children Ever Born for Women Aged 45– 49 Years 1976–1981
Figure 7.5 Metropolitan South Australia: Average Number of Children 236 Ever Born to Women Aged 45–49 Years, 1986 Census
Figure 7.6 Metropolitan South Australia: Positional Change in the 238 Average Number of Children Ever Born to Women Aged 45– 49 Years, 1981–1986
Figure 7.7 Metropolitan South Australia: Average Number of Children 241 Ever Born to Women Aged 45–49 Years, 1996 Census
Figure 7.8 Metropolitan South Australia: Positional Change in the 242 Average Number of Children Ever Born for Women Aged 45– 49 Years, 1986–1996
Figure 7.9 Metropolitan South Australia: Age Standardised Average 247 Number of Children Ever Born to Ever Married Women Aged 15–44 Years, 1976 Census
Figure 7.10 Metropolitan South Australia: Age Standardised Average 248 Number of Children Ever Born to Women Aged 15–44 Years, 1981 Census
Figure 7.11 Metropolitan South Australia: Positional Change in the Age 250 Standardised Average Number of Children Ever Born to Women Aged 15–44 Years, 1976–1981
Figure 7.12 Metropolitan South Australia: Age Standardised Average 252 Number of Children Ever Born to Women Aged 15–44, Years 1986 Census
Figure 7.13 Metropolitan South Australia: Positional Change in the Age 254 Standardised Average Number of Children Ever Born for Women Aged 15–44 Years, 1981–1986
Figure 7.14 Metropolitan South Australia: Age Standardised Average 256 Number of Children Ever Born to Women Aged 15–44 Years, 1996 Census
Figure 7.15 Metropolitan South Australia: Positional Change in the Age 258 Standardised Average Number of Children Ever Born to Women Aged 15–44 Years 1986–1996
List of Figures xx
Figure 7.16 Metropolitan South Australia: Percentage Distribution of 261 Children Ever Born to Women Aged 15–44 Years by Mapping Category, 1996 Census
Figure 7.17 Metropolitan South Australia: Age Standardised Average 264 Number of Children Ever Born to Women Aged 15–24 Years, 1981 Census
Figure 7.18 Metropolitan South Australia: Age Standardised Average 265 Number of Children Ever Born to Women Aged 20–29 Years, 1986 Census
Figure 7.19 Metropolitan South Australia: Age Standardised Average 266 Number of Children Ever Born to Women Aged 30–39 Years, 1996 Census
Figure 8.1 Percentage Distribution of Women Aged 45–49 and 15–44 282 Years by Parity, Far North SSD 1976, 1981, 1986, and 1996 Censuses
Abstract xxi
Abstract
In the past the identification and explanation of spatial variations in fertility was seen as an important contribution to the field of population geography. By the 1980s with the substantial declines in fertility and the ‘end’ of the demographic transition came the belief low fertility equated with little variation between groups and across space. Recent evidence however suggests the interaction of various factors including place-specific factors has led to spatio-temporal changes in fertility that have not been expected based on theoretical and national patterns of fertility.
The objective of this thesis was to investigate if spatial differentials in fertility still exist, and have relevance in a low fertility setting. The study examines the geography of fertility in the State of South Australia from the mid 1970s to the mid 1990s using unpublished issue data from the 1976, 1981, 1986 and 1996 Australian Censuses for women aged 45– 49 years and 15–44 years. In addition to identifying the patterns trends towards convergence or divergence in the patterns over time and the reasons for the patterns were also identified.
The findings of this study indicate strong spatial variations in fertility still exist, have persisted over time and there are localised conditions which temper overall expectations from theory. While it is assumed declines in fertility equate with a convergence in differentials, the statistical parameters used in this study showed trends towards convergence or divergence varied by geographical scale and age group. Despite the limited attention socio-economic factors have received in the examination of population issues in Australia, they remain central to explaining the fertility patterns and trends found in this study. In fact in metropolitan Adelaide fertility may be a significant contributor and influence on social polarisation.
Candidate Declaration xxii
Candidate Declaration
This work contains no material which has been accepted for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made in the text.
I give consent to this copy of my thesis, when deposited in the University Library, being available for loan and photocopying.
Full Name: …………………………………….……………………………………………
Signed: …………………………………..……………………………………………….
Date: ……………………………………………………………………………………
Acknowledgments xxiii
Acknowledgments
Many people have provided support and encouragement throughout the course of this thesis and I thank them very much. There are a number of people I would especially like to thank.
I would like to thank my supervisors: Professor Graeme Hugo for his long standing support, understanding, advice, guidance and encouragement. His support over many years has been invaluable. Professor Martin Bell for his constructive criticism, guidance and advice to be forthright. I also greatly appreciate his willingness and commitment to a supervisory role after moving to the University of Queensland.
I would like to thank the staff at the Australian Bureau of Statistics for providing the data in a friendly, helpful and timely manner.
There are a number of people within the Geography Departments of Flinders and Adelaide Universities who have assisted in enabling me to undertake this research: The map librarians David Johnson and Sue Murray for their invaluable help in the early stages to establish the geographic areas; Margaret Young for creating data files from available Census data on the characteristics of the population; Robert Keane for producing the final copies of all of the maps; Janet Wall for providing help and assistance with references and printing; and Kylie Lange for her valuable statistical advice. Special thanks to Cecile Cutler not only for continually reading chapters and providing advice on structure, content and grammar but especially for her friendship and support always.
Finally my family, my husband Craig, children Rebecca and Hayden and my parents have been integral in making it possible for me to undertake this research. I thank them dearly for their patience, support, encouragement, help and love.
Glossary xxiv
Glossary
ABS Australian Bureau of Statistics ARIA Accessibility/Remoteness Index for Australia ASD Adelaide Statistical Division CD Collection District CBR Crude Birth Rate CWR Child Woman Ratio GFR General Fertility Rate ISBR Indirect Standardised Birth Rate LGA Local Government Area SAHT South Australian Housing Trust SLA Statistical Local Area SSD Statistical Sub Division TFR Total Fertility Rate
Chapter One: Introduction 1
Chapter One
Introduction
1.1. Introduction
This study investigates whether spatial variations in fertility still have significance and relevance not only in their own right, but as a means to understanding the wider societal influences governing individual decision making and behaviour regarding fertility. In order to do this it examines spatial variations in fertility in South Australia. In trying to unravel the causes of the very low and unanticipated levels of fertility in industrialised societies, the focus of theoretical developments and research has been on trying to understand individual attitudes, behaviour and decision making in relation to child bearing through specially designed surveys. The traditional geographic approach to fertility has been largely ignored.
The identification and explanation of spatial variations in fertility has been considered an important component of the sub-disciplines of population geography and spatial demography (Boyle 2003; Coward 1986a; Jones 1984; Woods 1986a, Woods 1986b). As exemplified in the classical work of the Princeton Project (Coale and Watkins 1986) the analysis of spatial variations in fertility contributes to the understanding and ‘predicting of regional population dynamics and the testing of hypotheses concerning the general determinants of fertility…’ (Coward 1986a, 45). Yet concerns have been raised since the 1950s (Clarke 1977; Coward 1986b; Findlay 1991; Jones 1981, 1984, 1990; Ogden 1984; Trewartha 1953; Woods 1979, 1982) and more recently (Boyle 2003; Ogden 2000) about the relative neglect of spatial analyses of fertility. In the 1980s Jones (1984, 173) when discussing the work of British population geographers claimed the neglect was ‘serious and indefensible’, and almost twenty years later Graham and Boyle (2001, 391) went as far as to question whether population geography as an identifiable sub-discipline has a future without studies of fertility and mortality. Chapter One: Introduction 2
Issues of data availability and methodological limitations are some of the reasons that have been suggested as to why the geographic approach to fertility, even more so than for mortality, remains a neglected area of study. A contributing factor also was the empirical evidence emerging in the 1960s, 1970s and 1980s of the convergence of social and spatial differentials with the attainment of low levels of fertility as proposed in traditional demographic transition theory (Beegle 1966; Coward 1986b; Jones 1990; Wilson 1984, 1990). This led to a belief by some that ‘both the nature and the magnitude of the behavioural and societal dynamics underpinning [the fertility] trends have been effectively described and analysed’ (Wilson 1984, 202). In addition low fertility equated with little variation between groups and across space (Compton 1991; Wilson 1990). The need for a geographical approach to fertility appeared to have lost much of its relevance. Nevertheless regional variations in fertility continue to exist (Bell 1989; Brunetta and Rotundi 1991; Chesnais 1996; Coleman 1999; Hank 2001, 2002). The interaction of various factors including place-specific factors, has led to spatio-temporal changes in fertility that have not been expected based on theoretical and national patterns of fertility (Chesnais 1996).
The notion of convergence has also been called into question. Demographic convergence is an expected outcome of the demographic transition generally on the basis of convergent socio-economic trends, and of ideational factors outweighing socio-economic factors in the so called second demographic transition (Lesthaeghe 1995; Lesthaeghe and van de Kaa 1986; van de Kaa 1987). Outside demography, convergence also appears to be a predominant view in many fields—in Marxist and neo-classical economic theories; as an outcome of globalisation and the flow of trade, information and migration; and even as a deliberate aim of economic and welfare policy (Coleman 2002). Peoples’ lives however have become much more diverse than in the past when the sanctions and attitudes of society were considerably more restricting and rather than moving towards demographic uniformity some evidence suggests the pattern may be more one of divergence (Boseveld 1996; Kuijsten 1996; Reher 1998). As for Wilson’s statement that the behavioural and societal dynamics underlying fertility trends had been sufficiently analysed, current debates on fertility (Coleman 2000; Hakim 2001; McDonald 2000a; Manne 2001) clearly indicate we truly do not understand the complexity of the fertility decline and the reasons for the current low levels of fertility. Chapter One: Introduction 3
Part of the reason for the neglect of studies may be that human geographers have for a long time waxed and waned over the importance and meanings of ‘space’ and the term ‘spatial’ (Gregory and Urry 1985; Massey 1985). Massey (1985, 9) claims the critique of the discipline in the 1970s, ‘went far too far overboard in its rejection of the importance of the spatial organisation of things, of distance and perhaps above all, of geographical differentiation’.
In addition it has been claimed that the behavioural and environmental influences governing fertility are less important, or less intrinsically geographical than factors governing mortality and migration (Coward 1986b; Jones 1981, 1990). In fact from his analyses of fertility differentials in Australia, Wilson (1990, 64) went so far as to conclude any explanations of fertility differentiation are ‘space or place neutral’ simply reflecting variations in demographic structure and socio-economic composition of the population and therefore he questioned whether geographic approaches were ever fruitful (Wilson 1990, 53).
Within other fields of the social sciences and the humanities however, space and place have become increasingly important (Boyle 2003; Graham 2000; Murphy 1991; Thrift 1983). The increasing evidence of the ‘persistence and, in some instances, intensification of localist attachments and influences’ (Murphy 1991, 22) has led to the questioning of the view of Wilson (1990) and others that conducted social science research in the twentieth century. Space, place and context do have meaning and effect. As outlined and discussed by Sayer (1985, 57) the greatest misconception about space ‘is the implicit assumption that because space only exists where it is constituted by objects it is wholly reducible to them’, or in other words, ‘although space can only exist in and through objects, it is independent of the particular types of object present’ (Sayer 1985, 52). Space and place are discussed in terms of ‘regional social contexts’ reflecting regional opportunity structures and local patterns of social interaction (Hank 2002; Macintyre, Ellaway and Cummins 2002). Regions are seen as historic and geographic entities whereby the ‘inhabitants interpret and respond to events and processes in particular ways as a consequence of the unique social and physical contexts in which they are situated’ (Murphy 1991, 25).
Chapter One: Introduction 4
Despite the rejection of the idea of space as just a reflection of social and demographic structure, and the recognition of the importance of regions, places and localities, concerns have been raised about the lack of interest shown by population geographers. Graham (2000, 266) for example states,
Concerns with the nature and dynamics of space and place are archetypically geographical concerns and, arguably, fundamental to the identity of human geography…Yet, beyond an empirical attention to spatial distribution and the spatial movement of people (migration studies), there is surprisingly little evidence that population geographers see the nature and dynamics of space and place as a focus of concern.
The contribution of population geographers to fertility analyses specifically has been minimal (Boyle 2003). Considering the usefulness of a geographical perspective in identifying and contributing to an understanding of variations or lack of them in fertility, Boyle (2003, 622) states, ‘It seems rather remarkable that geographers are not more interested in contemporary fertility issues when they are capturing the minds of politicians, the press and those in some other disciplines’.
The situation in Australia is no different. Hugo (2002a, 1) in a review of Australia’s population at the millennium claimed that the ‘spatial dimension of Australia’s demography has received limited research attention’. This he finds curious not only because the population continues to grow and change in terms of its composition and distribution, but also because there has been unprecedented concern among policy makers over population issues, and in particular, the perceived widening of differentials in well- being between different groups in society and between different parts of the country.
Since the negative views of the utility of a geographic approach to fertility were expressed by Wilson (1990), few attempts have been made to document and examine in detail the influence of the overall decline in fertility, and the wider societal changes in recent decades, on the geography of fertility at the regional and local level in Australia. This is despite the findings of the persistence in spatial variations in fertility in other developed countries and conclusions that the anticipated convergence in patterns may not yet be occurring.
Chapter One: Introduction 5
1.2. Study Objectives
The major objective of this research is to identify if spatial differentials in fertility still exist and have relevance in a low fertility setting. More specifically the study seeks to:
• Review the role of spatial and socio-economic differentials in theories and models of fertility change. The study of variations in fertility levels between different groups in society and over regional areas has a long tradition in demographic research as an avenue for gaining a greater understanding of the processes and determinants of fertility and in the testing of hypotheses and theoretical constructs.
• Establish the extent of spatial differentials in fertility at various levels of disaggregation The spatial patterning of phenomena is affected by the scale at which it is mapped. A common problem for geographers is the lack of available data at a geographic scale or in appropriate spatial units that allow meaningful analyses. Official statistics are often only available for administrative areas that may not necessarily be the most appropriate for the object of investigation. With advances in computer technology, however, the scope for data management has become much more flexible resulting in the greater availability of many forms of data at a more disaggregated level. To a degree this allows the construction of ‘tailor made’ areal frameworks where the population is potentially more homogeneous in respect of the factors affecting, in this case fertility. Does increasing the degree of spatial refinement provide further insights into the patterns observed?
• Establish any trends towards convergence, stability or divergence in the spatial patterns. While the description of fertility patterns at a point in time can be informative, of greater interest is the change in spatial patterns through time. Demographic convergence is an expected outcome of the demographic transition generally on the basis of convergent socio-economic trends, and the greater importance of ideational factors in the second demographic transition (Lesthaeghe 1995; Lesthaeghe and van de Chapter One: Introduction 6
Kaa 1986; van de Kaa 1987). While a twenty year period is not a long time from which to make definitive statements, it is a period over which trends can be established and it can be the starting point for further analyses in subsequent years, depending on data availability.
• Based on widely cited correlates of spatial patterns of fertility, establish from the Census data available, ‘explanations’ to account for the observed patterns and trends. Fertility is a complex phenomenon and there is a wide range of identified factors that govern fertility levels and spatial patterns. In recent times the focus in understanding fertility has been the study of individual behaviour through specially designed surveys. While a valued, worthwhile and complementary endeavour this methodology cannot, in and of itself, explain the aggregate patterns identified here. It is necessary to return to the conventional characteristics and methods of explanation. Socio-economic factors have most often been the major explanatory variable, however in recent times the relationship between social class and demographic trends and issues has been largely ignored (Hugo 2002a). This is most likely part of the outcome of the expectation of convergence in economic characteristics and social institutions across the developed world. In the context of this study what particular characteristics of the population, or of the area, influence the patterns and trends identified?
1.3. Context: South Australia as a Case Study
The study is set in the State of South Australia. There are a number of reasons for this. First and foremost South Australia is a low fertility setting and therefore most appropriate for testing the validity of expectations concerning spatial and socials differentials in fertility. During the post-war period there have been dramatic changes in the patterns of fertility and family formation in Australia and the trends for South Australia reflect those of the nation as a whole.
There are numerous detailed studies on various aspects of Australian fertility but general overviews of the trends can be found in a number of publications including ABS Demography, Australian Demographic Trends, Bureau of Immigration Research’s Chapter One: Introduction 7
Australia’s Population Trends and Prospects (1983 to 1995) and articles and books by Adam (1991, 1992); Carmichael (2002); Choi and Ruzicka (1987); Hugo (1983, 1986, 1999, 2002b, 2002c, 2004); Hugo and Wood (1983); Jain and McDonald (1996); Khoo and McDonald (1988); McDonald (1983); Merlo (1995); Merlo and Rowland (2000) and Ruzicka (1979) .
Following almost eighty years of declining fertility the end of the Second World War heralded a new and unanticipated trend in Australia—a significant increase in fertility that is commonly referred to as the baby boom (Hugo 1983, 2002b). During this period the total fertility rate (TFR) (Figure 1.1) rose continually reaching a peak in 1961 in South Australia at 3.75 (Hugo 1983, 14). The baby boom was especially pronounced in this State such that the TFR rose above the national average. From the early 1960s the TFR dropped sharply to 2.84 in 1966, remained relatively stable until the early 1970s (in 1971 the TFR was 2.702) before once again declining sharply. By 1976 the TFR had fallen below replacement level to 1.873. Since the mid 1970s the TFR has continued a gradual decline reaching a low of 1.67 in 2001 (ABS 2002a, 19) and 1.72 in 2002 (ABS 2004a, 3) and 2003 (ABS 2004b, 16).
Figure 1.1: Total Fertility Rates, South Australia and Australia, 1947 to 2003
4
3.5
3 South Australia Australia 2.5
2 Rate
1.5
1
0.5
0 1947 1954 1961 1966 1971 1976 1981 1986 1991 1996 2001 2002 2003 Year
Source: ABS Births South Australia, Births Australia; Demography South Australia Various Issues Chapter One: Introduction 8
The trends in fertility in South Australia have been the result of several changes in marriage patterns (declining marriage rates, increase in median age at first marriage, increase in the incidence of divorce) and family formation patterns (increasing delays between marriage and the first confinement, increase in ex-nuptial fertility, shifts to later ages at birth) (see ABS 1992, 1997a, 2002a, 2004b; and Carmichael 2002 for example for trends).
The decline in fertility in Australia, as in other parts of the world, since the early 1970s is due to a number of complex, often inter-related, social and economic factors that have transformed society. These factors, for example, include the changing role of women with increasing proportions participating in the workforce, in post school education and achieving financial independence. The introduction of the contraceptive pill in the 1960s, the liberalisation of abortion laws and the removal of the legal discrimination against illegitimate children and unwed mothers with the introduction of social security benefits have also been important factors (Hugo 2004; Ruzicka 1974). Such developments give women much greater control over the number and spacing of their children. All of these factors led to changes in societal attitudes towards the institution of childbearing and rearing (Carmichael 1998; Hugo 1986, 1999; Jain and McDonald 1997; McDonald 1983).
These changes have been accompanied by changes in marriage patterns (declining marriage rates, increase in median age at first marriage, increase in the incidence of divorce) and family formation patterns (increasing delays between marriage and first confinement, increases in ex-nuptial fertility and shifts to later ages at birth).
In addition to being a low fertility setting South Australia has a mix of area types (a major urban centre, regional centres of varying sizes, and remote areas) that enable the testing of some of the key differentials highlighted in the literature such as the urban-rural differential in fertility.
Data issues also often play a role in the designation of a study area. The data utilised in this study is unpublished data collected by the Australian Bureau of Statistics (ABS) on the number of children ever born to women. This information, at a small area level, is available for the 1976, 1981, 1986 and 1996 Censuses. As it is data only available upon request from the ABS it is expensive to obtain and therefore there was a need to restrict Chapter One: Introduction 9 the case study area. Confining the analysis to one State in the country however allows more in-depth analyses to be undertaken, and in addition, familiarity with the research area assists in ‘explaining’ the patterns observed.
1.4. Outline of Study
To establish the relevance of spatial and socio-economic differentials in fertility theory, Chapter Two examines the main theoretical approaches. The chapter begins with demography’s predominant fertility theory, demographic transition theory. This theory provides an overall view or description of the movement of societies from a situation of high fertility and high mortality and slow population growth to a modern society experiencing low fertility and low mortality levels. The intervening period is a time of considerable change and the appearance of differentials both socially and spatially.
Stemming from demographic transition theory, to gain a greater insight into the declines in fertility, has been the development of a number of theories that examine more specifically the causality of fertility decline. These theories, for example, the home economics approach and socio-economic approaches, as discussed in Chapter Two, initially focussed on economic development and changes in the cost of children and childbearing with the progression of the transition. These explanations however were viewed by some researchers to be inadequate and ideational or cultural theories of fertility were proposed. The most relevant to the current situation of below replacement fertility is van de Kaa’s (1987) so called second demographic transition theory. This ‘new’ transition is distinguished from the first by different attitudes towards the importance of children. In the first transition the welfare and prospect of children (quality replaced quantity) governed norms and attitudes initially towards childbearing, however in the second demographic transition this goal is balanced with a number of options including what can be called consumerism, careerism and other lifestyle choices towards self fulfilment. While many different types of lifestyle arise from the focus on individualism, demographic uniformity throughout the developed world is an expected outcome.
Chapter One: Introduction 10
Arising from the development of theory has been the recognition of relationships between fertility decline and the demographic, socio-economic and cultural characteristics of a population. The study of variations in fertility levels between different groups in society and over regional areas has a long tradition in demographic research as an avenue for gaining a greater understanding of the processes and determinants of fertility. The aim of Chapter Three is to establish the evidence for regional and socio-economic fertility differentials focusing particularly on the patterns and trends in Australia. The chapter begins with a focus on spatial analyses followed by an examination of socio-economic differentials (age, marital status, education, employment, income) and cultural differences that may arise from religion, ethnicity or Aboriginality. A number of other issues are also discussed including the role of migration and the macro social, economic and political conditions before concluding with a review of attitudinal changes and preferences.
Chapter Four provides an outline and discussion of the specific research undertaken in this study. It provides the details upon which the major analyses in subsequent chapters are based. Specific issues discussed include the scope of the study, data availability and limitations; choice of measurement, design of geographic areas, mapping techniques, measures of variability and finally the methods used to provide explanations for the patterns and trends identified.
Chapters Five, Six and Seven provide the empirical evidence to establish the extent of spatial variability and trends towards convergence or divergence in the patterns over time. Chapter Five examines fertility patterns and trends for the statistical sub-divisions (SSDs) of South Australia. As these areas however are often very large, containing people with an array of characteristics, Chapters Six and Seven examine fertility trends at a more disaggregated level. Chapter Six discusses the patterns for non-metropolitan South Australia while metropolitan Adelaide is the focus of Chapter Seven.
Providing explanations for the patterns identified is the aim of Chapter Eight. Fertility is a complex phenomenon and there is a wide range of identified and inter-related factors that govern fertility levels and spatial patterns. Clearly while it would be desirable to include as many variables as possible (embracing demographic, biological, cultural, socio-economic and psychological factors) in any approach towards explanation this is impossible. This study is restricted by the data principally available from the Australian Census but it does Chapter One: Introduction 11 allow an examination of the importance of socio-economic factors. The role of such factors has largely been ignored in recent times as the focus on understanding fertility levels has centred on analysing individual behaviour.
Chapter Nine, as the final chapter, reviews the overall findings of the study in the context of previous research and expectations from theory. In addition the implications for further research are discussed.
Chapter Two: Differential Fertility—Theoretical Background 12
Chapter Two
Differential Fertility—Theoretical Background
2.1. Introduction
There is no single detailed and comprehensive theory, or coherent set of theories, of reproductive behaviour despite repeated calls for, and efforts to, develop them (Burch 1996; Hohn and Mackensen 1982; Johansson 1993; Leridon 1982; Mackensen 1982; McNicoll 1982; Murphy 1987). Instead, there is a variety of theoretical approaches each emphasising particular independent variables or sets of variables as being fundamental to fertility decline (Burch 1996). Unfortunately the different approaches are often seen to be in conflict with one another (Burch 1996; Johansson 1993; McNicoll 1982) and as Hirschman (1994, 214) finds there is ‘a plethora of contending theoretical frameworks none of which has gained wide adherence’. This lack of ‘development’ in theories of fertility decline is due to a number of factors including a lack of orientation in demography and population geography towards the development of theory (Graham 2000). As a consequence much of the empirical work undertaken has not been designed to specifically test theory (Burch 1996; Mason 1992). In addition a major thesis of Burch (1996, 61) is that there is ‘a general lack of clarity and precision in theoretical statements on fertility decline’ among social demographers. Johansson (1993, 383) also argues that the development of a more insightful theory of fertility is restricted by the different backgrounds and methodologies of researchers and in particular by the limited research funds available to academics:
…where research funds constitute a crucial but limited resource, rival groups of specialists compete with one another for the right to dominate fertility research and for the requisite funds that allow them to do so. Unfortunately for demographic knowledge the prevalence of ‘communication problems’ means that pervasive conflict precludes synthetic interdisciplinary research.
Chapter Two: Differential Fertility—Theoretical Background 13
Some researchers (Andorka 1978; Burch 1996; Farooq and DeGraff 1988; Johansson 1993; Mackensen 1982; Mason 1992, 1997; Woods 1982) hold a different point of view. They argue that no single approach could possibly account for the wide array of variables influencing reproductive decisions and behaviour and be applicable in any setting at any given time. Mackensen (1982, 251) maintains, moreover, that ‘a need for such a general theory is appropriate neither to the state of demographic research or population analysis, nor to the subject matter as such’.
This chapter examines the main theoretical approaches to fertility with a particular focus on the role they accord to differential (especially spatial and socio-economic) fertility. In the course of development of theories on fertility the path has been one of movement from a higher level of aggregation towards concepts of individual behaviour. In addition the approaches have become more interdisciplinary incorporating demographic, economic, sociological and psychological perspectives. The chapter begins with discussion of the most widely recognised theory, classical demographic transition theory. This discussion is followed by an examination of economic approaches to fertility behaviour, and then the theories classified as ideational are outlined. The chapter concludes by summarising the extent of recognition of differential fertility within the various theories and the themes that arise.
2.2. Classical Demographic Transition Theory
The most well known of all population theories, and the one demographers can lay most claim to, is demographic transition theory. This theory, based originally on the experience of European countries and their ‘New World’ colonies, describes the transition of populations from traditional societies where mortality and fertility are relatively high to modern societies characterised by low levels of mortality and fertility.
Key elements of the theory can be found in several early discussions, including Thompson’s (1929) general historical theory of the relationship between population and economic growth and Blacker (1947), but it is Notestein who is credited with formulation of the definitive version of the classical theory (1945, 1948, 1950, 1953). Notestein not Chapter Two: Differential Fertility—Theoretical Background 14 only provided a description of population change but also an explanation for the changes and as such he viewed the theory ‘not merely as a historical generalization but also as a predictive instrument for the control of future events and for policy formulation’ (Szreter 1993, 671).
According to Notestein, and earlier formulations (Kirk 1996) demographic transition is characterised by a number of stages as outlined in Table 2.1. The first stage, lasting centuries, is one of high mortality, high fertility, minimal population growth and minimal differential fertility. High mortality is the result of low living standards, famines, wars and infectious diseases while fertility is assumed to be high as a response to high levels of mortality. In peasant societies strong pressures exist on the population to reproduce. Notestein argued that in traditional societies fertility had to be high as the ‘economic organisation of relatively self-sufficient agrarian communities turns almost wholly about the family and the perpetuation of the family is the main guarantee of support and elemental security’ (Notestein 1953, 15). When death rates are high life is precarious and the individual is relatively unimportant. Life is about existence rather than striving for individual or personal advancement. This was particularly so for women whose role was as a wife and mother. The situation is perpetuated by the religious doctrines, moral codes, laws, education, community customs, marriage habits, breast feeding customs and family organisation within society (Notestein 1945).
The second stage of the transition is initiated when long term mortality declines as a result of the onset of what Notestein (1945) terms ‘modernisation’—agricultural, industrial and commercial revolutions which bring about rising standards of living, improved nutritional diets and improvements in sanitary and medical knowledge. Fertility rates according to Notestein, however, were slower to be affected by the processes of modernisation and this resulted in an increase in the growth rate of the population. He suggested in pre- transitional society the props to support high fertility (religious doctrines, moral codes, laws and community customs, for example) are an integral part of that society and as a consequence are slow to change. The decline of fertility however ‘awaited the gradual obsolescence of age-old social and economic institutions and the emergence of a new ideal in matters of family size’ (Notestein 1953, 16).
Chapter Two: Differential Fertility—Theoretical Background 15
Table 2.1: Phases of the Classical Demographic Transition Theory and Assumed Fertility Differentials Phase Fertility Mortality Population Differentials Growth Pre Transitional High and Variable High Zero or Minimal Low and Growth insignificant; 60- 80% population employed in agriculture and reside in rural areas Early Expanding High Declining slowly Slow As above Late Expanding Falling Falling Faster than Rapid Significant and Dramatically Birth Rates increasing differentials between urban rural population, by education, occupation, income and religion Low Stationary Low Low Zero or Very Low Differentials decline – may persist or become non-existent Source: Bengtsson and Ohlsson 1998; Chesnais 1996; Livi-Bacci 1999
Progression into the third stage of the transition occurs when societies are motivated to adopt lower levels of fertility as a response to the decline in mortality, the increasing growth in the population, and the social and economic changes associated with the process of modernisation. Notestein (1953, 16) states that this motivation arose in urban industrial society and that a variety of causal factors were important:
Urban life stripped the family of many functions in production, consumption, recreation, and education. In factory employment the individual stood on his own accomplishments. The new mobility of young people and the anonymity of city life reduced the pressures toward traditional behaviour exerted by the family and the community. In a period of rapidly developing technology new skills were needed, and new opportunities for individual advancement arose. Education and a rational point of view became increasingly important. As a consequence the cost of child-rearing grew and the possibilities for economic contributions by children declined. Falling death-rates at once increased the size of the family to be supported and lowered the inducements to have many births. Women, moreover, found new independence from household obligations and new economic roles less compatible with child-rearing. Chapter Two: Differential Fertility—Theoretical Background 16
In the theory, it is at this stage of the transition, that differentials (socio-economic and spatial) in fertility begin to emerge as the old ideals and beliefs give way to the new ideal of the small family. Declines in the number of births, according to Notestein, began ‘in the urban upper classes and gradually moved down the social scale and out to the countryside’ (Notestein 1953, 16). Studies by Haines (1992), Livi-Bacci (1986, 1999), Schneider and Schneider (1984) and Sharlin (1986) of historical data for Western Europe and America found general support for Notestein's statement.
Livi-Bacci's (1986, 1999) studies of social status groups experiencing fertility decline found it was the privileged groups, the ruling families, nobility, bourgeoisie, (the higher social classes by income, profession or education) that were, as he calls them, the ‘forerunners’ of fertility decline in Western Europe. He found the fertility of these groups declined decades or in some cases centuries before the general population began to restrict fertility. Other than their privileged position in society and moderate levels of mortality, Livi-Bacci commented that their common attribute was that all these groups resided in urban areas. The relevance of the urban factor, though, is little understood. Livi-Bacci (1986, 199) asks ‘are these groups forerunners of fertility control because they live in the special cultural, social, and economic environment of the city, or is the city environment special because of the existence and role of the forerunners?’ Similarly Sharlin (1986), in examining the role of urbanisation in fertility decline and urban and rural differences in fertility in Europe, found that while the level of urbanisation was not a good predictor of the initiation of the demographic transition, whenever it was possible from the data to note a difference between urban and rural areas prior to the onset of the transition, the decline in fertility always began in urban areas. As the transition progressed the urban-rural differential widened with urban fertility declining more rapidly in the initial stages. In addition Sharlin (1986) found a consistent inverse relationship between the size of a city and marital fertility. ‘The large cities begin a decline in fertility several years before the small cities, which in turn begin a decline several years before rural areas’ (Sharlin 1986, 253). Sharlin (259) argues, the urban-rural fertility differentials may be attributable to variations in social and economic characteristics—variations in the occupational and industrial structure across urban and rural areas.
Chapter Two: Differential Fertility—Theoretical Background 17
Livi-Bacci (1999, 153-154) in his study of the history of the population of Europe further states that the urban-rural differential needs to be understood in the context of the peculiarities inherent to urban areas at the time and the close ties that existed between urban and rural areas. He lists a number of influential factors such as the urban population included a high percentage of people for whom marriage and therefore having children was unlikely (people in military service, belonging to religious orders, living in institutions; or as domestic servants); there was a large proportion of people in urban areas who had left their families behind in the rural hinterland; and the ratio of men to women in urban areas was often out of balance. Even though these factors make interpretation of the data difficult, Livi-Bacci (1999, 155) concludes ‘over the course of the nineteenth century fertility seemed to come under control earlier and more quickly in cities than in the country’.
In the latter stages of the transition folk methods of contraception were used to restrict childbearing. Later, in response to increasing demands and the development and acceptance of more modern and efficient methods of contraception, fertility levels declined substantially to stabilise at a level just above the mortality rate. Population growth slowed and the year to year fluctuations in mortality and fertility were small. For Notestein (1953, 17) the ‘efficient recruitment of life on the basis of low birth-rates and low death-rates’ meant the transition was virtually complete. In this period of relative equilibrium (Table 2.1) fertility differentials decline and may persist or they may be expected to disappear altogether as similar values, attitudes and birth control practices are adopted and become the norm.
Opinion varies as to whether fertility differentials have converged in this last stage of the demographic transition (Coleman 2002; Wilson 2001) or whether this is even the last stage. Although examined in more detail in Chapter Three (particularly in relation to the Australian experience) as early as the 1940s the issue of convergence in fertility differentials was being disputed for socio-economic groups in Great Britain (Hopkin and Hajnal 1947).
In the 1940s and early 1950s demographic transition theory was believed to be ‘the culminating theoretical revelation that clarified the nature of modern population Chapter Two: Differential Fertility—Theoretical Background 18 dynamics’ (Hodgson 1983, 7). The theory stimulated a great deal of empirical research. While some studies provided support for the premises of the theory, others challenged its tenets. It has been criticised on a number of grounds both in terms of its content and its structure (Bongaarts and Watkins 1996; Coale 1973; Farooq and DeGraff 1988; Graham 2000; Mackensen 1982; Mason 1997; Watkins 1986; Woods 1982, 1986a). The most general criticism made of the theory is that it is inductive in origin being based on the experience of European countries and it has a deterministic linear path along which changes in the population are expected to proceed. For example Graham (2000, 262) states demographic transition theory
provides a general picture of change over time which is predicated on a linear view of history in which the West sets the standard and is further along a given path of progress than the Third World. Like modernisation theory, demographic transition theory, in some of its versions, denies the Third World a history and assumes that progress consists of achieving the characteristic conditions of the West.
The most elaborate test of demographic transition theory in the European context was the Princeton European Fertility Study led by Ansley Coale (Coale and Watkins 1986). In this study spatial fertility data were crucial. In collaboration with other researchers Coale examined provincial level data for the period 1870 to 1960. His general conclusion from this study was ‘certain aspects of fertility trends … appear inconsistent with, or at least more complex than, the description of fertility changes provided in the demographic transition’ (Coale 1973, 56).1 Such inconsistencies included the fact that in pre-modern societies fertility levels varied considerably among populations of particular countries and regions. In addition although data on socio-economic differences in fertility in the period prior to the modern period are scarce, Andorka’s (1978) review of the various studies that have been undertaken using family reconstitution and genealogies indicate that religious, cultural and socio-economic differentials in fertility existed in pre-modern societies. On the basis of the studies reviewed, however, it was not possible to ascertain any clear relationships. For example, Andorka (1978, 64) states:
1 Notestein was not unaware that there were situations that did not ‘easily fit’ into the model. He noted that birth rates had declined outside the urban industrial areas and that some birth rates, for example, in France had begun to decline in the eighteenth century (Notestein 1953, 17). Chapter Two: Differential Fertility—Theoretical Background 19
In some communities the negative correlation of fertility and socio-economic status, well known in the period after the industrial revolution, seems to have prevailed. In others, the wealthier couples had somewhat higher fertilities, i.e., as has been found in recent decades in some studies of advanced societies. However, the fact that differences existed before the industrial revolution seems to prove that there was some conscious limitation of births in some parts of the populations and that it might have been influenced by economic and social conditions.
Another identified inconsistency was that the decline in mortality did not always precede the decline in fertility. In some societies fertility declined before mortality while in other populations these elements declined almost simultaneously (Coale 1973, 61). Reher (2004, 25) however would argue differently. From his review of the available literature and his own analysis of data for 145 countries he states, ‘nowhere in the world, independent of the time period, the levels of wealth or the degree of modernisation, has fertility change taken place without significant prior mortality change’. Recent research both medical and historical into the decline in mortality in Europe in the late nineteenth century has identified that the ‘fall in mortality was not the start of something new but the end of something old’ (Bengtsson and Ohlsson 1998, 13). Improvements in nutrition, housing, hygiene, child care and sanitary systems in urban areas eventually were sufficient to halt the fluctuations in mortality and to stop mortality from returning to a period of increase after a period of low mortality.
It was also found there is no fixed threshold of development that initiates fertility decline.2 The transition began under a wide range of socio-economic circumstances. In parts of France and Hungary, for example, the decline in fertility had already begun at the beginning of the eighteenth century, well before the Industrial Revolution and the emergence of modern cities (Livi-Bacci 1986; Sharlin 1986). In contrast, in other places it was a long time after industrialisation that fertility eventually began to decline (Farooq and DeGraff 1988, Livi-Bacci 1999). For some regions the decline in fertility appeared to be more closely linked to diffusion within specific cultural or linguistic regions (Kirk 1996). Despite this Livi-Bacci (1999, 157) states:
2 Research to establish socio-economic thresholds of fertility decline has gained impetus with recent efforts to compile standardised historical indices (Caldwell and Caldwell 2001). Chapter Two: Differential Fertility—Theoretical Background 20
The process of fertility decline, for the most part, followed the geographical path of economic development and declining mortality, but with many deviations and exceptions attributable to culture and tradition, religions and institutions, permanence and change, all of which can only be understood at local and specific levels of analysis.
Careful analysis of spatial variations in fertility is important in understanding the complexity of the demographic transition.
The criticisms of demographic transition theory do not only apply to the developed world. Its applicability to developing countries has also been questioned, particularly in relation to the causal mechanisms postulated by Notestein (Caldwell 1976, 2000; Teitelbaum 1975). Teitelbaum (1975, 422, 425) states
When the cluster of socio-economic variables to which transition theory attributes the European fertility decline (industrialization, urbanization, education, mortality decline, and other factors) are quantified and examined, it becomes quickly apparent that a number of confident propositions in some versions of transition theory are overly facile…Close examination of transition theory in both historical and modern perspective shows that policymakers would be ill-advised to adopt such a simplistic and deterministic view – that in all circumstances development will ‘take care of’ population matters.
Some now question the attitude that the demographic transition of Europe has little relevance to the experience of developing nations. Reher (2004) would argue that the research community has been too hasty in its criticisms of the basic tenets of demographic transition theory. If viewed in the context of a historical process taking one hundred years or more, and if it is accepted that the precipitating factor for fertility decline is a decline in mortality then Reher (2004, 19) argues the current significant declines in vital rates in the developing world have many similarities to the demographic transition of Europe. In Reher’s view (2004, 19):
the process of transition in Europe has much to teach us about the recent past and present of the demographic transition under way in Africa, America and Asia, and also gives us a plausible blueprint for the future. The theory has also been criticised because of its function as a grand theory of population change. In relation to contemporary patterns of fertility behaviour the theory is seen as Chapter Two: Differential Fertility—Theoretical Background 21
‘not specific enough; it formulates its theses on too high a level of aggregation; it disguises precisely all those shades of reproductive behaviour which we would hope to understand’, (Mackensen 1982, 256).
In the opinion of others (Coward 1986b; Hirschman 1994; Leibenstein 1974; Woods 1982), however, it is precisely these features which are to its advantage. The theory is applicable to various settings and because of its broad structure is able to incorporate many variables affecting fertility. As Woods (1982, 158) outlines:
It lacks the precision of a more limited causal theory, yet one strength may lie in this very weakness for it holds out the promise of a general historical theory which will deal with temporal and spatial changes in population structures together with their main economic and social causes.
In a similar vein Livi-Bacci (1999, 157) argues that although there is considerable evidence for and against the transition model, ‘ultimately the debate fails to suggest an explicative model which incorporates the many variables and produces convincing results…’. Similarly Bengtsson and Ohlsson (1998, 2) argue the theory of demographic transition:
Still has a strong influence over our understanding both of how today’s industrial countries have developed and of developments in those countries which just now are in the midst of such a process. The reason why the theory still survives despite all criticism is that there is a great need for a general theory of population trends during the transition from an agrarian to an industrial society and that no sensible alternative to the demographic transition theory has been presented.
In spite of its general structure and the recognition that change through time occurs differentially between social groups and regional areas, differential fertility is not a central feature of the theory but merely one of the consequences of the theory’s main focus on the development and progression of a population to the final stage of the transition. For example in relation to space Graham (2000, 266) states: …initial formulations of demographic transition theory included a very crude conceptualisation of space. Space was seen as a container of national populations, and Chapter Two: Differential Fertility—Theoretical Background 22
these populations were divided into three basic types, each at a different stage of the transition.
Due to a lack of other options (indeed there appears to have been little explicit attention to socio-economic differentials in theory since Notestein) and the fact that differential fertility is a part of the demographic transition, this theory has been used most frequently as the basis upon which social and spatial differential fertility analysis has been undertaken. As the theory suggests that by the final stage differentials no longer exist, or are minimal, then for contemporary developed societies, the theory’s ability to explain the existence and persistence of differential fertility is limited.
Demographic transition theory has been the basis for, and prompted the development of a number of theories that examine more specifically the causality of fertility decline. As there is an emphasis on economic development and changes in the cost of children and childbearing with the progression of the transition, the discussion now turns to the economic theories of fertility decline.
2.3. Economic Theories of Fertility Behaviour
Although broad based theories such as demographic transition theory are useful in providing an overall picture of fertility decline, by the 1950s and 1960s it was believed necessary to develop a more focused approach in order to gain a more complete understanding of fertility behaviour. Such a focus entails theoretical models of fertility which examine demographic behaviour at the individual or household level. Various approaches have been pursued but it is the micro-economic approach which has ‘organised much fertility research’ (Pollak and Watkins 1993, 467) and has developed the most in theoretical terms (McNicoll 1992; Pollak and Watkins 1993). McNicoll (1992, 403) comments that the ‘power of the microeconomic model of household behavior is demonstrated by its absorption into the ‘common sense’ of thinking about family decision making with respect to fertility and migration’. There are two major schools of thought, the new home economics approach (the Chicago- Columbia Model) and the socio-economic approach (Pennsylvania School Model) (Pollak Chapter Two: Differential Fertility—Theoretical Background 23 and Watkins 1993). These two approaches are derived from neo-classical micro-economic analyses of consumer behaviour at the household level. Both are based on the household consumption model in which households derive utility (or wellbeing) from the purchase of market goods. This purchasing power is restricted or constrained by the price of the goods and the resources (financial) available to the household. These budget constraints determine the quantity of each market good that the household is able to purchase (Farooq and DeGraff 1988; Jones 1982; Pollak and Watkins 1993).
2.3.1. Home Economics Approach
Becker (1960, 231) was one of the first economists to equate children with both providing utility and having a cost so that decisions about fertility appear similar to the choices or decisions made regarding market goods:
Children are viewed as a durable good, primarily a consumer’s durable, which yields income, primarily psychic income, to parents. Fertility is determined by income, child costs, knowledge, uncertainty, and tastes. An increase in income and a decline in price would increase the demand for children, although it is necessary to distinguish between the quantity and quality of children demanded. The quality of children is directly related to the amount spent on them. Each family must produce its own children since children cannot be bought and sold in the market place. This is why every uncertainty in the production of children (such as their sex) creates a corresponding uncertainty in consumption. It is only when the number of children in a family depends not only on its demand but also on its ability to produce or supply them. Some families are unable to produce as many children as they desire and some have to produce more than they desire. Therefore, actual fertility may diverge considerably from desired fertility.
In Becker’s model therefore the factors which can affect fertility are household income, net cost of children, supply-uncertainty in production, and tastes. ‘Taste’ is the only avenue through which non-economic factors can affect fertility (Becker 1960, 211): The shape of the indifference curves is determined by the relative preference for children, or, in other words, by “tastes”. These tastes may, in turn, be determined by a family’s Chapter Two: Differential Fertility—Theoretical Background 24
religion, race, age, and the like. This framework permits, although it does not predict, fertility differences that are unrelated to “economic” factors.
Although this initial model of Becker’s was criticised it did provide, as Pollak and Watkins (1993, 475) comment ‘the conceptual framework that allows economists to apply tools developed for studying the behaviour of households including their fertility’.
This basic model was refined by the idea that time had a value (Willis 1973). Not only was the expenditure of income important in the fertility decision-making process but also the expenditure of time on children—the opportunity costs. These opportunity costs vary from household to household. The primary carer of children (usually the mother) can either use time to care for children or to become part of the labour force and earn income. If female wage rates increase (thus increasing the price of time) this is likely to reduce the optimal number of children. For women with higher income earning potential (those with higher education levels for example) these opportunity costs are even greater. Although the model recognises that increases in wages can have an income effect by increasing the potential family income thus making more children affordable, empirical findings have found a negative relationship between women’s wages and fertility in industrialised countries (Jackson 1995).
The household production model was further enhanced by the introduction of the concept of quality (Becker and Lewis 1973). This refinement revolves around the notion that a good has a differing price, based on its quality. A better quality product increases in cost and thus its utility to the consumer. In terms of reproductive behaviour parents must balance their desires and resources between quantity and quality of children, and as income is increased it is assumed to be diverted towards increases in quality rather than quantity.
Many criticisms have been levelled at the demand theory of fertility (Jones 1982; Murphy 1992; Pollak and Watkins 1993). Such criticism arises, for example, from the theory’s assumption of rationality that all parents make rational decisions about childbearing and more particularly the assumption of optimising behaviour (Jones 1982; Pollak and Watkins 1993). The theory assumes that couples make decisions at the outset of marriage Chapter Two: Differential Fertility—Theoretical Background 25 about the utility of childbearing and other activities. The model ignores the reality that decisions about childbearing are probably sequential and family size may be a continuing decision.
The demand theory of fertility has also been questioned because it ignores the issue of supply. The theory is unable to account for situations where there is no demand for children and where the demand for children is unable to be met due to biological factors. Situations are influenced by factors other than economic and the normal economic explanatory variables will be inadequate in explaining any influence such situations may have on fertility trends and differentials (Jones 1982). A further weakness and one of the major criticisms of the household model of fertility is the issue of preferences.3 In this model preferences are taken as fixed and thus attitudinal, cultural and even technological factors are not included. Preferences or aspirations about the demand for children are seen to be formed from privately derived parental desires without any influence from the wider social and economic environment (Johansson 1993; Murphy 1992). The household models of fertility were designed to explain the variations in completed family size of individual couples. How desires/preferences and fertility varies throughout the community (i.e., between classes or groups) or through time and space is not pertinent to this theory. Any small differences found between groups in society, are in these models, explained solely by economic factors (variations in individual couples’ status and economic prospects) or by the lack of suitable or sophisticated economic explanatory variables.
2.3.2. Socio-Economic Approaches
The second school of thought, the socio-economic approach is an attempt by Easterlin (1969) to build upon the economic theory of fertility by incorporating sociological dimensions.4 Easterlin’s theory has the same basis as other economic approaches in that fertility behaviour is seen as the result of choices within the household to maximise utility, subject to the constraints of incomes and prices. Easterlin’s theory, however, departs from
3 The terms, ‘preferences’, ‘tastes’, ‘desires’, ‘needs’, ‘aspirations’, appear interchangeable in the literature. 4 For a detailed discussion of the Easterlin Hypothesis see the collection of papers in Population and Development Review 1976, volume 2 pp 411-477. Papers are by Easterlin, Freedman, Lee, Leibenstein, Oppenheimer, Sanderson. Chapter Two: Differential Fertility—Theoretical Background 26 the neo-classical economic theories in relation to three concepts—income, prices and tastes.
Rather than a couple’s decision about fertility being governed by their income at a particular point in time, Easterlin proposed that it is potential income over the couple’s lifetime that is most important in the decision making process. In addition he believed that the costs of children were basically the same for all families and differentials in the expenditures of households on children occurred because of differences in income and tastes.
Easterlin addresses the neglected issue of supply in the new home economics model of fertility. He believes that it is necessary to apply the theory of consumer choice to the issue of birth control as well as to the desired number of children. The decision to use contraception rests upon the relationship between the loss of utility due to an unwanted birth and the costs of fertility, both monetary and psychological.
The most important distinguishing development of Easterlin’s theory is the concept of tastes. In contrast to the new home economics models of fertility where tastes/preferences are deemed to be determined by exogenous factors and fixed (and are therefore seen as irrelevant), Easterlin believes tastes are different for individuals, families and groups in society and that these tastes change over time. The formation of these tastes begins at birth and they are ‘moulded by heredity and past and current environment’ (Easterlin 1969, 135).
The development of one’s preference system continues throughout life. Easterlin believes that ‘an adequate framework for fertility analysis calls for explicit attention to preference phenomena and the factors entering into their formation’ (Easterlin 1969, 135). Thus it is important to consider such factors as religion, ethnicity, occupation, income, social status, education, place of residence and family background, for example, as all these factors may be important in the shaping of preferences. So although economic factors remain as the overriding influence on fertility behaviour in this model, many of the variables commonly analysed in empirical studies of differential fertility are considered to be important in influencing economic decisions. Chapter Two: Differential Fertility—Theoretical Background 27
In studying the pattern of American fertility during the baby boom and the subsequent baby bust, Easterlin (1973) proposed his relative income hypothesis. The basis of this hypothesis is that the difference between the income earning capacities of young couples and their desired living levels influences fertility (Easterlin 1973, 181):
The basic idea is that if young men–the potential breadwinners of households–find it easy to make enough money to establish homes in the style desired by them and their actual or prospective brides, then marriage and childbearing will be encouraged. On the other hand, if it is hard to earn enough to support the desired style of life, then the resulting economic stress will lead to deferment of marriage and, for those already married, to the use of contraceptive techniques to avoid childbearing, and perhaps also to entry of wives into the labor market.
Easterlin found that the dominant influence on the formation of young peoples’ material aspirations was the living standards they experienced while growing up in their parents’ household. These aspirations, in conjunction with the opportunities available to the individual as an adult, determine completed family size. Linked to relative income are the ideas of relative population size and age structure as important co-determinants of relative economic status (Easterlin 1976, 418). 5
This measure, [relative cohort size] though not itself a measure of the relative affluence of young persons, can be thought of as one of the important determinants of that variable. As an example of the way cohort size influences income, if a particular cohort in the labor force is substantially larger than earlier cohorts, this is likely to have a negative influence on its competitive position in the labor market. As a result, the cohort’s income is likely to be relatively lower at each age than was the case for the earlier cohorts. Leibenstein (1975, 3) developed the relative income assumption further by suggesting that social status groups have different tastes or aspirations and this may lead to different desires for children:
…populations are divided into social status groups that have different tastes, who may to some degree have different desires for children (but not simply because of an income
5 See also Easterlin and Condran (1976) and Lee (1976). Chapter Two: Differential Fertility—Theoretical Background 28
difference), and who especially see the whole cost structure of their expenditures, including expenditures for children, from the viewpoint of vastly different preference structures.
In addition households are affected by peer standards and these can have a significant influence on household or individual preferences (Jones 1982, 284):
Economic changes (e.g., a sharp rise in a family’s income) influence the social status of families. As a consequence of this change, tastes also change, not only for children but simultaneously for goods that compete with children and for goods and services involved in the nurture of children. The costs of raising children are related to the socioeconomic reference group of the parents.
Opinions on the value of the contribution of economic models of fertility and the family vary. While the review by Willis (1987, 78) concludes that there ‘is no alternative theory of demographic behavior that comes close in terms of either scope or power’ and ‘economic variables do influence family behaviour, often in the direction suggested by the theory’, others (Burch 1996; Hirschman 1994) suggest the approach remains too narrow. Despite the fact that over the years there has been greater availability of data, the theories have been refined and much more sophisticated techniques of analysis have been employed, Murphy (1992, 237) found that even in the 1990s he still had to agree with Leibenstein’s 1970s belief that though ‘a number of attempts have been made to “test” empirically the capacity of the theory to explain fertility differentials … the basic answer that emerges is that for the most part the facts do not appear to be in conformity with the theory’ (Leibenstein 1974, 457). Although this statement was made more in relation to the new home economics model than Easterlin’s theories, Cleland and Wilson (1987, 8)6 along with others (Smith 1981; Ermisch 1979) would agree it also applies to Easterlin’s theory:
Easterlin provides a model of much greater flexibility and scope than the strict micro- economic approach allows…However, the idea that economic considerations of costs and
6 Burch (1996, 63) and Mason (1992, 60) believe Cleland and Wilson’s dismissal of all theories which give a central role to economic factors as a fundamental force in fertility change or reproductive behaviour is narrow-minded. Chapter Two: Differential Fertility—Theoretical Background 29
benefits are of primary importance remains central, and in this regard the broader socio- economic approach is little different from simpler demand theory models.
In addition because the focus of these models is on explaining differences in fertility at the level of the individual couple, it is difficult to fit collectively changing norms and aspirations within these models and to thus comprehensively provide any guidance to the understanding of spatial and temporal patterns of fertility—the common problem of bridging the gap between individual and collective behaviour.
2.4. Ideational Theory
Emerging from the discontent with economic theories of fertility, has been the development of ideational or cultural theories of fertility. While some authors (Cleland and Wilson 1987) dismiss economic theories altogether in favour of cultural explanations, most researchers acknowledge the contribution and importance of these theories and the interdependence of the two approaches (Burch 1996; Hirschman 1994; Lesthaeghe and Surkyn 1988). The hypothetical basis of ideational theories is that cultural variables significantly influence fertility behaviour. Culture is a very general term and as Hirschman (1994, 216) points out ‘culture spans a wide variety of phenomena, and there are quite varied meanings of the term in the demographic literature’. Two main theoretical streams have emerged.
2.4.1. Diffusion Theory
The first theoretical stream has developed as a consequence of the limited power socio- economic variables or indicators seem to have had according to some researchers (Anderson 1986; Bongaarts and Watkins 1996; Cleland and Wilson 1987; van de Walle Chapter Two: Differential Fertility—Theoretical Background 30 and Knodel 1980) in predicting the onset and pace of the demographic transition in Europe and in contemporary developing countries. This deficiency has led to the development of the ‘diffusion of innovation’ theory. Such theory has a long history in areas other than fertility (for example see Hagerstrand 1967). According to Carlsson (1966, 150) this theory has a number of elements, the most important of which is that ‘the decline of fertility started in a setting where there was no, or at most very limited, previous practice of birth control. The theory stresses the importance of the spread of information about contraception and perhaps abortion’. The decline in fertility therefore begins when there are changes in the value and attitudes of married couples towards family limitation and this occurs often independently of social and economic circumstances.
The timing of the transition is related to cultural and/or linguistic boundaries and the process of social interaction (Bongaarts and Watkins 1996). This spread of information, skills and changing attitudes about fertility limitation begins in the metropolitan centres, and in time spreads to other urban places before later reaching rural areas. Corresponding with this ‘trickle down’ effect is a regional factor in that certain regions are reached before others, or are quicker to react. The final major element of this theory is class differentiation in the timing of the decline and the acceptance of birth control. The use of birth control whether it be contraception, abortion or abstinence is believed to start with the high or middle class groups7 before spreading to labourers and the farm population. According to Carlsson (1966, 165) ‘birth control behaviour is contagious and the fertility behaviour of a population is not the simple aggregate of isolated individual decisions but the end product of complex social interactions’.
Van de Walle and Knodel’s (1980) and Knodel and van de Walle’s (1982) review of the historical analyses of marital fertility decline in many countries of Europe is presented in the context of Carlsson’s (1966) diffusion of innovation theory. They believed many of the criteria of the theory were met in Europe’s fertility decline—the decline began under a diverse range of socio-economic and demographic conditions, there was little or no fertility control prior to the onset of the transition, and there was geographic diffusion of Chapter Two: Differential Fertility—Theoretical Background 31 family limitation practices within cultural boundaries. Thus van de Walle and Knodel’s interpretation of the fertility transition in Europe, succinctly summarised by Friedlander, Schellekens and Ben-Moshe (1991, 334) was that although:
long-term socioeconomic and demographic developmental changes that had started much earlier set the socioeconomic stage for the fertility transition [it was only when] the idea and practice of fertility limitation ‘were innovated’ [that] a process of diffusion within cultural boundaries could have started. Hence according to these arguments, the characteristics of marital fertility decline in European countries can best be explained by cultural variables and by the force of diffusion while socioeconomic variables had only marginal importance, if any at all.
In contrast to these observations and interpretations other studies of the European fertility transition, Carlsson (1966), Friedlander (1983), Friedlander, Schellekens and Ben-Moshe (1991), Teitelbaum (1984) question the validity of geographical and social class diffusion models and place much more emphasis on the socio-economic context of the fertility transitions. The issue remains unresolved, and the camps remain divided with their own peculiar interpretations of the available evidence.
2.4.2. Second Demographic Transition
The second ideational approach and that which is most relevant here is built around the changes that have occurred in the industrialised societies in the post-war period. The basis of the approach is that economic growth and increasing affluence in western society has led to a shift in value orientations, preferences and aspirations from essential needs to higher order needs. In conjunction with this, frustrations emerge with the role and functioning of existing institutions. The two most conspicuous features of ideational change have therefore been the processes of secularisation and individualism. These terms can be defined in various ways but basically secularisation involves the erosion of the
7 According to Livi-Bacci (1999) in the eighteenth and even in the seventeenth century the control of fertility was common practice among the privileged classes of Europe, some Jewish communities in Italy and among certain urban groups. Chapter Two: Differential Fertility—Theoretical Background 32 powers of the religious system. As stated by Lesthaeghe and Surkyn (1988, 13) the historical role of the churches has been that they:
…systematically engage in institutional regulation of individuals lives through the collective assertion of norms that restrict individualism and the externalities that individualism may produce, and through the psychological internalization of sanctions ranging in format from guilt to damnation.
As this power is weakened, individualism is able to grow and it has arisen in many sectors of life including reproductive behaviour and family structure. While historically, changing ideas and preferences are thought to have been initiated by the upper social strata of society due to their wealth, privilege and education and then filtered down to the lower social status groups, the increasing importance placed on education for males and females has meant that particularly in the post-war period, education has become a powerful force in fuelling ideational change. According to Lesthaeghe and Surkyn (1988) education shapes individuals’ values, aspirations and preferences prior to adulthood and these beliefs continue through life.
While some believe these ideational changes and the resulting changes in fertility and the family are just part of the long term secular trends accompanying the fertility transition (Bumpass 1990, for example),8 others believe the advanced societies are now in a new era of demographic change, a post transitional phase. This requires the development of different theoretical approaches to fertility and thus Lesthaeghe and van de Kaa (Lesthaeghe 1995; Lesthaeghe and van de Kaa 1986; van de Kaa 1987) propose a second demographic transition. Their second demographic transition endeavours to explain the post-transitional changes in fertility based on the experience of European countries since the mid 1960s. Changes in the family have resulted in the principal demographic feature of the second transition—declines in fertility to below and well below replacement level. Lesthaeghe and van de Kaa believe this transition is distinguishable from the first transition in terms of the motivation for fertility decline. The first was the result of ‘altruism’ or concerns for children and the family (van de Kaa 1987, 5) while the second
8 Bumpass believes that the demographic transition in the West is not over. He states ‘we tend to think of the transition in the relative value of childbearing as a past event for today’s low-fertility societies and Chapter Two: Differential Fertility—Theoretical Background 33 transition is due to the profound shifts in the norms and attitudes of people towards what van de Kaa terms ‘progressiveness’ and ‘individualism’—equality of opportunities in education and income, for example, and freedom of choice in behaviour.
This motivation Lesthaeghe (1995) identifies as resulting from various inter-related sources. First is a change in how adults view and evaluate their relationships. The rising incidence of divorce early in the transition and the results of value studies about relationships and marriages leads Lesthaeghe to believe there has been a rise in the minimum standards for adult relationships and greater tolerance towards unacceptable forms of behaviour. In addition individuals are seen to be governed less by external norms and morals and are thus more willing and free to make choices. This corresponds to the underlying factor Lesthaeghe terms ‘nonconformity as anti-authoritarianism’. Emerging in the 1960s was increasing protestation against forms of external authority whether in the social, religious or political domain. Lesthaeghe's final source underlying the change in motivations is ‘advanced consumerism coupled with increased market orientations’. He suggests that his own research and that of Inglehart (1977, 1985, 1990) and Crimmins, Easterlin and Saito (1991) show that ‘ “post-materialist values” continued to develop throughout the 1960s, 1970s and 1980s in all western countries…and that such changes follow essentially a cohort dominated model’ (Lesthaeghe 1995, 27).9
All of these changes in society have greatly influenced the changing roles of men and women and in particular the economic independence of women which is seen as being directly related to the changes in the family system that have occurred in industrialised countries since the 1960s. In the transition the changes in family formation are seen as the consequence of a sequence of events that countries pass through at different rates, to reach a common base of below replacement fertility. Van de Kaa (1987) identifies four related shifts in society: from marriage to cohabitation, from children to adults as the main focus of a family, the use of contraception to plan the number and spacing of births rather than just as a means to prevent unwanted pregnancies and a shift from uniform families to diversified families and households (van de Kaa 1987, 11). implicitly assume that we have reached a final equilibrium. We have no theoretical basis, however, for this assumption’ (1990, 484). Chapter Two: Differential Fertility—Theoretical Background 34
These changes are assumed to be far reaching throughout society thus there is no explicit discussion of differential fertility in this second demographic transition. To test the theory and illustrate the widespread nature of the demographic characteristics associated with the transition Lesthaeghe (1995) undertook a macro-scale analysis of twenty-four countries with a small number of variables representing demographic, economic, cultural and political (in terms of aspects associated with individual autonomy and female emancipation) characteristics. He found that the regional pattern of the second transition has been clearly different from that which occurred in the first transition and that the latter occurred in a much shorter period of time. The differential progression of countries through the transition could, from Lesthaeghe's analysis, be explained in terms of a small number of variables. While economic indicators (for example GNP/capita) were important predictors of the onset of the second transition, the ‘historical role of Protestantism’ (promotion of secondary education and employment for females) was also very significant. The continuation of the transition throughout the 1970s and 1980s appeared to be due to rising political autonomy for women and again the role of Protestantism (for promoting a reduction in the employment gap between males and females).
Although there are differences between countries as they experience the transition at different rates, the theory anticipates they will all relatively rapidly (in relation to the pace of the first transition) reach a common base of below replacement fertility and there will be increasing demographic uniformity throughout the industrialised world (Lesthaeghe 1995; Jones 1993). Lesthaeghe believes that any explanations for the second demographic transition must not rely solely on either ideational theory or economic theory. ‘The record of the second demographic transition and its correlates shows that economic and sociological theories are far more complementary than mutually exclusive’ (Lesthaeghe 1995, 58).
2.4.3. Gender Equity Models
9 In Lestheaghe (1995) ‘materialist needs’ are the basic economic and security needs (adequate income, job security, physical security, law and order). ‘Post-materialist needs’ refer to non-material needs (personal Chapter Two: Differential Fertility—Theoretical Background 35
The combination of economic and cultural factors is seen in the latest developments in theory for the industrialised countries where the concern is about persistent low levels of fertility. The growing field of research in this area (see for example, Chesnais 1996; Esping-Andersen 1996; Lesthaeghe and Surkyn 1988; McDonald 1996, 1997, 2005; Oppenheimer 1994; Pinnelli 1995; Rindfuss, Benjamin and Morgan 2000; Rindfuss, Brewster and Kavee 1996; Rindfuss, Morgan and Offutt 1996; Ryder 1990; Tsuya and Mason 1995) has as its basis the major changes in the status of women that have occurred as a result of the structural and ideational transformations in society in recent decades.
Although varying across and within countries, there have been, since the 1960s, enormous changes in the lives and roles of women, particularly in relation to employment and educational opportunities. Changes in the industrial structure of the economy with a decline in the importance of agriculture and manufacturing and an increase in finance, business and other service industries resulted in an increasing demand for female labour. This demand had to be increasingly met by married women, first those with older children and then by married women with younger children. The demand for labour has been complemented by the reduction of barriers to female employment and the promotion of the position of women in the workforce (Chafetz 1995; Daly 1990; Rindfuss, Brewster and Kavee 1996). In addition increasing living costs and increasing demands for goods and services have made it difficult for one income to be sufficient in a family situation.
Educational opportunities for women have also increased. Education of women is no longer for the purposes of their family role (Rindfuss, Morgan and Offutt 1996) but is now an important pre-requisite for occupational attainment and advancement. Women are increasingly moving into career-oriented jobs which leaves less time for traditional family roles.
These changes alone, however, cannot fully explain the decline in fertility to very low levels. Recent theory (McDonald 1997, 2005) suggests it is the degree to which other social and economic institutions of life, for example, family support services, the tax transfer system, industrial relations, attitudes of the family itself to the roles of men and women, have moved towards gender equity that is the force dominating fertility levels. In development, individual autonomy, self-fulfilment and recognition). Chapter Two: Differential Fertility—Theoretical Background 36 many instances these institutions have been much slower to move towards a gender equity situation, thus making it very difficult for women to combine work and childrearing responsibilities.
Fertility levels therefore vary within and between countries depending on the degree to which both social and economic institutions have adapted to the gender equity model. In addition McDonald (1997) acknowledges that ‘gender equity, intergenerational equity and social equity are not independent’. He comments that current government policies of goods and services taxes; reductions in the provision of public services such as education and health; lack of appropriate childcare facilities; as well as unemployment, low wages and job insecurity provide disincentives for people to have children. These disincentives however, vary across social strata and are at their strongest for people in the broad and middle range of incomes. The very wealthy are able to pay for services while for those on low incomes ‘nothing is lost by having children because they have no opportunity to succeed in the mainstream economy’ (McDonald 1997, 14).
2.5. Synthesis
All of the theories examined in this chapter have provided important developments in the understanding of fertility decline and family change. In attempting to explain the decline in fertility very few of the causal theories incorporate a perspective of differential fertility.
The new home economics models of fertility provide a useful framework for identifying the influence of particular factors (economic determinants) on the individual decision making of couples. While children cannot be equated with other market goods, economic factors do play a role in the short term. A major shortcoming of these models, however, is the neglect of other influences on the reproductive decision making process. The exception to this are the models of Easterlin and Leibenstein who proposed that economic decisions are influenced by preferences that develop and change throughout life, although many tastes are set by the time individuals reach adulthood. It is thus important to examine the characteristics of couples that may be influential in the shaping of preferences. These models therefore acknowledge the influence of many of the socio- Chapter Two: Differential Fertility—Theoretical Background 37 economic and cultural variables examined in empirical studies of differential fertility. Economic theories of fertility, however, are unable to explain the long term changes in fertility that have occurred in advanced countries.
Diffusion theory developed to explain the spread of changes in reproductive behaviour in particular linguistic or cultural areas in Europe irrespective or independently of socio- economic development, does incorporate urban-rural and socio-economic variations in the spread of birth control innovations and changing attitudes. More recent, culturally based theories, however, see change as being pervasive. Lesthaeghe and van de Kaa’s (Lesthaeghe 1995; Lesthaeghe and van de Kaa 1986; van de Kaa 1987) second demographic transition describes and explains the decline in fertility to below and well below replacement level in western societies since the mid 1960s but excludes any discussion of fertility differentials per se. This transition is seen to be the result of a multitude of changes that have occurred in western societies. These changes are assumed to be far reaching throughout society thus leading to increasing demographic uniformity throughout the industrialised world (Jones 1993; Lesthaeghe 1995). Such broad based theories of demographic transition provide very few if any theoretical underpinnings for analyses of differential fertility in industrialised countries today.
The newer theoretical field which centres round the relationship between fertility, social policies and gender equity, like demographic transition theory, is broad based. It has developed to explain the overall decline of fertility to very low levels in industrialised societies and because it is a macro theory it does not provide any specific framework for analysing regional variations in fertility although it is acknowledged that there will be variations in fertility within and between countries and between broad based socio- economic groups within society.
The theoretical framework upon which most research into differential fertility has been based, and the theory that has been tested most extensively, often by examining variations in the socio-economic and regional characteristics of populations, is demographic transition theory. This theory has survived because there is no theory of ‘equal value which can be used to forecast future population trends, or act as a guide to empirical research’ (Kirk 1996, 383). The theory also provides the most specific description of Chapter Two: Differential Fertility—Theoretical Background 38 fertility differentials. As part of Notestein’s overall description of, and explanation for, fertility decline he specifically noted variations in the initiation, direction and progression of the fertility decline between social class groups and regional areas. In this theory differentials in fertility are expected to diverge and then converge as the process of modernisation eventually leads to a similarity for all groups of material living conditions, social structure, values and norms.
In reviewing the major theories of fertility, while it is evident differential fertility does not play a central role in any of them, a number of important themes have emerged:
• There is geographic variability in the timing of the fertility decline within and between countries. • Analysis of the decline in fertility at different spatial scales has highlighted the complexity of the demographic transition. • There is a relationship between fertility decline and the demographic, socio- economic and cultural characteristics of a population. • The adoption of successful birth control behaviour eventually diffuses or spreads to other sectors of society. • The emergence and proposed diminution of spatial and socio-economic variability is related to the characteristics of the transition in particular, the stage of the transition. • In the last twenty years it has been proposed that variations in ideational change and the role of the social and economic institutions of society (rather than the more traditional factors) may be the forces influencing any remaining variations in fertility levels.
All of the theories examined in this chapter identify particular characteristics of the individual, community or wider society as being associated with the changes in fertility patterns and trends over time. The next chapter therefore examines in detail spatial, socio- economic and cultural differentials in fertility with an emphasis on Australian patterns and trends.
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 39
Chapter Three
The Spatial, Socio-Economic and Cultural Dynamics of Fertility in Australia
3.1. Introduction
As was identified in the previous chapter the development of theory on fertility has recognised, either explicitly or implicitly, a relationship between fertility decline and the demographic, socio-economic and cultural characteristics of a population. The study of variations in fertility levels between different groups in society and over regional areas has a long tradition in demographic research as an avenue for gaining a greater understanding of the processes and determinants of fertility (see for example Andorka 1978; Coale 1969; Coale and Watkins 1986; Friedlander, Schellekens and Ben-Moshe 1991; Gillis, Tilly and Levine 1992; Livi-Bacci 1986; Sharlin 1986; van de Walle and Knodel 1967, 1980).
Many studies have examined the initiation of fertility decline in various settings and it has been the careful analysis of spatial variations in fertility that has led to the questioning of several fundamental theoretical constructs. In a more contemporary context, geographic variability in fertility levels has helped in the development of new theoretical models. Despite the appearance of demographic stabilisation and uniformity in Europe substantial regional variations in fertility levels persist (Brunetta and Rotondi 1991; Coleman 2002; Hank 2001, 2002; Noin and Chauvire 1991) and there has been a striking reversal in fertility differences between the north and south of Europe (Chesnais 1996). This reversal in a long standing geographic pattern was ‘wholly unforseen by demographers’ (Chesnais 1996, 729). The recognition of such spatial differences even when overall fertility levels are very low helped to focus research attention upon gaining a greater understanding of the factors involved in fertility falling to record low levels.
Despite playing an influential role in the development and testing of theories, the analysis of regional fertility patterns in the last 30 to 40 years has remained limited and in the 1980s was characterised as ‘a relatively neglected area of study’ (Coward 1986a, 58). Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 40
There are a number of reasons for this including the availability and reliability of data, particularly at a spatially disaggregated level.
From another perspective the study of fertility and the development of fertility models or frameworks has not been a dominant pursuit of population geographers (Coward 1986b, Weber 1991, Wilson 1990).1 Fertility is perceived to be ‘less geographical’ than other population issues such as mortality and migration. The fact that spatial variations are a reflection of socio-economic, cultural and other variations in society has often been seen as a weakness of the geographical approach. Compton (1991, 75) for example states spatial patterns of fertility ‘are incidental outcomes of aspatial processes’. Weber (1991, 229) however then claims that such a view would mean that quite a few traditional research fields of geography should be left to other sciences…’. Yet this could be seen as a strength. Spatial analyses of fertility can highlight or be indicators of what is going on in society—how the population is changing, of the wider issues affecting society. In addition spatial analyses can reflect how these changes differ and impact from place to place (Champion 1993, 5-6):
Regional and local populations do not by any means constitute faithful microcosms of the national population…Nor do places change over time in their characteristics and demographic behaviour in the way that would be expected from the national statistics alone.
Due to the lack of enthusiasm towards the development of theory concerning fertility, much of the empirical work that has been undertaken has not been designed to specifically test theory and often therefore social and spatial studies of fertility are conducted without any reference to theory. The theoretical framework upon which most research into differential fertility is based has been demographic transition theory. From this theory, a widely held and accepted notion is the convergence of demographic patterns, and therefore spatial variations, with the finality of the transition process.
1 In relation to fertility the lack of theory development is also a concern of demographers (Caldwell 1997; Wilson 2001). In addition there is general concern about the lack of enthusiasm towards theory development in population geography altogether (Findlay and Graham 1991; Graham 2000; White and Jackson 1995). Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 41
This convergence results from socio-economic characteristics, attitudes to childbearing and ways of life becoming similar across regions and countries. For Notestein even in 1953 (17) ‘the transition was virtually complete’.
Some empirical support in the 1950s, 1960s and 1970s (Coward 1986a) for the hypothesised convergence of socio-economic and spatial differentials with the attainment of low levels of fertility, led some geographers (Compton 1991; Wilson 1990, 1991) to believe there was no reason, point or profit in continuing to monitor and analyse regional, and consequently, socio-economic variations in fertility in post-transitional societies. Their beliefs reflect those of other researchers developing theories to explain post- transitional fertility decline (Lesthaeghe 1995; Lesthaeghe and van de Kaa 1986; McDonald 1996, 1997; van de Kaa 1987). The ideational theories developed in the 1980s and 1990s (Chapter Two) assume the changes that have occurred in the post-war period have been so rapid and far reaching throughout society that little attention is given to differential fertility. So whether one subscribes to the importance of socio-economic factors for the decline in fertility or to ideational factors the expected convergent outcome remains similar.
Research on interstate variability in fertility in the 1950s, 1960s and early 1970s in the United States questioned the premise of convergence in a post-transitional society as expected in demographic transition theory. Alonso (1980) found fertility either remained constant or diverged over time. This happened when regional incomes had and were dramatically converging, an indication of the ‘progressive integration of the national socio-economy’ (Alonso 1980, 406). He found that although fertility declined in all states to some extent the decline in fertility was greatest in those states where fertility was already low. He hypothesised this was the result of two effects—a development effect and a cyclical effect. By development effects Alonso (1980) was referring to the long-term secular decline in fertility to low levels brought about by the changes in incomes, educational levels, urbanisation, and female status in society, for example, which have all led to an increase in direct and opportunity costs of raising children.
Superimposed on the developmental decline in fertility are cyclical effects or short-term changes in fertility which result from periodic changes, it was argued, in employment, income, economic growth and contraceptive technology (Alonso 1980). Spatially, Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 42 developmental effects will theoretically lead to a convergence of fertility rates as the economically backward regions catch up with the more progressive regions in terms of economic growth, education levels, incomes, female workforce participation and so on. Differences between areas, it was thought would eventually only be the result of variations in attitudes, ethnicity or religion and the effect of these factors diminish with rising inter- regional migration (Pandit 1992a).
The spatial implications of the cyclical effects however are different and of greater importance, as elaborated by O’Connell (1981a, 11):
…population groups that are at a high level of socioeconomic development (and have low fertility levels) appear to experience greater sensitivity or elasticity to cyclical factors affecting fertility than populations that are at a low level of socioeconomic development. The predicted outcome of this differential elasticity of response would mean that regional fertility levels will have a tendency to converge in times of cyclical upturns in fertility and diverge in times of cyclical downturns in fertility.
O’Connell (1981a, 13) suggests that although the gap between low and high developmental areas may decline over time, (Figure 3.1) ‘a persistent cyclical pattern of converging and diverging rates between regions can be expected to be the normal state of affairs’. Research into the validity of O’Connell’s suggestions has been limited; however research by Coward (1986b) and Compton (1991) for England and Wales, 1950–1980 and 1974–1986 found no evidence that convergence and divergence are related to the overall trend in fertility. They therefore concluded the O’Connell thesis was place specific to the United States and does not have general validity, although research into its applicability to other regions has been limited.
Research in the last decade or so on post-war demographic trends, in Europe in particular, (Boseveld 1996; Coleman 1996; 2002; Tomka 2002) has again renewed interest in the convergence hypothesis. This has led to the questioning of the notion of demographic convergence in a post-transitional regime. Coleman has been the most notable researcher in this field. In one of his latest analyses of demographic trends for selected groupings2 of
2 Groupings are Northern Europe, Western Europe, Southern Europe, Central/Eastern Europe, Balkans, Former Soviet Union, Neo-Europes (Australia, New Zealand, Canada, USA), Asia. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 43 industrialised countries (Coleman 2002, 327) he concluded, in relation to fertility trends, that from 1960 to the mid to late 1970s there ‘was a modest trend towards statistical convergence’ between groupings of countries. From then on however there was relatively little change and in fact in the last decade ‘a more divergent pattern’ has dominated. He concludes from this that ‘either the expectation of convergence is wrong, or we are looking for it too soon’ (Coleman 2002, 319).
Figure 3.1: O’Connell’s Model of Developmental (D) and Cyclical (C) Components of Fertility Change
Note: DL and DH represent the long-term path of fertility for two dichotomous areas at low and high levels of socio-economic development. CL and CH represent the low development and high development curves during the peaks and troughs of fertility cycles.
Source: O’Connell 1981a, 12
The major aim of this chapter is to review the research into regional and the traditional socio-economic fertility differentials focussing particularly on patterns and trends in Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 44
Australia. What evidence is there to support the arguments of convergence or cyclical variability in regional and socio-economic differentials in fertility?
3.2. Spatial Analyses
3.2.1. Introduction
This section of the chapter reviews the spatial patterning in fertility identified in Australia for different time periods, at varying geographic scales and for a range of fertility measures. Any analysis, with the aim of assessing convergence or divergence, or a cyclical pattern of both, is influenced by the scale at which the fertility pattern is mapped, the measurement of fertility used and how long a period of time is necessary to ascertain trends. Generally, relative variation increases with an increasing degree of spatial refinement or the use of crude rates of measurement (Compton 1991; Coward 1989). The length of time needed to establish trends is open to debate.
In Australia, relatively little attention has been directed at examining spatial variations in fertility. Much of the work undertaken has been by Wilson (1971a, 1971b, 1978a, 1984, 1990, 1991), but valuable contributions have also been made by Bell (1989, 1990), Borrie (1948), Day (1983), and Hugo and Wood (1983).
3.2.2. Urban-Rural Residence
One of the most enduring differentials in fertility in the world is that of urban and rural residence (Andorka 1978; Heenan 1967; Long and Nucci 1996; Rindfuss and Sweet 1977). Although this differential theoretically appears to have its origins in the process of demographic transition, Borrie (1948, 99) suggests it is a feature of fertility dating to well before modern times. In recent history and according to demographic transition theory, urban-rural differentials appear as the move to smaller family sizes initially begins in urban areas before filtering into the countryside. Although in most developed countries of the world the demographic transition was completed by the early decades of the twentieth Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 45 century, the inverse relationship between urbanisation and fertility has persisted and the Australian trends are no exception (Bell 1990; Glover, Harris and Tennant 1999).3
Studies of historical trends and patterns in fertility in Australia show that the differential between urban and rural areas was already apparent at the beginning of the twentieth century. The earliest study of geographic variations in fertility in Australia, Coghlan’s (1903) analysis of fertility in New South Wales at the 1901 Census, though not specifically examining the urban-rural differential, identified considerable variability throughout the State in the proportion of childless marriages. Fecundity was found to be at its highest level in the three rural divisions of the Coast Districts, the Tablelands and Western Slope, while the largest proportion of childless marriages at all ages occurred in the city and suburbs of Sydney. The rural counties containing the towns of Cobar and Broken Hill (important for copper and silver mining) had levels approaching the metropolitan area while the plains near the Darling River had a rate intermediate between the rural and urban levels.
Looking at more widespread patterns, Hicks (1971, 58–59) analysed child/women ratios in 1891, 1901 and 1911 for designated areas by population density in New South Wales, Victoria and South Australia. His work showed the decline in fertility in the 1890s ‘was most marked in the most densely populated areas and least in the sparsely settled districts of each State’. Between 1901 and 1911 the ‘geographical incidence of restriction was more uniform’. Similarly Stevenson’s (1982) detailed analysis of the fertility transition in South Australia showed that both overall fertility and marital fertility (Coale indices) initially began to fall in County Adelaide around 1881. While the greatest percentage declines in fertility occurred in this county during the 1890s, the fall in fertility diffused rapidly outwards from County Adelaide and the regional urban centres of Mount Gambier and Port Lincoln through the old settled contiguous and mostly urban counties during the period 1891–1901. The more recently settled rural areas and ‘frontier’ counties lagged behind in this fertility decline.
3 While the relationship between urbanisation and fertility persists in terms of the urban-rural continuum the inverse size of settlement fertility relationship has been largely discredited in the United States (Slesinger 1974) and in Australia (Wilson 1971a). Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 46
From these studies and others (Jones 1971; Ruzicka and Caldwell 1977, 1982) it is clear that on the basis of the historical statistics available the urban-rural differential in fertility was apparent in the later years of the nineteenth century. Declines in fertility started in the metropolitan areas of Australia and initially progressed at a faster rate here than in other urban and rural areas. As stated by Ruzicka and Caldwell (1982) though, with time, the rate of decline in family size became almost identical, irrespective of place and when the transition to small families was complete the differential was preserved. This differential has persisted to the present day.
Analysis of urban-rural differences in fertility in Australia appears to have first been undertaken by Borrie (1948) who examined data from the 1911 and 1921 Censuses. From the 1911 Census he reported that the urban-rural differential was clearly evident. While the national average completed family size was 5.25 children to all mothers aged 45–49 the average for metropolitan regions was 4.57 and for areas outside the metropolitan areas 5.78 (National Population Inquiry 1975, 49). While the definitions of urban, non-urban and the women whose issue is counted has changed over time, data presented by Gilligan (1954) for the 1911, 1921 and 1947 Censuses, Borrie (1948) for the 1911 and 1921 Censuses, the National Population Inquiry (1975, 1978) for the Censuses spanning the period 1921 to 1971 and data provided by Bell (1990) from the 1981 Census show the differential persisted into the early 1980s (Table 3.1). To gain some understanding of the age at which urban or rural residence has an influence on family size Borrie (1948) (for 1911 and 1921 Censuses), and Bell (1990) (for 1921, 1961 and 1981 Censuses) examined average issue by age. They found that the urban-rural differential appeared at an early age and was maintained into the older age groups.
Data from the 1986 Census (ABS 1992) similarly shows that the differential between settlement types persists. Although not directly comparable4 with the data presented by Borrie and Bell, the ABS found that compared with major urban areas the average number of children born to all women was 19 per cent higher in other urban areas and 25 per cent higher in rural areas. The comparison by age group (Table 3.2) shows the pattern identified by Borrie and Bell of the urban-rural differential appearing for the early childbearing age groups.
4 The ABS presents average number of children ever born for all women and for the five year age groups to 45 years and over. The data have also been standardised for differences in age distribution. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 47
Table 3.1: Australia: Average Issue of Women Aged 45–49 Years(a) by Urban- Rural Residence, Selected Censuses Year Total Metropolitan Extra- Other Urban Rural Australia Areas Metropolitan Areas 1911(b) 5.25 4.57 5.78 — — 1921(b) 4.19 3.65 4.65 4.35 4.82 1947(c) 2.77 2.39 3.22 — — 1954(c) 2.43 2.09 2.87 — — 1961(c) 2.50 2.23 2.89 2.71 3.15 1966(c) 2.66 2.44 2.99 2.84 3.22 1971(c) 2.96 2.75 3.31 3.18 3.55 1981(d) 2.97 2.80 3.30 3.23 3.42 1986(d) 2.87 2.56 — 2.96 3.16
a In 1986 for the age groups 45 years and over b From all marriages c From existing marriages d All women Source: National Population Inquiry 1975, 49–51, 1978; Bell 1990, 3; ABS 1992, 27
Table 3.2: Australia: Average Issue of Women Aged 15 Years and Over By Urban-Rural Residence, 1986 Age Group Major Urban Other Urban Rural 15-19 0.041 0.069 0.061 20-24 0.297 0.521 0.541 25-29 0.940 1.359 1.415 30-34 1.686 2.101 2.155 35-39 2.110 2.433 2.522 40-44 2.353 2.679 2.760 45 years and over 2.558 2.960 3.159 Source: ABS 1992, 27
Analysis of Total Fertility Rates (TFRs) for the period 1992 to 1995 by metropolitan and non-metropolitan areas indicates that the metropolitan/non-metropolitan difference remains (Table 3.3). In the first half of the 1990s fertility in non-metropolitan Australia was around 19 per cent higher than in the capital cities. Classification of non-metropolitan Australia according to the degree of access to services5 indicates there is a correlation
5 Classification of areas according to their accessibility to services is known as ARIA (Accessibility/Remoteness Index of Australia). ARIA includes five categories of remoteness: (a) highly accessible – locations with relatively unrestricted accessibility to a wide range of goods and services and opportunities for social interaction; (b) accessible – locations with some restrictions to accessibility of some goods, and opportunities for social interaction; (c) moderately accessible – locations with significantly restricted accessibility of goods, services and opportunities for social interaction; (d) remote – locations with Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 48 between the pattern of fertility in non-metropolitan areas and the degree of accessibility/remoteness of areas. For the period 1992 to 1995 the fertility of women in the very remote category was 40 per cent higher than for women living in areas classified as very accessible (Table 3.4). According to Hugo (2002d) this is partly the result of intrinsically higher fertility in rural areas and partly the higher representation of Indigenous populations in remote areas.
