DHS ANALYTICAL 

STUDIES 42

Trends in Marriage and 

Contraception in Sub-Saharan Africa:

A Longitudinal Perspective on

Factors of Fertility Decline

JANUARY 2014

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Michel Garenne, IRD, UMI Résiliences, Paris, France; Institut Pasteur, Epidémiologie des Maladies Emergentes, Paris, France; 

Witwatersrand University, Johannesburg, South Africa.

DHS Analytical Studies No. 42 

Trends in Marriage and Contraception in Sub-Saharan Africa: 

A Longitudinal Perspective on Factors of Fertility Decline 

Michel Garenne 

ICF International 

Rockville, Maryland, USA 

January 2014 

Corresponding author: Michel Garenne, IRD, UMI Résiliences, Paris, France; Institut Pasteur, 

Epidémiologie des Maladies Emergentes, Paris, France; Witwatersrand University, Johannesburg, South 

Africa. Email: Michel.Garenne@pasteur.fr 
Acknowledgment: The author acknowledges the support of USAID for this study, and would like to thank 

the reviewers, in particular Dr. John Casterline, as well as the text editor for numerous comments, 

corrections, and suggestions.

Editor: Bryant Robey 

Document Production: Yuan Cheng 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through the MEASURE DHS project (#GPO-C-00-08-00008-00). The views 

expressed are those of the authors and do not necessarily reflect the views of USAID or the United States 

Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Additional information can be obtained by contacting The 

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850; Telephone: 301-

407-6500; Fax: 301-407-6501; E-mail: reports@DHSprogram.com; Internet: www. DHSprogram.com. 

Recommended citation: 

Garenne, Michel. 2014. Trends in Marriage and Contraception in Sub-Saharan Africa: A Longitudinal 

Perspective on Factors of Fertility Decline. DHS Analytical Studies No. 42. Rockville, Maryland, USA: 

ICF International. 
iii 

Contents 

List of Tables ............................................................................................................................................. vii 

List of Figures ............................................................................................................................................. ix 

Preface ........................................................................................................................................................ xv 

Executive Summary ................................................................................................................................ xvii 

1. Introduction ......................................................................................................................................... 1 

1.1 Fertility Changes ............................................................................................................................ 1 

1.2 Economic Development and Social Change ................................................................................... 1 

1.3 Change in Marriage Patterns ......................................................................................................... 1 

1.4 Family Planning Programs and Contraceptive Use ....................................................................... 2 

1.5 Context of the Study ........................................................................................................................ 2 

2. Framework for Analysis ..................................................................................................................... 3 

2.1 Theoretical Framework of Proximate Determinants ...................................................................... 3 

2.2 Fertility Trends in Africa ................................................................................................................ 4 

2.3 Marriage and Fertility .................................................................................................................... 4 

2.4 Contraception and Fertility ............................................................................................................ 5 

2.5 Period and Cohort Effects .............................................................................................................. 5 

2.6 Longitudinal versus Cross-Sectional Approach ............................................................................. 5 

2.7 Socioeconomic Correlates of Marriage and Contraception ........................................................... 6 

2.8 Objectives of the Study .................................................................................................................... 6 

2.9 Open Questions ............................................................................................................................... 6 

3. Data and Methods ............................................................................................................................... 7 

3.1 Survey Data .................................................................................................................................... 7 

3.2 Available Variables ......................................................................................................................... 7 

3.3 Data Issues .................................................................................................................................... 10 

3.4 Trends in Cohort Age at Marriage ............................................................................................... 10 

3.5 Converting Period Estimates of Contraceptive Use into Cohort Estimates ................................. 10 

3.6 Estimating the Average Age Pattern of Contraceptive Use .......................................................... 11 

3.7 Period Estimates Reconstructed from Cohort Data ..................................................................... 11 

3.8 Other Data Used ........................................................................................................................... 12

 
iv 

4. Trends in Age at Marriage ............................................................................................................... 13 

4.1 Long-Term Trends in Cohort Age at Marriage ............................................................................ 13 

4.2 Country Variations in Age at Marriage since 1940 ...................................................................... 13 

4.3 Reversals in Age at Marriage Trends ........................................................................................... 14 

4.4 Period Trends in Age at Marriage ................................................................................................ 15 

4.5 Trends in Fertility and in Age at Marriage .................................................................................. 16 

4.6 Reversal in Age at Marriage and Fertility Trends ........................................................................ 17 

4.7 Effect of Severe Crises on Age at Marriage and Fertility............................................................. 17 

4.8 Overall Trends in Age at Marriage and in Education .................................................................. 18 

4.9 Diversity in Dynamics of Age at Marriage and Level of Education ............................................. 19 

4.10 Correlation between Changes in Age at Marriage and Changes in GDP .................................. 20 

4.11 Conclusions on Trends in Age at Marriage ................................................................................ 22 

