Modernization and Contraception in Kenya

MODERNIZATION AND CONTRACEPTION IN KENYA

FROM 1998 TO 2008-2009

DIANA ALAKA OPOLLO

Presented to the Faculty of the Graduate School of Social Work

The University of Texas at Arlington in Partial Fulfillment

of the Requirements

for the Degree of

DOCTOR OF PHILOSOPHY

THE UNIVERSITY OF TEXAS AT ARLINGTON

December 2011

DEDICATION

This dissertation is dedicated to the most special people in my life, with whom I have been able to achieve my dreams: my father, the Late Mr. Phillip Ochara Opollo

and my mother, Mrs. Joyce A. Opollo.

ACKNOWLEDGEMENTS

Pursuing a doctorate degree requires extreme dedication, tenacity, and perseverance. Equally

required and certainly very important, more times than others, are support and the right guidance. While this page is not enough to mention each person who has supported me in my academic journey, I would like to specially thank a few who significantly influenced my life, and helped me to complete my higher education and achieve my dreams. My academic journey began at a very young age; the vision and desire of pursuing a doctorate degree was instilled in me by my family at an early age, but that was just a dream. With the consistent support and unconditional love from my family; I was able to complete my education. I would like to start by thanking my immediate family for their constant support and encouragement throughout the years; my father, the late Phillip Ochara Opollo, my mother Mrs. Joyce

Opollo, my sisters Nancy Achieng, Winnie Aguko, Leah Vaughn, Rachel Tschann, Jakki Opollo, and my brothers Ken Opollo and Bobby Opollo.

Successful completion of a dissertation requires guidance and support from one’s committee. I was especially blessed and highly favored to have had an extremely supportive and well-wishing committee, who encouraged me from the beginning and guided me to the end. I thank my dissertation chair Dr. Vijayan Pillai, whose dedication and support far exceeded the expectations of a dissertation committee chair. He is the best mentor any student could ask for. Extra special thanks also go to rest of my committee members who together served as an excellent team: Dr. Randall Basham, Dr. Maria

Scannapieco, Dr. Doreen Elliott, and Dr. Rashmi Gupta. Additionally, I would also like to thank Rita Hay,

John Dillard, and Jaime Palma for their constant assistance and support throughout the program.

Outside academics, a great support system of friends and family enable you to go on, cheering

you on and encouraging you when you are down. I have been blessed with such great and wonderful

friends, a few of whom require special mention: Dr. Sisay Teketele, Fatma Gheorghe, Lydia Adundo,

Dr. Grace Virtue, Dr. Consoler Teboh, Roshanda McLemore, Benjamin Gant, and Fangshun Wei.

Lastly, I would like to give special thanks to my fiancé Michael Salako, whose patience and continued support enabled me to successfully pursue my doctoral studies.

November 26, 2011

ABSTRACT

MODERNIZATION AND CONTRACEPTION IN KENYA

FROM 1998 TO 2008-2009

Diana Alaka Opollo, PhD

The University of Texas at Arlington, 2011

Supervising Professor: Dr. Vijayan K. Pillai

The objective of this study is to examine the factors that influence contraceptive use in Kenya.

More specifically, the study focuses on the determinants of membership in two categories of contraceptive use: modern contraception methods and other contraception methods. Additionally, the study will describe the differences in contraceptive use between 1998 and 2008-2009 and attempt to examine what factors caused and propelled changes. In order to explain the categorical variations, this study uses two theories: modernization theory and human capital theory. In describing changes between

1998 and 2008-2009, this study will examine both individual as well as societal factors related to contraceptive use in Kenya, using Ryder’s theory on social change.

This study utilizes two data sets from the 1998 Kenya Demographic Health Survey (KDHS) which surveyed a total of 7,881 women, and the 2008-2009 KDHS which surveyed 8,444 women. This study utilizes several data analyses techniques such as descriptive analysis, binary logistic regression, and decomposition analysis. The findings of these analyses will be reported followed by a discussion on the findings. Additionally, this study will address implications for social work practice, policy, research, and education. Moreover, this study will address utilize the National Association of Social Workers (NASW) values and ethics assessments. Lastly, this paper will address the limitations faced, future directions, and a conclusion tying together the entire study.

The study results indicate that, women with some education and higher education were found to

be more likely to use modern contraceptives than women with no education. Women who live in urban

areas are more likely to use modern contraceptives compared to women who live in rural areas.

Additionally, women who earn an income are more likely to use modern contraceptives compared to

women who do not earn an income. The proposed hypotheses are strongly supported by the chi square

associations of the selected determinants on contraceptive use. The hypotheses are also supported by

the regression analyses net effects with independent variables only, and net effects with both independent and control variables. Additionally, statistically significant compositional changes addressed

by the Phi Coefficient values supported the compositional changes within cohorts of selected variables

over the two time periods, 1998 and 2008-2009. Moreover, the effect or processual changes indicated

support for the proposed hypotheses showing change in the selected determinants over time across

cohorts, between the two time periods, 1998 and 2009. Lastly, the decomposition analysis suggests that

all variables contributed to the overall change of selected determinants over the two survey periods 1998

and 2008-2009.

vii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... ……………..iv

ABSTRACT ...... vi

LIST OF ILLUSTRATIONS ...... x

LIST OF TABLES ...... xii

Chapter Page

1. INTRODUCTION……………………………………..………..…...... 1

2. LITERATURE REVIEW...... 5

2.1 Social and Cultural Factors ...... 15

2.2 Policy and Political Factors ...... 22

2.3 Legal Factors ...... 25

3. CONCEPTUAL FRAMEWORK ...... 29

3.1 Modernization Theory ...... 30

3.1.1 Urban versus Rural Residence ...... 34

3.1.2 Age at Marriage ...... 36

3.1.3 Income ...... 38

3.2 Human Capital Theory ...... 40

3.2.1. Education ...... 44

3.3 Social Change Theory ...... 45

4. METHODOLOGY ...... 49

4.1 Operationalization of Variables ...... 50

4.2 Data Analysis Techniques ...... 56

5. DATA ANALYSIS AND RESULTS ...... 60

5.1 Section I Descriptive Analysis ...... 61

5.2 Section II Chi Square Associations ...... 98

viii

5.3 Section III Logistic Regression ...... 101

5.4 Section IV Decomposition Analysis ...... 119

5.5 Compositional Analysis ...... 119

5.6 Processual Changes ...... 135

5.7 Decomposition Analysis Formula ...... 139

6. DISCUSSION AND IMPLICATIONS ...... 144

6.1 Discussion ...... 144

6.2 Social Work Implications ...... 148

6.3 NASW Values and Ethics Assessment ...... 153

7. CONCLUSION ...... 157

APPENDIX

A. SUMMARY OF LITERATURE REVIEW TABLE ...... 159

B. HISTORY OF FAMILY PLANNING POLICY AND PROGRAMS IN KENYA ...... 171

C. QUESTIONS FROM KDHS 1998 AND 2008-2009 ...... 174

REFERENCES ...... 184

BIOGRAPHICAL INFORMATION ...... 194

LIST OF ILLUSTRATIONS

Figure Page

3.1 Diagram Depicting the Relationship Among the Theories, Selected Determinants and Outcome Variable ...... …………….48

5.1 Histogram of Women with Different Levels of Education in Kenya in 1998 ...... 62

5.2 Bar Graph of Women with Different Levels of Education in Kenya in 1998 ...... 63

5.3 Histogram of Women with Different Levels of Education in Kenya in 2008-2009 ...... 63

5.4 Bar Graph of Women with Different Levels of Education in Kenya in 2008-2009 ...... 64

5.5 Histogram of Women Who Earned or Did Not Earn an Income in Kenya in 1998 ...... 66

5.6 Bar Graph of Women Who Earned or Did Not Earn an Income in Kenya in 1998 ...... 66

5.7 Histogram of Women Who Earned or Did Not Earn an Income in Kenya in 2008-2009 ...... 67

5.8 Bar Graph of Women Who Earned or Did Not Earn an Income in Kenya in 2008-2009 ...... 67

5.9 Histogram of Women Categorized According to Age at Marriage in Kenya in 1998 ...... 69

5.10 Bar Graph of Women Categorized According to Age at Marriage in Kenya in 1998 ...... 69

5.11 Histogram of Women Categorized According to Age at Marriage in Kenya in 2008-2009 ...... 70

5.12 Bar Graph of Women Categorized According to Age at Marriage in Kenya in 2008-2009 ...... 70

5.13 Histogram of Women in Urban and Rural Areas in Kenya in 1998 ...... 72

5.14 Bar Graph of Women in Urban and Rural Areas in Kenya in 1998 ...... 72

5.15 Histogram of Women in Urban and Rural Areas in Kenya in 2008-2009 ...... 73

5.16 Bar Graph of Women in Urban and Rural Areas in Kenya in 2008-2009 ...... 73

5.17 Histogram of Other Methods and Modern Contraceptive Use in Kenya in 1998 ...... 75

5.18 Bar Graph of Other Methods and Modern Contraceptive Use in Kenya in 1998 ...... 75

5.19 Histogram of Other Methods and Modern Contraceptive Use in Kenya in 2008-2009 ...... 76

5.20 Bar Graph of Other Methods and Modern Contraceptive Use in Kenya in 2008-2009 ...... 76

5.21 Histogram of Total Number of Children Born to Women Aged 15-49 in Kenya in 1998 ...... 79

5.22 Bar Graph of Total Number of Children Born to Women Aged 15-49 in Kenya in 1998 ...... 80

5.23 Histogram of Total Number of Children Born to Women Aged 15-49 in Kenya in 1998 ...... 81

5.24 Bar Graph of Total Number of Children Born to Women Aged 15-49 in Kenya in 2008-2009 ...... 81

5.25 Histogram of Women Categorized According to Their Marital Status in Kenya in 1998 ...... 83

5.26 Bar Graph of Women Categorized According to Their Marital Status in Kenya in 1998 ...... 83

5.27 Histogram of Women Categorized According to Their Marital Status in Kenya in 2008-2009 ...... 84

5.28 Bar Graph of Women Categorized According to Their Marital Status in Kenya in 2008-2009 ...... 84

5.29 Histogram of Women Categorized According to Marriage Type in Kenya in 1998 ...... 86

5.30 Bar Graph of Women Categorized According to Marriage Type in Kenya in 1998 ...... 86

5.31 Histogram of Women Categorized According to Marriage Type in Kenya in 2008-2009 ...... 87

5.32 Bar Graph of Women Categorized According to Marriage Type in Kenya in 2008-2009 ...... 87

5.33 Histogram of Women Who Heard of Contraceptive Methods Through the Media in Kenya in 1998 ...... 89

5.34 Bar Graph of Women Who Heard of Contraceptive Methods Through the Media in Kenya in 1998 ...... 89

5.35 Histogram of Women Who Heard of Contraceptive Methods Through the Media in Kenya in 2008-2009 ...... 90

5.36 Bar Graph of Women Who Heard of Family Planning Through the Media in Kenya in 2008-2009 ...... 90

5.37 Histogram of Women Categorized According to Their

Religion in Kenya in 1998 ...... 92

5.38 Bar Graph of Women Categorized According to Religion in Kenya in 1998 ...... 92

5.39 Histogram of Women Categorized According to Their Religion in Kenya in 1998 ...... 93

5.40 Bar Graph of Women Categorized According to Religion in Kenya in 1998 ...... 93

5.41 Histogram of Women in the 7 Provinces in Kenya in 1998...... 95

5.42 Bar Graph of Women in the 7 Provinces in Kenya in 1998 ...... 96

5.43 Histogram of Women in the 7 Provinces in Kenya in 2008-2009 ...... 97

5.44 Bar Graph of Women in the 7 Provinces in Kenya in 2008-2009 ...... 97

5.45 Diagram Showing the Strength of Strong, Moderate, Weak, and Contradictory Hypotheses ...... 102

LIST OF TABLES Table Page

2.1 Contraceptive Prevalence Using Any Method, Comparing the World, Africa, Sub-Saharan Africa, Northern, Eastern, Middle, Southern, and Western Africa Regions from 1980 to 2009 ...... 10

2.2 Contraceptive Prevalence Using Any Modern Method, Comparing the World, Africa, Sub-Saharan Africa, Northern, Eastern, Middle, Southern, and Western Africa Regions from 1980 to 2009 ...... 11

2.3 Contraceptive Prevalence Using Any Traditional Method, Comparing the World, Africa, Sub-Saharan Africa, Northern, Eastern, Middle, Southern, and Western Africa Regions from 1980 to 2009 ...... 12

2.4 The Unmet Need for Family Planning in the World, Africa, Sub-Saharan Africa, and the Different Regions in Africa from 1990 to 2009 ...... 13

2.5 Contraceptive Prevalence Using Any Method, Modern and Traditional Contraceptive Methods in Kenya from 1977-1978 to 2003 of Women Aged 15-49 ...... 14

2.6 Modern Contraceptive Methods in Kenya from 1977-1978 to 2008-2009 Among Women Aged 15-49 ...... 14

2.7 Traditional Contraceptive Methods in Kenya from 1977-1978 to 2008-2009 Among Women Aged 15-49 ...... 15

4.1 Summary of Selected Variables, Corresponding Hypotheses, and Projected Direction ...... 51

4.2 Variables and Corresponding Missing System Data for Both Survey Periods 1998 and 2008-2009 ...... 58

5.1 Total Number and Percentage of Women with Different Levels of Education in Kenya in 1998 ...... 61

5.2 Total Number and Percentage of Women with Different Levels of Education in Kenya in 2008-2009 ...... 62

5.3 Total Number and Percentage of Women who Earned and Did Not Earn an Income in Kenya in 1998 ...... 64

5.4 Total Number and Percentage of Women Who Earned and Did Not Earn an Income in Kenya in 2008-2009 ...... 65

5.5 Women Categorized According to Their Age at Marriage in Kenya in 1998 ...... 68

5.6 Women Categorized According to Their Age at Marriage in Kenya in 2008-2009 ...... 68

5.7 Total Number and Percentage of Women in the Urban and Rural Areas in Kenya in 1998 ...... 71

5.8 Total Number and Percentage of Women in the Urban and Rural Areas in Kenya in 2008-2009 ...... 71

5.9 Women Who Have Used Other Contraceptives and Those Who Have Used Modern Contraceptives in Kenya in 1998 ...... 74

5.10 Women Who Have Used Other Contraceptives and Those Who Have Used Modern Contraceptives in Kenya in 2008-2009 ...... 74

5.11 Total Number and Percentage of Children Born to Women Aged 15-49 in Kenya in 1998 ...... 77

5.12 Total Number and Percentage of Children Born to Women Aged 15-49 in Kenya in 2008-2009 ...... 78

5.13 Total Number and Percentage of Women Categorized According to Their Marital Status in Kenya in 1998 ...... 82

5.14 Total Number and Percentage of Women Categorized According to Their Marital Status in Kenya in 2008-2009 ...... 82

5.15 Total Number and Percentage of Women Categorized According to Marriage Type in Kenya in 1998 ...... 85

5.16 Total Number and Percentage of Women Categorized According to Marriage Type in Kenya in 2008-2009 ...... 85

5.17 Percentage of Women Who Heard of Contraceptive Methods Through the Media in Kenya in 1998 ...... 88

5.18 Percentage of Women Who Heard of Contraceptive Methods Through the Media in Kenya in 2008-2009 ...... 88

5.19 Total Number and Percentage of Women Categorized According to Their Religion in Kenya in 1998 ...... 91

5.20 Total Number and Percentage of Women Categorized According to Their Religion in Kenya in 2008-2009 ...... 91

5.21 Total Number and Percentage of Women in the 7 Provinces in Kenya in 1998 ...... 94

5.22 Total Number and Percentage of Women in the 7

Provinces in Kenya in 2008-2009 ...... 95

5.23 Chi Square values and Significance Levels of Selected Independent Variables on Contraceptive Use for Survey Periods 1998 and 2008-2009 ...... 100

5.24 Key on Strength and Direction of Hypotheses ...... 103

5.25 Regression Net Effects with Independent Variables Only, Net Effects with Independent and Control Variables and Gross Effects for Survey Period 1998 ...... 104

5.26 Summary of the Independent Variables, Hypotheses, and Strengths Table for Survey Period 1998 ...... 110

5.27 Regression Net Effects with Independent Variables Only, Net Effects with Independent and Control Variables, and Gross Effects for Survey Period 2008-2009 ...... 111

5.28 Summary of the Independent Variables, Hypotheses, and Strengths Table for Survey Period 2008-2009 ...... 116

5.29 Summary of the Independent Variables, Hypotheses, and Strengths Table for Survey Periods 1998 and 2008-2009 ...... 118

5.30 Phi Coefficient Interpretation Rules ...... 120

5.31 Phi Coefficient Table Showing Significance of Having Some Education on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 120

5.32 Phi Coefficient Table Showing Significance of Higher Education on Modern Contraceptive Use for Both Survey Periods1998 and 2008-2009 ...... 121

5.33 Phi Coefficient Table Showing Significance of Income on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 121

5.34 Phi Coefficient Table Showing Significance of Place of Residence on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 122

5.35 Phi Coefficient table showing significance of old age (15-20 years) at marriage on modern contraceptive use for both survey periods 1998 and 2008-2009 ...... 123

5.36 Phi Coefficient Table Showing Significance of Older Age (21-49 years) at Marriage on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 123

5.37 Phi Coefficient Table Showing Significance of Being Married on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 124

5.38 Phi Coefficient Table Showing Significance of Being Married on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 125

5.39 Phi Coefficient Table Showing Significance of Women Using Other Methods and Using Modern Contraceptive Methods for Both Survey Periods 1998 and 2008-2009 ...... 125

5.40 Phi Coefficient Table Showing Significance of Type of Marriage in Relation to Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 126

5.41 Phi Coefficient Table Showing Significance of Media in Relation to Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 127

5.42 Phi Coefficient Table Showing Significance of Religion on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 128

5.43 Phi Coefficient Table Showing Significance of Nairobi Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 129

5.44 Phi Coefficient Table Showing Significance of Central Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 129

5.45 Phi Coefficient Table Showing Significance of Coast Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 130

5.46 Phi Coefficient Table Showing Significance of Eastern Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 130

5.47 Phi Coefficient Table Showing Significance of Nyanza Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009 ...... 131

5.48 Phi Coefficient Table Showing Significance of Rift Valley Province on Modern Contraceptive Use for Both Survey Periods1998 and 2008-2009 ...... 131

5.49 Summary of Phi Coefficient Values for Survey Periods 1998 and 2008-2009 Showing Compositional Change Over Time ...... 133

5.50 Mean Differences on Total Children Ever Born to Women Aged 15-49 in Kenya for Survey Periods 1998 and 2008-2009 ...... 134

5.51 Interaction Effects of All Determinants on Modern Contraceptive Use with Survey Periods 1998 and 2008-2009 Data ...... 136

5.52 Decomposition Analysis Results Showing the Processual, Compositional and Interaction Changes of All Selected Determinants Including Control Variables for Survey Periods 1998 and 2008-2009 ...... 140

5.53 Summary of All Significant Processual, Compositional, and Interaction Changes of All Selected Determinants Including Control Variables for Survey Periods 1998 and 2008-2009 ...... 141

CHAPTER 1

INTRODUCTION

Sub-Saharan Africa is facing serious political, economic and social challenges, including increased population growth and numerous deaths caused by the HIV/AIDS pandemic (UNFPA, 2010). According to the Population Reference Bureau, Africa’s population was 1.030 billion in 2010 (PRB, 2010). Sub-

Saharan Africa’s population was 865 million with a projected increase to 1.207 billion in 2025 and 1.831 billion in 2050 (PRB, 2010). Currently, Sub-Saharan Africa has the world’s highest birth rate, with 39 per

1000 people and a 2.2 to 2.5 percent annual growth rate (PRB, 2010; UNFPA, 2010), and fertility rate of approximately 5.2 to 5.5 children (PRB, 2010, Cook & Kalu, 2008). Sub-Saharan Africa also has the highest death rate of 14 per 1000 deaths, the lowest life expectancy of 49 years, highest percentage of population under 15 years of age at 43 percent, and the lowest percentage of people aged 65 or older-- three percent, compared to the rest of the world (PRB, 2010). High fertility rate and population growth rates in Sub-Saharan Africa are outpacing economic growth, resulting in exacerbated social and economic problems, such as high unemployment and deepening poverty (UNFPA, 2010). The social and economic problems Sub-Saharan African countries are facing, points to the need for population control through increasing awareness and use of advanced family planning methods (Oliver, 1995).

Another devastating effect on the population of Sub-Saharan Africa is the prevalence of the

HIV/AIDS pandemic (UNFPA, 2010). The number of people living with HIV in Africa has increased from

19.7 million in 2001 to 22.4 million in 2008 (UNFPA, 2010; UNAIDS, 2009). Women accounted for over

61 percent of those living with HIV/AIDS in 2008 (UNFPA, 2010). According to UNAIDS/WHO, in 2008, there were a total of 33.4 million people living with HIV/AIDS globally, with adults accounting for 31.3 million. Close to 16 million were women and 2.1 million were children under 15 (UNAIDS/WHO, 2009). In

2008, the number of new infections globally totaled 2.7 million with adults accounting for 2.3 million and children under 15, 430,000 (UNAIDS/WHO, 2009). Globally, the number of HIV/AIDS related deaths totaled two million. Adult deaths were 1.7 million, and deaths of children under 15 were 280,000

(UNAIDS/WHO, 2009). The current HIV/AIDS epidemic in African countries, such as Kenya, calls for universal use of condoms to prevent HIV/AIDS transmission.

According to the Population Reference Bureau (2010), Kenya’s population, in 2010, was approximately 40 million with birth rate at 37 per 1,000 births, death rate at 10 per 1,000, and total fertility rate at 4.6 percent (PRB, 2010). Forty two percent of Kenya’s population is under 15 and three percent is

65 or older (PRB, 2010). In Kenya, 2.3 million people live with HIV/AIDS while an estimated 1.5 million people have died from the HIV virus (Milkowski, 2004). Approximately 200,000 Kenyans develop the

AIDS syndrome annually and an alarming 300 people die daily of AIDS related diseases such as

Tuberculosis (TB) or Malaria (Milkowski, 2004). The predominant mode of HIV/AIDS transmission is through heterosexual intercourse (Milkowski, 2004; Omungo, 2008; Erulkar et al, 2004). Other factors that increase the prevalence and incidence of HIV/AIDS, include incidence of other sexually transmitted infections (STIs), female circumcisions, and wife inheritance practices, which involves widow marriage to the brother of the deceased, in several Kenyan tribes (Milkowski, 2004).

Contraceptive use programs are gaining significant attention in Sub-Saharan Africa in an effort to curb the rapid population growth (Oliver, 1995; National Research Council, 1993). Family planning was popularized to lower fertility. The implementation rate and scope of family planning programs is slow, however, evidenced by the low use rate of only10 percent by those living in Sub-Saharan Africa (UNFPA,

2010; Milkowski, 2004). A strong resistance to fertility control by governments in Africa contributed to failure of programs to provide contraception to those who need it, especially in rural areas (KDHS, 1998;

KDHS, 2003; KDHS, 2009). There is an enormous need for contraceptive use among many Kenyans who want plan their families but do not have access to it (KDHS, 1998; KDHS, 2003; KDHS, 2009).

As part of the cultural norms, Kenyans consider raising children as investments in addition to continuing the family lineage (Pillai, 1992; Robinson, 1997). In addition to the strong pronatalist norms resulting in high fertility, social structural conditions such as poverty contribute to high fertility levels.

Approximately 56 percent of the 40 million Kenyan citizens live below the poverty line (Milkowski, 2004) and over 90 percent lack the financial resources to gain adequate levels of sexual and reproductive health services (Milkowski, 2004). In general, literature on Kenyan fertility show that strong pronatal

norms encourage large families and discourage contraceptive use for birth control (Ntozi, 2002; Crane,

2005; Cook & Kalu, 2008; Robinson, 1997; Pillai, 1992).The high fertility rates in Sub-Saharan Africa reflect the high demand for children (Van de Walle & Foster, 1990).The likelihood of current contraceptive use is determined not only by the demand for children, but also by the historical trends in the strong pronatalist norms over time in Kenya. Additionally, as the HIV/AIDS epidemic spreads among the Kenyan population, resulting in significant increases in mortality, the likelihood of contraceptive use may increase over time (Onyango & Mugo, 2008). Even though these explanations of contraceptive use have been offered by the popular media and political groups, very few studies have empirically tested these notions with respect to contraceptive use in reducing HIV/AIDS transmission (Njogu, 1991).

The purposes of this study are:

1. To examine the effects of selected determinants on contraceptive use in Kenya between 1998

and 2008-2009.

2. To describe the changes in the selected determinants of contraceptive use in Kenya between

1998 and 2008-2009.

1.1 Significance of Study

Recent studies on contraceptive use in Kenya did not include available data such as the Kenya

Demographic and Health Survey 2008-2009 data set. The only study that examined the trends in

contraceptive use in Kenya was done in the 1980s using the 1977-1978 Kenya Fertility Survey (KFS) and

the 1989 Kenya Demographic Health Survey (by Njogu, 1991). For most African countries, including

Kenya, this type of analysis has been hampered by the lack of comparable time-series data (Njogu, 1991;

Pillai & Teboh, 2010). Furthermore, it is very likely that contraceptive use patterns may have changed,

owing to several forces of social change related to both changes in values, as well as external realities

such as the spread of HIV/AIDS in the Kenyan population (Onyango & Mugo, 2008; Omungo, 2009).

Cross-sectional studies utilized older data sets such as the KDHS 1989, 1993, 1998, and 2003 to report

the impact of contraceptive use on women. Increased modernization, change in government

administrations, diversity in religious beliefs and practices and increased technology and media exposure

to western influences changed the use of contraceptives in the Kenyan population resulting in the need for this study.

Though there have been many studies on contraceptive use in Sub-Saharan Africa, very few have focused on its social determinants (Njogu, 1991). Previous studies suffered from several drawbacks. First, existing studies generally focus on medical issues related to contraceptive use in Kenya (Omungo, 2009;

Onyango & Mugo, 2008). Second, in spite of governmental and non-governmental efforts, supported by foreign donors, to promote contraceptive use, there is relatively little known about causal factors that bring about changes in contraceptive use over time. Third, prior studies on contraceptive use have been mostly descriptive and have not adequately assessed or estimated the independent influences of several social and economic variables. Finally, very few of the existing studies are adequately grounded in theories that contribute to a framework of contraceptive behavior in Kenya.

This current study is unique because it compares and addresses the factors affecting contraceptive use between 1998 and 2008-2009. Additionally, social change is always occurring, but its impact on people, and what they value in terms of their fertility, has mostly been ignored. Fortunately, more recent data sets have become available, such as the KDHS 2008-2009, which give an in-depth view of the current level of contraceptive use among women in Kenya. There are few studies utilizing longitudinal methods that address contraceptive use from a social change perspective such as that of Njogu (1991), where she highlights the role of social change as a component of understanding contraceptive use dynamics in Kenya, utilizing a sociological and demographical perspective.

The two research questions used in this study are:-

1. What are the characteristics of, and differences in the trends of contraceptive use between

women using the following methods of contraception in 1998 and 2008-2009 in Kenya?

a) Only modern contraception methods

b) Other contraception methods

2. What factors describe the overall changes in contraceptive use and what caused or propelled

these changes in Kenya?

CHAPTER 2

LITERATURE REVIEW

Contraceptives or birth control methods are defined as any form of modern, traditional, or mixed method used to delay or prevent pregnancy among women of child bearing age (WHO, 2011). Modern methods of contraceptives include female and male sterilization; intrauterine devices (IUDs); hormonal methods such as oral pills, injectables and hormone-releasing implants, skin patches, and vaginal rings; condoms; and vaginal barriers such as the diaphragm, cervical cap, spermicidal foams, jellies, creams, and sponges (WHO, 2011). Traditional contraceptive methods include rhythm, withdrawal, abstinence and lactational amenorrhea WHO, 2011). Contraceptive use among women in a society is often measured using age, such as the adolescent period, marital status, method of contraception, place of residence (urban/rural, major regions or provinces), and socioeconomic characteristics such as level of education and wealth index (WHO, 2011).

This study describes the differences in contraceptive use among women of child bearing ages between 15 and 49 in Sub-Saharan Africa with an in-depth focus on Kenya. The literature review includes the social, cultural, political, and legal factors influencing contraceptives and contraceptive use in Kenya.

2.1 Review Methods

Research articles were retrieved from various databases through the University of Texas at

Arlington library. These include; PsycINFO, Academic Search Complete, EBSCO Host, J-Stor, Science

Direct, Elsevier and Social Work Abstracts. Other databases used were Medline, BMJ Journals, PubMed

and ProQuest for dissertation abstracts. Additional sources include: conference presentations, working

papers, and textbooks from diverse disciplines such as sociology, demography, public health and economics. Websites of national and international organizations such as the Joint United Nations

Program on HIV/AIDS (UNAIDS) and World Health Organization (WHO), United Nations Economic

Commission for Africa (UNECA), United Nations Fund Development for Women (UNIFEM), United

Nations Fund Development for Population Activities (UNFPA), Population Reference Bureau (PRB),

Kenya National Bureau of Statistics and the Guttmacher Institute were also used. Key search words such as “contraceptive use in Africa,” “reproductive health,” “family planning in Sub-Saharan Africa and Kenya,”

“birth control,” and "women’s sexual health" were used interchangeably to access useful and current literatures from the databases.

A table listing all the literature used for this review is attached as Appendix A. The table includes the author (s), hypothesis and/or research questions (s), sample size, theory, methodology, and findings.

2.2 Overview

Research on the control of women’s reproduction through national and global health policies and

programs is gaining significant attention in the social sciences (Maternowska, 2006; Crane, 2005; Cook &

Kalu, 200). Smith-Oka (2009) stressed the importance of taking into consideration how the structural and cultural factors interact to influence women and their reproductive activities and their effects on reproductive and sexual health on certain groups in societies. In Kenya alone, 2.3 million people live with

HIV/AIDS while an estimated 1.5 million have died from the virus (Milkowski, 2004). Approximately

200,000 Kenyans develop the AIDS syndrome annually and an alarming 300 people die daily of AIDS related diseases such as Tuberculosis (TB) or Malaria (Milkowski, 2004).The predominant mode of

HIV/AIDS transmission is through heterosexual intercourse (Milkowski, 2004; Omungo, 2008; Erulkar et al, 2004).

Acquired Immuno-Deficiency Syndrome (AIDS) has taken a heavy toll on the Kenyan population. It has increased the number of orphans and widows, led to the deterioration of the health sector and adversely affected the outcomes on education and fertility levels (Ntozi, 2002; Cook & Kalu, 2008;

Omungo, 2008). In 2006, the HIV/AIDS pandemic was responsible for 2.9 million deaths globally; more than two million occurred in Sub-Saharan Africa (Kaiser Family Foundation, 2007; UNFPA, 2010). In

2007 alone, an estimated 2.1 million people died of HIV/AIDS and 1.6 million were in Sub-Saharan Africa

(Cook & Kalu, 2008). This translates into 76 percent of deaths as a result of HIV/AIDS.

Sub-Saharan Africa has the highest adult HIV/AIDS prevalence rate of 8.8 percent in the world, according to UNAIDS/WHO (2009), and 61 percent of patients with HIV/AIDS are women (Cook & Kalu,

2008; UNFPA, 2010). Women attending antenatal clinics in Uganda, for example, had infection rates as

high as 30 percent (UNAIDS/WHO, 2009). As of 2008, the prevalence rates increased in Southern

African countries with rates as high as 35.6 percent in Botswana, 25.3 percent in Swaziland, 25.1 percent in Zimbabwe, 23.6 percent in Lesotho, 20 percent in Zambia, 19.9 percent in South Africa, 19.5 percent in

Namibia and 16 percent in Malawi (UNAIDS/WHO, 2009). Women make up 55 - 61 percent of the total

HIV/AIDS infections, since the primary mode of HIV transmission in Sub-Saharan Africa is through heterosexual relationships (UNAIDS/WHO, 2009; Cook & Kalu, 2008; Omungo, 2008; Milkowski, 2004;

Erulkar et al, 2004). The negative impacts of HIV/AIDS on fertility are immense since women are more concerned with child bearing (Ntozi, 2002; Crane, 2005; Cook & Kalu, 2008). Sub-Saharan Africa has led other regions in levels of fertility, averaging about 6 children per woman (Ntozi, 2002; Cook & Kalu, 2008).

However, many countries have experienced a decline in women’s fertility to rates below 5.0 from

8.1(Ntozi, 2002). Botswana’s fertility decreased from 7.1 in 1981 to 4.4 in 1995-2000, Ghana 7.2 in 1960 to 4.5 in 1998, Kenya from 8.0 in 1975-1978 to 4.8 in 2003 (KDHS, 2003; United Nations, 2001), and

Zimbabwe from 8.3 in 1969 to 4.0 in 1996-99 (United Nations, 2001).

As of 2004, approximately 56 percent of the 40 million Kenyans were living below the poverty line

(Milkowski, 2004). Consequently, the high cost of public and private health services made access to quality reproductive health services unaffordable for the majority of the population (Milkowski, 2004). Over

90 percent of the Kenyan population lacks the financial resources and local access to adequate reproductive health services, resulting in poor sexual and reproductive health levels (Milkowski, 2004).

