Migration in the Murray-Darling Basin Australia during the Millennium Drought Period
BY
ERICK HANSNATA
March 2017
THESIS
Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Economics at the University of Canberra Australian Capital Territory, Australia
Doctoral Committee:
Professor Laurie Brown, Chair Professor Anne Daly Associate Professor Riyana Miranti
Abstract
The latest prolonged drought or the Millennium Drought period in the Murray Darling Basin (MDB) highlights several key issues for the people, businesses, and authorities in the Basin. Studies related to the event have mostly focused on water management, environmental issues and policy assessment. However, the direct socio economic impact during the drought also raises the question of population issues, particularly migration activities. Thus, identifying and understanding patterns of out migration as well as in migration into the Basin during the drought in the MDB are essential to fully appreciate the debates on MDB water policies and water for the future. This thesis examines the relationship between migration activities and socio economic factors, with a particular focus on the environmental shock of the drought. This has not been undertaken before in the literature on the MDB. In order to examine migration in the MDB, the study develops a unique dataset drawing on three different sources to capture migration data, socio economic indicators and environmental factors. The analysis assessing internal migration by age groups, area classifications, and remoteness index shows that the migration follows the fundamental concept of the gravity model. In the empirical estimation, the key environmental variable of rainfall data is applied indirectly as an instrument for agricultural production, where as an endogenous parameter it affects migration activity. Several econometric tests are also conducted by age group classifications and areas outside the Basin for comparison purposes. Besides highlighting fundamental determinants of migration such as the new classical concept of income differentials, the findings confirm that environmental factors influenced people’s mobility within the period, and the estimation is validated with a strong instrument.
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Acknowledgments
I would like to express my deepest appreciation to my chair, Professor Laurie Brown, for your endless support to completing my PhD journey. It has been a long winding road for me and your encouragement and wisdom have helped to grow my skills as a researcher. I also would like to extend my sincere gratitude to Professor Anne Daly and Dr. Riyana Miranti for your tremendous support, guidance and continuous advice since the beginning of my study that allowed me to conduct my research and to deal with hardship. My family and I will be forever indebted to you. I also would like to thank my previous committee members, Professor Alan Duncan, Dr. Brenda Dyack, Professor Helen Berry, A/Professor Xiaodong Gong and Dr. Neil Byron for the support and advice, particularly during the early stage of my PhD.
Special thanks to my wife, Mayada Hansnata, who at the same time was also pursuing her PhD. Words cannot express my appreciation for your support and Love during our arduous PhD journeys. My children, Aidan Hansnata and Adrien Hansnata, thank you for your understanding during demanding times. You all are my rock and keeping my spirit up. To my parents, Ana Indrawati and M. Nasir and my parents in laws Sultana Faradz and M. Hussein Gasem, thank you for your continuous words of encouragement and prayers that have sustained and kept me going to see the light at the end of the tunnel. Last but not least, to my sisters and my sister in laws and brother in laws, thank you for your prayers and for being all ears.
Thank you to my friends and colleagues throughout my PhD journey; Dr. Bimo Wijayanto, Dr. Dewa Wisana, Dr. Adek Muchtar, Dr. Tri Mulyaningsih, Lorna Evans, Adrienne McKenzie Rebecca Cassells, Annie Abello and all the NATSEM team for the good times we shared and the friendship. You have made this journey more bearable. Finally, thank you to A/Professor Budy P Resosudarmo and Dr. Yogi Vidyattama for being my mentors and for your advices that enabled me to pursue further studies.
This thesis would not be possible without funding and the support of the wonderful people from the Collaborative Research Network of Murray Darling Basin Futures (CRN MDB futures ) and the Institute for Governance and Policy Analysis (IGPA) at the University of Canberra who have spared their time to support my research.
This thesis was edited by Dr. Justine McNamara of Next Version Editing, and editorial intervention was restricted to Standards D and E of the Australian Standards for Editing Practice .
Last but not least, thank you to all my bicycles for the companionship of almost 10,000 km during my PhD journey. You “guys” always made my mind clear!
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Table of Contents
Abstract ...... i Acknowledgments ...... ii List Tables and Figures ...... viii List of Abbreviations ...... xi Chapter 1: Migration and Environmental Issues in the Murray-Darling Basin during the Millennium Drought Period ...... 1 1.1. Overview ...... 1 1.2.1. Population Change and Indications of Migration in the MDB ...... 5 1.2.2 Employment and Income Conditions in the Murray Darling Basin...... 8 1.2.3. Business and Industry in the Murray Darling Basin ...... 10 1.3. Environmental Problems in the Murray Darling Basin ...... 12 1.3.1. Environmental Events and Weather Anomalies in the MDB ...... 13 1.4. Research Framework: Literature Gap Related to the MDB ...... 17 1.4.1. Research Aims ...... 19 1.4.2. Research Questions...... 20 1.5. Methodology: Theoretical Framework and Empirical Analyses ...... 20 1.6. Thesis Structure ...... 21 1.7. Conclusion ...... 21 Chapter 2: Literature Review: the Evolution of Migration Theory ...... 23 2.1. Definition and Brief History of Migration ...... 23 2.2. The Economics of Migration: Basic Theoretical Framework ...... 26 2.2.1. Labour Mobility and the Selectivity Process ...... 26 2.2.2. Human Capital Investment ...... 29 2.3. New Classical Approach: Determinants and Impacts of Migration ...... 31 2.3.1. Labour Market Equilibrium ...... 32 2.3.2. Push–Pull Migration Model and Intervening Factors ...... 34 2.3.3. Individual Characteristics as Control Variables ...... 36 2.3.4. The Role of Information in Migration ...... 39 2.3.5. Social Capital and Risk Analysis: Feedback to the New Classical Approach ...... 41 2.4. Global Migration Pattern ...... 44 2.4.1. Incentives and Impacts of Global Migration ...... 45
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2.5. Migration in Australia ...... 47 2.6. Environmental Migration ...... 50 2.6.1. Rapid Onset Hazard and Slow Onset Hazard within the Environmental Migration Framework ...... 51 2.6.2. The Discussion of Environmental Migration based on Empirical Studies ...... 53 2.6.3. Environmental Migration and the Climate Change Issue ...... 58 2.6.4. Enhanced Framework and Methods related to Environmental Migration: Research Gap...... 61 2.7. Link between the Literature Gap to Migration in the Murray Darling Basin (MDB) .... 64 Chapter 3: Data Construction and Methodology ...... 69 3.1. Introduction ...... 69 3.2. Sources and Scope ...... 69 3.3. Migration data ...... 72 3.3.1. Migration based on ‘One Year Mobility’ ...... 74 3.3.2. Migration based on ‘Five Year Mobility’...... 75 3.3.3. Constructing migration activities: Net Migration, In Migration, and Out Migration76 3.4. Socio Economic Data ...... 77 3.5. Environmental Data ...... 81 3.6. Developing the Datasets ...... 85 3.6.1. Dataset I: Cross Section Analysis of Migration 2001–2006 ...... 86 3.6.2. Dataset II: Cross Section Analysis of Migration 2006–2011 ...... 87 3.7. Specification for the analysis ...... 87 3.8. Summary ...... 88 Chapter 4: Internal Migration in the Murray-Darling Basin during the Millennium Drought Period: Evidence of Mobility and Regional Patterns from the Census of Population and Housing in 2006 and in 2011 ...... 89 4.1. Overview: Internal Migration in the Murray Darling Basin (MDB) ...... 89 4.2. Internal Migration in the First Phase of 2001–2006 (five year mobility) ...... 94 4.3. Internal Migration in the Second Phase of 2006–2011 (five year mobility) ...... 100 4.4. One Year Mobility 2005–2006 and 2010–2011 ...... 105 4.5. The Migration Pattern in the Murray Darling Basin: The Gravity Model of Internal Migration...... 107 4.5.1. Mobility Pattern in Toowoomba Regional ...... 109 4.5.2. Mobility Pattern in Tamworth Regional City ...... 114
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4.5.3. Mobility Pattern in Wagga Wagga ...... 119 4.5.4. Mobility Pattern in Greater Bendigo and Greater Shepparton ...... 123 4.5.5. Mobility Pattern in Mildura Rural City (RC) ...... 129 4.6. Conclusion ...... 134 Chapter 5: Empirical Analysis (1): Environmental Migration in the Murray- Darling Basin (2001–2006) ...... 137 5.1. Introduction ...... 137 5.2. Theoretical Framework ...... 141 5.2.1. The Model: Migration Decision at the Individual Level ...... 142 5.3. Data Specification ...... 145 5.3.1. Explanatory Variables ...... 147 5.4. Empirical Strategy ...... 151 5.4.1. Instrument Variables ...... 152 5.4.2. Effect on Income ...... 154 5.5. Estimation Model ...... 155 5.5.1. Estimation with a Single Endogenous Regressor and Multiple Instruments...... 156 5.5.2. Estimation Properties and Instrument Validity Test...... 159 5.5.3. Multicollinearity Issues ...... 160 5.6. Estimation Result ...... 161 5.6.1. Endogenous Variables Estimation and the Endogeneity Test ...... 163 5.6.2. Outcome for General Migration ...... 167 5.6.3. Migration Pattern by Age Groups in the MDB ...... 174 5.6.4. Comparison Analysis by Age Groups in the Area outside the MDB ...... 179 5.7. Conclusion ...... 182 Chapter 6: Empirical Analysis (2): Environmental Migration in the Murray- Darling Basin (2006–2011) ...... 185 6.1. Overview: Conditions in the Recurrent Drought Years ...... 185 6.2. Data Specification ...... 190 6.2.1. Trend of Explanatory Variables ...... 192 6.3. Empirical Strategy ...... 196 6.3.1. Effect on Income ...... 198 6.4. Estimation Model ...... 199 6.5. Estimation Result ...... 201 6.5.1. Multiple Instruments Estimation and Endogeneity Test ...... 203 vi
6.5.2. Outcome for General Migration ...... 205 6.5.3. Migration Patterns by Age Groups in the MDB and Outside the MDB...... 210 6.6. Migration in the Irrigation Area and the Impact of Policy ...... 217 6.7. Conclusion ...... 221 Chapter 7: Conclusion and Discussion ...... 223 7.1. Overview ...... 223 7.2. Key Findings and Contributions ...... 224 7.2.1. Migration Patterns in the MDB during the Millennium Drought Period ...... 224 7.2.2. Socio Economic Drivers...... 225 7.2.3. The Role of the Environmental Variables on Migration in the MDB ...... 227 7.2.4. Irrigation and the Impact of Water Policy ...... 228 7.3. Migration Issues and the Basin Plan ...... 228 7.4. Migration Issues and the Agricultural Competitiveness White Paper ...... 230 7.5. Caveats and Further Research ...... 231 Appendices ...... 235 Appendix IA: Migration Rates at LGA Level in the MDB (2001 2006) ...... 235 Appendix IB: Migration Rates at LGA Level in the MDB (2006 2009) ...... 239 Appendix IIA: Barrios, Bertinelli, and Strobl Model (2006) ...... 244 Appendix IIB: Marchiori, Maystadt, and Schumacher Model (2012) ...... 245 Appendix IIC: Reuveny and Moore Model (2009) ...... 247 Appendix IIIA: Non ABS Structures in Australian Statistical Geography Standard ...... 249 Appendix IIIB: Questions 9 and 10 on the Census Household Form 2011 ...... 249 Appendix IIIC: Sample of Selected Weather Stations (LGAs in NSW) ...... 250 References ...... 261
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List Tables and Figures
Tables
Table 1.1 Expansionary Phase and Mature Phase in the MDB 4 Table 1.2 Population Change in the Murray Darling Basin Compared with States and National Figures 5 Table 1.3 Migration in MDB and Rest of Australia 2001 2011 7 Table 1.4 Employment Status in the MDB and Australia 1996 2011 9 Table 1.5 Gross Valued of Agricultural Production 2000 2006 11 Table 1.6 Employed Persons by Industry in the Murray Darling Basin 12 Table 2.1 Family and Demographic Variables in Migration 38 Table 2.2 Phases of Research and Policies towards Migration and Development 42 Table 2.3 Socio Economic of Immigrants and Natives in the U.S 47 Table 2.4 Empirical Evidence of Environmental Induced Migration 56 Table 2.5 Studies to Link Climate Change, Income Effect, and Migration 60 Table 2.6 Studies Assessing the Murray Darling Basin during the Millennium Drought Period 67 Table 3.1 Mobility Matrix Dataset (LGA of Usual Residence Five Years Ago 2011) 76 Table 3.2 Migration Matrix (Example from 5 year Mobility) 79 Table 3.3 Summary of Applied Explanatory Variables 81 Table 3.4 Average LGA Annual Rainfall (mm) States and MDB Area (2000 2013) 84 Table 4.1 Population Change in Selected Urban Centres in the MDB, 2001 2011 90 Table 4.2 The Proportion (%) of Area Classification in 2006 and 2011 91 Table 4.3 Main origin LGAs entering Toowoomba Regional 111 Table 4.4 Main origin LGAs entering Tamworth Regional 117 Table 4.5 Main origin LGAs entering Wagga Wagga 121 Table 4.6 Main origin LGAs entering Bendigo and Shepparton 127 Table 4.7 Main origin LGAs entering Mildura 132 Table 5.1 Summary Statistics of Estimation Data in the First Phase Mobility at LGA level 149 Table 5.2 Collinearity between variables in the main model in the first phase analysis161 Table 5.3 OLS Estimation Result of Migration 2001 2006 163 Table 5.4 Comparison of Endogenous Regressors 165 Table 5.5 Residual Comparison of Endogeneity Test 167 Table 5.6 2SLS (IV estimation) Result of Migration 2001 2006 169 Table 5.7 Migration Rates with Average Wage and Salary in the MDB 2001 2006 170 Table 5.8 Migration Rates with Average House Value and Approved Dwelling Number in the MDB 2001 2006 171 Table 5.9 Migration Rates with Educational Level Value and Information Service in the MDB 2001 2006 173 Table 5.10 LGAs with Highest GVAP with Rainfall level 2002 2003 and 2004 2005 in the MDB 174 Table 5.11 2SLS (IV estimation) Result of Migration in the MDB by Age Groups 2001 2006 177 Table 5.12 OLS Estimation for Young Age Groups Migration in the MDB 178 Table 5.13 2SLS (IV estimation) Result of Migration Outside the MDB Area by Age Groups 2001 2006 181 Table 6.1 Summary Statistics of Estimation Data in the Second Phase Mobility 194 Table 6.2 Collinearity between variables in the main model in the 2nd phase analysis197
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Table 6.3 OLS Estimation Result of Migration 2006 2009 202 Table 6.4 Instrument Estimation and Validation of Endogenous Variables 204 Table 6.5 2SLS (IVestimation) Result of Migration 2006 2009 206 Table 6.7 Migration Rates with Average Monthly Mortgage Payment and Total Approved Dwelling Number in the MDB 2006 2009 208 Table 6.8 LGAs with Highest GVAP with Rainfall level 2006 and 2007 2008 in the MDB 209 Table 6.9 2SLS (IV estimation) Result of Migration in the MDB by Age Groups 2006 2011 211 Table 6.10 OLS Estimation for Young & Working Age Groups Migration in the MDB 213 Table 6.11 OLS Estimation for Young Age Groups Migration in the OUTSIDE MDB area with Agricultural Production 214 Table 6.12 Irrigated and Non Irrigated area Classification by LGA 218 Table 6.13 OLS estimation for Irrigated lands in the MDB and National Level 220
Figures
Figure 1.1 Murray Darling Basin Area 2 Figure 1.2 Spatial Analysis of Net Migration Rate in the MDB 7 Figure 1.3 Young Age Workers (25 34) Decline in Some Towns in the MDB 8 Figure 1.4 Spatial Pattern of Young Age Migration (15 24) 8 Figure 1.5 Declining Agricultural Workers in the MDB 9 Figure 1.6 Water Storage in the Large Dams in the MDB 13 Figure 1.7 The Fluctuation of Long Term Annual Inflow: Frequent Droughts and Floods 15 Figure 1.8 Rainfall Anomalies during the Early Period of the Millennium Drought 16 Figure 1.9 Research Framework: Linking Literature and MDB 18 Figure 2.1 Market Equilibrium and Efficiency Gains from Migration 32 Figure 2.2 Push Pull Migration Model 35 Figure 2.3 Migration with Age Group 37 Figure 2.4 Environmental Decision Framework: Rapid Onset and Slow Onset 52 Figure 2.5 Enhanced Environmental Migration Framework 63 Figure 3.1 Climate Data Online provide by Bureau of Meteorology Australia 83 Figure 4.1 Population Composition by Age Group and Gender in Census 2011 and Census 2006 92 Figure 4.2 Mean of net migration rates at LGA level of five year mobility by age groups 2001 2006 96 Figure 4.3 Mean of net migration rates at LGA level of five year mobility by area type, remoteness, and age groups 2001 2006 98 Figure 4.4 Mean of net migration rates at LGA level of five year mobility by age groups 2006 2011 102 Figure 4.5 Mean of net migration rates of five year mobility by area type, remoteness, and age groups 2006 2011 104 Figure 4.6 Average net migration of one year mobility at LGA level by age groups (2005 2006 and 2010 2011) 106 Figure 4.7 Toowoomba Regional (R) in the MDB Map of LGAs 110 Figure 4.8 Average Annual Wage in Toowoomba and Surrounding LGAs 113 Figure 4.9 Tamworth Regional (A) in the MDB Map of LGAs 114 Figure 4.10 Average Annual Wage in Tamworth Regional and Surrounding LGAs 118 Figure 4.11 Wagga Wagga (C) in the MDB Map of LGAs 119
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Figure 4.12 Average Annual Wage in Wagga Wagga and Surrounding LGAs 123 Figure 4.13 Greater Bendigo and Greater Shepparton in the MDB Map of LGAs 124 Figure 4.14 Average Annual Wage in Bendigo and Shepparton Surrounding LGAs 128 Figure 4.15 Mildura in the MDB Map of LGAs 130 Figure 4.16 Average Annual Wage in Mildura and Surrounding LGAs 133 Figure 5.1 Mean Economic, Social, and Environmental Value in MDB and Non MDB/ROA Area in the First Phase Mobility at LGA level 150 Figure 5.2 Average Annual Rainfall Australia and MDB 153 Figure 5.3 Scatter Plot of Migration ( y-axis ) and Personal Income ( x-axis ) 154 Figure 5.4 Scatter Plot of Endogenous Variables ( y-axis ) and Rainfall ( x-axis ) 164 Figure 6.1 Economic Indicator between 2007 and 2010 in the MDB 188 Figure 6.2 Mean Economic, Social, and Environmental Value in MDB and Non MDB/ROA Area in the Second Phase Mobility 195 Figure 6.3 Scatter Plot of Migration and Personal Income 199
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List of Abbreviations
ABS Australian Bureau of Statistics ACT Australian Capital Territory ARIA Accessibility/Remoteness Index of Australia ASGC Australian Standard Geographical Classification ASGS Australian Statistical Geography Standard ATO Australian Taxation Office BoM Bureau of Meteorology CRN MDB futures Collaborative Research Network Murray Darling Basin Futures CPI Consumer Price Index DWH Durbin Wu Hausman GDP Gross Domestic Product GVAP Gross Value of Agricultural Production ILO International Labour Organisation IOM International Organisation for Migration IPPC Intergovernmental Panel on Climate Change IV Instrumental Variable LGA Local Government Area LPS Legalized Population Survey MDB Murray Darling Basin MDBA Murray Darling Basin Authorities MDBC Murray Darling Basin Commission NATSEM National Centre for Social and Economic Modelling NELM New Economics of Labour Migration NHS National Health Service NLS National Longitudinal Survey NRP National Regional Profile NSW New South Wales NT Northern Territory NWI National Water Initiative OLS Ordinary Least Squares PUR1P Place of Usual Residence One Year Ago PUR5P Place of Usual Residence Five Years Ago PV Present Value PWT Penn World Tables QLD Queensland RCS Reference Climate Stations RoA Rest of Australia RSMS Regional Sponsored Migration Scheme SA South Australia SA1 Statistical Area Level 1 SD Statistical Division SLA Statistical Local Area SSD Statistical Subdivision SSRM State Specific Regional Migration TAS Tasmania UAI1P Usual Address One Year Ago UAI5P Usual Address Five Years Ago UNHCR United Nations High Commissioner for Refugees VIC Victoria WA Western Australia 2SLS Two Stage Least Squares
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Chapter 1: Migration and Environmental Issues in the Murray- Darling Basin during the Millennium Drought Period
1.1. Overview
This thesis examines migration in the Murray Darling Basin (MDB), Australia. First, the analysis investigates migration patterns within the MDB area during the prolonged drought in the early 21st century, known as the Millennium Drought or the Big Dry. Moreover, this examination also attempts to validate a fundamental theory of rural–urban migration 1. Second, the thesis extends the analysis of the migration patterns to identifying and quantifying the role of not only socio economic determinants, but also environmental factors during the Millennium Drought period defined as 2001 to 2009 2. Two empirical studies were undertaken. The first estimates migration drivers during 2001–2006, representing the first half of the Millennium Drought. This study identifies the extent to which environmental aspects play a role in the migration activity, utilising census data in 2006. The second empirical estimation examines the drivers and their impact during 2006–2009, the latter half of the drought. This analysis uses migration data from the 2011 census.
The main aim of the thesis is to investigate migration drivers related to social and economic factors, demographic characteristics, development indicators and specifically the role of environmental aspects in the Basin. The analysis includes some benchmark variables that affect migration such as personal income and business income, which are main determinants in the context of the new classical theory of migration.
The MDB is the icon of Australia’s river system and characterised as the key centre of agricultural production in Australia (Figure 1.1). The Basin area is around 14 per cent of the continent, covering four states (Queensland, New South Wales, Victoria and South Australia) and the entire Australian Capital Territory. Total Basin area is over 1 million km 2, extending approximately 750 kilometres from west to east, and 1,450 kilometres from north to south
1 Ravenstein (1885) 2 Many studies describe the drought as starting in the late 1990s (Leblanc et al. 2012), however this study follows van Dijk (2013), who dates the duration of the Millennium Drought as being between 2001 and 2009. The description is also based on precipitation data from the Bureau of Meteorology (BoM) and a study by Timbal (2010), which confirms that in the period between 2001 and 2009 Australia had its lowest average rainfall. Timbal (2010) utilises the precipitation data of, when from three severe drought events were: the Federation drought, the War period drought, and the Millennium drought. Of these events, the Millennium Drought period has the lowest average rainfall..
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(ABS, 2009). The river system consists of several rivers, with the Murray and the Darling Rivers as the main branches, and the Murrumbidgee, Lachlan, Goulburn, Campaspe and the Namoi Rivers as main tributaries. Currently, there are around 2.1 million people living in the MDB, unchanged between the most recent census in 2011 and the 2006 census (MDBA 2012).
Figure 1.1: Murray-Darling Basin Area
Source : Murray Darling Basin Authority (MDBA), Ben Spraggon Map
In brief, the ‘white’ history of the MDB starts at the beginning of the 19th century, with the expansion of early colonial settlers requiring more farmland and pastures for agricultural production. Colwell and Finch (1978) note that within several years, journeys by private explorers and wandering pastoralists reached the Lachlan River and Murrumbidgee River, and by 1829 official explorers attained the eastern extremities of the Darling River. The discoveries encouraged other explorers, such as Captain Charles Sturt, to travel along the Murrumbidgee River and in 1830 Sturt encountered ‘a broad and noble river’ which he named the Murray. Since then, the basin areas around these river systems have been settled by people for inland farming, and for vital transportation of pastoral pioneers. The gold rush in Victoria and New South Wales in the 1850s attracted workers, which then created many small towns to support a
2 distribution line from mining activities, as well as the increasing agricultural commodities like wool destined for an expanding English market.
The expanding agricultural activity in the basin generated a high dependency on unregulated water utilisation, which could result in conflict (Leblanc et al. 2012). This marked the involvement of government in river development and water management (Table 1.1) as stated in Quiggin (2001). This involvement was initiated with the Irrigation Act in 1886 and the Water Rights Act in 1896, which generally provided control over irrigation while maintaining the river as a transport channel. Regulation became substantial after federal and state governments signed the River Murray Agreement in 1915–1917, including the establishment of the River Murray Commission (RMC) to control river water use. This remained in place until 1982 when the issue of salinity led to a revision to the agreement.
Following the amendment, all states and territory covered by the Basin area established the MDB Commission (MDBC) in 1993 to promote sustainable water utilisation. Although policies were intended to support irrigation industries and stimulate economic growth, several issues were also raised such as water competition, environmental factors and pressure from communities for water infrastructure (Connell, 2007).
Clearly, water has been a crucial issue in the MDB, including during the Millennium Drought, i.e. between 2001 and 2009. The growing population increased the demand on water resources over time, and created water entitlements in many areas in the MDB. Moreover, in 2004–2005, people in the MDB mostly (84%) used surfaced water derived from the river system (ABS 2009). This fact highlighted the issue of over allocation of water in the Basin, and although human water needs were seen as a priority, water management of the Basin also needed to consider economic and environmental outcomes.
Therefore, the Authority had to develop a plan to balance the extraction of water resources, so that sufficient water would be available for human needs, economic activity and for the environment. Thus, the Water Act 2007 was implemented, followed by the Basin Plan 2010 (the Plan), providing a strategic and integrated framework for water management. In general, the Plan ensures water quality improvement, sufficient water for the environment, a consistent framework for water trading, and continuous monitoring (MDBA 2012).
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Table 1.1: Expansionary Phase and Mature Phase in the MDB
Year Events Government Involvement (Policies) First Significant Conflict Diversion for Irrigation SA and Victorian Government of 1880s threatened the important Chaffey Brothers transport of the Murray River Corowa Conference of 1901 1902 Federation Drought Management Waters of Murray Expansionary River Phase Negotiation between States 1915 River Murray Agreement (NSW, VIC, SA) Established River Murray 1917 Commission 1930 Construction of dams, weirs,
1970s locks and Irrigation area
Government Involvement Year Events (Policies) Environmental problems and 1980s competition for water use 1982& River Murray Agreement Murray Darling Basin Agreement 1987 Amendment 1987 Full implementation of new Mature Phase 1993 agreement Established MDB Act 1993 of Over- MDBC, MDBMC, and CAC. allocation, Introduced a moratorium on Land 1995 1997 the future growth in diversions Degradation, Water Cap & Water Trading Salinity Millennium Drought Problem 2001 2009 National Water Initiative 2004 (The Big Dry) Initiate Water Buyback for the Water Act 2007, Water Act 2008 environment from willing Amendment 2008 sellers 2010 Introducing the Basin Plan Source : Quiggin (2001)
This chapter provides a brief overview of social, economic and environmental conditions in the last decade, particularly when the MDB and Australia experienced one of the longest drought periods in recorded history. The analysis below tries to provide a rationale for the aims and the framework of this thesis, which are outlined in section 1.4, followed by a brief description of methods in section 1.51.2. Social and Economic Conditions in the MDB. In the last two decades, social and economic conditions in the MDB have changed compared with areas outside the MDB or Rest of Australia (RoA). Data from the Australian Bureau of Statistics (ABS), including from the Census of Population and Housing, shows that the socio economic
4 conditions in the MDB are below national averages. The figures presented include population, industry, and employment conditions.
1.2.1. Population Change and Indications of Migration in the MDB
Based on the recent 2011 census, more than 2 million people live in the MDB or almost 10 per cent of the Australian population. However, population figures from the census show that the average population growth in the MDB is somewhat lower than for other states and the national figure (Table 1.2). Between 1996 and 2011, the number of people living in the Basin rose by only 3.2 per cent, less than one half of national growth at 8.7 per cent.
Table 1.2: Population Change in the Murray-Darling Basin Compared with States and National Figures Area POPULATION CHANGE (%) Average 1996 2001– 2006– 1996 2001 2006 2011 (1996 2001 2006 2011 2011) NSW 6038700 6371700 6549175 6917656 5.5 2.8 5.6 4.6 VIC 4373500 4645000 4932423 5354040 6.2 6.2 8.5 7.0 QLD 3368900 3655100 3904532 4332737 8.5 6.8 11.0 8.8 SA 1427900 1467300 1514341 1596570 2.8 3.2 5.4 3.8 NT 195100 202729 192898 211944 8.2 4.8 9.9 4.4 ACT 299200 311900 324036 357218 4.2 3.9 10.2 6.1 WA 1726095 1851252 1959087 2239171 7.3 5.8 14.3 9.1 MDB 1905600 1921840 2004560 2094997 0.9 4.3 4.5 3.2 RoA 15986800 17050560 17850727 19412722 6.7 4.7 8.8 6.7 Australia 17892400 18972400 19855287 21507719 6.0 4.7 8.3 8.7 Sources : Census of Population and Housing 1996 2011(usual residence on census night) Notes: MDB=Murray Darling Basin, RoA=Rest of Australia
The data also shows that the states of Queensland and Western Australia experienced an increase in average growth between 1996 and 2011 above national growth, indicating people move from other areas to these states. A report of the statistical profile of the MDB (Pink 2008) shows that, although the MDB area experienced a decline in population growth, there was a significant population growth in the major urban centres inside the Basin. For example, several areas increased their population by more than 20 per cent such as Bendigo (27%), Mildura (25%) and Shepparton (22%), and other areas were also above the national figure such as Bathurst (12%) and Toowoomba (13%). Based on population data between censuses at the national level, it can be seen that there is an indication of internal migration, with significant mobility between states. More specifically, the analysis from the 2006 Census of Population
5 and Housing and the 2011 census can detect people’s mobility, and shows the activity of in migration and out migration, therefore obtaining the net migration rate.
In terms of migration, ABS (2011) defines Internal Migration as ‘ the movement of people from one defined area to another within a country ’. In the 2006 and 2011 Census of Population and Housing, the activity of migration can be obtained from one year mobility or five year mobility 3. The standard calculation of net migration follows a study by Greenwood (1975) that net migration is in migration minus out migration or , where ( = ∑ − ∑ ) is net migration from i to j and GM is gross migration.
Table 1.3 shows a comparison between migration activity within and outside the MDB area based on the Local Government Area (LGA). The data shows that within the framework of 5 year mobility, migration activity was common for both areas (MDB and RoA). The table also displays that although the MDB population seems less mobile than the RoA, there is an indication that the Basin area experienced a negative net migration for both periods of census in 2006 and 2011, where negative net migration stood at 2.68 per cent between 2001 and 2006 and 2.78 per cent between 2006 and 2011 (Appendix IA and IB provide the detail of migration rates in the MDB area at LGA level).
Furthermore, in terms of spatial analysis at LGA level, the majority of areas in the MDB experienced either zero net migration rates or negative net migration rates, and only limited areas had a positive net migration rate. Based on the preliminary report of the migration team at the National Centre for Social and Economic Modelling (NATSEM) at the University of Canberra, which is part of the Collaborative Research Network of Murray Darling Basin Futures (CRN MDB futures )4, the areas with negative net migration spread from the north to the south of the Basin, including remote areas in Queensland and irrigation areas in New South Wales and Victoria (Figure 1.2.) 5. Consistent with a report from ABS (2009), the areas with a
3 The method to construct data from the Census will be explained in chapter 3. 4 The collaborative research network, led by the University of Canberra, was built on collaboration between cross disciplinary research leaders across four Australian universities and partnership with key government agencies. CRN MDB futures is a $14m investment which includes $6.3m in funding from the Department of Education (previously Department of Innovation, Industry, Climate Change, Science, Research and Tertiary Education) over four years commencing in 2011. http://www.canberra.edu.au/research/collaborations/murray darling crn/about
5 The report can be accessed from the link: http://natsem.canberra.edu.au/storage/1 Cassells%20 %20Internal%20Migration%20in%20the%20MDB_workshop.pdf#page=31&zoom=auto,0,479
6 positive net migration rate are those major urban centres with high average population growth, such as Bathurst (NSW) and Bendigo (Victoria).
Table 1.3: Mean Migration Rates in MDB and Rest of Australia at LGA level 2001–2006 and 2006-2011 based on five year mobility
in-migration out-migration net migration Period Area rates rates rates MDB 20.87 23.54 2.68 2001 2006 Non MDB/RoA 24.00 26.64 2.64 MDB 17.24 20.02 2.78 2006 2011 Non MDB/RoA 23.33 23.81 0.48
Sources : Census of Population and Housing 2006 and 2011 Notes: MDB=Murray Darling Basin, RoA=Rest of Australia
Figure 1.2: Spatial Analysis of Net Migration Rate in the MDB
Source : NATSEM Interactive Maps, Cassells, R and Berry, H (2013). The map can be accessed at http://web.natsem.canberra.edu.au/maps/MDB/LGAinMDB/atlas.html
In a context where youth are basically more mobile, the ABS report of Socio Economic Context in the Murray Darling Basin (2009) also indicates that some areas in the Basin lose a considerable number of younger workers. The largest decline in the MDB between 2001 and 2006 was the age group between 25–34 years of age, which comprised approximately 11.7 per cent of the Basin population in 2006. For example, several small–medium towns (2,000–10,000 residents) in the MDB show a significant decline of this young age group between the 2001 and 2011 censuses (Figure 1.3). Moreover, the report also highlights that several towns in the irrigation areas experienced a loss of young workers such as Deniliquin (Murray region), Hay (Murrumbidgee region), Beechworth (Ovens region) and Bourke (Barwon Darling region).
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Figure 1.3: Young Age Workers (25-34) Decline in Some Towns in the MDB
Decline in Population Age 25 34 (Medium Small Towns [2000 9999]) 1600 Deniliquin (Murray) 1400 Hay (Murrumbidgee) Beechworth (Ovens) 1200 Bourke (Bar Darling) 1000 St Arnaud (Lod Avoca) Moree (Gwydir) 800 Stawell (Wimmera) 600 Pittsworth (Cond Balo) No.Person Kyabram (Goul Broken) 400 Cobar (Bar Darling) Oakey (Cond Balo) 200 Gunnedah (Namoi) 0 2001 2006 2011 Sources : Census of Population and Housing 2001, 2006, 2011. ABS report (2009) Figure 1.4: Spatial Pattern of Young Age Migration (15–24)
Sources: NATSEM Interactive Maps, Cassells, R and Berry, H (2013). The map can be accessed at http://web.natsem.canberra.edu.au/maps/MDB/LGAinMDB/atlas.html
The spatial pattern of net migration among the young age worker group in the MDB area also shows that almost all areas experienced negative net migration, and only limited areas had positive net migration rates, including the Canberra region, Bathurst and Wagga Wagga (Figure 1.4). These figures suggest that the mobility of younger people (15–24) relates to pursuing higher education in areas that have universities or other higher education facilities (ABS 2009).
1.2.2 Employment and Income Conditions in the Murray-Darling Basin
Many economic indicators in the MDB are also below the national averages. In terms of labour force, the employment status in the last 15 years was always lower than the national level. Total employed persons in the MDB had an average growth of 6.4 per cent, compared with the
8 national level at 10.6 per cent between 1996 and 2011. In the same period, the average growth of full time employment in the Basin was around 4 per cent, compared with Australia’s full time worker growth at 8.3 per cent. Moreover, the figure for part time employment is well below the national level, with the MDB average growth only 7 per cent compared with Australia at 17.2 per cent (Table 1.4)
Table 1.4: Employment Status in the MDB and Australia 1996–2011
Employment 1996 2001 2006 Average 1996 2001 2006 2011 1996 Status 2001 2006 2011 2011 Murray-Darling Basin ( person ) Change (%) Full time 550760 552580 590890 620641 0.3 6.9 5.0 4.1 Part time 239470 272900 268980 292047 14.0 1.4 8.6 7.0 Total 810760 850900 921300 974979 5.0 8.3 5.8 6.4 Employed Australia ( person ) Change (%) Full time 6250000 6541200 7339500 7929000 4.7 12.2 8.0 8.3 Part time 2073800 2537500 2919000 3331600 22.4 15.0 14.1 17.2 Total 8323800 9078800 10258500 11260700 9.1 13.0 9.8 10.6 Employed Sources: Census of Population and Housing 1996–2011, ABS (2008)
Agricultural production from the Basin area accounts for 40 per cent of Australia’s agricultural output, and is estimated to be around $15 billion annually (MDBA 2012). However, census data shows there was a substantial decrease in agricultural employment in the MDB. Between the three censuses, employment in agriculture consistently declined, by 12.4 per cent in the period 2001–2006, and by 13.9 per cent in the period 2006–2011 (Figure 1.5).
Figure 1.5: Declining Agricultural Employment in the MDB
120000 103360 100000 90520 77913 80000 12.4 % Growth 60000 -13.9 % Growth 40000
20000
0 2001 2006 2011
Source : Census of Population and Housing 2001–2011
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In terms of income, the average annual personal and household income within the MDB remains below areas outside the MDB, and Australia in general. The ABS Socio Economic Context report (2009) notes that the average personal income between 2001 and 2005 in the MDB was $38,625, compared with an average outside the MDB/RoA of $44,164 and $43,607 for Australia as a whole.
Moreover, the National Regional Profile (NRP) report between 2006 and 2010 shows that the average wage and salary in the MDB at the LGA level, excluding the Canberra Region, was $35,439, compared with outside the MDB where the average was above $40,000. In the same period, the annual average growth rate of wage and salaries was only 4.5 per cent within the MDB (excluding Canberra ACT) and remained lower than outside the MDB/RoA, at 5.3 per cent. One of the reasons that average personal income in the MDB had a slower path while other areas outside the MDB/RoA had steady growth relates to business activity. The ABS report (2009) states that while most of the rest of Australia showed an increase in business income for the period 2001 2002 to 2003 2004, the MDB experienced a decrease in business income.
1.2.3. Business and Industry in the Murray-Darling Basin
The trend of business and industry in the MDB area also experienced a slower growth compared with outside the MDB and also Australia. Data from the National Regional Profile (NRP) between 2004 and 2011 shows that the average growth rate of total business numbers in the MDB was less than 5 per cent, which is lower than the national level which had an average growth rate of around 7–8 per cent. The Millennium Drought period has had a significant impact on declining business units in the MDB. The ABS report (2009) identified two major economic reasons. First, the prolonged drought certainly impacted agricultural production; and second, it was also followed by a substantial decrease in the number of persons who work in the agricultural sector.
Generally, the Gross Value of Agricultural Production (GVAP) in the MDB was growing during the first five years of the Millennium Drought period. Figures from the Agricultural Census (ABS 2008) show that the GVAP grew by 7.3 per cent, or from $13.97 billion to $15 billion, between the periods of 2000–2001 and 2005–2006 (Table 1.5). However, comparing the MDB area, which is described as Australia’s ‘food bowl’, with Australia as a nation, the
10 growth rate was considered to be below expectations. At the national level, GVAP growth in the same period was 12.8 per cent, or increased from $34.2 billion to $38.5 billion.
Table 1.5: Gross Value of Agricultural Production 2000–2006 MDB Australia MDB Australia in millions 2000/ 2005/ 2000/ 2005/ Growth% Growth% 2001 2006 2001 2006 Dairy Farming 1037 1172 3283 3603 13.0 9.7 Other Livestock 2817 4225 8364 10987 50.0 31.4 Rice 349 274 350 274 21.5 21.7 Cereals (exc.Rice) 3565 3436 7327 7320 3.6 0.1 Cotton 1184 861 1305 933 27.3 28.5 Grapes 874 777 1517 1377 11.1 9.2 Fruit 839 1111 2020 2627 32.4 30.0 Vegetables 603 602 2251 2923 0.2 29.9 Others 2695 2533 7723 8494 6.0 10.0
Total Agricultural 13972 14991 34164 38541 7.3 12.8 Commodities Source : ABS (2008)
Furthermore; at a more detailed level, and due to the prolonged drought period; there were agricultural commodities whose production significantly dropped, such as rice, cotton, and grapes, as these are water intensive commodities. This decline was experienced not only in the MDB area, but also in Australia. Table 1.5 shows that one of the strategies used by farmers to compensate for the potential loss was to intensify livestock and fruit production.
The slower growth in agricultural production is consistent with the figures related to workers by industry in the MDB. Based on the three census data points (2001–2011), the number of employed persons in the agricultural industry dropped by 24.4 per cent between 2001 and 2011. The ABS report (2009) states that the declining employment in this sector has impacted other related sectors like retail and wholesale (–19.5%), and manufacturing (–7.3%). However, some industries experienced a significant increase of workers (Table 1.6). For example, between 2001 and 2011 construction workers in the MDB grew almost 50 per cent and mining workers grew by 190 per cent.
However, although there are a limited number of migration or population analyses for the MDB related to social and economic conditions in the last decade, environment issues are also crucial, as these create a significant impact for people and communities, including business activities.
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Environmental issues became a concern when the degradation started to affect agricultural production in the late 1970s (Quiggin 2001).
Table 1.6: Employed Persons by Industry in the Murray-Darling Basin
Change Change Change INDUSTRY 2001 2006 2011 2001– 2006– 2001 2006 2011 2011
Agriculture, Forestry and Fishing 111400 98100 84197 11.9 14.2 24.4 Construction 47900 60500 71257 26.3 17.8 48.8 Manufacturing 81300 83900 75359 3.2 10.2 7.3 Mining 4500 7200 13038 60.0 81.1 189.7 Retail and Wholesale 158000 161100 127199 2.0 21.0 19.5 Transport and Storage 30000 32900 38990 9.7 18.5 30.0 Education 64200 71600 79984 11.5 11.7 24.6 Health Care and Social Assistance 80600 97600 112622 21.1 15.4 39.7 Finance and Insurance 16100 17900 17700 11.2 1.1 9.9 Electricity, Gas, and Water 7100 8500 11913 19.7 40.2 67.8 Accommodation & Food Service 41100 43600 61833 6.1 41.8 50.4 Communication Services 10700 9900 10903 7.5 10.1 1.9 Government and Defence 69100 94500 121042 36.8 28.1 75.2 Personal and Other Services 28600 31100 35227 8.7 13.3 23.2 Murray-Darling Basin Total* 850800 921600 974981 8.3 5.8 14.6 Source: Census of Population and Housing 2001, 2006, 2011
1.3. Environmental Problems in the Murray-Darling Basin
The development process of agriculture 6, including infrastructure expansions to support higher output, has been connected with a range of environmental issues. Quiggin (2001) summarises the inter related environmental problems in the MDB into five main categories: land degradation, river water salinity, land salinity, water quality problems, and loss of biodiversity. These problems relate to the climatic characteristics of the MDB, which includes low average rainfall and very high variability in precipitation.
Quiggin (2001) further states, the role of irrigation is very important for the people in the Basin, where it can greatly improve the value of agricultural production. However, uncontrollable irrigation may increase salinity. The primary cause is not only from rising water tables, but also because it reduces total flows downstream and infiltrates salt water into the river. These problems became evident in the 1970s and 1980s, and thus initiated the need for coordination between all stakeholders. One of the actions implemented was the amendment of the Murray
6 Crase (2004) identifies that widespread clearing of deep rooted native perennial tree and grass species resulted in rising water tables and salinity that now affects large tracts of agricultural land. Previously, institutional arrangements encouraged exploitative development rather than conservative management. 12
Darling Basin Agreement, signed in 1987, to promote and coordinate effective planning and management for efficient and sustainable water use and other environmental resources of the MDB (MDBC 2000). This was followed by another agreement, which in general manages the actions related to continuing environmental degradation. In order to understand environmental aspects in the MDB, this section provides a brief history and relevant data related to environmental conditions where these contribute to the current socio economic conditions.
1.3.1. Environmental Events and Weather Anomalies in the MDB
The Basin has a long history of frequent droughts and floods. Severe droughts were identified as a recurrent experience in MDB communities, including the Federation Drought (1895), the Pre and Post War Drought (1930s and 1940s), and the recent Millennium Drought (2001– 2009). However, drought is not the only repeated environmental event: Gorman (2012) highlighted that Australia is also prominent as a flood country, which is changing people’s understanding of, and relationship to, the river and floodplains in the MDB river system (Figure 1.7). As well as drought, flood has been documented before the Federation era, including floods in Gundagai (1852), Bourke (1890), Mildura and the Murray River (1956), and the recent flood in Cunnamulla (1990). Those episodes and the growing concern about weather anomalies in the MDB (Connor et al. 2009) certainly contribute to the decision making process of people in the MDB as to whether they remain in the Basin or migrate for better living conditions. The concept of weather anomalies was introduced when the Basin experienced continuing dry weather in the early 2000s. In that period, there was an abnormality in the average rainfall, measured by the deviation from the long term average level. Pink (2008) and Geoscience Australia (2004) confirmed these weather anomalies by conducting spatial analysis, displayed through an interactive map of rainfall patterns and temperature patterns (Figure 1.8).
Figure 1.6: Water Storage in the Large Dams in the MDB
Source: ABS (2008)
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The direct impact of rainfall anomalies has decreased water storage in large dams in the MDB (Figure 1.6). As shown in the figure, the period of 2002–2003 was considered as the most severe dry year in the first five years of the Millennium Drought, where the total capacity of water was in the critical level at around 20 per cent. Horridge, Madden and Wittwer (2005) confirm that this was an extreme dry year and impacted communities and businesses around the Basin, and was most likely connected with the slower growth in agricultural production.
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Figure 1.7: The Fluctuation of Long-Term Annual Inflow: Frequent Droughts and Floods
Source : Murray Darling Basin Authority (MDBA) factsheet and the Basin Plan (2011)
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Figure 1.8: Rainfall Anomalies during the Early Period of the Millennium Drought
Sources : Bureau of Meteorology (2008) and Geoscience Australia (2004)
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1.4. Research Framework: Literature Gap Related to the MDB
Several studies building on innovations in migration theory are relevant to studying migration in the MDB, and emphasise the significant influence of environmental factors on the decision making process of migrants 7. The interest in the association between migration and environmental change emerged in the early 1990s when migration was associated with ecological transformation and food security (McGregor, 1994). Environmental migration is defined as one of the options of the adaptation strategies when people leave an affected area. Reuveny (2007) further describes the issue of weather anomalies induced migration activity. Recent empirical evidence highlights how climate variability affects other drivers of migration. Lilleor and Van den Broeck (2011) investigate how economic drivers of income differentials and climate variability of rainfall may affect migration. Several studies found that environmental variables, such as rainfall or temperature, were a significant determinant for urbanization and also encouraged people to migrate internationally (Marchiori et al. 2012). However, there is no clear explanation of the involvement of all drivers (e.g. economic, social, political and environmental) on the pattern of migration.
The evolution of migration theory has reached a point where the importance of all determinants, including environmental factors is recognised, and it is a relatively coherent framework in the context of the MDB. Several key studies in the literature elucidate what really happened in the MDB, however this also creates gaps in the literature that this thesis will make a contribution to filling. First, inside the MDB, as suggested in many studies, economic and social factors certainly play a role in migration as well as demographic characteristics (McManus et al. 2012; Crase 2012; Howard 2008; Horridge et al. 2005). Second, environmental factors often became the lead variables in analysis of the MDB, particularly climate change and climate variability, even though these have not been related to population issues (Gallant et al. 2012; Jiang and Grafton 2012; Connor et al. 2009). A framework has been developed in this thesis to link migration theories and previous studies to understand migration in the MDB and the role of environmental factors (Figure 1.9)8.
7 Chapter 2 provides a comprehensive explanation of the evolution of modern migration theory 8 Studies related with the research framework such as new classical migration theory and new economics of labour migration (NELM) will be explained in chapter 2. 17
Figure 1.9: Research Framework: Linking Literature and the MDB •Land degradation. Environmental •Water salinity and land salinity. •Loss biodiversity. Problems in the •Derived by income differential. Linking MDB since New Classical •Cost Benefit Analysis of doing migration. •Water quality and water management 1970s Migration Migration issues. Theory •Rural urban migration pattern. Theory to the •Focus at individual level in decision Making. Conditions in •Based on census data during. the Murray- the Millennium Drought Indication of New •Recognise social capital change. Darling Basin period. Migration in the MDB Economics of •Involving family and social impact on •Declining trend in social and Labour economic indicators. migration decisions. Migration •Risk management in migrating.
•ABS report (2008, •Migration drivers Theory of 2009) of consecutive involve combination Weather Cumulative dry period and of social, economic, Anomalies in Causation of rainfall anomalies. demographic, and the MDB Migration •Impact on the political aspects. agriculture sector.
Potential of Environmental Environmental Migration Induced Migration
i. Current literature of migration considers environme ntal aspect as one of the key drivers. ii. MDB case can potentially contribute to the empirical analysis of environmental migration, in particular the case in developed countries. iii. The study may fill research gaps as many recen t studies analyse the environmental aspect in the context where there is no environmental managem ent on the impacted area. MDB has a long history on the issue of environmental management.
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1.4.1. Research Aims
This thesis is motivated by two reasons. First, it is important to note that the documented history of the MDB since white settlement highlights four essential aspects: environmental management, social and economic changes, the evolution of policy intervention, and population dynamics in the Basin. Studies related to the MDB in the last decade have focused on many aspects, including water issues, environmental degradation, socio economic impacts, and policy implications. These factors are inter connected and the relationship is complex. However, reports from key authorities (MDBA 2009&2012; ABS 2008) identify that studies of demographic and population issues in the Basin, including migration, are very limited. Therefore, research exploring the relationship between these aspects is valuable.
For example, environmental management of water not only involves the supply side, which fluctuates and depends on given environmental factors such as rainfall and climate conditions, but also from the demand for water, whether it is used for agriculture, industry or household purposes. In general, environmental management affects socio economic conditions. At first, regulations appear to accommodate the economic interests of all stakeholders. However, when it was challenged by environmental trade off for water security, a concern about reducing economic benefit from the Basin was raised which resulted in policy interventions.
Policy interventions before and during the Drought make Australia, and the MDB in particular, a unique empirical case of environmental migration. Many current empirical studies are being conducted in developing countries 9 which are heavily reliant on water. Therefore, the impact of environmental aspects like rainfall is direct and significant, especially through agricultural production. Thus, this impact decreases per capita income and encourages migration. In the MDB, there has been an indication of migration activity during the prolonged drought period. Further, there has also been a slower production growth, below the national level, which raises the question as to whether environmental factors play a role in this event.
Second, to analyse migration activity in the MDB will require a comprehensive approach incorporating all variables associated with migration, including the environmental aspect. This approach will elucidate the determinants of migration , and why they stay in the Basin. Additionally, the symptoms of transformation in the Basin can help explain whether the migration model is consistent with the latest theory of environmental migration (Marchiori et
9 Literature review of this issue is described in detail in chapter 2. 19 al. 2012), or reflects a new pattern which may contribute to the literature. Overall, understanding people’s mobility in the MDB, specifically internal migration activities, will help all stakeholders, including scholars and policy makers, to better understand and better plan for the Basin communities.
1.4.2. Research Questions
This thesis will examine migration – both in and out migration in the MDB including all determinants such as economic, social and demographic characteristics, as well as environmental factors. The key research questions are:
i. To what extent are new classical drivers of migration affecting migration patterns in the MDB? ii. Were environmental factors, driven by rainfall and water utilisation, involved in the migration pattern over the Millennium Drought period? iii. Dividing the Millennium Drought period into two phases, 2001–2006 (first phase) and 2006–2009 (second phase), did the determinants of migration play the same role in the first phase of the drought compared to the second phase ?
1.5. Methodology: Theoretical Framework and Empirical Analyses
This thesis applies enhanced migration decision theory, in that it considers socio economic factors and environmental aspects. In terms of empirical methodology, a migration model will be developed and analysed using the econometrics technique of cross sectional regression.. This thesis develops three analyses to examine migration patterns and drivers in the MDB.
The first stage of the analysis studies migration patterns in the MDB, using several key urban centres in the MDB as a sample. The analysis aims to validate the fundamental framework of rural–urban migration, i.e. the gravity model (Ravenstein 1885), where people’s migration is basically motivated by seeking better living conditions.
The second stage of the analysis conducts an empirical estimation of migration activities in the MDB during the period between 2001 and 2006, by considering socio economic drivers, including the contribution of environmental factors. The third analysis also conducts a similar empirical analysis of migration in the MDB during the second period of 2006 to 2009 .
In terms of the analyses, the study calculates people’s migration at the spatial level of the Local Government Area (LGA). Although the LGA is part of the non ABS geographical structure
20
(Australian Statistical Geography Standard), LGAs are approximated by aggregates of whole mesh blocks, which are the smallest spatial unit within the ABS structure 10 . Migration rates are calculated by examining usual address in one year and five year mobility captured in the census data, calculating in migration, out migration, and net migration rates. A comparison of internal migration between the MDB area and outside the MDB or the rest of Australia (RoA) is also presented.
In the first empirical analysis, the study utilises data from the 2006 Census of Population and Housing. The census data is combined with other data from the Australian Bureau of Statistics (ABS), including the National Regional Profile (NRP). Data on environmental factors is obtained from the Bureau of Meteorology (BoM) by developing a method to collect precipitation or rainfall data from credible and reliable weather stations. The second empirical study applies data from the 2011 Census of Population and Housing data with a similar methods and comparable datasets.
1.6. Thesis Structure
This thesis has seven chapters. Chapter 1 has described the research structure and provided an overview of the MDB, particularly the social and economic conditions and environmental impacts during the Millennium Drought period. Chapter 2 explores the evolution of the migration literature, investigating the history of migration theory from the modern migration framework of the gravity model to recent environmental migration theories. Chapter 3 describes the data construction. Chapter 4 identifies migration patterns in the MDB, including the pattern from key urban centres. Chapter 5 and Chapter 6 present the empirical analyses, examining socio economic drivers and whether environmental influences play a role in the migration activities in the MDB in a significant manner or not. Chapter 7 provides a discussion of the results and conclusions, including suggestions for further research.
1.7. Conclusion
The history of the MDB is marked by numerous examples of socio economic and environmental problems. The latest prolonged drought period has raised questions about migration and the impact on the sustainability of the Basin population. The recent introduction of the Basin Plan 2010 generated an intense debate about the MDB’s sustainability, especially the economic impacts on people and communities. Motivated by the dynamic conditions in the
10 The details of data construction is explained in chapter 3. 21
Basin and current studies of migration that tend to include environmental aspects in the analyses, this thesis examines the drivers and the decision making processes involved in migration in the Basin. The main objective is to provide a better understanding of population mobility in the MDB, and through this analysis contribute to advancing migration theory and research.
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Chapter 2: Literature Review: the Evolution of Migration Theory
2.1. Definition and Brief History of Migration
In terms of human behaviour, migration can be defined simply as the movement of people from one place to another chiefly for the particular reason of finding better living conditions. In one of the key studies of migration, Lee (1966) states that the definition of migration generally is a change in residence permanently or temporarily, without restriction upon the distance, the nature of the act (voluntary or involuntary), and the type of movement (internal or external migration). This basic definition can to some extent include various types of migration. For example, movement between units in one apartment block can be counted as an act of migration, just as well as a move from Shanghai, China to Sydney, Australia, though the reasons and consequences may be vastly different. Therefore, no matter the difficulties, or whether the distance involved is short or long, every migration activity involves an origin, a destination, and intervening obstacles (Lee 1966, p.49).
However, studies by Betts (2013) and Darcy (1993) suggest a difference between voluntary migration and involuntary migration. Forced migration caused by conflict, extreme environmental degradation, natural disaster, and poverty should be defined as involuntary migration (Darcy 1993). Betts (2013) and Archer (in Darcy 1993) argue that the increasing number of refugees since the 1970’s fleeing civil war demonstrates a difference between voluntary migration to improve living standards and involuntary migration for the sake of survival.
The Australian Bureau of Statistics (ABS) in its census website defines internal migration as the movement of people from one defined are to another within a country 11 . The ABS data does not distinguish between the two types of migration (voluntary and non voluntary) but principally the migration in the MDB is categorised as voluntary migration based on census series of questions. Migration in this thesis is simply defined as movement between local government areas (LGAS).
Migration activity has occurred in the history of human communities since 150,000–200,000 years ago. Goldin, Cameron and Balarajan (2011) note that the earliest migration followed a
11 The definition of internal migration in the ABS website is in this link: http://www.abs.gov.au/websitedbs/censushome.nsf/home/factsheetsim?opendocument&navpos=450
23 pattern of expansion to settle a new group in a better environment to increase food supply and improve production methods. The earliest movement of people is also identified with cross community migration. The purpose is to connect with members from other groups by settling in new ecologies for innovation and adaptation. Manning (2005) advises that in the earliest migration era young people left their home to join with other communities as part of the learning process about culture, social interactions, and technology to develop their hunting or construction techniques. When archaeologists argue that farming was introduced about 6,000 years ago by migration activity, it was followed by the intensive exploitation of local resources and increased population growth (Goldin 2011, p.19). The growing of agricultural production produced by farming innovation created interregional networks for the exchange of valuable goods, and thus stimulated trade and migration afterwards. While trade developed occupational specialisation among relatively large numbers of people, long distance trade also distributed people with superior skills as a result of specialisation and people’s movement (McNeill 1984). These series of people with high level skills dispersing and their division of labour are considered to be a way responding to change and increasing economic output. This remains as true today as it did in early human history.
In terms of behavioural analysis, the prior experience of migration is considered to have a systematic effect upon people’s behaviour, as the first migration decision at the individual level is often based on limited knowledge of its costs and benefits. Bailey (1993) suggests that there are two explanations for this phenomenon. First, migration is considered as a learning strategy. The history of migration induces people and communities to experience spatial and temporal movement, where they must respond to the demand for labour market participation. Second, migration is a selection process. This explanation is based on the direct impact on migrants, where the most successful migrants are the least likely to re migrate as they have achieved their expectation. In contrast, people with multiple experiences of migration reflect unsuccessful migration events and have inefficient labour market outcomes. Morrison (1967) has termed these people as chronic migrants. Cassarino (2004) provides another perspective, noting the selection process of multiple migrations relate to the comparison of income at the destination and the origin. A higher level of income at the destination than at the origin reflects a successful example of migration. Thus, the association between migration history and migration behaviour has formed the basis of exploratory studies to increase knowledge and understanding of how labour markets operate in a spatial analysis.
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The literature shows that migration activities before the industrial revolution in the 18 th century have various and combined motives, which are not only to improve wealth through exploration and trade, but also to expand power and political influence through conquest and forming colonies (McNeill 1984). Nevertheless, migration decisions during and after the industrialisation era had altered from individual into household decisions, which shows a strategy to maintain not only individual income but also household income, and the rates increased dramatically in the last quarter of the 19 th century. Empirical evidence by Nicholas and Shergold (1987) shows that during industrialisation era the demand for labour increased in many industrial centres and they heavily relied on migration from surrounding areas. Most of the workers were family members that acted to support household income. The empirical analyses by Hatton and Williamson (1998) on the mass migration from Europe to the New World in the period 1850–1914 showed that migration worked not only to achieve an equilibrium in wages and employment structures between origin and destination countries, but also additional factors encouraged people to move such, as access to land and the ‘friends and relatives effect’. The difference in development stages between countries has contributed significantly to the convergence of living standards and reducing labour market disparities in the destination country. However, a few scholars have argued that identifying the end of mass migration in 1914 is not accurate, instead considering the 1920s as a new peak in the world’s migration. McKeown (2004) stated that the period 1846–1940 was the most significant migration period, and despite wars and politics, the movement was recognised as an important aspect of expansion and industrialisation, particularly in North America.
The effects of mass migration in the 19 th century encouraged scholars to conduct early studies of migration. Greenwood and Hunt (2003) state that although migration activity has been a regular aspect of human behaviour, scientific analyses were relatively limited at the end of 19 th century. The research on migration by Ravenstein (1885) was considered as the first systematic study, analysing internal migration in the United Kingdom and introducing ‘the law of migration’ which in general described that the formation of great towns and centres of industry was caused by the impact of frequent migration activities from surrounding rural districts.
In relation to the thesis this literature review chapter has the objective of exploring and discussing the evolution of modern migration studies from the fundamental theories of migration into a new approach incorporating the influence of cumulative drivers, in particular the role of environmental factors in migration. Based on identified gaps in the literature, this
25 chapter creates a link from the past and current studies to the specific case of migration in the Murray Darling Basin (MDB), Australia.
2.2. The Economics of Migration: Basic Theoretical Framework
Migration issues have been extensively investigated from the perspective of sociology and demography. In terms of the economic context, migration studies are very prevalent as migration involves issues around labour structure, wage differentials, human capital improvement, and social well being using the concept of cost benefit analysis. McConnel, Brue and Macpherson (2003) state that one of the most important aspects that economists have contributed to the study of people’s mobility is the theoretical construction and testing by empirical studies of the human capital migration model in various spatial contexts. Recent literature shows that spatial analysis has evolved from rural–urban migration and internal migration between cities, through to the complexity of international migration. Modern migration literature has also analysed several migration models, from the new classical concept such as push pull migration, policy interventions approach like cross border migration, up to current issues of environmental migration.
2.2.1. Labour Mobility and the Selectivity Process
As stated previously, the empirical study by Ravenstein (1885) constructs the first structural theory of migration known as ‘the law of migration’, which essentially emphasises that the main objective of migration is to improve welfare. In brief, his seven laws describe distance, outcome, dispersion of population, and demographic characteristics in migration activity. He used the 1881 British census data and found a short distance labour movement from rural areas to the centre of commerce and industry, in order for people to find employment in factories or to search for work in domestic services. The empirical evidence from Ravenstein’s study provides a fundamental theory of migration, where people’s mobility to industrial centres from surrounding rural areas is based on searching for better living condition. Many studies refer to this basic concept of migration as the Gravity model.
Furthermore, there are two important findings in Ravenstein’s study that are also mentioned in Greenwood and Hunt (2003): first, based on geographical information, he was able to recognise the areas of absorption (in migration) and dispersion (out migration), which became crucial for further analysis in identifying migration drivers. Second, his study had already explored the
26 gender composition of British migration patterns, where females were more migratory than males 12 .
The rationale behind migration was identified by Hicks (1932) when he witnessed the period of the Great Depression in the early 1930s. Viewing the intensity of migration, he emphasised that the reason for labour mobility is the differences in net economic advantages, mainly differences in wages or labour income. Following studies explored the terms of net economic advantages by calculating the costs and benefits of migration. Sjaastad (1962) and Greenwood (1975) presume that potential migrants have an expected net benefit by estimating the difference between current income and cost at the origin with potential earnings and cost at the destination over some period of time.
Despite the fundamental migration reason being to improve welfare, migration is also a selectivity process. Becker (1962) describes this selectivity as skilled workers in an industry or occupation being less likely to leave than other workers, meaning that workers without ‘specific’ training would be the last absorbed and therefore this encourages them to find another labour market (via migration). Becker also highlights the relationship of human capital investment with migration, where the impact of education creates flexibility for workers to find better earnings. Furthermore, Schultz (1961) describes this selectivity process in the relationship between human capital, migration, and demographic advantage. He states that economic growth requires more internal migration of workers to adjust to the changes in job opportunity, so the aggregate output can be optimised. He also emphasises the importance of young age migration after finishing formal education, since young people have more years ahead of further human capital investment and they have less consideration of wage differentials to make it economically beneficial for them to move. This highlights that young migrants expect better returns on their human capital investment rather than an income differential in migration compared with older people, and this may explain the selectivity process.
Based on Greenwood (1975) and McConnel, Brue and Macpherson (2003), following the notion of expected returns to the migration decision, the formula for the net present value of migration is:
12 Ravenstein describes that the migration of females from county to county was more active than for the males, complemented by the fact that the proportion of women was higher in county than in rural areas. Moreover, the migration of females had a main objective of supporting the industrial sector through domestic services.
27
− − = − (1 + ) (1 + ) [1]
is the expected present value of doing migration from i to j. The expected benefit from i to j is represented by while the expected costs from i to j by , where r ∑ ( ) ∑ ( ) is a discount rate. Thus, people will migrate from i to j if , implying that the expected > 0 benefits exceed the potential costs. In contrast, people will consider staying if . From < 0 this simple equation, studies have explored particular factors defined as benefit or cost, for example potential costs of migrating would be greater as family size increases.
Labour mobility is also essential in the fundamental theory of migration. The idea is, labour mobility via rural–urban migration has created an equilibrium where the oversupply of unskilled labour in rural areas can be absorbed in urban industries. This basic notion of Ravenstein’s (1885) gravity model has been enhanced by Todaro’s (1969) behavioural model of rural–urban migration. Todaro assumes that natural change in the urban labour force is a result of migration, determined by the differential between expected urban and rural income. Therefore, labour mobility via rural–urban migration can be described as:
( ) − ( ) ( ) = , > 0 ( ) [2]
Where represents net rural–urban migration, and S is the existing urban labour force, hence is the portion of labour mobility or the ratio between migration and labour force in a time ( ) horizon. is the expected urban income over an unskilled labour and is expected ( ) ( ) rural income. Thus, labour mobility will happen if the difference between expected incomes in the urban area is higher than in the rural area, or in margin the value is positive. [ = > 0] Moreover, empirical studies have also been conducted on the relationship between migration and job mobility. Bartel (1979) investigates this association using data from the U.S. National Longitudinal Survey (NLS) of Mature Men 1966–1971 and NLS of Young Men 1971–1973. The
28 findings were interesting where at the first stage the variable wage has no significant effect on the migration of mature men, and this can be explained by job tenure and length of residence, as they have built up a stock of capital. However, in the NLS of Young Men, wage and job mobility have a strong relationship with migration since young individuals receive gains when they migrate by being transferred or promoted. Wage gains from the migration of young men also depends on the nature of job transfer, which leads to higher wages. The empirical evidence from Bartel (1979) validates Todaro’s theory of the expectation of better income from labour mobility.
In the case of Australia, Newbold (2001) applied the 1991 census data to analyse the relationship between migration and job mobility by detecting the frequency of interstate migration. He found that the individuals aged between 15 and 34 years were the most active group to migrate to find better occupations and income. The intensity of migration in this particular age group relates to the individual migrant’s qualifications and occupation, as repeating migrants tend to settle in the destination where the job they find meets their expectations.