5. Trends in Contraception .................................................................................................................. 23 

5.1 Age Pattern of Ever-Use of Contraception ................................................................................... 23 

5.2 Reconstructed Cohort Trends in Ever-Use of Contraception ....................................................... 24 

5.3 Trends in Period Ever-Use of Contraception ............................................................................... 24 

5.4 Trends in Fertility and Contraception .......................................................................................... 25 

5.5 Stagnation of Contraceptive Use and Fertility Stalls ................................................................... 26

5.6 Conclusions on Contraceptive Use ............................................................................................... 27 

6. Synthesis on the Longitudinal Relationships of Age at Marriage and Contraception on 

Fertility .............................................................................................................................................. 29 

6.1 Basic Regression Model ................................................................................................................ 29 

6.2 Country Heterogeneity .................................................................................................................. 30 

6.3 Full model with Extent of Contraception and Premarital Fertility .............................................. 30 

6.4 Univariate versus Multivariate Analysis ...................................................................................... 31 

7. Discussion........................................................................................................................................... 33 

7.1 Contraceptive Use ......................................................................................................................... 33 

7.2 Missing Information on Abortion.................................................................................................. 33 

7.3 Other Proximate Determinants ..................................................................................................... 33 

7.4 Marriage Trends ........................................................................................................................... 34 

7.5 Relation with Individual Data Analysis ........................................................................................ 34 

7.6 Conclusion .................................................................................................................................... 34 

References .................................................................................................................................................. 35 
v 

Appendix A: Surveys Included in the Study .......................................................................................... 41 

Appendix B: Country Studies .................................................................................................................. 43 

Presentation of the Country Studies ........................................................................................................ 43 

1. Benin ............................................................................................................................................... 44 

2. Burkina Faso .................................................................................................................................. 47 

3. Burundi ........................................................................................................................................... 50 

4. Cameroon ....................................................................................................................................... 53 

5. Central African Republic ................................................................................................................ 56 

6. Chad ................................................................................................................................................ 59 

7. Congo-Kinshasa (Democratic Republic of Congo) ........................................................................ 62 

8. Congo-Brazzaville (Republic of Congo) ......................................................................................... 65 

9. Côte d’Ivoire ................................................................................................................................... 68 

10. Ethiopia ......................................................................................................................................... 71 

11. Gabon ........................................................................................................................................... 74 

12. Ghana ........................................................................................................................................... 74 

13. Guinea ........................................................................................................................................... 80 

14. Kenya ............................................................................................................................................ 83 

15. Lesotho .......................................................................................................................................... 86 

16. Liberia ........................................................................................................................................... 90 

17. Madagascar .................................................................................................................................. 93 

18. Malawi .......................................................................................................................................... 96 

19. Mali ............................................................................................................................................... 99 

20. Mozambique ................................................................................................................................ 102 

21. Namibia ....................................................................................................................................... 105 

22. Niger ........................................................................................................................................... 108 

23. Nigeria ........................................................................................................................................ 111 

24. Rwanda ....................................................................................................................................... 114 

25. Senegal ........................................................................................................................................ 119 

26. Sierra Leone ................................................................................................................................ 114 

27. South Africa ................................................................................................................................ 119 

28. Swaziland .................................................................................................................................... 129 

29. Tanzania ..................................................................................................................................... 132 

30. Togo ............................................................................................................................................ 135 

31. Uganda ....................................................................................................................................... 138 
vi 

32. Zambia ........................................................................................................................................ 141 

33. Zimbabwe .................................................................................................................................... 144 
vii 

List of Tables 

Table 1. Basic framework of proximate determinants of fertility decline in Africa ................................... 3 

Table 2. List of contraceptive methods recorded in African DHS surveys ................................................ 9 

Table 3. Distribution of African countries according to the type of correlation between dynamics of 

level of education and dynamics of age at marriage ................................................................... 20 

Table 4. Cases of reversal in age-standardized contraceptive use between successive surveys ............... 27 

Table 5. Results from the regression analysis linking fertility with ever-use of modern 

contraception and change in age at marriage (aggregate data) ................................................... 30 

Table 6. Results from the regression analysis linking fertility with contraceptive use, age at 

marriage, and confounding factors (aggregate data, urban and rural combined) ....................... 31 

Table A-1. List of DHS and related surveys included in the study ........................................................... 41 

Table B-1.1. Survey characteristics, Benin ............................................................................................... 44 

Table B-2.1. Survey characteristics, Burkina Faso ................................................................................... 47 

Table B-3.1. Survey characteristics, Burundi ........................................................................................... 50 

Table B-4.1. Survey characteristics, Cameroon ........................................................................................ 53 

Table B-5.1. Survey characteristics, Central African Republic ................................................................ 56 