Reproductive and sexual health is influenced by various factors, including: HIV/AIDS status, marriage, contraception, premature and unwanted pregnancies, abortion, breastfeeding, and sterility (Ntozi, 2002;

Blacker et al., 2005; Njogu, 1991) and high maternal mortality rates (Milkowski, 2004). Additionally, fertility levels may decline in the new era of HIV/AIDS, due to the delayed onset of first sexual relations, age at first union, decreased premarital sexual relations and remarriage. Additionally, increased marital resolution (Ntozi, 2002; Njogu, 1991), increase in condom use and controlling for family size (Ntozi, 2002;

Kabiru & Orpinas, 2009) have aided in the decline in fertility levels. Women infected with HIV experience reduced pregnancy rates and rising levels of induced abortions (Ntozi, 2002; Crane, 2005; Onyango &

Mugo, 2008). Additionally, HIV induces sterility in some women and decreases frequency for sexual

intercourse (Ntozi, 2002). Several areas of Sub-Saharan African countries that were hard hit by HIV/AIDS include: Tanzania, Uganda, Kenya, and Zambia, where fertility has drastically declined (Ntozi, 2002;

Blacker et al., 2005; United Nations, 2001).

In 2000, Glynn, Buve, Carael, et al., found that increase in the incidence of HIV/AIDS has led to increased use of contraceptives. Women infected with HIV/AIDS, who had one child, were more likely to use contraceptives than women without HIV (Glynn, et al., 2000). In Yaoundé, Cameroon, the proportion of HIV positive women using modern contraceptives were 34.5 percent compared to 17.5 percent among

HIV negative women (Glynn et al., 2000). In Zambia, HIV positive women using contraceptive were 20.3 percent compared to 14.8 percent who did not have HIV and they favored reducing their family sizes in order to prevent HIV/AIDS transmission to their spouses and children (Rutenberg, Biddlecom et al., 2000;

Baylies, 2000). Baylies (2000) reported HIV positive women had fewer children than desired for several reasons including overwhelming concerns about leaving orphans behind. Additionally, HIV positive women have an overall desire to reduce the number of orphans left with the hope that they would receive better care from their guardians (Baylies, 2000).

Poor health status of a population is correlated with economic development, especially in developing countries such as those in Sub-Saharan Africa. The changing economic, social, and health climates in several African countries, made the use of contraception a challenging issue especially for young women (Cook & Kalu, 2008; Nzioka, 2004). Reproductive and sexual health matters in Kenya and other African countries, such as Zambia, have been highly charged social, moral and controversial topics for decades (Warenius et al., 2006).

Studies addressing contraceptive use in developing countries confirm that use is highest where access to a variety of choices is high (Ross et al., 2002; Ibisomi & Odimwegu, 2008). To increase contraceptive use, family planning programs should offer a variety of safe, effective, acceptable, and affordable methods to help women prevent unwanted pregnancies and sexually transmitted diseases, and to help them achieve their childbearing goals (Magadi & Curtis, 2003; Ross et al., 2002; Blacker et al., 2005). The use of more effective contraceptive methods, by a smaller group of people, can produce a greater decline in fertility than the use of less effective methods by a larger population (Magadi & Curtis,

2003; Berer, 2009; Blacker et al., 2005). A proper grasp of factors linked to types of contraceptive use is important for numerous reasons, such as improvement of the quality of care, program planning and management in areas of logistics, training, and financial planning and enables a country to realize the impact of family planning policies and programs (Magadi & Curtis, 2003; Cook & Kalu, 2008).

Sub-Saharan African countries have generally low contraceptive use (Magadi & Curtis, 2003;

Boonstra, 2007; Erulkar et al., 2004; Nzioka, 2004; Berer, 2009; Salo, 2002). In Kenya, modern contraceptive methods have been available since 1957 through the Ministry of Health and international non-governmental organizations (Magadi & Curtis, 2003; Becker et al., 2005). However, it was not until

1967 that Kenya became the first country in Sub-Saharan Africa to adopt a national population policy

(Magadi & Curtis, 2003). Kenya had the highest fertility rate in the world (8.1 children per woman) in the late 1970s. Due to efforts of both the public and private sectors, Kenya has experienced one of the most drastic declines in fertility rates in recent history (Magadi & Curtis, 2003; KDHS, 2003). Fertility fell by about 40 percent between 1980 and 2000, from eight births per woman to five (Blacker et al., 2005;

KDHS, 2003). By comparison, the Ugandan government did not adopt the National Population Policy for

Sustainable Development until 1995 (Blacker et al., 2005). Until this point, all family planning efforts had been promoted through urban-based non-governmental organizations (NGOs) led mainly by the Family

Planning Association of Uganda , which was not part of the Ministry of Health network. This had adverse effects on accessibility to contraceptive services, as most of Uganda’s population lived in rural areas

(Blacker et al., 2005). As a result, fertility fell by 10 percent due to the fact that Uganda had a greater unmet need for contraception and experienced pathological sterility (Blacker et al., 2005; Ibisomi &

Odimwegu, 2008).

As of 1999, 17 percent of women in East Africa and eight percent in West Africa were using modern methods of contraception (Stephenson, Baschieri, Clements, et al., 2007). The main methods used in Kenya are: the pill, injectables, intrauterine devices (IUDs), hormonal implants, male condoms, sterilization and natural family planning methods (Magadi & Curtis, 2003; Blacker et al., 2005; Nzioka,

2004).

The following Table (2.1) shows contraceptive prevalence using any method, comparing the different regions of the world, Africa as a whole, Sub-Saharan Africa, Northern, Eastern, Middle,

Southern, and Western Africa from 1980 to 2009.

Table 2.1

Contraceptive Prevalence Using Any Method, Comparing the World, Africa, Sub-Saharan Africa, Northern, Eastern, Middle, Southern, and Western Africa Regions from 1980 to 2009

Contraceptive Prevalence, Any Method Annual Change (Percentage Points) 1980- 1990- Region 1980 1985 1990 1995 2000 2005 2009 2000-2009 1990 2000 World 49.2 51.8 55.4 59.2 61.4 62.7 62.7 0.6 0.6 0.1

Africa 14.4 15.7 18.7 23.2 26.9 27.9 28.6 0.4 0.8 0.2 Sub- Saharan 11.2 12.1 13.4 17.3 20.1 21.1 21.8 0.2 0.7 0.2 Africa Northern 30.4 33.3 44.0 50.8 58.8 59.8 60.5 1.4 1.5 0.2 Africa Eastern 9.9 11.2 13.2 16.9 21.4 25.9 28.4 0.2 0.8 0.8 Africa Middle 17.5 18.4 20.1 21.5 24.1 20.1 18.6 0.3 0.4 -0.6 Africa Southern 45.6 46.0 47.6 51.5 54.7 57.9 58.4 0.2 0.7 0.4 Africa Western 6.3 6.7 7.0 12.5 14.3 13.9 14.4 0.1 0.7 0.0 Africa

The following Table (2.2) shows contraceptive prevalence using any modern method), comparing the different regions of the world, Africa as a whole, Sub-Saharan Africa, Northern, Eastern, Middle,

Southern, and Western Africa from 1980 to 2009.

Table 2.2

Contraceptive Prevalence Using Any Modern Method, Comparing the World, Africa, Sub-Saharan Africa, Northern, Eastern, Middle, Southern, and Western Africa Regions from 1980 to 2009

Annual Change (Percentage Contraceptive Prevalence, Any Method Points) 1980- 1990- 2000- Region 1980 1985 1990 1995 2000 2005 2009 1990 2000 2009 World 40.6 44.1 48.6 52.5 55.0 56.2 56.1 0.8 0.6 0.1

Africa 9.3 10.4 13.1 16.4 19.6 21.6 22.4 0.4 0.7 0.3 Sub- Saharan 5.6 6.3 7.7 10.3 12.8 14.7 15.7 0.2 0.5 0.3 Africa Northern 27.3 29.8 38.9 45.5 51.8 53.5 54.4 1.2 1.3 0.2 Africa Eastern 5.6 6.4 8.3 11.9 16.1 20.3 22.9 0.3 0.8 0.8 Africa Middle 3.8 4.0 4.3 4.6 5.4 6.4 6.6 0.1 0.1 0.1 Africa Southern 43.8 44.3 46.1 50.1 53.8 57.6 58.1 0.2 0.8 0.5 Africa Western 1.7 2.3 3.5 5.8 7.9 8.3 8.7 0.2 0.4 0.1 Africa

The following Table (2.3) shows contraceptive prevalence using any traditional method, comparing the different regions of the world, Africa as a whole, Sub-Saharan Africa, Northern, Eastern, Middle,

Southern, and Western Africa from 1980 to 2009.

Table 2.3

Contraceptive Prevalence Using Any Traditional Method, Comparing the World, Africa, Sub-Saharan Africa, Northern, Eastern, Middle, Southern, and Western Africa Regions from 1980 to 2009

Contraceptive Prevalence, Any Method Annual Change (Percentage Points) 1980- 1990- 2000- Region 1980 1985 1990 1995 2000 2005 2009 1990 2000 2009 World 8.6 7.7 6.8 6.7 6.4 6.5 6.6 -0.2 0.0 0.0

Africa 5.1 5.3 5.6 6.8 7.3 6.3 6.2 0.1 0.2 -0.1 Sub- Saharan 5.6 5.8 5.7 7.0 7.3 6.4 6.1 0.0 0.2 -0.1 Africa Northern 3.1 3.5 5.1 5.3 7.0 6.3 6.5 0.2 0.2 -0.1 Africa Eastern 4.3 4.8 4.9 5.0 5.3 5.6 5.5 0.1 0.0 0.0 Africa Middle 13.7 14.4 15.8 16.9 18.7 13.7 12.0 0.2 0.3 -0.7 Africa Southern 1.8 1.7 1.5 1.4 0.9 0.3 0.3 0.0 -0.1 -0.1 Africa Western 4.6 4.4 3.5 6.7 6.4 5.6 5.7 -0.1 0.3 -0.1 Africa

Until the mid-1990s, family planning services were supposed to be provided to married women

only. However, married women wishing to use these services had to get consent from their husbands in

person or in writing (Becker et al., 2005). Health providers were forbidden to offer services to young,

single women and shunned married women who requested contraceptives (Becker et al., 2005). In 1994,

the International Conference on Population and Development (ICPD) was instrumental in promoting new policies on reproductive health in developing countries in Africa through the Cairo Consensus (Ibisomi &

Odimwegu, 2008). Hence, family planning services were introduced in all clinics supported by the Ministry of Health (Becker et al., 2005).

The following table (2.4) shows the unmet need for family planning in the world, Africa, Sub-

Saharan Africa, and the different regions in Africa from 1990 to 2009.

Table 2.4

The Unmet Need for Family Planning in the World, Africa, Sub-Saharan Africa, and the Different Regions in Africa From 1990 to 2009

Unmet Need for Family Planning Annual Change (Percentage Points)

Region 1990 1995 2000 2005 2009 1990-2000 2000-2009

World 13.1 12.3 11.5 10.9 11.2 0.2 0.0

Africa 25.0 23.5 22.1 22.3 22.7 -0.3 0.1 Sub- Saharan 26.0 24.8 24.0 24.4 24.9 -0.2 0.1 Africa Northern 19.4 15.7 11.3 10.4 9.6 -0.8 -0.2 Africa Eastern 30.9 29.2 27.5 27.5 27.6 -0.3 0.0 Africa Middle 21.6 21.4 21.7 22.5 22.6 0.0 0.1 Africa Southern 16.6 16.5 16.1 15.5 15.5 -0.1 -0.1 Africa Western 23.8 22.9 22.3 23.0 24.2 -0.2 0.2 Africa

According to WHO (2007), data from the seven provinces in Kenya, in 2003, showed a high

percentage of women using modern contraceptive methods, with Central province being the highest at

57.9 percent followed by Nairobi at 44.3 percent. The other provinces were: Coast 19.1 percent, Eastern

33.2 percent, Nyanza 21 percent, Rift Valley 24.5 percent and Western province at 27.3 percent (WHO,

2007).

The following table (2.5) shows contraceptive prevalence using any method, modern and traditional contraceptive methods in Kenya from 1977-1978 to 2003 of Women aged 15-49.

Table 2.5

Contraceptive Prevalence Using Any Method, Modern and Traditional Contraceptive Methods in Kenya From 1977-1978 to 2003 of Women Aged 15-49

Year Any Method Any Modern Method Any Traditional Method

1977/78 7.0 4.4 2.5

1984 17.0 9.7 7.3

1988/89 26.9 17.9 9.0

1993 32.7 27.3 5.5

1998 39.0 31.5 7.5

2003 39.3 31.5 7.8

The following table (2.6) shows modern contraceptive methods in Kenya from 1977-1978 to 2008-

2009 among Women, aged 15-49.

Table 2.6

Modern Contraceptive Methods in Kenya from 1977-1978 to 2008-2009 Among Women aged 15-49

Sterilization Percentage Any Vaginal Any Male of any Year Modern Female Male Pill Injectables IUD Barrier Implant Method Condom Modern Method Method Methods 1977/78 7.0 4.4 1.0 0.0 2.0 0.6 0.7 0.1 0.0 62.9

1984 17.0 9.7 2.6 0.0 4.1 0.5 3.0 0.3 0.1 57.1

1988/89 26.9 17.9 4.7 0.0 5.2 3.3 3.7 0.5 0.4 0.0 66.5

1993 32.7 27.3 5.5 0.0 9.5 7.2 4.2 0.8 0.1 0.0 83.5

1998 39.0 31.5 6.2 0.0 9.5 11.8 2.7 1.3 0.0 0.8 80.8

2003 39.3 31.5 4.3 0.0 7.5 14.3 2.4 1.2 0.0 1.7 80.2

2008/09 45.5 38.9 4.8 0.0 7.2 21.6 1.6 1.8 0.0 1.9 85.5

The following table (2.7) showing traditional contraceptive methods in Kenya from 1977-1978 to

2008-2009 among women aged 15-49.

Table 2.7

Traditional Contraceptive Methods in Kenya from 1977-1978 to 2008-2009 Among Women Aged 15-49

Any Traditional Any Other Any Method as a Year Traditional Rhythm Withdrawal Traditional Method Percentage of any Method Methods Method 1977-78 7.0 2.5 1.1 0.2 1.2 35.7

1984 17.0 7.3 3.8 0.6 2.9 42.9

1988-89 26.9 9.0 7.5 0.2 1.3 33.5

1993 32.7 5.5 4.2 0.4 0.8 16.8

1998 39.0 7.5 6.1 0.6 0.8 19.2

2003 39.3 7.8 6.3 0.6 0.8 19.8

2008-09 45.5 6.6 4.7 0.7 1.2 14.5

2.3 Social and Cultural Factors

The silence surrounding issues of female reproductive health and family planning reflect a social

and cultural conditioning that offers women no option but to cope with their circumstances, no matter how

distressing (Kamara, 1997; Boonstra, 2007; Nzioka, 2004; Salo, 2002). Women in Kenya and Zambia, as well as other Sub-Saharan African countries, are experiencing social turmoil resulting from conflicting

values, fueled by urbanization and industrialization (Warenius et al., 2006; Boonstra, 2007; Horwitz,

2001). Most ethnic groups and tribes in Africa stipulate that young women should not have children out of wedlock (Warenius et al., 2006; Nzioka, 2004; Boonstra, 2007; Salo, 2002; Erulkar, 2004; Kabiru &

Orpinas, 2009). There are variations in societal and cultural norms on women’s sexuality, such as the Luo tribe in Kenya and Bemba in Zambia, where women are expected to be virgins until marriage (Warenius et al., 2006). Other tribes in Kenya, the Akamba and Kikuyu , traditionally prohibit premarital sex and show strong disapproval for it, especially among young women (Nzioka, 2004; Guttmacher, 2008). The Akamba associate youthfulness with purity and sex is socially constructed as a form of evil defilement of the body,

mind, and character of the young person (Nzioka, 2004; Nzioka, 2001). Young women who engage in premarital sex are viewed as suffering from poor socialization and upbringing (Nzioka, 2004), immature, irresponsible, having selfish sexual motivations and are sexually promiscuous (Salo, 2002; Omungo,

2008). However, the Cewa tribe in Zambia, allow limited discreet sexual relations among their young people (Warenius et al., 2006).

2.3.1 Premarital Relations

Sex with multiple partners is common in Kenya (Cook & Kalu, 2006; Nzioka, 2004; Obiero et al.,

2000; Magadi &Curtis, 2003; Milkowski, 2004; Tenkorang & Maticka-Tyndale, 2008). A study done by

Obiero, Nyakero, Mwikali and colleagues (2000) in Nyamira district of Western Kenya, showed that five percent of girls, aged 18-24, had experienced a Sexually Transmitted Infections (STI). In a study of young people’s knowledge and experience with STIs/HIV/AIDS, 60 percent of women, aged 15-24, in Western

Kenya, acknowledged having sex with multiple partners (Obiero et al., 2000).

Premarital pregnancy is viewed as shame to the woman and her parents (Nzioka, 2004; Boonstra,

2007) and may lead to loss of friends to the point of ostracism from the community (Nzioka, 2004). The pain and burden of bearing an unwanted child are enormous and thus, may result in a young girl dropping out of school (Nzioka, 2004; Erulkar et al., 2004; Boonstra, 2007; Onyango & Mugo, 2008; Salo, 2002).

This is viewed by others as a sign of immorality, lack of discipline and irresponsibility (Nzioka, 2004; Salo,

2002).

Despite the strong disapproval of premarital relations from parents and the community, young women engage in premarital sex because of love; desire for sex; peer pressure; the inability to resist sexual advances from older, more educated, and financially stable men; taking drugs such as alcohol or marijuana; the desire to get married; and as a result of poverty, survival, and a means for economic gain

(Nzioka, 2004; Nzioka, 2001; Leach & Machakanja, 2001; Salo, 2002; Milkowski, 2004; Onyango &

Mugo, 2008; Kabiru & Orpinas, 2009; Luke, 2005; Tenkorang & Maticka-Tyndale, 2008).

In most African countries, social and cultural norms often encourage men to engage in sexual relationships with younger women (Luke, 2005; Erulkar et al., 2004; Leach & Machakanja, 2002), thus creating differences in power relations and making it difficult for the women to demand condom use (PRB,

2007; Nzioka, 2004; Onyango & Mugo, 2008). Such practices increase the risks for unplanned pregnancies and HIV infection (Boonstra, 2007; Omungo, 2008; Onyango & Mugo, 2008; Ibisomi &

Odimwegu, 2008). A sugar daddy, as defined by the Merriam Webster dictionary, is “a wealthy older man who gives a young person expensive gifts in return for friendship or intimacy” (Merriam-Webster, 2010).

Sugar daddies are common in Sub-Saharan Africa and aid in the spread of HIV/AIDS by seeking out young, single women who are believed to be less likely infected with HIV (Luke, 2005; Leach &

Machakanja, 2002; Blacker et al., 2005; Warenius et al., 2006). In the era of HIV/AIDS, sugar daddy relationships have been branded a major health concern usually termed as the “sugar daddy syndrome,” the “sugar daddy trap,” or “sugar daddy phenomenon” with young unmarried women bearing the burden

(Luke, 2005; Tenkorang & Maticka-Tyndale, 2008; Leach & Machakanja, 2002). Additionally, older men gain more social and economic power due to their ability to monopolize younger women’s sources of income (Luke, 2005). Young women are groomed to succumb to male dominance and economic dependence (Tenkorang & Maticka-Tyndale, 2008; Milkowski, 2004; Leach & Machakanja, 2002).

In Kenya, 21 percent of young women have received money, gifts, or favors for sex, and 17 percent of young men have paid for sex (Warenius et al., 2006). The KDHS (1998) indicates that 21 percent of unmarried young women (aged 15-19) reported receipt of money, gifts, or favors for sex, compared to 17 percent of males in the same age group. In Zambia, 27 percent of young women have received money, gifts, or favors for sex and 40 percent of young men have paid for sex (Warenius et al.,

2006; Tenkorang & Maticka-Tyndale, 2008).

2.3.2 Male Dominance

Peer pressure was reported to be the main reason why young women engage in unprotected sex, despite the dire consequences (Nzioka, 2004; Leach & Machakanja, 2001; Omungo, 2008). Young women, in a study by Nzioka (2004), used various non-confrontational strategies to avoid getting pregnant such as using the pill, persuading the partner to use a condom or running away (Nzioka, 2004).

In the Akamb a tribe, dominant masculinity is the norm for men while women are required to show obedience and docility toward men (Nzioka, 2004). Other factors, such as gender norms emphasizing

submissiveness, make women reluctant and timid to discuss condoms with their partners (PRB, 2007;

Nzioka, 2004; Kabiru & Orpinas, 2009).

2.3.3 Myths Regarding Modern Contraceptives

Young women in Kenya associate condoms with negative meanings, such as revealing the sexual history of those using them (Nzioka, 2004; Erulkar et al., 2004; Omungo, 2008). Young men who carry condoms were viewed as responsible and cautious whereas young women carrying condoms or insisting on condom use are viewed as reckless, promiscuous or prostitutes (Nzioka, 2004; Omungo, 2004; Kabiru

& Orpinas, 2009). For young men, having multiple partners and being sexually experienced, are important marks of masculinity and lead to more risky behavior (Nzioka, 2001; Warenius et al., 2006) whereas it is shunned among young women.

Young women have misguided ideas regarding the low risk of contracting STIs/HIV/AIDS infections

(Nzioka, 2004; Omungo, 2008; Kabiru & Orpinas, 2009). These beliefs tend to compromise the ability to take protective measures against infections even when risks are apparent (Nzioka, 2004; Omungo, 2008;

Kabiru & Orpinas, 2009). Another factor preventing young women from taking cautionary measures, when negotiating sexual encounters, is the negative connotation associated with condoms (Nzioka, 2004; Salo,

2002; Omungo, 2008). To many young women in Kenya, South Africa, and other Sub-Saharan African countries, condoms symbolize promiscuity, infidelity or the presence of STIs/HIV/AIDS (Nzioka, 2004;

Omungo, 2008; Kabiru & Orpinas, 2009; Salo, 2002).

Young women reported using various traditional methods to prevent unwanted pregnancy, such as periodic abstinence, which is defined as having sex during ‘safe days,’ withdrawal by the male before ejaculation (Boonstra, 2007; Nzioka, 2004; Nzioka, 2001), and the use of natural herbs, concentrated tea, cow dung and goat droppings (Nzioka, 2004; Nzioka, 2001). These methods were preferred by young women as they believed that they have no noticeable effects, and because of a desire to follow religious or cultural norms. In addition, traditional methods were utilized due to lack of access to modern contraceptives (Nzioka, 2004). Oral sex and masturbation were also used less often as methods of pregnancy prevention (Nzioka, 2004).

2.3.4 Religion

Both Kenya and Zambia’s dominant religions are Christianity and traditional beliefs, with Islam practiced by a minority (Warenius et al., 2006). Premarital sex is prohibited regardless of religious affiliation (Agadjanian, Fawcett, & Yabiku, 2009; Warenius et al., 2006). Sex education was formerly taught by the elders within families, (Erulkar et al., 2004; Nzioka, 2004). However, this responsibility has been taken over by schools, churches, and non-governmental organizations (NGOs) (Warenius et al.,

2006; Erulkar et al., 2004; Boonstra, 2007; Omungo, 2008). These different institutions may have conflicting messages, with NGOs advocating condom use for the highly sexually active youth and religious organizations and family elders stress abstinence before marriage (Warenius et al., 2006;

Erulkar et al., 2004; Omungo, 2004).

2.3.5 Social Networks

Behrman, Kohler, and Watkins (2002) state that the use of social networks, among the Luo tribe in rural Kenya, enhances the awareness and use of modern contraceptive methods among young women.

Behrman and colleagues (2002) and Musalia (2005), assert that knowledge and information on contraceptive use is acquired through the evaluation of the pros and cons of having many versus fewer children, or learning of modern contraceptive methods from the experiences of a relative, friend, or neighbor. Additionally, women gossiping about other women’s experiences, and the support or opposition to the new reproductive method amongst women in the same community, help spread information and knowledge on contraceptives (Behrman et al., 2002; Musalia, 2005).

Musalia (2005) also addresses the value of the social network approach in promoting the understanding of fertility transition in developing countries such as Kenya. Musalia (2005) found that immediate family members have more influence on directing fertility behavior, since they are more involved in the process and cost of raising a child, than other community wide networks. However, interactions with, and getting advice from healthcare and family planning providers and friends, had a positive impact on contraceptive use in both Central and Nyanza provinces in Kenya (Musalia, 2005).

Social influence may impede or enable adoption and practice of family planning methods (Musalia, 2005).

Behrman and colleagues (2002) counter that social networks provide information via social learning as opposed to exerting social influence.

2.3.6 Abortions

Since young women have limited access to contraceptive services, the issues of increasing teenage pregnancies and abortions are on the rise (Warenius et al., 2006; Onyango & Mugo, 2008;

Ibisomi & Odimwegu, 2008). In Kenya, approximately 300,000 induced abortions occur annually among women aged 15-49 (Onyango & Mugo, 2008; Guttmacher Institute, 2008). Onyango and Mugo (2008) state that approximately 20,000 women are treated in Kenya’s public hospitals with abortion related complications. More than 40 percent of births in Kenya are unplanned; among adolescents aged 15-19,

47 percent are unplanned (Guttmacher Institute, 2008). Female adolescent childbearing has been identified as a major reason for dropping out of school (Warenius et al., 2006; Nzioka, 2004; Guttmacher

Institute, 2008; Onyango & Mugo, 2008). As a result, unmarried adolescent girls resort to induced abortions (Warenius et al., 2006; Blacker et al, 2005; Onyango & Mugo, 2008; Mitchell et al., 2006) to avoid dropping out of school. Young women most frequently cite the stigma of childbirth outside of marriage, the inability to support a child and the possibility of having to quit school as reasons for having an abortion (Guttmacher Institute, 2008; Onyango & Mugo, 2008; Kabiru & Orpinas, 2008; Mitchell et al.,

2006). National-level information on Kenyans’ attitudes toward abortion is lacking (Guttmacher, 2008;

Onyango & Mugo, 2008; Blacker et al., 2005).

A recent study in Nyeri District found that older women see abortion in practical terms—a response to the socioeconomic burden of having too many children, or as a way to space births (Guttmacher,

2008). Older men generally view it as a way to conceal the consequences of premarital or extramarital sex (Guttmacher, 2008). A 1990 study in Nairobi, found that medical professionals were evenly divided in their support for liberalizing Kenya’s abortion law but held generally negative attitudes about abortion

(Guttmacher, 2008; Onyango & Mugo, 2008; Ibisomi & Odimwegu, 2008). Almost half of nurses and nursing students, surveyed in two Nairobi hospitals, thought that emergency contraception was an abortion-inducing drug and were less likely to recommend it than those who did not hold this misperception (Guttmacher Institute, 2008).

In Uganda, a survey carried out in 1998 of young women aged 15-24, found that 23 percent of the women who had ever been pregnant, had had one or more abortions (Blacker et al., 2005). The major traditional abortion methods include utilizing crude measure such as physical removal of the fetus using makeshift tools by licensed and unlicensed medical practitioners, overdosing on anti-malarial tablets or drinking a concentrated concoction made from herbs or animal droppings (Nzioka, 2004; Onyango &

Mugo, 2008). The effectiveness of these traditional methods has not been established (Boonstra, 2007;

Nzioka, 2004). Other strategies for dealing with unwanted pregnancies include young women forcing themselves to be married by the partners who impregnated them, getting married as second wives to older men, or getting married to anyone who needed a wife immediately (Nzioka, 2004).

2.3.7 Media

There is increased media attention on reproductive health issues ranging from pregnancy,

HIV/AIDS, contraceptive use to abortion among the youth. However, much of the information is contradictory (Mitchell et al, 2006; Ligaga, 2005; Omungo, 2008). Magazines and newspapers often feature negative stories of single young women’s sexual and reproductive health matters, for example

(Ligaga, 2005). These stories reflect a myriad of viewpoints, some stemming from colonialism or from the social prescriptions (societal rules and norms) and stigma associated with sexual activity among the youth in many different Kenyan dialects (Nzioka, 2004; Ligaga, 2005). There is a stark contrast by the youth who are exposed to western media through comedies, talk shows and soap operas which portray liberal sexual rules (Folke-Frederiksen, 2000; Ligaga, 2005; Magadi & Curtis, 2003) and are more inclined to use long-term and permanent contraceptive methods (Magadi & Curtis, 2003).

Most women were aware of male condoms and their uses in preventing STIs/HIV/AIDS and unwanted pregnancies, but had no conception of female condoms, thus, further limiting access and use of sexual and reproductive health services (Nzioka, 2004). Local sources for accessing male condoms include hospitals, clinics, community-based distributors, shops, and friends (Nzioka, 2004; Behrman et al., 2002; Erulkar et al., 2004; Omungo, 2008; Kabiru & Orpinas, 2009). Over 50 percent of young people in Uganda stated that they need information on contraceptive and STIs/HIV/AIDS to come from reliable

sources such as teachers, the mass media, or health care providers because the information is more reliable as opposed to getting it from friends and family (Boonstra, 2007).

2.4 Policy and Political Factors

Kenya was the first country in Sub-Saharan Africa to adopt a National Population Policy-- the

National Family Planning Program in 1967 (Blacker et al., 2005). By the 1980s, the number of outlets where contraceptives were distributed, increased to include government and non-governmental facilities

totaling 3,500, offering maternal and child health and family planning services (MCH/FP) (Blacker et al.,

2005). The publication of the National Population Advocacy and IEC for Sustainable Development 1996-

2010, in 1996 by the National Council for Population and Development (NCPD), aimed to promote the use of modern contraceptive methods among less educated women by 2010 (Blacker et al., 2005).

Between 1998 and 2003, the family planning programs in Kenya faced major budget cuts. As a result, the implementation of the Information, Education, and Communication (IEC) Strategy underwent

massive cuts from its funding source—the United Nations Fund for Population Activities (UNFPA). A shift

in UNFPA’s organizational policy, which focused primarily on gender issues and HIV/AIDS/STIs

programs, further slowed the process (Blacker et al., 2005; Aloo-Obunga, 2003; Cleland et al., 2006).

These changes resulted in interruption in the distribution of basic contraceptives, increased the unmet

needs, and decreased contraceptive use among women evidenced by the results of the KDHS 2003

(Blacker et al., 2005).

Policies dealing with family planning, contraceptives and reproductive health received minimal

support from government and policymakers in the higher echelons of government, leading to slow

progress and weakening execution and application (Crichton, 2008). In the1990s, Kenya’s reproductive

health was insignificant in both national and international efforts, limiting access to contraceptive services

and decreasing fertility rates (Crichton, 2008). Reproductive health and population policies have not been

at the top of policy makers’ agenda, especially due to shifting international attention and concern for

assistance and development of HIV/AIDS programs (Cleland et al., 2006; Aloo-Obunga, 2003).

Globalization and industrialization pushed women into the sexual economy through Structural

Adjustment Programs (SAPs) sponsored by entities such as World Bank and the International Monetary

Fund (IMF) in the 1970s (Kamara, 1997; Cook & Kalu, 2008; Maternowska, 2006). The impact of SAPs was detrimental to African countries such as Kenya, affecting women, who constitute the majority of the poor, and have consistently seen a decline in income levels, healthcare and educational services

(Kamara, 1997; Cook & Kalu, 2008). In Kenya, the economy is largely agricultural. Women are at the center of the development process as a result of their significant contribution to food production and processing (1997). The introduction of SAPs in Kenya, and several other African countries, resulted in a decline in social services and further decline in general health and child care services. Women, who are often the primary healthcare providers in the family (Kamara, 1997; Cook & Kalu, 2008), are again significantly impacted.

The impact of SAPs is especially evident in areas of female reproductive health (Kamara, 1997).

During the 1980s and 1990s, the Kenyan government showed commitment to family planning by developing national policies and guidelines, involving high powered politicians in the policy making process, and through the establishment of the National Council for Population and Development (NCPD) department in the office of the vice president (Crichton, 2008). By the mid 2000s, family planning policies gained significant attention through both public and private advocacy efforts (Blacker et al., 2005;

Crichton, 2008; Onyango & Mugo, 2008). Additionally, there was increased distribution of contraceptives through government and nongovernmental health facilities, increased education and information and communication campaigns (Blacker et al., 2005; Crichton, 2008). The international body was instrumental in expanding and shaping Kenya’s family planning policies by advocating and supporting the implementation of these policies and by covering the costs of all government and nongovernmental contraceptives and IEC campaigns (Crichton, 2008; Aloo-Obunga, 2003). Unfortunately, the Kenyan government was slow in responding to the shifting of international aid and coupled with poor management of officials within the Ministry of Health and the Kenya Medical Supplies Agency (KEMSA); there was evident decline in the provision and distribution of contraceptive services in the governmental and nongovernmental sectors (Crichton, 2008).