Table 3.3: Total Fertility Rates by State and Territory, 1992 to 1995 Section of State NSW Vic Qld SA WA Tas NT ACT Total Capital City 1.81 1.70 1.73 1.64 1.76 1.79 2.06 1.72(a) 1.75 Other Major Urban 1.91 1.86 1.73 — — — — — 1.84 Areas(b) Rest of State/Territory 2.24 2.15 2.07 2.12 2.22 2.08 2.66 (c) 2.16 Whole of State/Territory 1.91 1.79 1.86 1.75 1.87 1.95 2.38 1.69 1.86 a Includes Queanbeyan (C) b Includes Newcastle and Wollongong (NSW); Geelong(Vic); and Gold Coast-Tweed Heads and Townsville- Thuringowa (Qld) c Data included with ACT total Source: Glover, Harris and Tennant 1999, 182
Table 3.4: Australia, Total Fertility Rate By the Accessibility/Remoteness Index of Australia (ARIA), 1992 to 1995 ARIA Very Accessible Moderately Remote Very Remote Accessible Accessible 1.79 2.15 2.30 2.43 2.51 Source: Glover, Harris and Tennant 1999, 183; and Hugo 2002d, 11
Analysis by the ABS (2002b) using the ARIA classification to examine TFRs using average births for 1999–2001 reinforce this pattern of fertility increasing with distance from the major cities (Figure 3.2). The ABS (2002b, 36) found women aged 15–49 years living in the major cities had the lowest TFR at 1.65 followed by women living in Inner Regional Australia (1.90) and then women in Outer Regional Australia (2.04). Women
very restricted accessibility of goods, services and opportunities for social interaction; (e) very remote – locationally disadvantaged – very little accessibility of goods, services and opportunities for social interaction (Hugo 2002d, 3). Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 49 living in Remote and Very Remote Australia had the highest TFRs at 2.27 and 2.28 respectively.
Figure 3.2: Australia: Total Fertility Rate(a) by Remoteness of the Area
a Using average births 1999-2001 and estimated female resident population at 30 June 2000 Source: ABS 2002b, 36
Again this differential was largely due to fertility differentials at the younger ages. From Figure 3.3 it is evident that in major cities the peak childbearing age was 30–34 years. For all other areas the peak age ranges were younger.
The urban-rural differential in fertility has been examined in various ways. In their analysis of 1966 Census data Ruzicka and Caldwell (1982) believed it was more informative to examine the distribution of women by parity between metropolitan, other urban and rural areas in addition to average issue. From this disaggregation it was clear that for currently married women aged 35–39 (at the 1966 Census) those living in the metropolitan area most often had two or three children while for rural women there was a more equal distribution of women with two, three and four children. Also significant were the higher proportions of childless and one child families in the metropolitan areas and, in contrast, the relatively higher proportion of women with five or more children in rural areas.
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 50
Figure 3.3: Australia: Age Specific Fertility Rates by Remoteness Area 2000
Source: ABS 2002b, 37
The most detailed study of urban-rural fertility differences in Australia is still that of 1971 Census data by Day (1983) using generational fertility measures. His analysis was confined to currently married women aged 40–64 years who married before they turned 26 years of age. From a background of increasing differences between urban and rural areas in median issue for cohorts of women born before 1909, Day found that for the subsequent five birth cohorts (1907–11 through to 1927–31) there were marginal declines in the differential with successive cohorts—a pattern consistent with demographic transition theory. Further examination, however, of these urban-rural differences in relation to a number of other characteristics (religion, age at marriage categories, husband and wife’s level of schooling, birthplace, and husband’s occupation) found not so much a narrowing of the urban-rural differential but more a consistency or stability in the differential over time. As Day (1983, 47) summarises:
What stands out in this more detailed analysis of fertility differences by urban-rural residence is how little change there was during the period of childbearing represented by the cohorts of 1907-11 to 1927-31…Whether in terms of median issue or the proportions bearing few (zero to one) or many (five or more, or six or more) children, when standardised for age at marriage, differences by residence were not only persistent and pervasive; they were also remarkably stable in extent. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 51
Whether using Section of State categories such as urban, other urban, rural or the remoteness index, the differential between urban and non-urban areas remains. Bell’s (1990) analysis of spatial variations in fertility examined urban-rural fertility differentials in the context of O’Connell’s convergence/divergence thesis. Using mostly cohort measures but also some period measures he examined the trends in urban-rural fertility measures in relation to overall changes in the total fertility rate for the period 1911 to 1981. While the analysis was hampered by the ‘severe’ limitations of the available data, Bell (1990, 36) concluded ‘the results provide clear support for the hypothesis of periods of cyclical convergence and divergence in fertility differentials’.
From studies undertaken in advanced societies two opposing viewpoints or hypotheses exist to explain the urban-rural differential. The first based on research, particularly in the United States in the 1950s and 1960s (Beegle 1966; Slesinger 1974; Bogue 1969; Johnson, Stokes and Warland 1978; Rindfuss and Sweet 1975), suggests much of the variance between localities can be explained in terms of the socio-economic characteristics of the people residing in the different areas. Petersen (1975, 525) in his discussion of urban-rural patterns argues ‘One should not stress too much direct influence of habitat on family size… rural-urban contrasts are in large part an index of the differences by social class’, while Slesinger (1974, 360) states from the results of her study ‘… it can be concluded that the differential found in the rural-urban continuum… can be explained better by compositional factors of the residents in the different geographic areas’. Thus once factors such as duration of marriage, religion, education and workforce status are controlled, very little variance between urban and rural areas remains.
To the contrary, however, studies from European countries (Andorka 1978, 1982), Canada (Travato and Grindstaff 1980) and Day’s (1983) research for Australia indicate that urban- rural differentials are real, that rural residence per se is important and a direct determinant of fertility. It was found in these studies that the difference between areas in levels of fertility was not simply the result of the different composition of the populations. Travato and Grindstaff (1980, 463, 466) conclude in their analysis of Canadian patterns at the 1971 Census ‘The data presented in this study demonstrate that residence has a strong positive independent effect on childbearing among rural women in Canada… the influence of residence per se, may be of greater importance than has been suggested in previous studies’. It is not suggested by this second hypothesis that ‘place’ in and of itself affects Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 52 fertility but rather the characteristics of rural areas are more conducive to higher levels of fertility—more relaxed lifestyle, better environment for raising children, lower perceived costs (financial and psychological) of rearing children in rural areas as opposed to urban areas.
The two different explanations for the persistence of urban-rural differentials were formulated on the basis of results of studies based on data from the 1950s, 1960s and early 1970s. How they relate to present patterns, however, is unclear. In Australia for example, in the last twenty years there have been significant changes in the nature of urban centres and rural areas. With agricultural and industrial restructuring, improvements in transport and communications, and centralisation of services many rural areas and service centres have experienced declines in job opportunities. Such changes often impact on certain groups. For example, many young people wishing to further their education, with career aspirations or to avail themselves of greater employment opportunities move to major urban centres. In contrast, other urban centres or areas, for example, the peri-urban fringe of major centres, have become attractive areas for people and families to settle. This movement of people to the fringe has been facilitated by the sub-division of land in these areas into residential blocks (Bell 1997a, 1997b; Hugo 1996). The selective migration of particular groups to certain regions may influence urban-rural fertility patterns (Long and Nucci 1996; Pandit 1992a, 1992b).
3.2.3. State Variations
From the limited number of studies undertaken in Australia it is clearly evident that spatial differentials in fertility continue to persist between urban and rural areas. Similarly, at the broadest level of the states and territories fertility differentials also exist and, as was the case for the urban-rural differential, these differences began to appear early (Borrie 1948; Forster 1974; Jones 1971; Ruzicka and Caldwell 1977) and have continued throughout the post-war period (ABS 1986a; Bell 1990; Hugo and Wood 1983; Linford 1950). From Table 3.5 it can be seen that the most prominent feature of the interstate fertility levels has been the consistently higher fertility rates in the Northern Territory. This is a function of the disproportionate representation of Aboriginal and Torres Strait Islander people in this Territory in relation to the rest of Australia. The Indigenous population have historically Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 53 had substantially higher fertility rates than the total Australian population (Tesfaghiorghis 1996).
The order of the other states and the Australian Capital Territory (ACT) with regard to fertility has varied over time. While the fertility of the New South Wales (NSW) population was the lowest in the country in the first two decades of the post-war period, in the 1970s the Total Fertility Rate (TFR) converged towards the national average and in the 1980s, 1990s and early in the 2000s it has remained relatively stable but slightly above the national average. In contrast, TFRs for Victoria generally remained slightly below the national average throughout the post-war period.
Table 3.5: Index of Total Fertility Rates, States and Territories, 1947 to 2001 (Australia=100) Year NSW Vic Qld SA WA Tas NT ACT 1947 95 96 109 104 112 116 132 151 1954 93 99 107 105 113 113 138 104 1961 95 97 108 106 104 114 129 109 1966 94 103 106 99 107 105 113 105 1971 98 100 106 92 106 101 140 101 1976 99 98 106 90 104 102 148 102 1981 100 96 101 93 102 106 136 108 1986 101 94 104 95 107 101 127 92 1991 101 98 101 93 103 103 122 101 1992 104 96 102 90 99 103 126 91 1993 103 96 102 96 101 104 124 91 1994 102 97 101 94 102 106 127 93 1995 102 96 100 96 102 105 133 93 1996 102 95 103 98 101 105 122 93 1997 103 95 102 96 101 101 122 91 1998 102 96 102 97 101 103 125 89 1999 104 93 101 97 101 107 122 96 2000 103 93 102 98 102 102 127 92 2001 102 93 104 97 100 120 131 87 Source: ABS Births Australia, Demography South Australia; Bell 1990
The trends in TFRs for Queensland and Western Australia have been almost identical. Although higher than the national average in the immediate post-war years they have converged such that by 2001 Queensland was just slightly higher and the TFR in Western Australia matched that for Australia as a whole. Of all the states, Tasmania had the highest fertility levels in the early post-war period. Although declining to slightly above the Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 54 national average in the 1970s and 1980s, in the 1990s fertility levels in Tasmania were variable and generally slightly higher than for many of the other states. In fact in 2001 the TFR of 2.066 was the highest figure recorded in Tasmania since 1978 and the first time the figure had exceeded 2 children per woman since 1985. No explanation is provided by the ABS for this upturn in fertility (ABS 2002b). Fertility levels in South Australia were slightly above the national average from 1947 to 1961 but since then the rate has been consistently below the national average. Throughout the 1970s, 1980s and early 1990s fertility levels in South Australia were generally the lowest in the country but this status now belongs to the ACT. Low rates for the ACT reflect, according to the ABS (1992), the generally higher percentage of employed women with their higher level of educational attainment and higher incomes compared with Australia as a whole.
Although various suggestions have been put forward to partly explain the differences between the states and territories and the changes over time (Hugo 1986), no detailed examination has been undertaken. Bell (1990) found through correlation analysis that the factors associated with the variations in fertility in 1986 included the proportion of the population living in major urban areas, Aboriginal origin, incidence of marriage for women aged 15–34 years and the labour force participation of married women 15–44 years of age.
3.2.4. Regional Spatial Patterns
Many studies have used regional data to test demographic transition theory and to gain insights into historical and contemporary fertility trends, patterns and differentials. In the post-war period in developed countries, although studies have been undertaken in numerous settings (Bahr and Gans 1991; Coleman 1993; Engelen and Hillebrand 1987), the most prominent studies in this field have been the works of Coward (1980, 1986a, 1986b), Compton (1978) and Wilson (1978b) on the British Isles and the work of Wilson (1971a, 1971b, 1971c, 1978a, 1984, 1990, 1991) on Australian fertility patterns. In addition to describing the areal patterns these studies attempted to statistically explore some of the factors involved in the explanation of the patterns identified.
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 55
Whatever the level of aggregation, the simplicity or sophistication of the fertility indices and/or methods of analysis used in regional studies, a common finding is the persistence of spatial variability in fertility patterns and the importance of factors other than population structure in the explanation of the patterns. For example, a study of Scottish fertility (Jones 1975) found considerable place to place variability in fertility for counties and major cities in 1971–72. Due to the simplicity of the fertility index (Crude Birth Rate) used, however, much of the variability was explained in terms of variations in age structure between the areas, although some socio-economic factors were also important. To establish more precisely the extent, and explanation of areal variations in fertility in Scotland, Wilson (1978b) re-examined fertility levels for the same areas using a cumulative standardised marital fertility rate that takes into account variations in age, age at marriage and duration of marriage. Despite the use of a more complex measure of fertility similar spatial patterns were observed, although Wilson (1978b) believed the use of cumulative standardised fertility ratios allowed the areal patterning to be far more precisely and meaningfully displayed. In addition Wilson found the same socio-economic explanatory factors—religion and female work activity rates—(consistent with demographic transition theory) to be important and that the patterns and explanatory variables persisted over four marriage cohorts.
Coward (1986a) attempted to update the work of Jones and Wilson by using Coale’s indices to examine fertility patterns in 1981. Although he noted the difficulty of comparing 1981 patterns with 1971 because of boundary changes and different data, he found broadly similar spatial patterns of fertility and the continuation of female work activity and religion were important explanatory factors. Thus, as stated by Wilson (1978b, 130), ‘areal differences in Scottish reproductive behaviour reflect well defined deeply ingrained and temporally persistent differences within Scottish society’. This quote could equally apply to the Irish Republic and Northern Ireland where marked spatial variations in fertility continue to exist even at fairly broad levels of aggregation (Coward 1986b). For the Republic of Ireland though there was some evidence of convergence. Coward’s (1980) study of average family size for 1946, 1961 and 1971 by county showed that while there were marked regional differences, the division between the west coast and the rest of the country so evident in the 1940s, ‘was less recognisable’ in the 1960s and 1970s. He believed this was the result of the decline in socio-economic differences across the country. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 56
Work on spatial patterns of fertility in England and Wales has identified a reduction in regional fertility differentials over time but when 1981 fertility levels were examined for local levels (districts) quite pronounced variations were evident. At the regional level Coward (1986b) examined the average family size of three marriage cohorts, 1926–31 (1951 Census), 1936–41 (1961 Census) and 1946–51 (1971 Census). While the spatial pattern of completed or near completed fertility remained much the same over time there was a convergence in the variation in the pattern. This, Coward hypothesised, was mainly the result of two processes—a convergence in social class differentials over time and the regional convergence of such population characteristics as female workforce participation, which influences fertility by exposing women to changing ideas and attitudes about family size and marriage and provides them with greater independence.
Despite convergence in regional fertility patterns, Coward’s examination of age- standardised fertility rates for local authority districts in England and Wales in 1981 found that around one quarter of all districts differed from the national rates by ten per cent or more. He assessed the effects of nuptiality (marital fertility, proportion married, extra marital fertility) on the spatial patterns identified and found that varying nuptiality was the dominant factor influencing fertility. He found areas with relatively high fertility and high nuptiality corresponded to small towns and outlying suburbs with housing developments attractive to young couples. Low marital and extra-marital fertility was also found to occur in areas of relatively high status. In addition to the outlying suburbs other areas of relatively high marital or extra-marital fertility were characterised by large immigrant populations or where levels of male and female youth unemployment were high. Finally Coward assessed the overall fertility of the districts in relation to particular demographic and socio-economic variables available from the 1981 Census. A stepwise regression analysis indicated the most important explanatory variables other than marital status were female unemployment, presence of immigrant groups, female educational attainment and participation of married women in the work force.
In the relatively limited body of work on the geographical study of fertility differentials in Australia most attention has been directed towards spatial variations at the level of the Local Government Area (LGA). Other than the historical studies of the fertility decline around the turn of the century (Section 3.2.2) by county, one of the first studies of regional fertility patterns in Australia was undertaken by Andrews (1940). In an atmosphere of Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 57 concern over population growth and economic development in the 1930s, Andrews in an address to the Annual Meeting of the Geographical Society of New South Wales stressed the need for regional patterns of population change to be identified and the reasons for the patterns to be understood before an appropriate national policy for the conservation of population could be formulated. As an example Andrews mapped fertility for rural areas and country towns in New South Wales and metropolitan Sydney. He found a significant degree of variability throughout the State and particularly within metropolitan Sydney. Although not very specific in his explanations of the patterns observed for the non- metropolitan areas Andrews saw a high degree of association between regions of high fertility and rapid population growth and low fertility and stable or declining rates of growth. He stated ‘that pioneer conditions and the pioneer mentality are closely connected with high fertility’ (Andrews 1940, 10). For metropolitan Sydney he found a correlation between fertility and income status and he stated the different patterns could be explained in terms of the attitudes of different social classes.
The first study to try and test the relationship between geographic patterns of fertility and associated factors was Wilson’s (1971b) study of fertility patterns in Victoria. The main emphasis of this research was to test the ability of a number of hypotheses to explain the patterns identified rather than just describe the spatial patterns. The most obvious spatial pattern, from his examination of 1966 TFRs, was the well known differential in fertility between metropolitan, urban and rural areas. Based on factors known to influence fertility Wilson tested a number of hypotheses separately for each of the three regions in an effort to explain the spatial variations identified. Though employing simple correlation, multiple correlation and regression and factor analysis he was less than satisfied with the outcome of the study. It was only in the metropolitan context that the analytical techniques employed could successfully account for the spatial patterns identified. Wilson believed more meaningful data and the use of additional variables, or the substitution of more appropriate variables (particularly in relation to urban and rural areas), may have resulted in a better outcome.
Continuing in this vein Wilson (1978a) examined child-woman ratios from the 1971 Census for Collectors’ Districts (CDs) in the LGAs of Wollongong and Shellharbour in NSW to assess the continuing relevance of socio-economic factors in explaining spatial variations in fertility. Within this urbanised area Wilson found quite marked spatial Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 58 differences in fertility levels. While much of the variance was accounted for by the age and marital status composition of the population, socio-economic factors still retained some significance. Wilson’s further studies in 1984 and 1990 continued to examine the importance of socio-economic factors in the explanation of geographical variability in fertility and in addition examined the convergence of regional/local fertility differentials and the relevance of Alonso’s (1980) cyclical model of change. First using Coale’s indices of overall and marital fertility Wilson (1984) described the changing pattern of fertility in 96 urban centres in Queensland, New South Wales, Australian Capital Territory, Victoria and South Australia for two inter-censal periods 1966–71 and 1971–76. He found that fertility levels dramatically declined with substantial reductions in inter-regional and inter- urban differentials. In addition his findings provided only ‘equivocal’ support for Alonso’s model, and in terms of the explanatory power of socio-economic variables, Wilson (1984, 218) concluded ‘the analytical significance of ecological differentials was so reduced as to cast serious doubts on their continuing utility in studies with a spatial frame of reference’. Secondly, using Coale’s indices again, Wilson (1990) examined the convergence of overall and marital fertility in non-metropolitan and metropolitan LGAs in NSW between 1933 and 1986 and the relevance of the traditional ecological approach. This study reinforced his views on the lack of utility of a geographical and ecological approach to fertility analysis.
Despite Wilson’s views other studies have found significant intra-state and intra-city variations in fertility. Hugo’s (1983) presentation of spatial variations in fertility in South Australia in the mid 1970s showed that particularly for the metropolitan area of Adelaide quite distinctive variations in fertility continued and these reflected very different family and lifestyle patterns in the various areas. Bell’s (1990) study of spatial variations in fertility in Australia examined fertility at a number of spatial levels. In addition to examining interstate variations, he also tested the applicability of Alonso’s (1980) and O’Connell’s (1981a) model of convergence and divergence as well as examining spatial variations in fertility for LGAs in Queensland. Bell found that trends in the pattern of urban-rural differentials in Australia since the 1930s were consistent with expectations from O’Connell’s model and thus indicate that differentials are unlikely to disappear in the foreseeable future. His investigation of spatial variations in age-specific rates and TFRs in the mid 1980s at the local area level also showed that substantial variations continued to exist. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 59
Although Wilson has been critical of the geographical approach and one of the main antagonists in questioning the continued relevance of such an approach he nevertheless produced a paper (Wilson 1991) in which he examined fertility (birth data) in New South Wales for the period 1986–87 by birth order and maternal age. In this study he still found ‘strongly marked, statistically significant’ patterns of fertility variation at the regional and local level throughout the State. With the use of correlation and regression analysis Wilson (1991, 15) was able to demonstrate that the variability in fertility was ‘substantially explicable (in a statistical sense) in terms of particular and essentially unique combinations of logically related socio-economic contextual and behaviourally relevant variables’.
To enhance his analysis Wilson adopted an approach of Woods (1986b). Woods stated the simple mapping and classification of demographic components can be important in alluding to avenues for further research in our understanding of the patterns and processes of demographic change. The value of the spatial analysis, however, can be enhanced by ‘attempting to regionalize characteristics into discrete multi-unit entities’ or ‘distinctive demographic regimes’ (Woods 1986b, 10). Wilson categorised his areas into what he called ‘regional reproductive syndromes’ on the basis of fertility levels and their associated social factors. Table 3.6 outlines the characteristics of his profiles. As Wilson found a ‘considerable degree of spatial contiguity’ (Wilson 1991, 13) in the way LGAs fitted into his profiles, he believed the approach provided ‘enhanced understanding of the factors underlying spatial variations in the family formation process’ (Wilson 1991, 15).
The latest study of regional fertility differentials in Australia is a special article in Births Australia 2001 (ABS 2002b) using Indirect Standardised Birth Rates (ISBR) for women aged 15–49 years for the period 1999–2001. The ABS divided the Statistical Local Areas (SLAs) of Australia into three categories; those with an ISBR higher than, similar to, or lower than the national ISBR. The ABS (2002b, 32–34) found 60 per cent of the SLAs in Australia had average ISBRs which were not significantly different when compared with Australia as a whole, 18 per cent were significantly higher than the national ISBR and 13 per cent were significantly lower. SLAs with lower than average fertility were likely to be located in Victoria and South Australia while SLAs with high fertility were found in New South Wales and the Northern Territory. Within metropolitan areas SLAs with low ISBRs were concentrated in inner city areas with higher fertility in the outer suburban areas. No explanations were provided for the variations in fertility. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 60
Table 3.6: Regional Reproductive Syndromes for Metropolitan Sydney, 1986–87 Name and Description of Reproductive Syndrome Characteristics of the Syndrome
The Early Flushers high levels of early second births single low income households home purchasing households young wives not in workforce low levels ethnicity low levels childlessness (low income, working class, home purchasing/owning suburbs)
The Late Starting, Long Continuers to Large Families low levels of early higher order childbearing and high levels of double income households high levels of late childbearing of all orders home owning/purchasing households non-ethnic (two parent older ‘working mother with children’ families)
Prolonged Childbearing to Large Families high levels of early and late higher order high levels of tenancy especially in public childbearing sector high levels of ethnicity (working class areas)
Very Late Starting Small Families high levels of late first and second births high levels of ethnicity medium density (young working couple’s suburbs of Sydney’s inner western region)
Careerist Small Families low levels of early childbearing high status households very high levels of late first and second births mixed tenure non-ethnic (delayed maternity, tertiary educated careerist residents of city’s most exclusive inner, harbour and beach-side suburbs)
Source: Derived from Wilson 1991
3.2.5. Synthesis
From this review of spatial analyses of fertility in ‘post-transitional’ societies it is clear that spatial differentials continue to exist. Supporting evidence for the hypothesis of convergence in patterns with declining fertility varies across studies.
Judgements are influenced by the time scale of the study, the robustness of the patterns at varying geographic scales, and the measures of fertility used. While some geographers, Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 61 based on empirical evidence of the 1950s, 1960s and 1970s, were keen to quickly accept the convergence hypothesis and the minimisation of differentials to the extent they would no longer be a viable area of investigation, the continuing persistence of spatial differentials has raised a number of issues and questions: • The expectation of convergence may be wrong (Coleman 2002); • Instead of just convergence a cyclical pattern of divergence and convergence is to be expected (O’Connell 1981a); • There is no theoretical basis that advanced societies today have reached a final equilibrium and demographic transition will continue into the foreseeable future (Bumpass 1990), therefore we may be looking for convergence too soon (Coleman 2002); • The theories available inadequately describe and predict trends in spatial patterns of fertility. What are the spatial and temporal scales upon which judgements and conclusions can be made? Compton (1991, 83) based on his comparison of spatial patterns in two countries at various geographic scales over time would argue, ‘the expectation of being able to construct anything other than ad hoc hypotheses and theories that reflect specific circumstances is rather unrealistic’. • Spatial variations exist because of, or are representative of, the uneven distribution across space of groups within the population with varying characteristics. Therefore while there is incomplete regional uniformity of socio-economic and demographic characteristics and cultural norms then it is unrealistic to expect spatial variations to disappear.
The extent to which there are differentials in fertility for different sub-groups of the population is the focus of the next section of this chapter.
3.3. Socio-Economic Differentials
The previous section indicated that considerable spatial variations in fertility persist, and in some instances may have increased, in the more developed countries of the world including Australia despite declining overall levels of fertility and some expectations to the contrary from theory. Spatial variations in fertility are a reflection of the wider relationship between reproductive behaviour and the characteristics of populations and Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 62 individuals. Although not precise in strength, direction, or specific influence of particular factors, theories suggest a number of characteristics that have a role.
Demographic transition theory, for example, identifies economic and social changes associated with modernisation, in particular education and new economic roles for women (Section 2.2). Economic theory (Section 2.3.1) sees only income as being important while socio-economic theory (Section 2.3.2) recognises that economic decisions can be influenced by a range of variables including religion, ethnicity, occupation, education and social structure. Even ideational theory (Section 2.4) sees particular factors as being especially important in the recent declines in fertility—rising educational attainment and the economic independence of women.
Theory and empirical analyses predict that fertility patterns are influenced by a wide range of factors embracing demographic, biological, socio-economic, cultural and psychological variables. Any understanding of, or expectations of, trends in spatial patterns in fertility is reliant on knowledge of the trends and changes in the relationships between fertility levels and these variables.
This section of the chapter therefore examines the characteristics of populations that have conventionally been used to explain spatial variations in fertility, beginning with a discussion of the demographic factors of age and marital status. When these factors are taken into account socio-economic factors have most often been the major explanatory variables. The term socio-economic can incorporate many variables so the most important ones—educational attainment, employment and income are examined here. Cultural variables such as religion, birthplace and race have also been important in influencing fertility differentials and are reviewed here. Spatial variations in fertility can be affected by wider economic and political circumstances in society that cannot be derived by aggregating individual characteristics so the chapter includes a discussion of such factors before presenting some concluding comments about how trends in fertility differentials discussed in this chapter may be expected to influence spatial variations in fertility.
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 63
3.3.1. Demographic Characteristics
Regional variations in fertility may occur as a result of differences in the demographic characteristics of a population, in particular age and marital status.
3.3.1.1. Age
Age is one of the most important variables influencing reproduction through its association with menarche, marriage, divorce, widowhood, fecundity and menopause. It is also related to social and economic variables which can affect fertility and are characterised by life-cycle patterns (Farooq and DeGraff 1988). The age structure of a population significantly influences fertility rates and therefore specific measures or methods of standardisation are used to control for the effect of age.
3.3.1.2. Marital Status
Marital status has always been an important intermediate determinant of fertility levels by ‘mediating the influence of the more basic economic and social forces that affect both marriage and fertility’ (Andorka 1982, 23). In essence marital status has represented exposure to sexual intercourse although the nature of this relationship has changed over time.
In explaining variations in fertility the marital characteristics of the population (for example, the proportion ever married, age at marriage, duration of marriage, prevalence of widowhood, divorce, remarriage) have often been much more relevant than fertility within marriage itself. For example, the decline in fertility and relatively low levels of fertility in Western Europe in the eighteenth and nineteenth centuries were significantly influenced by the late age at marriage and high proportion of women who never married (Andorka 1982; Farooq and DeGraff 1988). Similarly after World War II the younger age at marriage and an increase in the proportions of women marrying in Australia (as in many other developed countries) were factors of higher fertility, the so called ‘baby boom’, while the subsequent increases in age at marriage and declining proportions of people Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 64 marrying has been a major cause of the significant declines in fertility over the last thirty or so years (Hugo 1986).
Marital status, age at marriage and duration of marriage, have often been used as defining parameters in the study of fertility. Age at marriage in particular has been one of the most important factors influencing fertility levels with a consistently negative association between wife’s age at marriage and completed fertility (Bracher 1981; Day 1983; Westoff and Ryder 1977).
In Australia much of the work on differential fertility up to the 1970s was only for currently married women as the Census only collected information about children born to existing marriages and indeed this information accounted for nearly all births (Day 1970, 1983; Ruzicka and Caldwell 1977). With the significant changes in societal attitudes and practices towards divorce, cohabitation and childbearing outside marriage in the last twenty to thirty years marital patterns do not have as much impact on fertility trends as they did in the past. This is reflected in the focus of the Census which no longer asks age at marriage and collects issue data for all women, regardless of marital status. Even so as can be seen from Table 3.7 legal marriage still remains the major basis for family formation and thus married women still have on average larger families than women widowed, separated, divorced, living in a de facto relationship or women who have never married. Spatial variations in marital patterns may therefore continue to influence spatial patterns in fertility.
Table 3.7: Australia: Women Aged 35–49 Years By Marital Status and Children Ever Born, 1996 Age Never Married Separated Divorced Widowed Total Married Per Cent with No Children 35-39 65.7 8.0 9.2 18.4 12.8 16.8 40-44 69.2 6.6 6.6 13.6 10.3 12.8 45-49 75.6 6.2 5.8 10.9 8.8 10.7 Average Issue 35-39 0.70 2.27 2.31 1.88 2.16 2.02 40-44 0.62 2.39 2.46 2.06 2.32 2.21 45-49 0.50 2.44 2.56 2.20 2.43 2.31
Source Hugo 2004, 29 Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 65
3.3.2. Socio-Economic Factors
Socio-economic factors have long been identified as strong determinants of fertility and as the preceding discussion shows are often the major explanatory variables in spatial analyses of fertility. Spatial variations in fertility are conceptualised to exist as long as there is a connection between socio-economic status and fertility levels and there are variations in the socio-economic characteristics of the constituent population of regions.
The relative importance however of socio-economic factors in influencing trends and patterns in fertility in post-transitional societies has been questioned as differentials are expected to diminish considerably. The following discussion examines the traditional and current nature of the relationships between the most important of these socio-economic variables (education, employment, income, social status) and fertility generally, and then specifically for Australia.
3.3.2.1. Educational Attainment and Fertility
A great deal of research attention has focussed on female educational attainment as it has long been recognised as a key factor influencing reproductive behaviour (Andorka 1982; Farooq and DeGraff 1988; United Nations 1996). Although the nature of the relationship has varied, the general finding of empirical studies is of a negative association between fertility and education levels. Even though some research (Andorka 1978, 1982) suggests this relationship existed prior to the demographic transition, the importance of the relationship was reinforced during the transition with the parallel growth of education and the decline in fertility (Andorka 1978, 1982). Caldwell and Caldwell (2001, 107) state that in ‘nearly every world region, fertility decline began as primary schooling became almost universal, and secondary schooling substantial’. Education is regarded as a good proxy for a variety of fertility related conditions including—knowledge of birth control, level of aspiration for oneself and one’s children, social class, lifestyle and income (Blake and Del Pinal 1982; Day 1983).
Educational attainment in western societies has historically been measured in terms of number of years of schooling. Unlike many other social and economic characteristics of a Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 66 person’s life, this characteristic has generally remained constant throughout an individual’s adult lifetime, although the importance of educational attainment as a consistent lifetime characteristic is also changing as many women return to school and go on to gain post school qualifications as mature age students. Establishing a relationship between educational levels and fertility has a very recent history in Australia as questions on age left school or completed schooling and educational qualifications have only been asked regularly since the 1966 Census.6
In Australia educational attainment has generally been measured in terms of highest qualification attained although Day (1983) and Hugo and Wood (1983) examined years of schooling. In Australia, until recently it has been compulsory for all children to attend school until age 15 (now 16) and many go beyond this age. In the last three to four decades there have been dramatic changes in the participation of women in education in Australia as has been the case in other western countries. For example from 1977 to 1997, the retention rate of women to year 12 increased from just 36.6 per cent to 77.8 and by 2002 to 80.7 per cent (ABS 2003a, 18).
Analysis of 1966 Australian Census data by the National Population Inquiry (1975) and by Ruzicka and Caldwell (1977) identified a U-shaped relationship between fertility and education as measured by highest achievement (Table 3.8). This relationship existed for all age groups 35–39, 40–44, 45–49 and 50–54 showing that fertility was at its lowest for wives with an intermediate level of schooling or matriculation and higher for wives with university or other tertiary education as well as among those with only some high school or primary education. The ambiguous group ‘nil or not stated’ had the highest fertility of all. When the data are standardised for duration of marriage the pattern remains the same.
Day’s (1983) analysis of data from the subsequent 1971 Census found a different relationship between fertility and education levels. Here educational achievement was measured in terms of years of completed schooling (fewer than six years, 6–7, years, 8 years and 9 years and over). Although he examined cohorts spanning generally similar time periods (1907–11 to 1927–31) as Ruzicka and Caldwell (1977) for the 1966 Census Day found ‘an almost consistently negative association between schooling and fertility’
6 The 1911 Census included a question where respondents reported their highest level of educational achievement and Censuses prior to 1966 only asked about current schooling (ABS 1991a). Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 67
(Day 1983, 148). While this relationship persisted for all cohorts and age at marriage categories, the differential declined markedly over time due to increases in fertility for all schooling categories.
Table 3.8: Australia: Average Issue of Wives by Education and Year of Birth Education Level Generation Born In 1911-16 1916-21 1921-26 1926-31 R S R S R S R
University 2.24 2.37 2.57 2.71 2.76 2.91 2.74 Other Tertiary 2.32 2.44 2.50 2.66 2.75 2.87 2.80 Matriculation 2.16 2.28 2.33 2.46 2.56 2.66 2.72 Intermediate 2.29 2.32 2.48 2.51 2.65 2.68 2.77 Some High/Primary School 2.57 2.53 2.74 2.70 2.89 2.85 2.98 Nil, Not Stated 3.11 3.08 3.30 3.20 3.52 3.41 3.38
All Wives 2.49 2.66 2.82
R: recorded issue of the existing marriage S: standardised for marriage duration using the average issue of all wives as standard Source: Ruzicka and Caldwell 1977, 236; data for 1926-31 from National Population Inquiry 1975, 57
This inverse relationship between fertility and education levels persisted into the 1980s and 1990s (ABS 1992; Betts, Diemer and Hiller 1995; Hugo and Wood 1983; Jain and McDonald 1996, 1997) whether examining age left school or highest qualification obtained. In Table 3.9 for example, the relationship between age left school/qualification level and fertility is shown for women at the 1986 Census. It is clear that irrespective of age the average number of children born to women declines with each level of achievement such that women with the highest qualifications and those who stayed at school the longest had the smallest families. It also appears that, in terms of qualification levels, the differential increased with each cohort. The large difference in family size for women aged 25–34, however, must be treated with caution, as many of these women, especially those with qualifications, would not have completed their childbearing at the time of this Census.
In 1991 the question regarding children ever born was dropped from the Census thus hampering attempts to examine differential fertility into the 1990s. Using the one per cent Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 68 sample tape from the 1991 Census, however, Betts, Diemer and Hillier (1995) used the own child method of fertility estimation to match offspring with their mothers aged 20 to 39 years. Betts, Diemer and Hillier, examined various characteristics associated with differential fertility and found the strongest relationship to be that between education levels and fertility.
Table 3.9: Australia: Average Number of Children Ever Born to Women: Qualification Attained and Age Left School by Current Age, 1986 Age Groups (Years) All Ages Education Level and Age 25-29 30-34 35-39 40-44 45 Observed Age Left School and Standardised(a) over Qualification Degree 0.35 1.08 1.62 1.88 2.17 1.14 1.35 Diploma or Certificate 0.82 1.66 2.09 2.29 2.43 1.66 1.64 Other Qualifications 0.99 1.73 2.12 2.34 2.45 1.67 1.71 No Qualifications 1.30 2.01 2.34 2.58 2.81 1.99 1.99
Per Cent Difference(b) 271.4 86.1 44.4 37.2 29.5 47.4
Age Left School (yrs) No School 1.32 2.06 2.66 2.93 3.69 3.24 2.46 Less than 13 1.88 2.33 2.56 2.66 3.00 2.84 2.29 13-16 1.37 2.04 2.33 2.54 2.74 2.18 1.99 17-18 0.74 1.53 1.99 2.20 2.39 1.25 1.60 19 and over 0.69 1.39 1.79 2.01 2.26 1.35 1.50
Per Cent Difference2 91.3 48.2 48.6 45.8 63.3 64.0 a. According to the age distribution of all women b. Per cent difference between the categories—degree and no qualification; not go to school and 19 years and over Source: ABS 1992, 6, 19, 21
As was the case for the 1986 Census they found family size diminished rapidly as educational qualifications increased. The information available from the 1996 Census (McDonald 1998) shows that fertility declines as educational level increases. For women aged 35–39 years at the 1996 Census, women with no qualifications had on average 2.15 children while women with a bachelor degree or higher had 1.55 children.
Since data on education levels have been collected in Australia there has been a clear relationship between level of educational attainment and family size. Although this differential was quite narrow at first, and of a U-shaped nature, it soon became an inverse Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 69 relationship and this has persisted, remained stable and may now even be increasing. It is difficult to establish any definitive longitudinal trends in Australia from the studies undertaken so far and because of the recency of the data collection on the topic. It is reasonable, however, to expect that trends in the United States are applicable to the Australian situation. A study by Retherford and Luther (1996) set out to specifically examine the long-term relationship between education levels and fertility in the US in the context of demographic transition theory. They analysed trends in fertility differentials by education (defined as years of schooling: 0–8, 9–11, 12, 13 years and over) for a greater part of the twentieth century (1925–90). In addition to examining trends for the total population they also looked at trends by racial group and two regional types (urban and rural). No matter what measure of fertility used (age-specific rates, total fertility rates, number of children ever born, and parity progression ratios) or for what group examined they found (1996, 21, 32);
…differential period fertility by education was strongly negative during the entire period 1925-29 to 1985-89, and this negative relationship showed no sign of disappearing, much less becoming positive.… By some measures — most notably the total fertility rate calculated from period parity progression ratios (TFRppr) — differential fertility by education is becoming even more negative over time.
This trend is not surprising considering the changes that have occurred in the role and position of women in western societies over the last three decades. In the 1950s and early 1960s the prevailing attitudes were that men and women should marry and with marriage and the birth of a child, women should cease employment. According to Betts, Diemer and Hillier (1995), in explanation of the U-shaped relationship between education and fertility at this time, one income or wage was sufficient for highly educated women married to highly educated, high income men and for poorly educated women with lower living standard aspirations. For these households a single income was sufficient, allowing the wife to stay at home and raise a family. For women with a ‘reasonable’ standard of education and more middle class aspirations, however, it was necessary for the wife to remain in the work force, resulting in lower fertility levels.
Since the 1960s there have been massive social changes affecting the lives of women. Education has become increasingly important for both sexes for a number of reasons Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 70 including income support, attainment of employment, career advancement and self- improvement. This has a depressive influence on fertility. Research indicates that the more education one desires and attains, the later the onset of childbearing and ultimately lower fertility. This relationship is evident in all societies (Bloom and Trussell 1984; De Wit and Embree 1993; Merlo 1995; Rindfuss, Morgan and Offutt 1996; Statistics New Zealand 2001; United Nations 1996).
With a strong relationship between educational levels and fertility it is likely spatial variations in education will influence spatial patterns of fertility. Figure 3.4 for example shows the distribution of people who left school at age 15 or less or who did not go to school for metropolitan Adelaide as reported in the 1996 Census. It is clear that at this time there were striking variations across the metropolitan area. Although the figure represents all adult age groups and both sexes there is no reason to believe the pattern would be markedly different just for women of childbearing age.
It is clear from Figure 3.4 that throughout the higher status eastern, south eastern and coastal suburbs the number of people who had left school at age 15 or less was well below the expected figure. This contrasts with the pattern in the north and north west of the metropolitan area in particular where the numbers were significantly above what was expected. From the mapping of other characteristics the South Australian Health Commission (SAHC) (1990) found the areas whose populations had low levels of educational attainment to also be characterised by low incomes, low female participation in the workforce, high percentage of low skilled workers, single parent families and high percentages of public rental dwellings. In fact the SAHC (1990, 42) states ‘the pattern of variation within Metropolitan Adelaide in age-standardised educational participation serves as a striking illustration of the links between education, occupation, income and wellbeing’.
3.3.2.2. Employment and Fertility
One of the traditional fields of investigation in demographic analyses has been the relationship between social status and fertility and mortality. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 71
Figure 3.4: Metropolitan Adelaide: People Who Left School at Age 15 Years or Less, Or Who Did Not Go to School, 1996 Census (Standardised Ratio – Number of People in Each Statistical Local Area Compared With Number Expected)
Source: Adapted from Glover, Harris and Tennant 1999, 47
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 72
Although the term ‘social status’ can be represented by many socio-economic characteristics in the past it often has referred to occupational status (Andorka 1978; Hicks 1971; Ni Bhrolchain 1993).
The study of fertility levels by occupational groupings dates back to the early twentieth century in developed countries (Andorka 1978, 1982; Haines 1992; Kiser, Grabill and Campbell 1968).
Unlike educational status, however, occupational status is less easily defined in quantitative terms and in more recent times has become a less stable lifetime characteristic. As outlined by Kiser, Grabill and Campbell (1968, 179) there has been ‘much heterogeneity of specific occupations and of the education levels of people within a broad occupational class’. In addition there have been problems in the ‘proper placement of certain broad occupational classes on any type of scale’. In relation to the United States, although the same problems are relevant to Australian data (Borrie 1948; Day 1983), Kiser, Grabill and Campbell (1968) use the example of the group named ‘service workers’ which represented many different types of people, thus making it difficult to specify a social status ranking. These problems have been reduced or partly overcome in recent times with revisions of the classifications of occupations resulting in more appropriate, better defined occupational groupings (ABS 1996a).
It is only with the increasing emancipation of women that the variable, occupational status, as a measure of socio-economic status, has referred to women’s occupations. Previously the relationship was between fertility and the husband’s occupation as the majority of wives did not work outside the home in paid employment.
Despite these problems occupational status has been an important characteristic in the past in the study of fertility differentials. The broad occupational groupings have provided meaningful delineations of socio-economic status and lifestyle and because of the recording of occupational status in early censuses it allows analysis of the long-term relationship between socio-economic status and fertility. The general findings of studies throughout Europe, and America has been a U-shaped or J-shaped relationship or more recently an inverse relationship between fertility and occupational status (Andorka 1978; Coleman and Salt 1992; Kiser, Grabill and Campbell 1968). Consequently, at the broadest Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 73 level manual workers have higher fertility than non-manual workers and in particular farmers and farm workers had considerably higher fertility than families in which the husband was employed in non-agricultural occupations (Andorka 1978, 1982; Kiser, Grabill and Campbell 1968). Similar relationships are evident between women’s occupation and fertility. For example, O’Connell and Rogers (1982) study of 1980 data in the United States found that for 18–44 year olds the lowest fertility rates were for women who were managers or administrators, followed by sales and clerical workers, then professionals with the highest rates being recorded among farm workers.
With the shift to the study of the occupation of women has come the analysis of work force status as well. The extent of female labour force participation often varies throughout women’s working lives with movement in and out of the work force as well as movement between full time and part time employment. This makes it very difficult without a working life history to make clear definitive statements about the relationship between fertility and occupational status. However, the general findings are that employed women have lower fertility than women not in the labour force and for employed women the fertility rate for part time workers is higher than the rate for women working full time (O’Connell and Rogers 1982; Statistics New Zealand 2001).
The first detailed analyses7 of the relationship between fertility and occupational status in Australia were possible following the 1911 Census.8 Borrie’s (1948) analysis of the average number of children by husband’s occupation at the 1911 and 1921 Censuses and of male reproduction rates in 1933 showed that fertility was highest for husbands engaged in rural occupations, followed by industrial, transport and communication occupations with commercial, professional and domestic occupations having the lowest completed family size.
Day’s (1970) analysis of a sample of records from the 1961 Census continued to find the relationship identified by Borrie of higher fertility among the farmers and blue collar workers in comparison to the professional and clerical classes. According to this study analysis of fertility by cohort suggested there had been a substantial narrowing of the
7 Hicks (1971) did try to analyse fertility trends by father’s occupation using data from the South Australian birth registers for the years 1891, 1901 and 1911. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 74 differentials by husband’s occupation both in terms of median issue and the proportion of women having five or more children. By the 1966 Census the differential appeared to have further narrowed (Ruzicka and Caldwell 1977).
The most detailed analysis of the relationship between occupation and fertility in Australia is Day’s (1983) cohort analysis of data from the 1971 Census. Following the work of Borrie (1948), Day looked not only at fertility differences by occupational grouping but also further differentiated the analysis by studying occupational differences in relation to other attributes—urban-rural residence, nativity, religion and schooling.
Employing the seven major occupational categories given at the 1971 Census he found the general pattern of lowest fertility for the broad professional, administrative, clerical and sales group and the highest fertility for farmers and graziers. The magnitude of the differences between these groups continued to narrow over time for both median issue and distribution by issue. Day points out that if the high fertility of the farmers and graziers is excluded then the fertility differential by husband’s occupational grouping is less than that associated with the other characteristics studied—fertility differentials by urban-rural residence, religion and country of birth. Despite this and on the basis of Day’s analysis of fertility differences by occupation and rural-urban residence, schooling and so on, he (Day 1983, 104) concluded:
…occupation would appear to have a bearing on fertility independent of that arising from its association with other personal attributes, for fertility differences associated with occupational groupings are to be found not simply between the various categories of the population, but also within them.
Day’s (1983) study was the last to examine the traditional relationship between fertility and occupation in terms of the husband’s occupation.9 Studies in the 1980s and 1990s concerned themselves only with the relationship between fertility and the work force and occupational status of women, although the National Population Inquiry (1975) had already established trends in these relationships from the 1954, 1961 and 1966 Censuses. In the National Population Inquiry (1975) Borrie stated that fertility was found to be
8 Although detailed occupational details of people were recorded in earlier Censuses the data were not cross- tabulated with other characteristics to enable construction of a fertility index. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 75 higher for wives not in the labour force than for those in the labour force in the 1950s and 1960s. This difference was at its greatest for the most recently married (and therefore younger) wives but the difference remained even for wives who had completed their childbearing. From the 1966 Census data Borrie (National Population Inquiry 1975) also examined the average issue of wives aged 35–39 years according to the occupation of the wife. He found significant differences with the lowest fertility for wives in ‘career’ type occupations (professional, administrative and clerical workers) and the highest fertility among women classified as farmers and farm workers (Table 3.10).
Table 3.10: Australia: Average Issue of Wives Aged 35–39 Years by Occupation of Wife 1966 Occupation of Wives Average Issue Professional, Technical and Related Workers 2.38 Administrative, Executive and Management Workers 2.32 Clerical Workers 2.17 Sales Workers 2.40 Farmers, Farm Workers 3.33 Transport, Communication 2.51 Craftsmen etc 2.28 Services, Sport etc 2.69 Other, Not Stated 3.08
In the Workforce 2.51 Not in the Workforce 3.11
Total 2.91 Source: National Population Inquiry 1975, 58
Analyses of the one per cent sample tape from the 1981 Census (Hugo and Wood 1983) and 1986 Census data (ABS 1992; Hugo 1992) show a continuation of these trends. In terms of labour force status Hugo and Wood found whether looking at distribution of total issue, average issue or distribution by parity, women in all age categories who were wage and salary earners had substantially lower fertility than women not in the labour force. One factor of interest was the very small difference in the average issue of women who were self employed, employers or worked as unpaid helpers and that of women not in the labour force. This point was noted by Betts, Diemer and Hillier (1995) in their analysis of 1991 Census data. They suggest it may be a reflection of women working in family
9 Day (1983) briefly examined fertility and wife’s workforce participation from the 1971 data. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 76 businesses or a trend for women with larger families to organise their own home based employment and thus perhaps make it easier to combine child rearing and care with employment.
Examination of survey and Census data for the 1990s show some interesting results. Jain and McDonald’s (1996) examination of fertility differentials measured at the time of the 1992 Family Survey conducted by the ABS, showed that although wage and salary earning women had lower fertility than other groups and differences could be established between occupational groups, the differences were not statistically significant. Using 1996 Census data, McDonald (1998) continued to examine the relationship between working and non-working women. In terms of labour force status he compared 1986 and 1996 data in relation to mean number of children ever born and percentage of women with no children and three or more children for the age groups 25–29 and 35–39 (Table 3.11). Although fertility declined over the ten year period the differential between employed women and women not in the labour force increased over this time.
Table 3.11: Australia: Percentages of Women with No Children and Three or More Children, and Mean Children Ever Born, Age Group 25–29 and 35–39 by Labour Force Status of the Women, 1986 and 1996. Age 25-29 35-39
No. 0 3+ Mean 0 3+ Mean Labour Force Status Children Year Per Per Per Per Cent Cent Cent Cent Employed 1986 64.3 5.3 0.62 15.5 33.3 2.05 1996 72.7 2.9 0.43 21.1 28.0 1.82
Unemployed 1986 44.4 12.7 1.09 14.0 38.7 2.22 1996 54.7 8.7 0.83 19.2 31.0 1.92
Not in Labour Force 1986 11.4 22.2 1.79 6.0 47.2 2.55 1996 18.9 20.0 1.60 8.3 46.6 2.48
Source: McDonald 1998, 8
In terms of specific occupations, at the 1996 Census, for women aged 35–39 years fertility was the lowest for professionals (1.61 children) and highest for women working in the trades or as production process workers or labourers (2.01). McDonald differentiated this Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 77 analysis by marital status. The highest fertility by occupational groupings was found among married women although the differences in fertility between occupational groups for married women were much less. Fertility levels were lower for all occupational groups where women lived in a de facto relationship or were neither married nor living in a de facto relationship but the differential between occupational groups was greater.
From this review it is evident fertility differentials by occupational grouping and occupational status exist in Australia and have done so since assessments of the relationships were first undertaken. At first an inverse relationship was established between the husband’s occupational grouping and fertility and in more recent times with the occupational and labour force status characteristics of women and their fertility.
In reference to the latter relationship the actual causal association between women’s work force participation and fertility is not completely understood. In the post-war period there have been very significant changes in the labour force participation of women, in particular married women with children (Daly 1990; Evans 1996; Young 1990). It is thought that it is the difficulty of reconciling the two roles of mother and worker that result in women in the labour force having lower fertility than women not in the labour force. This is particularly reflected in occupational grouping differentials. Professional women tend to have fewer children and delay having children a little longer than women in other occupational categories because it is harder to reconcile career occupations with child rearing (National Population Inquiry 1975; Ware 1976). High fertility is found among ‘blue collar’ workers due to less orientation towards a career, earlier marriage than among professional women and less contraceptive knowledge or dedication to use (O’Connell and Rogers 1982; Ware 1976). Recent research in New Zealand (Statistics New Zealand 2001), however, suggests occupation has a minimal effect on fertility. In a study of fertility and socio-economic factors at the 1996 Census of New Zealand the initial differences evident by broad occupational groupings were explained by ethnic, educational and geographic factors.
Clearly spatial variations in the distribution of female labour force participation or occupational status would influence the spatial patterning of fertility. Figure 3.5 presents the geographic variability in female labour force participation in metropolitan Adelaide at the 1996 Census. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 78
Figure 3.5: Metropolitan Adelaide: Female Labour Force Participation of Women Aged 20–54, 1996 Census (As a Percentage of All Females Aged 20–54 Years in Each Statistical Local Area)
Source: Adapted from Glover, Harris and Tennant 1999, 43
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 79
The Figure reveals that participation rates at this time were highest in the inner, inner southern and eastern suburbs of the city. This is in contrast to the outer northern and southern LGAs. The South Australian Health Commission (1990, 38) commented that a close relationship existed between the low participation rates and areas of public housing. In addition it is stated the low rates of participation:
…are an indication of concentrations of single parent families dependent on welfare benefits and rent rebates. The low rates also suggest the existence of hidden unemployment…Hidden unemployment is particularly likely to contribute to low participation rates in the outer suburban areas like Elizabeth which have poor local job opportunities for women, and low rates of car ownership.
All groups of women, and working women in particular, regardless of occupational classification, have experienced a decline in fertility over time. Much of the discussion in recent times concerning the declines in birth rates in western countries has focussed on the conflict and incompatibility women face in working outside the home and childbearing (Davis 1986; Hakim 2001; Hoem and Hoem 1989; Hugo 2000; Manne 2001; Pinnelli 1995; Presser 1986, 1989; Probert and Murphy 2001). This focus has been a major part of developments in the recent theory of gender equity and social institutions (McDonald 1997, 2000a, 2000b, 2001a, 2001b).
The inverse relationship between occupation and fertility has at times raised concerns. In the early twentieth century the pre-occupation was that socio-economic differentials in fertility, as measured by occupational class, were of eugenic significance. The argument was that the professional class were more educated and had ‘on the average, a greater intellectual capacity than the average manual worker’ (Agar 1928, 131). Thus ‘granting, then, that there is a more than average accumulation of natural talent (not merely better education) in the classes of the intellectual workers the argument is sound that if these classes are less fertile than the rest of the population, then the average intellectual capacity of the population will decline’ (Agar 1928, 132).
In the 1940s, while acknowledging that intelligence was dispersed evenly among all social classes, Gentilli (1948) was still greatly concerned about the cultural standards of the population. ‘The opportunities to develop intelligence and to acquire culture are not the Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 80 same for all social groups and classes’, thus the inverse relationship between fertility and social class could lead to a ‘danger of cultural deterioration and spreading vulgarization’. His remedy to overcome this was the ‘cultural uplift of the lower sections of the population, not by enabling them to obtain culture if they so wish, but by taking culture to them and by developing their culture by every possible educational means’ (Gentilli 1948, 104).
During the 1980s and 1990s concern was raised that although declines in fertility occurred for all sub-groups of the population, in socio-economic terms (occupation, education, income), it is the ‘better off’ groups of women who have smaller numbers of children and are disproportionately represented among women who remain childless (Hugo 1992). Hugo suggested that in contrast to other differentials the differences in fertility between poor and well off women may have widened in the 1980s and therefore resulted in a greater proportion of all children born, being born into poor families than was the case previously. This leads to concerns about the disparity in the quality of life chances of children from different socio-economic backgrounds (Gilley and Taylor 1995; Morrison 1991; O’Hare 1996; Rindfuss, Morgan and Offutt 1996). The next section will explore this fact further by examining fertility differentials by income.
3.3.2.3 Income and Fertility
As a determinant of fertility, income has been one of the most widely studied variables and according to Andorka (1982) one of the ‘most hotly debated’ variables in demographic research (though this has not been the case in Australia). Most theoretical and empirical research into fertility trends and determinants whether at an aggregate or individual level, has incorporated some aspect or measure of income.
There are different theoretical perspectives on the nature of the relationship between fertility and income. At an aggregate level the historical decline in fertility tended to occur in parallel with a rise in per capita income. Demographic transition theory therefore proposes that there is a negative relationship between income and fertility. Analyses of absolute income data for a national or regional population support this proposed negative relationship (Andorka 1978). According to economic theory, though, the relationship Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 81 between fertility and income is positive, as more wealthy couples are able to afford a greater number of children. As mentioned earlier, many survey and census results of household and individual level data have shown a positive or U-shaped relationship between fertility and income (Andorka 1978, 1982; Rindfuss and Sweet 1977).
The appearance of a positive association between income and fertility has led to the formulation of other hypotheses concerning this relationship. It has been suggested that relative income (an individual’s income as compared with the average income level the person ought to have on the basis of socio-economic status, age, education and residence) is far more relevant as a factor influencing fertility than actual income. Analysis of the relationship of fertility and income in homogeneous groups has generally shown positive or U-shaped relationships (Andorka 1978, 1982; Bernhardt 1972; Easterlin 1973; Freedman 1963). For, example, while initial analyses of the major American surveys (Indianapolis Survey, Growth of American Families Surveys and National Fertility Studies) showed a negative relationship between income and fertility, re-analyses of the work when relative income was taken into account, indicated positive associations.
The varying contradictory relationships between fertility and income are the result, it is suggested (Andorka 1982; Jones 1990), of the strength of the influence of income. At both an aggregate and micro level income appears to be strongly correlated with several other variables representative of living standards, for example occupation, educational attainment and urbanisation.
Examining the relationship between income and fertility is not only a complex problem because of the influence of other social factors, but there are a number of compounding issues. In contrast to educational attainment and to a lesser extent occupational standing, income levels can vary considerably across a lifetime. Unless looking at detailed survey data which examine income, labour force status and occupation levels of women throughout the childbearing period (as was undertaken in The Australian Family Project, Bracher 1987, for example), there is difficulty in interpreting the income/fertility relationship. The use of data which measure contemporary income levels, as occurs in Censuses, may not reflect the true economic situation of the family. For example, women currently involved in childbearing may withdraw from the labour market temporarily and so their current or family’s current income may not be a true reflection of previous or Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 82 expected levels of income (O’Connell and Rogers 1982). At the other end of the scale, for women who are near the end of their reproductive period or who have completed childbearing, present income levels have little bearing on fertility decisions made ten to twenty years or so ago (Rindfuss and Sweet 1977). It should also be noted, that unlike most other variables, income is influenced by government actions and policies.
Further problems in this income/fertility nexus are that income can be measured in a number of ways (for example, in Australian Censuses, individual, family and household income data are collected) and the relationship between fertility levels and income can vary depending on the specific variables chosen. The study of this relationship is also hampered by the fact that the only available data are generally Census data. In the Census income is specified in broad categories, and these categories are not directly comparable over time and may not be totally appropriate for the analyses required.
In Australia, studies of fertility differentials have only been able to include income as a variable since 1976 when it was first collected in the Census. Borrie (1948) very briefly discussed data from the 1933 Census and from a social survey of Melbourne in 1942 but believed the data were inadequate to form any general conclusions about the fertility/income relationship. Hugo and Wood’s (1983) analysis of the 1981 Census one per cent sample tape appears to be the first more detailed analysis of the relationship. They examine all three types of income available. For household income, the not stated category for both income and issue is large and so this limits the relevance of the findings. Nevertheless, for women aged 20–29 a clear inverse relationship between issue and income was found when examining the proportion of women with zero parity and those with three or more children. For older women aged 40–49 and 50–59, there was no pattern, or an inverse relationship. Similarly in examining family income Hugo and Wood found a negative relationship for women aged 20–29 years but the differential in fertility disappeared for women in their thirties and forties. Interpretation of the differential between issue and individual income is confounded by the large number of women not earning income outside the home. From the information available, however, the authors found a U-shaped relationship.
Analysis by the ABS (1992) of income fertility differentials at the 1986 Census examined both individual and family income. A negative relationship for both income characteristics Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 83 was identified although the relationship was much sharper for individual income. The largest differences in fertility occurred at the younger ages. Women aged 25–29 years with high individual incomes on average were found to have 0.4 children compared with 1.6 children for women with no individual income.
Hugo (1992, 1993) also examined 1986 Census data. From the one percent sample tape he examined fertility levels by family income and though the three broad income categories are similar, but not comparable over time, he tried to assess whether the differential between low and high income families widened over the 1981–86 inter-censal period. Hugo identified a negative relationship between average number of children born and family income and commented further (Hugo 1992, 58) ‘It is very clear that for all age groups up to age 40 there has been a widening of the gap in the average number of children of women in poor families compared to those in high income families’. This he believed is additionally supported when patterns of childlessness by income are examined. Between the 1981 and 1986 Censuses Hugo found a large reduction in the percentage of women from low income families who were childless and the opposite trend for women from high income families.
As the question on issue was removed from the 1991 Census other methodologies were used to assess the validity of Hugo’s findings five years later. Betts, Diemer and Hillier (1995) analysed fertility differentials at the 1991 Census using a ‘resident-offspring’ method. Their analysis is not directly comparable to Hugo’s and they divided income into four categories—low income, low-middle income, middle-high and high income. With this breakdown of family income they found a U-shaped relationship with the mean family size of the lowest income group equivalent to that of the highest income group although they commented the very low income group may have included many young single people. For women aged 30–34, the mean family size for women in the second lowest family income group (low-middle income) was 28.3 per cent higher than for women in the high income category (Betts, Diemer and Hillier 1995, 22). The trend in childlessness by income was less clear from their analysis.
McDonald (1998) briefly analysed fertility and family income at the 1996 Census both for women in registered marriages and those in de facto relationships. If the lowest income category is ignored (for which mean family size is middle of the range) McDonald found a Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 84 general negative relationship between income and fertility for women aged 35–39. Although the differential was relatively small for wives (0.29 of a child) it was much wider for women in de facto relationships (1.13 children).
The income/fertility association has also been studied indirectly through spatial analyses of fertility. Borrie (1948) found that in 1921 and 1933 the wealthier residential areas of Sydney (Kuring-gai, Mosman, Manly and Vaucluse) and Melbourne (Kew, Hawthorn and Prahran) had much lower fertility ratios than the poorer suburbs such as Waterloo, St Peters, Leichhardt and Balmain in Sydney and Port Melbourne, Williamstown and Coburg in Melbourne. Wilson’s (1991) analysis of fertility in metropolitan Sydney identified what he believed to be an emerging ‘curvilinear relationship between parity and economic status,… with the propensity to produce a third or higher birth being best developed among less affluent or very well-off suburban dwellers…’ (Wilson 1991, 11).
In summary, it is clearly evident that socio-economic differentials in fertility in Australia and South Australia have not disappeared. The role of socio-economic factors in influencing spatial variations in fertility must remain significant. Figures 3.6, 3.7 and 3.8 present a composite variable of socio-economic status (IRSD)10 for metropolitan Adelaide and non-metropolitan South Australia and these clearly show the geographic variation across the State in the socio-economic characteristics of the population. Figure 3.6 shows that although there have been some fluctuation from Census to Census, overall there has been little change in this index over the 1986 to 2001 period. The index scores for Adelaide, at all Censuses have been considerably higher than those in non-metropolitan South Australia.
For metropolitan Adelaide (Figure 3.7) it is clear that in 1996 the least disadvantaged areas were to the east and south of the city while the most disadvantaged areas were to the north west, north and in the outer southern areas.
10 The Index of Relative Socio-Economic Disadvantage (IRSD) includes variables collected in the Census that either reflect or measure disadvantage. Such variables include low income, low educational attainment and high unemployment (Glover and Woollacott 1992, 90; Hetzel, Page, Glover and Tennant 2004, 31). Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 85
Figure 3.6: South Australia, Index of Relative Socio-Economic Disadvantage, 1986 to 2001
1015
1010
1005
1000
e 995 1986 or
c 1991 S 990 1996 ndex I 985 2001 980 975
970
965 Adelaide Rest of State
Source: Hetzel, Page, Glover and Tennant 2004, 31
For non-metropolitan South Australia in 1996 the most disadvantaged areas were in the north of the State while the least disadvantaged were in areas adjoining the metropolitan area.
The next two characteristics to be considered are cultural factors and these are often discussed jointly, in conjunction with the socio-economic variables already examined.
3.4. Values and Norms
Reproductive behaviour is influenced by certain values, attitudes and norms of the population that may be unrelated to socio-economic characteristics. These values vary among particular groups in society, for example, religious, ethnic, indigenous and regional locational groups (rural versus urban location). Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 86
Figure 3.7: Metropolitan Adelaide, Index of Relative Socio-Economic Disadvantage, 1996 Census (IRSD Index Number for Each Statistical Local Area)
Source: Adapted from Glover, Harris and Tennant 1999, 75
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 87
Figure 3.8: Non-Metropolitan South Australia: Index of Relative Socio-Economic Disadvantage, 1996 Census (IRSD Index Number for Each Statistical Local Area)
Source: Adapted from Glover, Harris and Tennant 1999, 77
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 88
Studies indicate the characteristics of regional populations in relation to these factors can be important in explaining spatial differentials in fertility. In Australia, variations in fertility by religion, birthplace and for the Indigenous population, have long been apparent.
3.4.1. Religion and Fertility
The study of the influence of religion on fertility in Europe, the United States and Canada has been well documented by Andorka (1978). In the past religion has been a strong and stable influence on fertility differentials but over the last fifty years the differential has greatly diminished.
The term ‘religion’ in this context in Australia and other Western countries generally refers to the difference between Catholics and Protestants. Catholics have been shown to have consistently higher fertility.11 The differences between the two religious groups have been attributed to the values and teachings of the Catholic church, in prohibiting many methods of birth control and in favouring a large number of children.
This differential between the two major Christian religious groups has not been a universal feature in all developed countries. According to Day (1968), based on international comparison of Catholic-Protestant fertility differentials, and Van Heek (1956) on Dutch fertility, Catholic fertility was higher only in situations where there was a high level of economic development and where the group constituted a significant but threatened minority. In these circumstances fertility was maintained at a relatively high level to increase the population’s political and physical presence within society. The high Catholic fertility in a number of countries (Canada, United States, Northern Ireland, Australia) can be interpreted in terms of this theory.
In addition, not all other Christian faiths have had lower fertility than the Catholic population. In the United States, for example, the more fundamentalist groups like the Mormons, Nazarenes, Pentecostals and Jehovah’s Witnesses have displayed fertility levels
11 In some studies other religious groups, for example the Jewish population, have been shown also to have variable fertility levels (Andorka 1978, 1982). Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 89 as high as Catholics (Jones 1990). It has been argued by some that it is not religion per se that results in differential fertility but the over-riding influence of other socio-economic factors that distinguish the groups. For example Petersen in the later 1960s (1969, 538) stated:
The effect of religion per se on the reproductive behaviour of most persons in the West is now probably close to nil. What may seem to be a religious influence often reflects the fact that the members of any denomination are typically concentrated in a very few places in the social structure as defined by occupation, education, income, or any other of the usual indices.
While the Protestant, Catholic and Jewish populations may have been over-represented in particular social strata (for example, Catholics in low status groups, Jews in high status urban groups and Protestants mirroring the national average) studies in the United States which attempted to standardise for socio-economic status seemed to contradict Petersen’s assertion leading Andorka (1978, 313) to conclude ‘denomination is such a strong influencing factor that it modifies the impact of other socio-economic factors on fertility’.
In a relatively short period of time, however, the Catholic/Protestant differential in fertility has significantly diminished to the point of becoming a relatively minor or insignificant determinant of fertility. Westoff and Jones (1979, 216-217) suggested in relation to the United States, though having wider applicability, this was the result of
the combination of all of the social forces encouraging low fertility in the United States coupled with the undermining of the authority of the church on the birth control issue [as well as] the great increase in the proportion of Catholics in the middle class, the suburbanization of Catholics—in short, the rapid blurring of distinctive identities.
Variations in fertility by religion have long been apparent in Australia (Day 1983; National Population Inquiry 1975). Using data from the 1911 Census on specific religious groups, Borrie (National Population Inquiry 1975) was able to show that for women who completed their childbearing in the 1880s and 1890s those of non-Catholic adherence had a higher average family size than women of Catholic or more specifically Roman Catholic faith. As can be seen in Table 3.12, though the differences for all age groups were not great, the decline in average family size between cohorts was greater for the non-Catholic Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 90 groups. For those women aged 35–39 at the 1911 Census Catholic women had slightly larger families than the non-Catholic groups. It is interesting to note in the Table that for all age groups average issue was the highest for wives belonging to the Salvation Army and at its lowest for Jewish women.
Table 3.12: Australia: Average Issue of Wives by Religion, 1911 and 1966 Censuses 1911(a) 1966 Religion of Wives Age of Wives 65-69 55-59 45-49 35-39 65-69 55-59 45-49 35-39 Catholic 6.96 6.76 5.57 3.88 2.82 2.63 2.77 2.94 Roman Catholic 6.56 6.70 5.67 3.87 2.95 2.90 3.22 3.32 Catholic Group — — — — 2.91 2.81 3.06 3.18
Church of England 7.00 6.37 5.15 3.74 2.33 2.24 2.51 2.81 Methodist 7.38 6.75 5.42 3.86 2.42 2.40 2.67 2.94 Presbyterian 7.04 6.17 4.95 3.49 2.27 2.26 2.56 2.80 Salvation Army 7.45 6.79 5.96 4.12 2.83 3.00 3.15 3.30 Hebrew 6.72 5.87 4.51 3.06 1.51 1.46 1.83 2.22 Non-Catholic Group — — — — 2.35 2.29 2.56 2.82
All Religions 7.03 6.44 5.25 3.75 2.45 2.39 2.66 2.91 a Issue from all marriages; 1966 Census issue from existing marriages only Source: National Population Inquiry 1975, 55
Day’s (1970) analyses of data from the 1954 and 1961 Censuses examined only the two broad categories of religion—Catholic and non-Catholic. He found that for every age group examined, Catholic fertility exceeded non-Catholic fertility both in terms of median issue and the proportion of wives with five or more children. The stability of the differential over the inter-censal period, in contrast to the contraction in other characteristics associated with fertility, led Day (1970, 19) to conclude that fertility differences may be found ‘to be more closely associated with religious affiliation and practices than with any other group attribute’. By the 1966 Census, however, although Catholic fertility was still greater than non-Catholic fertility it was reported in the National Population Inquiry (1975) and by Ruzicka and Caldwell (1977) that a considerable narrowing of the differential between older (65–69 years) and younger (35–39 years) age groups had occurred.
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Day’s (1983) more detailed study of Catholic/non-Catholic fertility at the 1971 Census similarly found Catholic fertility to be higher than non-Catholic fertility in all the sub- groups of the population he examined. He continued to find a ‘consistency’ in the differential rather than a narrowing. Such persistence in the differential since his analyses of the 1954 Census (Day 1970) led him to conclude ‘the association of religious identification with group fertility differences would appear to be unusually direct’ (Day 1983, 79). He elaborated by stating that religion is a good ‘indicator of the likely existence of a set of conditions that are themselves of some causal significance to fertility’ (Day 1983, 83). These other factors included values, lifestyle, levels of aspiration, social status and contact with family and friends. Despite his beliefs in the strength of the influence of religion, results of the 1971 Melbourne Family Survey (Young 1977, 143) indicated that, although Catholic fertility was higher than non-Catholic fertility, with successively more recent cohorts there appeared to be indications of a decline in the difference between the two groups of women.
By the 1981 Census (Hugo and Wood 1983) there appeared to be very little difference between Catholic and non-Catholic fertility with the greatest differences being apparent at the older ages. By the 1986 Census (ABS 1992) emphasis was more on the differential between Christian and non-Christian fertility although once again the influence of religion was small. This emphasis reflects the growing diversity and relative growth in ‘Other’ religious groups in Australia in recent years (Bouma 1997). For women aged 45–54 years the average number of children born to Christians was 2.9 and to non-Christians 2.7 (ABS 1992, 9) but for younger women (under 25 years) the fertility of non-Christian women was higher than that of Christian women. Within the non-Christian group the fertility of women aged 45–54 of Buddhist or Muslim faiths was considerably higher than that of Christian women while the fertility of Jewish women was lower (Table 3.13). Similar patterns were evident from the 1991 Census data (Betts, Diemer and Hillier 1995).
In terms of the traditional Catholic/non-Catholic differential in fertility the ABS found Catholic women (45–54 years) had slightly larger families than Anglican women (3.1 and 2.8 children respectively) but at the younger ages (15–34) Anglican women had slightly higher fertility. Betts, Diemer and Hillier’s (1995) study matching of offspring to mothers at the 1991 Census showed Catholic fertility to be slightly higher than Anglican fertility for women aged 20–39 but at both Censuses the differences were so slight that ‘women Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 92 described as Catholic are almost indistinguishable from other Christian women’ (Betts, Diemer and Hillier 1995, 23).
Table 3.13: Australia: Average Number of Children Ever Born to Women: Religious Affiliation by Current Age 1986 Census Age Group (Years) All Ages Religious Affiliation 15-19 20-24 25-34 35-44 45-54 Observed Standardised(a) Christian 0.05 0.36 1.52 2.40 2.88 1.96 1.92 Anglican 0.05 0.39 1.52 2.33 2.79 1.94 1.86 Catholic 0.04 0.34 1.51 2.50 3.07 1.97 2.03
Other 0.07 0.43 1.25 2.07 2.74 1.48 1.77 Buddhist 0.04 0.37 1.30 2.26 3.54 1.87 n.a. Jewish 0.03 0.14 1.19 2.06 2.34 1.63 n.a. Muslim 0.13 0.91 2.33 3.08 3.77 2.28 n.a.
No Religion 0.07 0.40 1.19 2.00 2.66 1.40 n.a. a According to the age distribution of all women Source: ABS 1992, 10
In the past religion was an important factor influencing fertility levels. By the 1960s however, it was becoming apparent that the traditional Catholic/non-Catholic variations in family size were declining as both groups of women adopted similar patterns of childbearing. Over the post-war period Australia has become increasingly diversified in terms of ethnicity and religious faith (Bouma 1997, 12):
Since 1947 the religious dimension of Australian society has been transformed through migration, conversion and profound changes in the relations between religious groups and Australian society.
Of significance over this time has been a growing trend in the proportion of Australians reporting in the Census that they have ‘no religion’, a decline in the mainstream Christian groups (except for the Catholics), and significant growth in the non-Christian religions. Over the 1991 to 1996 inter-censal period, Buddhist and Muslim adherents increased by nearly 43 and 36 per cent respectively to become the eighth and ninth largest religious groups in Australia (Bouma 1997, 13). Over the 1996 to 2001 inter-censal period growth was even more pronounced at 79 per cent and 42 per cent respectively (Bouma 2002, 21). Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 93
While the proportion of Australians who identify or belong to a Christian religion is dominant—70.6 per cent (Bouma 1997, 13), in Australian society the continual growth of more recent non-Christian groups with their traditional values and focus on family life may yet again lead to variations in fertility on religious grounds. With religious affiliation closely related to birthplace and ethnicity the relationship between non-Christian religions and fertility will be influenced by the background of the non-Christian groups.
In conclusion while the traditional Catholic/non-Catholic differential in fertility is now only slight and consequently would appear to have little influence on spatial patterns of fertility, the influence of some of the non-Christian groups such as the Muslims and Buddhists may be increasing for particular localised areas. Although only mapped at the level of LGAs Figures 3.9 and 3.10 show the distribution of Buddhist adherents and Islam adherents respectively in Adelaide at the 1991 Census. Among Buddhists there is a high degree of concentration in the north and northwest LGAs while the Muslim population is spread more evenly across the metropolitan area. The highest numbers of Muslims though were found in Salisbury, Marion and Mitcham LGAs. Beer and Cutler (1995, 148) suggest the settlements in Marion and Mitcham may be students studying at the nearby universities while the settlement in Salisbury, to the north of the city, is more likely to be linked to permanent migration.
3.4.2. Birthplace and Fertility
In countries where immigration has played a major role as an instrument of population growth, and the ethnicity of the population is diverse, the relationship between fertility and birthplace has been intensely investigated. In such countries as Canada, the United States and Australia important differences along ethnic or racial lines have been found and these differences impact upon the demographic structure of the society.
Past research has shown that the relationship between immigrant and non-immigrant fertility is variable. In some countries the fertility of the immigrant population is higher than that of the native-born population and in other countries it is lower.
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Figure 3.9: Distribution of Buddhist Adherents in Adelaide, 1991 Census
Source: Adapted from Beer and Cutler 1995, 149
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Figure 3.10: Distribution of Islam Adherents in Adelaide, 1991 Census
Source: Adapted from Beer and Cutler 1995, 150
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In addition, with the passage of time, the fertility levels of certain birthplace groups converge on those of the native population whether initially higher or lower than the native population, while the fertility of other birthplace groups remains the same or even rises with duration of residence.
Studies in Canada show the fertility of immigrant women in the post-war period was lower than that of the Canada-born (Basavarajappa 1993; Ram and George 1993). Since 1961 however the differential between the immigrant population and the Canada-born has narrowed. At the 1961 and 1971 Canadian Censuses the fertility of immigrant women aged 15–44 years was about 20 per cent lower. At the 1981 Census this gap had been reduced to just 4.3 per cent and data from the 1991 Census showed the fertility of immigrant women to be 3.4 per cent higher than that observed among Canada-born women (Ram and George 1993, 6). While traditionally the differentials in fertility by ethnicity were between the people of French ethnic origin with high levels of fertility, followed by the Irish and then people of British origin (excluding the Irish) who had the lowest fertility (Andorka 1978), with increasing ethnic diversity over the last thirty years this pattern has changed. It is this change in ethnic diversity and, in particular, the increasing proportion of immigrants arriving from Asian, African and Latin American countries that is believed to be responsible for the reversal in the differential (Basavarajappa 1993).
Such narrowing of the differential in the fertility levels of immigrant and native-born populations has also occurred in the United States over the last thirty years and again data for the 1990s suggests overseas-born women now have slightly higher fertility than American-born women (Kahn, Marcote and Maguire 1997). The term ‘overseas-born’ is very broad encompassing many individual birthplace groups with widely varying levels of fertility. In the United States the highest fertility levels are found among the Hispanic groups while many Asian groups have very low fertility (Kahn, Marcote and Maguire 1997). It must also be noted that a long standing differential in fertility in the United States is that between the black and white populations. For over a century the black population has had significantly higher fertility than the white population although the extent of this differential varies across the country (Andorka 1978; Rindfuss and Sweet 1977).
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Research into ethnic and racial differentials in fertility has provided some explanations to account for the differentials. Three different hypotheses exist (Beaujot, Krotki and Krishnan 1983; Forste and Tienda 1996; Krishnan and Krotki 1992). The social characteristics hypothesis attributes the differentials in fertility not to ethnicity per se but to the socio-economic characteristics of the women that belong to specific birthplace or racial groups. Different groups vary in regard to these factors, in particular in their income and education levels (Andorka 1978; Beaujot, Krotki and Krishnan 1983). Although some support has been found for this hypothesis, other studies indicate the differentials in fertility continued to persist when social and economic characteristics were statistically controlled. These results gave rise to the minority group status hypothesis. This explanation centres around an independent effect on fertility of the marginality and insecurity of minority groups. While in some cases the effect has been to increase fertility (as in Catholic fertility) generally it results in depressed fertility as women limit fertility size in an attempt to achieve upward social mobility. The final hypothesis, the cultural hypothesis, attributes differentials in fertility between groups to the values and norms of specific groups which either promote or depress fertility. Religion plays a role here. Ideas about family size are seen as a component of ethnic identity.
In addition to these three hypotheses are those of selectivity, disruption and adaptation. The thrust of the selectivity hypothesis is that the migration process itself is selective of persons with specific characteristics thus a migrant’s fertility ideals are likely to be different from those of the general population. In addition to the forces of selection are the disrupting influences of migration in perhaps delaying marriage or separating partners for a period of time. Once settled or established in a country however the adaptation theory holds that with time the migrant population will adopt the values and norms of the host society and thus fertility levels will converge with those of the host country (Abbasi- Shavazi and Mc Donald 1996; Khoo and Shu 1996; Young 1991).
While considerable progress has been made in trying to understand and unravel the mechanisms involved in variations in fertility between birthplace and racial groups Forste and Tienda (1996, 110) believe there are still many questions to be answered such as:
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how or why membership in an ethnic or racial group should influence fertility, and whether and how social contexts (family, church, neighborhoods, peers) influence sexual behavior. To date, a great deal has been learned about the evolution of racial and ethnic differences in fertility, but much less about the forces that sustain and modify such trends in reproductive behavior over time.
Australia’s demographic history is basically one of immigration particularly in the post- war period. Immigration has played an important role not only through its immediate impact on population numbers but also in its continuing influence on population growth through the fertility of migrant women resident in Australia. Given such a significant influence on Australia’s population it has been important to study in detail the fertility behaviour of the various immigrant groups and consequently this differential in fertility is the most extensively researched in Australia.
Although reports of variations in immigrant fertility were made in the 1930s and 1940s (Borrie 1948, 124) it was not until the late 1960s and early 1970s, with increasing cultural diversity as a result of the large scale immigration after 1947 initially of Europeans and then of non-Europeans, that considerable attention was given to birthplace differentials in fertility. Studies examined these differentials from a number of angles—comparisons are made between particular birthplace groups and the Australia-born population; changes in fertility with increasing duration of residence of individual birthplace groups; the fertility of migrant groups in Australia is compared with the population in the country of origin; and where the history of immigration has been long enough for particular countries, changes or variations in fertility patterns between the first generation (the immigrants) and the second generation (Australian-born children of immigrants) are examined.
Using retrospective data from the 1911 Census, Ruzicka and Caldwell (1977) provide an historical look at the patterns of fertility for immigrant women prior to, and during, the fertility transition in Australia. For the pre-transitional stage of the demographic transition they examined the fertility of women born in the periods 1831–36, 1836–41 and 1841–46. At this time, however, only a minority of the population were actually Australia-born, although the proportion increased over each cohort from a low of just nine per cent to 15 per cent and then to 30 per cent (Ruzicka and Caldwell 1977, 134). The distribution of the overseas-born population by birthplace group remained relatively stable over this time Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 99 period. The vast majority of immigrants came from the United Kingdom, in particular England (45 per cent) followed by Ireland (33 per cent) and Scotland (12 per cent). The remaining 10 per cent of the population comprised people from Wales, Other Europe, Asia, Africa, America and Polynesia (Ruzicka and Caldwell 1977, 134, 136).
Considering the cultural similarity between the major birthplace groups it is not surprising that the differentials in average family size were not very large and according to Ruzicka and Caldwell (1977, 137) may have been the result of different marriage patterns. Australia-born women had the highest average issue at 7.72, 7.78 and 7.75 for the three cohorts respectively. Women born in the United Kingdom followed with 6.96, 6.90 and 6.70, while women born in ‘Other Europe’ had on average 6.38, 6.68 and 6.94 children.
Analysis of the average issue of wives of subsequent generations, 1846–51 to 1871–76, shows that only for the generation of 1846–51 did Australia-born women continue to have the largest families. From 1851–56 onwards the women born in ‘Other European’ countries (other than United Kingdom) had the largest families as the Australia-born and the United Kingdom-born women began to start limiting the number of children. ‘Other European’ groups entered the demographic transition considerably later (Ruzicka and Caldwell 1977, 167). Employing data from the 1954 Census to examine the fertility of cohorts born in 1899–04 and 1904–09 Ruzicka and Caldwell (1977) concluded that as the fertility transition neared its end the family size of Australia-born wives and the overseas- born wives was practically the same.
Although issue was not a topic included in the 1933 Census, Borrie (1948) examined the average number of dependent children under 16 years of age for specific immigrant groups in Australia at the time of the Census to ascertain their influence on future population growth. The lowest average number of dependent children was found for the Irish-born (1.76) followed by the New Zealand-born (1.89), All Europe (2.10), England (2.11), Wales (2.12), Scotland (2.15) and Italy with the largest families at 2.58. He believed these variations were significantly influenced by residential location and marriage patterns. At the 1933 Census, 55.5 per cent of Italy-born women were living in rural areas and only 34.9 per cent in the capital cities. For the UK-born women 62.3 per cent were living in the capital cities in 1933 and this increased to 71.5 per cent for the New Zealand-born women. In addition marriage rates varied considerably between the groups. For the Irish-born a Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 100 relatively high proportion of women of reproductive age were not married, while the majority of the Italy-born women appeared to have migrated to Australia as the brides or fiancees of Italy-born men (Borrie 1948, 127–129).
As the majority of immigrants had family sizes below the Australian average, and as the groups had shown a tendency to conform to the Australian patterns of fertility with time, Borrie (1948, 129) concluded that immigration was probably not the solution to a decline in the population, unless migrants were attracted from countries where fertility was high, or where immigrants were predominantly less than thirty years of age.
The immediate post-war period heralded significant changes to the structure of Australia’s population. Australia launched an immigration programme designed to increase the population by an average of one per cent per year (Price 1975a, 306). So successful was this programme that nearly 60 per cent of Australia’s post-war increase to 1974 was due to post-war immigrants and their children (Price 1975b, 303). In terms of fertility, while in 1947 13.5 per cent of all nuptial confinements were registered to couples where at least one parent was born overseas, by 1971 this had increased to 36 per cent (Ruzicka and Caldwell 1977, 244). Not only was the increase in numbers significant but the programme changed the focus of the migrant intake away from Britain towards firstly European refugees, then all Europeans and finally to non-Europeans. The dismantling of the ethnic barriers to migration meant that throughout the post-war period Australia has become a much more culturally diverse population. It was initially at the Census of 1954 that a more expanded group of birthplaces could be examined in relation to fertility. While Ruzicka and Caldwell (1977, 185) found the average issue of women born in Australia and post- war immigrants was fairly similar (the average issue of wives aged 45–49 was 2.47 for Australia-born wives and 2.18 for immigrants) this varied when individual birthplace groups were examined. Analysis of the average issue of post-war immigrants married for 25–34 years in 1954 showed wives born in Malta and the Netherlands had significantly larger families than the Australian average while wives born in the Baltic countries, Germany, USSR, England and ‘Other Europe’ all had smaller families (Ruzicka and Caldwell 1977, 187).
From the 1961 Census, Day (1970) examined differential fertility from a 20 per cent random sample of women aged 40 years and over who were currently married and entered Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 101 the marriage before the age of 26. He looked at five specific birthplace groups (Australia, British Isles, Italy, Netherlands, Germany, Greece) and found results similar to Ruzicka and Caldwell (1977). Both in terms of median issue and family size distribution (per cent with five or more children) the issue of wives born in the Netherlands and Southern Europe was greater than that of wives born in Australia while the issue of wives born in the British Isles and Germany was less.
A number of studies have examined birthplace differentials utilising data available in the 1960s and 1970s. Ware (1975) examined trends in birthplace differentials using data from the 1966 Census as the background to an examination of the motivations, attitudes and values of immigrants and how these impacted upon family size as reported in the Melbourne Family Formation Survey of 1971. Comparison of the number of children by age, by the birthplace of the wife (Australia, United Kingdom and Republic of Ireland, Greece, Italy, Yugoslavia, Netherlands) indicated that the Netherlands-born women continued to have the highest fertility albeit at a lower level than previously, and that the Southern Europe-born women also had higher fertility levels than the Australia-born. This was particularly so at the older age groups but the differential between the Southern Europe-born and the Australia-born was being rapidly eroded among the younger generations. The analysis of data from the survey indicated that the Southern Europeans had distinctive patterns in terms of fertility behaviour and attitudes. Ware (1975, 376) however concluded ‘the most significant explanation of differences between the Southern- European-born immigrants and the native-born population is not through culture conflict in the more restricted sense of the term, but through differences in socio-economic status’.
A more detailed examination of birthplace differentials from data available from the 1966 and 1971 Censuses is Ruzicka and Caldwell’s (1977, 1982) analysis of differences in family size for the generations of Australia-born and overseas-born women who were born between 1906–11 and 1926–31. For all cohorts Australia-born women had larger families than all the overseas-born groups. Average family size and the distribution of wives by the number of children showed considerable variation between individual birthplace groups and over time. Over the twenty year period the UK and Ireland-born and Australia-born populations experienced an increase in average family size as two and three child families became the norm. Germany-born wives also showed an increase in average Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 102 family size over the period but of all the groups examined they had the lowest fertility throughout the period at 1.88 in 1906–11 to 2.26 in 1926–31.