Table B-6.1. Survey characteristics, Chad ................................................................................................ 59 

Table B-7.1. Survey characteristics, Congo-Kinshasa .............................................................................. 62 

Table B-8.1. Survey characteristics, Congo-Brazzaville .......................................................................... 65 

Table B-9.1. Survey characteristics, Côte d’Ivoire ................................................................................... 68 

Table B-10.1. Survey characteristics, Ethiopia ........................................................................................... 71 

Table B-11.1. Survey characteristics, Gabon .............................................................................................. 74 

Table B-12.1. Survey characteristics, Ghana .............................................................................................. 77 

Table B-13.1. Survey characteristics, Guinea ............................................................................................. 80 

Table B-14.1. Survey characteristics, Kenya .............................................................................................. 83 

Table B-15.1. Survey characteristics, Lesotho ........................................................................................... 86 

Table B-16.1. Survey characteristics, Liberia ............................................................................................. 90 
viii 

Table B-17.1. Survey characteristics, Madagascar ..................................................................................... 93 

Table B-18.1. Survey characteristics, Malawi ............................................................................................ 96 

Table B-19.1. Survey characteristics, Mali ................................................................................................. 99 

Table B-20.1. Survey characteristics, Mozambique ................................................................................. 102 

Table B-21.1. Survey characteristics, Namibia ......................................................................................... 105 

Table B-22.1. Survey characteristics, Niger ............................................................................................. 108 

Table B-23.1. Survey characteristics, Nigeria .......................................................................................... 111 

Table B-24.1. Survey characteristics, Rwanda ......................................................................................... 114 

Table B-25.1. Survey characteristics, Senegal .......................................................................................... 119 

Table B-26.1. Survey characteristics, Sierra Leone .................................................................................. 123 

Table B-27.1. Survey characteristics, South Africa .................................................................................. 126 

Table B-28.1. Survey characteristics, Swaziland ...................................................................................... 129 

Table B-29.1. Survey characteristics, Tanzania ........................................................................................ 132 

Table B-30.1. Survey characteristics, Togo .............................................................................................. 135 

Table B-31.1. Survey characteristics, Uganda .......................................................................................... 138 

Table B-32.1. Survey characteristics, Zambia .......................................................................................... 141 

Table B-33.1. Survey characteristics, Zimbabwe ..................................................................................... 144 
ix 

List of Figures 

Figure 1. Empirical relationships between proportions ever-married and cohort median age at first 

marriage in African DHS surveys ............................................................................................... 8 

Figure 2. Reconstructed trends in cohort median age at marriage, 33 African countries ......................... 13 

Figure 3. Average change in median age at marriage since cohort 1940 (years per decade) ................... 14 

Figure 4. Reconstructed trends in median age at marriage of synthetic cohorts, 33 African countries ... 15 

Figure 5. Correlation between change in age at marriage and fertility decline, urban areas .................... 16 

Figure 6. Correlation between change in age at marriage and fertility decline, rural areas ..................... 17 

Figure 7. Dynamics of cohort age at marriage and cohort level of education, 33 African countries, 

cohorts 1930-1985 .................................................................................................................... 19 

Figure 8. Correlation between changes in income per capita and change in age at marriage .................. 21 

Figure 9. Underlying average age pattern of ever-use of modern contraception, 33 African 

countries (from linear logistic regression, after controlling for cohort) .................................... 23 

Figure 10. Underlying cohort trends of ever-use of modern contraception, net of age effect, 33 

African countries. (from linear logistic regression, after controlling for age) .......................... 24 

Figure 11. Reconstructed trends in age-standardized ever-use of modern contraception of synthetic 

cohorts, 33 African countries .................................................................................................... 25 

Figure 12. Reconstructed path in ever-use of contraception and fertility of synthetic cohorts, 33 

African countries, 1950-2005 ................................................................................................... 26 

Figure B-1.1. Reconstructed trends in median age at first marriage, Benin ............................................... 44 

Figure B-1.2. Relationship between level of education and age at first marriage, Benin ........................... 45 

Figure B-1.3. Cohort trends in age-specific ever-use of contraception, Benin ........................................... 46 

Figure B-2.1. Reconstructed trends in median age at first marriage, Burkina Faso ................................... 47 

Figure B-2.2. Relationship between level of education and age at first marriage, Burkina Faso ............... 48 

Figure B-2.3. Trends in age-specific ever-use of contraception, Burkina Faso .......................................... 49 

Figure B-3.1. Reconstructed trends in median age at first marriage, Burundi ............................................ 50 

Figure B-3.2. Relationship between level of education and age at first marriage, Burundi ....................... 51 

Figure B-3.3. Trends in age-specific ever-use of contraception, Burundi .................................................. 52 
x 