Kenya’s fertility declined to 4.8 from 1998 to 2003 and rose for women who had not completed primary school education (Blacker et al., 2005; KDHS, 2003). Additionally, the 2003 KDHS revealed an

increased need for contraception and decrease in contraceptive usage rates (Blacker et al., 2005; KDHS,

2003). This new data increased prospects for reproductive health policy and services and ignited a cycle of awareness campaigns whose priority was to persuade the government to address contraceptive services as one of its major concerns by allocating public funding to distribute contraceptives and family planning services (Crichton, 2008). These efforts paid off, evidenced by the inclusion of contraceptive products and services as a line item in the 2005 national budget, allocating 200 million Kenya Shillings-- the equivalent to US$2.62 million by 2005 exchange rates (Crichton, 2008). This was the first time in

Kenya’s history that the government allocated funds specifically for contraceptive products and services

(Crichton, 2008). The inclusion of contraceptive funds in the budget was evidence of national commitment

(Shiffman, 2006). This approach enhanced the chance for sustaining such programs in cases where external funding shrunk (Crichton, 2008; Shiffman, 2006). In the 2006-2007 national budgets, the government allocation for contraceptives increased to 300 million Kenya Shillings—equivalent to US$4.17 million dollars (Crichton, 2008). Family planning and reproductive health advocates continue to lobby for even more funding from the government to support nationwide distribution of contraceptive products and services (Crichton, 2008; Blacker et al., 2005; Onyango & Mugo, 2008).

Over the years, past administrations failed to address the rapid decline of family planning policy implementation in the 1980s and 1990s in Kenya. A new administration, under President Mwai Kibaki, brought in a new perspective and mobilized action toward this issue by bringing in and placing supporters of family planning policies in strategic positions in the government (Crichton, 2008). The administration created the National Coordinating Agency for Population and Development (NCAPD) through an act of parliament in 2004 (Crichton, 2008). Additionally, the administration had an advocacy mandate and issued a memorandum urging the government to increase efforts and support for family planning policies and services (Crichton, 2008). Appendix B shows the history of family planning policies and programs in

Kenya.

The United States plays a major role in assisting developing countries to fight HIV/AIDS (Boonstra,

2007; Cleland et al., 2006). The President's Emergency Plan for AIDS Relief (PEPFAR) emphasizes abstinence, and mandates that one-third of all U.S. assistance should be used for abstinence until

marriage only programs. This limits access to reproductive and sexual health services to young people and adolescents (Boonstra, 2007; Cleland et al., 2006).

Western European countries are more accepting of teenage sexual behaviors and contraceptive use among those who are sexually active. As a result, levels of teenage pregnancy, childbearing and abortions are remarkably lower than that of most countries. For instance, pregnancy rates in the United

States are twice as high as those in Britain and approximately four times higher than those in France and

Sweden (Boonstra, 2007).

2.5 Legal Factors

The International Conference on Population and Development (ICPD) conference held in Cairo,

Egypt in 1994, was a landmark meeting that was instrumental in bringing together over 179 countries with

the common mission of addressing the public health impact of unsafe abortions (Ibisomi & Odimwegu,

2008; UNIFEM, 2010; UNAIDS/WHO, 2009). This has resulted in active support from governments in

promoting the availability and distribution of family planning and contraceptive services through the

government, private, and nongovernmental channels (Ibisomi & Odimwegu, 2008). This has also resulted

in provision of reproductive and sexual health services for various groups, including adolescents.

However, abortion laws in most Sub-Saharan countries are yet to undergo any substantial reforms

(Blacker et al., 2005; United Nations, 2002; Ibisomi & Odimwegu, 2008; Onyango & Mugo, 2008). In

Kenya, and in most Sub-Saharan countries under the Commonwealth rules, induced abortion is illegal and prohibited, except in cases where it is necessary to save the life of the mother or preserve her physical or mental health, and can only be performed by a qualified doctor (United Nations, 2002; Blacker et al., 2005; Onyango & Mugo, 2008; Guttmacher Institute, 2009). According to the abortion law in Kenya,

Tanzania, and Uganda, the woman obtaining an abortion, and the provider of the abortion, are both subject to up to 14 years in prison if found guilty (United Nations, 2002; Onyango & Mugo, 2008). With

Kenya, Tanzania, and Uganda being former British colonies, the abortion laws were drawn from the former British law that have undergone several changes to adopt a more liberal stance and provide more services that promote safe abortions (Onyango & Mugo, 2008; United Nations, 2002). In Zambia, abortion is legal, but adolescents undergo illegal abortions because legal abortions are inaccessible and

unacceptable (Warenius et al, 2006; United Nations, 2002). In 2003, young women under the age of 20 in

Kenya accounted for 16 percent of the 22,000 abortion-related complications treated in Kenya’s public hospitals (Gebreselassie et al., 2004; Guttmacher Institute, 2008).

These abortion laws are discriminatory in terms of socioeconomic status and accessibility, in that poor women are denied access and forced to utilize the unsanitary facilities or crude methods at the hands of untrained practitioners, often resulting in complication or death (Onyango & Mugo, 2008). On the contrary, women who are able to pay have access to safe services within the country and in other countries where abortions are legal (Onyango & Mugo, 2008). With such restrictive and rigid laws, young women are placed at further risk of obtaining unsafe abortions due to cost and accessibility. The pressure is to terminate the pregnancy due to the stigma of having an unwanted pregnancy and the possibility of dropping out of or being expelled from school (Onyango & Mugo, 2008). In a study by Mitchell, Halpern,

Kamathi, and Owino (2006), 29 percent of students surveyed incorrectly believed that abortion was never permitted in Kenya and 14 percent were not sure whether it was legal or not. Government policies in both

Kenya and Zambia require the provision of contraceptives to sexually active men and women (Warenius et al., 2006).

However, reform is needed in Kenya to shape abortion policies to be more liberal and to be more accessible to those that need it. This will decrease the high morbidity and mortality rates associated with unsafe abortions such as in countries like South Africa, which has showed a significant decrease in abortion related maternal morbidity and mortality after the passing of the Choice on Termination of

Pregnancy Act in 1996 (Onyango & Mugo, 2008). Denying pregnant women the right to make independent and non-coerced decisions regarding their bodies, in relation to abortion, violates and threatens human rights such as: women’s right to life and survival, women’s right to health and women’s right to non-discrimination (Onyango & Mugo, 2008). Coercing a woman to undergo an unsophisticated and crude abortion threatens her rights to life and survival thereby violating the most fundamental of rights (Onyango & Mugo, 2008). Unsafe abortions can kill or injure the woman whereas safe abortion protects women’s right to health. Governments, therefore, should ensure that all women have access to affordable and safe abortion services and that they are not exposed to the risks and humiliations of

unsafe abortions (Onyango & Mugo, 2008). Gender equality is also a fundamental human right. Denying women the right to safe and affordable abortions is a form of discrimination (Onyango & Mugo, 2008) and a violation of that right.

Organizations such as the Reproductive Health and Rights Alliance (RHRA) in Kenya, formed in

July 2004, under the direction of the Kenya Human Rights Commission (KHRC), strive to promote women’s right to reproductive self-determination including the right to make decisions concerning her body without coercion (Onyango & Mugo, 2008). Others included the right to physical integrity, the right to privacy and the right to decide the number and spacing of one’s children (Onyango & Mugo, 2008). The main purpose of this organization was to address the Sexual and Reproductive Health (SRH) and rights, particularly abortion, in Kenya (Onyango & Mugo, 2008). The organization strives further to raise public awareness on the prevalence of abortion and consequences of unsafe abortions, the need for safe abortion services in securing and ensuring women’s sexual and reproductive health and rights and contributes to the reduction of abortion related maternal mortality and morbidity in Kenya (Onyango &

Mugo, 2008). The RHRA firmly believes that no government or religious body should dictate how many children a woman can have or deny them access to safe abortions and contraceptive services (Onyango

& Mugo, 2008). The RHRA advocates and lobbies to create awareness amongst policymakers, the public, and opinion leaders seeking to gain support for policy and law review/reform through public debates, forums, and mock tribunals (Onyango & Mugo, 2008).

Organizations such as the German Development Cooperation (GDC) in Kenya have made RSH issues a priority on its agenda (Milkowski, 2004). One of their major priorities is to improve the use and access of affordable and adequate reproductive health services; promote the living conditions of women, men and children by improving their reproductive health status (Milkowski, 2004). Additional goals of the

German Development Cooperation (GDC) in Kenya are to expand community-based family planning services, including the prevention of unwanted pregnancies; enhance adolescent health promotion through information, education, communication (IEC), behavioral change communication (BCC), peer counseling, and prevention of STIs/HIV/AIDS; advocating for more sexual and reproductive health rights

for women; guaranteeing sustainable financing of essential reproductive and sexual health services; and establishing a social health insurance scheme (Milkowski, 2004).

CHAPTER 3

CONCEPTUAL FRAMEWORK

The objective of this study is to examine the factors that influence contraceptive use in Kenya.

1998 and 2008-2009, this study will examine both individual as well as societal factors related to contraceptive use in Kenya, using Ryder’s theory on social change.

Over the years, contraceptive use has gained popularity due to migration from rural to urban cities, accessibility and availability, and through modernization and industrialization of societies and communities. Forces of modernization reduce family size and change the pattern of family formation

(Pillai, 1984; Schuster, 1979). Previous studies have shown that education and type of residence (rural versus urban) are important determinants of contraceptive use (Njogu, 1991; NRC, 1993; Gage, 1995).

These factors coupled with increases in education, income, media influence and exposure, further increase contraceptive use among women in Sub-Saharan African countries. Additionally, when societies undergo change, shifts in proportions of women using contraceptives in the groups with education, and the different types of residences such as rural versus urban, are bound to occur. If women in these subgroups have different tendencies in contraceptive use, the overall rates of use may change. Other factors that have contributed to the contraceptive use debate include different religious influences

(Agadjanian, Yabiku, & Fawcett, 2009; Warenius et al., 2006), income and age at marriage. These factors will be used in examining the similarities and differences in characteristics between modern contraceptive users and users of other contraceptive methods between 1998 and 2008-2009 in Kenya. This paper will examine these changes, utilizing modernization and human capital theories, and describe the changes in

contraceptive use over time using Ryder’s social change theory. This study will use four variables to explain modern contraceptive use in Kenya. The selected variables are: place of residence (rural versus urban), age at marriage, income, and education.

3.1 Modernization Theory

The term “modern” is applied and used to describe several different things such as man, nations,

politics, economies, housing, cities, schools and ways of being (Inkeles, 1983; Inkeles & Smith, 1974).

The term modern refers to “anything which has more or less replaced something which in the past was

the accepted way of doing things” (Inkeles & Smith, 1974, p.15). Other researchers, Inglehart and Wetzel

(2005), define modernization theory as a conceptual scheme involving socioeconomic development,

cultural change and democratization under the theme of human development. This socioeconomic

development brings with it major social, cultural, political changes and values and beliefs from developed

societies (Inglehart & Wetzel, 2005). These changes have an effect on how societies are governed, the

promotion of gender equality, democratic freedom and leadership in society, resulting in massive

transformations politically, economically and socially (Inglehart & Wetzel, 2005).

Modernization increases demand for modern goods and services (Inkeles, 1983; Inkeles & Smith,

1974). Increased modernization also alters the desire and tastes for having and raising children as

traditional goods. As a result, the value and time invested in children decreases with the desire to pursue

a more modern lifestyle (Schuster, 1979; Pillai, 1984; Njogu, 1991; NRC, 1993; Pillai & Teboh, 2010).

Several studies address modernization as a movement that represents a societal force that was

set in motion by industrialization in the 18 th century where new technological innovations brought about

deep and extensive changes in how people lived their lives (Inglehart & Wetzel, 2005; Abraham 1980;

Martinelli, 2005; Inkeles & Smith, 1974; Inkeles, 1983). New technologies not only called for changes in

the organization of production consumption and distribution, but also changes in existing values systems

and norms, supportive of a new way of life (Inkeles, 1983; Inkeles & Smith, 1974; Morgan & Kickham,

1997; Inglehart & Wetzel, 2005). The emergence of new technologies called for new skill sets, requiring

prolonged durations of preparation for entry into the labor market (Inkeles, 1983; Inkeles & Smith, 1974).

This requirement brought about changes in the social calendars regarding age at first marriage, when to have children and desire to control family size (Macunovich, 2000; Pillai, 1981). The norms that characterized work life in modern economies and societies demanded similar organization of personal lives as well (Inkeles, 1983; Inkeles & Smith, 1974). The modern way of life was mainly founded on socioeconomic mobility supported by the growth of industrialization and urbanization (Inkeles & Smith, 1974;

Inkeles, 1983; Morgan & Kickham, 1997). These were based primarily on the values of effectiveness, democratization and cost- benefit analysis both from a societal and individual level (Inkeles & Smith,

1974; Inkeles, 1983; Inglehart & Wetzel, 2005).

A society’s cultural heritage continues to shape existing beliefs and motivations over time (Inkeles

& Smith, 1974; Inkeles, 1983). Socio-cultural change is not a linear process but a multidimensional process. Industrialization of a society, for example, brings with it rationalization, secularization and bureaucratization (Inglehart & Wetzel, 2005; Bean, 1983). The rise of knowledge in a society brings new changes such as an emphasis on individual autonomy, self-expression and free choice (Inglehart &

Wetzel, 2005; Inkeles, 1983; Inkeles & Smith, 1974). The emerging self-expression values of individuals transform modernization into a process of human development, giving rise to a new humanistic and people-centered society (Abraham, 1980; Inglehart & Wetzel, 2005; Salvadori, 2008). Modernization has two phases: The first involves the masses of people making democracy possible, and the second, postindustrial phase, produces increasingly powerful mass demands for democracy involving the kind of government that accords the citizens flexibility on choosing how to live their lives (Inglehart & Wetzel,

2005).

Inglehart and Wetzel (2005) address changes taking place in the political, religious, social and sexual norms in postindustrial societies. They present a model of modernization and social change that explains how the value system in any given society will evolve over the coming decades (Inglehart &

Wetzel, 2005; Abraham, 1980; Inkeles, 1983). Inglehart and Wetzel (2005) define democracy as a

"deeply-rooted orientation amongst people that motivates them to demand freedom and responsive government in a society by ensuring that the government leaders remain responsive to the citizens of that society, p 15." As modernization fosters the strengthening of civil societies and democratic forms of

government, modern methods begin to play an important role in the transmission of new information

(Inkeles & Smith, 1974; Abraham, 1980; Salvadori, 2008).

Socioeconomic modernization of a society reduces the external constraints on human choice by increasing people’s material, cognitive and social resources. This, in turn, leads to mass demands for individual self expression values leading to public demand for civil and political liberties, gender equality and responsive government—enhancing the establishment and sustenance of human choice-democracy

(Inglehart & Wetzel, 2005). As modernization becomes more pervasive and dominant in a society, cultural change becomes a major institutional outcome. As one aspect of the society changes, other aspects are forced to change in either direction, positive or negative (Martinelli, 2005; Salvadori, 2008; Inkeles &

Smith, 1974).

Culture is passed on from generation to generation. Individuals’ basic values not only reflect what they have been taught, but also their firsthand experiences (Inglehart & Wetzel, 2005). Economic growth, rising levels of education and information, and diversifying human interactions, increase people’s material; cognitive; and social resources; making them materially, intellectually and socially more independent (Inglehart & Wetzel, 2005; Inkeles, 1983; Inkeles & Smith, 1974). Individual liberty, human diversity and individual autonomy can be collectively classified as self-expression values. These values encourage individuals in a society to pursue freedom of expression and self-realization (Inglehart &

Wetzel, 2005; Inkeles & Smith, 1974; Abraham, 1980). These rising self-expression values also transform modernization in a society into a process of human development, giving rise to a society that is people centered—a humanistic transformation of modernity (Inglehart & Wetzel, 2005; Inkeles & Smith, 1974;

Inkeles, 1983).

Longitudinal perspectives of value changes over time show that rich post industrial societies show large intergenerational differences, with younger cohorts generally placing stronger emphasis on secular- rational values and self-expression values, than do older cohorts (Inglehart & Wetzel, 2005). Low income societies that have not experienced significant economic growth, over the past five decades; do not display intergenerational differences, meaning that younger and older cohorts are about equally likely to display traditional or modern values (Inglehart & Wetzel, 2005). A birth cohort’s value orientations do not

change, become more traditional or survival oriented over time. The changes are enduring attributes of given cohorts that have experienced successive growth under various favorable conditions (Inglehart &

Wetzel, 2005; Martinelli, 2005; Pillai, 1988). These intergenerational differences among cohorts may be attributed to the long term socioeconomic changes (Inglehart & Wetzel, 2005; Martinelli, 2005).

The theory of modernization addresses how variables such as income, age at first marriage, and place of residence, can influence contraceptive use among women in a society. Modernization theory explains how a society progresses from one time period to another, what factors contribute to this progress, and how individuals and communities react to change (Inglehart & Wetzel, 2005; Inkeles, 1983;

Inkeles & Smith, 1974). Additionally, modernization theory asserts that individuals are affected by the changes in their society, adopt new patterns of thinking and make decisions based on the current changes, comparing costs and benefits (Kalipeni, 1995; Gage, 1995). Kalipeni (1995) and the NRC

(1993), assert that fertility rates tend to decline as a country develops economically. Women in these societies are more modern and are more likely to use contraceptives. Modernization theory explains social, economic, cultural, or political changes that take place over time (Inglehart & Wetzel, 2005; Inkeles

& Smith, 1974). Martinelli (2005) highlights other important components of modernization theory to include:

1. unit of analysis

2. characteristics of the societies that are affected by the process of modernization

3. factors, mechanisms and processes

4. form sequence and direction of the modernization process

5. intentional and the unintentional characteristics of the modernization process

6. duration and consequences of modernization.

As societies evolve and become modern, individuals are also affected by these changes often by adopting new patterns of thinking and becoming more rational (Inglehart & Wetzel, 2005; Inkeles & Smith,

1974) and basing their decisions on opportunity cost (Macunovich, 2000). Additionally, structural changes such as industrialization in developed countries bring about the high to low fertility transition (Pillai, 1984).

Based on modernization theory, this study focuses on three factors: place of residence (urban versus rural), age at marriage, and income.

3.1.1 Urban versus Rural Residence

Place of residence is described as where people live as part of a community. In this study, the type or place of residence compares the effects of the characteristics found in the urban and rural settings such as access to social services, information, technology, the cost of living and their relation to contraceptive use.

Rural areas are typically plagued by inadequate education and illiteracy, lack of water sources, lack of employment, lack of electricity and lack of resources to gratify the inhabitants of these rural areas

(Molefe, 1996). Bester (1994) describes rural areas as being characterized by primitive agriculture, low income, lack of finances and technical skills and knowledge. Djukanovic and Mach (1975) continue to describe rural areas as being plagued with economic stagnation, lack of health care facilities and sanitation, and isolation, caused by distance and lack of communication. On the contrary, urban areas have higher employment rates, modern water sources and more schools (Molefe, 1996). Additionally, urban areas have more technology, more access to information, more awareness of health and social services and better educational systems compared to the rural areas (Molefe, 1996).

Rural to urban migration has been propelled by several factors such as urbanization, modernization, new and advanced technology, and communication (Morgan & Kickham, 1997; Schuster,

1979; Martinelli, 2005). With modernization and the evolution of societies, rural area inhabitants that desire a chance at modernization or advancement, migrate to urban areas (Schuster, 1979; Martinelli,

2005). Some motivations involved in the rural to urban migration, include the demand for more amenities such as running water and electricity, health care and social services, employment and higher education

(Schuster, 1979; Abraham, 1980).

One way to view the changes in contraceptive use over the years is through Schuster’s (1979) lens of urbanization and through the changing roles of women in developing countries, as outlined in the book, the New Women of Lusaka. This perspective illustrates how developing countries have undergone

social change in response to evolving economic and political changes (Schuster, 1979). Additionally

through this lens, Schuster (1979) discusses how moving to the urban areas has increased the accessibility, awareness and use of modern contraceptives in developing countries, such as Zambia.

In the past, region of residence has received significant attention as a major macro factor affecting contraceptive use in many less-developed countries such as those in Sub-Saharan Africa

(Cleland et al., 1984; Freedman, 1981; Gage, 1995; Njogu, 1991; NRC, 1993). Njogu (1991) discusses a socioeconomic hypothesis that asserts that, regions where women have low education, limited formal- sector employment, and limited access to health and family planning services, are expected to have low rates of contraceptive use. A Zairian study by Shapiro and Tambashe (1994), indicate that women who were self-employed, and those who were employees of government and non-governmental organizations, were more likely to use contraceptives than those women who were not employed (Gage, 1995). Rural and urban types of residences have been found to have an impact on contraceptive use with urban areas having an increased rate of using modern contraceptives than rural areas (Lightbourne, 1980; Tuladhar,

1985; Njogu, 1991; Gage, 1995; NRC, 1993). Urban migration and dwelling is characterized by high cost of living. Inhabitants of urban areas, therefore, adjust their lifestyles according to their resources and the cost of living in these areas. Over time, those living in urban areas make rational decisions regarding family size based on a cost benefit analysis (Macunovich, 2000).

In many developing countries, urban areas are associated with higher proportion of formal-sector employment, access to medical care, family planning and other social services. Those who live in urban areas are exposed to more access to health and family planning services than those living in rural areas

(Pillai & Teboh, 2010; Njogu, 1991). The widespread availability of contraceptive techniques and dissemination of contraceptive knowledge, which accompany modernization, bring about changes in fertility in society and also allow couples to make decisions regarding their fertility and family size formation (Pillai, 1984). Additionally, large scale changes in a society’s social and economic organization may radically alter the social and economic characteristics of the population (Pillai, 1984). The trends in the compositional variables are likely to bring changes in fertility, for instance, as the proportion of households living in urban areas increase, the proportion of households with small family size increase

(Pillai, 1984). The rates of modern contraceptive use are higher in urban area households than

contraceptive use in rural areas (Njogu, 1991; NRC, 1993; Pillai & Teboh, 2010). Since urbanization increases the cost of living, those who live in urban areas are more likely to use contraceptives (Becker &

Barro, 1998). The National Research Council found that urbanization is an important determinant affecting modern contraceptive use in Africa (1993).

Hypothesis 1 : The odds of using modern contraception methods, compared to the use of other contraception methods, are higher for those women who live in urban areas compared to those women who live in rural areas.

3.1.2 Age at Marriage

Modernization has extended the process of formal training and extended the age at marriage

(Warenius et al., 2006; Omungo, 2008; Kabiru & Orpinas, 2009). The age-at-family formation has a negative effect on family size (Pillai, 1984). The greater the age-at-family formation, the more the woman is likely to be exposed to extra familial interests such as exposure to school and to participation in the labor force (Pillai, 1984). Blake (1968) asserts that increase in social and economic participation outside the home, decreases the preference for motherhood.

The changes within the African marriage patterns are essential in the discussion of women’s social positions and contraceptive use. In many traditional African societies, women had little freedom of choice in matters pertaining to marriage and divorce (Gage, 1995; Pillai, 1992). Parents played an active role in finding spouses for their daughters and sons from a young age, and maintaining kinship obligations through promotion of cross-cousin marriages, which was a major practice (Gage, 1995).

However, as societies developed and women were able to access educational, vocational, and training opportunities, women had more choices to negotiate whether they wanted to start families or aspire toward their personal goals (Schuster, 1979). Additionally, as women produced and consumed more goods, they were faced with more options of where to live, where to work, what type of clothes to wear and were generally not confined to the traditional gender roles (Schuster, 1979). The success achieved in school, and in the training programs, gave women new found self worth, confidence and increased self esteem (Schuster, 1979). This base gave educated women entering the urban areas not only financial independence but also high social status and social mobility (Schuster, 1979). Like the

women in Zambia, education was seen as a gateway to happiness, independence, the acquisition of personal possessions such as a new wardrobe, electrical appliances, and even travel to another city or country (Schuster, 1979; Trussell & Reinis, 1989). Finding a handsome man that was wealthy and educated to marry was set up for later—after they are comfortable and advanced in their careers

(Schuster, 1979).

As women grow older, they are more likely to delay marriage and delay having children because of the desire to pursue higher education, advance their careers and pursue opportunities for social mobility (Schuster, 1979). Women desire advanced training and social mobility in their careers for two major reasons: to make more money and also to have less physically demanding jobs so that they may continue working as they grow older (Schuster, 1979). Another reason offered for advancement in career is to reduce dependency on their husbands or other family members (Schuster, 1979). Schuster gives the examples of registry clerks who desire to study typing, nurses who want to advance to midwives, vernacular broadcasters who want to broadcast in English and journalists who want to become editors

(Schuster, 1979).

Modernization gives rise to adoption of modern attitudes, values and beliefs which influence the way people make rational decisions and choices (Abrahams, 1980; Inkeles, 1983; Macunovich, 2000;

Inglehart & Wetzel, 2005; Inkeles & Smith, 1974). These choices include the decision to get married, the time or interval to get married and the desired number of children to have (Pillai, 1984; Amin &

Bajracharya, 2011). Women who choose to have children early may marry much younger than those who choose to postpone having children (Trussell & Reinis, 1989). The time to get married is often influenced by what the woman has to forego in order to have children (Becker, 1993; Becker, 1973) such as education and time spent in school (Sunil & Pillai, 2004). Women living in urban areas are more likely to delay marriage than their rural counterparts with length of time spent in school being a major factor (Sunil

& Pillai 2004). Becker (1973; Becker, 1993) attributes the delay in marriage to the intricate nature of modernization. Sunil and Pillai (2004) assert that increased modernization of a society gives educated women more options such as social mobility and independence, which may downplay the importance or urgency of marriage and having children. Women who choose to delay marriage and delay having

children are more likely to use modern methods of contraception compared to those women who opt to get married at a younger age (Sunil & Pillai, 2004). These women who choose to avoid having unwanted children, while pursuing an education, also postpone marriage, facilitating the consideration of a number of social choices (Sunil & Pillai, 2004) such as pursuing career advancement, pursuing the pleasures of life, consuming material goods and services or traveling to other cities or countries (Schuster, 1979;

Trussell &Reinis, 1989). Jones (2007) and Oliver (1995) assert that as women delay entry into marriage, they are more likely to plan toward family formation and are more likely to use modern methods of contraceptives to have fewer children.

Hypothesis 2: The odds of using modern contraception methods, compared to the use of other contraception methods, are higher for those women who are marry between 15-20 years (old) compared to those women who marry under 14 years (young).

Hypothesis 3: The odds of using modern contraception methods, compared to the use of other contraception methods, are higher for those women who marry between 21-49 years (older) compared to those women who marry under 14 years (young).

3.1.3 Income

The relationship between income levels and fertility has gained strong support from economists, demographers, statisticians and social scientists. In societies around the world, there is continued discussion regarding inequality in the distribution of earnings, income, and wealth among individuals and families (Becker & Tomes, 1993; 1976). Kalipeni (1995) and the NRC (1993), assert that fertility rates tend to decline as a country develops economically since women in these societies are more modern and are more likely to use contraceptives. Evidence from different parts of the world shows that growth in income is associated with lower fertility rates over time (World Bank, 1984; Berger et al., 1992; Becker &

Barro, 1988). Both rich and poor parents have similar demand patterns for children. However, the rich undergo high societal pressure to limit the number of children they have, to raise high quality children and to lead lifestyles consistent with their social rank (Blake, 1968; Pillai, 1981).

Women who participate in the labor force are less likely to want large families and are more likely to use modern methods of contraception than women who are not employed outside the home (Pillai &

Teboh, 2010). Economic theory suggests that as income increases, fertility rates increase as well, asserting that additional resources allow families to take care of more children (NRC, 1993; Berger et al.,

1992). However, over a period of time, fertility rates decline even as income grows, due to the complex nature of income and its relationship with education, participation of women in the labor force, the value of time, and accessibility to consumer goods (NRC, 1993; Oliver, 1995; Berger et al., 1992). This indicates that as women make more money, they are more likely to spend more time in the labor force, they value the time spent in shaping and launching their careers and desire more consumer goods and services, which in turn, affects the importance attached to having children. Women in these positions are more likely to use modern methods of contraception to prevent having children which may interfere with mobility in their careers and the income they earn (NRC, 1993; Becker, 1976; Pillai & Teboh, 2010)

Becker (1976) ascertains that an increase in income must increase the amount spent on the average good but not on each good specifically. For example, in the West, a Chevrolet may be considered an inferior brand of a car to a Mercedes Benz and margarine considered an inferior spread to butter. However, since children are not inferior members of a broader class of people, it is likely that a rise in income over time would increase the amount spent on children (Becker, 1976). Another illustration regarding consumer goods shows that families, at higher income levels, purchase more and better quality units of a certain product. If these similar expenditures translated to children, increased expenditure on children would consist of, or result in an increase in the quality of children (Becker, 1976). However, economic theory does not guarantee that the quantity of children will increase. Thus, an increase in income should increase both the quality and quantity of children, but the quantity elasticity would be small compared to the quality elasticity (Becker, 1976; Becker & Tomes, 1976; NRC, 1993). This shows a quality-quantity trade off whereby women with more income will choose to have fewer children and invests more time and resources on them to produce more quality children. Similarly, to avoid having unwanted children or increasing the quantity of children, women with higher incomes will most likely use modern contraceptive methods to limit the number of children they have (Becker & Tomes, 1976; Oliver,

1995). Having a large family can be viewed as an obstacle to achieve socially desirable goals because of the high cost of missed opportunities brought about by raising a large family. Blake (1968) argues that

given the cost of missed opportunities as a constant, and as long as having children is socially desirable, there is a positive relationship between income and family size. Oliver (1995) asserts that increase in income, lowers the demand for children and therefore raises the demand for contraceptive use.

Hypothesis 4 : The odds of using modern contraception methods, compared to the use of other contraception methods, are higher for those women who have high income compared to those women who have no income.

3.2 Human Capital Theory

Economic theory has been used to address different issues such as fertility, crime, politics, racial

discrimination and education, among others, to explain behavior outside the monetary market sector

(Becker, 1976). In societies around the world, economists continue to discuss inequality in the distribution

of earnings, income and wealth among individuals and families (Becker & Tomes, 1993). According to the

economic theory, the demand for children is viewed as a function of the economic contribution these

children make to the household, the cost of each child or children in regards to the value of the woman’s

time, and household income (Becker, 1976; Oliver, 1995). A woman’s decision to use contraception is assumed to be a function of these variables and also the cost of contraceptives (Oliver, 1995).

Economists use education in research models of fertility as a substitute for the cost of missed opportunities (Pillai, 1981). Economic models suggest that opportunity costs and family size formation are negatively related; that is, both husband’s and wife’s educational levels have a negative effect on the family size (Pillai, 1981; Pillai, 1984).

Current research shows evidence of a strong association between investments in human capital and population growth. Human capital embodies knowledge, skills, and economic development dependent upon advancement in technology and scientific knowledge. Development depends on the accumulation of human capital (Becker, 1976; Becker, 1993).

The concept of human capital is essential for addressing micro investments in education, training, and knowledge acquisition, by individuals and societies (Becker, 1976). The economic growth and inequality present in a society are closely dependent upon investments in different forms of human capital (Becker,

1976). Becker (1976) addresses parental investment in the human capital of their children, as being

dependent on their children’s abilities, available resources, and their own human capital. Human capital theory uses these associations between parents and their children to assess how parental background especially their income, abilities and human capital, determines the human capital and earnings of their children (Becker, 1976). Additionally, parents not only choose how much to invest in each child, but also the number of children they have (Becker, 1976). In many developing countries, which are primarily agricultural, the typical pattern is to have many children and to invest little in each child (Becker, 1976;

NRC, 1993). This strengthens the belief that educational investments are not highly regarded in these environments as children can begin to contribute to the farm output and labor at an early age (Becker,

1976; Pillai, 1992).

As an economy develops through industrialization and urbanization, parents’ time becomes scarcer and more expensive and the advantages of having many children begin to decline (Becker, 1976;

NRC, 1993; Kalipeni, 1995). Both industrialization and modern agricultural methods increase the return of education and advanced job skills. This shifts parental responsibility from having many children to investing much more in each child they have (Becker, 1976). With increase in levels of technology, societal forces influence parents towards lower fertility levels and greater investments in the human capital of each child (Becker, 1976; (Becker, Murphy, & Tamaura, 1976). This change can propel an underdeveloped country from a level of low per capita incomes and high birth rates, to an economy with continued growth, low birth rates and growing levels of human capital (Becker, Murphy, & Tamaura,

1976).

Becker (1993) asserts that the rates of returns on investments in human capital rise rather than fall as the stock of human capital increases. Education is used more intensively in sectors that produce consumption goods and physical capital. From this, emerge two steady states: an undeveloped state with little human capital and low rates of return on investments in human capital, or a developed state with a much higher rate of return and a steadily growing stock of human capital (Becker, 1993).

Becker (1993) views fertility as endogenous; fertility rates on investments in physical capital decline as the stock increases. This leads to multiple steady states: an undeveloped state with high birth rates and low levels of human capital or a developed steady state with much lower fertility rates and

abundant human and physical capital (Becker, 1993). Higher investment of human capital reduces the demand for children because that raises the cost of the time spent on childcare (Becker, 1993). When human capital is insignificant, fertility rate is high whereas if human capital is widespread and growing steadily over time, fertility rates tend to be low. Raising children is time consuming, implying that at higher income levels caused by greater human and physical capital per worker; there is a decrease in satisfaction in having many children, by raising the cost of children (Becker, 1993). Societies can save across generations by birthing many children, making great investments in each child, and by long-term accumulation of physical capital (Becker, 1993).

When human capital is abundant, the rates of return on human capital investments are high relative to rates of return on children. When human capital is low, the rates of return on human capital investments are low, relative to rates of return on children (Becker, 1993). Societies with limited human capital choose large families and invest little in each child, whereas societies with abundant human capital have fewer children and invest more in each child (Becker, 1993) and are more likely to use modern methods of contraception.