2.2.2. Human Capital Investment
In addition to the fundamental framework of viewing migration as a selectivity process, labour mobility and wealth improvement, the factor of human capital investment has a specific role in migration decisions. In this context, human capital investment variables consist of education, training, and migration activity itself. This view, that change in occupation by migration is an upgrading process, was supported by Sjaastad (1962). In this context, the experience of migration has been considered as natural training for a wider and more competitive employment market, and therefore the agents experience human capital development. Moreover, although those individuals may migrate temporarily, the return to the origin can potentially increase the wage level as an impact of occupational upgrading.
Education, in turn, is considered as a main determining factor of wages for migrants. More educated people may have more ability to adapt to new places (Greenwood 1975). The accumulation of educational attainment creates special skills that encourage people to move for higher earnings. Largely, the decision to migrate is related to education and skill acquisition and expected lifetime earnings. Dustmann and Glitz (2011) and Borjas and Bratsberg (1996) have developed economic models by using an optimisation method in the case where the decision to migrate is permanent. They argue that there are two possibilities as to whether
29 people decide to migrate or not. First, agents accumulate human capital at the origin, but as human capital is often not fully transferable, this can create loss as the costs of migration may be higher than the expected income, and therefore the propensity to migrate is less. Second, agents choose to develop additional human capital at the destination as part of skill enhancement in which migration is not only to meet the expected income, but also the possibility of an increasing their lifetime earnings, and thus increasing the propensity to migrate.
However, as migration is a strategy to improve income, individuals make a number of choices and perform temporary migration. The migration model from this scenario is life divided into three periods. In the first period, individuals make an optimal investment in their education at the origin. In the second period, they decide to migrate and develop additional human capital. In the third period, they return home for improved earnings. This scenario is based on the optimal calculation of human capital investment of lifetime income with migration as a key strategy. In a simple model of the migration decision conditional on human capital investment, Dustmann and Glitz (2011) state that the choice of whether to stay or migrate will depend on the comparison of the optimal value function:
= [ − + , + ]
= [3]
Where is the optimal value at the destination and is the optimal value in the origin, is the cost of migration, and , where j=O,D , are the errors terms capturing heterogeneity in the choice to migrate or not. In this function, people will migrate if the optimal value of human capital at the destination is higher than in the origin. Therefore, the probability to migrate is only a half if the optimal value in origin and destination is equal or . [ + = ] The young population is considered as an age group who intensively build their human capital stock and thus will have a higher probability of moving. A case study by Schwartz (1976) using the U.S. Census of the Population 1960 conducts an analysis by dividing the population into age groups based on years of schooling. The age classifications are from 25 years until 59 years of age with 7 groups of years of schooling, starting from 0 4 years of schooling to 16 years of schooling. The age classifications are constructed to analyse life cycle and the process of human
30 capital development such as education, married, and working experience. He found that increasing years of schooling create a higher probability to migrate, and moreover the proportion of migration increases with education if a long distance to the destination is involved. The literature also highlights that human capital mobility in the context of international migration may shape a country’s economic development stage. Di Maria and Stryszowski (2009) develop a theoretical model that migration distorts price signals and stimulate changes in human capital accumulation in a country’s development process, and thus this can explain the detrimental experience for developing countries and enhancement for developed countries.
2.3. New Classical Approach: Determinants and Impacts of Migration
The modern approach to the analysis of migration activity started when mass migration occurred from Europe to around the world in the 19 th and 20 th centuries. This phenomenon attracted social scientists to observe the fundamental motivation of the dynamics of labour mobility, which are not only income differentials, but other determinants that may improve individual wealth. The new classical approach, as stated in Keynes’s General Theory, perceives migration as a constituent part of the development process, by which the surplus of labour in rural sectors like agriculture supplies a workforce for urban industries (Lewis 1954). The two sector model, formed by Harris and Todaro (1970), of rural–urban migration has remained as the basis of the new classical approach of economic development. The basic model similarly emphasises that rural–urban migration occurred based on the wage differential between agricultural income and manufacturing income. With the assumption on the probability of employment, Harris Todaro model clearly postulates that migration will cease only when the expected income differential is zero 13 .
However, many factors contribute to the decision to migrate. Studies focusing on the determinants of migration have commonly been constructed based on the efforts of maximising individual utility (Greenwood 1985). Recent studies also recognise the decision making process being placed on the family unit, not merely on the individual level. Thus, migration involves a number of life cycle considerations such as completion of education, marriage, birth and growing children, and retirement. Other socio economic circumstances include age, gender,
13 In the paper, Harris and Todaro (1970) describe this when the sum of workers in agricultural and urban areas is equal with the sum of initial endowments of rural and permanent urban, which in turn equals with the total labour endowments. [ ] Therefore + =in equilibrium, + = the wage level between the agricultural sector equals with expected wages in urban areas
[ ]. = 31 health, employment status, and level of income. One of the studies that tries to include all of these determinant factors was held by Johnson (1978) in his ‘theory of job shopping’ which aims to handle the uncertainty and imperfect information about mobility between jobs. The basic model analyses the behaviour of a worker who seeks an optimal lifetime return from working, and therefore the person must select among many jobs and might migrate between jobs several times.
2.3.1. Labour Market Equilibrium
Numerous studies have stressed the income differential as a main determinant of migration (Dustmann 2003; Leach 1996) and even recent literature (Li and Zhou 2013; Nanos and Schluter 2014) utilise this instrument for their analyses of dynamic migration impact. Further studies show that the geographical mobility of labour creates labour market equilibrium, known as the efficiency gains from migration. There are several assumptions to describe this framework: there are two labour markets with different geographic locations with each market perfectly competitive, a fixed number of workers, no barrier on labour movement, full employment, and perfect information (McConnel, Brue and Macpherson 2003). From here, as presented in Figure 2.1, migration can create equilibrium in the quantity of labour and wage rate from markets A and B.
Figure 2.1: Market Equilibrium and Efficiency Gains from Migration
a
h Wu b
Wage rate Wage We c We i
Wage rate Wage Wm m j
Du Dm 0 e f 0 k l Quantity of Labour Market A Quantity of Labour Market B
Source : McConnel, Brue et al. (2003)
Suppose that the initial wage and employment in market A is at b, migration will be costless since information is perfect, thus labour will flow from market B to A to fill the demand. As a result, wages in market B will increase from Wm to We to adjust with the reduction of labour supply (l to k) , and conversely wages in market A will decrease from Wu to We as the impact
32 of additional labour (e to f). The equilibrium of labour mobility will cease when the wage level from market A and B is equal at We . Moreover, the efficiency gain is obtained because the total value of the combined output, produced by markets A and B, is higher than in the pre migration condition, displayed by the sum of areas Oacf and Ohik (ex-post ), which is larger than area Oabe plus Ohjl (ex-ante ). This simple framework is very useful, where more complex analyses in migration, whether internal or international migration, use this framework as a basic model.
Chiswick (1978), using data from the U.S. Census of Population 1970, analysed the earnings pattern between United States citizens and immigrants. He found that, although immigrants initially have less earnings compared with citizens, their income rises more rapidly than that of the US workers and even exceeds it after 15 years. Borjas (1987) discovered more specific information about earning patterns by using the U.S. Census of Population 1980. He found that labour supply from outside the United States tends to be a substitute and complement for some markets, filling the gap which the domestic labour market cannot supply, and therefore the market reaches efficiency and increases the total value of production. Furthermore, he also finds a small impact on earnings for local workers because of migration, however a greater supply of immigrants does have a sizeable impact on the income of immigrants themselves. Thus, the main competitors for workers from outside the United States are other immigrants.
In the Australian case, Hugo and Smailes (1985) investigate labour transformation from internal migration, using data from the census. They argue that internal migration after 1971 caused a reversal effect, where gain from positive in migration into two larger cities (Sydney and Melbourne) was transformed into a net migration loss (out migration). As a result, the smaller metropolitan areas showed a higher net in migration, reflecting labour market equilibrium.
However, there are caveats on migration analysis using only labour market conditions. First, with a static mechanism framework, it will be difficult to explain an equilibrium of workers in the dynamic mobility pattern. This view is emphasised by Topel (1986) who analyses wage and employment dynamics in the context of spatial equilibrium that is supported by incentives to migrate by offering greater present value of future income. The empirical test used Population Surveys from 1977–1979 and he found that wages are sensitive to inter area differences. Increasing current migration will affect expectation of future labour demand and thus reduces current wages.
Recent literature by Glaeser (2008) supports this view by applying dynamic spatial equilibrium to analyse agglomerations and cities development caused by migration activity from
33 surrounding areas. Second, using wage differentials between areas certainly cannot explain the entire process of migration decisions since one area has various types of workers, and therefore other determinants must be included to represent a sufficient individual utility function. Moretti (2010), following the Rosen Roback model of the spatial equilibrium model, attempts to capture this limitation by constructing spatial analysis of labour migration with two labour classifications: spatial equilibrium with homogenous labour and spatial equilibrium with heterogeneous labour (represented by skilled and unskilled workers).
Other than wages, the determinants include factors that can capture workers’ mobility, and these are: cost of living or housing, amenities, and individual preferences. For homogenous labour, the indirect utility model is as follows:
= − + + [4]
Where is nominal wage at city c; is cost of housing in c; is local amenities at c, and represent individual idiosyncratic preferences for city c. From this model, preference may contribute a greater role if wages, cost of living and amenities are similar between cities. Therefore, workers’ willingness to move can arbitrage away other factors. For instance, a worker may choose city A rather than B merely by preference factor, even though wages and amenities have been included for consideration 14 .
Workers’ mobility becomes complex when migration involves a heterogeneous labour utility model. Real wages and cost of living are different between origin and destination. Moreover, skilled and unskilled workers value local amenities unequally. This model highlights that besides earnings and other social economic factors, individuals have significant preferences for specific locations.
2.3.2. Push–Pull Migration Model and Intervening Factors
The new classical approach extends the analytical migration framework by observing the migration process from origin to destination. Determinants are not only attracting aspects from the destination (pull factors ), but elements from the origin that encourage people to move ( push
14 Moretti (2010) reflects this as where disparity between individual idiosyncratic remains higher than wages[ and− ameniti> ( es.− ) − ( − ) + ( − )]
34 factors ). In an analysis of the push pull model of migration, Lee (1966) attempts to revise Ravenstein’s theory of migration by proposing four factors on the process of the migration decision, which are factors associated with the area of origin, factors associated with the area of destination, intervening obstacles, and personal factors.
Figure 2.2: Push–Pull Migration Model
+-o+-o+-o+- +-o+-o+-o+- o+-o+-o+-o+- o+-o+-o+-o+- o+-o+-o+-o+- o+-o+-o+-o+-
Intervening Obstacles ORIGIN DESTINATION Plus (+), Minus ( ), and Zero (0) Factors Plus (+), Minus ( ), and Zero (0) Factors Source : Lee (1966)
With this new approach, Lee tried to assess every aspect from origin and destination and marked these as having a positive or negative value, and also zero as considerations (Figure 2.2). Some of these factors affect most people in the same way, and some specific factors affect individuals differently. Negative factors at the origin, such as declining industries, deteriorating of public services, and even bad climate, are defined as push factors. On the other hand, positive values at the destination such as good quality of schools, a higher employment rate, and better civic amenities are defined as pull factors. Personal factors include individual characteristics like age, education, occupation and family. The significant contribution made by Lee’s model is the aspect of an intervening obstacle, such as physical barriers or immigration law. This particular factor strengthens studies of international migration as labour movement is no longer fully transferable.
On the other hand, a prior study by Stouffer (1940) has described the intervening factors as opportunities rather than obstacles. The study introduced the relationship between mobility and distance, where intervening factors play an important role. It proposed that a given distance to migrate is proportional to the number of opportunities. Stouffer’s argument relies on the various opportunities of geographical, economic, social, and economic factors between origin and destination. In the context of internal migration, the intervening opportunities have a possibility of changing migrants’ destinations.
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Stouffer’s theory complements Lee’s model by adding the notion of competition between migrants. Galle and Taeuber (1966) re examined the theory by considering the context of inter metropolitan migration, where migrants tend to modify or improve in terms of labour force vacancies during their movement from origin to main destination. The empirical evidence by Denslow and Eaton (1984) highlight the distance variable and intervening opportunities. The internal migration data from 5 countries shows that a longer distance of migration is compensated for by its benefits.
However, studies also highlight some limitations to this model. Hass (2008) states that the model tends to ignore heterogeneity in the society, such as stratification of income. Therefore, push pull factors are likely to impact in a differentiated way at the individual level. Furthermore, it potentially becomes arbitrary and open to subjectivity to establish whether the pull or push factor is dominant. Nevertheless, this model essentially provides a more broadened analysis on migration, which focuses not only on the labour market, but also other aspects like political tension, demographic pressure, and environmental degradation.
2.3.3. Individual Characteristics as Control Variables
Following many migration models that mainly put economic factors as the key determinant, further regression analyses have started to include demographic characteristics, such as age, education, gender, and job status, as control variables. Moreover, personal factors are not merely individual idiosyncratic preferences, but include instrument of an individual’s life cycle like marriage, divorces, completing school, job transfers, or even retirement. This makes the decision process to migrate a family or household consideration (Greenwood 1985).
An empirical study by Bartel (1979) has underlined the association of age to migration activity. Basically, increasing age will reduce the probability to migrate since it involves many considerations and may increase migration costs. Bartel describes job tenure and capital stock accumulation, like assets and community ties, in the current location as barriers. In addition, Johnson (1978) finds a similar result by estimating people with different years of education and working years. He shows that the probability of people with more than 20 years of working experience to migrate is far less than that of people at the beginning of their careers.
Meanwhile, young age workers are perceived as the most active group to migrate. In Australia, people aged 15–34 years who moved interstate were 21.7 per cent from total internal migration between 1986 and1990, and from that figure 41 per cent were more likely to make a second
36 migration (Newbold 2001). A recent empirical model by Raymer, Smith and Giulietti (2011) which combined 1991 National Health Service (NHS) registration and 2001 census data in England finds that young people aged from 15 to 34 represents the main group of inter regional migrants in England, and this diminishes steadily with older age groups (Figure 2.3).
Figure 2.3: Migration with Age Group
Source : Raymer et al. (2011), Age pattern of inter regional Migration in England of National Health Service Central Register 1991 and 2007
Another two important findings from Raymer et al. (2011) are that first, females had higher levels of migration, with 52.3 per cent on average compared with males at 47.7 per cent; and second, at ages over 70 there is an indication of a slight increase in migration. A study from Lundholm (2012) can possibly confirm this as a returning home, or migration to the birthplace. Her empirical study in Sweden during the period 2003–2005 of persons aged 55–70 finds that one out of five in this group return to their origins in rural areas.
Gender certainly plays a key role in migration patterns. Empirical findings by Raymer et al. (2011) are consistently similar with Ravenstein’s findings in the United Kingdom census in 1881, where the proportion of female migration was larger than the proportion of male migration. Ravenstein states that the high demand for manufacturing workers promotes other sectors like domestic services, filled by female workers. However, there is no clear explanation from Raymer’s observation of higher level of female migration. Nivalainen (2004) argues that the reason for the difference of migration pattern on gender is mainly due to males being less likely to register with the National Health Survey (NHS) in Europe. In relation to earnings, Nivalainen (2004) also highlights a different with Ravenstein’s finding that female workers are often considered to have lower labour participation after migrating to the destination, thus females migrants have less income level than male migrants.
In an empirical estimation conducted by Fleischmann and Höhne (2013) from the most recent German micro census, these authors confirm that the variations in gender gaps in the labour
37 market across ethnic groups may possibly be one of the main causes of income disparity between male and female. However, using a sample from East West migrants in Germany during the period 1990–2001, Zaiceva (2010) found that, after migration, female migrant workers did not experience a significant income disparity as they found a new job at the destination.
In terms of another life cycle event – marriage the joint decision to migrate can be seen as the influence of family factors. McConnel, Brue and Macpherson (2003) state that married workers have a lesser tendency to migrate than single workers. The reason is simply that the mobility can increase the potential costs of migration as family size is larger for a couple or family with kids than a single worker. A costs and benefits calculation becomes a main cause again when family migration includes the presence of school age children, which can be associated with larger psychological costs relative to the expected monetary gain (Mincer 1978). Mincer also states that the migration propensity becomes stronger when a family experiences disintegration, such as divorce or separation.
Table 2.1: Family and Demographic Variables in Migration
NLS Young Men Coleman Rossi NLS Mature Men 1971 1973 1963 1969 1966 1971 Coeff t-stat Coeff t-stat Coeff t-stat Probability of Migration Education 0.0125 2.39 0.0105 2.17 0.0074 3.99 Experience 0.011 2.91 0.0112 1.44 0.009 1.22 Wage 0.0062 0.9 0.0002 0.31 0.0027 0.9 Marriage 0.0202 0.68 0.0121 0.22 0.0209 0.88 Years Married 0.0008 1.74 Wife works 0.0516 1.7 0.0266 0.78 0.0187 1.31 Wife's Education 0.0034 1.64 Wife's Earnings 0.001 1.53 0.0011 0.28 School age children 0.0184 0.36 0.0957 2.06 0.0275 1.556 Sample size 1608 581 1790 Migrants (N) 315 73 106 Source: Mincer (1978)
Moreover, Mincer’s empirical estimation became a major reference in terms of the relationship between demographic characteristics and migration (Table 2.1). From the table, it can be seen that several demographic factors play important roles in migration, such as education and work experience. This finding is consistent with several empirical studies (Borjas, Bronars and Trejo 1992; Costa and Kahn 2000; Long 1973). In addition, people with working experience, have a
38 marriage status, working wife, and have school age children seem more reluctant to migrate than single workers.
Based on the literature, there is an ambiguity in the relationship between migration and job status. The economic rationale behind migration is that people tend to move because of a significant unemployment rate or lower earnings at the origin. However, they are being unemployed in order to be absorbed into a growing labour market at the destination. Studies using survey data from the 1960s and 1970s showed that certain unemployed workers are more likely to migrate than employed workers (Da Vanzo 1978; Lansing and Mueller 1967; Saben 1964).
More detailed analysis of migration with respect to unemployment was undertaken in the context of agglomeration by observing the transitional process. Epifani and Gancia (2005) show a different pattern between the short run and long run impact of migration from peripheries to the core area in the context of regional unemployment. In the short run, it follows that migration from regional areas to towns may lower unemployment disparities, but in the long run, unemployment in the regional area will be intensified because of the congestion effect and uncompensated job destruction. The congestion effect and uncompensated job destruction refer to the condition where many workers from regional areas migrate to urban centre for better income and reduce business activities in the peripheries. The condition creates no incentive for workers in the regional areas to stay and tends to increase unemployment rates. Lee (2008) suggests in his model that migration from non urban to urban areas is often motivated by wage differentials, even though urban areas have higher unemployment rates. A recent study by Eggert, Krieger and Meier (2010) stresses the effect of an unemployment benefit programs through transfer payments and other government initiatives, which may provide an incentive for workers to stay.
2.3.4. The Role of Information in Migration
Information is an essential aspect of migration activity. Decisions to migrate are often affected by previous migrants, and also more symmetric information will be required to calculate accurate migration costs (Greenwood 1970; Nelson 1959; Renshaw 1970). Information about distance is important given that longer distance is associated with increasing transportation costs. Additionally, the greater the distance, the less information migrants will have about potential labour opportunities (McConnel, Brue and Macpherson 2003). Many migration studies apply perfect information as one of the assumptions, particularly when migration is
39 associated with labour mobility. However, this stringent assumption hardly accords with the realities, where limited information can reduce the advantages of migration and lead to miscalculation of the incurred cost (DaVanzo 1981). Da Vanzo also highlighted the role of information in repeated migration behaviour. Recurrent migrations are seen as a cycle to reduce ‘information costs’, and therefore many migrants perceive the initial move as an investment in minimising imperfect information. This creates a framework such that the propensity to migrate should be positively associated with the number of previous moves.
Taking information as part of the costs of migration was analysed by Herzog and Schlottmann (1981) by decomposing three components of relocation costs: moving costs, non monetary costs, and information costs. The model combines benefits and costs and can be represented as follows:
= + − ( + + )
∗ = , ′ > 0, [5]
Where is the benefit of relocation from i to j, measured by private benefit respectively from monetary benefit ( ), and non monetary benefit like the quality of life ( ) at location j. The components of the costs describe the private benefit of monetary and non monetary costs attributable to the move. is all the moving costs like transportation (based on distance), belongings, and lodging. is representing non monetary costs, and is information cost, including the additional cost of obtaining further knowledge relevant to specific economic and social conditions at the destination. The likelihood to migrate ( ∗ ) can occur positively when at the margin the function is greater than zero, reflecting the net benefit value of relocation. Applying this model into an empirical estimation, using U.S. Labor Force Migration 1965 1970, these authors found that the positive influence of information on allocative shares of friends, colleagues and relatives living outside the place of origin has become a non monetary cost instead of an information cost phenomenon, as the information provides additional uncertainty about committing to migration. The empirical case highlights that information from friends and colleagues as ‘migrant stocks’, or the pool of information related to conditions at the destination, can determine migration costs more precisely.
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A specific approach of access and asymmetric information on migration was constructed by Katz and Stark (Katz and Stark 1984; Katz and Stark 1987), as a comment on an empirical study by Kwok and Leland (1982) on US–Taiwan worker migration. At first, the reluctance of Taiwanese workers to return after completing their advanced studies in the United States was assumed to be based on income and productivity disparities between the US and Taiwanese labour markets. However, Katz argued that this was only one of several possible scenarios through which asymmetric information potentially reduced Taiwanese workers’ expectations. A recent study by Dequiedt and Zenou (2013) re confirms Katz’s hypothesis of asymmetric information between two countries where skilled workers are imperfectly observed by firms in the host country, thus many companies discriminate against highly skilled migrants in terms of wages. Dolfin and Genicot (2010) developed empirical tests using a dataset of undocumented Mexican migrants to the US from 1968–2004 and found that networks provide information about jobs, crossing the border, and destination, which encourage people to migrate illegally.
2.3.5. Social Capital and Risk Analysis: Feedback to the New Classical Approach
Migration scholars have extended the analysis of information’s role in migration in the framework of social capital and risk analysis. Palloni, Massey, Ceballos, Espinosa and Spittel (2001) describe information from the perspective of the social capital of networks. Although the impact from social capital may have negative and positive consequences, it has been particularly positive and strong in migration research. These authors state that network connections increase the likelihood of internal and international migration because such connections lower the costs and risks that may be incurred through movement, and increase the expected net returns. The term ‘network migration’ has commonly been used to define this process of ‘chain migration’ as sets of connections between migrants, former migrants, kinship, and shared community origin (Hass 2008). Massey (1989) states that a network connection in the origin area influences migrants in their cost benefit calculation.
The perspective of social capital draws attention to the role of institutions, social networks, and cultural and historical aspects in examining migration patterns. Hass (2008) describes the influence of networks as migration system theory, where the fundamental assumption is that migration does not solely impact on the personal aspects (income and wealth), but alters all the factors (social, economic, cultural, environmental and institutional) at both the sending and receiving areas. This view provides a significant response to the new classical approach, where factors attached to the individual are the main factors influencing the migration decision. One
41 of several significant reviews to the new classical approach is the phenomenon of remittances 15 on any migration pattern (internal and international) where migration activity is a strategy for income smoothing (Borjas 1995; Borjas 1999).
The critical response was introduced by a new analysis framework known as the new economics of labour migration (NELM). NELM 16 evaluates the new classical approach as too individualistic and inflexible in dealing with diverse realities of the interaction between migration and development (Massey, Arango, Hugo, Kouaouci, Pellegrino and Taylor 1993). In this perspective, the migration decision in the wider societal context is not limited to the scope of individual behaviour, but also captures the family or household level as the decision making unit.
Table 2.2: Phases of Research and Policies towards Migration and Development Period Research Community Policy Field Development views, capital and knowledge Development and Migration transfer by migrants would help developing Until 1973 Optimism countries in development take off. Development significantly linked to return. Growing scepticism; concerns about brain drain; after experiments with return migration Development and Migration policies focused on integration in receiving 1973 1990 Pessimism (Dependency, brain countries. Migration largely out of sight in drain) development field, tightening of immigration policies. Readjustment to more subtle perspectives under influence Persistent scepticism and near neglect of the 1990 2001 empirical studies (NELM, issue. More tightening of immigration policies livelihood strategy) Resurgence of migration and development Boom in research, especially optimism under influence of remittance boom, remittances. In general, positive and a sudden turnaround of views: remittances, >2001 views, re connected of brain drain, diaspora. Development contribution development with return of migration often framed within renewed hopes put on circular and return migration. Source : Hass (2007, 2010)
NELM as a new approach to labour migration has evolved based on the impact of migration on the development process. Hass (2010) has divided the phases of research and policies toward migration and development into several perspectives over around five decades. As shown in
15 Explained in more detail in the section on International Migration 16 NELM was initially introduced by Stark and Bloom (1985 p.175) in the American Economic Review . In that paper they stated that ‘...Risk handling provides another illuminating example in which a wider social entity is collectively responsible for individual migration. Clearly, the family is a very small group within which to pool risks. But the disadvantages of small scale may be made up by an ability to realize scale economies yet remain a cohesive group. Such scale economies are achieved by the migration of one or more family members into a sector where earnings are either negatively correlated, statistically independent, or not highly positively correlated with earnings in the origin sector..’. The statement reflects migration as a family strategy to achieve sufficient earnings due to minimising the risk of consumption smoothing failure.
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Table 2.2, the discussion about migration creates some research communities based on empirical studies. The debate between pessimism and optimism of migration on development occurred when many empirical studies were focusing on internal migration during the 1960s until the late 1970s. Initially, it suggested an optimistic view as migration transfers capital and knowledge, which became the catalyst for the development process. However, when international migration became more intense, concerns grew based on the ‘brain drain’ and the dependency phenomenon.
As NELM attempts to capture the impact of migration activity at both the individual and social level, the framework has been elaborated into more detailed models. First, NELM became a tool to explore risk sharing behaviour. At the family or household level, migrants seem able to diversify the resources of family members, to maintain their consumption (consumption smoothing) and minimise income risk by allocating them to improve their earnings. Hence, a family’s migration is not only perceived as maximising income, but also as minimising income risk behaviour (Stark and Bloom 1985; Stark and Levhari 1982; Taylor 1999). This strategy can be implemented following one of the family members migrating. The earnings become an additional household income, known as remittances. The income from migrating family member may also become an investment that may be used to create new economic activities at the place of origin.
Moreover, this economic or business activity may become an instrument for the family at the place of origin when they require income support in a declining economic situation. The studies discussed above also observe this more as a household livelihood strategy, which is frequently combined with other strategies, like agricultural intensification or non farm business diversification.
Second, despite income stability as a risk sharing strategy, the implementation of NELM can also overcome market constraints, especially in terms of remittances from international migration. Seasonal return migrants mostly return their earnings directly to the place of origin. Taylor (1999) describes this by considering labour as an export, then the payment to the workers (migrant remittances) can flow directly with less market constraint, showing that a large share of remittances is not channelled through the formal banking system.
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2.4. Global Migration Pattern
Research on modern global migration became of interest in the early 20 th century, when Europe was in the middle of a period of mass migration. The analysis was also important as determinants in internal migration correspond with the global migration pattern, as it follows the fundamental migration theory, i.e. the gravity model (Beine et al. 2015).When Tait (1927), as one of the British members of the Migration Service, addressed the phenomenon of global migration at the International Labour Office in 1926, he mentioned many factors that were encouraging people to move between countries, such as population increases that affect unemployment, economic crises, and civil or political conflict, and therefore global migration highlights the difference between voluntary migration and involuntary migration. He also identified that those migrants who leave their country permanently or temporarily create global migration activity, which can bring benefit to both the origin and host country.
Countries in the middle of industrialisation and in need of extra labour were gaining economically from immigration, and the emigrant countries were benefiting from the fact that their workers often returned after a number of years, bringing not only capital, but also new experience and more modern methods to be applied to the profit of the origin country. However, Tait also emphasised the need for international protection for these workers, as the international labour movement faced many restrictions and regulations from host countries.
Furthermore, besides the advantages in the destination countries, immigrants also coped with difficulties and dangers, such as the risks of different labour market systems, wages inequality between local workers and immigrants, and socio cultural clashes and language barriers. At the end of his address, Tait underlined the importance of cooperation between countries supported by the International Labour Organization (ILO), because an uncontrollable flow of immigrants can create new problems for both origin and host countries, and thus global migration not only has economic dimensions, but also a political element as well.
In terms of a theoretical approach, international migration activity can be linked into key migration theories, which also apply in internal migration activity. Immigrants certainly consider cost benefit analysis, expected revenue and information in their decision to migrate . Lee’s push pull model (1966), which fundamentally applied to rural urban migration, extends intervening obstacles in the immigrants’ consideration, particularly border policy of the destination country. Massey, Arango, Hugo, Kouaouci, Pellegrino and Taylor (1993) summarised this into an expectation model as follows,
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(0) = [ ( ) ( ) ( ) − ( ) ( )] − (0) [5]
Where is the decision making process on the expected return of migration at time 0 or (0) before departure, is probability of avoiding border or deportation since immigrants can ( ) be classified as legal migrants (P=1 ) or undocumented migrants ( P<1 ). is the probability ( ) of getting a job at the destination, and is the earnings from doing migration. ( ) ( ) ( ) represents potential earnings at the origin, r is the discount factor, and is the sum of (0) immigration costs, including information and psychic costs. The international movement occurs when >0. From this Massey et al. (1993) note several important conclusions, which are (0) important for further global migration studies. The important points from the macroeconomic formulation of the expected global migration model are that international movement stems from international differentials in earnings and employment rates: i. Individual human capital characteristics that increase linearly with the remuneration rate or the probability of employment in the destination relative to the origin country (education, experience, language, etc.) will increase the likelihood of international migration, all else being equal. ii. Individual characteristics, social conditions, or technologies that lower the costs increase the migration returns and thus raise the propensity of international migration. iii. Aggregate migration flows between nations are a simple sum of people moving on the basis of cost benefit analysis. iv. International migration does not occur in the absence of differences in earnings and employment rates between countries, and the size of those factors determines the migration size. v. Migration decisions stem from disequilibria between labour markets, and other markets influence directly to the decision process. vi. Governments control immigration mainly through policies that affect expected earnings for the origin and host country.
2.4.1. Incentives and Impacts of Global Migration
In the context of modern global migration that correspond with internal migration studies, Ghatak, Levine and Price (1996) identify two more specifically individual characteristics of
45 international migrants, which are being young and well educated. The rationale behind this is based on the argument that young immigrants will have a longer time frame to find the best opportunity, experience more migration, or even return to their native country. In terms of education, the level of education is crucial, particularly when immigrants compete with local skilled labour at the destination.
The pattern of global migration, mainly flows from less developed countries to developed countries, is primarily motivated by seeking better earnings as with rural urban migration. One comprehensive study to analyse the disparity of earnings between native born people and immigrants by country of origin was conducted by Chiswick (1978). Applying the US labour experience of working age people in the 1970s, he found that in general immigrants initially received less than the native born. The country of origin affected the initial earnings differences, with foreign born workers from Europe having a smaller disparity compared with immigrants from Latin America, Asia, and Africa. While immigrants with very specific skills can receive above their expected income, evidence using different data and labour markets confirm that generally the initial relative wage levels of immigrants are known to be less than those of the equivalent local workers (Borjas 1985; McManus 1985). This may be observationally due to the lack of location specific human capital or asymmetric information about a worker’s true ability (Ghatak, Levine and Price 1996).
Another empirical study by Borjas (1987), using the working age population from the United States census data in the 1980s, confirmed that at the first stage immigrants tend to be substitutes in some labour markets and complements in others, which confirms the misallocation of human capital and unequal earnings as human capital or immigrants skill is not fully transferable. Interestingly, in terms of wages, he found no significant differences between natives and immigrant workers. However, increasing the supply of immigrants did have a sizeable impact on their earnings, meaning the competition for earnings is within the immigrant group, as noted previously in section 2.3.1.
In another study, Borjas (1995) summarised main effects by investigating the socio economic characteristics of natives and immigrants in the US in 1970 1990 (Table 2.3). The trend confirms that the human capital of immigrants, represented by average years of educational attainment, converges with the native trend even though it falls slightly in 1990. In addition, the table also provides interesting figures showing that immigrant households receive more
46 public assistance than local households, and this can explain another motive for global migration, that of receiving better public facilities and social security support.
Table 2.3: Socio-Economic Characteristics of Immigrants and Natives in the US Group/Parameter 1970 1980 1990 Native: Mean Educational Attainment (years) 11.5 12.7 13.2 Household Receiving Public Assistance (%) 6 7.9 7.4 All Immigrants: Mean Educational Attainment (years) 10.7 11.7 11.6 Wage Differential between Immigrants and Natives (%) 0.9 9.2 15.2 Household Receiving Public Assistance (%) 5.9 8.7 9.1 Recent Immigrants (less than 5 years in U.S.): Mean Educational Attainment (years) 11.1 11.8 11.9 Wage Differential between Immigrants and Natives (%) 16.6 27.6 31.7 Household Receiving Public Assistance (%) 5.5 8.3 8.3 Source : Borjas (1995)
Numerous studies focus on the immigration impact on receiving countries, not only in macroeconomic terms of whether it contributes to a country’s economic growth, but also in relation to microeconomic and policy aspects, in particular when immigrant flows affect the national welfare system (Borjas and Hilton 1996; Coleman and Rowthorn 2004; Dettlaff 2012; Kymlicka and Banting 2006).
Another important aspect of global migration besides the dominant pattern of young and more educated people is the language skill aspect. Language ability, in particular English, often becomes the first screening in the assessment of immigrant workers. Empirical evidence shows that language deficiency will create an economic cost in occupation specific income and occupational mobility at the destination (Kossoudji 1988). More specific evidence was presented by Chiswick and Miller (1999) based on the U.S. Legalized Population Survey (LPS) 1989, where immigrants with good English language proficiency earned higher wages than their less proficient peers by 8 per cent for men and by 17 per cent for women.
2.5. Migration in Australia
In the case of migration activity in Australia, literature that observes the pattern and impact of migration can be divided into two categories. First, there are studies that focus on internal migration, particularly mobility from rural areas to major cities in Australia. Second, with Australia being perceived as a migrant receiving country, many recent studies explore the impact of immigration on socio economic conditions.
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In the context of internal migration patterns, a study by Bell, Blake, Boyle, Duke Williams, Rees, Stillwell and Hugo (2002) implies that Australians have relatively high propensities to migrate. A comparison of population figures from the Australian census and the British census in the 1980s and 1990s provides an intensity scale of migration. One of the measurements of crude migration intensity, which is the total number of internal migrants divided by population at risk, suggests that Australians are more migratory than Britons, and the distances they migrate are also longer, even though migration effectiveness is higher in Britain in the context of cost benefit analysis. Moreover, the study also asserts that in terms of the migration expectancy concept, which is the average number of migrations that people could expect during their lifetime, both male and female Australians have a higher mobility compared with their British counterparts, indicating that internal migration is a more common event for Australians than Britons during their lifetime.
The internal migration trend in Australia generally follows the basic concept of labour mobility from peripheries to core or from surrounding small areas to the urban centres. However, the congestion in urban centres and most major cities in Australia raise the concern of future population growth and its distribution (McGuirk and Argent 2011). A study highlights the ongoing trend between metropolitan and non metropolitan areas (Garnett and Lewis 2007). While cities focus on the issue of employment opportunity, affordable housing, and the provision of infrastructure and services that attract in migration, rural areas face depopulation, particularly of young and productive people, and also slow or stagnant economic development that becomes a push factor for out migration. Hence, the study recommends that the interconnected settlement system should be part of government action, with the expectation that, in the future, both metropolitan and rural areas are part of the solution for population growth pressures.
A study by Garnett and Lewis (2007) show that there is a connection between the distribution of people and the structure of the economy in rural areas and metropolitan cities in Australia. Based on their study, the regional population growth between 1991 and 2005 was around 6 per cent for capital and metropolitan cities, while for regional areas (inland and remote) it was about 2 per cent. The trend was followed by a negative net migration in rural areas and in contrast a significant positive net migration for metropolitan cities. Meanwhile, the trend for economic structure remains unchanged based on official data in 2001, with employment in the agricultural sector concentrated in rural areas (15%), and employment in services mostly in capital and metropolitan cities.
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Addressing the issue of the declining trend of population in rural areas of Australia, some actions have been taken by both the government sector and the private or business sector, focusing on development in rural areas. A study by Argent, Tonts, Jones and Holmes (2013) observes this from the perspective of creative businesses and the tourism sector. An empirical estimation of rural amenity and internal migration which increases the presence of the ‘creative class’ shows a robust association, indicating that the activity of creative businesses can stimulate economic development and reverse the declining economic trend, which also provides employment opportunities for people to enter rural or regional areas.
Another body of literature suggests that the common trend of migration from rural to urban areas or major cities in Australia can be addressed by creating some incentives for domestic skilled labour. The theory of reverse core to peripheries was initially developed by Krugman (1998) who postulated an interdependent association between large urban centres or major cities and surrounding regions. The concept is essentially that, although core areas offer better public facilities, with some appropriate incentives, peripheral regions can offer more specialised opportunities. Carson (2011) applies this framework in the case of limited locational advantages in the Northern Territory. A policy intervention to attract migration for skilled labour has shown results in some rural areas, in particular in relation to young people early in their careers.
In the context of international migration, Australia is very well known as a receiving migrant country, and after the World War II period the government realised that the country had low birth rates and the Prime Minister warned that Australia’s future could only be secured if population growth was sufficient (Walsh 2012). Thus, policy to encourage massive immigration was introduced. Walsh (2012) also highlights that the impact of this policy affected the supply of labour , in particular the demand for skilled workers. With the increasing number of migrants, Australian immigration policy started to tighten the selection process and promote skill intensive immigration patterns (Islam and Fausten 2008). Like the pattern of immigration in other developed countries, a study by Islam and Fausten (2008) also asserts that there is no evidence that skill intensive workers would undermine domestic labour supply.
In the context of international migration, the rapid growth from the skilled migrant intake in the last several decades in Australia required the government to implement migration distribution, in particular to those states with low economic growth and a shortage of skilled labour (Taylor, Bell, and Gerritsen 2014). Two national programs – the Regional Sponsored Migration Scheme (RSMS) and the State Specific Regional Migration (SSRM) scheme – have resulted in a positive
49 effect for rural areas and states. The outcome can be seen from the lowering age profile in states and rural workforces, as the impact of these schemes, and increasing employment opportunities, can promote business activity in the area (Taylor et al. 2014).
However, the national skilled migration program and regional sponsored programs do not always increase the supply of labour, as many rural areas experience chronic labour shortages. Argent and Tonts (2013) argue that, although the scheme to facilitate international labour migration to rural areas is more than sufficient, the impact on industries, particularly the agricultural industry, in terms of global competitiveness is still very small. Thus, it required a comprehensive strategy for the whole migration framework to ensure the scheme brings sustainable benefits for rural and regional Australia.
2.6. Environmental Migration
Environmental migration was initially defined as a displacement of people who experienced environmental degradation 17 in which the event involves a large number of people, with those involved in this movement being described as environmental refugees (Lonergen 1998; Renaud, Bogardi and Dun 2007). The UNHCR (United Nations High Commissioner for Refugees 1993) has already acknowledged environmental degradation to be one of the main root causes of migration. Myer and Kent (1995, cited in Castle 2002) estimate that around 25 million refugees were created by environmental degradation in the mid 1990s. Myers (1992, cited in Lonergen 1998) projected that in 2050 the number of environmental refugees in a greenhouse affected world could be as many as 150 million persons 18 , and this would become a global humanitarian issue.
As shown in this chapter, numerous studies of migration have considered a wide array of factors that influence the movement of people, such as fundamental economic reasons, as well as social and political issues and environmental factors. Evidence of environmental degradation such as soil erosion, deforestation, and desertification also shows that environmental conditions are also part of the determinants of migration (Castles 2002). However, the study of migration did not start to pay greater attention to this particular aspect until the mid 1990s. McGregor’s (1994)
17 Homer Dixon (1991, cited in Lonergen 1998) states that environmental degradation is likely to create ‘waves of environmental refugees’ that can produce a destabilising effect on both domestic and international relations. Furthermore, the consideration of those people who have been moved because of environmental degradation has reached above a humanitarian concern and could become a ‘threat to security’. 18 Myers (1992) projection is based on the estimation of all environmental disaster events. Another study such as Kolmannskog (2008) estimates the affected person by specific event. For example, 26.5 million are affected by 12 droughts or 20 million people are affected by climate change related events.
50 study began to conceptualise the link between migration and environmental change. Following the push pull migration model, he started to observe that in some cases, environmental degradation, disasters, and climate change can be the main driver in which migration is the autonomous response. This study focused specifically on the relationship between declining agricultural production, food security issues, and migration.
In terms of theoretical frameworks for the study of migration, the influence of environmental factors is often not considered. One of the reasons that the concept of environmental migration is not included in analysis is that the impact may be insignificant (Renaud, Dun, Warner and Bogardi 2011). Moreover, the framework is challenged because of the complications of identifying the contribution of individual factor’s to the decision to migrate. Some critiques also argue that environmental aspects are difficult to include, as migration drivers based on several factors such as lack of data or poor observation and has a minor impact on migration activity (Kibreab 1997).
Environmental factors in migration attracted much greater attention when scholars began to consider the issue of climate change (Barrios, Bertinelli and Strobl 2006; Reuveny 2007), or when the intensity of rainfall made a considerable impact on production and a country’s GDP (Henry and Beauchemin 2004). Nevertheless, these studies raise some questions on what the environmental aspect implies in the context of migration. Environmental refugees’ displacement occurs because a sudden natural event such as an earthquake, volcano, or cyclone forces people to move, and thus the decision to migrate is not based on a decision (by the individual or family unit) to improve wealth as in the new classical theory of migration, but as an immediate response for survival reasons. Moreover, when an environmental event is happening in a slow path as is the case with pollution, land degradation, and poor water quality, the analysis of migration can be complex, considering that the activity relates to other drivers (economic, socio political, demographic). Therefore, it is crucial to identify the classification of environmental events that may relate to migration.
2.6.1. Rapid-Onset Hazard and Slow-Onset Hazard within the Environmental Migration Framework
Identifying the level of an environmental event is crucial, particularly when the analysis measures how significant this aspect is in influencing decisions to migrate. Renaud, Dun, Warner and Bogardi (2011) have constructed a conceptual decision framework of environmental migration by dividing the event into two categories: Rapid Onset hazards and
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Slow Onset hazards (Figure 2.5). Environmental events that are categorised as Rapid Onset hazards, such as floods, volcanic eruptions, earthquakes, and tsunamis, tend to be unexpected and have a direct impact on people’s livelihood in affected areas.
In the Renaud et al. (2011) decision framework, people affected by Rapid Onset hazards are highly likely to migrate, and also have a lower probability of returning. If there is an effective disaster management and recovery process, the migration decision is defined as environmentally motivated migrants, and those who decide to migrate because of a slow recovery are described as environmentally forced migrants (Figure 2.4). The framework decision becomes more complex when the environmental event occurs in the context of a Slow Onset hazard such as land degradation, pollution, increasing sea levels, or soil erosion. Although the degradation process can be a gradual process or an accelerated process, affecting people in the area, migration activity depends on how dominant this environmental factor is in the decision to move. In this framework, Renaud et al. (2011) describe the mobility as an environmental migration if the determinant can be seen as the dominant push factor.
Figure 2.4: Environmental Decision Framework: Rapid-Onset and Slow-Onset
Source : Renaud et al. (2011)
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One example of when environmental reasons become dominant is a recurring event like frequent droughts and floods. Migration is described as environmentally forced migration if the affected people consider the impacted area will require a significant time to recover, and thus mobility becomes inevitable. Conversely, if the area can provide an alternative place to sustain livelihoods, those people who decide to migrate can be described as environmentally motivated migrants. Figure 2.4 shows in detail the decision framework of environmental induced migration for both Rapid Onset and Slow Onset hazards.
2.6.2. The Discussion of Environmental Migration based on Empirical Studies
Analysing how environmental aspects contribute to the decision to migrate, and thus being able to define migration as environmental migration, has become an extensive debate in the recent literature on migration. Clearly, in the context of Slow Onset hazards there could be many factors involved in the migration decision. One of the challenges is to identify what kind of data can represent the environmental aspect, and whether such data are available. A number of empirical migration studies that include environmental factors do not mention specifically whether the migration is defined as environmental migration. However, to acknowledge the contribution of environmental aspects in the study of migration, in particular in the framework of Slow Onset hazards, many current studies refer to this evidence as environmentally induced migration.
Many studies that explore environmental factors have been conducted since the mid 1990s (Reuveny 2007), with many focused on developing countries in Asia, Africa, and South America (Table 2.4), and limited studies in developed countries. Several points can be summarised from the evidence. First, environmental factors that are involved in most migration activities are classified as recurrent Slow Onset events, and in some cases become annual occasions. Drought, flood, land degradation and water scarcity are the most common environmental aspects which clearly affect productivity in the impacted areas. The reason behind this is that many developing countries rely heavily on rainfall for their agricultural sector and also have limited support from technology and innovation. There are other migration determinants that also play roles. These factors are food availability and healthcare, overpopulation, poverty levels, and underdevelopment. In addition, some countries experience the impact of policy intervention by either policy failures or authoritarian governments, such as North Korea, Russia and Somalia.
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Second, most of the mobility is categorised as rural–urban migration. This is parallel with the Harris Todaro model (1970) where migrants are looking for a better socio economic area in order to seek employment opportunities. However, in some cases the mobility crosses a country’s border. For example, a large portion of migrants from North Korea depart to China as the impact is not only regular environmental events like drought and flood, but also because of government repression. Other cases of international migration occurred from Mexico to the US or from South America to North America (US and Canada). This implies that spatial patterns of environmentally induced migration are not limited only to internal migration, but are also involved in the study of international migration. A report from the International Organization for Migration (IOM) in 2011 19 recognised that the limitation is not only the lack of data collection, which has become a common problem in environmental migration studies, but also the methodological approach on how to construct a model for migration that can distinguish between environmental issues and socio economic factors. Much of the empirical evidence (Table 2.5) analyses environmental events in terms of how often they occur. The caveat to this approach is that the estimation cannot measure the real impact of any particular event. In many environmental migration cases, the studies are unable to differentiate between the intensity of droughts or floods from one period to another. Therefore, to improve the methodology for analysing environmentally induced migration, better data collection that can accurately measure the intensity of trends of environmental events is required.
One of the strategies to address the limitations of environmental migration studies is by using a regular event, such as rainfall, that can represent environmental aspects and display fluctuations over time. Hence, measuring precipitation levels or rainfall and correlating them with migration is one empirical method of analysing environmental migration, given that rainfall can have a direct or indirect relationship to migration. For example, a study by Henry and Beauchemin (2004) has attempted to estimate a direct link between rainfall intensities with migration on first departure in the case of Burkina Faso, a country which is characterised by intense mobility to its neighbours such as Code d’Ivoire and Ghana. Using a longitudinal survey dataset on migration and global monthly rainfall from the Climatic Research Unit at the University of East Anglia, the study focuses on how significant the rainfall variable is in influencing the decision to migrate in the first place compared with other control variables (education, ethnic group, economic sector). The results show that the rainfall variable is not the
19 The report published in IOM Journal; Renaud, et al. (2011), ‘A Decision Framework for Environmentally Induced Migration’ International Migration , 49(s1).
54 main driver of migration, but it is indeed linked to migratory behaviour. Another finding shows that rainfall becomes more significant in mobility when it connects with the agricultural sector. Furthermore, observing the nexus between precipitation levels and migration becomes more useful when the rainfall variable is used as a proxy for climate change. The change in rainfall pattern is a key variable to explain declining agricultural production in rural areas and therefore encourages people to migrate.
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Table 2.4: Empirical Evidence of Environmentally Induced Migration
No Time Origin Destination Environmental Factors Other Factors People Sources Migration Type Movement Environmental Induced Migration in Asia
1 1970s Rural areas in Urban centres Frequent Drought, Poverty and Baechler (1999), Hafiz and Rural–urban 1990s Bangladesh in Bangladesh Floods, Water Scarcity Overpopulation Islam (1993) Migration
2 1980s Rural areas in Urban centres Land degradation, Poverty, Malnutrition, 20 30 Baechler (1999), Brown et Rural–urban 1990s China in China Floods, Water Scarcity government incentives million al. (1994), Smil (1995) Migration to move
3 1995 2000 North Korea Urban centres Droughts and Floods, Poverty, Repression of 300k 400k Chu Whan (1999), Lee International in China Land Degradation, government (2001) Migration Deforestation
4 1978 1983 East India Madras and Droughts Underdevelopment Jacobson (1989) Rural–urban (Rajasthan) Pradesh Migration
5 1980s Northeast of Urban centres Land degradation, Underdevelopment Cropper, Griffiths, Mani Rural–urban 1990s Thailand of Thailand Deforestation (1997), Bilsborrrow (2001) Migration (Rural) Environmental Induced Migration in South America
6 1970s Southern Northern Droughts, Deforestation, Underdevelopment UN (2001), Bilsborrow Internal Migration, 1990s Ecuador Ecuador Land Degradation, (2001) Geographical (Highland) Water Scarcity Migration (South North)
7 1950 1980s Rural areas of Urban Centres Floods, Land Overpopulation, 100k Bilsborrow and DeLargy Rural–urban and Guetemala of Guetemala, Degradation, Underdevelopment, (1990), UN (2001) International Pacific Coast, Deforestation, Water Government policy Migration and US Scarcity failure
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8 1940s Rural Urban Santiago's Land degradation, Poverty, Commodity Several Bilsborrow (2001), UN Rural–urban 1980s Dominican Urban centres, Deforestation price. tens of (2001) Migration Republic Dominican thousands Rep
9 From late Rural areas Urban centres Droughts, Land Inequality, 600k 900k Arizpe (1981), Liverman Rural–urban and 1970s Mexico Mexico, US degradation, water overpopulation, annually (2001), NHI (1997) International scarcity. underdevelopment Migration Environmental Induced Migration in Africa
10 Late 1980s Somalia Kenya, Drought, Erosion, Civil war in Somalia, 2.8 million Kibreab (1994), Cooper International Mid 1990s Ethiopia, Deforestation population growth (2001) Migration Djibouti
11 1960s Northern and Kenya Urban Drought, Land Inequality, 150k 200k Gould (1994), IOM (1996) Rural–urban 1990s Western kenya Centres Degradation, Land Unemployment, Migration Scarcity Overpopulation
12 1960s Burkina Faso, South Burkina Frequent Drought Overpopulation, n/a Binama (1996), Henry et al. Geographical 2000s Mossi Plateau Faso Underdevelopment (2003, 2004) Migration
13 1980s Lowlands Highland Drought Poverty n/a Lonergan (1998), Scoones Geographical Zimbabwe Zimbabwe (1992) Migration Environmental Induced Migration in Developed Countries
14 1931 1939 Canada, Great Canada, Droughts, Land Great Depression 300k IISD/EARG (1997), NHI Internal & Rural– Plains Urban Areas degradation (1997), Liverman (2001) urban Migration
15 n/a Russia, Kola Russia, Air pollution Poor healthcare, social 5% of Kane (1995), Specter Internal Migration Peninsula various issues population (1994) regions Sources: Reuveny (2007), Henry et al. (2004)
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2.6.3. Environmental Migration and the Climate Change Issue
The concern that climate change may encourage migration has been put forward by the Intergovernmental Panel on Climate Change (IPCC) 20 and IOM 21 in the early 1990s, with a warning that the impact could displace millions of people due to shoreline erosion, severe drought, and coastal flooding. The term climate change itself is referred to in the Stern Review (2006) 22 , and is defined as a permanent change in environmental factors such as a shifting of average temperature. Studies focusing on directly linking the climate change issue and migration have been conducted only since the early 2000s. The new approach, which uses changes in rainfall, temperature, and sea level, has developed the analysis of environmental migration more broadly, and is not limited only to developing countries, but also applies to advanced economies (Laczko and Aghazarm 2009). For example, the impact of Hurricane Katrina in the US in 2005 resulted in out migration, even years after the disaster.
The conceptual framework of environmental migration via the issue of climate change again needs to reassess the identification of environmental events. A direct relationship can be considered, as climate change has produced some catastrophic events (Rapid Onset hazards) and therefore motivates people in the affected area to move. However, scholars prefer to identify climate change as a slow process, which gradually has a significant impact to socio economic activity, and subsequently encourages people to move.
A study by Dell, Jones and Olken (2008) is one of the empirical studies that analyses the relationship between climate change (represented by temperature and precipitation) in almost all countries for half a century (1950–2006) and economic growth. The findings support the link between climate change and migration. First, higher temperatures will reduce economic growth, especially in poor countries but with a small effect in rich countries. Second, higher temperatures also reduce agricultural and industrial output, aggregate investment, and also political stability. If climate change reduces economic growth this means also a decrease in per capita income, and linking the reduction in per capita income to the theory of wage differentials, this could promote a migration experience.
20 IPCC published its first report in 1990 (First Assessment Report) and the concern remains stated in its following reports, including the 2007 report of climate change impacts, adaptation, and vulnerability. 21 In 1992 IOM together with the Refugee Policy Group stated that a ‘large number of people are moving as a result of environmental degradation that has increased dramatically in recent years. The number of such migrants could rise substantially as larger areas of the earth become uninhabitable as a result of climate change’ 22 The Stern Review Report on the Economics of Climate Change was produced in 2006 by a team led by Sir Nicholas Stern, as a report to the Prime Minister and the Chancellor of the Exchequer on the Economics of Climate Change. 58
A study by Barrios, Bertinelli and Strobl (2010) is considered as one of key contributions to the investigation of the role of climate change and income differentials in rural–urban migration, as it was published concurrently with the Stern Review and the IPCC report. The conceptual and empirical models (App. IIA) have developed the connection between production functions, sectors of the economy (particularly agriculture) and migration. From here, the model developed the important proposition that a decline in rainfall raises rural–urban migration (by urbanization). Estimated using data from 78 countries, the regression provides strong support for the hypothesis that decreasing rainfall encourages people to move to urban areas. Other studies (Table 2.6) follow this approach by applying combined data from rainfall and temperature as a proxy of climate change to study migration activity.
However, based on the definition of climate change, there is a strong debate on how climate change can contribute to recent migration activity. Barrios, Bertinelli and Strobl (2010) provide a slight correction of the Dell, Jones and Olken (2008) findings. Using the same data from the Penn World Tables (PWT) they find that rainfall affects GDP growth in poor countries (mostly in sub Saharan countries) with a relatively large positive impact, and has no impact on rich countries. This re affirms that poor countries with less technological support are heavily reliant on rainfall for their production, particular the agricultural sector. Moreover, the study also highlights that the impact of climate change on GDP could be indirect in developed countries.
Following the findings, Lilleor and Van den Broeck (2011) advise that the effect of climate change on migration has to be driven by its impact on prevalent migration drivers. Therefore, many recent papers link migration to climate change by constructing a channel or an instrument where the impact can relate to migration (Joseph and Wodon 2013).
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Table 2.5: Studies Linking Climate Change, Income Effect, and Migration No Study Finding Climate Change Proxy Country Climate Change impact on Income
1 Deschenes and Greenstone The effect of climate change on agriculture outcomes and profit. The impact will be Variation of rainfall and temperature USA (Census of (2007) much smaller when the adaptive responses of farmers are taken into account. levels. agriculture)
2 Dell, Jones and Olken Higher temperatures reduce economic growth in poor countries and less in rich Temperature and Precipitation World Countries (Penn (2008) countries. Higher temperatures reduce agricultural output, industrial output, (Rainfall) from 1950 to 2006 World Tables/WDI) aggregate investment and political stability which directly affect per capita income.
3 Dell, Jones and Olken Negative relationship between income and temperature, both between and within Mean Temperature and Rainfall Americas 12 countries (2009) countries (taking country fixed effects into account). Suggest that half of the strong (household surveys) negative short term effects are offset in the long run through adaptation. Climate Change link to Migration
4 Barrios et al. (2006) Climate change was a significant driver of rural–urban migration via urbanisation. Rainfall (1960 1990) 78 Sub Saharan Nations
5 Yang and Choi (2007) Rainfall variations are used to instrument income changes. Positive shocks increase Rainfall shock (change in local Philippines international migration, while negative shocks increase remittances rainfall constructed as rainfall in time t minus rainfall in t 1)
6 Gray (2009) Less precipitation is associated with more internal and international rural out Mean annual precipitation Ecuador (own data) migration
7 Marchiori et al. (2011) Climate variations increase the incentive to migrate internationally via wage Rainfall and temperatures anomalies 43 Sub Saharan Nations
8 Joseph and Wodon (2013) Climate variables do affect internal migration, but in a limited way as socio Temperatures and rainfall variability. Yemen economic and cost factors are included in the estimation Sources: Joseph and Wodon (2013), Bie Lilleor and Broeck (2011), Dell et al. (2008)
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Moreover, Lilleor and van den Broeck (2011) argue that there is a limitation to using the term climate change since, based on the definition, the climate variable needs to be permanently changed, and thus the data requires a long period of series. They suggest that a short term period of anomalous weather can be applied as an instrument to the main migration drivers which are income differentials and income variability. Therefore, the framework may explore the study of environmental migration, not only in less developed countries, but also in more advanced economies like the United States, Western European nations and Australia.
2.6.4. Enhanced Framework and Methods related to Environmental Migration: Research Gap
Focusing solely on environmental drivers to assess environmental migration will be biased, as people’s mobility has been analysed as a socio economic phenomenon. Recent studies try to consider the environmental aspect within the framework of weather anomalies. The model of environmental migration by Marchiori, Maystadt and Schumacher (2012) constructs this parameter based on rainfall deviation and temperature deviation from the data means (App. IIB). The empirical estimation, which is based on sub Saharan countries, includes as a control variable the ratio between the agricultural sector and total productivity, reflecting how weather anomalies affect agricultural output and thus induce migration. The model also includes wage differentials with agents optimising their potential wage when moving from rural to urban areas. As predicted in the theoretical model, the estimation confirms propositions that there is significant and robust evidence of out migration activity in agriculture dependent countries, confirming the impact of weather anomalies. The findings also highlight that, at equilibrium, a larger weather anomaly induces international migration directly and indirectly via rural–urban migration.
The theoretical model developed by Reuveny and Moore (2009) includes environmental factors with social, economic, and political aspects in migration (App. IIC). The model uses the effect of productive land and disaster as environmental factors and tries to calculate the costs and benefits of migration. The advantage of this model is that Reuveny and Moore (2009) develop a decision framework by setting all determinants (social, economic, political, environmental) both at the origin (push) and at the destination (pull). The limitation of this model is that it does not include demographic characteristics in the framework.
A new conceptual framework by Black, Adger, Arnell, Dercon, Geddes and Thomas (2011) developed an enhanced model to capture all factors in migration, including environmental
61 aspects (Figure 2.5). First, the framework captures the evolution of migration theories summarised from the neoclassical approach (labour market equilibrium), the new economics of labour migration (NELM), social capital theory, and the theory of cumulative causation. Second, the framework’s components 23 create a systematic distinction between social influences from five main drivers of migration into an individual or family decision whether they should stay or go. Black et al. separate this into a macro process and a micro process.
From the figure, Black et al. (2011) propose that current migration activity ideally can be analysed using all five drivers. This is because many countries have established specific policies to control migration, in relation to both internal and international mobility. The combination of the main drivers on the macro side and the process of decision making that involve individual and family considerations are relatively adequate to assess migration in a comprehensive manner. In terms of analysing environmental migration, the conceptual framework suggests two methodological possibilities as follows:
i. Environmental drivers as one of main drivers directly affecting migration. Therefore, in the empirical estimation, an environmental driver becomes a key variable. In this framework, many empirical cases come from developing countries, for example in regions of Asia and Africa where production relies heavily on rainfall. ii. The influence of the environmental aspect changes the other four main drivers. In this context, the environmental variable is identified as an instrumental parameter. For example, when the analysis is applied to a panel data set, the environmental variables such as rainfall or temperature are formed as an instrument for economic or social drivers.
23 Black et al . summarise the components as follows (p.3): i. A distinction between type of migration (micro process – right hand side of the figure), rather than types of migrants ii. The identification of five primary of drivers of migration (social, economic, political, demographic and environmental), and the recognition that it is the differences across space in these drivers which influence migration iii. The incorporation of agency in determining how drivers translate into outcomes, and specifically the representation of barriers and facilitators to movement iv. The incorporation of environmental change to environmental drivers, and also as an indirect influence through changes to the other four drivers. 62
Figure 2.5: Enhanced Environmental Migration Framework
MACRO MICRO
Political: Personal/Household Discrimination/persecution, Characteristics: Environmental: Exposure to conflict/insecurity, policy Age, Sex, Education, Wealth, Marital hazard, Ecosystem services (land incentives, governance. Status, Preferences, Ethnicity, productivity, habitability, Religion, Language food/water security/energy
Spatial or Temporal variability and difference Migrate in sources and destination DECISION
Actual Gradual Demographic: Stay Population size/density, Perceived Sudden population structure, Disease prevalence Intervening Obstacles and Social: Facilitators: Political farmework, Seeking cost of moving, social networks, education, diasporic links, recruitment agencies, family/kin Economic: techologies obligation Employment opportunities income/wages/well being
Source : Black et al. (2011)
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In addition, Black et al. (2011) state that there remain many gaps, with very few studies having explicitly examined the effect of environmental drivers on people’s mobility, especially from the perspective of other drivers. For example, political aspects in the framework which can be described as policy intervention emphasise that current migration activity is a complex function with a wide range of drivers. Local government intervention, such as policy in urban development, improving agricultural production and water management policy certainly would need to be taken into consideration in understanding future migration. With many environmental migration studies having been applied mostly to situations in developing countries, this framework indirectly suggests that observing environmental migration in developed countries will be more conducive to gaining a better understanding of why people move, and thus it can contribute to the evolution of the study of migration.
2.7. Link between the Literature Gap to Migration in the Murray-Darling Basin (MDB)
The MDB’s status as the core of agricultural production in Australia makes it important that any environmental shock should be responded to effectively. With the latest prolonged dry period (2001–2009) 24 being considered as the worst drought on record for the MDB area (van Dijk, Beck, Crosbie, de Jeu, Liu, Podger, Timbal and Viney 2013) many studies have been conducted since the early 2000s. The analysis started by identifying an environmental economics problem characterised by a low precipitation level and detecting market failure in the existing water market (Ladson and Finlayson 2002; Quiggin 2001). Afterwards, many papers reviewed the social and economic impact on the MDB and the issue of water management.
Policy intervention and political approaches are part of the MDB’s history and contribute more on water management in this Millennium Drought period. Studies examining policy responses related to water management in the MDB, have been conducted after the two policy milestones of the National Water Initiative (NWI) in 2004 and the Water Act in 2007. Some papers have suggested that water reform needs to engage with the many inconsistencies between an adaptive management approach and policy interventions (Connel 2007; Crase 2012).
Environmental aspects in the MDB have also been analysed from the perspective of its water issues, climate variability and climate change issues. Most of the studies are focused on the anomaly in the latest drought period by assessing temperature and precipitation to develop a
24 The period is also known as the Millennium Drought period or the Big Dry. 64 climate model which can be useful to deal with future environmental shocks (Maxino, McAvaney, Pitman and Perkins 2008; Potter and Chiew 2011). The issue of climate change has also been intensely assessed, both in the context of an environmental framework and its impact on social and economic conditions (Adamson, Mallawaarachchi and Quiggin 2009; Jiang and Grafton 2012). Table 2.6 presents studies on the MDB categorised by social economic, political and policy aspects, as well as environmental issues.
However, research on population issues as an impact of the Millennium Drought period is limited. The Commonwealth Government actually has voiced its concern about migration and declining population based on the census data in 2001 and 2006 (ABS 2009). The report of the socio economic context in the MDB (ABS 2009) clearly shows that, compared with national figures, there is out mobility from the Basin between 2001 until 2006, in particular a decline of the working group aged 25–34 (p.23). So far, the study by McManus, Walmsley, Argent, Baum, Bourke, Martin, Pritchard and Sorensen (2012) is one of a limited number of studies that highlight concerns about population issues by assessing a sample from rural areas in the MDB. The findings generally show that negative net migration is caused by deteriorating public services and that environmental quality plays an important role in positive net migration.
Recognising the gaps in environmental migration studies, the research presented in this thesis intends to apply the environmental variable as an instrument to assess the main drivers in the migration model in the MDB. Several supporting arguments can be specified as below:
i. The Murray Darling Basin is characterised by extreme variability of annual water flows which reflect high variability in rainfall. Ryan (2009) provides a comparison between Australian rivers and other rivers abroad. It shows that in 2006 the Murray River ratio of maximum to minimum annual flows was 15.5 and the Darling River 4,705.2, compared with the Rhine River in Europe (1.9), the Yangtze in China (2.0), and the Potomac in the US (3.9). This reflects that Australia is subject to weather anomalies. ii. A numbers of studies have been conducted, even before the severe drought period began in 2001. The research comprises almost all aspects of the five main migration drivers (Table 2.7). iii. However, studies that focus on the issue of population changes, in particular internal migration during the Millennium Drought are very limited, although the Australian government has stated its concern about declining numbers of young people in the
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MDB. Therefore, the study of environmental migration in the case of the MDB would be significant and would contribute to the study of migration more generally.
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Table 2.6: Studies Assessing the Murray-Darling Basin during the Millennium Drought Period
No Study Research Focus Study Research Focus Study Research Focus
Social Economic Political and Policy Aspect Environmental Aspect 1 Quiggin Overview of environmental Crase et al. (2004) Explore present growth of water (2001) economics problems at the MDB. market and legislative backgrounds Measuring externality using in the MDB, in particular the i. Water Issues production function derivative of implication of stronger property intertemporal consumption. rights.
2 Horridge et Assessing the impact of the 2002 Heaney et al. (2006) Examines policy response on third Reid and Brooks Detecting effects of environmental al. (2005) 2003 drought in Australia. It states party effects on water trading. Third (2000) water allocation in the MDB that on average agricultural output party effects of trade do not warrant declined around 30% as the impact policy intervention at the national of drought. or state level, but do at local level.