Figure B-4.1. Reconstructed trends in median age at first marriage, Cameroon ........................................ 53 

Figure B-4.2. Relationship between level of education and age at first marriage, Cameroon .................... 54 

Figure B-4.3. Trends in age-specific ever-use of contraception, Cameroon ............................................... 55 

Figure B-5.1. Reconstructed trends in median age at first marriage, Central African Republic ................ 56 

Figure B-5.2. Relationship between level of education and age at first marriage, Central African 

Republic ............................................................................................................................... 57 

Figure B-5.3. Trends in age-specific ever-use of contraception, Central African Republic ....................... 58 

Figure B-6.1. Reconstructed trends in median age at first marriage, Chad ................................................ 59 

Figure B-6.2. Relationship between level of education and age at first marriage, Chad ............................ 60 

Figure B-6.3. Trends in age-specific ever-use of contraception, Chad ....................................................... 61

Figure B-7.1. Reconstructed trends in median age at first marriage, Congo-Kinshasa .............................. 62 

Figure B-7.2. Relationship between level of education and age at first marriage, Congo-Kinshasa .......... 63 

Figure B-7.3. Trends in age-specific ever-use of contraception, Congo-Kinshasa ..................................... 64 

Figure B-8.1. Reconstructed trends in median age at first marriage, Congo-Brazzaville ........................... 65 

Figure B-8.2. Relationship between level of education and age at first marriage, Congo-Brazzaville ...... 66 

Figure B-8.3. Trends in age-specific ever-use of contraception, Congo-Brazzaville ................................. 67 

Figure B-9.1. Reconstructed trends in median age at first marriage, Côte d’Ivoire ................................... 68 

Figure B-9.2. Relationship between level of education and age at first marriage, Côte d’Ivoire ............... 69 

Figure B-9.3. Trends in age-specific ever-use of contraception, Côte d’Ivoire .......................................... 70 

Figure B-10.1. Reconstructed trends in median age at first marriage, Ethiopia ......................................... 71 

Figure B-10.2. Relationship between level of education and age at first marriage, Ethiopia ..................... 72 

Figure B-10.3. Trends in age-specific ever-use of contraception, Ethiopia ................................................ 73 

Figure B-11.1. Reconstructed trends in median age at first marriage, Gabon ............................................ 74 

Figure B-11.2. Relationship between level of education and age at first marriage, Gabon ........................ 75 

Figure B-11.3. Trends in age-specific ever-use of contraception, Gabon ................................................... 76 

Figure B-12.1. Reconstructed trends in median age at first marriage, Ghana ............................................ 77 

Figure B-12.2. Relationship between level of education and age at first marriage, Ghana ........................ 78 
xi 

Figure B-12.3. Trends in age-specific ever-use of contraception, Ghana ................................................... 79 

Figure B-13.1. Reconstructed trends in median age at first marriage, Guinea ........................................... 80 

Figure B-13.2. Relationship between level of education and age at first marriage, Guinea ....................... 81 

Figure B-13.3. Trends in age-specific ever-use of contraception, Guinea .................................................. 82 

Figure B-14.1. Reconstructed trends in median age at first marriage, Kenya ............................................ 83 

Figure B-14.2. Relationship between level of education and age at first marriage, Kenya ........................ 84 

Figure B-14.3. Trends in age-specific ever-use of contraception, Kenya ................................................... 85 

Figure B-15.1. Reconstructed trends in median age at first marriage, Lesotho .......................................... 87 

Figure B-15.2. Relationship between level of education and age at first marriage, Lesotho ...................... 88 

Figure B-15.3. Trends in age-specific ever-use of contraception, Lesotho ................................................ 89 

Figure B-16.1. Reconstructed trends in median age at first marriage, Liberia ........................................... 90 

Figure B-16.2. Relationship between level of education and age at first marriage, Liberia ....................... 91 

Figure B-16.3. Trends in age-specific ever-use of contraception, Liberia .................................................. 92 

Figure B-17.1. Reconstructed trends in median age at first marriage, Madagascar ................................... 93 

Figure B-17.2. Relationship between level of education and age at first marriage, Madagascar ............... 94 

Figure B-17.3. Trends in age-specific ever-use of contraception, Madagascar .......................................... 95 

Figure B-18.1. Reconstructed trends in median age at first marriage, Malawi ........................................... 96 

Figure B-18.2. Relationship between level of education and age at first marriage, Malawi ...................... 97 

Figure B-18.3. Trends in age-specific ever-use of contraception, Malawi ................................................. 98 

Figure B-19.1. Reconstructed trends in median age at first marriage, Mali ............................................... 99 

Figure B-19.2. Relationship between level of education and age at first marriage, Mali ......................... 100 

Figure B-19.3. Trends in age-specific ever-use of contraception, Mali .................................................... 101 