Over the years, the decision making process evolved not only from the decision to control births but also the decision to engage in sex (Becker, 1976; NRC, 1993).

Parents view children as a source of income or satisfaction. However, from an economic standpoint, children are consumer goods and when they provide income, they are a production good as well (Becker, 1976; Blake, 1968). Children, referred to as goods in this case, helps to show the kind of satisfaction they provide to their parents by relating the “demand” for children to a well developed framework of economic theory. Becker proposes a theory of consumer durables as a useful framework for analyzing the demand for children (1976; Blake, 1968).

Taste : Becker defines “taste” as the relative preference for children. These tastes or preferences may be determined by a family’s religion, age, or race. Therefore, this framework permits fertility differences that are unrelated to economic factors (1976; Agadjanian, Yabiku, & Fawcett, 2009).

Quality of Children : A family does not only determine how many children they can have but also the amount to be spent on each child such as, time, money, school, resources or extracurricular activities

(Becker, 1976). Parents divide their children into “higher quality” and “lower quality”. For instance, if parents spend more resources on one child, the assumption is that they are obtaining additional utility from them. This is what is termed as “higher quality” in this context of economic theory (Becker, 1976;

Becker, 1993).

Cost : It is sometimes argued that social pressures in a society may force richer families to spend more on children thereby increasing the cost of children (Becker, 1976). This high cost of children explains why richer families have fewer children than poor families, and why richer societies have fewer children than poorer societies (Becker, 1976).

Supply : Children cannot be bought in an open market as other consumer goods but must be produced at home. The number of children available to a family is determined by income, cost of each child and the ability to produce children (Becker, 1976). The average number of live births, produced by married women in societies with little knowledge of contraceptive use, is very high. Families with excess children will spend less on each child than other families with equal income and tastes that have fewer children (Becker, 1976). An increase in knowledge of contraceptive use would raise the quality of children and reduce their quantity (Becker, 1976; Becker, 1993). With increased education, the market value of time is likely to increase (Becker, 1976). As the time becomes increasingly expensive, women choose between this time for making money or raising a child. Women are more likely to forego the option to raise children because their return such as satisfaction and pleasure are not equal to monetary value (Becker,

1976). Therefore, as women’s education increases, opportunity cost increases, the desire to have children decreases, and increases the likelihood of using modern contraceptives.

Economists have long been aware that there are two effects when income rises: an “income effect” that causes us to purchase more of all “normal” goods, and a “price effect” as rising hourly wages raise the “opportunity cost” of any activities requiring an expenditure of our time (Becker, 1976; Becker,

1993). The opportunity cost of any activity is the value to us of the “next best” thing we might have done with our time; thus the opportunity cost of time spent with children might be thought of as the wage that is foregone by not working outside the home (Becker, 1976). In this context, women’s time – its value and whether or not it’s spent in labor market activities – is crucial in determining levels of fertility.

Becker & Mincer (1976) applied the concepts of “income” and “price” effects of a person’s wage in the context of “production” within the home, pointing out that households produce utility for their members using inputs of purchased goods in combination with the time of the household’s members. For example, a pair of skis gives utility only if we spend the time to use them. Further, the cost of obtaining utility from them includes not only just the price of the skis and lift tickets, but also the opportunity cost of our time spent skiing. In this sense, all goods that we purchase and use involve an expenditure of our time. Becker and Mincer (1976) pointed out that children are more time intensive than other goods; the opportunity cost of children represents a higher proportion of their total cost, than does the opportunity cost of most other goods and services.

3.2.1 Education

Over the years, studies have generated considerable interest in Sub-Saharan Africa regarding the relationship between women’s socioeconomic status in a society, reproductive decision making, and contraceptive use (Gage, 1995; Njogu, 1991; Rutenberg et al., 1991; NRC, 1993). A study by Ketkar

(1978) indicates that the amount of primary education of the wife, to a certain point, results in a larger family size at completion of the primary school education, but beyond this threshold level, results in a smaller family size. In other words women with low or no education have larger families and as they obtain higher education, they are more likely to have smaller families (Ketkar, 1978; Pillai, 1981; Baylies,

2000). The National Resource Center (1993) shows evidence that in some regions, increase in childrearing costs, educational aspirations and deteriorating economic conditions show increase in marital closeness and shared decision making between couples. These changes are more evident in urban areas, among more educated persons more likely to use contraceptives (NRC, 1993). Pillai (1981) found that wife’s education has a curvilinear (inverted U) relationship with completed family size. Up to a threshold of 4.4 years, an increase in wife’s education results in a larger family size. Beyond this point, increased education has a declining effect on family size (Pillai, 1981). Additionally, an increase in development of educational institutions may increase the proportion of college-educated women (Pillai,

1984). College-educated women are more likely to prefer smaller family sizes than those who have no college education (Pillai, 1984; Baylies, 2000).

Numerous research studies show that educated women have higher levels of contraceptive use than uneducated women (Njogu, 1991; Rutenberg, et al., 1991; DeRose, 2007; NRC, 1993; Baylies,

2000). The National Research Council found that female education is an important determinant affecting modern contraceptive use in Africa (1993). A study done in Kenya by Njogu (1991) states that women with better education are more likely to use modern contraceptives than women who have low or no education.

Hypothesis 5 : The odds of using modern contraception methods, compared to the use of other contraception methods, are higher for those women who have some education compared to those women who have no education.

Hypothesis 6 : The odds of using modern contraception methods, compared to the use of other contraception methods, are higher for those women who have higher education compared to those women who have no education.

3.3 Social Change Theory

Ryder’s theory of social change will be used in describing the changes in contraceptive use and

to examine both individual level as well as societal factors related to contraceptive use in Kenya between

1998 and 2008-2009.

Changes in contraceptive use in a society, over time, are likely to be a component of broad-based

social changes. Perspectives on social change may provide a useful lens to determine not only the

current influence on contraceptive use but also the changes in contraceptive use over time. Ryder’s

theory of demographic behavior points to the effect of structural variables on contraceptive use, as well as

the changes in structural variables over time, as they influence trends in contraceptive use as a

component of broad social change processes (1965).

Ryder’s 1965 perspective provides a useful framework to consider the influences of a number of socio-economic factors and structural determinants on contraceptive use. Ryder (1965) discusses social

change from a demographic perspective, emphasizing structural renovation of societies and

organizations. Ryder also addresses the interdependence and interrelation of social change and the

population growth process (1965). Additionally, Ryder (1965) addresses the concept of the birth cohort,

describing those born within the same time interval going through life stages together, eventually aging together. Ryder (1965) asserts that cohorts do not cause or effect change but allow it to occur. These changes differentiate cohorts from one another and studying their diverse careers and developments gives an opportunity to study social change.

Pillai’s (1988) cohort-historical model proposes the processes whereby people are born, live out their lives, and are replaced in a society. Members of the same cohort have underlying social changes such as fertility shifts, changes in opinions and values over time (Pillai, 1988). From this perspective, social change is seen as arising from the new connections and contacts the new cohorts make with contemporary social heritage (Ryder, 1965). The interaction between the new cohort members and the existing social system brings about the possibility for the members’ characteristics to be influenced by the social system and bring about interaction effects (Pillai, 1988; Pillai & Teboh, 2010). On the contrary, members of the cohorts may bring with them new or different rules and characteristics, which may alter or be altered by the environment so as to adopt and accomplish the social and economic needs of that society (Pillai, 1988; Pillai & Teboh, 2010). Such environmental and normative changes coupled with changes in the size of cohorts, can bring about inter-cohort differences (Pillai, 1988; Pillai & Teboh,

2010).

Given the individual-level cross-sectional determinants of the interval, the cohort-historical model provides various explanations of mean differences in proportions of modern contraceptive use across cohorts (Pillai & Teboh, 2010; Pillai, 1988). The first explanation focuses on differences in composition of the cohorts and the second explanation focuses on the differences in the effects of determinants influencing or affecting modern contraceptive use across cohorts (Pillai, 1988; Pillai & Teboh, 2010). The first explanation is referred to as the compositional explanation and the second referred to as the processual explanation (Pillai, 1988).

The compositional explanation concentrates on the differences in composition of members of each cohort (Pillai & Teboh, 2010; Pillai, 1988). The compositional explanation assumes that the independent effects (coefficients) of determinants on the first birth interval remain stable across cohorts.

Thus, the changes in mean first birth intervals are due to the changes in the mean levels of determinants

across marriage cohorts. The compositional changes in the characteristics of the cohorts take place as a result of cohort replacement. New cohorts are formed under new circumstances and face different and unique historical experiences (Pillai, 1988). Therefore, each new cohort differs in compositional characteristics and historical experiences that account for different fertility behavior (Pillai, 1988).

The processual changes explain the effects of the variables on modern contraceptive use across cohorts (Pillai & Teboh, 2010; Pillai, 1988). Additionally, Pillai and Teboh’s (2010) processual explanation also suggests that “even if cohort composition with respect to the determinants of modern contraceptive use remains stable across cohorts, changes in contraceptive use would result in changes in the effects of the selected determinants, p.7” (Pillai & Teboh, 2010). Compositional as well as processual changes are expected to describe changes in modern contraceptive use (Pillai, 1988; Pillai & Sunil, 2007; Pillai &

Teboh, 2010).

Figure 3.1 below depicts the relationship among the following: three theories (modernization, human capital, and social change); variables (place of residence, age at first marriage, income, and education); and outcome variables (other contraceptive methods and modern contraceptive methods), the processual and compositional changes and their relationship to each other.

Type of Residence

Other Contraceptive Methods Modernization Theory Age at Marriage

Income Modern Contraceptives

Human Capital Theory Education

Processual Changes

Social Change Theory Compositional Changes

Figure 3.1: Diagram Depicting the Relationship Among the Theories, Selected Determinants and Outcome Variable

CHAPTER 4

METHODOLOGY

4.1 Purpose of the Study

The purpose of this study is to explain the characteristics and differences between three groups

of women, aged 15-49, who have used other methods of contraceptives, and those who only use modern methods of contraceptives in Kenya, between 1998 and 2008-2009. This study will describe the differences in the trends of contraceptive use from 1998 to 2008-2009 and explain contraceptive use in

Kenya.

4.2 Data Source

Kenya has had a total of six demographic health surveys (DHSs) conducted in the country. The

first was conducted in 1977-1978, the second in 1989, the third in 1993, the fourth in 1998, the fifth in

2003 and the most recent, in 2008-2009. The 1998 Kenya Demographic Health Survey was the fourth

carried out in Kenya while the 2008-2009 was the sixth and most recent conducted. These six surveys

obtained detailed data on contraceptive use and on background information for a large and nationally

representative sample of women. The surveys provided information on a range of variables relating to

fertility such as background characteristics, reproductive history, marriage and sexual activity, use of

family planning methods, and knowledge, attitudes and behavior regarding HIV/AIDS and other Sexually

Transmitted Diseases (STDs). Three types of questionnaires were used in the 1998 KDHS: the

household questionnaire, the women’s questionnaire and the men’s questionnaire. The women’s and

men’s questionnaires were based on the DHS model “A” questionnaire designed for use in countries with relatively high levels of contraceptive use. The data collected on the 2008-2009 KDHS updated the demographic and health indicators derived from the previous DHSs.

4.3 Sampling

The 1998 KDHS surveyed a sample of 8,380 households including 7,881 women aged 15 to 49.

The most recent KDHS carried out in 2008-2009 surveyed 9,057 households including 8,444 women

aged 15 to 49.

4.4 Operationalization of Variables

For the purpose of this study contraceptive use is defined as any method used to delay or avoid

child birth.

The dependent variable in this study is contraceptive use among Kenyan women, divided into two

categories: those who have used other methods of contraceptives and those who use only modern

contraceptive methods. The focus is on modern contraceptive methods since this is a rapidly growing

trend in Kenya. To address this trend, we examined several social, economic and demographic variables

that may influence it. The independent variables in this study are: place of residence (rural versus urban),

income, age at marriage (old and older), and education (some education and higher education). The

control variables used are: media, religion, marriage type, total number of children born, region and

current marital status.

4.5 Dependent Variable

For this study, those women who use any type of modern contraceptives such as the pill,

intrauterine devices (IUDs), sterilization, injectables, condoms, and Norplant will be coded 1 and those

who have used other methods of contraceptives are considered the reference group and will be coded 0.

The dependent variable contraceptive use in both datasets had four categories: no contraception, folkloric methods, traditional methods and modern methods. Due to limited data under no contraceptive use, folkloric and traditional contraception methods, these categories were fused into one under the heading

“other methods.” The second category consists of modern contraceptive users. Therefore, the two main categories are those women who have used other contraceptive methods and women who have used only modern contraceptives. The category “other” methods is coded 0 and is the reference group and modern methods is coded 1.

4.6 Independent Variables

This study uses four independent variables: type of place of residence (urban versus rural),

income, age at marriage (old and older), and education (some education and higher education). Below are specific questions regarding these four variables used in this study.

4.6.1 Type of place of residence: Urban versus Rural

Place of residence is a dichotomous variable with urban coded 1, and rural, considered the reference group, is coded 0. An urban area is defined as an area with more than 100,000 inhabitants whereas a rural area is defined as an area with less than 100,000 inhabitants.

Question : Do you live in an urban area or a rural area? Urban (1) Rural (2)

The corresponding hypothesis for those women who live in rural or urban areas on modern contraceptive

use is as follows;

Hypothesis 1 : The odds of using modern contraception methods compared to the use of other contraception methods are higher for those women who live in urban areas compared to those women who live in rural areas (Positive).

4.6.2 Income

In the 1998 KDHS, the question regarding income asked whether the women earn cash for their work and presented two options: “yes” coded 1 and “no” coded 0. In 2008-2009, the question regarding income added more options: not paid is coded 1; cash only is coded 2; cash and kind coded 3; and in kind only, coded 4. Income was computed into a dichotomous variable asking women whether they earned income for their work or not. The options “not paid”, “in kind only,” and “cash” and “kind,” were computed into a different variable, “no,” showing that the women were not paid for the work they did.

Additionally, this new income variable was re-coded into those who said “yes” to earning cash for their work coded 1, and those who do not earn cash for their work, coded 0. Income is defined as monetary value earned for doing any type of work. Those who earn cash for their work are coded 1, and those who do not earn any cash for their work are the reference group and coded 0. The question asked regarding income is as follows:

Question : Do you earn cash for your work? Yes (1), No (2)

The corresponding hypothesis for those women who earn cash and those who do not earn cash on modern contraceptive use is as follows:

Hypothesis 2 : The odds of using modern contraception methods compared to the use of other

contraception methods are higher for those women who earn cash for their work compared to those who

do not (Positive).

4.6.3 Age at Marriage

The age at marriage was a continuous variable divided into three categories: women who are

under 14 years of age classified as “young” and assigned as the reference group, coded 0; women

between 15 and 20, classified as “old” and coded 1, and women aged 21-49, classified as “older” and

coded 2.

Question : How old were you when you first started living with him? Age_____

The corresponding hypotheses for women in the three different age groups in relation to contraceptive

use are as follows;

Hypothesis 3 : The odds of using modern contraception methods compared to the use of other

contraception methods are higher for those women who marry between 15-20 years (old) compared to

those who marry under 14 years (young) (Positive).

Hypothesis 4 : The odds of using modern contraception methods compared to the use of other

contraception methods are higher for those women who marry between 21-49 years (older) compared to

those who marry under 14 years (young) (Positive).

4.6.4 Education

The respondents’ level of education is categorized into three bivariate variables: no education (no

education); some education (some education), which constitutes those women who have more than

primary and less than secondary education; and higher education (higher education) constitute those

women with above secondary education. Those in the group with some education (some education) are

coded 1, those in the group with higher education (higher education) are coded 2, and those in the group

with no education (no education) are coded 0 and designated the reference group. In this study, the

woman’s or wife’s education is reported as number of years completed in school.

Question 1 : What is the highest level of school you attended? Primary (1), Secondary (2),

Higher (3)

Question 2 : What is the highest standard/form/year you completed at that level? ______

The corresponding hypotheses for women in the 3 different age groups on modern contraceptive use are as follows:

Hypothesis 5 : The odds of using modern contraception methods compared to the use of other contraception methods are higher for those women who have some education (less than primary or greater than secondary education) compared to those women who have no education (Positive).

Hypothesis 6 : The odds of using modern contraception methods compared to the use of other contraception methods are higher for those women who have higher education (greater than secondary education) compared to those women who have no education (Positive).

Table (4.1) below shows a summary of the selected determinants, corresponding hypotheses, and projected direction.

Table 4.1

Summary of Selected Variables, Corresponding Hypotheses, and Projected Direction

Variable Hypothesis Direction

Place of The odds of using modern contraception methods compared to the Positive (+) residence use of other contraception methods are higher for those women who Urban vs. Rural live in urban areas compared to those women who live in rural areas.

Age at Marriage The odds of using modern contraception methods compared to the Positive (+) use of other contraception methods are higher for those women who marry between 15-20 years (old) compared to those women who marry under 14 years (young).

The odds of using modern contraception methods compared to the use of other contraception methods are higher for those women who marry between 21-49 years (older) compared to those women who marry under 14 years (young).

Income The odds of using modern contraception methods compared to the Positive (+) use of other contraception methods are higher for those women who have high income compared to those women who have no income.

Education The odds of using modern contraception methods compared to the Positive (+) use of other contraception methods are higher for those women who have some education (less than primary or greater than secondary) compared to those women who have no education.

The odds of using modern contraception methods compared to the use of other contraception methods are higher for those women who have higher education (greater than secondary education) compared to those women who have no education.

4.7 Control Variables

There are six control variables used in this study: access to media, religion, marriage type, total

number of children born, region, and current marital status.

4.7.1 Media (Audio or Visual)

The questions asked related to access to media are;

Question 1 : Do you listen to the radio and/or watch television? Yes (1), No (2)

Question 2 : Do you read any print material (pamphlets, newspapers, magazines)? Yes (1), No (2)

4.7.2 Religion

Religion, as a variable, was divided into three categories: Christian, Muslims and “other.”

However, due to limited data and low percentages, those in the Muslim and “other” categories were fused to form the category “other”. Therefore the two categories under religion are Christians and “other”.

Christian is coded 1, and “other” is the reference group, coded 0

The question asked about the woman’s religion is:

Question : What is your religion? Christian (1) Other (0)

4.7.3 Marriage Type

The marriage type variable was continuous, asking women how many other wives their husbands had besides them. This variable was categorized to form two major categories to include: those women who were the only wives, indicating they were in monogamous marriages coded 1, and women whose husbands had other wives were categorized as being in polygamous marriages and coded 0, the reference group.

Question 1 : How many other wives does your husband have besides you? _____

4.7.4 Total Number of Children (EverBorn)

This question inquires about the total number of children born to a woman between the ages of

15 to 49 and whether they live together or elsewhere.

Question 1 : Do you have any sons or daughters to whom you have given birth to who are living with you or elsewhere? Yes (1), No (2)

Question 2 : How many children do you have living with you? ______

4.7.5 Current Marital Status (MaritStatus)

Marital status was added as a control variable (MaritStatus) to examine differences in

contraceptive use among women according to their marital status. This variable was initially divided into

six categories: never married, married, those who were living together, widowed, divorced, and those

women not living with their partners. This variable was fused into three main categories to include: those

who have never been married coded 0, those who are married coded 1, and those who are in other forms

of marriages (othermarry) to include those women who were living with their partners, widowed, divorced, and women not living together coded 2.

4.7.6 Region

Region was added as a control variable and divides the country into provinces. In the 1998 dataset, this variable divided the country into seven provinces to include: Nairobi, Central, Coast, Eastern,

Nyanza, Rift Valley and Western provinces. In the 2008-2009 dataset, an eighth province, North Eastern, was added. For this study, North Eastern province was combined with Eastern province due to proximity and also because the percentage of women in North Eastern was low.

Appendix C shows a list of questions from the Kenya demographic health surveys for 1998 and 2008-

2009.

4.8 Data Analysis Techniques

This study will use three types of data analysis techniques: univariate analysis, binary logistic

regression analyses, and decomposition analysis. Vogt (2005) defines univariate analysis as a technique

used to study the distribution of cases of one variable. First, univariate analysis will be used to report the

mean variance, standard deviation and distribution for each variable. For all categorical variables, percentages in each category will be described and reported. The results will be presented in tables, bar

graphs, and histograms. Kurtosis and skewedness will be reported for continuous variables.

Second, binary logistic regression analysis will be used to estimate the effects of the selected

determinants on modern contraceptive use among a sample of women respondents in the 1998 and

2008-2009 demographic health surveys in Kenya respectively. For the binary logistic regression analysis,

each independent and control variable was coded into a dummy variable for comparison with the

reference groups. For example, the variable income is divided into two categories. Earning an income

“yes” is coded 1, and not an earning an income “no,” is coded 0. For the variable religion, Christian is coded 1 and “other,” coded 0.

Third, decomposition analysis will also be used as it allows us to delineate factors that may have contributed to the observed increase and not merely describe the trends in contraceptive use. The differences in the effects of the determinants in the proposed contraceptive use models in the two

samples 1998 and 2008-2009, will be decomposed into processual, compositional and interaction components, suggested by the cohort-effect model of Norman Ryder (1965).

The compositional differences will be addressed using the Phi Coefficient for all binary categorical variables and using an independent sample-t-test for continuous variables to address the differences within cohorts for both survey periods 1998 and 2008-2009. The processual changes will be addressed, using the regression interaction variables created by multiplying the variable name and the two data sets for survey periods 1998 and 2008-2009. For example, the interaction variable for income is, income x data = incomeinter. The interaction variable for urban is urban x data = urbaninter.

4.8.1 Decomposition Analysis Formula

Lastly, the results will be decomposed into the formula below to address magnitude changes in the compositional, processual and interaction effects over the two time periods 1998 and 2008-2009.

4.8.2 Decomposition Analysis Formula

LnP[ /1− P ] = Σ β x Ln[ P /1− P ] x i i ii where i i is the logit or log-odds of contraceptive use, i is a vector of

β determinants and i is a vector of regression coefficients

Logitc( 08)− log itc ( 98) =−+Σ [ββ0(08) 0(98) ] P ij (98) ( ββ ij (08) −+ ij (98) )

Σβij(98)(PP ij (08) −+Σ− ij (98) ) ( PP ij (08) ij (98) )( β ij (08) − β ij (98) ).

P ij (98) Proportion of the jth category of the ith determinant in DHS 1998

P ij (08) Proportion of the jth category of the ith determinant in DHS 2008

β ij (98) =Coefficient of the jth category of the ith determinant in DHS 1998

β ij (08) =Coefficient of the jth category of the ith determinant in DHS 2008

β 0(98) = Intercept in the regression equation fitted to DHS 1998

β 0(04) =Intercept in the regression equation fitted to DHS 2008

4.8.2 Missing Data

Three variables in both data sets had large missing values. The table below shows the variables and corresponding missing system data. For the variable MarriageType, respondents who answered

“don’t know” to their husbands having other wives besides them, were combined with the missing data.

All missing values were re-coded and declared as missing 99.00.

Table 4.2

Variables and Corresponding Missing System Data for Both Survey Periods 1998 and 2008-2009

Variable 1998 2008-2009

AgeMarried 30.1 Percentage 30.1 Percentage

MarriageType 38.5 Percentage 40.3 Percentage

EverBorn 48.2 Percentage 44.3 Percentage

4.8.3 Conclusion

The objectives of this study are to examine the effects of selected determinants on contraceptive

use in Kenya between 1998 and 2008-2009, and to describe the changes in the selected determinants on

contraceptive use in Kenya between 1998 and 2008-2009.

This study will use modernization and human capital theories to explain the categorical variations in

contraceptive use among the two comparison groups: those women using other contraception methods

and those women using only modern contraception methods.

In describing the changes in contraceptive use between 1998 and 2008-2009, this study will

examine both individual level as well as societal factors related to contraceptive use in Kenya using

Ryder’s theory on social change. The data used for this study are derived from the Kenya Demographic

Health Surveys in 1998 and 2008-09. Three types of data analysis methods will be used: univariate

analysis, binary logistic regression, and decomposition analysis.

In many Sub-Saharan Africans, population control has been achieved through various family

planning programs and policies regarding fertility and population control. The use of the three theories—

modernization, human capital, and social change—give us insight into how development in a society

affects income, age at marriage, and the type of residence—urban versus rural. Additionally, we are able to explain how human capital and education affect the use of modern contraceptives in determining family size formation. Several studies have focused on the role of family planning programs; only a few have focused on structural sources of change in contraceptive use. Broad-based knowledge of structural changes that enhance modern contraceptive use is important in the shaping of population policies in a society. In this study, we will decompose these changes in modern contraceptive use between 1998 and

2008-2009 in Kenya using DHS data.

CHAPTER 5

DATA ANALYSIS AND RESULTS

This chapter addresses the data analysis techniques used and the findings of this study. The

1998 Kenya Demographic Health Survey (KDHS) surveyed a total of 7,881 women, and the 2008-2009

KDHS surveyed 8,444 women—an increase of 563 women. A systematic approach to the empirical test of hypothesis necessitates several progressive analysis steps. The data analysis in the study progresses through three main stages: first, descriptive analysis: second, bivariate association between the selected independent variables and the dependent variable—use of modern contraceptives, and last, an assessment of the hypothesized net effects of the selected determinants on modern contraceptive use.

The results from the data analysis techniques from each stage are presented in the following four sections of this chapter.

The first section presents statistical descriptions of all the independent, dependent and control variables used in this study. All the variables, except one (EverBorn), are categorical and are explained in terms of their percentages in different categories, such as earning an income or not earning income and having no education, some education, or higher education. The descriptions of all the selected determinants are performed separately for the two survey periods, 1998 and 2008-2009, respectively.

The results of the dependent variable, all selected independent and control variables for the two survey periods, 1998 and 2008-2009, are presented in tables and bar graphs below. Additionally, the histograms and bar graphs depict a visual illustration of the selected variables for the two time periods, 1998 and

2008-2009, respectively.

The second section, presents the association between the selected independent variables and the dependent variable, modern contraceptive use. A Chi-square analysis is used to examine the associations of these variables and presented for the two survey periods, 1998 and 2008-2009, respectively. The third section presents the results from two binary logistic regression analyses, assessing the empirical validity of the proposed hypotheses presented in this study, or the two survey

periods—1998 and 2008-2009 respectively. The fourth section examines the contributions of the selected variables to changes in modern contraceptive use in Kenya between the two survey periods, 1998 and

2008-2009 respectively.

5.1 Section I: Descriptive Analysis - Independent Variables

5.1.1 Education

The education variable is divided into three categories: those with no education (no education),

those with some education (more than primary school education and less than secondary school

education (some education) and those with higher education to include women with more than a

secondary school education (higher education). Those women with no education are assigned the

reference group.

Table 5.1

Total Number and Percentage of Women with Different Levels of Education in Kenya in 1998

Education

Frequency Percent Valid Percent Cumulative Percent

No Education 1010 12.8 12.8 12.8

Some Education 6723 85.3 85.3 98.1

Higher Education 148 1.9 1.9 100.0 Total 7881 100.0 100.0

In 1998, there were 12.8 percent of women without an education, 85.3 percent with some

education, including those with more than a primary school education and less than a secondary

education. Only 1.9 percent of the women surveyed had higher education.

Table 5.2

Total Number and Percentage of Women with Different Levels of Education in Kenya in 2008-2009

Education

Frequency Percent Valid Percent Cumulative Percent

No Education 1242 14.7 14.7 14.7

Some Education 6488 76.8 76.8 91.5

Higher Education 714 8.5 8.5 100.0 Total 8444 100.0 100.0

In 2008-2009, 14.7 percent of women had no education, 76.8 percent had some education to include those with more than a primary school education but less than a secondary school education, and

8.5 percent of the women surveyed had higher education.

Figure 5.1: Histogram of Women With Different Levels of Education in Kenya in 1998

Figure 5.2: Bar Graph of Women With Different Levels of Education in Kenya in 1998

Figure 5.3: Histogram of Women With Different Levels of Education in Kenya in 2008-2009

Figure 5.4: Bar Graph of Women with Different Levels of Education in Kenya in 2008-2009

5.1.2 Income (Type of Earnings for Work)

The income variable is divided into two categories: those earning cash are coded “yes” and those not earning cash are coded “no”.

Table 5.3

Total Number and Percentage of Women Who Earned and Did Not Earn an Income in Kenya in 1998

Earns cash for work Frequency Percent Valid Percent Cumulative Percent No 797 10.1 19.5 19.5 Yes 3280 41.6 80.5 100.0 Total 4077 51.7 100.0 Missing 99.00 3804 48.3 Total 7881 100.0

From the above table, it is observed that in 1998, 41.6 percent of women earned cash for the work they did and 10.1 percent stated they did not earn any cash for the work they did. Missing data accounted for 48.3 percent of the total women surveyed.

Table 5.4

Total Number and Percentage of Women Who Earned and Did Not Earn an Income in Kenya in 2008-2009

Earns cash for work

Frequency Percent Valid Percent Cumulative Percent

No 1499 17.8 31.9 31.9

Yes 3202 37.9 68.1 100.0

Total 4701 55.7 100.0

Missing 99.00 3743 44.3 Total 8444 100.0

In 2008-2009, 37.9 percent of women stated they were paid for the work they did and 17.8

percent of women stated that they were not paid for the work they did. Missing system data accounted for

44.3 percent of the total women surveyed.

Figure 5.5: Histogram of Women Who Earned or Did Not Earn an Income in Kenya in 1998

Figure 5.6: Bar Graph of Women Who Earned or Did Not Earn an Income in Kenya in 1998

Figure 5.7: Histogram of Women Who Earned or Did Not Earn an Income in Kenya in 2008-2009

Figure 5.8: Bar Graph of Women Who Earned or Did Not Earn an Income in Kenya in 2008-2009

5.1.3 Age at Marriage

The AgeMarried variable is divided into three categories: young (under 14), old (15-20), and older (21-49) age group .

Table 5.5

Women Categorized According to Their Age at Marriage in Kenya in 1998

AgeMarried

Frequency Percent Valid Percent Cumulative Percent Less than 14 713 9.0 12.9 12.9

15-20 old 3419 43.4 62.1 75.0

21-49 older 1374 17.4 25.0 100.0

Total 5506 69.9 100.0

Missing 99.00 2375 30.1

Total 7881 100.0

In 1998, 9.0 percent of the total number of women surveyed was in the young age group (under

14), 43.4 percent in the “old” group (15-20), and 17.4 percent in the “older” group

(21-49). There was 30.1 percent system missing data in 1998.

Table 5.6

Women Categorized According to Their Age at Marriage in Kenya in 2008-2009

AgeMarried

Frequency Percent Valid Percent Cumulative Percent

Under 14 628 7.4 10.6 10.6

15-20 old 3578 42.4 60.6 71.2

21-49 older 1698 20.1 28.8 100.0

Total 5904 69.9 100.0

Missing 99.00 2540 30.1 Total 8444 100.0

In 2008-2009, 7.4 percent of the total number of women surveyed was in the young age group

(under 14), 42.4 percent in the “old” group (15-20), and 20.1 percent in the “older” group (21-49). There was 30.1 percent system missing data in 2008-2009.

Figure 5.9: Histogram of Women Categorized According to Age at Marriage in Kenya in 1998

Figure 5.10: Bar Graph of Women Categorized According to Age at Marriage in Kenya in 1998

Figure 5.11: Histogram of Women Categorized According to Age at Marriage in Kenya in 2008-2009

Figure 5.12: Bar Graph of Women Categorized According to Age at Marriage in Kenya in 2008-2009

5.1.4 Type of place of Residence (Urban versus Rural)

The type of residence variable is divided into two categories: urban and rural.

Table 5.7

Total Number and Percentage of Women in the Urban and Rural Areas in Kenya in 1998

Type of place of residence

Frequency Percent Valid Percent Cumulative Percent

Urban 1466 18.6 18.6 18.6

Rural 6415 81.4 81.4 100.0 Total 7881 100.0 100.0

In 1998, 18.6 percent of women lived in urban areas and 81.4 percent lived in rural areas of the

total number of women surveyed.

Table 5.8

Total Number and Percentage of Women in the Urban and Rural Areas in Kenya in 2008-2009

Type of place of residence

Frequency Percent Valid Percent Cumulative Percent

Urban 2615 31.0 31.0 31.0

Rural 5829 69.0 69.0 100.0 Total 8444 100.0 100.0

In 2008-2009, the number of women living in urban areas was 31.0 percent and those living in

rural areas were 69.0 percent of the total number of women surveyed.

Figure 5.13: Histogram of Women in Urban and Rural Areas in Kenya in 1998

Figure 5.14: Bar Graph of Women in Urban and Rural Areas in Kenya in 1998

Figure 5.15: Histogram of Women in Urban and Rural Areas in Kenya in 2008-2009

Figure 5.16: Bar Graph of Women in Urban and Rural Areas in Kenya in 2008-2009

5.2 Dependent Variable

5.2.1 Ever Use

Table 5.9

Women Who Have Used Other Contraceptives and Those Who Have Used Modern Contraceptives in Kenya in 1998

EverUse

Frequency Percent Valid Percent Cumulative Percent

Other 4705 59.7 59.7 59.7

Modern 3176 40.3 40.3 100.0 Total 7881 100.0 100.0

In 1998, women who used other methods of contraception accounted for 59.7 percent and women who used modern methods of contraception accounted for 40.3 percent of the total number of women surveyed.