Average family size declined over the period under investigation for the other five birthplace groups. While initially (1906–11) having higher fertility than the Australia-born wives by 1926–31 the Greece, Italy and Yugoslavia-born women all had smaller families. Although the Netherlands-born population continued to have higher fertility than Australia-born wives throughout the twenty year period, by 1926–31 the differential had declined to 0.27 children. With the examination of a slightly broader range of countries the Netherlands-born were also eclipsed by the Maltese as the birthplace group with the largest families. Day’s (1983) analysis of birthplace differentials at the 1971 Census accords with the results of Ruzicka and Caldwell’s (1977) study and his cross classification of birthplace differentials with other characteristics showed that the patterns identified existed within every sub-division by religion, residence, occupation and schooling. To place the Australian trends in a wider context, Ruzicka and Caldwell also briefly discuss patterns of fertility in the countries of origin. They found trends towards greater homogeneity of family size were also occurring in the nations of Europe and some birthplace groups had on average larger families in Australia while others had smaller families.
One of the criticisms or restrictions of the analysis of retrospective data12 from the Census is there is no way of determining fertility of women before or after immigration, that is did they have most or all of their children in Australia or did they migrate after having started a family. Studies of birth registration data by Yusuf and Eckstein (1980), Yusuf and Rockett (1981) and Yusuf (1986) have attempted to overcome this problem. All three papers follow the same course of investigation. The initial work, Yusuf and Eckstein (1980) examined birth data for 1971–72 to inquire into post-immigration fertility of women from nine individual countries. Yusuf and Rockett (1981) expanded upon this work by comparing the fertility behaviour of women in 1971 and 1976. In addition to the nine individual countries, six country groups were introduced. Yusuf (1986) includes two more country groups into the study for the 1976 to 1981 inter-censal comparison.
12 Criticisms have also been made of the use of period measures of fertility in relation to immigrant fertility levels, see Young (1991, 69). Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 103
The data for 1971 and 1976 indicated that overseas-born women had higher fertility than Australia-born women however there was a tendency towards convergence over the inter- censal period. In terms of individual birthplaces the patterns identified mirrored past trends. In particular, fertility for the Southern European groups (Greece-born, Yugoslavia- born, Italy-born and Malta-born) and the Netherlands-born was higher than for the Australia-born population, although converging over time. Women born in Arab countries had very high fertility reflecting their long period of childbearing which actually increased over the inter-censal period. The lowest fertility occurred among the Poland and other East Europe-born groups and the Chinese. All other groups had fertility levels similar to the Australia-born.
By the early 1980s some changes in the usual trends appeared to be occurring (Yusuf 1986). Overall the fertility of the immigrant-born population was now slightly lower than the fertility of the Australia-born. The Southern Europe-born (except for the Malta-born) no longer had higher levels of fertility than the Australia-born. The highest fertility was found among the Arabs (generally Lebanon-born women) followed by more recently arrived immigrants from South and South East Asia with the Malta-born and the Netherlands-born populations still having slightly higher fertility than the Australia-born. Similar patterns of fertility were found from the analysis of retrospective data using for the first time the one per cent sample tape of the whole population at the 1981 Census (Gunn 1986; Hugo and Wood 1983). Different interpretations are made of whether some individual birthplace groups are slightly above or below the Australia-born trends but this is probably due to the use of different fertility indices and the limitations encountered in using the one per cent sample.
The continuing trend for overseas-born women to have lower fertility than the Australia- born, was evident from 1986 Census data (ABS 1992). In terms of individual birthplace groups low levels of fertility were found among women from Poland, Germany, Greece and Yugoslavia, levels similar to the Australia-born were found for women originating from Italy, New Zealand, the United Kingdom and Ireland, and high levels of fertility still existed for women born in the Netherlands, Malta, as well as very high levels of fertility for women from Turkey and in particular Lebanon and Vietnam (ABS 1992; Khoo and Shu 1996; Young 1991).
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Studies undertaken in the 1990s not only identified the trends in birthplace differentials but examined the fertility of immigrant groups in much greater detail. Young (1991) examined the extent to which the demographic and socio-economic experiences of particular birthplace groups converged towards the Australia-born population patterns over the period 1966 to 1986.
In relation to fertility Young (1991) found the persistence over time of distinct fertility patterns among migrant groups, with only a few having converged to the Australian level—women from the Netherlands, Malta and possibly Italy and China—and concluded that the cultural traditions of the country of origin remained very significant among the migrant community in Australia. The fertility levels of the second generation however, appeared to be converging to some extent to the Australia-born levels.
In 1991 no question on issue was included in the Census and so other data sources and methodologies were used to identify levels of fertility. Abbasi-Shavazi and McDonald (1996) applied the ‘own children technique’ to the 1991 Census in order to investigate birthplace differentials in fertility during the period 1977–91. In addition to the presentation of the general trends in fertility for twenty-one individual birthplace groups for which fertility in Australia and origin country are compared, they selected six migrant groups (British, Dutch, Greeks, Italians, Poles and Lebanese) to examine in more detail the effect of age at arrival in Australia and the fertility of their second generation. In general for the twenty-one birthplace groups they found declining fertility across the period but there were still marked variations between immigrant groups. Comparison of age-specific fertility rates of the migrant population with those in the country of origin showed that most migrant groups had lower fertility in Australia than the populations in their home countries. In addition among the second generation immigrants studied, indications of convergence towards Australian levels of fertility were found.
Khoo and Shu (1996) used three different ABS data sources, vital registration data of marriages and births 1986 to 1993, Censuses, and the 1992 Survey of Families in Australia, to examine not only the overall fertility rates of birthplace groups but also age patterns of childbearing, ex-nuptial fertility, marriage patterns and de facto relationships among migrants. The results of these analyses were that overseas-born groups from mainly English speaking countries had family formation patterns similar to the Australia- Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 105 born while there was considerable variation in the patterns for migrant groups from non- English speaking countries. In addition, groups from similar regions of origin tended to have similar family formation patterns suggesting the influence of social and cultural factors. This general conclusion was reinforced by statistical analyses that showed that the birthplace differentials in fertility were still significant even after differences in age and socio-economic status were taken into account.
The ABS (2002b, 24–31) for their bulletin, Births Australia 2001, produced a special chapter on fertility by country of birth. Based on TFRs the ABS examined overall trends in the TFR of the overseas-born population and Australia-born women, age specific fertility rates, the median age of mothers and compared the level of fertility of women born overseas living in Australia to that of women in their own country of birth. From this study the ABS noted that during most of the 1990s the TFR of women born overseas was slightly higher than that for women born in Australia. Between 1998 and 2000 however there was a reversal in this trend. In terms of age specific rates it was found overseas-born women aged 15–49 years living in Australia had lower fertility at the younger ages and higher fertility at older ages than women born in Australia. This fact was also reflected in the median age of mothers which was a little higher for overseas-born women (31.3 years) than for Australian-born women (29.5 years). Suggested factors that may be related to this pattern include a delay in childbearing after arrival in Australia due to the influence of the settlement process; the age of women when they migrated to Australia and the fact the TFR does not take into account any children born to the women before their arrival in Australia.
In terms of specific birthplace groups (Table 3.14) the ABS found the TFR for women aged 15–49 years in 2000 varied considerably ranging from 3.5 children per woman for those born in Lebanon to just under 1.0 for women born in Hong Kong. In general it was found the country of birth groups with the lowest TFRs were from South East and North East Asia, groups which contain a high proportion of female students in their populations in Australia. The ABS indicates that it appears women born overseas appear to adopt similar fertility levels to those of women born in Australia. As indicated in Table 3.14 there are exceptions to this, in particular for women born in Lebanon, Laos, Papua New Guinea and Spain.
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Table 3.14: Australia, Selected Countries of Birth,(a) Total Fertility Rates, 1990 to 2000 Country of Birth TFR in Year Country of Birth 1990 1992 1994 1996 1998 2000 Argentina 2.0 1.8 1.7 1.7 1.5 1.8 2.4 Australia (exc Norfolk Island) 1.9 1.9 1.8 1.8 1.8 1.7 1.7 Austria 1.9 1.8 1.6 1.4 1.3 1.7 1.2 Burma (Myanmar) 1.6 1.2 1.7 1.3 1.8 1.6 2.8 Cambodia 2.3 2.4 2.6 2.4 2.1 2.6 4.8 Canada 1.7 1.5 1.6 1.6 1.5 1.8 1.6 Chile 1.8 1.7 1.7 1.7 1.6 1.8 2.4 China (exc SARs &Taiwan Province) 1.5 1.8 2.3 2.4 2.0 2.0 1.8 Cyprus 2.3 2.2 2.0 2.0 1.7 1.5 1.9 Denmark 1.9 2.2 2.2 1.7 2.0 1.7 1.7 Egypt 2.4 2.4 2.7 2.8 2.6 2.3 2.9 Fiji 2.2 2.1 2.1 2.0 2.2 2.2 3.0 Former Yugoslav Republics(b) 1.9 2.0 1.8 1.7 1.6 1.5 1.6 France 1.9 2.0 1.6 1.6 1.6 1.6 1.8 Germany 1.8 1.7 1.6 1.6 1.6 1.6 1.3 Greece 1.5 1.6 1.3 1.3 1.5 1.3 1.2 Hong Kong (SAR of China) 1.5 1.3 1.3 1.3 1.3 1.0 1.2 India 1.9 1.9 2.0 2.0 1.9 1.7 3.0 Indonesia 1.9 1.9 1.8 1.8 1.8 1.4 2.3 Iran 1.5 1.7 1.5 1.6 1.5 1.5 2.8 Israel 2.9 2.3 2.0 1.8 2.3 1.9 2.7 Italy 1.7 1.6 1.5 1.4 1.5 1.5 1.2 Japan 1.5 1.2 1.2 1.2 1.7 1.7 1.3 Korea (South) 2.0 2.0 1.5 1.2 1.4 1.6 2.1 Laos 2.0 2.2 1.8 1.9 1.8 1.9 4.8 Lebanon 3.9 3.8 3.7 3.7 3.7 3.5 2.2 Malaysia 1.7 1.6 1.5 1.4 1.2 1.3 2.9 Malta 2.3 2.0 1.9 1.6 1.3 1.7 1.8 Mauritius 1.9 1.8 1.8 1.6 1.5 1.5 1.9 Netherlands 1.8 1.7 1.6 1.6 1.7 1.6 1.5 New Zealand 1.9 2.0 2.0 1.9 1.8 1.8 2.0 Papua New Guinea 1.9 1.7 1.8 1.9 1.8 1.8 4.3 Philippines 2.4 2.1 2.0 2.0 2.0 2.0 3.2 Poland 2.0 1.8 1.5 1.4 1.3 1.1 1.3 Portugal 1.8 1.9 1.4 1.5 1.8 1.7 1.5 Romania 2.2 2.4 2.0 1.8 1.8 1.9 1.3 Singapore 1.6 1.6 1.5 1.4 1.4 1.3 1.5 South Africa 1.6 1.6 1.5 1.5 1.4 1.4 2.9 Spain 1.7 1.9 1.9 1.5 1.9 1.7 1.1 Sri Lanka 1.7 1.8 1.9 1.9 1.9 1.9 2.1 Switzerland 2.1 1.8 1.6 1.6 1.9 1.6 1.4 Thailand 1.6 1.8 1.5 1.5 1.5 1.4 2.0 Turkey 2.7 2.7 2.6 2.5 2.5 2.5 2.3 United Kingdom & Ireland 1.8 1.8 1.7 1.6 1.5 1.5 1.6 United States America 2.0 2.0 1.9 1.8 1.8 1.8 1.9 Uruguay 1.5 1.6 1.7 1.6 1.8 1.9 2.3 Viet Nam 2.2 2.2 2.3 2.2 1.7 2.2 2.3 Total Overseas Born 1.9 1.9 1.9 1.8 1.7 1.7 Total 1.9 1.9 1.8 1.8 1.8 1.7 a Excludes countries with less than 100 births registered in 2000. b Former Yugoslav Republics consists of Bosnia-Herzegovina, Croatia, the former Yugoslav Republic of Macedonia, Slovenia, the former Yugoslav Republics of Serbia and Montenegro, and Yugoslavia n.f.d. Source: ABS 2002b, 27, 30 Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 107
Immigration has played a major part in Australia’s post-war growth and significantly contributed to the residential differentiation of Australia’s cities and regional areas (Atlas of the Australian Peoples Series 1986, 1991, 1996).13
While the difference in fertility between the Australia-born and overseas-born is small, individual birthplace groups have variable levels of fertility as the cultural traditions of the country of origin continue to be very significant among migrant communities in Australia. While many of the traditional post-war immigrant groups have lower or similar levels of fertility, higher levels exist for women born in the Netherlands, Malta, and in particular for the more recent arrivals from Turkey, Lebanon and Viet Nam. In general many of these groups as Figures 3.11, 3.12 and 3.13 show for Adelaide (for example) tend to concentrate in particular locations and therefore, at a local level, birthplace is of importance in influencing spatial variations in fertility.
3.4.3. Indigenous Population and Fertility
This section cannot be concluded without an examination of the Indigenous population of Australia. In any aspect considered, and fertility is no exception, the Aboriginal population differs considerably from the total population. Although the National Population Inquiry (1975) (based on detailed work later published as Smith [1980]) provided an exhaustive analysis of the fertility of the Aboriginal population up to the 1970s it was only in the 1980s and 1990s with the work of Gray (1982, 1990) initially and later others (for example, Dugbaza 1994; Gaminiratne 1992; Gray and Tesfaghiorghis 1993; Jain 1989; Taylor 1997; Tesfahgiorghis 1996) that the ‘true’ nature of Aboriginal fertility has been documented.
Establishing the true size or demographic characteristics of the Aboriginal population has long been a problem and continues to be so.
13 These atlases were produced for the Joint Commonwealth/State/Territory Population, Immigration and Multicultural Research Program by the Bureau of Immigration, Multicultural and Population Research, Commonwealth of Australia. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 108
Figure 3.11: Population Distribution of the Netherlands-born in Adelaide, 1991 Census
Source: Adapted from Beer and Cutler 1995, 42
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 109
Figure 3.12: Population Distribution of the Malta-born in Adelaide, 1991 Census
Source: Adapted from Beer and Cutler 1995, 36
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 110
Figure 3.13: Population Distribution of the Viet Nam-born in Adelaide, 1991 Census
Source: Adapted from Beer and Cutler 1995, 56
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 111
This is due in part to the problems associated with enumeration via the Census and the inconsistencies in the differentiation of Aboriginal people in birth and death registration systems. With time though, there have been genuine improvements in the range and quality of available statistics (Taylor 1997). Despite such developments a major concern in the enumeration of the Aboriginal population is the ‘social construction of Aboriginal and Torres Strait Islander identity’ (Gardiner and Bourke 2000; Taylor 1995, 4). Whereas birthplace groups are identified by the country of the individual’s birth, in the case of the Aboriginal and Torres Strait Islander people the critical question is the population group with whom the individual identifies. Depending on the political and social climate of the time, Aboriginal people have been either discouraged or encouraged to identify. Recently, however, there has been an increasing propensity for Aboriginal people to identify themselves in official statistics (Altman 1992; ABS 1993a, ABS 1993b; Gaminiratne 1993; Taylor 1995). The continual presence of such problems has meant the ‘description of demographic characteristics of the Aboriginal population has been built largely around constructive estimation from the available data’ (Gray and Tesfaghiorghis 1993, 81).
Estimated Total Fertility Rates for South Australia are indicative of the general trend for Australia (ABS 2002a) in fertility levels of Aboriginal women over this century. As can be seen from Table 3.15, Indigenous fertility reached its highest levels in the late 1950s and 1960s and declined significantly in the 1970s to reach levels only previously recorded in the early years of this century.
Table 3.15: South Australia: Indicative Estimates of Total Fertility Rates of Aboriginal Women Period(Years) Estimated TFR Period(Years) Estimated TFR 1906-11 3.4 1971-76 3.8 1928-33 4.7 1976-81 3.3 1942-47 5.8 1981-86 3.2 1956-61 7.0 1986-91 3.3 1961-66 6.6 1993 2.6 1966-71 6.3 2001 2.0 2003 1.95 Source: 1906–81 Gray 1982, 9; 1981–91 Gray and Tesfaghiorghis 1993, 89; 1993 Tesfaghiorghis 1996, 162; 2001, ABS 2002b 72; .2003 ABS 2004b, 58
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 112
The unexpected and significant declines in the TFR in the 1970s according to Taylor (1997, 92) were not the result of family planning programmes but ‘of increased participation by indigenous people, particularly women, in non-indigenous institutional structures that have altered the costs and benefits of children’. Throughout the 1970s and 1980s the TFR remained around 3.0. Such a level of fertility was well above that for the total Australian population and Tesfaghiorghis (1996, 165) credits most of this moderately high fertility to the very high fertility of teenagers and young women. By 1993 Tesfaghiorghis (1996) estimated the TFR in South Australia had declined to 2.6 and the latest data available from the ABS (2004b, 54, 58) indicates Indigenous fertility in South Australia in 2003 was 1.95 (2.145 for Australia as a whole). While overall the fertility of Indigenous women has declined, the continuing high fertility at the younger ages (less than 30 years) contributes to the relatively higher fertility of Indigenous women compared with that of all women (Table 3.16).
As with other characteristics of the population the geographic distribution of the Indigenous population in Australia is not even. Figures 3.14 and 3.15 presents the patterns for metropolitan Adelaide and non-metropolitan South Australia at the 1996 Census. Within Adelaide the Indigenous population is concentrated in the northern and western suburbs with some settlement to the south of the city. In non-metropolitan South Australia, the Indigenous population is found predominantly in the non-metropolitan urban centres and discrete Aboriginal communities that are not generally open to residence by the non- Indigenous population in the northern parts of the State (Beer and Cutler 1995, 70).
Table 3.16: South Australia: Age Specific Fertility Rates for Indigenous Women Compared to All Women, 2003 Age Group Indigenous Rate All Women 15-19 67.5 14.9 20-24 114.0 51.9 25-29 91.9 103.3 30-34 64.6 110.6 35-39 39.5 52.1 40-44 9.6 10.6 45-49 3.1 0.5 TFR 1.951 1.720 Source: ABS 2004b, 58
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 113
Figure 3.14: Metropolitan Adelaide Population Distribution of the Indigenous Population 1996 Census (As a Percentage of the Total Population in Each Statistical Local Area)
Source: Adapted from Glover, Harris and Tennant 1999, 51
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 114
Figure 3.15: Non-Metropolitan South Australia Population Distribution of the Indigenous Population 1996 Census (As a Percentage of the Total Population in Each Statistical Local Area)
Source: Adapted from Glover, Harris and Tennant 1999, 53
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 115
3.5. Migration
Major shifts in population out of, or into an area can significantly influence an area’s demographic structure. According to Pandit (1992a) interregional migration influences spatial variations in fertility such that areas that have close migration links will have a lower differential in fertility than areas that are relatively isolated from one another. Migration influences the fertility of both the origin and destination areas by acting to homogenise the regions in terms of socio-economic and cultural characteristics, and secondly migration helps to balance labour supply across areas and as a consequence reduces the impact of differentials in macroeconomic conditions on regional fertility differences. Although migration can reduce the effects a particular region’s characteristics can have on fertility the research of Pandit suggests it does not eliminate them.
Locally there is a relationship between life cycle stages and migration. The birth, or intending birth, of a child has been identified as a factor inducing mobility, for example intra-urban migration from higher density housing in the inner and coastal suburbs to low density housing in the outer suburbs (Faulkner 1980). The availability of appropriate and affordable housing in relation to family size and socio-economic characteristics can be an influential factor in population mobility and distribution (Hugo 1980).
3.6. Macro Social, Economic and Political Conditions
Spatial variations in fertility can also be affected by wider economic and political circumstances in society that cannot be derived by aggregating individual characteristics. In historical analyses of fertility trends, Pandit (1992a) notes links between long-term labour supply and demand with fertility (Andorka 1978; Lee 1973, 1978; Schultz 1981) while studies of more contemporary reproductive behaviour show a response between employment and economic conditions and fertility (Butz and Ward 1979a, 1979b; Easterlin 1961; Kirk 1960; Nam and Philliber 1984; Silver 1966). The theoretical framework of Alonso (1980) and O’Connell (1981a) incorporates the influence of macro conditions on regional variations in fertility. The connection between macroeconomic conditions and fertility has spatial dimensions in that at any given point in time different Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 116 regions are affected by, and respond differently to, varying levels of economic prosperity and decline. This is seen no more clearly than in the effects the processes of economic restructuring and globalistion have had on Australia and other advanced countries over at least the last two to three decades. The most visible features of economic restructuring are major declines in manufacturing employment with concomitant growth in service sector employment, but a bifurcation of this service sector employment into high and low paid jobs; increasing levels of unemployment, part-time and temporary work and an increase in the participation of women in the labour force (Sassen 1991; Badcock 1995).
An increasing body of research has assessed the social, economic and political impact economic restructuring has had on cities (Badcock 1995, 1997; Brown and Compton 1994; Marcuse 1996; Sassen 1991) although its impact has not been confined just to cities. This impact has varied from country to country depending on the unique character of each country’s urban, political, welfare and economic systems. In Australia, as in other places, the impact of economic restructuring has been compounded by a range of demographic changes. These changes include increases in the number and proportion of single parent families, in the aged population and in the number of migrants from the non-English speaking regions of the world. There is increasing evidence that the economic, social, technological, political and demographic changes that have occurred in society over the last three decades or so, are leading to greater inequality, inequity and social polarisation between and within cities and between cities and country areas (Beer and Forster 1998; Hugo and Smailes 1992).
In Australia since the 1980s there appears to have been increasing polarisation between those households where income is high and job security is relatively secure and those households where there is little or no employment and jobs are insecure and poorly paid (Birrell, Maher and Rapson 1997). It appears that the gap between the rich and poor has widened (Gregory 1993; McNamara, Lloyd, Toohey and Harding 2004). Current research has shown this widening is particularly so for families with children. Dawkins, Gregg and Scutella’s (2002) study on employment distribution in Australia found that even though employment levels in Australia are considered to be ‘healthy’ the work available has become increasingly polarised between households where all adults work or households where no one works. The most striking finding of this study (Dawkins, Gregg and Scutella, abstract) was the Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 117
large shift in patterns of employment in households with children, away from a dominant single male earner model toward more dual-earner and no-earner (couple and single) households with children. This dramatic polarisation of work and incomes for families with children is likely to have consequences for welfare costs and child opportunities in the next generation.
This increasing inequality is expressed both socially and spatially. Numerous writers in Australia have studied the impact of economic restructuring on Australia’s urban structure (see for example Badcock 1995; Baum and Hassan 1993; Beer and Forster 1996, 1998; Daly 1988; Forster 1991, 1995; Houghton 1987; Hunter and Gregory 1996; Murphy and Watson 1994; Stilwell 1980, 1989) and found it has had an uneven impact between and within cities. In general the major urban centres more reliant on a manufacturing/industrial base—Sydney, Melbourne, Adelaide and the major regional centres of Newcastle, Wollongong, Geelong and Whyalla have been the most adversely affected by the processes of economic restructuring. For urban centres themselves the general results of the studies undertaken have been as Beer and Forster (1998 8) so clearly state that:
The economic and demographic changes have combined to have major impacts on Australia’s low density, suburbanised and highly decentralised cities, with their long- established patterns of residential differentiation and contrasts in well-being between high and low-income suburbs….one impact is that our cities are becoming more unequal in the sense that the contrasts in incomes, employment levels and well-being between the rich and poor suburbs are increasing.
Within the major urban centres of Australia economic restructuring has most adversely affected the inner, middle and outer working class suburbs, in particular western Sydney, western and northern Melbourne, and northern and western Adelaide (Baum and Hassan 1993; Beer and Forster 1998; Forster 1991; Stilwell 1989). Many of the affected areas were developed in the 1950s and 1960s with the rapid expansion in manufacturing employment and have suffered the most with the significant declines in the importance of manufacturing employment to the economy. Economic restructuring has led to high levels of youth and total unemployment, low rates of labour force participation and low incomes. These areas are also characterised by a large proportion of public housing built to accommodate the growing blue collar work force associated with the expansion of industrial employment. Public housing in Australia has increasingly become welfare Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 118 housing and so these areas now house an increasing proportion of disadvantaged people (those on low incomes, single parent families, Indigenous Australians, refugee migrants). Thus the areas that have most adversely been affected by the processes of economic restructuring are also the areas which ‘have experienced the greatest decline in terms of social well-being’ (Baum and Hassan 1993, 164).
The changes that have affected the metropolitan centres of Australia over the last quarter of a century have also impacted on non-metropolitan Australia. With agricultural and industrial restructuring, the rural crisis, improvements in transport and communications, rationalisation and centralisation of services and shifts in lifestyle preferences, there has been considerable variation in the patterns of population change within non-metropolitan areas (Bell 1997a, Bell 1997b; Hugo 1996; Hugo and Smailes 1992). While non- metropolitan population change is becoming more complex, in its simplest form areas of growth are characterised by proximity to a metropolitan centre, an attractive scenic environment, or of tourism potential while areas more distant from the city or heavily reliant on a specific industry or service—manufacturing or farming (particularly wheat/sheep)—have experienced net losses of population (Hugo 1996). This dichotomy between the declining and growing regions of non-metropolitan Australia appears to have been ‘growing and sharpening’ since the 1970s (Hugo 1994), a trend Hugo (1996) in the mid 1990s expected to continue.
The increasing separation of particular groups of the population in metropolitan and non- metropolitan Australia is likely to be a significant factor in the widening of spatial differentials in fertility.
3.7. Attitudinal Changes and Preferences
While individual socio-economic factors and wider macro conditions influence patterns of family formation, the rapidly expanding literature on the causes of very low fertility, emphasise the importance of attitudinal changes and preferences. The traditional perception of the roles of motherhood and fatherhood have undergone considerable change. In the 1950s there was a ‘remarkably homogeneous and widely shared Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 119 view…revolving around almost stereotypical conceptions of men as breadwinners, and women as mothers and homemakers attached to individual breadwinners’ (Probert 2002, 10). Parenthood has become a much more calculated decision than it was in the past. There is a wide array of influences/pressures that affect women and men’s attitudes to family formation. These factors (Figure 3.16) including cost, childcare arrangements, work force aspirations, degree of family friendliness in the workplace, and responsibility for children and the home, for example, shape women’s and men’s preferences and choices.
The process of gender equality was perceived to result in men and women’s roles in society becoming more symmetrical (Manne 2001; Probert 2002) but in fact women have diverse lifestyle preferences, views and experiences. Understanding what women want, how this affects fertility and what policy responses should be instigated is creating considerable debate in the literature (Hakim 2001; Maher and Dever 2004; Manne 2001; McDonald 1998, 2000a, 2001a, 2001b; Probert 2002; Probert and Murphy 2001).
Another perspective that has received very little attention is the influence of the ‘embodied and socio-psychological experiences of childbearing and childrearing’ (Newman 2004a, 1), the fact that some women find decreasing satisfaction and difficulties with the role of mothering and motherhood (Hays 1996; Maushart 1997; Newman 2004a). Research by Newman (2004a, 2004b, forthcoming) on parenting suggests the pressures that come from raising a newborn child (physical and emotional) can be as ‘influential on fertility and family size as issues of work-family compatibility, the desire to return to work, and financial costs (Newman 2004a, 24). These pressures in many respects are seen to be a result of changes in society that have seen the burden of childbearing falling more heavily on the nuclear family. Newman (2004a, 24) states:
To a large extent the dominance of negative experiences over positive ones is seen to stem from decreased levels of social knowledge and social support for parenting, resulting from the loss of extended family and community without suitable replacements.
Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 120
Figure 3.16: Pressures on Family Formation
Source: Barnes 2001, 20 Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 121
Women’s tensions about family formation are also exacerbated by the contradictions in Australian society for what Probert and Murphy (2001, 31) state is our ‘enthusiasm for mothers working as well as our enthusiasm for mothers mothering’, roles many women see as incompatible or difficult to balance.
Regional and local differentiation in fertility may be important in identifying, and be partly explained by clusters of individuals with differing attitudes and preferences to family formation. Norms and value systems, regarding reproductive behaviour, are influenced by the lifecourse experience and current spheres of social interaction. Reproductive behaviour therefore may vary for groups whose sphere of interaction is wide, or increasingly located at the workplace, compared with those households whose reference groups may be more limited to their immediate environment, family of origin, local school, or local neighbourhood. This may provide further understanding to variations in spatial patterns of fertility.
3.8. Synthesis
This chapter has shown that despite the limitations of theory, studies of social and spatial differentials in fertility show differences continue to exist. While it is assumed within broad based theories that in the post-transitional era in most developed countries with low fertility, low mortality and low population growth that there is a great deal of uniformity throughout the population, studies have shown a considerable degree of variability exists between localities and that this variability increases with increasing areal disaggregation. Past patterns of fertility, mortality and migration have left local areas with distinctive profiles in terms of population size and composition (Champion 1993). These profiles influence the degree to which national demographic trends and social, economic and political events impact upon the area.
The economic, technological and social changes that have occurred in the past thirty years have considerably expanded the opportunities available in life, however the extent to which different groups are able to take advantage of these opportunities varies across the community. In addition, changing government policy, ‘the new liberal economic Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 122 approach’, (McDonald 1997, 13) also has a variable impact on different groups in society such that ‘present policy directions may lead to both those at the very top and those at the very bottom of the income distribution being more likely to have children while childbearing is restricted for those in the broad, middle range of incomes’ (McDonald 1997, 15). These changes in society have a spatial expression. There is increasing evidence the economic, social, technological and political changes that have occurred over the last two to three decades in society are leading to greater inequality, inequity and social polarisation between and within cities and between cities and country areas (Beer and Forster 1998; Hugo and Smailes 1992).
While Australia does not have the heritage of a distinctive class structure that exists in much older societies such as Britain, Australian cities have very distinct patterns of residential segregation in terms of socio-economic status. In describing Adelaide as it existed in 1976 Forster (1991, 50) stated:
Since the mid-nineteenth century there has been a striking contrast between the western, working class industrial suburbs of Hindmarsh, Thebarton, Woodville and Port Adelaide, and the pleasant middle and upper class residential environments to the east of the city on the slopes of the Adelaide Hills, and on the metropolitan coast at Brighton. Twenty-five years of rapid postwar suburban expansion, shaped by high rates of immigration, a booming manufacturing sector and the growth of almost universal car-ownership, perpetuated this contrast. Manufacturing industry and the accompanying public rental housing estates built by the South Australian Housing Trust (SAHT) dominated development on the flat land to the west and north in Woodville, Enfield, Salisbury and the satellite city of Elizabeth, and in the southern suburbs of Marion and Noarlunga. The more elevated east and south-east, on the other hand, developed as archetypal Australian owner-occupied middle class suburbia and by 1976 had high percentages of high income residents and almost no non-residential land use or public rental housing…. Adelaide, like all Australia’s capital cities, owed much of its postwar growth to immigration. By 1976, 27.6 per cent of the population was overseas-born and distinctive ethnic enclaves had developed. Migrants from Britain dominated the Housing Trust areas in the northern and southern suburbs, whereas people from Southern and Eastern Europe had settled in the inner suburbs immediately to the west and east of the city, before spreading outwards into middle suburbs such as Campbelltown, Payneham and Woodville. Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 123
The high status south-eastern suburbs, in contrast, had above average proportions of Australian born. Adelaide in 1976 therefore exhibited strong patterns of residential differentiation, which were reflected in significant variations in measures of well-being….
Studies of the impact of the processes of economic restructuring on Australian cities, including Adelaide, suggest it has had an adverse and unequal impact intensifying patterns of residential segregation and leading to greater inequality in social well-being (Beer and Forster 1998; Baum and Hassan 1993; Forster 1991; Murphy and Watson 1994). As Gleeson (2003, 183) states in relation to Western Sydney the growth of social polarisation ‘has been accompanied by, and partly realised through, increasing spatial polarisation, notably a heightened tendency towards residential segregation of socio-economic groups manifesting principally at the local scale’.
This social polarisation would have little influence on spatial patterns of fertility if, as assumed in demographic transition theory, socio-economic differentials between groups in terms of fertility had disappeared. As shown in studies of the direct link between socio- economic and cultural variables and fertility and in spatial studies of fertility using the ecological approach many of the traditional relationships continue to exist.
The strongest of these relationships currently is between educational attainment and fertility. Women with high educational qualifications tend to be career oriented, to be employed in high status, better paying occupations and tend to delay marriage, childbearing and ultimately have smaller families than other women. Conversely, women with lower levels of education are likely to have children earlier in their adult lives perhaps restricting their opportunities to improve educational attainment and possible economic prosperity. It is hypothesised here that women with higher levels of education are more able to fulfil aspirations to live in the more affluent higher status residential areas. As the educational differential in fertility is believed to have increased over time, it is hypothesised this characteristic would lead to a divergence in spatial patterns of fertility.
Labour force status and occupational classification have long been the defining characteristic in studies of the relationship between socio-economic status and fertility. While overall levels of fertility have declined, clear distinctions still exist between women Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 124 who are employed and those not in the labour force, and between women working in professional occupations as compared with women working in the trades or as production process workers or labourers. In Australian cities there are clear regional variations along occupational lines. As there is evidence the differential in fertility between occupational groups may have widened in the last decade or so it is suggested this factor is more likely to have contributed to a divergence or at least stability in spatial patterns of fertility than a narrowing of the differential.
Inter-related with education and occupation is income. The influence of income on fertility is difficult to interpret. The general finding of socio-economic analyses of fertility or spatial analyses is of a negative relationship with the lowest income groups having the highest fertility or a curvilinear relationship with higher order births occurring for those at the very top or very bottom of the income scale. Income is most useful as a further indicator of socio-economic status and thus it seems difficult to assume it would have led to a convergence in spatial patterns of fertility in recent times.
Whether socio-economic status is measured by these three variables or others such as housing type and tenure or motor vehicle ownership it is difficult to conclude spatial variations in fertility in the last two to three decades will have significantly narrowed.
In the United States in the 1990s it appeared birthplace differentials in fertility were more important than the traditional socio-economic variables in explaining spatial variations in fertility at least at the state level (Pandit and Bagchi-Sen 1993). Immigration has played a major part in Australia’s post-war growth and significantly contributed to the residential differentiation of Australia’s cities and regional areas (Atlas of the Australian Peoples Series 1986, 1991, 1996). While the difference in fertility between the Australia-born and overseas-born is small individual birthplace groups have variable levels of fertility as the cultural traditions of the country of origin continue to be very significant among migrant communities in Australia. Some of these groups tend to concentrate in particular locations and thus it is hypothesised that at a local level birthplace is of importance in influencing spatial variations in fertility. Similarly, while the traditional Catholic/Protestant differential in fertility is now only slight and consequently would appear to have little if any influence on spatial patterns of fertility, the influence of some of the non-Christian Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 125 groups such as the Muslims and Buddhists may be increasing for particular localised areas.
The fertility of the Indigenous population still remains higher than that of the rest of the population. In non-metropolitan areas the Indigenous population is highly concentrated in specific towns and Aboriginal settlements while in the cities they are found in areas of lower socio-economic status. Localised concentrations of the Aboriginal population may lead to higher rates of fertility in these areas compared to the surrounding areas.
Based on the influence of macro-economic conditions, the persistence of many of the traditional associations between socio-economic and cultural factors and fertility, and the evidence on spatial patterns earlier in the chapter, it would appear that at least for Australia, space still has some relevance in the analysis of fertility, particularly at a regional or local level.
Figure 3.17 presents a model summarising the factors discussed in this chapter that influence family formation. As can be seen from Figure 3.17 spatial patterns of fertility are the result of the complex inter-relationship of a wide range of variables. Whether spatial variations in fertility converge or diverge over time depends on the interaction and importance of these factors at various levels of space at a particular time. Each area, whether national, regional or local has a specific demographic profile. This profile, in conjunction with societal and governmental elements that may directly or indirectly affect women and families, leads to certain attitudes and values that result in particular fertility ideals and practices for people of that area. The resulting spatial patterns of fertility however, are also influenced by wider issues that affect overall population distribution. Some factors which influence the number and spacing of children, for example, educational qualification and income, also play a significant role in the decision making process about localities to live. The geographic pattern of fertility is mediated by historical settlement patterns (the level and persistence of residential segregation) and the degree of population mobility into and out of an area.
Clearly one or all of these factors at any given time may have an important influence on the distribution of the population and therefore the way changes in fertility for specific groups are reflected spatially. It is proposed the interacting influence and fluctuating Chapter Three: The Spatial, Socio-Economic and Cultural Dynamics of Fertility 126 importance or relevance of any or all of these factors over time for any given space can result in significant spatial differentials in fertility.
The following chapters of this thesis explore the extent of spatial variability in fertility in the State of South Australia, 1976–1996, and the relationship between the spatial patterns and some of the factors identified in this chapter that appear to still be important in explaining fertility patterns and trends. While the inclusion of a wide range of possible influential elements would be most desirable, as outlined in the next chapter on data, techniques and methods of analysis, this study will be confined to those variables available from the Census for small areas.
Figure 3.16 Toward a Spatial Model of Fertility for Australia SOCIETAL/GOVERNMENTAL ELEMENTS AFFECTING
Education Policy Restructuring of the Workplace Conditions Costs Childcare Gender Roles Motherhood vs -availability of Economy -workplace norms -direct costs (public, private, -sharing child Working -distribution of -lost family) Mother places -leave policies and house employment opportunity -availability responsibilities Contradiction -HECS fees -types of employment -flexibility in costs -affordability employment
Socio-Economic Factors Demographic Factors Cultural Factors
Education Birthplace/Nationality -age left school Age -qualification -ability to conceive -number of children Labour Force Status/Occupation Race -employed/unemployed CTING CHILDBEARING
E -part-time/full-time ‘Marital Status’ F -occupational status -partnering
AF -career orientation -stability of partnering
Religion Income -level -permanency
Values, Attitudes, Preferences, Choices INDIVIDUAL ELEMENTS
Spatial Location Fertility Level (modified by housing availability)
Historical Settlement CONVERGENCE / DIVERGENCE / STABILITY IN GEOGRAPHIC Degree of Population Patterns PATTERNS OF FERTILITY Mobility Chapter Four: Data and Research Methods 128
Chapter Four
Data and Research Methods
4.1. Introduction
To examine the existence and relevance of spatial differentials in fertility and the importance of socio-economic variables as explanatory factors in a ‘post-transitional’ society the remainder of this study focuses on the State of South Australia as a case study. The purpose of this chapter is to outline the data and methods of analysis used to assist in the interpretation of the findings presented in Chapters Five to Eight.
The chapter begins with a discussion of the wide array of fertility measures available and the choice of the Census variable—number of children ever born to women—as the measure of fertility in this study. This is followed by information on the design of the geographic units of analysis, mapping techniques and the measures used to establish convergence or divergence in the patterns over time. Finally the choice of independent variables and the use of multivariate techniques as a means of examining the relationships between the fertility patterns identified, and a number of socio-economic factors, are discussed.
4.2. Data
The study of fertility is far more complex technically than analyses of mortality. Any review of fertility studies or perusal of manuals of fertility analysis shows there is a wide array of measures available. ‘No single measure can express all quantitative aspects of human reproduction. Instead, there are many different measures, each suited to particular purposes, each possessing certain limitations’ (Campbell 1983, 1). Fertility measures can, however, be divided into two groups, calendar based indices or cohort based indices. The first group of measures, period measures of fertility, indicate the reproductive experience Chapter Four: Data and Research Methods 129 of a population during a specified period of time, generally a year. The defining quality of period fertility measures is that fertility is examined cross-sectionally without regard to the previous childbearing of the women concerned. They measure fertility at a point in time and are generally based on vital statistics (birth registration data) although they can also be calculated from Census data.
Period measures of fertility have been the most commonly employed in spatial analyses of fertility. Due to the unavailability or difficulty in obtaining fertility data from either birth registrations or from the Census for small area populations, early studies of the geographic patterns of fertility most commonly used the Child:Woman Ratio (CWR) or Marital Fertility Ratio which is a measure of the ratio of children under five years of age per 100 women or married women of reproductive age (15–44 or 15–49) (Heenan 1967; Hicks 1971; Wilson 1971a, 1971c, 1978a). Like indicators such as the Crude Birth Rate (CBR), General Fertility Rate (GFR), and Marital Fertility Rate (MFR), the CWR is easy to calculate but such measures do not account for variations in the demographic characteristics (age structure and often marital status) of populations. These may vary considerably across geographic areas so affecting spatial-temporal comparisons.
Over time more refined measures of fertility have been adopted, including Coale’s indices of fertility (Coale 1967, 1969) which compare the fertility of the population under consideration (actual births) to natural fertility (expected births) derived from the age- specific fertility rates of married Hutterite women during 1921–30. Coale’s indices have been specifically used in studies which analyse geographic variations in fertility over time (Coale 1969; Coward 1986a; Noin and Chauvire 1991; Stevenson 1982; Tsubouchi 1970; Wilson 1990). Even Coale’s indices though can be influenced by the age structure of the populations examined (Woods 1979). The most appropriate period measures to use if data are available are age specific fertility rates and total fertility rates (see Bell 1989; Weeks 1981) or some type of age standardised rate such as Wilson’s (1991) Age Standardised Order Specific Marital Fertility Rate. This is because these measures adjust for differences in the age structure (or if required marital status) of different populations.
As births are not independent of one another, an alternative approach based on cohort measures of fertility was developed to measure the fertility of a group of women as they Chapter Four: Data and Research Methods 130 pass through the childbearing years of life (Campbell 1983). These women are normally identified by the year of their birth or marriage. These measures typically examine fertility longitudinally and are normally calculated from Census and survey data. They have been increasingly used in general national level analyses of fertility but have received limited attention in areal studies as the data required are generally unpublished and therefore not readily available. Where cohort indices of fertility have been used, the most common measure has been the mean or average number of children ever born to women in particular age groups (Compton 1978; Coward 1980, 1986b). When this measure is calculated for women aged over 40 years it represents average completed family size. The measure can be calculated for all women or a subset of the population defined by marital status, such as those ever married, those currently married, or women married at particular ages as in Day’s (1983) analysis of urban/rural fertility differentials in Australia. Along similar lines, Wilson, in his 1978 study of the spatial patterns of Scottish fertility derived for each area a Cumulative Marital Fertility Ratio for currently married women aged 15 to 44 who had married only once, standardising for age at, and duration of, marriage.
4.2.1. Fertility Data
The source of fertility and other population data used in this current study is the Australian Population Census conducted every five years since 1961 and at irregular intervals prior to this. Since the passing of the Census and Statistics Act 1905 a question on the number of children ever born had been asked in every Census up until and including the 1986 Census, with the only exception being the Census held in 1933. After the 1986 Census, however, the ABS decided justification only existed to continue collecting these data at ten yearly intervals and thus the question was excluded from the 1991 Census (ABS 1989). Although included again for the 1996 Census the views of the ABS on the time frame for inclusion of this question in the Census have not changed and the question was excluded from the 2001 Census. The question will be included in the 2006 Census (ABS 2003b).
Over time the way in which the question has been asked has varied. Prior to the 1971 Census, all married persons were asked the number of children born to the existing Chapter Four: Data and Research Methods 131 marriage (although in 1911 and 1921 the number of children born in previous marriages was also requested). In the 1971 and 1976 Censuses only married women were asked to state the number of children born to the existing marriage as well as to all marriages. With the decline in marriage as a ‘principal fertility regulating mechanism’ (Statistics New Zealand 2001) resulting in an increasing numbers of children born outside legal marriage (Hugo 1986), at the 1981, 1986 and 1996 Censuses the issue question applied to all women aged 15 years and over irrespective of their marital status. (ABS 1991, 1992).
For the first stage of the research undertaken here a request was submitted to the ABS for tables derived from individual record data from the Censuses of 1976, 1981, 1986 and 1996 of age of female (in five year age groups) by issue for specially designed geographic areas (see section 4.3.2). This data set only allows an examination of fertility trends over a twenty year period. While it would be preferable to examine trends over a longer time period such individual record data is only available for the 1976 and subsequent Censuses.
As with all sources of data there are limitations on the accuracy of the data and Census data is no exception. The Census is subject to a number of inaccuracies as the result of collection or processing mistakes or from errors by the respondent (ABS 1997b). Whilst most errors are corrected by careful processing procedures, some still remain though their effect on the overall results of the census are very slight (ABS 1997b, 52).
Of particular relevance to this study is the non-response rate to the question on issue at each of the Censuses. Unfortunately in the data supplied from the 1976 Census the ‘not stated’ and ‘not applicable’ (women never married) were given as one category so it was impossible to calculate the extent of the non-response. Nationally the non-response rate for the female population aged 15 years and over was 6.8 per cent (Doyle in Hugo and Wood 1983, 3). Table 4.1 presents the non-response rate to the issue question at the 1981, 1986 and 1996 Censuses for the population of South Australia. The non-response rate was high in 1981 with much of the increase to women aged 15-24.1 At each of the following Censuses included in this study the non-response rate had decreased noticeably.
1 Doyle’s (1982) study of the national situation indicated tests on data quality showed much of the overall non-response rate was for women who had not had children. Chapter Four: Data and Research Methods 132
Due to the uncertainty of the distribution of not stated responses by issue, the population who failed to provide an answer to this question in the Censuses have been omitted from this study. This may mean that the number of women who are childless may be less than the actual level, particularly in the earlier Censuses as there has been a tendency for women who have never had a child to not answer the question (Mc Donald 1998).
It should be reiterated here that in 1976 information on children born was only collected for ‘ever married’ women so the data for 1976 and subsequent Censuses are not directly comparable. Hugo and Wood (1983, 3) state, however, that in 1976 ‘many of the women who had borne children outside marriage would have reported the births and many women who were or had been in de facto relationships would have been included in the “ever married” category.’ Due to budget cuts at the time of processing only one half of the 1976 Census forms were processed. These records were then weighted so that they could represent the entire population.
Table 4.1: South Australia: Not Stated Issue by Age, 1981, 1986 and 1996 Censuses 1981 1986 1996 Age ASD Non State ASD Non State ASD Non State Metro Metro Metro 15-19 34.8 37.9 35.6 17.0 17.9 17.2 9.1 11.3 9.6 20-24 13.6 11.7 13.1 9.7 7.5 9.2 4.6 5.5 4.8 25-29 6.7 5.1 6.3 5.8 4.8 5.5 3.5 4.1 3.6 30-34 5.4 4.4 5.2 5.0 3.9 4.7 3.2 3.6 3.3 35-39 5.3 4.4 5.0 5.1 4.5 4.9 3.1 3.1 3.1 40-44 5.1 5.1 5.1 5.7 5.4 5.6 3.1 3.3 3.1 45-49 5.4 5.1 5.3 5.9 5.6 5.8 2.9 3.3 3.0 50-54 5.4 5.5 5.4 6.2 5.8 6.1 3.4 3.3 3.4 55-59 5.5 5.4 5.5 6.2 6.1 6.2 4.2 4.6 4.3 60-64 6.2 6.5 6.3 6.4 6.0 6.3 5.4 5.8 5.5
Total 10.6 10.3 10.5 7.6 6.9 7.4 4.2 4.6 4.3 Total 12.8 12.3 12.7 8.2 7.4 8.0 4.3 4.8 4.5 (15-44) Source: ABS unpublished data
The data provided for 1976 are therefore estimates of the population and consequently are subject to sampling error. The more detailed the level of information, that is the smaller Chapter Four: Data and Research Methods 133 the population estimate, the greater the sampling error and this may be an important concern in the analysis of fertility at the small area level in this study (ABS 1979).
4.3. Method of Analysis
4.3.1. Choice of Measurement
In this study the observed and age-standardised average number of children ever born were calculated for specific geographic areas for the 1976, 1981, 1986 and 1996 Censuses.2 Rates were calculated for women of childbearing age (15–44 years), representing average but incomplete fertility, and for women who most recently completed their fertility, those aged 45–49 years. Crude measures of fertility are affected by the demographic characteristics of the population for which rates are calculated with age composition being a major factor. Where the prime interest, as in this study, is in relative fertility, a comparison of fertility between areas and over time, it is important to eliminate or control for the effects of any differences in the age structure of the populations/areas being compared. This control of demographic characteristics is achieved with the process of standardisation. This process creates a fictitious rate or index, which has no intrinsic value by itself. The rate only has meaning when compared with other similarly computed rates (Kitawaga 1964; Shryock and Siegel and Associates 1973).
According to Shryock, Siegel and Associates (1973, 419) if the appropriate data are available the direct method of standardisation ‘is the preferred procedure’ and provides ‘the best basis for determining the relative differences’ in mortality or fertility between areas and over time. In the direct method a chosen standard population age distribution is used to create weighted average age-specific fertility rates for the subsets of the population to be compared (Kitawaga 1964; Shryock, Siegel and Associates 1973). This method allows all rates computed using the standard population to be directly comparable.
2 These rates are estimates of average fertility. The question on ever born is not open ended or is coded to a maximum number. For this study data were supplied for the categories, 0, 1, 2, 3, 4, 5 or more, and not stated. To calculate the number of children the 5 plus category was assumed to be 5. This means that rates may in fact be slightly higher than are actually stated in this study. Chapter Four: Data and Research Methods 134
In this study, age standardised mean numbers of children ever born for women aged between 15–44 years have been calculated using this direct method.
The standard population employed in this study for the direct method of age standardisation is the sum of the populations and children of all the areas for South Australia included in this study. The formula for direct standardisation is
F = ∑ fa.Pa /P where fa = the mean number of children per woman in each five year age group, 15–19 to 40–44 for each area Pa = the standard population at each age P = the total standard population aged 15–44 years
4.3.2. Design of Geographic Areas
Geographically studies of fertility and mortality using ABS data have generally been undertaken for Statistical Local Areas (SLAs) or Local Government Areas (LGAs) as these areas have been the standard classification level for the release or publication of data. In the 1990s unit record data for smaller administrative units (Collection Districts CD) became available (upon request and at a cost) for the 1976 and subsequent Censuses. For the first time the potential exists to examine fertility patterns and trends in relative detail at the sub SLA level.
Based on the nature and pattern of settlement, South Australia has been divided into two sectors—metropolitan Adelaide and non-metropolitan South Australia—for the detailed analysis of fertility patterns and trends. The spatial units of analysis are different for each sector.
4.3.2.1 Metropolitan Adelaide
Chapter Four: Data and Research Methods 135
Below the level of the SLA in the ABS Australian Standard Geographical Classification (ABS 1986b)3 is the CD.4 To analyse fertility at a sub SLA level required the amalgamation of CDs into customised areas. The aim in formulating these areas was to establish areas of a reasonable size (total population of 5000–7000 persons) that appeared to represent neighbourhoods, that experienced development at the same time and that were comparable over time. The specification of the sub SLA areas began with the examination of CD maps for all LGAs of the Adelaide Statistical Division (ASD) at the 1976 Census. The boundaries of these CDs were then compared with their equivalent CD or group of CDs at the 1981, 1986 and 1996 Censuses. In many cases, particularly in long established and developed LGAs, CD boundaries or the boundary of a group of adjacent CDs had not altered since the 1976 Census.
Around ten per cent of all CDs undergo some type of change between Censuses (splitting, amalgamation, boundary variation) (ABS 1996b) and this is most prevalent in newly developing areas. In the Adelaide Statistical Division quite significant development occurred in a number of SLAs/LGAs (in particular, Gawler, Salisbury, Tea Tree Gully, Happy Valley, Noarlunga, Willunga) over the 1976 to 1996 period. In these SLAs one CD in 1976 may have been divided into ten CDs by the 1996 Census. While the sub SLA areas in this study have been designed to sit within SLA boundaries this was not always possible because of changes in the boundaries of SLAs over time (Appendix 4.1). From this matching procedure metropolitan Adelaide was divided into 155 regions. A base map of these areas is provided in Appendix 4.2 and the suburbs in each area are listed in Appendix 4.3. In the vast majority of cases (over 97 per cent) the areas created are directly comparable across the 1976 to 1996 period. Those areas where problems were encountered are listed in Appendix 4.1. The fertility data supplied by the ABS was for these custom designed regions at the 1976, 1981, 1986 and 1996 Censuses. In summary, the sub SLA areas for metropolitan Adelaide designed for this study are not based on any identifiable or defining characteristics but from an administrative base and the feasibility of the exercise of creating comparable areas over the 1976 to 1996 period.
3 To classify areas for which it can collect data the ABS developed a hierarchically structured classification system. In 1985 this classification system was standardised and called the Australian Standard Geographical Classification. The ASGC is updated annually (1986b). 4 The Census CD is the smallest geographic area defined in the ASGC. In urban areas a CD represented around 225 dwellings at the 1996 Census (ABS 1996b). In rural areas the number of dwellings per CD declines as population densities decrease. Chapter Four: Data and Research Methods 136
The areas vary widely in terms of physical and population size, and degree of development, but in most cases represent clusters of neighbourhoods or suburbs that overall would seem to be more homogeneous in their characteristics than the SLA as a whole of which they form a part.
4.3.2.2 Non-Metropolitan South Australia
The spatial units of analysis for non-metropolitan South Australia were also based on the geographic structure of areas designated by the ABS. Initially the feasibility of using SLAs as the geographic base was examined. With only about 30 per cent of South Australia’s population (or around 380 000 people at the 1996 Census) living outside the ASD and with 89 SLAs plus unincorporated areas many of the SLAs, particularly those more distant from the ASD, had total populations considerably smaller than the sub SLA regions designed for metropolitan Adelaide. The ABS groups neighbouring SLAs into Statistical Sub Divisions (SSDs). The ABS (2004c) defines an SSD as an ‘…intermediate level, general purpose regional type geographic unit. Statistical Subdivisions consist of one or more SLAs’. The SSDs form the first tier of spatial units of analysis in this study.
The SSDs have remained fairly stable over the 1976 to 1996 period despite considerable variation in SLA boundaries. Although consistency in boundaries over time was a specific objective of this research it was too difficult to make adjustments to the SSD boundaries so in some cases the SSDs are not exactly the same from Census to Census. Appendix 4.1 lists the SSDs where a change in the boundary occurred between the 1976 and 1996 Censuses and Appendix 4.4 provides a base map of the SSDs. It should be noted the SSDs of Barossa and Onkaparinga were combined because of boundary changes and Kangaroo Island was also included with these two SSDs as its total population numbered only 3902 in 1996 (ABS 1997c). In addition the SSDs of Pirie and the Flinders Ranges were also combined for the non-metropolitan analysis because of the changes in boundaries over time. Over the 1976 to 1996 period there was considerable change in the population in the non- metropolitan sector. In particular the Outer Adelaide Statistical Division experienced considerable growth resulting from an influx of people particularly from the metropolitan area while many of the other divisions had declining growth over the years or actually lost Chapter Four: Data and Research Methods 137 population (Table 4.2). These losses, according to Bell (1997a, 1997b), were particularly selective of young people moving to the metropolitan area for employment, educational and vocational training.
As well as experiencing considerable population change the distribution of the population and nature of settlement in the SSDs is not evenly distributed and this sector can itself be characterised as urbanised in parts with urban centres forming the focus of non- metropolitan settlement. Based on the debate over the relationship between fertility levels and urban size (section 3.2.2) a second tier of spatial units was devised for the non- metropolitan sector. Each State and Territory in Australia can be broken into Section of State categories. These categories represent (ABS 1986b, 134) ‘an aggregation of non- contiguous geographic areas of a particular urban type, or the rural balance (constituting another Section of State)’. In broad terms the Section of State categories are Major Urban (all centres with a population of 100,000 and over); Other Urban (all centres with a population of 1000–99,999 persons and holiday resorts of more than 250 dwellings of which at least 100 are occupied on Census night; this broad category can be broken down into smaller population size categories); Locality (all population clusters of 200–999 persons); and Rural Balance (the rural remainder of the State or Territory) (ABS 1986b). Research into non-metropolitan growth and decline has been based on this classification (Beer, Bolam and Maude 1994; Bell 1997a, 1997b).
Table 4.3 outlines the distribution of the population by Section of State for South Australia from 1976 to 1996 and it is clear that over time there was growth and decline in certain categories. These changes are often due to the reclassification of urban centres from one category to another as their population fluctuates. The physical definition of each urban centre is specified by the ABS and each urban centre or locality like other administrative units of the ABS, comprises one or more CDs. As population density is a major criterion in the designation of boundaries they are redefined at each Census and at times the boundaries are altered as a result of the growth or decline of the urbanised areas. Table 4.2: Population Growth, Statistical Divisions of South Australia, 1976 to 1996 Statistical Division 1976 1981 1986 1991 1996 Estimated Resident Population Adelaide 923 868 953 696 1 003 802 1 057 161 1 079 112 Chapter Four: Data and Research Methods 138
Outer Adelaide 60 648 69 839 81 894 93 200 104 385 Yorke & Lower North 40 646 41 721 43 592 43 881 44 058 Murray Lands 60 658 63 267 65 520 67 443 67 456 South East 59525 61 628 62 893 62 855 62 707 Eyre 32 962 34 454 34 935 33 165 33 011 Northern 95 763 94 164 89 914 88 594 83 524 Total 1 274 070 1 318 769 1 382 550 1 446 299 1 474 253 Population Distribution (Per Cent) Adelaide 72.5 72.3 72.6 73.1 73.2 Outer Adelaide 4.8 5.3 5.9 6.4 7.1 Yorke & Lower North 3.2 3.2 3.2 3.0 3.0 Murray Lands 4.8 4.8 4.7 4.7 4.6 South East 4.7 4.7 4.5 4.3 4.3 Eyre 2.6 2.6 2.5 2.3 2.2 Northern 7.5 7.1 6.5 6.1 5.7 Total 100.0 100.0 100.0 100.0 100.0 Inter-censal Growth 1976-81 1981-86 1986-91 1991-96 Adelaide 29 828 50 106 53 359 21 951 Outer Adelaide 9 191 12 055 11 306 11 185 Yorke & Lower North 1 075 1 871 289 177 Murray Lands 2 609 2 253 1 923 13 South East 2 103 1 265 -38 -148 Eyre 1 492 481 -1 770 -154 Northern -1 599 -4 250 -1 320 -5 070 Total 44 699 63 781 63 749 27 954 Average Annual Growth Rate (Per Cent) Adelaide 0.64 1.03 1.04 0.41 Outer Adelaide 2.86 3.24 2.62 2.29 Yorke & Lower North 0.52 0.88 0.13 0.08 Murray Lands 0.85 0.70 0.58 0.004 South East 0.70 0.41 -0.01 -0.05 Eyre 0.89 0.28 -1.03 -0.09 Northern -0.34 -0.92 -0.30 -1.17 Total 0.69 0.95 0.91 0.38
Source ABS 1998a; Bell 1997b, 15 Table 4.3: Non-Metropolitan South Australia: Population Change By Urban Centre Size and Section of State, 1976 to 1996 Census Size Category (Persons) Year Urban Centres Rural Rural Total Localities Balance Chapter Four: Data and Research Methods 139
>10 000 5000-9999 1000-4999 200-999 <200 Population 1976 91 087 14 211 76 149 37 976 147807 367230 1981 90 466 13 919 79 877 37790 155667 377719 1986 100 409 15 763 76 310 42083 162286 396851 1991 99 454 17 287 86 451 41433 163478 408103 1996 97 475 15 251 98 669 39503 156935 407833 Per Cent of Total Population 1976 24.8 3.9 20.7 10.3 40.2 100 1981 24.0 3.7 21.1 10.0 41.2 100 1986 25.3 4.0 19.2 10.6 40.9 100 1991 24.4 4.2 21.2 10.2 40.1 100 1996 23.9 3.7 24.2 9.7 38.4 100 Number of Urban Centres 1976 5 2 38 83 1981 5 2 38 81 1986 6 3 37 89 1991 6 3 42 90 1996 6 2 46 86 Population Change 1976-81 -621 -292 3728 -186 7860 10489 1981-86 9943 1844 -3567 4293 6619 19132 1986-91 -955 1524 10141 -650 1192 11252 1991-96 -1979 -2036 12218 -1930 -6543 -270
Source: adapted from Bell 1997b, 20
The fertility data requested from the ABS for this study was provided by the Section of State categories of urban centres of 10,000 persons and over; urban centres of 5000–9999 persons; urban centres of 1000–4999 persons; localities and rural balance. In the initial examination of the fertility data it was found that the data for localities was unstable (numbers too low) and the considerable movement of these areas across Section of State categories complicated the analysis. To overcome these problems these localities were combined with the rural balance. In this study no adjustments were made to accommodate for changes in the geographic boundaries of urban centres from one Census to another. This, in addition to some SSD boundary changes (Appendix 4.1), potentially makes direct comparisons over time difficult. An adjustment to the data that could be made however, was if an urban centre moved from one Section of State category to another due to growth Chapter Four: Data and Research Methods 140 or decline of its population. Data was ordered for individual urban centres in this situation and the Section of State categories could be altered to account for, and to assess, the impact of this movement from one category to another over an inter-censal period. Appendix 4.5 provides a detailed listing of the SSDs and the centres in each settlement category.
4.3.3. Mapping
A review of spatial studies of fertility, or indeed of other demographic processes and events, shows a variety of procedures for the choice of, and number of, categories to be used in mapping. Rates are often ranked and separated into a number of categories based, for example, on quintiles (Wilson 1978a) or measures of statistical significance (Bell 1989; Wilson 1991) but more often there is no discussion or evidence of the decisions made in the choice of categories.
As a major aim of this research is comparison of patterns over time, a standard form of categorisation has been implemented following Armstrong (1969). Fertility rates are divided into categories based on the use of the standard deviation of the distribution of rates to calculate standard distances in terms of the standard normal curve.
This is achieved by: • Finding the standard deviation (σ) of the distribution of rates for a set of administrative units. • Selecting the number of categories for mapping. • Class intervals for the chosen categories are established using the relation
Mean +/- (z)(σ) The z values are standard normal deviates based on the standard normal curve. To create six standard categories six equal areas of the standard normal curve are defined by z values on either side of the mean of zero, respectively, +/- 0.431 and +/- 0.967. Any desired number of class intervals can be established by selecting the appropriate set of z values. Chapter Four: Data and Research Methods 141
Multiplying the standard deviation of the rate distribution (σ) by the z factors fixes the distances between categories; each of these products are then added and subtracted from the overall mean to reveal the rate values for the categories themselves.
• The standard deviation (σ) is calculated using the rate distribution mean—the mean of the distribution of rates calculated for each of the administrative units. The rate distribution mean is likely to differ from the overall mean calculated directly from total numbers. The difference between the overall mean and the mean of the rate distribution is due to rounding error in the case of crude rate computation; in the case of age-adjusted rates it is due to both rounding error and the weightings of the procedure itself.
This procedure, for establishing standard rather than arbitrarily chosen class intervals, reduces the bias and error in map interpretation and permits comparisons between maps. As Armstrong (1969, 384) states
With use of the standard deviation of each rate distribution to fix class interval, it follows that the number of rates falling into the six categories [for his example he chose six categories although a different number can be chosen] is a function of the nature and degree of dispersion of rates about the mean, as well as their actual value. Thus one of the aims in establishing categories is fulfilled-that of adequately representing the nature of the dispersion of values peculiar to each rate distribution…Such a procedure may point out ‘significant’ state or county units with consistently high or low rates over time, or clusters of such units in geographical contiguity.
Any number of categories can be chosen and in this study six mapping categories are used, three above and three below the mean rate. 4.3.4. Convergence, Divergence and Spatial Persistence Over Time
Measuring variability in mortality and fertility often relates to measuring convergence and establishing criteria for its attainment. Specific indices of convergence or divergence ‘do not seem to have been developed’ (Coleman 2002, 323) and therefore depending on the variable(s) under examination a number of different measures individually, or in Chapter Four: Data and Research Methods 142 combination, have been used. In studies of fertility the measures used to establish convergence or divergence are the standard deviation (Compton 1991; Wilson 1978b, 1990), relative variance (Compton 1991) and most commonly the coefficient of variation (Coward 1986a, 1986b; Wilson 1978b, 1990). Pandit and Bagchi-Sen (1993) used a much more sophisticated modelling technique to establish the pace and temporal changes in fertility in the United States from 1970 to 1990. Spatial persistence over time, the degree to which the spatial patterning of fertility from one year to another is similar, has been measured by the correlation coefficient (r) (Coward 1986b) and the coefficient of determination (r2) (Compton 1991).
Although these measures are used to establish trends, there has been little discussion of the measure or criteria to establish when an absolute level of convergence has been reached. Coleman (2002, 323) suggests a couple of measures that perhaps could be indicators of a trend towards or the attainment of convergence. He suggests five percentage points difference in the TFR between countries may be sufficient, although that seems a way off as in the 1990s in Europe, the TFR in some countries was 60 per cent higher than in others. Similarly Coleman (2002, 323) suggests a coefficient of variation around five within and between countries also seems ‘to be a reasonable minimum for differences…since zero variation is not to be expected’.
In this study a combination of measures are used to establish variability over time. In addition to the standard measures used in other studies such as the standard deviation, coefficient of variation (standard deviation /average x 100) the regression approach (Social Model of Change) of Congdon and Shepherd (Congdon 1989; Congdon and Shepherd 1985, 1988; Shepherd and Congdon 1990) was also used. The social model of change approach using linear regression has been developed to establish the relative importance of different components of change and reflect the causal influence of the initial value for an area on the latter value. In this model change is decomposed into structural change (shift in the average over all areas; changes in the scatter about the average- convergence/divergence) and localised change (change in the relative position, rank order, of individual areas) at the level of individual areas and as elements of total change (Congdon 1989; Congdon and Shepherd 1985, 1988; Shepherd and Congdon 1990).
Chapter Four: Data and Research Methods 143
The regression equation expresses two components of change—the change in averages and in scatter. The predicted value of Y′i in area i for given Xi is
Y′i = Y + B(Xi ─ X) (1) where:
Y′i = the predicted value for an individual area Y = the mean over all areas for the latter period/date X = mean over all areas for the initial period/date
Xi = value for individual area at initial period/date B = the regression slope which expresses the influence (feedback) of the earlier value on the latter value, and where
B = RSy/Sx (2) where:
Sy = the standard deviation of the latter period/date
Sx = the standard deviation of the initial period/date R = the correlation between the initial and latter period/date
Equation 1 defines the two basic elements of change, structural and positional, which added together, give total change. Structural change in area i given by Y′i ─ Xi is that part of total change expected from the knowledge of X and the total changes in average and scatter of the index under consideration.
Structural change can be broken down further into:
Y′i ─ Xi = ( Y ─ X) + (B ─ 1)(Xi ─ X) (3) where: Y ─ X = change in the overall mean Chapter Four: Data and Research Methods 144
(B ─ 1)(Xi ─ X ) = the effect of the initial period/date X in inducing convergence or divergence. This is known as feedback.
Feedback is positive if B is greater than one and an area is above average in the initial period/date or if B is less than one and an area is below average in the initial period/date. If there is negative feedback then structural increases greater than average change ( Y ─ X ) are occurring only in areas below average in the initial period/date.
According to Congdon (1989), Congdon and Shepherd (1985, 1988) the regression slope (B) indicates trends towards convergence or divergence between areas. A regression slope with a value greater than one indicates that the differential between areas is widening. If this occurs in combination with an increase over time in a scale free measure of dispersion such as the coefficient of variation, there is clear evidence of a divergence in spatial patterns. If the value of (B) is less than one, and the coefficient of variation declines between two periods, then there is clear evidence of converging differentials. A regression slope under one may occur even if the coefficient of variation increases if there is a low correlation between the two time periods.
The other type of change that can be calculated from this social change model is positional change. Positional change is the difference Yi ─ Y′i where Yi is the latter period value and
Y′i is the predicted value from equation 1. Such positional change or change in the relative ordering of areas for an index according to (Congdon and Shepherd 1988, 174):
occur only when the end period value in an area is not what would be expected on the basis of structural changes and the area’s start period value. Changes in relative position reflect localised conditions which mean actual change does not match that expected on the basis of general structural shifts. Positional change has wider significance as a ‘true’ measure of change. It is the residual when the feedback effect of origin value on change has been controlled, whereas percentage point differences or ratios are not independent of origin.
Chapter Four: Data and Research Methods 145
If the difference between the actual value (Yi) and the predicted value (Y′i) is positive then the area has increased more than expected or decreased less than expected and therefore the area has risen in position relative to other areas. If the difference between (Yi) and 5 (Y′i) is negative then an area has declined in relative position (Congdon and Shepherd 1985).
Total change is the sum of structural and positional elements and is
Yi ─ Xi = ( Y ─ X ) + (B ─ 1)(Xi ─ X ) + (Yi ─ Y′i) (4)
To assess the aggregate amount of change, the variances of the components of change are used since the averages sum to zero over all areas. Squaring both sides of equation (4):
2 2 2 2 2 2 ∑ (Yi ─ Xi) /N = ( Y ─ X) + (B ─ 1) S x + S y (1 ─ R ) (5)
4.4. Explanation of Fertility Patterns
4.4.1. Introduction
Chapter Three highlighted a wide range of factors that are associated with fertility and spatial patterns of fertility as identified in the development of theory and from empirical studies. Clearly while it would be desirable to include as many variables as possible (embracing demographic, biological, cultural, socio-economic and psychological factors) in any approach towards explanation this is impossible. As Coward (1986a, 46) states
5 An area may experience a positional change without a change in rank order. For example, an area may have the highest value of the index being measured at time ‘a’ and time ‘b’ but if the increase in the index between time ‘a’ and time ‘b’ increases more than expected that area will be even more above average in time ‘b’ than at time ‘a’. Chapter Four: Data and Research Methods 146
…. the range and diversity of potentially important variables and possible levels of analysis ensures it is virtually impossible for any single approach or research design to offer anything but a partial view on the general explanation of fertility.
This section discusses the data utilised and the method of analysis.
4.4.2. Multivariate Analyses
In spatial analyses of demographic data the most commonly used research design, and the one used in this study, is the ecological approach (Macintyre, Ellaway and Cummins 2002; Woods 1986b). In this approach multivariate analyses (including correlation analysis and regression models) are used to examine the sign and strength of relationships between dependent variables, for example, measures of fertility, and a range of measurable independent variables that represent such attributes, for example as social class, that are associated with differential fertility.
Like all methodologies this approach has limitations and has therefore received much criticism (Banks 1972; Compton 1991; Wilson 1990). One of the limitations of this approach is that only measurable variables can be used and these should be in a normalised form. In addition the independent variables should not contain highly related variables. As the approach can only include measurable variables and generally these are obtained from Census data, it can normally only examine demographic, socio-economic and cultural variables. The approach is unable to include information on childbearing norms and couples’ decision making processes.
In a similar vein the most common criticism is that of ecological fallacy which is seen to restrict the value of conclusions made (Compton 1991; Robinson 1950; Woods 1986b) because only inferences can be drawn from the results rather than concrete causal connections. Conclusions about individual behaviour cannot be made from aggregate spatial relationships. Woods (1986b) however suggest that often there are valid causal links particularly if the relationships are strong. The methodology therefore highlights relationships that can or should be explored further through other complementary research methods, such as survey data. Despite the limitations the ecological approach remains a Chapter Four: Data and Research Methods 147 convenient, efficient methodology. This is particularly so for the study of small areas which are likely to be more homogeneous in their characteristics (Compton 1991).
The nature of multivariate analysis in demographic research is described in non-technical terms in Woods (1986b) but it essentially involves a number of stages. The first step is to identify the overall categories (demographic, economic, social, cultural, and environmental for example) and data sources from which important independent variables can be chosen. The next step involves identifying specific quantifiable variables and the expected or hypothesised associations of each individual variable with the dependent variable to create a model of explanation. The third stage of the process is the use of multivariate techniques to test the model.
The first analysis generally begins with a correlation matrix (Pearson r) to ascertain the strength and direction of associations not only between the dependent variable (fertility) and the independent variables, but between the independent variables themselves to identify those variables that may be highly correlated and may need to be excluded from the regression analyses. Correlation matrices were constructed for each age group (for example 15–44, 45–49) by geographic sector (metropolitan Adelaide, non-metropolitan South Australia) for data from the 1981, 1986 and 1996 Censuses.
Block linear regressions, where variables are entered simultaneously, were then undertaken to test the strength of the variables in combination and to gain an indication of the significance of individual variables over time. While R Square (R2) indicates the amount of variance in the dependent variable explained by the combined effects of the independent variables, the Adjusted R Square value is the most useful measure of the success of the regression model. As explained by (Kemp, Snelgar and Brace 2003, 209):
R Square tends to somewhat over-estimate the success of the model when applied to the real world, so an Adjusted R Square value is calculated which takes into account the number of variables in the model and the number of observations (participants) our model is based on.
Chapter Four: Data and Research Methods 148
In these analyses all residuals were checked for normality and variance and these showed no departure from the assumptions of the models.
4.4.3. Independent Variables
The analysis in this research is bound by the availability of data at the level of the CD which restricts the data source to the Census of Population and Housing. While for general purposes the Census provides a comprehensive range of information on the nature of Australia’s population, for small geographic areas there are a number of limitations. The full Census results are not publicly available as a unit record file. Data are only available from the official tables released by the ABS, from a number of software packages that can be purchased or as customised data available upon request to be purchased from the ABS. Obtaining customised data however is an expensive exercise. This means that the most appropriately specified variable may not be available in many cases because in the tables released by the ABS not all characteristics, for example, are cross referenced by age or sex. In addition the randomising of small numbers within the Census to protect the confidentiality of individuals can make the analysis of multiple characteristics for individuals or households unreliable for many areas (Lloyd and Harding 2004).
The independent variables used in this study are based on the identified associations between population characteristics and fertility identified in the literature. The specificity of variables was guided by the need to achieve a sufficient number of cases for each variable by area as well as where possible, to provide comparability over time. The explanatory analysis focuses on the more detailed non-metropolitan and metropolitan patterns of fertility rather than the spatial analysis at the level of the SSD (Chapter Five). In addition the lack of available data from the 1976 Census restricts this part of the study to the Census years of 1981, 1986 and 1996. The overall characteristics and specific variables available from the released data (ABS CDATA81, CDATA86, CDATA96 software packages), to be examined in providing a key to the explanation of the spatial patterns of fertility are outlined in Table 4.4.
Chapter Four: Data and Research Methods 149
It is clear from Table 4.4 that for most of the variables available the data are only available for all females, or for women aged 15 years and over and not just for women aged 15–44 or 45–49 years for whom measures of fertility were calculated and mapped. As a test of the effect this lack of differentiation by age may have on the explanatory power of the variables a request for customised data was made to the ABS for age related data (for women aged 15–44 years) from the 1996 Census for the geographic areas used in this study. The variables for which this customised data was provided are highlighted in Table 4.4.
In addition to the availability of individual variables, the ABS has developed a number of composite variables to represent different aspects of socio-economic status by geographic areas. The Socio-Economic Indexes for Areas (SEIFA), in their present form were first produced in 1990 from 1986 Census data (ABS 1998b). This study was able to access the SEIFA for the 1996 Census. As the indexes are provided at CD level, indexes were calculated for the geographic areas used in this study for fertility rates. The index score for these aggregated areas were formed by taking the weighted average, using population counts from the 1996 Census, across all CDs in the larger geographic areas.
The three main indexes used in this study are the Index of Relative Socio-Economic Disadvantage (IRSD), the Index of Economic Resources (IER) and the Index of Education and Occupation (IEO). The IRSD is derived from characteristics such as low income, low educational attainment, high unemployment and jobs in relatively unskilled occupations and variables that reflect disadvantage rather than measure specific aspects of disadvantage such as Aboriginal and Torres Strait Islander identification and persons aged 15 years and over separated or divorced. The higher an area’s index value the less disadvantaged that area is compared with other areas. High scores, for example, occur when the area has few families of low income and few people with little training and in unskilled occupations. Low scores on the index occur when the area has many low income families and people with little training and in unskilled occupations (ABS 1998b, 3). The IER reflects the profile of economic resources of families within the areas. This index reflects the income and expenditure of families such as income, rent and home ownership. Other variables which reflect non-income assets are also included such as dwelling size and number of cars. The income variables are specified by family structure, since this Chapter Four: Data and Research Methods 150 affects disposable income. Occupation and education variables are not included in this index as they are not directly related to economic resources. A high score on this index indicates an area has a higher proportion of families on high income, more households purchasing or owning dwellings and more families living in large houses. A low value reflects the opposite, a lack of economic resources (ABS 1998b, 3).
The IEO reflects the educational and occupational characteristics of areas. The education variables in this index show either the level of educational qualification achieved or whether further education is being undertaken. The occupation variables classify the work force into the Australian Standard Classification of Occupations (ASCO) major groups and the unemployed. The index does not include any income variables. An area with a high score on this index would have a high proportion of people with higher education qualifications or undergoing further education and being employed in the higher skilled occupations. A low score would indicate an area with concentrations of people with a low educational attainment and involved in unskilled occupations or unemployed (ABS 1998c, 3-4).
The relationship of these composite variables with the fertility measures for metropolitan Adelaide and non-metropolitan South Australia at the 1996 Census will be tested via correlation and may provide further evidence of the association between fertility and socio-economic factors.
The hypotheses of the proposed association of all the variables with the fertility patterns established in Chapters Six and Seven are discussed in Chapter Eight.
Table 4.4: Key Characteristicsa of the Population Used in the Multivariate Analyses 1981–1996 Characteristic Census Variable Demographic Marital Status 1981-96 Per cent of women aged 15 years and over now married* Aboriginality 1981-96 Per cent of females of Aboriginal and/or Torres Strait Island Chapter Four: Data and Research Methods 151
descent* Socio-Economic Educational Status 1981-96 Per cent of women aged 15 years and over who left school aged 15 years or less or who did not attend school* 1981-96 Per cent of women with diploma, bachelor degree graduate diploma or higher degree* 1981-96 Per cent of women with no qualifications* Labour Force 1981-96 Labour force participation rate for women aged 15-34 years* 1981-96 Labour force participation rate for women aged 35-54 years 1981-96 Per cent of women in the labour force unemployed* Occupational Status 1981 Per cent of women professional, technical and related workers and administrative executive and managerial workers 1986-96 Per cent of women managers, administrators, professionals and associated professionals* Industry (non- 1981-96 Per cent of women employed in agriculture, forestry, fishing and metropolitan analysis mining* only) Income – Individual 1981 Per cent of women with an annual income over $8001 1986 Per cent of women with an annual income over $12001 1996 Per cent of women with an annual income over $20800* Income – Household 1981 Per cent of households with an annual income less than $6000 Per cent of households with an annual income over $22001 1986 Per cent of households with an annual income less than $12000 Per cent of households with an annual income over $32000 1996 Per cent of households with an annual income less than $15548 Per cent of households with an annual income over $52000 SEIFA Indexes IRDS, IEO, IER Housing Occupancy 1981-96 Per cent of households owner/purchaser 1981-86 Per cent of households tenant – housing authority Dwelling structure 1981-96 Per cent of households in semi-detached, row, terrace house, flat or other medium density Cultural Birthplace 1981-96 Per cent of overseas born women from a mainly non English speaking country* 1981-96 Per cent of women born in the Netherlands, Malta, Turkey, Lebanon and Vietnam* Religion 1981-96 Per cent of women Catholic* 1981-96 Per cent of women Non Christian* 1981-96 Per cent of women no religion* Other Mobility 1981-96 Per cent of women in a different residence to 5 years previously* Accessibility/remoteness 1981-96 ARIA Index (non-metropolitan analysis only) *Variables at the 1996 Census available for women aged 15-44 years a More detailed information on these characteristics is provided in Appendix 4.6
Source: ABS Censuses 1981, 1986, 1996
4.5. Conclusion
This chapter has presented the data and methods of analysis used in this study to ascertain if spatial differentials in fertility still have relevance in Australia today and if socio- Chapter Four: Data and Research Methods 152 economic factors continue to play an important role in ‘explaining’ these pattens. The next Chapter examines fertility for SSDs in South Australia while Chapters Six and Seven provide more detailed analyses. Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 153
Chapter Five
Spatial Analysis of Fertility in South Australia 1976 to 1996 Statistical Sub Divisions
5.1. Introduction
Although there is ongoing discussion and analysis of the societal transformation in fertility there have been very few recent attempts that focus on the way local regions and communities have reflected, or varied from, national trends and why. There are however a number of justifications for such studies. As outlined in previous chapters, there is debate and conflicting viewpoints, both from a theoretical and empirical stance about the existence, persistence and relevance of differentials including spatial fertility differentials in advanced countries today. This has renewed interest in the convergence hypothesis for ‘post-transitional’ societies. In addition, understanding and projecting regional population dynamics has not lost its importance and, as Rowland (2003, 348) states, ‘studying population at a small scale deepens understanding of society-wide developments, especially where the national situation arises from a mosaic of trends and characteristics’.
In light of the changes that have occurred in fertility trends in Australia over the last thirty years or so (Chapter One) the objective of the next three chapters is to establish the extent of spatial differentials in fertility for the State of South Australia from the mid 1970s to the mid 1990s. In particular to identify the patterns; the changes in these patterns over time; and establish trends towards convergence or divergence. As a major aim is comparison over time, a standard form of categorisation has been implemented following Armstrong (1969). Fertility rates are divided into categories based on the use of the standard deviation of the distribution of rates to calculate standard distances in terms of the standard normal curve. So although the parameters of categories vary from map to map the categories generally represent the same area on the normal curve.
As scale can be a significant influence (Champion 1993; Compton 1991) in identified patterns this analysis will be undertaken at three geographic levels. First spatial patterns of fertility across the State as a whole at a broad level, SSDs, are presented to provide an Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 154 overall view. These areas however are large (both in terms of area and population) and contain a characteristically diverse population. To gain an understanding of how local regions and communities have conformed to State wide trends, Chapters Six and Seven examine fertility levels and trends at a more disaggregated geographic level. Fertility patterns by Section of State divisions for non-metropolitan South Australia are presented in Chapter Six while patterns for custom designed small areas (as outlined in Chapter Four) in metropolitan Adelaide forms the focus of Chapter Seven.
The data for this analysis are drawn from the 1976 and subsequent Censuses. Disaggregated data at the level of the CD is only available back to 1976. In analyses of change over time it is preferable to have time series data over as long a period as possible (Coleman 2002; Compton 1991). Although the period 1976 to 1996 is a relatively short period of time for an analysis of demographic trends, the data are of the previous issue of women and therefore cover a much longer time period than is first apparent. The data represent the experience or part experience of eleven five year cohorts of women. As the Lexis diagram (Figure 5.1) shows, the collective reproductive behaviour of these women extends from the immediate post-war years to the mid 1990s and therefore reflects the sweep of changes in family formation that have occurred over this time.
5.2. Geographic Patterns of Fertility by Statistical Sub-Division (SSDs) 1976 to 1996
This chapter examines fertility patterns for women aged 45–49 years and 15–44 years by statistical sub-division. This chapter therefore provides an overview, at a relatively broad level of the distribution of average issue for women, and whether and how the patterns change over the twenty year period, 1976 to 1996. Through the following three chapters average issue is referred to in various ways including ‘family size’ and generally as ‘fertility levels’.
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 155
Figure 5.1: Period-Cohort Space of Study for Women Aged 15–49 Years, 1976 to 1996
45-49 Period-Cohort Space 40-44
35-39
30-34
25-29
20-24 Age
15-19 10-14
5-9
0-4
6 1 6 1 1 6 1 6 1 6 1 6 1 6 2 3 3 4 5 5 6 6 7 7 8 8 9 9 46 - 1921- 1926- 1931- 1936- 1941 1946- 1951- 1956- 1961- 1966- 1971- 1976- 1981- 1986- 1991-
Cohort
5.2.1. Women Aged 45–49 Years
The analysis of the average number of children born to women aged 45–49 years examines the fertility trends of the group of women who at the time of the Census had ‘biologically’ most recently finished their childbearing. The information presented here is therefore for completed fertility, not a point in the ongoing fertility experience of this age cohort. The peak childbearing years for these women range from the early 1950s to the early to mid 1960s for the women aged 45–49 years in 1976 through to the early 1970s to early 1980s for women aged 45–49 in 1996 (Figure 5.1).
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 156
5.2.1.1 Description of Patterns
From Figures 5.2 to 5.5 and Table 5.1 it is evident that the most striking feature across all the maps is the contrast in fertility levels between SSDs in non-metropolitan South Australia compared with the SSDs in the metropolitan area. At all four Censuses the average issue of women living in the non-metropolitan SSDs was above the overall State mean (though not necessarily the distributional mean, Table 5.3). In the metropolitan sector of the State this was the case for the Northern SSD only at the 1981, 1986 and 1996 Censuses.
Table 5.1: South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years for Statistical Sub-Divisions, 1976, 1981, 1986 and 1996 Censuses (per 100 women) Census Year SSD 1976 1981 1986 1996 Av Pop Av Pop Av Pop Av Pop Issue Issue Issue Issue Northern 293.25 6291 294.29 6510 274.96 7291 226.90 11377 Western 274.63 6483 273.95 5182 257.01 5190 205.99 6492 Eastern 276.25 5314 259.86 4734 245.50 5226 198.66 7999 Southern 282.71 6083 271.83 5662 253.97 6261 210.09 11799 Barossa 314.59 638 298.50 665 279.58 769 220.96 1417 Onk, Fleurieu, K. I. 318.39 749 297.97 789 275.05 1018 220.27 2028 Yorke 314.87 565 306.02 482 289.22 529 239.82 756 Lower North 323.91 513 324.02 408 288.04 460 243.04 625 Murray Mallee 332.46 659 329.92 635 306.24 753 247.85 1072 Riverland 327.14 850 325.07 722 287.25 816 245.04 1150 Upper South East 346.33 436 321.29 418 290.45 440 254.05 568 Lower South East 330.60 921 319.34 853 298.26 922 247.91 1313 Lincoln 339.52 640 341.78 529 310.87 598 246.99 830 West Coast 345.44 139 330.43 92 310.48 105 262.83 191 Whyalla 325.10 764 325.57 704 311.23 739 241.87 695 Pirie 323.23 851 316.62 680 299.37 633 243.82 858 Flinders Ranges 336.37 438 328.82 399 303.49 516 242.94 687 Far North 314.85 131 302.90 138 274.25 167 234.63 257 Source: ABS calculated from unpublished data
Within the non-metropolitan region the main contrast is between the inner regions and outer SSDs. The SSDs close to, or adjoining, the metropolitan area had consistently lower levels of fertility. Although the areas of highest fertility in non-metropolitan South Australia varied from Census to Census (1976: Upper South East, West Coast, Lincoln, Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 157
Flinders Ranges; 1981: Lincoln, West Coast, Whyalla, Flinders Ranges; 1986: Whyalla, Lincoln, West Coast, Murray Mallee; 1996: West Coast, Upper South East, Lower South East, Murray Mallee) most are at a considerable distance from the major urban centre of Adelaide. Of particular note, however, is the Far North SSD. While the average issue for women aged 45–49 years living in this area was above the State mean, fertility in this area was comparatively low for an outer non-metropolitan SSD. In 1976 and 1986 it recorded the second lowest average issue and in 1981 and 1996 the third lowest average issue of all non-metropolitan regions (Table 5.1).