Figure B-20.1. Reconstructed trends in median age at first marriage, Mozambique ................................ 102 

Figure B-20.2. Relationship between level of education and age at first marriage, Mozambique ........... 103 

Figure B-20.3. Trends in age-specific ever-use of contraception, Mozambique ...................................... 104 

Figure B-21.1. Reconstructed trends in median age at first marriage, Namibia ....................................... 105 
xii 

Figure B-21.2. Relationship between level of education and age at first marriage, Namibia ................... 106 

Figure B-21.3. Trends in age-specific ever-use of contraception, Namibia ............................................. 107 

Figure B-22.1. Reconstructed trends in median age at first marriage, Niger ............................................ 108 

Figure B-22.2. Relationship between level of education and age at first marriage, Niger ....................... 109 

Figure B-22.3. Trends in age-specific ever-use of contraception, Niger .................................................. 110 

Figure B-23.1. Reconstructed trends in median age at first marriage, Nigeria ......................................... 111 

Figure B-23.2. Relationship between level of education and age at first marriage, Nigeria .................... 112 

Figure B-23.3. Trends in age-specific ever-use of contraception, Nigeria ............................................... 113 

Figure B-24.1. Reconstructed trends in median age at first marriage, Rwanda ........................................ 115 

Figure B-24.2. Relationship between level of education and age at first marriage, Rwanda ................... 116 

Figure B-24.3. Trends in age-specific ever-use of contraception, Rwanda .............................................. 117 

Figure B-24.4. Trends in period first marriage rates, women age 14-29, Rwanda ................................... 118 

Figure B-25.1. Reconstructed trends in median age at first marriage, Senegal ........................................ 120 

Figure B-25.2. Relationship between level of education and age at first marriage, Senegal .................... 121 

Figure B-25.3. Trends in age-specific ever-use of contraception, Senegal ............................................... 122 

Figure B-26.1. Reconstructed trends in median age at first marriage, Sierra Leone ................................ 123 

Figure B-26.2. Relationship between level of education and age at first marriage, Sierra Leone ............ 124 

Figure B-26.3. Trends in age-specific ever-use of contraception, Sierra Leone ....................................... 125 

Figure B-27.1. Reconstructed trends in median age at first marriage, South Africa ................................ 126 

Figure B-27.2. Relationship between level of education and age at first marriage, South Africa ............ 127 

Figure B-27.3. Trends in age-specific ever-use of contraception, South Africa ....................................... 128 

Figure B-28.1. Reconstructed trends in median age at first marriage, Swaziland .................................... 129 

Figure B-28.2. Relationship between level of education and age at first marriage, Swaziland ................ 130 

Figure B-28.3. Trends in age-specific ever-use of contraception, Swaziland ........................................... 131 

Figure B-29.1. Reconstructed trends in median age at first marriage, Tanzania ...................................... 132 

Figure B-29.2. Relationship between level of education and age at first marriage, Tanzania .................. 133 
xiii 

Figure B-29.3. Trends in age-specific ever-use of contraception, Tanzania ............................................. 134 

Figure B-30.1. Reconstructed trends in median age at first marriage, Togo ............................................ 135 

Figure B-30.2. Relationship between level of education and age at first marriage, Togo ........................ 136 

Figure B-30.3. Trends in age-specific ever-use of contraception, Togo ................................................... 137 

Figure B-31.1. Reconstructed trends in median age at first marriage, Uganda ........................................ 138 

Figure B-31.2. Relationship between level of education and age at first marriage, Uganda .................... 139 

Figure B-31.3. Trends in age-specific ever-use of contraception, Uganda ............................................... 140 

Figure B-32.1. Reconstructed trends in median age at first marriage, Zambia ......................................... 141 

Figure B-32.2. Relationship between level of education and age at first marriage, Zambia .................... 142 

Figure B-32.3. Trends in age-specific ever-use of contraception, Zambia ............................................... 143 

Figure B-33.1. Reconstructed trends in median age at first marriage, Zimbabwe .................................... 144 

Figure B-33.2. Relationship between level of education and age at first marriage, Zimbabwe ............... 145 

Figure B-33.3. Trends in age-specific ever-use of contraception, Zimbabwe .......................................... 146 

xv 

Preface 

One of the most significant contributions of The DHS Program is the creation of an internationally 

comparable body of data on the demographic and health characteristics of populations in developing 

countries. 

The DHS Comparative Reports series examines these data across countries in a comparative framework. 

The DHS Analytical Studies series focuses on analysis of specific topics. The principal objectives of both 

series are to provide information for policy formulation at the international level and to examine 

individual country results in an international context. 

While Comparative Reports are primarily descriptive, Analytical Studies provide in-depth, focused 

studies on a variety of substantive topics. The studies are based on a varying number of data sets, 

depending on the topic being examined. These studies employ a range of methodologies, including 

multivariate statistical techniques. 