Table 5.10

Women Who Have Used Other Contraceptives and Those Who Have Used Modern Contraceptives in Kenya in 2008-2009

EverUse Frequency Percent Valid Percent Cumulative Percent Other 4186 49.6 49.6 49.6 Modern 4258 50.4 50.4 100.0 Total 8444 100.0 100.0

In 2008-2009, women who used other methods of contraception accounted for 49.6 percent and those who use modern methods of contraception accounted for 50.4 percent of the total number of women surveyed.

Figure 5.17: Histogram of Other Methods and Modern Contraceptive Use in Kenya in 1998

Figure 5.18: Bar Graph of Other Methods and Modern Contraceptive Use in Kenya in 1998

Figure 5.19: Histogram of Other Methods and Modern Contraceptive Use in Kenya in 2008-2009

Figure 5.20: Bar Graph of Other Methods and Modern Contraceptive Use in Kenya in 2008-2009

5.3 Control Variable

5.3.1 Total Number of Children Born (EverBorn)

This variable addressed the total number of children born to each woman aged between 15 and

49. This is a continuous variable.

Table 5.11

Total Number and Percentage of Children Born to Women Aged 15-49 in Kenya in 1998

Total children ever born

Frequency Percent Valid Percent Cumulative Percent

Valid 0 2164 27.5 27.5 27.5

1 1099 13.9 13.9 41.4

2 992 12.6 12.6 54.0

3 777 9.9 9.9 63.8

4 690 8.8 8.8 72.6

5 542 6.9 6.9 79.5

6 509 6.5 6.5 85.9

7 359 4.6 4.6 90.5

8 275 3.5 3.5 94.0

9 227 2.9 2.9 96.9

10 139 1.8 1.8 98.6

11 53 .7 .7 99.3

12 38 .5 .5 99.8

13 12 .2 .2 99.9

14 3 .0 .0 100.0

15 2 .0 .0 100.0 Total 7881 100.0 100.0

In 1998, majority of the women –27.5 percent—had no children, followed by 13.9 percent who had one child and 12.6 percent who had two children. Women who had three children accounted for 9.9

percent, women with four children accounted for 8.8 percent, women with five children accounted for 6.9 percent, and women with six children accounted for 5.6 percent. Women with seven children accounted for 4.6 percent; women with eight children, 3.5 percent; women with nine children, 2.9 percent; and women with 10 children, 1.8 percent. Women with 11 children accounted for 0.7 percent; women with 12 children, 0.5 percent; and women with 13 children, 0.2 percent. There were women with 14 and 15 children but the percentages were negligible. In 1998, the mean number of children was, 2.96; median.

2.0; and mode, 0.

Table 5.12

Total Number and Percentage of Children Born to Women Aged 15-49 in Kenya in 2008-2009

Total children ever born Cumulative Frequency Percent Valid Percent Percent Valid 0 2342 27.7 27.7 27.7

1 1182 14.0 14.0 41.7

2 1211 14.3 14.3 56.1

3 1040 12.3 12.3 68.4

4 785 9.3 9.3 77.7

5 564 6.7 6.7 84.4

6 474 5.6 5.6 90.0

7 325 3.8 3.8 93.8

8 232 2.7 2.7 96.6

9 141 1.7 1.7 98.2

10 75 .9 .9 99.1

11 41 .5 .5 99.6

12 19 .2 .2 99.8

13 9 .1 .1 100.0

15 4 .0 .0 100.0 Total 8444 100.0 100.0

In 2008-2009, 27.7 percent of women had no children, 14.0 percent had one child, 14.3 percent had two children, and 12.3 percent had three children. Women with four children accounted for 9.3 percent; women with five children, 6.7 percent; and women with six children, 5.6 percent. Women with seven children accounted for 3.8 percent; women with eight children 2.7 percent; women with nine children,1.7 percent; and women with 10 children,0.9 percent, women with 11 children accounted for 0.5 percent, and women with 12 children, for 0.2 percent. There were women with 13 and 15 children but the percentages were negligible—0.1 and 0.0 percent respectively. In 2008-2009, the mean number of children was 2.67; median, 2.0; and mode, 0.

Figure 5.21: Histogram of Total Number of Children Born to Women Aged 15-49 in Kenya in 1998

Figure 5.22: Bar Graph of Total Number of Children Born to Women Aged 15-49 in Kenya in 1998

The skewedness of the EverBorn is 0.929 and the standard error of skewedness is 0.028., not a

normal distribution. The kurtosis is 0.109 and the standard error of kurtosis is 0.055. The distributional properties of the variables in terms of skewedness and kurtosis will be determined by the following rules.

Variables with kurtosis values between -3 and +3 are considered to have kurtosis properties of a normal

distribution (Brown, 2008). A distribution with kurtosis exactly 3 is called mesokurtic. A distribution with

kurtosis less than 3 is called platykurtic with a broader and lower peak and shorter and thinner tails. A

distribution with kurtosis greater than 3 is called leptokurtic, its peak is higher and sharper, and its tails

are longer and fatter (Brown, 2008).

For skewedness, the following rules apply: if it is less than -1 and greater than +1 it is highly

skewed. If between -1 and -0.5 or between 0.5 and 1, the distribution is considered to be moderately

skewed. If the values are between -0.5 and +0.5, the distribution is considered to be approximately

symmetrical (Brown, 2008). By these norms, the distribution of EverBorn is moderately skewed and the kurtosis is leptokurtic.

Figure 5.23: Histogram of Total Number of Children Born to Women Aged 15-49 in Kenya in 2008-2009

Figure 5.24: Bar Graph of Total Number of Children Born to Women Aged 15-49 in Kenya in 2008-2009

The skewedness of the EverBorn is 1.030 and the standard error of skewedness is 0.027, not a normal distribution. The kurtosis is 0.635 and the standard error of kurtosis is 0.053. By the norms stated in the paragraph above, the distribution of EverBorn is moderately skewed and the kurtosis is leptokurtic.

5.3.2 Marital Status

The marital status (MaritStatus) variable is divided into three categories: never married, married, and othermarry.

Table 5.13

Total Number and Percentage of Women Categorized According to Their Marital Status in Kenya in 1998

MaritStatus

Frequency Percent Valid Percent Cumulative Percent

Never Married 2375 30.1 30.1 30.1

Married 4631 58.8 58.8 88.9

Other 875 11.1 11.1 100.0 Total 7881 100.0 100.0

In 1998, married women accounted for 58.8 percent, never married 30.1 percent, and all others

accounted for 11.1 percent to include all those women living together, widowed, divorced, and not living

together of the total number of women surveyed.

Table 5.14

Total Number and Percentage of Women Categorized According to Their Marital Status in Kenya in 2008-2009

MaritStatus

Frequency Percent Valid Percent Cumulative Percent

Never Married 2540 30.1 30.1 30.1

Married 4682 55.4 55.4 85.5

Other 1222 14.5 14.5 100.0 Total 8444 100.0 100.0

In 2008-2009, the number of never married women accounted for 30.1 percent. The percentage of married accounted for 55.4 percent. Women in the othermarry category, include those who were living together, widowed, divorced, and not living together, accounted for 14.5 percent of the total number of women surveyed.

Figure 5.25: Histogram of Women Categorized According to Their Marital Status in Kenya in 1998

Figure 5.26: Bar Graph of Women Categorized According to Their Marital Status in Kenya in 1998

Figure 5.27: Histogram of Women Categorized According to Their Marital Status in Kenya in 2008-2009

Figure 5.28: Bar Graph of Women Categorized According to Their Marital Status in Kenya in 2008-2009

5.3.3 Marriage Type

The marriage type variable is divided into two categories: monogamous and polygamous.

Table 5.15

Total Number and Percentage of Women Categorized According to Marriage Type in Kenya in 1998

MarriageType

Frequency Percent Valid Percent Cumulative Percent

Monogamous 4057 51.5 84.0 84.0

Polygamous 773 9.8 16.0 100.0

Total 4830 61.3 100.0

Missing 99.00 3051 38.7 Total 7881 100.0

In 1998, majority of the women, 51.5 percent stated that their husbands had no other wives

besides them, indicating that they were in monogamous marriages. Women who stated their husbands

had one or more wives besides them, indicating that they were in polygamous marriages, accounted for

9.8 percent of the total number of women surveyed. In 1998 the missing system data accounted for 38.7

percent.

Table 5.16

Total Number and Percentage of Women Categorized According to Marriage Type in Kenya in 2008-2009

MarriageType

Frequency Percent Valid Percent Cumulative Percent

Monogamous 4156 49.2 83.8 83.8

Polygamous 805 9.5 16.2 100.0

Total 4961 58.8 100.0

Missing 99.00 3483 41.2 Total 8444 100.0

In 2008-2009, 49.2 percent of the women stated that their husbands had no other wives besides

them indicating that they were in monogamous marriages. Women who stated their husbands had one

other or more wife besides them, indicating that they were in polygamous marriages accounted for 9.5 percent of the total number of women surveyed. In 2008-2009, the missing system data accounted for

41.2 percent.

Figure 5.29: Histogram of Women Categorized According to Marriage Type in Kenya in 1998

Figure 5.30: Bar Graph of Women Categorized According to Marriage Type in Kenya in 1998

Figure 5.31: Histogram of Women Categorized According to Marriage Type in Kenya in 2008-2009

Figure 5.32: Bar Graph of Women Categorized According to Marriage Type in Kenya in 2008-2009

5.3.4 Media

The media variable is divided into two categories: audio or visual to include those women who heard of contraceptive use methods through the radio or television, and “other” which primarily consists of women who heard of contraceptive use methods primarily through the newspaper and word of mouth.

Table 5.17

Percentage of Women Who Heard of Contraceptive Methods Through the Media in Kenya in 1998

Media

Frequency Percent Valid Percent Cumulative Percent

Audio or Visual 3758 47.7 81.8 81.8

Other 836 10.6 18.2 100.0

Total 4594 58.3 100.0

Missing 99.00 3287 41.7 Total 7881 100.0

In 1998, women who heard of contraceptive methods through the radio or television, accounted

for 47.7 percent and those women who heard of contraceptive methods through other medium, primarily

newspapers and word of mouth, accounted for 10.6 percent of the women surveyed. There was 41.7 percent of system missing data in 1998.

Table 5.18

Percentage of Women Who Heard of Contraceptive Methods Through the Media in Kenya in 2008-2009

Media

Frequency Percent Valid Percent Cumulative Percent

Audio or Visual 3354 39.7 59.4 59.4

Other 2296 27.2 40.6 100.0

Total 5650 66.9 100.0

Missing 99.00 2794 33.1 Total 8444 100.0

In 2008-2009, women who had heard of contraceptive methods through the radio or television accounted for 39.7 percent and those women who heard of contraceptive methods through other methods, primarily newspapers and word of mouth, accounted for 27.2 percent of the total number of women surveyed. There was 33.1 percent of system missing data in 2008-2009.

Figure 5.33: Histogram of Women Who Heard of Contraceptive Methods Through the Media in Kenya in 1998

Figure 5.34: Bar Graph of Women Who Heard of Contraceptive Methods Through the Media in Kenya in 1998

Figure 5.35: Histogram of Women Who Heard of Contraceptive Methods Through the Media in Kenya in 2008-2009

Figure 5.36: Bar Graph of Women Who Heard of Family Planning Through the Media in Kenya in 2008-2009

5.3.5 Religion

The religion variable is divided into two categories; Christians and “other”.

Table 5.19

Total Number and Percentage of Women Categorized According to Their Religion in Kenya in 1998

Religion

Frequency Percent Valid Percent Cumulative Percent

Other 658 8.3 8.4 8.4

Christian 7154 90.8 91.6 100.0

Total 7812 99.1 100.0

Missing 99.00 69 .9 Total 7881 100.0

In 1998, 90.8 percent of women identified themselves as Christians and 8.3 percent identified as

being of other religious affiliation. In 1998, missing system data accounted for 0.9 percent of the total

number of women surveyed.

Table 5.20

Total Number and Percentage of Women Categorized According to Their Religion in Kenya in 2008-2009

Religion

Frequency Percent Valid Percent Cumulative Percent

Other 1599 18.9 19.0 19.0

Christian 6836 81.0 81.0 100.0

Total 8435 99.9 100.0

Missing 99.00 9 .1 Total 8444 100.0

In 2008-2009, 81.0 percent women identified as being Christians and 18.9 percent as other. In

2008-2009, missing system data accounted for 0.1 percent of the total number of women surveyed.

Figure 5.37: Histogram of Women Categorized According to Their Religion in Kenya in 1998

Figure 5.38: Bar Graph of Women Categorized According to Religion in Kenya in 1998

Figure 5.39: Histogram of Women Categorized According to Their Religion in Kenya in 2008-2009

Figure 5.40: Bar Graph of Women Categorized According to Religion in Kenya in 2008-2009

5.3.6 Region

The region variable is divided into seven categories; Nairobi, Central, Coast, Eastern, Nyanza,

Rift Valley, and Western provinces.

Table 5.21

Total Number and Percentage of Women in the 7 Provinces in Kenya in 1998

Region

Frequency Percent Valid Percent Cumulative Percent

Nairobi 419 5.3 5.3 5.3

Central 787 10.0 10.0 15.3

Coast 1226 15.6 15.6 30.9

Eastern 1186 15.0 15.0 45.9

Nyanza 1390 17.6 17.6 63.5

Rift Valley 1977 25.1 25.1 88.6

Western 896 11.4 11.4 100.0 Total 7881 100.0 100.0

In 1998, the percentages of women in each province were as follows: 5.3 percent in Nairobi; 10.0

percent in Central; 15.6 in Coast; and 15.0 percent in Eastern province. Nyanza province accounted for

17.6 percent of women; 25.1 percent in Rift Valley; and 11.4 percent in Western province of the total women surveyed.

Table 5.22

Total Number and Percentage of Women in the 7 Provinces in Kenya in 2008-2009

Province

Frequency Percent Valid Percent Cumulative Percent

Nairobi 952 11.3 11.3 11.3

Central 973 11.5 11.5 22.8

Coast 1149 13.6 13.6 36.4

Eastern 1735 20.5 20.5 57.0

Nyanza 1318 15.6 15.6 72.6

Rift Valley 1278 15.1 15.1 87.7

Western 1039 12.3 12.3 100.0 Total 8444 100.0 100.0

In 2008, the percentages of women in each province were as follows: 11.3 percent in Nairobi;

11.5 percent in Central; 13.6 percent in Coast; and 20.5 percent in Eastern. Nyanza province accounted for 15.6 percent of women: Rift Valley, 15.1 percent; and Western province, 12.3 percent in of the total women surveyed.

Figure 5.41: Histogram of Women in the 7 Provinces in Kenya in 1998

Figure 5.42: Bar Graph of Women in the 7 Provinces in Kenya in 1998

The skewedness of the region variable is -0.345 and the standard error of skewedness are 0.028, not a normal distribution. The kurtosis is -0.928 and the standard error of kurtosis are 0.055. For skewedness, if the values are between -0.5 and +0.5, the distribution is considered to be approximately symmetrical. A distribution with kurtosis less than 3 is called platykurtic with a broader and lower peak and shorter and thinner tail. The kurtosis of the region variable is platykurtic and the skewedness is between -0.5 and +0.5, thus, it is approximately symmetrical close to a normal distribution.

Figure 5.43: Histogram of Women in the 7 Provinces in Kenya in 2008-2009

Figure 5.44: Bar Graph of Women in the 7 Provinces in Kenya in 2008-2009

The skewedness of the Region variable is -0.108 and the standard error of skewedness are

0.027. The kurtosis is -1.028 and the standard error of kurtosis is 0.053. For skewedness, if the values are between -0.5 and +0.5, the distribution is considered to be approximately symmetrical. A distribution

with kurtosis less than 3 is called platykurtic with a broader and lower peak and shorter and thinner tail.

The kurtosis of the region variable is platykurtic and the skewedness is between -0.5 and +0.5, thus, it is approximately symmetrical close to a normal distribution.

5.4 Section II: Chi Square Associations

This section utilizes the chi-square test to determine the association of modern contraceptive use

on the independent variables to include; some education, higher education, income, place of residence

urban, old and older women for both survey periods 1998 and 2008-2009.

5.4.1 Chi Square Associations

The chi-square test is a statistic test for categorical data. It is used as a test of independence and

association but is also used as a goodness of fit test (Vogt, 2005). There are 6 independent categorical

variables in this study; some education, and higher education (education), income (earn income and do

not earn income), old and older (age married) and type of residence urban.

5.4.2 Some Education

A chi-square test of independence was calculated to determine the association of women who

had some education on modern contraceptive use. Women with some education were compared to

women with no education on modern contraceptive usage in 1998 and in 2008-2009. The 1998 chi

square value = 28.063, df =1, p<0.05 and the 2008-2009 chi square = 225.922, df =1, p<0.05, show there is a statistically significant difference among women who have some education compared to women with no education on modern contraceptive use in 1998 and in 2008-2009.

5.4.3 Higher Education

A chi-square test of independence was calculated to determine the association of women who had higher education on modern contraceptive use. Women with higher education were compared to women with no education on modern contraceptive usage in 1998 and in 2008-2009. The 1998 chi square value =24.667, df =1, p<0.05 and the 2008-2009 chi square=130.378, df =1, p<0.05, show there is a statistically significant difference among women who have higher education compared to women who have no education on modern contraceptives use in 1998 and in 2008-2009.

5.4.4 Income

A chi-square test of independence was calculated to determine the association of women who earn an income on modern contraceptive use. Women who earned an income were compared to women who did not earn an income on modern contraceptive use in 1998 and in 2008-2009. The 1998 chi square value = 303.902, df =1, p<0.05 and the 2008-2009 chi square value = 638.669, df =1, p<0.05, show there

is a statistically significant difference in modern contraceptive use among women who earn an income

compared to women who do not earn an income in 1998 and in 2008-2009.

5.4.5 Old

A chi-square test of independence was calculated to determine the association of women who

are between 15-20 years at marriage on modern contraceptive use. Women who marry between 15-20

years (old) were compared to women classified as marrying under 14 years (young) on modern

contraceptive use in 1998 and in 2008-2009. The 1998 chi square value = 898.335, df =2, p<0.05 and the

2008-2009 chi square value =1052.464, df =2, p<0.05, show there is a statistically significant difference

among women who are categorized as marrying between 15-20 years (old) compared to women who are

categorized as marrying under 14 (young) on modern contraceptives use in1998 and in 2008-2009.

5.4.6 Older

A chi-square test of independence was calculated to determine the association of women

classified as marrying between 21-49 (older) on modern contraceptive use. Women who were classified

as marrying between 21-49 (older) were compared to women classified as marrying under 14 years

(young) on modern contraceptive use in 1998 and in 2008-2009. The 1998 chi square value = 952.281, df

=2, p<0.05 and the 2008-2009 chi square value = 1118.210, df =2, p<0.05, show there is a statistically

significant difference among women who are categorized as marrying between 21-49 years (older)

compared to women who are categorized as marrying under 14 (young) on modern contraceptives use in

1998 and in 2008-2009.

5.4.7 Urban

A chi-square test of independence was calculated to determine the association of urban

residence on modern contraceptive use. Women who live in urban areas and women who live in rural

areas were compared on modern contraceptive use in 1998 and in 2008-2009. The 1998 chi square

value = 97.386, df =1, p<0.05 and 2008-2009 chi square value = 119.637, df =1, p<0.05, show there is a

statistically significant difference among women who live in urban areas compared to women who live in

rural areas on modern contraceptives use in 1998 and in 2008-2009.

Table 5.23

Chi Square Values and Significance Levels of Selected Independent Variables on Contraceptive Use for Survey Periods 1998 and 2008-2009

Variables Chi-Square Significance

1998 2008 1998 2008

Some Education 28.063 225.922 .000 .000 Higher Education 24.667 130.378 .000 .000

Income 303.902 638.669 .000 .000

Old 898.335 1052.464 .000 .000

Older 952.281 1118.210 .000 .000 Urban 97.386 119.637 .000 .000

From the results above, the significant chi squared values and hypothesized directions of the

selected determinants show preliminary support for the hypotheses in this study. Therefore, the

variables—some education, higher education, old, older, income, and urban—are significant at the level p

< 0.05, in the hypothesized direction, and support the proposed hypotheses.

5.4.8 T-Test

This section utilizes the independent sample-t-test to compare the mean difference of the total number of children born (EverBorn) to women aged 15-49 in Kenya on modern contraceptive use, for both survey periods 1998 and 2008-2009. The t value, degrees of freedom df , and the p value of significance are reported. The test was conducted to inquire whether there was a difference in number of children between women who used modern contraceptives compared to women who used other contraceptive methods.

In 1998, the mean number of women who used modern contraceptive methods was ( M=3.82) and the mean for women who used other methods of contraceptives was (M=2.38).

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Women, who used modern contraceptive methods, were compared to women who used other methods of

contraceptives, on total number of children born. It is observed that there is a statistically significant

difference ( p <0.05, t=-21.912, df =7879) in total number of children born between women who use

modern contraceptive methods and women who use other contraceptive methods.

In 2008-2009, the mean number of women who used modern contraceptive methods was

(M=3.15) and the mean for women who used other methods of contraceptives was ( M=2.18). Women,

who used modern contraceptive methods, were compared to women who used other methods of

contraceptives, on total number of children born. It is observed that there is a statistically significant

difference (p <0.05, t=-17.115, df =8442) in total number of children born between women who use

modern contraceptive methods and women who use other contraceptive methods.

5.5 Section III: Logistic Regression

This section examines the 1998 gross effects, net effects with independent variables only, and

net effects with independent and control variables.

5.5.1 Binary Logistic Regression

Logistic regression is a technique for making predictions when the dependent variable is

dichotomous—scored as 0 and 1—and the independent variables can be continuous and/or categorical

(Vogt, 2005). Logistic regression is usually used for predicting whether the odds of something will happen or not, (Vogt, 2005)—in this case, the likelihood of using or not using modern contraceptive use. Logistic

regression is used to assess the effects of independent and control variables on the dependent variable,

modern contraceptive use for the two time periods 1998 and 2008-2009 respectively.

The regression analyses for both survey periods, 1998 and 2008-2009, were conducted as

follows. First, modern contraceptive use is regressed on each of the independent variables proposed in

this study, yielding the gross effects of the variables. Second, modern contraceptive use is regressed on

all independent variables proposed in this study. The net effect of each independent variable is evaluated

in terms of its hypothesized direction as well as the level of significance. Lastly, the net effects of the

proposed independent variables on modern contraceptive use are examined with the addition of control

variables such as type of marriage, media, religion, total number of children born, marital status and

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region (Table 5.25). The systematic process of testing the hypotheses beginning with assessing gross effects, to the net effects with independent variables only, and net effects with independent and control variables, provides a more rigorous test with each. The following section depicts the regression net effects with independent variables only, the regression net effects with independent and control variables, and gross effects of each individual variable used in the survey period 1998 and 2008-2009 respectively.

Figure 5.45 below is used to determine the strength and direction of the hypotheses in this study.

Additionally, table 5.24 below describes the strength and the direction of the hypotheses as shown in figure 5.45.

Figure 5.45: Diagram Showing the Strength of Strong, Moderate, Weak, and Contradictory Hypotheses

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Table 5.24 below is a summary of the strength and direction of the hypotheses shown in the diagram above.

Table 5.24

Key on Strength and Direction of Hypotheses

Hypothesis Support Description

Strong support If the gross effects, net effects with only independent variables and net effects with independent and control variables are significant, slope and odds ratios are in the right direction

Moderate Support Gross effects are significant, net effects with controls are significant and without control variables are not significant and in the direction hypothesized.

Weak Support If net effects with only independent variables and net effects with independent and control variables are not significant but both odds and slope are in the right direction

No support When gross effects, net effects with independent variables or net effects with independent and control variables are not significant.

Contradictory Support The gross effects, net effects with independent variables, and net effects with independent and control variables are significant but the odds ratio and slope are in the wrong direction.

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Table 5.25

Regression Net Effects with Independent Variables Only, Net Effects with Independent and Control Variables and Gross Effects for Survey Period 1998 1998 Data Regression Net Effects Regression Net Effects with Regression Gross with Independent Independent and Control Effects Variables Only Variables Odds Odds Odds Variables B S.E. B S.E. B S.E. Ratio Ratio Ratio Some 0.961* 0.077 2.613 0.914* 0.087 2.495 0.354* 0.067 1.425 Education No education Higher 1.480* 0.204 4.393 1.436* 0.212 4.206 0.820* 0.169 2.270 Education No education Income Yes 0.531* 0.051 1.701 0.481* 0.054 1.617 0.814* 0.047 2.258 No income -0.104* -0.018* Old 0.033 0.901 0.413* 0.093 1.512 0.001 0.982 young -0.018* Older 0.085* 0.033 1.088 0.682* 0.107 1.977 0.001 0.982 young Urban 0.623* 0.065 1.865 0.851* 0.084 2.341 0.571* 0.058 1.770 Rural

Married 109.040* 18.444 2.267E47 1.159* 0.050 3.186 Never married

OtherMarry 109.407* 18.447 3.273E47 0.488* 0.072 1.628 Never married

Monogamous 0.446* 0.083 1.563 1.207* 0.049 3.345

EverBorn 0.144* 0.011 1.154 0.169* 0.008 1.184

Audio or 0.342* 0.053 1.408 0.470* 0.046 1.600 Visual

Christian 0.295* 0.108 1.343 0.386* 0.083 1.472 Other

Nairobi 0.379* 0.156 1.461 0.747* 0.102 2.110

Central 0.989* 0.133 2.688 0.772* 0.076 2.165 -0.313* Coast -0.078 0.113 0.925 0.065 0.731

Eastern 0.405* 0.102 1.499 0.260* 0.063 1.298

Nyanza -0.108 0.099 0.897 -0.248* 0.061 0.780 Rift Valley -0.178 0.093 0.837 -0.328* 0.054 0.720 *Denotes significance level at p < 0.05

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Women with some education are compared to those with no education. From the results above, it is observed that there is a statistically significant difference between women who have some education and women who have no education on modern contraceptive use p<0.05. The odds of using modern contraception, compared to the use of other contraception methods, are higher for those women who have some education (less than primary or greater than secondary education) compared to those women who have no education (positive). The odds of modern contraceptive use among women with some education are about 2.495 times the odds of modern contraceptive use among women with no education.

This hypotheses is strongly supported

Women with higher education are compared to those with no education. From the results above, it is observed that there is a statistically significant difference between women who have higher education and women who have no education on modern contraceptive use p<0.05. The odds of using modern methods of contraception compared to the use of other contraception methods, are higher for those women who have higher education (greater than secondary education) compared to those women who have no education (positive). The odds of modern contraceptive use among women with some education are about 4.206 times the odds of modern contraceptive use among women with no education. This hypothesis is strongly supported.

Women with income are compared to those with no income. From the results above, it is observed that there is a statistically significant difference between women who have income and women who have no income on modern contraceptive use p<0.05. The odds of using modern contraception compared to the use of other contraception methods, are higher for those women who earn cash for their work, compared to those women who do not earn cash for their work (positive). The odds of modern contraceptive use among women with income are about 1.617 times the odds of modern contraceptive use among women with no income. This hypothesis is strongly supported.

(young) on modern contraceptive use ( p<0.05). However, the gross effects are significant but in the

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wrong direction. The odds of using modern contraception, compared to the use of other contraception methods, are higher for those women who marry between 15-20 years (old) compared to those women who marry under 14 years (young) (positive). The odds of using modern contraception compared to the use of other contraception methods, are higher for those women who marry between 15-20 years compared to those women who marry under 14 years (young) (positive). The odds of modern contraceptive use among old women are about 1.512 times the odds of modern contraceptive use among young women. The gross effects are significant but in the wrong direction. This hypothesis has contradictory support.

Women categorized as marrying between 21-49 years (older), are compared to those who marry under14 years (young). From the results above, it is observed that there is a statistically significant difference between women who marry between 21-49 years (older) and women who marry under 14 years (young) on modern contraceptive use ( p<0.05). The odds of using modern contraception compared to the use of other contraception methods, are higher for those women who marry between 21-49 years

(older) compared to those women who marry under 14 years (young) (positive). The odds of modern contraceptive use among women who marry older are about 1.977 times the odds of modern contraceptive use among women who marry young. The gross effects are significant but in the wrong direction. This hypothesis has contradictory support.

Women who live in urban areas are compared to those who live in rural areas. From the results above, it is observed that there is a statistically significant difference in modern contraceptive use between women who live in urban areas and women who live in rural areas ( p<0.05). The odds of using modern contraception compared to the use of other contraception methods, are higher for those women who earn cash for their work compared to those women who do not earn cash for their work (positive).

The odds of modern contraceptive use among women who live in urban areas are about 2.341 times the odds of modern contraceptive use among women who live in rural areas. This hypothesis is strongly supported.

Women who are married are compared to those women who have never been married. From the results above, it is observed that there is a statistically significant difference in modern contraceptive use

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between women who are married and women who have never been married (p <0.05). The odds of

modern contraceptive use among women who are married are about 2.267 times the odds of modern

contraceptive use among women who have never been married.

Women who are in other forms of marriage (divorced, living with their partners, or widowed,

categorized as other marry) are compared to those women who have never been married. From the

results above, it is observed that there is a statistically significant difference in modern contraceptive use

between women who are in other forms of marriage and women who have never been married ( p<0.05).

The odds of modern contraceptive use among women who are in other forms of marriage are about 3.273 times the odds of modern contraceptive use among women who have never been married.

Women who are in monogamous marriages are compared to those who are in polygamous marriages. From the results above, it is observed that there is a statistically significant difference in modern contraceptive use between women who are in monogamous marriages and women who are in polygamous marriages, ( p<0.05). The odds of modern contraceptive use among women who are in

monogamous marriages are about 1.563 times the odds of modern contraceptive use among women who

are in polygamous marriages.

From the results above, it is observed that there is a statistically significant difference in modern

contraceptive use between women who have more children compared to those women who have no

children ( p<0.05). The odds of modern contraceptive use among women who have more children are

about 1.154 times the odds of modern contraceptive use among women who have no children.

Women who have heard of family planning through the radio or television (audio or visual) are

compared to women who have heard of family planning through other methods—predominantly

newspapers and word of mouth. It is observed that there is a statistically significant difference in modern

contraceptive use between women who heard of family planning via audio or visual methods and women,

who heard of family planning through other medium, (p <0 .05). The odds of modern contraceptive use

among women who have heard of family planning through audio or visual methods are about 1.408 times

the odds of modern contraceptive use among women who have heard of family planning through other

methods.

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Women who identified as Christians are compared to women who identified themselves as being

of other religious affiliations. It is observed that there is not a statistically significant difference in modern

contraceptive use between Christians and women in other religious groups ( p=0.06). The odds of modern

contraceptive use among women who identified as Christians are about 1.343 times the odds of modern

contraceptive use among women who identify as being of other religions.

Women who live in Nairobi province are compared to women who live in Western province. It is

observed that there is a statistically significant difference in modern contraceptive use, between women

who live in Nairobi and women who live in Western province (p <0.015). The odds of modern

contraceptive use among women who live in Nairobi are about 1.343 times the odds of modern

contraceptive use among women who live in Western province.

Women who live in Central province are compared to women who live in Western province. It is

observed that there is a statistically significant difference in modern contraceptive use, between women

who live in Central province and women who live in Western province ( p<0.05). The odds of modern

contraceptive use among women who live in Central province are about 2.688 times the odds of modern

contraceptive use among women who live in Western province.

Women who live in Coast province are compared to women who live in Western province. It is

observed that there is not a statistically significant difference in modern contraceptive use between

women who live in Coast province and women who live in Western province ( p=0.491). The odds of modern contraceptive use among women who live in Coast province are about 0.925 times the odds of modern contraceptive use among women who live in Western province

Women who live in Eastern province are compared to women who live in Western province. It is observed that there is a statistically significant difference in modern contraceptive use between women who live in Eastern province and women who live in Western province ( p<0.05). The odds of modern

contraceptive use among women who live in Eastern province are about 1.499 times the odds of modern

contraceptive use among women who live in Western province.

Women who live in Nyanza province are compared to women who live in Western province. It is

observed that there is not a statistically significant difference in modern contraceptive use between

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women who live in Nyanza province and women who live in Western province ( p=0.276). The odds of modern contraceptive use among women who live in Nyanza province are about 0.897 times the odds of modern contraceptive use among women who live in Western province.

Women who live in Rift Valley province are compared to women who live in Western province. It is observed that there is not a statistically significant difference in modern contraceptive use between women who live in Rift Valley province and women who live in Western province ( p=0.056). The odds of modern contraceptive use among women who live in Rift Valley province are about 0.837 times the odds of modern contraceptive use among women who live in Western province.

5.5.2 Hosmer and Lemeshow Test

The Hosmer and Lemeshow test for the regression net effects with only independent variables is chi-squared values = 44.327, df = 6, and p < 0.05. This is not a good model fit as non significant numbers indicate a good model fit. Hosmer and Lemeshow test for the regression net effects with independent and control variables is chi-squared value = 9.972, df = 6, and p = 0.267. Non significant Hosmer and

Lemeshow test shows the model is a good fit, thus, this it is a good model fit.

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The table below presents a summary of the independent variables, hypotheses, and strengths for survey period 1998.