Within metropolitan Adelaide a consistent pattern of fertility levels is also evident (Figures 5.2 to 5.5). From 1976 to 1996 women living in the Northern SSD had on average more children than women living in other areas of Adelaide. Women living in the Eastern SSD had close to the lowest fertility in the State at the 1976 Census and the lowest fertility at the 1981, 1986 and 1996 Censuses. (Table 5.1).
5.2.1.2 Change Over Time
Using Congdon’s model of the ‘components of social change’ (see Chapter 4) it is possible to quantify change in specific areas over time. With this model it can be established to what extent the change in fertility in individual areas is a result or consequence, of structural changes that equally affect all areas, and to what extent it is the consequence of localised conditions1 (Congdon 1989; Congdon and Shepherd 1985, 1988; Shepherd and Congdon 1990).
1976–1981 With the knowledge that the rates for 1976 and 1981 are not exactly comparable over time (see Chapter 4), most areas followed the State trend and experienced a decline in fertility between 1976 and 1981, although there were some exceptions (Table 5.2). For example, based on the State wide trends a decline of 7.56 children per 100 women was expected in the Northern SSD but instead there was an increase of 8.6 children per 100 women over what was expected.
1 Localised conditions could include the effect on the population and fertility of changes in the boundaries of some SSDs between Censuses (Appendix 4.1). As can be seen from this section the overall consistency in the patterns over time suggest the effect of boundary changes has been minimal. Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 158
Figure 5.2: South Australia: Average Number of Children Ever Born to Ever Married Women Aged 45–49 Years for Statistical Sub-Divisions, 1976 Census
Source: ABS unpublished data
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 159
Figure 5.3: South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years for Statistical Sub-Divisions, 1981 Census
Source: ABS unpublished data
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 160
Figure 5.4: South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years for Statistical Sub-Divisions, 1986 Census
Source: ABS unpublished data
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 161
Figure 5.5: South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years for Statistical Sub-Divisions, 1996 Census
Source: ABS unpublished data
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 162
This resulted in fertility being slightly higher in 1981 than in 1976. Other areas to experience a similar trend were the Lower North SSD, Lincoln SSD and Whyalla SSD.
In a number of areas the decline in fertility was less than expected, although the decline was sufficient for the 1981 rate to be lower than in 1976 (Table 5.2). For some areas (Eastern SSD, Upper South East SSD and Barossa SSD) the decline in fertility between 1976 and 1981 was greater than expected based on State wide trends (Table 5.2). The Eastern and Barossa SSDs were already areas of comparatively lower rates.
1981–1986 Over this five year period all areas experienced a decline in the average number of children per woman. Whereas between 1976 and 1981 fertility across all areas was expected to decline by between 6.9 and 9.4 children per 100 women, the pace of the decline in fertility increased substantially over the next five year period 1981 to 1986. Based on overall State trends fertility across all areas was expected to decline by between 20.6 and 24.4 children per 100 women. Of note over this period was a reversal in the degree of change in the Lower North SSD. In this period fertility declined by a greater margin than expected. With a rate of 324 per 100 women in 1976 it was predicted to fall to 300 but fell to 288. The Riverland, Upper South East, Lincoln and Far North SSDs also declined by a greater margin than expected. In contrast, the average issue of women living in the Eastern, Yorke, Whyalla and Pirie SSDs declined less than expected.
1986–1996 This period of course covers ten years and so the decline in fertility over this time was substantial. Overall the decline ranged from 12 to 22 per cent but generally it was between 17–22 per cent. The decline in fertility in most areas was close to that expected based on the State wide trends. There were notable exceptions. In the Upper South East, Riverland, West Coast and Far North SSDs fertility remained considerably higher than expected. This was the first time period in which fertility in the Far North was above that expected. In the Barossa, Lincoln and Whyalla SSDs the decline in fertility was greater than expected by at least seven children per 100 women.
Table 5.2: South Australia: Measures of Change Over Time (Components of Social Change) in Average Number of Children Ever Born to Women Aged 45–49 Years for Sub-Statistical Divisions, 1976–1981, 1981–1986 and 1986–1996 (per 100 Women) SSD 1976–1981 1981–1986 1986–1996 Rates Change Rates Change Rates Change 1976 Pred 1981 St Pos Fdb Pred 1986 St Pos Fdb Pred 1996 St Pos Fdb
Northern 293.25 285.68 294.29 -7.57 8.61 0.85 272.05 274.96 -22.23 2.90 2.50 226.17 226.90 -48.78 0.72 2.42 Western 274.63 267.71 273.95 -6.92 6.24 1.49 252.67 257.01 -21.28 4.34 5.88 212.02 205.99 -45.00 -6.03 6.20 Eastern 276.25 269.27 259.86 -6.98 -9.41 1.44 239.25 245.50 -20.61 6.25 8.21 202.94 198.66 -42.57 -4.28 8.63 Southern 282.71 275.51 271.83 -7.20 -3.68 1.21 250.65 253.97 -21.18 3.31 6.23 209.62 210.09 -44.35 0.48 6.85 Barossa 314.59 306.28 298.50 -8.30 -7.78 0.11 276.07 279.58 -22.43 3.52 1.80 229.82 220.96 -49.76 -8.86 1.44 Onk, 318.39 309.96 297.97 -8.44 -11.99 -0.02 275.57 275.05 -22.41 -0.52 1.89 226.25 220.27 -48.80 -5.98 2.40 Fleurieu, K. I. Yorke 314.87 306.55 306.02 -8.31 -0.54 0.10 283.23 289.23 -22.78 5.99 0.55 237.43 239.82 -51.80 2.38 -0.60 Lower 323.91 315.28 324.02 -8.63 8.74 -0.21 300.39 288.04 -23.63 -12.34 -2.44 236.50 243.04 -51.55 6.54 -0.35 North Murray 332.46 323.54 329.92 -8.92 6.38 -0.51 306.01 306.24 -23.91 0.23 -3.42 250.85 247.85 -55.39 -3.00 -4.19 Mallee Riverland 327.14 318.40 325.07 -8.74 6.67 -0.33 301.39 287.25 -23.68 -14.13 -2.61 235.88 245.04 -51.38 9.17 -0.18 Upper 346.33 336.93 321.29 -9.40 -15.64 -0.99 297.79 290.45 -23.50 -7.33 -1.98 238.40 254.05 -52.05 15.65 -0.86 South East Lower 330.60 321.74 319.34 -8.86 -2.39 -0.44 295.93 298.26 -23.41 2.33 -1.66 244.56 247.91 -53.70 3.34 -2.50 South East Lincoln 339.52 330.35 341.78 -9.17 11.42 -0.75 317.31 310.87 -24.47 -6.44 -5.38 254.51 246.99 -56.36 -7.52 -5.17 West 345.44 336.07 330.43 -9.37 -5.64 -0.96 306.50 310.48 -23.93 3.97 -3.50 254.19 262.83 -56.28 8.63 -5.08 Coast Whyalla 325.10 316.43 325.57 -8.67 9.13 -0.25 301.86 311.23 -23.70 9.37 -2.69 254.79 241.87 -56.44 -12.92 -5.24 Pirie 323.23 314.63 316.62 -8.60 1.99 -0.19 293.33 299.37 -23.28 6.03 -1.21 245.43 243.82 -53.94 -1.61 -2.74 Flinders 336.37 327.31 328.82 -9.06 1.51 -0.64 304.96 303.49 -23.86 -1.48 -3.23 248.68 242.94 -54.81 -5.74 -3.61 Ranges Far North 314.85 306.53 302.90 -8.31 -3.63 0.10 280.26 274.25 -22.64 -6.01 1.07 225.62 234.63 -48.63 9.01 2.57 St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Rate Source: ABS calculated from unpublished data Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 164
5.2.1.3 Convergence/Divergence
As outlined in Chapter Four a number of measures can be used to establish variability over time. Table 5.3 presents some conventional summary statistics of change between each Census as well as measures derived from Congdon and Shepherd‘s (Congdon 1989; Congdon and Shepherd 1985, 1988; Shepherd and Congdon 1990)) ‘social model of change’ (regression value, changes in means, feedback, positional change). For the State as a whole, at the level of the SSD, a number of measures indicate a trend towards convergence over the twenty year period 1976 to 1996.
On first inspection the value of measures for the inter-censal period 1976–1981 provide mixed indications of convergence or divergence. An increase in the range from highest to lowest value (71.7 to 81.9), a slight increase in dispersion of values around the mean (standard deviation increased from 22.27 to 22.95) and an increase in the coefficient of variation (from 7.02 to 7.42) indicate a slight trend towards divergence. According to the regression value generated from the model of social change these slight increases are not sufficient enough to imply increased dispersion of areas with regard to fertility levels as the regression slope was under one. The inclusion of only married women in the 1976 data as compared to all women regardless of marital status in subsequent Censuses may have affected these measures of variability.
For the period 1981–86 all the indicators strongly imply convergence in the fertility patterns (declines in the range, standard deviation, coefficient of variation, regression slope less than one and 91 per cent of the change over time being due to a change in the means). For 1986–96 all the indicators, excluding the coefficient of variation, indicate a trend towards convergence. While there is variability in the different indicators in terms of implying convergence or divergence, when the SSDs are divided into metropolitan and non-metropolitan sectors, overall the regression slopes for both regions indicate a trend towards convergence. The exception to this is the metropolitan sector for 1976–81. Between 1976–81 the range, standard deviation and coefficient of variation all increased. In the metropolitan sector this increase was great enough to result in the regression slope generated from the model of social change to be significantly greater than one, implying divergence in the patterns.
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 165
Table 5.3: South Australia: Summary Statistics and Components of Social Change of the Average Number of Children Ever Born to Women Aged 45–49 Years for Statistical Sub-Divisions, 1976, 1981, 1986 and 1996 Censuses (per 100 Women) Summary Statistics Census Year 1976 1981 1986 1996 Total State Highest Value 346.33 341.78 311.23 262.83 Lowest Value 274.63 259.86 245.50 198.66 Range 71.70 81.92 65.73 64.17 Overall Mean1 293.43 287.06 268.26 218.64 Distributional Mean2 317.76 309.34 286.40 235.20 Standard Deviation 22.27 22.95 20.09 17.58 Coefficient of Variation 7.01 7.42 7.01 7.47 Percentage difference in 16.06 15.22 13.44 12.53 overall mean between metro/non-metro sectors 1976-1981 1981-1986 1986-1996 Correlation 0.94 0.95 0.90 Regression 0.97 0.83 0.79 Per cent of Variance Due To: Change in Means 52.1 91.1 97.2 Feedback 0.4 2.5 0.7 Positional Change 47.5 6.4 2.1 1976 1981 1986 1996 Metropolitan Adelaide SSDs Highest Value 293.25 294.29 274.96 226.90 Lowest Value 274.63 259.86 245.50 198.66 Range 18.62 34.43 29.46 28.24 Overall Mean1 281.87 276.38 259.17 212.03 Distributional Mean2 281.71 274.98 257.86 210.41 Standard Deviation 8.44 14.28 12.39 11.96 Coefficient of Variation 3.00 5.19 4.81 5.69 1976-1981 1981-86 1986-96 Correlation 0.85 0.998 0.97 Regression 1.45 0.87 0.94 1976 1981 1986 1996 Non-Metropolitan SSDs Highest Value 346.33 341.78 311.23 262.83 Lowest Value 314.59 297.97 274.25 220.27 Range 31.74 43.81 36.98 42.56 Overall Mean1 327.13 318.46 294.00 238.61 Distributional Mean2 328.06 319.16 294.56 242.29 Standard Deviation 10.87 13.22 13.03 11.30 Coefficient of Variation 3.31 4.14 4.43 4.66 1976-1981 1981-86 1986-96 Correlation 0.78 0.85 0.70 Regression 0.95 0.83 0.61
1 Overall mean: the rate for the State as a whole; all units combined 2 Distribution mean: mean of the rate distribution across areas Source: ABS calculated from unpublished data
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 166
While the values may provide a true indication of trends, I suspect the differences in the population counted at the 1976 and 1981 Censuses (married women compared to all women), has affected the measures. Between 1981–86 all indicators (except the coefficient of variation for the non-metropolitan sector) imply a trend towards convergence of the spatial differential in completed fertility. For the following inter-censal period the measures of variability provide conflicting indicators of convergence or divergence. In the metropolitan sector the range and standard deviation declined while the coefficient of variation and regression value increased. In the non-metropolitan sector the range and coefficient of variation increased while the standard deviation and regression value decreased. If the regression slope is taken as the most accurate indicator, then for both regions, and in particular the non-metropolitan region, there was a trend towards convergence over the 1986–96 period.
While the general trend over time has been towards convergence, the correlation coefficients, particularly for the metropolitan sector indicate a high correlation in the spatial patterns of fertility. As evident from Figures 5.2 to 5.5 usually areas with low or high fertility maintained their distinctiveness over time. This is despite the fact that many of these women may have completed their childbearing ten years before providing information to the Census and their circumstances, including their place of residence, may have changed over time.
There is a contrast in this pattern however when the State is divided into its metropolitan and non-metropolitan sectors. For metropolitan Adelaide the correlation between the 1981–86 and 1986–96 patterns was very high but the similarity between the patterns of fertility in the non-metropolitan sector actually declined over the 1986–96 period. In addition it appears the differential in family size between the two sectors of the State declined over time. In 1976 the average issue for women living in the non-metropolitan regions was 16 per cent above the metropolitan average and this percentage declined slightly at each Census point in time to reach 12.5 per cent by 1996.
To gain a greater insight into this metropolitan/non-metropolitan pattern, Figure 5.6 provides the distribution of women by parity for the two major sectors of the state. The disaggregation of these data show that a greater and more even proportion of women living in the non-metropolitan area had three, four or five or more children while women Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 167 living in metropolitan Adelaide most often had two or three children. Also significant is the higher proportions of childless or one child families in the metropolitan area. While these trends were evident throughout the 1976 to 1996 period they were much less distinctive for women aged 45–49 years in 1996 than in previous years. Much more significant for both sectors in 1996 compared in particular with 1976 and 1981 was the increase in childlessness, the decline in families of four or more children and the emergence of the two child family as the dominant parity. In 1996, 44.3 and 40.2 per cent of women in the metropolitan and non-metropolitan sector respectively had two children.
Figure 5.6: Percentage Distribution of Women Aged 45–49 Years by Parity, Metropolitan Adelaide and Non-Metropolitan Regions, 1976 to 1996
100
90
80
70 parity 5 or more parity 4 t 60 n e parity 3
C 50 r parity 2 Pe 40 parity 1 30 parity 0 20
10
0
6 n n n n n n an ta a ta an ta a ta lit li lit li 986 lit li lit li 197 o 1981 1 o 1996 p p o opo opo opo o opo opo opo tr r tr tr r r etr etr et me met m me me met m m
non- non- non- non-
Source: ABS, unpublished data
These patterns are reflected in the trends in fertility by age and parity for the SSDs.
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 168
Figures 5.7 to 5.9 take women aged 45–49 years in 1996 and examine their cumulative fertility2 since 1976, the proportion of women childless and the proportion of women with three or more children. It is clear that the trends in fertility were basically set in place at least by ages 25-29. The relative position of each area in terms of cumulative fertility and the percentage remaining childless or having three or more children remained relatively stable over time with few fluctuations. This provides a good explanation for the consistency in the spatial patterns observed.
Figure 5.7: South Australia: Cumulative Number of Children Ever-Born by Successive Age Group by Statistical Sub-Division, 1976 to 1996 2.8 SSD North
2.6 SSD West SSD East 2.4 SSD South SSD Barossa 2.2 SSD Onl, Fl, K.I. 2 SSD Yorke SSD Lower N 1.8 SSD Murray M
Cumulative Issue 1.6 SSD Riverland SSD U.S.E. 1.4 SSD L.S.E. 1.2 SSD Lincoln SSD West Coast 1 SSD Whyalla 25-29 30-34 35-39 45-49 SSD Pirie 1976 1981 1986 1996 SSD Flinders R Year and Age SSD Far North
Source: ABS, calculated from unpublished data
2 Although the women in this study are not a closed sample as the migration of women in and out of SSDs cannot be accounted for, it is not unreasonable to assume the level of migration for an SSD is not sufficient enough to significantly change the characteristics of the core group of women that are counted in the SSD from Census to Census. In fact the close association between the cumulative fertility pattern for SSDs and Figures 5.2 to 5.5 provide support for this assertion. Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 169
Figure 5.8: South Australia: Per Cent of Women with Three or More Children by Statistical Sub-Division by Successive Age Group, 1976 to 1996
60
North West 50 East South Barossa 40 Onk. Fl, KI Yorke Lower N 30 Murray M Per Cent Riverland USE 20 LSE Lincoln West Coast 10 Whyalla Pirie Flinders R 0 Far North 1976 (25-29) 1981 (30-34) 1986 (35-39) 1996 (45-49) Census Year and Age Group
Source: ABS, calculated from unpublished data
Figure 5.9: South Australia: Per Cent of Women Childless by Statistical Sub- Division by Successive Age Group, 1976 to 1996
35 North West 30 East South 25 Barossa Onk. Fl, KI Yorke 20 Lower N Murray M Per Cent 15 Riverland USE 10 LSE Lincoln West Coast 5 Whyalla Pirie 0 Flinders R 1976 (25-29) 1981 (30-34) 1986 (35-39) 1996 (45-49) Far North Census Year and Age Group
Source: ABS, calculated from unpublished data Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 170
Figures 5.7 to 5.9 generally show a steep increase in fertility to ages 30–34 and then a more gradual increase in fertility levelling out at ages 45–49 years. Particularly noticeable is the much higher rate of increase between the 1981–86 and 1986–96 Censuses for women living in the Eastern SSD. Despite this increase, completed fertility for women in this SSD was the lowest in 1996, a consequence of the significant delay in childbearing until the later ages.
5.2.1.4 Summary
This analysis of fertility for women aged 45–49 years at the broad level of the SSD has highlighted a number of points, including women living in three of the four metropolitan SSDs had the lowest number of children ever born compared with women living in any other SSD in the State.
In addition there is a high correlation in the spatial patterns of the number of children ever born from one Census to the next; these patterns are established early, at the younger ages, and there is little variation in the pattern with increasing age. In establishing trends in convergence/divergence it depends on the measures of variability employed. Using the social model of change as the most accurate indicator of variability, the overall trend was one towards convergence between SSDs and the metropolitan/non-metropolitan sector of the State over time.
While an examination of the fertility patterns of women aged 45–49 years is able to provide a picture of completed fertility much of their family building began or occurred two to three decades ago and does not reflect more recent trends in fertility patterns. Therefore the next section of the analysis will examine trends for women aged 15–44 years.
5.2.2. Women Aged 15–44 Years
This part of the analysis examines fertility patterns for those women, who at the time of the Census, were still in the childbearing age groups and so may not have completed their childbearing. Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 171
5.2.2.1 Description of Patterns
Table 5.4 provides the age standardised and unadjusted average number of children ever born per 100 women for each area at the various Censuses. Age standardisation controls for the effects of any differences in the age structure of the population of areas. Figures 5.10 to 5.13 show the spatial patterns of the standardised rates at each Census. From a broad perspective the spatial distribution of the standardised average issue for women aged 15–44 is reflective of the patterns evident for women aged 45–49 years. Clearly in terms of the whole State there is a distinct contrast between the non-metropolitan sector of the State with above average fertility in every SSD and the metropolitan region where rates are more variable.
In 1976 for example, married women living in the non-metropolitan SSDs had on average over two children yet this was the case in only one of the four metropolitan SSDs. By 1981, with the inclusion of births to all women regardless of marital status, rates had dropped considerably, but overall, the spatial pattern was maintained. This trend persisted in 1986 and 1996. In all years the highest fertility was for women living in Lincoln, the West Coast and Flinders Ranges SSDs except in 1996 when the Lower North SSD replaced the Flinders Ranges in the top three. In 1976 and 1981, as was the case for women aged 45–49 years, average issue for women living in the Far North SSD was lower than for women living in the surrounding SSDs. By 1986 however, this difference had disappeared. Women living in the Eastern SSD consistently had the lowest issue per woman at all four Censuses.
While the patterns of fertility displayed here are for a broad age group, 15–44, the trends in fertility by area are established by the younger age groups. To show this, rather than providing maps of various age groups which in essence will look very similar to Figures 5.10 to 5.13, Table 5.5 ranks the areas from lowest to highest in terms of age standardised average issue per woman at ages 25–29 and 15–44 at the 1981 and 1996 Censuses. Although there may be some shuffling of areas once all the five year age groups are included, this shuffling is generally only of the order of one or two places and therefore the overall spatial pattern at ages 25–29 generally remained much the same for the whole group 15–44 years.
Table 5.4: South Australia: Standardised and Observed Average Number of Children Ever Born for Women Aged 15–44 Years for Statistical Sub Divisions, 1976, 1981, 1986 and 1996 Censuses (per 100 Women) Census Year 1976 1981 1986 1996 SSD Stn Av Obs Av Pop Stn Av Obs Av Pop Stn Av Obs Av Pop Stn Av Obs Av Pop Issue Issue 15-44 Issue Issue 15-44 Issue Issue 15-44 Issue Issue 15-44 Northern 206.75 206.42 43499 152.09 154.61 58752 138.12 137.07 66690 129.26 126.58 72450 Western 190.90 191.47 27187 132.05 125.71 39319 114.08 108.01 42091 100.74 98.02 41487 Eastern 176.60 179.87 26180 111.18 106.96 41383 94.86 92.01 46276 85.60 80.82 47086 Southern 185.39 185.24 34301 131.16 134.86 49981 118.48 123.98 59037 112.49 113.83 65235 Barossa 210.48 216.09 3536 149.18 153.73 5369 134.42 141.25 6774 129.20 140.89 8354 Onk, Fleurieu, K.I. 217.85 220.34 4494 152.05 158.63 7104 136.74 145.45 9192 127.88 138.08 11444 Yorke 228.87 234.81 2665 166.27 171.77 3447 148.06 153.33 3938 147.80 164.18 3624 Lower North 229.23 232.61 2465 165.06 173.96 3222 154.17 160.64 3653 148.82 165.45 3317 Murray Mallee 229.53 228.18 4245 171.31 176.97 5415 155.31 159.98 6080 147.74 155.01 5790 Riverland 220.34 219.88 4331 163.12 166.25 6026 151.63 154.52 6520 144.66 150.35 6417 Upper South East 228.71 229.09 2754 164.70 170.76 3601 151.25 157.71 3838 145.25 156.07 3426 Lower South East 231.04 223.48 6062 169.76 167.69 8040 153.55 152.72 8930 141.26 144.02 8676 Lincoln 239.80 234.01 3754 174.61 176.09 4977 156.85 158.84 5520 150.57 158.24 4811 West Coast 236.99 226.70 989 183.91 181.08 1348 172.58 173.13 1444 159.48 160.62 1234 Whyalla 231.65 228.84 5758 173.40 168.04 6603 154.25 145.12 6285 146.21 144.01 4938 Pirie 229.77 228.25 4451 167.51 169.74 5595 150.28 151.95 5434 147.18 149.96 4894 Flinders Ranges 237.77 229.61 2869 174.84 170.78 4161 158.21 154.75 5078 148.34 152.81 4213 Far North 211.23 210.11 1245 159.83 155.68 1584 153.18 150.89 1621 143.45 147.91 1845 Source: ABS calculated from unpublished data Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 173
Figure 5.10: South Australia: Age Standardised Average Number of Children Ever Born to Ever Married Women Aged 15–44 Years for Statistical Sub- Divisions, 1976 Census
Source: ABS unpublished data
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 174
Figure 5.11: South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years for Statistical Sub-Divisions, 1981 Census
Source: ABS unpublished data
Figure 5.12: South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years for Statistical Sub-Divisions, 1986 Census
Source: ABS unpublished data
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 175
Figure 5.12: South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years for Statistical Sub-Divisions, 1986 Census
Figure 5.13: South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years for Statistical Sub-Divisions, 1996 Census
Source: ABS unpublished data
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 176
Figure 5.13: South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years for Statistical Sub-Divisions, 1996 Census
Source: ABS unpublished data
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 177
Table 5.5: South Australia: Ranking of SSDs from Lowest to Highest Age Standardised Average Number of Children Ever Born Per Woman at Ages 25–29 and 15–44, 1981 and 1996 Censuses Rank 1981 Census Age Groups 1996 Census Age Groups 25-29 15-44 25-29 15-44 1 Eastern Eastern Eastern Eastern 2 Western Southern Western Western 3 Southern Western Southern Southern 4 Barossa Barossa Barossa Onk, Fleurieu, K.I. 5 Onk, Fleurieu, K.I. Onk, Fleurieu, K.I. Northern Barossa 6 Northern Northern Onk, Fleurieu, K.I. Northern 7 Riverland Far North Lower South East Lower South East 8 Lower North Riverland Upper South East Far North 9 Pirie Upper South East Far North Riverland 10 Upper South East Lower North Whyalla Upper South East 11 Far North Yorke Riverland Whyalla 12 Murray Mallee Pirie Pirie Pirie 13 Yorke Lower South East Murray Mallee Murray Mallee 14 Lower South East Murray Mallee Yorke Yorke 15 Whyalla Whyalla Lincoln Flinders Ranges 16 Lincoln Lincoln Flinders Ranges Lower North 17 Flinders Ranges Flinders Ranges Lower North Lincoln 18 West Coast West Coast West Coast West Coast Source: ABS unpublished data
5.2.2.2 Change Over Time
1976-1981 Overall change for 1976–1981 in most areas was close to what was predicted based on State wide trends and the area’s value in 1976 (Table 5.6). The areas of note where the greatest deviations occurred were in the Eastern SSD, the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs and Lincoln SSD where the decline in average issue was greater than expected. The Eastern SSD had the lowest average issue of all SSDs in both 1976 and 1981 and the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs has one of the lowest rates for a non-metropolitan SSD. In the Northern SSD, Southern SSD, West Coast SSD and Far North SSD the decline in average issue for women aged 15-44 years was less than expected.
1981-1986 Between 1981 and 1986 the age standardised average issue for women aged 15–44 years was predicted to fall by around 14 children per 100 women across all areas. Fertility decline in most areas was consistent with this, with most variation around this mark being in the order of 0 to 4 children per 100 women. The Far North SSD experienced the Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 178 greatest variation with an increase of 8 children per 100 women more than expected. This trend over two periods, 1976–81 and 1981–86 resulted in fertility levels in the Far North becoming similar to those of women living in adjoining SSDs.
1986-1996 Over the period 1986–1996 the overall mean issue for women for the State as a whole declined by 6.9 per cent, with the variation by SSD ranging from 0.17 to 12.3 per cent. The areas of highest fertility are not necessarily those that experience the greatest declines. Between 1986 and 1996 the decline in fertility was greater than expected in the Western, Lower South East and West Coast SSDs in particular, while less than expected declines in fertility occurred mainly for two SSDs, Pirie and Yorke. In fact in Yorke SSD there was very little difference in the average issue per 100 women aged 15–44 in 1986 and 1996.
5.2.2.3 Convergence/Divergence
Summary measures of overall change are presented in Table 5.7. For the State as a whole these measures (standard deviation, coefficient of variation, correlation, regression) indicate strong stability in the pattern over time, and in the 1980s and 1990s, a slight trend towards divergence in the spatial pattern of fertility for women aged 15–44 years. Most of the change in variance over time was as expected, based on State wide trends, indicating that this strong spatial pattern existed prior to 1976. This is supported by Figures 5.2 to 5.5 of the average issue for women aged 45–49 years.
It should be noted that according to the model of social change, while the declines in fertility in the SSDs did mainly follow State wide trends, this was less so with subsequent inter-censal periods. The movement, by some areas, away from the overall State trends was enough to imply stability or a slight divergence in the spatial patterns (regression slope of 1.0 in 1981–86 and 1.01 in 1986–96). At each subsequent inter-censal period a greater proportion of the change was due to positional change.
Table 5.6: South Australia: Measures of Change Over Time (Components of Social Change) in Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years for Statistical Sub-Divisions, 1976–1981, 1981–1986 and 1986–1996. SSD 1976–1981 1981–1986 1986–1996 Rates Change Rates Change Rates Change 1976 Pred 1981 St Pos Fdb Pred 1986 St Pos Fdb Pred 1996 St Pos Fdb
Northern 206.75 147.23 152.09 -59.52 4.86 0.52 137.28 138.12 -14.81 0.84 -0.03 130.27 129.26 -7.85 -1.01 -0.06 Western 190.90 132.04 132.05 -58.86 0.01 1.19 117.17 114.08 -14.88 -3.09 -0.10 105.98 100.74 -8.10 -5.24 -0.32 Eastern 176.60 118.35 111.18 -58.25 -7.17 1.80 96.23 94.86 -14.95 -1.37 -0.18 86.55 85.60 -8.31 -0.95 -0.52 Southern 185.39 126.77 131.16 -58.62 4.39 1.43 116.28 118.48 -14.88 2.20 -0.10 110.43 112.49 -8.05 2.06 -0.27 Barossa 210.48 150.79 149.18 -59.69 -1.61 0.36 134.36 134.42 -14.82 0.06 -0.04 126.53 129.20 -7.89 2.67 -0.10 Onk, 217.85 157.86 152.05 -59.99 -5.81 0.05 137.25 136.74 -14.80 -0.51 -0.03 128.88 127.88 -7.86 -1.00 -0.08 Fleurieu, K. I. Yorke 228.87 168.40 166.27 -60.47 -2.13 -0.42 151.52 148.06 -14.75 -3.46 0.03 140.31 147.80 -7.75 7.49 0.04 Lower 229.23 168.75 165.06 -60.48 -3.69 -0.43 150.31 154.17 -14.75 3.86 0.02 146.49 148.82 -7.68 2.33 0.10 North Murray 229.53 169.04 171.31 -60.49 2.27 -0.44 156.58 155.31 -14.73 -1.27 0.05 147.64 147.74 -7.67 0.10 0.12 Mallee Riverland 220.34 160.23 163.12 -60.11 2.89 -0.05 148.36 151.63 -14.76 3.27 0.02 143.92 144.66 -7.71 0.74 0.08 Upper 228.71 168.25 164.70 -60.46 -3.55 -0.41 149.94 151.25 -14.76 1.31 0.02 143.54 145.25 -7.71 1.71 0.07 South East Lower 231.04 170.49 169.76 -60.55 -0.73 -0.51 155.03 153.55 -14.73 -1.48 0.04 145.87 141.26 -7.68 -4.61 0.10 South East Lincoln 239.80 178.88 174.61 -60.92 -4.27 -0.88 159.89 156.85 -14.72 -3.04 0.06 149.20 150.57 -7.65 1.37 0.13 West 236.99 176.18 183.91 -60.81 7.73 -0.76 169.22 172.58 -14.69 3.36 0.09 165.09 159.48 -7.49 -5.61 0.30 Coast Whyalla 231.65 171.07 173.40 -60.58 2.33 -0.53 158.68 154.25 -14.72 -4.43 0.05 146.57 146.21 -7.68 -0.36 0.11 Pirie 229.77 169.26 167.51 -60.51 -1.75 -0.45 152.76 150.28 -14.75 -2.48 0.03 142.56 147.18 -7.72 4.62 0.06 Flinders 237.77 176.93 174.84 -60.84 -2.09 -0.79 160.12 158.21 -14.72 -1.91 0.06 150.57 148.34 -7.64 -2.23 0.15 Ranges Far North 211.23 151.51 159.83 -59.72 8.32 0.33 145.05 153.18 -14.78 8.13 0.00 145.49 143.45 -7.69 -2.04 0.09 St =Structural Change; Pos=Positional Change; Fdb=Feedback Source: ABS calculated from unpublished data Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 180
Table 5.7: South Australia: Summary Statistics and Components of Social Change for Women Aged 15–44 Years for Statistical Sub-Divisions, 1976, 1981, 1986 and 1996 Censuses Summary Statistics Census Year 1976 1981 1986 1996 Total State Highest Value 239.80 183.91 172.58 159.48 Lowest Value 176.60 111.18 94.86 85.60 Range 63.20 72.73 77.72 73.88 Overall Mean1 201.70 142.13 126.88 118.09 Distributional Mean2 219.05 159.00 144.22 136.44 Standard Deviation 18.69 18.44 18.79 19.28 Coefficient of Variation 8.53 11.60 13.03 14.13 Percentage difference in 18.60 24.27 26.41 28.52 overall mean between metro/non-metro sectors 1976-1981 1981-1986 1986-1996 Correlation 0.97 0.985 0.98 Regression 0.96 1.00 1.01 Percent of Variance Due To: Change in Means 99.4 95.5 84.3 Feedback 0.0 0.0 0.1 Positional Change 0.6 4.5 15.6
Metropolitan Adelaide SSDs Highest Value 206.75 152.09 138.12 129.26 Lowest Value 176.60 111.18 94.86 85.60 Range 30.15 40.91 43.26 43.66 Overall Mean1 191.92 133.59 118.70 110.20 Distributional Mean2 189.91 131.62 116.38 107.02 Standard Deviation 12.68 16.70 17.75 18.47 Coefficient of Variation 6.68 12.69 15.25 17.25 1976-1981 1981-86 1986-96 Correlation 0.97 0.99 0.99 Regression 1.28 1.05 1.03
Non-Metropolitan SSDs Highest Value 239.80 183.91 172.58 159.48 Lowest Value 210.48 149.18 134.42 129.20 Range 29.32 34.73 38.16 30.28 Overall Mean1 227.60 166.01 150.05 141.62 Distributional Mean2 227.38 166.83 152.18 144.84 Standard Deviation 9.16 9.15 9.08 8.07 Coefficient of Variation 4.03 5.49 5.96 5.57 1976-1981 1981-86 1986-96 Correlation 0.89 0.93 0.93 Regression 0.89 0.92 0.82
1 Overall mean: the rate for the State as a whole; all units combined. 2 Distribution mean: mean of the rate distribution across areas Source: ABS Calculated from unpublished data
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 181
As outlined in Chapter Four positional change refers to changes in areas that are greater than, or less than, that expected based on the trend for the State as a whole. It represents a change in the rank order of areas and in their deviation from the average (Congdon and Shepherd 1988). These areas are therefore affected more so by localised population and area characteristics.
From Table 5.6 it is clear that the degree of variance between the expected change in fertility in an area and the actual change was greater for a number of areas in the period 1986–96 than it was in 1981–86. For example, positional change for the Western SSD was -3.09 in 1981–86 but -5.24 in 1986–96, for Yorke -3.46 in 1981–86 but 7.49 in 1986–96. While the trend in positional change is less for some areas, overall the variance was great enough to imply a trend towards divergence between areas in fertility for women aged 15– 44 years.
When the State is divided into sectors it is clear that although patterns remained strong in both the metropolitan and non-metropolitan sectors over time, the divergence in the pattern over time was due to the influence of fertility trends in the metropolitan sector of the State (increasing standard deviation, coefficient of variation, correlation coefficients and regression parameter greater than one). In addition, in contrast to the trend for women aged 45–49 years, it appears the differential between the metropolitan and non- metropolitan sectors of the State increased over time. In 1976 average issue for women living in the non-metropolitan sector was 18.6 per cent above the overall metropolitan mean. By 1996 the difference had increased to 28.5 per cent.
The variation in the differential between the metropolitan and non-metropolitan sector of the State is explained partly by the smaller proportion of women in the metropolitan sector having three or more children but more so by the increasing difference in the proportion of women who have no children (or issue) between the two sectors (Figure 5.14). In 1981, 40.1 per cent and 32.0 per cent of women aged 15–44 years in the metropolitan and non- metropolitan sectors respectively had no issue. By 1996 these proportions had increased to 48.8 and 35.4 percent respectively.
Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 182
Figure 5.14: Percentage Distribution of Women Aged 15–44 Years by Parity, Adelaide and Non-Metropolitan Regions, 1976 to 1996
100
90
80
70 parity 5 or more 60 parity 4 t n
e parity 3
C 50 r parity 2 Pe 40 parity 1 30 parity 0
20
10
0
6 1 6 7 an n an n 86 an n an n it ta 98 t ta 9 t ta 99 t ta 19 li 1 i 1 li li 1 li i pol poli ol po po ol po o p o po o p tro ro tr ro tr ro tr ro et et e et et me -m me -m m -m me -m on non non non n
Source: ABS calculated from unpublished data
5.2.2.4 Summary
This analysis of the standardised average number of children for women aged 15–44 years has shown that the patterns identified strongly resemble those identified for women aged 45–49 years. In addition these patterns are generally established by the ages of 25–29 years. A number of the statistical parameters suggest a slight divergence in the patterns of fertility between the metropolitan and non-metropolitan sectors of the State and between SSDs from one Census to the next.
5.3. Synthesis
This chapter has presented the patterns of fertility at the broad level of the SSD, identified changes in the pattern over time and provided some indications of trends towards convergence or divergence in these patterns over inter-censal periods from 1976 to 1996. Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 183
What is immediately apparent is the strong contrast between the metropolitan SSDs and the non-metropolitan SSDs and the consistency in the spatial pattern over time for women in the childbearing years and for women who, in the main, have most recently passed this stage of life (biologically).
Within the metropolitan and non-metropolitan sectors of the State there are also contrasts between the inner and outer SSDs. This is particularly noticeable within the non- metropolitan sector of the State (due to the physical size of the SSDs). Here the SSDs closer to, and generally adjoining the Adelaide Metropolitan Area, consistently had lower fertility than the SSDs more distant from the Adelaide Metropolitan Area and its influence. At this geographic level however, there is very little differentiation across the rest of the non-metropolitan sector.
Trends towards convergence or divergence varied by age group. For women aged 45–49 years the statistical parameters indicated a convergence in the spatial pattern of fertility both by individual SSD and between the two major sectors—metropolitan and non- metropolitan. For women aged 15–44 years the opposite was the case with a divergence in the patterns. Clearly, trying to present the most optimal representation of spatial patterns and spatial trends in fertility is difficult. In addition to the variation in results that may arise due to the fertility measures used and the statistical parameters to measure change, as outlined in Chapter 4, different age groups also present problems.
While women aged 45–49 at each Census most closely represented completed fertility, much of their family building may have begun or occurred two to three decades before the Census in which they were classified as aged 45–49 years. Over the period of study examined here, and up to today, the family life course has undergone considerable changes. No longer is the idea of a job for life and consequently a single home for life the norm resulting in greater levels of residential mobility in cities and between urban and rural areas than in the past (Bell 1997a, 1997b; Ford 1997; Hugo 1996, 2002d, Hugo and Smailes 1992; Williams 2003). Therefore it is highly likely that these women may not reside in the neighbourhood in which they did when their children were born, thereby ‘diluting’ the spatial pattern of fertility. With the younger age group, 15–44 there is likely to be a greater correlation between current place of residence and neighbourhood at the time of the birth of their children, but these spatial patterns are compromised by the fact Chapter Five: Spatial Analysis of Fertility in South Australia 1976 to 1996 184 that this age group represents incomplete fertility. Variation in the timing of childbearing has the potential to change fertility patterns depending on the particular age groups being examined, although the analysis of cumulative fertility earlier in this chapter (Figure 5.7) indicated that the trends in fertility were set by the younger age groups.
Whether trends of convergence or divergence occurred, there was a strong correlation from year to year in the spatial patterns of fertility. Usually areas with low or high fertility maintained their distinctiveness over time. This suggests there are strong locational factors operating in South Australia.
While the analysis of fertility levels for SSDs has been very informative of the persistence and variability of fertility patterns over time, these geographic administrative regions are not the most appropriate units for such an analysis. They are often very large, containing people with an array of characteristics. Average values for such large areas may disguise significant sub-area variation. Chapter 6 and Chapter 7 therefore examine fertility patterns and trends at a more disaggregated level. Chapter 6 begins with the patterns for non- metropolitan South Australia.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 185
Chapter Six
Spatial Analysis of Fertility in South Australia 1976 to 1996 Non-Metropolitan South Australia
6.1. Introduction
South Australia’s population is highly urbanised with only a little over 30 per cent living in the non-metropolitan sector. Over the last two decades there has been considerable change in the population in this part of the State (Chapter 4). For this part of the analysis the non-metropolitan sector of the State is treated as a separate entity.
The population in the non-metropolitan sector is not evenly distributed and this sector can itself be characterised as urbanised with urban centres forming the focus of non- metropolitan settlement. As outlined in Chapter 4, Section of State categories form the basis of the more detailed analysis of fertility for this region of the State. As the growth and decline in the population in some categories has been sufficient to result in the reclassification of urban centres from one category to another, this hampers comparisons over time. This is in addition to the effect changes to some SSD boundaries over the 1976 to 1996 period (Chapter 4) have at a more disaggregated level.
6.2. Women Aged 45–49 Years
6.2.1. Description of Patterns 1976–1981
Table 6.1 and Figure 6.1 present the fertility pattern for women aged 45–49 years at the 1976 Census. Although the population numbers in some areas are very low (and will have been affected by the ABS process of randomisation to maintain confidentiality of the data), and therefore must be treated with caution, some discernable and interesting patterns are evident.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 186
Table 6.1: Non-Metropolitan South Australia: Average Number of Children Ever Born to Ever Married Women Aged 45–49 Years By Section of State by SSD at the 1976 Census (per 100 Women) SSD 1976 Urban Centres (persons) Rural balance Over 10 000 5000-9999 1000-4999 Less than 1000 Av ch Pop Av ch Pop Av ch Pop Av ch Pop Barossa 321.42 226 310.83 412 O, Fl, KI1 320.49 257 317.30 491 Yorke 282.15 209 334.03 357 Lower Nth 298.17 96 329.84 417 Murray Mal 311.12 196 308.13 101 350.88 362 Riverland 331.19 321 324.68 529 Upper SE 340.29 205 351.70 231 Lower SE 308.57 450 346.73 113 338.75 37 354.89 321 Lincoln 324.33 282 351.45 359 West Coast 325.66 69 364.77 70 Whyalla 326.12 737 297.27 27 Pirie 307.34 410 310.34 134 353.76 388 Flinders R 330.60 324 316.83 34 Far North 271.24 81 386.33 50 1. Onkaparinga, Fleurieu and Kangaroo Island
Source: ABS calculated from unpublished data
In the SSDs adjacent to metropolitan Adelaide (Barossa; and the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs) fertility was below average in relation to the mean for the total non-metropolitan sector in both the urban centres and rural balance. In fact the average number of children ever born was higher for women living in the urban centres than in the rural balance. While classified as rural, rural in these SSDs is very different to rural in the northern and far western SSDs where the population is very dispersed and there are few local centres. The rural sectors of the Barossa and combined Onkaparinga, Fleurieu and Kangaroo Island SSDs are closely settled and contain many rural localities (centres of 200–999 persons). For people in these SSDs there is considerable interaction with metropolitan Adelaide with many people being daily commuters (Ford 1997).
As the distance from the metropolitan area increases the completed family size is generally lower in the urban centres of an SSD than for the rural balance. Many of the women living in the urban centres have below average fertility compared with those living in the rural balance. The largest urban centres do not have the lowest fertility. The urban centres with the highest fertility were situated in the Lower South East and Upper South East SSDs.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 187
Figure 6.1: Non-Metropolitan South Australia: Average Number of Children Ever Born to Ever Married Women Aged 45–49 Years by Section of State by SSD, 1976 Census
Metropolitan Adelaide is excluded Source: ABS unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 188
The urban centre category 1000–4999 persons on the Yorke Peninsula and in the Far North SSD recorded the smallest completed family size across the non-metropolitan sector. The Far North SSD is one of complete contrasts. The urban centre category 1000– 4999 (urban centres of Coober Pedy and Woomera) recorded the lowest fertility (271.2 children per 100 women) of all Section of State categories while the rural balance of the SSD recorded the highest fertility (386.3/100). The high rate for the rural balance is likely to be explained by the presence of the Indigenous population while the special nature of these urban centres (mining and rocket range) may be responsible for the low levels of fertility.
Table 6.2 and Figure 6.2 present the average number of children per 100 women for women aged 45–49 years by Section of State at the 1981 Census. These data are not directly comparable with the 1976 data as the base population is different with all women regardless of marital status counted and there were some changes in the membership of the Section of State categories of the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs and the West Coast SSD.
Table 6.2: Non Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years By Section of State by SSD at the 1981 Census (per 100 Women) SSD 1981 Urban Centres (persons) Rural balance Over 10 000 5000-9999 1000-4999 Less than 1000 Av ch Pop Av ch Pop Av ch Pop Av ch Pop Barossa 278.43 204 307.38 461 O, Fl, KI1 301.59 315 295.57 474 Yorke 286.34 183 318.06 299 Lower Nth 312.50 104 327.96 304 Murray Mal 322.96 196 364.00 75 326.65 364 Riverland 323.27 275 326.17 447 Upper SE 306.67 195 334.08 223 Lower SE 312.53 431 327.68 112 275.00 20 329.31 290 Lincoln 323.81 231 355.70 298 West Coast 321.62 37 336.36 55 Whyalla 325.86 669 320.00 35 Pirie 312.66 316 317.27 110 322.86 315 Flinders R 330.36 303 314.29 35 Far North 264.79 71 343.28 67 1. Onkaparinga, Fleurieu and Kangaroo Island
Source: ABS calculated from unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 189
Figure 6.2: Non-Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years by Section of State by SSD, 1981 Census
Metropolitan Adelaide is excluded Source: ABS unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 190
It is evident from Figure 6.2 that the pattern of fertility is quite variable across the non- metropolitan sector and for some SSDs the pattern bares little resemblance to that of 1976. In general however, as was the case for 1976, fertility is most often lower for women living in the urban centres of the SSD than in the rural hinterland and the area surrounding the metropolitan region has reasonably lower levels of fertility. The lowest fertility in 1981 was recorded in the urban centre category of 1000–4999 persons in the Yorke SSD (286.34 per 100 women), Lower South East SSD (275.00) and Far North SSD (264.79).
The highest fertility levels were recorded in the rural balances of the Lincoln SSD (355.70 per 100 women) and Far North SSD (343.28) and the urban centre category 1000-4999 persons in the Murray Mallee SSD (364.00). The contrast between the urban centres and rural balance in the Far North SSD remained.
6.2.2. Change Over Time 1976–1981
To try to gauge some idea of change over time, of whether by the time women complete their childbearing the spatial distribution of family size is the same or different, the Section of State categories have been adjusted at each Census to make the data as comparable as possible over the 1976 to 1981 period. Table 6.3 presents measures of change using the components of social change model (Congdon 1989; Congdon and Shepherd 1985, 1988; Shepherd and Congdon 1990). In this model the expected value at the 1981 Census can be calculated based on a Section of State’s initial (1976) value and the trend for the total non-metropolitan area in the average number of children ever born to women aged 45–49 years.
It is clear from Table 6.3 there has been considerable variation across the non- metropolitan sector with the average number of children ever born per 100 women predicted to decline, stay the same or actually increase in particular Section of State categories. For example, in the areas adjacent to metropolitan Adelaide (Barossa and the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs) fertility was predicted to decline and it did so by greater margins than was expected.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 191
Table 6.3: Measures of Social Change Over Time (Components of Social Change) in the Average Number of Children Ever Born to Women Aged 45–49 Years for Section of State by SSDs, 1976–1981 (per 100 Women)1 SSD/Section of State 1976-1981 Rates Change 1976 Pred 1981 St Pos Fdb
Barossa UC 1000-4999 321.42 315.37 278.43 -6.05 -36.90 2.41 Rural Balance 310.83 309.78 307.38 -1.05 -2.40 7.40 Onkaparinga, Fleurieu, K.I UC 1000-4999 324.53 317.02 301.59 -7.52 -15.40 0.94 Rural Balance 314.75 311.85 295.57 -2.90 -16.30 5.55 Yorke UC 1000-4999 282.15 294.62 286.34 12.47 -8.28 20.93 Rural Balance 334.03 322.03 318.06 -12.00 -3.97 -3.54 Lower North UC 1000-4999 298.17 303.09 312.50 4.92 9.41 13.37 Rural Balance 329.84 319.83 327.96 -10.82 8.14 -1.57 Murray Mallee UC 5000-9999 311.12 309.93 322.96 -1.19 13.00 7.26 UC 1000-4999 308.13 308.35 364.00 0.22 55.70 8.67 Rural Balance 350.88 330.94 326.65 -19.94 -4.29 -11.50 Riverland UC 1000-4999 331.19 320.53 323.27 -10.66 2.74 -2.20 Rural Balance 324.68 317.09 326.17 -7.59 9.08 0.87 Upper South East UC 1000-4999 340.29 325.34 306.67 -14.95 -18.70 -6.49 Rural Balance 351.70 331.37 334.08 -20.33 2.71 -11.90 Lower South East UC Over 10000 308.57 308.58 312.53 0.01 3.95 8.47 UC 5000-9999 346.73 328.75 327.68 -17.99 -1.06 -9.53 UC 1000-4999 338.75 324.53 275.00 -14.22 -49.50 -5.77 Rural Balance 354.89 333.06 329.31 -21.83 -3.75 -13.40 Lincoln UC Over 10000 324.33 316.91 323.81 -7.42 6.90 1.03 Rural Balance 351.45 331.24 355.7 -20.21 24.50 -11.80 West Coast UC 1000-4999 325.66 317.61 301.89 -8.05 -15.70 0.41 Rural Balance 364.77 338.28 369.23 -26.49 31.00 -18.00 Whyalla UC Over 10000 326.12 317.86 325.86 -8.27 8.00 0.19 Rural Balance 297.27 302.61 320.00 5.34 17.40 13.80 Pirie UC Over 10000 307.34 307.93 312.66 0.59 4.73 9.05 UC 1000-4999 310.34 309.52 317.27 -0.82 7.75 7.63 Rural Balance (combined with 353.76 332.46 322.86 -21.30 -9.60 -12.80 Flinders Ranges) Flinders Ranges UC Over 10000 330.6 320.22 330.36 -10.38 10.10 -1.92 UC 1000-4999 316.83 312.95 314.29 -3.88 1.34 4.57 Rural Balance (see Pirie) Far North UC 1000-4999 271.24 288.86 264.79 17.62 -24.10 26.07 Rural Balance 386.33 349.67 343.28 -36.66 -6.39 -28.20 1 For the data to be directly comparable in terms of Section of State categories, Hahndorf in the Onkaparinga, Fleurieu and Kangaroo Island SSDs for 1976 was included in the urban category 1000-4999 (instead of in rural balance). At the 1981 Census Streaky Bay in the West Coast SSD was included in the urban centre 1000-4999 category (instead of in rural balance). St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value Source: ABS calculated from unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 192
In the larger urban centres of over 10 000 people, little change occurred in the fertility rates with two areas declining slightly and two areas experiencing slight increases in fertility. Some of the largest actual changes in family size, and predicted changes in family size, occurred for the urban centre category of 1000–4999 persons and for the rural balance. For these areas rates at a point in time and changes over one five year period can be very deceiving as the population being studied is very small and slight changes of 5 to 10 people in a category can considerably change the rates.
6.2.3. Convergence/Divergence, 1976–1981
Clearly based on the preceding discussion it is difficult to establish trends towards convergence or divergence. There is very little correlation (Table 6.4) between the two distribution patterns. There is a tendency towards convergence with the standard deviation and coefficient of variation decreasing and a regression value considerably below one.
Table 6.4: Non-Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 45–49 Years for Section of State1 by SSD, 1976–1981 Summary Statistics Census Year Summary Statistics Census Year 1976 1981 1976-1981 Highest Value 386.33 369.23 Correlation 0.55 Lowest Value 271.24 264.79 Regression 0.53 Range 115.09 104.44 Percent of Variance Due To: Overall Mean2 327.13 318.46 Change in Means 12.5 Distributional Mean3 326.52 317.66 Feedback 22.7 Standard Deviation 24.11 21.20 Positional Change 64.8 Coefficient of Variation 7.38 6.68 1. Section of State categories the same as identified in Table 5.10 2.Overall mean: the rate for the state as a whole; all units combined 3.Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 193
6.2.4. Description of Patterns 1986
Between the 1981 and 1986 Censuses, there was some movement in the membership of the Section of State categories. These changes occurred as the result of the growth of three urban centres in particular which resulted in the movement of two centres from the urban centre category of 1000–4999 persons to the urban centre category of 5000–9999 (Victor Harbor and Mount Barker in the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs) and one centre from urban centre 5000–9999 to over 10 000 (Murray Bridge in the Murray Mallee SSD).
As evident from Figure 6.3 these changes had little effect on the relative position of these areas. Fertility levels in the SSDs adjoining the metropolitan area remained low and fertility for Murray Bridge (297.10 per 100 women, Table 6.5) remained around the non- metropolitan average of 294.00 per 100 women as it has for the other larger centres except Mount Gambier in the Lower South East.
Table 6.5: Non-Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years By Section of State by SSD at the 1986 Census (per 100 Women) SSD 1986 Urban Centres (persons) Rural balance Over 10 000 5000-9999 1000-4999 Less than 1000 Av ch Pop Av ch Pop Av ch Pop Av ch Pop Barossa 275.14 185 280.99 584 O, Fl, KI1 276.16 172 264.07 231 278.86 615 Yorke 273.08 208 299.69 321 Lower Nth 274.49 98 291.71 362 Murray Mal 297.10 276 308.70 92 312.21 385 Riverland 282.24 304 290.23 512 Upper SE 291.90 210 289.13 230 Lower SE 283.25 424 313.08 107 286.21 29 312.43 362 Lincoln 300.40 251 318.44 347 West Coast 338.00 50 285.45 55 Whyalla 310.10 723 362.50 16 Pirie 301.21 331 288.70 115 301.58 317 Flinders R 302.96 338 316.67 48 Far North 231.51 73 307.45 94 1. Onkaparinga, Fleurieu and Kangaroo Island Source: ABS calculated from unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 194
Figure 6.3: Non-Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years by Section of State by SSD, 1986 Census
Metropolitan Adelaide is excluded Source: ABS unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 195
Overall there still appears to be a general pattern of lower fertility in urban centres compared to the rural hinterland, although there are exceptions to this trend (West Coast SSD, Flinders Ranges SSD). Of note are the continuing higher levels of fertility in the urban centre category 1000–4999 in the Murray Mallee SSD and the category 5000–9999 in the Lower South East. The relative position of the West Coast and Flinders Ranges small urban centres changed, moving from having fertility levels around or just below the non-metropolitan average in 1981 to being amongst the highest rates in the sector in 1986. This may just be a consequence of the small numbers of women involved and therefore minor increases or decreases in the population can significantly affect average family size. The trend of very low fertility in the urban centre category 1000–4999 in the Far North SSD, identified in 1976, continued at the 1986 Census. In fact this category recorded the lowest average number of children born per woman for the non-metropolitan sector in 1986.
6.2.5. Change Over Time, 1981–1986
The variation between the patterns of fertility in Figure 6.2 (1981) and Figure 6.3 (1986) suggests considerable change over time in a number of areas. Table 6.6 indicates that the change in average family size for the Section of State categories was not only quite variable but was predicted to be quite variable.
There do not appear to be any significant patterns by Section of State categories, although for the urban centre categories over 10 000 the change in fertility was generally close to that expected based on the social model of change. The wide fluctuations in fertility (difference between actual and predicted values–positional change) are more likely to occur in the Section of State categories of urban centres of 1000–4999 persons and for the rural balance. This is likely to be partly an artefact of the low numbers involved in a number of these areas.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 196
Table 6.6 Measures of Social Change Over Time (Components of Social Change) in the Average Number of Children Ever Born to Women Aged 45–49 Years for Section of State by SSD, 1981–1986 (per 100 Women)1 SSD/Section of State 1981-1986 Rates Change 1981 Pred 1986 St Pos Fdb
Barossa UC 1000-4999 278.43 270.16 275.14 -8.27 4.97 13.60 Rural Balance 307.38 288.95 280.99 -18.43 -7.96 3.45 Onkaparinga, Fleurieu, K.I UC 5000-9999 305.73 287.88 276.16 -17.85 -11.72 4.02 UC 1000-4999 298.88 283.44 264.07 -15.45 -19.37 6.43 Rural Balance 294.92 280.87 278.86 -14.06 -2.00 7.82 Yorke UC 1000-4999 286.34 275.29 273.08 -11.04 -2.23 10.83 Rural Balance 318.06 295.88 299.69 -22.18 3.80 -0.30 Lower North UC 1000-4999 312.50 292.28 274.49 -20.22 -17.79 1.65 Rural Balance 327.96 302.31 291.71 -25.65 -10.60 -3.78 Murray Mallee UC Over 10000 322.96 299.06 297.10 -23.89 -1.96 -2.02 UC 1000-4999 364.00 325.70 308.70 -38.30 -17.01 -16.42 Rural Balance 326.65 208.53 312.21 -25.19 10.75 -3.32 Riverland UC 1000-4999 323.27 299.27 282.24 -24.00 -17.03 -2.13 Rural Balance 326.17 301.15 290.23 -25.02 -10.92 -3.15 Upper South East UC 1000-4999 306.67 288.49 291.90 -18.18 3.42 3.69 Rural Balance 334.08 306.28 289.13 -27.80 -17.15 -5.93 Lower South East UC Over 10000 312.53 292.29 283.25 -20.23 -9.04 1.64 UC 5000-9999 327.68 302.13 313.08 -25.55 10.96 -3.68 UC 1000-4999 275.00 267.93 286.21 -7.07 18.27 14.81 Rural Balance 329.31 303.19 312.43 -26.12 9.24 -4.25 Lincoln UC Over 10000 323.81 299.62 300.40 -24.19 0.78 -2.32 Rural Balance 355.70 320.32 318.44 -35.39 -1.88 -13.51 West Coast UC 1000-4999 321.62 298.20 338.00 -23.43 39.80 -1.55 Rural Balance 336.36 307.77 285.45 -28.60 -22.31 -6.73 Whyalla UC Over 10000 325.86 300.95 310.10 -24.91 9.15 -3.04 Rural Balance 320.00 297.14 362.50 -22.86 65.36 -0.98 Pirie UC Over 10000 312.66 292.38 301.21 -20.28 8.83 1.59 UC 1000-4999 317.27 295.37 288.70 -21.90 -6.68 -0.03 Rural Balance (combined with 322.86 299.00 301.58 -23.86 2.58 -1.99 Flinders Ranges) Flinders Ranges UC Over 10000 330.36 303.87 302.96 -26.49 -0.91 -4.62 UC 1000-4999 314.29 293.43 316.67 -20.85 23.23 1.02 Rural Balance (see Pirie) Far North UC 1000-4999 264.79 261.31 231.51 -3.48 -29.80 18.39 Rural Balance 343.28 312.26 307.45 -31.03 -4.81 -9.16 1. For the data to be directly comparable in terms of Section of State categories at both Censuses, Victor Harbor and Mount Barker in the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs were included in the category urban centres 5000–9999 persons and Port Elliot was included in the urban centre category 1000–4999. Murray Bridge in the Murray Mallee SSD was counted as an urban centre over 10 000 persons. St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value Source: ABS calculated from unpublished data Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 197
Although as outlined in the previous section (6.2.4) fertility in the urban centre category 1000–4999 in the Murray Mallee was relatively high at 308.70, it actually declined by more than expected. It remained relatively high as fertility in a number of areas declined by a greater margin. The same cannot be said however about the urban centre category 5000–9999 in the Lower South East. At all three Censuses, 1976, 1981 and 1986, fertility levels here have consistently been relatively high. The decline in fertility over the 1981– 1986 inter-censal period was less than expected. The urban centre category 1000–4999 in the Far North continued its trend of fertility declining by more than expected resulting in this category maintaining its record of the lowest fertility recorded in the non-metropolitan sector at all three Censuses. This is mainly the result of the low rate originally recorded in 1976.
6.2.6 Convergence/Divergence, 1981–1986
Table 6.7 presents summary statistics on the overall change in fertility levels at the 1981 and 1986 Censuses. From initial indications it appears that over the inter-censal period there has been a slight movement towards a divergence in the patterns with an increase in the range, standard deviation and coefficient of variation. According to the model of social change these measures however are deceiving and when the influence of the initial value (1981) is taken into account there was actually a convergence in the pattern with a regression value 0.65. So even though there was a slight increase in the scatter of areas around the mean in 1986 as compared with 1981 this was not sufficient to suggest increased polarisation.
6.2.7 Description of Patterns 1996
By 1996 average issue had declined considerably and all areas recorded fertility levels below the mean for 1986. However, it appears from the mapping of the data for Section of State categories as they existed in 1996 (Figure 6.4) that a number of areas have not experienced the decline in average family size that would be expected.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 198
Table 6.7: Non-Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 45–49 Years for Section of State1 by SSD, 1981–1986 Summary Statistics Census Year Summary Statistics Census Year 1981 1986 1981-1986 Highest Value 364.00 362.50 Correlation 0.60 Lowest Value 264.79 231.51 Regression 0.65 Range 99.21 130.99 Percent of Variance Due To: Overall Mean2 318.46 294.00 Change in Means 54.9 Distributional Mean3 317.19 295.32 Feedback 6.3 Standard Deviation 21.06 22.91 Positional Change 38.8 Coefficient of Variation 6.64 7.76 1. Section of State categories the same as identified in Table 5.13 2.Overall mean: the rate for the state as a whole; all units combined. 3.Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
In 1996 a greater number of areas fall within the top category of the distribution (Figure 6.4). It is less likely this can be the result of low population numbers as this is a much larger cohort of women compared with the other Censuses (Table 6.8) and therefore the calculated rates should be more stable. It may however be partly the result of the considerable movement in the membership of the Section of State categories over the 1986–1996 period (see Appendix 4.5) and the Lower North, Pirie and Riverland SSDs may have been affected by boundary changes (Appendix 4.1).
The major patterns discernable in 1996 were that women aged 45–49 years living in the major urban centres (over 10 000 people) had fertility levels around the average for the non-metropolitan area as a whole. In contrast women living in the SSDs immediately adjacent to the metropolitan area continue to record the least number of children ever born per woman. The Barossa and combined Onkaparinga, Fleurieu and Kangaroo Island SSDs have seen a considerable influx of people particularly from metropolitan Adelaide.
In many respects these SSDs have just become an extension of the Adelaide Metropolitan Area. Although women and couples may choose these areas as better places to raise children (Ford 2001) it appears their fertility levels relate more to the influences of the metropolitan region than to those of the wider non-metropolitan area. Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 199
Table 6.8: Non-Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years by Section of State by SSD at the 1996 Census (per 100 women) SSD 1996 Urban Centres (persons) Rural balance Over 10 000 5000-9999 1000-4999 Less than 1000 Av ch Pop Av ch Pop Av ch Pop Av ch Pop Barossa 223.53 442 219.79 975 O, Fl, KI1 230.96 436 221.16 501 215.58 1091 Yorke 235.29 323 243.19 433 Lower Nth 236.00 175 245.78 450 Murray Mal 245.72 409 250.46 109 248.92 554 Riverland 244.44 450 234.00 700 Upper SE 252.92 257 254.98 311 Lower SE 241.43 700 251.01 149 256.68 464 Lincoln 243.24 377 271.43 28 248.71 425 West Coast 257.66 111 270.00 80 Whyalla 241.55 669 250.00 26 Pirie 238.71 465 253.49 129 246.36 453 Flinders R 244.62 437 227.87 61 Far North 236.81 163 230.85 94 1. Onkaparinga, Fleurieu and Kangaroo Island Source: ABS calculated from unpublished data
The adjoining SSDs of the Murray Mallee, Upper and Lower South East are areas of relatively high fertility. The rural balance of Lincoln SSD and the two categories of the West Coast SSD recorded the highest fertility in 1996. In terms of relative position a number of the urban centres have moved into a higher category between 1986 and 1996, i.e., fertility has not declined as much in these centres as elsewhere. These centres include those of 1000–4999 persons in the Upper South East SSD, the Riverland SSD, the Lower North SSD and Pirie SSD.
For the first time there was a change in the pattern of fertility for the Far North SSD. The urban centre did not record the lowest fertility and the rural balance, previously having one of the highest rates, at this Census has one of the lower rates. This change can be explained by a change in the membership of the Section of State categories. In the ten years 1986 to 1996 there was considerable growth in the urban locality (classified as rural balance in this study), Roxby Downs, such that by 1996 it had moved from the rural balance to the urban centre category of 1000–4999 persons.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 200
Figure 6.4: Non-Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years by Section of State by SSD, 1996 Census
Metropolitan Adelaide is excluded Source: ABS unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 201
This category previously had only included Woomera and Coober Pedy. If based on the previous classification then the pattern in 1996 would have been much the same as in 1986.1
6.2.8. Change Over Time, 1986–1996
Establishing change over the 1986–1996 inter-censal period is compounded by the number of changes in membership of the Section of State categories and some boundary changes (see Appendices 4.1 and 4.5). It is possible to make adjustments to the data that allow the 1986 distribution of urban centres to match 1996, or vice versa for the 1996 distribution to match 1986. Table 6.9 presents the former option however it appears to make only minor differences to the pattern of change over time (Appendix 6.1 presents a similar table matching 1996 data to the 1986 Section of State categories).
It is clear from Table 6.9 the declines in fertility over the 10 year inter-censal period were substantial across all areas. The exception to this trend was the Far North SSD urban centre category 1000–4999 persons where the average issue of women only declined by a very small margin. The greatest deviations between the predicted value and the actual rate (positional change) to result in greater declines in fertility than expected occurred in the SSDs adjoining the metropolitan area and in the less stable (due to low population numbers) rural balance of Whyalla and the urban centre category of 1000–4999 persons in the Flinders Ranges SSD. For example for the Section of State categories in the Barossa SSD, average issue declined by an extra 33 per cent and 42 per cent respectively for the urban centres 1000–4999 persons and the rural balance. Similar deviations occurred for the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs.
1 Based on the 1986 classification the rate for the urban centre category 1000-4999 would have been 225.42 and for the rural balance 242.45. In 1996 if rates are calculated separately for Roxby Downs then the number of children ever born in the rural balance would be 230.85 and in Roxby Downs would be 266.67 (45 women). Data were not available separately for Roxby Downs in 1976, 1981or 1986. Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 202
Table 6.9: Measures of Social Change Over Time (Components of Social Change) in the Average Number of Children Ever Born to Women Aged 45–49 Years for Section of State by SSD 1986–1996 (per 100 Women)1 SSD/Section of State 1986-1996 Rates Change 1986 Pred 1996 St Pos Fdb
Barossa UC 1000-4999 280.43 237.82 223.53 -42.62 -14.29 10.43 Rural Balance 279.22 237.43 219.79 -41.80 -17.63 11.25 Onkaparinga, Fleurieu, K.I UC 5000-9999 276.16 236.43 230.96 -39.73 -5.47 13.31 UC 1000-4999 272.62 235.28 221.16 -37.34 -14.13 15.70 Rural Balance 275.81 236.32 215.58 -39.49 -20.74 13.55 Yorke UC 1000-4999 276.67 236.60 235.29 -40.07 -1.30 12.97 Rural Balance 299.65 244.06 243.19 -55.60 -0.87 -2.56 Lower North UC 1000-4999 274.49 235.89 236.00 -38.60 0.11 14.44 Rural Balance 291.71 241.48 245.78 -50.23 4.30 2.81 Murray Mallee UC Over 10000 297.10 243.23 245.72 -53.87 2.49 -0.83 UC 1000-4999 308.70 246.99 250.46 -61.71 3.47 -8.66 Rural Balance 312.21 248.13 248.92 -64.08 0.79 -11.04 Riverland UC 1000-4999 282.24 238.40 244.44 -43.83 6.04 9.21 Rural Balance 290.23 241.00 234.00 -49.23 -7.00 3.81 Upper South East UC 1000-4999 291.90 241.54 252.92 -50.36 11.38 2.68 Rural Balance 289.13 240.64 254.98 -48.49 14.34 4.55 Lower South East UC Over 10000 283.25 238.73 241.43 -44.52 2.69 8.52 UC 1000-4999 307.35 246.55 251.01 -60.80 4.45 -7.76 Rural Balance 312.43 248.20 256.68 -64.23 8.48 -11.19 Lincoln UC Over 10000 300.40 244.30 243.24 -56.10 -1.06 -3.06 UC 1000-4999 295.45 242.69 271.43 -52.76 28.74 0.28 Rural Balance 320.00 250.66 248.71 -69.34 -1.95 -16.30 West Coast UC 1000-4999 307.25 246.52 257.66 -60.73 11.14 -7.68 Rural Balance 316.67 249.58 270.00 -67.09 20.42 -14.05 Whyalla UC Over 10000 310.10 247.45 241.55 -62.65 -5.89 -9.61 Rural Balance 362.50 264.45 250.00 -98.05 -14.45 -45.01 Pirie UC Over 10000 301.21 244.56 238.71 -56.65 -5.85 -3.61 UC 1000-4999 288.70 240.50 253.49 -48.20 12.99 4.85 Rural Balance (combined with Flinders Ranges) 301.58 244.68 246.36 -56.90 1.68 -3.85 Flinders Ranges UC Over 10000 302.96 245.13 244.62 -57.83 -0.51 -4.79 UC 1000-4999 316.67 249.58 227.87 -67.09 -21.71 -14.05 Rural Balance (see Pirie) Far North UC 1000-4999 231.51 221.94 225.42 -9.57 3.48 43.48 Rural Balance 307.45 246.59 242.45 -60.86 -4.14 -7.82 (a) 1986 Section of State categories match 1996 Section of State categories. For the data to be directly comparable in terms of Section of State categories at both Censuses: Barossa SSD Freeling, Lyndoch and Williamstown move from rural balance in 1986 into the urban centre category 1000–4999; in the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs Nairne and Woodside move from rural balance in 1986 into urban centre category 1000–4999; for Yorke SSD Ardrossan moves from rural balance in 1986 into urban centre category 1000–4999; Lower South East SSD Millicent moves from the urban centre category 5000–9999 in 1986 into the urban centre category 1000–4999; Lincoln SSD Tumby Bay moves from rural balance 1986 into the urban centre category 1000–4999; West Coast SSD Streaky Bay moves from rural balance in 1986 to the urban centre category 1000–4999. At both censuses Roxby Downs is included in rural balance. St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value Source: Calculated from unpublished ABS data Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 203
In a number of other areas average issue for women aged 45–49 years did not decline by as much as expected and these areas, in particular, included the urban centres 1000–4999 persons in the Lincoln SSD and Pirie SSD and all Section of State categories in the West Coast, Murray Mallee, Upper South East and Lower South East SSDs.
6.2.9 Convergence/Divergence, 1986–1996
The summary statistics presented in Table 6.10 clearly indicated much less correlation between the patterns over a ten year period (0.60 1981–1986 compared to 0.53 1986– 1996). All of the values (range, standard deviation, coefficient of variation, regression slope) indicate a convergence in the pattern over this inter-censal period.
Table 6.10: Non-Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 45–49 Years for Section of State1 by SSD South Australia, 1986–1996 Summary Statistics Census Year Summary Statistics Census Year 1986 1996 1986–1996 Highest Value 362.50 271.43 Correlation 0.53 Lowest Value 231.51 215.58 Regression 0.32 Range 130.99 55.85 Percent of Variance Due To: Overall Mean2 294.00 238.61 Change in Means 89.3 Distributional Mean3 295.87 242.83 Feedback 6.6 Standard Deviation 21.40 13.23 Positional Change 4.1 Coefficient of Variation 7.23 5.45 1. Section of State categories the same as identified in Table 5.16 2.Overall mean: the rate for the state as a whole; all units combined 3.Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
6.2.10 Summary
This section has examined the spatial patterning of fertility for women aged 45–49 years in non-metropolitan South Australia and the changes in these patterns over the 1976–1996 period. Some general trends were identified. The SSDs (and the Section of State categories within) adjoining metropolitan Adelaide recorded some of the smallest average Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 204 family sizes while the areas in the remotest parts of the state often recorded larger average family sizes. Of interest is the consistently high levels of fertility in the urban centre category 1000–4999 in the Murray Mallee SSD (1981–1996) and the urban centre category 5000–9999 in the Lower South East SSD (1976–1986). The large urban centres (over 10 000 persons) often recorded fertility levels around the mean for the non- metropolitan sector.
The Far North presented an interesting case in that the rural balance generally recorded the highest value and the urban centre category 1000–4999 often recorded the lowest rate. In addition the fertility rate for this category for both the 1976–1981 and 1981–1986 inter- censal period declined by a greater amount than expected.
Overall the average issue across the areas converged at each inter-censal period. Although there were some consistencies across time, it is clear however that small variations produced considerable changes in the rank ordering of areas, leading to random changes of patterns from one year to the next. Analysing fertility by Section of State for this age group has provided more detail and shown that there were variations in fertility within an SSD when it is broken down into separate sections representing urban and rural areas. While there was a general trend of lower fertility in an urban centre classification than for the rural balance, urban centre size did not appear to be a significant influence on overall average number of children ever born to women aged 45–49 years. Table 6.11 presents rates for the Section of State categories as they existed at each Census irrespective of SSD classification. The largest and smallest average issue by category varies from Census to Census and there is no continuum in values from low to high from the largest urban centres category in terms of population size to rural balance. This appears to be contrary to the findings of Borrie in the National Population Inquiry (1975, 1978), Bell (1990) and the ABS (1992) (Section 3.2.2) although the categories used in these studies were broader.
The analysis of the pattern for women aged 15–44 may provide a clearer picture of any distinctions across the non-metropolitan sector and whether urban centre size has an influence on fertility levels.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 205
Table 6.11: Non-Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years By Section of State Categories, 1976, 1981, 1986, 1996 (per 100 Women) Section of State1 Census Year (number of persons) 1976 1981 1986 1996 Over 10000 319.47 321.23 300.38 242.30 5000–9999 324.12 324.68 290.32 230.96 1000–4999 315.88 304.68 280.95 237.41 Rural Balance 336.54 322.15 295.79 236.56 1. Section of State as they existed at each Census. No adjustment has been made to make them comparable over time. Source: ABS calculated from unpublished data
6.3. Women Aged 15–44 Years
Establishing patterns in fertility for women aged 45–49 is hampered by the small numbers in some areas and the fact that the women may have completed their childbearing many years prior to the time of the Census. This section will look at the spatial distribution of fertility for women who were more likely to be in the childbearing stages of their life at each Census. Of course the limitation of this age group is that they are at various stages of the family formation process and variations in the timing of fertility could be an influential factor.
6.3.1 Description of Patterns, 1976–1981
In 1976 the spatial patterning of fertility in non-metropolitan South Australia for women aged 15–44 (Figure 6.5) varied from the pattern for the women aged 45–49 years (Figure 6.1). This is noticeable for many of the urban centres that fall within a different category. For a number of centres where fertility was below the non-metropolitan average for women aged 45–49 years, fertility levels were above average for women aged 15–44 or vice versa. Similarly there is some contrast across the rural sector. This is not unexpected as the fertility for the broad age group 15–44 is incomplete and reflects differences in the timing of childbearing.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 206
Figure 6.5: Non-Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Ever Married Women Aged 15–44 Years by Section of State by SSD, 1976 Census
Metropolitan Adelaide is excluded Source: ABS unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 207
Women aged 45–49 years in 1976 were making decisions about childbearing predominantly in the social and economic environment of the 1950s and 1960s; and depending on the age at which women aged 45–49 had their children, their residence at the time of childbearing and then at the 1976 Census is possibly less likely to correspond than for women aged 15–44.
The highest age standardised levels of fertility in 1976 (Table 6.12) were in the rural balance sections of the West Coast, Lincoln, Whyalla and Lower South East SSDs. Of note is the high level of fertility in the urban centre category 1000–4999 persons in the Murray Mallee SSD. The relatively higher rate in this category was evident for women aged 45–49 years at the 1981, 1986 and 1996 Censuses.
Table 6.12: Non Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Ever Married Women Aged 15–44 Years by Section of State by SSD at the 1976 Census (per 100 Women) 1976 Urban Centres (persons) Rural balance SSD Over 10 000 5000-9999 1000-4999 Less than 1000 Rates (aver no. Pop Rates (aver no. Pop Rates (aver no. Pop Rates (aver no. Pop childn) childn) childn) childn) Stnd Obs Stnd Obs Stnd Obs Stnd Obs Barossa 210.26 211.37 1020 210.72 218.01 2516 O, Fl, KI 223.79 221.41 1624 214.44 219.74 2869 Yorke 220.51 227.66 924 233.15 238.61 1741 Lower 228.70 233.86 597 229.33 232.21 1867 Nth Murray 217.98 213.94 1222 245.93 241.62 586 231.22 232.09 2436 Mal Riverland 221.46 216.22 1698 219.95 222.24 2633 Upper SE 225.71 225.22 1336 232.00 232.74 1418 Lower 221.45 212.17 2998 234.08 220.99 886 215.53 205.03 169 244.73 243.01 2009 SE Lincoln 228.06 225.79 1495 248.39 239.45 2259 West 228.22 209.83 505 246.63 244.30 484 Coast Whyalla 231.09 228.90 5519 246.26 227.43 238 Pirie 232.33 229.96 2191 221.54 206.96 749 236.50 241.34 1874 Flinders 236.23 225.43 2199 222.98 220.91 307 R Far North 197.73 199.34 764 235.20 227.22 481 Stnd = Age Standardised Rate; Obs = Observed Rate Source: ABS calculated from unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 208
The lowest rates of fertility were recorded in both Section of State categories in the Barossa SSD, in the rural balance of the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs and the urban centre categories 1000–4999 persons in the Lower South East and Far North SSDs. It is interesting that the low rate in the Far North urban centre replicates the findings for women aged 45–49 years.
Table 6.13 and Figure 6.6 present the data and spatial patterns for women aged 15–44 years at the 1981 Census. As outlined for women aged 45–49 years, these data are not directly comparable with the 1976 data as the base population is different with all women regardless of marital status counted and there were some changes in the membership of the Section of State categories of the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs and the West Coast SSD.
Table 6.13: Non Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 15–44 Years by Section of State by SSD at the 1981 Census (per 100 Women) 1981 Urban Centres (persons) Rural balance SSD Over 10 000 5000-9999 1000-4999 Less than 1000 Rates (aver no. Pop Rates (aver no. Pop Rates (aver no. Pop Rates (aver no. Pop childn) childn) childn) childn) Stnd Obs Stnd Obs Stnd Obs Stnd Obs Barossa 152.01 147.70 1478 147.86 156.03 3891 O, Fl, KI 152.43 151.40 2825 151.95 163.40 4279 Yorke 156.49 158.83 1246 171.68 179.10 2201 Lower 145.40 143.42 896 172.40 185.73 2326 Nth Murray 162.72 159.54 1641 178.14 182.27 626 174.08 185.01 3148 Mal Riverland 157.38 155.07 2506 167.12 174.20 3520 Upper SE 159.05 155.27 1802 170.22 186.27 1799 Lower 161.06 153.75 4121 181.19 170.58 1074 166.11 172.64 212 178.44 187.92 2633 SE Lincoln 165.77 162.46 2099 180.66 186.03 2878 West 181.54 176.05 572 185.95 184.79 776 Coast Whyalla 173.56 167.76 6296 170.34 173.94 307 Pirie 164.92 161.90 2803 175.33 170.63 960 168.69 178.98 2393 Flinders 174.74 169.80 3076 175.66 175.76 524 R Far North 141.97 152.42 763 179.59 158.71 821 Stnd = Age Standardised Rate; Obs = Observed Rate Source: ABS calculated from unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 209
Figure 6.6: Non-Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years by Section of State by SSD, 1981 Census
Metropolitan Adelaide is excluded Source: ABS unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 210
The patterns of note in 1981 were that fertility varied considerably within an SSD based on Section of State categories. In general, although there were exceptions, fertility levels were at their highest in the rural balance section of an SSD particularly on the West Coast, in the Far North, in Lincoln and in the Lower South East. Relatively higher rates of fertility are also evident in the urban centre category of 1000–4999 persons in the West Coast SSD, in Murray Mallee SSD and in the urban centre category 5000–9999 persons in the Lower South East. The relatively higher rates in these centres, in most cases, mirror the 1976 pattern and the patterns identified for women aged 45–49 years.
As was the case for women aged 45–49 years, and in 1976 for women aged 15–44 years, fertility was at its lowest for women living in the SSDs (regardless of Section of State categories) adjacent to the metropolitan area and again in the urban centre category of 1000–4999 persons in the Far North SSD. In fact average issue at 141.97 per 100 women in the Far North SSD was the lowest rate recorded in the non-metropolitan sector of the State.
6.3.2 Change Over Time, 1976–1981
Table 6.14 presents the measures of change over time for the 1976 to 1981 period. It is clear that the decline in fertility over this time was considerable for all areas, of the order of 50–60 children per 100 women. In most instances the change was as expected based on the area’s initial rate in 1976 and the overall changes to the non-metropolitan area. The greatest changes from the expected rates occurred in a number of the smaller urban centres (category of 1000–4999 persons). For example in the Lower North SSD the urban centre category of 1000–4999 persons actually declined by 36 per cent more than expected while some urban centres of this size further from the city (West Coast SSD, Pirie SSD, Flinders Ranges SSD) declined by around 20 per cent less than expected.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 211
Table 6.14: Measures of Social Change Over Time (Components of Social Change) in the Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years for Section of State by SSD, 1976–19811 SSD/Section of State 1976-1981 Rates Change 1976 Pred 1981 St Pos Fdb
Barossa UC 1000-4999 210.26 153.54 152.01 -56.72 -1.53 4.06 Rural Balance 210.72 153.90 147.86 -56.82 -6.03 3.95 Onkaparinga, Fleurieu, K.I UC 1000-4999 221.53 162.21 152.43 -59.32 -9.77 1.46 Rural Balance 215.44 157.52 151.95 -57.91 -5.57 2.87 Yorke UC 1000-4999 220.51 161.43 156.49 -59.09 -4.94 1.69 Rural Balance 233.15 171.15 171.68 -62.01 0.53 -1.23 Lower North UC 1000-4999 228.70 167.72 145.40 -60.98 -22.32 -0.20 …Rural Balance 229.33 168.21 172.40 -61.12 4.19 -0.35 Murray Mallee UC 5000-9999 217.98 159.48 162.72 -58.50 3.24 2.28 UC 1000-4999 245.93 180.97 178.14 -64.96 -2.83 -4.18 Rural Balance 231.22 169.66 174.08 -61.56 4.42 -0.78 Riverland UC 1000-4999 221.46 162.16 157.38 -59.30 -4.78 1.47 Rural Balance 219.95 161.00 167.12 -58.96 6.13 1.82 Upper South East UC 1000-4999 225.71 165.42 159.05 -60.29 -6.37 0.49 Rural Balance 232.00 170.26 170.22 -61.74 -0.04 -0.96 Lower South East UC Over 10000 221.45 162.15 161.06 -59.30 -1.09 1.48 UC 5000-9999 234.08 171.86 181.19 -62.22 9.33 -1.44 UC 1000-4999 215.53 157.60 166.11 -57.94 8.51 2.84 Rural Balance 244.73 180.05 178.44 -64.68 -1.61 -3.90 Lincoln UC Over 10000 228.06 167.23 165.77 -60.83 -1.47 -0.05 Rural Balance 248.39 182.86 180.66 -65.52 -2.20 -4.75 West Coast UC 1000-4999 228.22 167.35 178.24 -60.87 10.89 -0.09 Rural Balance 246.63 181.51 190.73 -65.12 9.22 -4.34 Whyalla …UC Over 10000 231.09 169.56 173.56 -61.53 3.99 -0.75 Rural Balance 246.26 181.22 170.34 -65.03 -10.89 -4.25 Pirie UC Over 10000 232.33 170.51 164.92 -61.81 -5.59 -1.04 UC 1000-4999 221.54 162.21 175.33 -59.32 13.11 1.46 Rural Balance (combined with 236.50 173.72 168.69 -62.78 -5.03 -2.00 Flinders Ranges) Flinders Ranges UC Over 10000 236.23 173.51 174.74 -62.72 1.23 -1.94 UC 1000-4999 222.98 163.33 175.66 -59.66 12.34 1.12 Rural Balance (see Pirie) Far North UC 1000-4999 197.73 143.91 141.97 -53.82 -1.94 6.96 Rural Balance 235.20 172.72 179.59 -62.48 6.87 -1.70 1. For the data to be directly comparable in terms of Section of State categories, Hahndorf in the Onkaparinga, Fleurieu and Kangaroo Island SSDs for 1976 was included in the urban category 1000–4999 (instead of in rural balance). At the 1981 Census Streaky Bay in the West Coast SSD was included in the urban centre 1000–4999 category (instead of in rural balance). St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value
Source ABS calculated from unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 212
6.3.3 Convergence/Divergence, 1976–1981
The summary statistics provided in Table 6.15 reinforce the fact that the decline in fertility in most areas was according to that expected. Over 98 per cent of the overall change was due to structural elements (the overall non-metropolitan trend) than to any strong localised effects. While the coefficient of variation suggests a slight tendency towards divergence in the two patterns, the regression value, at under one, implies a degree of convergence in the pattern. This reflects the trends found for women aged 45–49 years, although there appears to be a greater correlation between the patterns of 1976 and 1981 for women aged 15–44 than for women who had completed their childbearing.