The DHS Program staff, in conjunction with the U.S. Agency for International Development (USAID), 

selects the topics covered in Analytical Studies. 

It is anticipated that the DHS Analytical Studies will enhance the understanding of analysts and 

policymakers regarding significant issues in the fields of international population and health. 

Sunita Kishor 

Director, The DHS Program 

xvii 

Executive Summary 

This study presents an analysis of the relationships among age at marriage, contraceptive use, and fertility 

in 33 sub-Saharan African countries. The Demographic and Health Surveys (DHS) Program provided all 

of the data on marriage, contraception, and fertility. Median age at marriage was defined as the age at 

which half of a cohort is ever-married. Contraceptive use was defined as ever-use of modern 

contraception by age 40. Fertility was defined as cumulated fertility by age 40. All calculations were done 

for urban and rural areas separately, for cohorts and periods. The study makes extensive use of conversion 

of cohort estimates into period estimates for reconstructing characteristics of synthetic cohorts. Period 

estimates of age at marriage were calculated from cohort estimates and age patterns of fertility. Cohort 

estimates of contraceptive use were calculated from period estimates and an estimated age pattern. 

Median age at marriage of synthetic cohorts increased by some 3.3 years between 1950 and 2005 in urban 

areas, and some 1.1 years in rural areas. Age at marriage increased in all urban areas, but in only half of 

the rural areas in countries studied. Trends in age at marriage were not always steady, and reversed in 

seven countries. Some of these reversals were associated with economic crises, others with political crises 

or civil wars. Trends in age at marriage had little correlation with trends in level of education, or with 

trends in income per capita. Fertility trends also had virtually no correlation with trends in age at marriage 

in countries studied. Trends in age at marriage could explain only a tiny fraction of fertility trends. 

Contraceptive use increased in all countries studied, earlier and more so in urban areas than in rural areas. 

Ever-use of contraception also occurred at an earlier age in urban areas than in rural areas. Trends in 

contraceptive use were highly correlated with fertility trends. Overall, in urban areas fertility was reduced 

by 49% by year 2005, while ever-use of contraception reached 49% of the population. In rural areas, 

fertility was reduced by 14% by year 2005, while ever-use of contraception reached 28% of the 

population. Contraceptive use could potentially explain all the fertility decline. However, it was 

compounded by a relatively low level of current-use, on average 43% of ever-use. 

In a multivariate analysis, trends in ever-use of contraception explained about half of fertility trends. The 

percentage was increased to about 64% when the extent of contraception was added, measured by the 

ratio of current-use to ever-use, and was reduced to about 22% in settings with low current-use of 

contraception. In contrast, whatever the model, the role of age at marriage appeared small, explaining less 

than 10% of fertility decline. The impact was further reduced by the emergence of premarital fertility, 

especially in urban areas. 

The longitudinal analysis of the relationships among trends in marriage, contraception, and fertility leads 

to different conclusions than cross-sectional analysis. While in cross-sectional analysis age at marriage 

had a significant effect on fertility at the individual level; in longitudinal analysis trends in age at 

marriage had virtually no effect on fertility trends. 

1 

1. Introduction 

1.1 Fertility Changes 

The 20th century witnessed a major decline in fertility worldwide, although fertility had started to decline 

earlier in Europe, but had not yet started to decline in some remote rural areas of sub-Saharan Africa. 

According to the United Nations Population Division, for the world as a whole, the total fertility rate 

(TFR) declined from 4.97 children per woman in 1950-1954 to 2.53 in 2005-2010. For developing 

countries together, the changes were even more dramatic over this period, from a TFR of 6.08 to 2.69, 

while in sub-Saharan Africa the changes were much smaller, from a TFR of 6.53 to 5.39 (UNPD, 2012). 

Fertility changes in developing countries are attributed to extensive use of modern contraception, which 

occurred in the context of economic development and social change, in particular rising levels of modern 

education. In this respect, sub-Saharan Africa appears as the region of the world where levels of 

contraceptive use, economic development, and education are the lowest, which could explain the slower 

or later fertility decline and the higher fertility level in 2010 (Bongaarts and Casterline, 2012; Caldwell et 

al., 1992; Caldwell, 1994; Casterline, 1999; Cleland et al., 1994; Garenne and Joseph, 2002; Garenne, 

2008; Gould and Brown, 1996; Kirk and Pillet, 1998; Lestaeghe and Jolly, 1995; Machiyama, 2010; 

Ngom and Fall, 2005; Sneeringer, 2009). 