Table 5.26

Summary of the Independent Variables, Hypotheses, and Strengths for Survey Period 1998

Variable Hypothesis Strength

Some The odds of using modern contraception compared to the use of other Strong Education contraception methods, are higher for those women who have some education compared to those women who have no education (Positive +)

Higher The odds of using modern methods of contraception compared to the Strong Education use of other contraception methods, are higher for those women who have higher education compared to those women who have no education (Positive+).

Income The odds of using modern contraception compared to the use of other Strong contraception methods, are higher for those women who earn cash for their work compared to those women who do not earn cash for their work (Positive +).

Old The odds of using modern contraception compared to the use of other Contradictory contraception methods, are higher for those women who marry between 15-20 years compared to those women who marry under 14 years (Positive +).

Older The odds of using modern contraception compared to the use of other Contradictory contraception methods, are higher for those women who are older (21-49) compared to those women who marry under 14 years (Positive +).

Residence The odds of using modern contraception compared to the use of other Strong contraception methods, are higher for those women who live in urban areas compared to those women who live in rural areas (Positive +).

From the regression analyses conducted for survey period 1998, the results above, the significant p values, positive odds ratios, and hypothesized directions of three selected determinants show very strong support for the hypotheses in this study. Therefore, the variables –some education, higher education, income, and urban residence—are significant at the level p<0.5, in the hypothesized direction, and strongly support the proposed hypotheses for both survey periods 1998. On the contrary, the results of the variable age at marriage, old and older are contradictory whereby the gross effects are significant but the slope is in the wrong direction.

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This section examines the 2008-2009 gross effects, net effects with independent variables only, and net effects with independent and control variables on modern contraceptive use.

Table 5.27

Regression Net Effects with Independent Variables only, Net Effects with Independent and Control Variables, and Gross Effects for Survey Period 2008-2009 2008-2009 Data Regression Net Effects Regression Net Effects with with Independent Independent and Control Regression Gross Effects Variables Only Variables Odds Odds Odds Variables B S.E. B S.E. B S.E. Ratio Ratio Ratio

Some 2.371* 0.087 10.703 1.866* 0.102 6.464 0.793* 0.053 2.210 Education

Higher 3.040* 0.133 20.902 2.529* 0.146 12.536 0.947* 0.085 2.579 Education

Income Yes 0.784* 0.055 2.190 0.642* 0.057 1.901 1.174* 0.047 3.233

Old -0.018 0.035 0.982 0.263* 0.107 1.300 -0.017* 0.001 0.983

Older -0.003 0.035 0.997 0.357* 0.120 1.429 -0.017* 0.001 0.983

Urban 0.330* 0.060 1.391 0.401* 0.073 1.494 0.519* 0.048 1.681

Married 62.655* 21.192 1.624E27 0.999* 0.045 2.175

OtherMarry 62.792* 21.193 1.863E27 0.679* 0.064 1.971

Monogamous 0.298* 0.084 1.348 01.162* 0.045 3.196

EverBorn 0.112* 0.014 1.119 0.145* 0.009 1.156

Audio or 0.195* 0.055 1.215 0.535* 0.045 1.708 Visual

Christian 1.047* 0.090 2.850 1.380* 0.063 3.974

Nairobi 0.435* 0.124 1.545 0.657* 0.072 1.928

Central 0.677* 0.112 1.967 0.878* 0.073 2.406

Coast 0.431* 0.111 1.539 -0.123* 0.064 0.885

Eastern -0.089 0.100 0.915 -0.921* 0.057 0.398

Nyanza -0.027 0.097 0.973 0.221* 0.060 1.248

Rift Valley -0.107 0.100 0.898 -0.238* 0.061 0.788 *Denotes significance at p < 0.05 level

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Women with some education are compared to those with no education. From the results above, it is observed that there is a statistically significant difference in modern contraceptive use between women who have some education and women who have no education ( p<0.05).The odds of using modern contraception compared to the use of other contraception methods, are higher for those women who have some education compared to those women who have no education (positive). The odds of modern contraceptive use among women with some education are about 6.464 times the odds of modern contraceptive use among women with no education. This hypothesis is strongly supported.

Women with higher education are compared to those with no education. From the results above, it is observed that there is a statistically significant difference in modern contraceptive use between women who have higher education and women who have no education ( p<0.05). The odds of using modern methods of contraception compared to the use of other contraception methods, are higher for those women who have higher education compared to those women who have no education (positive).

The odds of modern contraceptive use among women with some education are about 12.536 times the odds of modern contraceptive use among women with no education. This hypothesis is strongly supported.

Women who earn an income are compared to those with no income. From the results above, it is observed that there is a statistically significant difference in modern contraceptive use between women who have income and women who have no income ( p<0.05). The odds of using modern contraception methods compared to the use of other contraception methods, are higher for those women who earn cash for their work compared to those women who do not earn cash for their work (positive). The odds of modern contraceptive use among women with income are about 1.901 times the odds of modern contraceptive use among women with no income. This hypothesis is strongly supported.

Women categorized as marrying between 15-20 years (old) are compared to those who marry under14 years (young). From the results above, it is observed that there is a statistically significant difference in modern contraceptive use between women who marry between 15-20 years (old) and women who marry under 14 years (young) (p<0.05). The odds of using modern contraception methods compared to the use of other contraception methods, are higher for those women who marry between 15-

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20 years compared to those women who marry under 14 years (young) (positive). The odds of modern

contraceptive use among old women are about 1.300 times the odds of modern contraceptive use among

young women. The gross effects are significant but in the wrong direction. The hypothesis has

contradictory support.

Women categorized as marrying between 21-49 years (older) are compared to those who marry

under 14 years (young). From the results above, it is observed that there is a statistically significant

difference in modern contraceptive use between women who marry between 21-49 years (older) and

women who marry under 14 years (young) (p<0.05). The odds of using modern contraception methods compared to the use of other contraception methods, are higher for those women who marry between 21-

49 years compared to those women who marry under 14 years. The odds of modern contraceptive use among older women are about 1.429 times the odds of modern contraceptive use among young women.

The gross effects are significant but in the wrong direction. The hypothesis has contradictory support.

modern contraception compared to the use of other contraception methods, are higher for those women

who live in urban areas compared to those women who live in rural areas (positive). The odds of modern

contraceptive use among women who live in urban areas are about 1.494 times the odds of modern

contraceptive use among women who live in rural areas. This hypothesis is strongly supported.

Women who are married are compared to those women who have never been married. From the

results above, it is observed that there is a statistically significant difference in modern contraceptive use

between women who are married and women who have never been married ( p< 0.05). The odds of modern contraceptive use among women who are married are about 1.624 times the odds of modern contraceptive use among women who have never been married.

Women who are in other forms of marriage (OtherMarry; divorced, living with their partners or widowed) are compared to those women who have never been married. From the results above, it is observed that there is a statistically significant difference in modern contraceptive use between women

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who are in other forms of marriage and women who have never been married ( p<0.05). The odds of modern contraceptive use among women who are in other forms of marriage are about 1.863 times the odds of modern contraceptive use among women who have never been married.

Women who are in monogamous marriages are compared to those who are in polygamous marriages. From the results above, it is observed that there is a statistically significant difference in modern contraceptive use between women who are in monogamous marriages and women who are in polygamous marriages ( p<0.05). The odds of modern contraceptive use among women who are in monogamous marriages are about 1.348 times the odds of modern contraceptive use among women who are in polygamous marriages.

From the results above, it is observed that there is a statistically significant difference in modern contraceptive use between women who have more children compared to those women who have no children ( p<0.05). The odds of modern contraceptive use among women who have more children are about 1.119 times the odds of modern contraceptive use among women who have no children.

Women who have heard of family planning through the radio or television (audio or visual) are compared to women who have heard of family planning through other methods— predominantly newspapers and word of mouth. It is observed that there is a statistically significant difference in modern contraceptive use between women who heard of family planning through audio or visual methods and women who heard of family planning through other medium ( p<0.05). The odds of modern contraceptive use among women who have heard of family planning through audio or visual methods are about 1.215 times the odds of modern contraceptive use among women who have heard of family planning through other methods.

Women who identified as Christians are compared to women who identified themselves as being of other religions. It is observed that there is a statistically significant difference in modern contraceptive use between Christians and women in other religions ( p<0.05). The odds of modern contraceptive use among women, who identified as Christians, are about 2.850 times the odds of modern contraceptive use among women who identify as being of other religions.

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Women who live in Nairobi province are compared to women who live in Western province. It is observed that there is a statistically significant difference in modern contraceptive use between women who live in Nairobi and women who live in Western province ( p<0.05). The odds of modern contraceptive use among women who live in Nairobi are about 1.545 times the odds of modern contraceptive use among women who live in Western province.

Women who live in Central province are compared to women who live in Western province. It is observed that there is a statistically significant difference in modern contraceptive use between women who live in Central province and women who live in Western province ( p<0.05). The odds of modern contraceptive use among women who live in Central province are about 1.967 times the odds of modern contraceptive use among women who live in Western province.

Women who live in Coast province are compared to women who live in Western province. It is observed that there is a statistically significant difference in modern contraceptive use between women who live in Coast province and women who live in Western province

(p <0.05). The odds of modern contraceptive use among women who live in Coast province are about

1.539 times the odds of modern contraceptive use among women who live in Western province.

Women who live in Eastern province are compared to women who live in Western province. It is observed that there is not a statistically significant difference in modern contraceptive use between women who live in Eastern province and women who live in Western province ( p=0.377). The odds of modern contraceptive use among women who live in Eastern province are about 0.915 times the odds of modern contraceptive use among women who live in Western province.

Women who live in Nyanza province are compared to women who live in Western province. It is observed that there is not a statistically significant difference in modern contraceptive use between women who live in Nyanza province and women who live in Western province ( p=0.780). The odds of modern contraceptive use among women who live in Nyanza province are about 0.973 times the odds of modern contraceptive use among women who live in Western province.

Women who live in Rift Valley province are compared to women who live in Western province. It is observed that there is not a statistically significant difference in modern contraceptive use between

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women who live in Rift Valley province and women who live in Western province ( p=0.283). The odds of modern contraceptive use among women who live in Rift Valley province are about 0.898 times the odds of modern contraceptive use among women who live in Western province.

Table (5.28) below presents a summary of the selected independent variables, hypotheses, and strengths for survey period 2008-2009.

Table 5.28

Summary of the Independent Variables, Hypotheses, and Strengths Table for Survey Period 2008-2009

Variable Hypothesis Strength

Older The odds of using modern contraception compared to the use of other Contradictory contraception methods, are higher for those women who are marry between 21-49 years compared to those women who marry under 14 years (Positive +).

5.5.3 Hosmer and Lemeshow Test

Hosmer and Lemeshow test for the regression net effects without control variables is chi-square

values = 48.276, df = 7, and p<0.05. A non significant Hosmer and Lemeshow test shows the model is a

good fit, thus, this is not a good model fit. Hosmer and Lemeshow test for the regression net effects with

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independent and control variables is chi-square value = 37.685, df = 8, and p<0.05. A non significant

Hosmer and Lemeshow test shows the model is a good fit, thus, this is not a good model fit.

The table below presents a summary of the selected independent variables, hypotheses, and strengths for survey periods 1998 and 2008-2009.

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Table 5.29

Summary of the Independent Variables, Hypotheses, and Strengths for Survey Periods 1998 and 2008- 2009

Variable Hypothesis 1998 2008-2009 Conclusion

Some The odds of using modern Strong Strong Very Strong Education contraception methods compared to Positive (+) Positive (+) Support the use of other contraception methods, are higher for those women who have some education compared to those women who have no education.

Higher The odds of using modern Strong Strong Very Strong Education contraception methods of Positive (+) Positive (+) Support contraception compared to the use of other contraception methods, are higher for those women who have higher education compared to those women who have no education.

Income The odds of using modern Strong Strong Very Strong contraception methods compared to Positive (+) Positive (+) Support the use of other contraception methods, are higher for those women who earn cash for their work compared to those women who do not earn cash for their work.

Old The odds of using modern Contradictory Contradictory No Support contraception methods compared to the use of other contraception methods, are higher for those women who marry between 15-20 years compared to those women who marry under 14 years.

Older The odds of using modern Contradictory Contradictory No Support contraception methods compared to the use of other contraception methods, are higher for those women who marry between 21-49 years compared to those women who marry under14 years.

Urban The odds of using modern Strong Strong Very Strong contraception methods compared to Positive (+) Positive (+) Support the use of other contraception methods, are higher for those women who live in urban areas compared to those women who live in rural areas.

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From the regression analyses conducted, the results above, the significant p values, positive odds ratios, and hypothesized directions of three selected determinants show very strong support for the hypotheses in this study. Therefore, the variables –some education, higher education, income, and urban residence—are significant at the level p<0.5, in the hypothesized direction, and strongly support the proposed hypotheses for both survey periods 1998 and 2008-2009. On the contrary, the results of the variable age at marriage, old and older are contradictory whereby the gross effects are significant but the slope is in the wrong direction. These hypotheses, therefore, have contradictory support for both survey periods 1998 and 2008-2009.

5.6 Section IV: Decomposition Analysis - Compositional and Processual Changes

5.6.1 Compositional Changes: Phi Coefficient of Binary Categorical Variables

A binary variable is one that can have only two possible values and the coding system uses only

two digits, generally 0 and 1 (Vogt, 2005). There are several binary categorical variables in this study.

These include: type of place or residence (urban or rural), age married (old or young, older or young),

earn cash for work income (yes or no), media users (audio or visual or other), religion (Christian or other),

and ever used (other methods or modern methods). For these binary categorical variables, we run a chi

square test of independence and report the Phi coefficient.

The Phi coefficient is a type of correlation or measure of association between two variables used

when both are categorical (Vogt, 2005). The Phi coefficient is a symmetric measure based on the chi-

square test statistic (Vogt, 2005). The table below shows the interpretation rules for the Phi coefficient.

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Table 5.30

Phi Coefficient Interpretation Rules

Score Strength and direction of association

-1.0 to -0.7 Strong negative association

-0.7 to -0.3 Weak negative association

-0.3 to +0.3 Little or no association

+0.3 to +0.7 Weak positive association

+0.7 to +1.0 Strong positive association

5.6.2 Education

The Education variable is divided into three categories: those women with some education (more

than a primary school education but less than a secondary school education), those with higher education

(above a secondary school education), and those women with no education. Regressed variables were

used whereby each variable was dummy coded. For this variable, those women with no education are designated the reference group.

Table 5.31

Phi Coefficient Table Showing Significance of Having Some Education on Modern contraceptive use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

2008 1998 data data Total 1998 2008 Phi Sig. Some No Education 1158 1956 3114 12.8 14.7 Education Some Education 6723 6488 13211 85.3 76.8

Total 7881 8444 16325 -0.108 0.000

Women without some education increased in 2008-2009 by 1.9 percent, and there was a

decrease in women with some education from 1998 to 2008-2009 by 8.5 percent. The Phi coefficient is -

0.108, p<0.05 indicating that there is a statistically significant difference in modern contraceptive use, between women who have some education and those who have no education in 1998 and 2008-2009.

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However, from the table above, a Phi coefficient score of -0.108 suggests that the strength of the

association ranges from slight to no association.

Table 5.32

Phi Coefficient Table Showing Significance of Higher Education on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

1998 2008 Total 1998 2008 Phi Sig. data data Higher No Education 7733 7730 15463 12.8 14.7

Education Higher Education 148 714 862 1.9 8.5

Total 7881 8444 16325 0.147 0.000

The percentage of women with higher education increased from 1.9 percent in 1998 to 8.5

percent, a 6.6 percent increase, in 2008-2009. This observed increase in women with higher education may be due to modernization, pursuit of better employment opportunities, and desire for higher income

and wages in urban areas. The Phi coefficient is 0.147, p<0.05, indicating that there is a statistically

significant difference in modern contraceptive use between women who have higher education and those

who have no education in 1998 and 2008-2009. However, from the table above, a Phi coefficient score of

0.147 suggests that the strength of the association ranges from slight to no association.

5.6.3 Income

Table 5.33

Phi Coefficient Table Showing Significance of Income on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value 1998 2008 Total 1998 2008 Phi Sig. data data Income Yes No Income 4601 5242 9843 10.4 17.8 Income Yes 3280 3202 6482 41.6 37.9 Total 7881 8444 16325 -0.038 0.000

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The number of women who said yes to earning income for the work they did decreased from 41.6

percent in 1998 to 39.9 percent, a 1.7 percent decrease, in 2008-2009, and those who said no to earning

income for their work increased from 10.1 percent in 1998 to 17.8 percent, a 7.7 percent increase, in

2008-2009. In 1998, there was 48.3 percent of system missing data and 44.3 percent in 2008-2009. The

Phi coefficient is -0.038, p<0.05, indicating that there is a statistically significant difference in modern

contraceptive use between women who have some education and those who have no education in 1998

and 2008-2009. However, from the table above a Phi coefficient score of -0.038 suggests that the

strength of the association ranges from slight to no association.

5.6.4 Type of Place of Residence

Table 5.34

Phi Coefficient Table Showing Significance of Place of Residence on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

1998 data 2008 data Total 1998 2008 Phi Sig.

Urban Rural 6415 5829 12244 18.6 31.0

Urban 1466 2615 4081 81.4 69.0

Total 7881 8444 16325 0.143 0.000

The variable type of place or residence is divided into two categories urban and rural. Between

the two datasets we observe the number of women living in urban areas increased from 1998 to 2008-

2009 by 12.4 percent and the number of women living in rural areas decreased in 2008-2009 from 1998

by 12.4 percent. These observed changes in the increase and decrease in urban and rural women’s

population between the two periods may have been due to increased modernization and the rural-urban

migration in search of better jobs, education, and living conditions. The Phi coefficient is 0.143, p<0.05 indicating that there is a statistically significant difference in modern contraceptive use between women who live in urban areas compared to those who live in rural areas 1998 and 2008-2009. However, from the table above a Phi coefficient score of 0.143 suggests that the strength of the association ranges from slight to no association.

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5.6.5 Age Married.

Table 5.35

Phi Coefficient Table Showing Significance of Old Age (15-20) at Marriage on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

1998 data 2008 data Total 1998 2008 Phi Sig.

Old Less than 14 2087 2326 4413 9.0 7.4

15-20 group 3419 3578 6997 43.4 42.4

99.00 2375 2540 4915 30.1 30.1

Total 7881 8444 16325 0.013 0.261

From the results, it is observed that there was a decrease in the number of women surveyed in

the under 14 age group, from 9 percent in 1998 to 7.4 percent, a 1.6 percent decrease in 2008-2009 and

a slight decrease of women in the 15-20 age group—43.4 percent in 1998, to 42.4 percent, a1.0 percent decrease, in 2008-2009. The Phi coefficients is 0.013, p<0.05, indicating that there is a statistically

significant difference in modern contraceptive use, between women who are old (15-20), compared to

those who are young (under 14) in 1998 and 2008-2009. However, from the table above, a Phi coefficient

score of 0.013 suggests that the strength of the association ranges from slight to no association.

Table 5.36

Phi Coefficient Table Showing Significance of Older Age (21-49) at Marriage on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

1998 data 2008 data Total 1998 2008 Phi Sig

Older Under 14 4132 4206 8338 9.0 7.4

21-49 group 1374 1698 3072 17.4 20.1

99.00 2375 2540 4915

Total 7881 8444 16325 0.036 .000

From the results above, it is observed that there was a slight increase in the number of women

surveyed in the 21-49 age group in 1998, from 17.4 percent to 20.1 percent, a 2.7 percent increase, in

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2008-2009. The Phi coefficient is 0.036, p<0.05, indicating that there is a statistically significant difference

in modern contraceptive use between women who are older (21-49) compared to those who are young

(under 14) in 1998 and 2008-2009. However, from the table above a Phi coefficient score of 0.036

suggests that the strength of the association ranges from slight to no association.

5.6.6 Marital Status

Table 5.37

Phi Coefficient Table Showing Significance of Being Married on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

Total 1998 data 2008 data 1998 2008 Phi Sig Married Never 3250 3762 7012 30.1 30.1 Married Married 4631 4682 9313 58.8 55.4

Total 7881 8444 16325 -0.033 0.000

From the results above, we observe the percentage of never married women stayed the same at

30.1 percent for both years. The percentage of married women decreased from 58.8 percent in 1998 to

54.5 percent, a 3.4 percent increase, in 2008-2009. The Phi coefficient is -0.033, p<0.05, indicating that there is a statistically significant difference in modern contraceptive use between women who are married compared to those women who have never been married, in 1998 and 2008-2009. However, from the table above, a Phi coefficient score of -0.033 suggests that the strength of the association ranges from slight to no association.

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Table 5.38

Phi Coefficient Table Showing Significance of Being Married on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

Total 1998 data 2008 data 1998 2008 Phi Sig. OtherMarry Never 7006 7222 14228 30.1 30.1 Married OtherMarry 875 1222 2097 11.1 14.5 Total 7881 8444 16325 0.050 0.000

From the results above, we observe the percentage of never married women stayed the same at

30.1 percent for both years. Women in the other category increased from 11.1 percent in 1998 to 14.5

percent, a 3.4 percent increase in 2008-2009. The Phi coefficients is 0.050, p<0.05, indicating that there is a statistically significant difference in modern contraceptive use between women who are in other forms of marriage (othermarry) compared to those women who have never been married in 1998 and 2008-

2009. However, from the table above a Phi coefficient score of 0.050 suggests that the strength of the association ranges from slight to no association..

5.6.7 Ever Use

The variable EverUse is divided into two categories: those women who have used other contraceptives (other methods) and those women who have used modern contraceptives (modern methods).

Table 5.39

Phi Coefficient Table Showing Significance of Women Using Other Methods and Using Modern Contraceptive Methods for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

Total 1998 data 2008 data 1998 2008 Phi Sig EverUse Other 4705 4186 8891 59.7 49.6

Modern 3176 4258 7434 40.3 50.4

Total 7881 8444 16325 0.102 0.000

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From the results, it is observed that the mean number of children decreased from 2.96 in 1998 to

2.67, a 0.29 percent increase in 2008-2009. The Phi coefficient is=0.102, p<0.05, indicates that there is a

statistically significant difference between women who have used other contraceptive methods compared

to those women only used modern contraceptive methods in1998 and 2008-2009. However, from the

table above a Phi coefficient score of 0.102 suggests that the strength of the association ranges from

slight to no association.

5.6.8 Marriage Type

The variable “marriage type” is divided into two categories: those women whose husbands had

no other wives (Monogamous) and those husbands had one or more wives (Polygamous).

Table 5.40

Phi Coefficient Table Showing Significance of Type of Marriage in Relation to Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value 1998 2008 data Total data 1998 2008 Phi Sig. Monogamous Polygamous 3824 4288 8112 51.5 49.2

Monogamous 4057 4156 8213 9.8 9.5

Total 7881 8444 16325 -0.023 0.004

In 1998, the mean number of wives was 0.23, median was 0, and the mode was 0. In 2008-2009,

the mean number of wives was 0.20, median was 0, and mode was 0. In 1998, the number of women

who were in monogamous marriages, decreased from 51.5 percent to 49.2 percent, a 2.3 percent

decrease in 2008-2009. Women in polygamous marriages decreased slightly from 9.8 percent in 1998 to

9.5 percent, a 0.3 percent decrease in 2008-2009. The Phi coefficients is -0.023, p<0.05, indicating that there is a statistically significant difference in modern contraceptive use between women who are in monogamous marriages compared to those women who are in polygamous marriages in 1998 and 2008-

2009. However, from the table above a Phi coefficient score of -0.023 suggests that the strength of the association ranges from slight to no association.

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5.6.9 Media

The media variable is divided into two categories: those women who heard of family planning

through the radio or television (audio or visual) and those who heard of family planning through other

methods such as newspapers or word of mouth in this study.

Table 5.41

Phi Coefficient Table Showing Significance of Media in Relation to Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

Total 1998 data 2008 data 1998 2008 Phi Sig. Audio or Other 4123 5090 9213 47.7 39.7 Visual Audio or Visual 3758 3354 7112 10.6 27.2

Total 7881 8444 16325 -0.080 0.000

From the above data, it is observed that women who heard of contraceptive methods through the

radio or television, decreased from 47.7 percent in 1998 to 39.7 percent, an 8 percent decrease in 2008-

2009. Those women who heard of contraceptive methods through other sources, primarily through

newspapers and word of mouth, increased from 10.6 percent in 1998 to 27.2 percent, a 16.6 percent

increase, in 2008-2009. The Phi coefficient is=-0.080 , p <0.05, indicating that there is a statistically significant difference in modern contraceptive use between women who heard of contraceptive methods through the radio or television (audio or visual), and those who heard of contraceptive methods through

other media, such as newspapers or word of mouth for this study (Other) in 1998 and 2008-2009.

However, from the table above a Phi coefficient score of -0.080 suggests that the strength of the

association ranges from slight to no association.

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5.7 Religion

Table 5.42

Phi Coefficient Table Showing Significance of Religion on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value 2008 1998 data Total data 1998 2008 Phi Sig Christian Other 727 1608 2335 8.3 18.9

Christian 7154 6836 13990 90.8 81.0

Total 7881 8444 16325 -0.140 0.000

The variable “religion” is divided into two categories: Christian and “other.” The percentage of women who identified as Christian decreased from 90.8 percent in 1998, to 81.0 percent, a 9.8 percent decrease, in 2008-2008. In 2008-2009, women who identified as “other” increased to 18.9 percent from

8.3 percent in 1998 by 10.6 percent. The Phi coefficient is = -0.140, p<0.05, indicating there is a statistically significant difference in modern contraceptive use among women who identified as Christians and other in 1998 and in 2008-2009. However, from the table above a Phi coefficient score of -0.140 suggests that the strength of the association ranges from slight to no association.

5.8 Region

The region variable is divided into seven provinces in 1998 and 2008-2009. These are: Nairobi,

Central, Coast, Eastern, Nyanza, Rift Valley, and Western. In the 2008-2009 dataset, North Eastern province was fused with Eastern province due to proximity and negligible numbers in North Eastern province. Western province was designated the reference group.

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Table 5.43

Phi Coefficient Table Showing Significance of Nairobi Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

Total 1998 data 2008 data 1998 2008 Phi Sig. Nairobi Western 7462 7492 14954 11.4 12.3

Nairobi 419 952 1371 5.3 11.3

Total 7881 8444 16325 0.107 0.000

Those women living in Nairobi province increased from 5.3 percent in 1998 to 11.3 percent in

2008-2009—a six percent increase, compared to those women living in Western province with an

increase from 11.4 percent in 1998 to 12.3 percent, a 0.9 percent increase in 2008-2009. The Phi

coefficient is 0.107, p<0.05, indicating that there is a statistically significant difference in modern

contraceptive use between women living in Nairobi province compared to women living in Western

province in 1998 and in 2008-2009. However, from the table above a Phi coefficient score of 0.107

suggests that the strength of the association ranges from slight to no association.

Table 5.44

Phi Coefficient Table Showing Significance of Central Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

Total 1998 data 2008 data 1998 2008 Phi Sig. Central Western 7094 7471 14565 11.4 12.3

Central 787 973 1760 10.0 11.5

Total 7881 8444 16325 0.025 0.002

Those women living in Central province increased from 10 percent in 1998 to 11.5 percent in

2008-2009, by 1.5 percent increase, compared to those women living in Western province with an

increase from 11.4 percent in 1998 to 12.3 percent in 2008-2009, a 0.9 percent increase. The Phi

Coefficient is 0.025, p<0.05, indicating that there is a statistically significant difference in modern

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contraceptive use between women living in Central province compared to women living in Western

province, in 1998 and in 2008-2009. However, from the table above a Phi coefficient score of 0.025

suggests that the strength of the association ranges from slight to no association.

Table 5.45

Phi Coefficient Table Showing Significance of Coast Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

Total 1998 data 2008 data 1998 2008 Phi Sig. Coast Western 6655 7295 13950 11.4 12.3 Coast 1226 1149 2375 15.6 13.6 Total 7881 8444 16325 -0.028 0.000

Those women living in Coast province decreased from 15.6 percent in 1998 to 13.6 percent in

2008-2009, a two percent decrease compared to those women living in Western province with an

increase from 11.4 percent in 1998 to 12.3 percent, a 0.9 percent increase in 2008-2009. The Phi

coefficient is -0.028, p<0.05, indicating that there is a statistically significant difference in modern contraceptive use between women living in Coast province compared to those women living in Western province in 1998 and in 2008-2009. However, from the table above a Phi coefficient score of -0.028 suggests that the strength of the association ranges from slight to no association.

Table 5.46

Phi Coefficient Table Showing Significance of Eastern Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

Total 1998 data 2008 data 1998 2008 Phi Sig. Eastern Western 6695 6709 13404 11.4 12.3 Eastern 1186 1735 2921 15.0 20.5 Total 7881 8444 16325 0.072 0.000

The women living in Eastern province increased from 15.0 percent in 1998 to 20.5 percent in

2008-2009, a 5.5 percent increase compared to those women living in Western province with an increase from 11.4 percent in 1998 to 12.3 percent, a 0.9 percent increase in 2008-2009. The Phi coefficient is

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0.072, p<0.05, indicating that there is a statistically significant difference in modern contraceptive use

between women living in Eastern province compared to Western province in 1998 and in 2008-2009.

However, from the table above a Phi coefficient score of 0.072 suggests that the strength of the

association ranges from slight to no association.

Table 5.47

Phi Coefficient Table Showing Significance of Nyanza Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

Total 1998 data 2008 data 1998 2008 Phi Sig. Nyanza Western 6491 7126 13617 11.4 12.3 Nyanza 1390 1318 2708 17.6 15.6 Total 7881 8444 16325 -0.027 0.000

The women living in Nyanza province increased from 17.6 percent in 1998 to 15.6 percent in

2008-2009, a two percent increase, compared to those women living in Western province with an

increase from 11.4 percent in 1998 to 12.3 percent, a 0.9 percent increase in 2008-2009. The Phi

coefficient is -0.027 , p <0.05, indicating that there is a statistically significant difference in modern contraceptive use between women living in Nyanza province compared to Western province in 1998 and in 2008-2009. However, from the table above a Phi coefficient score of -0.027 suggests that the strength

of the association ranges from slight to no association.

Table 5.48

Phi Coefficient Table Showing Significance of Rift Valley Province on Modern Contraceptive Use for Both Survey Periods 1998 and 2008-2009

Data Percentage Value

1998 data 2008 data Total 1998 2008 Phi Sig.

Rift Valley Western 5904 7166 13070 11.4 12.3

Rift Valley 1977 1278 3255 25.1 15.1

Total 7881 8444 16325 -0.124 0.000

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The number of women living in Rift Valley province decreased from 25.1 percent in 1998 to 15.1 percent in 2008-2009, a 10 percent increase, compared to those women living in Western province with an increase from 11.4 percent in 1998 to 12.3 percent, a 0.9 percent increase in 2008-2009. The Phi coefficient is -0.124, p<0.05, indicating that there is a statistically significant difference in modern contraceptive use between women living in Rift Valley province compared to Western province in 1998 and in 2008-2009. However, from the table above a Phi coefficient score of -0.124 suggests that the strength of the association ranges from slight to no association.

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Table 5.49

Summary of Phi Coefficient Values for Survey Periods 1998 and 2008-2009 Showing Compositional Change Over Time

Variable Phi Coefficient Sig. Values

Some Education -0.108 0.000

Higher Education 0.147 0.000

Income -0.038 0.000

Old 0.013 0.261

Older 0.036 0.000

Urban 0.143 0.000

Married -0.033 0.000

OtherMarry 0.050 0.000

EverUse 0.102 0.000

Monogamous -0.023 0.004

Audio or Visual -0.080 0.000

Religion -0.140 0.000

Nairobi 0.107 0.000

Central 0.025 0.002

Coast -0.028 0.000

Eastern 0.072 0.000

Nyanza -0.027 0.000

Rift Valley -0.124 0.000

From the above table, the results indicate that there were statistically significant compositional changes over time on the selected determinants within cohorts for both survey periods 1998 and 2008-

2009. The only variable with no significant changes is the age group categorized as marrying between

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15-20 years (old) with p= 0.261 indicating that there were no significant compositional changes within this age group for both survey periods 1998 and 2008-2009.

5.8.1 T-Test of Continuous Variables

This section addresses the mean differences over time of the continuous variables. An independent sample t test was carried out to compare the mean difference of the total number of children born (EverBorn) to women aged 15-49 in Kenya in 1998 and in 2008-2009.

5.8.2 EverBorn

An independent sample t test comparing the mean scores of the total number of children born in

1998 and 2008-2009 found a significant difference between means of the two groups of data sets t=6.737, p<0.05, df =16,323. The mean of the total number of children born (EverBorn) in 1998 was slightly higher ( M=2.96, SD =2.942) than the mean of the total number of children born in 2008-2009

(M=2.67, SD =2.639).

Table 5.50

Mean Differences on Total Children Ever Born to Women Aged 15-49 in Kenya for Survey Periods 1998 and 2008-2009

Levene's Test t-test for Equality for Equality of of Means Variances Std. Sig. Mea Std. Mean Data N Error F Sig. t df (2- n Deviation Difference Mean tailed) Equal 1998 variances 7881 2.96 2.942 .033 121.318 .000 6.737 .000 .294 assumed data Equal 2008 variances not data 8444 2.67 2.639 .029 6.711 15827.140 .000 .294 assumed

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5.9 Processual Changes

5.9.1 Interaction Effects

This section looks at the different interaction effects of each variable using the two data sets for survey periods 1998 and 2008-2009 in relation to the dependent variable, contraceptive use (EverUse).