3 Turral et al . Assess the success of surface water Quiggin (2006) Policy option for the National Ladson and Investigating market failure on (2005) market in the MDB . Despite Water Initiative in the MDB, Finlayson (2002) existing water market under relatively low re allocation of water particularly the possibility of ineffective water allocation for the the markets performs well. repurchasing the renewal rights for environment: A case in the irrigation licenses. Goulburn River Victoria 4 Humpries Historical indigenous use of water Quiggin (2008) Managing the problems in the Crase et al. (2011) Managing environmental water in (2007) resources in MDB and its impact MDB as a response to climate the MDB by focusing on the for river management. change policy by assessing policy institutional arrangement. success and failure to design a more effective, cost effective policy. 5 Howard The importance of amenity in the Garrick et al . (2009) Policy reforms in the water market: Crase et al . (2012) Water reform program in Australia (2008) MDB, considering environmental implementation in the MDB and by enhancing agri environmental damage as the economic value is Columbia basin. outcomes. very significant for the community. 6 Connor et al. Economic impacts on climate Connel (2011) Water reform in the MDB (2009) change in lower Murray irrigation by adopt the adaptation strategy on ii. Rainfall, Temperature, and Climate cost effectiveness.
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7 Crase (2010) Perspective to the Basin Plan and its Hatton and Young Connecting science into public Maxino et al. Simulate temperature and impact to the socio economic (2011) policy. By translating policy (2008) precipitation in the MDB for factors. domain into future water security. qualitative analysis and climate model. 8 Dixon et al. Analysing the economic effects of Crase (2012) Assessing the MDB Plan. The Potter and Chiew Investigate the change in the (2011) buyback of irrigation water in the inconsistencies between an adaptive (2011) climate in the MDB using rainfall southern MDB. management approach and water runoff model. policy. 9 Grafton et al . Optimal dynamic water allocation van Dijk et al . (2013) The Millennium Drought in Gallant et al . (2012) The Characteristics of seasonal (2011) and environmental trade off in the southeast Australia (2001 2009) drought in Australia from 1911 to MDB implication for ecosystem, 2009. economy, society and government intervention 10 Grafton et al . Economic effects on water recovery (2011) in irrigated agriculture in the MDB. iii. Climate Change Issue 11 Wittwer and Prolonged drought 2006 2007 and Adamson et al. Analyse the impact of climate Griffith 2008 2009 has delivered a (2009) change in the MDB. Climate (2011) significant impact. Drought reduces change is manifested in the real GDP in some regions by up to increasing frequency of drought. 20%. 12 Jiang and Economic effect of climate change Quiggin (2010) Climate change, uncertainty and Grafton in the MDB adaptation in the MDB. (2012) Source : Author’s Observation
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Chapter 3: Data Construction and Methodology
3.1. Introduction
The main objective of this thesis is to examine migration drivers in the Murray Darling Basin during the Millennium Drought period of 2001–2009 (also known as the Big Dry period). The analysis takes into account the potential of environmental factors, examining whether these play a significant or only a minor role in the migration decision’s framework. In terms of empirical analysis, the study chiefly utilises data from the Australian Bureau of Statistics (ABS). In terms of environmental factors, the study constructs an instrumental variable based on data provided by the Bureau of Meteorology (BoM) Australia where access could be obtained from its Climate Data Online service.
In order to conduct an empirical estimation, the study develops a dataset which comprises migration activities as the main dependent variable. Following studies discussed in the previous literature chapter, the first group of control variables consists of parameters that represent economic activity, in particular labour market conditions such as personal income and wages, personal investment income, personal business income; and economic performance indicators represented by business activity in general and in agricultural production. The second group of control variables comprises a social and civic amenities factor, represented by a development indicator parameter 25 , including factors that can represent individual asset accumulation. The third group of control variables includes social and demographic conditions, such as education levels and access to information, as previous studies highlight these factors in the decision making process to migrate.
3.2. Sources and Scope
Empirical studies of migration have often applied census data to analyse the patterns of people’s mobility and its determining factors (Schwartz 1976; Bartel 1979; Borjas et al. 1992). The main advantage of applying census data in migration studies is that this data records the movement of people from one place to another place and provides socio economic information that attaches to those individuals. In the context of Australia, census data is the only dataset which records people’s movement at the national level, and has also been applied to explain labour
25 Development indicators are represented by a variable that can correspond with the area’s development progress. This is described in detail in the section below.
69 transformation as an impact of internal migration (Hugo and Smailes 1985). The thesis is also using the Census of Population and Housing in Australia to investigate internal migration.
On the ABS website 26 , the description of the Census of Population and Housing is:
“… the largest statistical collection undertaken by the ABS and one of the most important. Its objective is to accurately measure the number and key characteristics of people who are in Australia on Census Night, and of the dwellings in which they live. This information provides a reliable basis for estimating the population of each of the states, territories and local government areas, primarily for electoral purposes and for planning the distribution of government funds. Census data are also used by individuals and organisations in the public and private sectors to make informed decisions on policy and planning issues that impact on the lives of all Australians”
Moreover, with a long history of conducting the census based on the Census and Statistics Act 1905 , every person 27 in Australia has a legal requirement to complete a census form to ensure that the data can accurately portray the nation’s demographic conditions.
In order to obtain a comprehensive picture of migration activity within and outside the Murray Darling Basin area during the Millennium Drought period (2001–2009), the thesis utilises the latest two censuses, which are the Census of Population and Housing in 2006 and in 2011. The technical method to obtain migration activity in the census data is by using TableBuilder , an online self assistance tool for census users. More precisely, the study utilises TableBuilder Pro to extract migration data from the census database of ‘ Counting Persons, Place of Usual Residence’ , from both the 2006 and 2011 Census of Population and Housing. This is discussed in more detail in section 3.3.
Socio economic indicators are obtained from the ABS release of serial National Regional Profile (NRP) data. The National Regional Profile is a standard dataset to record trends in information over time, covering population, the economy, industry, and the environment. The dataset covers a range of geographical units, formerly consisting of Local Government Area (LGA), Statistical Local Area (SLA), Statistical Subdivision (SSD), and Statistical Division (SD). The datasets are constructed from various sources, not only from ABS’s data collection,
26 http://www.abs.gov.au/websitedbs/censushome.nsf/home/what?opendocument&navpos=110 27 The person here refers to those that can complete the census form. Children or people with special needs require some other person to complete the form for them.
70 but also from non ABS sources such as the Department of Social Services and the Australian Taxation Office (ATO) 28 . To this point, NRP has been released eight times; NRP 2000–2004, NRP 2002–2006, NRP 2004–2008, NRP 2005–2009, NRP 2006–2010, NRP 2007–2011, NRP 2008–2012, and NRP 2009–2013. However, the ABS states that users should validate between releases of NRP for two reasons. First, some data will have been subject to revision. Second, current and previous releases may refer to different geographical boundaries 29 .
In the empirical estimation, the study utilises the combination of four NRP datasets. In the first analysis, which assesses migration in the first phase of the Millennium Drought period between 2001and 2006, the estimation applies NRP 2000–2004 and NRP 2002–2006. The second analysis, where the investigation of migration covers the second phase of drought between 2006 and 2009 30 , the estimation uses the NRP 2007–2011 and NRP 2008–2012.
Additionally, to include environmental aspects in the estimation, this thesis develops a methodology to obtain environmental data from the BoM Australia. The methodology is needed to select a reliable weather station 31 to provide accurate environmental data. Based on the information page of the BoM website 32 , there are thousands of weather stations, operated by 6,000 observers in Australia, with many of them working on a voluntary basis. Some of the stations have been classified as Reference Climate Stations (RCS) to provide long term and high quality climate data and are a main source for data collection. On the other hand, some stations may not be operated or may be closed for a number of reasons such as the property in which the station operates being sold by the observer or the station having had a history of poor observations. The technical method to obtain environmental data is basically extracting the data online from the BoM website, by selecting the specific location of reliable weather stations.
In terms of spatial analysis, and to examine comparative statistics between areas in the empirical estimation, all the collected data are formed into a specific geographical classification. In this
28 Explanatory notes about NRP can be found in detail at http://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/1379.0.55.001Explanatory%20Notes12008%20to%202012?OpenDoc ument 29 The geographical standard notes that some of the early NRP releases are based on the Australian Standard Geographical Classification (ASGC), and the latest release follows the new Australian Statistical Geography Standard (ASGS). The utilisation of this classification is explained in the section which deals with the development the empirical dataset. 30 The partition of period for analysis will be described in the section on time frame conditions. 31 Based on information from BoM, Bureau weather stations (also called sites), including most Bureau of Meteorology offices, record a variety of weather phenomena, including temperature, humidity, rainfall, pressure, sunshine, wind, cloud and visibility. Weather balloons are used at selected stations (most of which are airports) to measure wind in the upper atmosphere, with many of the balloon flights also recording pressure, temperature, and humidity. The majority of stations in BoM's network do not observe all weather phenomena, and the elements observed at any particular station may change over time. 32 http://www.bom.gov.au/climate/cdo/about/sites.shtml
71 thesis, the data are based on Local Government Areas (LGAs). In the Australia Statistical Geography Standard (ASGS 2011a), LGAs are non ABS structures that are defined by the Department of Local Government, or its equivalent, in each State and Territory. An LGA is constructed from mesh blocks, and from approximately 347,000 mesh blocks across Australia, 577 LGAs are identified in the census in 2011 (App. IIIA).
The main reason why this thesis aggregates data to the LGA level is because LGAs cover incorporated areas in Australia, and therefore are administered by local governing bodies (ASGS 2011b), which are the focus of some policy instruments. Also, the LGA is large enough to include a range of activities and diversities. The incorporated areas of LGAs exclude the area of the Australian Capital Territory (ACT), northern parts of South Australia, and Other Territories (ASGS 2011a). With this structure, the estimated variables are comparative with control variables and it provides an accurate interpretation for the empirical estimation.
From the perspective of geographical standards, the datasets follow both the Australian Statistical Geography Standard (ASGS), which is the latest ABS main structure, and the Australian Standard Geographical Classification (ASGC). The first dataset, covering the period of migration between 2001 and 2006, is based on the ASGC standard. However, the second dataset, which covers migration between 2006 and 2011, uses the latest ASGS to correspond with other variables in the census data in 2011, and with the latest NRP series.
3.3. Migration data
Migration data are key in this thesis. Internal migration itself, based on the ABS description (2011), is defined as the movement of people from one defined place or area to another within a nation. In terms of measurement, this thesis specifies internal migration in Australia in two ways, in order to analyse mobility patterns in the Murray Darling Basin area:
i. Migration of one year mobility based on the ‘Usual Address One Year Ago’ Indicator (UAI1P) or ‘Place of Usual Residence One Year Ago’ (PUR1P) from the Census of Population and Housing in 2006 and in 2011. From these two censuses, the analysis can capture people’s mobility between 2005–2006 and 2010–2011. ii. Migration of five year mobility based on ‘Usual Address Five Years Ago’ Indicator (UAI5P) or ‘Place of Usual Residence Five Years Ago’ (PUR5P) also from the Census of Population and Housing in 2006 and in 2011. From these two censuses, the analysis can capture people’s mobility between 2001–2006 and 2006–2011.
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There are some advantages and disadvantages in the use of census data, in this manner which the ABS also draws attention to in the TableBuilder website data quality declaration. The advantages of utilising census data are:
i. Census data has been regularly used and has become a primary source in many studies in order to examine population changes, including migration activity (Schwartz 1976, Bartel 1979, Borjas et al. 1992). ii. Census data is collected based on individual data, which is very comprehensive in terms of describing attributes that attach to the person. In addition, it has a purpose of observing the country as a whole, to obtain a reliable basis to estimate population numbers33 . iii. Migration in the census can identify the place of origin and destination of the person’s mobility, therefore the data can measure the intensity of migration in a particular area from the activity of entering migrants and people who leave the area. Moreover, it can calculate migration activity in more specific ways, such as in migration, out migration, and net migration.
On the other hand, the disadvantages of applying census data are:
i. One of the main limitations of most census data is that the data is not collected on an annual basis. Some countries, including Australia, conduct a census every five years, and other countries do so every ten years. Hence, there is a constraint on being able to capture migration rates annually. The data is also unable to observe the exact year when people decide to migrate, and thus the census can only detect migration between ranges of census times. ii. With the limitation of time frames, associating migration with other indicators, in particular those derived from annual data, would need an adjustment in interpretation. For example, in a census which is held every five years, the data can
33 In the ABS website where the Census of Population and Housing objectives are outlined, it states clearly that the five year census is a program to accurately and efficiently estimate the number and key characteristics of people in Australia. In one of the output descriptions it states that, ‘The Census sample files are comprehensive 1% (Basic) and 5% (Expanded) confidentialised Unit Record Files. These files contain Census characteristics for a random sample of respondents on a person, family, household and dwelling basis’ .
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only explain the movement during a five year period, except where the census enquires specifically into what year the respondent migrated iii. The indicator of one and five year mobility can only capture a single migration activity from origin at the start of the period and destination at the end of the period. The ABS states this limitation and is discussed in section 3.3.1. and 3.3.2.
3.3.1. Migration based on ‘One Year Mobility’
ABS collects one year mobility data in the Census of Population and Housing every five years. The question about ‘Place of Usual Residence One Year Ago’ (PUR1P) was first asked in the census in 1976. According to ABS records, the response rate of this particular question is very high in comparison with other variables. The ABS, on their census website (2014), claims that the response rate for this particular question in the Census of Population and Housing in 2011 was 94.7 per cent and for the Census in 2006 it was 93.9 per cent. In the Census Dictionary 2011, PUR1P is an indication of a person’s place of usual residence one year before the census, and the variable is available hierarchically from the highest level to the lowest level, based on the ASGS.
The question of usual address one year ago (UAI1P) appears on the Census Household Form. In the recent census in 2011, the Census Household Form is divided into three sections. The first section is the census introduction, explaining the objective of the census and its confidentiality provisions, 34 and also noting that the census was to be held on 8 August 2011. The second section is the instructions for completing the form, and last section is the list of 51 questions related to person and household conditions. The single form can record up to six people within the same dwelling. The one year mobility indicator is in question number 9. The question is a continuation of the previous question (number 8), which asks where the person usually lives. Persons who are eligible to answer the one year mobility question are those who respond in question 8 by providing the address where that person has lived or intends to live for a total of six months or more in 2011.
The question is: ‘Where did the person usually live one year ago at 9 August 2010 ?’ Persons less than one year old are not counted as they were not yet born on
34 In the Census Introduction 2011: Left hand panel: Australian Bureau of Statistics: Census 100 years 1911 2011. There are several explanations: Why a Census? The census is the only practical way to get information on how many people there are in each part of Australia, what they do and how they live. Confidentiality: Under the Census and Statistical Act 1905 the ABS must not release any information you provide in a way which would enable an individual’s or household’s data to be identified. http://www.abs.gov.au/websitedbs/censushome.nsf/home/2011hhftranscript?opendocument&navpos=310&#q9
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9 August 2010. If the person has not experienced migration, they choose the option ‘ same as in question 8’ . Conversely, if they did not live at the current address, they can choose the option ‘elsewhere in Australia ’ where they must provide the address, or they can choose ‘ Other country’ if one year ago they lived abroad (Appendix 3B).
There is a limitation in this data since the indicator is derived from the usual address at certain dates. The ABS (2014) notes that it can only capture the net effect of people’s mobility where there were any multiple movements between these dates. For example, if person A lived in rural Victoria last year, then three months later he/she moved to Sydney for six months before they resided in their current address in Adelaide, person A can only be reported as having migrated from rural Victoria to Adelaide.
3.3.2. Migration based on ‘Five Year Mobility’
The question about ‘Place of Usual Residence Five Years Ago’ (PUR5P) was first used in the census in 1971. Although the response rate of this question is relatively high, it is slightly lower compared with the one year mobility question. The ABS, in its census website (2014), shows that the response rate in the Census of Population and Housing in 2011 was 93.5 per cent and in 2006 was 92.6 per cent. In the Census Dictionary 2011, PUR5P is an indication of a person’s place of usual residence five years before the census, meaning the person’s usual address at 9 August 2006.
In the 2011 Census Household Form, the five year mobility question is in question number 10 and has a similar structure to the previous question (number 9). The question structure for five year mobility is ‘ Where did the person usually live five years ago at 9 August 2006 ?’ Persons less than five years old are not counted as they were not yet born on 9 August 2006. If the person does not experience migration they choose the option ‘ same as in question 8’ or ‘same as in question 9’ . Similar to PUR1P, if they do not live at the current address, they can choose the option ‘ elsewhere in Australia ’ where they must provide the address, or they can choose ‘Other country’ if they lived outside Australia five years ago.
Similarly to question number 9, this question has a limitation in that it can only capture the net effect of people’s mobility anywhere they have had multiple movements within five years. The probability of not reflecting individuals’ real migration activity is greater than for the one year mobility indication. However, the question of ‘usual address five years ago’ (PUR5P) can provide additional information if it is combined with the question of ‘usual address one year
75 ago’ (PUR1P). For example, if a person answers ‘elsewhere in Australia’ in question number 9 and number 10, this means he or she has had a multiple migration experience.
3.3.3. Constructing migration activities: Net Migration, In-Migration, and Out-Migration
As previously stated, one of the advantages of utilising census data is that it can construct mobility data into specific migration activities. The ABS TableBuilder can provide a matrix dataset of people’s mobility from the Census of Population and Housing in both 2006 and in 2011. In extracting data from the census of one year mobility and five year mobility, the LGA areas are set into columns and rows as displayed in Table 3.1. From the sample of five year mobility in the 2011 census, it can be seen in the table that the data are able to form three important elements, which are the total population in 2011 (right side of the table), the total population five years ago in 2006 (bottom of the table), and the people who remain in the area since 2006 (diagonal).
Table 3.1: Mobility Matrix Dataset (LGA of Usual Residence Five Years Ago 2011)
Counting: Persons, Place of Usual Residence LGAU5P LGA of Usual Albury (C) Armidale Ashfield Auburn (C) Ballina (A) Balranald Bankstown Residence Five Years Ago Dumaresq (A) (A) (C) Total (A)
Local Government Areas (2011 Boundaries) (UR) LGA Code/Name LGA10050 LGA10110 LGA10150 LGA10200 LGA10250 LGA10300 LGA10350 ………. Population in LGA10050 …….. Albury (C) 32693 11 13 6 18 13 21 Albury 2011 Population in LGA10110 …….. Armidale Dumaresq (A) 7 15072 9 7 41 0 11 Armidale 2011 Population in LGA10150 …….. Ashfield (A) 4 13 22656 138 11 0 169 Ashfield 2011 Population in LGA10200 ……. Auburn (C) 12 9 245 41045 0 0 791 Auburn 2011 Population in LGA10250 ……. Ballina (A) 6 33 13 6 27528 0 23 Ballina 2011 Population in LGA10300 ……. Balranald (A) 5 4 0 0 0 1605 15 Balranald 2011 Population in LGA10350 …….. Bankstown (C) 20 3 271 1279 9 0 128114 Bankstown 2011 ……….. …………….. …….. ………. ………. ………….. …………. ……….. …………… ………. Population Population Population Population Population Population Population in People who Stay in in Albury 5 in Armidale in Ashfield in Auburn 5 in Ballina 5 in Balranald Bankstown 5 particular LGA Total Year Ago 5 Year Ago 5 Year Ago Year Ago Year Ago 5 Year Ago Year Ago (2006) (2006) (2006) (2006) (2006) (2006) (2006) Source : TableBuilder ABS (Census of Population and Housing 2011)
From these three elements, the matrix can be used to estimate migration into a specific mobility class as shown in the complete formula in Table 3.2. The specific mobility consists of in migration, out migration, and net migration. For example, the rate of in migration of five years in LGA i is calculated from the total population in 2011 in i, minus the people who stay at i in 2006, divided by total population in i five years ago in 2006 or in a complete mathematical form for all mobility:
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∑ ( ) , − ∑ [ →( )], = ( , ) (3.1.)
In terms of out migration of five years in LGA i, the rate is calculated from the total population of LGA i five years ago in 2006, minus people who stay at i in 2006, divided by total population five years ago at i in 2006, or in the complete formula:
( , ) − ∑ [ →( )], = ( , ) (3.2.)
At the end, calculating net migration at LGA i is simply in migration at i minus out migration at i, or in mathematical form:
∑ ( ) , − ∑ [ →( )], = ( , ) ( , ) − ∑ [ →( )], − ( , )
(3.3.)
The same formulae are also applied when this thesis constructs migration for one year mobility in both censuses in 2006 and in 2011, including when matrices are created for particular age groups in order to analyse the pattern of young migration. This thesis develops the migration data not only in a general classification, but also based on several age groups namely, young age group (15–24 years), young workers group (20–34 years), and a working age group (15–64 years).
3.4. Socio-Economic Data
As already indicated, the empirical approach of this study follows the current migration theoretical framework where the decision to migrate is divided into a micro structure and a
77 macro structure (Black, Adger et al . 2011). The micro structure involves personal characteristics such as age group and education level. The macro structure is correlated with aspects of community such as social, economic and development factors, as well as environmental factors. Most social and economic parameters that are applied in this study are derived from the NRP series. As introduced in section 3.2, ABS releases this dataset regularly and provides configuration data based on the LGA level.
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Table 3.2: Migration Matrix (Example from 5 year Mobility)
( ) , = ( , ) + ( ( → ), ) + ( ( → ), ) + … … … … +( [( )→ ], ) , ( → ), ) + … … ( ) , = ( ( → ), ) + ( ) + ( … … +( [( )→ ], ) ⋮ ⋮ ⋮ ⋮ … … … … ⋮ ⋮ ⋮ ⋮ ⋮ … … … … ⋮ ( )[ ], = ( [ →( )], ) + ( [ →( )], ) + ( [ →( )], ) + … … … … +( [( )→ ], ) = = = = … … … … = , = [ →( )], + [ →( ) , + [ →( )], + … … … … + [( )→( )],
( ) , = ; [ →( )], = ℎ ; ( , ) = 5
∑ ( ) , − ∑ [ →( )], − ( ) = ( , )
( , ) − ∑ [ →( )], − ( ) = ( , )
∑ ( ) , − ∑ [ →( )], ( , ) − ∑ [ →( )], ( ) = − ( , ) ( , ) 79
First, as stated in the literature section, the main purpose of voluntary migration is to improve the quality of life (Sjaastas 1962; Greenwood 1975). Migration studies have continuously and consistently identified income differentials as the main determinant, reflected by net economic advantages between origin and destination. The association of migration and income differentials highlights that migration is not only based on individual motives, but also on family strategies to reduce income risk, as per the theory of the New Economics of Labour Migration (Massey et al. 1993, Hass 2010). Moreover, economic factors began to include business conditions and public facilities such as business income, development progress at the destination, and employment opportunities as other key determinants which motivate people to migrate.
Second, the evolution of migration studies has also acknowledged the important role of information access and services in order to make mobility more efficient (DaVanzo 1981), particularly from the perspective of cost benefit analysis.
Therefore, this thesis captures these parameters by categorising socio economic variables into three clusters;
i. The first cluster covers economic factors related to individual conditions such as personal income from wages and salary and personal investment income. ii. The second cluster of variables comprises business conditions that can represent economic performance and social conditions in the area, and are related to the people and community. The variables include business income, total business numbers in the area, the number of business entries and exits in the area, the proportion of individuals in the area with a Bachelor’s degree or higher, the percentage of households with internet access and level and, and as the MDB is a core of agricultural production, data related to the Gross Value of Agricultural Production (GVAP) are also included to reflect business conditions and performance. iii. The third cluster aims to capture the level of development and asset value. From the NRP series, the parameters that can represent these aspects consist of the number of residential building approvals and total dwelling units, used to reflect a development indicator for the particular area. The value of private houses or mortgage payments reflect other development indicators and housing affordability. The summary of all explanatory variables is provided in Table 3.3, which also provides additional notes for
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the utilisation of the variables. A more detailed application of these parameters will be described in the analysis chapters.
Table 3.3: Summary of Applied Explanatory Variables (Socio-Economic)
First Cluster (Individual Economic Factor) Personal Income: Wage and Salary Investment Income Second Cluster (Area Economic, Business, and Social Condition) Unincorporated Business Income Average Total Number Business 1 Average Number of Business Entry 1 Average Number of Business Exit 1 Gross Value of Agricultural Production (GVAP) Employment Growth Rates 2 Unemployment Rates 2 Number of Bachelor Degree or Higher per Total Population Percentage of Internet Access Level Third Cluster (Development Factor) Total Building Approval for Dwelling Unit (ASGC) 3 Total Approved Residential Building (ASGS) 3 Average Value of Private Houses (ASGC) 4 Mortgage Payment (ASGS) 4 1Business figures are formed into average values. It applies to both periods of analysis. In the period of first phase, the average number is between 2004 and 2005 as the dataset utilises two NRP datasets. In the period of the second phase, the average number is between 2007 and 2009 as the dataset also utilises two NRP datasets. 2 The utilisation of these variables is not included in the main structural empirical model, yet the parameters are applied as instruments. The details can be found in the analysis chapters. 3 Both parameters are assumed to be comparable where data of building approvals for dwelling units is obtained from NRP series 2000–2004 and utilised in the first stage of analysis. Meanwhile, the variable of approved residential buildings is collected from NRP series 2007–2012 and applied for the second stage of the analysis. 4 Similarly to point number 2, these variables of average value of private houses and mortgage payment are assumed to be comparable , which can be found in the next analysis chapters.
3.5. Environmental Data
One of the main objectives of this study is to examine the contribution of environmental factors on migration in the MDB during the Millennium Drought period. In the previous literature review chapter, it was emphasised that current studies consider the contribution of environmental factors in migration activity. However, the main caveat with this approach is the lack of data. As stated by the International Organisation for Migration (IOM) in 2011, many empirical studies of environmental migration use environmental events as one parameter, an approach which has limitations in terms of the occurrence number. To address this limitation, one of the key strategies recommends using an ongoing temporal event that can represent environmental factors, such as temperature or rainfall, in the study of migration.
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In Australia, the BoM provides abundant information about weather and climate data including temperature, humidity, pressure and wind. The climate statistics also include rainfall observations at the daily, monthly and annual level. In recording climate data, not all stations transmit observations electronically to the Bureau, thereby the availability of the data for analysis or forecasting purposes needs to be validated with other stations.
In relation to this study, the abundant supply of weather observations in Australia creates challenges in constructing reliable rainfall data based on LGAs. Hence, this thesis develops a methodology to obtain rainfall data from BoM sites, and this is considered as one of the contributions of the study.
Climate Data Online provided by BoM offers two options for getting rainfall data. The first option provides for the selection to be made using a text box, where users can select a weather station in the area of interest based on geographical coordinates. The system responds by displaying matching selected towns and the nearest bureau stations. In the second option, users can opt to select climate data by using a map of Australia, where the map displays all available weather stations (Figure 3.4). This option is quite useful in validating the exact location of weather stations.
The methodology used to obtain rainfall data is based on several properties and conditions:
i. The rainfall data used is the annual reported amount, measured in millimetres 35 , accumulated from monthly data from 2000 until 2013, and has been validated and confirmed by BoM. ii. The key observation for weather stations is the nearest and the most reliable Bureau station 36 . As the geographical unit of analysis is at the LGA level, the weather station should be located within an LGA or be the nearest to it. The most reliable stations are those that record rainfall continuously, including during the drought period. The stations can be identified based on BoM information, where many stations may be closed or terminated for a number of reasons, such as the site being sold or having a history of poor observations.
35 International standards to measure the precipitation level or the intensity of rainfall is expressed in millimetre per day (mm/day) which represents the total depth of rainwater (mm) during 24 hours in a given square metre field. For example a rainfall of 1 mm supplies 0.001 m 3 or 1 litre of water to each square metre of the field. 36 To define the most reliable weather station, the study refers to the BoM RCS (Reference Climate Stations) where the data is continuously updated and validated. 82
Figure 3.1: Climate Data Online provide by Bureau of Meteorology Australia (BoM)
Source : http://www.bom.gov.au/climate/data/
iii. The validation of the data uses the option of the BoM map to identify an LGA location, and afterward the location is compared with an additional map provided by a Google search. For example, to confirm whether the weather station is accurately within the LGA, the location was matched with the council location or the relevant LGA area. iv. If the LGA name is not listed in BoM’s climate data online, the validation utilises Google maps and the LGA official website, and then converts this into the BoM map for the nearest reliable weather station. v. Some reliable stations had only been operating after the year 2000, and thus if the rainfall data from the nearest station was not complete, the next nearest reliable weather station with complete data was utilised. For example, the LGA of Eurobodalla in NSW used the Narooma weather station, and for Greater Hume Shire in NSW used the Culcairn weather station. vi. Sequencing rainfall data from two reliable weather stations was possible during the period 2000–2013. The reason again was due to termination of a weather station within a particular LGA, with the data then being covered by the next nearest reliable weather station. vii. In congested areas, such as a greater metropolitan area, one weather station can cover more than one LGA. For example, in Sydney, the LGA of Bankstown and the LGA of Fairfield utilise the same weather station, and therefore the rainfall data for both areas is similar.
83 viii. Rainfall data can be used from interstate if this is the most reliable station in the area. This method was applied in the case of Murray in NSW and Echuca in VIC where rainfall data for both LGAs came from the Echuca Aerodrome Station. ix. The monthly data was often incomplete in some particular years between 2000 and 2013. The strategy to overcome this issue was by choosing only those stations where the data covered at least 10 months in one year or 83.3 per cent of the recording period. x. In terms of rainfall data for unincorporated LGA areas, the data is based on the NRP classification. xi. Rainfall data for ‘unincorporated other territory’ used the weather station in the Christmas Island which is based on the NRP. The summary of data can be seen in Table 3.4, showing the average precipitation level or rainfall per LGA in the states, Northern Territory, MDB, rest of Australia and averaged across all LGAs in Australia. Appendix IIIA shows the list of weather stations in all States and Territories. At a national level, it confirms that the Millennium Drought period occurred from 2001 until 2009 where rainfall level was below the average level (van Dijk et al . 2013; Grafton et al . 2011). Moreover, during this period the years of 2002 and 2006 were also considered as the worst years (Horridge et al . 2005).
Table 3.4: Average LGA Annual Rainfall (mm) – States and MDB Area (2000-2013) Year NSW VIC QLD SA WA TAS NT MDB Non MDB Australia 2000 731.2 658.6 1345.8 502.7 556.8 762.0 1576.4 609.7 796.7 757.4 2001 762.8 631.1 886.3 492.0 460.9 837.1 1390.4 483.4 726.7 675.5 2002 558.3 472.7 696.1 307.4 417.4 686.1 861.1 354.6 555.1 513.0 2003 780.9 604.3 855.4 454.0 525.2 792.2 1410.1 541.3 716.9 680.0 2004 727.6 633.4 1062.5 417.0 454.8 789.4 1261.7 509.5 713.3 670.4 2005 716.2 638.2 854.7 457.3 556.3 887.5 1051.0 567.7 696.7 669.6 2006 594.1 396.4 1078.3 280.0 435.0 464.8 1415.9 300.2 637.9 566.9 2007 894.7 601.7 1025.1 411.9 470.7 697.4 1321.7 539.5 751.4 706.8 2008 831.1 502.5 1151.5 342.2 535.3 640.1 1386.8 499.2 751.5 698.4 2009 742.3 550.2 1116.8 435.8 470.8 904.5 1043.5 464.1 731.6 675.3 Average 734.2 558.9 969.6 399.7 480.7 744.3 1238.0 473.3 697.9 650.7 2001 2009 2010 993.8 868.3 1629.4 532.8 346.5 819.4 1594.5 856.7 853.9 854.5 2011 968.6 851.0 1290.5 494.6 616.8 891.1 1781.8 694.8 915.0 868.7 2012 839.9 653.1 1109.5 387.1 461.6 756.4 1138.2 567.8 740.2 704.0 2013 830.8 641.4 954.1 448.4 526.0 904.0 1193.7 471.3 814.7 742.5 Average 908.3 753.4 1245.9 465.7 487.7 842.7 1427.1 647.7 831.0 792.4 2010 2013 Sources: BoM and Author Calculation
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From the summary table of rainfall, it can also be concluded that all the states followed the national trend, and only the Northern Territory and Queensland had rain intensity above the average level. In comparing MDB and non MDB areas, the rainfall gap is significant, which indicates that the MDB area was likely to experience a severe impact during the drought period. The States that are included in the MDB area also experienced low rainfall. In terms of the MDB river system, the state of NSW, which is the upstream catchment of many rivers, had a higher average rainfall compared with the state of Victoria. South Australia (where the rivers flow downstream through the state ending at the mouth of the Murray in Lake Alexandrina) experienced the lowest rainfall level.
3.6. Developing the Datasets
The empirical analysis in this thesis is divided into two datasets. The first dataset applies migration data from the Census of Population and Housing in 2006, and therefore variables in the data must follow a similar unit of analysis. The second dataset uses migration data from the Census of Population and Housing in 2011 and thus variables in the dataset must adjust with the changes in the unit of analysis.
The empirical study applies an adjustment in the dataset as a result of the differences in data characteristics, particularly in the unit of analysis. The main constraint is due to changes in the geographical standard in data releases published by the ABS. In 2011, the ABS started releasing data using the Australian Statistical Geography Standard (ASGS) as a replacement of the previous Australian Standard Geographical Classification (ASGC). The new standard changed regions and boundaries, including the boundaries of LGAs, which are applied in this thesis.
The ABS argues that the latest standard has some benefits, such as the structures being more consistent in terms of population size, more optimal for statistical data, and will remain stable until the Census of Population and Housing in 2016. However, it creates obstacles for users when the analysis requires some comparison with previous dataset publications. In the context of this thesis, the empirical estimation applies data from several publications. The data consists of two series of the Census of Population and Housing (2006 and 2011), several series of the National Regional Profile (NRP) dataset, and also the construction of environmental data from Climate Data Online. In order to resolve the issue of data consistency, and to conduct the analysis in a comprehensive manner, a strategy and adjustments have been implemented, which are:
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i. The analysis during the Millennium Drought is divided into two time frames based on the origin of data characteristics. For example, migration data at the LGA level in the censuses of 2006 and 2011 may be different in the context of area boundaries, thus the socio economic data from NRP data series follows the unit boundaries in the census data. In general, the time frame of analysis follows the standard in each census. The main reason is for consistency, so the result can minimise bias in the estimation. ii. In order to merge the data into a single geographical standard, this thesis applies the concordance data guided by the ABS in combining the dataset with a different geographical standard, in particular the analysis that used census data in 2011. The concordance result will be based on the latest standard of the ASGS. As a result, this study applies two datasets in the empirical estimation.
3.6.1. Dataset I: Cross-Section Analysis of Migration 2001–2006
The first dataset assesses migration activities in the period between 2001 and 2006. The main key variable or the dependent variable to be reviewed is the five year mobility from the census in 2006, comprising of net migration, in migration, and out migration. The migration data is de composed into four categories: first, migration data for the general population; second, migration data of a young age group of people aged 15–24 years; third, migration data of a young workers group of people aged 20–34 years; and finally migration data of a working age group of people aged 15–64 years.
To correspond with all variables and also for consistency reasons, and to align with other data series, the first dataset uses the previous geographical standard of ASGC. A concordance is needed as the Census of Population and Housing in 2006 did not provide migration data at the LGA level, and rather presented the census data at the Statistical Local Area (SLA) level. A concordance from 1426 SLAs into 674 LGAs based on this ASGC edition was required. Socio economic parameters use two NRP series, which are NRP 2000–2004 and NRP 2002–2006 to cover the first phase of the Millennium Drought period. Variables use the average value of the two NRPs. These NRP series are already provided at the LGA level, so the data aligns with the migration data. The description of data for the analysis, including specification and summary statistics, will be explained in the chapter which presents the analysis of migration in the first phase period.
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3.6.2. Dataset II: Cross-Section Analysis of Migration 2006–2011
The second dataset examines migration during the period 2006–2011. Similarly to the first dataset, mobility is also classified into the four population categories. In terms of socio economic variables, the dataset uses two NRP series: NRP 2007–2011 and NRP 2008–2012. The second NRP series of 2008–2012 is useful to validate data from the previous series, or to verify if there is a revision in a particular variable. The dataset is complemented with the environmental aspect of rainfall data from Climate Data Online. Moreover, the dataset follows the latest geographical standard of ASGS 2011 where the number of LGAs is 577, including unincorporated areas and the areas defined as ‘no usual address’ and ‘unknown’ in each state.
The data is compiled in a cross sectional framework and as a consequence it requires some modification in relation to the socio economic variables from the NRP series. As the main objective of the empirical analysis is to examine migration in the drought period which ended in 2009, the NRP data has been formed into an average value. For example, the time series data of personal income in the NRP 2007–2011 is constructed into a single variable of average personal income between 2007 and 2009.
The description of data for the analysis, including specification and summary statistics, will be explained in the chapter dealing with the analysis of migration in the second phase period.
3.7. Specification for the analysis
In using the data, some conditions and assumptions are applied to align with the analysis. The specifications consist of:
i. Time frame of analyses: In the first phase period, the period of migration analysis is coherent with the main source of data, which is the 2006 Census of Population and Housing. The migration data from this census covers five year mobility between 2001 and 2006, which corresponds with the starting year of the Millennium Drought period in 2001. However, in the second phase period, with the drought years ending in 2009, the analysis applies the Census of Population and Housing in 2011, thus the five year mobility between 2006 and 2011 is used to represent patterns of migration from 2006 until 2009. This temporal inconsistency leads to the assumption that migration patterns in 2010 and 2011 are in keeping with those between 2006 and 2009 and therefore the five year mobility patterns 2006 2011 are an adequate representation of the last three
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years of the drought (2006 2009). This is the best possible approach, given data availability and limitation. ii. The exclusion of the Australian Capital Territory (ACT): The analysis sections covering internal migration patterns and the empirical estimation for environmental migration in the MDB in the following chapters consistently exclude ACT, which includes Canberra, from the MDB area. There are several reasons for doing this, based on official reports from MDBA and ABS. The MDB authority report of the socio economic implications of the proposed Basin Plan (MDBA 2012) states that there is a large population size difference between Canberra and other LGAs within the MDB, which may lead to a biased comparison. Data from the 2006 Census indicates that the population of Unincorporated ACT was 323,325. In 2011, the population size increased to 356,586. Meanwhile, the average population at the LGA level within the MDB area was below 50,000 persons. In terms of the distribution of people employed by sector, most of the people in Canberra are in the government sector or public administration, while most people in the Basin are in agriculture, forestry and fishing. Moreover, by excluding Canberra, the Basin’s economic figures indicate that in 2006 the MDB population aged over 15 years with earnings above $1,000 per week was 13 per cent, which was lower than the national level of 16 per cent (ABS 2009). Most reports exclude Canberra and the ACT from the analysis of people and communities in the Basin, and this thesis follows the same approach.
3.8. Summary
This chapter explains the construction of the migration data from the Census of Population and Housing, including the unit of analysis. It also describes the construction of the environmental data, including how the rainfall data was collected for all areas. The constructed dataset for the analysis has also been specified with some conditions based on previous studies and for consistency reasons, such as the different geographical standards available for each period of analysis. The next three chapters detail the analysis of migration in the MDB from the context of basic migration theory (gravity model) and in the context of a quantitative approach using econometrics estimations.
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Chapter 4: Internal Migration in the Murray-Darling Basin during the Millennium Drought Period: Evidence of Mobility and Regional Patterns from the Census of Population and Housing in 2006 and in 2011 37
4.1. Overview: Internal Migration in the Murray-Darling Basin (MDB)
The issue and impact of migration in the Murray Darling Basin (MDB) during the Millennium Drought period (2001–2009) became a crucial concern for communities, businesses, and authorities in the Basin. The Murray Darling Basin Authority (MDBA) and ABS Report in 2009 of the socio economic context indicates a population change in the Basin as an impact of the prolonged drought. The Report states that, although the Basin area experienced a growth in population, the trend was lower compared to the national level. As stated in Chapter 1, during the Millennium Drought the MDB population only grew by 4.3 per cent between 2001 and 2006, while at the national level the population increased by 4.7 per cent. The gap became wider between 2006 and 2011, as the MDB population grew by 4.5 per cent and the nation increased significantly by 8.3 per cent 38 .
The Report highlights that population change in the Basin is influenced by the intensity of internal migration activity (ABS 2009, p.17). In general, the mobility follows the natural pattern of rural–urban migration where many urban centres experience a fast growing population, while localities, small towns, and more remote areas are declining in population. Data from a series of the Census of Population and Housing in 2001, 2006 and 2011 show that several key urban centres in the MDB experienced a consistent population growth at a faster rate than the national average, in particular in the period of 2001–2006 (Table 4.1).
Although urban centres in the MDB follow the trend of the fastest growing centres in Australia for the period 1976–2001 (ABS 2009, p.13), the population growth pattern declined in the period 2006–2011. Table 4.1 presents data for selected urban centres in the MDB, and shows that the average rate of population change in this period dropped, when compared to the previous period of 2001–2006.
37 A substantial part of this chapter was presented at the 59 th National Australian Agricultural and Resource Economics Society (AARES) Conference, 10 th 13 th February 2015, Rotorua, New Zealand. 38 The data is from the last update of Census of Population and Housing 2011 and 2006. The figures are displayed in Table 1.1. 89
For example, some key urban areas which are agricultural centres experienced a slower population growth, such as Toowoomba in Queensland (QLD), which experienced a 7.9 per cent growth in population from 2001 2006 but a 1.4 per cent population growth from 2006 2011, and Wagga Wagga in New South Wales (NSW), which went from 8 per cent population growth from 2001 2006 to 0.4 per cent population growth in 2001 2006. In the state of Victoria (VIC), urban agriculture centres had a similar pattern of declining population growth, for example Mildura, where the growth declined from 11.5 per cent to 4.5 per cent, and Echuca Moama where growth fell from 16.8 per cent to 2 per cent.
Table 4.1: Population Change in Selected Urban Centres in the MDB, 2001–2011 States Urban Centres, Population Population Murray Darling Basin Change (%) Change (%) 2001 2006 2011 2001–2006 2006–2011
QLD Toowoomba 88318 95264 96568 7.9 1.4
Warwick 11598 12563 13379 8.3 6.5 NSW Bathurst 26597 28990 31292 9.0 7.9
Wagga Wagga 43283 46736 46913 8.0 0.4
Albury Wodonga 68918 73498 77229 6.6 5.1 VIC Bendigo 69345 76048 82795 9.7 8.9
Horsham 13291 14121 15261 6.2 8.1
Shepparton Mooroopna 36085 38770 42742 7.4 10.2
Mildura 26923 30018 31363 11.5 4.5
Echuca Moama 10583 12364 12610 16.8 2.0 SA Mount Barker 9183 11540 14452 25.7 25.2
Murray Bridge 12783 14049 15968 9.9 13.7 Sources : Census of Population and Housing 2001, 2006, 2011. ABS Report of Socio Economic Context (2009).
Although urban centres have a high probability of being the destination of rural–urban mobility, the declining growth rates of these areas can be an indication that one of the major causes of population change in the Basin is the intensity of both internal migration activities, which comprise migration within the MDB area and migration out from the MDB area. Moreover, by looking at the structure of area classification in the Basin between the two censuses of 2006 and 2011 39 , it can be seen that the MDB has a declining trend of the number of small areas, in particular towns and small urban areas. Table 4.2 shows the comparison of area classification between MDB with non MDB areas/Rest of Australia (RoA). From the table, in the MDB, the
39 ABS’s Census Dictionary for the current geographical standard of ASGS and the previous standard of ASGC (catalogue number 2901.0 and 1216.0) states that a locality is between 200 and 999 people. Above this number, the area can be classified as a small urban centre or a major urban centre. However, following the dictionary’s delimitation of urban centres, the study creates a more specific area classification in terms of population numbers in every LGA (Table 4.2.). The provided classification is based on several ABS reports, including ‘Water and the Murray Darling Basin: A Statistical Profile 2000–2001 to 2005– 2006’, published by the ABS in 2008. 90 proportion of area classified as Towns was decreasing from around 30 per cent to 26.3 per cent and Small Urban areas from 30.2 per cent to 28.8 per cent. The trend was followed by a declining trend of the proportion of population of Town (7.3% to 5.4%) and Small Urban (17.4% to 14%). Meanwhile, following the trend in the RoA, the area classified as a City in the MDB and RoA shows an increase for both the proportion of area and population.
Table 4.2: The Proportion (%) of Area Classification in 2006 and 2011
MDB Non-MDB/RoA % of area % population a % of area % population b Area Area Range 2006 2011 2006 2011 2006 2011 2006 2011 Classification Localities 0 999 1.5 0.9 0.1 0.04 21.9 13.9 0.3 0.2 Town 1000 4999 30.2 26.3 7.3 5.4 21.8 23.3 1.6 1.3 Small Urban 5000 9999 30.2 28.8 17.4 14.0 8.4 8.7 1.8 1.5 Urban/Small City 10000 49999 34.6 39.0 54.5 53.4 26.2 28.1 18.8 16.8 City >50000 3.7 5.1 20.6 27.2 21.8 25.9 77.5 80.2 a percentage of population per total of the MDB b percentage of population per total of outside MDB/rest of Australia Sources : Census of Population and Housing 2006 and 2011
The concern of population shifts in the MDB in part reflects the nature of migration, where a large portion of the migrants consist of productive people, especially young people. The ABS Report (2009) states that a relatively large number of young working aged individuals left the Basin in the drought period. The argument is supported by examining demographic composition by age group (Figure 4.1). In the 2006 Census of Population and Housing, the MDB population aged between 20 and 34 years was 5.8 per cent of the total population in the Basin. This proportion is notably below the national level at 6.8 per cent in 2006.
Moreover, based on the 2011 Census of Population and Housing, the proportion of the age group 20–34 years in the MDB population only increased by one point to 5.9 per cent, while the national level was at 6.9 per cent. Figure 4.1 shows the demographic composition between the population in the MDB and the whole of Australia between 2006 and 2011, where the MDB pyramid is clearly short of young people.
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Figure 4.1: Population Compositions by Age Group and Gender in Census 2011 and Census 2006
A. Murray Darling Basin 2011 2006
80+ Female 3.4 5.2 80+ Female 2.8 4.8 75-79 Male 2.8 3.0 75-79 Male 2.8 3.2 70-74 3.8 3.9 70-74 3.4 3.5 65-69 4.9 4.8 65-69 4.3 4.2 60-64 6.3 6.1 60-64 5.3 5.1 55-59 6.5 6.4 55-59 6.7 6.4 50-54 7.0 7.0 50-54 6.9 6.8 45-49 6.8 7.0 45-49 7.4 7.4 40-44 6.7 6.9 40-44 7.0 7.2 35-39 6.2 6.4 35-39 6.7 6.9 30-34 5.6 5.8 30-34 6.1 6.3 25-29 5.8 5.9 25-29 5.5 5.5 20-24 6.2 5.8 20-24 6.1 5.8 15-19 7.1 6.5 15-19 7.3 6.7 10-14 7.1 6.6 10-14 7.7 7.2 5-9 6.8 6.3 5-9 7.3 6.8 0-4 6.9 6.5 0-4 6.7 6.3 10.0 5.0 0.0 5.0 10.0 10.0 5.0 0.0 5.0 10.0
B. Australia 2011 2006
80+ Female 3.1 4.7 80+ Female 2.8 4.5 75-79 2.4 2.7 75-79 2.5 2.9 Male 70-74 3.2 3.4 70-74 Male 3.0 3.2 65-69 4.3 4.3 65-69 3.8 3.8 60-64 5.6 5.6 60-64 4.9 4.7 55-59 6.0 6.1 55-59 6.3 6.2 50-54 6.7 6.8 50-54 6.6 6.6 45-49 7.0 7.0 45-49 7.3 7.3 40-44 7.1 7.2 40-44 7.4 7.5 35-39 7.0 7.1 35-39 7.3 7.5 30-34 6.8 6.8 30-34 7.0 7.1 25-29 7.1 7.0 25-29 6.5 6.4 20-24 7.0 6.6 20-24 7.0 6.6 15-19 6.8 6.3 15-19 7.1 6.6 10-14 6.6 6.1 10-14 7.2 6.6 5-9 6.5 6.0 5-9 6.9 6.3 0-4 6.9 6.4 0-4 6.6 6.1 10.0 5.0 0.0 5.0 10.0 10.0 5.0 0.0 5.0 10.0
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Another issue related to migration in the Basin is the ageing population. Figure 4.1 also confirms that, although the population of Australia between 2006 and 2011 is ageing (ABS 2009, p.21), this trend is more pronounced for the MDB. The portion of older persons (aged>=65 years) is slightly lower for the rest of Australia, where in 2001 the figure was 12.6 per cent, increasing to 13.3 per cent in 2006 and 14.1 per cent in 2011. According to census data, in 2001 the MDB population aged over 65 years was 13.1 per cent, increasing to 14.5 per cent in 2006 and 15.9 per cent in 2011. The ageing of the population can be seen as one of the determinants for people to migrate. A recent comprehensive analysis by Titan and Otoiu (2014) asserts that there is a strong direct link between ageing and emigration. An ageing population has a low proportion of young people in the structure of the population. As a result, it creates a lower demand for employment and provides a greater incentive for younger people to migrate.
Following the method described in the previous chapter, this section examines the pattern of migration in the Basin area based on the average of Local Government Areas (LGAs), and therefore the average migration rates are based on the total of LGAs. Several propositions are proposed based on the fundamental theory of internal migration (Ravenstein 1885; Sjaastad 1962; Lee 1966; Todaro 1969; Greenwood 1975), which are:
i. Following the fundamental concept of the gravity model (Ravenstein 1885; Beine et al . 2015), the type of rural urban migration can be classified as a purely ‘rural–urban’ migration or ‘small urban–larger urban/city’ migration in the MDB during the Millennium Drought period. ii. There are different patterns between age group categories, in the working age group (15 64 year) and young age groups (15 24 year and 20 34 year), area classifications, and the degree of remoteness. iii. There are differences across the two periods (first phase and second phases) of the Millennium Drought.
Additionally, to obtain more complete figures, the analysis also includes regional migration patterns from several key urban areas, classified as cities within the MDB. For consistency reasons, in the analysis of people’s mobility from the 2006 and 2011 census, this section develops the spatial units based from the urban and locality structure of the ASGS 2011 40 . The similarities of urban structure from these two censuses are related to the definition of locality
40 In the ASGS geographical unit volume 4, urban and locality structure coincide with the new statistical unit where it consists of statistical area level 1 (SA1s), combined together according to population density and other criteria. 93 and urban centre. Locality is defined as an area with a population fewer than 1,000 people, while an urban centre is an area with a population between 1,000 and 19,999. In the ASGS 2011, urban centres are classified not only by their population size, but also with additional criteria such as the labour market conditions and road distances in the area.
In order to correspond with both statistical standards and the unit of analysis, this thesis modifies the ABS classification, and thus the area classifications are: LGAs with a population between 0 and 999 are defined as localities; towns are areas with a population between 1,000 and 4,999; and small urban areas are LGAs with a population range between 5,000 and 9,999. Above 10,000 to 49,999 people, the areas are described as urban or small cities, and areas with above 50,000 people are classified as cities.
Since the composition of the MDB area also includes a substantial regional and remote area, the analysis applies a remoteness structure. The ASGC standard in 2001 was the first edition to insert a structure describing Australia in terms of remoteness measurement. The structure categorises 5 remoteness areas with an average Accessibility/Remoteness Index of Australia (ARIA) 41 . A larger ARIA index denotes poor accessibility to the area. The classification consists of Major Cities of Australia with an ARIA index value of 0–0.2, Inner Regional (ARIA Index 0.2–2.4), Outer Regional (ARIA Index 2.4–5.9), Remote Area (ARIA Index 5.9–10.5), and above that value the areas are defined as Very Remote. In addition, as stated in the previous chapter, the analysis of internal migration and mobility patterns in the MDB excludes the LGA of Unincorporated Australian Capital Territory (ACT).
4.2. Internal Migration in the First Phase of 2001–2006 (five year mobility)
In 2006, based on the Census of Population and Housing and an ABS Report (2008) 42 , more than two million people were living within the Basin area. The majority of the communities were in NSW (38.7%) and Victoria (28.7%), followed by Queensland (10.8%), and South Australia (5.6%). In the context of population changes, all the states covering the MDB experienced a steady population growth between the three censuses of 1996, 2001 and 2006. However, in comparison with Australia’s population changes, all the states were below the national level of 5.7 per cent between 1996 and 2001. In the period 2001–2006, the average
41 ABS defines the ARIA index as “an important dimension of policy development in Australia”, based on distances that people need to travel outside major cities or metropolitan areas. The index starts from zero (major cities) and 10.53 (Remote Australia). Above 10.53 is classified as Very Remote Australia. 42 ABS catalogue number 4610.0.55.007. Pink, B, 2008, ‘Water and the Murray Darling Basin A Statistical Profile 2000 2001 to 2005–2006’, ABS, Canberra. Published on 15 August 2008. 94 population change in the MDB area was 4.3 per cent, which is 1.5 per cent below the national level (5.8%). At this period, only the states of Queensland (6.3%) and South Australia (8.5%) had a population growth above nationwide rates.
In relation to the Millennium Drought event, studies have confirmed that the consecutive years of dry, in particular in 2002 and 2003 (Horridge et al. 2005), contributed to population mobility, where people tended to move out from the Basin. The calculation of five year mobility from the 2006 census at LGA level shows that of the 136 LGAs in the MDB, 94 areas, or almost 70 per cent, had negative net migration rates. In general, the net migration based on five year mobility at LGA level in the MDB between 2001 and 2006 is –2.68 per cent (App. 1A), which indicates that the number of people who experience out migration in the Basin was larger than the number of people who came in (in migration). The three LGAs that had the biggest loss are Bourke (–19.3%), Urana (–17.5%), and Walgett (–16.6%), all of which are located in the remote area of NSW.
On the other side, the areas that had the largest positive net migration rates are Crow’s Nest (16.2%), Bungil (14.3%), and Cambooya (13.8%), all located in the state of Queensland. In terms of population size, of LGA areas with number of residents above 50,000, only Greater Shepparton in the state of Victoria had a negative net migration rate at –2.9 per cent. Other areas such as Greater Bendigo (VIC), Toowoomba (QLD), Wagga Wagga (NSW) and Tamworth (NSW) had positive migration rates.
Following key migration studies age is treated as one of the main determinants (Bartel 1979; Raymer 2011), in which increasing age will reduce the propensity to migrate, since people have to deal with increasing migration costs and maintaining asset accumulation at the original location the pattern of five year mobility in the Basin corresponds with those empirical studies. The working age population (15–64 years) on average had a negative net migration of 3.4 per cent, slightly larger than that of the general population.
The rate of negative net migration becomes higher when examining only younger age groups. Figure 4.2 of the mean net migration at LGA level by age group between MDB and Non MDB confirms the intensity of younger workers’ negative mobility. The young workers age group (20–34 years) has an average net migration rate larger than the working age group, with average negative net migration at –9.8 per cent. The mobility pattern of the young age group (15 24 years) confirms the concern that the MDB experienced a significant number of young people leaving the Basin or relocating within the Basin (ABS, 2009). The negative net migration of
95 this group reaches –20.7 per cent, more than six times that of the general population. However, the trend of negative net migration during this period is not only in the Basin area; the tendency of higher out migration than in migration also occurs outside the MDB at LGA level. The evidence shows that areas outside the MDB have a similar trend, even though the negative net migration rates are lower in all age groups. The main reason for this mobility coincides with population changes in Australia during 2001–2006, where the main contribution of population growth is chiefly from major urban centres and cities (Table 4.2), while many small areas and towns in regional Australia experience out internal migration (Pink, 2008).
Figure 4.2: Mean net migration rates at LGA level of five year mobility by age groups (2001–2006)
Net Migration 5 year Mobility 2001-2006 by age group and MDB/Non-MDB
MDB
Non-MDB
-20 -15 -10 -5 0
Net Migration Rate All Net Migration Rate Age 15-24 Net Migration Rate Age 20-34 Net Migration Rate Age 15-64
Source: Census of Population and Housing 2006
The ASGC classification of 136 LGAs in the MDB, excluding Unincoporated Australian Capital Territory (ACT/Canberra), comprises 2 localities, 41 towns, 41 small urban areas, 47 urban/small cities, and 5 cities. In relation to these classifications, the five year mobility pattern is consistent with the main reason for Australia’s population change, that is that in general the incidence of declining population in the MDB occurred in the areas where population is below 10,000 people. In general, towns and small urban areas are the main contributors to the average negative net migration at LGA level.
Moreover, 41 towns and 41 small urban areas in the MDB experience more people going than people coming in or staying, with on average towns having a negative net migration of –6.7 per cent and small urban areas of –2.1 per cent. Meanwhile, LGAs defined as large urban areas or small cities seem to have a steady population with a 0.3 per cent net migration rate (Figure 4.3). Within the MDB, there are five LGAs described as cities or areas with a population size above 50,000. Three LGAs, which are Tamworth and Wagga Wagga in NSW, and Toowoomba in
96
Queensland, contribute to the positive net migration rates. The other two LGAs, Shepparton and Bendigo, seem to have interesting figures. In terms of geography, the Bendigo region is next to the Shepparton region, however Bendigo has the highest positive rate of net migration in the classification of cities in the MDB at 4.6 per cent, while Shepparton is the only city that experiences negative net migration rate at –2.9 per cent. For the Bendigo region, the proximity to the major city of Melbourne could be one of the causes of high population growth and high in migration rate, as Bendigo is considered as a commuter city, while the location of Shepparton is more northerly and close to the agricultural yield region of Goulburn Broken 43 .
In terms of age groups, except for the city, all the classified areas in the MDB experienced negative net migration rates (Figure 4.3), and, consistent with the general trend, the age group of 15–24 years had the largest rate, with a negative net migration rate above –10 per cent. The young workers group (20–34 years) appears to have a significant negative mobility, with a negative trend occurring in all areas, including cities and urban areas/small cities. The working age group (15–64 years) also has a similar pattern with the negative trend becoming smaller in the larger LGA areas. In comparison, the area outside the MDB seems to have the same trend, yet the data shows that the significant increase of in migration in the city areas explains the contribution from rural–urban migration 44 . The figures emphasise that during this period, the MDB area population loss is not only with the population in general, but more precisely people of productive working ages.
43 Goulburn Broken is part of MDB catchment areas. The catchment occupies 2 per cent of MDB area, but provides 11 per cent of Basin’s water. The water mostly used for people and businesses, in particular agriculture services and productions. http://www.mdba.gov.au/discover basin/catchments/goulburn broken
44 The census household form (App IIIB) of one year and five year mobility provides three options for where a person usually lives: 1. Same with current address, 2. Elsewhere in Australia, 3. Other country. The in migration and out migration to major cities in Australia can be derived from these three options. 97
Figure 4.3: Mean net migration rates at LGA level of five year mobility by area type, remoteness, and age groups (2001–2006)
Net Migration 5 year Mobility 2001-2006 Net Migration 5 year Mobility 2001-2006 by area type and MDB/Non-MDB by area type and MDB/Non-MDB 5 10 0 0 -10 -5 -20 -30 mean of netrate5y of mean -10 n ty ty ies it Ci ities C Tow l C Town l Ci l Urban al al Localities al Sm Localities n/ -15 Sm Small Urban ba rban/Sm s n ty Ur U ies ie ity ies a i lit C it rb own C Cit Town ll C T l ca l U o ma L Localities Smal MDB Non-MDB n/ Small Urban an/S Smal b Ur Urba net migration 15-24 years net migration 20-34 years net migration 15-64 years MDB Non-MDB Source: Census of Population and Housing 2006 Source: Census of Population and Housing 2006
Net Migration 5 year Mobility 2001-2006 Net Migration 5 year Mobility 2001-2006 by remote area type and MDB/Non-MDB by remote area type and MDB/Non-MDB 20 5 10 0 0 -10 -5 -20 -30 -10
mean of netrate5y mean of ia lia lia ia lia lia a ia a a a a tral r tralia ral st st us u ustr ustr us ustr ustralia A A A l f A A l A A o e a of -15 s nal iona e o ion es ti gi mot g ti a a a a a e e emote Au li lia lia li li e Remote Austral R Ci Remote R a a a a Reg R y R r Regional Australi y trali trali tra tr tr tralia r o r s s s str str ter er u u u u ne u V ner Ver Aus Aus A A In Major Ci In Maj l f l Aus O Oute a o a te s o nal Australia s of ote A ote A o e ional Au m m gion gion iti e e MDB Non-MDB Re Re Remote Australia Rem C R R r r Regi r r Reg ry er e e jo e n Very V In Major Citie Out Inn Ma Oute net migration 15-24 years net migration 20-34 years net migration 15-64 years MDB Non-MDB Source: Census of Population and Housing 2006 Source: Census of Population and Housing 2006
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In terms of remote classification based on the ARIA index (Figure 4.3), the areas with a high remoteness index (Outer Regional, Remote, Very Remote) experienced a negative rate of migration. The tabulation from the 2006 census data shows that 66 Outer Regional LGAs had a negative percentage of 4.7 on average for the 14 LGAs defined as remote areas in the MDB. Most of these are located in the states of NSW and Queensland, only one LGA (Warroo) has a positive net migration rate. On average, remote areas experience a significant net number of people leaving the area at 8.8 per cent. Two LGAs, Central Darling in NSW and Paroo in Queensland, which are classified as Very Remote, have a much greater negative rate of nearly 15 per cent. Conversely, the Inner Regional and Major City areas have positive rates, which suggest that migrants are flowing from remote areas to regions that are close to major urban areas or cities.
The examination of productive age groups emphasises the outflow of young people from the Basin. The young age group once again has the highest proportion of negative net migration, followed by the young workers group and working age people in general. The factor of remoteness increases the intensity of out migration rates. From the Figure 4.3, young people seem to have a high intensity of leaving remote areas, or in other words the more remote an area, the higher negative rates are for young people. The trend is also parallel with the rest of Australia outside the MDB, even though the rates are lower than in the Basin.
To sum up, the pattern of five year mobility in the MDB during 2001–2006 indicates that the population experience more negative net migration than the people outside the Basin, especially young people. The analysis using age groups and geographical classifications results in two main points. First, the intensity of negative net migration rates follows the fundamental theories of migration, that increasing age will reduce the probability of people migrating. In the case of the MDB, the young age group (15–24 years) and the young workers group (20–34 years) have the highest propensity to migrate compared with other age groups. Second, the area’s type and the classification of location have a significant contribution in influencing the decision making process to migrate. The mobility also confirms another basic migration framework of the gravity model, specifically the model of rural–urban migration. In the context of the MDB, the mobility pattern is from towns and small urban areas to large urban centres or cities. Moreover, the cross tabulation between the migration and remoteness indices highlights the main cause of the gravity model, that is, that people are migrating to find better living conditions. The figures confirm a study by Garnett and Lewis (2007), which shows there is a relationship between rural
99 economic structure and the distribution of people. A slower rate of economic development in outer regional Australia than in inner regional Australia has encouraged people to migrate.
4.3. Internal Migration in the Second Phase of 2006–2011 (five year mobility)
The 2011 Census of Population and Housing indicates a substantial population growth in Australia between 2006 and 2011 at 8.3 per cent, as stated in Chapter 1. Nevertheless, in comparison with all states, the MDB area continues to have the slowest population growth at 4.5 per cent, a slight increase of 0.2 points from the previous census period of 2001–2006. In the states inside the MDB area, the proportion of the population seems to be unchanging, with the state of NSW remaining the largest contributor at 38 per cent, followed by Victoria at 28 per cent. Queensland recorded a small increase in population growth of 1 per cent during this period.
This thesis considers the period of 2006–2011 as the second phase of the drought period. The year of 2006 was perceived as the most severe year during this prolonged drought period, together with the year of 2002 (Table 3.4). The rainfall data from the Bureau of Meteorology (BoM) based on the LGA level validates this perspective, with total monthly average annual precipitation data showing the rainfall level in 2006 was 300 mm, slightly lower than 2002, which was 355 mm. The difference between the first phase and the second phase of the drought period was that, in the second phase, people and communities in the Basin had already adapted through some consecutive dry years in the first phase, thus there was a possibility of resilience and an adjustment process (Grafton, Chu, Stewardson and Kompas 2011; Jiang and Grafton 2012; McManus, Walmsley, Argent, Baum, Bourke, Martin, Pritchard and Sorensen 2012). Moreover, the intervention from federal and state governments, and from local authorities to mitigate the impact of drought were more substantial than in the early years. For example, the federal government established the Water Act in 2007, followed by the implementation of the Water Buyback policy in 2008. Those policies were put in place for environmental reasons and to support local farmers and communities in the Basin.
However, the trend of more people who experience out migration in the Basin area was continuing in the second phase of five year mobility. The average negative net migration at LGA level in this period is –2.78 per cent (App. 1B), an increase of 0.1 point from previous phase. In terms of spatial unit, the new ASGS standard has reduced the number of LGAs in the MDB from 136 LGAs to 118 LGAs. With this new benchmark there are 87 LGAs in the MDB, or almost 74 per cent, experiencing negative net migration. Based on the data, it also appears
100 that the highest percentage of population loss is not only in the remote or outer regional areas, but also LGAs in the agricultural yield regions or in the irrigation areas. For example, the LGA of Hay in NSW, which is part of the Murrumbidgee irrigation area, has the largest rate of negative net migration at –15.3 per cent, followed by Jerilderie (–14.4%), Urana (–14%), and Bourke (–13.6%). In terms of LGAs with a large population size, Shepparton in Victoria continues to lose people while Bendigo (VIC), Toowoomba (QLD), and Tamworth (NSW) remain as the growing major urban areas in the Basin. In contrast, the LGA of Wagga Wagga, which is described as the satellite urban area for surrounding agricultural towns, experienced a slight negative net migration at –0.2 per cent.