1.2 Economic Development and Social Change 

The relationship of fertility decline with economic development is complex, but overall in developing 

countries and over a long period of time (1950-2008) both occurred simultaneously. Worldwide, fertility 

decline occurred while income per capita rose by a factor of 3.6 between 1950 and 2008. In Asia the 

impressive fertility decline occurred while income per capita rose by a factor of 7.8, while in Africa the 

smaller fertility decline occurred while income per capita doubled (Maddison, 2010). In some countries 

however, fertility declined while income per capita stagnated, or even fell, as in several African countries 

such as Côte d’Ivoire, Gabon, Liberia, Madagascar, Sierra Leone, and Zimbabwe. 

In developing countries, fertility decline usually has occurred earlier and faster in urban areas than in rural 

areas, for a variety of reasons, and in particular because of wider availability of modern contraception. 

Urban areas are also associated with higher levels of income, higher levels of education, and better access 

to mass media (Rustein, 2002; Westoff et al., 2011). 

In cross-sectional studies, the level of education is universally found to be negatively associated with 

fertility (women with more education have lower fertility), and this is true also in international 

comparisons: countries with higher levels of education tend to have lower fertility (Castro-Martin, 1995; 

Chimere-Dan, 1993; Cleland and Rodriguez, 1988; Bongaarts, 2010; Kravdal, 2002; Lloyd et al., 2000; 

United Nations, 1995 and 2007). However, the relationship between trends in level of education and 

trends in fertility appears complex (Shapiro, 2011; Garenne, 2012). Studies also found exposure to mass 

media to be closely associated with desire for fewer children and with higher levels of contraceptive use 

(Westoff et al., 2011). 

1.3 Changes in Marriage Patterns 

Marriage is often considered an important fertility determinant associated with economic development 

and social change. With economic development, age at marriage tends to rise, although the relationships 

between both are complex and remain poorly investigated (Bledsoe and Pison, 1994; van de Walle, 1968 

and 1993; Doddo, 1998). Economic development could occur for long periods of time without much 
2 

change in age at marriage, and age at marriage could change rapidly even without economic development. 

In cross-sectional studies, early marriage is associated with high fertility, and later age marriage is 

associated with lower fertility (Althaus, 1991; Burch, 1983; Quisumbing and Hallman, 2003; Smith, 

1983; Udjo, 2001; Westoff et al., 1994). However, little research has been conducted on the relationship 

between marriage trends and fertility trends in a longitudinal perspective (Hardwood-Lejeune, 2000). In 

Africa the matter is further complicated by the emergence of premarital fertility, and by new forms of 

living arrangements, such as informal cohabitation (Garenne et al., 2000; Garenne and Zwang, 2004, 

2005 and 2006; Wawire and Jensen, 2013). 

1.4 Family Planning Programs and Contraceptive Use 

Most fertility declines in developing countries appear to be closely associated with the growing use of 

modern contraception, and with family planning programs. In fact, there is no documented case of 

sustained fertility decline without extensive use of modern contraception, and this rarely occurs without 

large-scale family planning programs. 

In sub-Saharan Africa family planning clinics (public and private) and family planning programs started 

in the 1960s in selected countries, extended their reach in the 1970s, and became widespread in the 1980s, 

reaching all major urban areas, and also many rural areas. Even by year 2005, however, some rural areas 

remained poorly served. The dynamics of family planning programs and services seem quite independent 

of economic development and social change in Africa, and have been determined primarily by national 

health policies and by international aid and cooperation (Bongaarts, 2011; Cleland et al., 2010; David, 

1992; Mbacke, 1994; Ross et al., 2005). 

1.5 Context of the Study 

This study was undertaken in the context of economic development, social change, and family planning 

programs in sub-Saharan Africa. Its aim is to better understand the long-term fertility trends that have 

occurred since 1960 and their relationships with changing marriage patterns and the emergence of modern 

contraceptive use. 

 
3 

2. Framework for Analysis 

This section presents the approach followed for the study of the relationships among marriage, 

contraception, and fertility trends in sub-Saharan Africa. 

2.1 Theoretical Framework of Proximate Determinants 

The classic theoretical framework for studying fertility levels and trends is that of proximate 

determinants, presented by Kingsley Davis and Judith Blake over half a century ago (Davis and Blake, 

1956). The framework was further refined and expanded, although it remained similar in principle to the 

original version (Bongaarts, 1978; Bongaarts et al., 1984; Bongaarts and Jones, 1982; Bryant, 2007). The 

proximate determinants framework presents all the known mechanisms of exposure to conception and 

fertility regulation. 