The table below depicts the regression coefficient (B), standard error (SE), and significance values of all the net effects with independent variables only, and net effects with independent and control variables in relation to contraceptive use. All interactions were computed with all the main effects. A binary logistic

regression analysis was conducted to determine the interaction effects of the independent and control

variables on modern contraceptive use. For example, the interaction effect examines the effect of a

selected variable, such as education, on modern contraceptive use changes over time. Table 5.51 below presents the interaction effects of all determinants on modern contraceptive use with survey periods 1998

and 2008-2009.

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Table 5.51

Interaction Effects of All Determinants on Modern Contraceptive Use with Survey Periods 1998 and 2008- 2009 Data

Interaction Variable B Standard Error Sig.

Someinter 1.377 0.111 0.000 Higherinter 1.315 0.214 0.000

Incomeinter 0.359 0.067 0.000

Oldinter -0.014 0.044 0.755 Olderinter 0.015 0.044 0.735

Urbaninter -0.051 0.075 0.495

Everborninter -0.024 0.012 0.045

Marriedinter -0.135 0.085 0.113 OtherMarryinter 0.014 0.117 0.908

Monogamousinter -0.045 0.066 0.495

Audiovisualinter 0.066 0.064 0.308

Christianinter 0.994 0.104 0.000

Nairobiinter -0.284 0.151 0.061

Centralinter -0.074 0.136 0.589

Coastinter -0.002 0.125 0.986

Easterninter -1.120 0.121 0.000

Nyanzainter 0.230 0.122 0.058

Riftvalleyinter -0.116 0.118 0.324 Variable Incomeinter=income x data

The results from the table above indicate that, the effect of having some education as opposed to

having no education on modern contraceptive use in 1998 is significantly different from the effects of

having some education on modern contraceptive use in 2008-2009

(B=1.377, SE =0.111, p<0.05).

The effect of having higher education, as opposed to having no education, on the use of modern contraceptive use in 1998, is significantly different from the effects of having higher education on modern contraceptive use in 2008-2009 ( B=1.315, SE =0.214, p<0.05).

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The effect of having an income, as opposed to having no income, on the use of modern

contraception in 1998, is significantly different from the effects of having income on using modern

contraceptives in 2008-2009 ( B=0.359, SE =0.067, p<0.05).

The effect of marrying between 15-20 years (old) as opposed to marrying under 14 years

(young), on the use of modern contraception in 1998, is not significantly different from the effects of

having marrying between 15-20 years (old) on using modern contraceptives in 2008-2009 ( B=-0.014,

SE =0.044, p=0.755).

The effect of marrying between 21-49 years (older) as opposed to marrying under 14 years

(young), on the use of modern contraception in 1998, is not significantly different from the effects of

marrying between 21-49 years (older) on using modern contraceptives in 2008-2009 ( B=0.015,

SE =0.044, p=0.735).

The effect of living in an urban area, as opposed to living in a rural area on the use of modern

contraception in 1998, is not significantly different from the effects of living in an urban area on using

modern contraceptives in 2008-2009 ( B=-0.051, SE =0.075, p=0.495).

The effect of having children, as opposed to not having children on the use of modern contraception in 1998, is significantly different from the effects of having children on using modern contraceptives in 2008-2009 ( B=-0.024, SE =0.012, p<0.05).

The effect of being married, as opposed to never being married on the use of modern contraception in 1998, is not significantly different from the effects of being married on using modern contraceptives in 2008-2009 ( B=-0.135, SE =0.085, p=0.113).

The effect of being in other forms of marriage (othermarry), as opposed to never being married on the use of modern contraception in 1998, is not significantly different from the effects of being other forms of marriage on using modern contraceptives in 2008-2009

(B=0.014, SE =0.117, p=0.908).

The effect of being in monogamous marriage, as opposed to being in a polygamous marriage on the use of modern contraception in 1998, is not significantly different from the effects of being in a monogamous marriage on using modern contraceptives in 2008-2009

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(B=-0.045, SE =0.066, p=0.495).

The effect of hearing of contraceptive methods through audio or visual methods, as opposed to

other methods such as newspapers and word of mouth on the use of modern contraception in 1998, is

not significantly different from the effects of hearing of contraceptive methods through audio or visual on

using modern contraceptives in 2008-2009 ( B=0.066, SE =0.064, p=0.308).

The effect of being a Christian, as opposed to other religious affiliation on the use of modern

contraception in 1998, is significantly different from the effects of being a Christian on using modern

contraceptives in 2008-2009 ( B=0.994, SE =0.104, p<0.05).

The effect of living in Nairobi province, as opposed to Western province on using modern

contraception in 1998, is not significantly different from the effects of living in Nairobi province on the use

of modern contraceptives in 2008-2009 ( B=-0.284, SE =0.151, p=0.061).

The effect of living in Central province, as opposed to Western province on the use of modern

contraception in 1998, is not significantly different from the effects of living in Central province on using

modern contraceptives in 2008-2009 ( B=-0.074, SE =0.136, p=0.589).

The effect of living in Coast province, as opposed to Western province on the use of modern

contraception in 1998, is not significantly different from the effects of living in Coast province on using

modern contraceptives in 2008-2009 ( B=-0.002, SE =0.125, p=0.986).

The effect of living in Eastern province, as opposed to Western province on the use of modern

contraception in 1998, is significantly different from the effects of living in Eastern province on using

modern contraceptives in 2008-2009 ( B=-1.120, SE =0.122, p=0.058).

The effect of living in Nyanza province as opposed to Western province on the use of modern

contraception in 1998 is not significantly different from the effects of living in Nyanza province on using

modern contraceptives in 2008-2009 ( B=0.230, SE =0.122, p=0.058).

The effect of living in Rift Valley province, as opposed to Western province on the use of modern contraception in 1998, is not significantly different from the effects of living in Rift Valley province on using modern contraceptives in 2008-2009 ( B=-0.116, SE =0.118, p=0.324).

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5.9.2 Decomposition Analysis Formula

LnP[ /1− P ] = Σ β x The decomposition analysis formula is as follows: i i ii , where

Ln[ P /1− P ] x β i i is the logit of contraceptive use, i is a vector of determinants and i is a vector of regression coefficients. The logit, shall then be the difference between the 2008-2009 data and the 1998 data or K08 – K98 where K08 is the 2008-2009 KDHS variable proportion and K98 is the 1998 KDHS variable proportions as explained in the formula below.

Logitc( 08)− log itc ( 98) =−+Σ [ββ0(08) 0(98) ] P ij (98) ( ββ ij (08) −+ ij (98) ) Σβ(PP −+Σ− ) ( PP )( β − β ). ij(98) ij (08) ij (98) ij (08) ij (98) ij (08) ij (98)

P = ij (91) Proportion of the jth category of the ith determinant in DHS 1998

P = ij (04) Proportion of the jth category of the ith determinant in DHS 2008

β = ij (91) Coefficient of the jth category of the ith determinant in DHS 1998

β = ij (04) Coefficient of the jth category of the ith determinant in DHS 2008

β 0(91) = Intercept in the regression equation fitted to DHS 1998

β = 0(04) Intercept in the regression equation fitted to DHS 2008

The above procedure results in three components: the first is the processual component reflecting the differences in the slopes for 2008 and 1998 multiplied by the proportion of the variable

(1998); the second is the composition component, which is the difference produced by the proportions of

2008-1998 variables multiplied by the slope (1998); and the third is the interaction component, which is the product of the processual (effect) and the composition changes. Table 5.52 below presents results from the decomposition analysis showing the processual, compositional and interaction changes of all selected determinants including control variables for survey periods 1998 and 2008-2009.

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Table 5.52

Decomposition Analysis Results Showing the Processual, Compositional and Interaction Changes of All Selected Determinants Including Control Variables for Survey Periods 1998 and 2008-2009

Variable Processual Compositional Interaction

Some Education 0.812 0.829 -0.082

Higher Education 0.021 0.095 0.072

Income Yes 0.007 -0.018 -0.006

Old -0.065 -0.004 0.002

Older -0.057 0.018 -0.005

Urban -0.084 0.106 -0.056

Married -27.274 -0.436 1.577

OtherMarry -5.174 3.719 -1.585

Monogamous -0.076 -0.010 0.003

EverBorn 0.001 0.000 0.000

AudioorVisual -0.070 -0.027 0.012

Christian 0.678 -0.027 -0.101

Nairobi 0.003 0.023 0.003

Central -0.031 0.015 -0.005

Coast 0.079 0.002 0.010

Eastern -0.074 0.022 -0.017

Nyanza 0.024 0.002 -0.003

Rift Valley 0.072 0.018 0.029

Constant 46.031 46.031 46.031

From the table above, it is observed that there were more statistically significant compositional changes than effect or processual changes. There were also many interaction changes. The table also shows many negative values. The magnitudes of the processual changes are much larger, given that the composition and processual changes are statistically significant at the 0.05 level. It is observed that all the variables in the table contributed to the overall change over time.

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Table 5.53 below presents a summary of all significant processual, compositional, and interaction

changes of all selected determinants including control variables for survey periods 1998 and 2008-2009.

Table 5.53

Summary of All Significant Processual, Compositional, and Interaction Changes of All Selected Determinants Including Control Variables for Survey Periods 1998 and 2008-2009

Sig. of Effect Sig. of Sig. of and Variable Processual Variable Compositional Variable Compositional Changes Changes Changes Higher 0.021* Income -0.018* Old 0.002 Education Some 0.812* Education Old -0.004 Older -0.005 0.007* Income Yes EverBorn -0.001* Older 0.018* Monogamous 0.003

Christian 0.003* Married -0.436* EverBorn 0.000

Eastern -0.031* Monogamous -0.010* AudioorVisual 0.012

EverBorn -0.001* Christian -0.101

AudioorVisual -0.027* Nairobi 0.003

Christian -0.027* Central -0.005

Nairobi 0.023* Coast 0.010

Central 0.015* Eastern -0.017

Coast 0.002* Nyanza -0.003

Eastern 0.022* Rift Valley 0.029

Nyanza 0.002*

Rift Valley 0.018*

Results from the table above indicate statistically significant compositional changes in the

following variables: some education, higher education, income, older, urban, ever born, married,

othemarry, monogamous, audio or visual, Christian, Nairobi, Central, Coast, Eastern, Nyanza, and Rift

Valley between 1998 and 2008-2009. The negative values for the compositional change include the following variables: Christian, income, old, married, monogamous, audiovisual, and Christian.

The results from the table above also indicate statistically significant processual or effect changes

in the following variables: Some education, higher education, income, ever born, Christian, and Eastern

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across the two time periods. The processual change in variables such as old, older, urban, married, othermarry, monogamous, audiovisual, Central, and Eastern had negative values.

The interaction effects indicate that the variables, audio or visual, Christian, other marry, some education, higher education and type of marriage tended to show less magnitude of change over time.

The negative values for the interaction effect include the following variables: some education, income, older, urban, married, Christian, Central, Eastern and Nyanza provinces.

Based on the results from the table above, it can be concluded that more of the change that took place in Kenya from 1998 to 2008-2009 were due to compositional changes within cohorts rather than processual changes across cohorts because the compositional magnitudes were larger than processual magnitudes. However, the compositional changes were more statistically significant than processual changes, showing that compositional changes between the two time periods were statistically significant.

5.9.4 Conclusion

From the results above, it is observed that a statistically significant increase in contraceptive use occurred in Kenya from 1998 to 2008-2009. The change in contraceptive use was evident in all groups but the amount of change in each group differed. The hypothesized relationship among education, type of place of residence, and income was supported by this study. However, the hypothesized relationship of the variable, “age at marriage,” for both old and older groups, yielded contradictory results for both survey periods. Women, with some education and higher education, were found to be more likely to use modern contraceptives than women with no education. Women who live in urban areas are more likely to use modern contraceptives compared to women who live in rural areas. Additionally, women who earn an income are more likely to use modern contraceptives compared to women who do not earn an income.

The proposed hypotheses are strongly supported by the chi square associations of the selected determinants on contraceptive use. The hypotheses are also supported by the regression analyses net effects with independent variables only, and net effects with both independent and control variables.

Additionally, the statistically significant compositional changes addressed by the Phi Coefficient supported the compositional changes within cohorts of selected variables over the two time periods, 1998 and 2008-

2009. Moreover, the effect or processual changes indicated support for change in the selected

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determinants over time across cohorts, between the two time periods 1998 and 2009. Lastly, the decomposition analysis suggests that all variables contributed to the overall change of selected determinants on modern contraceptive use over the two survey periods 1998 and 2008-2009.

The analysis of this study is important to women’s reproductive health in Kenya. Due to the availability of several and more recent Kenya Demographic Health Surveys, more comparisons can be made utilizing different fertility surveys over a period of time. The comparison of the two survey periods

1998 and 2008-2009 not only enabled us to view overall changes across cohorts on modern contraceptive use, but also trends within cohorts. Additionally, the variables education, income, and residence in this study are important variables in providing a comprehensive view of how modernization and urbanization influence modern contraceptive use.

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CHAPTER 6

DISCUSSION AND SOCIAL WORK IMPLICATIONS

6.1 Discussion

The results of this study add to the knowledge base by supporting the proposed hypotheses as well as exploring areas for further research. The use of secondary data sets inhibits the ability to phrase questions directly as intended and results in inferences which may be subjective and biased in nature.

The contrast and comparison between the two survey periods, 1998 and 2008-2009, provides an in-depth look at how social change has influenced the selected determinants on modern contraceptive use. The two time periods also give insight on how variables such as income, education, and residence are important factors in the study of modernization and urbanization. The two data points also give insight on not only changes within groups and cohorts but also across cohorts over time.

6.1.1 Education

Having some education and higher education, compared to no education, positively influences increased modern contraceptive use among women in both survey periods—1998 and 2008-2009. Ketkar

(1978) indicates that the amount of primary education of the wife, to a certain point, results in a larger family size at completion of the primary school education, but beyond this threshold level, results in a smaller family size. Women with low or no education have larger families and as they obtain higher education, they are more likely to have smaller families (Ketkar, 1978; Pillai, 1981).

Pillai (1981) asserts that a wife’s education has a curvilinear (inverted U) relationship with completed family size. Up to a threshold of 4.4 years, an increase in wife’s education results in a larger family size. Beyond this point, increased education has a declining effect on family size (Pillai, 1981;

Ketkar, 1978). College-educated women are more likely to prefer a smaller family than those who have no college education (Pillai, 1984).

Several research studies show that educated women have higher levels of contraceptive use than uneducated women (Njogu, 1991; Rutenberg, et al., 1991; DeRose, 2007; NRC, 1993). Female

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education is an important determinant affecting modern contraceptive use in Africa (NRC, 1993). Women with better education are more likely to use modern contraceptives than women who have low or no education (Njogu, 1991; Baylies, 2000).

The hypothesis, the odds of using modern contraception methods compared to the use of other contraception methods, are higher for those women who have some education (less than primary and greater than secondary education) compared to those women who have no education, is strongly supported by this study.

The hypothesis, the odds of using modern methods of contraception compared to the use of other contraception methods, are higher for those women who have higher education (greater than secondary education) compared to those women who have no education, is strongly supported by this study.

6.1.2 Income

Moreover, earning an income, compared to not earning an income in the form of cash, strongly influences the modern contraceptive use among women in both survey periods. However, women who earned an income in 2008-2009 were fewer than expected and there was a substantial amount of missing data related to income. A plausible explanation would be that there were semantic differentials on how the question was framed. Some women may equate earning an income as being solely tied to having a government, office job, or being a salaried employee and may discount income earned from odd jobs.

Earning an income is related to the use of modern contraceptives and is supported by several researchers. Women who are self-employed, business owners and employees of government and non- governmental organizations are more likely to use modern contraceptives than those women who are not employed (Gage, 1995; Shapiro & Tambashe, 1994). In economically developed countries, fertility rates tend to decline as women in these societies are more modern and are more likely to use modern contraceptives (Kalipeni, 1995; NRC, 1993). Women who participate in the labor force are more likely to use modern methods of contraceptives and less likely to want large families than women who are not employed outside the home (Pillai & Teboh, 2010, NRC, 1993; Oliver, 1995; Berger et al., 1992). Several researchers ascertain that women with higher incomes will most likely use modern contraceptive methods to limit the number of children they have (Becker & Tomes, 1976; Oliver, 1995).

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The hypothesis, the odds of using modern contraception, compared to the use of other contraception methods, are higher for those women who have an income compared to those women who have no income, is strongly supported for both survey periods 1998 and 2008-2009.

6.1.3 Residence

Living in an urban area strongly influences modern contraceptive use among women in Kenya.

Migrating from the rural areas to the urban areas has increased the accessibility, awareness, and use of modern contraceptives in developing countries (Schuster, 1979).

Several researchers have found that rural and urban types of residences have an impact on contraceptive use, with urban areas having an increased rate of using modern contraceptives compared to rural areas

(Lightbourne, 1980; Tuladhar, 1985; Njogu, 1991; Gage, 1995; NRC, 1993). Additionally, modern contraceptive use rates are higher in urban areas than modern contraceptive use in rural areas (Njogu,

1991; NRC, 1993; Pillai & Teboh, 2010). Urban migration and urban dwelling is characterized by high cost of living, therefore, inhabitants of urban areas adjust their lifestyles according to their resources and the cost of living in these areas, making rational decisions regarding family size based on a cost benefit analysis (Macunovich, 2000).

In many developing countries such as Kenya, urban areas are characterized with higher proportion of formal-sector employment, and access to healthcare and family planning services, thus, those living in urban areas are more likely to use modern contraceptives (Pillai & Teboh, 2010; Njogu,

1991). Urbanization is an important determinant influencing increase in modern contraceptive use in

Africa (NRC, 1993).

The hypothesis, the odds of using modern contraception methods compared to the use of other contraception methods, are higher for those women who live in urban areas, compared to those women who live in rural areas, is strongly supported for both survey periods 1998 and 2008-2009.

6.1.4 Age at Marriage

From this study, the age at marriage variable negatively influences the use of modern contraceptives, thus it provides contradictory support to the proposed hypotheses for survey periods,

1998 and 2008-2009. The process of modernization has extended the process of formal training and

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extended the age at marriage (Warenius et al., 2006; Omungo, 2008; Kabiru & Orpinas, 2009). The age at family formation has a negative effect on family size (Pillai, 1984), thus, greater the age-at-family formation, the more the woman is likely to be exposed to school and to participation in the labor force

(Pillai, 1984). The increase in social and economic participation outside the home, decreases the preference for motherhood, resulting in increased use of modern contraception (Blake, 1968).

Increased development of societies led to women’s ability to access educational, vocational, and training opportunities (Schuster, 1979). Therefore, women had more choices to negotiate whether they wanted to start families or aspire toward their personal goals (Schuster, 1979). Modernization comes with more choices, such as when to have children, time and interval when to get married, and how many children to have ((Pillai, 1984; Amin & Bajracharya 2011). Women who choose to have children early may get married much younger than those who choose to postpone having children (Trussell & Reinis, 1989).

As a society evolves to become more modern, modernization increases—giving educated women more options, which may downplay the importance or urgency of marriage and having children (Sunil & Pillai,

2004). Women who choose to delay marriage and delay having children are more likely to use modern methods of contraception compared to those women who opt to marry at a younger age (Jones, 2007;

Sunil & Pillai, 2004; Oliver, 1995).

The hypothesis, the odds of using modern contraception methods compared to the use of other contraception methods, are higher for those women who marry between 15-20 years (old), compared to those women who marry under 14 years (young), is not supported in this study; it shows contradictory support, Additionally, the hypothesis, the likelihood of using modern contraception methods compared to the use of other contraception methods, are higher for those women who marry between 21-49 years

(older) compared to those women who marry under 14 (young), is not supported in this study; it shows contradictory support. A plausible explanation for these hypotheses would be that, younger generations of women are more technologically savvy and have more access to information on contraceptives compared to their older counterparts.

These changes may also be attributed to political and value changes in Kenya. Over the 10 year period, from 1998 to 2008-2009, Kenya underwent several political changes. These include change in

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government administration from President Moi to President Kibaki and to ethnic and civil clashes in 2007

due to elections discrepancies, with voters demanding more health and social services, higher wages,

more jobs, and better housing conditions .These changes affected the social and economic atmosphere

in Kenya—from how much funding was allocated to different social programs, to economic hardship,

unemployment, and inflation.

6.2 Implications for Social Work Practice, Policy, Research, and Education

6.2.1 Practice

Based on the findings of this study, women in urban areas are more likely to use modern

contraception methods compared to their rural counterparts. Efficient and effective service delivery should

be concentrated in rural areas and enhanced in urban areas Social service providers and practitioners in the field of family planning should be adequately trained in the areas of reproductive health services, programs, and products, to be able to provide effective, efficient services and information to their clients who consist of women both in urban and rural areas.

The government and local organizations should collaborate to provide affordable contraceptives, and reproductive health services, especially to those women in rural areas, women who do not earn an income, women who have no education, and increase similar services in the urban areas, as evidenced by the findings of this study that women in rural areas, women who do not earn an income, and women who have no education are less likely to use modern contraceptives.

Service providers should make contraceptives, condoms in particular, available and accessible to women in both rural and urban areas, in areas such as schools, public restrooms, shops and kiosks, and vending machines. By promoting the use of modern contraceptives, such as condoms to prevent or delay pregnancy, prevent the transmission of sexually transmitted diseases as well as protect and reduce the incidence and prevalence of HIV/AIDS, which ultimately leads to death. This would contribute to the overall wellbeing of the society as a whole. Based on the findings of this study, rural areas are less likely to have available and accessible contraceptives.

Social service providers and professionals in the field of reproductive health services should work on breaking the stigma regarding the use of modern contraceptives, especially condoms. They should

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educate both men and women on the importance of using contraceptives and on the consequences of not using any. Women should be empowered to understand that carrying condoms does not make one promiscuous or morally rebellious, but is a sign of empowerment and protection.

Practitioners and social service providers should conduct outreach programs, such as seminars and workshops, to educate community members on the pros of using contraceptives and cons of not using modern contraceptives. These activities should be culturally sensitive, provided in local dialects, and involve local leaders, to increase acceptance and reception by other community members. This not only increases participation but also increases ownership and sustenance of such programs. Having access to such information empowers women to make informed choices.

Reproductive health providers and professionals working in rural areas should encourage the formation of social networks among women in smaller communities. These networks would provide information on contraceptive use and contraceptive services as well as support among women. Women in these networks can share their experiences of using or not using contraceptives and they may share ideas on how to delay child birth, space children, how to go back to school or vocational training, to acquire skills to improve their economic conditions. Due to rural-urban differences in availability of information, technology, and communication methods, social networks in rural areas act as the primary mode of information transmission channels especially amongst women. Service providers in rural areas should promote the formation and usage of social networks to encourage women to share information regarding the use of modern contraceptives.

Primary and secondary schools should include contraceptive use and services into their curriculum to educate girls from an early age and to give them options when they get pregnant they can return to school and not have to drop out. Findings from this study indicate that higher education is strongly associated with modern contraceptive use. Therefore, introducing the concepts of contraceptive use, services and products at an early level of education such as secondary school, will increase the use of modern contraceptives over time.

Social service providers working in family planning organizations should work closely with the media such as radio personalities, television anchors, newspaper journalists, and other media outlets

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such as creating billboards to advertise and give positive publicity to the use of modern contraceptives.

These campaigns should be geared toward rural areas with dense populations, limited technology and communication tools. Additionally, from the findings of this study, exposure to media outlets such as television and radio increase women's likelihood of using modern contraception methods.

Provision of informal education channels such as advice from elders and community leaders in practices such as “Senga,” practiced in Uganda, or during rites of passage, are important in the sharing of contraceptive use knowledge as well as educating the community members on making better informed decisions.

The media can play a vital role in the promotion of modern contraceptive use. Through collaboration with family planning organizations in setting up a themed month such as “National

Reproductive Health Month” the media can create increased awareness, support, and participation from members of the community. This can be in the form of scheduled 5, 10, or 15 kilometer walks or runs or cycling contests, all in an effort to raise awareness, educate members, and raise funds to promote modern contraceptive use. The media can play a crucial role in these events by positively portraying information regarding contraceptive use and advertising.

Agencies and schools providing reproductive health services and products should advocate for better technology and communication channels for their clients and students. This should include providing computers and Internet services in providing information on different types of contraceptives and services.

Agencies and organizations providing reproductive health services should train their social workers for different roles such as advocate, policy analyst, program developer, educator, community organizer, and researcher. This will enable social workers to provide more specialized services in their areas of expertise and ensure that the profession is represented well across the board.

6.2.2 Policy

Reproductive health advocates should lobby for more funding from the government to develop new and increase programs in rural areas and sustain already existing programs operating in urban areas. This funding will help with provision of family planning services such as pregnancy tests,

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contraceptive products like condoms, and training health workers. Additionally, the funding may be used to provide educational materials and pamphlets and to set up mobile clinics in rural areas to increase services due to limited transportation, technology, and advanced communication methods; evidenced by the findings of this study showing that women who live in rural areas are less likely to use modern contraception methods.

Government officials should include reproductive health programs and services in their annual budget and equally distribute resources to all local and national family planning clinics. There needs to be proper and transparent channels of fund allocation to ensure equitable distribution of resources to organizations dealing with reproductive health programs especially in rural areas.

Agency policies should be inclusive of young women who choose to use birth control, and older women who wish to stop or delay child birth. This will encourage women to seek services, be productive members of the society, and break the negative stigma associated with contraceptives and contraceptive use.

Findings from this study may help in shaping up reproductive health policies, developing new, and improving existing family planning programs and services both in urban and rural areas.

Public school and secondary governing bodies should advocate for inclusive policies regarding reproductive health into school curricula. This gives teachers and school counselors an opportunity to openly discuss sex education with their students and also provides an avenue for those students who, for example, may become pregnant, to access services and be in a position to finish their studies, as opposed to dropping out, being suspended, or expelled from school. The finding that higher education is associated with an increased likelihood of using modern contraceptive methods supports initiatives for introducing concepts of contraceptive use as early as late primary and secondary school.

Policy makers and the department of education should collaborate to develop alternative options such as charter schools, which provide continuing education to young students who end up pregnant before completing their studies. This gives the young mothers an opportunity to complete their education and also provides services to care for their young children. With this type of support, the young students

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may complete their education, be able to compete for better job opportunities, and earn better income to be able to take care of themselves and their dependents.

6.2.3 Research

Research should focus on modern contraceptive use over time and have available comparable data with other regions and countries. Information from these types of researches can be helpful in drafting local and national policies and programs aimed at increasing availability and accessibility to contraceptives especially in rural areas. Apart from this current study, only one other study conducted by

Njogu (1991) addressed trends and determinants of contraceptive use in Kenya over time.

Increase in Community Based Participatory Research (CBPR) will greatly increase the awareness and use of contraceptives especially in rural areas where there are strong conservative and pronatalist norms. Additionally, this type of research will increase participation from local community members who are not often represented in decision making processes. If members of a community are involved in some level of research or program development, they are more likely to have ownership of the project and maintain it over a period of time, as opposed to the program being introduced by outsiders.

External monitoring and evaluation agencies need to be set up to evaluate what programs and services and those that are not. Such services can be helpful in giving recommendations to those programs not meeting their goals and also enhance those that are running effectively. This will increase quality and efficiency of service delivery. Additionally, programs can have measureable goals and outcomes and agency personnel can ensure that programs and services are functioning as intended.

Research should also focus on women who have never used any form of contraception, folkloric and traditional contraception methods. This type of research should focus on the reasons why these methods have been used and if there are ways to increase the use of modern contraception. This type of research also needs to address educational, income, and regional characteristics of women using these types of contraceptive methods and data compared with those using modern contraceptives. The comparison and contrast of these data may help in developing specific programs and services to cater to the different tribes and regions.

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6.2.4 Education

Social service providers in the education field should advocate for the inclusion of sex education in primary and secondary schools. Additionally, service providers and teachers should advocate for inclusive reproductive health policies in primary and secondary schools. Lastly, school officials and school board policy makers should advocate for curriculum inclusion of reproductive health rights.

6.3 NASW Value and Ethics Assessment

The National Association of Social Workers’ (NASW) primary mission for the social work profession is to improve human wellbeing and help meet the basic human needs of all people, with particular attention to the needs and empowerment of people who are vulnerable, oppressed, and living in poverty (NASW, 2008). Additionally, social workers promote social justice and social change with and on behalf of clients such as individuals, families, groups, organizations, and communities. Moreover, NASW advocates for informed decision making through research and evaluation of programs to improve the effectiveness and efficiency of service delivery.

Social workers must be sensitive to cultural and ethnic diversity and strive to end discrimination, oppression, poverty and any other form of social injustice (NASW, 2008). Social workers may advocate for their clients through providing direct practice services, community organizing, advocacy, social and political action, policy development and implementation, education, and through research and evaluation

(NASW, 2008). Additionally, social workers should seek to enhance the capacity of people to address their own needs and to promote the responsiveness of organizations, communities, and governments to individuals’ needs and social problems (NASW, 2008).

Social workers are guided by a set of six core values that give the profession its unique purpose and perspective. These six core values include; service, social justice, dignity and worth of the person, importance of human relationships, integrity, and competence (NASW, 2008).

6.3.1 Service

Social workers’ primary goal is to help people in need and to address social problems.

Social workers should elevate service to others above self-interest and should draw on their knowledge, values, and skills to help people in need and to address social problems. Social workers working in the

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areas of reproductive health or family planning, should seek to help those in need by providing the best

service to their clients and linking their clients to resources within their communities (NASW, 2008). Social

workers are encouraged to volunteer some portion of their professional skills with no expectation of

significant financial return; therefore social workers should volunteer their time and expertise to agencies

and areas that are undeserved such as rural areas or to urban areas where there may be a shortage of

professionals due to high client ratios.

6.3.2 Social Justice

Social workers must challenge social injustice. Social work professionals should pursue social

change, with and on behalf of vulnerable and oppressed individuals and groups. This effort should focus primarily on issues of poverty, unemployment, or discrimination in under developed areas such as rural areas. Social work service providers should seek to promote sensitivity to, and knowledge about oppression, cultural and ethnic diversity, and strive to ensure access to needed information, services, and resources; equality of opportunity; and participation in decision making (NASW, 2008). Social workers involved in reproductive health services should advocate for funding to develop programs and services in rural areas. They should also advocate for participation in decision making at all local and national policy making bodies.

6.3.3 Dignity and Worth of a Person

Social workers respect the inherent dignity and worth of the person and exhibit this by treating each person in a caring and respectful manner, mindful of individual differences and cultural and ethnic diversity. Social workers promote clients’ self determination by enhancing their capacity and opportunity to change and to address their own needs. For instance, due to the stigma associated with contraceptive use, social workers must be accommodating to women seeking contraceptive use. This will encourage women old and young, married or single to access contraceptive use services without feeling shame, embarrassment, or guilt. Social workers should be cognizant of their dual responsibility to clients and to the broader society by seeking to resolve conflicts between clients’ interests and those of the broader society’s interests in a socially responsible manner consistent with the values, ethical principles, and ethical standards of the profession (NASW, 2008).

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6.3.4 Importance of Human Relationships

Social workers should recognize the central importance of human relationships by understanding that relationships between and among people are an important vehicle for change. Social workers should engage individuals and their families as partners in the helping process, and they should involve their client’s family members and other support network in their care. For example, if a 16-year-old girl is sexually active and approaches a social worker for contraceptives, the social worker should involve the young girl’s family members to ensure that she receives all the support and care needed. This will encourage others to seek contraceptive use services and will reduce feelings of shame, guilt, or embarrassment. Additionally, social workers should seek to strengthen relationships among an individual’s family or support network in an effort to promote, restore, maintain, and enhance the wellbeing of individuals, families, social groups, organizations, and communities (NASW, 2008).

6.3.5 Integrity

Social workers must behave in a trustworthy manner and should continually be aware of the profession’s mission, values, ethical principles and standards, and practice in a manner consistent with them. Social workers should act honestly and responsibly, and promote ethical practices on the part of the organizations with which they are affiliated (NASW, 2008). Social workers in agencies providing family planning services, should be truthful and open in the provision of service and should not engage in any acts that may jeopardize the wellbeing of a client, the agency or the profession. For instance, social workers should not engage in favoritisms over specific clients or groups in the delivery of family planning and reproductive health services. Social workers should treat each client with respect and provide the same level of quality service to each and every person they serve.

6.3.6 Competence

Social workers should practice within their areas of competence, work on developing, and enhancing their professional expertise (NASW, 2008). Social workers should continually strive to increase their professional knowledge, skills, and to apply them in their practice. Social workers should aspire to contribute to the knowledge base of the profession. Social work providers in reproductive health programs should strive to keep up with current research and statistics on contraceptives and contraceptive use.

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6.4 Limitations

First, the use of secondary data limits measurement of certain variables such as income, leading

to simplification of the variable which may lead to oversight of important details. Second, the lack of a

variable on women’s rights hinders our ability to address the sense of status of women within the family

and knowledge of their rights. Third, the lack of a variable addressing mixed contraception method as

originally intended restricts us from comparing those women who use mixed contraceptive methods with

those who use only modern contraceptive methods.