Figure 4.4 confirms the continuing trend of the average negative net migration at LGA level in all population segments. The young age group (15–24 years) remains as the highest rate of negative mobility. The difference from the first phase period is that the negative net migration of the young age group decreases slightly from –20.7 per cent to –18.6 per cent. Meanwhile, the young workers group (20–34 years) seems to have an increasing portion of negative net migration rate, with an increasing rate from –9.8 per cent in the first phase to –11.1 per cent in the second phase. This trend has been highlighted by the ABS (2009), which shows that this pattern had already started from 2001, with the group’s proportion in the population showing a substantial decrease. Another explanation from the report is a widening disparity between regions, with outer regional and remote areas appearing to have less economic and business opportunities, compared with several major urban centres that keep growing and provide better facilities. Furthermore, the working age group of 15–64 years has a similar trend as in the previous phase, where this group experiences a slight increase (to –3.6 per cent) of negative net migration, stressing that the trend of negative mobility at LGA level in the Basin continues and is larger in the second phase.
In comparison, the tendency of persistent negative net migration seems to have a reverse trend for the area outside the MDB or the rest of Australia. For the young age group of 15–24 years, the rate of negative mobility dropped from almost 10 per cent in the first phase to –8.5 per cent. For other age groups and the population as a whole, although on average they still have a negative mobility, the percentage decreased by a significant amount. In other words, the migration trend outside the Basin experienced a rebound trend, where the negative net migration decreased compared with the previous period. For example, the working age group of 15–64 years shows almost zero net mobility, compared with almost –2 per cent in the first phase period. The declining trend of negative net migration can be also an indication of labour
101 movement from rural to urban centres, as the new classical migration model states that a deficit of people in a particular area, caused by a labour movement, must be compensated by other areas with a surplus in their labour force.
Figure 4.4: Mean net migration rates at LGA level of five year mobility by age groups 2006–2011
Net Migration 5 year Mobility 2006-2011 by age group and MDB/Non-MDB
MDB
Non-MDB
-20 -15 -10 -5 0
Net Migration Rate All Net Migration Rate Age 15-24 Net Migration Rate Age 20-34 Net Migration Rate Age 15-64
Source: Census of Population and Housing 2011
As stated above, the new geographical standard of the ASGS in 2011 reduces the number of LGAs inside the MDB area, excluding Canberra and the ACT. It means some areas have merged with new boundaries. Related with area classification of population size, the LGAs within the Basin now consist of 31 towns, 34 small urban areas, 46 large urban areas or small cities, 6 major cities, and only 1 locality of unincorporated area in the state of Victoria. In terms of the cities, classified as an LGA with a population size above 50,000, the LGA of Mildura in Victoria has grown in size even though it experienced a negative net migration. This may have occurred as the methodology of migration rates excludes people who are categorised as ‘not stated’ and people who were living overseas.
From Figure 4.5, it appears that there are two important changes in the migration trend based on area classification. First, the tendency of negative mobility in the Basin continues in this second phase, with higher rates for towns and small urban areas. The average negative net migration in towns increased from –6.7 per cent to –7.1 per cent, while in small urban areas it grew from –2.1 per cent to –2.8 per cent. These figures suggest the general trend in the second phase that the impact of environmental shocks may continue influencing people to migrate outside the affected areas. The intensity of out migration in the Basin area in the second phase period spreads to larger districts, including significant urban areas or small cities, and the data tabulation shows that on average the negative rate for these areas is –0.5 per cent.
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The age group figures in the area classification analysis show a similar pattern of mobility to the general population (Figure 4.5). The figure indicates that negative net migration at LGA level is higher in the Basin during the second phase period. The pattern remains consistent with the new classical approach between age and mobility, where the intensity level of migration gets lower when age is increasing. In this period, the young age group of 15–24 years remains with the highest percentage of negative net migration, followed by the young workers group (20–34 years) and the working age group (15–64 years). Following the trend in the general population, the prevalence of negative mobility based on age groups and area classifications is contributed to by towns and small urban areas. The main difference compared with the first phase, excluding locality 45 , is that all area classifications in the Basin with those three productive age groups experienced negative net migration. In the first phase the data showed that there was a positive net migration rate in cities, particularly for the young age group and the young workers group. This could be an indication of rural–urban mobility where people in the Basin do out migration from small areas to the larger areas.
In addition, the five year mobility pattern in the second phase within the MDB area highlights another important finding. In terms of migration for the general population, the data shows a positive net migration to the cities, because of the propensity of people leaving towns and small urban areas to enter major urban centres in the Basin, such as Toowoomba, Tamworth, Wagga Wagga, and Bendigo. However, when the figures are classified into more specific groups, such as the young age group (15 24 years) and the working age group (15 64 years), all areas in the MDB, including cities, experience negative net migration.
45 As the number of localities in the MDB based on the ASGS standard is only one LGA, which is Unincorporated VIC and has a total population of 802 people with a high percentage of positive net migration, the analysis of five year mobility disregards this pattern as in general the five year mobility trend is negative net migration. 103
Figure 4.5: Mean net migration rates of five year mobility by area type, remoteness, and the age groups effects (2006–2011)
Net Migration 5 year Mobility 2006-2011 Net Migration 5 year Mobility 2006-2011 by age group and MDB/Non-MDB by age group and MDB/Non-MDB 2 40 20 0 0 -2 -20 -4 -40 mean of meannetrate5y -6 s s n y e wn ty e an t ities Ci ies b o lit r ow l Ci C T Citi a U T l c l mall o l Localiti S L -8 /Sma ma n Small Urban an/ S b y Ur Urba ies an an t rb b own Cities Town ll City Cities T ll Cit cali U a Ur a o ll L a Localities Sm /Sm MDB Non-MDB Sm Small an Urban/ Urb net migration 15-24 years net migration 20-34 years net migration 15-64 years MDB Non-MDB Source: Census of Population and Housing 2011 Source: Census of Population and Housing 2011
Net Migration 5 year Mobility 2006-2011 Net Migration 5 year Mobility 2006-2011 by age group and MDB/Non-MDB by age group and MDB/Non-MDB 5 10 0 0 -10 -20 -5 -30
meanof netrate5y a lia lia lia ia lia li a a a a a r r ralia r tralia tral r tralia st s s s str ust ust u ust ust u u A A A A Au A A l f e e l -10 a o t a te A n s n io ional mo io mo g ie e emot g a a a e R e Remote ia i lia lia lia li ia R Cit R R Re ral ra rali a ral r r Reg r Regional Au ry t tral t r t er jo e e s st st n a Very ter V Aus Aus Au Aus In M Inne Major Cities of Australia l Austra Au Out Ou of te of al te Australia nal Au o o es o mote Austra es ion m i gi e ti e Cit e R Ci R Remote MDB Non-MDB y Rem Regiona Reg y jor r R jor a e er a ter Ver nn u Ver Inner RegionalM Australia Out I M O net migration 15-24 years net migration 20-34 years net migration 15-64 years MDB Non-MDB Source: Census of Population and Housing 2011 Source: Census of Population and Housing 2011
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In the remoteness classification of the ARIA index, there are no major changes in the five year mobility between 2006 and 2011 compared with the first phase period. Based on the ASGS standard in the Census of Population and Housing 2011, there are 13 LGAs classified as very remote and remote areas in the MDB, where on average both experience negative net migration of –9 per cent and –8.9 per cent. The outer regional areas in the Basin consist of 55 LGAs and these also continue to have a negative mobility at –4.4 per cent.
Another interesting finding is that LGAs with highest negative rates of migration are around irrigation areas such as the LGAs of Murrumbidgee, Urana, Hay, and Jerilderie, where the region is within or close to Murrumbidgee irrigation and Coleambally irrigation areas. The inner regional areas, which had a significant surplus of mobility in the first phase, now only experience a small positive net migration of 0.6 per cent, while only one LGA classified as a major city in this period (Greater Queanbeyan) that also had negative net migration. The tabulation between different age groups with area classification and remoteness index emphasises the higher intensity of people to migrate.
To summarise, five year mobility in the second phase (2006–2011) has a similar trend to the first phase period in terms of the new classical pattern, in which young people have the largest propensity to migrate and the rates diminish with increasing age. The crucial difference in this period is the negative mobility within the Basin area remains higher, while the rest of Australia indicates a decreasing trend. The main reason could be the consecutive years of environmental shock from the drought that encourages people to leave affected areas. This will be explored in the following chapters.
4.4. One Year Mobility 2005–2006 and 2010–2011
The indicator of one year mobility from both censuses follows the trend of negative mobility of the internal migration pattern in the Basin area. However, in comparison with five year mobility, the migration rates have a smaller percentage, as they only cover people’s mobility in a one year period. In the period of 2005–2006, as derived from the Census of Population and Housing in 2006, people’s mobility at LGA level experiences an average negative rate of 0.72 per cent, which is lower than the five year mobility (2001–2006) figure of 2.68 per cent.
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The young age group of 15–24 years remains with the highest portion of negative mobility with net migration at –5.41 per cent. Moreover, other age groups of young age workers (20–34 years) and the overall working age group (15–64 years) follow the whole population pattern with average net migration of –0.74 per cent and –0.87 per cent respectively.
Figure 4.6: Mean net migration rates of one year mobility at LGA level by age groups (2005–2006 and 2010–2011)
Net Migration 1 year Mobility 2005-2006 by area type and MDB/Non-MDB
MDB
Non-MDB
-6 -4 -2 0
Net Migration Rate All Net Migration Rate Age 15-24 Net Migration Rate Age 20-34 Net Migration Rate Age 15-64
Source: Census of Population and Housing 2006
Net Migration 1 year Mobility 2010-2011 by age group and MDB/Non-MDB
MDB
Non-MDB
-4 -3 -2 -1 0 1
Net Migration Rate All Net Migration Rate Age 15-24 Net Migration Rate Age 20-34 Net Migration Rate Age 15-64
Source: Census of Population and Housing 2011
The pattern of one year mobility in the period of 2010–2011 in the MDB, observed in the Census of Population and Housing in 2011, displays a decreasing negative net migration. In the whole population, the mobility indicates a minor negative net migration of 0.26 per cent, reflecting a balance between people who leave and enter LGAs in the Basin.
The trend is also opposite to the mobility figures between the two censuses. From the previous analysis of five year mobility in both the 2006 and 2011 censuses, the mobility indicates an increase of negative net migration at LGAs around the Basin. Conversely, the pattern of one year mobility is the opposite, where the mobility seems to be lessening (2010 2011 – end of
106 drought period). The trend is consistent with the age groups (Figure 4.6), where migration for young age workers of 20–34 years appears to have almost a zero net migration rate, and the overall working age group of 15–64 years only shows 0.36 per cent of average net migration.
In addition, areas outside the Basin area show an interesting trend. Although on average these areas follow the tendency of negative mobility at LGA level with a very small rate, the young age worker group (20–34 years) shows consistent positive net migration rates. In the year 2005– 2006 mobility figures for areas outside the Basin have a surplus net migration at 0.3 per cent, and the trend increases in the period 2010–2011, with 0.76 per cent growth. The young age group (15–24 years) as usual still has the largest tendency for out migration activity in both periods.
As stated in Chapter 3, this study only applied five year mobility data for the empirical analysis, as one year mobility migration data has a limitation in terms of covering the period of analysis compared to five year mobility migration data. The migration figure for one year mobility is chiefly unable to capture migration activity during the Millennium Drought period. On the other hand, although five year mobility data has the limitation of being unable to identify the actual year when people move, the data is capable of capturing migration during the Millennium Drought period.
4.5. The Migration Pattern in the Murray-Darling Basin: The Gravity Model of Internal Migration.
In the literature review of modern migration studies, the study by Ravenstein (1885) is a fundamental reference for migration studies, particularly in relation to labour mobility and rural–urban migration. Further key studies followed Ravenstein’s gravity model by analysing the rationale behind the movement, which then created the concept of the new classical approach, where the main reason for people’s mobility is an opportunity for labour to find better income or other economic advantages (Hicks, 1932; Sjaastad, 1962; Todaro, 1969). The essential theory of migration extends the concept by creating the mobility framework in more detail. Lee (1966) enhanced the gravity model by proposing the process of the migration decision. As stated in Chapter 2, the concept known as the push–pull migration model emphasises that people’s mobility is determined by plus and minus factors for both origin and destination areas, including other mediating aspects or intervening obstacles.
The factors that intervene between origin and destination are also part of fundamental theories of migration, following Stouffer (1940), who stresses intervening opportunities. The concept
107 complements Lee’s model of push pull theory, where distance of migration is proportional to the number of opportunities. In the context of rural urban migration or small urban city migration in the MDB, the combination of theories may reflect the mobility pattern during the Millennium Drought period.
This framework is essential for the empirical analysis of internal migration. Despite many determinants, comprising the combination of socio economic and environmental factors, the main aim of this analysis is to observe whether the fundamental theory of migration can be applied to people’s mobility in the Basin during the Millennium Drought period. The observation utilises the matrix table of five year mobility from the Census of Population and Housing in 2006 and 2011, where it can detect people’s movement when they experience in migration and out migration, and it solely investigates this mobility regardless of the effect of migration determinants, which will be examined in the next chapter.
In order to implement this framework, this thesis utilises the area classification as categorised above (Table 4.2) to differentiate which LGAs are described as central urban areas and rural LGAs around those urban centres within the MDB. The analysis investigates all LGAs that have a direct boundary with the core area (classified as a city). The core area is the migration destination, and the surrounding LGAs are the origin areas. The analysis assumes that the effect of distance has already been captured by the direct boundary between origin and destination.
Moreover, this thesis uses the classification of cities, defined as LGAs with populations above 50,000 people, as the sample of the gravity model. Based on this selection, there are six LGAs from both censuses which are: Toowoomba (QLD), Tamworth (NSW), Wagga Wagga (NSW), Shepparton (VIC), Bendigo (VIC), and Mildura (VIC). The last LGA of Mildura has been transformed from a large urban area to a city as the population is on the classification’s boundary. The Mildura population in the 2006 census was 49,814 people, and increased 2.3 per cent in the 2011 census, with a population of 50,979 people. The side by side LGAs of Albury in NSW and Wodonga in Victoria are not part of the selection samples, even though the combination of these two LGAs generates population numbers above 50,000 people. The fundamental reason is for consistency in the analysis with a single LGA as the destination of mobility while the surrounding LGAs are viewed as the origin areas.
In addition, the selection of city as the core area or a destination for migration activity was based on the level of population growth. Table 4.2 shows that the average population growth of cities within the MDB are the highest at 38 per cent, compared with large urban areas at 12.8
108 per cent, and negative percentage in others of area classification such as localities, towns, and small urban areas. Thus, the population growth in the cities can be an initial indicator that the areas become a destination of internal migration activity.
4.5.1. Mobility Pattern in Toowoomba Regional
In the MDB communities map published by Geoscience Australia (2002), the regional city of Toowoomba is located at the Basin’s north east boundary. Moreover, a small part of Toowoomba Regional City is outside the MDB area. The geographical location of remoteness index categorised the city as Inner Regional Australia. The proximity to the state’s capital city of Brisbane influences the urban development of this LGA with an indication of average population growth above 2 per cent during the period of 2001–2010 (ABS, National Regional Profile series). Based on the 2006 census, the LGA of Toowoomba had a population of 90,198. However, the population size changed in the census 2011 since the ABS formed the new geographical standard of the ASGS, which impacted the Toowoomba boundary. As the new boundary of Toowoomba creates a wider area, the population figure increases to 151,189 in the 2011 census.
Based on the LGAs map, Toowoomba Regional City has a direct boundary with several small urban areas and towns. Based on the 2006 census, on the south side those LGAs include Warwick and Cambooya, meanwhile Goondiwindi, Jondaryan, and Dalby are on the west side of Toowoomba. To the north side, the neighbouring LGAs comprise Crow’s Nest and Lockyer Valley (outside the MDB area) 46 . The LGAs were changed in the 2011 census, with those LGAs in 2006 being merged into two big areas: the LGA of Southern Downs, consisting of urban areas and towns in the south, and Western Downs to the east and north side of Toowoomba.
The mobility analysis of the Toowoomba Regional city uses the comparison of net migration rates between Toowoomba as the migrant’s destination, with the neighbouring LGAs as the origin areas. This approach is applied as in the states of Queensland the transformation of the geographical standard from ASGC to ASGS has resulted in a merging process of LGAs, and therefore the analysis will focus on migration of five year mobility. In the period of 2001–2006, the analysis uses the ASGC standard, while in the period of 2006–2011 the analysis applies the ASGS standard.
46 The LGAs around Toowoomba region have changed in structural way from the ASGC standard to the latest geographical standard of ASGS. The changes involve a merge between LGAs. 109
In the first phase of five year mobility (2001–2006), the migration activity figure from the nearby LGAs displays a positive net migration rate, especially from the closest LGAs and only two LGAs (Dalby and Goondiwindi) have negative net migration rates. The LGA of Toowoomba itself experiences a positive net migration rate at 1.4 per cent, reflecting that more people were entering rather than leaving this major urban area.
Figure 4.7: Toowoomba Regional (R) in the MDB Map of LGAs
Source : Geoscience Australia (2002)
The migration matrix from the Census of Population and Housing in 2006 shows that the contributor areas of in migration activity in Toowoomba were from those closest LGAs (Table 4.3), reflecting a pattern of the gravity model. The LGAs of Jondaryan and Crow’s Nest are the main contributors within the MDB area, followed by Rosalie, Cambooya, Warwick, and Dalby. Meanwhile, the only LGA that is classified as Outer Regional Australia, which is Goondiwindi, does not show up as one of the main origin areas of in migration activity.
In the second phase of five year mobility (2006–2011) with the newest geographical standard of the ASGS, three nearby LGAs to the north west of Toowoomba Regional (Dalby, Jondaryan and Rosalie) have been amalgamated into one LGA of Western Downs. Another amalgamation also occurred in the southern area, converting Warwick as part of Southern Downs and Cambooya into a non MDB LGA of Lockyer Valley. The migration figures from those new LGAs that have a direct boundary with Toowoomba also show a similar trend with the first phase. The LGA of Western Downs has a positive net migration at 3.1 per cent, Southern
110
Downs at 2.2 per cent and Lockyer Valley at 6.9 per cent. Nevertheless, they remain as the major contributor of origin areas in Toowoomba’s in migration activity.
Table 4.3: Main origin LGAs of Migrants entering Toowoomba Regional City
Five Year Mobility 2001–2006 Five Year Mobility 2006–2011
No Origin Area Entering No Origin LGAs Area Entering LGAs Toowoomba Toowoomba (persons) (persons)
1 Brisbane Non MDB 1834 1 Brisbane Non MDB 2359 Western Downs 2 Jondaryan MDB 1347 2 MDB 1207 (Dalby,Jondaryan,Rosalie) 3 Crow's Nest MDB 990 3 Lockyer Valley (Cambooya) Non MDB 1185 4 Gatton Non MDB 912 4 Moreton Bay Non MDB 984 5 Gold Coast Non MDB 661 5 Gold Coast Non MDB 920 6 Rosalie MDB 650 6 Sunshine Coast Non MDB 869 7 Cambooya MDB 606 7 Southern Downs (Warwick) MDB 840 8 Warwick MDB 524 8 Logan Non MDB 688 9 Dalby MDB 396 9 Ipswich Non MDB 592 10 Maroochy Non MDB 371 10 Townsville Non MDB 556 ……….. …….. …………. ………. 26 Goondiwindi MDB 180 12 Goondiwindi MDB 487 Sources : Census of Population and Housing 2006 and 2011
The matrix tabulation of five year mobility (2006–2011) confirms that those amalgamated LGAs that have a direct boundary with Toowoomba continue as the major contributor of in migration activity. Western Downs, which includes Dalby, Jondaryan, and Rosalie, becomes the second largest originated LGA of migrants. Southern Downs, which includes Warwick in the south area, is in seventh place of origin LGAs. Moreover, although Lockyer Valley is classified as a non MDB LGA, the area remains as one of main origins from the east side of Toowoomba. These figures validate that the fundamental concept of the gravity model, where people from periphery areas tend to move to the core area, has occurred in the internal migration within this part of the MDB area.
In terms of the mobility pattern in Toowoomba, the positive net migration rates from the nearby LGAs can be caused by two factors. First, in area classification, several LGAs based on the ASGC in 2006 are categorised as large urban areas with populations above 10,000 people. These areas include Warwick (21,534), Jondaryan (14,098), and Crow’s Nest (12,639), and even Dalby (9,776) and Rosalie (9,037) are close to becoming significant urban areas. In addition, all these LGAs are also classified as Inner Regional Australia in the remoteness index. Only the LGA of Goondiwindi, classified as a town with a population below 5,000 people, has
111 a negative net migration rate relative to Toowoomba. The possible reason was the trend of low population LGA that tends to have negative net migration rates in the MDB during the Drought period. From these figures, the area around Toowoomba Regional City can possibly become a ‘satellite area’ where it has the capacity as a sub destination from rural areas, indicated by positive net migration rates. The amalgamation of the LGAs from the ASGC to the ASGS standard also continues the trend of migrants sub destination, with the population of the Southern Downs becoming 33,883 and the Westerns Downs turning to 31,591. A second factor relates to the closeness of the major city of Brisbane. The analysis from out migration patterns from Toowoomba shows Brisbane as the top LGA of origin and destination in the 2006 and 2011 census, indicating that people from Brisbane are very mobile and have many destination options if they want to move a short distance. The migration destination is not only Toowoomba, but also large urban areas around the Toowoomba region.
As the movement from surrounding LGAs to Toowoomba confirms the gravity model, additional data for income differentials between these LGAs needs to be compared, following Stouffer’s model (1940) and Lee’s model (1966), of intervening factors between origin and destination, to reflect the compensation from the distance effect. Referring to Stouffer’s intervening opportunities, Toowoomba is the first option as an urban centre for migrants from surrounding LGAs in their rural urban migration.
Although there was a fluctuation in the average income during the first phase (average annual wage in 2003 and in 2004 2005), Toowoomba had the highest average annual wage compared to other surrounding LGAs in 2003 (Figure 4.8a). The figures confirm that there is a gap of annual income levels between surrounding LGA’s (especially Dalby, Crow’s Nest, and Warwick) and Toowoomba to compensate for distance, even though the levels drop by 1.5 per cent in 2004 2005.
Brisbane, as the closest major city, indicates a significant gap for annual wage in 2003 and 2004 2005. The gap reflects the intervening opportunities as the distance effect on migration displays proportional opportunity. The figures for income differentials also show that Toowoomba was one of the main destinations for internal migration from Brisbane. Table 4.3 has confirmed that Brisbane had the highest number of in migration of people to Toowoomba in the first phase period.
The average annual wage in Toowoomba and its surrounding LGAs in the second phase shows that the income differentials between origin and destination remain the key factor determining
112 the decision of people to migrate. The average wage in 2007 and 2008 2009 in Toowoomba was higher than surrounding LGAs (Figure 4.8b). The income gap between Brisbane and Toowoomba continues to reflect the intervening opportunity model, as distance is proportional to the number of opportunities. The in migration pattern was also consistent, as people from Brisbane were the largest cohort to enter Toowoomba in the second phase, indicating Toowoomba was one of the main destinations for internal migration of people from Brisbane.
Figure 4.8: Average Annual Wage in Toowoomba and Surrounding LGAs
a. Average Annual Wage in the First Phase
Average Annual Wage Toowoomba and Surrounding LGAs in the First Phase 29369 WARWICK (S) 27,916 CROW'S NEST (S) 31487 28,745 Av. Wage 30696 2003 DALBY (T) 28,367 GOONDIWINDI … 30735 Av. Wage 30,504 2004/2005 31108 ROSALIE (S) 31,909 31165 CAMBOOYA (S) 31,909 31651 JONDARYAN (S) 31,909 32399 TOOWOOMBA … 31,909 37932 BRISBANE (C) 38,853 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
b. Average Annual Wage in the Second Phase
Average Annual Wage Toowoomba and Surrounding LGAs in the Second Phase SOUTHERN DOWNS 31484 (R) 33559 Av. Wage 33983 LOCKYER VALLEY 2007 (R) 36402 Av. Wage WESTERN DOWNS 34108 2008/2009 (R) 37718
36642 TOOWOOMBA (R) 39527
44902 BRISBANE (C) 48619
0 10000 20000 30000 40000 50000
Sources: National Regional Profile (NRP) series 2000 2004 and 2004 2007
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4.5.2. Mobility Pattern in Tamworth Regional City
The LGA of Tamworth Regional City is classified as an area (A) based on the ABS area structure, as it consists of 12 municipalities. A report from Tamworth Regional Council in 2011 highlights that the area has experienced a strong population growth in the period 2001–2009, and has become the major contributor to population growth in the Northern Statistical Division (SD). Based on the MDB communities’ map, the LGA of Tamworth has a direct boundary with five LGAs within the MDB area. Those LGAs are Liverpool Plains in the south west, Gunnedah in the west, Narrabri in the north west, Gwydir in the north, and Uralla in the west.
Figure 4.9: Tamworth Region (A) in the MDB Map of LGAs
Source : Geoscience Australia (2002)
In the 2006 Census of Population and Housing, the population in Tamworth was 53,592 which denotes the area as the fifth most populous LGA in the MDB. The city is also classified as an Inner Regional area in the remoteness index, and the location is between the two major metropolitan cities of Sydney and Brisbane. The migration figures indicate that in general Tamworth receives more people or experiences positive net migration at 1.38 per cent between 2001 and 2006. However, the city appears to experience a loss of people in the lower age groups, in particular young people between 15–24 years and the young workers group 20–34 years. The young age group displays a negative mobility at –9.8 per cent, suggesting the possibility of leaving the city to pursue university education in the major Australian cities, as stated in the ABS report (2009). Meanwhile the young workers group also has a negative rate
114 at –5.1 per cent, following the general trend in the MDB during the first phase of the prolonged drought period.
Conversely, in general for the working age group (15–64 years), Tamworth Regional City seems to receive more people, indicated by a slight surplus between people who leave and enter the city, or has a positive net migration rate at 0.3 per cent. The mobility figure suggests that, while the general trend in the MDB in the first phase of the Millennium Drought (2001–2006) has a negative mobility pattern, Tamworth Regional City, as a major city in the Basin, seems to be one of main destinations for people to migrate to, especially from surrounding LGAs.
In the second phase period of 2006–2011, the 2011 Census of Population and Housing figures for migration activity in Tamworth Regional City appear to have no significant changes. In terms of the whole population, the net migration rate remains positive at 1.4 per cent, suggesting Tamworth continues as one of the destination areas for migration. Young people (15–24 years) persist as the group with the highest intensity to migrate out from the city, with the percentage of net migration declining slightly at –8.5 per cent. However, the proportion of young workers (20–34 years) lost decreased to –3.4 per cent, even though the MDB’s trend shows an increase in negative net migration. On the other hand, the surplus from in and out migration for the working age group (15–64 years) grows in this second phase at 0.5 per cent, following the trend of steady population growth in Tamworth.
Meanwhile, the neighbouring LGAs are mostly defined as small urban areas, with most experiencing a stagnant or declining population growth. Liverpool Plains, with a population of 7,537 in 2006, slightly declining in 2011 to 7,469, is defined as a small urban area and classified as Outer Regional Australia in the remoteness index. Gunnedah is a large urban area with population in 2006 of 11,524 and 12,065 in 2011, and is also categorised as Outer Regional Australia. Narrabri is among the urban areas that experience a declining trend in population from 13,113 in 2006 to 12,926 in 2011, and is categorised as Outer Regional Australia. To the north side of Tamworth, another outer regional LGA of Gwydir is at the classification border between town and small urban area with a population of 5,310 in 2006, decreasing to 4,965 in 2011. Another outer regional area of Uralla, with a population size very similar to Gwydir, has a positive population growth with 5,737 people in 2006, slightly increasing to 6,032 in 2011.
Based on these characteristics, it can be seen that there is a high possibility of migration for people from LGAs near Tamworth as the major urban area in the region. The opposite trend of
115 population growth in Tamworth, which experiences a steady positive growth in both periods, can be seen as another indication of people’s mobility from surrounding areas to enter the city.
The migration figures for all surrounding LGAs suggest evidence of the gravity model, indicated by negative net migration during the first phase and second phase of five year mobility. In the period 2001–2006, the outer regional areas to the west of the city display the highest rate of negative mobility such as Moree Plains at –14.3 per cent and Narrabri at –8.1 per cent. Other LGAs also experience a deficit net mobility, such as Liverpool Plains at –6.6 per cent, followed by Gunnedah (–4.9%), Gwydir (–1.1%), and Uralla (–0.3%). The figures change slightly in the period 2006–2011, but all the LGAs continue to have negative net migration. Moree Plains remains as the highest at –9.2 per cent and Narrabri declines by more than a half at –3.8 per cent. Other LGAs like Gwydir experience an increase of negative net migration to –4.6 per cent and Uralla to –1.3%.
The tabulation of five year mobility from census data between 2001 and 2006 indicates that the nearby LGAs are the main contributors to migration activity to enter Tamworth, reflecting the activity of rural–urban migration. Table 4.4 shows in the five year mobility 2001–2006 that three neighbouring LGAs (Liverpool Plains, Gunnedah and Narrabri) are in the top five contributors of people entering Tamworth. Another nearby LGA of Moree Plains is also a main area of origin. Moreover, the other two areas that have a direct boundary with Tamworth, which are the LGAs of Gwydir and Uralla, are not part of the main contributors of in migration, indicated by their lower ranking of 22 and 30 respectively. Since those LGAs are located to the north of Tamworth, one of the main possibilities to explain this finding is the closeness with another major urban area of Armidale Dumaresq, which is classified outside the MDB area. Other main contributor areas to enter Tamworth are mostly from the coastal region outside the boundary of the MDB, such as Lake Macquarie, Gosford, Wyong, and the urban centre of Newcastle.
The mobility pattern of Tamworth Regional City in the second phase (2006–2011) has similar figures as the previous phase. The areas to the south and west of Tamworth continue to contribute in migration flow. Liverpool Plains and Gunnedah remain as the main origin LGAs of people entering Tamworth. Narrabri and Moree Plains are also in the top list of major origin areas of migration activity. Meanwhile, as in the first phase period, the LGAs to the north of Tamworth of Gwydir and Uralla also continue as minor contributors of migrants entering Tamworth. In terms of major areas outside the MDB that have a significant amount of people
116 entering Tamworth, the LGAs in the coastal region, such as Lake Macquarie and Newcastle, are consistently at the top of the list in this period, even though in terms of geographical distance those areas do not have a direct boundary with Tamworth. One possible explanation for this is a temporary migration between Tamworth and these LGAs. Based on the 2006 and 2011 census, Newcastle and Lake Macquarie are the major destinations of out migration from Tamworth.
Table 4.4: Main origin LGAs of Migrants entering Tamworth Regional City
Five Year Mobility 2001–2006 Five Year Mobility 2006–2011
No Origin LGAs Area Entering No Origin LGAs Area Entering Tamworth Tamworth (persons) (persons)
1 Liverpool Plains MDB 451 1 Liverpool Plains MDB 360 2 Gunnedah MDB 345 2 Gunnedah MDB 339 3 Lake Macquarie Non MDB 233 3 Lake Macquarie Non MDB 211 4 Narrabri MDB 231 4 Armidale Dumaresq Non MDB 204 5 Armidale Dumaresq Non MDB 213 5 Newcastle Non MDB 195 6 Moree Plains MDB 210 6 Narrabri MDB 187 7 Newcastle Non MDB 191 7 Moree Plains MDB 177 8 Blacktown Non MDB 179 8 Brisbane Non MDB 172 9 Gosford Non MDB 162 9 Gold Coast Non MDB 136 10 Wyong Non MDB 157 10 Coffs Harbour Non MDB 134 …. ……… ………. …… …. ……… ………. …… 22 Gwydir MDB 104 23 Gwydir MDB 86 30 Uralla MDB 81 34 Uralla MDB 67 Sources : Census of Population and Housing 2006 and 2011
The five year mobility out migration figure for Tamworth Regional City in both periods also emphasises that the migration pattern in this region follows the gravity model of migration. Between 2001 and 2006 out migration from Tamworth mostly goes to other major urban centres in the coastal area such as Newcastle, Brisbane, Gold Coast and Lake Macquarie. The pattern only changed slightly in the period 2006–2011, when Armidale Dumaresq becomes one of the major destinations. Further analysis from the mobility matrix implies that those nearby LGAs have only received a small number of people from Tamworth, and the net mobility still reflects the tendency of a ‘periphery to core’ migration pattern.
The figure of average annual wages in Tamworth Regional and its surrounding LGAs also confirms that the push pull migration model and the intervening opportunity model have explanatory power in analysing migration activity. Data from the National Regional Profile in 2000 2004 and 2002 2006 show that annual wage income in Tamworth is relatively higher than the surrounding LGAs, reflecting the pull factor for migrants to enter Tamworth (Figure 4.10a).
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The average annual wage in the second phase also indicates that the LGA of Tamworth still has the highest wage level compared with surrounding LGAs (Figure 4.10b), reflecting income differentials as one of key factors for people moving to Tamworth.
Figure 4.10: Average Annual Wage in Tamworth Regional and Surrounding LGAs
a. Average Annual Wage in the First Phase
Average Annual Wage Tamworth and Surrounding LGAs in the First Phase 27,933 GWYDIR (A) 27,933 LIVERPOOL PLAINS 28,825 (A) 28,825 Av. Wage 29,217 2003 URALLA (A) 29,418 30,042 Av. Wage GUNNEDAH (A) 31,260 2004/2005 30,225 NARRABRI (A) 31,687 32,084 MOREE PLAINS (A) 32,707 TAMWORTH 32,251 REGIONAL (A) 33,072 36,900 NEWCASTLE (C) 36,805 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
b. Average Annual Wage in Second Phase
Average Annual Wage Tamworth and Surrounding LGAs in the Second Phase 30,536 GWYDIR (A) 32,799 URALLA (A) 32,623 34,231 Av. Wage LIVERPOOL PLAINS 34,226 2007 (A) 36,618 34,945 Av. Wage NARRABRI (A) 37,479 2008/2009 34,621 GUNNEDAH (A) 37,749 35,171 MOREE PLAINS (A) 37,230 TAMWORTH 35,914 REGIONAL (A) 38,279 42,807 NEWCASTLE (C) 45,490 0 10,000 20,000 30,000 40,000 50,000
Sources: National Regional Profile (NRP) series 2000 2004 and 2004 2007
The number of people entering and leaving Tamworth from and to the nearest major urban centre, which is the LGA of Newcastle, confirms Stouffer’s model of distance and opportunity. The significant income differential between Tamworth Regional and Newcastle reflects that migrants from areas surrounding Tamworth would gain a higher annual wage if they migrate further. However, potential migrants must also consider skills and qualifications to derive more
118 benefit from migration (Todaro 1969; Bartel 1979). Overall, the figure for average annual wages around Tamworth Regional for both periods of mobility is consistent with fundamental theories of migration.
4.5.3. Mobility Pattern in Wagga Wagga
In the local government classification and remoteness index, Wagga Wagga is described as a city (C) and Inner Regional Australia. The area is located in the middle of two major cities, approximately 460 km north of Melbourne and 450 km south west of Sydney. The compilation from the data series of the National Regional Profile (NRP) shows that the population growth in Wagga Wagga in the period 2001–2010 is 0.7 per cent, which is below the national average. The census in 2006 and 2011 also confirms this slow growing pattern, where the population size in 2006 was 57,012 and increases slightly to 59,459 in 2011, or grows 4.3 per cent in five years. However, the migration figures reflect a dynamic mobility in this region. In the first phase of five year mobility (2001–2006), the city experiences positive mobility with a net migration rate of 2.3 per cent, representing 10,523 people entering the city and 9,410 leaving the area. In the second phase of five year mobility (2006–2011), the number of migrants drops slightly, with net migration rates becoming negative at –0.2 per cent. The number of migrants entering Wagga Wagga during this time is 9,405, and 9,501 people leave the area.
Figure 4.11: Wagga Wagga (C) in the MDB Map of LGAs
Source : Geoscience Australia (2002)
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Geographically, the LGA of Wagga Wagga is surrounded by agricultural regions including the Riverina, Leeton, and Griffith, and irrigation areas such as Murrumbidgee irrigation and Coleambally irrigation. There are eight LGAs that have a direct border with Wagga Wagga: Coolamon, Junee, and Gundagai in the north; Narrandera and Lockhart in the west; Greater Hume in the south; and Tumut and Tumbarumba in the east of the area. In terms of population size, most of these LGAs are classified as towns and small urban areas or have a population size between 1,000 and 9,999, indicating those areas are peripheries of Wagga Wagga. The migration pattern also shows a deficit trend of mobility in this region in the first phase period (2001–2006) with Narrandera having the highest negative net migration at –6 per cent, followed by Tumut (–4.8%), and Junee (–4.7%). The negative pattern also occurs in the large urban areas close to Wagga Wagga such as Griffith (–5.7%) and Leeton (–4%).
The trend of negative net migration continues in the second phase period (2006–2011) with all the LGAs around Wagga Wagga still having significant rates of people leaving the areas. The LGA of Narrandera persists in having the highest negative net migration rate at –5.2 per cent, followed by Tumut (–4.3%), Tumbarumba (–4%), Junee (–3.3%), Lockhart (–2.9%), Coolamon (–2.6%), Greater Hume (–2.5%), and Gundagai (–1.6%). Moreover, the LGA of Griffith, one of the major urban areas in the region, also experienced an increasing negative mobility to –6.6 per cent.
In terms of whether the mobility pattern follows the fundamental concept of the gravity model, the analysis of origin areas of migration can confirm this pattern. The five year mobility of 2001–2006 shows that the nearby LGAs, mostly from the north side of Wagga Wagga, are on the top of the list of the origin districts. These LGAs comprise Junee, Coolamon, Lockhart, Tumut and Narrandera (Table 4.5). However, other LGAs to the south of Wagga Wagga, such as Greater Hume and Tumbarumba, appear to have insignificant amounts of migrants entering the city. Moreover, other close LGAs classified as large urban areas like Griffith and Leeton are among the top contributors of in migration.
The second period of five year mobility (2006–2011) continues the pattern from the previous phase, Canberra (ACT) remaining the main contributor of people entering Wagga Wagga. The mobility figure in the second phase also confirms that the origin areas for the in migration pattern in Wagga Wagga are from the north side region, with the LGAs of Junee, Coolamon, Lockhart and Tumut still on the top list of origin areas of migrants.
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In addition, Wagga Wagga is also well known as a destination city in the centre of the Basin area for educational purposes 47 , in particular for young people to pursue higher education as the city has a university. The presence of Charles Sturt University in this city certainly attracts young people from the surrounding rural and urban areas to pursue higher education. In the first phase of five year mobility, the net migration figure for the young age group (15–24 years) confirms this activity with a positive rate of 14.7 per cent, the highest of all cities in the MDB area (excluding Canberra). The trend continues in the second phase of five year mobility, with the young age group net migration rate dropping slightly at 13.2 per cent.
Table 4.5: Main origin LGAs of Migrants entering Wagga Wagga
Five Year Mobility 2001–2006 Five Year Mobility 2006–2011
No Origin LGAs Area Entering No Origin LGAs Area Entering Wagga Wagga Wagga Wagga (persons) (persons)
1 ACT MDB 463 1 ACT MDB 376
2 Junee MDB 300 2 Junee MDB 242
3 Coolamon MDB 291 3 Coolamon MDB 228
4 Lockhart MDB 283 4 Lockhart MDB 215
5 Tumut MDB 277 5 Tumut MDB 212
6 Griffith MDB 213 6 Brisbane Non MDB 207
7 Temora MDB 202 7 Leeton MDB 203
8 Bland MDB 189 8 Townsville Non MDB 187
9 Narrandera MDB 184 9 Albury MDB 173
10 Leeton MDB 177 10 Griffith MDB 155
….. ……… ………. …… 13 Narrandera MDB 129
17 Greater Hume MDB 120 23 Greater Hume MDB 90
35 Tumbarumba MDB 81 35 Tumbarumba MDB 72 Sources : Census of Population and Housing 2006 and 2011
Other population groups, particularly those of young age worker and the general working age group, seem to follow the general trend of migration in Wagga Wagga. For example, young age workers (20–34 years) have a steady positive net migration in both phases: 2.1 per cent in 2001– 2006 and 0.5 per cent in 2006–2011. Furthermore, in the general working age group of 15–64 years, the net mobility displays a positive net migration rates in the first phase period at 2.4 per cent, and a slight decline in the second phase at 0.3 per cent.
47 Canberra (ACT) is a part of the MDB area and relatively is the main destination for young age persons (15–24 years) to pursue higher education, with several universities and vocational institutions. However, this study follows previous literature where Canberra (ACT) is excluded from the empirical estimation as the effect of including it could potentially bring a bias in the average analysis due to a large gap in the population and economic profile. 121
The mobility pattern in Wagga Wagga during the Millennium Drought reflects the general condition of the MDB, where the intensity of migration corresponds to the prolonged drought. It is hypothesised that in the first phase, communities in this region appeared to handle the effect of the drought, indicated by positive movement to the nearest urban centre as part of the adjustment process. The consecutive years of shock appear to reduce the community’s resilience, where the city starts to lose people in the second phase. In terms of the migration pattern, the gravity model can be seen from the LGAs to the north side of Wagga Wagga, where most of the origin areas come from this region. Wagga Wagga also has a specific characteristic as a main destination for young people aged 15–24 years to pursue further education given the presence of Charles Sturt University’s Wagga campus. The net migration rate of this group confirms a significant number of young people entering Wagga Wagga in both periods.
While the data of people entering Wagga Wagga in the first phase and second phase confirms the gravity model, the income differentials between Wagga Wagga as destination and its surrounding LGAs as origins reflect Lee’s model of push pull migration and Stouffer’s model of intervening opportunities. The average annual wage in Wagga Wagga in the first phase of the drought period was relatively higher than its neighbouring LGAs. The annual wage gap is substantial for several LGAs such as Junee, Temora, Narrandera, and Coolamon (Figure 4.12a).
In the second phase, the average annual wage had a similar pattern, with the wage level in Wagga Wagga remaining as the key pull factor for people from surrounding LGAs to enter the city (Figure. 4.12b). In terms of Stouffer’s model of distance and opportunity, the nearest urban centre, which is Canberra ACT, describes those migrants who compare opportunities between urban centres. Based on the data in Table 4.5, people from Canberra were the highest number entering Wagga Wagga in both periods. The data also shows that the first destination of out migration from Wagga Wagga was Canberra in both the first phase and second phase. This mobility pattern confirms that migrants from this area were fully informed that opportunity, i.e. wage level, has an effect on migration.
The higher average annual wage in Canberra ACT (Figure 4.12) attracted those people from Wagga Wagga and its surrounding LGAs if they had relevant skills and qualifications (Todaro 1969; Bartel 1979). This also explains that for some people the migration to Wagga Wagga from its surrounding LGAs is simply rural urban migration, and confirms the gravity model. However, for other migrants, Wagga Wagga may become the intervening factor to their main destination. Overall, the migration pattern in Wagga Wagga validated the gravity model, i.e.
122 people move from periphery to urban centres to seek improved opportunities. However, other fundamental theories (Lee’s model and Stouffer’s model), also allowed that pull factors, such as wage levels, play an important role in people settling at the destination or moving again to further pursue opportunities.
Figure 4.12: Average Annual Wage in Wagga Wagga and Surrounding LGAs
a. Average Annual Wage in the First Phase
Average Annual Wage Wagga Wagga and Surrounding LGAs in the First Phase 28,419 JUNEE (A) 30,690 28,491 TEMORA (A) 29,353 Av. Wage 28,628 2003 NARRANDERA (A) 30,434 COOLAMON (A) 28,947 Av. Wage 29,390 2004/2005 29,945 LEETON (A) 32,968 31,668 GRIFFITH (C) 31,362 29,998 LOCKHART (A) 30,120 33,319 TUMUT SHIRE (A) 34,002 33,680 WAGGA WAGGA (C) 34,075 43,256 CANBERRA 44,890 0 10,000 20,000 30,000 40,000
b. Average Annual Wage in Second Phase
Average Annual Wage Wagga Wagga and Surrounding LGAs in the Second Phase 33,577 NARRANDERA (A) 35,183 Av. Wage COOLAMON (A) 34,069 36,936 2007 34,432 LOCKHART (A) 37,408 Av. Wage 34,483 2008/2009 GRIFFITH (C) 36,111 35,078 LEETON (A) 36,769 37,503 JUNEE (A) 38,513 37,056 TUMUT SHIRE (A) 40,211 37,613 WAGGA WAGGA (C) 40,505 49,725 CANBERRA 53,540 0 10,000 20,000 30,000 40,000 50,000
Sources: National Regional Profile (NRP) series 2000 2004 and 2004 2007
4.5.4. Mobility Pattern in Greater Bendigo and Greater Shepparton
Greater Bendigo and Greater Shepparton have some specific characteristics in the MDB area. First, in terms of geographical location and distance, these LGAs are very close, separated merely by the large urban LGA of Campaspe (Figure 4.9.). Second, the proximity of these
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LGAs does not undermine the other city. The population trends of Bendigo and Shepparton during the first decade of the 21 st century confirm this proposition.
The estimated annual population data from the ABS shows that between 2001 and 2010, the population growth in Bendigo was at a steady rate of 1.3 per cent. Meanwhile, population changes between the 2006 and 2011 census validate the trend, with 7.9 per cent growth or an increase from 93,254 people to 100,617 people. Meanwhile, Shepparton’s population growth in the same period also experienced a constant growth at 0.7 per cent with the population changes between the 2006 and 2011 census from 57,090 to 60,448 or 5.9 per cent. Third, the area also represents one of the central agricultural regions in the MDB consisting of the Loddon Campaspe and Goulburn Murray yield regions.
Figure 4.13: Greater Bendigo and Greater Shepparton in the MDB Map of LGAs
Source : Geoscience Australia (2002)
However, in terms of migration activity, the five year mobility in these two cities appears to have a different pattern. The first phase of migration in Bendigo implies the city is a destination area for surrounding regional towns. Between 2001 and 2006 there were 13,693 people entering Bendigo, while 10,147 were left the city. This figure put Bendigo as an LGA in the MDB that experienced a net migration rate of 4.6 per cent. The trend remained consistent in the period of 2006–2011 with the number of people entering Bendigo almost the same as from the previous
124 phase at 13,094, while 9,748 people moved out from the city were. This makes the net migration rate in Bendigo slightly decreased at 4 per cent.
Conversely, the mobility story in Shepparton in the Millennium Drought period follows the majority of LGAs in the MDB that experience a loss of people. In the first phase of 2001–2006, there were 6,286 people entering Shepparton and 7,722 were leaving the area, which put Shepparton’s net migration rate at –2.9 per cent. The consecutive years of drought in the second phase period of 2006–2011, shows that 5,988 migrants entered Shepparton and 7,008 departed the city. This mobility means the net migration figure in Shepparton improves marginally to – 2 per cent.
In terms of periphery areas in the region, Bendigo and Shepparton share the surrounding LGAs (Figure 4.9). In geography of Bendigo, the areas that have a direct boundary with the city include Loddon, Campaspe, Mitchell and Mount Alexander. Shepparton itself is one of the closer areas in the north east, which potentially becomes an origin or destination of migration activity. Meanwhile, the LGAs around Shepparton comprise Moira, Campaspe, Strathbogie and Benalla. There are also several LGAs in the north of both cities that potentially contribute to the in migration activity such as Gannawarra, Murray, Berrigan and Corowa.
Most of these nearby LGAs areas are classified as large urban areas with populations above 10,000 people. Campaspe and Mitchell are the most populous neighbours with populations above 30,000, followed by the LGA of Moira with a population size around 28,000. Other LGAs like Gannawarra, Mount Alexander and Benalla have a population range from 10,000 to 20,000, indicating that they can also be described as significant urban areas. Only two LGAs from this region have a population size below 10,000 or are defined as small urban areas, and these are Loddon (close to Bendigo) and Strathbogie, which has a direct border with Shepparton. In addition, in terms of the remoteness index, almost all LGAs in this part of the MDB area are categorised as Inner Regional Australia. Only two LGAs located to the west of Bendigo, Loddon and Gannawarra, are classified as Outer Regional Australia.
Related to the dynamics of mobility between periphery areas to core cities, the data seems to reflect the concept of the gravity model only for the remote areas of Loddon and Gannawarra. In the five year mobility of 2001–2006, negative net migration occurs in Loddon at –6.1 per cent and in Gannawarra at –4.9 per cent. Several LGAs seem to experience a minor net migration activity such as Benalla (–0.5%), Campaspe (0.4%) and Mount Alexander (1%).
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Meanwhile, the rest of the surrounding LGAs appear to have a surplus from five year mobility, with Mitchell as the highest at 6.3 per cent, followed by Strathbogie (2.1%) and Moira (2.1%).
The net migration trend in this area has a minor change in the next five year mobility of 2006– 2011 with Gannawarra becoming the highest area to lose more people with net migration at – 6.2 per cent, and Loddon at –5.8 per cent. While Mitchell remains in surplus with positive net migration of 5.3 per cent, Campaspe, as the LGA in between Bendigo and Shepparton, experiences more people departing from the area, with net migration rates becoming negative at –2.1 per cent. Other LGAs have changed slightly from the previous phase of net migration activity such as Strathbogie (1.8%), Moira (0.4%), Mount Alexander (2.2%) and Benalla (– 0.4%).
However, the gravity model of migration can also be detected by analysing the origin areas of people entering Bendigo and Shepparton (Table 4.6). The tabulation from the 2006 census of five year mobility in Bendigo shows that almost all the nearby LGAs are main contributors for in migration activity. The LGAs of Mount Alexander, Campaspe and Loddon are in the top three origin areas, and also the outer regional area of Gannawarra is one of the sources of incoming people.
Migrants from Shepparton also contribute considerably to the positive net migration in Bendigo during this period. The contributor areas remain almost the same in the second period of 2006– 2011. From the 2011 census, those top three LGAs continue as the main contributors, and all LGAs that have a direct border with Bendigo are in the top list, including Mitchell. Moreover, during this second phase of mobility, more people from Shepparton are coming into Bendigo.
On the other hand, although Shepparton experienced negative net migration in both periods of five year mobility, the origin of people entering Shepparton reflects periphery to core migration activity. In migration activities during 2001 to 2006 implies that all the surrounding areas of Shepparton are main contributor areas. The LGAs of Moira, Campaspe, Strathbogie and Benalla are among those at the top of the list for the number of people entering Shepparton. Moreover, people from Bendigo are also a substantial source of migrants. The pattern is almost similar in the second phase of five year mobility between 2006 and 2011, with all nearby LGAs remaining as the key contributors of in migration, but with decreasing migrants from Bendigo.
In addition, with a further analysis of destination areas of people who leave Bendigo and Shepparton, out migration from Bendigo to nearby LGAs is not substantial. Thus, the impact
126 is reflected on their positive net migration rates. The trend did not occur in Shepparton by observing its out migration trend. The numbers of people who leave Shepparton and go to the nearest LGAs appear to be significant and even exceed people who enter the city. For example, in 2006 there were 572 people entering Shepparton from Campaspe, while 574 people left Shepparton to go to Campaspe. This figure could be one of the causes for Shepparton experiencing negative net migration in both periods of five year mobility.
Table 4.6: Main origin LGAs of Migrants entering Bendigo and Shepparton a. Greater Bendigo Five Year Mobility 2001–2006 Five Year Mobility 2006–2011
No Origin LGAs Area Entering No Origin LGAs Area Entering Greater Greater Bendigo Bendigo (persons) (persons)
1 Mount Alexander MDB 711 1 Mount Alexander MDB 623 2 Campaspe MDB 688 2 Campaspe MDB 597 3 Loddon MDB 629 3 Loddon MDB 538 4 Swan Hill MDB 362 4 Swan Hill MDB 392 5 Macedon Ranges Non MDB 336 5 Shepparton MDB 378 6 Shepparton MDB 334 6 Macedon Ranges Non MDB 308 7 Gannawara MDB 300 7 Geelong Non MDB 269 8 Geelong Non MDB 264 8 Gannawarra MDB 251 9 Mildura MDB 262 9 Mildura MDB 243 10 Hume Non MDB 250 10 Mitchell MDB 225 b. Greater Shepparton Five Year Mobility 2001–2006 Five Year Mobility 2006–2011
No Origin LGAs Area Entering No Origin LGAs Area Entering Greater Greater Shepparton Shepparton (persons) (persons)
1 Moira MDB 643 1 Moira MDB 628 2 Campaspe MDB 572 2 Campaspe MDB 550 3 Bendigo MDB 206 3 Strathbogie MDB 186 4 Strathbogie MDB 189 4 Bendigo MDB 144 5 Mitchell MDB 133 5 Benalla MDB 110 6 Geelong Non MDB 110 6 Casey Non MDB 110 7 Benalla MDB 104 7 Yarra Ranges Non MDB 107 8 Casey Non MDB 101 8 Wodonga MDB 98 9 Hume Non MDB 97 9 Auburn MDB 95 10 Yarra Ranges Non MDB 93 10 Mitchell MDB 93 Sources : Census of Population and Housing 2006 and 2011
The figure of income differentials between Bendigo, Shepparton and the surrounding LGAs explains the mobility pattern in this area. In the first phase, the average annual wage in Bendigo was the highest when compared to surrounding LGAs and Shepparton (Figure 4.14a). Therefore
127 people entering Bendigo originated from its neighbouring LGAs, Shepparton, and LGAs around Shepparton (Table 4.6). The pattern is also similar in Shepparton, although the numbers entering from Bendigo were lower. In the second phase, Bendigo remained the LGA with the highest average annual wage, and the mobility pattern was similar with the first phase, where it attracted migrants from surrounding LGAs, including Shepparton. The income differentials between peripheral areas and urban centres consistently validate fundamental theories of migration that gravity model is determined by greater opportunity (income level).
Figure 4.14: Average Annual Wage in Greater Bendigo and Greater Shepparton and Surrounding LGAs
a. Average Annual Wage in the First Phase
Average Annual Wage Bendigo and Shepparton and Surrounding LGAs in the First Phase 27,550 GANNAWARRA (S) 26,219 27,691 LODDON (S) 25,584 29,247 STRATHBOGIE (S) 28,567 Av. Wage 30,657 2003 BENALLA (RC) 29,956 30,072 MOIRA (S) 29,096 Av. Wage 30,761 2004/2005 MOUNT … 29,985 30,120 MITCHELL (S) 30,112 31,115 CAMPASPE (S) 30,547 30,858 SHEPPARTON (C) 30,331 31,320 BENDIGO (C) 31,115 44,084 MELBOURNE (C) 43,279 0 10,000 20,000 30,000 40,000
b. Average Annual Wage in the Second Phase
Average Annual Wage Bendigo and Shepparton and Surrounding LGAs in the Second Phase 28,587 LODDON (S) 30,848 29,536 GANNAWARRA (S) 31,806 31,625 STRATHBOGIE (S) 34,036 Av. Wage 32,346 2007 MOIRA (S) 34,393 33,955 Av. Wage MOUNT … 35,760 34,241 2008/2009 MITCHELL (S) 35,573 33,375 CAMPASPE (S) 35,343 33,483 BENALLA (RC) 35,838 33,487 SHEPPARTON (C) 35,687 35,101 BENDIGO (C) 37,581 47,880 MELBOURNE (C) 50,399 0 10,000 20,000 30,000 40,000 50,000 Sources: National Regional Profile (NRP) series 2000 2004 and 2004 2007
The small gap between the average annual wage in Greater Bendigo and Greater Shepparton in both periods validate Stouffer’s concept of intervening opportunity with distance. The close
128 proximity of Shepparton and Bendigo provides several options for migrants from rural LGAs around these two urban centres. Referring to fundamental theories (Stouffer 1940; Lee 1966; Todaro 1969; Bartel 1979), the first option was to move to these two urban centres (Shepparton and Bendigo), as shown in Table 4.6. However, migrants with superior skills and qualifications have another option, to migrate to Melbourne, where the wage levels are higher than Bendigo or Shepparton (Figure 4.14). The income differential confirms Stouffer’s intervening obstacle of distance and opportunity. The data for out migration from Bendigo and Shepparton shows that Melbourne was a high priority destination for people to leaving these areas.
To sum up, population changes in the region of Greater Bendigo and Greater Shepparton seem not to affect each other from the perspective of population changes over time, as they are both experience a steady population growth. However, by observing their migration patterns, particularly the origin areas of people who enter these cities, Bendigo, as the bigger city, was absorbing more people than Shepparton. One of the potential reasons could be the proximity of Bendigo to Melbourne, a major city, which is in easy commuting distance (Wilkinson and Butt 2013). Moreover, even though Bendigo and Shepparton are main contributors of in migration for each other, the observation from out migration from Bendigo and Shepparton to the nearby LGAs seems to have an impact more in Shepparton than in Bendigo, indicated by higher number of out migration in Shepparton than Bendigo and this could contribute to its negative net migration rate.
4.5.5. Mobility Pattern in Mildura Rural City (RC)
Based on the ABS classification of local government area, Mildura is defined as a rural city (RC), and from a geographical perspective the city is located at the intersection of three states: Victoria, NSW and South Australia. In the context of the agricultural industry, Mildura is one of the key areas of central agricultural production in the MDB area. Moreover, the area is also well known as the junction between the two major iconic rivers: the Murray River and the Darling River. This study puts Mildura as a city (population size above 50,000) based on the transformation process from the 2006 census, with a population of 49,814 becoming 50,979 in the 2011 census or growing at 2.3 per cent in five year period. The sluggish population growth is also shown in the ABS’s population estimate in the period 2000–2010, with an average population growth only at 0.4 per cent.
In terms of migration activity during the Millennium Drought period, represented between two censuses, Mildura reflects the MDB’s general condition of negative net migration in this period.
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In the first phase of five year mobility (2001–2006), the net migration rate stood at 0.5 per cent, and increases significantly in the second phase to 2.2 per cent. The migration profile also indicates a key issue in the MDB, namely, that young and productive people experience negative mobility intensively during the consecutive dry years. Between 2001 and 2006, the net migration rate for the young age group (15–24 years) and young workers group (20–34 years) was at a significant level at –9 per cent and –4.4 per cent respectively. The net mobility trend for these specific groups continued and was exacerbated in 2006 2011 period, with young people’s net migration rate at –13.6 per cent and young workers at –9.2 per cent.
Figure 4.15: Mildura in the MDB Map of LGAs
Source : Geoscience Australia (2002)
In terms of the remoteness index, Mildura and all nearby LGAs that have a direct boundary with it are defined as Outer Regional Australia. The surrounding LGAs in this region are mostly classified as small urban areas such as Wentworth, Buloke, Hindmarsh, Renmark Paringa, and Yarriambiack (Figure 4.10). Two LGAs, which are Swan Hill in the east and Loxton Walkarie in the west, are categorised as large urban areas, and the LGA of Balranald in the north east is the only area categorised as a town.
The migration figures of all periphery LGAs correspond with Mildura as a core area, and in aggregate with the Basin’s trend. In the first phase of five year mobility (2001–2006), all nearby LGAs experience negative net migration, reflecting that more people were leaving this region than were entering it. Balranald is the LGA that has the highest negative net migration at –11.2
130 per cent, followed by Yarriambiack (–7.7%), Buloke (–5.6%), Swan Hill (–4.4%), Hindmarsh (–4.1%), Renmark Paringa (–3%), Loxton Walkarie (–2.8%) and Wentworth (–2%). Considering the core and surrounding areas have a negative mobility, it is premature to assume the gravity model of migration in this area without additional data about the mobility pattern. Furthermore, although the mobility pattern can validate the movement from surrounding areas to Mildura, the negative figure in the core area can be a sign of multiple stages of migration, where at the first stage people are moving to Mildura, and at the second stage people move from Mildura to migrate to other cities in another region.
The negative trend continues in the second phase of mobility (2006–2011), and similar with the migration pattern in the Basin, the LGAs around Mildura also experienced a worsening negative net migration such as the LGA of Renmark Paringa that increased by almost double to –5.6 per cent, followed by Wentworth (–3.1%), Buloke (–6%), Hindmarsh (–5.5%), Swan Hill (–5.3%) and Loxton Walkarie (–3.8%). The LGAs with the highest rates of negative net migration in the first phase declined slightly in the second phase, such as Balranald (–9.9%) and Yarriambiack (–6.6%).
Based on a matrix tabulation to detect the origin areas of people entering the city of Mildura, the data can imply that there is an indication of the gravity model where the surrounding LGAs are in the top list of origin areas. In the five year mobility of 2001–2006, the LGAs of Wentworth and Swan Hill are the top two origin areas that contribute to in migration activity. However, only Yarriambiack in the south of Mildura is among the main contributors, while other nearby LGAs like Buloke and Hindmarsh display a minor contribution for people mobility inflowing to Mildura. Moreover, Mildura also becomes a main destination for people from Broken Hill, a historic mining town that also suffers from the latest prolonged drought.
In terms of destination LGAs of people who leave Mildura, other cities within and out of the MDB area are among those on the top of the list, such as Bendigo, Geelong and the Gold Coast. These figures are possibly an indication that moving to the nearest city within the region cannot meet migrants expectations for looking for better conditions, and therefore this causes another migration activity to other cities.
In the second phase of five year mobility in 2006–2011, more surrounding LGAs are included on the top list of origin areas, such as Balranald and Renmark Paringa (Table 4.5.), while others follow the trend from the previous phase. Furthermore, the mobility pattern in this period emphasises the likelihood of multi stage migration activity based on the out migration pattern
131 from Mildura, where cities outside the region are on the top of the list of main destinations. In this time frame, the cities include Bendigo, Ballarat, Geelong and Melbourne.
Table 4.7: Main origin LGAs of Migrants entering Mildura
Five Year Mobility 2001–2006 Five Year Mobility 2006–2011
No Origin LGAs Area Entering No Origin LGAs Area Entering Mildura Mildura (persons) (persons)
1 Wentworth MDB 691 1 Wentworth MDB 608
2 Swan Hill MDB 401 2 Swan Hill MDB 276
3 Broken Hill MDB 197 3 Broken Hill MDB 223
4 Bendigo MDB 176 4 Bendigo MDB 125
5 Ballarat Non MDB 91 5 Geelong Non MDB 88
6 Yarriambiack MDB 86 6 Onkaparinga Non MDB 87
7 Onkaparinga Non MDB 84 7 Balranald MDB 84
8 Geelong Non MDB 81 8 Ballarat Non MDB 70
9 Casey Non MDB 74 9 Yarriambiack MDB 70
10 Salisbury Non MDB 73 10 Renmark Paringa MDB 69 ….. …… …. …… ….. …… …. …… 19 Buloke MDB 51 27 Buloke MDB 42 Hindmarsh
30 Hindmarsh MDB 42 58 MDB 24 Sources : Census of Population and Housing 2006 and 2011
Mildura, as one of the key agricultural cities, reflects the general condition of LGAs in the MDB during the Millennium Drought period. The environmental shock, with consecutive years of severe drought, may have caused people and communities in the Basin to reconsider their living conditions, with individuals and many families undertaking internal migration. Mildura follows the pattern in other cities within the MDB, where the people from nearby LGAs are moving into the city, which is evidence of the gravity model.
Following other cities in the MDB, the average annual wage in Mildura and its surrounding LGAs validates the rural urban migration pattern, where income level is the key determinant for people entering Mildura. In the first phase, the wage level in the neighbour LGAs such as Balranald, Wentworth, Swan Hill, and Hindmarsh was below Mildura’s wage level (Figure 4.16a). The wage level in the area in the second phase had a similar pattern, with Mildura remaining higher than its surrounding LGAs (Figure 4.16b).
However, one particular LGA, Broken Hill, is different, with the wage level higher than Mildura, or even other urban centre such as Bendigo. In the first phase, the average annual wage in Broken Hill was $ 33,495 or almost 13 per cent higher than Mildura. Meanwhile, in the
132 second phase, the gap increased and reached 18 per cent more than Mildura. Although the data indicates that the income level in Broken Hill was higher than Mildura, numerous migrants from this LGA entered the LGA of Mildura. The out migration from Mildura also shows that Broken Hill was not a destination for migrants in the Millennium Drought period.
Figure 4.16: Average Annual Wage in Mildura and Surrounding LGAs
a. Average Annual Wage in the First Phase
Average Annual Wage Mildura and Surrounding LGAs in the First Phase 33,349 BROKEN HILL (C) 33,640 26,882 YARRIAMBIACK (S) 25,185 27,435 Av. Wage SWAN HILL (RC) 26,926 2003 28,187 RENMARK PARINGA (DC) 28,121 Av. Wage 28,363 2004/2005 BALRANALD (A) 27,883 28,413 WENTWORTH (A) 27,950 28,661 HINDMARSH (S) 26,418 29,966 MILDURA (RC) 29,421 31,320 BENDIGO (C) 31,115 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000
b. Average Annual Wage in the Second Phase
Average Annual Wage Mildura and Surrounding LGAs in the Second Phase 38,947 BROKEN HILL (C) 41,517 28,135 YARRIAMBIACK (S) 30,052 30,133 Av. Wage HINDMARSH (S) 31,694 2007 30,575 SWAN HILL (RC) 32,438 Av. Wage 30,664 2008/2009 BALRANALD (A) 32,763 31,396 RENMARK … 33,022 31,615 WENTWORTH (A) 34,246 32,871 MILDURA (RC) 35,040 35,101 BENDIGO (C) 37,581 0 10,000 20,000 30,000 40,000
Sources: National Regional Profile (NRP) series 2000 2004 and 2004 2007
One possible reason might be the socio economic conditions in Broken Hill, which are lower than its surrounding LGAs. Referring to the Socio Economic Indexes for Areas (SEIFA) 48 in
48 Based on ABS (2006) SEIFA socio economic indexes using six main variables. The variables are income, education, employment, occupation, housing and advantage or disadvantage indicator. The index is classified into four indexes and categorised by decile. It reflects areas with the most advantages and most disadvantages in terms of socio economic factors. 133
2011, the SEIFA score in Broken Hill was 888 (Decile 2) while Mildura was 924 (Decile 3). Moreover, Broken Hill was categorized in decile 2 within Australia and decile 1 in state. Based on ABS figures (2006), this means that the socio economic condition in Broken Hill were comparatively extremely low at both state and national levels.