Table 6.15: Non-Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 15–44 Years for Section of State1 by SSD South Australia, 1976–1981 Summary Statistics Census Year Summary Statistics Census Year 1976 1981 1976–1981 Highest Value 248.39 190.73 Correlation 0.77 Lowest Value 197.73 141.97 Regression 0.77 Range 50.66 48.76 Percent of Variance Due To: Overall Mean2 227.60 166.01 Change in Means 98.3 Distributional Mean3 227.84 167.06 Feedback 0.2 Standard Deviation 11.74 11.79 Positional Change 1.5 Coefficient of Variation 5.15 7.06 1. Section of State categories the same as identified in Table 5.21 2.Overall mean: the rate for the state as a whole; all units combined 3.Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
6.3.4 Description of Patterns 1986
While overall fertility levels continue to fall it is evident from Table 6.16 and Figure 6.7 that the general patterns are maintained. This indicates that the factors producing/maintaining the patterns in each area from 1981 (and 1976) to 1986 remain significant. In all cases, where a change in the mapping category had occurred between 1981 and 1986, movement has been into the adjacent category.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 213
Table 6.16 Non Metropolitan South Australia: Average Number of Children Ever Born to Women aged 15–44 Years by Section of State by SSD at the 1986 Census (per 100 Women) 1986 Urban Centres (persons) Rural balance SSD Over 10 000 5000-9999 1000-4999 Less than 1000 Rates (aver no. Pop Rates (aver no. Pop Rates (aver no. Pop Rates (aver no. Pop childn) childn) childn) childn) Stnd Obs Stnd Obs Stnd Obs Stnd Obs Barossa 139.68 144.15 1839 132.38 140.16 4935 O, Fl, KI 143.35 149.83 2001 133.55 135.60 1837 135.46 147.20 5354 Yorke 141.03 142.60 1547 152.33 160.27 2391 Lower 141.66 147.88 992 158.82 165.39 2661 Nth Murray 148.59 145.48 2535 159.42 163.49 682 159.93 171.99 2863 Mal Riverland 149.72 144.97 2695 152.74 161.25 3825 Upper SE 146.29 143.56 1942 155.32 172.20 1896 Lower 147.75 142.87 4635 166.18 160.27 1105 151.17 148.43 223 157.57 165.62 2967 SE Lincoln 151.12 145.71 2459 161.01 169.39 3061 West 172.95 164.46 681 170.19 180.87 763 Coast Whyalla 154.07 144.61 6137 157.07 166.22 148 Pirie 145.49 143.91 2863 159.08 158.04 951 154.32 162.08 2653 Flinders 158.77 151.07 3372 155.12 163.15 673 R Far North 145.72 150.96 785 160.98 150.84 836 Stnd = Age Standardised Rate; Obs = Observed Rate Source: ABS calculated from unpublished data
The West Coast, the rural balance of Lincoln SSD, the rural balance of the Far North SSD and the urban centre category 5000–9999 in the Lower South East SSD maintained their position as areas where women aged 15–44 recorded the greatest number of children ever born per 100 women. The major non-metropolitan centres remained as middle range areas, particularly Port Lincoln (Lincoln SSD) and Whyalla (Whyalla SSD) whose fertility rates were very close to the State average (150.05 per 100 women).
The urban centre category 1000–4999 in the Far North lost its position as the category of lowest fertility, although in relation to the surrounding and adjoining areas, fertility levels in this category were very low. By 1986 the smallest average family sizes were for women living in the rural balance of the Barossa SSD followed closely by the urban centre category 1000–4999 and rural balance of the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs. Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 214
Figure 6.7: Non-Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years by Section of State by SSD, 1986 Census
Metropolitan Adelaide is excluded Source: ABS unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 215
6.3.5 Change Over Time, 1981–1986
Table 6.17 presents the expected and actual changes over time in the age standardised average issue for women aged 15–44 years between the 1981 and 1986 Censuses. Based on the overall change for the non-metropolitan area as a whole, and the initial value for each Section of State category, the predicted change ranged from a decline in the average number of children from 9 to 19 children per 100 women aged 15–44 years. Actual change ranged from a decline of around 19 children to an increase of close to four children. In the urban centre category 1000–4999 persons in the Far North SSD average issue increased from 142 in 1981 to 146 in 1986, despite a predicted decline of 8.5 children.
The Section of State categories (Barossa and the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs) adjacent to the metropolitan area experienced declines in fertility greater than expected. These were the areas already with the lowest levels of fertility in the non-metropolitan sector. Generally the Section of State categories identified in the previous section as continuing to have high fertility from 1981 to 1986 were the areas that did not experience as much decline in fertility as predicted. This was particularly so for the West Coast SSD. For the urban centre category 1000–4999 fertility only declined by 50 per cent of that expected. Of the largest urban centres (over 10 000 persons) three declined as predicted (Mount Gambier, Lower South East SSD; Port Lincoln, Lincoln SSD; Port Augusta, Flinders Ranges SSD) and average family size declined by more than expected in Whyalla (Whyalla SSD) and Port Pirie (Pirie SSD).
6.3.6 Convergence/Divergence, 1981–1986
In section 6.3.4 it was pointed out that the spatial patterns between 1981 and 1986 seemed very similar. This is confirmed by the correlation coefficient in Table 6.18 which indicates a strong correlation between the two spatial distributions. In relation to convergence or divergence, all the components in the Table imply a slight trend toward convergence over this time. There was a decline in the range, standard deviation, and coefficient of variation and the regression value remained below one.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 216
Table 6.17: Measures of Social Change Over Time (Components of Social Change) in the Age Standardised Average Number of Children Ever Born to Women Aged 15-44 Years for Section of State by SSD, 1981–19861 SSD/Section of State 1981-1986 Rates Change 1981 Pred 1986 St Pos Fdb
Barossa UC 1000-4999 152.01 141.04 139.68 -10.97 -1.36 3.49 Rural Balance 147.86 137.89 132.38 -9.97 -5.50 4.49 Onkaparinga, Fleurieu, K.I UC 5000-9999 155.72 143.86 143.35 -11.86 -0.50 2.60 UC 1000-4999 148.21 138.15 133.55 -10.06 -4.61 4.41 Rural Balance 152.10 141.11 135.46 -10.99 -5.64 3.47 Yorke UC 1000-4999 156.49 144.44 141.03 -12.04 -3.41 2.42 Rural Balance 171.68 155.98 152.33 -15.69 -3.65 -1.23 Lower North UC 1000-4999 145.40 136.02 141.66 -9.38 5.64 5.08 Rural Balance 172.40 156.53 158.82 -15.87 2.29 -1.41 Murray Mallee UC Over 10000 162.72 149.18 148.59 -13.54 -0.59 0.92 UC 1000-4999 178.14 160.89 159.42 -17.24 -1.47 -2.78 Rural Balance 174.08 157.81 159.93 -16.27 2.12 -1.81 Riverland UC 1000-4999 157.38 145.12 149.72 -12.26 4.60 2.20 Rural Balance 167.12 152.52 152.74 -14.60 0.22 -0.14 Upper South East UC 1000-4999 159.05 146.39 146.29 -12.66 -0.10 1.80 Rural Balance 170.22 154.87 155.32 -15.34 0.45 -0.88 Lower South East UC Over 10000 161.06 147.91 147.75 -13.14 -0.17 1.32 UC 5000-9999 181.19 163.21 166.18 -17.98 2.97 -3.52 UC 1000-4999 166.11 151.75 151.17 -14.36 -0.58 0.11 Rural Balance 178.44 161.12 157.57 -17.32 -3.55 -2.86 Lincoln UC Over 10000 165.77 151.49 151.12 -14.27 -0.37 0.19 Rural Balance 180.66 162.81 161.01 -17.85 -1.80 -3.39 West Coast UC 1000-4999 181.54 163.48 172.95 -18.06 9.47 -3.60 Rural Balance 185.95 166.83 170.19 -19.12 3.36 -4.66 Whyalla UC Over 10000 173.56 157.41 154.07 -16.15 -3.35 -1.68 Rural Balance 170.34 154.97 157.07 -15.37 2.11 -0.91 Pirie UC Over 10000 164.92 150.85 145.49 -14.07 -5.36 0.39 UC 1000-4999 175.33 158.76 159.08 -16.57 0.32 -2.11 Rural Balance (combined with 168.69 153.72 154.32 -14.98 0.60 -0.52 Flinders Ranges) Flinders Ranges UC Over 10000 174.74 158.31 158.77 -16.43 0.46 -1.97 UC 1000-4999 175.66 159.01 155.12 -16.65 -3.89 -2.19 Rural Balance (see Pirie) Far North UC 1000-4999 141.97 133.41 145.72 -8.56 12.31 5.90 Rural Balance 179.59 162.00 160.98 -17.59 -1.02 -3.13 (a) For the data to be directly comparable in terms of Section of State categories at both Censuses, Victor Harbor and Mount Barker in the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs were included in the category urban centres 5000–9999 persons and Port Elliot was included in the urban centre category 1000–4999. Murray Bridge in the Murray Mallee SSD was counted as an urban centre over 10 000 persons. St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value
Source ABS calculated from unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 217
While structural change remains of most significance in explaining the pattern between 1981 and 1986 (accounting for 89.7 per cent of all change overall), localised factors (positional change) played more of a role in 1981–1986 than for the 1976–1981 period.
The next section explores the patterns for women aged 15–44 years a decade later in 1996.
Table 6.18: Non-Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 15–44 Years for Section of State1 by SSD South Australia, 1981–1986 Summary Statistics Census Year Summary Statistics Census Year 1981 1986 1981–1986 Highest Value 185.95 172.95 Correlation 0.91 Lowest Value 141.97 132.38 Regression 0.76 Range 43.98 40.57 Percent of Variance Due To: Overall Mean2 166.01 150.05 Change in Means 89.7 Distributional Mean3 166.55 152.09 Feedback 3.4 Standard Deviation 11.79 9.82 Positional Change 6.9 Coefficient of Variation 7.08 6.46 1. Section of State categories the same as identified in Table 5.24 2.Overall mean: the rate for the state as a whole; all units combined 3.Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
6.3.7 Description of Patterns 1996
As outlined in Section 6.2.7, for women aged 45–49 years, between 1986 and 1996 there were a number of SSD boundary changes and considerable movement in the membership of the Section of State categories. The Figures present the spatial distribution for Section of State categories as they existed at the time of the Census and therefore it is a little more difficult to directly compare Figure 6.8 to Figure 6.7. There are however a number of patterns that are consistent with previous Censuses. For example the rural balance of SSDs generally recorded above average fertility. Women living in the rural balance of the West Coast SSD had the greatest number of children at 166.44 per 100 women (Table 6.19). Some urban centres of relatively high fertility in 1986 maintained their position, for example the urban centre category of 1000–4999 persons in the West Coast SSD, Murray Mallee SSD, and Pirie SSD.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 218
Table 6.19: Non Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 15–44 Years by Section of State by SSD at the 1996 Census (per 100 Women) 1986 Urban Centres (persons) Rural balance SSD Over 10 000 5000-9999 1000-4999 Less than 1000 Rates (aver no. Pop Rates (aver no. Pop Rates (aver no. Pop Rates (aver no. Pop childn) childn) childn) childn) Stnd Obs Stnd Obs Stnd Obs Stnd Obs Barossa 130.56 138.91 2912 128.47 141.96 5442 O, Fl, KI 131.16 134.01 2961 128.52 139.47 3180 125.74 139.52 5303 Yorke 140.04 153.21 1774 155.01 174.70 1850 Lower 138.31 146.07 953 153.15 173.27 2364 Nth Murray 144.33 143.03 2547 146.92 153.63 565 150.68 166.69 2678 Mal Riverland 141.01 142.31 2678 143.38 151.85 3649 Upper SE 140.47 145.35 1808 150.12 168.05 1618 Lower 136.82 134.43 4845 143.59 147.24 1124 147.63 159.84 2707 SE Lincoln 143.55 142.37 2393 138.98 157.23 166 158.24 175.18 2252 West 153.20 148.36 730 166.44 178.37 504 Coast Whyalla 146.02 143.54 4839 158.81 166.67 99 Pirie 140.53 136.77 2695 157.53 161.61 818 150.34 166.74 2327 Flinders 149.29 149.35 2865 142.52 151.74 402 R Far North 143.97 152.78 1167 141.18 139.53 678 Stnd = Age Standardised Rate; Obs = Observed Rate Source: ABS calculated from unpublished data
Port Pirie (urban centre over 10 000 persons) remained with below average fertility in contrast to the two other large centres in the Whyalla SSD and Flinders Ranges SSD respectively. The SSDs adjoining metropolitan Adelaide continued to record low fertility rates. The rural balance of the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs recorded the smallest average number of children ever born to women at 125.74.
One of the most noticeable features of Figure 6.8 is the change in the relative fertility rate in the Far North SSD. At every other Census there has been a stark contrast between the urban centre category 1000–4999 persons and the rural balance of the SSD with the urban centre category having considerably lower fertility than the rural balance.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 219
Figure 6.8: Non-Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years by Section of State by SSD, 1996 Census
Metropolitan Adelaide is excluded Source: ABS unpublished data
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 220
By 1996 this had changed with the urban centre category 1000–4999 having higher fertility than the rural balance. As for women aged 45–49 years, this change in relative fertility can be explained by the rapid growth of the mining centre of Roxby Downs over the 1986–1996 period and therefore the movement of this centre from the rural balance to the urban centre category. The average number of children ever born to women aged 15– 44 years in Roxby Downs in 1996 was 142.41 per 100 women. Also partly responsible is the very little change in the fertility rate of the urban centre category 1000–4999 even if Roxby Downs is not included. For example in 1986 the urban centre category had a fertility rate of 145.72 per 100 women. If Roxby Downs is excluded from the category in 1996 the fertility rate was 144.84, a very slight decline over a ten year period (Table 6.20).
6.3.8 Change Over Time, 1986–1996
As stated in section 6.2.8 for women aged 45–49 years, making comparisons over a ten year period is compounded by the number of changes in membership of the Section of State categories and some boundary changes (see Appendices 4.1 and 4.5). It is possible to make adjustments to the data that allow the 1986 distribution of urban centres to match 1996, or vice versa for the 1996 distribution to match 1986. Table 6.20 presents the former option however it appears to make only minor differences to the pattern of change over time (Appendix 6.2 presents a similar table matching 1996 data to the 1986 Section of State categories).
What is significant about change over this ten year period is the minimal decline in the age standardised number of children for women aged 15–44 years. Fertility for all areas was predicted to decline by between 4 to 11 children per 100 women. Between 1981 and 1986 fertility was predicted to decline by between 9 to 19 children per 100 women. In complete contrast was the trend for women aged 45–49 years. In the ten year period 1986–1996 the predicted decline in fertility was between 40 to 60 children per 100 women.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 221
Table 6.20: Measures of Social Change Over Time (Components of Social Change) in the Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years for Section of State by SSD, 1986–19961 (Rates per 100 Women) SSD/Section of State 1986-1996 Rates Change 1986 Pred 1996 St Pos Fdb
Barossa UC 1000-4999 138.86 133.62 130.56 -5.24 -3.06 2.32 Rural Balance 132.02 127.98 128.47 -4.04 0.50 3.53 Onkaparinga, Fleurieu, K.I UC 5000-9999 143.35 137.32 131.16 -6.03 -6.16 1.53 UC 1000-4999 133.24 128.99 128.52 -4.25 -0.47 3.31 Rural Balance 135.68 131.00 125.74 -4.68 -5.26 2.88 Yorke UC 1000-4999 142.20 136.37 140.04 -5.83 3.66 1.74 Rural Balance 152.37 144.76 155.01 -7.62 10.25 -0.05 Lower North UC 1000-4999 141.66 135.93 138.31 -5.73 2.38 1.83 Rural Balance 158.82 150.07 153.15 -8.75 3.08 -1.19 Murray Mallee UC Over 10000 148.59 141.63 144.33 -6.95 2.70 0.61 UC 1000-4999 159.42 150.56 146.92 -8.86 -3.65 -1.29 Rural Balance 159.93 150.98 150.68 -8.95 -0.30 -1.38 Riverland UC 1000-4999 149.72 142.57 141.01 -7.15 -1.56 0.42 Rural Balance 152.74 145.06 143.38 -7.68 -1.68 -0.12 Upper South East UC 1000-4999 146.29 139.74 140.47 -6.55 0.72 1.01 Rural Balance 155.32 147.19 150.12 -8.14 2.93 -0.57 Lower South East UC Over 10000 147.75 140.94 136.82 -6.80 -4.12 0.76 UC 1000-4999 163.48 153.91 143.59 -9.57 -10.31 -2.01 Rural Balance 157.57 149.04 147.63 -8.53 -1.41 -0.97 Lincoln UC Over 10000 151.12 143.72 143.55 -7.40 -0.17 0.17 UC 1000-4999 153.07 145.33 138.98 -7.74 -6.35 -0.17 Rural Balance 161.41 152.20 158.24 -9.21 6.04 -1.64 West Coast UC 1000-4999 170.49 159.69 153.20 -10.80 -6.48 -3.24 Rural Balance 172.85 161.63 166.44 -11.22 4.80 -3.65 Whyalla UC Over 10000 154.07 146.15 146.02 -7.92 -0.13 -0.35 Rural Balance 157.07 148.62 158.81 -8.44 10.17 -0.88 Pirie UC Over 10000 145.49 139.08 140.53 -6.41 1.45 1.16 UC 1000-4999 159.08 150.28 157.53 -8.80 7.25 -1.23 Rural Balance (combined with Flinders Ranges) 154.32 146.36 150.34 -7.96 3.98 -0.39 Flinders Ranges UC Over 10000 158.76 150.03 149.29 -8.74 -0.73 -1.18 UC 1000-4999 155.12 147.02 142.52 -8.10 -4.50 -0.53 Rural Balance (see Pirie) Far North UC 1000-4999 145.72 139.27 144.84 -6.45 5.57 1.12 Rural Balance 160.98 151.85 142.70 -9.13 -9.15 -1.57 1 1986 Section of State Categories match 1996 Section of State categories. For the data to be directly comparable in terms of Section of State categories at both Censuses, for Barossa SSD Freeling, Lyndoch and Williamstown move from rural balance in 1986 into the urban centre category 1000–4999; in the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs Nairne and Woodside move from rural balance in 1986 into urban centre category 1000–4999; Yorke SSD Ardrossan moves from rural balance in 1986 into urban centre category 1000–4999; the Lower South East SSD Millicent moves from the urban centre category 5000–9999 in 1986 into the urban centre category 1000–4999; Lincoln SSD Tumby Bay moves from rural balance 1986 into the urban centre category 1000–4999; West Coast SSD Streaky Bay moves from rural balance in 1986 to the urban centre category 1000– 4999. At both Censuses Roxby Downs is included in rural balance. St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value Source: Calculated from unpublished ABS data Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 222
This indicates a slowing of the decline in fertility across the non-metropolitan sector over this period. The rate of this decline is compounded by the fact that in two areas, the rural balance of Yorke SSD and the rural balance of Whyalla SSD the average issue per woman actually increased between 1986 and 1996 against the predicted decline. In some other Section of State categories the decline in fertility was minimal (urban centre category 1000–4999 in the Far North, Pirie and Yorke SSDs).
As has been the case for other inter-censal periods the declines in average issue were generally greater than expected in the inner SSDs adjoining the Adelaide Metropolitan Area however there are some different trends to previous periods. Decline in the rural balance of the Far North SSD was double what was expected and declines in the urban centre category 1000–4999 in the Flinders Ranges, Lower South East and on the West Coast were considerably greater than expected. Clearly some areas with reasonably high levels of fertility experienced the greatest declines in fertility, at least more so than in the past.
6.3.9 Convergence/Divergence, 1986–1996
Table 6.21 presents statistical data analysing the trend in spatial patterning of fertility between 1986 and 1996. The range and standard deviation declined only marginally suggesting stability in the pattern over time. A correlation coefficient of 0.85 indicates a strong association between the two Censuses. The regression slope value of 0.82 implies the areas are still converging, but it is to a lesser extent than in the past (the values for 1976–1981, 1981–1986 were 0.77 and 0.76 respectively). Increasingly local factors are influencing changes in the average issue of women aged 15–44 years with nearly 30 per cent of the change being due to positional change.
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 223
Table 6.21: Non-Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 15–44 Years for Section of State1 by SSD South Australia, 1986–1996 Summary Statistics Census Year Summary Statistics Census Year 1986 1996 1986–1996 Highest Value 172.85 166.44 Correlation 0.85 Lowest Value 132.02 125.74 Regression 0.82 Range 40.83 40.70 Percent of Variance Due To: Overall Mean2 150.05 141.62 Change in Means 66.7 Distributional Mean3 152.08 144.51 Feedback 3.4 Standard Deviation 9.75 9.50 Positional Change 29.9 Coefficient of Variation 6.41 6.57 1. Section of State categories the same as identified in Table 5.27 2.Overall mean: the rate for the state as a whole; all units combined 3.Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
6.3.10 Summary
This section of the chapter has examined the spatial patterns of fertility for women aged 15–44 years in non-metropolitan South Australia. The analysis has shown there is a greater consistency in the patterns from Census to Census than was the case for women aged 45–49 years.
In general the highest rates of fertility were found in the rural balances of SSDs in the outer reaches of the non-metropolitan area—the West Coast SSD, Lincoln SSD and Far North SSD for example, while the lowest rates of fertility were in the SSDs adjoining the metropolitan area. In addition often the areas of higher fertility did not experience as great a decline in fertility as expected while areas of low fertility initially experienced greater declines in fertility than expected.
Of particular note are a number of urban centre categories. Continual high rates of fertility were recorded in the urban centre category 1000–4999 in the Murray Mallee SSD at all Censuses. This category includes the urban centres of Tailem Bend and Mannum (due to the nature of the data requested from the ABS to restrict the costs of data purchase it is not possible to separate out these towns). Similarly the urban centre category 5000–9999 in the Lower South East SSD, comprising the urban centre of Millicent also consistently recorded higher levels of the fertility than the other urban centres in the South East region. Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 224
By the 1996 Census the population of Millicent had declined by over 300 people and as a result it lost its status as a centre of 5000–9999 persons. It became a member of the urban centre category 1000–4999 persons and so is not shown as a separate centre in Figure 6.8. From Appendix 6.2 however where the Section of State categories for 1996 match 1986 Section of State categories it is clear that even though the decline in fertility over the 1986–1996 inter-censal period was greater than expected, this centre still recorded the highest fertility of any of the urban centre categories in the South East region. The higher rates of fertility in these centres may be the result of the changing nature of the population of these towns over the 1976 to 1996 period.
Another urban centre of consistently high fertility was the Section of State category 1000– 4999 persons on the West Coast, particularly in the 1980s and 1990s. In contrast the ‘neighbouring’ urban centre category of 1000–4999 persons in the Far North SSD (the distinctive townships of Coober Pedy and Woomera) for the period 1976 to 1986 had unexpected low levels of fertility. This changed in 1996. Note should also be made of the urban centre category of 1000–4999 in the Pirie SSD. In 1981 and 1986 it had the third highest rate for this urban size category and in 1996 it recorded the highest rate. This category represents the townships of Crystal Brook, Jamestown and Peterborough, urban centres located in the declining wheat/sheep belt of South Australia.
The SSDs adjoining the metropolitan area stand out as consistently different in terms of fertility rates to the rest of the non-metropolitan area. Fertility in these SSDs was always below average. Beside the aberration of the urban centre category in the Far North SSD the lowest numbers of children ever born were always recorded in these SSDs. It is likely however that representation of Kangaroo Island as having fertility levels equating to those of the Barossa and Fleurieu and Onkaparinga SSDs is probably a little misleading. Due to the small population of Kangaroo Island (and the need to limit the cost associated with obtaining the data from the ABS) it was included with the Fleurieu and Onkaparinga SSDs in this study. Examination of the average Total Fertility Rate for 1997 calculated from birth registration data indicates that fertility on Kangaroo Island (2.26) was considerably higher than for the Barossa (1.79), Onakparinga (1.99) and Fleurieu (1.92) areas (ABS 1998a).
Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 225
The statistical parameters used to provide an indication of convergence or divergence in the fertility patterns for women aged 15–44 years imply a trend over each inter-censal period towards convergence in the pattern with only a slight tendency towards convergence over the 1986–1996 period. This is reinforced by the strong degree of correlation between the patterns at each inter-censal period, particularly between 1981– 1986 and 1986–1996 indicating that in many instances areas of high or low fertility maintained their distinctiveness between Censuses.
The variations in fertility overall by Section of State for women aged 15–44 almost exactly replicate the pattern for women aged 45–49 years. As evident in Table 6.22 the lowest fertility in 1976, 1981 and 1986 was recorded in the urban centres of 1000–4999 persons. In 1996 the lowest rate was recorded in the urban centre category of 5000–9999 persons. The highest rate for three of the four Censuses was recorded in the urban centre category of 10 000 people or more, going against expectations.
Table 6.22: Non-Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years By Section of State Categories, 1976, 1981, 1986, 1996 (per 100 Women) Section of State1 Census Year (number of persons) 1976 1981 1986 1996 Over 10000 229.69 168.80 151.40 143.05 5000–9999 224.68 169.87 151.07 131.06 1000–4999 221.36 158.08 146.69 138.77 Rural Balance 229.24 167.50 150.29 142.68 1. Section of State as they existed at each Census. No adjustment has been made to make them comparable over time. Source: ABS calculated from unpublished data
6.4. Synthesis
This chapter has examined the distribution of fertility at a disaggregated level across the non-metropolitan sector of the State from 1976 to 1996. It is clear that for women aged 45–49 years and 15–44 years the patterns are relatively similar although the small numbers involved for women aged 45–49 years is probably responsible for some of the random changes of pattern from one Census to the next. Chapter Six: Spatial Analysis of Fertility in Non-Metropolitan South Australia 226
As stated at the outset of this chapter, due to the nature of settlement in non-metropolitan South Australia the region was divided into Section of State categories allowing differentiation between urban settlements and predominantly rural areas. In an effort to gain meaningful results areas were aggregated and this complicates interpretation of the patterns for some areas. This is particularly the case when an urban centre category comprising more than one individual centre stands in contrast to patterns within the SSD and adjoining SSDs. For example, is the relatively high fertility for the urban centre category 1000–4999 persons in the Murray Mallee the result of similar fertility patterns in each of the two centres (Tailem Bend and Mannum) or due to very different patterns in just one of the centres? Similarly the below average fertility in the urban centre category 1000–4999 in the Far North SSD could be due to differing fertility patterns in Woomera and/or Coober Pedy or an aberration of the data for one or both of these centres.
The most noticeable variation across the non-metropolitan sector is the consistently low fertility for the SSDs adjacent to metropolitan Adelaide. In many respects these areas are atypical of much of non-metropolitan South Australia. For example they contain close settlements, and they are seen as increasingly attractive areas to live resulting in inmigration, increasing residential development and increasing interaction with the metropolitan area. In fact these areas could potentially be classified as expanding areas of the metropolitan area with increasing proportions of the population having more in common with the population living in the ‘city’ than with the farming and more remote settlements of much of ‘country’ South Australia.
Overall there has been a tendency towards convergence in the pattern of fertility however for women aged 15–44 this appeared to slow over the 1986 to 1996 inter-censal period. This fact, in conjunction with the lower levels of fertility recorded in the metropolitan sector indicates further declines in this sector of the State could be expected with continuing declines in the overall total fertility rate for the State.
The next chapter provides a detailed examination of fertility in metropolitan Adelaide over the 1976 to 1996 period. Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 227
Chapter Seven
Spatial Analysis of Fertility in South Australia 1976 to 1996 Metropolitan South Australia
7.1. Introduction
Over two thirds of the State’s population live in metropolitan Adelaide so this region has a significant bearing on fertility patterns and trends in South Australia. In Chapter Five it was identified that fertility rates in the SSDs comprising the Adelaide Statistical Division (ASD) were generally lower than for other SSDs in the State. The spatial representation of fertility, and the trends over time, have particular relevance here if one subscribes to the notion that low fertility equates with little variation between groups and across space (Compton 1991).
For this part of the study metropolitan Adelaide is divided into 155 smaller localities. These localities unfortunately, like SLAs, are of varying sizes as their designation was governed by the size of the smallest data collection unit of the ABS, the CD. The size of the CDs reflects population density and land use. Some areas therefore remain very large as the population is very dispersed and only small concentrations can be found in particular areas. Figure 7.1 for example shows the extent of urban development in the Adelaide Metropolitan Area over time and it is clear that in some areas included in this study population at particular times is sparse.
7.2. Women Aged 45–49 Years
This Chapter begins by examining the fertility of women aged 45–49. Figures 7.2, 7.3, 7.5 and 7.7 (note data for each area are available in Appendix 7.1) show the overall average family size for women aged 45–49 years at the 1976, 1981, 1986 and 1996 Censuses respectively.
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 228
Figure 7.1: Adelaide Urban Development 1836–1994
Source: Beer and Cutler 1995, 19 Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 229
Comparison of these figures with Figures 5.2 to 5.5 shows the complexity of variation in fertility when examined at a sub SSD level. Although the general pattern of above and below average fertility is clearly evident across the metropolitan region at the small area level, what is immediately apparent is the widely scattered distribution of values. The SSDs (and their constituent Statistical Local Areas, SLAs) contain a mixture of values when divided into smaller neighbourhood regions.
7.2.1. Description of Patterns, 1976 and 1981
1976 The average issue for women aged 45–49 years at the 1976 Census ranged from a high of 350.05 per 100 ever married women to a low of 227.24 (Appendix 7.1). Although areas of high and low fertility are scattered across the metropolitan region, there is a general pattern of lower fertility in the inner suburbs extending west to the coast. A band of low fertility follows the River Torrens (areas 61, 72, 73) to the coast at Henley Beach (areas 48, 49). Women with higher levels of fertility generally live in the outer and fringe suburbs of the metropolitan region. The highest average completed family size in 1976 for this age group was in the south of the metropolitan region, in the Willunga SLA (areas 155, 156). This is mainly a farming district, and at the time of this Census more closely resembled the adjoining non-metropolitan areas in terms of population and settlement density. In fact at the 1976 Census the ABS included much of area 156 in this study in the non-metropolitan Fleurieu SSD. It was shifted to the metropolitan region specifically in this study to make the metropolitan and non-metropolitan areas comparable over the 1976 to 1996 period.
Of interest from Figure 7.2 is the juxtaposition of high and low fertility levels. For example, the northern part of the Port Adelaide SLA compared to the southern sector, the north western parts of Enfield SLA compared to the south and eastern sectors of the SLA and there are many more examples across the metropolitan region. This shows that fertility can vary considerable within an SLA, probably reflecting populations with varying characteristics living within the SLAs.
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 230
Figure 7.2: Metropolitan South Australia: Average Number of Children Ever Born to Ever Married Women Aged 45–49 Years, 1976 Census
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 231
1981 By the 1981 Census the overall average fertility rate for the metropolitan region as a whole had declined slightly from 282 children per 100 women aged 45–49 years in 1976 to 276 children in 1981 (Appendix 7.1). This trend however did not occur for all small areas (Figure 7.3). In fact in 45 per cent of the small areas, average family size increased over the inter-censal period. This may reflect real changes in fertility but may also be a consequence of the limitations of the data at the 1976 Census.
In comparing Figures 7.2 and 7.3 the spatial pattern in 1981 appears slightly more distinctive as areas of similar fertility levels are clustered together. Particularly noticeable is the dominance of above average fertility in the northern sector of the metropolitan region. The largest average families, at 342.33 children per 100 women, were recorded in area 36 in the Enfield SLA.
Much of the Noarlunga SLA and Willunga SLA to the south recorded above average fertility. Areas of lowest average family size tend to be concentrated to the immediate east, north and south of the city. A pattern of low fertility is also noticeable along the River Torrens west to the coast (bordered by areas 50, 70, 61, 72, 73, 62, 48, 49) along the main highway (Anzac Highway) leading from the City to Glenelg (areas 129, 128, 77, 76, 126, 121 120); and along the coast in Henley and Grange (areas 47, 48, 49), Glenelg (areas 119, 120, 121) and Brighton (areas 116, 117, 118). Some of these patterns exist across SLA boundaries as areas of similar fertility in adjoining SLAs form along or around particular features of the urban and physical landscape.
7.2.2. Change Over Time, 1976–1981
As in Chapters Five and Six, the extent of change over time has been analysed using Congdon and Shepherd’s (Congdon 1989; Congdon and Shepherd 1985, 1988; Shepherd and Congdon 1990) Social Model of Change. Using a regression approach to measurement, this model is able to provide a projected or predicted value for each area in 1981 based on the 1976 value for an area, and the overall change in fertility for the metropolitan region over the inter-censal period. In addition the model is able to provide an indication of how closely the change in a specific area aligns with that expected. Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 232
Figure 7.3: Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years, 1981 Census
Source: ABS unpublished data Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 233
As there are considerably more areas to make sense of in this chapter, in comparison to the geographic breakdown in Chapters Five and Six, Figure 7.41 provides a picture of the variation in positional change. Areas in which there are large positional increases, exceeding one standard deviation above mean positional change (which is zero) appear in the top category (see Chapter 4 for further details). In these areas the decline in fertility has been considerably less (in fact fertility may have increased) than expected based on the metropolitan wide trends. The lowest category highlights areas where the decline in average issue was greater than expected (more than one standard deviation below average).
It is clear from Figure 7.4 that there is a definite split across the metropolitan area in terms of the areas where positional change was the greatest. All the areas in the top category (over 20.15) experienced an increase in fertility over the inter-censal period. For some of these areas an increase in fertility was predicted, but the increase was substantially greater than that expected. For example in area 16, in the Salisbury SLA, average issue was predicted to increase by 6.36 children per 100 women but it actually increased by an additional 27.02 children. The areas experiencing declines in fertility considerably greater than expected tend to be small inner suburban areas with a few outliers to the south. Comparing Figure 7.4 with Figures 7.2 and 7.3 also highlights that in many cases the areas originally with relatively high fertility experienced the greatest increase in fertility, and the areas initially with low fertility experienced the greatest declines in fertility.
7.2.3 Convergence/Divergence, 1976–1981
The above pattern suggests a move towards divergence of the spatial distribution of the average issue of women aged 45–49 years living in the metropolitan region. Table 7.1 provides some summary statistics to indicate whether this is the case. The range, standard deviation and coefficient of variation suggest little has changed over the 1976 to 1981 period. The correlation coefficient however indicates there is only a weak correlation between the two patterns.
1 The calculated degree of structural and positional change for each small area is provided in Appendix 7.2. Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 234
Figure 7.4: Metropolitan South Australia: Positional Change in the Average Number of Children Ever Born for Women Aged 45–49 Years 1976– 1981
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 235
This may be a result of the different population bases for each Census and the low numbers involved in a number of areas. In terms of whether the pattern has diverged over time, a regression value of 0.57 indicates the pattern has not diverged, but converged, over the inter-censal period.
Table 7.1: Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 45–49 Years, 1976–1981 Summary Statistics Census Year Summary Statistics Census Year 1976 1981 1976-1981 Highest Value 350.05 342.33 Correlation 0.54 Lowest Value 227.24 219.80 Regression 0.57 Range 122.81 122.53 Percent of Variance Due To: Overall Mean1 281.87 276.38 Change in Means 8.1 Distributional Mean2 282.06 275.41 Feedback 17.4 Standard Deviation 22.73 23.98 Positional Change 74.5 Coefficient of Variation 8.06 8.71 1. Overall mean: the rate for the state as a whole; all units combined 2..Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
7.2.4. Description of Patterns, 1986
Figure 7.5 and Appendix 7.1 present the average issue for women aged 45–49 years at the 1986 Census. Overall fertility across the metropolitan region declined, although again, as was the case in 1981, not all areas participated in this decline. The spatial patterning of fertility, particularly the pattern to the north of the metropolitan region is remarkably similar. While there is a scattering of values across Adelaide there is a distinct concentration of above average fertility in the northern suburbs (the highest value of 328.13 was recorded in area 13 in the Salisbury SLA) and in Noarlunga SLA to the south. The older established inner and coastal suburbs remain areas of lower fertility. The lowest average family size was recorded in area 76 in the West Torrens SLA at 204.59. The larger, more sparsely settled areas along the eastern border of the metropolitan area and to the far south recorded average family sizes around the metropolitan mean.
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 236
Figure 7.5: Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years, 1986 Census
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 237
Of interest in 1986 is the continuation of low fertility levels in area 63, Port Adelaide SLA to the north west of the city, and relatively higher fertility in the adjacent areas 64 and 67. A similar situation occurs in the Marion SLA for areas 132, 133 and 134 which have higher fertility than the adjacent areas. For example the average issue of women in areas 132 and 133 were 296.67 and 271.51 respectively, yet the fertility in the adjacent areas to the north, areas 127 and 131 were 248.72 and 254.93
7.2.5. Change Over Time, 1981–1986
Figure 7.62 presents those areas that experienced a change in fertility greater than one standard deviation of the mean of positional change. The figure provides a number of patterns. In some areas of relatively high fertility in 1981, area 155 (Willunga), area 106 (Stirling), area 94 (East Torrens), and area 27 (Salisbury) the decline in fertility was greater than one standard deviation and this was sufficient for these areas to move from above average fertility in 1981 to below average fertility in 1986.
Similarly there were areas of below average fertility in 1981, areas 81, (Burnside) and area 137 (Mitcham) for example, where the decline in fertility was considerably less than expected and these areas moved from having below average fertility in 1981 to above average fertility in 1986. In a sense these areas went against expected trends. On the other hand in areas to the north of the city and some of the inner suburbs, the decline in fertility just reinforced the areas with very high or very low fertility in 1981.
Over this inter-censal period women living in all the areas were expected to have smaller families in terms of their issue than women of the same age in 1981. Average issue was predicted to decline by anywhere between 4 and 31 children per 100 women, with most areas expected to experience a decline of between 18 and 26 children (Appendix 7.3). The difference between the predicted value and the actual value in 1986 (positional change) ranged however from a decline of 38 children per 100 women to an increase of 42 children.
2 The calculated degree of structural and positional change for each small area is provided in Appendix 7.3. Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 238
Figure 7.6: Metropolitan South Australia: Positional Change in the Average Number of Children Ever Born to Women Aged 45–49 Years, 1981– 1986
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 239
7.2.6. Convergence/Divergence, 1981–1986
Table 7.2 shows that the difference in average issue across the metropolitan area was still over one child per woman in 1986. The correlation coefficient of 0.76 indicates a greater degree of similarity between the 1981 and 1986 distribution than was the case for the previous inter-censal period. A slight increase in the standard deviation and coefficient of variation suggests a slight movement towards divergence in the spatial pattern over time. The regression value of 0.78 however still indicates convergence, though to a lesser extent than in the 1976–1981 inter-censal period. Overall 44 per cent of the change in the average issue of women was due to localised factors—factors affecting individual areas which altered the influence of the overall metropolitan wide decline in fertility.
Table 7.2: Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 45–49 Years, 1981–1986 Summary Statistics Census Year Summary Statistics Census Year 1981 1986 1981-1986 Highest Value 342.33 328.13 Correlation 0.76 Lowest Value 219.80 204.59 Regression 0.78 Range 122.53 123.54 Percent of Variance Due To: Overall Mean1 276.38 259.17 Change in Means 50.7 Distributional Mean2 275.41 258.40 Feedback 4.9 Standard Deviation 23.98 24.55 Positional Change 44.4 Coefficient of Variation 8.71 9.50 1. Overall mean: the rate for the state as a whole; all units combined 2..Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
7.2.7 Description of Patterns, 1996
The spatial patterning for 1986 (Figure 7.5) and 1996 (Figure 7.7) look remarkably similar with what appears to be a more distinct solid pattern across the metropolitan region of relatively high and low fertility. Average family size continued to decline such that by 1996 no area recorded average fertility above three children and the lowest average number of children ever born had dropped to less than two children (Appendix 7.1).
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 240
Despite having the greatest potential for fertility decline, the areas of highest fertility in 1996 remained those in the northern part of the metropolitan area. They encompassed all of Munno Para, Elizabeth and Salisbury SLAs and area 36 of Enfield. In addition, fertility in the Noarlunga region (areas 147-154) was considerably higher than for the surrounding district.
Areas of relatively low fertility form a fairly distinct pattern radiating out from the City of Adelaide (area 80). Particularly noticeable is the band of very low fertility in the areas adjacent to the southern edge of the city (in Unley SLA) and the radiating spine of low values from the south west of the city to the coast. Some of the outer areas of the metropolitan region, where there was significant urban development and growth in the population aged 45–49 years between 1986 and 1996, had average fertility levels close to the metropolitan mean of 212.03.
7.2.8 Change Over Time, 1986–1996
Figure 7.83 presents the areas that experienced the least and greatest declines in fertility over the ten years, 1986 to 1996. These areas are scattered across the metropolitan area but often two to three adjoining areas are grouped together. The dominant feature of Figure 7.8 is the conglomeration of areas to the north. These were areas that experienced the least decline in fertility. For example, for area 2 in the Elizabeth SLA, average issue was expected to decline by 57 children per 100 women but it actually only declined by 25 children per 100 women (Appendix 7.4). Many of the areas to the north were also those where in 1986 average issue was already relatively high.
Some areas of relatively high fertility in 1986 [areas 66, 67 (Port Adelaide SLA); area 37 (Enfield SLA), area 53 (Hindmarsh Woodville SLA) and area 132 (Marion SLA)] did experience significant declines in fertility over the 1986–1996 period. These declines were great enough to result in a change of category from 1986 to 1996. For example some of these areas moved from having the average issue of women greater than the metropolitan average in 1986 to recording an average issue below the metropolitan average at the 1996 Census.
3 The calculated degree of structural and positional change for each small area is provided in Appendix 7.4. Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 241
Figure 7.7: Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years, 1996 Census
Source: ABS unpublished data Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 242
Figure 7.8: Metropolitan South Australia: Positional Change in the Average Number of Children Ever Born for Women Aged 45–49 Years, 1986– 1996
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 243
All the areas that recorded the greatest declines in fertility over the inter-censal period were confined to the inner, older established sections of the city (Figure 7.8). Many of these areas were areas of relatively low fertility in 1986, thus reinforcing their low fertility status. In contrast to the situation for area 2 discussed above, in many of these areas, (for example areas 95, 96, 98, 103, 109, 111, 114, 119, 120, 136) average issue was expected to decline by between 30 and 50 children per 100 women when in fact it declined by an additional 50 to 90 per cent.
7.2.9 Convergence/Divergence, 1986–1996
Table 7.3 shows the significant decline in average issue for women aged 45–49 years over the 1986 to 1996 inter-censal period. Although the metropolitan mean was around 212 children per 100 women, in some areas average issue was only around 150 children per 100 women. Comparison of Figures 7.7 and 7.5 shows a degree of similarity between the two distributional patterns (correlation of 0.69, Table 7.3). In addition, overall, much of the change over time followed that expected based on metropolitan wide trends (change in means of 87 per cent). This resulted in a decline in the range and standard deviation over time and some degree of convergence in the pattern over the inter-censal period (regression value less than one).
Table 7.3: Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 45–49 Years, 1986–1996 Summary Statistics Census Year Summary Statistics Census Year 1986 1996 1986–1996 Highest Value 328.13 261.75 Correlation 0.69 Lowest Value 204.59 152.10 Regression 0.65 Range 123.54 109.65 Percent of Variance Due To: Overall Mean1 259.17 212.03 Change in Means 87.2 Distributional Mean2 258.40 209.60 Feedback 2.7 Standard Deviation 24.55 23.05 Positional Change 10.1 Coefficient of Variation 9.50 11.00 1. Overall mean: the rate for the state as a whole; all units combined 2..Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 244
7.2.10 Summary
This section has examined fertility levels for women aged 45–49 years at each Census individually and the trends in fertility by area from one consecutive Census to another where the question on issue has been included in the Census. Over the twenty year period of examination the spatial pattern of fertility has remained relatively constant during a period of time that has included significant declines in fertility and noteworthy societal changes in the structure of families and the roles of women.
Women living in the northern areas of the metropolitan region and Noarlunga to the south of the city have the greatest number of children ever born. The high levels in the north are particularly striking not only because of their concentration but also because of the size of the areas. These areas are large because urban development is not widespread across these SLAs (see Figure 7.1). These areas however have experienced considerable population growth over the twenty year period under study here. The inmovement of people has only reinforced the higher than average levels of fertility that were present in 1976.
Lower than average fertility is prevalent in the older, inner, middle and coastal areas. Very low fertility is centred in and immediately around the City of Adelaide and tends to branch off in various directions. Most noticeable is the branch radiating from the south west corner of the city along the highway to the coast.
Despite the broad striking patterns the spatial pattern is fluid with many areas changing category from one point in time to another. Even at the extremes no one area has the highest or lowest rate continually over time. Some areas of interest though are 149 in Noarlunga, 64 in Port Adelaide and 132 in Marion. These small pockets have persistently recorded high average family sizes over time in contrast to surrounding areas. From various social atlases of Adelaide (ABS 1988, 1993c; Division of National Mapping and ABS 1984) it is evident these areas have a relatively higher proportion of their population comprising people with socio-economic characteristics that are associated with higher levels of fertility (for example, Aboriginality, no post school educational qualifications, low income households, and public housing tenants).
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 245
Table 7.3 indicated despite the declines in fertility since 1976 there was still a difference of over one child per woman across the metropolitan area in 1996. Over each inter-censal period however indications were of a convergence in the spatial pattern of fertility. However, as commented in Chapter Five, while an examination of the fertility patterns of women aged 45–49 years is able to provide a picture of completed fertility much of their family building began or occurred two to three decades ago and does not reflect more recent trends in fertility. In addition it is more likely that these women may no longer reside in the neighbourhood in which they lived when their children were born, thereby ‘diluting’ the spatial pattern of fertility. Therefore the next section of the analysis will examine trends for women aged 15–44 years.
7.3. Women Aged 15–44 Years
7.3.1. Description of Patterns, 1976 and 1981
1976 Figure 7.9 presents the spatial distribution of age standardised fertility for ever married women aged 15–44 years at the 1976 Census. The patterns clearly reflect those identified for women aged 45–49 years with the band of high fertility to the north and north west of the city and then again in the southern suburbs and the low fertility in the middle section of the map—the older more established suburbs.
Many areas in the high band of fertility rates recorded fertility levels well above the top category definition of above 211.64, and well above the mean of 191.91. For example, in area 1 (Elizabeth SLA) the rate was 238.50, in areas 10 and 11 (Gawler-Munno Para SLAs) the fertility rates were 243.43 and 237.82 respectively and in area 36 (Enfield SLA) there were 242.84 children ever born per 100 ever married women aged 15–44 (Appendix 7.5). Of particular note are area 71 in Thebarton and area 132 in the Marion SLA. While not recording the very high rates of many of the northern areas, the fertility levels in these areas were high enough for them to fall into the highest fertility category. These areas Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 246 stand in contrast to the adjoining and nearby areas where fertility is more likely to be below average.
Areas along the eastern fringe of the metropolitan area had fertility levels around average although the sparsely settled SLAs of East Torrens and Stirling had relatively lower rates. The areas of lowest fertility were the City of Adelaide (area 80) at 151.09 children per 100 ever married women and the adjoining areas to the east and south of the city. Fertility remains low along the highway axis from the south west corner of the city to the coast. The coastal areas recorded some of the lowest fertility across the metropolitan area. The northern sections of the Happy Valley SLA also recorded low levels of fertility in 1976 (Appendix 7.5)
1981 At the 1981 Census, when all women regardless of marital status are counted in the data, fertility levels declined in all areas to below two children ever born per woman (Appendix 7.5), but the pattern remained very similar to 1976 (Figure 7.10). The northern areas continue to have relatively high fertility, well above the metropolitan mean of 133 children per 100 women, while in contrast the City of Adelaide and the areas to the immediate north, east and south of the city recorded relativley low fertility levels.
Area 10 in Munno Para adjacent to the Elizabeth SLA, recorded the highest fertility at 192.42 while the age standardised average number of children ever born to women aged 15–44 years in the City of Adelaide (area 80) was only 75.26 per 100 women. Areas along the coast also recorded low levels of fertility. Although all areas in the Port Adelaide SLA had above average fertility, area 64 remained in the category of highest fertility. The small area in Marion SLA, area 132 also stands in contrast to the surrounding areas. It recorded an age standardised fertility rate of 150.42 compared to the adjoining areas of 131 at 121.07, 133 at 132.93 and 145 at 123.49 for example (Appendix 7.5). The SLAs to the south of the city, Noarlunga and Willunga, also recorded high fertility. In particular areas 149 and 151 in Noarlunga recorded relatively high fertility at 167.94 and 159.30 children ever born per 100 women.
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 247
Figure 7.9: Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Ever Married Women Aged 15–44 Years, 1976 Census
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 248
Figure 7.10: Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years, 1981 Census
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 249
7.3.2. Change Over Time, 1976–1981
This study uses Congdon’s model of the ‘components of social change’ to gauge change over time. Figure 7.11 provides a picture of the variation in positional change. Areas in which there are large positional increases, exceeding one standard deviation above mean positional change (which is zero) appear in the top category while the lowest category highlights areas where the decline in average issue was greater than expected (more than one standard deviation below average). The predicted value and measures of structural and positional change for each individual area are listed in Appendix 7.6
It is immediately apparent that it was the inner and some coastal areas that experienced the greatest declines over that expected between 1976 and 1981. Some of these areas however were areas in 1976 to have above average fertility while others like Adelaide (area 80) and areas 95 and 96 in Kensington and Norwood already had low fertility in 1976. Area 80 for example, began with a fertility rate in 1976 of 151.09. This was predicted to decline by 57.88 to 93.20. In fact by 1981 the fertility rate had dropped an additional 17.90 children to reach 75.26 (Appendix 7.6). Of course the opposite also occurred with a number of areas experiencing less change than expected. Some of these areas had relatively high levels of fertility in 1976 (for example areas 8, 12, and 150).
What is perhaps more surprising is that many of the areas that had the highest levels of fertility in 1976 are not highlighted in Figure 7.11. These areas, for example (1, 2, 3 and to a lesser extent 4 and 5) which form the SLA of Elizabeth actually experienced close to the metropolitan trend in the decline in fertility, yet still in 1981 they maintained their relative position as the areas of high fertility (Appendix 7.6).
7.3.3. Convergence/Divergence, 1976–1981
Table 7.4 provides some statistical parameters to gain an insight into a convergence or divergence in the spatial pattern of fertility between the 1976 and 1981 Censuses. Overall the change in fertility across the individual areas was close to the pattern for metropolitan Adelaide as a whole with the change in means accounting for 96 per cent of the difference in fertility from one year to another. Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 250
Figure 7.11: Metropolitan South Australia: Positional Change in the Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years, 1976–1981
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 251
An increase in the difference between the highest and lowest values (range) from 96.93 to 117.16, an increase in the standard deviation, and an increase in the coefficient of variation from 10.74 to 17.51 all indicate divergence in the pattern over this inter-censal period. A regression value of 0.98 indicates only a slight degree of convergence and there is a strong correlation between the patterns (correlation coefficient of 0.87). In general those areas with high and low fertility maintained their distinctiveness between the two Censuses as clearly evident from Figures 7.9 and 7.10.
Table 7.4: Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 15–44 Years, 1976–1981 Summary Statistics Census Year Summary Statistics Census Year 1976 1981 1976–1981 Highest Value 243.43 192.42 Correlation 0.87 Lowest Value 146.50 75.26 Regression 0.98 Range 96.93 117.16 Percent of Variance Due To: Overall Mean1 192.49 133.03 Change in Means 96.4 Distributional Mean2 189.81 131.24 Feedback 0.0 Age Standardised Mean3 191.92 133.59 Positional Change 3.6 Standard Deviation 20.40 22.98 Coefficient of Variation 10.74 17.51 1. Overall mean: the rate for the state as a whole; all units combined 2..Distribution mean: mean of the rate distribution across areas 3. Age standardised mean: mean of the age standardised rates across all areas
Source: ABS calculated from unpublished data
7.3.4. Description of Patterns, 1986
By 1986 very little appears to have changed other than fertility levels are lower. Although some of the areas changed category the overall pattern remained much the same with the distinct separation of the metropolitan area into ‘fertility zones’ (Figure 7.12). The north and south of the metropolitan area are dominated by above average age standardised fertility while the inner central core of the metropolitan region records below average age standardised fertility for women aged 15–44 years.
While much of the north and north west of the metropolitan area was in the highest category of fertility (fertility exceeding 141.48 children per 100 women), a small conglomerate of areas around the northern border of the Elizabeth SLA and the southern border of the Munno Para SLA continued (since 1976) to have much higher fertility. Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 252
Figure 7.12: Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44, Years 1986 Census
Source: ABS unpublished data Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 253
The three adjoining areas numbers 10, 11 and 1 recorded the highest fertility levels at the 1986 Census at 181.46, 175.34 and 170.04 children ever born per 100 women aged 15–44 years (Appendix 7.5).
In contrast the lowest fertility levels were again found in the City of Adelaide at 64.56 children per 100 women, in the two areas comprising the Kensington and Norwood SLA (areas 95 and 96) with 63.29 and 65.19 children respectively and in area 110 of Unley SLA at 68.30 (Appendix 7.5).
Of note again is the much higher fertility recorded in area 64 of Port Adelaide SLA compared to the surrounding areas and area 132 (and to a lesser extent area 134) in the Marion SLA where fertility is above average while women living in the adjoining areas had below average fertility.
7.3.5. Change Over Time, 1981–1986
Change over the inter-censal period was predicted to be fairly even across all areas with the decline in fertility to range only between 15 and 16 children (Appendix 7.7). Figure 7.13 highlights those areas that experienced a greater or much less than expected decline in fertility (exceeding one standard deviation above or below mean positional change- which is zero). For the areas that exceeded one standard deviation above the mean of positional change the decline in fertility was generally only 60 per cent or less of that expected. Conversely the areas that exceeded one standard deviation below the mean of positional change experienced a decline in fertility anywhere between 36 per cent and 130 per cent more than expected. It is particularly noteworthy that areas 1 and 11, the areas of highest fertility in 1986 are highlighted.
7.3.6 Convergence/Divergence, 1981–1986
The appearance of little changing over the 1981 to 1986 period (Section 7.3.4) is supported by the statistics in Table 7.5. The correlation between the two patterns was 0.97 and the regression value of 0.99 indicates stability in the pattern. Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 254
Figure 7.13: Metropolitan South Australia: Positional Change in the Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years, 1981–1986
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 255
Clearly the decline in fertility overall has occurred relatively evenly across the metropolitan area and therefore had little impact on the geographic pattern of fertility.
Table 7.5: Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 15–44 Years, 1981-1986 Summary Statistics Census Year Summary Statistics Census Year 1981 1986 1981–1986 Highest Value 192.42 181.46 Correlation 0.97 Lowest Value 75.26 63.29 Regression 0.99 Range 117.16 118.17 Percent of Variance Due To: Overall Mean1 133.03 118.01 Change in Means 88.1 Distributional Mean2 131.24 115.66 Feedback 0.0 Age Standardised Mean3 133.59 118.70 Positional Change 11.9 Standard Deviation 22.98 23.55 Coefficient of Variation 17.51 20.36 1. Overall mean: the rate for the state as a whole; all units combined 2..Distribution mean: mean of the rate distribution across areas 3. Age standardised mean: mean of the age standardised rates across all areas
Source: ABS calculated from unpublished data
7.3.7 Description of Patterns, 1996
By 1996 the mean age standardised average number of children ever born to women aged 15–44 had declined from 118.70 to 110.20 per 100 women. Despite this ongoing decline in fertility over time the starkness of the spatial pattern identified in Figure 7.14 is remarkable. Instead of convergence in the pattern with declining fertility it appears as if the metropolitan area has become more divided.
As for the previous Censuses, areas 10, 11 and 1 in the north of the metropolitan area had the highest fertility at 178.49, 177.57 and 170.48. The next highest rate was for area 2 at 157.35 (Appendix 7.5). In contrast the lowest fertility rates were recorded in the City of Adelaide (area 80) at 55.80, and in the two areas that comprise the SLA of Kensington and Norwood (areas 95 and 96) at 54.78 and 59.65. This means that the age standardised average number of children ever born to women in the three northern suburbs is more than three times the rate for women living in the inner suburbs.
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 256
Figure 7.14: Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years, 1996 Census
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 257
Area 64 in Port Adelaide SLA remained in the category of highest fertility even though areas immediately to its south continued to move into categories of lower fertility. For the first time though women living in area 132 in the Marion SLA had fertility levels similar to women living in the adjacent districts and fertility below the overall metropolitan mean.
7.3.8. Change Over Time, 1986–1996
Appendix 7.8 presents the measures of social change for the inter-censal period 1986– 1996. The decline in fertility for each individual area over this time was expected to be very even across the metropolitan area at between 9.52 and 9.55 children per 100 women.
Figure 7.15 provides an interesting pattern of those areas that experienced a change in fertility greater or less than one standard deviation from that expected based on the change in the metropolitan mean and scatter and an area’s value in 1986. Such change has occurred in a number of areas grouped together, often across SLA or council boundaries. This suggests that over the inter-censal period localised factors in these small regions influenced the pattern of fertility. Such localised factors may have included the development of a housing estate or the gentrification of an area and the influx of people with different behaviours in terms of childbearing.
Appendix 7.8 indicates that for fifteen of the twenty areas where the decline in fertility was less than expected, the age standardised average number of children ever born per woman aged 15–44 years actually increased over the 1986 to 1996 inter-censal period. This was the case for areas 1 and 11, the areas of highest fertility in both 1986 and 1996.
As noted in the previous section in Figure 7.15, area 132 in Marion experienced a greater decline in fertility than expected. This decline resulted in a change in the rank order of this area so that in 1996 it was no longer an outlier of higher fertility but had a rate similar to adjacent areas.
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 258
Figure 7.15: Metropolitan South Australia: Positional Change in the Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years 1986–1996
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 259
7.3.9 Convergence/Divergence, 1986–1996
Not unexpectedly Table 7.6 indicates that over the 1986 to 1996 inter-censal period the spatial pattern of fertility remained stable.
Table 7.6: Metropolitan South Australia: Summary Statistics and Components of Social Change For Women Aged 15–44 Years, 1986–1996 Summary Statistics Census Year Summary Statistics Census Year 1986 1996 1986–1996 Highest Value 181.46 178.49 Correlation 0.96 Lowest Value 63.29 54.78 Regression 1.00 Range 118.17 123.71 Percent of Variance Due To: Overall Mean1 118.01 108.14 Change in Means 65.2 Distributional Mean2 115.66 106.13 Feedback 0.0 Age Standardised Mean3 118.70 110.20 Positional Change 34.8 Standard Deviation 23.55 24.55 Coefficient of Variation 20.36 23.14 1. Overall mean: the rate for the state as a whole; all units combined 2..Distribution mean: mean of the rate distribution across areas 3. Age standardised mean: mean of the age standardised rates across all areas
Source: ABS calculated from unpublished data
The range between the highest and lowest rates continued to increase. The 1986 and 1996 patterns are highly correlated and an increased proportion (as compared to the 1981–1986 period) of the change in fertility was due to localised factors.
7.3.10 Summary
As fertility for women aged 15–44 years is incomplete, the spatial patterns presented here could be considered to be misleading in terms of any ‘real’ patterns, particularly considering the trend towards later ages of commencing childbearing (Chapter One). Clearly however the similarity in the spatial patterns for women aged 45–49 years indicates this is not the case.
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 260
From 1976 to 1996 the spatial patterns of fertility for women aged 15–44 years at a small area level remained very distinctive in metropolitan South Australia. Many areas remained in the same category over three, if not all four, of the time periods. This stability has reinforced the contrast of higher fertility in the northern and southern suburbs and much lower fertility in the inner suburbs. Over time there was a strengthening of the contrast between the areas of high and low fertility.
Of particular note over this time are areas 1, 10 and 11 in the SLAs of Elizabeth and Munno Para. These areas consistently had fertility levels considerably higher than any other areas in the metropolitan area and fertility was even higher here than in many of the non-metropolitan areas. In fact over the inter-censal period 1986 to 1996 there was an increase in the age standardised average number of children ever born in two of these three areas.4 At the other end of the scale there was also consistency in the areas of lowest fertility, area 80 the City of Adelaide and areas 95 and 96 comprising the Norwood and Kensington SLA. By 1996 the fertility rate for women living in area 10 was three times that of women living in area 95.
To provide further insights into this variation Figure 7.16 shows the percentage distribution of children ever born at the 1996 Census by the mapping categories used. It is immediately clear that women in region 1 are less likely to remain childless and are also more likely to go on and have three or more children than women in categories four, five and six. This pattern may be said to reflect differences in the timing of births however there is evidence that postponing childbearing ultimately reduces the number of eventual children even though Australian women seem on average to still prefer two children (Bryson, Stefani and Brown 1999; McDonald 2000b). To explore the timing of births Table 7.7 outlines the number of children ever-born by age for the six categories of spatial distribution at the 1996 Census for women aged 15–44 (Figure 7.14). There is a clear pattern as one moves through the Table from region 1 (highest fertility) to region 6 (lowest fertility) reflecting the timing of childbearing.
4 Within some of the northern suburbs of Adelaide represented by areas 1, 10 and 11 there is a culture of teenage pregnancies that has been described as ‘endemic’ (Jory 2001, 18). Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 261
Figure 7.16: Metropolitan South Australia: Percentage Distribution of Children Ever Born to Women Aged 15–44 Years by Mapping Category, 1996 Census
100 90 80 70
t Number of children 3 or 3+
n 60 e
C 50
r Number of children 2
Pe 40 30 Number of children 1
20 Number of children 0 10 0 123456 Regions (Region 1 of Highest Fertility to Region 6 of Lowest Fertility)
Source: ABS calculated from unpublished data
Regions 1 and 2 are characterised by an early start to childbearing with around 23 and 15 per cent of 15–24 year olds respectively having at least one child. Generally at all ages and parities a greater proportion of the female population have children. Between the ages of 15–34, 38 per cent of the population had two children and this was the case for 40 per cent of women aged 35–44. Many women in these two regions go on to have three or more children. In region 1 nearly 60 per cent of the population aged 25–44 years had 3 or more children. The proportion of women at all ages with no children was comparatively low in these regions compared with the other regions.
In contrast to regions 1 and 2, regions 5 and 6 present a very different pattern of childbearing. Very few women aged 15–24 had begun childbearing (around 6 per cent) and the proportion of women at all ages remaining childless is considerably higher than in regions 1 and 2. While at ages 35–44 the proportion with 1 or 2 children is little different to regions 1 and 2 (suggesting some catching up of births) it is the differences at the younger ages for these parities and the reluctance of women in regions 5 and 6 to go on and have 3 or more children that creates the variation in overall fertility. Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 262
Table 7.7: Metropolitan South Australia: Number of Children Ever Born to Women Aged 15–44 Years for Each Mapping Category, 1996 Census Area Age Number of Children Ever Born No Children 1 Child 2 Children 3 or 3+ Total Not Stated Total Children No. % No. % No. % No. % No. % No. % No. 1 15-24 8419 77.0 1546 14.1 711 6.5 260 2.4 10936 100 812 6.9 11748 25-34 3236 25.4 2638 20.7 4077 31.9 2810 22.0 12761 100 513 3.9 13274 35-44 1050 9.1 1424 12.3 4624 40.0 4474 38.7 11572 100 484 4.0 12056 Total 12705 36.0 5608 15.9 9412 26.7 7544 21.4 35269 100 1809 4.9 37078
2 15-24 7646 85.2 839 9.3 368 4.1 125 1.4 8978 100 630 3.7 9608 25-34 3505 33.1 2355 22.2 3131 29.6 1604 15.1 10595 100 338 3.1 10933 35-44 1177 10.9 1270 11.8 4888 45.3 3465 32.1 10800 100 290 2.6 11090 Total 12328 40.6 4464 14.7 8387 27.6 5194 17.1 30373 100 1258 4.0 31631
3 15-24 15455 91.7 995 5.9 318 1.9 92 0.5 16860 100 1272 7.0 18132 25-34 7144 39.5 3923 21.7 4901 27.1 2138 11.8 18106 100 515 2.8 18621 35-44 2436 11.4 2818 13.2 9938 46.5 6197 29.0 21389 100 545 2.5 21934 Total 25035 44.4 7736 13.7 15157 26.9 8427 15.0 56355 100 2332 4.0 58687
4 15-24 7581 90.8 510 6.1 193 2.3 68 0.8 8352 100 641 7.1 8993 25-34 4472 48.6 1807 19.6 1991 21.6 934 10.1 9204 100 336 3.5 9540 35-44 1754 18.4 1374 14.4 3751 39.3 2661 27.9 9540 100 313 3.2 9853 Total 13807 51.0 3691 13.6 5935 21.9 3663 13.5 27096 100 1290 4.5 28386
5 15-24 10393 93.8 472 4.3 150 1.4 68 0.6 11083 100 766 6.5 11849 25-34 6422 54.8 2319 19.8 2107 18.0 869 7.4 11717 100 420 3.5 12137 35-44 2745 21.9 1848 14.7 4894 39.0 3069 24.4 12556 100 359 2.8 12915 Total 19560 55.3 4639 13.1 7151 20.2 4006 11.3 35356 100 1545 4.2 36901
6 15-24 29146 94.0 1256 4.1 413 1.3 192 0.6 31007 100 2352 7.1 33359 25-34 18418 55.7 6058 18.3 6092 18.4 2479 7.5 33047 100 1207 3.5 34254 35-44 7294 21.4 5043 14.8 13381 39.2 8401 24.6 34119 100 1160 3.3 35279 Total 54858 55.9 12357 12.6 19886 20.3 11072 11.3 98173 100 4719 4.6 102892 Source: ABS calculated from unpublished data Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 263
The proportion of women aged 25–44 having 3 or more children in regions 5 and 6 was half that of women in regions 1 and 2 (32 per cent compared to 60 per cent).
The small area geographic patterns of fertility for women aged 15–44 years in metropolitan South Australia are strongly influenced by the patterns established early, by the age groups 15–24 and 25–34, the peak childbearing ages. For example Figures 7.17, 7.18 and 7.19 present the spatial pattern of fertility for a ‘cohort’5 of women through from 1981 to 1996.6
It is clear immediately from Figure 7.17 that already for the youngest age group, 15–24 years the division between the northern and southern suburbs as areas of high fertility, and the inner suburbs as areas of considerably lower fertility, are quite distinct. Already areas 1, 10 and 11 (along with area 13) have the highest fertility levels.
Five years on in 1986 (Figure 7.18) when the women are now 20–29 years old the pattern has become even more distinct. There is a generalised pattern of women living in the areas to the north and south of the central inner city area having the greatest number of children ever born while women living in the inner suburbs and some coastal districts have the least number of children ever born. Areas 1, 10 and 11 have the highest rates and the inner areas 95, 96, 80 and 110 recording the lowest levels of fertility (Appendix 7.9). Table 7.8 indicates that there is a close association between the 1981 and 1986 distributions (correlation coefficient of 0.91) and in fact with an increasing standard deviation and regression value of 1.86 there has been a divergence in the pattern. These statistics therefore reinforce the visual image.
By 1996 little has changed although as Table 7.8 indicates a degree of stability had been reached by the ages of 30–39 years. From the evidence of the patterns for women aged 45–49 years from 1976 to 1996, the pattern in 2006 for these women aged 30–39 years in 1996, may be a degree of convergence or dilution, as these women age and the period of time between actually having children and reporting in the Census increases.
5 This is not a closed cohort of women and therefore in and outmigration of women is not accounted for. 6 This cohort analysis begins in 1981 instead of 1976 as the parameters for the data set in 1976 differed to the later censuses – see Chapter 4 for further details. Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 264
Figure 7.17: Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–24 Years, 1981 Census
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 265
Figure 7.18 Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 20–29 Years, 1986 Census
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 266
Figure 7.19: Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 30–39 Years, 1996 Census
Source: ABS unpublished data
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 267
Table 7.8: Metropolitan South Australia: Selected Summary Statistics and Components of Social Change For Women Aged 15–24, 20–29 and 30– 39 Years, 1981–1996 Summary Statistics Census Year 1981 1986 1996 15-24 Years 20-29 Years 30-39 Years Highest Value 69.59 144.96 249.31 Lowest Value 1.95 14.90 78.71 Range 67.64 130.06 170.59 Distributional Mean1 22.79 61.18 155.83 Age Standardised Mean 23.33 64.21 162.79 Standard Deviation 13.95 28.65 33.09 1981-1986 1986-1996 Correlation 0.91 0.86 Regression 1.86 1.00 1 Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
Data for different cohorts of women present similar patterns and trends. As the overall geographic patterns are much the same, the maps and more detailed data by individual area are not presented here. Included however, is a summary table of selected characteristics. Table 7.9 shows that beginning with the cohort of women aged 20–29 years in 1981, the spatial pattern becomes more diverse by 1986 (with an increase in the range, standard deviation and a regression value over one). By the time the women reach 35–44 years in 1996, a degree of convergence in the distribution has occurred but the distributions remain closely correlated.
Gauging the trends for a later cohort (women aged 15–24 years in 1986) is restricted by the unavailability of issue data since the 1996 Census. It was noted in Sections 7.3.7 to 7.3.9 that the patterns for women aged 15–44 years in 1996 appeared to be trending towards divergence and the high regression value of 2.53 between the 1986 and 1996 Censuses for women aged 15–24 and then 25–34 respectively would indicate this may have happened. It is unfortunate that the patterns emerging here are unable to be further examined until at least 2007 when data from the 2006 Census becomes available.
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Table 7.9: Metropolitan South Australia: Selected Summary Statistics and Components of Social Change For Women of Various Ages, 1981–1996 Summary Statistics Census Year 1981 1986 1996 20–29 Years 25–34 Years 35–44 Years Highest Value 158.09 215.80 256.51 (area 10) (area 10) (area 10) Lowest Value 23.29 46.12 112.25 (area 96) (area 95) (area 80) Range 134.80 169.68 144.26 Distributional Mean1 71.82 124.14 186.89 Age Standardised Mean 74.62 129.59 192.49 Standard Deviation 28.52 35.85 28.20 1981–1986 1986–1996 Correlation 0.94 0.90 Regression 1.19 0.71
1986 1996 15–24 Years 25–34 Years Highest Value 60.21 204.60 (area 10) (area 11) Lowest Value 1.60 37.84 (area 86) (area 95) Range 58.61 166.76 Distributional Mean1 17.32 100.43 Age Standardised Mean 18.06 106.48 Standard Deviation 11.95 35.13 1986–1996 Correlation 0.86 Regression 2.53 1 Distribution mean: mean of the rate distribution across areas
Source: ABS calculated from unpublished data
Overall it is evident that for women aged 15–44 years contrasts across the metropolitan region in the average number of children ever born have existed at least since 1976. Though fertility has declined considerably over time it appears the forces at play in 1976 have been reinforced and amplified by 1996. Clearly there are strong influences governing the patterns identified and while this will be the subject of the next chapter, it appears the patterns are strongly related to socio-economic status and the provision of public or low cost housing.
Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 269
7.4. Synthesis
Chapters Five, Six and Seven have analysed spatial patterns of fertility in South Australia at various geographic scales over the two decades from 1976 to 1996. In Chapter Five it was clear that at the broad division of SSDs, there was a strong contrast between the metropolitan SSDs and the non-metropolitan SSDs continuing a long established trend of a differentiation between major urban areas and more sparsely settled rural areas. While for women aged 45–49 years the differential appeared to be converging, the opposite was true for women aged 15–44 years.
In Chapter Six the non-metropolitan sector of the State was taken as a separate entity and fertility patterns were established for Section of State categories to allow differentiation between urban settlements and predominantly rural areas. Spatially some interesting and consistent patterns for some areas were identified in the non-metropolitan sector of the State. Overall there has been a tendency towards convergence in the pattern of fertility however for women aged 15–44 this appeared to slow over the 1986 to 1996 inter-censal period. This fact, in conjunction with the lower levels of fertility recorded in the metropolitan sector indicates further declines in the non-metropolitan sector of the State could be expected with continuing declines in the total fertility rate.
This current chapter analysed fertility patterns for smaller ‘customised’ areas in metropolitan Adelaide and strong divisions across the metropolitan region in the average number of children ever born were identified. While a degree of convergence in the spatial patterns could be established over time for women aged 45–49 years this was not the case for women aged 15–44 years.
It is clear from this study that trends towards convergence in the patterns of fertility over time varied by geographic scale and age group. The trends towards convergence however appeared to be influenced by the time frame between the birth of children and the reporting of the number of children by women in the Census and the likelihood of residential mobility. What varied little over time though, were the overall patterns. In most cases these remained strong and distinctive, and at times atypical of state wide or regional trends, and expectations from theory. Generally at most times, all areas participated in the Chapter Seven: Spatial Analysis of Fertility in Metropolitan South Australia 270 decline in fertility but in many instances, the areas already with relatively low levels, experienced the greatest declines in fertility.
The patterns identified for South Australia reflect those identified by Alonso (1980) for the states in the US—although over time fertility declined in all states to some extent the greatest declines in fertility occurred in those states where fertility was already low. From his analyses, (as outlined here in Chapter 3), he hypothesised this was due to two effects— developmental and cyclical—and according to O’Connell (1981a) in discussing Alsono’s model in relation to further trends in the US, it is the cyclical effects (periodic changes in employment, economic growth, and incomes for example) that have the greatest influence on differentials in fertility, particularly regional fertility levels. This is because the population in the higher socio-economic groups react more strongly to these cyclical changes occurring in society.
This study has not covered a long enough period of time to form any assessment about the validity of regional fertility levels tending to converge in times of cyclical upturns in fertility and diverge in times of cyclical downturns in fertility. It has however established that strong patterns still exist and it appears the role of socio-economic factors is important.
In recent times as the changes that have been identified as being influential in the decline in fertility have been accepted as far reaching throughout society, there has been little explicit attention either in research or theory of the effect of these changes on local regions and communities, or of the significance of some differentials especially class or socio- economic differentials. Yet reference to social atlases of Adelaide and South Australia (ABS 1988, 1993c; Division of National Mapping and Australian Bureau of Statistics 1984; Glover and Woollacott 1992; Glover, Harris, and Tennant 1999; Hetzel, Page, Glover, Tennant 2004) tend to suggest socio-economic differentials are very relevant in explaining the fertility patterns identified here. This is explored further in Chapter Eight.
Chapter Eight: Explaining Spatial Variations in Fertility 271
Chapter Eight
Explaining Spatial Variations in Fertility
8.1. Introduction
The previous three chapters have shown that space, in the context of South Australia, still has relevance in the analysis of fertility, particularly at a regional or local level. The aim of this chapter is to provide some explanation for the observed patterns. Fertility is a complex phenomenon and Chapter Three identified the interrelationships of a wide range of factors that govern fertility levels and spatial patterns.
In recent times the focus of understanding fertility has been the study of individual behaviour through specially designed surveys. While a valued, worthwhile and complementary endeavour, this methodology cannot, in and of itself, explain the aggregate patterns identified here. It is necessary to return to the traditional methods of explanation. As outlined in Chapter Four, explanations for the patterns observed are mainly sought through correlation and regression analyses. Ecological analysis in the 1960s, 1970s and 1980s was as Woods (1986b, 15) comments ‘all but standard practice for the exploration of causal associations among demographic and non-demographic variables’. Although criticised on a number of levels it has been stated that ‘as a technique, it is most effective and persuasive when relationships are derived from spatial units that are homogeneous in their population characteristics’ (Compton 1991 28). The care taken in deriving the geographic divisions utilised in this study means that the units of analysis should more closely approximate homogenisation than in many previous studies.
Before examining in detail explanations for the fertility patterns identified the next section provides information on the independent variables and their hypothesised association with variations in fertility.
Chapter Eight: Explaining Spatial Variations in Fertility 272
8.2. Independent Variables and Hypothesised Association with Fertility
8.2.1. Introduction
In explaining differentials in fertility three main groups of factors have conventionally been used. These are demographic factors such as age, marital status, duration of marriage, population growth, net migration and Aboriginality; socio-economic factors representative of class or social status such as labour force participation, educational attainment, occupational status, income, housing structure and tenure; and cultural variables such as religion, birthplace and ethnicity. In addition, to explain geographic patterns, spatially defined variables such as distance from a metropolitan centre, population density and urbanisation measures have been used (Bell 1989). For this study a number of Census based variables descriptive of the above factors are used to seek ‘explanations’ to account for the observed patterns and trends.
As outlined in Chapter Four the specificity of the variables was guided by identified associations between the characteristics of the population and fertility identified in the literature, the need to achieve a sufficient number of cases for each variable by area, as well as where possible to provide a degree of comparability over time. Initially a bi-variate analysis is undertaken using Pearsons Correlation Coefficient. This is to elucidate initial associations between the independent variables and the spatial patterns of fertility and between the independent variables themselves to identify those variables that may be highly correlated and may need to be excluded from the regression analyses. Based on the outcomes of this analysis, expectations from theory and findings of previous research, a selection of variables are included in simultaneous or enter regressions. This is the most robust approach particularly for a relatively low number of cases and the recommended approach when there is no reason to believe one variable is likely to be more important than another (personal communication, Kylie Lange, Statistical Consultant Flinders University 2005; Kemp, Snelgar and Brace 2003).
This analysis excludes data for the 1976 Census and concentrates on the patterns identified for the two sectors of South Australia—metropolitan Adelaide and non-metropolitan South Australia.
Chapter Eight: Explaining Spatial Variations in Fertility 273
8.2.2. Hypothesised Relationship
Based on the literature review in Chapter Three the hypothesised relationships between the variables and fertility are outlined below.1
8.2.2.1 Demographic Factors
Marital Status: Percentage of Women Aged 15 Years and Over Married In the past marital status (including age at marriage and duration of marriage) was one of the most important factors influencing fertility levels. With the significant changes in societal attitudes and practices towards cohabitation and childbearing outside marriage, marital status does not have the influence it did in the past. It is hypothesised the relationship for married women and fertility will be positive although the strength of the relationship may decline over time.
Race: Percentage of Women Who Identify as of Aboriginal or Torres Strait Islander Descent As the fertility rate of the Indigenous population continues to be higher than for all women in Australia it is expected the relationship between this variable and the spatial distribution of fertility in both sectors of the State (metropolitan and non-metropolitan) will be positive and significant.
Mobility: Percentage of Women at a Different Address to Five Years Previously The literature suggests that major shifts in population in and out of an area can significantly influence an area’s demographic structure and culture of ideas and beliefs regarding fertility and therefore spatial patterns of fertility. Areas that have close migration links will have a lower differential in fertility than areas that are relatively isolated from one another. Therefore it is expected areas where there is a relatively higher proportion of women at a different address than five years previously will have lower fertility than
1 For further details on individual characteristics and their association with fertility see Chapter Three. For further details on the specific variables from the Australian Censuses see Chapter Four. Chapter Eight: Explaining Spatial Variations in Fertility 274 women who have not moved. This variable may have more relevance for non-metropolitan South Australia than for metropolitan Adelaide.
8.2.2.2 Socio-Economic Factors
Educational Attainment: Percentage of Women Aged 15 Years and Over Who Left School at Age 15 Years or Less or Who Did Not Attend School; Percentage of Women With A Degree; Percentage of Women with No Qualifications Three variables are initially included in the analysis to establish the importance of educational attainment in explaining the spatial patterns of fertility. Research indicates one of the strongest relationships currently is between educational attainment and fertility. It is hypothesised that the association with the per cent of women aged 15 years and over who left school at age 15 years or less and the per cent of females with no qualifications will be positive while the relationship between the spatial patterns of fertility and females with a degree will be negative. It is expected the associations will be statistically significant.
Employment Status: Labour Force Participation Rate for Women Aged 15–34 (to be used with fertility measures for women aged 15–44 years) and 35–54 (to be used with fertility measures for women aged 45–49 years); Percentage of Women in the Labour Force Unemployed Two variables are used to represent the employment status of females. The interpretation of employment status is complicated by women’s varying commitments to the labour force at different stages of their life. However, based on previous findings, it is expected the association between fertility and labour force participation of women will be negative. In contrast, it is hypothesised there will be a positive correlation between unemployment and fertility.
Occupational Status: Percentage of Women Professional, Technical and Related Workers and Administrative, Executive and Managerial Workers (1981); Percentage of Women Managers, Administrators, Professionals and Associated Professionals (1986, 1996) The classification of this factor, occupational status, has changed between Censuses and so the categories are not comparable over time but they represent professional ‘career’ type Chapter Eight: Explaining Spatial Variations in Fertility 275 occupations. Consistent findings from research are that professional women have lower fertility than women employed in other occupational categories and it is expected the results will be the same for this study.
Income: Percentage of Women with an Annual Income in the Upper 20 Per Cent of Personal Incomes in the State; Percentage of Households with an Annual Income in the Upper 20 Per Cent of Household Incomes in the State; Percentage of Households with an Annual Income in the Lower 20 Per Cent of Household Incomes in the State Three variables are available from the Census to measure income. This study utilises individual income and household income as family income was defined differently across the Censuses. In the literature the relationship between income and fertility has provided the most contradictory findings of all the socio-economic variables. It is anticipated that the relationship of fertility with high individual income and high household income will be negative while there will be a positive relationship between fertility and low household income.
Religion: Percentage of Women of Catholic Denomination; Percentage of Women of Non- Christian Faith; Percentage of Women Stating No Religion It is hypothesised there will be a positive relationship between fertility and the per cent of women in an area nominating Catholicism as their religion. Based on the literature however, this relationship is at best, expected to be weak. The growth of non-Christian groups in society, with their traditional values and focus on family life, may have some influence on fertility levels. As the broad category ‘non-Christian’ however incorporates a range of religious groups in Australia with contrasting fertility levels it may be difficult for any meaningful associations to be identified. Hugo and Wood (1983) and Hugo (2004) identified women stating no religion at the 1981 and 1996 Censuses had lower fertility than other groups or the highest rates of childlessness. It is therefore anticipated there may be an inverse relationship between the per cent of women stating ‘no religion’ and fertility.
Birthplace: Percentage of Women from a Predominantly Non-English Speaking Country; Percentage of Women Born in Malta, Lebanon, the Netherlands, Turkey and Vietnam Birthplace differentials are the most extensively researched in Australia. As for religion, great diversity exists between groups in terms of their fertility patterns. The low numbers involved however particularly for non-metropolitan South Australia means it is impossible Chapter Eight: Explaining Spatial Variations in Fertility 276 to choose individual groups. Two groups are included in this analysis—a collective group of countries (Malta, Lebanon, Netherlands, Turkey, Vietnam) that had some of the highest fertility rates in Australia over the 1990s (Table 3.14) and a broad category representing women from non-English speaking countries. Selecting such a broad category inclusive of many different groups presents difficulties in assessing what the relationship with fertility would be. However on the basis that over time the overseas-born have tended to have higher fertility than the Australia-born it is anticipated the relationship will be positive.
Housing Occupancy: Percentage of Households Tenant of Government Housing Authority (at 1996 Census is all tenants, public and private, as no differentiation in the data) Over the period of time covered by this study and as an ongoing policy today, public housing in South Australia, as in other parts of the country, has increasingly been targeted at those persons in greatest need. These people are characterised by disengagement from the labour force, unemployment, lower levels of educational attainment, and low incomes (with many reliant on welfare) for example, all characteristics associated with higher levels of fertility. Therefore it is hypothesised there will be a positive relationship between fertility and the proportion of households in an area who are renting from a government authority.
Dwelling Structure: Percentage of Households in Semi-Detached, Row, Terrace House, Flat or Other Medium Density As portrayed in the conceptualisation of housing careers (Kendig 1984, 1990) the arrival of children or the decision to have children in the near future, is often a catalyst to entry into the property market and the purchase of a detached dwelling. It is hypothesised there will be an inverse relationship between fertility and the per cent of households in semi- detached, row, terrace or other form of medium density housing.
Composite Variables: For the 1996 metropolitan analysis three composite variables representing different aspects of socio-economic status are included in the analysis. These variables are the Index of Relative Socio-Economic Disadvantage (IRDS), the Index of Economic Resources (IER) and the Index of Education and Occupation (IEO).2 It is
2 For further details on these Indexes see Chapter Four. Chapter Eight: Explaining Spatial Variations in Fertility 277 hypothesised there will be an inverse relationship between these three variables and fertility.
8.2.2.3 Specific Non-Metropolitan Factors
While all the variables chosen so far provide a useful basis upon which to assess the influence of socio-economic factors on the spatial patterns of fertility they may not be as effective in explaining patterns in the non-metropolitan areas of the State since as Wilson (1991, 8) states these factors
…have little direct relationship to such matters as the regional variety of agricultural and pastoral production system…, nor do they involve such oft-reported upon tendencies as the inverse relationship between reproductive activity and urban settlement size, location in the diffusion chain, and so on, all of which might be thought of as potentially significant in the search for explanation beyond the metropolitan area.
Therefore three additional variables have been added to the analysis of the non- metropolitan patterns. These variables are:
Industry Status: Percentage of Women Employed in Agriculture, Forestry, Fishing and Mining Research findings on fertility and occupational status identify farmers and farm workers as having some of the highest levels of fertility. It is therefore hypothesised there will be a positive relationship between fertility and the proportion of women in an area employed in agriculture, forestry, fishing or mining.
Accessibility/Remoteness Index of Australia (ARIA) This is a measure of the accessibility/remoteness of urban settlements derived from measures of road distance between populated localities and service centres. As distance is seen as a factor in delaying the diffusion of new ideas from major centres, it is hypothesised there will be a positive relationship between ARIA and fertility.
Chapter Eight: Explaining Spatial Variations in Fertility 278
Population Change: Percentage Change in the Total Population over the Inter-censal Period 1976–81, 1981–86, 1986–96 This variable is inter-related with the mobility variable and is an indication of the stability of the population in an area. It is anticipated there will be an inverse relationship between inter-censal population change and fertility.
8.3. Non-Metropolitan South Australia
8.3.1. Introduction
In Chapter Six it was found that the spatial patterns of fertility in non-metropolitan South Australia were quite variable from Census to Census although greater consistency in the patterns was evident for women aged 15–44 years than for women aged 45–49 years. This variability is almost certainly a consequence of the considerable changes in population in the non-metropolitan sector over the post-war period and particularly since the 1970s. With the exception of the LGAs that form an arc around metropolitan Adelaide most of the remainder of the non-metropolitan area has experienced net out-migration. As outlined by Hugo and Smailes (1992, 39) these areas include the wheat/sheep areas of the mid and upper north which were closely settled in the nineteenth century but have consistently recorded net out-migration for over one hundred years and overall declines in population since 1945; the wheat/sheep areas of the middle Eyre Peninsula settled in the early twentieth century; the marginal wheat farming areas of the Murray Mallee district again initially settled in the early twentieth century, and the grazing and afforestation areas of the South East.3
In addition to the loss of people from the rural districts, many urban centres that were established to support and service the farming community, as well as centres with specialised economic functions such as Port Pirie, Whyalla (mineral export and steel works), Millicent (timber based industries), Peterborough (railways) and Woomera (weapons research) for example, have also mainly experienced decline over the last thirty years. Migration from the non-metropolitan sector has been in response to the economic
3 For a detailed discussion of population movement in South Australia see Bell (1997a and 1997b). Chapter Eight: Explaining Spatial Variations in Fertility 279 constraints that exist in this sector and the wider range of educational, training and employment opportunities in Adelaide. This migration is selective of a number of population groups, in particular young adults, especially women (Bell 1997a). This change, in a small population base, over inter-censal periods must have an influence on overall patterns of fertility.
Although the spatial patterns of fertility were variable, some consistent trends over time however were identified. This consistency indicates the population in these areas had particular characteristics, which over time continued to influence levels of fertility. The highest rates of fertility were most often found for women living in the rural balances of the SSDs particularly in the remote outer reaches of the non-metropolitan area—the West Coast SSD, Lincoln SSD and Far North SSD. These areas of the State are sparsely settled, have lost considerable population over the last 25 to 30 years (Bell 1997a, 1997b), are in many respects quite isolated from metropolitan Adelaide (the centre of new ideas and opportunities) and contain a considerable proportion of the State’s Indigenous population (Beer and Cutler 1995). As outlined in Chapter Three, the Indigenous population of the State continues to have higher levels of fertility than the total population.