6.5 Future Directions

Future directions in the study of modern contraceptive use among women should also incorporate

factors that are important in determining the relationship between women and their partners and the fertility goals for both husbands and wives. Additionally, studies on the husbands’ level of education and income should be addressed to find out if they influence the wife’s use of modern contraceptives. In addition, future studies should address women’s sense of status within the family in regards to knowledge of women’s rights. Further studies should also address women’s perceptions of decision making and the level of decision making within the family, and address ways to empower women about their reproductive

rights. Last, further studies need to look into ways of improving communication between husbands and

wives in relation to increasing the use of modern contraceptives.

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CHAPTER 7

CONCLUSION

The objectives of this study were to examine the effects of selected determinants on contraceptive use in Kenya between 1998 and 2008-2009, and to describe the changes in the selected determinants on contraceptive use during that period. This study utilized modernization and human capital theories to explain the categorical variations in contraceptive use among the two comparison groups; those using no contraception and those women using only modern contraceptive methods. In describing the changes in contraceptive use between 1998 and 2008-2009, this study examined both individual level as well as societal factors related to contraceptive use in Kenya using Ryder’s theory on social change. The data used for this study are derived from the Kenya Demographic Health Surveys for survey periods 1998 and 2008-2009. Three types of data analysis methods were be used; univariate analysis, binary logistic regression, and decomposition analysis.

In many Sub-Saharan African countries, population control has been achieved through various family planning programs and policies regarding fertility and population control. The use of the three theories: modernization, human capital, and social change give us insight on how development in a society has effects on income, age at marriage, and the type of place of residence, urban versus rural.

Additionally, we are able to explain how human capital and education affect the use of modern contraceptives in determining family size formation. Several studies have focused on the role of family planning programs; only a few have focused on structural sources of change in contraceptive use. Broad- based knowledge of structural changes that enhance modern contraceptive use is important in the shaping of population policies in a society.

From the results of this study, it is observed that a statistically significant increase in contraceptive use occurred in Kenya from 1998 to 2008-2009. The change was evident in all groups but the amount of change in each group differed. The hypothesized relationships among the selected determinants education, type of place of residence, and income on modern contraceptive use were

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supported by this study. However, one hypothesized relationship of the variable, “age at marriage” for both old and older groups, yielded contradictory results for both survey periods 1998 and 2008-2009. This study found that women who marry between 15-20 years (old) and between 21-49 years (older) are less likely to use modern contraceptives compared to women who marry under 14 years (young), these hypotheses are contradictory.

Women with some education and higher education were found to be more likely to use modern contraceptives than women with no education. Women who live in urban areas are more likely to use modern contraceptives compared to women who live in rural areas. Additionally, women who earn an income are more likely to use modern contraceptives compared to women who do not earn an income.

Additionally, statistically significant compositional changes addressed by the Phi Coefficient values supported the compositional changes within cohorts of selected variables over the two time periods, 1998 and 2008-2009. Moreover, the effect or processual changes indicated support for the proposed hypotheses showing change in the selected determinants over time across cohorts, between the two time periods, 1998 and 2009. Lastly, the decomposition analysis suggests that all variables contributed to the overall change of selected determinants over the two survey periods 1998 and 2008-2009.

The analysis of this study is important in the area of women’s reproductive health in Kenya. Due to the availability of several and more recent Kenya Demographic Health Surveys, more comparisons can be made utilizing different fertility surveys over a period of time. The comparison of the two survey periods, 1998 and 2008-2009, not only enabled us to view overall changes across cohorts on modern contraceptive use, but also trends within cohorts. Additionally, the variables “education,” “income,” and

“residence” in this study, are important variables in providing a comprehensive view of how modernization and urbanization influence modern contraceptive use.

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APPENDIX A

MODERNIZATION AND CONTRACEPTION IN KENYA FROM 1998 TO 2008-2009: LITERATURE

REVIEW ARTICLES

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Comparison Authors/Year Hypothesis/Res Question Sample Theory Findings Group More intensified advocacy is needed to put the family planning program back prominently on the agenda of the Ministry of Health (MOH) and all other relevant player. 2). The division of reproductive health services needs to take the lead in implementing an integrated approach of FP/RH and HIV/AIDS. 3). Improved and expanded management of sexually This study was designed to transmitted infections (STIs) has document the extent to increasingly become an HIV/AIDS which Kenya has managed control strategy rather than an RH both its family component, as was the case about planning/reproductive health 10

160 160 (FP/RH) and HIV/AIDS years ago. The Division of programs in the context of Reproductive Health should continue Aloo-Obunga, C. the high HIV prevalence the None No theory to keep the issue of STIs at the top of (2003) country is experiencing. 16 Interviews mentioned mentioned its agenda as an RH issue too.

Women who were younger and had higher levels of education preferred a Examining the way in which smaller number of children. Women concerns about HIV infection Surveys of 150 who were older and had lower levels impacts family size adults and 7 None No theory of education preferred a larger Baylies, C. (2000) preference in Zambia groups mentioned mentioned number of children.

1. Social networks have substantial effects even after unobserved factors e.g. homophily that may determine social networks are controlled for. 2. Controlling for unobserved factors may substantially alter the estimated effects of networks. 3. Network effects are important for both men Behrman, J. R., Addressing the impact of and women. 4. Network effects are Kohler, H. P., & social networks on changes 1993 Kenya Social Network nonlinear and asymmetric; networks Watkins, S. C. in contraception in rural 497 Women Demographic Model, Local provide information primarily through (2002). Kenya. and 324 Men Health Survey Luo Model social learning. 1), Kenya Demographic Health Survey Blacker, J., 1998, 1993, 1). Fertility differences in Kenya and Opiyo, C., 1988, 1983, Uganda Uganda are attributed to different Jasseh, M., 1978, Demographic economic paths followed by each

161 161 Sloggett, A., & To compare the trends and 2). Fertility Health Surveys country after independence. 2). Ssekamatte- determinants of fertility Surveys 1977, 1988, 1969, Social Kenya government promoted family Sebuliba, J. between Kenya and 1958, 1963, 1974, 1979, Networks planning from the 1960s compared to (2005). Uganda. 1968, 1973 1984 Model Uganda in 1995. 1). Family planning should be a top priority in dealing with population growth.2). Countries should provide Cleland, J., safe and affordable social and health Bernstein, S., services to catch up with the rest of Ezeh, A., To encourage political the world. 3). Other Western Foundes, A., willingness in incorporating countries other than USA should take Glasier, A., & family planning in the 17 developing Countries in Political the lead in providing aid to Innis, J. (2006). development arena. countries South Asia Economy developing countries. Discussing the health status 1). Political 1). Governments should work on of Sub-Saharan African Economy of implementing development policies in countries focusing on Health Policy Africa to deal with issues of maternal infectious and parasitic Model 2). and child health, HIV/AIDS infections, diseases, HIV/AIDS, Sub-Saharan Demographic and reproductive rights. 2). Africa Cook, C., & Kalu, maternal and child health, Africa in None Transition should also invest in itself by training K. (2008). famine, and political general mentioned Theory and retaining their professionals.

instability.

1). SRHR organizations must find ways to generate their own funding and not be totally dependent on external funding. 2). Groups at the 1). Advocating for global local, national, and global levels economic justice. 2). should be allied together and share Drawing attention to the ideas and resources. 3). Reach the neglected sexual and mass media to reframe information reproductive health and Global Political on SRHR and invoke wider public rights (SRHR) in the Sub-Saharan Economy of support.4). Engage progressive Crane, B. B. millennium Development Africa in None Reproductive donors and require their input when

162 162 (2005). Goals (MDGs) general mentioned Rights issues arise. 13 semi- 1). Policy space expanded due to structured changing contextual factors and the interviews and actions of different individuals. 2). 3 unstructured Proponents of family planning within discussions two government ministries played an with high important role in expanding the policy officials and top space through public and intra- government government advocacy activities. 3). and agencies, Policy Space Analysis can provide academic, and useful insights into the dynamics of international routine policy and program evolution Analyzing fluctuating policy non- and the challenge of sustaining Crichton, J. space for family planning in governmental None Policy Space support for issues after they have (2008). Kenya. organizations. mentioned Model made it onto the policy agenda.

1). Reproductive decisions in rural Changes in existing supply areas reflect entrenched male and demand-side inequities dominance, showing that gender should enable rural women KDHS 1989 inequality in decision making must be Dodoo, F. N., & to attain their reproductive and 1993 total addressed if rural women are to Tempenis, M. goals to the same extent as sample of None Joint Decision realize their fertility goals fully as their (2002). their urban counterparts. 2,358 women mentioned Model urban counterparts. 21). 21% of females and 11% of males had experienced sex under coercive conditions. 2). Most of the perpetrators were intimate partners such as husbands, boyfriends, and 2,712 married girlfriends. 3). Sexual coercion was and unmarried associated with having had multiple Erulkar, A. S., young people sexual partners and with having had Ettyang, L. NL. were Peer Education a reproductive tract infection. 4). A., Onoka, C., interviewed. Model, Life Females who had ever been married Nyaga, F. K., & To address the experience Sample was Planning Skills and those who did not live with a

163 163 Muyonga, A. of sexual coercion among limited to 10-24 None of Adolescent parent or souse, had a significantly 2004). young people in Kenya. year olds. mentioned Curriculum elevated risk of sexual coercion.

Addressing young women’s Popular culture images and and men’s ideals of local Case study narratives influence values and life and global culture popular Urban slum in perspectives of the young people in culture, gender relations, Eastlands the Eastlands neighborhood. Current Folke- and democratization of Nairobi, Kenya. representations of love, romance, Frederiksen, B. equality and authority in Surveys and Young men Sociological and marriage are conveyed through (2000). Kenya. interviews and women theory music, visual, and printed media. Over 80% of women presented with incomplete abortions. The annual number of women with abortion complications admitted to public Gebreselassie, hospitals in Kenya is 20,893. The H., Gallo, M. F., To estimate and describe Cross-sectional annual incidence of incomplete Monyo, A., & the magnitude of abortion descriptive abortion and other abortion related Johnson, B. R. complications presenting at study of 809 None None complications per 1000 women aged (2005). public hospitals in Kenya. patients. mentioned mentioned 15 to 49 years is projected to be

3.03.

1). Rising unemployment and social inequalities leave some groups especially poor women extremely 1). Increased women's rural-urban vulnerable.2). Greatly migration and the sex-money reduced marital rates and exchange have fueled the spread of the subsequent increase of AIDS. 2). Research is needed to one person's households. Political provide more statistical analysis over Hunter, M. 3). Rising levels of women's None Economy of historical and ethnographic

164 164 (2007). rural-urban migration. South Africa mentioned Sex perspectives. Age and marital status are Examines the levels and 17 Sub- associated with and are critical Ibisomi, L., & patterns of as well as factors Saharan predictors of having ever had a Odimwegu, C. associated with pregnancy African None No theory terminated pregnancy in all the (2008). termination. Countries mentioned mentioned countries. 1). 53 % of respondents reported having used condoms. 2). Consistent condom users engaged in sexual Examination of intercourse at an older age and sociodemographic, reported higher condom use behavioral, and compared to non-users and sporadic psychosocial characteristics users. 3). Sporadic users had more of adolescents' knowledge sexual partners and stronger on using condoms as a Nonusers, attitudes that males and females Kabiru, C. W., & strategy to reduce the risk of sporadic users, have separate roles. 4). The three Orpinas, P. sexually transmitted and consistent Socioecological groups perceived their risk of (2009). diseases. 931 males users. Framework contracting and STD as low.

1). Churches should help women out Addressing the effects of of their poverty and help them Structural Adjustment achieve fulfilling lives. 2). Programs on female Government agencies should also Kamara, E. K. reproductive health in None No theory rethink policies that promote (1997). Kenya. Case study mentioned mentioned women's reproductive health. 1). The mean age difference between nonmarital sexual partners was 5.5 years, and 47% of men's female Confronting the sugar daddy partners were adolescents. 2). 14% stereotype and addressing of partnerships involved an age the age and economic difference of at least 10 years, 23% asymmetries and risky 1,614 recent involved more than the mean amount sexual behavior in urban non-marital No theory of male-to-female material Luke, N. (2005). Kenya. 1,052 men partnerships mentioned assistance. 1). The use of modern contraceptive methods, especially long-term methods, is higher in urban areas

165 165 than rural areas, whereas, the pattern is reversed for traditional methods. 2). Use of barrier methods among unmarried women is steadily rising but remains relatively low in the wake of the HIV/AIDS epidemic. 3). Higher use of injectables especially among rural women, whose To address the trends and Data from husbands disapprove of family Magadi, M. A., & determinants of KDHS 1989, planning, uneducated women, less Curtis, S. L. contraceptive method choice 1993, and None No theory exposed to media messages, (2003). in Kenya. 1998. mentioned mentioned compared to their Arab counterparts. To address the poverty and politics of population in Haiti, address broader issues affecting women beyond the Government and donor agencies individual level; aligning Political need to provide more support for reproductive rights with the Economy of family planning, healthcare, to Maternowska, M. fight for human rights and Focus groups, None Fertility increase employment, and to reduce C. (2006). dignity case study mentioned Framework poverty.

To increase engagement to combat HIV/AIDS through Improve fight against HIV/AIDS, and supporting a multisectorial reproductive health. Increase approaching the hardest hit awareness and funding to fight areas in Sub-Saharan poverty and unemployment. To Africa. To advocate for Political and create a social health insurance HIV/AIDS control measures Socio- program to help the poor have Milkowski, A. to be integral parts of a None economic affordable and accessible health and (2004). country's programs. Case study mentioned Framework social services. This study explores The rhetorical devices, moralizing students’ narratives and social scripts and dubious health discourses about adolescent claims about pregnancy and abortion abortion in students’ online narratives Mitchell, E. M. H., elicited via internet-based mirror the tenor and content of their Halpern, C. T., open-ended questions academic curricula as well Kamathi, E. M., & posed in response to a None No theory as Kenyan media presentation of the Owino, S. (2006). cartoon vignette. 614 students mentioned mentioned issue 1) encourage girls to act as a group

166 166 to discuss problems, support each other and learn about their rights 2) hold school-based workshops with teachers and parents to raise awareness about abuse and to develop school-based action plans to address it 3) invite girls who have dropped out of school because of pregnancy to talk about the difficulties they now face 4) create a helpline and/or message box at regional ministries for pupils to report abuse Leach, F., & Addressing the sugar daddy 5) include awareness raising and Machakanja, P. trap and how peer pressure None No theory discussion of ethical behavior in all (2001) pushes girls into sex. Case study mentioned mentioned pre- and in-service teacher training

1). Respondents in Central province are supportive of fertility innovations. 2). Community wide networks are not as influential in directing fertility 1). A total of behavior as direct family members 591 individuals, are. 3). Family members are at the 323 Women forefront of encouraging behavior and 268 Men in adjustment. 4). Not all social Situating the role of Murang'a.2). A networks had significantly influence geography and interaction total of 434 contraceptive use, interacting with with significant others, people 234 healthcare workers, family planning community and family in women and Social networks regarding contraceptive Musalia, J. M. understanding Kenya's 268 men in None Networks use, influenced contraception in both (2005). fertility. Kakamega Mentioned Theory regions. 1977-1978 1). A substantial increase in Kenya Fertility contraceptive use occurred in Kenya Survey and over the decade. 2). There is an Examining the trends and 1989 Kenya increase in better educated women 167 167 determinants of Demographic None Social Change and in the proportion of those who Njogu, W. (1991). contraceptive use in Kenya. Health Survey. Mentioned Theory want to cease child bearing. 1). Findings from the study suggest that, despite knowledge of the protective value of condoms and other contraceptives, the use of these methods by girls is hampered by inability to access them, the fear of the side effects of contraceptives, and the desire by girls to remain faithful to their religious calling. 2). Most girls also resort to the use of traditional methods such whose potency and efficacy is unproven. 3). Address the levels of These findings suggest the need to knowledge concerning the 8 focus groups make condoms more easily protective value of condoms consisting of Social accessible to girls in rural areas, and Nzioka, C. and other contraceptives of 15-19 year old None Constructionist also for education in the proper use (2004). young women. girls. mentioned Perspective of ‘natural’ family planning methods.

4).Young women may also benefit from training in how to be more assertive in sexual negotiations.

Obiero, C. O., Nyagero, J. M., Mwikali, T., Tendo, W., Nyamongo, M., & Omurwa, T. (2000). 1). Condoms are accepted by 93.2%. 2). About 50% of the student 168 168 population believe that condoms are meant for single people while 21.1% believe it's for those who are promiscuous. 3). Correct and To determine the impact that consistent use of condoms 39.5% the perceived threat of together with abstinence 49.6% is by HIV/AIDS has on condom far the most effective way of Omungo, P. A. use at the University of 337 university None Health Belief preventing pregnancy and disease in (2008). Nairobi. students mentioned Model a relationship. 1). The media was responsible for reporting perspectives of the reproductive-rights groups and the anti-choice group. 2).Governments Addressing the human rights should increase advocacy efforts and Onyango, S., & violations and abortion rights None No theory funding in supporting reproductive Mugo, C. (2008). in Kenya. Case study mentioned mentioned rights' groups.

To address the history and effects of race and oppression on reproductive Government should invest more in health services during the Political the rural areas by proving better pre-apartheid period and the None Economy of housing, more employment, and Salo, E. (2002). transition to democracy. Case study mentioned Race better education. 1). Western countries impose their An analysis of how politics of ideas on indigenous people and reproduction shape eliminate alternative meanings to indigenous women's family reproductive health. 2). Doctors and planning choices., Political nurses should communicate reproductive rights within a Economy of information on programs and policies Smith-Oka, V. larger struggle for human None Fertility without judgment of a woman's (2009). rights and dignity. 53 women mentioned Framework socioeconomic status. Stephenson, R., Baschieri, A., Aspects of a community's Clements, S., To identify factors sociocultural and economic Hennink, M., & contributing to geographic Six countries environment influence a woman's

169 169 Madise, N. variations in women's use of from Sub- None None use of modern contraceptive (2007). modern contraceptives. Saharan Africa mentioned mentioned methods. Both males and females who rejected myths about HIV transmission, those who experienced less sexual pressure and those who did not know anyone who had died of AIDS, as well as males who had a stronger belief in their Tenkorang, E. Y., To examine the ability to abstain, were more likely to & Maticka- postponement of first sex as Information postpone sexual intercourse than Tyndale, E. a strategy to prevent Standard 6 and Motivation were young people who lacked those (2008). infection. 8,183 students 7 PUPILS Behavioral Skill characteristic

1). Findings revealed that nurse- midwives disapproved of adolescent sexual activity, including The aim of this study was to masturbation, contraceptive use and investigate abortion, but also had a pragmatic attitudes among Kenyan and attitude Warenius, L. U., Zambian nurse-midwives to handling these issues. 2). Those Faexlid, E. A., toward adolescent sexual with more education and those who Chishimba, P. N., and had received continuing education on Musandu, J. O., reproductive health adolescent sexuality and Ong'any, A. A., & problems, in order to reproduction showed a tendency Nissen, E. B. M. improve services for Kenya and No theory towards more youth friendly (2006). adolescents 820 nurses Zambia mentioned attitudes.

170 170

APPENDIX B

THE HISTORY OF FAMILY PLANNING POLICY AND PROGRAMS IN KENYA

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1958 Local Family Planning associations receive international financial assistance from the

Pathfinder Fund

1960 Local family planning associations open clinics to serve multiracial populations in Nairobi

and Mombasa.

1962 Family Planning Association of Kenya (FPAK) formed and becomes affiliated with the

International Planned Parenthood Federation.

The first post-independence census reveals an extremely high population growth

1965 Sessional Paper Number 10 is issued and calls for moderating the rate of population

growth.

The government invites the Population Council to send an advisory mission on population

policy.

1966 The Ministry of Health issues a circular to provincial and district medical officers

announcing the establishment of the national family planning program.

1967 Government of Kenya’s first population policy. Contraceptive services and Information,

Education, and Communication (IEC) mainly provided by the private sector Ministry of

Health issues a second circular on family planning, stipulating that family planning

providers should be trained and that services should be offered free of charge.

1969 A new census indicates that the rate of population growth is extremely high.

1971 The government asks the World Bank to help develop a nationwide family planning

program.

1975 The government launched a 5 year Family Planning Program (1975-1979) with an overall

budget of $39 million.

1979 A new census indicated that the annual population growth was approaching 4 Percent.

1982 The National Council for Population and Development was formed in the Office of the

Vice President 1984.

First National Leader’s Population Conference in Nairobi.

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1994 United Nation’s International Conference on Population and Development conference,

Cairo, Egypt.

1996 NCPD published National Population Advocacy and IEC Strategy for Sustainable

Development 1996-2010.

1996 Choice on Termination of Pregnancy Act.

1997 National Reproductive Health Strategy published.

2000 NCPD published the second Population Policy for Sustainable Development.

2001-2005 Condom Policy and Strategy Initiated.

2003 Kenya Demographic Health Survey (KDHS) generates declining indicators on fertility.

2004 NCPD became the National Coordinating Agency for Population and Development

(NCAPD) under the Ministry of Planning and Economic Development.

2004 Reproductive Health and Rights Alliance (RHRA).

2005 Kenyan government allocates funding for family planning programs and services for the

first time in Kenya’s history.

2007 Kenya National Reproductive Health Policy Published.

Sources: Blacker et al., 2005; Aloo-Obunga, 2000; NCAPD, 2010; Crichton, 2008; Heisel, 2007; Milkowski, 2004; Onyango & Mugo, 2008.

173

APPENDIX C

KDHS QUESTIONS REGARDING CONTRACEPTION AND CONTRACEPTIVE USE IN 1998 AND 2008-2009

174

Dependent Variables

Contraception

1998 2008 Yes No

301 301 Now I would like to talk about family planning the various ways or methods a couple can use to delay or avoid a pregnancy.

07 01 FEMALE STERILIZATION Women 1 2 Have you ever can have an operation to avoid had an operation having any more children. to avoid having any more children? Yes 1 No 2

08 02 MALE STERILIZATION Men can 1 2 Have you ever have an operation to avoid having had a partner any more children. who had an operation to avoid having any more children? Yes 1 No 2

01 03 PILL Women can take a pill every 1 2 day to avoid becoming pregnant.

02 04 IUD Women can have a loop or 1 2 coil placed inside of them by a doctor or a nurse.

03 05 INJECTABLES Women can have 1 2 an injection by a health provider that stops them from becoming pregnant for one or more months.

04 NORPLANT Women can have several small rods placed in their upper arm by a doctor or nurse which can prevent pregnancy for one or more years.

06 IMPLANTS Women can have 1 2 several small rods placed in their upper arm by a doctor or nurse which can prevent pregnancy for one or more years.

05 None DIAPHRAGM, FOAM, JELLY Women can place a sponge, suppository, diaphragm, jelly, or

175

cream inside themselves before intercourse.

06 07 CONDOM. Men can put a rubber 1 2 sheath on their penis

08 FEMALE CONDOM Women can 1 2 place a sheath in their vagina before sexual intercourse.

09 LACTATIONAL AMENORRHEA 1 2 METHOD (LAM)

09 NATURAL METHODS Every month that a woman is sexually active she can avoid pregnancy by not having sexual intercourse on the days of the month she is most likely to get pregnant.

10 RHYTHM METHOD Every month 1 2 that a woman is sexually active she can avoid pregnancy by not having sexual intercourse on the days of the month she is most likely to get pregnant.

10 11 WITHDRAWAL Men can be careful 1 2 and pull out before climax.

12 EMERGENCY CONTRACEPTION 1 2 As an emergency measure after unprotected sexual intercourse, women can take special pills at any time within five days to prevent pregnancy.

11 13 Have you heard of any other ways 1 2 or methods that women or men can use to avoid pregnancy? 1 2

305 304 Have you ever used anything or 1 2 tried in any way to delay or avoid getting pregnant?

309 307 Now I would like to ask you about Number of Children the first time that you did something or used a method to avoid getting pregnant. How many living children did you have at the time, if any? IF NONE, RECORD “00”

307 What have you used or done?

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308 Now I would like to ask you about Pills 01 the first time that you did IUD 02 something or used a method to Injections 03 avoid getting pregnant. Norplant 04 What was the first method you ever Diaphragm/Foam/Jelly 05 used? Condom 06 Female Sterilization 07 Male Sterilization 08 Natural Methods 09 Withdrawal 10 Other (Specify) 96 Yes No

313 310 Are you currently doing something 1 2 or using any method to delay or avoid getting pregnant?

314 Which method are you using? Pills 01 IUD 02 Injections 03 Norplant 04 Diaphragm/Foam/Jelly 05 Condom 06

314A Circle “07” for Female Sterilization Female Sterilization 07 Male Sterilization 08 Natural Methods 09 Withdrawal 10 Other (Specify) 96

311 Which method are you using? Female Sterilization A Circle all mentioned Male Sterilization B If more than one method Pills C mentioned, follow skip instruction IUD D for highest method in list. Injectables E

311A Circle “A” for Female Sterilization Implants F Condom G Female Condom H Lactational I Amenorrhea (LAM) L Rhythm Method M Withdrawal X Other (Specify)

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323 None How do you determine which days of Based on Calendar 01 your monthly cycle not to have sexual Based on Body 02 relations? Temperature Based on Cervical 03 Mucus (Billings Method) Based on Body Temperature and 04 Cervical Mucus No Specific System Other (Specify) 05 96

Questions From the KDHS Questionnaires on Education, Type of Place of Residence, Age at Marriage, and Income for Survey Periods 1998 and 2008-2009

Independent Variables

1998 2008 Education

107 108 Have you ever attended school? Yes 1 No 2

108 109 What is the highest level of school you attended; Primary 1 primary, secondary, or higher? Secondary 2 Higher 3

109 110 What is the highest (standard/form/year) Standard/Form/Year __ completed at that level Are you currently attending school? Yes 1 111 None5 No 2

113 Have you ever participated in a literacy program Yes 1 or any other program that involves learning to No 2 read or write (not including primary school)? Type of Residence: Rural vs. Urban

102 102 First I would like to ask some questions about Nairobi/Mombasa 1 you and your household. For most of the time Other City/Town 2 until you were 12 years old, did you live in Countryside 3 Nairobi or Mombasa, in another town or city, or in the countryside?

103 103 How long have you been living continuously in Years (Name of Current Place of Residence)? Always 95 Visitor 96

104 104 Just before you moved here, did you live in Nairobi/Mombasa 1 Nairobi or Mombasa, in another city or town, or Other City/Town 2 in the countryside? Countryside 3

178

121 None Now I would like to ask about the place in which Nairobi/Mombasa 1 you usually live. Do you usually live in Nairobi or Other City/Town 2 Mombasa, another town or city, or in the Countryside 3 countryside

122 None In which district is that? District______(Print District Name)

Income

708 Does/did your husband/partner work mainly on His Land 1 his own land or on family land, pr does/did he Family Land 2 rent land, or does/did he work on someone else’s Rented Land 3 land? Someone Else’s Land 4

709 Aside from your own housework, are you Yes 1 currently working? No 2

710 808 As you know some women take up jobs for which they are paid in cash or kind. Others sell things, have a small business or work on the family farm or in the family business. Are you Yes 1 currently doing any of these things or any other No 2 work?

711 810 Have you done any work in the last 12 months? Yes 1 No 2

712 811 What is your occupation, that is, what kind of work do you mainly do?

713 None Works in agriculture Does not work in agriculture

714 813 Do you work mainly on your own land or on Own Land 1 family land, or do you rent land, or work on Family Land 2 someone else’s land? Rented Land 3 Someone Else’s Land 4

715 814 Do you do this work for a member of your family, For Family Member 1 for someone else, or are you self-employed? For Someone Else 2 Self-employed 3

716 816 Do you usually work throughout the year, or do Throughout the year 1 you work seasonally, or only once in a while? Seasonally/Part of the year2 Once in a while 3

817 Are you paid in cash or kind for this work or are Cash Only 1 you not paid at all? Cash and Kind 2 In Kind Only 3 Not Paid 4

720 None Do you earn cash for your work? Yes 1 PROBE: Do you make money for working? No 2

179

721 None How much do you earn for this work per month? Is it less than 1,000 shillings? Less than 1,000 1 1,000-5,000 shillings? 1,000-5,000 2 5,000-10,000 shillings? 5,000-10,000 3 Or more than 10,000 shillings? More than 10,000 4

723 815 Do you usually work at home or away from Yes 1 home? No 2

Age at Marriage

502 Are you currently married or living with a man? Yes, Currently Married 1 No, Living with a Man 2 No, Not in Union 3

601 Are you currently married or living together with a Yes, Currently Married 1 man as if married? Yes, Living with a Man 2 No, Not in Union 3

504 Have you ever been married or lived with a man? Yes, Formerly Married 1 Yes, Lived with a Man 2 No 3

602 Have you ever been married or lived together Yes, Formerly Married 1 with a man as if married? Yes, Lived with a Man 2 No 3

513 How old were you when you started living with Age him?

616 How old were you when you first started living Age with him?

None 616A When you got married or lived with a man, was it Own Choice 1 your choice or was it arranged? Arranged 2

None 616B When you first got married or lived with a man, Older 1 was the man older than you, younger than you, Younger 2 or the same age as you? About the same age 3 Don’t Know/Don’t Remember8

None 616C Would you say this person was ten or more Ten or more years older 1 years older than you or less than ten years older Less than ten years older2 than you? Older, unsure how much3

Questions from the KDHS Questionnaires on Access to Media, Religion, Type of Union, and Total Number of Children for Survey Periods 1998 and 2008-2009

Control Variables

Access to Media

114 None Can you read and understand a letter or Easily 1 newspaper easily, with difficulty, or not at all? With difficulty 2

180

Not at all 3

115 Do you usually read a newspaper or magazine at Yes 1 least once a week? No 2

115 Do you read a newspaper or magazine almost Almost every day 1 every day, at least once a week, less than once a At least once a week 2 week, or not at all? Less than once a week 3 Not at all 4

116 Do you usually listen to a radio every day? Yes 1 No 2

116 Do you listen to the radio almost every day, at least Almost every day 1 once a week, less than once a week, or not at all? At least once a week 2 Less than once a week 3 Not at all 4

117 Do you usually watch television once a week? Yes 1 No 2

117 Do you watch television almost every day, at least Almost every day 1 once a week, less than once a week, or not at all? At least once a week 2 Less than once a week 3 Not at all 4

Yes No 616 In the last six months have you heard about family planning? Radio 1 2 On the radio? Television 1 2 On the television? Newspaper or 1 2 In a newspaper or magazine? Magazine 1 2 From a billboard? Billboard 1 2 At a live drama? Live Drama 1 2 At a community event? Community 1 2 Event

Yes No 715 In the last few months have you, 1 2 Heard about family planning on the radio? Heard about family planning on the television? 1 2 Have you heard of family planning in a newspaper or magazine? 1 2

Religion

118 118 What is your religion? Catholic 1 Protestant/Other Christian 2 Muslim 3 No religion 4 Other (specify) 6

Type of Union

181

502 Are you currently married or living with a man? Yes, Currently Married 1 No, Living with a Man 2 No, Not in Union 3

601 Are you currently married or living together with a Yes, Currently Married 1 man as if married? Yes, Living with a Man 2 No, Not in Union 3

503 Do you currently have a regular sexual partner, an Regular Sexual Partner 1 occasional sexual partner, or no sexual partner at Occasional Sexual Partner 2 all? No Sexual Partner 3

504 Have you ever been married or lived with a man? Yes, Formerly Married 1 Yes, Lived with a Man 2 No 3

602 Have you ever been married or lived together with Yes, Formerly Married 1 a man as if married? Yes, Lived with a Man 2 No 3

506 603 What is your marital status now, are you widowed, Widowed 1 divorced, or separated? Divorced 2 Separated 3

507 604 Is your husband/partner living with you now or is he Living with Me 1 staying elsewhere? Staying Elsewhere 2

508 Does your husband/partner have other wives Yes 1 besides you? No 2

None 606 Does your husband/partner have other wives or Yes 1 does he live with other women as if married? No 2 Don’t Know 8

509 How many other wives does he have? Number Don’t Know 98

607 Including yourself, in total, how many wives or Total number of wives and live-in partners does your husband live with now as if partners married? Don’t Know 98

None 608 Are you the first, second……wife? Rank

511 609 Have you been married or lived with a man only Once 1 once or more than once? More than once 2 Number of Children

201 201 Now I would like to ask about all the births you Yes 1 have had during your life. Have you ever given No 2 birth?

202 202 Do you have any sons or daughters to whom you Yes 1 have given birth who are now living with you? No 2

182

203 203 How many sons live with you? Sons at home And how many daughters live with you? Daughters at home If none, record “00”

204 204 Do you have any sons or daughters to whom you Yes 1 have given birth who are alive but are not living No 2 with you?

205 205 How many sons are alive but do not live with you? Sons Elsewhere And how many daughters are alive but do not live Daughters Elsewhere with you? If none, record “00”

183

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BIOGRAPHICAL INFORMATION

Diana Alaka Opollo completed her undergraduate studies in psychology in 2005 from the

University of Texas at Arlington. She pursued her master's degree and graduated in 2007 and begun her doctoral studies in 2008 both at the University of Texas at Arlington, school of social work. Her teaching interests are social welfare policy, community and administrative practice, generalist macro practice, program evaluation, research I and II, statistics, and human behavior and diverse populations. Her research interest areas are women's reproductive health (HIV/AIDS, Contraception), international social work education, program evaluation of INGO's, technology use in human services, social capital and resource development, immigrants and refugee's resiliency and coping mechanisms, and social policy analysis and research. She hopes to charter a career path in academia with a heavy focus on research.

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