In terms of intervening factors and distance, the next migration option for people in this area was an urban area within the MDB, the LGA of Greater Bendigo. Data from Table 4.7 shows that migrants from Bendigo are on the top list to enter Mildura. On the other hand, Bendigo was one of the main destinations for migrants from Mildura. The pattern confirms Stouffer’s intervening opportunities concept, which would suggest that people from surrounding LGAs at first chose Mildura as their destination, while those migrants with adequate skills and qualifications would move to Bendigo, as that LGA has higher wage levels than Mildura (Figure 16). Overall, the migration pattern in Mildura validates the assumptions of fundamental theories of migration, i.e. that people move from peripheral areas to urban centres, where income plays an important role as the key pull factor.
4.6. Conclusion
In summary, the analysis of internal migration and mobility patterns in the MDB during the Millennium Drought period highlights several points that also address the propositions above:
i. First, in terms of migration from five year mobility data, many LGAs in the Basin area experienced negative net migration. The data indicated that the negative mobility already started in the first phase between 2001 and 2006. The intensity of negative mobility continued in the second phase of 2006–2011, which featured consecutive dry years. In this period, the areas affected by the prolonged drought widened, with more LGAs in the Basin experiencing negative net migration. ii. Second, corresponding with the theoretical framework of migration, the intensity of migration follows the view that increasing age reduces the propensity to migrate (Bartel 1978). In the MDB context, the Millennium Drought period was associated with more young people experience out migration in the Basin area. The young age group between 15–24 years was the group with the highest negative net migration. The trend of negative net migration is smaller for other age groups. iii. Third, the migration matrices using classification area reflects that small population LGAs in the MDB were the main contributors of negative net migration, particularly those that are classified as towns and small urban areas. The pattern confirms the ABS
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Report (2009) that one of the significant impacts from the severe drought was that many small areas and towns were gone. The figures also confirmed the basic concept of the gravity model (Ravenstein 1889), where people move from rural or small urban to larger areas to seek better opportunities. iv. Fourth, by utilising the remoteness index to observe whether the distance from major urban centre also affected people’s mobility in remote areas, it was seen that LGAs categorised as remote or very remote in the MDB experienced significant negative net migration. Moreover, the people in areas classified as Outer Regional Australia also have a tendency to leave the LGAs rather than to stay, indicated by negative net migration in average for outer regional areas and thus contribute significantly to the negative mobility in the MDB v. Fifth, validating the fundamental theory of migration, the mobility pattern follows the gravity model, meaning that people tended to move from periphery areas to the core or urban centres. The sample from LGAs in the Basin classified as cities (above 50,000 people) shows that the areas surrounding the cities were mostly among the top contributors of in migration. The pattern implies that the internal migration activity in the MDB has a similarity with the concept of rural–urban migration (Todaro 1969) and the intervening factors (Stouffer 1940, Lee 1966), even though many origin LGAs are classified as large urban areas.
In the next chapter, empirical analyses will be conducted where the migration activities (net migration, out migration, and in migration) in the MDB and outside the MDB are assessed relative to possible determining factors. The determinants include socio economic variables and environmental aspects, as the main aim of the thesis is to confirm whether environmental variables also play a role in explaining mobility during the drought period.
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Chapter 5: Empirical Analysis (1): Environmental Migration in the Murray-Darling Basin (2001–2006)49
5.1. Introduction
Periods of drought are serial events in the Murray Darling Basin (MDB). Helman (2009) notes that the MDB, along with most of eastern Australia, has recorded recurring droughts since the late 1700s, or from the early years of European settlement in Australia. As stated in Chapter 1, these recurring events include the Settlement Drought Periods or the Great Drought (1790– 1830), the Goyder’s Line Drought (1861–1866) which impacted almost the whole of Australia, the Federation Drought Period (1895–1903), the Second World War Drought (1935–1945) with featured devastating stock losses, the Dust Cloud Drought in the early 1980s and the recent Millennium Drought or the Big Dry (2001–2009). Based on climatic phases of drought history over the 221 years since European settlement, Helman (2009, p.21) asserts that there have been two prolonged drought periods that delivered an extensive alteration to the water cycle. These episodes are the Great Drought Periods, which extended over 35 years, and the Millennium Drought Periods over 29 years from 1980 to 2009.
The definition of drought itself, based on the Intergovernmental Panel on Climate Change (IPCC) in 2007 is ‘a prolonged absence or marked deficiency of precipitation resulting in water shortage for many uses, or a period of abnormally dry weather sufficiently prolonged for the lack of precipitation to cause a serious hydrological imbalance’ . This definition corresponds with several studies that explore the characteristics of seasonal drought in Australia, where the fluctuation of the precipitation level became the key indicator of dry seasons (Gallant, Reeder, Risbey and Hennessy 2012; Leblanc, Tregoning, Ramillien, Tweed and Fakes 2009). Moreover, from a global perspective, the impact of prolonged drought is more dispersed than any other natural hazard as it is characterised by a water deficit, and thus studies have difficulty in quantifying the effect over large areas (Bryant, 2005).
The Millennium Drought period from the late 1990s to 2009 recorded the lowest rainfall, where the precipitation level was 73 mm or 12.4 per cent below the 20 th century mean (Gale, Edwards,
49 Substantial parts of this chapter were presented at the 59 th National Australian Agricultural and Resource Economics Society (AARES) Conference, 10 th 13 th February 2015, Rotorua, New Zealand. The findings have also been presented at the Australian Agricultural and Resource Economics Society (AARES) monthly seminar of the Australian Capital Territory (ACT) Branch, 2 nd June 2015.
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Wilson and Greig 2014). The lack of water availability in the MDB in the early years of the 2000’s caused severe difficulties for both dryland farmers and irrigation farmers. Wittwer and Griffith (2011) found that the impact of drought reduced the real GDP of small regions in the Basin by up to 20 per cent. Moreover, farmer and regulator strategies to tackle this issue by applying efficient water allocation and water trading still resulted in losses. Wittwer and Griffith also estimated that more than five thousand jobs were lost across the southern Basin.
The observations based on data provided by the Australian Bureau of Statistics show the effects from this event. First, the significant drop of average rainfall has a direct impact on water consumption for both private use and business use. In the context of the MDB, water usage for agricultural production has clearly shown a consecutive reduction from the period 2002–2003 to 2004–2005. Based on the study by Wittwer and Griffith (2011) total water consumption for agriculture declined from 10,000 gigalitres in 2001–2002 to around 7,000 gigalitres between 2002 and 2005. Second, following the limitation of water availability for production, the Gross Value of Agricultural Production (GVAP) between 2000–2001 and 2005–2006 in the MDB had growth rates below the national average. Within the five year period, the GVAP in the MDB grew only 7.3 per cent or from $13.9 billion to $15 billion. Meanwhile, at the national level, GVAP increased 12 per cent or from $34.1 billion to $38.5 billion. The growth rate in the MDB was considerably lower as the “all groups” Consumer Price Index (CPI) in this period increased by 14.7 per cent, showing that people in the Basin pay more for their goods and services than people outside the MDB 50 . This figure in the MDB reflects that the Millennium Drought period may not only have had an impact on businesses, but also on people and communities.
As the main centre of agricultural production in Australia, the employment proportion in agriculture in the MDB is higher than the national figure. As introduced in Chapter 1, based on the Census of Population and Housing in 2006, the number of employed persons in the agricultural sector in the MDB was 10 per cent, compared with Australia at 2.7 per cent. Hence, the slow growth of GVAP during the Millennium Drought period impacted on components of the agricultural economy, while other industries seem in line with the national trend. For example, total workers in the agriculture sector between 2001 and 2006 dropped significantly by 12.4 per cent, or from 103,360 to 90,520 (Pink, 2008). Moreover, the number of people who identified themselves as farmers or farm managers also declined by 10 per cent in the period between the two censuses (2001–2006), or from 74,000 to 67,000. One of the main concerns in
50 Based on an ABS report by Pink (2008, p.110) ‘Water and the Murray Darling Basin –A Statistical Profile Australia 2000– 01 to 2005–06’. 138 regard to this trend is the distribution of farmers by age group. Figures from the census clearly show that between 1996 and 2006 farmers in the age group of 15–34 years dropped by 33.1 per cent and the age group of 35–49 years also declined by 23.6 per cent, but farmers in the age group between 50–64 years and above 65 years increased at 3.1 per cent and 25.7 per cent respectively. Thus, many young farmers have departed the Basin area, while those ageing farmers with lower mobility remain.
The substantial change in environmental conditions, followed by a declining trend in agricultural production in the MDB during this period, also delivered a significant change in the demographic composition. As discussed in Chapter 4, based on the Census of Population and Housing in 2006, the MDB has experienced significant migration activities. An indication of people’s mobility inside the MDB was the decline of economic factors in some parts of the Basin area. In terms of general labour conditions, the trend of employment status in the MDB between 2001 and 2006 remained below the national level 51 . In that period, the average growth of total employment in the Basin increased by only 8.3 per cent, while the national level increased at 13 per cent.
However, studies to explore the impact of drought on the people and communities in the Basin by incorporating socio economic effects are very limited. Many of the studies focus on the environmental issues as the result of the prolonged drought (Adamson, Mallawaarachchi and Quiggin 2009; Chowdhury, Beecham, Boland and Piantadosi 2015; Connor, Schwabe, King, Kaczan and Kirby 2009; Gallant, Reeder, Risbey and Hennessy 2012; Maxino, McAvaney, Pitman and Perkins 2008), water management issues (Crase, O'Keefe and Kinoshita 2012; Crase, Pagan and Dollery 2004; Grafton, Chu, Stewardson and Kompas 2011), and policy evaluation or policy recommendations (Connel 2007; Connell and Grafton 2011; Heaney, Dwyer, Beare, Peterson and Pechey 2006; Wittwer 2011).
In terms of migration studies, it is important to highlight that the case of migration in the MDB may contribute significantly to the latest developments in the migration literature where factors include not only the new classical element, such as the differential of social and economic benefits, but also the identification of the environmental aspect as one of the drivers of migration (Black, Adger, Arnell, Dercon, Geddes and DSG 2011; Joseph and Wodon 2013; Lilleor and Van den Broeck 2011; Marchiori, Maystadt and Schumacher 2012). Furthermore, the concept of environmentally induced migration differentiates the analysis approach based
51 Figures displayed in Chapter 1. 139 on the event characteristics, which are identified as a sudden environmental event or slow environmental degradation 52 (Renaud, Dun, Warner and Bogardi 2011). The serial drought events in the MDB, in particular the latest Millennium Drought, can be observed as a gradual process that encouraged people in the MDB to migrate, and therefore the analysis of migration drivers during the Millennium Drought period will provide not only a better understanding of the people and communities in the Basin, but also will contribute to the general study of migration.
In this chapter, the study attempts to examine the determinants that cause people in the MDB to migrate in the period 2001–2006. The analysis follows the segregation of the period in line with the previous chapter defining the first phase of migration as a period between 2001 and 2006. The empirical analyses propose several hypotheses:
i. First, the new classical approach that income differentials between origin and destination are a key driver of migration activity; ii. Second, in terms of an internal migration framework, the empirical analysis also attempts to confirm that better economic condition in the destination area is one of the main pull variables for people to migrate; iii. Third, following the studies of migration focusing on demographic factors, in particular age, increasing age will reduce the propensity to migrate, including in the MDB area; iv. Fourth, that social factors and better facilities in the destination are crucial drivers of migration; and v. Fifth, the last proposition and as the key objective of this study, the estimation attempts to see that there is evidence of environmental factors contributing to migration activity in the MDB, in particular to agricultural production as the core economic activity. The concept of how this factor influences mobility will be specified in the estimation model.
Data for the analysis for the first phase of migration in the MDB comes from the Census of Population and Housing 2006. The explanatory variables are mostly from regional data provided by Australian Bureau of Statistics (ABS), and the environmental data of rainfall is constructed from data available online from the Bureau of Meteorology (BoM). As discussed
52 Several migrations that can be classified as slow onset migration occurred mostly in developing countries such as migration caused by drought in India (Jacobson, 1989), rural–urban migration caused by land degradation and drought in Mexico (Liverman, 2001), and migration in Somalia as a result of deterioration of forest and land (Cooper, 2001). 140 in chapter 3, the level of migration analysis uses Local Government Areas (LGAs) 53 with the benchmark of the Australian Standard Geographical Classification (ASGC).
5.2. Theoretical Framework
As discussed in Chapter 2, key literature in modern migration studies persistently focus on the new classical approach of differential income between two areas as the main migration driver (Becker 1962; Greenwood 1975; Hicks 1932; Ravenstein 1885; Schultz 1961; Sjaastad 1962; Todaro 1969). Migrants are always described as rational economic agents by looking for better living condition with the decision based on the calculation of expected benefits and costs (Greenwood 1975). Moreover, Lee (1966) developed this concept into a push–pull model where he calculated all determinants from origin and destination, including intervening obstacles into monetary values.
The determinants of migration also consider a life cycle strategy for human capital investment. Greenwood (1975) formulates the concept into a simple mathematical model, which is discussed previously (Chapter 2, p.32).
− − = − (1 + ) (1 + )
This model estimates the expected net benefit of migration over time, with net expected earnings or benefits minus net expected costs , and people will decide to ∑ ( ) ∑ ( ) move from origin to destination if the present value of discounted benefits minus costs is greater than zero ( ). > 0 However, the evolution of the theory of migration as stated in Chapter 2 has involved to include other determining factors such as social, economic, public amenities, and politics (Reuveny and Moore 2009), and environmental aspects (Black et al. 2011). A recent study by Lilleor and Van den Broeck (2011) reviews the new classical theory and the contribution of environmental issues such as climate change to the migration decision. The study suggests that current migration analyses should start to consider environmental factors as one of the important determinants of migration.
53 The explanation of this has been stated in the data construction information provided in the methodology section. 141
5.2.1. The Model: Migration Decision at the Individual Level
The theoretical framework of how the individual makes the decision to migrate follows four key studies (Greenwood 1975; Reuveny and Moore 2009; Renaud et al. 2011; Black et al. 2011)54 . In the first key study, ‘Research on Internal Migration in the United States: A Survey’ in the Journal of Economic Literature, Greenwood (1975) composed a model by assuming that individuals apply utility maximisation of income at the aggregate level. The perspective of this model considers all factors that attach to individuals directly such as income differentials, the costs of undertaking migration, and expected income and cost for a period of time.
The second key study by Reuveny and Moore (2009) developed the model by considering indirect factors that complement individual motives to migrate. They developed a model by inserting specifically social and economic influences, political aspects, and also environmental factors. In the context of the cost of migration, the model proposes that migrants pay a one time cost by summing up various expenses which correspond with the Greenwood model. Additionally, the decision making process was constructed not only from the perspective of undertaking migration, but also for staying, by measuring the probability to stay or go.
The last two key studies emphasise the influence of environmental change in the decision framework of migration. Renaud et al. (2011) classify environmental change into two events, which are the Rapid Onset hazard and the Slow Onset hazard. Rapid Onset hazard is a sudden catastrophic event that forces people in the affected area to leave temporarily or permanently. Conversely, Slow Onset hazard, which investigates environmental changes in a long period (Renaud et al . 2011), was considered very limited in migration studies. Most environmental migration studies that use the Slow Onset framework usually estimate the environmental aspect as a direct impact (Barrios, Bertinelli and Strobl 2006; Henry and Beauchemin 2004; Joseph and Wodon 2013). However, those events mostly occurred in developing countries where environmental factors have a direct link to the aggregate output. Furthermore, Black et al. (2011) combine the concepts from all of the key literature by involving all the drivers (social, economic, political, demographic, and environmental drivers).
The contribution of this thesis is to enhance the model specification in more detail and in the context of environmental migration in the MDB. In this context, the environmental aspect follows the framework of Slow Onset hazard and influences the decision indirectly through
54 All this literature has been discussed in detail in chapter 2 142 economic activity. In addition, the advantage of the Census of Population and Housing in 2006 and 2011 as shown in Chapter 4 the data can construct mobility in terms of the inflow and outflow of people. This extends the analysis not only in the context of net migration, but also in migration and out migration. Moreover, the model specification detaches business activities from economic factors as the study constructs this specific aspect to be instrumented by the environmental factor.
The model specification is below:
Migration activities in the MDB consist of in migration (im), and out migration (om)55 .
(5.1) = ′ = , | ∉ ′
Migrants consider the net benefit (nB ) of going (G) and staying (S).
(5.2) [ ( ), ] = ( , , , , , )
Net benefits from in migration, and out migration are not fully informed, thus the model puts in low (L) and high (H) probabilities of individuals to stay ( ) or go , ( ). Thus, the benefits of migration are in the expected value. , , , The probabilities of migration activities are . Migration decision in time horizon (T), , and migrants pay a one time cost of migration (C). Therefore, for each period of time (t) in the migration decision making, people consider the expected benefit from migration ( nBG ) or the benefit from staying (nBS ).
(5.3) ( ) ( ) = ( ) ( ) + (1 − ( )) ( )
55 The model focuses on in migration and out migration as these activities consist of pull and push factors from origin and destination. The model does not include net migration as the concept is a net effect from in migration minus out migration (Greenwood 1975). 143
(5.4) ( ) ( ) = ( ) ( ) + (1 − ( )) ( )
The benefits and probabilities to migrate depend on several factors, which are Economic factors (Ec) , Social factors (So), Public Facilities or development (Pu), and Business activities indicators (Bu).
Additionally, business activity is a suspected endogenous variable and it is influenced by the environmental factor.
( ) = ( , , , )
ℎ (5.5) = ( ) The decision making process is a combination from all determinants and thus the function puts weights on each variable, w(x,y,x) for all migration activities, and s for stay.
( ) ( ) = [ ( ) . ( ) + ( ). ( ) + ( ). ( )
+ ( ). = ( ) ( ) − ( )] (5.6)
( ) ( ) = [ ( ) . ( ) + ( ). ( ) + ( ). ( ) + ( ). = ( ) ( )] (5.7)
In detail migration activities where = , In-migration
( ) ( ) = [ ( ) . ( ) + ( ). ( ) + ( ). ( ) + ( ). = ( ) ( )
− ( )] Out-Migration
( ) ( ) = [ ( ) . ( ) + ( ). ( ) + ( ). ( ) + ( ). = ( ) ( )
− ( )]
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All the present values of migration are assumed to be the future expected net benefits using time horizon (t) and discount rate (r).
( ) ( ) ( ) = (1 + ) (5.8)
( ) ( ) ( ) = (1 + ) (5.9)
Thus, the individual decides to migrate (Dm) when the present value of net benefits of migration is higher than the present value of net benefits of staying ( ) ( )
( ) ( ) ( ) ( ) = − > 0 (1 + ) (1 + ) (5.10)
Furthermore, the individual decides to migrate (Dm) from ( i) to (j), in time (t)
( ) ( ) ( ) ( ) [ ] = [ ] − [ ] > 0 (1 + ) (1 + ) (5.11)
In terms of the aggregate level, this thesis estimates migration activities by applying the econometric approach set out in section 5.5.
5.3. Data Specification
The primary source of migration data for the analysis is from the 2006 Census of Population and Housing, where the questions about ‘ Place of Usual Residence’ five years ago and one year ago can be derived into three types of migration measurements: in migration, out migration, and net migration. The explanatory variables are mostly obtained from the National Regional Profile (NRP) series 2002 2006, which was published by the ABS. The data from the NRP have been constructed in an average value between 2004 and 2005. In terms of environmental factors,
145 the rainfall data is compiled from Climate Data Online from the BoM, collected from reliable local climate stations around the country, as discussed in Chapter 3.
The selection of variables was based on the evidence that the environmental shock of the lowest rainfall level in 2002 had delivered a significant effect on income to people in the MDB area and businesses activity, in particular agricultural production (Horridge et al. 2005; Grafton et al. 2011). In classifying data for the estimation, the explanatory variables follow the individual framework on the migration decision making process. First, following the basic principle of the nexus between migration and income differentials, income data based at LGA level is constructed as the main economic variable. The data includes the different types of income such as average personal income from wages and salary, business income and average investment income. All the income is based on the individual unit. Focusing on the economic sector, the data also attempts to capture business activities, reflected by the number of business entries and business exits, and the total number of businesses in every area. Another key economic factor, particularly to capture agricultural activities, is the Gross Value of Agricultural Production (GVAP) where the data is part of the NRP series.
Second, in terms of social explanatory variables, the estimation includes the education level of an LGA population measured as the percentage of persons with bachelor degrees or higher, as education level and education facilities play a significant role in the decision to migrate (Dustmann and Glitz 2011; Borjas and Bratsberg 1996). The importance of public facilities are another social factor (Dolfin and Genicot 2010), and are represented by the percentage of the population with internet connection in every LGA. Other social factors include the value of private houses. In terms of development indicators, data from the NRP of the number of approved residential building is included, as this can reflect the level of development in the LGAs.
As the study focuses on the proposition of whether the environmental aspect during the drought played a role in migration activity, the developed model set the environmental factor as having an indirect effect on migration through the performances of either agricultural production (GVAP) or business activity in general, indicated by business income 56 . Thus, the instrument variables include rainfall, water use to represent water management issues in the MDB (Connel et al. 2011; Crase et al. 2011; Grafton et al. 2011), and the level of employment growth to
56 The analysis of the indirect effect of environmental factors to migration activity through economic activities has been described in Chapter 2, in the section of ‘ Link between literature gap to migration in the Murray-Darling Basin (MDB) ’. The detail of the empirical estimation whether GVAP or business income suit for instrument variable is described in section 5.5. 146 represent the new classical concept of labour mobility (Todaro 1969; Harris Todaro 1970). Table 5.1 presents summary statistics of the variables that are utilised in the estimation.
The characteristics of the key data seem to follow fundamental studies of migration. By looking at the average data of migration, it can be seen that the migration rate figures correspond with the concept that increasing age lowers the propensity to migrate, as people have more attachment at the origin such as the accumulation of capital or assets (Bartel 1979). In this estimation, net migration rates of five year mobility for the general population on average stood at –2.7 per cent, however the percentage is higher for the 15–24 year age group at –12.2 per cent, then decreases for the age group 20–34 years at –3.8 per cent and for the working age group (15–64 years) at –2.2 per cent.
5.3.1. Explanatory Variables
The explanatory variables follow the migration decision framework that consists of economic, social, and environmental aspects. In this first phase of analysis between 2001 and 2006, the distribution of data reflects a clear disparity in figures between the MDB area and the non MDB area. From the aspect of income, it can be seen that the level of all income types in the MDB are below those for outside the MDB. Figure 5.1 shows that the mean value of annual personal income in the MDB stood at $31,057, compared with outside the MDB with $33,393. The average business income shows a larger gap with the MDB area at $12,241 while outside the MDB the level is $16,128. The figure corresponds with the ABS Report in 2008, which shows that the drought period starting in the early 2000s had impacted directly on business activity in the Basin. The significant disparity of business income between the MDB and the non MDB areas seems unrelated with the dynamic business indicators. The mean number of business entries and business exits within the MDB during this first phase of drought is 365 and 324 respectively, compared with outside the MDB area at 650 and 578 respectively. This means that in terms of the ratio between entry and exit, the activity seems similar, with an average ratio of 1.1. The figure is consistent with the proportion of total business numbers, with the MDB area having a 15 per cent ratio for business entry and 13 per cent for business exit. Meanwhile, the area outside the Basin has 17 per cent for business entry and 15 per for business exit, suggesting that regardless of the number of businesses, the pattern of business between the MDB and non MDB areas is very similar.
In terms of asset valuation, although many LGAs in the Basin are classified as Outer Regional Australia or even Remote or Very Remote areas, the average private house price indicates a
147 higher valuation in the MDB at $170,000, compared with the average private house price outside the MBD area at $155,000. However, the component that represents the level of development, which is the variable of average building approvals for residential units, shows that the Basin region has a lower development level with an average number of approvals at 297 buildings compared with the rest of Australia at 364 buildings. Moreover, the environmental data corresponds with the ABS (2009) that the Millennium Drought has delivered a larger impact to the Basin area compared with outside the MDB. The driest year in the first phase of the drought period, which was 2002, shows that the rainfall level in the MDB was only 455 mm, compared with 578
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Table 5.1: Summary Statistics of Estimation Data in the First Phase Mobility at LGA level Variables Description Variables Code Unit Maximum Minimum Mean Standard Deviation IN Migration Rate of 5 Year Mobility inrate5Y % 79.4 0.0 23.4 9.9 OUT Migration Rate of 5 Year Mobility outrate5Y % 69.2 2.0 26.0 9.4 NET Migration Rate of 5 Year Mobility netrate5Y % 32.2 42.9 2.7 8.6 IN Migration Rate of 5 Year Mobility (15–24 years) inrate5Y_1524 % 191.3 0.0 24.2 18.7 OUT Migration Rate of 5 Year Mobility (15–24 years) outrate5Y_1524 % 74.8 0.0 36.5 14.8 NET Migration Rate of 5 Year Mobility (15–24 years) netrate5Y_1524 % 136.2 62.7 12.2 21.5 IN Migration Rate of 5 Year Mobility (20–34 years) inrate5Y_2034 % 101.2 0.0 36.5 17.3 OUT Migration Rate of 5 Year Mobility (20–34 years) outrate5Y_2034 % 79.2 0.0 40.3 13.0 NET Migration Rate of 5 Year Mobility (20–44 years) netrate5Y_2034 % 63.0 45.5 3.8 13.7 IN Migration Rate of 5 Year Mobility (15–64 years) inrate5Y_1564 % 89.8 0.0 25.7 10.8 OUT Migration Rate of 5 Year Mobility (15–64 years) outrate5Y_1564 % 69.2 0.0 28.0 9.5 NET Migration Rate of 5 Year Mobility (15–64 years) netrate5Y_1564 % 32.8 36.9 2.2 8.6 Average Wage and Salary 2003–2005 wage $ 68000.1 21316.0 33572.6 6889.8 Average Unincorporated Business Income 2004–2005 businc0405 $ 61007.5 14718.5 15376.0 7424.3 Average Investment Income 2004–2005 invest $ 38964.0 833.0 5501.7 3876.8 Unemployment Rates 2004–2005 unemploy0405 % 23.1 0.1 5.6 3.4 Average Number of Business Entry 2004–2005 bus_entry (busnumb)* no. 18087.0 0.0 588.8 1170.5 Average Number of Business Exit 2004–2005 bus_exit (busnumb)* no. 14520.0 0.0 523.4 990.7 Total Number of Business 2004–2005 tot_bus (busnumb)* no. 95946.0 0.0 3517.2 6316.6 Average Value of Private Houses 2004–2005 housevalue0405 $('000) 942.5 0.0 158.1 90.8 Gross Value of Agricultural Production 2002 2006 gvap $ (millions) 524.9 0.0 55.0 72.9 Building Approval Total Dwelling Unit 2004–2005 dwelling no. 9901.0 0.0 350.3 841.7 Average Rainfall in 2002–2003 rain0203 mm. 2474.5 57.6 633.9 375.4 Average Rainfall in 2004–2005 rain0405 mm. 3864.4 87 711.8 416.5 Water Use 2002 2006 watuse Mega litres 686289.0 0.0 28547.9 71312.4 Number of Bachelor Degree per Total Population 2006 educ % 27.3 0.0 6.2 4.5 Internet Access Level 2006 info % 75.2 0.0 40.9 14.1 *the utilisation of business number data is adjusted based on migration types
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Figure 5.1: Mean Economic, Social and Environmental Values in MDB and Non-MDB/ROA Area in the First Phase Mobility at LGA level*
Income Types ($) Business Condition (number) 33928.1 35000 4000 3805.4 30772.4 MDB Non MDB/ROA MDB Non MDB/ROA 30000 3500 3000 25000 2460.8 2500 20000 16127.5 2000 15000 12240.9 1500 10000 1000 5363.4 5534.9 650.0 324.1 577.7 5000 500 364.6 0 0 ave_wage0305 ave_businc ave_inv bus_entry bus_exit tot_bus
Development Indicators Environmental Indicators
500 900 MDB Non MDB/ROA MDB Non-MDB/ROA 450 800 (mm) (mm) 732.8 400 653.0 649.8 364.3 700 628.8 350 (No.) 558.8 296.6 600 300 ('00) ($ '000) 500 250 ML'00 169.9 400 200 155.2 150 300 169.9 100 200 50 100 0 0 ave_housevalue0405 ave_bldappr_dwell0405 rain0203 rain0405 watuse
Note: ROA (Rest of Australia) *Variable name in the bar charts refer to variables codes in Table 5.1.
150 mm for the rest of Australia. However, as the MDB area is the main agricultural centre, the mean level of water utilisation at LGA level was far higher with the MDB using 64,980 megalitres while areas outside the MDB only used 17,000 megalitres (Figure 5.1).
5.4. Empirical Strategy
The quantitative estimation in this study attempts to link the theoretical frameworks above with an econometric approach. In order to apply this method several assumptions and conditions are made to fit with the data collection. These are:
i. At the individual level, the model develops business activity as an endogenous value and a function of the environmental variable, . As the MDB is well known = ( ) as the main area of agricultural production in Australia, business activity is divided into two categories: business as a general activity reflected by business income, and agricultural business activity represented by GVAP. However, whether general business activity or GVAP better explains migration activity will be described in detail in the technical estimation below (section 5.5). ii. The model also considers costs of migration as part of the decision framework. With the estimation focus being on key drivers of migration, the study assumes that the costs of migration are already embedded within the economic variables such as personal income. iii. The estimation follows Stouffer (1940) and Lee (1966) who argue that migration in general is a change in residence and the effect of distance is proportional to opportunity. Although distance is part of the consideration to migrate, the study assumes no effect of distance on internal migration in the MDB. iv. The income variables and GVAP in the estimation are based on nominal values and without any adjustment for inflation or consumer price index (CPI). The reason is the GVAP data in the ABS Agricultural Census and NRP series only provide one year data. v. To capture business activities, the variable of business numbers is diversified into three categories, which consist of total business numbers, number of business entries, and number of business exits. To capture migration patterns, the application of these figures is adjusted on migration types (Table 5.1 summary statistics). Total business numbers are applied for net migration, while business entry is utilised for in migration activity and business exit for out migration. vi. The average rainfall in 2002–2003 and 2004–2005 are key instrumental variables. Another instrument for the environmental aspect is water availability to represent water
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issues in the MDB, yet due to data limitations for water flow based on the LGA level, the figure for water utilisation (watuse ) from the NRP is applied in the estimation as a representation of water availability. Moreover, as water trading is a common activity in the MDB and regulated by authorities, the estimation ignores the effect of water trading of both permanent and temporarily water trading schemes that could affect the amount of water use. vii. As the thesis follows the concept of Slow Onset hazard of environmental migration, which requires a relatively long period of time, the time frame of the analysis of 2001– 2009 is assumed to be relatively sufficient. viii. As stated previously in the methodology section in Chapter 3, the estimation for the MDB area excludes Canberra ACT.
5.4.1. Instrument Variables
Although the intermittent drought years have been occurring since the early 1990s, the consecutive years of drought that started from 2001 were based on a rainfall level that fell below the 20 th century climatological average (Timbal et al. 2010). The study even observed that the likelihood of such a long series of dry years between 2001 and 2009 by chance is less than 0.5 per cent. The significant declining trend of rainfall during the period is being utilised to explain economic activity, in particular agricultural production.
Moreover to set the rainfall as the key instrumental variable that indirectly affects migration activities, the estimation strategy uses a cumulative effect of rainfall during the Millennium Drought in the Basin. Based on the data collection from the BoM and supported by a review from Murphy and Timbal (2008), two points can be made to emphasise the irregular pattern of rainfall. First, during the Millennium Drought of 2001–2009 the average rainfall was below 550 mm (Figure 5.2a), which indicate the level was below the trend. The view is based on the comparison during 100 years of rainfall trend since Federation Drought in the early 1900s with several intermittent drought periods.
Second, the years 2002 and 2006 had the lowest amount of rainfall during the Millennium Drought period, with the rainfall level being below 400 mm (Figure 5.2b), which become the key instrument for both migration analyses in the first phase period and the second phase period. In this first phase period, the empirical analysis estimates the rainfall effect during the driest years whether the impact is different compared to the rainfall level in other years during the drought period.
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Figure 5.2: Average Annual Rainfall Australia and MDB a. Rainfall Trend in the Last Century (average mm/year)
b. Rainfall Trend during the Millennium Drought (average mm/year)
Rainfall Level During The Millennium Drought (2001 2009) 1000 MDB Australia 800 513.0 566.9 600
400(mm)
354.6 200 300.2 The Lowest Level 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Sources : Timbal et al. (2010) and Author Data Processing from Climate Data Online, Bureau of Meteorology (BoM) Australia
Following a study by Horridge et al. (2005), the average rainfall of 2002 2003 is formed as a key instrumental variable. The average rainfall of 2004 2005 is also formed to compare the effect of rainfall to the endogenous variable of production activities (general business and GVAP). The setting follows the theoretical framework of environmentally induced migration (Renaud et al. 2011) that the environmental aspect is a Slow Onset hazard and has lag years of impact.
In summary, both endogenous regressors in the main migration model, which are the average business income and GVAP, utilise similar multiple instruments. The instrumental variables consist of the average precipitation level in 2002–2003 (lnrain0203 ), the average rainfall in 2004–2005 (lnrain0405 ), and the unemployment rate between 2004 and 2004 ( unemploy0405 ) to capture the new classical concept of labour condition in the economic activity. The additional
153 instrument of water utilisation ( watuse ) is added to the GVAP regressor with the purpose of capturing water issues in the MDB.
5.4.2. Effect on Income
One of the questions in this Chapter is whether migration follows the new classical approach of income differentials being the key driver for people to change their residence. In the context of people’s migration in the Basin area during the first phase period, the variable of personal income is applied to explain migration, which is the average personal income in 2003 2005.
Figure 5.3: Scatter Plot of Migration (y-axis ) and Personal Income ( x-axis ) a. IN-Migration with Average Wage and Salary 2003-2005 80 60 40 20 0
10 10.5 11 lnwage
95% CI Fitted values inrate5Y
b. OUT-Migration with Average Wage and Salary 2003-2005 0 -20 -40 -60 -80 10 10.5 11 lnwage
95% CI Fitted values outrate5Y
Sources: Dataset Estimation (in logarithm and plot with standard deviation), Census of Population and Housing 2006 and NRP
The estimation expects that income has a positive association with in migration activity or positive net migration rates, denoting that people calculate a positive value when they are moving based on cost benefit analysis. Conversely, a negative association between migration and personal income is anticipated when people are experiencing out migration or negative net migration.
154
From the scatter plot based on the dataset between in migration and out migration, with the average income figures from 2003 2005, it can be seen that even though the relationship of migration to income follows the theoretical framework, where in migration has a positive relationship to income (Figure 5.3a) and there is a negative association with out migration to income (Figure 5.3b), the figures display a slightly different pattern between in migration and out migration. Thus, by including the parameter of income, the estimation is expected to provide a better understanding of people’s mobility within the Basin area.
5.5. Estimation Model
At the initial stage, the econometrics approach in the estimation assumes that all explanatory variables are exogenous, indicating no correlation between the parameter coefficients with the residual of the estimation 57 , thus the first estimation applied a simple ordinary least squares (OLS) method.
= + (5.12) = + + ⋯ + +
Where is the dependent variable of migration activities, is the explanatory variables, and is the error term. However, following the decision migration model above, the framework suggests that two key explanatory variables are endogenous which are business income and GVAP. The developed model suggests that parameters will explain the migration in a better way by estimating with these instrumental variables. The endogenous parameters use multiple instruments that include the environmental factor. Thus the method to estimate the model of interest uses two stage least squares ( 2SLS ), which is also known as an instrumental variable ( IV ) method. Therefore, consider the general model of 2SLS
= + +
57 Assumptions and properties of Ordinary Least Squares are explained in the following section 5.5.2.
155
= + (5.13) , = 0
The first equation is a standard ordinary least squares ( OLS ) model, however as it assumes that the model has two suspected endogenous variables, therefore is the regressor with multiple instruments of and estimated by the 2SLS . In the context of the migration model in this study, the estimation will test which one of the production variables, whether the general business activity or GVAP, can better explain migration in the MDB.
5.5.1. Estimation with a Single Endogenous Regressor and Multiple Instruments
The empirical analysis in this study attempts to validate that the environmental migration factor that may operate in the MDB area can be explained by economic performance of the LGAs. However, two possible channels are postulated through general business activity or GVAP. Although the hypothesis above stated that the rationale is from agricultural production, some statistical tests must be applied to justify the decision model. Therefore, prior to the main estimation that applies the IV method, the second step is to perform an endogeneity test for both general business activity and GVAP by using multiple instruments.
In terms of theoretical form, Verbeek (2008) asserts that to identify and in the general case, the estimation requires at least one additional moment condition. The multiple instrumental variables of can be assumed to be uncorrelated with the main model error of in the equation (5.13) but correlated with the endogenous regressor . The condition can be displayed by
(5.14) − + + = 0
The condition above can be referred to as exogenous, and the moment condition provided in
(5.14) is not a combination of the other regressor in the main model ( is not a linear combination of ) as this is sufficient to identify and The instrumental variables estimator . can be explained from;
156
1 − − = 0
1 − − = 0 (5.15)
The general solution can be expressed by the following IV model estimator,
= (5.16)
Where and . Also from the (5.16), it is clear that if the = ( , ) = ( , ) = expression will reduce to the OLS estimator.
In addition, the model will be estimated by applying a logarithm format to translate the result into elasticities. Therefore, in terms of the study’s estimation model, the configuration is as below: i. OLS estimation Model
= + ( ) + ( ) + ( 0405)
+ ( ) + ( ) + ( ℎ )
+ ( ) + ( ) + ( ) + (5.17)
Where is the three types of five year migration (net migration, in migration, and out migration), and data has already formed in the percentage for elasticity effect. ii . Post OLS estimation, the model proposes that and are endogenous ( 0405) ( ) parameters, and thus additional variables are added by inserting multiple instruments including environmental factors. The reduced form model then,
a. ( 0405) = + ( 0203 ) + ( 0405 ) + + 157 b. ( ) = + ( 0203 ) + ( 0405 ) + ( ) +
+ (5.18)
The instrumental variables consist of the rainfall level in 2002–2003 and 2004–2005, and an additional economic variable of unemployment rate to capture the labour market conditions in the area i. Moreover, the instrument for GVAP is added by the variable of water utilisation as a proxy of water flow in order to capture the influence of water impact in the agricultural production. iii. The endogeneity test is conducted for both variables of to opt for ( 0405 , ) which endogenous equation that has a significant residual with all explanatory variables and instrumental variables. The test is based on the study by Durbin (1954), Hausman (1978) and Wu (1973), where in the general 2SLS model the instruments are uncorrelated with the error of the structural model. The endogeneity test estimates the reduced form model, and then gets the fitted residual of The adds as an additional explanatory variable in the structural model, . and if the coefficient is statistically significant, then the endogenous variable is valid.
Moreover, the selection also considers the significance level of the instrumental variable estimation. The fitted value from the first step equation will be inserted in the main estimation or in the IV model, where it applies either GVAP as the endogenous regressor:
= + ( ) + ( ) + ( ) + ( )
+ ( ℎ ) + ( + ( ) + ( ) + (5.19) or business income as the endogenous regressor:
= + ( ) + ( ) + ( )
+ ( ) + ( ℎ ) + ( )
+ ( ) + ( ) + (5.20)
158
The main model has fitted the suspected endogenous variables. However, as composed above the selection of which one of the endogenous variable is suitable will be explained in the estimation result section (5.6).
5.5.2. Estimation Properties and Instrument Validity Test
The estimation using 2SLS basically follows the properties of the OLS estimator, characterised by Gauss Markov conditions of the distribution between regressor and error terms (Gujarati 2011; Verbeek 2008). Those properties consist of:
=0, =1,…, are independent , … , & , … ,
= , =1,…, , ≠
, =0, , =1,…, , ≠ The first property ( ) assumes that the expected value of the error term =0, =1,…, equals zero, implying that on average the trend line of regression should be correct. The second property denotes that the error terms and the regressors ( ) are not , … , & , … , correlated as a consequence of the first property. The third property ( ) states that all = error terms in the model have the same variance, referring to the condition of homoscedasticity. The last property ( ) indicates that between error terms is zero correlation or , = 0 covariance between different error terms equals zero. All these basic properties of OLS are attached to the estimation.
In terms of the estimation in this study that apply the instrumental variable, the additional properties should include,
, ≠ 0
, = 0 The first property implies that co variance of instrumental variables is not equal to zero with the endogenous regressor, and therefore they are correlated. On the other hand, the co variance between the instrumental variables with the residual or error term in the main structural model must be equal to zero, indicating there is no correlation between them.
159
Moreover, an additional test is also conducted at post estimation by applying another Durbin- Wu-Hausman (DWH) test to examine whether the result of the structural model has a strong instrument or a weak instrument. The basic idea of the test is simply to compare the difference between the OLS coefficient and the IV/2SLS coefficient of all variables in the structural model (Gujarati 2011).
= ( − )
Where is distributed as the chi square distribution with the degrees of freedom equal to the number of all coefficients. If the value of turns out to be equal to zero, it suggests that the estimation model has a weak instrument and the degree of correlation between the endogenous regressor is not correlated with the residual, and therefore the OLS method seems to be more efficient rather than estimating using IV method.
5.5.3. Multicollinearity Issues
The potential for multicollinearity issues arises when a individual control variable has a significant degree of linearity with other control variables. The estimation of a high degree of collinearity between control variables means that there is no longer a best linear approximation which means there is a potential bias in the result (Verbeek 2008). An econometric technique to address this issue replaces the variable that has the least impact on the dependent variable (Verbeek 2008; Gujarati 2011).
Because of data limitation, trade off was made between best linier fit and data availability. Variables were retained in the modelling despite having statistical significant correlation with other variables because they are identified in the literature on migration theories as being important, and the value of the correlation coefficients are relatively small, although reaching statistical significant at p=0.05 or p=0.01. The linier association are not strong and a large proportion of the variation is unexplained. Thus, suggesting each variable may made a unique contribution in the modelling.
The interpretation of collinearity based on the Pearson correlation coefficient holds that the correlation variables are close to being perfectly correlated if the coefficient is almost one, and therefore requires some adjustments in order to reduce bias and maintain linear approximations
160 in the main estimation model. Collinearity tests from the combination of all variables in the main model shows that there are no significant correlations between control variables (Table 5.4).
Table 5.2. Collinearity between variables in the main model in the first phase analysis
Correlation# netrate5y lnwage lnbusinc lninvest lntotbus lngvap lnhouseval lndwelling educ info
netrate5y 1.00 lnwage 0.14** 1.00 lnbusincome 0.07 0.03** 1.00 lninvest 0.10 0.16 0.42** 1.00 lntotbus 0.40** 0.40** 0.04 0.17 1.00 lngvap 0.03 0.41** 0.10* 0.12* 0.12 1.00 lnhouseval 0.25** 0.55** 0.10** 0.09** 0.60** 0.14** 1.00 lndwelling 0.49** 0.36** 0.12 0.24 0.78** 0.15** 0.52** 1.00 educ 0.11** 0.45** 0.18** 0.21** 0.34** 0.48** 0.37** 0.23** 1.00 info 0.35** 0.53** 0.15** 0.00** 0.54** 0.25** 0.42** 0.42** 0.57** 1.00 #The range of correlation coefficient is between 1 and 1. Coefficient 1 is a perfect positive correlation and correlation 1 is a perfect negative correlation. When correlation coefficient is zero, the variables are said to be uncorrelated. **p value 1%, *p value 5%. Based on calculation of performing hypothesis tests on correlation. However, to test the instruments’ degree of effectiveness, the endogeneity test is conducted for both endogenous regressors. The test estimates the endogenous parameters for all explanatory variables and instruments in order to obtain the residual. Subsequently, the residual is added as an additional explanatory variable to the structural model. If the p value of the residual coefficient is significant, the suspected variable is endogenous, and it is valid to conduct a second stage estimation of the IV method. Conversely, if the p value of the residual is insignificant, the application of the IV method is not effective.
5.6. Estimation Result
The OLS estimation based on equation (5.17) comprises 464 observations 58 from a total of 674 LGAs, as the model only contains those areas with agricultural production ( lngvap ); and from the 464 areas with agricultural production, LGAs within the MDB are 115 from a total of 136 LGAs. The simple regression for GVAP areas in the MDB (Table 5.3) implies that the expected nexus between migration and income differential likely occurred in the period 2003–2005, indicated by a significant level of negative coefficient of out migration for personal wage and
58 The first phase migration analysis applies the ASGC standard, comprising 674 LGAs. However, as the model estimates the areas with agricultural production, thus only 464 LGAs can be estimated as they have a GVAP. 161 salary, and positive coefficients of net migration and in migration for investment income. Meanwhile, for other LGAs outside the MDB, the activity of in migration and out migration shows a positive association and negative association with average income level 2003 2005 respectively.
The relationship between migration activities and other socio economic variables in the MDB is weak and unclear. Business conditions ( lnbusnumb) , represented by the variable of business numbers (total business for net migration, business entry for in migration and business exit for out migration), which is expected to have a positive association with all migration models, shows a weak positive relationship. Development indicators, represented by total dwelling units (lndwelling ) and average value of private houses (lnhouseval ), also display a limited association with migration. Only the variable of total dwelling numbers (lndwelling ) shows the expected positive association, even though the coefficients for all migration activities are not significant.
In terms of education level (educ ), where the estimation expects to show a positive relationship with net migration and in migration, and negative association with out migration, only the model of net migration and in migration support the rationale that people tend to move to as area with better educational levels. On the other hand, the internet access level (info ) in the area shows that it follows previous studies (Davanzo 1981; Katz and Stark 1984; Paloni et al. 2001) where better facilities at the destination can encourage migrants to enter as it represents better public services.
The standard OLS estimation is not capable of describing the economic performance of business activity ( lnbusin0405 ) and agricultural production ( lngvap ). The coefficient for these variables in the MDB is contrary to the expected result. The coefficient for GVAP in the MDB is significantly negative for in migration rates and positive with out migration, reflecting people entering a particular area in the Basin with lower agricultural production value. Similarly, with the business activity indicator, the figure also reflects an opposite result from the expected outcome, where business income is significantly negative with in migration rates.
The estimation can be clarified in that the incoming mobility in the MDB reflects people entering areas with a lower business income. Hence, the estimation using standard OLS without any involvement of the environmental aspect seems to be limited and insufficient to capture a clear relationship between migration and its drivers. The basic result envisages a suggestion that the economic performance indicators cannot reflect the actual impact to the mobility
162 pattern, which suggests an endogeneity test by applying multiple instruments to those variables of business income and agricultural production as reflected in the model (5.18).
Table 5.3: OLS Estimation Result of Migration 2001–2006
5 year MDB Outside MDB mobility Net In Out Net In Out Variables Migration Migration Migration Migration Migration Migration
lnwage 14.48*** 2.44 17.32*** 5.77* 16.09*** 21.36*** (4.75) (4.97) (4.49) (3.20) (3.49) (3.74) lnbusin0405 -0.09 -2.97 2.84 -1.49 -2.73** 1.63 (1.96) (1.79) (1.87) (1.30) (1.35) (1.23) lninvest 2.13 4.36*** 2.15 0.19 0.17 0.29 (1.62) (1.47) (1.83) (1.07) (1.15) (0.78) lnbusnumb# 0.27 0.24 0.13 0.43 2.05*** 2.27*** (0.72) (0.89) (0.79) (0.66) (0.65) (0.24) lngvap -0.61 -1.40*** 0.78 0.29 -0.002 0.29 (0.64) (0.41) (0.58) (0.21) (0.29) (0.24) lnhouseval 0.34 5.23** 4.91*** 2.01 1.08 1.98 (2.56) (2.22) (1.73) (1.71) (1.73) (1.63) lndwelling 0.78 0.85 0.13 1.82*** 0.99** 0.65** (0.56) (0.56) (0.62) (0.38) (0.41) (0.31) educ 0.41 0.19 0.62 0.19 0.03 0.19 (0.61) (0.56) (0.52) (0.16) (0.25) (0.19) info 0.46*** 0.36*** 0.10 0.18*** 0.02 0.18*** (0.12) (0.13) (0.11) (0.07) (0.08) (0.05) R2 0.44 0.38 0.17 0.30 0.13 0.43 All parentheses are robust standard error ***=1% significant level, **=5% significant level, *=10% significant level #busnumb represents the variable of total business number for net migration model, number of business entry for in migration model, and number of business exit for out migration model.
5.6.1. Endogenous Variables Estimation and the Endogeneity Test
The multiple instruments are applied to the endogenous variables for all LGAs with agricultural production, comprising two rainfall levels in 2002–2003 and 2004–2005, unemployment growth, and water utilisation for GVAP. The expected rainfall in 2002–2003 ( lnrain0203) and rainfall in 2004–2005 (lnrain0405) are positively associated where the increasing of rainfall should also increase both endogenous regressors. The expected association denotes that areas with more intense rainfall in 2002–2003 and 2004–2005 would increase the economic productivity of either GVAP or business income. Another instrument of unemployment growth (unemploy ) is expected to have a negative relationship as the improvement of economic
163 activities (GVAP and general business) will reduce unemployment. Meanwhile, the instrumental variable of water utilisation ( lnwatuse) is expected to have a positive association, as more water being used in the agricultural sector will increase production.
The scatter plot from the dataset shows that rainfall in 2002 2003 fits with the expected pattern of positive relationship with the endogenous variables, particularly GVAP (Figure 5.4). The trend is slightly less between rainfall in 2004 2005 and GVAP, indicating that rainfall levels have a higher association with GVAP in the driest years of the drought period (Figure 5.4a). In comparison, the other plot appears to have a lower of positive relationship between rainfall levels and business income (Figure 5.4b). Rainfalls in 2002 2003 show that it has a slight positive relationship with business income, but rainfall levels in 2004 2005 seem to have an opposite association where the plot becomes flat and slightly negative.
Figure 5.4: Scatter Plot of Endogenous Variables ( y-axis ) and Rainfall ( x-axis ) a. GVAP with Rainfall Level (Rainfall 2002-2003) (Rainfall 2004-2005) 6 6 4 4 2 2 0 0 -2 -2
4 5 6 7 8 4 5 6 7 8 lnrain0203 lnrain0405
95% CI Fitted values 95% CI Fitted values lngvap lngvap
b. Business Income with Rainfall Level (Rainfall 2002-2003) (Rainfall 2004-2005) 11 11 10 10 9 9 8 8
4 5 6 7 8 4 5 6 7 8 lnrain0203 lnrain0405
95% CI Fitted values 95% CI Fitted values lnbusin0405 lnbusin0405
Sources: Dataset Estimation (in logarithm and plot with standard deviation), NRP and BoM
164
Table 5.4: Comparison of Endogenous Regressors
Business Instruments GVAP Income
lnrain0203 0.51* 0.30*** (0.27) (0.08) lnrain0405 0.78*** 0.38*** (0.29) (0.08) unemploy 0.01*** 0.03*** (0.02) (0.005) lnwatuse 0.44*** (0.03) R2 0.43 0.12 ***=1% significant level, **=5% significant level, *=10% significant level
From the estimation (Table 5.3), the environmental aspect of rainfall in 2002–2003 appears to have a significant positive relationship for both endogenous parameters, yet the coefficients show that the effect is greater for GVAP rather than for business in general, reflecting that agricultural activities rely heavily on precipitation levels for production. However, the effect of the precipitation level in 2004 2005 is the opposite of the expected association, where the coefficients are significantly negative for both endogenous parameters.
The scatter plot from the dataset shows that rainfall in 2002 2003 fits with the expected pattern of positive relationship with the endogenous variables, particularly GVAP (Figure 5.4). The trend is slightly less between rainfall in 2004 2005 and GVAP, indicating that rainfall levels have a higher association with GVAP in the driest years of the drought period (Figure 5.4a). In comparison, the other plot appears to have a lower of positive relationship between rainfall levels and business income (Figure 5.4b). Rainfalls in 2002 2003 show that it has a slight positive relationship with business income, but rainfall levels in 2004 2005 seem to have an opposite association where the plot becomes flat and slightly negative.
From the estimation (Table 5.3), the environmental aspect of rainfall in 2002–2003 appears to have a significant positive relationship for both endogenous parameters, yet the coefficients show that the effect is greater for GVAP rather than for business in general, reflecting that agricultural activities rely heavily on precipitation levels for production. However, the effect of the precipitation level in 2004 2005 is the opposite of the expected association, where the coefficients are significantly negative for both endogenous parameters.
165
The result reflects that the inclusion of rainfall levels between 2002 2003 and 2004 2005, as the empirical analysis assesses the cumulative effect, may differentiate the impact of rainfall to economic activity (GVAP and general business). The positive result in 2002–2003 confirms that in those years the value of water to the economy is higher compared to other years in the first phase period.
The additional economic variable of unemployment growth reflects the conditions in the labour market, where the expected negative association appears to be statistically significant. The additional instrument variable of unemployment growth also shows no correlation issue with the main dependent variable of migration. However, the coefficient value is relatively small, which possibly indicates that economic activities during the drought period can only generate a small number of opportunities. The last instrument of water utilisation, specifically for the GVAP model, reflects consistent findings from many previous studies that water is a crucial aspect of agricultural production. The comparison between suspected regressors also reflects that agricultural production has a stronger association, indicated by the stronger R 2 level with GVAP compared to the business income model. Hence, the first stage estimation seems to show that agricultural production is a more appropriate variable to represent the environmental aspect of migration by using the GVAP variable.
However, to test the effectiveness of the instruments, the endogeneity test is conducted for both endogenous regressors. The test estimates the endogenous parameters with all explanatory variables and instruments to obtain the residual. Subsequently, the residual is added as an additional explanatory variable to the structural model. If the p value of the residual coefficient is significant, the suspected variable is endogenous, and it is valid to conduct a second stage estimation of the IV method. Conversely, if the p value of the residual is insignificant, the application of the IV method is not effective.
The endogeneity test is conducted for all models of all migration types (Table 5.5), categorised for the MDB area, and for LGAs outside the MDB with agricultural production, for comparative analysis. Based on the test, the endogenous regressor of GVAP appears to have a significant residual, which confirms that the variable is endogenous, and therefore it is valid to apply the IV estimation. In terms of the MDB area, the variable of GVAP consists of two migration models (in migration and out migration) that are valid as an endogenous variable. Meanwhile, the other suspected variable, i.e. business income, shows an insignificant residual on every types of migration. The test result emphasises that the GVAP is both sufficient and credible to
166 apply in the IV estimation inside the MDB area, based on the significance of the multiple instruments estimation and the residual of the endogeneity test.
Table 5.5: Residual Comparison of Endogeneity Test
MDB Other GVAP Areas Outside MDB Residual (p-value) Net In Out Net In Out Migration Migration Migration Migration Migration Migration
GVAP 0.11 3.61** 3.57*** 1.44** 3.41*** 1.90*** (1.51) (1.58) (1.00) (0.63) (0.79) (0.69) R2 0.53 0.42 0.35 0.37 0.17 0.49
Business Income 2.04 5.77 3.52 8.49* 11.97** 5.42 (6.30) (6.63) (4.44) (4.67) (5.60) (5.18) R2 0.43 0.34 0.13 0.30 0.14 0.43 All parentheses are robust standard error ***=1% significant level, **=5% significant level, *=10% significant level
Moreover, the test for all areas outside the MDB that have agricultural production consistently displays a more robust result on the GVAP model. The residual coefficients for all types of migration appear to be significant, reflecting that the IV method can be applied in all structural migration models. Alternatively, the test for business income indicates that only two of the migration types (net migration and in migration) have a significant residual. The test result for the areas outside the MDB highlights that GVAP is also more appropriate to apply to the analysis outside the MDB area.
Furthermore, after testing the instruments variables, another Hausman Test is applied for every IV model by comparing the difference in the coefficient between the fitted instrument variable and the original variable (Gujarati 2011; Verbeek 2008). If the difference in coefficients is not systematic (Prob>chi square), the IV estimation is valid when utilising the instrument, otherwise it results in weak or failed estimations.
5.6.2. Outcome for General Migration
The outcome for general migration using the 2SLS or the IV method reflects the relationship between economic performance, in particular agricultural production, with migration activities. However, the estimation model is slightly changed as a result of the adjustment from applying the multicollinearity and endogeneity tests. The new estimation excludes the variable of total passenger cars and a merger of income variables. The specification model for 2 SLS is as follow:
167
= + ( ) + + ( ) + ( ) + ( )
+ ( ℎ ) + ( ) + ( ) + ( ) + (5.21)
The inclusion of three types of business numbers 59 can replace the insufficient endogenous regressor of business income to capture the effect of business activity on migration. Meanwhile, the two explanatory variables of income are also capable of measuring the income effect on migration.
In terms of the MDB, the valid models that can explain the migration drivers between 2001 and 2006 are in migration estimation and out migration estimation (Table 5.6). The outcome from in migration and out migration estimations in the Basin is valid, as the Durbin-Wu-Hausman test to test the IV method reflects significant instrument variables, and is relatively robust in terms of significant level (R 2 0.36 for in migration model and R 2 0.33 for out migration model).
The parameter of income can represent the new classical theory (Todaro 1969; Greenwood 1975) that migration has a positive association with increasing income. The average personal income level in 2003 2005 has a positive association with the in migration model, where the coefficient is statistically significant. Moreover, in the out migration model, the average income level during the peak drought period between 2003 and 2005 shows a significant negative relationship, indicating that a higher propensity to leave a particular LGA was caused by a falling level of personal income. The tabulation from the estimation data (Table 5.7a) shows that among the LGAs with the highest average wage and salary in 2003 2005, most of them have positive net migration rates, such as Yass Valley, Bathurst, Albury, Blayney and Mitchell. Unlike other LGAs, the first two LGAs of Cobar ad Queanbeyan have specific conditions, as Cobar is well known as a mining area and Queanbeyan is basically a commutable outer suburb of Canberra, ACT. On the other hand, LGAs with the lowest average wage and salary all have a negative net migration rate, for example Swan Hill, Yarriambiack and Stanthorpe.
In terms of area and remoteness classification, most of the LGAs with the highest average personal income are categorised as urban centres or small cities in the state of NSW, with populations between 10,000 and 50,000. Moreover, the ARIA index for the top LGAs by
59 The estimation model consists of three types of migration activities: net migration model, in migration model, and out migration model. This allows the capture of the actual impact from figures of business numbers, the number of business entries on the in migration model, the number of business exits on the out migration model, and the total business numbers on the net migration model. 168 income shows that most of them are located in inner regional Australia. Conversely, the figure for LGAs with the lowest level of average wage and salary are classified as Towns or small urban areas, and located mostly in outer regional Victoria or South Australia.
Table 5.6: 2SLS (IV estimation) Result of Migration (2001–2006)
MDB Other GVAP Areas Outside MDB 5 year mobility
Net In Out Net In Out Variables Migration Migration Migration Migration Migration Migration
lnwage -8.01 11.75* -19.35*** 6.15 17.54*** -23.09*** (5.89) (6.42) (4.68) (3.75) (3.93) (3.80) lninvest 4.89* 0.39 4.59* 1.18 0.95 0.03 (2.85) (2.54) (2.45) (1.30) (1.23) (0.77) lnbusnumb# 0.45 0.62 0.93 1.17* 2.38*** 1.83*** (0.95) (0.88) (0.83) (0.69) (0.73) (0.47) lngvaph (fitted) -0.32 1.06 -1.43*** 1.25*** 1.84** -0.70 (0.69) (0.72) (0.47) (0.46) (0.65) (0.48) lnhouseval 0.88 7.18** 6.12*** 2.45 0.03 1.63 (2.21) (2.84) (1.95) (1.84) (1.88) (1.68) lndwelling 1.59* 0.09 1.45** 2.44*** 1.03* 1.24***** (0.83) (0.65) (0.68) (0.40) (0.45) (0.31) educ 0.08 0.20 0.28 0.34* 0.25 0.12 (0.41) (0.41) (0.42) (0.18) (0.27) (0.17) info 0.50*** 0.38** 0.11 0.22*** 0.05 0.15*** (0.10) (0.14) (0.11) (0.07) (0.08) (0.05) R2 0.50 0.36 0.33 0.38 0.15 0.48 Durbin-Wu- Hausman - 0.01 0.00 0.00 0.02 Prob>chi 2 Instrument - valid valid valid valid Validation All parentheses are robust standard error ***=1% significant level, **=5% significant level, *=10% significant level #busnumb consists of total business for net migration, business entry for in migration, and business exit for out migration.
The indicator of economic activities represented by the number of business entries (lnbusnumb ) appears to have a small positive association with the in migration model and negative relationship with the out migration model, showing that the growth of new business units at LGA level has a limited impact on migration.
Other socio economic and development indicators, represented by the variables of average value of private houses (lnhouseval ) and dwelling approval ( lndwelling ), display some
169 interesting findings. From the estimation, the average value of private houses in the MDB shows a significant negative association with in migration rates and a positive association with out migration rates. The result reflects that there is a tendency for migrants to enter areas where there has been a fall in house values, indicating their preference for affordable housing in the destination LGA. On the other hand, the positive relationship between house values with the out migration model suggests that the increase of house values may encourage people to sell their property, and benefit by moving to an area with lower house prices. The tabulation from the estimation data supports the finding where LGAs with the highest average value of private houses are mostly classified as urban centres or small cities with negative net migration rates (Table 5.8a).
Table 5.7: Migration Rates with Average Wage and Salary in the MDB 2001-2006 a. LGAs with Highest Average Wage and Salary
Net. Net. Net. Av. Wage Net. Pop. ARIA Migration Migration Migration and salary No LGAs State Area Classification Migration Number Classification Rates (15 24 Rates (20 Rates (15 2003 2005 Rates years) 34 years) 64 years) ($)
1 Cobar (A) NSW 4915 Town Remote 7.45 6.57 1.86 6.51 41222 2 Queanbeyan (C) NSW 35969 Urban/Small City Major Cities 5.02 9.94 15.02 6.86 41380
3 Yass Valley (A) NSW 13133 Urban/Small City Inner Regional 7.52 16.15 0.06 6.45 37147 4 Oberon (A) NSW 5029 Small Urban Inner Regional 4.12 16.16 12.23 3.95 36218
5 Bathurst (A) NSW 35845 Urban/Small City Inner Regional 3.60 14.95 0.50 3.26 35807
6 Orange (C) NSW 35338 Urban/Small City Inner Regional 4.76 8.98 10.56 5.93 37348 7 Albury (C) NSW 46285 Urban/Small City Inner Regional 1.86 9.27 2.36 2.17 34813
8 Tumut Shire (A) NSW 10798 Urban/Small City Inner Regional 4.83 24.75 9.96 5.86 34000 9 Blayney (A) NSW 6594 Small Urban Inner Regional 2.51 17.84 9.31 1.59 34282 10 Mitchell (S) VIC 30929 Urban/Small City Inner Regional 6.30 7.30 4.40 5.74 34616 b. LGAs with Lowest Average Wage and Salary
Net. Net. Net. Net. Av. Wage Pop. ARIA Migration Migration Migration No LGAs State Area Classification Migration and salary Number Classification Rate (15 24 Rate (20 34 Rate (15 64 Rate 2003 2005 years) years) years) 1 Swan Hill (RC) VIC 20631 Urban/Small City Outer Regional 4.35 16.73 12.76 5.27 27180 2 Wakool (A) NSW 4365 Town Outer Regional 7.29 35.46 20.18 8.69 27060
3 Coonamble (A) NSW 4212 Town Remote 12.95 29.52 15.70 12.34 26867
4 Goyder (DC) SA 4181 Town Outer Regional 1.97 29.87 15.04 3.30 26134 5 Yarriambiack (S) VIC 7515 Small Urban Outer Regional 7.72 36.64 25.96 9.52 26034
6 The Coorong SA 5666 Small Urban Outer Regional 3.30 29.58 4.77 2.95 26042 7 Stanthorpe (S) QLD 10126 Urban/Small City Outer Regional 1.22 26.67 16.29 2.35 27360
8 Southern Mallee SA 2138 Town Remote 7.34 28.88 7.49 7.88 25396 9 Buloke (S) VIC 6850 Small Urban Outer Regional 5.55 38.27 36.57 9.81 25296 10 Karoonda East SA 1163 Town Outer Regional 11.11 45.03 26.92 14.02 23328 Sources : Dataset First Phase estimation. Census of Population and Housing 2006 and NRP
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Moreover, the significant positive association between out migration and house value is related with the location of the areas, where LGAs with highest house value are mostly located in outer regional Australia such as Leeton, Dubbo, Wentworth and Griffith or even remote areas like Brewarrina.