In contrast the women living in areas adjacent to metropolitan Adelaide—the Barossa SSD and the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs consistently had below average fertility for both the urban centre and rural balance Section of State categories. The ‘rural’ sectors of these SSDs are more closely settled and contain many urban centres and rural localities (centres of 200–999 persons). These SSDs have experienced considerable growth over the last twenty-five years both from the retention of population and the in-movement of people from other areas in the State and particularly from metropolitan Adelaide (see Table 4.2). This in-migration has involved an array of socio-demographic groups and has occurred due to a range of motivations (availability of cheaper land and greater possibility of home ownership; lifestyle factors; employment opportunities; retirement) or as responses to different sets of forces (declining opportunities in the wheat/sheep belts of the State) (Bell 1997a; Ford 1997; Hugo and Smailes 1992). These SSDs have a strong connection with the metropolitan area. From her analysis of population trends in the peri-urban region of Adelaide for the 1986–91 inter- censal period, Ford (1997, 22) commented:
Chapter Eight: Explaining Spatial Variations in Fertility 280
…it was those SLAs within commuting range of Adelaide that experienced the greatest increases in population through in-migration. This reflects the desire of many peri-urban migrants to maintain functional linkages with the urban centre both in terms of employment locations and in their social and cultural connections.
In many respects these areas have become an extended arm of metropolitan Adelaide and therefore it is not surprising fertility levels of women here reflect those of women in the metropolitan area.
Some individual Section of State categories at times provided consistent, noticeable contrasting fertility patterns in relation to the rest of the SSD of which they are a part and adjoining SSDs. Trying to interpret why is difficult because the characteristics of the population by age and sex for small areas from the Census are only available upon request and at a high cost from the ABS. An examination of the population characteristics, (generally only available for all women or all households) used in the correlation/regression analyses, often provide little insight. For example, women living in the urban centre category 5000–9999 persons (town of Millicent) in the Lower South East SSD often had higher fertility than women living in the other urban centre categories (over 10 000 persons and 1000–4999 persons) and at times fertility levels higher than for women living in the rural balance of this SSD. In fact women living in Millicent at times had some of the highest fertility rates in the non-metropolitan sector. Yet the characteristics of the women in all the Section of State categories in this SSD at the 1986 Census for example, were not that different (Table 8.1).
Interpretation of the fertility patterns is also complicated by the methodological design of this study in which urban centres falling within the same urban centre size classification were grouped together. When inconsistencies or ‘outliers’ arise it is not possible to identify for which centre the fertility of women may be different. The very low fertility in the urban centre category 1000–4999 persons in the Far North SSD is a good example. This Section of State category comprises the urban centres of Coober Pedy and Woomera. Consistently this Section of State category recorded the lowest fertility or close to the lowest fertility rates in the non-metropolitan sector of the State.
Chapter Eight: Explaining Spatial Variations in Fertility 281
Table 8.1: Selected Characteristics of the Population by Section of State, Lower South East SSD, 1986 Census Characteristic Section of State Over 5000- 1000- Rural 10000 9999 4999 Balance persons persons persons Per Cent Females with a Degree, Diploma or Higher Degree 5.4 3.4 8.1 6.4 Females Married 58.4 57.6 59.3 70.8 Females of Aboriginal or Torres Strait Islander Descent 0.7 1.2 1.7 0.3 Females who Changed Residence in last Five Years 51.8 48.1 46.9 40.7 Females Born in a Mainly Non-English Speaking Country 5.8 6.1 1.3 3.5 Females non-Christian religion 0.3 0.3 0.0 0.1 Females Nominating No Religion 14.2 16.7 8.9 14.1 Labour Force Participation Rate of Females Aged 15-34 63.7 54.2 52.7 59.0 Labour Force Participation Rate of Females Aged 35-54 55.7 50.2 53.9 64.8 Females Unemployed 11.2 14.8 4.5 8.0 Females Managers, Administrators, Professionals and 22.9 19.7 30.0 39.7 Associated Professionals Households with High Income (over $32 000 per annum) 22.3 20.6 19.4 24.5 Households with a Low Income (less than $12 000 per 24.6 26.7 28.2 21.3 annum) Households Tenant State Housing Authority 21.2 21.5 13.4 3.0 Source: ABS 1986 Census unpublished data
In addition this was in stark contrast to the high rates of fertility recorded for the rural balance of the Far North SSD. It appears the urban centres are not conducive to family building. To investigate this further, Figure 8.1 shows the proportional distribution of women aged 15–44 and 45–49 years by parity for the Section of State categories in the Far North SSD. It is clear this was the case more so for women aged 45–49 years than for women aged 15–44 years. Women aged 45–49 years living in the urban centre were more likely to be childless or have one, two or three children but less likely to have four or more children than women of the same age living in the rural sector of the SSD.
While the pattern for women aged 15–44 years is similar (particularly for 1976) with a lower proportion of women living in the urban centres having four or more children, this differential lessened over time.
Chapter Eight: Explaining Spatial Variations in Fertility 282
Figure 8.1: Percentage Distribution of Women Aged 45–49 and 15–44 Years by Parity, Far North SSD 1976, 1981, 1986, and 1996 Censuses
100
9 90 4 - 5
4 80 d e 70 parity 5 or more g parity 4
n A 60 e parity 3 m 50 o parity 2
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100 90 44 80 15- parity 5 or more ed 70 g
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t 30 parity 0 n e 20 C r
e 10 P 0 6 1 6 a b e e e e e res res res res res 197 198 198 anc anc anc anc anc l l l l l 1996 1996 ent ent ent ent ent a a a a a B B B B B l l l l l a a a a a r r r r r ban C ban C ban C ban C ban C u u u u u r r r r r R R R R R U U U U U Section of State and Year
1996a Section of Sate categories as they were in 1986 1996b includes the urban centre of Roxby Downs
Source: ABS unpublished data
Chapter Eight: Explaining Spatial Variations in Fertility 283
In fact by the 1996 Census the pattern was reversed with a higher percentage of women (9.7 per cent) living in the urban centres having four or more children than women living in the rest of the SSD (7.4 per cent). A similar decline in the differential between the proportion of women with four or more children living in the urban centre category (1000–4999 persons) as compared to the rural balance of the SSD, was also evident for women aged 45–49 years. While these facts initially (in 1976 and 1981) support the hypothesis the urban centres may not be conducive to childbearing it is clear this is not so valid with subsequent Censuses.
Explanation for the contrasting fertility levels of the urban centre category and the rural balance most likely lies in the distinct unique characteristics of this region and the urban centres that comprise the urban centre category. The Far North SSD covers an expansive area of the State. It is sparsely settled though over ten per cent of the State’s Indigenous population live in this SSD many in discrete Aboriginal communities (Beer and Cutler 1995, 70). The townships of Coober Pedy and Woomera are also unique and different. Construction of the Woomera Village began in 1947 as the residential base and service centre for the activities undertaken as part of the British-Australian Joint Project for a rocket range (the Woomera Prohibited Area originally covered 270,000 square kilometres). Until 1982 Woomera was a restricted entry township, closed to the general public (BAE SYSTEMS 2005; Moreton 1989).
Coober Pedy, 900 kilometres from metropolitan Adelaide is an isolated opal mining centre settled mainly after World War Two by people from Southern Europe. As can be seen from Table 8.2 the characteristics of the females living in each town at the 1981, 1986 and 1996 Censuses were generally very different. Coober Pedy has a high degree of ethnic diversity (at the 1996 Census 30.7 per cent of the total population was born overseas, 20.8 per cent of females from a mainly non-English speaking country, and there were over 40 different nationalities listed in the Census); a significant proportion of the population is Indigenous (18.2 per cent of the female population); a relatively higher proportion of the population were unemployed (17.1 per cent of the workforce at the 1996 Census); and a much lower proportion of households had a significant income.
Chapter Eight: Explaining Spatial Variations in Fertility 284
Table 8.2: Selected Characteristics of the Female Population and Households of the Urban Centres of Coober Pedy and Woomera 1981, 1986 and 1996 Censuses Characteristic 1981 1986 1996 (Per Cent) Coober Woomera Coober Woomera Coober Woomera Pedy Pedy Pedy Females Left School Aged 15 43.6 36.5 n.a. n.a. 33.1 16.6 Years or Less Females with a Diploma, 9.3 10.5 7.5 9.8 28.2 39.1 Degree or Higher Degree Females Married 46.3 48.9 59.9 70.2 56.7 65.9 Females of Aboriginal or Torres 4.4 0.3 18.2 0.3 18.2 1.7 Islander Descent Females who Changed 42.5 76.7 48.3 82.6 29.8 66.0 Residence in the Last 5 Years Females Catholic 20.3 20.5 15.8 20.6 21.5 25.8 Females Non Christian Religion 0.4 1.1 1.3 1.0 1.8 1.4 Females Nominating No 14.6 12.3 13.7 14.4 12.4 15.6 Religion Females born in Mainly Non 77.9 18.7 21.2 6.6 20.8 4.6 English Speaking Country Labour Force Participation Rate 63.8 61.2 47.4 62.7 48.2 62.0 of Females Aged 15-34 Labour Force Participation Rate 49.6 62.7 50.6 62.0 52.4 62.5 of Females Aged 35-54 Females Unemployed 14.0 4.0 21.5 11.5 8.3 5.3 Households with High Income 5.0 35.8 13.4 38.7 6.3 27.1 (in Top 20% for South Australia) Households Medium Density 2.9 32.1 3.6 35.5 7.3 37.0 n.a. not available (information supplied incorrect) Source: ABS unpublished data
In fact in a study of Coober Pedy’s youth, Harvey (1996) indicated the town’s people were heavily dependent on social welfare. Many of the socio-economic characteristics of women in Coober Pedy are associated with higher levels of fertility.
It appears the characteristics of the women living in Woomera coupled with the highly mobile status of the women (over 60 per cent of females in all three Censuses were at a different address at the previous Census, Table 8.2) and the overall decline in the population of Woomera from a peak of 6000 persons in the 1960s to 4000 in 1971 to 1658 in 1981 to 1349 in 1996 (BAE SYSTEMS 2005; Bell 1997b) is most likely responsible for the dampening of fertility levels in this urban centre category in the Far North SSD.
Chapter Eight: Explaining Spatial Variations in Fertility 285
While this discussion gives some insight into possible reasons for fertility levels in particular areas it does not provide an overall explanation(s) for the patterns throughout the non-metropolitan sector. To explore this, a number of correlation and regression analyses were undertaken to examine mainly, the role of socio-economic factors, but also some locational measures and indicators of population change which it appears may have been influential.
8.3.2. The Role of Socio-Economic Factors—Women Aged 45–49 Years
Table 8.3 presents the correlation coefficients between the fertility rates (average number of children ever born) of women aged 45–49 years and a number of independent variables at the 1981, 1986 and 1996 Censuses. If the guide is that values of 0.71 and above represent a strong correlation, values of 0.50 to 0.70 represent a moderate association and values between 0.30 and 0.49 represent a weak association between two variables, then it is clear that independently these variables provide little explanation for the spatial patterns of fertility. Most correlations at all three Censuses are below 0.30. In a number of instances the direction of the relationship is the opposite to that expected.
In 1981 the only variable of significance in the bi-variate analysis was the proportion of females in professional and administrative occupations though the relationship was weak.4 The other variables with a value above 0.30 were the proportion of females who changed residence in the inter-censal period 1976 to 1981 and the proportion of females employed in agriculture.
As was the case for 1981 only one variable in 1986 was significant, the proportion of females with no qualifications, but again the relationship is weak. In 1996 slightly stronger bi-variate relationships appear and four variables are statistically significant in accounting for the variance in the fertility patterns—proportion of females with no qualifications, proportion of females with no religion, households with a relatively high income and the percentage change in the population over the 1986–1996 inter-censal period. The variables
4 Significance is related to the number of cases in the sample and as the non-metropolitan analysis involves a small sample only a very small number of the variables would be expected to be significant. Chapter Eight: Explaining Spatial Variations in Fertility 286 representing birthplace characteristics of the female population also show a weak association with fertility but the relationship is in the opposite direction to that expected.
Table 8.3: Non-metropolitan South Australia: Correlation Coefficients Between Fertility of Women Aged 45–49 Years and Selected Variables 1981, 1986, 1996 Censuses Variable (and Expected Relationship) Year 1981 1986 1996 Females Married + -.121 .042 .109 Females of Aboriginal or Torres Strait Islander Descent + .283 .188 .026 Females Changed Residence Last 5 Years - -.334 -.211 -.212 Females Left School Aged 15 Years or Less + .076 n.a .175 Females With at Least a Diploma or Degree - -.261 -.208 .061 Females With No Qualifications + .085 *.464 *.410 Labour Force Participation Rate of Females Aged 35-54 - .164 .012 .058 Females Unemployed + .012 -.014 -.127 Females in Professional and Administrative Occupations - *-.365 .113 .129 Females Employed in Agriculture, Forestry, Fishing or + .305 .213 .260 Mining Females Catholic Religion + -.091 .052 .189 Females Non-Christian Religion + .018 .061 -.226 Females No Religion - .115 .174 **-.465 Females Born in Mainly Non-English Speaking Country + .020 -.337 -.329 Females Born in Malta, Lebanon Netherlands, Turkey, + .288 -.156 -.320 Vietnam Household Tenant of Govt Housing Authority (1996 All + .114 .285 -.057 Rental) House Medium Density - .111 .029 -.032 Female Individual Income High - -.097 -.299 -.231 Household Income Low + -.282 -.282 .257 Household Income High - .198 .126 *-.384 ARIA (Measure of Distance from Main Centres) + .161 .218 .191 Total Inter-Censal Population Change - .042 .-282 **-.529 ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) n.a. not available (data supplied incorrect)
Source: ABS calculated from unpublished data
Overall from the bi-variate analysis the apparent importance of particular variables changes from Census to Census, though there appears to be some consistency in the association of fertility with the proportion of females who changed residence over the inter-censal period, with educational qualifications, with the proportion of females employed in agriculture and the total inter-censal population change, though the relationships are weak. Chapter Eight: Explaining Spatial Variations in Fertility 287
To assess whether in combination a number of the variables had a greater association with the fertility patterns, a limited number of variables was entered into a simultaneous regression (also known as an enter regression).5 The factors defining the variables chosen were the strength (and expected direction) of the relationship in the bi-variate analysis, expectations from theory and previous findings in the literature and the standard deviation of the individual variable. Tables 8.4, 8.5 and 8.6 outline the results of these analyses at the 1981, 1986 and 1996 Censuses.
Table 8.4: Non-Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 45–49 Years, 1981 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model .320 .015 Variables Females of Aboriginal or Torres Strait .392 .036 Islander Descent Females Changed Residence Last 5 -.103 .613 Years Females in Professional and -.415 .175 Administrative Occupations Females With at Least a Diploma or -.181 .391 Degree Females Employed in Agriculture, .213 .533 Forestry, Fishing or Mining Household Tenant of Govt Housing .413 .039 Authority
Block Enter Model Block Variables 1 Females in Professional and .347 .002 -.647 .001 Administrative Occupations Females of Aboriginal or .447 .007 Torres Strait Islander Descent Household Tenant of Govt .432 .016 Housing Authority Source: ABS calculated from unpublished data
5 Multiple regression requires a large number of observations. The number of cases must be substantially greater than the number of independent variables. The absolute minimum is five times as many cases as independent variables. A more acceptable ratio is 10:1 and some people would argue this ratio should be even higher for some statistical selection methods (Kemp, Snelgar and Brace 2003, 210). Chapter Eight: Explaining Spatial Variations in Fertility 288
In combination using enter or simultaneous regression the variables listed in Table 8.4 at the 1981 Census provided a significant model accounting for 32.0 per cent of the variance in fertility patterns of women aged 45–49 years. The significant variables in this model were the proportion of females that identified themselves as of Aboriginal or Torres Strait Islander descent and the percentage of households that were public housing tenants. If the variable with the highest correlation (Table 8.3) in 1981 is added to these two variables as block one in the regression and the rest of the variables as block two then the explanatory power of the model is only increased to 34.7 per cent (Table 8.4).
In 1986 a similar set of variables (Table 8.5) accounted for an increased amount of the variance (adjusted R square =0.567) in the fertility patterns of women aged 45–49 years. In this model all of the variables except the percentage of women who changed residence over the inter-censal period, were significant. Block enter regression analysis indicated that the two most significant variables in the first model, the per cent of females employed in agriculture, forestry, fishing or mining; and women with no post school qualifications accounted for 26.3 per cent of the variance. The addition of the proportion of households renting from a government authority added another 11.4 per cent and the proportion of women that identified themselves as of Aboriginal or Torres Strait Islander descent accounted for an additional 17.1 per cent (Table 8.5).
Based on the correlation coefficients in Table 8.3 four variables were included in the regression analyses for 1996—females employed in agriculture, forestry, fishing or mining; females with no qualifications, per cent of females who stated ‘no religion’; and the percentage change in the total population over the 1986–1996 period. In combination, using the enter method, a significant model emerged accounting for 45.3 per cent of the variance in fertility. From this model all but the employment variable were significant. An enter regression excluding the employment variable resulted in a significant model accounting for a slight increase in the adjusted R square value to .468.
8.3.2.1. Summary
It appears that the socio-economic variables available are relatively poor explanatory variables for the spatial patterns of fertility identified for women aged 45–49 years. Chapter Eight: Explaining Spatial Variations in Fertility 289
However the characteristics of women at ages 45–49 years recorded in the Census (such as income levels, educational qualifications, occupational and labour force status, home ownership) may not represent their characteristics when decisions governing the number, spacing and timing of their childbearing were made.
Table 8.5: Non-Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 45–49 Years, 1986 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model .567 .000 Variables Females of Aboriginal or Torres Strait .402 .005 Islander Descent Females Changed Residence Last 5 .290 .153 Years Females With No Qualifications .714 .000 Females Employed in Agriculture, .794 .000 Forestry, Fishing or Mining Household Tenant of Govt Housing .426 .011 Authority
Block Enter Model Block Variables 1 Females With No .263 .005 .525 .002 Qualifications
Females Employed in .314 .055 Agriculture, Forestry, Fishing or Mining 2 Females With No .377 .001 .397 .015 Qualifications Females Employed in .563 .003 Agriculture, Forestry, Fishing or Mining Household Tenant of Govt .467 .018 Housing Authority 3 Females With No .548 .000 .580 .000 Qualifications Females Employed in .603 .000 Agriculture, Forestry, Fishing or Mining Household Tenant of Govt .437 .010 Housing Authority Females of Aboriginal or .447 .002 Torres Strait Islander Descent Source: ABS calculated from unpublished data
Chapter Eight: Explaining Spatial Variations in Fertility 290
Table 8.6: Non-Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 45–49 Years, 1996 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model .453 .000 Variables Females No Religion -.381 .011 Total Inter Censal Population Change -.374 .021 Females With No Qualifications .314 .039 Females Employed in Agriculture, .076 .630 Forestry, Fishing or Mining
Block Enter Model Block Variables 1 Total Inter-Censal .468 .000 -.405 .006 Population Change Females No Religion -.399 .005 Females With No .291 .040 Qualifications Source: ABS calculated from unpublished data
In addition, the independent variables are the characteristics of all women and households in an area and not just those for women aged 45–49 years. How representative of the total population this age group is, is an unknown. This coupled with the often low number of females in an area with a particular characteristic (which leads to significant fluctuations from year to year and instability in the numbers) and the small number of cases overall (which has an influence on the strength and direction of relationships) makes the results presented here not all that surprising.
The next section will establish if stronger relationships exist with a group representing a wider cross section of the population, women aged 15–44.
8.3.3. The Role of Socio-Economic Factors—Women Aged 15–44 Years
The bi-variate analysis for women aged 15–44 years is, in terms of the strength of the relationships, similar to that for women aged 45–49 years. Most associations are very weak with the exception of a few in 1996 (Table 8.7).
Chapter Eight: Explaining Spatial Variations in Fertility 291
Table 8.7: Non-Metropolitan South Australia: Correlation Coefficients Between Fertility of Women Aged 15–44 Years and Selected Variables 1981, 1986, 1996 Censuses Variable (and Expected Relationship) Year 1981 1986 1996 Females Married + -.211 .105 .281 Females of Aboriginal or Torres Strait Islander Descent + .353 *.365 .195 Females Changed Residence Last 5 Years - -.248 -.143 -.399 Females Left School Aged 15 Years or Less + .050 n.a. -.022 Females With at Least a Diploma or Degree - -.254 -.044 .141 Females With No Qualifications + .265 .310 .233 Labour Force Participation Rate of Females Aged 15-34 - -.182 -.298 **-.525 Females Unemployed + .007 .118 -.137 Females in Professional and Administrative Occupations - -.264 .221 *.424 Females Employed in Agriculture, Forestry, Fishing or + .304 .290 **.486 Mining Females Catholic Religion + .096 .188 .209 Females Non-Christian Religion + -.101 .148 -.087 Females No Religion - .137 -.042 *-.420 Females Born in Mainly Non-English Speaking Country + .007 -.137 -.217 Females Born in Malta, Lebanon Netherlands, Turkey, + .229 -.223 -.291 Vietnam Household Tenant of Govt Housing Authority (1996 All + .190 .153 -.085 Rental) House Medium Density - -.064 -.052 -.169 Female Individual Income High - .093 -.143 -.035 Household Income Low + *-.437 -.058 .075 Household Income High - .334 -.006 -.189 ARIA (Measure of Distance from Main Centres) + *.399 **.595 .294 Total Inter-Censal Population Change - .021 **-.515 **-.721 ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) n.a. not available (data supplied incorrect) Source: ABS calculated from unpublished data
In 1981 two variables were significant—distance of a Section of State category from the main centres of activity and influence (ARIA) and low household income although this association is in the opposite direction to that expected (Table 8.7). Other variables portraying a weak association with fertility, though not significant, were the percentage of females of Aboriginal or Torres Island descent; women employed in agriculture, forestry, fishing, or mining; and a high household income although as for low household income the association is in the opposite direction to that expected. In rural areas the relationship between income and fertility is likely to be complex because the income of the household may rely solely on the success of the farming business and this can fluctuate considerably from year to year. Chapter Eight: Explaining Spatial Variations in Fertility 292
At the 1986 Census three variables showed a significant bi-variate relationship with fertility, two of them to a moderate degree (ARIA, total inter-censal population change). The only other variable to show a weak but non significant relationship was females with no qualifications. By the 1996 Census the association between fertility levels and a number of variables had intensified. There was a strong significant relationship with total inter-censal population change and moderate associations with labour force participation, females employed in agriculture, forestry, fishing and mining; and females with no religion. A weak and insignificant association existed with females who changed residence in the last five years.
An enter regression analysis for 1981 using the six most relevant variables from the bi- variate analysis indicates that in combination these variables only accounted for 36.4 per cent of the variance in the spatial patterns of fertility for women aged 15–44 years (Table 8.8). Only two of the variables in the model however were significant, ARIA and the per cent of females with a qualification. Entering the variables in blocks indicates together these two variables accounted for 29.9 per cent of the variance. The addition of females employed in agriculture, forestry, fishing and mining adds to the model increasing the adjusted R square value to .412. The addition of further variables reduces the explanatory power of the model.
In line with the increased strength in some of the associations in the bi-variate analysis the variance in fertility explained by a combination of these variables increased significantly at the 1986 Census. As shown in Table 8.9 the adjusted R square value from an enter regression of the six most appropriate variables was .722, indicating that the model accounts for over 70 per cent of the variance in the spatial patterns of fertility for women aged 15–44 years at the 1986 Census. Entering the variables in blocks based on the strength of their contribution in the first enter regression shows that block 1 accounted for 59.9 per cent of the variance. The inclusion of women of Aboriginal or Torres Strait Islander descent, and women employed in agriculture, forestry, fishing mining results in an additional 7.9 per cent of the variance being explained. The percentage change in the population of Section of State categories over the inter-censal period raises the adjusted R square value to .725.
Chapter Eight: Explaining Spatial Variations in Fertility 293
Table 8.8: Non-Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 15–44 Years 1981 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model
Variables .364 .008 Females of Aboriginal or Torres Strait -.005 .980 Islander Descent ARIA .577 .013 Females Changed Residence Last 5 -.186 .381 Years Females in Professional and .149 .625 Administrative Occupations Females Employed in Agriculture, .392 .225. Forestry, Fishing or Mining Females With at Least a Diploma or -.539 .016 Degree
Block Enter Model Block Variables 1 ARIA .299 .003 .571 .002 Females With at Least a -.465 .009 Diploma or Degree 2 ARIA .412 .001 .522 .002 Females With at Least a -.578 .001 Diploma or Degree Females Employed in .379 .018 Agriculture, Forestry, Fishing or Mining 3 ARIA .369 .005 .510 .017 Females With at Least a -.599 .005 Diploma or Degree Females Employed in .466 .136 Agriculture, Forestry, Fishing or Mining Females of Aboriginal or .009 .967 Torres Strait Islander Descent Females in Professional and .337 .739 Administrative Occupations Source: ABS calculated from unpublished data
At the 1996 Census a slightly different set of variables were included in the regression analyses. For the first time no variable representing educational status was included. Only a limited number of variables can be entered into the regression models and from the bi- variate analysis a number of other variables appeared to be more important.6
6 The inclusion of other combinations of variables including those important in other years such as those representing educational status did not improve the model. Chapter Eight: Explaining Spatial Variations in Fertility 294
Table 8.9: Non-Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 15–44 Years, 1986 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model
Variables .722 .000 Females of Aboriginal or Torres Strait .338 .021 Islander Descent ARIA .418 .005 Total Inter-Censal Population Change -.257 .031 Females with No Qualifications .544 .000 Females Employed in Agriculture, .261 .021 Forestry, Fishing or Mining Labour Force Participation Rate of -.091 .396 Females Aged 15-34
Block Enter Model Block Variables 1 Females with No .599 .000 .547 .000 Qualifications ARIA .765 .000 2 Females with No .610 .000 .587 .000 Qualifications ARIA .665 .000 Females of Aboriginal or .192 .188 Torres Strait Islander Descent 3 Females with No .678 .000 .645 .000 Qualifications ARIA .550 .000 Females of Aboriginal or .295 .038 Torres Strait Islander Descent Females Employed in .291 .014 Agriculture, Forestry, Fishing or Mining 4 Females with No .725 .000 .561 .000 Qualifications ARIA .422 .004 Females of Aboriginal or .374 .008 Torres Strait Islander Descent Females Employed in .248 .025 Agriculture, Forestry, Fishing or Mining Total Inter-Censal -.263 .026 Population Change Source: ABS calculated from unpublished data
Table 8.10 shows that via an enter regression the five variables most relevant from the bi- variate analysis accounted for 62.5 per cent of the variance in the fertility patterns of women aged 15–44 years at the 1996 Census. Only two variables though were significant Chapter Eight: Explaining Spatial Variations in Fertility 295 and a block enter regression indicated that these two variables (total inter-censal population change; and women professing no religion) alone explained 60.2 per cent of the variance. In particular the importance of the percentage change in the total population over the 1986–1996 period should be noted. It was the most influential variable (standardised beta of -.678). This finding provides support for the discussion earlier in section 8.3.1.
Table 8.10: Non-Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 15–44 Years, 1996 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model
Variables .625 .000 Females Employed in Agriculture, .184 .223 Forestry, Fishing or Mining Labour Force Participation Rate of -.196 .140 Females Aged 15-34 Total Inter-Censal Population Change -.588 .000 Females Changed Residence Last 5 .108 .476 Years Females with No Religion -.266 .033
Block Enter Model Block Variables 1 Total Inter-Censal Population .602 .000 -.678 .000 Change Females with No Religion -.331 .007 Source: ABS calculated from unpublished data
One of the criticisms of the ecological approach is the use of data on the characteristics of the total population as explanatory factors to represent the subset of the population under study. To test the effectiveness of using mostly non age differentiated data in the correlation regression analyses, customised data of a selected number of the socio- economic characteristics just for females aged 15–44 years at the 1996 Census were obtained from the ABS (See Chapter 4 for more details). Table 8.11 presents the correlation coefficients.
Chapter Eight: Explaining Spatial Variations in Fertility 296
Table 8.11: Non-Metropolitan South Australia: Correlation Coefficients Between Fertility and Selected Characteristics of Women Aged 15–44 Years, 1996 Census Variable and Expected Relationship Correlation Variable and Expected Relationship Correlation Coefficients Coefficients Females Left School Aged 15 + .064 Females Non-Christian Religion + -.086 Years or Less Females With at Least a - -.087 Females No Religion - **-.593 Diploma or Degree Females Married + .201 Females Born in Mainly Non- + -.190 English Speaking Country Females of Aboriginal or + .210 Females Born in Malta, + -.268 Torres Strait Islander Descent Lebanon Netherlands, Turkey, Vietnam Females Changed Residence - **-.497 Labour Force Participation Rate - *-.403 Last 5 Years of Females Aged 15-44 ARIA (Measure of Distance + .239 Females Employed in + *.439 from Main Centres) Agriculture, Forestry, Fishing or Mining Females in Professional - *.436 Occupations ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) Source: ABS calculated from unpublished data
From Table 8.11 it can be seen that the variables of importance (a significant association in the expected direction) were measures of population change over the 1991–1996 inter- censal period, females professing no religion, females employed in agriculture, forestry, fishing or mining and the labour force participation rate. These findings reflect those when non-age differentiated data was used (Table 8.7). Regression analysis indicates only two variables (women professing no religion and the per cent of females who changed residence in the last five years) were significant predictors of fertility. Together they accounted for 40.8 per cent of the variance.
8.3.3.1 Summary
Overall it appears the data for the general female population has greater applicability to women aged 15–44 and their fertility than for women aged 45–49 years. The most relevant variables changed from one Census to another. What is interesting however is the importance of the locational factor (ARIA) in 1981 and 1986 and the total inter-censal Chapter Eight: Explaining Spatial Variations in Fertility 297 population change over the 1986–1996 period. This was also the most influential variable in 1996 for women aged 45–49 years.
8.3.4 Summary
The search for ‘explanations’ of the spatial patterns of fertility in non-metropolitan South Australia is compromised by the need for the underlying assumption the characteristics of the population aged 45–49 and 15–44 do not depart too widely from the characteristics of the total female population, the reliability of small numbers and the small number of cases. Despite these inadequacies of the analysis some interesting relationships have been identified pointing to the influence of educational status, occupational status, industry classification, race, public housing, distance from centres of influence and the in and out migration of people in explaining the spatial patterns of fertility.
8.4. Metropolitan Adelaide
8.4.1. Introduction
In contrast to the spatial variability in patterns of fertility for non-metropolitan South Australia, it was identified in Chapter Seven that for the metropolitan sector of the State, there were strong patterns of spatial differentiation in fertility for both women aged 45–49 and 15–44 years. It was concluded that although fertility had declined considerably over time it appeared the forces at play in 1976 had only been reinforced and amplified by 1996, particularly for women aged 15–44. In Section 3.8 of this study it was identified that metropolitan Adelaide exhibited strong patterns of segregation in terms of socio-economic status in 1976. Reference to the social atlases of Adelaide and South Australia (ABS 1988, 1993c; Division of National Mapping and ABS 1984; Glover and Woollacott 1992; Glover, Harris and Tennant 1999; Hetzel, Page, Glover and Tennant 2004) tend to suggest little has changed and socio-economic variables appear very relevant in explaining the fertility patterns identified.
Chapter Eight: Explaining Spatial Variations in Fertility 298
Of importance in South Australia in influencing the form of urban Adelaide has been the South Australian Housing Trust (Division of National Mapping and ABS 1984, 34). In contrast to the other metropolitan areas of Australia, Adelaide has an unusually high percentage of public housing, around 11 per cent of private dwellings at the 1991 Census (ABS 1993c, 46; Forster 2004, 98).7 As its initial role was to assist in the industrial development of the State rather than act as a welfare housing authority the Trust traditionally housed a wide spectrum of income groups in contrast to the public housing authorities in the other States. These dwellings tended to be grouped together over relatively large areas to the north, south and west of the city rather than being distributed over a wide range of suburbs. With the changes in policy towards housing those in greatest need, public rental housing has become associated with low incomes, unemployment and welfare dependency. Visually the location of public housing estates is closely related to the high rates of fertility found in areas 1, 2, 3, 4, 5, 10, 11 to the north of the city centre, areas 149, 150, 151 in the south, area 36 to the north west and area 132 to the south of the city centre in the maps in Chapter Seven. The patterns of fertility for area 132 are very interesting. In 1976, 1981 and 1986 this area of high fertility stood in contrast to the surrounding areas but for the first time in 1996 women living in this area had fertility levels similar to women living in the adjacent districts. The late 1980s saw the beginning of the redevelopment of this public housing estate (Arthurson 1998) and therefore a slow change in its social and economic characteristics.
The following sections will explore the importance of public housing and other socio- economic factors in explaining the spatial patterns of fertility identified in Chapter Seven.
8.4.2. The Role of Socio-Economic Factors—Women Aged 45–49 Years
Table 8.12 presents the correlation coefficients between fertility and a number of independent variables for women aged 45–49 years at the 1981, 1986 and 1996 Censuses. It is clear for the smaller more homogeneous areas of the metropolitan area the
7 Since 1991 some major changes have occurred in the public housing sector resulting in a decline in the housing stock. In 2001 public dwellings represented 8 per cent of total dwellings in Adelaide (Forster 2004, 80). Chapter Eight: Explaining Spatial Variations in Fertility 299 relationships are stronger than they were for the Section of State categories in non- metropolitan South Australia.
Table 8.12: Metropolitan South Australia: Correlation Coefficients Between Fertility of Women Aged 45–49 Years and Selected Variables 1981, 1986, 1996 Censuses Variable (and Expected Relationship) Year 1981 1986 1996 Females Married + *.184 **.210 **.482 Females of Aboriginal or Torres Strait Islander Descent + **.413 **.544 **.431 Females Changed Residence Last 5 Years - .003 .106 **-344 Females Left School Aged 15 Years or Less + **.556 n.a **.583 Females With at Least a Diploma or Degree - **-.547 **-.600 **-.634 Females With No Qualifications + **.585 **.662 **.671 Labour Force Participation Rate of Females Aged 35-54 - *-.555 **-.684 **-.619 Females Unemployed + **.521 **.660 **.491 Females in Professional and Administrative Occupations - **-.610 **-.615 **-.662 Females Catholic Religion + -.051 .002 -.054 Females Non-Christian Religion + **-.235 .128 .063 Females No religion - **.354 **.423 **.365 Females Born in Mainly Non-English Speaking Country + **-.279 .056 -.009 Females Born in Malta, Lebanon, Netherlands, Turkey, + .100 **.393 **.254 Vietnam Household Tenant of Govt Housing Authority (1996 All + **.637 **.678 -.071 Rental) House Medium Density - .004 .059 **-.336 Female Individual Income High - **-.543 **-.683 **-.603 Household Income Low + -.156 .127 -.001 Household Income High - **-.256 **-.439 **-.329 Index Disadvantage (1996 Only) - **-.511 Index of Economic Resources (1996 Only) - **-.270 Index of Education and Occupation (1996 Only) - -.024 ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) n.a. not available (data supplied incorrect) Source: ABS calculated from unpublished data
In 1981 significant moderate associations (values between 0.50 and 0.70) were found with many of the traditional variables—education, employment and income. Of note for 1981 was that the strongest relationship is with the percentage of households renting from a government authority. Significant but weak associations were also found for women who identified as of Aboriginal or Torres Islander descent; and for women specifying no religion, although this association was in the opposite direction to that expected.
Chapter Eight: Explaining Spatial Variations in Fertility 300
For 1986 a similar outcome is evident from Table 8.12. The strongest significant relationships were with employment variables (labour force participation rate, unemployment, females in professional type occupations), education variables (women with a degree versus women with no qualifications), income variables (high female personal income), the proportion of females identifying as Indigenous and households renting from a government authority. Significant but weak associations were also evident for households where the income is relatively high, for females born in either Malta, Lebanon, the Netherlands, Turkey or Vietnam; and for women stating no religion. Again however this variable was positive rather than negative as expected and as was the case for non-metropolitan South Australia.
In 1996 the pattern is repeated (Table 8.12) but a number of other variables have become important. For the first time marital status, percentage of females who were at a different address in 1991 and the proportion of households that were classified as medium density showed a weak but significant relationship. In 1996 rental housing was not separated into public and private and is no longer relevant.
To explore the relationships identified in the bi-variate analyses further, and to assess the importance of the variables in combination in explaining the fertility patterns, a number of variables were entered into regression analyses. Tables 8.13, 8.14 and 8.15 present the results. For 1981 twelve variables were entered into the regression. Together they produced a significant model accounting for 66.6 per cent of the variance in the spatial fertility patterns of women aged 45–49 years in metropolitan Adelaide. From Table 8.13 it is evident that only four of the variables provide a significant contribution to the variance—proportion of households with a high income; females stating no religion, women with high annual personal incomes; and households renting from a government authority. The direction of the association for the first two of these variables however is opposite to that expected and they are therefore not included in further analysis of the data. A block enter regression analysis was undertaken to assess the relative importance of the remaining variables. This regression indicated that the variables, households renting from a government authority and women with high annual personal incomes accounted for 44 per cent of the overall variance in fertility. The addition of the proportion of females in professional type occupations resulted in these three variables explaining 51.6 per cent of the variance. The remainder of the variables accounted for only small incremental Chapter Eight: Explaining Spatial Variations in Fertility 301 increases in the adjusted R square value. Examination of the standardised beta values indicates that with the addition of the proportion of females in professional type occupations, individual income loses its relevance as a significant influential variable (Table 8.13).
Table 8.13: Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 45–49 Years, 1981 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model .666 .000 Variables Females of Aboriginal or Torres Strait .093 .221 Islander Descent Females Unemployed .145 .227 Female Individual Income High -.392 .000 Females With No Qualifications .165 .334 Females in Professional and -.185 .264 Administrative Occupations Labour Force Participation Rate of .151 .196 Females Aged 35-54 Household Tenant of Govt Housing .318 .000 Authority Females No Religion .331 .000 Females Born in Mainly Non-English .041 .620 Speaking Country Females Non Christian Religion -.044 .522 Females Married .086 .371 Household Income High .370 .000 Block Enter Model Block Variables 1 Household Tenant of Govt .440 .000 .485 .000 Housing Authority Female Individual Income -.254 .001 High 2 Household Tenant of Govt .516 .000 .455 .000 Housing Authority Female Individual Income .027 .765 High Females in Professional and -.411 .000 Administrative Occupations Source: ABS calculated from unpublished data
In 1986 the most significant variables in combination explained 64.3 per cent of the variance (Table 8.14). Four variables in this model were significant—females with high personal income, households renting from a government authority, households with a high Chapter Eight: Explaining Spatial Variations in Fertility 302 income (although direction of relationship opposite to that expected) and the per cent of females with no religion. This religion variable also produces relationships in the opposite direction to that expected. It has been left in the analysis as this relationship is consistent across all three Censuses.
Table 8.14: Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 45–49 Years, 1986 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model .643 .000 Variables Females Married .070 .555 Females of Aboriginal or Torres Strait .028 .746 Islander Descent Females Unemployed .133 .364 Females Individual Income High -.339 .011 Females With No Qualifications .292 .088 Females in Professional, and .183 .218 Administrative Occupations Labour Force Participation Rate of -.178 .235 Females Aged 35-54 Household Tenant of Govt Housing .210 .035 Authority Household Income High .263 .047 Females Born in Malta, Lebanon, -.001 .983 Netherlands, Turkey, Vietnam Females No Religion .214 .005
Block Enter Model Block Variables 1 Household Tenant of Govt .539 .000 .392 .000 Housing Authority Females Individual Income -.409 .000 High 2 Household Tenant of Govt .604 .000 .245 .002 Housing Authority Females Individual Income -.478 .000 High Females No Religion .281 .000 3 Household Tenant of Govt .620 .000 .256 .001 Housing Authority Females Individual Income -.264 .013 High Females No Religion .261 .000 Females with No .250 .007 Qualifications Source: ABS calculated from unpublished data
Chapter Eight: Explaining Spatial Variations in Fertility 303
A block enter analysis indicates the usefulness of the chosen variables in predicting fertility patterns. It is clear that socio-economic measures including no religion (in combination with the other dominant variables representing education, income, and tenancy) were reasonable predictors of fertility patterns in 1986. In fact any combination of the variables, females unemployed, labour force participation rate of females aged 35– 54 years, females with no qualifications, females with a relatively high annual income and the per cent of households renting publicly account for around 50 to 60 per cent of the variance in fertility patterns. Generally once two to three of these variables are included in a model then other variables are not important as they do not add to the power of the model.
For 1996 fourteen variables were initially placed in an enter regression to examine in combination their influence on the fertility patterns. Two variables, representing composite variables (Index of Relative Socio-Economic Disadvantage and Index of Economic Resources) that were significant in the bi-variate analysis, were also included. The variable representing tenancy however was excluded from the 1996 regression analysis as the data was not disaggregated into public and private rental and total rental showed no association with fertility in the bi-variate analysis (Table 8.12).
A review of the results indicates that there is a high degree of collinearity between the IRSD and a number of the variables including the labour force participation rate of females aged 35–54 years, females unemployed and individual and household income.8 It therefore has been excluded from further analyses. Reflecting the stronger bi-variate relationships identified for 1996 thirteen variables, in combination, produced a significant model accounting for 74.9 per cent of the variance in fertility (Table 8.15). The variables of note are the labour force participation rate for women aged 35–54 and the per cent of females married. This is the first time marital status has been a significant variable.
Block enter regressions were undertaken to test the variables to assess the relative contribution of specific variables in explaining the fertility patterns identified. While the
8 The tolerance value was .01. Tolerance values are a measure of the correlation between the independent variables and can vary between o and 1. The closer to zero the tolerance value is for a variable, the stronger the relationship between this and the other independent variables. While SPSS will not include variables in a model if it has a tolerance of less than 0.0001, it has been suggested a tolerance level of .01 is preferable (Kemp, Snelgar and Brace 2003, 217). Chapter Eight: Explaining Spatial Variations in Fertility 304 labour force participation rate of females accounted for 37.9 per cent of the variance in fertility, the addition of the per cent of females married into the model increased its explanatory power considerably.
Table 8.15: Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 45–49 Years, 1996 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model .749 .000 Variables Females Married .384 .002 Females of Aboriginal or Torres Strait .076 .295 Islander Descent Females Unemployed .165 .181 Females Individual Income High -.350 .057 Females With No Qualifications .264 .119 Females in Professional Occupations .226 .194 Labour Force Participation Rate of -.559 .000 Females Aged 35-54 House Medium Density -.040 .682 Household Income High .256 .154 Females Born in Malta, Lebanon, -.052 .341 Netherlands, Turkey, Vietnam Females No Religion .087 .163 Females Changed Residence Last 5 -.001 .984 Years Index of Economic Resources .250 .209
Block Enter Model Block Variables 1 Labour Force Participation .379 .000 -.619 .000 Rate of Females Aged 35- 54 2 Labour Force Participation .719 .000 -.708 .000 Rate of Females Aged 35- 54 Females Married .589 .000 3 Labour Force Participation .717 .000 -.703 .000 Rate of Females Aged 35- 54 Females Married .589 .000 Females Individual Income -.006 .952 High Source: ABS calculated from unpublished data
The addition of further variables has little bearing on the explanatory power of the model or the importance of the labour force participation rate and per cent of females married as Chapter Eight: Explaining Spatial Variations in Fertility 305 predictor variables. The substitution of the IRSD for the economic variables results in similar findings (adjusted R square IRSD =.261; IRSD plus per cent married =.687, IRSD plus per cent married plus females in professional type occupations =.699).
8.4.2.1 Summary
Clearly the variables representing socio-economic status used in this study are stronger predictors of fertility in metropolitan Adelaide for women aged 45–49 years than they were in non-metropolitan South Australia. This is so even though the same assumption applies that the characteristics of this age group do not differ too widely from the total population. In 1981 and 1986 they accounted for around 65 per cent of the variance and by 1996 this had risen to nearly 75 per cent. Of note for 1981 and 1986 was the importance of household tenancy and therefore the role of the South Australian Housing Trust (SAHT) in shaping the social and economic structure of Adelaide. It is unfortunate the data was not available for the 1996 Census so as to explore its continuing role.
Besides household tenancy the variables representing education, employment and individual income were the most important as predictors of fertility or in explaining variance in the fertility patterns. In 1986 the variable ‘no religion’ was also important. The fact that this variable in the bi-variate analysis and enter regressions was in the opposite direction to that expected, i.e., positive rather than negative, suggests that it may not be a true finding, though this variable would need to be explored further before conclusive statements could be made. As religious nomination is the only voluntary question in the Census it may have something to do with the quality of the data for this question. Another unusual finding was the importance in 1996 of the percentage of women married as one of the two most important predictors. It was expected that while a positive relationship would be identified for marital status it would not be a significant relationship and not become a major predictor of fertility.
Chapter Eight: Explaining Spatial Variations in Fertility 306
8.4.3. The Role of Socio-Economic Factors—Women Aged 15–44 Years
Table 8.16 presents the correlation coefficients between the socio-economic variables and the spatial patterns of fertility for women aged 15–44 years. For 1981 significant but weak associations exist for the non-economic factors such as marital status, race, religion and birthplace but it is many of the economic (including education) factors that present the strongest relationships. Significant strong associations (over .710) were found with females with no qualifications and the per cent of females in professional, administrative and technical occupations. Moderate associations (.050-.070) existed with the other two education variables, the employment related variables (labour force participation rate of females aged 15–34 years, females unemployed, females with high annual personal incomes) and the per cent of households renting from a government authority.
By the 1986 Census a number of the relationships had become stronger, not only for the economic type factors but also for other variables like females married, females of Aboriginal or Torres Islander descent, and the percentage of females professing no religion. At this Census the most influential predictor from the bi-variate analysis was the labour force participation rate of females aged 15–34 years, followed closely by the qualification variables and the percentage of females in professional occupations.
Ten years later in 1996 the strong significance of most of these variables continued. The strongest relationships occurred again with the educational and employment type variables. This provides support to the notion that for many women childbearing and work are not always compatible. Other variables to note in 1996 are the percentage of females who were at a different address in the last Census (1991) and the percentage of homes that were medium density. These two variables for the first time presented weak but significant relationships in 1996. Mention also needs to be made of the variable, women professing no religion. At the outset, based on previous findings, it was hypothesised this relationship, if it existed, would be negative. The analysis for non-metropolitan South Australia tended to substantiate this expectation, however, the pattern for metropolitan Adelaide is reversed. Here the relationship is positive and it also strengthened over the 1981 to 1996 period. The reasons for this remain unclear.
Chapter Eight: Explaining Spatial Variations in Fertility 307
Table 8.16: Metropolitan South Australia: Correlation Coefficients Between Fertility of Women Aged 15–44 Years and Selected Variables 1981, 1986, 1996 Censuses Variable (and Expected Relationship) Year 1981 1986 1996 Females Married + **.350 **578 **.469 Females of Aboriginal or Torres Strait Islander Descent + **.381 **.425 **.490 Females Changed Residence Last 5 Years - -.014 .090 **-.305 Females Left School Aged 15 Years or Less + **.649 n.a. **.663 Females With at Least a Diploma or Degree - **-.703 **-.712 **-.762 Females With No Qualifications + **.729 **.784 **.759 Labour Force Participation Rate of Females Aged 15-34 - **-.546 **-.836 **-.787 Females Unemployed + **.556 **.554 **.593 Females in Professional and Administrative Occupations - **-.770 **-.743 **-.772 Females Catholic Religion + -.007 -.099 **-.245 Females Non-Christian religion + **-.304 -.046 .026 Females No Religion - **.318 **.490 **.525 Females Born in Mainly Non-English Speaking Country + **-.321 -.106 *-.172 Females Born in Malta, Lebanon, Netherlands, Turkey, + .030 **.343 **.241 Vietnam Household Tenant of Govt Housing Authority (1996 All + **.597 **.610 -.018 Rental) House Medium Density - -.145 **-.218 **-.306 Female Individual Income High - **-.632 **-.702 **-.696 Household Income Low + **-.281 *-.172 .037 Household Income High - **-.284 **-.277 **-.417 Index Disadvantage (1996 Only) - **-.600 Index of Economic Resources (1996 Only) - **-.354 Index of Education and Occupation (1996 Only) - -.026 ** Correlation is significant at the 0.01 level (2-tailed) *Correlation is significant at the 0.05 level (2-tailed) n.a. not available (data supplied incorrect)
Source: ABS calculated from unpublished data
To explore the relationships identified in the bi-variate analyses further and to assess the importance of the variables in combination in explaining the fertility patterns, enter or simultaneous regression analyses were undertaken. The initial enter regression for the 1981 Census produced eight significant variables that accounted for a very high 89.7 per cent of the variance (Table 8.17).
Block enter regressions highlighted the importance of the employment variables, in particular the proportion of females in professional, administrative and technical occupations and the labour force participation rate of females aged 15–34 years (Table 8.17). Together, for example, these variables produce a significant predictor model accounting for 77.4 per cent of the variance in the spatial patterns of fertility. Chapter Eight: Explaining Spatial Variations in Fertility 308
Table 8.17: Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 15–44 Years, 1981 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model .897 .000 Variables Females Married .098 .058 Females of Aboriginal or Torres Strait -.057 .147 Islander Descent Females Unemployed .261 .000 Females Individual Income High -.302 .000 Females With No Qualifications .252 .009 Females in Professional and -.258 .006 Administrative Occupations Labour Force Participation Rate of -.232 .000 Females Aged 15-34 Females Non-Christian Religion -.067 .072 Household Income High .356 .000 Females Born in Mainly Non-English .000 .966 Speaking Country Females No Religion .166 .000 Household Tenant of Govt Housing .126 .003 Authority
Block Enter Model Block Variables 1 Females in Professional and .590 .000 -.770 .000 Administrative Occupations 2 Females in Professional and .774 .000 -.701 .000 Administrative Occupations Labour Force Participation -.435 .000 Rate of Females Aged 15- 34 3 Females in Professional and .782 .013 -.432 .000 Administrative Occupations Labour Force Participation -.453 .000 Rate of Females Aged 15- 34 Females With No .283 .013 Qualifications Source: ABS calculated from unpublished data
Although these variables were the most important, other variables such as females with no qualifications, females unemployed, females with high annual incomes and the proportion of households renting from a government authority individually could predict around 35 per cent of the variance in fertility. In combination with one or two of the other most relevant variables the explanatory power of the models increased to 60 to 70 per cent.
Chapter Eight: Explaining Spatial Variations in Fertility 309
Table 8.18 highlights the same process for the data from the 1986 Census. A slightly higher proportion of the variance is explained (94.3 per cent) and again it is clear, that in combination, many of the variables remain significant although the direction of the association for some is opposite to that expected.
Table 8.18: Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 15–44 Years, 1986 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model .943 .000 Variables Females Married .236 .000 Females of Aboriginal or Torres Strait -.081 .026 Islander Descent Females Unemployed .324 .000 Females Individual Income High -.190 .002 Females With No Qualifications .299 .000 Females in Professional and -.015 .804 Administrative Occupations Labour Force Participation Rate of -.207 .000 Females Aged 15-34 Females Non-Christian Religion -.102 .025 Household Income High .188 .001 Females Born in Mainly Non-English -.020 .641 Speaking Country Females No Religion .092 .005 Household Tenant of Govt Housing .166 .003 Authority House Medium Density -.103 .045
Block Enter Model Block Variables 1 Females With No .615 .000 .784 .000 Qualifications 2 Females With No .734 .000 .669 .000 Qualifications Females Married .368 .000 3 Females With No .899 .000 .454 .000 Qualifications Females Married .213 .000 Labour Force Participation -.505 .000 Rate of Females Aged 15- 34 4 Females With No .915 .000 .307 .000 Qualifications Females Married .360 .000 Labour Force Participation -.399 .000 Rate of Females Aged 15- 34 Females Unemployed .244 .000 Source: ABS calculated from unpublished data Chapter Eight: Explaining Spatial Variations in Fertility 310
Block enter regressions reveal that in essence two to three variables that represent education, employment, marital status or household tenancy account for over 80 per cent of the variance in the spatial patterns of fertility identified in 1986. It is interesting to note the relevance of marital status. It was hypothesised that though the relationship would be positive, its importance was likely to diminish over time. In fact it seems its relevance has increased.
In 1996 thirteen variables were initially placed in an enter regression to examine in combination their influence on the fertility patterns. Two variables, representing composite variables (Index of Relative Socio-Economic Disadvantage and Index of Economic Resources), that were significant in the bi-variate analysis, were also included. The variable representing tenancy however was excluded from the 1996 regression analysis as the data was not disaggregated into public and private rental and total rental showed no association with fertility in the bi-variate analysis (Table 8.16).
As for women aged 45–49 years in 1996, a review of the results indicates that there is a high degree of collinearity between the IRSD and a number of the variables including the labour force participation rate of females 15–34 years, females unemployed and individual and household income. It therefore has been excluded from further analyses. The remaining 12 variables from the bi-variate analysis with at least weak associations with fertility, through an enter regression, accounted for 92.9 per cent of the variance in fertility for women aged 15–44 years in metropolitan Adelaide (Table 8.19). The most important variables appear to be the labour force participation rate, marital status, educational qualifications, individual income, professional occupations and no religion.
Block enter regressions highlight the importance of the variables representing employment, individual income and education. In conjunction with these variables marital status is also important (on its own it accounts for around 20 per cent of the variance and it is a positive influence on fertility). The most influential variable however was the labour force participation rate of female aged 15–34 years. Clearly participation in the labour force (or non-participation) has a significant bearing on fertility levels and consequently the areas in which people are able, or chose to reside.
Chapter Eight: Explaining Spatial Variations in Fertility 311
Table 8.19: Metropolitan South Australia: Regression Analyses of Fertility Patterns for Women Aged 15–44 Years, 1996 Census Model Type and Variables Adjusted R Significance Standardised Significance Square Beta Enter Model .929 .000 Variables Females Married .287 .000 Females of Aboriginal or Torres Strait .001 .972 Islander Descent Females Unemployed .113 .063 Females Individual Income High -.218 .045 Females With at Least a Diploma or -.249 .004 Degree Females in Professional Occupations -.195 .021 Labour Force Participation Rate of -.293 .000 Females Aged 15-34 Females Changed Residence Last 5 .013 .684 Years Household Income High .130 .180 Females No Religion .188 .000 Index of Economic Resources .119 .245 House Medium Density -.035 .502
Block Enter Model Block Variables 1 Labour Force Participation .616 .000 -.787 .000 Rate of Females Aged 15- 34 2 Labour Force Participation .764 .000 -.744 .000 Rate of Females Aged 15- 34 Females Married .388 .000 3 Labour Force Participation .900 .000 -.473 .000 Rate of Females Aged 15- 34 Females Married .389 .000 Females With at Least a -.456 .000 Diploma or Degree Source: ABS calculated from unpublished data
As was the case for the non-metropolitan sector, data for women aged 15–44 years was obtained from the ABS for a selected number of socio-economic variables to test the effectiveness of using data for the total population rather than a more defined subset of the population in the correlation regression. Table 8.20 presents the correlation coefficients for this analysis and it is clear strong significant associations exist with the educational, occupational and labour force status variables. Regression analyses indicate these variables account for a slightly higher proportion of the variation (95.1 per cent) than was the case when the total population data was used (92.9 per cent) but at over 90 per cent for Chapter Eight: Explaining Spatial Variations in Fertility 312 both analyses it is really of little significance. The most influential variables from the regression analysis were females who left school at age 15 years or less, the labour force participation rate of females and marital status. Together these three variables accounted for 93.4 per cent of the variance.
Table 8.20: Metropolitan South Australia: Correlation Coefficients Between Fertility and Selected Characteristics of Women Aged 15–44 Years, 1996 Census Variable and Expected Relationship Correlation Variable and Expected Relationship Correlation Coefficients Coefficients Females in Professional - **-.801 Females Non-Christian Religion + -.034 Occupations Females Left School Aged 15 + **.874 Females No religion - **.356 Years or Less Females With at Least a - **-.812 Females Born in Mainly Non- + -.037 Diploma or Degree English Speaking Country Females Married + *.469 Females Born in Malta, + **.329 Lebanon, Netherlands, Turkey, Vietnam Females of Aboriginal or + **.399 Labour Force Participation Rate - **-.787 Torres Strait Islander Descent of Females Aged 15-34 Females Changed Residence - **-.426 Last 5 Years ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed)
Source: ABS calculated from unpublished data
8.4.3.1 Summary
It is clear that socio-economic variables are very relevant in explaining the spatial patterns identified for women aged 15–44 years in Chapter Seven. The data for the total female population (or females aged 15 years and over) and total households accounted for around 90 per cent of the variance in fertility at all three Censuses. The data specifically for women aged 15–44 years did not provide more powerful models of explanation. The findings are reflective of the strong patterns of socio-economic segregation/differentiation in Adelaide that has existed for decades.
At the three Censuses the most influential variables were those representing educational status, individual income, household tenancy (1981, 1986) and employment. In particular Chapter Eight: Explaining Spatial Variations in Fertility 313 the most influential variable appeared to be the labour force participation rate of females. Marital status was also of relevance in 1986 and 1996.
8.5. Conclusion
The aim of this chapter was to provide explanations for the spatial patterns of fertility identified in Chapters Six and Seven and to ascertain the role of socio-economic factors. This was achieved via correlation regression analyses for the two major sectors of the State—non-metropolitan and metropolitan.
In non-metropolitan South Australia the socio-economic variables were less significant or relevant in explaining the spatial differentiation in fertility than was the case in metropolitan Adelaide. While factors representative of economic and educational status (household tenancy, post school qualifications) were important, a number of other variables were also relevant. Such variables included the proportion of the female population of Aboriginal or Torres Islander descent; the accessibility/remoteness index (ARIA) and the change in the total population over inter-censal periods.
It is not surprising that the socio-economic variables have less explanatory power for the non-metropolitan sector of the State. It is a vast area that varies enormously in terms of for example, settlement patterns, settlement population size, distance to other centres including metropolitan Adelaide, economic focus and infrastructure and it is a sector that has undergone considerable change over the period of time covered by this study. Each Section of State has in a sense created or evolved its own microcosm and these can vary considerably from one region to another. Trying to generalise is therefore difficult.
The relevance of the socio-economic variables however is greater in metropolitan Adelaide. This is particularly so for women aged 15–44 years for whom it is more likely that their characteristics recorded in the Census equate more closely to their characteristics when they were having children than was the case for women aged 45–49 years.
Chapter Eight: Explaining Spatial Variations in Fertility 314
In 1981 and 1986, for women aged 45–49 years, household tenancy was a significant predictor of fertility particularly in conjunction with other education/economic type variables. Unfortunately this relationship could not be tested in 1996. In 1996 the most significant variables were the labour force participation rate of females aged 35–54 years and marital status.
For women aged 15–44 years the socio-economic variables included in this study accounted for over 90 per cent of the variance in fertility. While tenancy was a significant variable in 1981 and 1986 its power as an explanatory variable on its own was overshadowed by the stronger influence of the education and employment variables. A recurring variable of strength was the labour force participation rate of females. This finding suggests at least two possible things. One is that women in the labour force have a predisposition not to have children or secondly participation in the workforce and childbearing are not easily compatible. While there is a degree of truth in the first suggestion in that some women do not want children and the labour force provides them with alternative options, research suggests (Chapter Three) it is the later factor that is of greater significance. Such a finding suggests policies that promote an environment which encourages women to combine both roles, or at least supports both roles may be much more beneficial in raising the fertility rate than baby bonuses9 and forcing parents (women) back to work by reducing welfare.10
In light of the strength of the education and economic type variables in accounting for the spatial patterns of fertility the influence of marital status was interesting. Much of the literature suggests marital status is no longer the influence it was but even though this may be true the findings here reflect the fact legal marriage still in the mid 1990s was the major basis for family formation in Australia and it still influenced spatial patterns of fertility.
9 In the 2004 May Federal Budget a $3000 payment to be made available to new parents was announced (Office of the Prime Minister of Australia 2004). While a baby bonus cannot be criticised if it enables women to stay at home longer to care for their young children rather than return to work,. to portray the bonus as a means to raising the fertility rate may be a little shortsighted. 10 In the 2005 May Federal Budget the Treasurer announced the ‘Welfare to Work Package’. The aim of this package is to encourage (by reducing welfare payments) people into paid employment. Parents will only be able to receive a Parenting Payment from the government while their youngest child is less than six years old. After that they will be subject to an obligation to seek part-time work of at least 15 hours per week (Office of the Treasurer 2005). Chapter Eight: Explaining Spatial Variations in Fertility 315
In the context of this study, socio-economic factors remain as the predominant influence on spatial patterns of fertility in South Australia, particularly in the metropolitan area. Over the last twenty years the role of socio-economic factors in influencing fertility and spatial patterns of fertility has received little attention, however with the expectation that this study has wider applicability, the role and importance of socio-economic factors in influencing fertility levels should not be overlooked in post transitional societies.
Chapter Nine: Conclusion 316
Chapter Nine
Conclusion
9.1. Introduction
The objective of this study was to investigate if spatial variations in fertility still have significance and relevance, not only in their own right but as a means of understanding the wider societal influences governing decision making and behaviour regarding fertility. Although the identification and explanation of spatial variations in fertility has been considered an important component of the sub-disciplines of population geography and spatial demography, in recent years the subject has attracted little attention. Yet even in a ‘post transitional’ era the identification of spatial variations in fertility, in Europe in particular, has helped to focus research attention upon the factors involved in fertility falling to record low levels.
This study has examined the geography of fertility in the State of South Australia from 1976 to 1996 using Census issue data—how many children ever born to women aged 45– 49 years and 15–44 years. The specific aims of the study were: • to review the role of spatial and socio-economic differentials in theories and models of fertility change; • to establish spatial variations in fertility at various geographic scales; • to establish any trends towards convergence, stability or divergence in the spatial patterns; and • from the Census data of socio-economic characteristics available, provide explanations for the patterns and trends identified.
The purpose of this chapter is to review the theories and evidence presented, to summarise the findings of this study and to discuss their implications.
Chapter Nine: Conclusion 317
9.2. Review of Theory and Empirical Research
Chapter One discussed why the traditional geographical approach to fertility has been largely ignored in recent times although space and place have become increasingly important within other fields of social science. Part of the reason for this is the nature of the existent theories relating to differential fertility which are reviewed in Chapter Two. All the theories examined provided important developments in the understanding of fertility decline and family change but very few of the theories incorporate a perspective on differential fertility.
The most widely recognised and most widely debated theory in demography and the theory upon which much research, including social and spatial differential fertility analysis has been based, is demographic transition theory. In spite of the recognition in this theory that change through time occurs differentially between social groups and areas, differential fertility is not a central feature of the theory but merely one of the consequences of the theory’s focus on the development and progression of a population from a stage of high fertility to one of to low fertility. As the theory suggests that by the final stage of the transition differentials no longer exist, or are minimal, then for contemporary developed societies, the theory’s ability to explain the existence and persistence of differential fertility is limited. More recently developed broad based theories, such as the second demographic transition model and gender equity models, similarly provide little if any theoretical underpinnings for understanding differential fertility in industrialised countries today.
In reviewing the theories however a number of important themes emerged including variability in the decline of fertility at different spatial scales and that there is a relationship between fertility decline and the demographic, socio-economic and cultural characteristics of a population. In the last twenty years though, it has been proposed variations in ideational change and the role of the social and economic institutions of society, rather than the more traditional socio-economic factors may be the forces influencing any remaining variations in fertility levels.
Besides theory, another reason for the lack of interest in the study of differential fertility in recent times was the emergence in the 1950s, 1960s, 1970s and into the 1980s of empirical Chapter Nine: Conclusion 318 evidence of the hypothesised convergence of social and spatial differentials with lower levels of fertility as hypothesised in demographic transition theory. This led some geographers (Compton 1991; Wilson 1990, 1991) to argue that there was no reason, point or profit in continuing to monitor and analyse regional, and consequently, socio-economic variations in fertility in post transitional societies. However Chapter Three shows that evidence questions the premise of convergence in a post transitional society and establishes that social and spatial differentials in fertility continue to exist, and Australia, is no exception. In particular the review of the literature on spatial differentials showed that whatever the level of aggregation or disaggregation, the simplicity or sophistication of the fertility indices and/or methods of analysis used in regional studies, a common finding was the persistence of spatial variability in fertility patterns and the importance of factors other than population structure in the explanation of the patterns.
While a range of factors embracing demographic, biological, cultural and psychological variables of individuals influence fertility patterns and trends, socio-economic factors have long been identified as strong determinants of fertility. They are often the major explanatory variables which emerge in spatial analyses of fertility. Spatial variations in fertility are conceptualised to partly exist as long as there is a connection between socio- economic status and fertility levels and there are variations in the socio-economic characteristics of the constituent population of regions. The relative importance however of socio-economic factors in influencing trends and patterns in fertility in post transitional societies has been questioned as differentials were expected to diminish.
Spatial variations in fertility can also be affected by other influences such as migration and diffusion and wider economic and political circumstances in society that cannot be derived by aggregating individual characteristics. The connection between macroeconomic conditions and fertility has spatial dimensions in that at any given point in time different regions are affected by, and respond differently to, varying levels of economic prosperity and decline. This is seen no more clearly than in the effects the processes of economic restructuring and globalisation have had on Australia and other advanced countries over the last three decades.
While individual socio-economic factors and wider macro conditions influence patterns of family formation, the rapidly expanding literature on the causes of very low fertility Chapter Nine: Conclusion 319 emphasise the importance of attitudinal changes and preferences. Regional and local differentiation in fertility may be important in identifying, and be partly explained by clusters of individuals with differing attitudes and preferences to family formation. Norms and value systems regarding reproductive behaviour, are influenced by the life-course experience and current spheres of social interaction. Reproductive behaviour therefore may vary for groups whose sphere of interaction is wide, or increasingly located at the workplace, compared with those households whose reference group may be limited to their regional centre, immediate environment, family of origin, local school or local neighbourhood for example.
9.3. Research Findings
To investigate if spatial differentials in fertility still exist, and have relevance in a low fertility setting, fertility for women aged 45–49 and 15–44 years in the State of South Australia at the 1976, 1981, 1986 and 1996 Censuses was examined at three levels of geographic disaggregation.1 This allowed an exploration of some of the tenets underlying theory, (in particular demographic transition theory), allowed some insight into trends towards convergence and tested the robustness of the findings at different geographic scales.
One of the most enduring differentials in fertility throughout the world has been that observed between urban and rural areas. Theoretically this differential has its origins in demographic transition theory which sees the move to smaller families beginning in the urban areas before filtering into the countryside. Immediately apparent from the broad level analysis of fertility patterns by Statistical Sub Divisions (SSD) in South Australia was the distinct contrast between the non-metropolitan sector of the State with above average fertility in every SSD and the metropolitan area where for three of the four SSDs rates were below average. Within the metropolitan and non-metropolitan sectors however there were also distinctions. In the non-metropolitan sector there was a contrast between the inner and outer SSDs while in metropolitan Adelaide the Eastern SSD consistently had the lowest fertility and the Northern SSD the highest.
1 The 1996 Census was the last Australian Census at which a question on fertility was included. Chapter Nine: Conclusion 320
Trends towards convergence or divergence in the spatial patterns of fertility varied by age group. For women aged 45–49 years the overall trend was towards convergence between the SSDs and between the metropolitan and non-metropolitan sectors of the State over time (1976 to 1996). By examining the distribution of women by parity for the two major sectors of the State it was established the decline in the differential between the metropolitan sector and non-metropolitan sector was due to an increase in childlessness and a decline over time in the number of families of four or more children. For women aged 15–44 years however measures of overall change indicated strong stability in the patterns over time with a slight trend towards divergence in the spatial patterns in the 1980s and 1990s. This divergence over time was found to be due to the influence of fertility trends in the metropolitan sector of the State. Analysis of data by parity indicated that over time a smaller proportion of women in the metropolitan sector were having three or more children, but more importantly, there was an increasing difference in the percentage of women who had no children, by major sector.
As the SSDs are very large, containing people with a wide array of characteristics, average values for such areas disguise significant sub-area variation. The next part of the study therefore looked at each sector separately and in greater detail. For the non-metropolitan analysis the sector was divided into Section of State categories (as defined by ASGC) which represented non-contiguous geographic areas of particular urban population sizes and rural balances. The overall findings of this analysis for women aged 45–49 years and 15–44 years was a contrast between the fertility of women in the urban centres and rural balances of the inner SSDs (close to and adjoining metropolitan Adelaide) which was comparatively low, and the outer more remote urban centres and rural balances of SSDs which recorded higher levels of fertility. The areas adjacent to metropolitan Adelaide have a strong connection with the metropolitan area and in many respects they have become an extended arm of the metropolitan area (the peri-urban fringe). Many peri-urban migrants maintain functional linkages with the metropolitan area both in terms of employment locations and in their social and cultural connections (Ford 1997). It is therefore understandable fertility levels of women here reflect more closely those of women in the metropolitan region (though rates remained slightly higher in the peri-urban fringe areas than for areas in metropolitan Adelaide),2 rather than the overall non-metropolitan region of which they
2 Except for a select few areas in the northern suburbs of Adelaide that had comparatively high fertility. Chapter Nine: Conclusion 321 form a part. A number of outliers were also identified where fertility levels were consistently different from those in the surrounding areas. The Far North SSD was the most glaring example where the rural balance of the SSD often recorded the highest fertility in the non-metropolitan sector while the urban centre category recorded the lowest rate. Explanation for the contrasting fertility levels of the urban centre category and the rural balance lies in the distinct unique characteristics of this region and the urban centres that comprise the urban centre category, Coober Pedy and Woomera.
The statistical parameters used to provide an indication of convergence or divergence in the patterns indicated that over each inter-censal period for women aged 45–49 years there was a trend towards convergence in the spatial patterns. Although there were some consistencies across time, it was clear that small variations produced considerable changes in the rank ordering of areas, leading to random changes in the spatial patterns from Census year to Census year. While there was a general trend of lower fertility in an urban centre classification than for the rural balance, urban centre size per se did not appear to be a significant influence on the overall average number of children ever born to women aged 45–49 years. For women aged 15–44 years there was also a trend towards convergence in the spatial patterns however this tendency was only slight in the 1986–1996 period. This perhaps reflects, or is a consequence of the increasing urban development, sub-division of land and inmovement of people from metropolitan Adelaide occurring in the SSDs adjacent to metropolitan Adelaide.
Explanations for the spatial patterns of fertility in non-metropolitan South Australia, and the variability in these patterns over time was explored by examining the role and relevance of socio-economic factors available from the appropriate Censuses. Providing explanations for the patterns in non-metropolitan South Australia requires some knowledge and understanding of the unique character of this section of the State and the changes it has undergone over the last few decades, in particular the net migration gains and losses across the region. Finding generalised reasons for the patterns and trends identified for non-metropolitan South Australia is difficult because it is a large area that varies enormously in terms of settlement sizes, characteristics and economic foci and there is not the degree of socio-economic differentiation that exists in metropolitan Adelaide.
Chapter Nine: Conclusion 322
Overall for women aged 45–49 years socio-economic factors only accounted for a limited proportion of the variability in fertility, around 30 per cent, 57 per cent and 45 per cent respectively in 1981, 1986 and 1996. It is likely that this result was influenced by the limitations of the data, i.e., that the independent variables describe all women and households and not just those aged 45–49; the relatively low numbers recorded for each characteristic and the small number of cases. The socio-economic data had greater applicability for women aged 15–44 years and it was able to account for a greater degree of the variability in the spatial patterns of fertility at least in 1986 (72 per cent) and in 1996 (62 per cent). The most influential socio-economic factors were educational status, race, employment in agriculture and household tenancy.
Two other variables however were important. The first of these was ARIA, a measure of the accessibility or remoteness of a Section of State category. Clearly it appears that closeness to centres of innovation and opportunity, and interaction with those centres, in particular metropolitan Adelaide is a relevant explanatory factor for non-metropolitan South Australia. Similarly the other variable of importance was the change in the population over an inter-censal period. The non-metropolitan sector of the State has undergone considerable population change over the time period of this study. These changes included net migration gains and retainment of population in the inner SSDs and net migration losses from most of the other areas. Such change in areas that already have low population numbers can alter the reliability of the data to significantly influence the analysis here, and is probably partly responsible for the variability in the spatial patterns of fertility from one year to another, especially for women aged 45–49 years. While some possible reasons for the patterns and variation were identified in this study a greater understanding of the patterns, and in particular why some areas appeared to be ‘outliers’ requires closer examination of the areas’ populations than was possible in this study.
In metropolitan Adelaide the spatial patterns of fertility were more stark and consistent over the time period of this study, a time that included significant overall declines in fertility and noteworthy societal changes in the structure of families and the roles of women. For both age groups women living in the northern areas of the metropolitan area and in the Noarlunga LGA to the south of the city had the highest fertility. Lower than average fertility was prevalent in the older, inner, middle and coastal areas. For women aged 45–49 years while there was a broad striking pattern, the spatial patterns were varied Chapter Nine: Conclusion 323 with many areas changing rank from one point in time to another. Consequently a degree of convergence in the spatial pattern for women aged 45–49 years occurred over time. This was not unexpected as while an examination of the fertility patterns of women aged 45–49 years is able to provide a picture of completed fertility, much of their family building began or occurred two to three decades before the data was collected in the Census. It therefore does not necessarily reflect the neighbourhoods in which they lived when their children were born, thereby ‘diluting’ the spatial pattern of fertility.
For women aged 15–44 years the spatial patterns of fertility reflected those found for women aged 45–49 years but the patterns were very defined. Many areas were ranked in a similar position at three if not four of the Censuses. This stability reinforced the contrast of higher fertility in the northern and southern suburbs and much lower fertility in the inner suburbs. High correlations in the patterns were found from one Census year to the next. Analysis of the fertility for specific cohorts of women through from 1981 to 1996 showed the spatial patterns of fertility for women aged 15–44 years was strongly influenced by the patterns established early—by the age groups spanning 15–34 years. This analysis also indicated that over the 1981–1986 and 1986–1996 inter-censal periods the geographic patterns for these age groups had diverged and this divergence appeared to be increasing. Where the cohort could be followed through to the ages of 35–44 years then there appeared to be a degree of convergence in the distribution but the distributions remained closely correlated. This could reflect the influence of the delay in childbearing to later ages or the increasing movement of people to different areas as their children age.
Overall for women aged 45–49 years and 15–44 years there were strong contrasts across the metropolitan area in the average number of children ever born and these patterns have existed at least since 1976. Though fertility has declined considerably over time it appears the forces at play in 1976 had been reinforced and amplified by 1996. Clearly strong influences govern the patterns identified and explanation was sought through examining socio-economic factors.
The initial bivariate analysis for metropolitan Adelaide indicated that stronger relationships existed between the fertility patterns and many of the socio-economic variables, particularly for the traditional variables representing education, employment and income than was the case for non-metropolitan South Australia. For women aged 45–49 Chapter Nine: Conclusion 324 years regression analyses showed that in the 1980s the variables significantly accounted for around 65 per cent of the variance in fertility and by 1996 this had increased to just under 75 per cent. The most influential variables in 1981 and 1986 were individual income and the proportion of households renting from a government authority. Unfortunately, as previously stated, the importance of public housing tenancy could not be tested in 1996 as the ABS data did not distinguish between public and private tenancy. In 1996 the most influential variables were the labour force participation rate of females aged 35–54 and the percentage of females married.
For the broader age group of 15–44 years the socio-economic variables accounted for nearly all of the variance (90 per cent and over) to be found in the patterns of fertility. Again the most influential variables were those representing educational status, employment, individual income and household tenancy. In light of the strength of the education and economic type variables in accounting for the spatial patterns of fertility the influence of marital status was interesting. Within Australia, legal marriage remains the major basis for family formation and its importance still influenced the spatial patterns of fertility identified in this study.
9.4. Implications
This research has shown that space, in the context of the State of South Australia as representative of a low fertility setting, a post-transitional society, still has relevance in the understanding of fertility. This finding is not just based on the analysis of fertility patterns at a single point in time, but from four data recording points (Censuses) covering a twenty year period. Despite the considerable declines in fertility and the significant societal changes in the structure of families and the roles of women over this time, the geographic differentiation in fertility persisted and the overall patterns were preserved. Areal disaggregation allowed a closer examination of more specific variability. While it is assumed declines in fertility equate with a convergence in differentials the statistical parameters used in this study showed that trends towards convergence or divergence in the spatial patterns over time varied by geographical scale and age group.
Chapter Nine: Conclusion 325
Demographic convergence is an expected outcome of the demographic transition generally on the basis of convergent socio-economic trends, and of ideational factors outweighing socio-economic factors in the so called second demographic transition (Lesthaeghe 1995; Lesthaeghe and van de Kaa 1986; van de Kaa 1987). This study suggest that this view may be simplistic. People’s lives have become much more diverse and complex than in the past when the sanctions and attitudes of society were considerably more restricting. Rather than moving towards demographic uniformity and convergent socio-economic trends the changes in the last two to three decades have led to greater complexity in society and in the case of major urban centres, the pattern may be more one of demographic divergence.
The patterns identified in this study for South Australia reflect those identified by Alonso (1980) for the states in the US—although over time fertility declined in all states to some extent the greatest declines in fertility occurred in those states where fertility was already low. From his analyses, (as outlined here in Chapter 3), he hypothesised this was due to two effects—developmental and cyclical—and according to O’Connell (1981a), it is the cyclical effects (periodic changes in employment, economic growth, and incomes for example) that have the greatest influence on differentials in fertility, particularly regional fertility levels. This is because higher socio-economic groups react more strongly to these cyclical changes occurring in society. This study has not covered a long enough period of time to form any assessment about the validity of regional fertility levels tending to converge in times of cyclical upturns in fertility and diverge in times of cyclical downturns in fertility. It has however established that strong patterns exist over the period studied, that it was often the areas of low fertility which experienced greater declines in fertility than the areas of high fertility, and socio-economic factors play an important role in defining the patterns. It is evident that spatial studies should not be dismissed over fears of ecological fallacy or that some theoretical limit to change has been reached. In fact it could be argued that space should be included as a general element in theory regarding fertility past and present.
This research has shown that for metropolitan Adelaide in particular, socio-economic status (as represented by educational status, occupational status, housing tenancy and individual income) significantly affects, or is affected by, fertility levels and where people choose to, or are able to, reside. In fact it seems spatial patterns of fertility are faithful reflections of socio-economic differentiation within metropolitan Adelaide and differential Chapter Nine: Conclusion 326 fertility may be a contributory factor in widening of class differentials. Over the last two to three decades advanced societies have undergone what McDonald (2005) identifies as two waves of social change, social liberalism (also termed reflexive modernization) and economic deregulation (also termed new capitalism). These changes have had an enormous impact on society and the structure of cities including major repercussions on housing demand and affordability. The demand for housing has resulted in significant growth in house prices which are undermining housing affordability for low and moderate income households, particularly those relying on a single wage to pay for housing (Berry 2003; Randolph 2003) as is often the case with the birth of children. In discussing inner city revival in Australian cities Randolph (2003, 6) comments:
The emergence of a post-gentrified inner urban society, with life-style and consumer driven housing demand replacing more suburban concerns about family and domesticity have taken a strong hold. More than ever before, the inner city has become a pre-child and child-free area. Children are increasingly a suburban phenomena. At the same time lower income housing/households are all but excluded from inner city areas, pushed out into older and poorer quality housing in middle and outer suburbs.
This is a pattern increasingly reflected in the spatial patterns of fertility for metropolitan Adelaide identified in this study over the 1976 to 1996 period.
While overall from the research in this study the most significant individual factors were education and employment variables, for this State and in particular metropolitan Adelaide, the role of the South Australian Housing Trust (SAHT) has been significant in influencing the social gradient through its location of disadvantaged people. For example, in this study the northern suburbs of Adelaide, (Elizabeth and its surrounds), continually had the highest fertility levels and between 1986 and 1996 in some sections of this area the fertility of women aged 15–44 years actually increased. These northern suburbs have been identified as one of the most disadvantaged in Australia in terms of unemployment, poverty and welfare dependency (Baum, Stimson, Mullins and O’Connor 2000). The SAHT has been seen as partly responsible for this (Harris 2000) because of the relatively large rental estates located in these suburbs. and its policy of housing those in greatest need. In addition the SAHT is likely to locate large disadvantaged families in these suburbs as this is the location of its stock of four and five bedroom houses (personal Chapter Nine: Conclusion 327 communication, Manager, Development Opportunities SAHT Michael Findlay). Another locational factor to influence fertility levels in this area is a culture of teenage pregnancies which is associated with the lower levels of socio-economic status (Dekker in Jory 2001).3 This highlights the influence of local spheres of interaction.
It is unfortunate that the latest available data for this research is almost ten years old and that a number of questions that arise from this research cannot be adequately answered until further data on the number of children ever born is available from the 2006 Census. Such questions include: • Is marital status still an influential variable in defining spatial patterns of fertility in metropolitan Adelaide given the trend towards an increase in de facto relationships (Carmichael and Mason 1998), an increase in the number of marriages that end in divorce (Carmichael 2002; ABS 2002a) and an increase in the proportion of births outside marriage (ABS 2004b)? • Does the location of public rental housing still influence the spatial patterning of fertility in light of the changes in the policies of the SAHT that have led to a reduction in stock, redevelopment of previous estates and a move towards greater social mix in housing those in greatest need (Orchard and Arthurson 2005)? • What is the role of gentrification in some previously low socio-economic status areas? What impact does this have on fertility patterns? • Has the increasing divergence identified in the pattern of fertility of women aged 15–34 in particular continued since 1996 and therefore is fertility a significant contributor and influence on social polarisation in society?
The lack of available data to answer these questions is the result of the view of the ABS after the 1986 Census that justification only exists to collect issue data at ten yearly intervals (ABS 1989, 59):
With Australia’s fertility now remaining rather stable at a low level, and given that variations in fertility among most small groups of the population have considerably narrowed it is not essential for data on the number of children ever born to be collected every 5 years.
3 Through Commonwealth and State funding a number of programs have been put in place to improve the educational, social, health and wellbeing outcomes for teenage mothers and their infants in these areas through increased school retention and social inclusion (Salter 2005; ShineSA 2004). Chapter Nine: Conclusion 328
This point of view has not changed (ABS 1998c; 2003b). This is despite the movement of fertility onto the government policy agenda as the implications of an ageing population are realised (Commonwealth of Australia 2002; Office of the Prime Minister of Australia 2004) and, as more than ever before, the technological means is available (through such powerful tools as geographical information systems (GIS) technology) for the mapping and integration of an array of diverse data sets to gain a greater insight and understanding of the patterns and processes.
An identified area of need for research in understanding change in Australian cities (applicable also to regional areas) is the need to understand the impacts of policy at the small area level and therefore the availability of data at this level is essential. As Randolph (2003, 12) states:
Given that it is at the neighbourhood level that the interaction between the multi-scaled dynamic forces of urban change are played out – housing markets, job markets, household change, the impact of policy, for example – and the importance of the household itself as the fulcrum around which all these forces play out socially, then I suggest a strong line of research is needed to explore how our cities are changing from the local level upwards.
Efforts in this endeavour and in gaining a greater understanding of fertility trends and patterns over time may be improved if the ABS proposal to enhance the population Census is accepted and implemented. The ABS recently (April 2005) released a discussion paper proposing to enhance the data from the 2006 Australian Census by combining it with future Censuses (and possibly the 2001 Census) creating a Statistical Longitudinal Census Database (SLCD) (ABS 2005). This data set would also be available for use with a limited number of other data sets such as the ABS household surveys and birth register data upon request.
The logical next phase of the research undertaken in this study would be to examine the particular influences, constraints, desires, expectations and attitudes of women and couples living in the different fertility regions identified in this study. The geographic differentiation identified here has been used as a guide in another study exploring the socio-psychological experiences of childbearing and childrearing experienced by women Chapter Nine: Conclusion 329 with differing socio-economic characteristics in Adelaide (Newman 2004a, 2004b, forthcoming). This research will provide further detail to the picture of fertility in metropolitan Adelaide.
Clearly the geographic approach to fertility is a worthwhile, informative endeavour even in times of unprecedented low fertility in advanced countries. The changes that are deemed responsible for the decline in fertility and the perpetuation of low levels have not resulted in convergent socio-economic and ideational trends but have resulted in a more complex situation. Today there is considerable concern that low and very low fertility will have detrimental demographic and economic effects that will last for decades and that social policy intervention by governments is essential (McDonald 2005).
One of the consequences is that an increasing proportion of children are being born into poor families. International and national research (Duncan and Brooks-Gunn 1997, Bradbury, Jenkins and Micklewright 2001, Blanden, Goodman, Gregg and Machin 2002) indicates that low income families have greater difficulty providing adequate health care, affording school care costs (Taylor and Fraser 2003) and even a lack of knowledge of child development and child rearing principles (Cross and Lewis 1998). Research also indicates income of the family in childhood also matters for outcomes in later life. Ball and Wilson (2002, 93) state:
While not the only factor that shapes attainments and abilities, parental income in childhood has small to modest positive associations with cognitive development and levels of attainment as children move through the school system, and with employment propensities and incomes in adulthood, even after controlling for related factors such as maternal education. Importantly, it appears likely that the level of parental income has a strong effect on a child’s outcomes where income is low, and that the effects are even stronger where income is low for long periods.
It is important that government policy protects the quality of life of children in low income families by supporting families generally and more specifically by providing targeted programs of assistance in local areas of need. The continual monitoring of the spatial distribution of fertility is important in assisting in this endeavour.
Appendices 330
Appendix 4.1: Boundary Changes Affecting Comparability of Areas Over Time SLA/SSD Changes in Boundaries
Adelaide (c) The ABS divides the Adelaide SLA into four CD sub-divisions. Region 80 These divisions are comparable 1976 to 1986 but between the 1986 and 1996 Census the boundaries were reconfigured. The changes made direct comparison over time impossible so the Adelaide SLA was taken as one region in this study.
East Torrens Although covering a large land area East Torrens is only one sub- (DC) division of CDs. Region 94 3.8.1978 – small area transferred to Onkaparinga (DC) resulting in the loss of approximately 113 persons.
Elizabeth (c) 1.1.1991 – 0.8 of a hectare transferred to Munno Para (c) Regions 1-5 resulting in the loss of approximately 60 persons.