Table 5.8: Migration Rates with Average House Value and Approved Dwelling Number in the MDB 2001-2006 a. LGAs with Highest Average House ($’000)
Net. Net. Net. Av. Net. P o p. Area ARIA Migratio n Migratio n Migratio n House No LGAs State Migratio n Number Clas s ificatio n Clas s ificatio n Rate (15 24 Rate (20 Rate (15 Value Rate years ) 34 years ) 64 years ) ($ '000)
1 M oree Plains (A) QLD 13973 Urban/Small City Outer Regional -14.26 -20.82 -15.15 -13.70 943
2 Leeton (A) NSW 11109 Urban/Small City Outer Regional -4.06 -9.62 -16.39 -5.57 260
3 Queanbeyan (C) NSW 35969 Urban/Small City M ajor Cities of 5.02 9.94 15.02 6.86 233
4 Brewarrina (A) NSW 1943 Town Remote -14.42 -11.54 -11.68 -12.53 223
5 Parkes (A) NSW 14284 Urban/Small City Outer Regional -4.57 -19.58 -9.24 -5.75 222
6 Wentworth (A) SA 6778 Small Urban Outer Regional -2.01 -19.06 -2.62 -1.40 210
7 Griffith (C) NSW 23798 Urban/Small City Outer Regional -5.72 -11.29 -7.61 -6.12 210
8 Corowa Shire (A) NSW 10978 Urban/Small City Inner Regional 3.84 -14.86 -4.73 3.11 207
9 Dubbo (C) NSW 37845 Urban/Small City Inner Regional -3.23 -8.46 -5.80 -4.36 205 10 Wagga Wagga (C) NSW 57012 Cities Inner Regional 2.33 14.74 1.21 2.44 203 b. LGAs with Highest Approved Dwelling Number
Net. Net. Net. Net. No . o f P o p. Area ARIA Migratio n Migratio n Migratio n No LGAs State Migratio n Building Number Clas s ificatio n Clas s ificatio n Rate (15 24 Rate (20 34 Rate (15 64 Rate Appro val years ) years ) years )
1 Go o ndiwindi (T) QLD 4713Town Outer Regional 6.32 1.54 1.72 7.01 6841
2 Inglewo o d (S) QLD 2536Town Outer Regional 7.80 46.03 18.05 9.95 6841
3 Waggamba (S) QLD 2875Town Outer Regional 0.26 16.60 16.39 2.73 6841
4 Parkes (A) NSW 14284 Urban/Small City Outer Regional 4.57 19.58 9.24 5.75 1273
5 Narromine (A) NSW 6511Small Urban Outer Regional 6.73 27.64 16.97 8.66 1194
6 Southern Mallee (DC) SA 2138Town Remote 7.34 28.88 7.49 7.88 1129
7 Greater Bendigo (C) VIC 93254Cities Inner Regional 4.59 4.27 1.22 4.35 954
8 Lachlan (A) NSW 6672 Small Urban Outer Regional 10.30 24.09 11.94 10.94 876
9 Lockhart (A) NSW 3182Town Outer Regional 5.63 38.41 21.27 7.59 810 Hay (A) NSW 10 3379Town Outer Regional 5.80 28.92 18.54 7.55 652 Sources : Dataset First Phase estimation. Census of Population and Housing 2006 and NRP The significantly positive coefficients of approved dwelling numbers, which represent development indicators in the MDB, compliment the estimation result of house valuation. The can be inferred that an increase in residential building reflects a growing demand for houses, which is followed by higher house valuations. The data (Table 5.8b) also shows that most of the LGAs with the highest building approval number are located in the outer regional areas of
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Australia, which have negative net migration rates, and are classified as towns or small urban areas, such as Goondiwindi, Narromine, Lockhart, and Hay.
The estimation also suggests that the proportion of households with internet access is a key pull factor for migrants in the MDB, and can be reliably seen as a proxy parameter reflecting the provision of public facilities. The significant positive coefficient of variable info on the in migration model shows that migrants in the MDB prefer to enter an LGA with superior internet access services.
Furthermore, the significant variable of households with internet access on the in migration model is validated with the data from the Basin. The dataset shows that almost all of the LGAs with highest internet access level had positive net migration rates (Table 5.9). This indicates that the destination areas attract more people to migrate there than to leave, as they have better facilities. Moreover, most LGAs with the highest level of internet access are located in inner regional Australia, and are classified as urban centres or small cities. Some LGAs are even on the top list of both parameters, such as Mount Barker, Yass Valley, Palerang and Indigo (Table 5.9).
The endogenous variable of agricultural production produces a significant result. The fitted value of GVAP, containing the environmental aspects of rainfall and water utilisation, is able to capture a reason behind people leaving affected areas in the Basin (out migration model). The negative coefficient, with a significant estimation result, suggests that the out migration activity was associated with declining agricultural production. More precisely, the logarithm model can show the marginal effect that for every 1 percentage drop of GVAP, the out migration level is 1.43 per cent. This figure validates the proposition that environmentally induced migration occurred in the MDB and confirms key studies (Reuveny and Moore 2009; Renaud et al. 2011; Black et al. 2011; Marchiori et al . 2011).
The significant result of endogenous variables on the out migration model also suggests that the developed model of environmental migration validates the hypothesis that environmental factors, represented by rainfall, play a role on migration activities in the MDB through agricultural production during the period of 2001 2006.
The tabulation from the dataset shows that most of the LGAs with the highest GVAP experienced rainfall levels below the average of the MDB during the Millennium Drought period (473mm – Table 3.4). Table 5.9 shows that the average rainfall in 2002 2003 is lower
172 than the average rainfall in 2004 2005, which corresponds with the instrumental estimation that rainfall in 2002 2003 had a positive association with GVAP.
Table 5.9: Migration Rates with Internet Access Level in the MDB 2001-2006
Net. Net. Net. Net. Internet P o p. Area AR IA Migratio n Migration Migratio n No LGAs State Migratio n Access Number Clas s ificatio n Clas s ificatio n Rate (15 Rate (20 R ate (15 Rate (%) 24 years ) 34 years ) 64 years )
1 Cambooya (S) QLD 5815 Small Urban Inner Regional 13.80 4.25 10.54 13.25 63
2 Crow's Nest (S) NSW 12639 Urban/Small City Inner Regional 16.20 18.54 0.28 12.77 61
3 Mount Barker (DC) SA 26435 Urban/Small City Inner Regional 8.73 0.81 11.19 8.58 58
4 Yas s Valley (A) NSW 13133 Urban/Small City Inner Regional 7.52 16.15 0.06 6.45 57
5 Palerang (A) NSW 12313 Urban/Small City Inner Regional 12.88 10.57 9.88 12.84 57
6 Jondaryan (S) QLD 14098 Urban/Small City Inner Regional 5.20 13.40 6.77 3.93 56
7 Queanbeyan (C) NSW 35969 Urban/Small City Major Cities 5.02 9.94 15.02 6.86 56
8 Wo do nga (RC) VIC 33006 Urban/Small City Inner Regional 0.83 6.06 0.78 1.32 54
9 Wagga Wagga (C) NSW 57012Cities Inner Regional 2.33 14.74 1.21 2.44 52
10 Indigo (S) VIC 14801 Urban/Small City Inner Regional 2.46 27.61 10.49 1.56 51
Sources : Dataset First Phase estimation. Census of Population and Housing 2006 and NRP In terms of net migration rates in the LGAs with the highest GVAP, most of them experienced negative net migration rates, in particular LGAs that are located in the outer regional parts of Australia, such as Mildura, Moree Plains, Loxton Walkarie, Swan Hill, and Griffith. Meanwhile, LGAs with positive net migration rates were located in the inner regional, areas such as Campaspe and Moira.
The result of the 2SLS/IV estimation confirms that the method is capable of explaining not only migration with income and socio economic drivers, but also the role of environmental factors in the MDB during the first phase of the Millennium Drought period. The outcomes also show a better association between migration and its drivers in comparison with standard OLS estimation in Table 5.2.
In order to extend the analysis and for comparison, the 2SLS method also estimates for LGAs with GVAP outside the MDB. The selection of this area is based on the concept that the majority of LGAs within the MDB are agricultural centres, and therefore a comparable area should not consist of all LGAs outside the MDB, but rather areas containing the value of agricultural production 60 . All the models of migration types have a robust IV estimation with a significant Durbin-Wu-Hausman test. In terms of income, the outcome from in migration confirms the
60 The definition of areas containing the value of agricultural production is LGAs with GVAP > 0. 173 new classical nexus between income and migration, where income has a positive association with in migration in 2003 2005.
Table 5.10: LGAs with Highest GVAP with Rainfall level 2002-2003 and 2004-2005 in the MDB
Av. Av. Net. Net. Net. Net. GVAP R ainfall Rainfall P o p. Area ARIA Migratio n Migratio n Migratio n No LGAs State Migratio n ($ 2002 2004 Number C las s ificatio n Clas s ificatio n Rate (15 Rate (20 Rate (15 Rate millio n) 2003 2005 24 years ) 34 years ) 64 years ) (m m) (mm)
1 Mildura (R C) VIC 49814 Urban/Small City Outer Regional 0.45 8.90 4.40 1.29 525 248 226
2 Moree Plains (A) NSW 13973 Urban/Small City Outer Regional 14.26 20.82 15.15 13.70 514 353 397
3 Campas pe (S) VIC 36209 Urban/Small City Inner Regional 0.38 18.09 10.52 1.17 421 353 397
4 Shepparton (C) VIC 57090Cities Inner Regional 2.89 12.80 7.90 4.00 412 400 462
5 Mo ira (S) VIC 27083 Urban/Small City Inner Regional 2.05 17.57 10.16 0.84 398 426 466
6 Lo xto n Waikerie (DC ) SA 11607 Urban/Small City Outer Regio nal 2.78 19.86 8.16 4.60 364 337 452
7 Swan Hill (RC ) VIC 20631 Urban/Small City Outer Regional 4.35 16.73 12.76 5.27 342 253 364
8 Carrathool (A) NSW 2817Town Remote 13.79 27.45 24.49 14.39 318 213 203
9 Griffith (C) NSW 23798 Urban/Small City Outer Regional 5.72 11.29 7.61 6.12 291 581 620 Bulo ke (S) VIC 10 6850 Small Urban Outer Regional 5.55 38.27 36.57 9.81 284 273 351 Sources : Dataset First Phase estimation. Census of Population and Housing 2006 and BoM
Only two models of migration outside the MDB (net migration and in migration) can describe the fitted variable of GVAP in a more comprehensive manner, but only the net migration estimation show a significant relationship between the mobility pattern and agricultural production This finding implies that environmental migration also occurred outside the MDB during 2001 2006, where the precipitation level and water utilisation have made a strong impact on GVAP, and subsequently this influences people to migrate from the affected areas.
In terms of socio economic drivers, the agricultural areas outside the MDB show a strong positive association between development indicators and migration. The variable of dwelling approvals displays a significant positive relationship, especially for net migration and in migration. Moreover, the role of information access is confirmed again as an important driver for migrants’ decision making processes, indicated by a significant coefficient in the net migration model.
5.6.3. Migration Pattern by Age Groups in the MDB
Further migration models have been estimated to analyse whether the mobility pattern for different age groups has a different outcome than for the general population, in particular for the young age group of 15–24 years. A similar procedure is applied in the estimation as for the general population model. First, the age groups are comprised of the three classifications of 15–
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24 years, 20–34 years and 15–64 years. Therefore, with all of the migration type models, the endogeneity tests are implemented for each of the age groups. Second, a post estimation check of the Durbin-Wu-Hausman test is also conducted to confirm whether the multiple instruments are sufficient or weak for the model.
As shown in Table 5.11, from nine models of migration in the MDB by age group, the endogeneity test for a significant residual was only successful for six models of migration. However, from six credible models containing the endogenous regressor of GVAP, only three models of net migration in the young age group of 15 24 years, in migration in the working age group of 15 64 years, and out migration also in the working age group are credible, and have valid instrumental variables to describe the migration pattern in the MDB. Other estimations in the age groups of in migration in 15–24 years and all models of 20–34 years appear inadequate and have weak instrumental variables to elucidate migration activity.
The factor of income level appears to be important for the young age group (15 24 years) as a determining factor for migration, indicated by a significantly positive coefficient of wage factors. For the working age group of 15–64 years, the out migration model seems to meet the expected outcome. Based on the model, the effect of income on migration rates is consistent with the previous finding in the general population, where the coefficient for both models (in migration and out migration) are statistically significant. An improved income in the destination area attracted people to enter that, area and lower income levels in the origin encouraged people to leave. The coefficient value of income (lnwage) appears to be statistically significant, and very elastic for both valid models, where an increasing income in the destination LGA may encourage working age migrants to enter the area, or a small decrease in personal income in an LGA may initiate a substantial number of people leaving the affected area. In addition, a significant positive coefficient of average wage and salary in 2003 2005 in the in migration model, and a significant negative wage and salary in the out migration model, suggest that income level was very responsive to changes for the working age group in the MDB.
The mobility pattern for the working age group also shows that LGAs with the highest level of income become main destinations, and experience a surplus of migration, indicated by positive net migration rates, such as the LGA’s of Yass Valley, Bathurst, Albury, Blayney and Mitchell (Table 5.7a). Meanwhile, all of the LGAs with the lowest level of income, most of them being classified as towns or small urban areas, and located in outer regional Australia, experienced negative net migration (Table 5.7b).
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The variable of lnbusnumb , which represents the number of business entry on in migration and business exit on out migration, still has a limited association with migration activities for the young age group and working age group in the MDB, and was unable to explain people’s mobility. The socio economic and development variables have a similar impact on migration for the working age group. In terms of average value of private houses, working people in the MDB appear to migrate to an LGA with a lower house value, showing a preference for house affordability. The increasing in house value may also provide an incentive for people to sell their private houses and move to other LGAs.
Moreover, the provision of information access remains an important factor for migrants in selecting their destination area. The positive coefficient of variable info on net migration in the young age group of 15 24 years, and in migration model in the working age group, highlight that these age groups in the MDB prefer a destination with better internet access level. In addition, these two factors can be considered to be substantial aspects, with most of the LGAs with the highest level of information access experiencing positive net migration rates (Table 5.9)
The fitted values of GVAP as a key estimated variable are also associated with net migration in the young age group (15 24 years) and out migration in the working age group. The result shows that the 2SLS method is able to estimate environmental migration in the MDB for the age group of 15 24 and 15 64 years. Environmentally induced migration may have occurred, with rainfall and water utilisation playing a role in GVAP, which eventually influenced people to leave. The marginal effect for net migration in the young age group of 15 24 suggests that for every 1 percentage point increase of GVAP, net migration goes up by 1.34 per cent. Meanwhile, for out migration in the working age group, it suggests that for every 1 percentage point drop of GVAP, out migration goes up by 1.32 per cent (Table 5.11).
The tabulation from the estimation data displays a consistent pattern in relation to the general population, where most of the LGAs with the highest GVAP have rainfall levels below the MDB’s average during the Millennium Drought period. The negative net migration rates of the working age group are also higher compared with the general population. For example, one of the LGAs with the highest GVAP Campaspe in the state of Victoria experienced a positive net migration (0.38%), however the migration activity became negative ( 1.17%) for the working age group (Table 5.10). This confirms that age affects mobility patterns, as argued in Chapter 4.
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Table 5.11: 2SLS ( IV estimation) Result of Migration in the MDB by Age Groups (2001–2006)61
MDB MDB MDB 5 year mobility Age Group 15–24 Age Group 20–34 Age Group 15–64 (young people) (young working age) (working age) Variables Net In Net In In Out Migration Migration Migration Migration Migration Migration
lnwage 38.01*** 23.97*** 23.49** 16.41* 11.44* 13.61*** (11.56) (7.80) (10.64) (9.25) (6.70) (4.71) lninvest 2.20 0.73 2.64 1.25 0.30 4.25* (5.24) (3261) (3.75) (3.53) (2.63) (2.42) lnbusnumb# 0.22 1.12 2.84** 0.65 0.46 1.13 (1.36) (1.17) (1.36) (1.27) (0.90) (0.84) lngvaph (fitted) 1.40 1.90** 1.21 1.99* 1.18 -1.32*** (0.99) (0.90) (0.98) (0.99) (0.75) (0.46) lnhouseval 2.73 3.37 1.30 4.95* 6.79** 5.37*** (4.45) (2.94) (3.72) (2.53) (2.77) (1.94) lndwelling 1.56 0.15 2.19*** 0.17 0.19 1.56** (0.98) (0.91) (0.82) (0.82) (0.67) (0.68) educ 0.20 0.15 0.62 0.72 0.37 0.34 (1.09) (0.83) (0.64) (0.64) (0.40) (0.41) info 0.45** 0.39** 0.67*** 0.64*** 0.35** 0.09 (0.19) (0.16) (0.17) (0.17) (0.14) (0.10) R2 0.47 0.38 0.45 0.40 0.33 0.30 Endogeneity Test -8.17*** -7.74*** -4.58** -5.12** -4.08** 3.19*** Residual p Durbin-Wu- Hausman fail fail fail 0.44 0.00 0.00 Prob>chi 2 Instrument valid weak weak weak valid valid Validation All parentheses are robust standard error, ***=1%level, **=5%level, *=10%level #busnumb consists of total business for net migration, business entry for in migration, and business exit for out migration. However, the estimation using 2SLS/IV methods seems unable to capture the mobility pattern in the younger age workers of 20–34 years. The Durbin-Wu-Hausman test for instrument effectiveness clearly shows a weak relationship between migration drivers and migration activities for the young age groups. Thus, based on Verbeek (2008) and an example test from Gujarati (2011), the option to obtain an adequate result requires returning to the OLS method. However, using this estimation method has substantial implications, as the explanation for young age workers migration in the MDB does not warrant the inclusion of environmental
61 The table only contains 6 models of migration that have a significant residual of endogeneity test. Other models: out migration model in age 15–24, out migration model in age 20–34, and net migration model in age 15–64 do not pass the endogeneity test for significant residual. 177 variables, and thus cannot be defined as environmentally induced migration, as other factors play stronger roles in the migration activities for this group.
As the OLS estimation is an effective way to estimate the migration model for the young workers age group, the results suggest some different patterns from the IV model (Table 5.12). For example, in terms of personal income, the young workers group of 20–34 years shows a positive association between two migration models (net migration and in migration) and the average wage for 2003 2005, but both coefficients are not statistically significant as in the IV models.
Table 5.12: OLS Estimation for Young Age Groups Migration in the MDB
MDB 5 year mobility Age 20–34 (young working age) Variables Net In Out Migration Migration Migration
lnwage 6.76 4.14 3.71 (8.91) (8.23) (6.10) lninvest 1.75 3.86* 2.19 (2.40) (2.28) (2.23) lnbusnumb# 3.15** 1.64 0.85 (1.37) (1.47) (0.94) lngvap 0.60 1.06 0.36 (0.57) (0.64) (0.60) lnbusin0405 0.19 4.17 4.06** (2.60) (2.77) (1.96) lnhouseval 1.67 2.79 1.23 (4.33) (2.57) (2.74) lndwelling 2.41*** 1.82* 0.45 (0.81) (0.94) (0.60) educ 0.22 0.25 0.46 (0.79) (0.85) (0.61) info 0.73*** 0.64*** 0.07 (0.18) (0.17) (0.12) R2 0.42 0.38 0.08 All parentheses are robust standard error ***=1%level, **=5%level, *=10%level Another indicator of personal income, investment income (lninvest ), also follows the new classical theory of the migration–income relationship for this age group. The estimation shows a significant association in the net migration and in migration models for young workers aged 20 34 years. In line with the 2SLS estimation for general migration, the parameter of business
178 activity ( lnbusnumb ) is capable of capturing the expected relationship with migration activities, in particular with net migration activity.
As predicted, the economic performance variables (GVAP and business income) are unable to display a relationship for net migration and in migration, as these two models have a stronger relationship with all migration models. The business income parameter, which was included in the OLS model, is also not capable of capturing the association with migration activities.
In terms of development and social indicators, the young working age group (20–34) seems to take into their migration decision the level of development in the destination area as this age range may represent a migrant with a young family. The significant estimation of dwelling variables in the net migration and in migration models reflects this view. Other social factors, such as the role of information (info) , appears to have a significant relationship in net migration and in migration models.
5.6.4. Comparison Analysis by Age Groups in the Area outside the MDB
Similar to the analysis in the general population, the estimation by age groups also includes a comparison with LGAs outside the MDB with agricultural production (Table 5.12). From the nine models of migrations (three types of migration and three age groups), the endogenous regressor of GVAP seems to be efficient in capturing environmental influence on migration, reflected by seven models qualifying for the IV estimation. Furthermore, although post estimation shows that one model has resulted in a weak instrument, every age group contains a credible model to describe mobility of people living outside the MDB.
The out migration model in the age group of 15–24 years, which has a strong instrument for the IV estimation, confirms that a low income in the years 2003–2005 in some LGAs may have influenced decisions to leave the area. Interestingly, another income indicator of investment income appears to be a significant driver for young people to experience out migration. Another important finding is the substantial coefficient of GVAP, which validates the contribution of environmental migration in the young age group outside the MDB. Other parameters related to social and development factors have a similar pattern with the MDB area such as a positive coefficient of average house value, which may impact young people for affordable accommodation. However, other parameters related to social and development factors seem to be insignificant and poorly related to the mobility pattern of young people.
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The large value of the coefficients for in migration models in all age groups suggests that young people and workers were very responsive to the income level of the destination LGA. The variable of business numbers shows a significant relationship with migration, showing the influence of business activity on these groups’ migration behaviour. Moreover, the development indicator of the average dwelling numbers also shows a significant correlation with migration activity, in particular with the in migration model for the working age group.
The mobility pattern in the working age group (15–64 years) outside the MDB area can be explained in a more comprehensive manner, as all migration models are robust and have strong instrumental variables. In terms of income, the in migration and out migration model confirms that the average income level in 2003–2005 is significantly positive for migrants entering a destination area, and negatively related with the activity of leaving affected areas during the first phase period.
Another important finding is that the GVAP follows the expected pattern. Net migration, in migration, and out migration activities seem to reflect the significant effect of agricultural production for people entering and leaving LGAs outside the MDB. The importance of this parameter is that it suggests that environmental factors also contributed to where people lived in agricultural LGAs outside the MDB.
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Table 5.13: 2SLS (IV estimation) Result of Migration Outside the MDB Area by Age Groups (2001–2006)
Other GVAP Areas Other GVAP Areas Other GVAP Areas Outside MDB Outside MDB Outside MDB Age Group 15–64 5 year mobility Age Group 15–24 Age Group 20–34 (working age) (young people) (young working age) Variables In Out In Out Net In Out Migration Migration Migration Migration Migration Migration Migration
lnwage 30.36*** 2.43 33.31*** 13.33*** 2.55 19.35*** 21.96*** (5.98) (5.13) (7.56) (4.56) (3.85) (4.17) (3.71) lninvest 1.78 3.90*** 1.08 0.70 1.62 1.05 0.36 (1.66) (1.46) (1.90) (1.06) (1.35) (1.29) (0.78) lnbusnumb# 1.55 3.52*** 3.43*** 1.67*** 1.51* 2.42*** 1.63*** (1.14) (0.69) (1.17) (0.54) (0.78) (0.76) (1.07) lngvaph (fitted) 1.62* -2.35*** 2.40** -2.24*** 1.18** 1.86*** -0.81* (0.92) (0.70) (1.04) (0.57) (0.48) (0.70) (0.47) lnhouseval 1.18 1.86 0.55 0.25 2.62 0.33 1.97 (3.92) (2.54) (3.34) (2.13) (2.12) (2.08) (1.63) lndwelling 0.31 1.03** 0.51 0.37 2.32*** 1.00** 1.12*** (0.49) (0.45) (0.66) (0.36) (0.44) (0.46) (0.29) educ 0.85 0.24 0.05 0.41 0.23 0.04 0.22 (0.84) (0.20) (0.59) (0.22) (0.18) (0.08) (0.18) info 0.09 0.14* 0.07 0.16 0.14* 0.05 0.17*** (0.15) (0.08) (0.12) (0.07) (0.08) (0.08) (0.05) R2 0.15 0.59 0.17 0.21 0.29 0.14 0.43 Endogeneity Test Residual p -3.39*** 2.42** -4.72*** 3.31*** -1.37** -3.49*** 2.06*** Durbin-Wu-Hausman Prob>chi 2 fail 0.00 0.00 0.00 0.02 0.00 0.00 Instrument Validation weak valid valid valid valid valid valid
All parentheses are robust standard error; ***=1%level, **=5%level, *=10%level; #busnumb consists of total business for net migration, business entry for in migration, and business exit for out migration.
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5.7. Conclusion
The MDB may be defined as a food bowl and a key centre of agricultural production for Australia. However, the area is also well known for its history of serial environmental events of floods and drought. These fluctuations and environmental problems have raised concerns not only in relation to agricultural issues such as water management, but also in regard to the wellbeing of the people and communities in the Basin. The last drought (the Millennium Drought period) has raised the issue of migration, where the prevalence of people leaving affected areas could impact the socio economic sustainability of the MDB. This chapter focused on analysing the migration drivers in the Basin. The analysis uses data from the Census of Population and Housing in 2006 and only observes mobility during the first phase of the drought between 2001 and 2006. Several propositions were specified at the beginning of this chapter, which are based on the literature review of migration studies and the constructed research questions.
The new classical nexus of income and migration as a fundamental theory in migration studies consistently appears as an important driver of migration in the MDB. The observation from the overall population and from separate age groups both in the Basin and in comparable areas outside the MDB confirm some expected relationship with the migration variables. The young age group tend to displays more responsiveness to the income variable than the other groups.
Other socio economic and development indicators of the value of private houses and the average number of dwellings also remain crucial drivers within the analysis of mobility patterns, and confirm that people will move to areas with affordable housing or accommodation and a growing economy.
Another key finding is the role of information: although the marginal effect seems to be small in magnitude, the estimation in several model classifications shows that information is statistically significant, indicating that migrants in the MDB prefer an LGA with better information access (Da Vanzo 1981; Dolfin and Genicot 2010).
The main outcome from this chapter was to identify whether environmental migration occurred in the MDB in the first phase of the Millennium Drought period. The proposition that an economic activity variable better explains the pattern of migration by using environmental instruments confirms this hypothesis. By implementing the procedure of the Durbin-Wu- Hausman test to perform the 2SLS/IV estimation, the significance and negative impact of the
182 fitted variable of GVAP in the out migration model, and positive impact of the fitted variable of GVAP in the in migration model, confirm that the drought represented by rainfall and water utilisation have some impact on people’s mobility in the Basin.
Furthermore, the estimation does contain some caveats that will be described in Chapter 7. The subsequent chapter will assess and analyse the mobility pattern in the second phase of the Millennium Drought period between 2006 and 2009.
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Chapter 6: Empirical Analysis (2): Environmental Migration in the Murray-Darling Basin (2006–2011) 62
6.1. Overview: Conditions in the Recurrent Drought Years
The trend of negative net migration rates in the Murray Darling Basin (MDB) continued to occur at the level of Local Government Area (LGA) during the second phase of the Millennium Drought period between 2006 and 2009 63 . In the general population, data from the 2011 Census of Population and Housing shows that during the second phase period negative net migration increased slightly, as described in Chapter 4. The recurring drought years affected agricultural activities more severely for both irrigated land and dry land production than in the first phase period (Grafton et al. 2011; Wittwer and Griffith 2011). Quiggin (2008) asserts that severe conditions caused by the drought years in 2006 and 2007 reduced water flow to the lowest level on record in many rivers in the MDB, and delivered a significant impact through the water system.
Moreover, economic modelling by Wittwer and Griffith (2011) to analyse the impact of the consecutive dry years between 2006 and 2009 showed that in the southern MDB the drought reduced employment by approximately 6,000 jobs. The model also provides a calculation based on an increase of water prices as water availability dropped significantly to agricultural commodities. The study found that commodities that relied heavily on water for production, such as rice and cereal, dropped in production.
The first year of the second phase period was also marked by one of the driest years during the Millennium Drought period. As discussed in Chapter 3, rainfall data from the Bureau of Meteorology (BoM) shows that the MDB’s precipitation level in the year 2006 (300 mm) was even lower than in the year 2002 (355 mm). From the pattern of rainfall data, after the environmental shock of 2002, the next three years remained as a severely dry period, where the
62 Substantial parts of this chapter were presented at the 59 th National Australian Agricultural and Resource Economics Society (AARES) Conference, 10 th –13 th February 2015, Rotorua, New Zealand, the Australian Consortium and Political Research Incorporated (ACSPRI) Social Science Methodology Conference, 7 th –10 th December 2014, at the University of Sydney and the Australian Population Association (APA) Biennial Conference, Hobart 3 rd –5th December 2014. The findings have also been presented at the Australian Agricultural and Resource Economics Society (AARES) monthly seminar of the Australian Capital Territory (ACT) Branch, 2 nd June 2015.
63 The study assumes that the five year mobility data from the Census of Population and Housing in 2011, which detects migration from 2006 to 2011, is the most suitable approach to analyse the Millennium Drought migration from 2006 to 2009. The thesis has acknowledged the limitations set out in the data and methodology chapter that the detection of mobility is unable to analyse the exact year of migration. 185 average annual precipitation level in the MDB was around 500 mm, while the level outside the Basin was already above 700 mm 64 . Thus, another severe drop in rainfall in 2006 became a substantial source of distress for people in the MDB, which might encourage those who had previously remained to consider leaving affected areas (MDBA 2012; van Dijk et al. 2013).
The second phase of the Millennium Drought also highlights more responsive action from the authorities, not only at the federal level, but also by state and local governments. In the first phase, there was the one major intervention of the National Water Initiative (NWI) in 2004. In the second phase, there were at least two major interventions at the federal level. Hatton and Young (2011) state that the prolonged drought years, which reduced water supplies in the key agricultural areas, resulted in policy responses where the Prime Minister, in coordination with all Premiers from affected states, discussed contingencies to mitigate the impact. Statement from the Prime Minister in the national media argued that:
‘… the serious water situation in the Murray-Darling Basin requires a collaborative response from both federal and state governments. This prolonged drought is having a terrible impact on farming communities across Australia especially in the Murray-Darling Basin, and has inevitable consequences for city dwellers.’ (Sydney Morning Herald, 5 November 2006).
The gesture from policy makers to be more involved with the conditions in the Basin sent the signal that the deteriorating situation in the second phase would entail more interventions and regulations, which ultimately could affect the living conditions of all the Basin’s stakeholders. The consecutive dry years motivated the government to pass the Water Act 2007 . Part of the Act was to establish the Murray Darling Basin Authority (MDBA), an independent regulator with the main task of undertaking water resources management in a sustainable way 65 .
Environmental degradation since the first phase period, where the fluctuation of water availability was the crucial factor, initiated government to take immediate intervention steps through the MDBA. The function of the MDBA related to this was to prepare a plan for setting sustainable limits on water that could be taken from both groundwater and surface sources. The notion was to provide a system such that water flow is distributed in a balanced way for people and the environment over a long term period. Another key duty of the MDBA is conducting research and engaging with communities in the Basin, and also communicating the program of
64 In climatology studies, there are many standards to describe low precipitation levels, which depend on the event. In the context of the MDB, the thesis follows the definition by Timbal et al. (2010), where, based on observations from 1900, a rainfall level below 500 mm is defined as a severe drought year. 65 The Commonwealth Department of Environment in the establishment of MDBA; http://www.mdba.gov.au/about mdba/governance/the water act 186 resource management. Although the missions of the Act not only focus on environmental aspects, but also on social factors, the issue of population changes and how to assist people affected by the drought are not stated clearly 66 .
Moreover, another response to restore the balance in the MDB and as part of the overall framework to provide water for the environment for long term sustainability, the federal government implemented the Water Buyback scheme (or Water for the Future) in 2008. The fundamental idea of this program is to obtain water from irrigators and those who intend to offer their water entitlement for sale. The water will then be restored to the rivers to meet the needs of the environment, and thus can sustain the supply of water in the future. The initial plan 67 provides a 10 year fund of $12.9 billion to secure water supply in Australia. The fund will also be allocated for development in cities and towns with population sizes of less than 50,000 people. The responsiveness of government in the second phase period of the drought means that policy factors become another aspect to be considered as a possible driver of migration activities, even though there is a limitation to measure policy impact on migration activities.
Another indication of the conditions during the second phase of the Millennium Drought period is given a comparison of socio economic figures in 2007, during the drought, and in 2010 when the drought period had largely ended. Based on the series of regional data at the LGA level, provided by the Australian Bureau of Statistics (ABS), the average wage and salary in the MDB in 2007 was $33,878 and when the drought period ended in 2010, the wage level had increased to $38,886, an increase of 14.8 per cent which was below the national average wage growth of 19.3 per cent.
Other economic indicators portray the economic condition in the MDB. From the same source, business activities showed an opposite trend, where the average total business numbers in the Basin at the LGA level in 2007 was 1,788 units and this dropped slightly in 2010 to 1,778 units. In terms of total workers who received regular earnings, the trend follows wages and salaries pattern, where in 2007 there were 7,560 total workers compared with 7,783 workers in 2010.
66 ibid 67 The statement is based on a joint media release from the Treasury, Minister for Climate Change and Water, and Senator the Hon Penny Wong. The release can be found in the link: http://ministers.treasury.gov.au/DisplayDocs.aspx?doc=pressreleases/2008/138.htm&pageID=003&min=wms&Year=&Doc Type=0 187
Figure 6.1: Economic Indicators between 2007 and 2010 in the MDB at LGA level a. Wage and Salary Earners by Age Group (Persons)
1800 1687 1726 1603 1670 1666 2007 2010 1600 1519 1474 1415 1400 1200 1122 1017 1000 800 600
400 232 Persons 215 200 0 15 24 25 34 35 44 45 54 55 64 ≥65 Age Groups
b. Number of Business by the Size of Employees (Number)
1200 1054 1087 1000 2007 2010 800
600 422 405 400 312 287 Business No Business 200
0 0 (owner operated) 1 to 4 ≥5 No. of Employees
Source : National Regional Profile Series 2007–2011
The combination of these three figures may indicate that economic conditions in the Basin during the second phase of the drought period continued to be distressed. The higher wage levels and worker numbers at the end of the drought period may indicate a recovery. The opposite trend in business numbers may reflect business activity in the Basin becoming concentrated in a smaller number of enterprises with firms requiring a period of time to assess whether conditions are back to normal or remain under pressure.
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Categorising the data by the age group of workers and the size of company based on the number of employees suggests another consideration Figure 6.1a shows that the number of total earners in the MDB between 2007 and 2010 follows a standard labour structure, where the distribution is concentrated in the age groups of 25–34 years to 45–54 years. However, for the youngest group of workers (15–24 years), there was a slight decrease during the period, suggesting that young people continued to leave the LGAs in the Basin area, even though the drought period had ended. The migration figures in Chapter 4 confirm that young people in the MDB represent the age group with the highest propensity to migrate.
Business activity in the Basin also shows an interesting pattern between these years. In terms of businesses with number of employees between 1 to 4 and ≥5, the average number of businesses at the LGA level declined from the drought period in 2007 to the recovery year in 2010. This figure is consistent with the trend of average total business numbers, indicating that a recovery time is required for normal economic conditions to return. However, business activity for owner operated firms shows a different pattern, where the average business numbers slightly increase from 1,054 units in 2007 to 1,087 units in 2010. Referring to the report of socio economic conditions in the Basin (ABS 2009), the figure may reflect the activity of single farmers who stayed in the Basin, and at the end of the Millennium Drought started to work in the agriculture sector again.
The substantial concern in the second phase period was how to optimise the scarcity of water for irrigated areas around the Basin. The intervention through water allocation systems, where the main solution was to allocate more to the environment, prompted discussion among stakeholders and scholars about the idea of a ‘win win solution’ for both the economy and environmental sustainability. Grafton, Chu, Stewardson and Kompas (2011) provide on optimal dynamic water allocation by balancing the trade off between the net benefit of water allocation for irrigated agriculture versus minimising costs of reduced surface water flow for the environment. Their model specifically considers the relationship between the length of drought and environmental damage, and thus the longest drought period would increase the costs and the limited water allocation should be used optimally in the irrigated area for maximum benefit.
More studies about the impact to irrigated agriculture were published in response to the government’s involvement in water allocation. Jiang and Grafton (2012) provide a scenario of water allocation to irrigated agriculture and the impact to the economy in the Basin. Moreover, Crase, O'Keefe and Kinoshita (2012) argue that the water buyback policy is positive for farmers
189 in the irrigated areas if they can adapt and use the opportunity provided by this program, which follows a market based approach.
Furthermore, the second phase of the Millennium Drought period highlights some key issues with respect to the condition of the people and communities in the MDB. First, the persistence of the drought remains as an important factor affecting the livelihood of people and communities in the Basin. Second, more involvement of government in water management issues highlights the role that policy can play in people’s consideration to stay or go from affected areas. The intervention, with a large fund for programs such as the Water Buyback, raised the participation of many groups to share ideas on the importance of optimising irrigated agriculture for a ‘win win solution’.
Hence, based on these factors, this chapter attempts to examine the migration drivers in the MDB during the second phase period between 2006 and 2009. In terms of the theoretical framework and methodology, there is no major difference from the previous analysis in the first phase of migration. Fundamental propositions are similar, focusing on the new classical nexus of migration and income differentials between origin and destination; better facilities in the destination that can attract migrants to enter the area; and certainly the role of environmental factors through economic performance that affects migration activities. The additional factors that differentiate this chapter from the previous one relates to the area classification analysis with the inclusion of the additional category of irrigated lands. The other feature was to add a variable that captures the impact of government intervention.
6.2. Data Specification
The migration data source for the analysis is from the Census of Population and Housing in 2011. The construction of the data is compiled from TableBuilder and based on the geographical unit of Local Government Area (LGA). Similar to the previous census in 2006, migration data can be constructed into the three categories of net migration, in migration, and out migration. Parameters of the migration drivers were obtained from the National Regional Profile (NRP) series 2007–2011 from the ABS Data by Region section. The rainfall data was constructed from reliable weather stations at LGA level from the Bureau of Meteorology (BoM) Australia Climate Data Online, as described previously.
The characteristics of the data are similar to the first phase dataset where the migration drivers follow the theoretical framework of the individual migration decision in (5.11). Personal wage
190 and salary data remains the main determinant as this reflects the new classical approach of income differentials between origin and destination. Other elements of income are also included, such as investment income and business income. Specifically, in relation to business income, the estimation applies the same method to the previous analysis, where the standard estimation has suspected endogenous regressors of business income and Gross Value of Agricultural Production (GVAP). The GVAP variable was provided as part of the NRP series 2007–2011. The GVAP is a key economic factor capturing agricultural production, and in relation to the aim of the study, the comparison of GVAP and business income is the key to observing environmental migration.
The variables that represent the accumulation of local assets and development progress, which influence the decision to migrate (Todaro 1969; Bartel 1979), are average house mortgages and the average number of approved residential buildings in 2008–2009. The variable of average house mortgages is utilised to substitute for the parameter of house valuation, which was applied in the first phase analysis, but was unfortunately not available for the second phase 68 . This particular parameter can represent individual asset valuation and accumulation, for as Bartel (1979) argues capital accumulation at the point of origin is an idiosyncratic value that may lower the individual propensity to migrate.
The social factor of educational level within an area is represented by the percentage of individuals with a bachelors degree or higher as a percentage of the total population. Meanwhile, the factor that representd public facility is indicated by the proportion of people who have internet access. These variables have been created so as to have a comparable set of analyses between the first phase period and the second phase period of migration.
In terms of the environmental factors in relation to the second phase, several studies (Jiang and Grafton 2012; Wittwer and Griffith 2011) highlight the year of 2006 as the most severe year of the prolonged drought period. One of the reasons for this, as well as the lowest rainfall being recorded in 2006, the year also saw a substantial fall in irrigated land from 1,654,000 ha to 958,000 ha (Jiang and Grafton 2012). Therefore, the rainfall data used in this analysis is rainfall in 2006 and the average rainfall between 2007 and 2009. The purpose of using two rainfall variables is to capture the dynamic variation of the precipitation level during the second phase
68 To conduct a comparable analysis for both the first phase period and the second phase period, the unavailable data for the second phase period has been replaced with another comparable measure. 191 period, and also to compare whether a different year of rainfall delivers a different impact on economic activities.
As this analysis implements a similar approach with the previous chapter to detect environmental migration, multiple instrumental variables are also applied in the estimation. However, to capture labour market condition in the second phase period, the instrumental estimation uses the variable of employment growth rates rather than unemployment rates due to the data availability. Hence, the instruments include water utilisation at the LGA level based on the NRP series 2007 2011 and employment growth rates in 2007 2008. The total water utilisation data is only applied for the instrumental model of GVAP in order to capture water use in agricultural areas, including the Murray Darling Basin.
Moreover, the analysis in the second phase period has a farther difference from the first phase period, in terms of empirical estimation. The geographical unit follows the latest Australian Statistical Geography Standard (ASGS), and, based on the ASGS structure (ABS 2011), the number of LGAs becomes 577, this is a smaller number than the 674 LGAs in the previous benchmark of the Australian Standard Geographical Classification (ASGC). The summary statistics of variables applied in the estimation are presented in Table 6.1.
6.2.1. Trend of Explanatory Variables
From the dataset, some figures reflect the worsening conditions in the MDB as presented in Figure 6.2. In terms of personal income composition, the average wage and salary at the LGA level outside the MDB area remained higher than inside the Basin area. However, the disparity shows a larger gap than the first phase, with personal income outside the MDB or in the rest of Australia (ROA) growing at 24.3 per cent, while within the MDB the income growth rates only reached 13.8 per cent.
The prolonged drought also exacerbated findings in relation to the level of personal business income. Within the Basin, the average business income declined from $12,241 to $9,071 or a drop of 25.9 per cent, while the area outside the MDB experienced an increasing trend of average business income of 11.2 per cent, or from $16,128 to $17,932. The investment income was the only income variable in the MDB that had a significant growth rate at 26.1 per cent. However, this rate is still well below the trend outside the Basin, which had a 42.1 per cent growth rate.
192
The second phase of drought also shows worsening business activity in the Basin. From the figures, it can be seen that the average total business numbers at the LGA level in the MDB was 1,582, well below the number in the first phase period of 2,461. A substantial drop in the average business entry variable of almost a half, from 365 units in the first phase to 170 units in the second phase, indicates why business activity in the Basin experienced a significant declining trend.
Moreover, one of the development indicators that represents economic activity – total vehicle numbers – also follows a worsening trend with the average number of vehicles at the LGA level dropping from 13,200 units to 7,900 units, while for areas outside the MDB these numbers grew from 20,800 units to 24,200 units. Meanwhile, environmental indicators show more water utilisation in the MDB during the second phase period with average water use at the LGA level increasing from 64,980 megalitres to 69,440 megalitres.
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Table 6.1: Summary Statistics of Estimation Data Set in the Second Phase Mobility at LGA level Variables Description Variables Code Unit Maximum Minimum Mean Standard Deviation IN Migration Rate of 5 Year Mobility inrate5Y % 82.2 45.6 21.9 10.9 OUT Migration Rate of 5 Year Mobility outrate5Y % 53.6 5.3 23.0 7.9 NET Migration Rate of 5 Year Mobility netrate5Y % 63.5 75.3 1.1 9.0 IN Migration Rate of 5 Year Mobility (15–24 years) inrate5Y_1524 % 168.0 34.7 23.6 19.8 OUT Migration Rate of 5 Year Mobility (15–24 years) outrate5Y_1524 % 100.0 0.0 34.2 13.8 NET Migration Rate of 5 Year Mobility (15–24 years) netrate5Y_1524 % 115.2 100.0 10.6 23.5 IN Migration Rate of 5 Year Mobility (20–34 years) inrate5Y_2034 % 157.8 21.8 36.4 20.8 OUT Migration Rate of 5 Year Mobility (20–34 years) outrate5Y_2034 % 71.8 0.0 39.2 11.0 NET Migration Rate of 5 Year Mobility (20 44 years) netrate5Y_2034 % 150.0 56.0 2.7 19.8 IN Migration Rate of 5 Year Mobility (15–64 years) inrate5Y_1564 % 101.2 43.3 24.6 12.3 OUT Migration Rate of 5 Year Mobility (15–64 years) outrate5Y_1564 % 56.9 4.3 25.4 8.0 NET Migration Rate of 5 Year Mobility (15–64 years) netrate5Y_1564 % 80.8 74.4 0.8 10.3 Average Wage and Salary 2007–2009 Wage2 $ 107544 24689 39714.9 9307.2 Average Unincorporated Business Income 2007–2009 businc0709 $ 69249.0 5872.3 16074.8 9790.7 Average Investment Income 2007–2009 invinc0709 $ 54945.3 2005.7 7631.8 5668.6 Employment Growth Rates 2007–2008 empgrwork0708 % 24.4 4.0 6.4 3.4 Average Number of Business Entry 2007–2009 busentry0709 no. 18756.0 0.0 544.2 1233.2 Average Number of Business Exit 2007–2009 busexit0709 no. 17506.0 0.0 564.7 1203.5 Total Number of Business 2007–2009 totbus0709 no. 106908.3 0.0 3602.7 7220.5 Average Mortgage Payment (monthly) 2006–2011 mortgage $ 5366.0 0.0 1592.1 687.8 Gross Value of Agricultural Production 2007 2011 gvap06 $ (millions) 663.4 0.0 75.9 95.7 Approved Total Residential Building 2007–2009 dwelling no. 199344 0.0 7236 13331.3 Average Rainfall in 2006 rain06 mm. 5944.6 42.6 567.3 520.5 Average Rainfall in 2007–2009 raon0709 mm. 3784.4 83.6 693.7 461.1 Water Use 2007 2011 watuse_tot Megalitres 686289.0 0.0 26227.3 72551.4 Number of Bachelor degrees per Total Population 2011 educ % 29.3 0.2 7.8 5.2 Internet Access Level 2011 info % 100.0 0.0 65.9 12.2
194
Figure 6.2: Mean Economic, Social, and Environmental Values in MDB and Non-MDB/ROA Areas in the Second Phase Mobility at LGA level*
Income Types ($) Business Condition(number) 45000 4500 41523.7 4139.9 40000 4000 35332.6 MDB Non-MDB/ROA MDB Non-MDB/ROA 35000 3500 30000 3000 25000 2500 20000 17931.9 2000 1581.5 15000 1500 9071.4 7862.6 1000 10000 6761.5 644.1 665.5 500 5000 169.9 189.4 0 0 ave_wage0709 ave_businc0709 ave_invinc0709 busentry0709 busexit0709 totbus0709
Social Indicators Environmental Condition 100 1000 89.6 MDB Non-MDB/ROA 90 900 80 800 744.8 638.1 694.4 700 70 (mm) 600 (mm) 60 MDB 500.1 50 500 Non-MDB/ROA ML'00 40 400 no. 299.3 30 300 $'00 20.0 16.6 20 13.5 200 114.5 10 100 0 0 ave_mortgage dwelling rain06 rain0709 watuse_tot
Note: ROA (Rest of Australia) *Variable name in the bar charts refer to variables codes in Table 6.1.
195
6.3. Empirical Strategy
The empirical analysis of migration drivers during the second phase period applies several assumptions and conditions: i. To correspond with the assumptions in the previous analysis, first, the applied model in the estimation follows the concept of suspected endogenous regressors on the economic indicators of business income and GVAP. The endogeneity tests of Durbin-Wu- Hausman are implemented to opt for sufficient and reliable endogenous regressors, and thus the instrumental variable method ( IV ) or the 2SLS estimation is applied as a single endogenous variable with multiple instruments. Second, the assumptions also include the embedded costs of migration, zero effect of distance on internal migration analysis, and no adjustment for real value in terms of variables with a current price unit. Third, the indicator of business numbers comprises three parameters for different types of migration. The variable of total business numbers is applied for the net migration estimation, business entry for the in migration model, and business exit for the out migration model. ii. To minimize bias in the estimation, and for the regression to follow the econometric properties (section 5.5.2) for best approximation, the analysis has undertaken multicollinearity test to ensure that there are no substantial correlation coefficients between control variables and dependent variables. The matrix correlation (Table 6.2) shows the adjustment from the data set. The data limitation also creates a trade off between best approximation and data availability. Although correlation coefficients are significant, the variables are retained as it follows migration theories, and since the variation is unexplained, variables in the estimation model may provide better understanding in the migration activity. iii. Similar to the first phase analysis, some adjustments are applied in the control variables. The income variable is represented by the average of wage salary between 2007 and 2009. In general, the test shows no substantial number of collinearity, the highest coefficients are between the number of business and the percentage of bachelor degree and the number of business and the percentage of internet access (Table 6.2). iv. The estimation consistently does not include Canberra ACT as part of the MDB area, as this area has a different pattern of demographic composition as discussed earlier in Chapter 5.
196
Table 6.2. Collinearity between variables in the main structural model in the first phase analysis
Correlation# netrate5y lnwage2 lninvinc0709 lntotbus0709 lngvap06 lnbusinc0709 lnmortgage lndwelling educ info
netrate5y 1
lnwage2 0.0493 1
lninvinc0709 0.134** 0.0188* 1
lntotbus0709 0.1642** 0.2599** 0.0986** 1
lngvap06 0.0559 0.389** 0.0717* 0.1174 1
lnbusinc0709 0.0094 0.3445** 0.2431** 0.1839** 0.305** 1
lnmortgage 0.3687** 0.4742** 0.0689** 0.5637** 0.326** 0.2183** 1
lndwelling 0.0559 0.0619** 0.1199 0.4297** 0.0214 0.0449 0.1938** 1
educ 0.0544 0.4284** 0.0301** 0.6593** 0.390** 0.3177** 0.4858** 0.2808** 1
info 0.1481 0.4199** 0.1585** 0.6069** 0.2111* 0.4324** 0.5127** 0.2074** 0.5407** 1 #The range of correlation coefficient is between 1 and 1. Coefficient 1 is a perfect positive correlation and correlation 1 is a perfect negative correlation. When correlation coefficient is zero, the variables are said to be uncorrelated. **p value 1%, *p value 5%. Based on calculation of performing hypothesis tests on correlation.
197
v. In terms of area classification for comparison analysis, the composition of areas only comprises those areas in the MDB and those outside the MDB with agricultural production. The NRP series of 2007 2011 contains a variable that can be used to classify LGA as an irrigation area. Hence, the analysis includes a comparison of migration activity between irrigated lands within the MDB, and irrigated lands at the national level 69 . This approach follows other MDB studies as policy implementation has raised concerns regarding the sustainability of irrigation areas. vi. Although measuring the impact of policy often requires caveats and additional methodology (Baker, Bloom, and Davis 2013) 70 , for this analysis additional empirical estimation is conducted to capture the intensity of policy interventions in the Basin area by using an additional parameter in the main structural model. vii. As stated above, the environmental instrumental variables include the rainfall in 2006 as this was the lowest rainfall in the Millennium Drought period (Pink 2008), and also the average rainfall between 2007 and 2009 as the study attempts to include the rainfall effect in all years during the second phase period.
6.3.1. Effect on Income
The effect of income in different years after the drought event, in particular after the driest year in 2006, is also applied in the second phase’s estimation. To measure the nexus between migration and income differentials, a variable of personal income is utilised, which is the average wage and salary level in 2007 2009. The expected relationship of income to migration activities has a positive association with net migration and in migration, and a negative relationship with out migration.
However, as presented in Figure 6.3, there is a significant change between the first and second phase periods in terms of income pattern. By observing the plot figure between the first phase period and the second phase period, the fitted values of income in the second phase of migration are slightly flatter for in migration compared with positive fitted values in the first phase. The graphic of fitted values with the inclusion of standard deviation may also suggest that the association of income differentials with migration activity in this period is a weaker relationship
69 The classification of irrigated lands is based on data of from the National Regional Profile 2007 2011, where the areas use water from irrigation (water >0). The details are describe in Section 6.6. 70 In their analysis, to measure economic policy, an index of policy uncertainty was constructed to investigate the impact of policy in the recovery period of the recession in 2007–2008. 198 compare with previous income level in 2003 2004. The detail of elasticities between migration types and income will be described in the empirical results section (6.5).
Figure 6.3: Scatter Plot of Migration and Personal Income a. In-Migration ( y-axis) with Average Wage between 2007 and 2009 100 50 0 -50 10 10.5 11 11.5 lnwage2
95% CI Fitted values inrate5y b. Out-Migration (y-axis) with Average Wage between 2007 and 2009 0 -20 -40 -60 10 10.5 11 11.5 lnwage2
95% CI Fitted values outrate5y
Sources: Data Set Estimation, Census of Population and Housing 2011 and NRP Series 2007 2011
6.4. Estimation Model
The econometric method in the empirical estimation implements the same procedure as in the previous analysis. The simple OLS model follows the equation in (5.12) to examine whether migration can be elucidated with a simple regression analysis. Subsequently, the results suspect endogenous variables of business income and GVAP, and by validating endogenous regressors with endogeneity tests an instrument variable ( IV ) method is applied with the model in (5.13).
The assumptions and properties of the analysis follow the condition as stated in section 5.5.2 section, including conditions for the instrument variables where co variance is not zero with the endogenous regressor and with the error term in the main structural model. Additionally, post IV estimation will be validated with another Durbin-Wu-Hausman test as to whether or not the instruments are sufficient enough to explain migration activities. The configuration of estimation follows this procedure:
199 i. The OLS estimation Model
= + ( 2) + ( 0709) + ( )
+ ( 0709) + ( ) + ( )
+ ( ) + ( ) + ( ) + (6.1)
Where is the migration types of five year mobility, and from the model it can be seen that the structure of the model is comparable with the first phase OLS model. The difference is in the geographical standard of analysis as the current analysis applies the ASGS standard. ii. Post OLS regression, the model suspects that business income and GVAP during the second phase period can explain more about environmental migration, and by adding multiple instruments, the reduced form model becomes:
a. ( 79) = + ( 06) + ( 0709) + + b. ( ) = + ( 06 ) + ( 0709 ) + ( ) +
+ (6.2)
From the model above, similar instruments are applied for both endogenous regressors, which are the rainfall in 2006, average rainfall 2007–2009 and the employment growth rates in 2007– 2008. An additional instrument variable of water utilisation is added into the GVAP model to capture the issues of water management. The notion is similar to and comparable with the previous analysis, where the instrument models try to reflect environmental aspects and additional labour market effects on migration. iii. The endogeneity test is implemented for both suspected endogenous variables ( in order to select a credible endogenous regressor with a ( 79) , ( ) ) significant residual coefficient. Subsequently, the main structural model can apply the 2SLS/IV method to estimate a migration model with fitted values. The general IV model therefore, can be applied to an endogenous variable of GVAP:
200
= + ( 2) + ( 0709) + ( ) + ( )
+ ( ) + ( ) + ( ) + ( ) + (6.3) or an endogenous variable of business income:
= + ( 2) + ( 0709) + ( )
+ ( ) + ( ) + ( )
+ ( ) + ( ) + (6.4)
The selection of which of the suspected endogenous regressors will be applied to the IV estimation model will be shown in the regression result section.
6.5. Estimation Result
The first procedure of the regression analysis estimates the OLS model in (6.1). From 577 LGAs listed in the ASGS standard, the migration matrix from the Census of Population and Housing in 2011 has constructed 568 areas, including those LGA’s classified as unincorporated in a particular state or territory. The reduced number is due to the exclusion of areas defined as ‘No Usual Address’, ‘Not Stated’ and ‘Unknown’ (ABS 2011). As the estimation utilises the GVAP value (areas with agricultural production), the observation numbers are further reduced to 435 LGAs. The area classification for the MDB therefore is 110 LGAs and 325 LGAs for areas outside the MDB.
In the standard OLS model (Table 6.2), the result simply cannot reflect any environmental impacts as the theoretical framework defines no direct association between environmental variables and mobility patterns. Therefore, the result can only reflect the drivers of income differentials, social and development factors and economic performance. The expected relationship with net migration and in migration is a positive association, as people enter a specific area with the motivation of better life conditions. Meanwhile, the out migration model postulates a negative association with the drivers, as people are encouraged to leave affected areas.
The exception is for the variable lnmortgage , which has the expectation of an opposite coefficient (negative association) for net migration and in migration. In the full marginal effect,
201 it is expected that low mortgage payments will promote migrants to enter an area. The expected outcome also corresponds and is comparable with the variable of average house value in the first phase period, where migrants prefer an LGA with affordable housing or accommodation.
Table 6.3: OLS Estimation Result of Migration 2006–2009
MDB Other GVAP Areas Outside MDB 5 year mobility Variables Net In Out Net In Out Migration Migration Migration Migration Migration Migration
lnwage2 3.86 5.02 7.72** 3.61* 3.62 6.69*** (4.02) (4.70) (3.62) (2.05) (2.66) (1.83) lninvinc0709 1.14 1.655 0.34 1.11 2.81*** 3.60*** (1.55) (1.69) (1.31) (0.79) (1062) (0.72) lnbusnumb# 1.89** 3.86*** 2.13*** 2.54*** 5.06*** 3.30*** (0.87) (0.91) (0.75) (0.52) (1.06) (0.49) lngvap06 -0.66 -0.77* 0.004 0.13 -0.03 0.05 (0.43) (0.45) (0.35) (0.22) (0.28) (0.19) lnbusinc0709 -1.07* -0.75 -0.18 -2.79*** 0.32 -2.72*** (0.56) (0.61) (0.47) (0.83) (1.06) (0.73) lnmortgage 3.68 2.13 1.00 8.01*** 7.73*** 1.12 (2.69) (2.93) (2.27) (1.41) (1.81) (1.24) lndwelling 3.30*** 2.75*** 0.55 2.53*** 1.91*** 0.22 (0.59) (0.67) (0.55) (0.40) (0.56) (0.38) educ -0.11 0.59 0.68*** -0.39*** 0.41** -0.87*** (0.25) (0.28) (0.21) (0.15) (0.19) (0.13) info 0.28*** 0.15 0.11 0.13** 0.05 0.06 (0.09) (0.01) (0.08) (0.06) (0.08) (0.06) R2 0.61 0.50 0.51 0.37 0.23 0.54 All parentheses are robust standard error ***=1% significant level, **=5% significant level, *=10% significant level #busnumb consists of total business for net migration, business entry for in migration, and business exit for out migration.
The OLS result for the MDB suggests that migration drivers are limited in their ability to sufficiently explain five year mobility migration activities. The main drivers of income variable (lnwage2) in net migration appear to be insignificant, and are indeed opposite to what was expected, while the out migration result follows the postulation that a lower income promotes people to leave. Another income variable ( lninvinc0709 ) also shows a limited association, which is only the net migration result has positive relationship, but statistically insignificant.
The associations between the migration variables and other socio economic variables display a strong relationship compared with the first phase estimation. Business conditions ( lnbusnumb ) show a positive coefficient with the in migration model, suggesting areas with higher business
202 entry attract people to enter destination areas. Moreover, the parameter of approved residential building (lndwelling) emphasises that the development level in an LGA remains as a pull factor for people in the MDB at the drought time by showing significantly positive coefficients for net migration and in migration.
Following the framework, the key suspected endogenous regressors of GVAP and business income persistently show a contrary result, even though the coefficients for GVAP are significant for the net migration and in migration models. Hence, like the estimation in the first phase, the OLS regression for the main structural model seems to be ineffective in explaining the role of economic performance in migration activities, which subsequently suggests an endogeneity test by inserting multiple instruments in the suspected endogenous regressors.
6.5.1. Multiple Instruments Estimation and Endogeneity Test
The estimation of the endogenous model by applying multiple instruments suggests that rainfall continues to have a significant effect on agricultural production (Table 6.4a). The positive coefficient indicates that increasing rainfall will deliver better agricultural production or GVAP. Moreover, the GVAP model also displays a robust positive association between employment growth and water utilisation. In comparison with the previous instrument model, the positive impact of rainfall on agricultural production appears to be slightly lower (0.51 in first phase and 0.33 in second phase), yet in terms of the degree of relationship ( R2), the instrument estimation in the second phase period can explain a larger variance than in the first phase period (0.43 in the first phase and 0.61 in the second phase).
The other instrument variable of rainfall between 2007 and 2009 appears to be negative, which reflects that the inclusion of all rainfall years in the second phase may result in a different impact on agricultural production. The positive result of rainfall in 2006 confirms that in the driest year the value of water is higher than other years in the drought period, and it also confirm the expected association that rainfall has a positive effect for agricultural production. Other instrument variables of employment growth and water utilisation also display a significant positive association, as expected, with both instrument models, reflecting that increasing numbers of workers will improve production and more water use will increase agricultural production.
Meanwhile, the endogenous regressor of business income suggests that the model is not capable of assessing the relationship between environmental conditions and economic performance,
203 even though the instrument of employment growth has a higher impact on business income. The essential factor is that the model cannot display a sufficient degree of association, indicated by the small R2 (only 15 per cent of the relationship between business income and instrumental variables is explained by the observed numbers).
Table 6.4: Instrument Estimation and Validation of Endogenous Variables a. Comparison of Endogenous Regressors
Business Instruments GVAP Income
lnrain06 0.33** 0.36*** (0.14) (0.08) lnrain 0709 0.54*** 0.38*** (0.16) (0.09) employgrwt 0.04** 0.06*** (0.02) (0.01) lnwatuse 0.57*** (0.02) R2 0.61 0.15 ***=1% significant level, **=5% significant level, *=10% significant level
b. Residual Comparison of Endogeneity Test
MDB Other GVAP Areas Outside MDB Residual (p-value) Net In Out Net In Out Migration Migration Migration Migration Migration Migration
GVAP 2.42*** 0.06 2.43*** 1.53** 1.79** 0.37*** (0.79) (0.83) (0.81) (0.61) (0.84) (0.05) R2 0.62 0.40 0.55 0.37 0.25 0.66
Business Income 3.46 1.14 2.23 1.73 24.70*** 22.66*** (2.63) (3.31) (2.08) (4.15) (5.78) (4.32) R2 0.60 0.38 0.45 0.30 0.28 0.42 All parentheses are robust standard error ***=1% significant level, **=5% significant level, *=10% significant level
Moreover, the endogeneity test to measure the effectiveness of the instruments for all three migration types in both the GVAP model and the business model emphasises that the regressor of GVAP is more efficient in capturing environmental migration. The estimation of both endogenous regressors to all explanatory variables and instruments to obtain a residual with a
204 significant p value suggests that the business income model only contains one valid model for out migration in the MDB (Table 6.4b).
Conversely, the estimation for the MDB area also results in two valid models that implement the instrumented variables of GVAP, which are net migration and out migration. Hence, the endogenous variable of business income does not reflect well the environmental aspects in the main structural model.
Therefore, in the procedure of validating suspected endogenous regressors in the second phase period, the migration analysis continues to apply GVAP as a valid instrument variable to describe the role of environmental aspects in the migration activity in the MDB area.
6.5.2. Outcome for General Migration
Based on the result of the test above, the 2SLS/IV estimation for the general migration model is capable of examining relationships between the economic performance of agricultural production/ GVAP and migration activities. The regression only applies for those models that have valid instruments based on the Durbin-Wu-Hausman test. Therefore, the estimation for the MDB area comprises the net migration model and the out migration model, and for the comparative areas with agricultural output outside the Basin, the valid models are the net migration model and the in migration model (Table 6.5).
= + ( 2) + ( 0709) + ( )
+ ( 0709) + ( ) + ( )
+ ( ) + ( ) + ( ) + (6.5)
The results for general migration in the Basin area show that the relationship between the personal income variable and migration has a strong relationship with out migration activity. The significant negative coefficient of income (lnwage2) in the out migration model reflect how a drop of wage and salary in the Basin area encourages people to leave. The robust result from the out migration model confirms that income factors remain a key determinant on the decision to migrate. This key finding also confirms fundamental migration theories (Hicks 1932; Becker 1952; Greenwood 1975).
The tabulation from the second phase estimation data (Table 6.6a) suggests that among the LGAs with highest average wages and salaries in 2007 2009, and with the exclusion of LGA
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Queanbeyan and Cobar as discussed in Chapter 5, most of them have positive net migration rates and are classified as urban areas or small cities, such as Yass Valley, Orange, Bathurst and Mitchell. In the remoteness classification, almost all LGAs with highest income levels are located in the inner regional zone of Australia, and only one in another regional zone, which is Broken Hill.
Table 6.5: 2SLS (IV estimation) Result of Migration 2006–2009
Other GVAP Areas 5 year mobility MDB Variables Outside MDB
Net Out Net In Out
Migration Migration Migration Migration Migration
lnwage2 8.92* 16.74*** 6.40*** 5.80* 10.15*** (5.29) (2.65) (2.43) (3.28) (2.15) lninvinc0709 1.64 1.17 0.85 3.09** 2.22*** (1.40) (1.80) (0.96) (1.37) (0.82) lngvaph ( fitted ) -1.11*** -0.88** 1.39*** 0.71 0.29 (0.31) (0.33) (0.41) (0.48) (0.35) lnbusnumb# 4.13** 1.68 7.31*** 8.18*** 2.44* (1.86) (2.02) (1.97) (1.75) (1.41) lnmortgage 0.73 1.04 7.37*** 6.61** 0.92 (3.06) (1.51) (3.40) (3.13) (1.92) lndwelling 3.37*** 1.64*** 1.72* 1.44* 0.46 (0.77) (0.53) (1.00) (0.86) (0.48) educ 1.56 2.23 4.76*** 1.81 0.41 (1.50) (1.74) (1.80) (2.75) (0.48) info 0.17*** 0.15 0.00 0.10 0.09 (0.08) (0.10) (0.89) (0.10) (0.06) R2 0.63 0.54 0.37 0.25 0.52 Durbin-Wu-Hausman Test 0.17 0.00 0.1 0.03 -11.26 Prob> chi 2 valid valid valid valid fail Instrument Validation (weak) All parentheses are robust standard error ***=1% significant level, **=5% significant level, *=10% significant level #busnumb consists of total business for net migration, business entry for in migration, and business exit for out migration. Conversely, most of the LGAs with the lowest average wage and salary continue to experience negative net migration rates, classified as towns or small urban areas and located in outer regional Australia such as Buloke, Goyder, Yarriambiack and Loddon (Table 6.6b)
Meanwhile, the development indicators that influence migrants in the MDB yield some interesting findings. The relationship between migration and the monthly average mortgage payment ( lnmortgage ) at LGA level appears to follow Lee’s migration theory (1966) of push pull factors. The positive association between mortgage payment and out migration suggests
206 that people in the Basin were leaving the affected area in the Basin as the level of mortgage increased. The finding also confirms a study by Bartel (1979) of the effect of asset accumulation and management in migration, as discussed in Chapter 2.