Happy Valley (c) Before 3.11.1983 Happy Valley was known as Meadows (pt A). With the reconstitution from Meadows to Happy Valley there was a slight change in the southern boundary between Happy Valley and Meadows (rural). It appears the change at the time had negligible impact on population numbers. In 1976 the ABS divided the whole SLA into two sub-divisions. By 1986 the SLA was split into three sub-divisions. With the significant developments in population, land development and housing stock by 1996 the ABS had redesigned the CDs into five regions. In this study 1986 and 1996 regions were used to redesign the area into four regions.
Hindmarsh (m) - 1.7.1994 – Hindmarsh SLA amalgamated with Woodville SLA to Woodville form the SLA of Hindmarsh and Woodville SLA. With the amalgamation the ABS changed the CD boundaries along the border of the separate Hindmarsh and Woodville SLA boundaries.
Gawler/Munno Gawler includes the area called Mudla Wirra (pt A) 1976 and Para renamed Light (pt A) 1977. Region 6 13.3.1985 – The ABS changes the official boundaries of Gawler. The ABS officially includes Light (pt A) as well as two small parts of Light (pt B). The ABS estimated the transfer of the area of Light (pt B) involved approximately 80 persons. In this study data was received separately for the CDs of Light (pt B) to transfer to Gawler to account, if necessary, for variations due to boundary changes. The ABS also transferred part of Barossa (DC) to Gawler. The ABS estimated this transfer involved 360 persons. This area was given its own CD number and in this study data was received separately so it could be assigned to either Gawler or Barossa. Appendices 331
SLA/SSD Changes in Boundaries Gawler/Munno The ABS also transferred part of Munno Para (m) to Gawler. The Para ABS estimated this boundary change involved approximately Region 8 3720 persons. The change in boundary involved a number of CDs. Some Munno Para CDs were completely transferred while only parts of other CSs were taken. To overcome this significant change in boundary, region 8 includes parts of Gawler and all the CDs split between the two SLAs.
Gumeracha At the 1981 Census Part A of Gumeracha was included in the Adelaide Statistical Division. For all Censuses looked at in this study the whole of Gumeracha has been included in non- metropolitan South Australia.
Marion (c) 1.7.1988 – Marion SLA lost 15 hectares to Noarlunga (c). The Region 135 ABS estimated a loss of 24 persons.
Noarlunga (c) 1.1.1987 – The ABS transferred 342 hectares (around 240 Region 154 persons) to Willunga (DC). In this study this area for the 1996 Census was transferred back to Noarlunga. Region 150 1.7.1988 – Noarlunga gained 15 hectares (24 persons) from Marion SLA.
Onkaparinga At the 1981 Census Part A of Onkaparinga SLA was included in (DC) the Adelaide Statistical Division. For all Censuses looked at in this study the whole of Onkaparinga has been included in non- metropolitan South Australia.
Port Adelaide (c) 26.8.1982 – The ABS transferred one hectare to Woodville affecting approximately 50 persons.
Salisbury (c) 1.7.1990 – Salisbury SLA lost 5.4 hectares to Tea Tree Gully SLA and gained 4.1 hectares from Tea Tree Gully SLA. This boundary change involved a loss of approximately 20 persons from Salisbury.
Tea Tree Gully 1.7.1990 – Tea Tree Gully SLA lost 4.1 hectares to Salisbury (c) SLA and gained 5.4 hectares from Salisbury SLA. This boundary change involved a gain of approximately 20 persons to Tea Tree Gully SLA. Region 29 3.7.1976 – The ABS transferred the ‘Hills’ ward to Gumeracha (DC). The ABS estimated this change involved approximately 732 persons. Approximately 640 of these people were in two CDs that were completely transferred. About one-fifth of the land area of a third CD was annexed which would have included about 92 people of the 704 in the CD. These 92 persons were left in Tea Tree Gully in this study.
Appendices 332
SLA/SSD Changes in Boundaries Willunga (DC) In 1976 and 1981 Willunga (DC) was divided into two parts, an Adelaide Statistical Division part and a non-metropolitan part. Between the 1981 and 1986 Censuses the ASD boundary was extended to include the whole of Willunga (DC). For the 1976 and 1981 Censuses separate figures were obtained for part B of Willunga.
Barossa SSD Includes Gumeracha (DC) at all Censuses. 3.8.85 – The ABS transferred part of Barossa to Gawler SLA. The ABS estimated this transfer involved approximately 360 persons. This transferred area was given its own CD number and in this study data was received separately for this area so it could be assigned to either Gawler or Barossa.
Onkaparinga, 3.8.1978 – A small area of East Torrens (DC) transferred to Fleurieu and Onkaparinga affecting approximately 113 persons. Kangaroo Island 1976 to 1996 – Willunga (pt B) included in the Metropolitan SSD Region.
Lower North 1.7.1988 – The SLA of Redhill (DC) was amalgamated with SSD Crystal Brook (DC) to form Crystal Brook-Redhill (DC). The boundary between the Lower North SSD and Pirie SSD altered to accommodate this amalgamation with Redhill being transferred to the Pirie SSD. The ABS estimated this change affected approximately 520 persons. 1.7.1992 – The SLA of Truro was amalgamated with a major part of Ridley (DC) to form Ridley-Truro (DC). This amalgamation resulted in an adjustment of the SSD boundary between the Riverland SSD and the Lower North SSD with Truro shifting from the Riverland SSD to the Lower North SSD. The ABS estimated this change affected approximately 850 people.
Riverland SSD 1.7.1992 – The SLA of Truro was amalgamated with a major part of Ridley (DC) to form Ridley-Truro (DC). This amalgamation resulted in an adjustment of the SSD boundary between the Riverland SSD and the Lower North SSD with Truro shifting from the Riverland SSD to the Lower North SSD. The ABS estimated this change affected approximately 850 people.
Whyalla SSD At the 1981 Census Whyalla SSD included some of Port Augusta. Population involved relatively small. Just for the 1986 Census a new subdivision of Lake Gilles was created and comprised all of the unincorporated area formerly in the Whyalla Sub-division. In this study Lake Gilles was amalgamated with the rest of the Whyalla SSD to make it comparable over time.
Appendices 333
SLA/SSD Changes in Boundaries Flinders Ranges In 1976 Port Augusta (c) LGA was contained wholly within the SSD Flinders Ranges SSD. By 1981 the LGA of Port Augusta had been expanded by the acquisition of part of Kanyaka-Quorn LGA from the Flinders Ranges SSD (+960 persons), part of Port Germein (Pirie SSD), part of Wilmington (Flinders Ranges SSD) and part of Unincorporated. The ABS estimated Port Augusta’s population increased by approximately 1490 persons. Port Augusta LGA was then divided between three SSDs - Whyalla, Pirie and the Flinders Ranges although around 95 percent of the population of Port Augusta was still in the Flinders Ranges SSD. Between the 1981 and 1986 Censuses all of Port Augusta’s land area was transferred back to the Flinders Ranges SSD. Between the 1976 and 1981 Censuses Flinders Ranges SSD gained part of the Far North SSD- (population change probably inconsequential). Between 1981 and 1986 the boundary between Pirie SSD and Flinders Ranges SSD altered so Flinders Ranges included the whole of the Mount Remarkable (DC)- (see Pirie SSD notes). 1.5.1988 – Rocky River (DC) gained 10680 hectares or 55 persons from Pirie (DC) in Pirie SSD.
Pirie SSD In 1976 Pirie SSD included Port Germein (DC) and Flinders Ranges SSD included Wilmington (DC). The boundary between these two adjoining LGAs was the boundary between Pirie and Flinders Ranges SSDs. Between 1976 and 1981 Port Germein (DC) lost a small area and population to the enlarged area of Port Augusta and the rest of the LGA was amalgamated with Wilmington (DC) to form Mount Remarkable (DC). The area of Mount Remarkable (approximately 20 per cent) that was previously Port Germein remained in the Pirie SSD and the Wilmington part remained in Flinders Ranges SSD. By the 1986 Census the boundary between the Pirie and Flinders Ranges SSDs was changed to include the whole of Mount Remarkable in the Flinders Ranges SSD. 1.7.1988 – Redhill (DC) was amalgamated with Crystal Brook (DC) to form Crystal Brook-Redhill (DC). The boundary between Pirie SSD and the Lower North SSD was altered to accommodate this amalgamation. The ABS estimated a gain of 520 persons. Between 1986 and 1996 Pirie (DC) (Pirie SSD) lost 10680 hectares (55 persons) to Rocky River (DC) (Flinders Ranges SSD).
Far North SSD Between 1976 and 1981 the Far North SSD lost a small amount of land and probably negligible population to the Flinders Ranges SSD.
Source: ABS 1982, 1987, 1990, 1991b, 1994, 1996c
Appendices 334
Appendix 4.2:
Metropolitan Adelaide: Customised Areas and Statistical Local Areas
Appendices 335
Statistical Local Areas
Appendices 336
Appendix 4.3:
Suburbs Included in Customised Areas Metropolitan Adelaide Area SLA Name of Suburb Number 1 Elizabeth Downs, Elizabeth North, Elizabeth Elizabeth Park (pt NW) 2 Elizabeth (pt N), Elizabeth Park (pt SE), Elizabeth Elizabeth West 3 Elizabeth Elizabeth East, Elizabeth Park (pt) 4 Elizabeth Elizabeth (pt),Elizabeth Grove, Elizabeth South 5 Elizabeth Elizabeth South, Elizabeth Vale
6 Gawler, Munno Para Gawler, Gawler East (pt), Gawler South, Gawler West, Willaston 7 Gawler, Munno Para Barossa (pt), Gawler East (pt) 8 Gawler, Munno Para Bibaringa, Blakeview, Dalkeith, Evanston Gardens, Evanston Park, Evanston South, Kudla, Munno Para Downs, Munno Para West, Uleybury, Yattalunga 9 Gawler, Munno Para Andrews Farm, Angle Vale, Buckland Park, Hillier, McDonald Park, Munno Para Downs, Penfield, Penfield Gardens, Virginia 10 Gawler, Munno Para Davoren Park, Elizabeth Field, Elizabeth West (pt N) 11 Gawler, Munno Para Elizabeth Downs (pt N), Smithfield, Smithfield Plains 12 Gawler, Munno Para Craigmore (pt S), Mt Crawford, One Tree Hill, Para Wirra Recreation Ground
13 Salisbury Salisbury North, Defence Science and Technology Organisation 14 Salisbury Paralowie 15 Salisbury Elizabeth Vale, Salisbury (pt W), Salisbury North 16 Salisbury Salisbury East, Salisbury Heights, Salisbury Park 17 Salisbury Brahma Lodge, Salisbury (pt E), Salisbury Plain 18 Salisbury Salisbury (pt W) 19 Salisbury Salisbury Downs 20 Salisbury Parafield Gardens (pt) 21 Salisbury Bolivar, Burton, Cavan, Direk, Dry Creek, Edinburgh, Greenfields, Parafield Airport, Parafield Gardens (pt), Paralowie, Pooraka, St Kilda, The Levels, Waterloo Corner 22 Salisbury Salisbury East
Appendices 337
Area SLA Name of Suburb Number 23 Salisbury Para Hills (pt W), Para Hills West (pt N), Pooraka (pt NE) 24 Salisbury Para Hills (pt S), Para Hills West (pt S) 25 Salisbury Ingle Farm (pt N), Para Vista (pt N) 26 Salisbury Ingle Farm, Para Vista (pt S), Valley View (pt W) 27 Salisbury Ingle Farm (pt S), Pooraka, Walkley Heights
28 Tea Tree Gully Modbury Heights, Para hills (pt SE), Wynn Vale (pt SW) 29 Tea Tree Gully Anstey Hill, Banksia Park (pt E), Fairview Park (pt E), Golden Grove, Greenwith, Modbury Heights (pt E), Oaklands, Petworth, Salisbury Heights, Upper Hermitage, Wynn Vale (pt), Yatala Vale 30 Tea Tree Gully Modbury, Modbury North, Ridgehaven (pt W), St Agnes (pt NE) 31 Tea Tree Gully Gilles Plains (pt E), Highbury (pt NW), Holden Hill, Hope Valley (pt SW), Hope Valley Reservoir 32 Tea Tree Gully Holden Hill (pt N), Hope Valley (pt W), Modbury (pt S), St Agnes (pt SW) 33 Tea Tree Gully Highbury, Hope Valley (pt E), Ridgehaven (pt S), St Agnes 34 Tea Tree Gully Ridgehaven (pt N), Redwood Park, Surrey Downs 35 Tea Tree Gully Banksia Park (pt most), Fairview Park (pt most), Tea Tree Gully, Vista
36 Enfield Angle Park, Mansfield Park, Regency Park, Wingfield, Woodville gardens (pt N) 37 Enfield Croydon Park (pt NW), Ferryden Park, Woodville Gardens 38 Enfield Croydon Park (pt SE), Devon Park, Dudley Park 39 Enfield Kilburn 40 Enfield Blair Athol (pt N), Clearview (pt N), Dry Creek, Enfield (pt N), Gepps Cross 41 Enfield Blair Athol (pt S), Broadview (pt N), Clearview (pt S), Enfield (pt S) 42 Enfield Greenacres (pt S), Manningham, Sefton Park 43 Enfield Gilles Plains (pt N), Hillcrest, Oakden 44 Enfield Greenacres (pt NW), Northfield, Valley View (pt W) 45 Enfield Windsor Gardens 46 Enfield Klemzig
Appendices 338
Area SLA Name of Suburb Number 47 Henley and Grange Grange, Henley Beach (pt NW) 48 Henley and Grange Henley Beach (pt), Henley Beach South (pt W) 49 Henley and Grange Henley Beach South (pt), West Beach (pt W)
50 Hindmarsh and Brompton (pt E), Bowden, Hindmarsh, West Woodville Hindmarsh 51 Hindmarsh and Brompton (pt), Croydon, Devon Park (pt Woodville small), Renown Park, Ridleyton 52 Hindmarsh and Royal Park, Semaphore Park, Tennyson, Woodville West Lakes, West Lakes Shore 53 Hindmarsh and Albert Park, Cheltenham, Hendon (pt N), Woodville Woodville 54 Hindmarsh and Athol Park, Cheltenham (pt), Pennington, Woodville Woodville North (pt N) 55 Hindmarsh and Hendon (pt), Royal Park (pt SE), Seaton Woodville 56 Hindmarsh and Findon, Seaton Woodville 57 Hindmarsh and Kilkenny (pt), Woodville North (pt S), Woodville Woodville Park 58 Hindmarsh and Findon (pt N), Kilkenny (pt), Woodville (pt Woodville S), Woodville Park (pt S), Woodville West (pt) 59 Hindmarsh and Allenby Gardens (pt), Beverley (pt), Woodville Kilkenny (pt), West Croydon 60 Hindmarsh and Allenby Gardens (pt N), Beverley (pt S), Woodville Findon (pt S), Welland (pt S) 61 Hindmarsh and Allenby Gardens (pt S), Flinders Park, Woodville Kidman Park (pt E), Welland (pt S) 62 Hindmarsh and Fulham Gardens, Kidman Park (pt SW) Woodville
63 Port Adelaide Birkenhead, Largs North, Peterhead 64 Port Adelaide Taperoo 65 Port Adelaide North Haven, Osborne, Outer Harbor 66 Port Adelaide Ethelton, Exeter, Glanville, Largs Bay (pt S), Semaphore, Semaphore South 67 Port Adelaide Largs North (pt N) 68 Port Adelaide Alberton, Port Adelaide (pt), Queenstown, Rosewater (pt W) 69 Port Adelaide Gillman, Ottoway, Port Adelaide, Rosewater
70 Thebarton Mile End (pt E), Thebarton, Torrensville (pt N) 71 Thebarton Mile End (pt), Torrensville (pt S)
Appendices 339
Area SLA Name of Suburb Number 72 West Torrens Brooklyn Park, Lockleys (pt E), Underdale 73 West Torrens Fulham, Lockleys, West Beach (pt E) 74 West Torrens Adelaide Airport, Brooklyn Park, Richmond (pt SW), West Beach, West Richmond 75 West Torrens Cowandilla, Hilton, Keswick Terminal, Mile End (pt S), Richmond, Torrensville (pt SW) 76 West Torrens Camden Park, Glenelg North (pt E), Novar Gardens, West Beach (sewage works, caravan parks) 77 West Torrens Camden Park (pt), Plympton North, Plympton (pt W) 78 West Torrens Marleston, Netley, North Plympton (pt E) 79 West Torrens Ashford, Keswick, Kurralta Park, Keswick, Marleston (pt E)
80 Adelaide Adelaide, North Adelaide
81 Burnside Beulah Park, Hazelwood Park, Kensington Park, Leabrook, Tusmore (pt E) 82 Burnside Kensington Gardens, Magill (pt SE), Rosslyn Park (pt W) 83 Burnside Beaumont, Burnside, Glen Osmond, Hazelwood Park (pt S), Leawood Gardens (pt W), Linden Park, Mount Osmond, St Georges, Stonyfell (pt SE), Waterfall Gully 84 Burnside Burnside (pt N), Erindale, Rosslyn Park (pt E), Stonyfell (pt W), Wattle Park 85 Burnside Dulwich, Eastwood, Glenside (pt W), Rose Park, Toorak Gardens, Tusmore (pt W) 86 Burnside Frewville, Glen Osmond (small pt W), Glenside (pt E), Glenunga
87 Campbelltown Campbelltown (pt W), Paradise 88 Campbelltown Campbelltown (pt E) 89 Campbelltown Athelstone (pt SW), Newton (pt E), Paradise 90 Campbelltown Athelstone 91 Campbelltown Rostrevor 92 Campbelltown Hectorville, Magill (pt N), Tranmere (pt N) 93 Campbelltown Magill (pt S), Tranmere (pt S)
94 East Torrens Ashton, Auldana, Basket Range, Black Hill Conservation Park, Carey Gully (pt), Castambul, Cherryville, Cleland Conservation Park, Greenhill, Marble Hill, Montacute, Morialta Conservation Park, Mt Crawford Forest, Norton Summit, Piccadilly (pt), Skye, Summertown, Teringe, Uraidla, Woodforde Appendices 340
Area SLA Name of Suburb Number 95 Kensington and Kent Town, Norwood (pt) Norwood 96 Kensington and Heathpool, Kensington, Marryatville, Norwood Norwood (pt SE)
97 Payneham Joslin, Marden, Payneham (pt W), Royston Park 98 Payneham Felixtow, Firle (pt N), Glynde, Payneham (pt E) 99 Payneham Firle (pt S), Payneham South, St Morris, Trinity Gardens
100 Prospect Prospect (pt N) 101 Prospect Fitzroy, Prospect (pt S), Thorngate 102 Prospect Broadview (pt S), Collingswood, Medindie Gardens, Nailsworth, Sefton Park (pt S)
103 St Peters Evandale, Maylands, Stepney (pt) 104 St Peters College Park, Hackney, St Peters, Stepney (pt W)
105 Stirling Aldgate (pt S), Crafers (pt S), Dorset Vale, Heathfield, Ironbank (pt), Longwood, Scott Creek, Stirling, Upper Sturt 106 Stirling Aldgate (pt E), Bridgewater (pt S), Mylor, Verdun 107 Stirling Aldgate (pt N), Bridgewater (pt N), Carey Gully (pt), Crafers, Piccadilly (pt), Stirling (pt N)
108 Unley Fullarton (pt E), Myrtle Bank 109 Unley Fullarton (pt W), Malvern (pt N), Parkside (pt E), Unley (pt E) 110 Unley Hyde Park (pt N), Parkside (pt W), Unley (pt) 111 Unley Goodwood (pt E), Millswood (pt E), Unley Park (small pt to NW), Wayville (pt E) 112 Unley Fullarton (pt S), Highgate, Hyde Park (pt S), Kings Park, Malvern (pt), Unley Park (pt) 113 Unley Clarence Park (pt E), Forestville, Keswick (pt SE-Military Barracks), Millswood (pt W), Wayville (pt W) 114 Unley Black Forest, Clarence Park (pt W), Everard Park
115 Walkerville Vale Park, Walkerville
Appendices 341
Area SLA Name of Suburb Number 116 Brighton North Brighton, Somerton Park 117 Brighton Kingston Park, Seacliff, South Brighton 118 Brighton Brighton, Hove, North Brighton (pt small)
119 Glenelg Glenelg North 120 Glenelg Glenelg (pt E), Glenelg East (pt N), Glenelg South (pt N) 121 Glenelg Glenelg East (pt S), Glenelg South (pt S)
122 Happy Valley Happy Valley, Happy Valley Reservoir, O’Halloran Hill (pt E) 123 Happy Valley Flagstaff Hill (pt N, W) 124 Happy Valley Aberfoyle Park, Chandlers Hill (pt W), Coromandel Valley (pt SW), Flagstaff Hill (pt SE) 125 Happy Valley Baker Gully, Chandlers Hill (pt E), Cherry Gardens, Clarendon, Coromandel East, Coromandel Valley (pt E), Kangarilla, Wickham Hill
126 Marion Glengowrie, Morphettville (pt E) 127 Marion Marion (pt N), Oaklands Park (pt N), Warradale (pt N) 128 Marion Morphettville (pt N), Parkholme (pt N), Plympton Park 129 Marion Ascot Park (pt N), Edwardstown (pt N), Glandore (pt S), South Plympton 130 Marion Ascot Park (pt SE), Edwardstown (pt- middle), Morphettville (pt E), Parkholme (pt S) 131 Marion Clovelly Park (pt N), Edwardstown (pt S), Marion (pt S), Mitchell Park (pt N) 132 Marion Clovelly Park (pt S), Mitchell Park (pt S) 133 Marion Bedford Park (pt W), Dover Gardens (pt N), Marion (pt S), Oaklands Park (pt S), Seacombe Gardens (pt N), Sturt (pt-most), Warradale (pt S) 134 Marion Dover Gardens (pt S), Seacombe Gardens (pt S), Seacombe Heights (pt NE), Sturt (pt-SW corner) 135 Marion Darlington, Hallett Cove, Marino, O’Halloran Hill, Seacliff Park (pt S), Seacombe Heights (pt S), Seaview Downs, Sheidow Park, Trott Park
136 Mitcham Clarence Gardens (pt N), Cumberland Park, Daw Park (pt N) 137 Mitcham Clarence Gardens (pt S), Colonel Light Appendices 342
Area SLA Name of Suburb Number Gardens (pt W), Daw Park (pt-most), Melrose Park, Pasadena (pt N), St Marys (pt N) 138 Mitcham Colonel Light Gardens (pt E), Lower Mitcham 139 Mitcham Hawthorn, Lower Mitcham (pt NE), Westbourne Park 140 Mitcham Kingswood, Mitcham (pt N), Netherby, Torrens Park (pt N), Urrbrae (pt W) 141 Mitcham Belair (pt NW), Clapham (pt E), Lower Mitcham (pt E), Lynton, Torrens Park (pt) 142 Mitcham Clapham (pt W), Panorama (pt N) 143 Mitcham Belair (pt), Belair National Park, Blackwood (pt N), Brownhill Creek, Mitcham (pt E), Springfield, Urrbrae (pt E) 144 Mitcham Blackwood (pt E), Coromandel Valley (pt NW), Glenalta (pt E), Hawthorndene, Upper Sturt 145 Mitcham Eden Hills (pt W), Panorama (pt S), Pasadena (pt S), St Marys (pt S) 146 Mitcham Bedford Park, Bellevue Heights, Blackwood (pt W), Eden hills (pt S)
147 Noarlunga Christies Beach (pt S), Onkaparinga Estuary, Port Noarlunga, Port Noarlunga South (pt N), Seaford Meadows 148 Noarlunga Christies Beach (pt N), O’Sullivan Beach 149 Noarlunga Christies Downs, Hackham West (pt N), Lonsdale, Morphett Vale (pt W), Noarlunga Centre, Noarlunga Downs (pt N) 150 Noarlunga Reynella (pt SE), Morphett Vale (pt W) 151 Noarlunga Morphett Vale (pt W, S) 152 Noarlunga Morphett Vale (pt E), Onkaparinga Hills (pt NW) 153 Noarlunga North-Happy Valley (pt S), Morphett Vale (pt E), Old Reynella, Onkaparinga Hills (pt E), Reynella (pt E), Reynella East (pt S), Woodcroft; South-Blewitt Springs, Hackham (pt N), Hacham West (pt), McLaren Vale area, Noarlunga Downs (pt) 154 Noarlunga Landcross Farm, McLaren Vale area and town, Moana, Old Noarlunga, Port Noarlunga South (pt S), Seaford, Seaford Heights, Seaford Rise
155 Willunga Aldinga, Aldinga Beach, Mclaren Vale area, Maslins Beach, Port Willunga, Willunga (pt W) Appendices 343
Area SLA Name of Suburb Number 156 Willunga Hope Forest, Munetta, Pages Flat, Sellicks Beach, Sellicks Hill, Willunga area and town, Yundi
Appendices 344
Appendix 4.4: Non-Metropolitan South Australia, Statistical Sub Divisions
Appendices 345
Appendix 4.5: Non-Metropolitan South Australia, Membership of Section of State Categories by Statistical Sub Division SSD Urban Name of Urban Centre Centre Size Census (Persons) 1976 1981 1986 1996 Barossa 1000-4999 Angaston Angaston Angaston Angaston Kapunda Kapunda Kapunda Freeling Nuriootpa Nuriootpa Nuriootpa Lyndoch Tanunda Tanunda Tanunda Nuriootpa Tanunda Williamstown
Rural Birdwood Birdwood Birdwood Birdwood Localities Freeling Freeling Freeling Greenock Greenock Greenock Greenock Gumeracha Gumeracha Gumeracha Gumeracha Houghton Lyndoch Lyndoch Lyndoch Kersbrook Mallala Mallala Mallala Mallala Mt Pleasant Mt Pleasant Mt Pleasant Mt Pleasant Two Wells Mt Torrens Mt Torrens Mt Torrens Williamstown Two Wells Two Wells Roseworthy Williamstown Williamstown Springton Two Wells Wasleys
Onkaparinga, 5000-9999 Mt Barker Fleurieu, Victor Harbor Kangaroo Island
1000-4999 Goolwa Goolwa Goolwa Goolwa Kingscote Harndorf Harndorf Harndorf Lobethal Kingscote Kingscote Kingscote Mt Barker Lobethal Lobethal Lobethal Strathalbyn Mt Barker Mt Barker Nairne Victor Harbor Strathalbyn Pt Elliot Pt Elliot Victor Harbor Strathalbyn Strathalbyn Victor Harbor Woodside
Rural Balhannah Balhannah Balhannah Balhannah Localities Brukunga Brukunga Brukunga Carricalinga Echunga Echunga Carricalinga Echunga Harndorf Meadows Echunga Macclesfield Meadows Nairne Macclesfield Meadows Nairne Normanville Meadows Middleton Normanville Oakbank Middleton Mt Compass Oakbank Pt Elliot Normanville Normanville Pt Elliot Woodside Oakbank Oakbank Woodside Yankalilla Yankalilla Yankalilla Yankalilla
Appendices 346
SSD Urban Name of Urban Centre Centre Size Census (Persons) 1976 1981 1986 1996 Yorke 1000-4999 Kadina Kadina Kadina Kadina Maitland Maitland Maitland Maitland Moonta Moonta Moonta Moonta Wallaroo Wallaroo Wallaroo Wallaroo Ardrossan
Rural Ardrossan Ardrossan Ardrossan Bute Localities Bute Bute Bute Edithburgh Edithburgh Edithburgh Edithburgh Minlaton Minlaton Minlaton Minlaton Pt Broughton Pt Broughton Pt Broughton Pt Broughton Pt Victoria Pt Victoria Pt Victoria Pt Victoria Pt Vincent Pt Vincent Pt Vincent Pt Vincent Stansbury Stansbury Stansbury Stansbury Warooka Warooka Warooka Warooka Yorketown Yorketown Yorketown Yorketown
Lower North 1000-4999 Balaklava Balaklava Balaklava Balaklava Burra Burra Burra Burra Clare Clare Clare Clare
Rural Auburn Auburn Auburn Auburn Localities Blyth Blyth Blyth Blyth Brinkworth Brinkworth Eudunda Eudunda Eudunda Eudunda Hamley Hamley Hamley Hamley Bridge Bridge Bridge Bridge Owen Owen Pt Wakefield Pt Wakefield Pt Wakefield Pt Wakefield Riverton Riverton Riverton Riverton Saddleworth Saddleworth Saddleworth Saddleworth Snowtown Snowtown Snowtown Snowtown Spalding Spalding Spalding Spalding
Murray 5000-9999 Murray Murray Bridge Murray Bridge Murray Mallee Bridge Bridge
1000-4999 Mannum Mannum Mannum Mannum Tailem Bend Tailem Bend Tailem Bend Tailem Bend
Rural Coonalypn Coonalypn Coonalypn Blanchetown Localities Karoonda Karoonda Karoonda Callington Lameroo Lameroo Lameroo Coonalypn Meningie Meningie Meningie Karoonda Pinnaroo Pinnaroo Pinnaroo Lameroo Tintinara Swan Reach Swan Reach Meningie Tintinara Tintinara Pinnaroo Swan Reach Tintinara
Appendices 347
SSD Urban Name of Urban Centre Centre Size Census (Persons) 1976 1981 1986 1996 Riverland 1000-4999 Barmera Barmera Barmera Barmera Berri Berri Berri Berri Loxton Loxton Loxton Loxton Renmark Renmark Renmark Renmark Waikerie Waikerie Waikerie Waikerie
Rural Glossop Glossop Moorook Cobdogla Localities Moorook Moorook Morgan Moorook Morgan Morgan Paringa Morgan Paringa Paringa Paringa
Upper South 1000-4999 Bordertown Bordertown Bordertown Bordertown East Keith Keith Keith Keith Kingston Kingston Kingston Kingston Naracoorte Naracoorte Naracoorte Naracoorte
Rural Lucindale Lucindale Lucindale Lucindale Localities Robe Robe Robe Robe
Lower South Over 10 000 Mt Gambier Mt Gambier Mt Gambier Mt Gambier East
5000-9999 Millicent Millicent Millicent
1000-4999 Penola Penola Penola Millicent Penola
Rural Beachport Beachport Beachport Beachport Localities Kalangadoo Kalangadoo Kalangadoo Kalangadoo Mt Burr Mt Burr Mt Burr Mt Burr Nangwarry Nangwarry Nangwarry Nangwarry Pt Pt Pt Pt MacDonnell MacDonnell MacDonnell MacDonnell Tantanoola Tantanoola Tantanoola Tantanoola Tarpeena Tarpeena Tarpeena Tarpeena Lincoln Over 10 000 Pt Lincoln Pt Lincoln Pt Lincoln Pt Lincoln
1000-4999 Tumby Bay
Rural Cleve Cleve Cleve Cleve Localities Cowell Cowell Coffin Bay Coffin Bay Cummins Cummins Cowell Cowell Kimba Kimba Elliston Cummins Lock Lock Kimba Elliston Minnipa Tumby Bay Lock Kimba Tumby Bay Wudinna Tumby Bay Pt Neill Wudinna Wudinna Wudinna
Appendices 348
SSD Urban Name of Urban Centre Centre Size Census (Persons) 1976 1981 1986 1996 West Coast 1000-4999 Ceduna Ceduna Ceduna Ceduna Streaky Bay Streaky Bay
Rural Streaky Bay Streaky Bay Yalata Localities
Whyalla Over 10 000 Whyalla Whyalla Whyalla Whyalla
Rural Iron Baron Iron Baron Iron Knob Iron Knob Localities Iron Knob Iron Knob
Pirie Over 10 000 Pt Pirie Pt Pirie Pt Pirie Pt Pirie
1000-4999 Crystal Brook Crystal Brook Crystal Brook Crystal Brook Jamestown Jamestown Jamestown Jamestown Peterborough Peterborough Peterborough Peterborough
Rural Gladstone Gladstone Gladstone Gladstone Localities Laura Laura Laura Laura Orroroo Napperby Napperby Napperby Terowie Orroroo Orroroo Orroroo
Flinders Over 10 000 Pt Augusta* Pt Augusta Pt Augusta Pt Augusta Ranges
1000-4999 Leigh Creek Leigh Creek Leigh Creek Leigh Creek Quorn Quorn Quorn Quorn
Rural Booleroo Booleroo Booleroo Booleroo Localities Centre Centre Centre Centre Hawker Hawker Hawker Hawker Maree Wilmington Pt Germain Pt Germain Wilmington Wirrabara Wilmington Wilmington Wirrabara Wirrabara Wirrabara
Far North 1000-4999 Coober Pedy Coober Pedy Coober Pedy Coober Pedy Woomera Woomera Woomera Woomera Roxby Downs
Rural Andamooka Andamooka Andamooka Andamooka Localities Oodnadatta Mintabie Minitabie Amata Fregon Indulkana Marla
Source: Bell 1997b
Appendices 349
Appendix 4.6: Information on Census Variables Included in Correlation/Regression Analyses
Demographic Variables
Marital Status This variable has remained much the same from one Census to another and so is directly comparable over time.
Aboriginality Since being included in Australian Censuses in 1966 this variable has remained the same. For the first time in 1996 Indigenous people could record their origin as both Aboriginal and Torres Strait Islander. Prior to this only one or the other could be recorded.
Socio-economic Variables
Education A number of questions are asked in the Census to gather information about a person’s education. Two variables are included in this study. The variable ‘age left school’ has remained the same over time and therefore is directly comparable over inter-censal periods. The variable ‘highest qualification obtained’ refers to the highest qualification obtained by a person since leaving school. In its current format it has been included in all Censuses since 1966. It is directly comparable over time.
Labour Force A number of questions are asked in the Census to establish labour force status. The ways in which the data is coded has varied from Census to Census. For example in Censuses prior to 1986 if a labour force question was unanswered an attempt was made to derive labour force status from other questions on the Census form; where this was not possible, a response was randomly allocated. Since 1991 no random allocation has been made and such cases are coded as ‘not stated’. In the basic profiles for geographic areas released by the ABS this variable is cross referenced with age.
Appendices 350
Income Three variables are available from the Census to measure income. This study utilises individual income and household income as family income was defined differently across the Censuses. The parameters of the income categories are revised at each Census to accurately represent the increased earning capacity of the population and are therefore not directly comparable over time. The individual income categories chosen for this study correspond to the categories that delimit approximately the upper 20 per cent of personal incomes in the State at the time of the Census while the household income categories delimit approximately the upper and lower 20 per cent of household income for the State.
Occupation The occupation categories for 1981 and in previous Censuses were classified according to the ‘Classification and Classified list of Occupations (CCLO)’. Since the 1986 Census occupation data has been coded from the Australian Standard Classification of Occupations (ASCO). The 1986 and 1996 Census categories are not directly comparable with the 1981 classifications.
Industry This variable describes the type of industry in which employed persons aged 15 years and over worked at the time of the Census. The industry classification is based on the Australian Standard Industrial Classification (ASIC). The major categories have remained much the same over time.
Nature of Occupancy This variable indicates whether households were renting, purchasing or owned the dwelling in which they were enumerated. This variable is comparable from Census to Census.
Dwelling Structure This variable records the type of dwelling structure (separate house, semi-detached house, row or terrace house, medium density housing, flats over three storeys) of all private dwellings. The major categories for this variable have remained consistent over time.
Appendices 351
Cultural Variables
Birthplace In the Census a range of data are collected on the birthplace characteristics of the population. Many of the classifications are comparable over time however the membership of the categories provided in the basic community profiles may change as the size and importance of particular birthplace groups over time increase and decline in the population.
Religion A question on religious denomination has been included in all Australian Censuses but response to the question is optional. The classification of religions has changed over time but for the broad categories included in this study, this should not be a problem.
Mobility The Census includes information on a person’s usual residence one year and five years prior to the Census. The question does not account for numerous moves one year and five years prior to the Census but the data are useful in providing up to date information on the usual resident population of an area and on internal migration patterns.
Index of Relative Socio-Economic Disadvantage This variable was constructed from the following variables in 1996. The variables are grouped by the value of their weight to indicate the contribution of each variable to the index. Variables with a weight between 0.2 and 0.3: per cent of persons aged 15 years and over with no qualification; per cent of families with income less than $15,600; per cent of families with offspring having parental income less than $15,600; per cent of females (in labour force) unemployed; per cent of males (in labour force) unemployed; per cent of employed females classified as ‘labourers and related workers’; per cent of employed males classified as ‘labourers and related workers’; per cent of employed males classified as ‘intermediate production and transport workers; per cent of persons aged 15 and over who left school at or under 15 years of age; per cent of one parent families with dependent offspring only; per cent of households renting (government authority). Variables with a weight between 0.1 and 0.2: per cent of employed females classified as ‘intermediate production and transport workers; per cent of employed females classified as ‘elementary Appendices 352 clerical, sales and service workers’; per cent of employed males classified as ‘tradespersons’; per cent of persons aged 15 and over separated or divorced; per cent of dwellings with no motor cars at dwelling; per cent of persons aged 15 and over who did not go to school; per cent Aboriginal and Torres Strait Islanders; per cent of occupied private dwellings with two or more families; per cent lacking fluency in English.
Index of Economic Resources This variable was constructed from the following variables in 1996. The variables are grouped by the value of their weight to indicate the contribution of each variable to the index. Variables with a weight between 0.2 and 0.4: per cent of households owning or renting a dwelling; per cent of dwellings with 4 or more bedrooms; per cent of families with family structure other than two parent or single parent with dependent offspring or consisting of a couple only, and income greater than $77,999; per cent of families consisting of a two parent family with dependent offspring, and income greater than $77,999; per cent of families consisting of a couple only, and with income greater than $62,399; per cent of families consisting of a single parent with dependent offspring, with income greater than $31,199; per cent mortgage greater than $1,300 per month; per cent rent greater than $249 per week. Variables with a weight between 0 and 0.2; per cent households purchasing dwelling; per cent households owning dwelling; per cent dwellings with 3 or more motor cars; average number of bedrooms per person. Variables with a weight between –0.2 and 0: per cent of households in improvised dwelling; per cent households renting (government authority; per cent households renting (non-government authority0; per cent dwellings with one or no bedrooms; per cent rent less than $74 per week; per cent families consisting of a single parent with dependent offspring, with income less than $15,600. Variables with weight between –0.3 and –0.2: per cent of families consisting of a couple only, and with income less than $15,600; per cent of families with family structure other than two parent or single parent with dependent offspring or consisting of a couple only, and income less than $26,000; per cent of families consisting of a two parent family with dependent offspring, and income less than $26,000; per cent of dwellings with no motor cars.
Index of Education and Occupation This variable was constructed from the following variables in 1996. The variables are grouped by the value of their weight to indicate the contribution of each variable to the Appendices 353 index. Variables with a weight between 0.2 and 0.4: per cent of employed males classified as ‘professionals’; per cent of employed females classified as ‘professionals’; per cent persons aged 15 and over at CAE or university. Variables with a weight between 0 and 0.2: per cent of employed males classified as ‘associate professionals’; per cent of employed females classified as ‘advanced clerical and social workers’; per cent of employed males classified as ‘advanced clerical and social workers; per cent of employed males classified as ‘intermediate clerical, sales and service workers’. Variables with a weight between –0.2 and 0: per cent of employed females classified as ‘tradespersons’; per cent of employed males classified as ‘tradespersons’; per cent of employed females classified as ‘elementary clerical, sales and service workers’; per cent of employed females classified as ‘intermediate production and transport workers’. Variables with a weight between –0.4 and –0.2: per cent of employed males classified as ‘intermediate production and transport workers’; per cent of employed females classified as ‘labourers and related workers’; per cent of employed males classified as ‘labourers and related workers’; per cent of males (in labour force) unemployed; per cent of females (in labour force) unemployed; per cent of persons aged 15 and over who left school at or under 15 years of age; per cent of persons aged 15 and over with no qualifications.
Accessibility/Remoteness ARIA+ (Accessibility/Remoteness Index of Australia Plus) is an index of remoteness derived from measures of road distance between populated localities and service centres. ARAI+ is a continuous varying index with values ranging from 0 (high accessibility) to 15 (high remoteness). This measure was developed by GISCA (National Centre for Social Applications of GIS) and it is the standard ABS endorsed measure of remoteness.
(www.gisca.adelaide.edu.au/products_services/ariav2-about.html; www.gisca.adelaide.edu.au/web_aria/aria/aria.html
For further information on all Census variables see the Census Dictionary 1986 (ABS 1986b) and the Census Dictionary 1996 (ABS 1996b).
Appendices 354
Appendix 6.1: Non-Metropolitan South Australia: Measures of Social Change Over Time (Components of Social Change) in the Average Number of Children Ever Born for Women Aged 45–49 Years for Section of State by SSD, 1986–1996(a) (Rates per 100 Women) SSD/Section of State 1986–1996 Rates Change 1986 Pred 1996 St Pos Fdb
Barossa UC 1000-4999 275.14 236.22 221.93 -38.92 -14.29 14.27 Rural Balance 280.99 237.94 220.65 -43.05 -17.29 10.13 Onkaparinga, Fleurieu, K.I UC 5000-9999 276.16 236.53 230.96 -39.64 -5.56 13.54 UC 1000-4999 264.07 232.98 216.98 -31.09 -16.00 22.09 Rural Balance 278.86 237.32 217.45 -41.54 -19.87 47.82 Yorke UC 1000-4999 273.08 235.62 236.88 -37.46 1.26 15.72 Rural Balance 299.69 243.42 241.76 -56.26 -1.67 -3.09 Lower North UC 1000-4999 274.49 236.04 236.00 -38.45 -0.03 14.72 Rural Balance 291.71 241.09 245.78 -50.63 4.69 2.55 Murray Mallee UC Over 10000 297.10 242.67 245.72 -54.44 3.06 -1.26 UC 1000-4999 308.70 246.07 250.46 -62.63 4.39 -9.45 Rural Balance 312.21 247.10 248.92 -65.11 1.82 -11.93 Riverland UC 1000-4999 282.24 238.31 244.44 -43.93 6.14 9.25 Rural Balance 290.23 240.65 234.00 -49.58 -6.65 3.60 Upper South East UC 1000-4999 291.90 241.14 252.92 -50.76 11.78 2.42 Rural Balance 289.13 240.33 254.98 -48.80 14.66 4.38 Lower South East UC Over 10000 283.25 238.61 241.43 -44.65 2.82 8.53 UC 5000-9999 313.08 247.35 250.44 -65.73 3.09 -12.55 UC 1000-4999 286.21 239.47 252.78 -46.74 13.31 6.44 Rural Balance 312.43 247.16 256.68 -65.27 9.52 -12.09 Lincoln UC Over 10000 300.40 243.63 243.24 -56.77 -0.40 -3.59 Rural Balance 318.44 248.93 250.11 -69.52 1.19 -16.34 West Coast UC 1000-4999 338.00 254.66 263.53 -83.34 8.87 -30.16 Rural Balance 285.45 239.25 262.26 -46.20 23.01 6.97 Whyalla UC Over 10000 310.10 246.48 241.55 -63.62 -4.92 -10.44 Rural Balance 362.50 261.85 250.00 -100.65 -11.85 -47.48 Pirie UC Over 10000 301.21 243.87 238.71 -57.34 -5.16 -4.16 UC 1000-4999 288.70 240.20 253.49 -48.49 13.29 4.68 Rural Balance (combined with Flinders Ranges) 301.58 243.98 246.36 -57.60 2.39 -4.42 Flinders Ranges UC Over 10000 302.96 244.38 244.62 -58.57 0.24 -5.40 UC 1000-4999 316.67 248.40 227.87 -20.54 -15.08 30.08 Rural Balance (see Pirie) Far North UC 1000-4999 231.51 223.43 236.81 -8.08 1.99 45.10 Rural Balance 307.45 245.70 230.85 -61.75 -3.25 -8.57 (a) 1996 Section of State Categories match 1986 Section of State categories. For the data to be directly comparable in terms of Section of State categories at both Censuses, for Barossa SSD Freeling, Lyndoch and Williamstown move from the urban centre category 1000–4999 in 1996 into rural balance; in the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs Nairne and Woodside move from the urban centre category 1000–4999 in 1996 into rural balance; for Yorke SSD Ardrossan moves from the urban centre category 1000–4999 in 1996 into rural balance; for the Lower South East SSD Millicent moves from the urban centre category 1000–4999 in 1996 into the urban centre category 5000–9999; for the Lincoln SSD Tumby Bay moves from the urban centre category 1000–4999 in 1996 into rural balance; for the West Coast SSD Streaky Bay moves from the urban centre category 1000– 4999 in 1996 to rural balance. St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value Source: ABS calculated from unpublished data Appendices 355
Appendix 6.2: Non-Metropolitan South Australia: Measures of Social Change Over Time (Components of Social Change) in the Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years for Section of State by SSD, 1986–1996(a) (Rates per 100 Women) SSD/Section of State 1986–1996 Rates Change 1986 Pred 1996 St Pos Fdb
Barossa UC 1000-4999 138.86 133.93 126.73 -4.93 -7.20 2.86 Rural Balance 132.02 128.56 129.91 -3.45 1.35 4.34 Onkaparinga, Fleurieu, K.I UC 5000-9999 143.35 137.45 131.16 -5.91 -6.29 1.89 UC 1000-4999 133.24 129.52 131.10 -3.72 1.58 4.07 Rural Balance 135.68 131.44 125.17 -4.25 -6.27 3.55 Yorke UC 1000-4999 142.20 136.54 138.62 -5.66 2.07 2.14 Rural Balance 152.37 144.52 154.97 -7.86 10.46 -0.06 Lower North UC 1000-4999 141.66 136.12 138.31 -5.54 2.18 2.25 Rural Balance 158.82 149.57 153.15 -9.25 3.58 -1.46 Murray Mallee UC Over 10000 148.59 141.55 144.33 -7.04 2.78 0.76 UC 1000-4999 159.42 150.04 146.92 -9.38 -3.12 -1.59 Rural Balance 159.93 150.44 150.68 -9.49 0.25 -1.70 Riverland UC 1000-4999 149.72 142.44 141.01 -7.28 -1.43 0.51 Rural Balance 152.74 144.81 143.38 -7.94 -1.43 -0.14 Upper South East UC 1000-4999 146.29 139.75 140.47 -6.54 0.71 1.25 Rural Balance 155.32 146.83 150.12 -8.49 3.29 -0.70 Lower South East UC Over 10000 147.75 140.89 136.82 -6.85 -4.07 0.94 UC 5000-9999 163.48 153.22 145.82 -10.26 -7.40 -2.47 UC 1000-4999 157.57 148.59 135.33 -8.98 -13.26 -1.19 Rural Balance 151.12 143.54 147.63 -7.59 4.09 0.21 Lincoln UC Over 10000 153.07 145.06 143.55 -8.01 -1.51 -0.21 Rural Balance 161.41 151.60 156.84 -9.81 5.24 -2.02 West Coast UC 1000-4999 170.49 158.71 156.21 -11.77 -2.51 -3.98 Rural Balance 172.85 160.56 160.64 -12.29 0.08 -4.49 Whyalla UC Over 10000 154.07 145.84 146.02 -8.22 0.17 -0.43 Rural Balance 157.07 148.20 158.81 -8.87 10.61 -1.08 Pirie UC Over 10000 145.49 139.12 140.53 -6.37 1.41 1.43 UC 1000-4999 159.08 149.77 157.53 -9.31 7.76 -1.51 Rural Balance (combined with Flinders Ranges) 154.32 146.04 150.34 -8.28 4.30 -0.49 Flinders Ranges UC Over 10000 158.77 149.53 149.29 -9.24 -0.24 -1.45 UC 1000-4999 155.12 146.67 142.52 -8.45 -4.15 -0.66 Rural Balance (see Pirie) Far North 1.38 UC 1000-4999 145.72 139.30 144.84 -6.42 5.53 1.38 Rural Balance 160.98 151.26 142.70 -9.72 -8.56 -1.93 (a) 1996 Section of State Categories match 1986 Section of State categories. For the data to be directly comparable in terms of Section of State categories at both Censuses, for Barossa SSD Freeling, Lyndoch and Williamstown move from the urban centre category 1000–4999 in 1996 into rural balance; in the combined Onkaparinga, Fleurieu and Kangaroo Island SSDs Nairne and Woodside move from the urban centre category 1000–4999 in 1996 into rural balance; for Yorke SSD Ardrossan moves from the urban centre category 1000–4999 in 1996 into rural balance; for the Lower South East SSD Millicent moves from the urban centre category 1000–4999 in 1996 into the urban centre category 5000–9999; for the Lincoln SSD Tumby Bay moves from the urban centre category 1000–4999 in 1996 into rural balance; for the West Coast SSD Streaky Bay moves from the urban centre category 1000– 4999 in 1996 to rural balance. At both censuses Roxby Downs is included in rural balance. St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value Source: ABS calculated from unpublished data Appendices 356
Appendix 7.1: Metropolitan South Australia: Average Number of Children Ever Born to Women Aged 45–49 Years, 1976, 1981, 1986, 1996 Censuses (Rates per 100 Women)
Area 1976 1981 1986 1996 Code Statistical Local Area Av Issue Pop Av Issue Pop Av Issue Pop Av Issue Pop 1 Elizabeth 314.07 292 317.08 281 313.69 241 260.87 207 2 Elizabeth 321.42 161 321.48 135 282.05 117 257.14 105 3 Elizabeth 314.03 113 316.78 149 314.09 149 244.44 144 4 Elizabeth 304.86 200 303.43 175 319.55 133 237.37 99 5 Elizabeth 312.71 271 301.28 235 301.64 183 239.29 140 6 Gawler, Munno Para 317.98 117 309.15 142 285.41 185 226.93 323 8 Gawler, Munno Para 328.55 109 298.56 139 286.85 213 253.09 405 9 Gawler,Munno Para 289.27 63 308.08 99 290.82 98 254.59 196 10 Gawler, Munno Para 325.36 274 331.22 221 319.47 190 257.24 145 11 Gawler, Munno Para 314.07 127 321.57 153 310.71 140 261.75 183 12 Gawler, Munno Para 288.14 17 295.38 65 266.19 139 244.05 336 13 Salisbury 280.31 71 341.94 62 328.13 96 247.20 161 14 Salisbury 306.21 17 322.92 48 285.71 56 229.61 179 15 Salisbury 310.63 154 323.14 121 318.75 144 260.26 151 16 Salisbury 251.62 68 285.00 100 250.75 134 220.67 329 17 Salisbury 283.47 189 293.57 171 285.14 249 244.21 233 18 Salisbury 272.28 75 280.88 68 258.24 91 235.09 114 19 Salisbury 306.66 30 286.54 52 308.79 91 230.22 182 20 Salisbury 312.08 83 284.31 102 288.13 160 238.46 312 21 Salisbury 299.27 49 316.84 95 301.48 135 241.67 360 22 Salisbury 263.02 184 280.95 252 265.52 261 237.75 347 23 Salisbury 303.03 127 281.43 140 264.38 160 219.59 245 24 Salisbury 263.36 204 275.83 211 264.50 231 207.11 225 25 Salisbury 254.22 39 259.04 83 249.15 118 225.65 347 26 Salisbury 304.22 113 291.08 157 261.83 186 222.64 296 27 Salisbury 301.83 172 304.81 187 254.22 249 225.78 287 28 Tea Tree Gully 274.27 58 277.27 88 254.74 137 224.44 311 29 Tea Tree Gully 236.37 57 275.73 103 267.79 149 211.12 1007 30 Tea Tree Gully 281.93 251 276.18 361 251.09 366 214.78 548 31 Tea Tree Gully 286.31 171 297.35 189 259.68 253 222.89 367 32 Tea Tree Gully 255.27 72 284.07 113 246.94 147 228.57 231 33 Tea Tree Gully 270.44 191 278.77 212 249.86 345 216.53 629 34 Tea Tree Gully 280.97 82 267.63 139 259.23 233 211.91 470 35 Tea Tree Gully 315.48 156 272.20 205 256.94 288 215.19 395 36 Enfield 329.74 278 342.33 189 301.69 177 252.91 189 37 Enfield 326.59 209 334.62 104 313.51 111 221.43 154 38 Enfield 272.31 178 282.24 107 271.29 101 206.20 129 39 Enfield 299.44 139 323.68 114 291.18 102 219.30 114 40 Enfield 287.34 408 298.18 220 275.00 164 208.29 205 41 Enfield 254.90 252 263.74 171 270.64 109 219.71 137 42 Enfield 251.39 220 259.62 156 263.27 98 221.28 141 43 Enfield 324.98 199 294.77 172 297.71 131 194.35 177 44 Enfield 295.38 249 308.37 203 260.40 202 214.05 242 45 Enfield 291.76 224 295.50 200 260.65 216 230.57 157 46 Enfield 292.16 238 276.47 221 248.51 202 188.21 195 Appendices 357
Area 1976 1981 1986 1996 Code Statistical Local Area Av Issue Pop Av Issue Pop Av Issue Pop Av Issue Pop 47 Henley & Grange 320.77 140 267.15 137 222.66 128 188.51 174 48 Henley & Grange 263.59 163 266.90 145 227.05 122 197.18 177 49 Henley & Grange 278.99 160 261.70 141 237.70 122 191.07 112 50 Hindmarsh, Woodville 281.21 97 255.67 97 250.72 69 189.52 105 51 Hindmarsh, Woodville 270.70 114 273.58 106 256.10 82 177.70 139 52 Hindmarsh, Woodville 268.30 294 270.93 344 257.28 515 205.37 913 53 Hindmarsh, Woodville 283.23 172 261.42 127 272.03 118 182.94 170 54 Hindmarsh, Woodville 304.69 169 294.26 122 287.61 113 233.33 150 55 Hindmarsh, Woodville 275.34 263 255.84 154 272.54 142 215.79 133 56 Hindmarsh, Woodville 276.05 299 279.17 240 253.19 235 238.03 213 57 Hindmarsh, Woodville 227.24 108 276.56 64 281.82 77 211.84 76 58 Hindmarsh, Woodville 268.14 391 257.01 214 233.52 179 204.13 242 59 Hindmarsh, Woodville 250.53 137 283.06 124 235.04 137 194.64 112 60 Hindmarsh, Woodville 261.11 179 279.85 134 266.20 142 205.56 126 61 Hindmarsh, Woodville 239.18 238 257.71 175 243.59 195 214.92 181 62 Hindmarsh, Woodville 265.09 210 251.98 202 255.45 321 229.55 335 63 Port Adelaide 271.66 195 270.51 156 247.02 151 203.59 167 64 Port Adelaide 313.89 172 312.58 151 290.00 160 241.22 131 65 Port Adelaide 306.41 47 283.02 53 273.20 97 205.72 297 66 Port Adelaide 269.52 147 278.36 134 268.38 117 196.50 200 67 Port Adelaide 269.51 120 292.50 120 271.15 104 193.25 163 68 Port Adelaide 267.93 132 275.56 90 291.38 116 230.99 142 69 Port Adelaide 283.64 158 306.72 119 282.56 86 209.15 153 70 Thebarton 255.52 145 269.03 113 234.48 87 195.56 90 71 Thebarton 304.16 106 270.99 131 267.33 101 210.87 92 72 West Torrens 253.73 193 255.63 160 251.90 158 207.91 177 73 West Torrens 254.02 257 261.47 218 262.43 173 210.42 144 74 West Torrens 266.37 169 274.67 150 246.90 145 196.30 162 75 West Torrens 269.33 194 269.75 162 250.39 129 196.92 130 76 West Torrens 264.57 195 238.00 100 204.59 109 183.14 172 77 West Torrens 260.13 151 255.04 129 214.56 103 175.00 148 78 West Torrens 263.42 192 261.73 162 232.37 173 181.94 144 79 West Torrens 257.76 115 259.26 108 245.26 95 171.33 150 80 Adelaide 255.90 233 241.87 203 224.34 267 170.83 408 81 Burnside 291.76 182 254.20 131 259.70 134 202.35 255 82 Burnside 265.38 190 259.59 146 243.40 159 198.68 228 83 Burnside 272.71 377 257.79 263 250.00 296 198.49 464 84 Burnside 283.09 147 270.73 164 223.46 162 202.19 228 85 Burnside 268.65 185 243.80 137 228.31 166 200.29 339 86 Burnside 263.20 99 219.80 101 217.39 92 179.33 179 87 Campbelltown 273.25 132 267.05 176 253.91 230 209.22 206 88 Campbelltown 298.64 190 278.61 187 270.22 178 233.56 149 89 Campbelltown 303.08 81 264.96 117 249.75 203 226.89 305 90 Campbelltown 250.46 88 272.00 125 241.57 166 228.22 287 91 Campbelltown 240.88 172 265.42 240 252.96 253 235.56 225 92 Campbelltown 303.85 255 276.14 197 255.05 198 225.70 179 93 Campbelltown 283.38 219 260.37 164 257.23 173 210.56 180 94 East Torrens 288.20 145 290.58 138 253.02 149 218.56 334 95 Kens & Norwood 291.49 125 234.72 72 267.05 88 155.03 169 Appendices 358
Area 1976 1981 1986 1996 Code Statistical Local Area Av Issue Pop Av Issue Pop Av Issue Pop Av Issue Pop 96 Kens & Norwood 256.68 81 246.15 78 226.44 87 166.01 153 97 Payneham 259.14 151 251.88 133 240.78 103 196.59 176 98 Payneham 263.49 194 255.43 184 264.36 188 187.41 135 99 Payneham 281.19 124 236.46 96 248.11 106 208.11 111 100 Prospect 247.29 165 270.59 119 248.00 100 196.70 182 101 Prospect 274.53 144 257.66 137 239.46 147 195.28 212 102 Prospect 278.17 140 250.78 128 265.14 109 179.49 195 103 St Peters 307.52 111 287.65 81 258.46 65 174.42 86 104 St Peters 279.20 112 224.51 102 253.04 115 194.32 176 105 Stirling 313.47 90 270.59 85 264.29 126 210.63 320 106 Stirling 309.72 80 294.83 58 235.56 90 229.78 225 107 Stirling 257.57 100 265.71 105 246.10 141 211.70 359 108 Unley 294.88 103 241.00 100 224.47 94 178.95 190 109 Unley 273.87 128 287.39 119 234.51 113 170.26 195 110 Unley 248.41 87 250.00 84 208.22 73 177.27 154 111 Unley 274.92 114 235.19 108 243.44 122 175.14 181 112 Unley 303.12 149 265.91 132 255.28 161 202.59 270 113 Unley 279.96 135 252.11 71 210.38 106 162.50 160 114 Unley 252.96 61 240.32 62 220.27 74 152.10 119 115 Walkerville 266.26 224 269.11 191 246.35 192 193.58 265 116 Brighton 293.92 217 266.26 163 236.36 132 180.00 220 117 Brighton 274.84 239 278.21 179 250.68 146 210.98 246 118 Brighton 287.82 252 246.37 179 248.30 147 201.62 185 119 Glenelg 260.52 113 250.00 90 237.08 89 167.86 140 120 Glenelg 285.83 82 230.67 75 218.95 95 161.59 138 121 Glenelg 254.49 82 264.41 59 246.97 66 178.10 137 122 Happy Valley 273.98 54 269.44 72 235.35 99 215.55 476 123 Happy Valley 243.89 53 263.04 92 228.67 143 217.71 401 124 Happy Valley 301.38 22 258.70 46 243.81 105 214.37 696 125 Happy Valley 282.09 52 278.79 66 256.25 96 204.15 241 126 Marion 266.95 253 271.13 142 253.15 111 194.79 192 127 Marion 279.41 320 277.92 240 248.72 195 199.10 221 128 Marion 274.13 206 263.40 153 240.50 121 200.00 118 129 Marion 282.54 304 248.50 167 239.05 169 181.40 242 130 Marion 278.93 156 265.94 138 228.13 128 193.38 136 131 Marion 272.93 187 261.11 180 254.93 213 180.56 144 132 Marion 303.81 137 330.46 151 296.67 150 214.17 127 133 Marion 282.74 301 287.02 262 271.51 186 217.43 218 134 Marion 298.34 251 292.02 213 263.64 154 203.57 140 135 Marion 285.39 238 262.28 289 254.86 401 207.02 968 136 Mitcham 266.10 106 227.27 77 238.57 70 179.20 125 137 Mitcham 296.84 268 252.47 223 265.77 149 207.78 180 138 Mitcham 268.57 46 264.15 53 244.90 49 203.74 107 139 Mitcham 286.66 101 258.24 91 234.91 106 190.05 191 140 Mitcham 270.69 132 269.16 107 235.90 78 201.16 173 141 Mitcham 259.83 173 258.20 122 231.19 109 201.43 210 142 Mitcham 274.64 121 253.13 96 241.35 104 204.10 122 143 Mitcham 275.74 178 279.35 184 249.08 218 200.93 431 144 Mitcham 303.27 152 263.16 152 235.19 233 212.70 315 Appendices 359
Area 1976 1981 1986 1996 Code Statistical Local Area Av Issue Pop Av Issue Pop Av Issue Pop Av Issue Pop 145 Mitcham 289.13 182 269.44 216 252.32 237 206.48 293 146 Mitcham 256.90 188 258.82 238 247.71 327 202.38 378 147 Noarlunga 278.06 94 273.91 115 272.73 143 225.00 184 148 Noarlunga 274.30 174 291.85 184 293.84 211 223.94 213 149 Noarlunga 338.39 143 306.63 196 279.75 237 235.87 513 150 Noarlunga 287.51 73 289.84 128 247.19 178 228.46 267 151 Noarlunga 271.94 42 291.53 59 280 115 229.07 227 152 Noarlunga 297.38 72 268.92 74 266.67 132 230.08 369 153 Noarlunga 285.33 136 296.89 161 276.78 267 225.22 1154 154 Noarlunga 287.54 85 268.14 113 256.17 162 222.2 491 155 Willunga 350.05 50 289.23 65 250.94 106 212.2 295 156/ Willunga 324.74 51 292.31 52 255.95 84 210.29 175
Source: ABS calculated from unpublished data
Appendices 360
Appendix 7.2: Metropolitan South Australia: Measures of Social Change Over Time (Components of Social Change) in the Average Number of Children Ever Born for Women Aged 45–49 Years 1976–1981 (Rates per 100 Women) Area Statistical Local Area 1976–1981 Code Rates Change 1976 Pred 1981 St Pos Fdb 1 Elizabeth 314.07 293.73 317.08 -20.34 23.35 -13.69 2 Elizabeth 321.42 297.94 321.48 -23.48 23.55 -16.83 3 Elizabeth 314.03 293.71 316.78 -20.33 23.07 -13.67 4 Elizabeth 304.86 288.46 303.43 -16.40 14.97 -9.75 5 Elizabeth 312.71 292.95 301.28 -19.76 8.33 -13.10 6 Gawler,Munno Para 317.98 295.97 309.15 -22.01 13.19 -15.36 8 Gawler,Munno Para 328.55 302.01 298.56 -26.53 -3.45 -19.88 9 Gawler,Munno Para 289.27 279.54 308.08 -9.74 28.55 -3.09 10 Gawler,Munno Para 325.36 300.19 331.22 -25.17 31.03 -18.51 11 Gawler,Munno Para 314.07 293.73 321.57 -20.34 27.84 -13.69 12 Gawler,Munno Para 288.14 278.88 295.38 -9.25 16.50 -2.60 13 Salisbury 280.31 274.41 341.94 -5.91 67.53 0.75 14 Salisbury 306.21 289.23 322.92 -16.98 33.69 -10.33 15 Salisbury 310.63 291.76 323.14 -18.87 31.38 -12.22 16 Salisbury 251.62 257.98 285.00 6.36 27.02 13.02 17 Salisbury 283.47 276.21 293.57 -7.26 17.36 -0.60 18 Salisbury 272.28 269.81 280.88 -2.47 11.07 4.18 19 Salisbury 306.66 289.49 286.54 -17.17 -2.95 -10.52 20 Salisbury 312.08 292.59 284.31 -19.49 -8.28 -12.84 21 Salisbury 299.27 285.26 316.84 -14.01 31.58 -7.36 22 Salisbury 263.02 264.51 280.95 1.49 16.44 8.14 23 Salisbury 303.03 287.41 281.43 -15.62 -5.98 -8.97 24 Salisbury 263.36 264.70 275.83 1.34 11.13 8.00 25 Salisbury 254.22 259.47 259.04 5.25 -0.44 11.90 26 Salisbury 304.22 288.09 291.08 -16.13 3.00 -9.47 27 Salisbury 301.83 286.72 304.81 -15.11 18.09 -8.45 28 Tea Tree Gully 274.27 270.95 277.27 -3.32 6.33 3.33 29 Tea Tree Gully 236.37 249.25 275.73 12.88 26.48 19.54 30 Tea Tree Gully 281.93 275.33 276.18 -6.60 0.85 0.06 31 Tea Tree Gully 286.31 277.84 297.35 -8.47 19.52 -1.82 32 Tea Tree Gully 255.27 260.07 284.07 4.80 24.00 11.45 33 Tea Tree Gully 270.44 268.75 278.77 -1.69 10.02 4.97 34 Tea Tree Gully 280.97 274.78 267.63 -6.19 -7.16 0.47 35 Tea Tree Gully 315.48 294.54 272.20 -20.95 -22.34 -14.29 36 Enfield 329.74 302.70 342.33 -27.04 39.63 -20.39 37 Enfield 326.59 300.90 334.62 -25.70 33.72 -19.04 38 Enfield 272.31 269.82 282.24 -2.49 12.42 4.17 39 Enfield 299.44 285.35 323.68 -14.09 38.33 -7.43 40 Enfield 287.34 278.43 298.18 -8.91 19.75 -2.26 41 Enfield 254.90 259.86 263.74 4.96 3.88 11.61 42 Enfield 251.39 257.85 259.62 6.46 1.77 13.11 43 Enfield 324.98 299.97 294.77 -25.00 -5.20 -18.35 44 Enfield 295.38 283.03 308.37 -12.35 25.34 -5.70 45 Enfield 291.76 280.96 295.50 -10.80 14.54 -4.15 46 Enfield 292.16 281.19 276.47 -10.97 -4.72 -4.32 Appendices 361
Area Statistical Local Area 1976–1981 Code Rates Change 1976 Pred 1981 St Pos Fdb 47 Henley & Grange 320.77 297.57 267.15 -23.21 -30.41 -16.55 48 Henley & Grange 263.59 264.83 266.90 1.25 2.07 7.90 49 Henley & Grange 278.99 273.65 261.70 -5.34 -11.94 1.31 50 Hindmarsh, Woodville 281.21 274.92 255.67 -6.29 -19.25 0.36 51 Hindmarsh, Woodville 270.70 268.91 273.58 -1.80 4.68 4.86 52 Hindmarsh, Woodville 268.30 267.53 270.93 -0.77 3.40 5.88 53 Hindmarsh, Woodville 283.23 276.07 261.42 -7.15 -14.66 -0.50 54 Hindmarsh, Woodville 304.69 288.36 294.26 -16.33 5.91 -9.68 55 Hindmarsh, Woodville 275.34 271.56 255.84 -3.78 -15.72 2.87 56 Hindmarsh, Woodville 276.05 271.97 279.17 -4.09 7.20 2.57 57 Hindmarsh, Woodville 227.24 244.03 276.56 16.78 32.54 23.44 58 Hindmarsh, Woodville 268.14 267.44 257.01 -0.70 -10.43 5.95 59 Hindmarsh, Woodville 250.53 257.36 283.06 6.83 25.71 13.48 60 Hindmarsh, Woodville 261.11 263.41 279.85 2.31 16.44 8.96 61 Hindmarsh, Woodville 239.18 250.86 257.71 11.68 6.86 18.34 62 Hindmarsh, Woodville 265.09 265.69 251.98 0.60 -13.71 7.25 63 Port Adelaide 271.66 269.45 270.51 -2.21 1.06 4.45 64 Port Adelaide 313.89 293.62 312.58 -20.26 18.96 -13.61 65 Port Adelaide 306.41 289.35 283.02 -17.07 -6.33 -10.41 66 Port Adelaide 269.52 268.23 278.36 -1.29 10.13 5.36 67 Port Adelaide 269.51 268.22 292.50 -1.29 24.28 5.37 68 Port Adelaide 267.93 267.32 275.56 -0.61 8.24 6.04 69 Port Adelaide 283.64 276.31 306.72 -7.33 30.41 -0.68 70 Thebarton 255.52 260.22 269.03 4.69 8.81 11.35 71 Thebarton 304.16 288.06 270.99 -16.11 -17.07 -9.45 72 West Torrens 253.73 259.19 255.63 5.46 -3.57 12.11 73 West Torrens 254.02 259.36 261.47 5.33 2.11 11.99 74 West Torrens 266.37 266.43 274.67 0.05 8.24 6.71 75 West Torrens 269.33 268.12 269.75 -1.21 1.64 5.44 76 West Torrens 264.57 265.40 238.00 0.82 -27.40 7.48 77 West Torrens 260.13 262.86 255.04 2.72 -7.82 9.38 78 West Torrens 263.42 264.74 261.73 1.31 -3.01 7.97 79 West Torrens 257.76 261.49 259.26 3.74 -2.24 10.39 80 Adelaide 255.90 260.43 241.87 4.53 -18.56 11.19 81 Burnside 291.76 280.96 254.20 -10.80 -26.76 -4.15 82 Burnside 265.38 265.86 259.59 0.48 -6.27 7.13 83 Burnside 272.71 270.06 257.79 -2.66 -12.26 4.00 84 Burnside 283.09 275.99 270.73 -7.09 -5.26 -0.44 85 Burnside 268.65 267.73 243.80 -0.92 -23.93 5.74 86 Burnside 263.20 264.61 219.80 1.41 -44.81 8.07 87 Campbelltown 273.25 270.36 267.05 -2.89 -3.32 3.77 88 Campbelltown 298.64 284.89 278.61 -13.74 -6.29 -7.09 89 Campbelltown 303.08 287.44 264.96 -15.64 -22.48 -8.99 90 Campbelltown 250.46 257.32 272.00 6.86 14.68 13.51 91 Campbelltown 240.88 251.84 265.42 10.95 13.58 17.61 92 Campbelltown 303.85 287.88 276.14 -15.97 -11.74 -9.32 93 Campbelltown 283.38 276.16 260.37 -7.22 -15.80 -0.57 94 East Torrens 288.20 278.92 290.58 -9.28 11.66 -2.62 95 Kens & Norwood 291.49 280.80 234.72 -10.69 -46.08 -4.03 96 Kens & Norwood 256.68 260.88 246.15 4.20 -14.73 10.85 97 Payneham 259.14 262.28 251.88 3.15 -10.40 9.80 Appendices 362
Area Statistical Local Area 1976–1981 Code Rates Change 1976 Pred 1981 St Pos Fdb 98 Payneham 263.49 264.77 255.43 1.29 -9.34 7.94 99 Payneham 281.19 274.91 236.46 -6.28 -38.45 0.37 100 Prospect 247.29 255.50 270.59 8.22 15.09 14.87 101 Prospect 274.53 271.10 257.66 -3.44 -13.43 3.22 102 Prospect 278.17 273.18 250.78 -4.99 -22.40 1.66 103 St Peters 307.52 289.98 287.65 -17.54 -2.33 -10.89 104 St Peters 279.20 273.77 224.51 -5.433 -49.26 1.22 105 Stirling 313.47 293.38 270.59 -20.08 -22.80 -13.43 106 Stirling 309.72 291.24 294.83 -18.48 3.59 -11.83 107 Stirling 257.57 261.39 265.71 3.82 4.33 10.47 108 Unley 294.88 282.74 241.00 -12.14 -41.74 -5.48 109 Unley 273.87 270.72 287.39 -3.15 16.67 3.50 110 Unley 248.41 256.15 250.00 7.73 -6.15 14.39 111 Unley 274.92 271.32 235.19 -3.60 -36.13 3.05 112 Unley 303.12 287.46 265.91 -15.66 -21.55 -9.00 113 Unley 279.96 274.20 252.11 -5.76 -22.09 0.90 114 Unley 252.96 258.75 240.32 5.79 -18.43 12.44 115 Walkerville 266.26 266.36 269.11 0.10 2.75 6.76 116 Brighton 293.92 282.19 266.26 -11.72 -15.94 -5.07 117 Brighton 274.84 271.27 278.21 -3.57 6.94 3.09 118 Brighton 287.82 278.70 246.37 -9.12 -32.33 -2.46 119 Glenelg 260.52 263.08 250.00 2.56 -13.08 9.21 120 Glenelg 285.83 277.56 230.67 -8.27 -46.90 -1.61 121 Glenelg 254.49 259.63 264.41 5.13 4.78 11.79 122 Happy Valley 273.98 270.78 269.44 -3.20 -1.34 3.45 123 Happy Valley 243.89 253.56 263.04 9.67 9.49 16.32 124 Happy Valley 301.38 286.46 258.70 -14.91 -27.77 -8.26 125 Happy Valley 282.09 275.42 278.79 -6.67 3.37 -0.01 126 Marion 266.95 266.76 271.13 -0.19 4.37 6.46 127 Marion 279.41 273.89 277.92 -5.52 4.03 1.13 128 Marion 274.13 270.86 263.40 -3.26 -7.47 3.39 129 Marion 282.54 275.68 248.50 -6.86 -27.18 -0.21 130 Marion 278.93 273.61 265.94 -5.32 -7.67 1.34 131 Marion 272.93 270.18 261.11 -2.75 -9.07 3.91 132 Marion 303.81 287.86 330.46 -15.96 42.61 -9.30 133 Marion 282.74 275.79 287.02 -6.95 11.23 -0.29 134 Marion 298.34 284.73 292.02 -13.62 7.29 -6.96 135 Marion 285.39 277.31 262.28 -8.08 -15.03 -1.42 136 Mitcham 266.10 266.27 227.27 0.17 -38.99 6.83 137 Mitcham 296.84 283.87 252.47 -12.97 -31.40 -6.32 138 Mitcham 268.57 267.68 264.15 -0.88 -3.53 5.77 139 Mitcham 286.66 278.04 258.24 -8.62 -19.80 -1.97 140 Mitcham 270.69 268.90 269.16 -1.79 0.26 4.86 141 Mitcham 259.83 262.68 258.2 2.85 -4.48 9.51 142 Mitcham 274.64 271.16 253.13 -3.48 -18.04 3.17 143 Mitcham 275.74 271.79 279.35 -3.95 7.56 2.70 144 Mitcham 303.27 287.55 263.16 -15.72 -24.39 -9.07 145 Mitcham 289.13 279.45 269.44 -9.68 -10.01 -3.02 146 Mitcham 256.90 261.00 258.82 4.10 -2.18 10.76 147 Noarlunga 278.06 273.12 273.91 -4.95 0.80 1.71 148 Noarlunga 274.30 270.96 291.85 -3.34 20.88 3.32 Appendices 363
Area Statistical Local Area 1976–1981 Code Rates Change 1976 Pred 1981 St Pos Fdb 149 Noarlunga 338.39 307.65 306.63 -30.74 -1.02 -24.09 150 Noarlunga 287.51 278.52 289.84 -8.98 11.32 -2.33 151 Noarlunga 271.94 269.62 291.53 -2.33 21.91 4.33 152 Noarlunga 297.38 284.18 268.92 -13.21 -15.26 -6.55 153 Noarlunga 285.33 277.28 296.89 -8.05 19.61 -1.40 154 Noarlunga 287.54 278.54 268.14 -9.00 -10.40 -2.344 155 Willunga 350.05 314.32 289.23 -35.73 -25.09 -29.07 156/ Willunga 324.74 299.84 292.31 -24.91 -7.53 -18.25 St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value
Source: ABS calculated from unpublished data
Appendices 364