Table 6.6: Migration Rates with Average Wage and Salary in the MDB 2006-2009 a. LGAs with Highest Average Wage and Salary
Ne t. Net. Ne t. Net. Av. Wage Av. Wa ge P o p. Are a AR IA Migra tio n Migratio n Migra tio n No LGAs State Migratio n a nd s a lary and s ala ry Number C la s s ific atio n C las s ifica tio n Rate (15 24 Rate (20 34 Rate (15 64 Rate 2007 2008 2009 yea rs ) ye ars ) yea rs ) 1 Queanbeyan (C) NSW 37994 Urban/Small City Major Cities 0.62 3.96 7.70 0.89 48854 52621
2Cobar (A) NSW 4713Town Remote 9.87 11.99 8.97 9.47 46456 51630
3 Palerang (A) NSW 14351Urban/Small City Inner Regional 9.28 14.71 0.96 9.16 45148 48582
4 Yas s Va lley (A) NSW 15020 Urba n/Sm a ll City Inne r R egio nal 8.05 11.35 4.58 8.69 42677 46581
5 Orange (C) NSW 38056 Urban/Small City Inner Regional 2.42 1.11 4.20 2.47 40241 43219
6 Bathurst Regional (A) NSW 38517 Urban/Small City Inner Regional 5.10 16.39 1.09 4.43 39777 41789
7Oberon (A) NSW 5041Small Urban Inner Regional 3.85 21.81 18.28 3.89 39775 42266
8 Broken Hill (C) NSW 18519 Urban/Small City Outer Regio nal 5.57 12.71 6.87 5.94 38947 41517
9Blayney (A) NSW 6985Small Urban Inner Regional 0.98 18.16 7.98 2.10 38421 41862 10 Mitchell (S) VIC 34637 Urban/Small City Inner Regional 5.32 4.69 3.81 5.31 38241 40573 b. LGAs with Lowest Average Wage and Salary
Ne t. Net. Ne t. Net. Av. Wage Av. Wa ge P o p. Are a AR IA Migra tio n Migratio n Migra tio n No LGAs State Migratio n a nd s a lary and s ala ry Number C la s s ific atio n C las s ifica tio n Rate (15 24 Rate (20 34 Rate (15 64 Rate 2007 2008 2009 yea rs ) ye ars ) yea rs )
1 Tenterfield (A) NSW 6809 Small Urban Outer Regional 1.13 24.59 14.32 0.38 29702 31898
2 Gannawarra (S) VIC 10366 Urban/Small City Outer Regiona l 6.15 30.35 23.82 9.22 29536 31806
3 Weddin (A) NSW 3665Town Outer Regional 3.87 33.99 20.56 5.64 28799 31006
4 Loddon (S) VIC 7460Small Urban Outer Regional 5.76 37.51 28.13 6.92 28587 30848
5 Goyder (DC) SA 4163Town Outer Regional 4.82 23.90 19.65 5.81 28341 31962
6 Yarriambiack (S) VIC 7090 Small Urban Outer Regional 6.61 30.54 26.19 8.73 28135 30052
7 The Coorong (DC) SA 5523 Small Urban Outer Regional 5.44 23.38 15.14 6.55 27601 30056
8 Buloke (S) VIC 6383Small Urban Outer Regional 5.99 33.18 24.42 7.60 27380 30096
9 Karoonda East Murray (DC)SA 1033Town Outer Regional 12.94 48.34 31.50 14.04 26701 29966
10 Southern Mallee (DC) SA 2101Town Remote 6.46 33.85 11.22 8.04 26313 29674
Sources : Dataset Second Phase estimation. Census of Population and Housing 2011 and NRP
Moreover, the significantly positive coefficient of approved total dwelling numbers (lndwelling ) in the net migration model can be connected with the finding for the mortgage variable in the out migration model. It suggests that migrants in the MDB during the second drought phase prefer to move to a growing LGA with affordable housing costs. The strong relationship between migration and total approved dwelling number that represent development in the area is also found in the net migration model and in migration model outside the MDB area. This finding corresponds with Garnett and Lewis’s (2007) argument discussed in Chapter 2 that the development level and the structure of the economy contribute to the rural urban migration in Australia.
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Table 6.7: Migration Rates with Average Monthly Mortgage Payment and Total Approved Dwelling Number in the MDB 2006-2009
Net. Net. Net. To tal. Av. Net. P o p. Area ARIA Migratio n Migratio n Migratio n Appro ved Mo nthly No LGAs State Migratio n Number Clas s ificatio n Clas s ificatio n Rate (15 Rate (20 Rate (15 Dwelling Mo rtgage Rate 24 years ) 34 years ) 64 years ) Number ($ )
1 To o wo o mba (R) QLD 151189 Cities Inner Regio nal 1.18 3.58 5.35 0.12 230 1638
2 Greater Bendigo (C) VIC 100617 Cities Inner Regional 4.04 4.50 1.52 3.72 165 1404
3 Alexandrina (DC) SA 23699 Urban/Small City Inner Regio nal 12.86 5.31 1.99 12.27 91 1533
4 Greater Shepparton (C) VIC 60448 Cities Inner Regional 2.00 11.18 8.67 3.31 90 1484
5 Wagga Wagga (C) NSW 59459Cities Inner Regional 0.19 13.21 0.47 0.31 86 1680
6 Bathurst Regional (A) NSW 38517 Urban/Small City Inner Regio nal 5.10 16.39 1.09 4.43 85 1740
7 Tamworth Regional (A) NSW 56291Cities Inner Regional 1.35 8.50 3.35 0.51 81 1582
8 Albury (C) NSW 47808 Urban/Small CityInner Regional 0.20 4.52 2.32 0.11 68 1567
9 Mildura (RC) VIC 50979Cities Outer Regional 2.17 13.64 9.15 3.46 67 1346
10 Wodonga (RC) VIC 35518 Urban/Small City Inner Regional 2.47 8.15 2.76 2.26 63 1553
Sources : Dataset Second Phase estimation. Census of Population and Housing 2011 and NRP
The tabulation data also supports the view (Table 6.7), where several LGAs in the Basin with the highest number of total approved dwelling numbers have experienced positive net migration rates, and the average mortgage payment are still around the average in the MDB ($1,347/monthly), that migrants prefer an area with better development and affordable accommodation. Most of these LGAs are categorised as cities/small cities and located in the inner regional part of Australia. These LGAs include Toowoomba, Bendigo, Alexandria, Bathurst, Tamworth and Albury. Only two LGAs in the top list experienced negative net migration, which are Mildura and Shepparton.
Another variable, educational level, has very limited explanatory power for explaining migration. However, the parameter of internet services, especially in the net migration estimation, has a significant effect for migrants in the MDB moving into specific LGAs. b. LGAs with Highest Internet Access Level In terms of endogenous variables, the IV estimation results in a robust model, and may emphasise the role of environmental factors in agricultural production, and how it effects migration. The out migration model indicates that there is a substantial negative relationship between GVAP and out migration rates in the MDB area, indicated by the significant coefficient variable of agricultural production ( lngvaph ). This implies that a one per cent drop in agricultural production, one of the causes of which is a decrease in rainfall, will increase the out migration rate by 0.88 per cent. The result is also relatively credible, with the degree of association above 50 per cent ( R2 of 0.54), and after the application of another Durbin-Wu- Hausman test of instrument validity, the model shows that it is a valid and strong instrument
208 for the 2SLS/IV estimation. This important finding confirms the observations of the key literature drawn on for this thesis concerning environmental factors and migration (Renaud et al . 2011; Reuveny and Moore 2009; Black et al. 2012; Marchiori et al. 2012).
In considering the previous analysis of migration in the first phase period, the outcome for the second phase period shows that the model of out migration with a fitted value of GVAP also indicates that environmental factors, represented by rainfall, have contributed to migration in the MDB during the latter part of the Millennium Drought period.
The findings also suggest that there is a slightly declining impact of the environmental factor between the first phase period and second phase period. The similar configuration of the econometric model shows that the coefficient of lngvaph (fitted) decreases from 1.46 (2001– 2006) to 0.88 (2006–2011), and in the context of elasticities, the impact of declining GVAP (and also the environmental factors) on migration is less in the second phase period.
The tabulation from the second phase dataset shows that most LGAs with the highest agricultural production value have rainfall levels below the average of MDB during the Millennium Drought period (473mm). Table 6.8 shows that several agricultural centres experience negative net migration rates with low rainfall levels such as Mildura, Moree Plains, Swan Hill, Carrathool and Buloke. This figure also corresponds with instrumental estimation that rainfall in 2006, which was the lowest level during the drought period, has a positive association with GVAP, suggesting the influence of environmental factors on agricultural production.
Table 6.8: LGAs with Highest GVAP and its Rainfall level 2006 and 2007-2008 in the MDB Av. Net. Net. Net. Av. Net. GVAP Rainfall P o p. Area ARIA Migratio n Migratio n Migratio n Rainfall No LGAs State Migratio n ($ 2007 Number Clas s ificatio n Clas s ificatio n Rate (15 Rate (20 Rate (15 2006 Rate millio n) 2008 24 years ) 34 years ) 64 years ) (mm) (mm)
1 To o wo o mba (R) QLD 151189 Cities Inner Regio nal 1.18 3.58 5.35 0.12 663 480 615
2 Moree Plains (A) NSW 13428 Urban/Small City Outer Regio nal 9.24 10.59 3.01 8.19 533 277 542
3 Greater Shepparton (C) VIC 60448 Cities Inner Regional 2.00 11.18 8.67 3.31 487 212 431
4 Mildura (RC) VIC 50979 Cities Outer Regio nal 2.17 13.64 9.15 3.46 471 192 237
5 Campaspe (S) VIC 36364 Urban/Small City Inner Regional 2.05 16.38 14.15 3.61 450 189 375
6 Mo ira (S) VIC 28124 Urban/Small City Inner Regio nal 0.33 19.91 13.22 1.14 439 251 446
7 Go o ndiwindi (R) QLD 10628 Urban/Small City Outer Regio nal 4.45 19.84 6.43 5.39 368 465 623
8 Lo xto n Waikerie (DC) SA 11288 Urban/Small City Outer Regio nal 3.79 22.73 17.79 6.69 366 121 243
9 Griffith (C) NSW 24363 Urban/Small City Outer Regio nal 6.63 12.69 12.11 7.56 343 193 288
10 Swan Hill (RC) VIC 20448 Urban/Small City Outer Regio nal 5.28 15.88 10.57 6.39 343 175 308
Sources : Dataset Second Phase estimation. Census of Population and Housing 2011 and BoM
209
Following the trend in the MDB that the environmental aspect appears to have a smaller effect on GVAP in the second phase, and therefore lowers the marginal impact to migration, the estimation of net migration outside the Basin also shows a declining coefficient from 2.31 (2001–2006) to 1.10 (2006–2011). However, this result may not necessarily suggest that environmental migration outside the MDB follows the trend inside the MDB area. A post estimation of instrument validity test shows that the model is weak, and thus can only be used with caution to explain the relationship between migration and its parameters.
The lowering impact of the migration drivers, including the endogenous regressor of GVAP, may indicate the possibility of an increasing resilience level among people and communities in the MDB, although this indication would require a further empirical study and evidence to be tested.
6.5.3. Migration Patterns by Age Groups in the MDB and Outside the MDB
The analysis of migration by age groups in the MDB area continues to follow the procedure of the valid 2SLS/IV estimation. First, the utilisation of multiple instruments must satisfy the condition of a significant p value of a residual for all migration models. Second, the post estimation must also be sufficient in the instrument validity test. If one of these procedures does not satisfy, the model simply cannot be applied and no relationship can be explained from the regression. Therefore, drawing on technical econometrics (Verbeek 2008; Gujarati 2011), a simple multivariate OLS method is more efficient than the IV method.
Based on that procedure for the estimation of age groups in the MDB, the results appear to be insufficient for credible IV models. The total of nine models (three age groups and three migration types) resulted in only two models (Table 6.9) with a reliable endogenous regressor (net migration and out migration for the 15–64 years age group). Moreover, a further instrument validity test has shown that only one of the estimations can reflect migration activity. However, the only valid IV model of out migration indicates that, in the second phase period, an environmental migration framework is also not suitable for migration by age groups. Furthermore, for the selected endogenous regressor of GVAP fitted value, the significant outcome seems to be opposite to the expected result, in particular the personal income variable. Therefore, the model is unreliable not only because the instruments are weak to explain the contribution of the environmental aspect in migration activity, but also because the coefficient displays a contrary association between economic performance and migration rates.
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In response to the results of the 2SLS/IV estimation for age groups in the MDB, a standard multivariate OLS is applied for all age groups. However, as a consequence of using the OLS method, as it discussed in Chapter 5, the result cannot reflect environmental migration for the young age group, young workers group, and working age group.
Table 6.9: 2SLS ( IV estimation) Result of Migration in the MDB by Age Groups (2006–2009) MDB 5 year mobility Age Group 15–64 (Working Age) Variables Net Out Migration Migration
lnwage2 3.22 12.02*** (5.80) (2.74) lninvinc0709 2.26 1.08 (1.56) (1.81) lngvaph ( fitted ) -0.98*** 0.63* (0.35) (0.36) lnbusnumb 4.91**** 1.87 (1.99) (2.08) lnmortgage 1.77 0.62 (3.19) (1.48) lndwelling 2.97*** 1.28** (0.86) (0.59) educ 2.64 2.76 (1.701) (1.79) info 0.13 0.16 (0.09) (0.10) R2 0.58 0.50 Endogeneity Test Residual p 1.83* 1.66** Durbin-Wu-Hausman Test -10.1 0.04 Prob> chi 2 Instrument Validity # fail valid All parentheses are robust standard error. ***=1% significant level, **=5% significant level, *=10% significant level #Although the instrumental variable of GVAP is sufficient based on endogeneity test, a further Durbin-Wu-Hausman Test for the instrument validity displays that endogenous regressor is weak and thus the estimation is not valid for analysis.
The result of the OLS estimation for age groups within the MDB area nevertheless shows some interesting findings (Table 6.10). Five year mobility for the young age group of 15–24 years appears to be responsive to economic indicators that may influence this group to migrate. The significant positive coefficients (net migration and in migration) of investment income in 2007 2009 ( lninvinc0709 ) reflect that young people migrate to an LGA with better opportunities for doing business a year after the lowest level of the drought in 2006, suggesting their
211 responsiveness to changes. The association between migration and other forms of income, such as investment income, confirms migration studies discussed in chapter 2 (Sjaastad 1962; Harris and Todaro 1970). On the other hand, the young workers of 20 34 years of age show a strong association between migration and personal income (lnwage2) , indicated by significant positive coefficients in the net migration and in migration models. It reflects that, for young workers aged 20 34 years, the factor of salary and income level is a key determinant in the decision to migrate. Lee (1966) emphasises this as a key pull factor from destination
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Table 6.10: OLS Estimation for Young Age Groups and Working Age Group Migration in the MDB
MDB MDB MDB 5 year mobility Age 15–24 Age 20–34 Age 15–64 (young people) (young working age) (working age) Variables Net In Out Net In Out Net In Out Migration Migration Migration Migration Migration Migration Migration Migration Migration
lnwage2 6.88 9.43 3.99 33.33*** 28.70*** 7.31 0.72 8.64 5.58 (18.43) (11.43) (6.01) (10.99) (7.65) (5.78) (5.14) (5.71) (4.73) lninvinc0709 8.23* 7.16** 0.69 8.55*** 7.72** 0.93 2.45 0.63 0.63 (4.48) (3.16) (2.73) (3.27) (3.02) (2.59) (1.89) (1.89) (1.89) lnbusnumb 10.21 2.72 5.07 4.12 4.48 0.73 3.60** 2.82 0.28 (7.12) (4.43) (3.08) (4.85) (4.00) (2.80) (1.77) (2.35) (2.44) lngvap06 2.98* 2.17* 1.34** 0.64 0.55 0.29 0.44 1.24** 0.63 (1.63) (1.24) (0.58) (1.10) (0.84) (0.53) (0.52) (0.54) (0.41) lnbusinc0709 1.25 0.05 1.42* 1.18 1.60 0.58 1.05* 0.96 0.01 (1.42) (1.09) (0.72) (1.29) (1.16) (0.76) (0.53) (0.64) (0.45) lnmortgage 1.78 1.98 2.87 1.80 3.38 1.91 4.61 3.18 1.37 (7.38) (5.23) (3.74) (5.72) (4.77) (3.86) (3.37) (3.92) (2.13) lndwelling 4.74** 2.50* 2.76** 2.11 1.36 0.92 2.82*** 2.61*** 0.37 (1.88) (1.33) (1.18) (1.45) (1.26) (1.18) (0.83) (0.97) (0.65) educ 9.23 1.15 6.19* 3.89 1.22 2.48 1.95 0.55 2.17 (5.60) (3.50) (2.48) (4.15) (3.39) (2.58) (1.62) (1.85) (1.97) info 0.43* 0.08 0.53** 0.19 0.37** 0.60*** 0.21** 0.25** 0.06 (0.23) (0.17) (0.15) (0.23) (0.17) (0.19) (0.09) (0.11) (0.09) R2 0.57 0.34 0.66 0.45 0.43 0.42 0.56 0.43 0.46 All parentheses are robust standard error ***=1%level, **=5%level, *=10%level #busnumb consists of total business for net migration, business entry for in migration, and business exit for out migration.
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Table 6.11: OLS Estimation for Young Age Groups and Working Age Group Migration in the OUTSIDE MDB area with Agricultural Production
MDB MDB MDB 5 year mobility Age 15–24 Age 20–34 Age 15–64 (young people) (young working age) (working age) Variables Net In Out Net In Out Net In Out Migration Migration Migration Migration Migration Migration Migration Migration Migration
lnwage2 0.84 6.76 2.35 1.80 10.81 3.06 5.61** 4.30 7.28*** (6.53) (5.62) (2.86) (6.40) (7.46) (2.87) (2.80) (3.82) (2.21) lninvinc0709 1.25 2.47 2.97*** 6.74*** 3.65 4.98*** 0.20 1.58 0.23 (2.30) (2.76) (0.86) (2.24) (2.82) (1.12) (0.82) (1.44) (0.89) lnbusnumb 1.36 6.12 0.76 9.64* 6.08 1.40 7.61*** 3.72 0.25 (7.76) (7.77) (2.08) (5.750) (6.46) (2.30) (2.32) (3.64) (1.94) lngvap06 2.11** 2.47** 0.16 1.28** 0.87 1.36*** 0.92** 0.66* 1.09*** (1.00) (0.91) (0.27) (0.54) (0.55) (0.33) (0.26) (01.37) (0.26) lnbusinc0709 0.84 0.99 3.27*** 2.63 1.00 1.42 2.41** 0.42 2.62*** (2.55) (2.21) (1.19) (2.20) (2.46) (1.19) (1.03) (1.37) (0.89) lnmortgage 6.31 2.33 4.66* 13.18 15.10* 0.33 8.38** 10.10* 0.59 (5.04) (4.66) (2.71) (8.75) (8.27) (2.81) (3.83) (3.93) (1.98) lndwelling 0.28 0.07 0.18 0.12 0.17 0.28 1.90* 1.22 0.88* (1.37) (1.03) (0.57) (2.41) (2.12) (0.62) (1.15) (1.05) (0.46) educ 5.39 5.01 6.06*** 5.88 0.98 0.14 4.29** 0.60 1.78
(6.32) (6.52) (1.72) (4.68) (5.42) (1.96) (2.07) (3.10) (1.63) info 0.52*** 0.31* 0.27*** 0.27* 0.28 0.085 0.05 0.06 0.03 (0.18) (0.18) (0.10) (0.16) (0.20) (0.12) (0.08) (0.12) (0.08) R2 0.47 0.12 0.75 0.17 0.16 0.27 0.31 0.21 0.45 All parentheses are robust standard error ***=1%level, **=5%level, *=10%level #busnumb consists of total business for net migration, business entry for in migration, and business exit for out migration.
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Development indicator of total approved residential building (lndwelling ) also play a substantial role in the age groups mobility patterns. The significant results from net migration and in migration models in the young age group of 15 24 years as well as in the working age group of 15 64 years indicate that the increasing development of new residential building in a particular LGA in the MDB attract people to enter the area. The results once more confirm previous studies discussed in chapter 2, in particular study by Argent et al. (2013) of economic development and migration.
Moreover, the strong positive relationship between total residential buildings with net migration and in migration rates for all age groups suggest that people in the Basin are looking for a prospective LGA that is developing more than their origin area. Several urban centre and cities reflect these findings that well developed LGAs with affordable housing experienced positive net migration for all age groups during the second phase period such as Bathurst, Wagga Wagga, Bendigo and Wodonga (Table 6.7).
Another socio economic indicator of the proportion of people with internet access (info ) appears to have significant results in several models of migration by age groups. . The factor of access to internet at the LGA level in the MDB seems play as a push factor in out migration activity in the young age group of 15 24 years, while it becomes a pull factor for the age groups of young workers (20 23 years) and general working age (15 64 years). The findings relate to study by Greenwood (1970) that provision of facilities influence the decision to migrate.
On the other hand, comparing the migration pattern of MDB areas with areas outside the MDB a several similar pattern emerges. The approach to detect whether environmental migration occurred outside the MDB areas appears to have limited findings. The first essential step of the Durbin-Wu-Hausman test for a suspected endogenous regressor, in particular the variable of GVAP, has resulted in two reliable IV estimation models, which are the net migration model for the young age group 15–24 years, and the net migration model for the working age group 15–64 years.
Subsequently, the instrument validity test comes up with net migration of the young age group 15–24 years as the only model that satisfied all components of the procedure. However, the coefficient is unable to establish the contribution of environmental factors to the mobility pattern, as indicated by the insignificant level of the fitted variable of GVAP. However, several parameters do display an important relationship with young people’s mobility. The variables of
215 educational level, the proportion of household that has internet access, and development indicators show a strong positive relationship with the young age group’s net migration rates.
Hence, since the only credible IV model cannot elucidate environmental migration, a similar approach is applied to describe migration for age groups outside the MDB area by applying the OLS method. From the estimation, several findings correspond with the mobility pattern in the MDB (Table 6.11).
Young people aged 15–24 years outside the MDB area are not as responsive as young people in the MDB to changes in personal income levels. The only model that is able to capture the effect of income to migration is out migration model, indicated by significant negative coefficient of investment income ( lninvinc0709 ). It reflects that young people outside the MDB leave the area as a result of dropping income from investment.
Economic activity represented by the average business income in 2007 2009 appears to be a strong driver for young people (15 24 year), indicated by the out migration model. It suggests that decreasing business income, in particular in LGAs outside the MDB, becomes one of the push factors for young people to leave a particular area. Furthermore, young age group outside MDB area also consider the factor of mortgage as show in the significant coefficient in out migration model. The positive association with out migration model reflects that the higher level of mortgage level in a particular LGA outside MDB increases the out migration rates.
The mobility pattern for the young workers group aged 20–34 years and the working age group of 15–64 years outside the MDB displays a similar pattern with the Basin area of the income effect on migration. For these groups, the investment income in 2007 2009 and the average personal income level in 2007–2009 affects five year mobility, with income having a significant negative association with the activity of out migration. The effect of personal income in the working age (15 64 years) appears to have stronger impact in influencing people to leave the area.
A slight effect for the working age group outside the Basin from agricultural production can be seen in relation to net migration activity. From the 0.92 coefficient, the estimation implies that as GVAP increases by one percentage point net migration rates will intensify by almost one percentage point. The mobility of these working age groups can also be explained by the development indicators. The coefficient of the average total number of approved total
216 residential buildings (lndwelling ) shows that the growth of residential building in the destination area attracts people to enter.
Moreover, the indicator of business income also affects both age groups (15 24 years and 15 64 years) in out migration activity. The significant negative coefficients indicate that areas outside MDB with low business income influencing people to leave the area. The finding follows empirical studies by Newbold (2001) and Greenwood and Hunt (2003) of labour mobility. The significant positive association between the proportion of bachelor degree ( educ) and net migration in the working age group may reflect migrant’s preference of destination area with high educational attainment. Meanwhile, the negative coefficient of the internet access (info ) proportion in the population in the young age group may indicate young people’s preferences for better facility.
6.6. Migration in the Irrigation Area and the Impact of Policy
The factor of policy interventions and the concern about the sustainability of irrigated areas during the second phase of the Millennium Drought are also included in the extension of the analysis. The method to capture this additional aspect was to add an extra variable that represents policy involvement. However, many studies show that measuring policy impact requires a specific methodology and a complex framework (Baker, Bloom, and Davis 2013). Since the focus of this study is analysing migration, and following studies about the MDB during the drought period that found that water issues are a crucial factor for policy, the additional variable of water utilisation for agricultural activity was added to the main structural estimation model as a proxy for policy intervention. Data on water utilisation provided in the NRP series 2007 2011.
The rationale for this parameter is that many policy studies in the MDB focus on water management issues for both people and the environment in order to balance water use for people, the environment, and also agricultural production in the MDB (Quiggin 2006; Garrick et al. 2009; Crase 2010; Connel and Grafton 2011; Wittwer 2011). Thus maintaining water utilisation for agriculture is likely to sustain development in the area. In the context of the estimation result, the expected outcome should be a positive relationship with net migration and in migration rates, and a negative relationship with out migration.
A complementary area classification based on the LGA level is also constructed to detect irrigated lands in the MDB and at the national level. The classification refers to the NRP data
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2007 2011 of ‘ Water use on Australian Farms-Area Irrigated ’, which sets the benchmark of a minimum 1,000 hectares (ha) of irrigated areas to be identified as irrigated lands. Subsequently, the cross tabulation reveals that there are 88 LGAs in the MDB classified as irrigated land or almost 74 per cent of all 119 LGAs. At the national level, there are 227 irrigated LGAs or around 40 per cent from 568 LGAs based on the ASGS (Table 6.12).
The estimation for the analysis also comprises general assumptions and conditions that were also applied in the previous estimation. The framework follows the procedures used for the previous environmental migration analysis where agricultural production is a preferred endogenous regressor for the instrument variable of rainfall. The analysis also entails the endogeneity test and instrument validity test, with the context being the irrigated lands classification.
The structural model has been slightly modified from (6.3) by adding an extra explanatory variable of water utilisation for agriculture at LGA level ( watuse_agri), which is to represent policy intervention. Thus the model becomes
= + ( 2) + ( 0709) + ( )
+ ( ) + + ( )
+ ( ) + ( ) + ( ) + (6.5)
Table 6.12: Irrigated and Non-Irrigated area Classification by LGA Irrigated Non Total Irrigated LGA
MDB 88 31 119 % 38.8 9.1 21 Non MDB 139 310 449 % 61.2 91 79 Total LGA 227 341 568 Sources : National Regional Profile Dataset (2007 2011)
The first stage of the instrument variable test for the MDB area has not resulted in a credible model with the residual sufficient enough to conduct an IV method for environmental migration. The robust estimation of whether environmental migration occurs in the 88 irrigated LGAs in the MDB shows that GVAP is significantly negative, which is opposite to the expected result that increasing GVAP is expected to encourage people to enter an area. The extra explanatory
218 variable of water use in farms for agricultural activity does not show a significant effect in the second phase of the drought period.
In order to observe the policy impact on migration activity, the standard OLS estimations are applied for all migration models of irrigated lands in the MDB and irrigated lands nationwide 71 . With the addition of a variable that represents policy during the second phase, there is a minor adjustment to the OLS main structural model, where the variable of business income is excluded. The exclusion of this particular parameter does not change the theoretical framework, as business activity is still reflected with the variable of the number of business activities. The construction of the OLS model is:
= + ( 2) + ( 0709) + ( )
+ ( 06) + + ( )
+ ( ) + ( ) + ( ) + (6.6)
From the OLS regression (Table 6.13), several findings describe the relationship between socio economic drivers and the effect of policy intervention on migration activity. The points are listed below:
i. Personal income level of the LGAs in the irrigation area within the MDB in the year 2007–2009 had a significant negative association with out migration model. From the perspective of irrigated lands at the national level, the income level in 2007 2009 also showed a strong association with out migration, indicating income was an important driver for migration. ii. The effect of agricultural production (GVAP) on the mobility pattern for all migration categories seems to have significant associations for all irrigated lands, both in the MDB and at a national level. However, the significant negative coefficients in the net migration model and the in migration model are opposite from that expected of a positive relationship between increasing agricultural production in the irrigated land and attracting people to come to, not to leave, the area. This suggests other factors are influencing migration decisions.
71 The comparison areas, which are irrigated lands at the national level, were selected with the consideration of higher sample size (227 LGAs) than irrigated lands outside the MDB (139 LGAs). The higher sample is expected to have a better variance, and thus is reliable to compare with the irrigated lands in the MDB. 219
Table 6.13: OLS estimation for Irrigated lands in the MDB and National Level
Irrigated Land (LGAs) Irrigated Land (LGAs) in the MDB at National Level (Australia) 5 year mobility
Net In Out Net In Out Variables Migration Migration Migration Migration Migration Migration
lnwage2 10.38* 1.42 10.48*** 9.22*** 1.30 7.43*** (6.22) (5.60) (3.14) (1.85) (2.65) (1.79) lninvinc0709 2.03 2.61 0.38 0.14 2.13* 1.60* (1.67) (1.82) (1.82) (0.77) (1.19) (0.95) lngvap06 -1.46* -2.04** 0.52 -0.64* -1.38*** 0.46 (0.75) (0.68) (0.39) (0.37) (0.41) (0.28) lnwatuse_agri 0.27 1.49*** -1.12*** 0.23 1.19** -0.94*** (0.46) (0.46) (0.34) (0.22) (0.29) (0.23) lnbusnumb# 3.36 3.46 1.40 3.13** 0.91 1.27 (2.26) (2.51) (2.18) (1.30) (1.78) (0.95) lnmortgage 3.58 2.80 0.84 0.54 1.66 2.14 (2.97) (2.56) (1.86) (2.57) (3.17) (1.85) lndwelling 3.30*** 2.74*** 0.95 4.41*** 4.09*** 0.48 (0.84) (1.05) (0.65) (0.52) (0.64) (0.33) educ 1.75 0.62 2.34 0.76 4.54** 3.03*** (1.60) (1.75) (1.72) (1.07) (1.32) (0.88) info 0.27*** 0.33*** 0.10 0.36*** 0.62*** 0.24*** (0.08) (0.09) (0.10) (0.08) (0.10) (0.06) R2 0.65 0.47 0.57 0.66 0.56 0.54 All parentheses are robust standard error, ***=1%level, **=5%level, *=10%level #busnumb are total business for net migration, business entry for in migration, and business exit for out migration.
iii. The variable of water use by agricultural farms in irrigated lands (as a proxy for the policy impact on migration) displays a credible result for both area classifications of irrigated lands. In the Basin area, the impact of policy seems to be substantial, with a positive significant relationship for in migration activity. The marginal effect suggests that increasing water use by one percentage point in the irrigated lands would increase the in migration rate by 1.49 per cent, suggesting that policy implementation to support irrigation in particular areas becomes a pull factor for people to enter. Meanwhile, the effect is also significant for all irrigated land nationwide, with a coefficient of 1.19 in the in migration activity model, indicating that the policy impact is stronger in the MDB compared with all irrigated areas.
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iv. The additional economic driver of business numbers appears to be unreliable in explaining migration, as the association is contrary to the expected result. A possible explanation is that workers in areas with higher numbers of businesses exiting are able to find alternative employment through irrigated agriculture. v. In terms of the development indicator, the variable of the average total number of residential building approvals reflects development progress in the destination LGA which may play a crucial role in attracting people. The estimation shows that the coefficients of this parameter for the net migration and in migration models are statistically significant for both the MDB area and all irrigated lands. vi. The impacts of households that have internet access on migration activities displays a strong positive association in the net migration model and in migration model for both the irrigated lands classifications (MDB and National level), meaning that people consider areas that have good internet access are attractive areas to move to.
6.7. Conclusion
This chapter has continued the analysis of migration drivers during the second half of the Millennium Drought period. The time frame of the second phase period is utilised as the estimation implements five year mobility data from the Census of Population and Housing in 2011, and therefore the data can capture migration activity in the MDB for the rest of the drought period from 2006 to 2009. In this second phase period, there were two major factors that differentiated the conditions from the first phase period. First, the consecutive dry years, started in year 2006 as the lowest rainfall during the drought period, raised concerns from all the MDB’s stakeholders about balancing water allocation, in particular to maintain the sustainability of irrigated lands. Second, the prolonged drought period motivated the authorities to intervene more by introducing policies related to water management and development in the Basin area.
These two additional factors led to an extended analysis by adding the area classification of irrigated lands and an extra explanatory variable to represent policy impact. With a similar methodological approach and comparable structural econometric model, the estimation obtained several important findings. First, to address the main aim of the study as to whether environmental migration occurred in the MDB, the analysis of the 2SLS/IV estimation validates that there is a significant relationship between people’s mobility with the instrumented GVAP
221 variable, thus reflecting the impact of the environmental factor. From the out migration model of the general population at the LGA level, the coefficient suggests that a drop in agricultural production will increase out migration rates. Moreover, from the main estimation, it can also be seen that the influence of environmental factors on agricultural production is lower compared with the first phase period.
The fundamental driver of income differentials continues to have a consistent effect in the MDB, particularly in terms of net migration and in migration. However, the income effect in the second phase is less significant than in the first phase period. Development indicators also play important roles in the decision making process of migrants, particularly a number of residential buildings that have a positive and significant impact on the net migration and in migration in the destination areas. This implies that the destination areas may provide migrants with a sense that the area has good prospects for their living conditions and may offer other economic benefits.
In terms of migration analysis by age groups of young people (15–24 years), young workers (20–34 years), and working age (15–64 years), the structural model of environmental migration appears to be limited and cannot provide a strong and valid confirmation that environmental factors contributed to these groups’ mobility patterns. It should not be interpreted that the slow onset environmental shock of the drought did not play a role in migration decisions, but rather the large statistical variance in the migration driver variables meant that any such effect could not be detected. The standard multivariate OLS approach did elucidate socio economic drivers that influence these groups to enter or leave an area.
The additional observation of irrigated lands in the MDB and at the national level provides an additional perspective. The additional variable as a proxy of policy impact with the same specification model, the instrumental estimations are unable to find a relationship between agricultural production and environmental factors on migration activities in the MDB’s irrigated lands.
The standard OLS procedure reveals a consistent effect of policy on migration activities, even though the non instrumental variable of agricultural production displays an opposite result to the expected outcome. In comparing the effect of policy impact and irrigated lands in the MDB and at the national level, it can be seen that the marginal effect of policy has a larger impact on in migration to the irrigation areas within the MDB. This may suggest that the purpose of policy to address the issues in the MDB area also influences migration.
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Chapter 7: Conclusion and Discussion
7.1. Overview
The study’s aim to analyse migration activity and its drivers in the MDB during the Millennium Drought period has resulted in the development of a research framework of environmental migration, which is based on the evolution of migration studies. As stated in the literature review section, the thesis has enhanced the migration model to fill a gap in the migration literature. In order to correspond with the main research question, that is, whether environmental migration played a role during the drought period in the MDB, the enhanced model includes an environmental factor as one of the migration drivers. However, this does not determine migration directly as is the case in developing countries in Asia and Africa, which rely heavily on agricultural production. The case of the Murray Darling Basin (MDB) in Australia provides a new perspective on environmental migration, where the factor determines migration activities indirectly, and also provides an example for developed countries.
The MDB is well known as the centre of irrigated agricultural production in Australia, the migration model in this thesis examined rainfall data as a key variable for explaining variation in agricultural production during the Millennium Drought. The expectation of the enhanced environmental migration model is not only to obtain a better understanding of which socio economic factors determine migration activity in the Basin, but also whether environmental aspects play a significant role in this migration.
Following the fundamental migration model and the new classical approach of migration, the thesis began the analyses from the fundamental theory (the gravity model) of rural–urban migration or from periphery areas to urban centres (Chapter 4). The method applied involved selecting major areas in the MDB classified as cities with population size above 50,000 people. The objective was to investigate not only migration movement but also specific patterns related to area characteristics.
The next two chapters (Chapter 5 and Chapter 6) examine migration drivers in the MDB during the drought period by dividing the time frame into two periods, as the analyses apply the Census of Population and Housing in 2006 and in 2011. The migration data in both censuses is reported using different geographical standards and also different spatial units, and therefore for reasons of consistency the study examines the data in different estimation models but with similar model
223 specifications. Aside from socio economic variables obtained from regional profile data (NRP), the study needs to provide data that represents the environmental aspect. Hence, in order to obtain environmental data with natural variances, the study also developed a methodology to obtain rainfall data from reliable and credible weather stations across Australia during the drought period.
The section below summarises findings and contributions of the study, including the limited capacity of this thesis’s migration model. Moreover, the expectation of the study is that its finding to become an additional reference in the process of the Basin Plan’s implementation and also for Australia’s recent strategy for agricultural competitiveness.
7.2. Key Findings and Contributions
This study of migration in the MDB during the Millennium Drought (2001–2009) has resulted in two key findings. The first major finding is related to the migration pattern within the MDB area. The second key finding is based on the quantitative approach by applying econometrics estimation which incorporates a single endogenous regressor, taking into account some environmental variables. The model specification included economic factors, social and development factors, and the environmental aspect to analyse the marginal effect of drivers of the three migration types: net migration, in migration, and out migration
7.2.1. Migration Patterns in the MDB during the Millennium Drought Period
Following the fundamental theory of migration that people initially move internally within a country from rural areas to urban centres to achieve better living conditions (Ravenstein 1885; Hicks 1932; Sjaastad 1962; Lee 1966; Todaro 1969; Harris and Todaro 1970), this study applied this approach inside the MDB area. The analysis started with general overview of migration during the drought years, where, based on the LGA level, the MDB area experienced negative net migration rates.
The objective was to obtain a comprehensive picture of migration patterns, including by classifications, such as age groups, area classification and degree of remoteness. From the analysis, several findings are apparent:
i. The evidence of a migration pattern from the surrounding areas or peripheries to urban centres occurred in cities in the MDB as shown from six LGAs classified as cities in the MDB. (Toowoomba Regional City, Tamworth Regional City, Wagga Wagga City,
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Greater Bendigo, Greater Shepparton and Mildura Rural City). This finding corresponds with other migration studies in Australia (Bell et al. 2002: Garnett and Lewis 2007) ii. The migration pattern reflects the theoretical framework that increasing age will reduce the propensity of people to migrate (Bartel 1978). From the analysis it can be seen that the young age group between 15 and 24 years is the most actively mobile group, indicated by having the highest negative net migration rates. iii. Internal migration in the MDB also follows Lee’s model of push–pull migration (1966) where the more negative aspects in the origin will encourage more people to leave the area. This can be seen with the migration pattern of LGAs based on the remoteness index. Those areas classified as ‘remote’ or ‘very remote’, with a low provision of public amenities, display a negative net migration. Moreover, the combination of the small population of the LGAs and their location in remote areas is the main contributor of negative net migration in the MDB, which corresponds with the ABS report (2009).
7.2.2. Socio-Economic Drivers
The new classical model of migration asserts that the migration decision is based on positive net expected benefit as the migrant is a rational economic agent (Greenwood 1975). Therefore, income differentials become a main driver for people to migrate. However, the evolution of migration studies also highlights that income is not the sole substantial aspect or driver for people to move. The inclusion of other social factors in many empirical cases shows that migration activity can be described in a more comprehensive manner and such an approach can better explain people’s behaviour.
The analysis of migration in the MDB during the drought period, both in the first phase (2001– 2006) and the second phase (2006–2009), also included social factors that represent the level of development of an area and also the provision of public facilities. Some key findings are listed below from the estimation:
i. In the context of personal income, income differentials remain an essential driver of migration in the MDB. In the first phase period of analysis, the income effect seems to be a more important driver than the income effect in the second phase period. The year after the driest year (2002) in the first phase, the impact of the variable of income is significantly positively related on the net migration, reflecting an immediate response from people. However, in the out migration model, the effect of income takes into account two years after the driest event.
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ii. The effect of the income differential remains significant in the second phase of the drought, but slightly lower in terms of regression result. This could possibly indicate that in the second phase period, people in the MDB had a better resilience level that made them less likely to migrate as the most vulnerable people had already left the Basin in the first phase. iii. The socio economic variable that represent Lee’s theory (1966) of pull push factors of migration, indicated by the average value of private houses in the first phase (2001 2006) and the average mortgage payment in the second phase (2006 2009), shows mixed result with the affordable housing and accommodation in the destination is only significant in the first phase as a substantial factor in attracting people in the MDB to enter areas (LGAs). iv. Another socio economic indicator that represents the level of development in the area (building approvals of dwelling units (2001–2006), and approved residential buildings (2006–2009)) indicates some significant associations with the pattern of migration. The coefficient shows positive values for net migration in the second phase and out migration activity in the first phase, reflecting the rationale that migrants move to areas with better prospects, where the level of development in the destination is higher than in the origin. v. In terms of educational level, even though most of the estimation models show insignificant coefficients, the value follows the expected outcome that the level of education level in an LGA affects the propensity of people to migrate. The finding follows the new classical approach of migration that migrants consider better educational attainment in the destination than the origin in their migration activity (Schultz 1961; Becker 1962). vi. The variable of public facility of household proportion that has internet access shows a relatively significant association with some migration models. Many of the estimation models in both the first phase and the second phase period of analysis display a strong relationship between internet access and migration, which aligns with the literature (DaVanzo 1981; Schlottmann 1981). In this study level of internet access maybe a summary measure capturing both the nature of the physical location of the LGAs as well as their degree of urbanization.
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7.2.3. The Role of the Environmental Variables on Migration in the MDB
The thesis follows the recent theoretical approach of the inclusion of socio economic and environmental factors in the study of human migration (Black et al. 2011). The enhanced model is based on the study by Marchiori et al. (2012) where weather anomalies contribute to migration activities. However, the empirical estimation of that study was applied in developing countries. This thesis develops the theory by implementing the concept, and in particular in the case of the MDB, which is in a developed country. Furthermore, the model of migration decisions extends the study by Renaud et al. (2011) and Reuveny and Moore (2009) by treating the potential environmental degradation from the drought as a Slow Onset process.
In the specification of the econometric model, the environmental factor of rainfall is formed as an instrumental variable for agricultural production (GVAP). The construction of the rainfall data is considered a major contribution of this thesis in terms of dataset development. The collection of rainfall data by selecting reliable weather stations from thousands of stations is a relatively complex process aimed at obtaining a credible figure which satisfies the natural variance of the data.
The expected outcome was that rainfall will have a positive relationship with agricultural production, and subsequently the changes in the agricultural production will influence people in their migration decision. From the empirical estimations, several findings are noteworthy:
i. From the 2SLS /IV estimation, the first stage regression showed that the rainfall data had a significant positive association with agricultural production in both the first phase and second phase period, in particular in the years that rainfall was at the lowest level (year 2002 and 2006). ii. In terms of the marginal effect, the rainfall impact on agricultural production in the first phase is higher than in the second phase period (0.51 compared to 0.33). iii. The estimation of the second stage of the 2SLS/IV model applies an instrument validity test ( Durbin-Wu-Hausman test ) to check whether the method is sufficient enough to explain environmental migration in the MDB. For the three types of migration activity, the out migration model in both the first phase and the second phase period shows a significant relationship between agricultural production and migration, which also indicates a valid instrumental variable. The negative coefficient reflects that areas with lower agricultural production had higher rates of people leaving. Like the marginal effect in the instrumental model, the coefficient for the first phase period is larger than
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the second phase period ( 1.43 to 0.88), which may again indicate the increasing resilience of people in the Basin. This result highlights the key finding of this study, in that it confirms the contribution of environmental factors to the migration patterns in the MDB during the Millennium Drought. iv. In terms of technical interpretation, in the first phase period (2001–2006), a decrease of 1 per cent of agricultural production in the LGAs of the MDB increased the out migration rate by 1.43 per cent. In the second phase period (2006–2009), the out migration elasticity reduced to 0.88 per cent. Both coefficients are statistically significant.F
7.2.4. Irrigation and the Impact of Water Policy
The policy intervention during the drought period, in particular in the second phase period, and the concern about irrigated lands in the MDB, formed an extension of the analysis. First, an additional explanatory variable was added to represent the policy impact (water use by agricultural farms), and second, the analysis used the spatial unit of irrigated lands. As stated in Chapter 6, the 2SLS/ IV model is unable to capture environmental migration for both the irrigation model and the policy impact model as the instrument is not valid, thus the estimation applied a standard OLS model.
However, from the estimation there is an interesting finding related to policy impact. The proxy variable of policy to migration corresponds with the expected result. In the irrigated lands within the MDB area, policy appears to be significantly positively associated with in migration activity, indicating that policy implementation may have been effective in retaining the rural population in the irrigation areas. The impact also has a similar result in the context of irrigated lands at the national level. However, to explore more about the effectiveness of policy during the drought would require another specific examination, as this study is focusing on migration issues.
7.3. Migration Issues and the Basin Plan
Following the prolonged drought in the MDB and the issue of water management, the Commonwealth Government of Australia introduced the Basin Plan in 2010 72 and started to
72 The Murray Darling Basin Authority (MDBA) proposed the Basin Plan in 2010 by introducing several publications for the Basin Plan’s Guidelines before it is implemented in 2012. Some guides to the proposed the Basin Plan can be accessed through online publications available on the MDBA website such as http://www.mdba.gov.au/sites/default/files/archived/Guide to proposed BP vol2 0 12.pdf
228 implement it in 2012 for a period of seven years until 2019 73 . In brief, the Basin Plan includes five strategies:
i. The watering plan for the environment to obtain the optimum environmental outcomes for the Basin. ii. A framework and management plan for water quality and salinity issues. iii. The plan of state water resources will need to comply with the Basin Plan. iv. Provide a mechanism to provide critical water for people. v. Requirements for evaluation and monitoring of the effectiveness in the implementation of the Basin Plan.
Furthermore, the draft of the Basin Plan also stated a concern related to population in the Basin. The Basin Plan draft page 185 points 50 and 51 state:
50. In the period from 1976 to 2001, the population of the Basin’s large cities and towns grew by 30%, much more quickly than most of the smaller towns and rural localities. From 2001 to 2006, coinciding with extended drought, the population in large and medium towns grew by 8% while the rural population declined by 1.7%. This reflects a continuation of the trend, since the beginning of the twentieth century, for the percentage of the population living in rural areas of the Basin to decline. 51. The Basin’s river systems are of critical importance to the social, cultural and economic life of Indigenous people. About 70,000 people in the Basin identify as Indigenous. Reflecting in part their much younger age profile, the Indigenous population in the Basin grew by 17% between 2011 and 2006, five times faster than the growth of the non-Indigenous population.
From point 50, the Plan recognises the decline of the population in rural areas in the MDB, particularly during the Millennium Drought period. The concern is reflected in the implementation of the Basin Plan in that it must consider communities’ development by improving socio economic conditions.
Moreover, point 51 considers the cultural and economic life of Indigenous people in the Basin. In contrary to the general population trend, Indigenous people in the MDB had a positive population growth at 17 per cent between 2001 and 2006, suggesting that even taking into
73 The complete draft of the Basin Plan, published by the Minister for Sustainability , Environment, Water, Population and Communities can be accessed at http://www.mdba.gov.au/sites/default/files/Basin Plan/Basin Plan Nov2012.pdf
229 account the higher rates of self identification of indigenous stakes between the two census dates, the environmental event of the drought did not encourage them to leave the affected areas as the river system and land are important parts of their lives. Therefore, the Plan must consider the empowerment of Indigenous communities in order to improve their living conditions.
The findings of migration patterns in the MDB during the Millennium Drought presented in this study can be a useful reference for the implementation of the Basin Plan, in particular in relation to the issue of population growth and socio economic development of communities in the Basin area.
7.4. Migration Issues and the Agricultural Competitiveness White Paper
In a recent update related to the Basin’s development, the Commonwealth Government of Australia published the Agricultural Competitiveness White Paper on 4 July 2015 74 . The aim of the White Paper generally is to create a practical design to increase competitive products from the agricultural sector in a sustainable way. The paper also represents a response from all stakeholders in the agricultural sector (farmers, industries, and communities) that are concerned about global competition.
The five key objectives of the paper are: a fair system for farm business; better infrastructure support of the agricultural sector in the 21 st century; the improvement of approaches for drought and risk management; stronger research and development and technological support for farmers; and government assistance for premium markets and international trade.
From the paper themes, it can be seen that the response to an exogenous shock like drought is important as the response is an embedded strategy for improvement in the agricultural sector, for supporting development and also for increasing profitability. Related to population issues and migration, the White Paper also highlights the concern on page 100 101:
‘To meet projected demand and exploit market opportunities, the agriculture sector needs both skilled farmers and a skilled and available workforce. However, agriculture has struggled to attract and retain the skilled labour it needs to prosper. This was particularly so when the mining industry was booming. Other factors contributing to this have been declining rural populations as people have moved to larger towns and cities, outdated perceptions of agricultural career paths, and relatively low rates of participation in agriculture-related education…'
74 The Agricultural Competitiveness White Paper can be accessed online at http://agwhitepaper.agriculture.gov.au/SiteCollectionDocuments/ag competitiveness white paper.pdf
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Thus, the White Paper highlights one of the factors affecting the declining trend of rural population is the perception that agricultural career paths lack of prospects. The recommendation from the White Paper is to provide support services for farmers in rural areas with better technology, training and skills to meet the increasing trend of global demand.
Related to this thesis, the estimation model showed that lower levels of development in an area is one of the key drivers for people to leave, and thus better development programs in rural areas, in particular related to the agricultural industry and new technologies will be very beneficial in promoting the growth of agricultural production. Therefore, in the future, rural areas will become a stronger basis for agricultural business, and may attract people into these areas.
7.5. Caveats and Further Research
The thesis acknowledges that research into migration in the MDB has many limitations. The limitations of the study are:
i. Although many studies apply census data for migration analysis (Schwartz 1976; Borjas 1987; Raymer et al . 2011) as it provides more detailed figures on the population, the limitation of census data is that these figures cannot capture the migration frequency or migration movement on an annual basis. Census data in most countries is collected every five or ten years as the census is costly and requires a period of time to present the data. The common approach and the one used in Australia is that the mobility over the previous year and inter census period. This thesis uses data from the Census of Population and Housing in 2006 and in 2011, particularly the five year mobility data, hence migration is defined as the movement between the periods of 2001–2006 and 2006–2011. ii. Given the limitations of the census data, in terms of its timeframes and that the Millennium Drought period was between 2001 and 2009, it had to be assumed that the five year mobility patterns from 2006 until 2011, provided in the 2011 census data, were unchanged from those in the second phase of the drought between 2006 and 2009. iii. Using LGAs as the spatial unit of analysis provided the figures on migration in the MDB in terms of population average values without being weighted to the total population of each LGA. As a result, there is no difference in the treatment of LGAs with a small population size and the LGAs classified as cities
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iv. Although the dataset was mostly constructed from ABS data, the different sources of data meant that unavailable data was for some LGAs. As a consequence not all LGAs could be included in the estimation. v. The division between the first phase and the second phase of the drought also reflected a change in the geographical standards to identifying LGAs between the two censuses, where the census in 2006 applied the Australian Standard Geographical Classification (ASGC) and the census in 2011 applied the new Australian Statistical Geography Standard (ASGS) standard. vi. As the estimations consistently apply the procedure to test the validity of the instruments, the estimations for different age groups appear to have an invalid instrumental variable, and therefore the estimation cannot explain environmental migration for the young age group, young workers group, and the working age group. This is likely to reflect the statistical nature of the data (e.g. large variances) rather than evidence that these age groups were immune in their decision making to the impact of the environmental factors. vii. In terms of the additional area classification of irrigated lands and policy impact, the study was limited in exploring both factors as the result for irrigation areas appears to have a non valid instrument, and thus the study cannot describe environmental migration in the irrigated lands. Moreover, as the policy impact is represented by a proxy variable, the analysis is unable to include policies specifically related to migration, even though the OLS model can show some impacts of the proxy variable on migration activity.
In general, the limitation of available data requires several assumptions to be made for the analysis. The improvement of data provision certainly would improve the analysis.
With respect to the latest migration studies and recent publications, some recommendations for further research are suggested below:
i. The creation of a concordance between the census data in 2006 and in 2011 could provide a panel dataset, giving a different perspective and perhaps a stronger result in terms of the relationships uncovered over the full course of the drought. However, the concordance process would need to include all variables, including the data from the National Regional Profile.
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ii. The implementation of the Basin Plan in 2012 for seven years to 2019 provides a time frame for further migration analysis. The result could inform the assessment as to whether the Basin Plan implementation is beneficial for people in the MDB, particularly those who are living in rural or remote areas, or if it still needs improvement. iii. As many interventions may reduce the marginal effect of environmental migration, a study of people’s and communities’ resilience in the Basin could be undertaken. This would help all stakeholders in the MDB to better understand and measure the effectiveness of different policy interventions. iv. Based on the Agricultural Competitiveness White Paper that the Indigenous population is one of the key groups in the Basin, research that focuses on the living conditions and migration activity of Aboriginal and Torres Strait Islander people should be conducted to develop a better and more comprehensive understanding of people in the MDB.