Appendix 7.3: Metropolitan South Australia: Measures of Social Change Over Time (Components of Social Change) in the Average Number of Children Ever Born for Women Aged 45–49 Years 1981–1986 (Rates per 100 Women) Area Statistical Local Area 1981–1986 Code Rates Change 1981 Pred 1986 St Pos Fdb 1 Elizabeth 317.08 290.89 313.69 -26.19 22.80 -9.19 2 Elizabeth 321.48 294.32 282.05 -27.16 -12.27 -10.16 3 Elizabeth 316.78 290.65 314.09 -26.13 23.44 -9.12 4 Elizabeth 303.43 280.25 319.55 -23.18 39.30 -6.18 5 Elizabeth 301.28 278.57 301.64 -22.71 23.07 -5.70 6 Gawler, Munno Para 309.15 284.71 285.41 -24.44 0.70 -7.44 8 Gawler, Munno Para 298.56 276.45 286.85 -22.11 10.40 -5.11 9 Gawler,Munno Para 308.08 283.87 290.82 -24.21 6.94 -7.20 10 Gawler, Munno Para 331.22 301.91 319.47 -29.31 17.56 -12.30 11 Gawler, Munno Para 321.57 294.39 310.71 -27.18 16.33 -10.18 12 Gawler, Munno Para 295.38 273.98 266.19 -21.41 -7.79 -4.40 13 Salisbury 341.94 310.26 328.13 -31.67 17.86 -14.67 14 Salisbury 322.92 295.44 285.71 -27.48 -9.72 -10.47 15 Salisbury 323.14 295.61 318.75 -27.53 23.14 -10.52 16 Salisbury 285.00 265.88 250.75 -19.12 -15.13 -2.12 17 Salisbury 293.57 272.56 285.14 -21.01 12.58 -4.00 18 Salisbury 280.88 262.67 258.24 -18.21 -4.43 -1.21 19 Salisbury 286.54 267.08 308.79 -19.46 41.71 -2.45 20 Salisbury 284.31 265.35 288.13 -18.97 22.78 -1.96 21 Salisbury 316.84 290.70 301.48 -26.14 10.78 -9.14 22 Salisbury 280.95 262.73 265.52 -18.23 2.79 -1.22 23 Salisbury 281.43 263.10 264.38 -18.33 1.28 -1.33 24 Salisbury 275.83 258.73 264.50 -17.10 5.77 -0.09 25 Salisbury 259.04 245.64 249.15 -13.40 3.51 3.61 26 Salisbury 291.08 270.62 261.83 -20.46 -8.79 -3.46 27 Salisbury 304.81 281.33 254.22 -23.49 -27.11 -6.48 28 Tea Tree Gully 277.27 259.86 254.74 -17.42 -5.11 -0.41 29 Tea Tree Gully 275.73 258.65 267.79 -17.08 9.13 -0.07 30 Tea Tree Gully 276.18 259.00 251.09 -17.17 -7.91 -0.17 31 Tea Tree Gully 297.35 275.51 259.68 -21.84 -15.83 -4.84 32 Tea Tree Gully 284.07 265.16 246.94 -18.91 -18.22 -1.91 33 Tea Tree Gully 278.77 261.03 249.86 -17.75 -11.17 -0.74 34 Tea Tree Gully 267.63 252.34 259.23 -15.29 6.89 1.72 35 Tea Tree Gully 272.20 255.90 256.94 -16.30 1.05 0.71 36 Enfield 342.33 310.57 301.69 -31.76 -8.88 -14.75 37 Enfield 334.62 304.56 313.51 -30.06 8.96 -13.05 38 Enfield 282.24 263.73 271.29 -18.51 7.56 -1.51 39 Enfield 323.68 296.04 291.18 -27.65 -4.86 -10.64 40 Enfield 298.18 276.16 275.00 -22.03 -1.16 -5.02 41 Enfield 263.74 249.31 270.64 -14.43 21.33 2.57 42 Enfield 259.62 246.09 263.27 -13.52 17.17 3.48 43 Enfield 294.77 273.49 297.71 -21.27 24.22 -4.27 44 Enfield 308.37 284.10 260.40 -24.27 -23.71 -7.27 45 Enfield 295.50 274.07 260.65 -21.43 -13.42 -4.43 46 Enfield 276.47 259.23 248.51 -17.24 -10.72 -0.24 Appendices 365
Area Statistical Local Area 1981–1986 Code Rates Change 1981 Pred 1986 St Pos Fdb 47 Henley & Grange 267.15 251.97 222.66 -15.19 -29.31 1.82 48 Henley & Grange 266.90 251.77 227.05 -15.13 -24.72 1.88 49 Henley & Grange 261.70 247.72 237.70 -13.98 -10.01 3.02 50 Hindmarsh, Woodville 255.67 243.02 250.72 -12.65 7.71 4.35 51 Hindmarsh, Woodville 273.58 256.98 256.10 -16.60 -0.88 0.40 52 Hindmarsh, Woodville 270.93 254.91 257.28 -16.02 2.37 0.99 53 Hindmarsh, Woodville 261.42 247.5 272.03 -13.92 24.54 3.08 54 Hindmarsh, Woodville 294.26 273.10 287.61 -21.16 14.51 -4.16 55 Hindmarsh, Woodville 255.84 243.15 272.54 -12.69 29.38 4.31 56 Hindmarsh, Woodville 279.17 261.33 253.19 -17.83 -8.14 -0.83 57 Hindmarsh, Woodville 276.56 259.3 281.82 -17.26 22.52 -0.26 58 Hindmarsh, Woodville 257.01 244.06 233.52 -12.95 -10.54 4.06 59 Hindmarsh, Woodville 283.06 264.37 235.04 -18.69 -29.34 -1.69 60 Hindmarsh, Woodville 279.85 261.87 266.20 -17.98 4.33 -0.98 61 Hindmarsh, Woodville 257.71 244.61 243.59 -13.10 -1.02 3.90 62 Hindmarsh, Woodville 251.98 240.14 255.45 -11.84 15.31 5.16 63 Port Adelaide 270.51 254.59 247.02 -15.93 -7.57 1.08 64 Port Adelaide 312.58 287.38 290.00 -25.20 2.62 -8.20 65 Port Adelaide 283.02 264.34 273.20 -18.68 8.86 -1.68 66 Port Adelaide 278.36 260.70 268.38 -17.66 7.67 -0.65 67 Port Adelaide 292.50 271.73 271.15 -20.77 -0.57 -3.77 68 Port Adelaide 275.56 258.52 291.38 -17.04 32.86 -0.03 69 Port Adelaide 306.72 282.81 282.56 -23.91 -0.26 -6.90 70 Thebarton 269.03 253.43 234.48 -15.60 -18.95 1.41 71 Thebarton 270.99 254.96 267.33 -16.03 12.37 0.97 72 West Torrens 255.63 242.98 251.90 -12.64 8.92 4.36 73 West Torrens 261.47 247.54 262.43 -13.93 14.89 3.07 74 West Torrens 274.67 257.83 246.90 -16.84 -10.93 0.16 75 West Torrens 269.75 253.99 250.39 -15.76 -3.61 1.25 76 West Torrens 238.00 229.24 204.59 -8.76 -24.65 8.25 77 West Torrens 255.04 242.52 214.56 -12.51 -27.96 4.49 78 West Torrens 261.73 247.74 232.37 -13.99 -15.37 3.02 79 West Torrens 259.26 245.81 245.26 -13.44 -0.55 3.56 80 Adelaide 241.87 232.26 224.34 -9.61 -7.92 7.39 81 Burnside 254.20 241.87 259.70 -12.33 17.83 4.68 82 Burnside 259.59 246.07 243.40 -13.52 -2.68 3.49 83 Burnside 257.79 244.67 250.00 -13.12 5.33 3.88 84 Burnside 270.73 254.76 223.46 -15.97 -31.30 1.03 85 Burnside 243.80 233.76 228.31 -10.04 -5.45 6.97 86 Burnside 219.80 215.06 217.39 -4.75 2.34 12.26 87 Campbelltown 267.05 251.88 253.91 -15.16 2.03 1.84 88 Campbelltown 278.61 260.90 270.22 -17.71 9.33 -0.71 89 Campbelltown 264.96 250.26 249.75 -14.70 -0.50 2.30 90 Campbelltown 272.00 255.75 241.57 -16.25 -14.18 0.75 91 Campbelltown 265.42 250.61 252.96 -14.80 2.35 2.20 92 Campbelltown 276.14 258.98 255.05 -17.17 -3.93 -0.16 93 Campbelltown 260.37 246.68 257.23 -13.69 10.55 3.32 94 East Torrens 290.58 270.23 253.02 -20.35 -17.21 -3.35 95 Kens & Norwood 234.72 226.69 267.05 -8.04 40.36 8.97 96 Kens & Norwood 246.15 235.60 226.44 -10.56 -9.16 6.45 97 Payneham 251.88 240.06 240.78 -11.82 0.72 5.19 Appendices 366
Area Statistical Local Area 1981–1986 Code Rates Change 1981 Pred 1986 St Pos Fdb 98 Payneham 255.43 242.83 264.36 -12.60 21.53 4.40 99 Payneham 236.46 228.04 248.11 -8.42 20.07 8.59 100 Prospect 270.59 254.65 248.00 -15.94 -6.65 1.06 101 Prospect 257.66 244.57 239.46 -13.09 -5.12 3.91 102 Prospect 250.78 239.21 265.14 -11.58 25.93 5.43 103 St Peters 287.65 267.95 258.46 -19.70 -9.49 -2.70 104 St Peters 224.51 218.73 253.04 -5.78 34.32 11.22 105 Stirling 270.59 254.65 264.29 -15.94 9.64 1.06 106 Stirling 294.83 273.54 235.56 -21.29 -37.99 -4.28 107 Stirling 265.71 250.85 246.10 -14.87 -4.75 2.14 108 Unley 241.00 231.58 224.47 -9.42 -7.11 7.59 109 Unley 287.39 267.75 234.51 -19.65 -33.23 -2.64 110 Unley 250.00 238.60 208.22 -11.40 -30.38 5.60 111 Unley 235.19 227.05 243.44 -8.14 16.40 8.87 112 Unley 265.91 251.00 255.28 -14.91 4.28 2.09 113 Unley 252.11 240.24 210.38 -11.87 -29.87 5.14 114 Unley 240.32 231.05 220.27 -9.27 -10.78 7.73 115 Walkerville 269.11 253.49 246.35 -15.62 -7.14 1.39 116 Brighton 266.26 251.27 236.36 -14.99 -14.91 2.02 117 Brighton 278.21 260.59 250.68 -17.62 -9.90 -0.62 118 Brighton 246.37 235.77 248.30 -10.60 12.53 6.40 119 Glenelg 250.00 238.60 237.08 -11.40 -1.52 5.60 120 Glenelg 230.67 223.53 218.95 -7.14 -4.58 9.86 121 Glenelg 264.41 249.83 246.97 -14.58 -2.86 2.43 122 Happy Valley 269.44 253.75 235.35 -15.69 -18.40 1.31 123 Happy Valley 263.04 248.76 228.67 -14.28 -20.09 2.73 124 Happy Valley 258.70 245.38 243.81 -13.32 -1.57 3.68 125 Happy Valley 278.79 261.04 256.25 -17.75 -4.79 -0.75 126 Marion 271.13 255.07 253.15 -16.06 -1.91 0.94 127 Marion 277.92 260.36 248.72 -17.56 -11.64 -0.55 128 Marion 263.40 249.04 240.50 -14.36 -8.55 2.65 129 Marion 248.50 237.43 239.05 -11.07 1.62 5.93 130 Marion 265.94 251.02 228.13 -14.92 -22.90 2.09 131 Marion 261.11 247.26 254.93 -13.85 7.67 3.15 132 Marion 330.46 301.32 296.67 -29.14 -4.66 -12.14 133 Marion 287.02 267.46 271.51 -19.57 4.05 -2.56 134 Marion 292.02 271.35 263.64 -20.67 -7.72 -3.66 135 Marion 262.28 248.17 254.86 -14.11 6.69 2.89 136 Mitcham 227.27 220.88 238.57 -6.39 17.69 10.61 137 Mitcham 252.47 240.52 265.77 -11.95 25.25 5.06 138 Mitcham 264.15 249.63 244.9 -14.52 -4.73 2.48 139 Mitcham 258.24 245.02 234.91 -13.22 -10.12 3.78 140 Mitcham 269.16 253.53 235.90 -15.63 -17.63 1.38 141 Mitcham 258.20 244.99 231.19 -13.21 -13.79 3.79 142 Mitcham 253.13 241.03 241.35 -12.09 0.31 4.91 143 Mitcham 279.35 261.47 249.08 -17.87 -12.39 -0.87 144 Mitcham 263.16 248.85 235.19 -14.30 -13.66 2.70 145 Mitcham 269.44 253.75 252.32 -15.69 -1.43 1.31 146 Mitcham 258.82 245.47 247.71 -13.35 2.23 3.66 147 Noarlunga 273.91 257.24 272.73 -16.68 15.49 0.33 148 Noarlunga 291.85 271.22 293.84 -20.63 22.62 -3.63 Appendices 367
Area Statistical Local Area 1981–1986 Code Rates Change 1981 Pred 1986 St Pos Fdb 149 Noarlunga 306.63 282.74 279.75 -23.89 -3.00 -6.88 150 Noarlunga 289.84 269.66 247.19 -20.19 -22.47 -3.18 151 Noarlunga 291.53 270.97 280.00 -20.56 9.03 -3.55 152 Noarlunga 268.92 253.34 266.67 -15.57 13.32 1.43 153 Noarlunga 296.89 275.15 276.78 -21.74 1.63 -4.74 154 Noarlunga 268.14 252.74 256.17 -15.40 3.43 1.60 155 Willunga 289.23 269.18 250.94 -20.05 -18.24 -3.05 156 Willunga 292.31 271.58 255.95 -20.73 -15.62 -3.73 St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value
Source: ABS calculated from unpublished data
Appendices 368
Appendix 7.4: Metropolitan South Australia: Measures of Social Change Over Time (Components of Social Change) in the Average Number of Children Ever Born for Women Aged 45–49 Years 1986–1996 (Rates per 100 Women) Area Statistical Local Area 1986–1996 Code Rates Change 1986 Pred 1996 St Pos Fdb 1 Elizabeth 313.69 245.67 260.87 -68.02 15.20 -19.22 2 Elizabeth 282.05 225.03 257.14 -57.02 32.11 -8.22 3 Elizabeth 314.09 245.93 244.44 -68.16 -1.49 -19.36 4 Elizabeth 319.55 249.49 237.37 -70.06 -12.12 -21.26 5 Elizabeth 301.64 237.81 239.29 -63.83 1.48 -15.03 6 Gawler, Munno Para 285.41 227.22 226.93 -58.19 -0.28 -9.39 8 Gawler, Munno Para 286.85 228.16 253.09 -58.69 24.92 -9.89 9 Gawler, Munno Para 290.82 230.75 254.59 -60.07 23.84 -11.27 10 Gawler, Munno Para 319.47 249.44 257.24 -70.03 7.80 -21.23 11 Gawler, Munno Para 310.71 243.73 261.75 -66.99 18.02 -18.19 12 Gawler, Munno Para 266.19 214.68 244.05 -51.51 29.37 -2.71 13 Salisbury 328.13 255.09 247.20 -73.04 -7.88 -24.24 14 Salisbury 285.71 227.42 229.61 -58.29 2.19 -9.50 15 Salisbury 318.75 248.97 260.26 -69.78 11.29 -20.98 16 Salisbury 250.75 204.61 220.67 -46.14 16.06 2.66 17 Salisbury 285.14 227.05 244.21 -58.10 17.16 -9.30 18 Salisbury 258.24 209.50 235.09 -48.74 25.59 0.06 19 Salisbury 308.79 242.47 230.22 -66.32 -12.25 -17.52 20 Salisbury 288.13 228.99 238.46 -59.13 9.47 -10.33 21 Salisbury 301.48 237.71 241.67 -63.78 3.96 -14.98 22 Salisbury 265.52 214.24 237.75 -51.27 23.51 -2.47 23 Salisbury 264.38 213.50 219.59 -50.88 6.09 -2.08 24 Salisbury 264.50 213.58 207.11 -50.92 -6.47 -2.12 25 Salisbury 249.15 203.57 225.65 -45.58 22.08 3.22 26 Salisbury 261.83 211.84 222.64 -49.99 10.80 -1.19 27 Salisbury 254.22 206.87 225.78 -47.35 18.91 1.46 28 Tea Tree Gully 254.74 207.22 224.44 -47.53 17.22 1.27 29 Tea Tree Gully 267.79 215.72 211.12 -52.06 -4.60 -3.26 30 Tea Tree Gully 251.09 204.83 214.78 -46.26 9.95 2.54 31 Tea Tree Gully 259.68 210.44 222.89 -49.25 12.45 -0.45 32 Tea Tree Gully 246.94 202.12 228.57 -44.82 26.45 3.99 33 Tea Tree Gully 249.86 204.03 216.53 -45.83 12.51 2.97 34 Tea Tree Gully 259.23 210.14 211.91 -49.09 1.77 -0.29 35 Tea Tree Gully 256.94 208.65 215.19 -48.29 6.54 0.51 36 Enfield 301.69 237.85 252.91 -63.85 15.06 -15.05 37 Enfield 313.51 245.56 221.43 -67.96 -24.13 -19.16 38 Enfield 271.29 218.01 206.20 -53.28 -11.81 -4.48 39 Enfield 291.18 230.98 219.30 -60.19 -11.68 -11.39 40 Enfield 275.00 220.43 208.29 -54.57 -12.14 -5.77 41 Enfield 270.64 217.59 219.71 -53.06 2.12 -4.26 42 Enfield 263.27 212.77 221.28 -50.49 8.50 -1.69 43 Enfield 297.71 235.25 194.35 -62.46 -40.90 -13.66 44 Enfield 260.40 210.90 214.05 -49.49 3.15 -0.69 45 Enfield 260.65 211.07 230.57 -49.58 19.51 -0.78 46 Enfield 248.51 203.15 188.21 -45.36 -14.95 3.44 Appendices 369
Area Statistical Local Area 1986–1996 Code Rates Change 1986 Pred 1996 St Pos Fdb 47 Henley & Grange 222.66 186.28 188.51 -36.37 2.22 12.43 48 Henley & Grange 227.05 189.15 197.18 -37.90 8.03 10.90 49 Henley & Grange 237.70 196.10 191.07 -41.61 -5.03 7.20 50 Hindmarsh, Woodville 250.72 204.59 189.52 -46.13 -15.07 2.67 51 Hindmarsh, Woodville 256.10 208.10 177.70 -48.00 -30.40 0.80 52 Hindmarsh, Woodville 257.28 208.87 205.37 -48.41 -3.50 0.39 53 Hindmarsh, Woodville 272.03 218.50 182.94 -53.54 -35.55 -4.74 54 Hindmarsh, Woodville 287.61 228.66 233.33 -58.95 4.68 -10.15 55 Hindmarsh, Woodville 272.54 218.82 215.79 -53.71 -3.03 -4.91 56 Hindmarsh, Woodville 253.19 206.20 238.03 -46.99 31.83 1.81 57 Hindmarsh, Woodville 281.82 224.88 211.84 -56.94 -13.04 -8.14 58 Hindmarsh, Woodville 233.52 193.37 204.13 -40.15 10.76 8.65 59 Hindmarsh, Woodville 235.04 194.36 194.64 -40.68 0.28 8.12 60 Hindmarsh, Woodville 266.20 214.69 205.56 -51.51 -9.13 -2.71 61 Hindmarsh, Woodville 243.59 199.94 214.92 -43.65 14.98 5.15 62 Hindmarsh, Woodville 255.45 207.68 229.55 -47.77 21.87 1.03 63 Port Adelaide 247.02 202.18 203.59 -44.84 1.42 3.96 64 Port Adelaide 290.00 230.22 241.22 -59.78 11.01 -10.98 65 Port Adelaide 273.20 219.25 205.72 -53.94 -13.53 -5.14 66 Port Adelaide 268.38 216.11 196.50 -52.27 -19.61 -3.47 67 Port Adelaide 271.15 217.92 193.25 -53.23 -24.67 -4.43 68 Port Adelaide 291.38 231.12 230.99 -60.26 -0.13 -11.46 69 Port Adelaide 282.56 225.36 209.15 -57.20 -16.21 -8.40 70 Thebarton 234.48 194.00 195.56 -40.49 1.56 8.32 71 Thebarton 267.33 215.42 210.87 -51.90 -4.56 -3.10 72 West Torrens 251.90 205.36 207.91 -46.54 2.55 2.26 73 West Torrens 262.43 212.23 210.42 -50.20 -1.81 -1.40 74 West Torrens 246.90 202.10 196.30 -44.80 -5.80 4.00 75 West Torrens 250.39 204.37 196.92 -46.01 -7.45 2.79 76 West Torrens 204.59 174.49 183.14 -30.09 8.65 18.71 77 West Torrens 214.56 181.00 175.00 -33.56 -6.00 15.24 78 West Torrens 232.37 192.62 181.94 -39.75 -10.67 9.05 79 West Torrens 245.26 201.03 171.33 -44.23 -29.70 4.57 80 Adelaide 224.34 187.38 170.83 -36.96 -16.55 11.84 81 Burnside 259.70 210.45 202.35 -49.25 -8.10 -0.45 82 Burnside 243.40 199.81 198.68 -43.58 -1.13 5.22 83 Burnside 250.00 204.12 198.49 -45.88 -5.63 2.92 84 Burnside 223.46 186.80 202.19 -36.65 15.39 12.15 85 Burnside 228.31 189.97 200.29 -38.34 10.32 10.46 86 Burnside 217.39 182.85 179.33 -34.54 -3.52 14.26 87 Campbelltown 253.91 206.67 209.22 -47.24 2.55 1.56 88 Campbelltown 270.22 217.31 233.56 -52.91 16.24 -4.11 89 Campbelltown 249.75 203.96 226.89 -45.79 22.93 3.01 90 Campbelltown 241.57 198.62 228.22 -42.95 29.60 5.85 91 Campbelltown 252.96 206.05 235.56 -46.91 29.50 1.89 92 Campbelltown 255.05 207.42 225.70 -47.63 18.28 1.17 93 Campbelltown 257.23 208.83 210.56 -48.39 1.72 0.41 94 East Torrens 253.02 206.09 218.56 -46.93 12.47 1.87 95 Kens & Norwood 267.05 215.24 155.03 -51.80 -60.21 -3.01 96 Kens & Norwood 226.44 188.75 166.01 -37.69 -22.74 11.11 97 Payneham 240.78 198.10 196.59 -42.67 -1.51 6.13 Appendices 370
Area Statistical Local Area 1986–1996 Code Rates Change 1986 Pred 1996 St Pos Fdb 98 Payneham 264.36 213.49 187.41 -50.87 -26.08 -2.07 99 Payneham 248.11 202.89 208.11 -45.22 5.22 3.58 100 Prospect 248.00 202.82 196.70 -45.18 -6.11 3.62 101 Prospect 239.46 197.24 195.28 -42.21 -1.96 6.59 102 Prospect 265.14 214.00 179.49 -51.14 -34.51 -2.34 103 St Peters 258.46 209.64 174.42 -48.82 -35.22 -0.02 104 St Peters 253.04 206.11 194.32 -46.94 -11.79 1.86 105 Stirling 264.29 213.44 210.63 -50.85 -2.82 -2.05 106 Stirling 235.56 194.70 229.78 -40.86 35.08 7.94 107 Stirling 246.10 201.58 211.70 -44.52 10.12 4.28 108 Unley 224.47 187.46 178.95 -37.00 -8.52 11.80 109 Unley 234.51 194.02 170.26 -40.50 -23.76 8.30 110 Unley 208.22 176.86 177.27 -31.36 0.41 17.44 111 Unley 243.44 199.84 175.14 -43.60 -24.70 5.20 112 Unley 255.28 207.56 202.59 -47.71 -4.97 1.09 113 Unley 210.38 178.27 162.50 -32.11 -15.77 16.69 114 Unley 220.27 184.73 152.10 -35.54 -32.62 13.26 115 Walkerville 246.35 201.74 193.58 -44.61 -8.16 4.19 116 Brighton 236.36 195.22 180.00 -41.14 -15.22 7.66 117 Brighton 250.68 204.57 210.98 -46.12 6.41 2.68 118 Brighton 248.30 203.01 201.62 -45.29 -1.39 3.51 119 Glenelg 237.08 195.69 167.86 -41.39 -27.83 7.41 120 Glenelg 218.95 183.86 161.59 -35.08 -22.27 13.72 121 Glenelg 246.97 202.14 178.10 -44.83 -24.04 3.97 122 Happy Valley 235.35 194.57 215.55 -40.79 20.98 8.01 123 Happy Valley 228.67 190.21 217.71 -38.46 27.50 10.33 124 Happy Valley 243.81 200.08 214.37 -43.73 14.29 5.07 125 Happy Valley 256.25 208.20 204.15 -48.05 -4.05 0.75 126 Marion 253.15 206.18 194.79 -46.98 -11.39 1.82 127 Marion 248.72 203.28 199.10 -45.43 -4.19 3.37 128 Marion 240.50 197.92 200.00 -42.58 2.08 6.22 129 Marion 239.05 196.98 181.40 -42.07 -15.57 6.73 130 Marion 228.13 189.85 193.38 -38.27 3.53 10.52 131 Marion 254.93 207.34 180.56 -47.59 -26.78 1.21 132 Marion 296.67 234.56 214.17 -62.10 -20.39 -13.30 133 Marion 271.51 218.15 217.43 -53.36 -0.72 -4.56 134 Marion 263.64 213.02 203.57 -50.62 -9.45 -1.82 135 Marion 254.86 207.29 207.02 -47.57 -0.27 1.23 136 Mitcham 238.57 196.67 179.20 -41.91 -17.47 6.89 137 Mitcham 265.77 214.41 207.78 -51.36 -6.63 -2.56 138 Mitcham 244.90 200.79 203.74 -44.11 2.95 4.69 139 Mitcham 234.91 194.27 190.05 -40.63 -4.22 8.17 140 Mitcham 235.90 194.92 201.16 -40.98 6.24 7.82 141 Mitcham 231.19 191.85 201.43 -39.34 9.58 9.46 142 Mitcham 241.35 198.48 204.10 -42.87 5.62 5.93 143 Mitcham 249.08 203.52 200.93 -45.56 -2.59 3.24 144 Mitcham 235.19 194.46 212.7 -40.73 18.24 8.07 145 Mitcham 252.32 205.63 206.48 -46.69 0.85 2.11 146 Mitcham 247.71 202.62 202.38 -45.08 -0.24 3.72 147 Noarlunga 272.73 218.95 225.00 -53.78 6.05 -4.98 148 Noarlunga 293.84 232.72 223.94 -61.12 -8.78 -12.32 Appendices 371
Area Statistical Local Area 1986–1996 Code Rates Change 1986 Pred 1996 St Pos Fdb 149 Noarlunga 279.75 223.53 235.87 -56.22 12.34 -7.42 150 Noarlunga 247.19 202.29 228.46 -44.90 26.18 3.90 151 Noarlunga 280.00 223.69 229.07 -56.31 5.38 -7.51 152 Noarlunga 266.67 214.99 230.08 -51.67 15.09 -2.87 153 Noarlunga 276.78 221.59 225.22 -55.19 3.63 -6.39 154 Noarlunga 256.17 208.15 222.20 -48.03 14.05 0.78 155 Willunga 250.94 204.74 212.20 -46.21 7.47 2.59 156 Willunga 255.95 208.00 210.29 -47.95 2.28 0.85 St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value
Source: ABS calculated from unpublished data
Appendices 372
Appendix 7.5: Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–44 Years, 1976, 1981, 1986, 1996 Censuses (Rates per 100 Women) Area Statistical Local Area Census Year Code 1976 1981 1986 1996 Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Issue Issue 44 Issue Issue 44 Issue Issue 44 Issue Issue 44 1 Elizabeth 238.50 235.81 1745 178.20 176.47 1955 170.04 160.84 1956 170.48 160.29 1453 2 Elizabeth 229.66 234.74 811 174.51 165.47 944 159.79 145.56 913 157.35 142.93 771 3 Elizabeth 232.12 241.21 677 170.55 164.53 1235 156.96 153.52 1237 154.98 150.56 987 4 Elizabeth 230.75 219.80 1001 180.31 168.40 1041 155.40 138.20 1034 148.38 140.90 841 5 Elizabeth 232.40 239.44 1119 180.54 167.11 1365 163.40 146.54 1300 156.55 149.85 1013 6 Gawler, Munno Para 198.12 191.63 712 150.79 146.79 1090 129.37 127.21 1518 126.52 130.98 2340 8 Gawler, Munno Para 213.85 210.76 693 171.19 169.34 1556 151.38 154.28 1879 142.35 142.97 3151 9 Gawler,Munno Para 226.87 249.67 391 165.45 166.85 543 149.30 142.63 814 145.39 145.19 1622 10 Gawler, Munno Para 243.43 249.79 1395 192.42 182.53 1591 181.46 160.14 1563 178.49 166.85 1104 11 Gawler, Munno Para 237.82 220.31 1324 185.20 175.21 1634 175.34 160.68 1625 177.57 169.20 1276 12 Gawler, Munno Para 184.86 203.53 167 153.28 149.59 740 138.93 133.16 1758 128.77 140.11 2598 13 Salisbury 240.56 201.52 849 184.14 179.65 1366 156.78 144.71 1702 150.04 143.58 1526 14 Salisbury 186.20 170.06 163 159.91 136.56 982 155.85 154.46 1212 142.23 141.04 1635 15 Salisbury 226.12 215.47 1075 175.87 162.31 1385 156.14 141.26 1384 152.55 137.13 1018 16 Salisbury 192.27 182.74 754 136.94 138.42 1421 133.57 138.89 1918 127.57 132.25 2158 17 Salisbury 213.29 214.35 1321 155.29 157.32 1727 137.11 134.15 1848 135.75 126.21 1511 18 Salisbury 188.34 179.42 431 142.57 136.23 726 126.85 118.40 761 124.88 112.03 748 19 Salisbury 216.44 215.21 529 154.62 154.37 767 153.22 154.76 1167 136.42 136.29 1276 20 Salisbury 202.62 195.88 1229 150.88 169.89 1335 139.84 143.99 1605 142.53 141.20 1796 21 Salisbury 215.80 226.85 465 160.66 145.37 820 146.65 133.45 1782 146.04 145.74 4405 22 Salisbury 187.50 183.18 1826 146.10 153.69 2155 131.35 135.06 2362 125.92 121.28 2044 23 Salisbury 192.00 202.43 701 151.53 150.91 1047 135.03 126.13 1416 120.68 117.24 1868 24 Salisbury 206.83 221.45 1170 146.03 153.11 1288 124.09 120.54 1344 118.61 116.44 1052 25 Salisbury 203.18 180.42 1384 155.41 176.79 1508 136.12 151.98 1620 126.50 119.02 1325 26 Salisbury 200.11 203.56 1450 142.71 167.17 1602 132.97 141.33 1650 121.71 109.53 1196 27 Salisbury 212.00 221.28 1641 148.62 165.65 1831 131.65 130.77 1934 118.79 110.83 1440 Appendices 373
Area Statistical Local Area Census Year Code 1976 1981 1986 1996 Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Issue Issue 44 Issue Issue 44 Issue Issue 44 Issue Issue 44 28 Tea Tree Gully 188.15 172.4 4 646 132.93 127.32 1574 129.39 140.51 1787 116.13 120.14 1589 29 Tea Tree Gully 199.60 191.95 841 137.01 157.92 1262 125.05 145.44 1622 118.33 124.46 6852 30 Tea Tree Gully 187.86 192.29 2396 135.53 149.42 2865 124.36 134.92 2981 113.32 108.09 2486 31 Tea Tree Gully 197.81 201.41 1627 145.43 161.94 1876 129.53 136.87 2029 114.67 106.77 1817 32 Tea Tree Gully 192.82 182.51 950 142.54 145.45 1463 130.66 133.67 1669 115.22 108.33 1513 33 Tea Tree Gully 183.87 191.05 1840 135.65 153.03 2708 121.85 134.26 3246 111.44 108.74 2826 34 Tea Tree Gully 185.23 170.27 1440 138.72 145.20 2354 128.79 141.43 2742 118.34 119.63 2659 35 Tea Tree Gully 190.48 203.20 1680 139.09 163.55 1981 124.84 135.71 2117 111.69 108.54 1662 36 Enfield 242.84 249.26 950 184.52 177.03 1171 162.48 151.94 1213 136.74 126.15 1170 37 Enfield 230.86 226.96 547 151.76 137.54 879 143.50 132.36 989 129.71 124.75 1014 38 Enfield 195.39 191.12 538 124.80 111.97 752 115.04 105.17 755 104.19 100.95 838 39 Enfield 225.27 213.36 592 158.25 153.69 814 141.39 128.71 930 123.26 118.29 875 40 Enfield 198.92 204.48 968 137.90 135.32 1260 126.63 124.69 1430 120.63 120.11 1392 41 Enfield 180.81 174.79 691 127.03 108.38 895 112.96 96.22 900 102.87 99.50 998 42 Enfield 187.56 189.18 625 121.00 107.81 845 95.98 87.21 899 94.82 92.58 916 43 Enfield 235.65 254.32 793 145.90 137.94 1099 139.64 133.72 958 103.59 102.09 1243 44 Enfield 203.81 214.61 1176 143.82 150.50 1400 122.04 123.50 1383 116.06 110.37 1128 45 Enfield 197.39 207.11 1158 138.01 137.76 1324 124.54 112.87 1298 108.91 104.02 1070 46 Enfield 191.09 199.07 1054 124.04 117.05 1378 111.11 101.22 1397 91.074 80.5 5 1270 47 Henley & Grange 172.21 173.80 706 114.18 109.04 1051 97.26 98.74 1029 88.81 92.34 1018 48 Henley & Grange 177.30 175.89 854 115.21 111.01 1163 103.38 97.67 1246 88.39 84.93 1088 49 Henley & Grange 171.84 173.49 642 111.59 107.21 860 99.47 92.81 793 90.40 84.04 683 50 Hindmarsh, Woodville 201.88 194.52 517 127.47 102.21 768 98.07 85.90 844 71.91 67.85 1011 51 Hindmarsh, Woodville 195.63 201.56 552 129.86 113.73 721 105.54 95.22 878 81.83 80.73 1095 52 Hindmarsh, Woodville 185.13 178.33 1666 129.98 142.35 3119 114.64 127.33 4146 103.78 104.49 3539 53 Hindmarsh, Woodville 177.48 169.19 734 127.42 117.74 1037 105.08 102.68 1118 107.26 110.55 1147 54 Hindmarsh, Woodville 205.38 201.49 624 152.88 151.86 808 129.35 123.39 945 124.73 121.51 1125 55 Hindmarsh, Woodville 195.47 196.97 711 142.07 132.40 1031 125.69 111.91 1125 114.74 110.40 1135 56 Hindmarsh, Woodville 194.66 202.97 1035 136.68 134.81 1310 123.40 112.42 1377 119.05 112.54 1292 57 Hindmarsh, Woodville 207.25 197.06 445 134.64 118.00 639 116.79 106.75 652 111.47 111.05 697 58 Hindmarsh, Woodville 183.44 174.69 1187 126.01 106.58 1747 109.93 98.57 1817 101.68 103.55 1745 Appendices 374
Area Statistical Local Area Census Year Code 1976 1981 1986 1996 Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Issue Issue 44 Issue Issue 44 Issue Issue 44 Issue Issue 44 59 Hindmarsh, Woodville 200.17 203.67 599 133.86 126.84 883 114.83 103.62 967 92.64 93.89 1048 60 Hindmarsh, Woodville 183.32 186.89 672 129.33 112.45 916 110.27 89.44 966 96.43 92.41 870 61 Hindmarsh, Woodville 178.60 190.97 1041 129.63 141.61 1264 117.28 115.93 1281 103.08 90.98 1108 62 Hindmarsh, Woodville 183.60 200.89 1162 135.34 154.33 1627 123.71 130.27 1675 109.07 101.00 1494 63 Port Adelaide 192.64 197.97 825 141.67 146.10 1000 127.38 120.30 1118 108.47 115.17 1121 64 Port Adelaide 219.64 244.73 782 166.01 160.89 969 151.04 138.31 1065 145.02 144.81 857 65 Port Adelaide 208.52 199.33 297 143.05 139.06 937 126.68 136.02 1302 112.51 123.97 1585 66 Port Adelaide 187.07 176.38 767 138.42 133.41 868 112.23 107.62 1115 88.55 96.73 1130 67 Port Adelaide 206.15 198.17 638 141.17 134.09 877 112.48 109.14 941 96.17 109.65 1047 68 Port Adelaide 190.04 180.41 682 146.95 134.87 869 120.75 112.55 996 108.19 106.58 1079 69 Port Adelaide 210.04 193.96 620 150.52 140.89 768 132.45 126.73 909 113.75 111.03 979 70 Thebarton 200.11 196.60 611 130.59 110.81 777 101.56 84.70 817 65.44 58.74 892 71 Thebarton 211.75 207.65 706 135.69 110.05 935 99.99 77.40 1031 73.05 64.61 1023 72 West Torrens 177.82 178.57 890 117.53 101.90 1317 101.92 89.60 1327 88.52 79.75 1368 73 West Torrens 180.79 218.86 773 120.53 120.06 1002 108.92 106.39 955 101.56 101.87 963 74 West Torrens 188.09 200.55 751 124.17 123.91 1075 107.40 102.40 1083 101.44 91.06 928 75 West Torrens 185.95 175.92 797 127.35 113.52 1058 103.73 88.57 1172 90.45 84.02 1183 76 West Torrens 166.54 166.80 589 116.03 108.30 879 96.13 96.69 967 88.77 74.57 881 77 West Torrens 156.42 149.02 713 103.69 87.44 1019 87.19 71.12 1094 78.02 67.59 1080 78 West Torrens 179.44 192.16 827 118.91 117.75 1110 101.47 95.37 1058 94.62 86.38 918 79 West Torrens 175.68 143.34 734 114.21 86.79 1113 86.99 68.91 1325 79.50 66.77 1336 80 Adelaide 151.09 138.24 1138 75.26 53.67 2763 64.56 47.82 3321 55.80 39.60 3697 81 Burnside 171.20 173.22 691 108.81 109.05 1127 89.97 88.73 1286 81.19 79.34 1331 82 Burnside 181.65 199.97 610 109.51 108.50 918 97.24 94.62 1022 83.92 78.10 1073 83 Burnside 164.12 189.87 989 110.52 120.27 1658 97.35 109.40 1798 98.31 97.07 1807 84 Burnside 172.98 214.54 553 108.04 130.59 693 93.45 105.23 765 88.88 92.38 892 85 Burnside 180.83 181.96 953 108.83 112.67 1476 92.96 97.49 1716 83.69 85.10 1577 86 Burnside 146.50 136.00 509 92.37 81.21 857 80.96 81.06 871 83.51 81.62 914 87 Campbelltown 191.52 203.85 1085 132.83 139.64 1405 114.15 103.57 1624 100.98 93.45 1251 88 Campbelltown 197.28 215.59 888 145.28 142.68 1052 120.79 106.83 1113 103.61 93.04 1077 89 Campbelltown 187.48 189.02 823 140.20 154.18 1410 126.94 132.45 1627 112.48 107.69 1872 Appendices 375
Area Statistical Local Area Census Year Code 1976 1981 1986 1996 Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Issue Issue 44 Issue Issue 44 Issue Issue 44 Issue Issue 44 90 Campbelltown 185.61 202.66 737 133.74 147.15 1105 122.53 132.37 1316 112.76 117.55 1601 91 Campbelltown 193.76 225.99 1147 135.47 151.90 1370 117.63 116.57 1340 106.06 96.47 1077 92 Campbelltown 197.39 209.24 994 135.14 130.48 1342 110.29 95.90 1366 93.73 84.38 1280 93 Campbelltown 179.95 200.57 730 113.68 111.43 1006 97.86 89.56 1025 85.17 79.71 1173 94 East Torrens 178.21 188.65 743 124.06 138.84 1071 109.26 122.90 1354 106.07 113.53 1353 95 Kens & Norwood 164.83 141.74 591 88.94 68.14 1067 63.29 52.84 1162 54.78 41.52 1221 96 Kens & Norwood 164.64 146.15 450 84.68 70.0 2 864 65.19 59.43 1050 59.65 46.77 1144 97 Payneham 174.80 164.48 706 108.02 90.83 1069 90.85 82.99 1082 81.73 77.54 98 4 98 Payneham 195.66 211.00 871 122.80 116.35 1162 104.13 86.9 2 1208 78.26 66.00 1203 99 Payneham 196.85 188.18 540 116.77 102.19 867 95.73 82.64 962 86.29 78.73 1053 100 Prospect 175.36 155.46 752 117.85 103.08 1168 96.62 86.00 1307 87.41 84.89 1423 101 Prospect 180.89 174.53 833 103.40 93.62 1316 91.36 85.22 1495 82.14 82.06 1449 102 Prospect 172.93 164.98 705 103.53 87.06 1198 94.69 90.87 1293 84.37 84.23 1496 103 St Peters 200.00 183.62 496 109.25 84.06 803 81.25 65.83 875 66.34 61.30 938 104 St Peters 164.46 169.49 576 102.45 95.28 975 88.49 88.04 978 85.80 80.96 998 105 Stirling 181.71 189.82 613 123.84 140.64 940 112.15 131.27 1225 107.47 123.76 1128 106 Stirling 176.79 166.33 510 127.55 139.81 824 113.30 133.23 1014 102.01 114.11 1049 107 Stirling 173.82 176.89 707 124.32 146.58 1095 111.01 136.93 1324 105.73 114.66 1228 108 Unley 158.07 158.99 489 95.78 95.89 803 85.77 89.50 924 88.81 90.63 971 109 Unley 174.47 167.45 746 98.37 87.26 1264 79.19 71.5 5 1476 69.39 63.77 1543 110 Unley 152.36 138.99 662 81.17 64.47 1064 68.30 62.33 1221 61.40 52.24 1250 111 Unley 171.15 154.73 650 100.46 88.28 1186 77.04 67.97 1302 67.22 56.93 1291 112 Unley 170.23 179.95 773 110.11 113.41 1298 95.99 102.66 1466 85.39 86.17 1432 113 Unley 161.42 140.18 636 95.48 80.33 1098 83.53 75.56 1199 71.16 68.95 1214 114 Unley 158.04 139.55 508 94.94 84.03 764 80.35 75.69 868 77.64 78.24 827 115 Walkerville 176.99 194.00 776 104.91 98.70 1305 89.81 88.47 1301 78.21 73.52 1269 116 Brighton 184.80 203.51 610 103.16 100.40 1009 83.74 91.27 1065 86.40 92.27 1086 117 Brighton 167.92 176.14 941 116.66 116.41 1109 98.72 101.94 1136 86.01 85.01 1174 118 Brighton 171.16 182.85 738 108.83 104.49 1024 92.45 93.79 998 94.41 98.73 945 119 Glenelg 152.94 150.76 527 95.88 85.37 779 84.82 76.11 858 74.19 65.63 902 120 Glenelg 167.69 151.51 445 94.31 80.96 751 77.23 64.96 879 65.25 56.48 933 Appendices 376
Area Statistical Local Area Census Year Code 1976 1981 1986 1996 Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Issue Issue 44 Issue Issue 44 Issue Issue 44 Issue Issue 44 121 Glenelg 162.71 154.65 498 105.52 93.70 682 90.15 87.79 786 79.16 81.47 788 122 Happy Valley 159.19 138.08 1354 128.65 151.52 1706 122.49 149.54 2063 117.57 112.79 1743 123 Happy Valley 157.25 139.01 837 128.08 153.56 1279 118.63 149.48 1534 111.26 104.83 1263 124 Happy Valley 162.49 148.22 249 133.84 120.19 1352 123.69 134.62 2874 117.03 132.65 4653 125 Happy Valley 184.33 185.26 408 127.39 138.95 742 119.21 139.49 899 115.61 131.32 926 126 Marion 185.26 201.30 600 117.55 115.06 830 99.93 97.81 958 83.90 84.97 998 127 Marion 182.01 189.44 1009 121.52 123.23 1214 99.65 96.53 1324 96.29 94.70 1170 128 Marion 191.23 203.21 625 128.00 120.21 846 109.56 98.61 933 101.93 100.55 905 129 Marion 181.34 169.33 991 116.43 105.53 1392 101.73 92.12 1598 87.78 87.91 1819 130 Marion 183.21 189.82 648 115.90 112.89 869 104.34 94.35 885 90.03 86.69 962 131 Marion 182.17 196.32 896 121.07 125.90 1139 104.90 97.89 1088 99.70 95.74 1057 132 Marion 219.27 247.02 679 150.42 153.45 827 132.63 127.86 858 108.28 106.18 857 133 Marion 186.33 203.71 1034 132.93 135.87 1196 117.91 117.22 1202 100.87 97.90 1094 134 Marion 199.00 213.66 906 138.75 136.71 1057 121.89 122.66 1015 111.55 111.60 991 135 Marion 175.91 179.34 1672 128.83 130.52 3195 115.71 124.09 4566 114.58 127.47 5795 136 Mitcham 171.08 162.90 452 101.33 93.67 727 88.87 87.49 823 90.17 89.13 938 137 Mitcham 189.03 190.62 869 122.75 110.75 1265 102.62 90.74 1360 93.44 94.71 1360 138 Mitcham 169.32 177.63 403 114.62 118.81 521 102.29 112.83 600 99.07 114.26 631 139 Mitcham 171.51 170.68 510 109.56 108.86 880 97.75 105.36 1007 99.19 109.34 985 140 Mitcham 175.83 189.52 432 106.40 103.51 684 99.21 107.30 767 89.51 95.47 816 141 Mitcham 174.80 186.11 547 101.31 98.56 904 90.79 99.90 978 86.75 85.37 998 142 Mitcham 182.57 210.82 406 110.51 119.68 554 102.85 106.46 573 97.66 93.79 515 143 Mitcham 175.36 204.28 1007 120.99 143.14 1435 104.70 126.19 1577 96.75 102.09 1385 144 Mitcham 178.18 200.18 1101 118.47 141.96 1442 107.86 122.21 1576 97.57 104.42 1267 145 Mitcham 185.69 202.71 984 123.49 135.64 1316 108.37 110.93 1445 99.63 88.18 1286 146 Mitcham 179.89 201.24 1384 121.44 133.13 2010 107.13 109.44 2162 97.29 79.18 1772 147 Noarlunga 202.33 204.87 658 140.83 150.06 849 130.45 135.14 925 121.11 117.46 842 148 Noarlunga 211.41 211.23 1232 155.43 161.15 1256 136.48 133.83 1209 128.22 118.45 970 149 Noarlunga 215.50 197.49 1769 167.94 176.96 2886 157.03 164.98 3264 148.00 141.10 2754 150 Noarlunga 186.01 178.06 1355 142.28 160.31 1469 133.81 141.72 1522 126.51 117.17 1246 151 Noarlunga 187.39 178.20 613 159.30 163.21 1264 150.37 160.91 1622 138.11 134.85 1300 Appendices 377
Area Statistical Local Area Census Year Code 1976 1981 1986 1996 Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Stn Av Obs Av Pop 15- Issue Issue 44 Issue Issue 44 Issue Issue 44 Issue Issue 44 152 Noarlunga 194.70 169.11 1197 149.75 168.66 1653 140.29 156.91 1875 134.34 129.52 1924 153 Noarlunga 191.48 166.47 2041 145.02 150.62 3135 136.49 145.02 4238 129.88 131.81 8488 154 Noarlunga 193.49 174.50 978 149.14 156.59 1433 129.11 139.25 1944 129.86 133.30 2913 155 Willunga 202.69 186.07 396 151.64 150.43 807 141.80 149.81 1337 134.61 144.60 1879 156 Willunga 184.44 198.05 299 136.99 157.81 493 124.55 137.82 714 125.30 140.77 905
Source: ABS calculated from unpublished data
Appendices 378
Appendix 7.6: Metropolitan South Australia: Measures of Social Change Over Time (Components of Social Change) in the Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years 1976–1981 (Rates per 100 Women) Area Statistical Local Area 1976–1981 Code Rates Change 1976 Pred 1981 St Pos Fdb 1 Elizabeth 238.50 179.06 178.20 -59.45 -0.86 -0.87 2 Elizabeth 229.66 170.37 174.51 -59.29 4.14 -0.71 3 Elizabeth 232.12 172.79 170.55 -59.33 -2.24 -0.76 4 Elizabeth 230.75 171.44 180.31 -59.31 8.87 -0.73 5 Elizabeth 232.40 173.06 180.54 -59.34 7.48 -0.76 6 Gawler,Munno Para 198.12 139.39 150.79 -58.73 11.40 -0.15 8 Gawler,Munno Para 213.85 154.84 171.19 -59.01 16.40 -0.43 9 Gawler,Munno Para 226.87 167.63 165.45 -59.24 -2.18 -0.66 10 Gawler,Munno Para 243.43 183.89 192.42 -59.54 8.52 -0.96 11 Gawler,Munno Para 237.82 178.38 185.20 -59.44 6.82 -0.86 12 Gawler,Munno Para 184.86 126.37 153.28 -58.49 26.90 0.09 13 Salisbury 240.56 181.07 184.14 -59.48 3.07 -0.91 14 Salisbury 186.20 127.69 159.91 -58.51 32.20 0.06 15 Salisbury 226.12 166.90 175.87 -59.23 8.98 -0.65 16 Salisbury 192.27 133.65 136.94 -58.62 3.30 -0.04 17 Salisbury 213.29 154.29 155.29 -59.00 1.00 -0.42 18 Salisbury 188.34 129.79 142.57 -58.55 12.80 0.03 19 Salisbury 216.44 157.39 154.62 -59.05 -2.77 -0.48 20 Salisbury 202.62 143.82 150.88 -58.81 7.06 -0.23 21 Salisbury 215.80 156.76 160.66 -59.04 3.90 -0.46 22 Salisbury 187.50 128.97 146.10 -58.54 17.10 0.04 23 Salisbury 192.00 133.39 151.53 -58.62 18.10 -0.04 24 Salisbury 206.83 147.95 146.03 -58.88 -1.92 -0.30 25 Salisbury 203.18 144.37 155.41 -58.82 11.00 -0.24 26 Salisbury 200.11 141.35 142.71 -58.76 1.36 -0.18 27 Salisbury 212.00 153.02 148.62 -58.97 -4.40 -0.40 28 Tea Tree Gully 188.15 129.60 132.93 -58.55 3.33 0.03 29 Tea Tree Gully 199.60 140.85 137.01 -58.75 -3.84 -0.17 30 Tea Tree Gully 187.86 129.32 135.53 -58.54 6.21 0.03 31 Tea Tree Gully 197.81 139.09 145.43 -58.72 6.33 -0.14 32 Tea Tree Gully 192.82 134.18 142.54 -58.63 8.35 -0.05 33 Tea Tree Gully 183.87 125.40 135.65 -58.47 10.20 0.11 34 Tea Tree Gully 185.23 126.74 138.72 -58.50 12.00 0.08 35 Tea Tree Gully 190.48 131.89 139.09 -58.59 7.20 -0.01 36 Enfield 242.84 183.32 184.52 -59.53 1.21 -0.95 37 Enfield 230.86 171.55 151.76 -59.31 -19.80 -0.73 38 Enfield 195.39 136.71 124.80 -58.68 -11.90 -0.10 39 Enfield 225.27 166.06 158.25 -59.21 -7.81 -0.63 40 Enfield 198.92 140.18 137.90 -58.74 -2.28 -0.16 41 Enfield 180.81 122.40 127.03 -58.42 4.64 0.16 42 Enfield 187.56 129.02 121.00 -58.54 -8.03 0.04 43 Enfield 235.65 176.26 145.90 -59.40 -30.40 -0.82 44 Enfield 203.81 144.98 143.82 -58.83 -1.16 -0.25 45 Enfield 197.39 138.68 138.01 -58.71 -0.67 -0.14 46 Enfield 191.09 132.49 124.04 -58.60 -8.45 -0.02 Appendices 379
Area Statistical Local Area 1976–1981 Code Rates Change 1976 Pred 1981 St Pos Fdb 47 Henley & Grange 172.21 113.95 114.18 -58.26 0.23 0.31 48 Henley & Grange 177.30 118.94 115.21 -58.35 -3.74 0.22 49 Henley & Grange 171.84 113.58 111.59 -58.26 -1.99 0.32 50 Hindmarsh, Woodville 201.88 143.09 127.47 -58.79 -15.60 -0.22 51 Hindmarsh, Woodville 195.63 136.95 129.86 -58.68 -7.09 -0.10 52 Hindmarsh, Woodville 185.13 126.63 129.98 -58.49 3.34 0.08 53 Hindmarsh, Woodville 177.48 119.13 127.42 -58.36 8.29 0.22 54 Hindmarsh, Woodville 205.38 146.52 152.88 -58.86 6.36 -0.28 55 Hindmarsh, Woodville 195.47 136.80 142.07 -58.68 5.27 -0.10 56 Hindmarsh, Woodville 194.66 136.00 136.68 -58.66 0.68 -0.09 57 Hindmarsh, Woodville 207.25 148.36 134.64 -58.89 -13.70 -0.31 58 Hindmarsh, Woodville 183.44 124.98 126.01 -58.46 1.03 0.11 59 Hindmarsh, Woodville 200.17 141.40 133.86 -58.76 -7.55 -0.19 60 Hindmarsh, Woodville 183.32 124.86 129.33 -58.46 4.47 0.12 61 Hindmarsh, Woodville 178.60 120.22 129.63 -58.38 9.41 0.20 62 Hindmarsh, Woodville 183.60 125.14 135.34 -58.47 10.20 0.11 63 Port Adelaide 192.64 134.01 141.67 -58.63 7.66 -0.05 64 Port Adelaide 219.64 160.53 166.01 -59.11 5.48 -0.53 65 Port Adelaide 208.52 149.60 143.05 -58.91 -6.55 -0.33 66 Port Adelaide 187.07 128.54 138.42 -58.53 9.88 0.05 67 Port Adelaide 206.15 147.28 141.17 -58.87 -6.11 -0.29 68 Port Adelaide 190.04 131.46 146.95 -58.58 15.50 -0.00 69 Port Adelaide 210.04 151.10 150.52 -58.94 -0.57 -0.36 70 Thebarton 200.11 141.35 130.59 -58.76 -10.80 -0.18 71 Thebarton 211.75 152.78 135.69 -58.97 -17.10 -0.39 72 West Torrens 177.82 119.45 117.53 -58.36 -1.92 0.21 73 West Torrens 180.79 122.37 120.53 -58.42 -1.84 0.16 74 West Torrens 188.09 129.54 124.17 -58.55 -5.38 0.03 75 West Torrens 185.95 127.44 127.35 -58.51 -0.09 0.07 76 West Torrens 166.54 108.38 116.03 -58.16 7.65 0.42 77 West Torrens 156.42 98.44 103.69 -57.98 5.25 0.60 78 West Torrens 179.44 121.05 118.91 -58.39 -2.14 0.19 79 West Torrens 175.68 117.35 114.21 -58.32 -3.14 0.25 80 Adelaide 151.09 93.20 75.26 -57.88 -17.90 0.69 81 Burnside 171.20 112.96 108.81 -58.24 -4.14 0.33 82 Burnside 181.65 123.22 109.51 -58.43 -13.70 0.15 83 Burnside 164.12 106.00 110.52 -58.12 4.52 0.46 84 Burnside 172.98 114.70 108.04 -58.28 -6.66 0.30 85 Burnside 180.83 122.42 108.83 -58.42 -13.60 0.16 86 Burnside 146.50 88.69 92.37 -57.80 3.68 0.77 87 Campbelltown 191.52 132.91 132.83 -58.61 -0.09 -0.03 88 Campbelltown 197.28 138.57 145.28 -58.71 6.72 -0.13 89 Campbelltown 187.48 128.94 140.20 -58.54 11.30 0.04 90 Campbelltown 185.61 127.11 133.74 -58.50 6.63 0.08 91 Campbelltown 193.76 135.11 135.47 -58.65 0.35 -0.07 92 Campbelltown 197.39 138.68 135.14 -58.71 -3.54 -0.14 93 Campbelltown 179.95 121.55 113.68 -58.40 -7.86 0.18 94 East Torrens 178.21 119.84 124.06 -58.37 4.22 0.21 95 Kens & Norwood 164.83 106.70 88.94 -58.13 -17.80 0.45 96 Kens & Norwood 164.64 106.52 84.68 -58.13 -21.80 0.45 97 Payneham 174.80 116.50 108.02 -58.31 -8.47 0.27 Appendices 380
Area Statistical Local Area 1976–1981 Code Rates Change 1976 Pred 1981 St Pos Fdb 98 Payneham 195.66 136.98 122.80 -58.68 -14.20 -0.10 99 Payneham 196.85 138.14 116.77 -58.70 -21.40 -0.13 100 Prospect 175.36 117.04 117.85 -58.32 0.81 0.26 101 Prospect 180.89 122.47 103.40 -58.42 -19.10 0.16 102 Prospect 172.93 114.66 103.53 -58.28 -11.10 0.30 103 St Peters 200.00 141.24 109.25 -58.76 -32.00 -0.18 104 St Peters 164.46 106.34 102.45 -58.12 -3.89 0.45 105 Stirling 181.71 123.27 123.84 -58.43 0.56 0.14 106 Stirling 176.79 118.45 127.55 -58.34 9.11 0.23 107 Stirling 173.82 115.53 124.32 -58.29 8.79 0.29 108 Unley 158.07 100.06 95.78 -58.01 -4.28 0.57 109 Unley 174.47 116.16 98.37 -58.30 -17.80 0.27 110 Unley 152.36 94.45 81.17 -57.91 -13.30 0.67 111 Unley 171.15 112.90 100.46 -58.24 -12.40 0.33 112 Unley 170.23 112.00 110.11 -58.23 -1.89 0.35 113 Unley 161.42 103.35 95.48 -58.07 -7.87 0.51 114 Unley 158.04 100.03 94.94 -58.01 -5.10 0.57 115 Walkerville 176.99 118.64 104.91 -58.35 -13.70 0.23 116 Brighton 184.80 126.31 103.16 -58.49 -23.20 0.09 117 Brighton 167.92 109.74 116.66 -58.19 6.93 0.39 118 Brighton 171.16 112.92 108.83 -58.24 -4.09 0.33 119 Glenelg 152.94 95.02 95.88 -57.92 0.86 0.66 120 Glenelg 167.69 109.51 94.31 -58.18 -15.20 0.40 121 Glenelg 162.71 104.61 105.52 -58.09 0.90 0.48 122 Happy Valley 159.19 101.16 128.65 -58.03 27.50 0.55 123 Happy Valley 157.25 99.26 128.08 -57.99 28.80 0.58 124 Happy Valley 162.49 104.40 133.84 -58.09 29.40 0.49 125 Happy Valley 184.33 125.85 127.39 -58.48 1.54 0.10 126 Marion 185.26 126.76 117.55 -58.50 -9.21 0.08 127 Marion 182.01 123.57 121.52 -58.44 -2.05 0.14 128 Marion 191.23 132.63 128.00 -58.60 -4.62 -0.03 129 Marion 181.34 122.92 116.43 -58.43 -6.49 0.15 130 Marion 183.21 124.76 115.90 -58.46 -8.86 0.12 131 Marion 182.17 123.72 121.07 -58.44 -2.66 0.14 132 Marion 219.27 160.16 150.42 -59.10 -9.74 -0.53 133 Marion 186.33 127.82 132.93 -58.51 5.11 0.06 134 Marion 199.00 140.26 138.75 -58.74 -1.51 -0.16 135 Marion 175.91 117.58 128.83 -58.33 11.30 0.25 136 Mitcham 171.08 112.83 101.33 -58.24 -11.50 0.34 137 Mitcham 189.03 130.46 122.75 -58.56 -7.72 0.01 138 Mitcham 169.32 111.11 114.62 -58.21 3.51 0.37 139 Mitcham 171.51 113.26 109.56 -58.25 -3.70 0.33 140 Mitcham 175.83 117.50 106.40 -58.33 -11.10 0.25 141 Mitcham 174.80 116.49 101.31 -58.31 -15.20 0.27 142 Mitcham 182.57 124.12 110.51 -58.45 -13.60 0.13 143 Mitcham 175.36 117.04 120.99 -58.32 3.94 0.26 144 Mitcham 178.18 119.81 118.47 -58.37 -1.33 0.21 145 Mitcham 185.69 127.18 123.49 -58.50 -3.69 0.07 146 Mitcham 179.89 121.49 121.44 -58.40 -0.05 0.18 147 Noarlunga 202.33 143.53 140.83 -58.80 -2.70 -0.22 148 Noarlunga 211.41 152.44 155.43 -58.96 2.99 -0.39 Appendices 381
Area Statistical Local Area 1976–1981 Code Rates Change 1976 Pred 1981 St Pos Fdb 149 Noarlunga 215.50 156.46 167.94 -59.04 11.50 -0.46 150 Noarlunga 186.01 127.50 142.28 -58.51 14.80 0.07 151 Noarlunga 187.39 128.85 159.30 -58.53 30.40 0.04 152 Noarlunga 194.70 136.04 149.75 -58.66 13.70 -0.09 153 Noarlunga 191.48 132.87 145.02 -58.61 12.10 -0.03 154 Noarlunga 193.49 134.84 149.14 -58.64 14.30 -0.07 155 Willunga 202.69 143.88 151.64 -58.81 7.76 -0.23 156 Willunga 184.44 125.96 136.99 -58.48 11.00 0.10 St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value
Source: ABS calculated from unpublished data
Appendices 382
Appendix 7.7: Metropolitan South Australia: Measures of Social Change Over Time (Components of Social Change) in the Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years 1981–1986 (Rates per 100 Women) Area Statistical Local Area 1981–1986 Code Rates Change 1981 Pred 1986 St Pos Fdb 1 Elizabeth 178.20 162.36 170.04 -15.84 7.68 -0.26 2 Elizabeth 174.51 158.69 159.79 -15.82 1.09 -0.24 3 Elizabeth 170.55 154.75 156.96 -15.79 2.20 -0.22 4 Elizabeth 180.31 164.46 155.40 -15.85 -9.06 -0.27 5 Elizabeth 180.54 164.69 163.40 -15.85 -1.29 -0.28 6 Gawler,Munno Para 150.79 135.11 129.37 -15.68 -5.74 -0.11 8 Gawler,Munno Para 171.19 155.39 151.38 -15.80 -4.02 -0.22 9 Gawler,Munno Para 165.45 149.69 149.30 -15.77 -0.38 -0.19 10 Gawler,Munno Para 192.42 176.50 181.46 -15.92 4.96 -0.34 11 Gawler,Munno Para 185.20 169.32 175.34 -15.88 6.02 -0.30 12 Gawler,Munno Para 153.28 137.59 138.93 -15.70 1.34 -0.12 13 Salisbury 184.14 168.27 156.78 -15.87 -11.50 -0.30 14 Salisbury 159.91 144.17 155.85 -15.74 11.70 -0.16 15 Salisbury 175.87 160.05 156.14 -15.82 -3.91 -0.25 16 Salisbury 136.94 121.34 133.57 -15.61 12.20 -0.03 17 Salisbury 155.29 139.58 137.11 -15.71 -2.47 -0.13 18 Salisbury 142.57 126.93 126.85 -15.64 -0.08 -0.06 19 Salisbury 154.62 138.91 153.22 -15.71 14.30 -0.13 20 Salisbury 150.88 135.19 139.84 -15.68 4.65 -0.11 21 Salisbury 160.66 144.92 146.65 -15.74 1.73 -0.16 22 Salisbury 146.10 130.44 131.35 -15.66 0.91 -0.08 23 Salisbury 151.53 135.84 135.03 -15.69 -0.81 -0.11 24 Salisbury 146.03 130.38 124.09 -15.66 -6.29 -0.08 25 Salisbury 155.41 139.70 136.12 -15.71 -3.57 -0.14 26 Salisbury 142.71 127.07 132.97 -15.64 5.90 -0.06 27 Salisbury 148.62 132.95 131.65 -15.67 -1.30 -0.10 28 Tea Tree Gully 132.93 117.35 129.39 -15.58 12.00 -0.01 29 Tea Tree Gully 137.01 121.40 125.05 -15.61 3.65 -0.03 30 Tea Tree Gully 135.53 119.93 124.36 -15.60 4.43 -0.02 31 Tea Tree Gully 145.43 129.77 129.53 -15.65 -0.24 -0.08 32 Tea Tree Gully 142.54 126.90 130.66 -15.64 3.76 -0.06 33 Tea Tree Gully 135.65 120.05 121.85 -15.60 1.80 -0.02 34 Tea Tree Gully 138.72 123.11 128.79 -15.62 5.68 -0.04 35 Tea Tree Gully 139.09 123.47 124.84 -15.62 1.37 -0.04 36 Enfield 184.52 168.65 162.48 -15.87 -6.17 -0.30 37 Enfield 151.76 136.07 143.50 -15.69 7.43 -0.11 38 Enfield 124.80 109.26 115.04 -15.54 5.78 0.04 39 Enfield 158.25 142.53 141.39 -15.73 -1.14 -0.15 40 Enfield 137.90 122.29 126.63 -15.61 4.34 -0.04 41 Enfield 127.03 111.48 112.96 -15.55 1.48 0.02 42 Enfield 121.00 105.48 95.98 -15.52 -9.50 0.06 43 Enfield 145.90 130.24 139.64 -15.66 9.40 -0.08 44 Enfield 143.82 128.17 122.04 -15.65 -6.13 -0.07 45 Enfield 138.01 122.40 124.54 -15.61 2.14 -0.04 46 Enfield 124.04 108.51 111.11 -15.53 2.60 0.04 Appendices 383
Area Statistical Local Area 1981–1986 Code Rates Change 1981 Pred 1986 St Pos Fdb 47 Henley & Grange 114.18 98.70 97.26 -15.48 -1.44 0.10 48 Henley & Grange 115.21 99.72 103.38 -15.48 3.66 0.09 49 Henley & Grange 111.59 96.13 99.47 -15.46 3.34 0.11 50 Hindmarsh, Woodville 127.47 111.92 98.07 -15.55 -13.90 0.02 51 Hindmarsh, Woodville 129.86 114.29 105.54 -15.57 -8.75 0.01 52 Hindmarsh, Woodville 129.98 114.41 114.64 -15.57 0.23 0.01 53 Hindmarsh, Woodville 127.42 111.86 105.08 -15.55 -6.78 0.02 54 Hindmarsh, Woodville 152.88 137.19 129.35 -15.70 -7.84 -0.12 55 Hindmarsh, Woodville 142.07 126.43 125.69 -15.64 -0.75 -0.06 56 Hindmarsh, Woodville 136.68 121.07 123.40 -15.61 2.32 -0.03 57 Hindmarsh, Woodville 134.64 119.04 116.79 -15.59 -2.25 -0.02 58 Hindmarsh, Woodville 126.01 110.47 109.93 -15.55 -0.53 0.03 59 Hindmarsh, Woodville 133.86 118.27 114.83 -15.59 -3.44 -0.01 60 Hindmarsh, Woodville 129.33 113.77 110.27 -15.56 -3.50 0.01 61 Hindmarsh, Woodville 129.63 114.07 117.28 -15.57 3.21 0.01 62 Hindmarsh, Woodville 135.34 119.75 123.71 -15.60 3.96 -0.02 63 Port Adelaide 141.67 126.04 127.38 -15.63 1.34 -0.06 64 Port Adelaide 166.01 150.24 151.04 -15.77 0.80 -0.19 65 Port Adelaide 143.05 127.41 126.68 -15.64 -0.73 -0.07 66 Port Adelaide 138.42 122.80 112.23 -15.61 -10.60 -0.04 67 Port Adelaide 141.17 125.54 112.48 -15.63 -13.10 -0.06 68 Port Adelaide 146.95 131.29 120.75 -15.66 -10.50 -0.09 69 Port Adelaide 150.52 134.84 132.45 -15.68 -2.39 -0.11 70 Thebarton 130.59 115.02 101.56 -15.57 -13.50 0.00 71 Thebarton 135.69 120.09 99.99 -15.60 -20.10 -0.02 72 West Torrens 117.53 102.03 101.92 -15.50 -0.11 0.08 73 West Torrens 120.53 105.02 108.92 -15.51 3.90 0.06 74 West Torrens 124.17 108.63 107.40 -15.54 -1.23 0.04 75 West Torrens 127.35 111.79 103.73 -15.55 -8.07 0.02 76 West Torrens 116.03 100.54 96.13 -15.49 -4.41 0.09 77 West Torrens 103.69 88.27 87.19 -15.42 -1.08 0.15 78 West Torrens 118.91 103.40 101.47 -15.51 -1.93 0.07 79 West Torrens 114.21 98.74 86.99 -15.48 -11.70 0.10 80 Adelaide 75.26 59.99 64.56 -15.26 4.56 0.31 81 Burnside 108.81 93.37 89.97 -15.45 -3.40 0.13 82 Burnside 109.51 94.05 97.24 -15.45 3.19 0.12 83 Burnside 110.52 95.06 97.35 -15.46 2.28 0.12 84 Burnside 108.04 92.60 93.45 -15.44 0.86 0.13 85 Burnside 108.83 93.38 92.96 -15.45 -0.42 0.13 86 Burnside 92.37 77.02 80.96 -15.36 3.95 0.22 87 Campbelltown 132.83 117.24 114.15 -15.58 -3.09 -0.01 88 Campbelltown 145.28 129.63 120.79 -15.65 -8.84 -0.08 89 Campbelltown 140.20 124.58 126.94 -15.62 2.36 -0.05 90 Campbelltown 133.74 118.15 122.53 -15.59 4.38 -0.01 91 Campbelltown 135.47 119.87 117.63 -15.60 -2.24 -0.02 92 Campbelltown 135.14 119.55 110.29 -15.60 -9.26 -0.02 93 Campbelltown 113.68 98.21 97.86 -15.48 -0.35 0.10 94 East Torrens 124.06 108.53 109.26 -15.53 0.73 0.04 95 Kens & Norwood 88.94 73.60 63.29 -15.34 -10.30 0.24 96 Kens & Norwood 84.68 69.36 65.19 -15.31 -4.18 0.26 97 Payneham 108.02 92.58 90.85 -15.44 -1.72 0.13 Appendices 384
Area Statistical Local Area 1981–1986 Code Rates Change 1981 Pred 1986 St Pos Fdb 98 Payneham 122.80 107.27 104.13 -15.53 -3.14 0.05 99 Payneham 116.77 101.28 95.73 -15.49 -5.55 0.08 100 Prospect 117.85 102.35 96.62 -15.50 -5.74 0.08 101 Prospect 103.40 87.99 91.36 -15.42 3.38 0.16 102 Prospect 103.53 88.11 94.69 -15.42 6.58 0.16 103 St Peters 109.25 93.80 81.25 -15.45 -12.50 0.12 104 St Peters 102.45 87.04 88.49 -15.41 1.45 0.16 105 Stirling 123.84 108.30 112.15 -15.53 3.85 0.04 106 Stirling 127.55 112.00 113.30 -15.55 1.30 0.02 107 Stirling 124.32 108.78 111.01 -15.54 2.23 0.04 108 Unley 95.78 80.41 85.77 -15.38 5.37 0.20 109 Unley 98.37 82.98 79.19 -15.39 -3.79 0.18 110 Unley 81.17 65.87 68.30 -15.29 2.43 0.28 111 Unley 100.46 85.06 77.04 -15.40 -8.02 0.17 112 Unley 110.11 94.66 95.99 -15.46 1.33 0.12 113 Unley 95.48 80.11 83.53 -15.37 3.42 0.20 114 Unley 94.94 79.56 80.35 -15.37 0.79 0.20 115 Walkerville 104.91 89.48 89.81 -15.43 0.33 0.15 116 Brighton 103.16 87.74 83.74 -15.42 -4.00 0.16 117 Brighton 116.66 101.17 98.72 -15.49 -2.45 0.08 118 Brighton 108.83 93.38 92.45 -15.45 -0.93 0.13 119 Glenelg 95.88 80.50 84.82 -15.38 4.32 0.20 120 Glenelg 94.31 78.94 77.23 -15.37 -1.71 0.21 121 Glenelg 105.52 90.08 90.15 -15.43 0.06 0.14 122 Happy Valley 128.65 113.09 122.49 -15.56 9.40 0.01 123 Happy Valley 128.08 112.53 118.63 -15.56 6.10 0.02 124 Happy Valley 133.84 118.25 123.69 -15.59 5.44 -0.01 125 Happy Valley 127.39 111.84 119.21 -15.55 7.37 0.02 126 Marion 117.55 102.06 99.93 -15.50 -2.13 0.08 127 Marion 121.52 106.00 99.65 -15.52 -6.35 0.05 128 Marion 128.00 112.45 109.56 -15.56 -2.89 0.02 129 Marion 116.43 100.94 101.73 -15.49 0.79 0.08 130 Marion 115.90 100.41 104.34 -15.49 3.93 0.09 131 Marion 121.07 105.55 104.90 -15.52 -0.65 0.06 132 Marion 150.42 134.74 132.63 -15.68 -2.12 -0.11 133 Marion 132.93 117.35 117.91 -15.58 0.56 -0.01 134 Marion 138.75 123.13 121.89 -15.62 -1.24 -0.04 135 Marion 128.83 113.27 115.71 -15.56 2.44 0.01 136 Mitcham 101.33 85.93 88.87 -15.41 2.94 0.17 137 Mitcham 122.75 107.22 102.62 -15.53 -4.60 0.05 138 Mitcham 114.62 99.14 102.29 -15.48 3.15 0.09 139 Mitcham 109.56 94.10 97.75 -15.45 3.64 0.12 140 Mitcham 106.40 90.97 99.21 -15.44 8.25 0.14 141 Mitcham 101.31 85.90 90.79 -15.41 4.89 0.17 142 Mitcham 110.51 95.05 102.85 -15.46 7.80 0.12 143 Mitcham 120.99 105.47 104.70 -15.52 -0.77 0.06 144 Mitcham 118.47 102.97 107.86 -15.50 4.89 0.07 145 Mitcham 123.49 107.96 108.37 -15.53 0.42 0.04 146 Mitcham 121.44 105.92 107.13 -15.52 1.21 0.06 147 Noarlunga 140.83 125.20 130.45 -15.63 5.25 -0.05 148 Noarlunga 155.43 139.72 136.48 -15.71 -3.25 -0.14 Appendices 385
Area Statistical Local Area 1981–1986 Code Rates Change 1981 Pred 1986 St Pos Fdb 149 Noarlunga 167.94 152.16 157.03 -15.78 4.87 -0.21 150 Noarlunga 142.28 126.65 133.81 -15.64 7.16 -0.06 151 Noarlunga 159.30 143.57 150.37 -15.73 6.80 -0.16 152 Noarlunga 149.75 134.08 140.29 -15.68 6.22 -0.10 153 Noarlunga 145.02 129.37 136.49 -15.65 7.12 -0.08 154 Noarlunga 149.14 133.46 129.11 -15.67 -4.35 -0.10 155 Willunga 151.64 135.95 141.80 -15.69 5.86 -0.11 156 Willunga 136.99 121.38 124.55 -15.61 3.17 -0.03 St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value
Source: ABS calculated from unpublished data
Appendices 386
Appendix 7.8: Metropolitan South Australia: Measures of Social Change Over Time (Components of Social Change) in the Age Standardised Average Number of Children Ever Born for Women Aged 15–44 Years 1986–1996 (Rates per 100 Women) Area Statistical Local Area 1986–1996 Code Rates Change 1986 Pred 1996 St Pos Fdb 1 Elizabeth 170.04 160.48 170.48 -9.56 10.00 -0.02 2 Elizabeth 159.79 150.23 157.35 -9.55 7.11 -0.02 3 Elizabeth 156.96 147.40 154.98 -9.55 7.58 -0.02 4 Elizabeth 155.40 145.85 148.38 -9.55 2.54 -0.02 5 Elizabeth 163.40 153.84 156.55 -9.55 2.70 -0.02 6 Gawler,Munno Para 129.37 119.83 126.52 -9.54 6.69 -0.01 8 Gawler,Munno Para 151.38 141.83 142.35 -9.55 0.53 -0.01 9 Gawler,Munno Para 149.30 139.76 145.39 -9.55 5.64 -0.01 10 Gawler,Munno Para 181.46 171.90 178.49 -9.56 6.59 -0.03 11 Gawler,Munno Para 175.34 165.79 177.57 -9.56 11.80 -0.02 12 Gawler,Munno Para 138.93 129.39 128.77 -9.54 -0.60 -0.01 13 Salisbury 156.78 147.23 150.04 -9.55 2.81 -0.02 14 Salisbury 155.85 146.30 142.23 -9.55 -4.10 -0.02 15 Salisbury 156.14 146.59 152.55 -9.55 5.96 -0.02 16 Salisbury 133.57 124.02 127.57 -9.54 3.55 -0.01 17 Salisbury 137.11 127.57 135.75 -9.54 8.18 -0.01 18 Salisbury 126.85 117.31 124.88 -9.54 7.57 0.00 19 Salisbury 153.22 143.67 136.42 -9.55 -7.20 -0.01 20 Salisbury 139.84 130.30 142.53 -9.55 12.20 -0.01 21 Salisbury 146.65 137.10 146.04 -9.55 8.94 -0.01 22 Salisbury 131.35 121.81 125.92 -9.54 4.11 -0.01 23 Salisbury 135.03 125.48 120.68 -9.54 -4.80 -0.01 24 Salisbury 124.09 114.55 118.61 -9.54 4.06 0.00 25 Salisbury 136.12 126.58 126.50 -9.54 -0.10 -0.01 26 Salisbury 132.97 123.43 121.71 -9.54 -1.70 -0.01 27 Salisbury 131.65 122.11 118.79 -9.54 -3.30 -0.01 28 Tea Tree Gully 129.39 119.85 116.13 -9.54 -3.70 -0.01 29 Tea Tree Gully 125.05 115.52 118.33 -9.54 2.81 0.00 30 Tea Tree Gully 124.36 114.82 113.32 -9.54 -1.50 0.00 31 Tea Tree Gully 129.53 119.99 114.67 -9.54 -5.30 -0.01 32 Tea Tree Gully 130.66 121.12 115.22 -9.54 -5.90 -0.01 33 Tea Tree Gully 121.85 112.31 111.44 -9.54 -0.90 0.00 34 Tea Tree Gully 128.79 119.25 118.34 -9.54 -0.90 -0.01 35 Tea Tree Gully 124.84 115.30 111.69 -9.54 -3.60 0.00 36 Enfield 162.48 152.92 136.74 -9.55 -16.00 -0.02 37 Enfield 143.50 133.96 129.71 -9.55 -4.20 -0.01 38 Enfield 115.04 105.51 104.19 -9.54 -1.30 0.00 39 Enfield 141.39 131.84 123.26 -9.55 -8.60 -0.01 40 Enfield 126.63 117.09 120.63 -9.54 3.54 0.00 41 Enfield 112.96 103.43 102.87 -9.53 -0.60 0.00 42 Enfield 95.98 86.45 94.82 -9.53 8.36 0.01 43 Enfield 139.64 130.09 103.59 -9.55 -26.00 -0.01 44 Enfield 122.04 112.50 116.06 -9.54 3.56 0.00 45 Enfield 124.54 115.00 108.91 -9.54 -6.10 0.00 46 Enfield 111.11 101.57 91.07 -9.53 -10.00 0.00 Appendices 387
Area Statistical Local Area 1986–1996 Code Rates Change 1986 Pred 1996 St Pos Fdb 47 Henley & Grange 97.26 87.73 88.81 -9.53 1.07 0.01 48 Henley & Grange 103.38 93.85 88.39 -9.53 -5.50 0.00 49 Henley & Grange 99.47 89.94 90.40 -9.53 0.47 0.01 50 Hindmarsh, Woodville 98.07 88.54 71.91 -9.53 -17.00 0.01 51 Hindmarsh, Woodville 105.54 96.01 81.83 -9.53 -14.00 0.00 52 Hindmarsh, Woodville 114.64 105.10 103.78 -9.54 -1.30 0.00 53 Hindmarsh, Woodville 105.08 95.55 107.26 -9.53 11.70 0.00 54 Hindmarsh, Woodville 129.35 119.81 124.73 -9.54 4.93 -0.01 55 Hindmarsh, Woodville 125.69 116.15 114.74 -9.54 -1.40 0.00 56 Hindmarsh, Woodville 123.40 113.86 119.05 -9.54 5.19 0.00 57 Hindmarsh, Woodville 116.79 107.26 111.47 -9.54 4.21 0.00 58 Hindmarsh, Woodville 109.93 100.40 101.68 -9.53 1.28 0.00 59 Hindmarsh, Woodville 114.83 105.29 92.64 -9.54 -13.00 0.00 60 Hindmarsh, Woodville 110.27 100.74 96.43 -9.53 -4.30 0.00 61 Hindmarsh, Woodville 117.28 107.75 103.08 -9.54 -4.70 0.00 62 Hindmarsh, Woodville 123.71 114.17 109.07 -9.54 -5.10 0.00 63 Port Adelaide 127.38 117.84 108.47 -9.54 -9.40 0.00 64 Port Adelaide 151.04 141.49 145.02 -9.55 3.52 -0.01 65 Port Adelaide 126.68 117.14 112.51 -9.54 -4.60 0.00 66 Port Adelaide 112.23 102.70 88.55 -9.53 -14.00 0.00 67 Port Adelaide 112.48 102.94 96.17 -9.53 -6.80 0.00 68 Port Adelaide 120.75 111.21 108.19 -9.54 -3.00 0.00 69 Port Adelaide 132.45 122.91 113.75 -9.54 -9.20 -0.01 70 Thebarton 101.56 92.03 65.44 -9.53 -27.00 0.01 71 Thebarton 99.99 90.46 73.05 -9.53 -17.00 0.01 72 West Torrens 101.92 92.39 88.52 -9.53 -3.90 0.01 73 West Torrens 108.92 99.39 101.56 -9.53 2.18 0.00 74 West Torrens 107.40 97.87 101.44 -9.53 3.58 0.00 75 West Torrens 103.73 94.20 90.45 -9.53 -3.70 0.00 76 West Torrens 96.13 86.60 88.77 -9.53 2.17 0.01 77 West Torrens 87.19 77.67 78.02 -9.52 0.36 0.01 78 West Torrens 101.47 91.94 94.62 -9.53 2.67 0.01 79 West Torrens 86.99 77.47 79.50 -9.52 2.04 0.01 80 Adelaide 64.56 55.04 55.80 -9.52 0.76 0.02 81 Burnside 89.97 80.44 81.19 -9.53 0.75 0.01 82 Burnside 97.24 87.71 83.92 -9.53 -3.80 0.01 83 Burnside 97.35 87.82 98.31 -9.53 10.50 0.01 84 Burnside 93.45 83.92 88.88 -9.53 4.96 0.01 85 Burnside 92.96 83.43 83.69 -9.53 0.25 0.01 86 Burnside 80.96 71.44 83.51 -9.52 12.10 0.01 87 Campbelltown 114.15 104.61 100.98 -9.54 -3.60 0.00 88 Campbelltown 120.79 111.25 103.61 -9.54 -7.60 0.00 89 Campbelltown 126.94 117.40 112.48 -9.54 -4.90 0.00 90 Campbelltown 122.53 112.99 112.76 -9.54 -0.20 0.00 91 Campbelltown 117.63 108.09 106.06 -9.54 -2.00 0.00 92 Campbelltown 110.29 100.75 93.73 -9.53 -7.00 0.00 93 Campbelltown 97.86 88.33 85.17 -9.53 -3.20 0.01 94 East Torrens 109.26 99.73 106.07 -9.53 6.34 0.00 95 Kens & Norwood 63.29 53.77 54.78 -9.52 1.01 0.02 96 Kens & Norwood 65.19 55.67 59.65 -9.52 3.97 0.02 97 Payneham 90.85 81.33 81.73 -9.53 0.40 0.01 Appendices 388
Area Statistical Local Area 1986–1996 Code Rates Change 1986 Pred 1996 St Pos Fdb 98 Payneham 104.13 94.59 78.26 -9.53 -16.00 0.00 99 Payneham 95.73 86.20 86.29 -9.53 0.09 0.01 100 Prospect 96.62 87.09 87.41 -9.53 0.32 0.01 101 Prospect 91.36 81.84 82.14 -9.53 0.30 0.01 102 Prospect 94.69 85.16 84.37 -9.53 -0.80 0.01 103 St Peters 81.25 71.73 66.34 -9.52 -5.40 0.01 104 St Peters 88.49 78.96 85.80 -9.53 6.83 0.01 105 Stirling 112.15 102.61 107.47 -9.53 4.86 0.00 106 Stirling 113.30 103.77 102.01 -9.53 -1.80 0.00 107 Stirling 111.01 101.48 105.73 -9.53 4.25 0.00 108 Unley 85.77 76.25 88.81 -9.52 12.60 0.01 109 Unley 79.19 69.66 69.39 -9.52 -0.30 0.01 110 Unley 68.30 58.78 61.40 -9.52 2.62 0.02 111 Unley 77.04 67.52 67.22 -9.52 -0.30 0.01 112 Unley 95.99 86.46 85.39 -9.53 -1.10 0.01 113 Unley 83.53 74.01 71.16 -9.52 -2.80 0.01 114 Unley 80.35 70.83 77.64 -9.52 6.81 0.01 115 Walkerville 89.81 80.29 78.21 -9.53 -2.10 0.01 116 Brighton 83.74 74.22 86.40 -9.52 12.20 0.01 117 Brighton 98.72 89.19 86.01 -9.53 -3.20 0.01 118 Brighton 92.45 82.92 94.41 -9.53 11.50 0.01 119 Glenelg 84.82 75.30 74.19 -9.52 -1.10 0.01 120 Glenelg 77.23 67.71 65.25 -9.52 -2.50 0.01 121 Glenelg 90.15 80.62 79.16 -9.53 -1.50 0.01 122 Happy Valley 122.49 112.95 117.57 -9.54 4.62 0.00 123 Happy Valley 118.63 109.09 111.26 -9.54 2.17 0.00 124 Happy Valley 123.69 114.16 117.03 -9.54 2.87 0.00 125 Happy Valley 119.21 109.67 115.61 -9.54 5.95 0.00 126 Marion 99.93 90.40 83.90 -9.53 -6.50 0.01 127 Marion 99.65 90.12 96.29 -9.53 6.17 0.01 128 Marion 109.56 100.02 101.93 -9.53 1.91 0.00 129 Marion 101.73 92.20 87.78 -9.53 -4.40 0.01 130 Marion 104.34 94.81 90.03 -9.53 -4.80 0.00 131 Marion 104.90 95.37 99.70 -9.53 4.33 0.00 132 Marion 132.63 123.08 108.28 -9.54 -15.00 -0.01 133 Marion 117.91 108.37 100.87 -9.54 -7.50 0.00 134 Marion 121.89 112.36 111.55 -9.54 -0.80 0.00 135 Marion 115.71 106.18 114.58 -9.54 8.40 0.00 136 Mitcham 88.87 79.35 90.17 -9.53 10.80 0.01 137 Mitcham 102.62 93.09 93.44 -9.53 0.35 0.01 138 Mitcham 102.29 92.76 99.07 -9.53 6.30 0.01 139 Mitcham 97.75 88.22 99.19 -9.53 11.00 0.01 140 Mitcham 99.21 89.69 89.51 -9.53 -0.20 0.01 141 Mitcham 90.79 81.27 86.75 -9.53 5.48 0.01 142 Mitcham 102.85 93.32 97.66 -9.53 4.34 0.00 143 Mitcham 104.70 95.17 96.75 -9.53 1.57 0.00 144 Mitcham 107.86 98.33 97.57 -9.53 -0.80 0.00 145 Mitcham 108.37 98.84 99.63 -9.53 0.79 0.00 146 Mitcham 107.13 97.60 97.29 -9.53 -0.30 0.00 147 Noarlunga 130.45 120.91 121.11 -9.54 0.20 -0.01 148 Noarlunga 136.48 126.93 128.22 -9.54 1.29 -0.01 Appendices 389
Area Statistical Local Area 1986–1996 Code Rates Change 1986 Pred 1996 St Pos Fdb 149 Noarlunga 157.03 147.48 148.00 -9.55 0.53 -0.02 150 Noarlunga 133.81 124.27 126.51 -9.54 2.24 -0.01 151 Noarlunga 150.37 140.82 138.11 -9.55 -2.70 -0.01 152 Noarlunga 140.29 130.75 134.34 -9.55 3.60 -0.01 153 Noarlunga 136.49 126.95 129.88 -9.54 2.93 -0.01 154 Noarlunga 129.11 119.57 129.86 -9.54 10.30 -0.01 155 Willunga 141.80 132.26 134.61 -9.55 2.36 -0.01 156 Willunga 124.55 115.01 125.30 -9.54 10.30 0.00 St =Structural Change; Pos=Positional Change; Fdb=Feedback; Pred=Predicted Value
Source: ABS calculated from unpublished data
Appendices 390
Appendix 7.9: Metropolitan South Australia: Age Standardised Average Number of Children Ever Born to Women Aged 15–24 Years in 1981, 20–29 Years in 1986 and 30–39 Years in 1996 (Rates per 100 Women)
Age Standardised Average Issue Area 1981 1986 1996 Code Statistical Local Area 15–24 Years 20–29 Years 30–39 Years 1 Elizabeth 59.23 139.35 231.17 2 Elizabeth 54.51 120.09 203.28 3 Elizabeth 46.67 114.76 218.25 4 Elizabeth 54.23 119.78 208.08 5 Elizabeth 54.61 120.76 211.18 6 Gawler, Munno Para 36.32 75.80 182.28 8 Gawler, Munno Para 56.17 101.48 205.44 9 Gawler,Munno Para 36.31 90.15 205.65 10 Gawler, Munno Para 68.90 144.96 244.43 11 Gawler, Munno Para 64.65 136.79 249.31 12 Gawler, Munno Para 30.78 89.36 197.04 13 Salisbury 69.59 113.84 207.90 14 Salisbury 48.90 115.95 197.01 15 Salisbury 51.73 108.51 214.50 16 Salisbury 22.38 85.83 190.72 17 Salisbury 31.20 78.45 195.01 18 Salisbury 38.33 76.43 165.36 19 Salisbury 32.97 111.37 196.83 20 Salisbury 27.51 96.10 206.73 21 Salisbury 32.38 102.30 207.82 22 Salisbury 26.48 74.50 187.43 23 Salisbury 41.63 89.54 173.48 24 Salisbury 22.70 67.89 179.60 25 Salisbury 34.75 84.35 190.35 26 Salisbury 16.87 80.74 184.35 27 Salisbury 27.92 73.38 171.72 28 Tea Tree Gully 22.10 79.71 179.21 29 Tea Tree Gully 20.84 71.84 180.60 30 Tea Tree Gully 22.54 65.93 167.78 31 Tea Tree Gully 28.45 68.59 167.79 32 Tea Tree Gully 31.90 76.96 171.92 33 Tea Tree Gully 20.76 65.66 173.27 34 Tea Tree Gully 28.78 75.06 176.27 35 Tea Tree Gully 20.04 65.77 172.49 36 Enfield 46.43 106.20 181.68 37 Enfield 38.98 106.21 171.76 38 Enfield 25.08 57.33 141.00 39 Enfield 39.59 90.34 167.78 40 Enfield 29.47 76.41 173.00 41 Enfield 20.18 46.17 146.16 42 Enfield 16.70 40.91 142.40 43 Enfield 28.22 92.52 145.07 44 Enfield 24.94 73.86 167.37 Appendices 391
Age Standardised Average Issue Area 1981 1986 1996 Code Statistical Local Area 15–24 Years 20–29 Years 30–39 Years 45 Enfield 25.88 61.47 145.36 46 Enfield 18.58 60.38 131.54 47 Henley & Grange 11.63 38.02 137.40 48 Henley & Grange 15.32 36.41 127.95 49 Henley & Grange 7.94 42.38 131.53 50 Hindmarsh, Woodville 21.82 44.13 94.55 51 Hindmarsh, Woodville 23.54 63.21 117.78 52 Hindmarsh, Woodville 16.81 59.95 158.90 53 Hindmarsh, Woodville 27.56 56.81 161.84 54 Hindmarsh, Woodville 36.82 85.28 173.33 55 Hindmarsh, Woodville 24.16 66.48 163.05 56 Hindmarsh, Woodville 18.01 59.64 173.29 57 Hindmarsh, Woodville 22.41 64.33 161.28 58 Hindmarsh, Woodville 16.58 53.66 145.78 59 Hindmarsh, Woodville 22.33 60.91 131.89 60 Hindmarsh, Woodville 18.77 44.56 139.75 61 Hindmarsh, Woodville 10.75 51.72 156.16 62 Hindmarsh, Woodville 18.32 51.76 166.66 63 Port Adelaide 29.12 74.91 155.21 64 Port Adelaide 41.93 98.82 193.11 65 Port Adelaide 28.21 80.14 172.50 66 Port Adelaide 35.74 61.61 136.47 67 Port Adelaide 27.07 56.45 135.41 68 Port Adelaide 36.53 73.22 161.01 69 Port Adelaide 39.74 84.56 166.47 70 Thebarton 22.47 47.25 91.46 71 Thebarton 20.67 36.81 101.13 72 West Torrens 14.68 40.15 133.84 73 West Torrens 10.91 37.87 155.39 74 West Torrens 15.02 42.72 145.42 75 West Torrens 21.42 48.37 133.00 76 West Torrens 12.48 32.62 130.88 77 West Torrens 13.10 28.44 115.22 78 West Torrens 11.51 99.38 133.69 79 West Torrens 13.74 34.60 110.61 80 Adelaide 6.62 19.60 82.31 81 Burnside 8.06 26.59 121.51 82 Burnside 7.08 28.70 129.84 83 Burnside 3.87 26.79 152.13 84 Burnside 1.95 27.17 144.25 85 Burnside 13.10 35.88 121.85 86 Burnside 9.22 23.21 132.03 87 Campbelltown 19.33 53.34 150.55 88 Campbelltown 19.02 53.26 153.05 89 Campbelltown 29.69 65.56 172.30 90 Campbelltown 17.11 61.97 174.45 91 Campbelltown 13.74 48.31 161.06 92 Campbelltown 18.39 47.47 135.56 Appendices 392
Age Standardised Average Issue Area 1981 1986 1996 Code Statistical Local Area 15–24 Years 20–29 Years 30–39 Years 93 Campbelltown 10.18 35.75 132.02 94 East Torrens 17.15 47.00 162.26 95 Kens & Norwood 10.84 16.83 78.71 96 Kens & Norwood 8.59 14.90 88.98 97 Payneham 13.00 27.45 119.10 98 Payneham 12.14 41.29 110.64 99 Payneham 11.56 39.00 125.83 100 Prospect 18.35 41.34 125.67 101 Prospect 10.00 38.89 124.09 102 Prospect 10.63 40.53 125.21 103 St Peters 11.02 25.06 105.64 104 St Peters 5.74 27.21 128.90 105 Stirling 17.80 57.90 176.21 106 Stirling 31.02 58.96 160.72 107 Stirling 12.68 41.22 162.77 108 Unley 3.80 22.32 141.70 109 Unley 12.28 25.20 106.89 110 Unley 10.14 17.52 89.21 111 Unley 7.59 20.79 103.72 112 Unley 5.54 27.35 128.05 113 Unley 9.85 34.74 109.44 114 Unley 20.30 34.12 118.26 115 Walkerville 7.02 25.12 122.43 116 Brighton 10.83 26.25 139.36 117 Brighton 9.63 42.73 129.37 118 Brighton 9.70 33.81 142.34 119 Glenelg 10.59 34.52 107.98 120 Glenelg 10.38 27.59 94.35 121 Glenelg 11.29 30.24 121.01 122 Happy Valley 18.82 66.77 180.49 123 Happy Valley 21.44 61.66 169.01 124 Happy Valley 26.22 70.92 180.98 125 Happy Valley 18.28 66.58 182.58 126 Marion 11.28 41.96 128.37 127 Marion 19.01 39.73 142.48 128 Marion 24.90 53.21 137.12 129 Marion 15.50 46.40 126.67 130 Marion 18.85 50.19 125.13 131 Marion 16.07 51.25 152.15 132 Marion 28.07 86.20 154.09 133 Marion 15.73 54.62 147.92 134 Marion 22.82 74.30 158.11 135 Marion 18.28 62.93 177.79 136 Mitcham 11.29 35.49 143.93 137 Mitcham 15.11 45.59 142.75 138 Mitcham 12.04 34.73 155.39 139 Mitcham 6.23 31.54 152.47 140 Mitcham 7.21 32.71 138.72 Appendices 393
Age Standardised Average Issue Area 1981 1986 1996 Code Statistical Local Area 15–24 Years 20–29 Years 30–39 Years 141 Mitcham 6.34 37.90 133.96 142 Mitcham 6.82 31.86 151.76 143 Mitcham 11.63 41.01 146.37 144 Mitcham 15.45 45.53 155.60 145 Mitcham 14.26 46.95 158.60 146 Mitcham 11.13 45.33 154.66 147 Noarlunga 16.11 73.80 180.17 148 Noarlunga 30.48 87.93 187.54 149 Noarlunga 54.93 118.68 206.02 150 Noarlunga 26.00 81.861 181.90 151 Noarlunga 47.12 111.78 198.63 152 Noarlunga 31.68 90.90 197.92 153 Noarlunga 31.02 89.30 188.91 154 Noarlunga 30.72 75.24 187.09 155 Willunga 39.49 98.18 192.64 156 Willunga 21.70 75.51 185.19 Source: ABS calculated from unpublished data
References 394
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