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Appendices
Appendix IA: Migration Rates at LGA Level in the MDB (2001-2006)
In Out net In Out net In Out net In Out net migration migration migration migration migration migration migration migration migration migration migration migration LGA ARIA Remoteness No LGA Name Area Classification rates rate rates rates rate rates rates rate rates rates rate rates Code Classification General Population Age group 15 24 years Age Group 20 34 year Age Group 15 64 years
1 10050 Albury (C) Urban/Small City Inner Regional Australia 20.77 18.92 1.86 38.42 29.15 9.27 39.30 36.94 2.36 23.69 21.51 2.17
2 10300 Balranald (A) Town Outer Regional Australia 13.51 24.73 11.22 11.66 42.33 30.67 24.88 42.58 17.70 15.52 27.12 11.60
3 10470 Bathurst Regional (A) Urban/Small City Inner Regional Australia 22.42 18.83 3.60 43.84 28.89 14.95 37.76 37.26 0.50 24.30 21.04 3.26
4 10650 Berrigan (A) Small Urban Outer Regional Australia 23.19 22.89 0.30 15.42 46.67 31.24 34.99 49.58 14.59 25.06 27.16 2.10
5 10800 Bland (A) Small Urban Outer Regional Australia 15.03 20.88 5.85 14.01 43.48 29.47 31.48 39.02 7.54 17.43 23.80 6.37
6 10850 Blayney (A) Small Urban Inner Regional Australia 24.58 22.08 2.51 17.72 35.56 17.84 34.85 44.16 9.31 26.14 24.54 1.59
7 10950 Bogan (A) Town Remote Australia 16.36 24.10 7.74 15.50 46.78 31.29 30.95 38.11 7.16 19.39 26.38 6.99
8 11050 Boorowa (A) Town Inner Regional Australia 18.19 20.45 2.26 12.37 47.77 35.40 20.00 41.59 21.59 18.00 22.07 4.07
9 11150 Bourke (A) Town Remote Australia 15.31 34.59 19.27 13.95 40.38 26.43 27.95 47.52 19.57 17.95 35.52 17.57
10 11200 Brewarrina (A) Town Remote Australia 13.69 28.11 14.42 19.62 31.15 11.54 22.16 33.83 11.68 16.32 28.85 12.53
11 11250 Broken Hill (C) Urban/Small City Outer Regional Australia 12.22 17.42 5.20 12.72 31.98 19.27 21.70 31.98 10.28 13.51 20.00 6.49
12 11400 Cabonne (A) Urban/Small City Inner Regional Australia 25.33 19.72 5.61 17.15 41.06 23.92 39.56 44.24 4.67 26.95 22.79 4.16
13 11600 Carrathool (A) Town Remote Australia 14.42 28.21 13.79 18.49 45.94 27.45 22.65 47.14 24.49 16.53 30.92 14.39
14 11700 Central Darling (A) Town Very Remote Australia 18.19 32.30 14.11 5.79 35.54 29.75 18.66 41.98 23.32 19.68 31.56 11.88
15 11750 Cobar (A) Town Remote Australia 24.49 31.94 7.45 28.95 35.52 6.57 42.31 44.17 1.86 26.41 32.92 6.51
16 11860 Conargo (A) Town Outer Regional Australia 21.82 23.50 1.68 24.39 43.29 18.90 51.32 34.92 16.40 25.13 23.68 1.45
17 12000 Coolamon (A) Town Inner Regional Australia 20.12 20.49 0.37 13.23 39.89 26.65 36.90 46.37 9.48 21.64 24.54 2.89
18 12050 Cooma Monaro (A) Small Urban Inner Regional Australia 21.17 21.76 0.59 20.08 40.08 20.00 34.05 46.21 12.17 23.09 25.27 2.18
19 12150 Coonamble (A) Town Remote Australia 11.83 24.77 12.95 16.42 45.94 29.52 27.18 42.88 15.70 14.63 26.97 12.34
20 12200 Cootamundra (A) Small Urban Inner Regional Australia 16.84 18.26 1.41 14.93 37.10 22.17 23.85 39.94 16.09 18.58 21.80 3.22
21 12300 Corowa Shire (A) Urban/Small City Inner Regional Australia 24.13 20.29 3.84 19.81 34.67 14.86 35.20 39.93 4.73 26.41 23.30 3.11
22 12350 Cowra (A) Urban/Small City Inner Regional Australia 16.65 19.39 2.73 14.85 35.71 20.86 23.96 38.79 14.83 18.64 22.16 3.52
23 12500 Deniliquin (A) Small Urban Inner Regional Australia 15.57 22.70 7.13 15.24 38.20 22.96 26.03 43.41 17.38 17.13 26.12 8.99
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24 12600 Dubbo (C) Urban/Small City Inner Regional Australia 17.60 20.83 3.23 22.42 30.88 8.46 29.86 35.66 5.80 18.82 23.17 4.36
25 12900 Forbes (A) Small Urban Outer Regional Australia 15.84 19.85 4.01 17.83 38.88 21.04 24.62 39.22 14.60 16.57 22.98 6.41
26 12950 Gilgandra (A) Town Outer Regional Australia 16.83 21.49 4.66 13.96 43.66 29.70 28.14 43.08 14.94 18.38 24.57 6.19
27 13010 Glen Innes Severn (A) Small Urban Outer Regional Australia 18.80 20.07 1.28 16.09 41.52 25.43 25.59 36.50 10.91 20.99 22.81 1.82
28 13370 Greater Hume Shire (A) Small Urban Inner Regional Australia 22.50 21.86 0.64 19.17 40.77 21.60 34.89 45.59 10.70 24.04 24.21 0.18
29 13450 Griffith (C) Urban/Small City Outer Regional Australia 12.19 17.91 5.72 16.96 28.25 11.29 22.82 30.43 7.61 13.94 20.06 6.12
30 13500 Gundagai (A) Town Inner Regional Australia 14.25 15.95 1.71 11.16 31.25 20.09 20.93 34.07 13.15 17.00 18.81 1.81
31 13550 Gunnedah (A) Urban/Small City Outer Regional Australia 16.53 21.42 4.89 16.24 39.97 23.73 26.88 41.13 14.24 17.55 24.55 7.00
32 13650 Guyra (A) Town Outer Regional Australia 17.53 21.20 3.67 20.04 38.51 18.47 27.18 34.60 7.41 18.68 22.79 4.11
33 13660 Gwydir (A) Small Urban Outer Regional Australia 21.81 22.91 1.10 21.22 44.87 23.65 40.46 45.19 4.73 24.02 25.48 1.46
34 13700 Harden (A) Town Inner Regional Australia 18.95 23.58 4.63 9.48 43.51 34.02 26.48 42.22 15.74 20.23 26.94 6.71
35 13850 Hay (A) Town Outer Regional Australia 15.20 21.00 5.80 11.52 40.44 28.92 25.44 43.98 18.54 16.91 24.46 7.55
36 14200 Inverell (A) Urban/Small City Outer Regional Australia 19.33 17.00 2.33 16.92 31.75 14.82 28.28 33.76 5.47 20.74 19.79 0.95
37 14250 Jerilderie (A) Town Outer Regional Australia 19.59 23.35 3.76 18.27 46.70 28.43 31.31 50.00 18.69 20.60 25.87 5.27
38 14300 Junee (A) Small Urban Inner Regional Australia 17.08 21.83 4.76 15.88 40.53 24.65 27.44 43.02 15.57 18.09 24.83 6.74
39 14600 Lachlan (A) Small Urban Outer Regional Australia 12.50 22.80 10.30 14.19 38.28 24.09 24.32 36.27 11.94 14.05 24.98 10.94
40 14750 Leeton (A) Urban/Small City Outer Regional Australia 15.17 19.23 4.06 24.82 34.44 9.62 21.21 37.60 16.39 16.28 21.85 5.57
41 14920 Liverpool Plains (A) Small Urban Outer Regional Australia 17.76 24.34 6.58 15.32 43.69 28.37 29.30 42.97 13.67 19.47 25.87 6.39
42 14950 Lockhart (A) Town Outer Regional Australia 18.85 24.48 5.63 12.02 50.43 38.41 26.97 48.25 21.27 20.51 28.10 7.59
43 15270 Mid Western Regional (A) Urban/Small City Inner Regional Australia 17.02 20.49 3.47 12.17 36.08 23.91 26.86 38.75 11.89 18.21 22.65 4.44
44 15300 Moree Plains (A) Urban/Small City Outer Regional Australia 14.08 28.34 14.26 18.85 39.67 20.82 25.31 40.47 15.15 16.02 29.72 13.70
45 15500 Murray (A) Small Urban Inner Regional Australia 39.18 26.69 12.49 38.47 48.21 9.74 55.41 52.32 3.09 42.67 30.42 12.25
46 15550 Murrumbidgee (A) Town Outer Regional Australia 19.15 29.62 10.47 17.52 43.40 25.88 30.92 45.61 14.69 19.04 31.63 12.59
47 15750 Narrabri (A) Urban/Small City Outer Regional Australia 12.99 21.09 8.10 13.13 35.40 22.27 24.12 38.09 13.97 13.74 23.70 9.96
48 15800 Narrandera (A) Small Urban Outer Regional Australia 14.73 20.77 6.04 13.57 41.44 27.87 23.61 39.00 15.38 16.43 24.14 7.71
49 15850 Narromine (A) Small Urban Outer Regional Australia 16.02 22.75 6.73 12.76 40.40 27.64 25.59 42.56 16.97 16.37 25.04 8.66
50 16100 Oberon (A) Small Urban Inner Regional Australia 18.95 23.08 4.12 17.07 33.23 16.16 25.35 37.58 12.23 20.18 24.13 3.95
51 16150 Orange (C) Urban/Small City Inner Regional Australia 17.96 22.72 4.76 23.62 32.59 8.98 28.28 38.84 10.56 19.27 25.19 5.93
52 16180 Palerang (A) Urban/Small City Inner Regional Australia 38.22 25.35 12.88 27.02 37.59 10.57 59.60 49.72 9.88 39.58 26.74 12.84
53 16200 Parkes (A) Urban/Small City Outer Regional Australia 16.33 20.90 4.57 16.78 36.35 19.58 28.26 37.50 9.24 18.12 23.87 5.75
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54 16470 Queanbeyan (C) Urban/Small City Major Cities of Australia 29.16 24.14 5.02 36.04 26.10 9.94 51.64 36.62 15.02 32.41 25.54 6.86
55 17310 Tamworth Regional (A) Cities Inner Regional Australia 18.40 17.02 1.38 20.79 30.59 9.79 28.32 33.44 5.12 19.63 19.36 0.27
56 17350 Temora (A) Small Urban Outer Regional Australia 13.93 17.65 3.72 9.29 40.60 31.31 23.56 41.61 18.05 14.65 20.93 6.29
57 17400 Tenterfield (A) Small Urban Outer Regional Australia 23.84 21.47 2.38 15.94 39.16 23.22 31.29 40.67 9.39 24.99 23.20 1.80
58 17450 Tumbarumba (A) Town Outer Regional Australia 20.37 23.16 2.79 16.59 50.00 33.41 30.86 44.24 13.37 22.34 26.87 4.53
59 17500 Tumut Shire (A) Urban/Small City Inner Regional Australia 13.45 18.28 4.83 10.21 34.96 24.75 22.92 32.88 9.96 14.78 20.63 5.86
60 17640 Upper Lachlan (A) Small Urban Inner Regional Australia 20.37 21.94 1.57 9.48 41.85 32.37 29.08 43.22 14.14 23.58 24.31 0.73
61 17650 Uralla (A) Small Urban Outer Regional Australia 24.64 24.96 0.32 19.60 43.34 23.74 33.29 46.27 12.97 25.14 26.91 1.77
62 17700 Urana (A) Town Outer Regional Australia 18.11 35.61 17.50 13.10 59.52 46.43 20.22 44.81 24.59 19.52 38.22 18.71
63 17750 Wagga Wagga (C) Cities Inner Regional Australia 22.01 19.68 2.33 43.69 28.95 14.74 38.49 37.28 1.21 24.89 22.45 2.44
64 17800 Wakool (A) Town Outer Regional Australia 18.41 25.69 7.29 14.38 49.84 35.46 32.43 52.61 20.18 20.71 29.40 8.69
65 17900 Walgett (A) Small Urban Remote Australia 14.98 31.55 16.57 16.38 45.50 29.13 26.91 43.92 17.00 17.22 32.76 15.54
66 17950 Warren (A) Town Outer Regional Australia 11.55 26.90 15.34 19.74 52.75 33.01 25.84 46.22 20.38 13.08 28.90 15.82
67 18020 Warrumbungle Shire (A) Small Urban Outer Regional Australia 17.44 23.41 5.96 12.75 49.39 36.64 27.95 45.98 18.03 18.94 26.68 7.74
68 18100 Weddin (A) Town Outer Regional Australia 17.35 20.94 3.59 11.25 47.03 35.79 26.68 42.65 15.97 18.92 24.10 5.18
69 18150 Wellington (A) Small Urban Outer Regional Australia 16.56 21.54 4.98 13.99 39.37 25.37 27.25 42.21 14.97 18.53 24.85 6.32
70 18200 Wentworth (A) Small Urban Outer Regional Australia 22.03 24.04 2.01 20.24 39.29 19.06 39.64 42.25 2.62 24.62 26.02 1.40
71 18710 Yass Valley (A) Urban/Small City Inner Regional Australia 29.91 22.39 7.52 20.87 37.02 16.15 44.44 44.39 0.06 31.40 24.95 6.45
72 18750 Young (A) Urban/Small City Inner Regional Australia 19.97 18.78 1.19 23.25 34.94 11.69 33.85 33.45 0.40 22.00 21.69 0.31
73 20110 Alpine (S) Urban/Small City Outer Regional Australia 19.63 20.15 0.52 11.28 37.05 25.76 32.14 46.79 14.65 21.70 23.21 1.50
74 21010 Benalla (RC) Urban/Small City Inner Regional Australia 16.26 16.31 0.05 11.78 33.78 22.00 24.62 40.63 16.01 17.25 19.56 2.31
75 21270 Buloke (S) Small Urban Outer Regional Australia 15.67 21.22 5.55 10.53 48.79 38.27 19.92 56.49 36.57 16.79 26.59 9.81
76 21370 Campaspe (S) Urban/Small City Inner Regional Australia 17.70 17.31 0.38 14.42 32.50 18.09 26.87 37.40 10.52 19.12 20.29 1.17
77 21670 Central Goldfields (S) Urban/Small City Inner Regional Australia 15.72 16.29 0.57 12.22 36.00 23.77 17.85 36.88 19.03 17.10 19.52 2.42
78 22250 Gannawarra (S) Urban/Small City Outer Regional Australia 13.71 18.58 4.87 12.91 39.31 26.41 22.94 40.96 18.02 15.43 21.71 6.29
79 22620 Greater Bendigo (C) Cities Inner Regional Australia 17.72 13.13 4.59 25.76 21.49 4.27 28.72 27.49 1.22 19.55 15.21 4.35
80 22830 Greater Shepparton (C) Cities Inner Regional Australia 12.64 15.53 2.89 15.10 27.90 12.80 22.62 30.52 7.90 13.94 17.94 4.00
81 22910 Hepburn (S) Urban/Small City Inner Regional Australia 20.83 22.00 1.17 11.75 37.97 26.22 29.63 44.85 15.22 23.22 24.91 1.68
82 22980 Hindmarsh (S) Small Urban Outer Regional Australia 14.31 18.38 4.07 11.47 44.94 33.47 25.76 42.55 16.79 16.50 22.79 6.29
83 23190 Horsham (RC) Urban/Small City Outer Regional Australia 15.67 15.49 0.18 19.54 29.59 10.05 28.28 31.95 3.67 17.55 18.18 0.63
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84 23350 Indigo (S) Urban/Small City Inner Regional Australia 23.15 20.70 2.46 12.59 40.20 27.61 38.83 49.32 10.49 25.20 23.64 1.56
85 23940 Loddon (S) Small Urban Outer Regional Australia 17.33 23.45 6.12 11.39 48.23 36.84 29.63 49.45 19.82 20.50 27.13 6.62
86 24250 Mansfield (S) Small Urban Outer Regional Australia 30.44 18.45 11.99 33.33 37.36 4.02 43.51 43.64 0.13 33.66 21.29 12.37
87 24780 Mildura (RC) Urban/Small City Outer Regional Australia 14.50 14.94 0.45 16.63 25.53 8.90 25.10 29.50 4.40 15.90 17.19 1.29
88 24850 Mitchell (S) Urban/Small City Inner Regional Australia 28.70 22.40 6.30 23.29 30.59 7.30 43.81 39.40 4.40 29.78 24.04 5.74
89 24900 Moira (S) Urban/Small City Inner Regional Australia 20.49 18.44 2.05 16.93 34.50 17.57 28.46 38.62 10.16 22.43 21.59 0.84
90 25430 Mount Alexander (S) Urban/Small City Inner Regional Australia 19.14 18.08 1.07 9.57 34.08 24.50 24.84 41.36 16.52 21.40 21.04 0.37
91 25620 Murrindindi (S) Urban/Small City Inner Regional Australia 23.35 22.73 0.63 13.51 38.05 24.54 38.34 42.97 4.63 26.00 25.15 0.85
92 25810 Northern Grampians (S) Urban/Small City Outer Regional Australia 13.93 19.86 5.94 11.42 37.95 26.53 25.47 42.78 17.31 15.73 22.96 7.23
93 26430 Strathbogie (S) Small Urban Inner Regional Australia 20.55 18.49 2.07 13.99 38.03 24.04 33.53 42.91 9.38 23.20 21.72 1.48
94 26610 Swan Hill (RC) Urban/Small City Outer Regional Australia 14.27 18.62 4.35 16.11 32.84 16.73 25.51 38.27 12.76 16.13 21.40 5.27
95 26670 Towong (S) Small Urban Outer Regional Australia 21.15 22.02 0.86 13.58 49.31 35.72 32.24 55.35 23.11 22.08 26.17 4.09
96 26700 Wangaratta (RC) Urban/Small City Inner Regional Australia 14.99 14.60 0.40 12.00 30.54 18.54 23.28 34.07 10.79 15.97 16.94 0.97
97 27170 Wodonga (RC) Urban/Small City Inner Regional Australia 24.77 23.94 0.83 39.40 33.35 6.06 42.95 42.17 0.78 27.29 25.96 1.32
98 27630 Yarriambiack (S) Small Urban Outer Regional Australia 12.96 20.68 7.72 8.16 44.80 36.64 21.81 47.77 25.96 15.00 24.52 9.52
99 29399 VIC Unincorporated Localities Outer Regional Australia 32.52 48.04 15.52 32.56 19.77 12.79 30.83 57.71 26.88 34.73 50.00 15.27
100 30300 Balonne (S) Town Remote Australia 19.99 35.53 15.54 30.84 51.57 20.73 36.66 50.16 13.50 22.06 37.31 15.25
101 30650 Bendemere (S) Localities Remote Australia 20.99 29.01 8.02 17.97 59.38 41.41 41.43 46.43 5.00 25.79 33.61 7.82
102 30850 Booringa (S) Town Remote Australia 23.68 30.15 6.47 23.74 44.95 21.21 39.92 50.19 10.27 26.98 32.08 5.09
103 31850 Bungil (S) Town Remote Australia 39.15 24.86 14.29 38.67 45.86 7.18 65.98 42.74 23.24 41.52 27.41 14.12
104 32150 Cambooya (S) Small Urban Inner Regional Australia 47.49 33.69 13.80 41.20 45.45 4.25 63.60 53.06 10.54 48.76 35.51 13.25
105 32350 Chinchilla (S) Small Urban Outer Regional Australia 25.08 25.28 0.20 24.24 43.74 19.50 44.22 42.19 2.03 28.38 28.02 0.36
106 32400 Clifton (S) Town Inner Regional Australia 32.94 29.40 3.53 29.07 53.67 24.60 51.32 56.60 5.28 35.43 33.63 1.80
107 32550 Crow's Nest (S) Urban/Small City Inner Regional Australia 45.93 29.73 16.20 29.86 48.40 18.54 59.32 59.60 0.28 45.54 32.77 12.77
108 32650 Dalby (T) Small Urban Inner Regional Australia 26.65 31.04 4.40 36.83 43.05 6.22 41.21 47.49 6.28 29.53 33.75 4.22
109 33600 Goondiwindi (T) Town Outer Regional Australia 30.11 36.43 6.32 42.47 44.02 1.54 48.97 50.69 1.72 32.81 39.83 7.01
110 33900 Inglewood (S) Town Outer Regional Australia 23.24 31.04 7.80 16.23 62.25 46.03 33.52 51.58 18.05 26.22 36.17 9.95
111 34200 Jondaryan (S) Urban/Small City Inner Regional Australia 37.64 32.43 5.20 33.00 46.40 13.40 50.90 57.67 6.77 38.48 34.55 3.93
112 35000 Millmerran (S) Town Outer Regional Australia 26.24 30.93 4.68 27.35 56.63 29.28 35.58 49.89 14.32 28.04 34.12 6.08
113 35550 Murilla (S) Town Outer Regional Australia 26.08 28.90 2.83 34.11 46.49 12.37 45.36 49.12 3.76 30.39 32.00 1.61
238
114 35600 Murweh (S) Town Remote Australia 20.43 32.01 11.58 20.93 46.35 25.42 35.30 49.94 14.64 22.60 35.05 12.45
115 35800 Paroo (S) Town Very Remote Australia 16.76 32.42 15.66 16.21 47.04 30.83 21.39 43.61 22.22 18.06 33.00 14.95
116 36050 Pittsworth (S) Town Inner Regional Australia 23.83 24.29 0.46 18.01 37.73 19.72 28.57 47.58 19.00 26.09 27.27 1.18
117 36400 Roma (T) Small Urban Outer Regional Australia 26.12 36.90 10.79 35.88 46.13 10.25 41.87 45.77 3.90 27.73 39.18 11.45
118 36450 Rosalie (S) Small Urban Inner Regional Australia 38.08 31.87 6.21 30.51 45.47 14.97 52.21 51.36 0.85 38.53 33.02 5.51
119 36600 Stanthorpe (S) Urban/Small City Outer Regional Australia 21.40 22.62 1.22 13.95 40.62 26.67 25.80 42.09 16.29 22.96 25.31 2.35
120 36700 Tara (S) Town Outer Regional Australia 25.12 36.29 11.17 26.29 59.63 33.33 46.68 53.32 6.64 27.66 37.69 10.03
121 36900 Toowoomba (C) Cities Inner Regional Australia 27.56 25.98 1.58 45.47 35.65 9.82 38.53 43.45 4.92 29.49 29.47 0.03
122 37120 Waggamba (S) Town Outer Regional Australia 34.81 35.07 0.26 44.40 61.00 16.60 66.39 50.00 16.39 38.76 36.02 2.73
123 37150 Wambo (S) Small Urban Outer Regional Australia 31.42 28.15 3.27 23.70 50.00 26.30 50.23 53.02 2.79 32.06 31.04 1.02
124 37200 Warroo (S) Town Remote Australia 29.49 26.46 3.03 22.83 55.43 32.61 65.83 39.17 26.67 30.96 26.90 4.05
125 37260 Warwick (S) Urban/Small City Inner Regional Australia 22.61 20.49 2.12 19.10 34.46 15.36 29.73 34.62 4.89 24.52 23.00 1.52
126 40220 Alexandrina (DC) Urban/Small City Inner Regional Australia 32.91 20.54 12.38 24.74 35.27 10.53 45.84 39.82 6.02 35.36 23.11 12.25
127 40520 Berri and Barmera (DC) Urban/Small City Outer Regional Australia 17.27 20.87 3.60 20.21 34.86 14.66 26.65 37.92 11.27 18.46 23.33 4.87
128 42110 Goyder (DC) Town Outer Regional Australia 22.09 24.06 1.97 15.31 45.18 29.87 28.62 43.66 15.04 24.00 27.31 3.30
129 43080 Karoonda East Murray (DC) Town Outer Regional Australia 17.01 28.12 11.11 5.26 50.29 45.03 22.44 49.36 26.92 17.22 31.24 14.02
130 43790 Loxton Waikerie (DC) Urban/Small City Outer Regional Australia 15.26 18.04 2.78 15.45 35.31 19.86 25.46 33.62 8.16 16.24 20.84 4.60
131 44210 Mid Murray (DC) Small Urban Inner Regional Australia 23.74 25.33 1.59 19.42 41.86 22.44 27.61 45.48 17.87 25.90 27.18 1.28
132 44550 Mount Barker (DC) Urban/Small City Inner Regional Australia 30.25 21.52 8.73 25.76 26.57 0.81 47.87 36.68 11.19 31.47 22.90 8.58
133 45040 Murray Bridge (RC) Urban/Small City Inner Regional Australia 19.69 17.50 2.19 21.96 27.72 5.76 27.08 31.49 4.41 20.70 19.44 1.27
134 46670 Renmark Paringa (DC) Small Urban Outer Regional Australia 15.58 18.54 2.96 15.71 32.71 16.99 23.28 35.06 11.78 17.18 21.41 4.23
135 47290 Southern Mallee (DC) Town Remote Australia 17.53 24.86 7.34 24.19 53.07 28.88 34.85 42.35 7.49 19.93 27.81 7.88
136 47800 The Coorong (DC) Small Urban Outer Regional Australia 20.27 23.57 3.30 15.83 45.42 29.58 34.00 38.77 4.77 23.41 26.37 2.95
Average Rate 20.87 23.54 -2.68 19.74 40.47 -20.73 32.86 42.64 -9.77 22.71 26.15 -3.44
Appendix IB: Migration Rates at LGA Level in the MDB (2006-2009)
in out net in out net in out net in out net LGA ARIA Remoteness No LGA Name Area Classification migration migration migration migration migration migration migration migration migration migration migration migration Code Classification rates rate rates rates rate rates rates rate rates rates rate rates
239
General Population Age group 15 24 years Age Group 20 34 year Age Group 15 64 years
1 10050 Albury (C) Urban/Small City Inner Regional Australia 18.56 18.36 0.20 33.09 28.57 4.52 35.82 38.14 2.32 21.14 21.25 0.11
2 10300 Balranald (A) Town Outer Regional Australia 13.70 23.64 9.94 17.41 42.32 24.91 26.33 47.34 21.01 15.82 27.56 11.74
3 10470 Bathurst Regional (A) Urban/Small City Inner Regional Australia 21.02 15.92 5.10 42.64 26.25 16.39 35.70 34.61 1.09 23.28 18.85 4.43
4 10650 Berrigan (A) Small Urban Inner Regional Australia 19.62 19.55 0.07 17.44 39.76 22.32 33.69 46.38 12.69 22.52 23.79 1.27
5 10800 Bland (A) Small Urban Outer Regional Australia 14.87 21.15 6.28 13.44 44.35 30.90 28.72 43.36 14.65 17.30 25.00 7.70
6 10850 Blayney (A) Small Urban Inner Regional Australia 18.57 19.55 0.98 15.60 33.76 18.16 31.72 39.70 7.98 19.95 22.05 2.10
7 10950 Bogan (A) Town Remote Australia 16.73 24.50 7.78 20.30 36.42 16.12 39.51 37.06 2.45 19.84 27.44 7.60
8 11050 Boorowa (A) Town Inner Regional Australia 19.12 19.17 0.05 12.54 39.55 27.01 27.92 42.76 14.84 20.48 24.01 3.53
9 11150 Bourke (A) Town Very Remote Australia 18.17 31.79 13.63 13.24 40.88 27.65 30.23 43.89 13.66 20.57 33.29 12.73
10 11200 Brewarrina (A) Town Very Remote Australia 14.96 23.38 8.41 13.13 24.71 11.58 23.51 30.79 7.28 17.86 23.02 5.16
11 11250 Broken Hill (C) Urban/Small City Outer Regional Australia 9.63 15.20 5.57 12.49 25.20 12.71 20.59 27.46 6.87 11.29 17.23 5.94
12 11400 Cabonne (A) Urban/Small City Inner Regional Australia 19.05 18.99 0.06 15.99 37.17 21.19 31.46 42.83 11.37 20.75 22.11 1.37
13 11600 Carrathool (A) Town Remote Australia 16.49 25.33 8.84 17.70 46.02 28.32 33.50 45.52 12.02 18.48 26.50 8.02
14 11700 Central Darling (A) Town Very Remote Australia 21.46 24.84 3.38 18.10 33.33 15.24 33.45 36.59 3.14 24.53 26.42 1.89
15 11750 Cobar (A) Town Remote Australia 20.05 29.92 9.87 24.16 36.15 11.99 32.76 41.72 8.97 21.75 31.21 9.47
16 11860 Conargo (A) Town Outer Regional Australia 18.64 21.74 3.09 22.58 40.65 18.06 43.02 45.35 2.33 20.07 21.86 1.79
17 12000 Coolamon (A) Town Inner Regional Australia 16.34 18.97 2.63 11.11 40.68 29.57 28.25 44.98 16.73 17.86 23.53 5.66
18 12050 Cooma Monaro (A) Small Urban Inner Regional Australia 18.57 19.51 0.94 15.69 38.19 22.50 28.54 41.85 13.31 20.48 22.36 1.88
19 12150 Coonamble (A) Town Remote Australia 12.55 21.11 8.55 14.75 38.31 23.56 26.92 37.19 10.28 14.83 23.11 8.28
20 12200 Cootamundra (A) Small Urban Inner Regional Australia 15.73 17.68 1.95 13.31 41.16 27.85 23.24 40.65 17.41 16.36 21.36 5.00
21 12300 Corowa Shire (A) Urban/Small City Inner Regional Australia 18.97 19.11 0.13 17.84 35.85 18.01 31.45 43.18 11.73 21.15 22.89 1.75
22 12350 Cowra (A) Urban/Small City Inner Regional Australia 13.92 17.66 3.74 14.16 34.06 19.89 20.55 38.09 17.54 15.49 20.89 5.40
23 12500 Deniliquin (A) Small Urban Inner Regional Australia 12.97 20.00 7.03 12.65 36.76 24.11 18.16 41.80 23.63 13.98 24.30 10.33
24 12600 Dubbo (C) Urban/Small City Inner Regional Australia 15.26 16.73 1.47 19.92 26.65 6.73 27.48 30.90 3.42 17.01 18.73 1.72
25 12900 Forbes (A) Small Urban Outer Regional Australia 14.80 18.30 3.50 18.57 33.71 15.13 28.08 35.08 7.00 16.18 21.16 4.98
26 12950 Gilgandra (A) Town Outer Regional Australia 13.85 16.77 2.91 9.15 34.58 25.42 22.64 38.11 15.47 14.77 19.86 5.10
27 13010 Glen Innes Severn (A) Small Urban Outer Regional Australia 15.34 18.47 3.13 12.67 35.60 22.94 22.50 36.27 13.77 17.45 20.98 3.52
28 13340 Greater Hume Shire (A) Small Urban Inner Regional Australia 18.83 21.28 2.45 15.11 41.74 26.63 31.15 49.11 17.96 20.16 24.73 4.56
29 13450 Griffith (C) Urban/Small City Outer Regional Australia 9.45 16.08 6.63 14.12 26.81 12.69 17.49 29.61 12.11 10.78 18.34 7.56
240
30 13500 Gundagai (A) Town Inner Regional Australia 14.08 15.67 1.59 16.11 31.57 15.45 22.61 33.20 10.59 16.74 18.04 1.30
31 13550 Gunnedah (A) Urban/Small City Outer Regional Australia 15.55 18.37 2.81 15.00 31.81 16.81 32.65 35.39 2.74 17.74 20.84 3.10
32 13650 Guyra (A) Town Outer Regional Australia 18.92 18.00 0.92 18.51 34.54 16.03 29.04 36.00 6.96 21.26 20.26 1.00
33 13660 Gwydir (A) Town Outer Regional Australia 15.90 20.46 4.55 17.47 46.40 28.94 29.65 44.39 14.74 17.76 23.89 6.13
34 13700 Harden (A) Town Inner Regional Australia 18.88 20.41 1.53 14.01 43.96 29.95 26.88 41.29 14.41 20.86 23.54 2.68
35 13850 Hay (A) Town Outer Regional Australia 9.05 24.39 15.34 7.57 44.54 36.97 18.51 47.86 29.35 9.82 27.67 17.84
36 14200 Inverell (A) Urban/Small City Outer Regional Australia 16.11 16.30 0.19 15.38 32.09 16.71 25.33 32.79 7.46 17.49 18.96 1.47
37 14250 Jerilderie (A) Town Outer Regional Australia 10.62 25.05 14.43 6.99 43.23 36.24 22.22 45.45 23.23 10.95 27.43 16.48
38 14300 Junee (A) Small Urban Inner Regional Australia 17.59 20.90 3.31 18.14 36.43 18.29 30.27 44.11 13.84 19.40 23.58 4.18
39 14600 Lachlan (A) Small Urban Remote Australia 11.49 19.13 7.64 13.87 36.93 23.07 22.20 33.11 10.91 13.74 21.65 7.91
40 14750 Leeton (A) Urban/Small City Outer Regional Australia 15.69 17.89 2.20 26.72 33.22 6.50 21.52 37.16 15.65 16.14 20.77 4.62
41 14920 Liverpool Plains (A) Small Urban Outer Regional Australia 18.85 19.46 0.61 21.18 38.00 16.82 35.80 40.48 4.68 22.17 22.65 0.48
42 14950 Lockhart (A) Town Outer Regional Australia 17.62 20.49 2.87 10.72 42.90 32.17 27.30 46.32 19.02 19.35 23.19 3.85
43 15270 Mid Western Regional (A) Urban/Small City Inner Regional Australia 17.53 16.73 0.80 15.35 31.87 16.52 31.78 33.16 1.38 19.79 19.25 0.54
44 15300 Moree Plains (A) Urban/Small City Outer Regional Australia 13.09 22.33 9.24 20.74 31.33 10.59 30.59 33.60 3.01 15.35 23.54 8.19
45 15500 Murray (A) Small Urban Inner Regional Australia 30.46 22.65 7.81 31.02 38.85 7.83 49.74 49.35 0.39 32.83 26.61 6.22
46 15550 Murrumbidgee (A) Town Outer Regional Australia 14.61 25.35 10.74 19.80 37.92 18.12 27.75 41.48 13.74 16.20 27.29 11.09
47 15750 Narrabri (A) Urban/Small City Outer Regional Australia 13.88 17.72 3.84 14.98 32.27 17.29 26.29 32.02 5.73 15.37 19.63 4.26
48 15800 Narrandera (A) Small Urban Outer Regional Australia 13.66 18.86 5.20 11.59 37.94 26.35 25.00 41.26 16.26 15.28 22.44 7.16
49 15850 Narromine (A) Small Urban Outer Regional Australia 14.43 19.42 4.99 14.68 38.11 23.42 25.63 39.08 13.45 15.50 22.65 7.15
50 16100 Oberon (A) Small Urban Inner Regional Australia 16.93 20.77 3.85 14.17 35.98 21.81 20.30 38.58 18.28 18.83 22.73 3.89
51 16150 Orange (C) Urban/Small City Inner Regional Australia 20.03 17.61 2.42 27.11 26.00 1.11 35.70 31.50 4.20 22.25 19.79 2.47
52 16180 Palerang (A) Urban/Small City Inner Regional Australia 33.26 23.98 9.28 22.60 37.31 14.71 50.42 49.45 0.96 35.17 26.01 9.16
53 16200 Parkes (A) Urban/Small City Outer Regional Australia 14.64 17.21 2.57 15.60 31.98 16.37 26.50 35.42 8.93 16.47 20.35 3.88
54 16470 Queanbeyan (C) Urban/Small City Major Cities of Australia 23.68 24.30 0.62 28.08 24.12 3.96 46.09 38.39 7.70 26.91 26.01 0.89
55 17310 Tamworth Regional (A) Cities Inner Regional Australia 16.26 14.90 1.35 19.26 27.76 8.50 27.21 30.56 3.35 17.92 17.41 0.51
56 17350 Temora (A) Small Urban Outer Regional Australia 12.09 15.77 3.68 10.86 35.86 25.00 19.48 41.81 22.33 13.04 19.62 6.58
57 17400 Tenterfield (A) Small Urban Outer Regional Australia 20.29 19.17 1.13 12.22 36.82 24.59 24.63 38.96 14.32 22.32 21.94 0.38
58 17450 Tumbarumba (A) Town Outer Regional Australia 16.88 20.87 3.99 14.36 42.56 28.20 32.99 45.88 12.89 19.54 24.26 4.73
59 17500 Tumut Shire (A) Urban/Small City Inner Regional Australia 12.21 16.53 4.32 10.48 33.07 22.59 21.07 34.54 13.47 13.03 19.41 6.38
241
60 17640 Upper Lachlan Shire (A) Small Urban Inner Regional Australia 18.31 17.32 0.99 12.43 36.59 24.17 26.27 43.25 16.99 21.31 20.28 1.03
61 17650 Uralla (A) Small Urban Outer Regional Australia 21.45 22.76 1.31 17.71 39.49 21.78 27.91 49.05 21.14 22.25 25.20 2.95
62 17700 Urana (A) Town Outer Regional Australia 11.18 25.22 14.04 11.54 51.28 39.74 28.87 47.18 18.31 13.62 28.37 14.75
63 17750 Wagga Wagga (C) Cities Inner Regional Australia 18.67 18.86 0.19 40.09 26.88 13.21 37.13 36.67 0.47 21.96 21.65 0.31
64 17800 Wakool (A) Town Outer Regional Australia 18.32 24.95 6.63 16.73 47.41 30.68 27.45 55.71 28.26 20.62 28.17 7.55
65 17900 Walgett (A) Small Urban Remote Australia 15.43 25.04 9.60 18.41 36.98 18.56 29.23 37.58 8.35 17.77 26.25 8.49
66 17950 Warren (A) Town Outer Regional Australia 17.00 21.89 4.90 26.41 42.96 16.55 39.01 40.66 1.65 19.80 23.08 3.28
67 18020 Warrumbungle Shire (A) Small Urban Outer Regional Australia 15.88 18.21 2.33 13.16 38.51 25.35 29.16 41.16 12.00 17.62 21.80 4.18
68 18100 Weddin (A) Town Outer Regional Australia 15.17 19.04 3.87 10.89 44.88 33.99 23.59 44.16 20.56 16.26 21.90 5.64
69 18150 Wellington (A) Small Urban Outer Regional Australia 16.45 20.16 3.72 13.02 36.19 23.18 25.35 39.00 13.65 18.31 22.86 4.55
70 18200 Wentworth (A) Small Urban Outer Regional Australia 19.55 22.60 3.05 17.74 39.05 21.31 34.87 45.32 10.45 22.12 25.67 3.55
71 18710 Yass Valley (A) Urban/Small City Inner Regional Australia 28.86 20.81 8.05 22.65 34.00 11.35 46.88 42.29 4.58 31.94 23.25 8.69
72 18750 Young (A) Urban/Small City Inner Regional Australia 15.46 17.30 1.84 17.09 32.38 15.29 24.83 35.96 11.13 17.40 20.24 2.84
73 20110 Alpine (S) Urban/Small City Outer Regional Australia 16.55 19.16 2.61 9.68 39.62 29.94 22.22 48.93 26.71 18.41 22.79 4.38
74 21010 Benalla (RC) Urban/Small City Inner Regional Australia 15.19 15.64 0.44 12.66 33.24 20.58 22.98 38.84 15.86 16.45 18.54 2.10
75 21270 Buloke (S) Small Urban Outer Regional Australia 13.67 19.66 5.99 14.07 47.25 33.18 27.92 52.34 24.42 16.92 24.52 7.60
76 21370 Campaspe (S) Urban/Small City Inner Regional Australia 14.44 16.49 2.05 14.65 31.03 16.38 23.88 38.04 14.15 15.97 19.59 3.61
77 21670 Central Goldfields (S) Urban/Small City Inner Regional Australia 16.60 14.65 1.96 12.45 31.36 18.91 19.91 35.48 15.57 18.01 17.22 0.79
78 22250 Gannawarra (S) Urban/Small City Outer Regional Australia 11.88 18.03 6.15 9.63 39.97 30.35 20.46 44.28 23.82 13.64 22.85 9.22
79 22620 Greater Bendigo (C) Cities Inner Regional Australia 15.79 11.76 4.04 24.14 19.64 4.50 27.24 25.72 1.52 17.64 13.93 3.72
80 22830 Greater Shepparton (C) Cities Inner Regional Australia 11.74 13.74 2.00 14.03 25.21 11.18 21.38 30.05 8.67 12.83 16.14 3.31
81 22910 Hepburn (S) Urban/Small City Inner Regional Australia 22.87 21.52 1.35 12.50 38.92 26.42 34.79 47.66 12.87 25.38 24.11 1.26
82 22980 Hindmarsh (S) Small Urban Outer Regional Australia 12.70 18.23 5.54 9.63 41.16 31.53 19.71 44.37 24.66 14.98 22.69 7.71
83 23190 Horsham (RC) Urban/Small City Outer Regional Australia 15.16 15.04 0.12 20.54 27.11 6.57 27.29 31.61 4.32 17.21 17.76 0.55
84 23350 Indigo (S) Urban/Small City Inner Regional Australia 20.15 20.54 0.39 11.44 40.32 28.87 31.38 53.43 22.05 20.91 23.66 2.75
85 23940 Loddon (S) Small Urban Outer Regional Australia 14.87 20.63 5.76 10.83 48.35 37.51 24.49 52.62 28.13 17.48 24.40 6.92
86 24250 Mansfield (S) Small Urban Outer Regional Australia 25.89 17.28 8.62 33.33 35.29 1.96 33.60 40.32 6.72 27.75 19.54 8.20
87 24780 Mildura (RC) Cities Outer Regional Australia 12.16 14.33 2.17 12.49 26.13 13.64 20.39 29.54 9.15 13.24 16.71 3.46
88 24850 Mitchell (S) Urban/Small City Inner Regional Australia 24.55 19.23 5.32 20.81 25.50 4.69 38.92 35.10 3.81 26.16 20.86 5.31
89 24900 Moira (S) Urban/Small City Inner Regional Australia 17.34 17.02 0.33 13.63 33.54 19.91 25.85 39.07 13.22 18.92 20.05 1.14
242
90 25430 Mount Alexander (S) Urban/Small City Inner Regional Australia 19.17 16.96 2.21 10.04 32.00 21.96 25.83 40.47 14.64 21.63 19.63 1.99
91 25620 Murrindindi (S) Urban/Small City Inner Regional Australia 19.03 24.84 5.82 13.63 40.56 26.93 33.68 46.01 12.33 21.08 27.50 6.42
92 25810 Northern Grampians (S) Urban/Small City Inner Regional Australia 15.48 17.11 1.63 15.85 35.92 20.07 26.60 39.13 12.52 17.62 20.19 2.57
93 26430 Strathbogie (S) Small Urban Inner Regional Australia 19.92 18.11 1.81 15.06 38.39 23.33 29.72 42.80 13.08 22.46 21.46 1.00
94 26610 Swan Hill (RC) Urban/Small City Outer Regional Australia 12.23 17.51 5.28 14.18 30.06 15.88 22.96 33.54 10.57 13.85 20.24 6.39
95 26670 Towong (S) Small Urban Outer Regional Australia 17.94 20.20 2.27 14.21 44.49 30.28 30.57 54.22 23.64 20.25 24.22 3.96
96 26700 Wangaratta (RC) Urban/Small City Inner Regional Australia 13.35 13.10 0.25 13.25 30.16 16.91 22.51 34.48 11.97 14.87 16.09 1.22
97 27170 Wodonga (RC) Urban/Small City Inner Regional Australia 23.26 20.79 2.47 37.86 29.71 8.15 41.89 39.13 2.76 25.47 23.21 2.26
98 27630 Yarriambiack (S) Small Urban Outer Regional Australia 14.02 20.63 6.61 12.47 43.01 30.54 23.36 49.55 26.19 15.49 24.22 8.73
99 29399 Unincorporated Vic Localities Outer Regional Australia 36.73 35.37 1.36 54.95 19.78 35.16 46.97 45.45 1.52 37.27 37.88 0.61
100 30300 Balonne (S) Town Remote Australia 17.57 30.19 12.63 26.39 48.51 22.12 32.24 42.82 10.58 20.41 31.75 11.34
101 33610 Goondiwindi (R) Urban/Small City Outer Regional Australia 19.14 23.58 4.45 22.62 42.46 19.84 35.15 41.58 6.43 20.98 26.37 5.39
102 34860 Maranoa (R) Urban/Small City Outer Regional Australia 20.09 24.60 4.51 27.34 37.95 10.61 44.22 37.66 6.56 23.56 26.26 2.70
103 35600 Murweh (S) Town Very Remote Australia 19.30 27.97 8.67 23.73 39.83 16.10 34.56 42.08 7.52 20.54 30.32 9.78
104 35800 Paroo (S) Town Very Remote Australia 17.09 28.10 11.01 19.81 42.92 23.11 31.79 37.42 5.63 19.36 29.21 9.85
105 36660 Southern Downs (R) Urban/Small City Inner Regional Australia 19.67 17.45 2.21 16.68 33.21 16.53 23.10 35.25 12.14 20.95 20.16 0.79
106 36910 Toowoomba (R) Cities Inner Regional Australia 17.81 16.62 1.18 22.08 25.67 3.58 26.06 31.41 5.35 18.88 19.00 0.12
107 37310 Western Downs (R) Urban/Small City Outer Regional Australia 22.98 19.90 3.09 24.80 33.28 8.48 42.29 35.52 6.77 26.04 22.30 3.74
108 40220 Alexandrina (DC) Urban/Small City Inner Regional Australia 32.24 19.38 12.86 27.70 33.00 5.31 43.17 41.18 1.99 34.77 22.50 12.27
109 40520 Berri and Barmera (DC) Urban/Small City Outer Regional Australia 15.09 19.08 4.00 16.93 36.43 19.50 25.24 39.89 14.65 16.22 22.76 6.53
110 42110 Goyder (DC) Town Outer Regional Australia 18.37 23.19 4.82 18.93 42.83 23.90 24.86 44.51 19.65 20.36 26.16 5.81
111 43080 Karoonda East Murray (DC) Town Outer Regional Australia 9.65 22.59 12.94 2.65 50.99 48.34 20.47 51.97 31.50 12.32 26.36 14.04
112 43790 Loxton Waikerie (DC) Urban/Small City Outer Regional Australia 13.31 17.09 3.79 12.60 35.33 22.73 20.46 38.25 17.79 13.68 20.38 6.69
113 44210 Mid Murray (DC) Small Urban Inner Regional Australia 18.69 20.21 1.52 14.96 35.95 20.99 27.10 42.52 15.42 21.58 22.59 1.01
114 44550 Mount Barker (DC) Urban/Small City Inner Regional Australia 26.72 20.30 6.43 22.72 26.37 3.66 41.31 37.03 4.28 28.24 22.12 6.12
115 45040 Murray Bridge (RC) Urban/Small City Inner Regional Australia 18.63 15.52 3.11 21.09 23.85 2.76 29.76 30.37 0.60 20.34 17.63 2.71
116 46670 Renmark Paringa (DC) Small Urban Outer Regional Australia 11.25 16.88 5.63 11.41 30.35 18.93 23.97 34.32 10.35 12.81 19.76 6.95
117 47290 Southern Mallee (DC) Town Remote Australia 14.27 20.73 6.46 11.92 45.77 33.85 26.28 37.50 11.22 15.62 23.67 8.04
118 47800 The Coorong (DC) Small Urban Outer Regional Australia 16.94 22.37 5.44 20.62 44.00 23.38 25.36 40.49 15.14 18.96 25.52 6.55
Average Rate 17.24 20.02 -2.78 17.49 36.08 -18.59 29.02 40.13 -11.11 19.15 22.79 -3.64
243
Appendix IIA: Barrios, Bertinelli, and Strobl Model (2006)
Climate change and rural–urban migration: The case of sub Saharan Africa’, Journal of Urban Economics , 60, pp. 357 371. Two industries: The rural/agricultural sector (A) The urban/manufacturing sector (M) Three factors of production: Land input (L); Capital (K); Labour (N) Thus, production functions are:
(1) = ( ) . (2) = .
is capital endowment of the economy and are the elasticity in both agricultural and manufacturing output with respect to ( w.r.t ) labour. Land is depending on the level of rainfall (R) , that is L(R ). Positive impact of rainfall on land input, we assume L’(R)>0 . pA and pM are exogenously given. For simplicity reasons, we also assume both sectors are constant return to scale (CRS), then FOC (3) and (4) are
̅ = → = ( ) ( ) ̅ ̅ = → = ( ) ̅
Where is the mass of workers in the agricultural/manufacturing sector. Market equilibrium / is or the equilibrium of wage + rate and= distribution of workers between two sectors.
And the equilibrium of urbanization is: ∗ ≡ / The impact of rainfall on urbanization is by determining the changes in agricultural and manufacturing employment (model below).
( ) 1 = . − ( ) 1 −
244
( ) ℎ = Ƞ ( ) ( )
1 = − 1 −
Proposition A . A decline in rainfall raises the urbanization rate, specifically, the elasticity of the urbanization rate w.r.t rainfall.
ln ln Ƞ ( ) = = − < 0 1 − ln ln + 1 − 1 − The empirical model
= + + + + μ +
Where U is log or urbanization, is a vector of potential determinants , R is log of rainfall, is year specific effect common to i, and is time invariant. μ
Appendix IIB: Marchiori, Maystadt, and Schumacher Model (2012)
The impact of weather anomalies on migration in sub Saharan Africa; Journal of Environmental Economics and Manageme nt, 63, pp. 355 374.
is mobile workers working in the urban sector and workers in the rural sector ∈ [ 0.1] is international mobile workers that only work(1 in − the ) urban sector but are mobile across ∈ [0,1] countries. Two sectors with weather variable (c) 1. The rural sector with production technology
2. The urban sector ( , 1 − )
Both are decreasing return to scale (DRS)( + , ) Measure weather through a random variable, say Z with ; 0 represents the best outcome while the worst . ∈ [0, ∞] ∞ on average we expect ; with as the probability function ( ) = ( ) ( ) thus we expect while the worse outcome would be ( = ( )) ( > ( )) 245
Capital and knowledge as given. Both rural and urban sectors priced competitively; prices in each sector are given.
Rural sector produces according to [ (1 − , ) = ] With < 0, < 0, lim → = ∞ Urban sector produces according to [ ( + , ) = ] With < 0, < 0
Thus, nationally mobile workers then decide to move from the rural to the urban,
= ( + , ) − (1 − , ) (1)
Assume that internationally mobile workers compare their wage with country intended
Denote as ∗ and direct weather g(c) with g(c)>0 [ (1 − )] Thus, the workers from the urban region migrate internationally according to
∗ = ( + , ) − (1 − ) − ( ) (2)
Additional assumptions:
∗ (1) → lim (1 − , ) < (1 − ) + ( ); (2) ( , 0) ∗ ∗ > (1) + ( ); (3) ( + 1, 1) < (0) + ( )
Proposition 1 . At equilibrium, a larger weather anomaly induces international migration directly and indirectly via rural–urban migration .
= = 0 ∗ (1 − ) + ( ) = (1 − , ) ∗ (1 − ) + ( ) > 0 In the equilibrium:
246
∗ ( + ) − = ∗ > 0 + ( + )
Weather anomalies increase rural urban migration as well as urban–international migration. The effect of amenity: since agglomeration effect would flatten the curve of urban wages (wu ), thereby it diminishes the change in international wages.
Urbanization defined as or the ratio between urban population with total population. = Proposition 2. Weather anomalies increase equilibrium urbanization if the amenity channel is weak enough and agglomeration forces are sufficiently small.
1 + 1 − = . + . > 0 (1 + ) (1 + )
Weather anomalies induce rural–urban migration.
Appendix IIC: Reuveny and Moore Model (2009)
Does Environmental Degradation Influence Migration? Emigration to Developed Countries in the Late 1980s and 1990s, Social Science Quarterly , 90, pp.461 479.
Expected net benefit from stay or go (migration).
(1) ( ) = ( ) ( ) + (1 − ) ( ) (2) ( ) = ( ) ( ) + (1 − ) ( ) The net benefit ( , , , ) Probabilities and determined factors ( , ) Migrants[ pay a (one time ), cost ( ), ( ), ( )] [( )] Factors in decision making process ae migrate and staying Hence, the expected net benefits from stay or go are: ( = 1,2. . )
( ) = [ ( ) . ( ) + ( ). ( ) + ( ) . ( ) + ( ). ( ) − . ( )
247
(3) and ((4) ) = [ ( ) . ( ) + ( ). ( ) + ( ) . ( ) + ( ). ( )]
The present value of expected net benefits from migrating and staying using time horizon (T) and discount rate (r) are equation (5) and (6) [ , ]
( ) ( ) = (1 + )
( ) ( ) = (1 + ) In migration, individual i in country j (j=1,2,…n) computes each period the expected net benefits. Thus, individuals maximise the difference across all destination k= 1, 2….n. (7) ( , − )
max[ ( − ) , ( − ) , … … , ( − ) ] Therefore ; Migrate and Stay [ − ] > 0 [ − ] < 0
The empirical model
Identifies push factors operating in j and pull in k . Equations (8) and (9). [ ( )]
=
= The migration flow form j to k and rises with . (10) [ ( )]
=
Where is a constant term, measuring the intrinsic propensity of people from j to k in the absence of other stimulants. Put together (8), (9) and (10).
( ) ln = ln + ln + ln + ln + ln + ln + ln ( ) + ln ( ) + ln ( ) + ln( ) 248
Appendix IIIA: Non-ABS Structures in Australian Statistical Geography Standard
Source: ABS (2011), ASGS: Volume 3–Non ABS Structure (2011)
Appendix IIIB: Questions 9 and 10 on the Census Household Form 2011
Source :ABS, http://www.abs.gov.au/websitedbs/censushome.nsf/home/statementspersonpur1p?opendocument&navpos=450
249
Appendix IIIC: Sample of Selected Weather Stations (LGAs in NSW)
Station Station LGA Weather Station LGA Weather Station Number Number NEW SOUTH WALES Albury 72160 Albury Airport AWS Lane Cove 66131 Riverview Observatory Armidale 56037 Armidale (Tree Group Nursery) Leeton 74037 Yanco Agricultural Institute Ashfield 66194 Canterbury Racecourse AWS Leichhardt 66194 Canterbury Racecourse AWS Ballina 58198 Ballina Airport AWS Lismore 58201 Tuncester (Leycester Creek) Balranald 49002 Balranald (Rsl) Lithgow 63164/63226 Lithgow (Kylie Park)/Lithgow(Cooerwull) Bankstwon 66137 Bankstown Airport AWS Liverpool Plains 55311 Duri Post Office Bathurst 63005 Bathurst Agricultural Station Liverpool 67117 Holsworthy Control range Bega 69139 Bega AWS Lockhart 74064 Lockhart Bowling Club Bellingen 59078 Promised Land (Bellingen (Crystal Creek) Maitland 61388 Maitland visitors centre Berrigan 74255 Berrigan (New Shiloh) Manly 66182 Frenchs Forest Blacktown 67026 Seven Hills (Collins St) Marrickville 66037 Sydney Airport Mid Western Bland 73032 Quandialla Post Office 62013 Gulgong Post Office Regional Blayney 63294 Blayney (Orange Road) Moree Plains 53115 Moree Aero Blue Mountains 63028 Faulconbridge (St Georges Crescent) Mosman 66062 Sydney Observatory Hill Bogan 50034 Nevertire (Beverley) Murray/Echuca 80015 Echuca Aerodrome Bombala 70005 Bombala (Therry Street) Murrumbidgee 74249 Coleambally Irrigation Boorowa 70220 Boorowa Post Office Muswellbrook 61168 Muswellbrook (Lindisfarne) Botany Bay 66037 Sydney Airport AMO Nambucca 59024 Nambucca Heads Bowling Club Bourke 48245 Bourke Airport AWS Narrabri 53030 Narrabri West Post Office Brewarrina 48015 Brewarrina Hospital Narrandera 74148/74221 Narrandera Airport/Narrandera Golf Club Broken Hill 47007 Broken Hill (Patton Street) Narromine 51037 Narromine (Alagalah Street) Burwood 66013 Concord Golf Club Newcastle 61055 Newcastle Nobbys Signal Station AWS Byron 54078 Inverell (Wandera) North Sydney 66062 Sydney (Observatory Hill) Oberon (Jenolan Caves Road)/Oberon (Gilholmes Cabonne 65010 Cudal Post Office Oberon 63293/63213 Rd)
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Camden 68192 Camden Airport AWS Orange 63254 Orange Agricultural Institute Campbelltown (Kentlyn Georges River Campbelltown 68160 Palerang 69132 Braidwood Racecourse AWS Roa) Canada Bay 66194 Canterbury Racecourse AWS Parkes 65068 Parkes Airport AWS Canterbury 66194 Canterbury Racecourse AWS Parramatta 66124 Parramatta North (Masons Drive) Groongal (Gundaline)/Hay Carrathool 75064/75047 Penrith 67113 Penrith Lakes AWS (Mulberrygong) Central Darling 46043 Wilcannia (Reid St) Pittwater 66141 Mona Vale Golf Club Cessnock 61260 Cessnock Airport AWS Port Macquarie 60139 Port Macquarie Airport AWS Clarence/Grafton 58130 Grafton Olympic Pool Port Stephens 61072 Tahlee (Carrington (Church St)) Cobar 48027 Cobar MO Queanbeyan 70072 Queanbeyan Bowling Club Coffs Harbour 59040 Coffs Harbour MO Randwick 66052 Randwick Bowling Club Conargo 75054 Conargo (Puckawidgee) Richmond Valley 58208 Casino Airport AWS Coolamon 74033 Coolamon Post Office Rockdale 66037 Sydney Airport AMO Cooma 70278 Cooma Visitors Centre Ryde 66013 Concord Golf Club Coonamble 51161 Coonamble Airport AWS Shellharbour 68241 Albion Park (Wollongong Airport) Cootamundra 73142 Cootamundra Airport Shoalhaven 68080 Greenwell Point Bowling Club Corowa Shire 74034 Corowa Airport Singleton 61092 Elderslie Cowra 65111/63022 Cowra Ag Research/Cowra Airport AWS Snowy River 70217 Cooma Airport AWS Deniliquin 74258 Deniliquin Airport AWS Strathfield 66048 Concord (Brays Rd) Dubbo 65070 Dubbo Airport AWS Surherland Shire 66078 Lucas Heights (ANSTO) Dungog 61017 Dungog Post Office Sydney 66062 Sydney (Observatory Hill) Euribodalla/Narooma 69022 Narooma (Marine Rescue) Tamworth Regional 55325 Tamworth Airport AWS Fairfield 66137 Bankstown Airport AWS Temora 73038/73037 Temora Research Stat/Temora Ambulance Forbes 65103 Forbes Airport AWS Tenterfield 56032 Tenterfield (Federation Park) Gilgandra 51018 Gilgandra (Chelmsford Ave) The Hills Shire 67100 Castle Hill (Kathleen Ave) Gloucester 60015 Gloucester Post Office Tumbarumba 72043 Tumbarumba Post Office Gosford 61319 Gosford North (Glennie St) Tumut Shire 72056 Blowering Dam Goulburn Mulwaree 70263 Goulburn TAFE Tweed 58158 Murwillumbah (Bray Park) Great Lakes/Forster 60013 Forster Tuncurry Marine Rescue Unincorp NSW 47039 Umberumberka Reservoir
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Greater 74188 Culcairn Bowling Club Upper Hunter Shire 61051 Murrurundi Post Office Hume/Culcairn Greater Taree 60141 Taree Airport AWS Upper Lachlan Shire 70080 Taralga Post Office Griffith 75041 Griffith Airport AWS Uralla 56238 Armidale Airport AWS Gundagai 73141 Gundagai (William St) Urana 74110/74026 Urana Post Office/Urana (Butherwah) Gunnedah 55023 Gunnedah Pool Wagga Wagga 73127 Wagga Wagga Agricultural Institute Guyra 56229 Guyra Hospital Wakool 75012 Wakool (Calimo) Gwydir 54004 Bingara Post Office Walcha 56236 Walcha (Inglewood) Harden 73109/73029 Murringo/Murrumburrah Post Office Walgett 52088 Walgett Airport AWS Hawkesbury 67105 Richmond RAAF Warren 51054 Warren (Frawley St) Hay 75031 Hay Miller Street Warringah 66188 Belrose (Evelyn Place) Holroyd 67111 North Parramatta (Burnside Homes) Warrumbungle 51088 Warrumbungle (Cheddington) Hornsby 66158 Turramurra (Kissing Point Road) Waverley 66052 Randwick Bowling Club Hunters Hill 66131 Riverview Observatory Weddin 73014 Grenfell (Manganese Rd) Hurstville 66058 Sans Souci (Public School) Wellington 65034 Wellington (Agrowplow) Inverell 56242 Inverell (Raglan Street) Wentworth 47053 Wentworth Post Office Jerilderie 74055 Jerilderie Treatment Works Willoughby 66080 Castle Cove (Rosebridge Ave) Junee 73025 Old Junee (Millbank) Wingecarribee 68045 Moss Vale (Hoskins Street) Kempsey 59017 Kempsey (Wide Street) Wollondily 68052 Picton Council Depot Kiama 68035 Jamberoo (The Ridge) Wollongong 68110 Berkeley (Northcliffe Drive) Kogarah 66058 Sans Souci (Public School) Woollahra 66052 Randwick Bowling Club Ku Ring Gai 66158 Turramurra (Kissing Point Road) Wyong 61380 Wyong (Jilliby (Jilliby Creek)) Kyogle 58032 Kyogle Post Office Yass 70091 Yass (Linton Hostel) Lachlan 50105 Trundle (Huntingdale) Young 73138 Young Airport Lake Macquarie 61393 Edgeworth WWTP VICTORIA Alpine 83024 Mount Buller Mansfield 83019 Mansfield (Post Office) Ararat 89085 Ararat Prison Maribyrnong 86039 Flemington Racecourse Ballarat 89002 Ballarat Aerodrome Melbourne 86071 Melbourne Regional Office
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Banyule 86351 Bundoora (Latrobe University) Melton 87040 Melton Reservoir Bass Coast 86127 Wonthaggi Mildura 76031 Mildura Airport Baw Baw 85305 Vesper Mitcham 86074 Mitcham Bayside 86018 Caulfield (Racecourse) Mitchell 88053 Seymour Shire Depot Benalla 82109 Molyullah (Killanoola) Moira 80109 Cobram (Goulburn Murray) Borondara 86018 Caulfield (Racecourse) Monash 86303 Glen Waverley (Golf Course) Brimbank 86282 Melbourne Airport Moonee Valley 86039 Flemington Racecourse Buloke 78072 Donald Moorabool 87147 Bannockburn (Hillside) Campaspe 80015 Echuca Aerodrome Moreland 86071 Melbourne Regional Office Mornington Cardinia 86394 Pakenham 86079 Mornington Peninsula Casey 86299 Berwick (Buchanan Road) Mount Alexander 88110 Castlemaine Prison Central Goldfields 88043 Maryborough Moyne 90175 Port Fairy AWS Colac Otway 90022 Colac Airport Murrindindi 88067 Yea Corangamite 90005 Beeac (Post Office) Nillumbik 86068 Viewbank Darebin 86068 Viewbank Northern Grampian 79105 Stawell Aerodrome East Gippsland 85279 Bairnsdale Airport Port Phillip 86071 Melbourne Regional Office Frankston 86079 Mornington Pyrenees 89005 Beaufort Gannawarra 80023 Kerang Queenscliffe 87178 Ocean Grove Glen Eira 86095 Prahran (Como House) South Gippsland 85049 Leongatha Sth Gippsland Water Glenelg 90184 Cape Nelson Lighthouse Southern Grampians 90173 Hamilton Airport Golden Plains 87147 Bannockburn (Hillside) Stonnington 86018 Caulfield (Racecourse) Greater Bendigo 81123 Bendigo Airport Strathbogie 82151 Seven Creeks at Strathbogie Greater Dandenong 86224/86111 Dandenong/Springvale Necropolis Surf Coast 87135/87160 Barwon Heads/Torquay Golf Club Greater Geelong 87034 Lovely Banks (Reservoir) Swan Hill 77094 Swan Hill Aerodrome Greater Shepparton 2012 Shepparton Airport Towong 82060 Towong upper Hepburn 88020 Daylesford Unincorporated VIC 83084 Falls Creek Hindmarsh 78040 Nhill (Woorak) Wangaratta 82138 Wangaratta Aero Hobsons Bay 87031 Laverton RAAF Warnambool 90186 Warrnambool Airport Ndb Horsham 79100 Horsham Aerodrome Wellington 85072 East Sale Airport
253
Hume 86282 Melbourne Airport West Wimmera 79011 Edenhope (Post Office) Indigo 82039 Rutherglen Research Whitehorse 86074 Mitcham Kingston 86077 Moorabbin Airport Whittlesea 86117 Toorourrong Reservoir (Toorourrong) Knox 86104 Scoresby Research Institute Wodonga 72146 Albury Airport Latrobe 85280 Morwell (Latrobe Valley Airport) Wyndham 87031 Laverton RAAF Loddon 80061 Wedderburn (Post Office) Yarra Ranges 86066 Lilydale Macedon Ranges 87171 Bullengarook South Yarra 86071 Melbourne Regional Office Manningham 86068 Viewbank Yarriambiack 78077 Warracknabeal Museum QUEENSLAND Aurukun 27000 Aurukun Shire Council Logan 40715 Shailer Park Oregon Drve Balonne 43109 St George Airport Longreach 36031 Longreach Aero Banana 39003 Banana Post Office Mackay 33045 Mackay Aero Barcaldine 36007 Barcaldine Post Office Mapoon 27042 Weipa Eastern Ave Barcoo 38063 Haughton Vale Maranoa 43091 Roma Airport Blackall Tambo 35259 Duneira Mckinlay 29150 Malvie Downs Station Boulia 38003 Boulia Airport Moreton Bay 40965 Clontarf Brisbane 40913 Brisbane Mornington 29039 Mornington Island Bulloo 45029 Orientos Station Mount Isa 29126 Mount Isa Mine Bundaberg 39128 Bundaberg Aero Murweh 44072/44078 Werrina/Yarronvale Burdekin 33001 Burdekin Shire Council Napranum 27045 Weipa Aero Burke 29004 Burketown Post Office North Burnett 40021 Biggenden Post Office Cairns 31011 Cairns Aero Northern Peninsula 27031 Bamaga Carpentaria 29004 Burketown Post Office Palm Island 32141/32117 Lucinda Point/Allingham Forrest Cassowary Coast 32025 Innisfail Paroo 44026 Cunnamulla Post Office Central Highlands 35264 Emerald Airport Pormpuraaw 29038 Kowanyama Airport Charters Towers 34084 Charters Towers Airport Quilpie 45037 Moble Cherbourg 40152 Murgon Post Office Redlands 40265 Redlands HRS Cloncurry 29141 Cloncurry Airport Richmond 30045 Richmond Post Office Cook 31017 Cooktown Mission Strip Rockhampton 39083 Rockhampton Aero
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Croydon 29012 Croydon Township Scenic Rim 40939/40407 Beaudesert Alert/Lumeah Diamantina 38000 Bedourie Police Station Somerset 27031 Bamaga Doomadgee 29000 Almora Station South Burnett 40922 Kingaroy Airport Etheridge 30018/30124 Georgetown Post Office/Airport Southern Downs 41013 Canning Downs Flinders 40094 Harrisville Post Office Sunshine Coast 40078 Eumundi Crescent Rd Fraser Coast 40430 Urangan Hibiscus St Tabellands 31075 Tinaroo Falls Dam Gladstone 39326 Gladstone Airport Toowoomba 41529 Toowoomba Airport Gold Coast 40160 Nerang Gilston Rd Torres Strait Island 27031 Bamaga Goondiwindi 41521 Goondiwindi Airport Torres 27058 Horn Island Gympe 40093 Gympie Townsville 32040 Townsville Aero Hinchinbrook 32192 Cardwell Range Weipa 27045 Weipa Aero Hope Vale 31129 Hazelmere Western Downs 41522 Dalby Airport Ipswich 40004 Amberley AMO Whitsunday 33247 Proserpine Airport Isaac 34086 Seloh Nolem Winton 37039/37006 Winton Airport/Bladensburg Kowanyama 29038 Kowanyama Airport Woorabinda 39004 Baralaba Post Office Lockhart River 28008 Lockhart River Airport Wujal Wujal 31012 Cape Tribulation Store Lockyer valley 40449 Placid Hills Yarrabah 31063 Mt Sheridan SOUTH AUSTRALIA Adelaide Hills 23731 Cudlee Creek (Millbrook) Mount Remarkable 19024 Melrose Adelaide 23011 North Adelaide Murray Bridge 24521 Murray Bridge Comparison Alexandrina 23718 Goolwa Council Depot Naracoorte 26099 Naracoorte Aerodrome Anangu 16088 Mintabie Northen Areas 21027 Jamestown Barossa 23309 Lyndoch Norwood Payneham 23090 Adelaide (Kent Town) Barunga West 21042 Port Broughton Onkaparinga 19034 Peterborough Berri Barmera 24008 Lyrup Orroroo 19032 Orroroo Burnside 23005 Adelaide (Glen Osmond) Peterborough 19034 Peterborough Campbelltown 23096 Adelaide (Hope Valley Reservoir) Playford 23083 Edinburgh RAAF Ceduna 18012 Ceduna AMO Port Adelaide Enfield 23079 Adelaide (Dry Creek Saltworks) Charles Sturt 23024 Adelaide (Seaton) Port Augusta 18201/19030 Port Augusta Aero/Quorn
255
Clare & Gilbert 21131 Clare High School Port Lincoln 18205/18192 Port Lincoln South/AWS Valley Cleve 18014 Cleve Port Pirie 19037 Port Germein Coober Pedy 16007 Coober Pedy Prospect 23011 North Adelaide Copper Coast 22011 Moonta Renmark Paringa 24003 Renmark Irrigation Elliston 18069 Elliston Robe 26026 Robe Comparison Flinders Ranges 19038 Quorn Roxby Downs 16096 Roxby Downs (Olympic Aerodrome) Franklin Harbour 18022 Cowell Salisbury 23023 Adelaide (Salisbury Bowling Club) Gawler 23021/23078 Roseworthy/Gawler Council Depot Southern Malee 25015 Pinnaroo Goyder 21035 Mount Templeton (Glenalbyn) Streaky Bay 18079 Streaky Bay Grant 26021 Mount Gambier Aero Tatiara 25519 Wolseley Holdfast Bay 23721 Happy Valley Reservoir Tea Tree Gully 23806 Upper Hermitage Kangaroo Island 22841 Kingscote Aero The Coorong 26049 Policeman Point Karoonda East 25006 Karoonda Tumby Bay 18086 Tumby Bay Murray Kimba 18040 Kimba Unincorporated SA 17132 Marree (Etadunna) Kingston 26012 Kingston SE Unley 23115 Adelaide (Keswick) Light 23307 Kapunda Victor Harbor 23804/23742 Encounter Bay/Port Elliot Lower Eyre Peninsula 18023 Cummins Wakefield 21044 Port Wakefield Loxton Walkerie 24024 Loxton Research Centre Walkerville 23090 Adelaide (Kent Town) Mallala 23009 Mallala Wattle Range 26101 Furner (Woomera Homestead) Maralinga 18114 Maralinga West Torrens 23011 North Adelaide Marion 23034 Adelaide Airport Whyalla 18120 Whyalla Aero Mid Murray 24531 Sedan Wudinna 18083 Wudinna Aero Mitcham 23704 Belair (State Flora Nursery) Yankalilla 23754 Yankalilla Mount Barker 23733 Mount Barker Yorke Peninsula 22009 Minlaton Mount Gambier 26021 Mount Gambier Aero WESTERN AUSTRALIA Albany 9500 Albany Kwinana 9194 Medina Research Centre Armadale 9239 Bedfordale Lake Grace 10911 Lake Grace
256
Ashburton 5017 Onslow Airport Laverton 12045/12305 Laverton/Aero Augusta Margaret 9574 Margaret River Leonora 12046 Leonora River Bassendean 9021 Perth Airport Mandurah 9977 Mandurah Bayswater 9225 Perth Metro Manjimup 9573 Manjimup Belmont 9021 Perth Airport Meektharra 7031 Hillview Bencubbin 10007 Bencubbin Melville 9127/9192 Mosman Park/Fremantle Beverley 10515 Beverley Menzies 12052/12043 Menzies/Kookynie Boddington 9575 Marradong Merredin 10092 Merredin Boyup Brook 9504 Boyup Brook Mingenew 8088 Mingenew Bridgetown 9510 Bridgetown Comparison Moora 8005 Barberton Brookton 10524 Brookton Morawa Airport 8296 Morawa Airport Broome 3003 Broome Airport Mosman Park 9127 Mosman Park Broomehill 10525 Broomehill Mount Magnet 7600 Mount Magnet Aero Bruce Rock 10118 Breakell Mukinbuddin 10030 Wattoning Bunbury 9965 Bunbury Mullewa 8095 Mullewa Busselton 9603 Busselton Aero Mundaring 9031 Mundaring Weir Cambridge 9215 Swanbourne Murchison 6099 Murchison Canning 9106 Gosnells City Nannup 9585 Nannup Capel 9992/9744 Capel North/Paynedale Narembeen 10612 Narembeen Carnamah 8025 Carnamah Narrogin 10614 Narrogin Carnarvon 6011 Carnarvon Airport Nedlands 9215 Swanbourne Chapman 8051 Geraldton Airport Comparison Ngananyatjarraku 12038 Kalgoorlie Boulder Airport Chittering 9221 Chittering Heights Northam 10111 Northam Claremont 9215 Swanbourne Northampton 8100 Northampton Cockburn 20002 Cockburn Nungarin 10112 Nungarin Collie 9628 Collie Perenjori 8107 Perenjori Coolgardie 12204 Dedari Peppermint Grove 9594 Peppermint Grove Coorow 8037 Coorow Perth 9225 Perth Metro
257
Corrigin 10536 Corrigin Pingelly 10626 Pingelly Cottesloe 9215 Swanbourne Pinjarra 9596 Pinjarra Cranbrook 10537 Cranbrook Plantagenet 9581 Mount Barker Cuballing 10614 Narrogin Port Hedland 4032 Port Hedland Airport Cue 7017 Cue Quairading 10628 Quairading Cunderdin 10286 Cunderdin Airfield Ravensthorpe 10869 Carlingup Dalwallinu 8297 Dalwallinu Rockingham 9256 Garden Island HSF Dandaragan 9054 Tambrey Roebourne 4035 Roebourne Dardanup 9527 Dardanup East Sandstone 12008 Booylgoo Spring Derby West 3032 Derby Aero Serpentine Jarrahdale 9242 Cloon Kimberley Denmark 9531 Denmark Shark Bay 6105 Shark Bay Airport Donnybrook 9534 Donnybrook South Perth 9225 Perth Metro Dowerin 10042 Dowerin Stirling 9225 Perth Metro Dumbleyung 10528 Bunkin Subiaco 9151 Subiaco Treatment Plant Dundas 12009 Norseman Aero Swan 9025 Midland East Fremantle 9215 Swanbourne Tammin 10121 Tammin East Pilbara 7176 Newman Aero Three Springs 8121 Three Springs Esperance 9789 Esperance Toodyay 10125 Toodyay Exmouth 5007 Learmonth Airport Trayning 10126 Trayning Fremantle 9215 Swanbourne Upper Gascoyne 6022 Gascoyne Junction Geraldton 8051 Geraldton Airport Comparison Victoria Park 9225 Perth Metro Gingin 9248 Moondah Brook Victoria Plains 10009 Bolgart Gnowangerup 10558 Gnowangerup Vincent 9151 Subiaco Treatment Plant Goomalling 10058 Goomalling Wagin 10647 Wagin Gosnells 9106 Gosnells City Wandering 10917 Wandering Halls Creek 2012 Halls Creek Airport Wanneroo 9105 Wanneroo Harvey 9812 Harvey Waroona 9614 Waroona Irwin 8276 Irwin House West Arthur 10542 Darkan
258
Jerramungup 10707 Jerramungup Westonia 12083 Westonia Joondalup 9249 Mariginiup Wickepin 10654 Wickepin Kalamunda 9216 Victoria Dam Williams 10655 Williams Kalgoorlie Boulder 12038 Kalgoorlie Boulder Airport Wiluna 13012 Wiluna Katanning 10916 Katanning Wongan Ballidu 8137 Wongan Hills Kellerberrin 10073 Kellerberrin Woodanilling 10659 Woodanilling Kent 10589 Kwobrup Wyalkatchem 10032 Cowcowing Kojonup 10530 Chamingup Wyndham Kimberley 1013 Wyndham Kondinin 10583 Kondinin Yalgoo 7027 Gabyon Koorda 10077 Koorda Yilgarn 12320 Southern Cross Airfield Kulin 10584 Kulin York 10311 York TASMANIA Brek O'Day 92120 St Helens Aerodrome Houn Valley 94089 Huonville (Tutton Avenue) Brighton 94005 Bridgewater (Treatment Plant) Kentish 91153 Barrington Post Office Burnie 91009 Burnie (Round Hill) King Island 98017 King Island Airport Central Coast 91102 Ulverstone (Knights Road) Kingborough 94098 Mount Nelson (Rialannah Road) Central Highlands 95008 Hamilton (Uralla) Latrobe 91048 Latrobe (Coal Road) Circular Head 91292 Smithton Aerodrome Launceston 91237 Launceston (Ti Tree Bend) Clarence 94161 Clifton Beach (Clifton Beach Road) Meander Valley 91236 Westbury (Birralee Road) Darwent Valley 95074 Magra (Black Hills Road) Northern Midlands 91054 Longford (Denton Close) Devonport 91126 Devonport Airport Sorell 94064 Wattle Hill Dorset 91219 Scottsdale (West Minstone Road) Southern Midlands 93014 Oatlands Post Office Flinders 99015 Whitemark Post Office Tasman 94053 Premaydena Hatchery George Town 91286 George Town (South Street) Waratah Wynyard 91107 Wynyard Airport Glamorgan Spring 92027 Orford (Aubin Court) West Coast 97091 Queenstown (South Queenstown) Bay Glenorchy 94025 Glenorchy Reservoir West Tamar 91262 Bell Bay (Temco) Hobart 94029 Hobart (Ellerslie Road) NORTHERN TERRITORY
259
Alice Springs 15590 Alice Springs Airport Macdonne 15590 Alice Springs Airport Barkly 15135 Tennant Creek Airport Palmerston 14265 CSIRO Berrimah Belyuen 14015 Darwin Airport Roper Gulf 14903 Katherine Aviation Museum Central Desert 15528 Yuendumu Tiwi Islands 14142 Pirlangimpi Airport Coomalie 14272 Batchelor Airport Unincorporated NT 14253 Channel Point Darwin 14015 Darwin Airport Victoria Daly 14850 Timber Creek East Arnhem 14508 Gove Airport Wagait 14238 Wagait Beach Katherine 14903 Katherine Aviation Museum West Arnhem 14198 Jabiru Airport Litchfield 14219 McMinns Lagoon ACT and UNINCORPORATED OTHER TERRITORY Unincorporated ACT 70247 Canberra (Australian National Botanic) Unincorporated Other 200790 Island Aero
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