The proximate determinant framework used for this study was adapted to the availability of data in DHS 

surveys and to the nature of the fertility transition in Africa, and is summarized in Table 1. Several points 

deserve special attention. The information on induced abortion, probably an important regulator of 

fertility in urban Africa, is not documented in DHS surveys and not available elsewhere. The cohort 

information on contraception is concentrated on ever-use of modern contraception. Current-use is also 

available at the dates of the surveys, but not in a long term longitudinal perspective. The issue of 

premarital fertility, that is, fertility before the first marriage, is important in Africa, and has been 

investigated in another report (Garenne and Zwang, 2006). In Africa, premarital fertility may compensate 

for the effect of increasing age at marriage, if women continue to have children early while marrying 

later, as in Southern Africa. This study will focus on the net effect of age at marriage, which may be 

negligible if premarital fertility remains high when age at marriage increases. 

Table 1. Basic framework of proximate determinants of fertility decline in Africa 

 Proximate determinant Confounder 

Selected Contraceptive use 

(ever-use modern methods) 

Periods of use 

(current use /ever use) 

Age at first marriage Premarital fertility 

Missing Induced abortion 

Others, changes 

assumed to be 

negligible 

Marital disruption, divorce, widowhood, spouse separation, 

coital frequency 

HIV/AIDS 

Primary and secondary infertility, puberty, menopause, 

breastfeeding, intra-uterine mortality, infertility due to emerging 

diseases 

HIV/AIDS 

The present study focuses on marriage and contraception, two of the leading proximate determinants, and 

simply ignores the other factors of natural fertility, because they are not documented (induced abortion), 

or because they are assumed to be constant or negligible. This was obviously not the case in sub-Saharan 

Africa over the years, and in particular it seems that fertility increased significantly in many countries in 

the 1950s and 1960s, probably because of a reduction in primary and secondary infertility (Garenne, 

2008). This is why some of the figures presented in this report show an increase in fertility in the early 
4 

years, despite slowly emerging contraceptive use. This later point is beyond the scope of this study and 

would require another analysis. 

Likewise, the net effect of HIV/AIDS on fertility is assumed to be small in comparison with that of 

marriage and contraception. For instance, in a population with 10% prevalence of HIV, fertility will 

plausibly be reduced by 5% because of the effect of HIV on fecundability, and probably by some 

additional small amount because of the effect on marital disruption. The effects of HIV/AIDS on fertility 

are also beyond the scope of this study. 

Nevertheless, as will be seen below, all cases of fertility decline in this study appeared primarily 

associated with increased contraceptive use, and there is no documented evidence that they were 

associated with major changes in any other known factors of natural fertility, although this last point 

remains poorly investigated with respect to induced abortion. Furthermore, fertility decline could also be 

associated with increasing age at marriage and declining proportions marrying. In fact, first marriage is 

usually the starting point for exposure to fertility, so that age at marriage and proportions marrying were 

considered as key determinants of fertility levels, at least until recently, when marriage and first birth 

have become increasingly independent of each other, and when premarital fertility emerged as a serious 

confounder. 

2.2 Fertility Trends in Africa 

Fertility trends in Africa have been the focus of synthetic work by the United Nations Population 

Division, as well as by several authors (United Nations, 2007; Bongaarts and Casterline, 2012; Casterline, 

1999; Garenne, 2008; Machiyama, 2010; Sneeringer, 2009). Without entering into details covered 

elsewhere, three features of fertility trends need to be mentioned here. First, fertility appeared to be 

increasing in the middle of the 20th century, largely because of a reduction in primary and secondary 

sterility, and in a number of cases because of lower age at marriage. Second, fertility started to decline in 

the 1960s, 1970s or 1980s depending on the country, and this decline was moderate, and differed between 

urban and rural areas in timing and speed. Third, fertility stalls occurred in a number of countries for a 

variety of reasons, and at various stages of the fertility transition (Bongaarts, 2006; Garenne 2009, and 

2011; Machiyama, 2010; Shapiro and Gebreselassie, 2008; Schoumaker, 2009). 

2.3 Marriage and Fertility 

The dynamic relationship between marriage and fertility is complex and has become even more complex 

in the recent years because marriage patterns have been changing. The fertility transition in Europe 

(roughly 1870-1930) occurred despite any large-scale change in age at marriage or proportions ever 

marrying; in fact, in the 1950s, women’s age at marriage was lower than before and proportions marrying 

were larger. In ancient Europe, as well as in 20th century United States, marriage rates could change 

rapidly for short periods of time following a crisis (famine, war, economic recession, etc.), but these 

changes were quickly followed by a recovery and did not affect long-term trends (Kirk, 1960). In Europe, 

the fertility transition occurred in a context of very low premarital fertility (births before first marriage), 

usually only a small percentage of all births. 

The situation is different in sub-Saharan Africa. First, age at marriage was very low in the 1950s, so low 

that it could hardly decline but could only increase (Garenne, 2004; van de Walle, 1968 and 1993). 

Second, age at marriage appeared susceptible to rapid changes, for a variety of reasons, as will be seen 

below. Third, premarital fertility exists in Africa, and could include more than half of women, as in 

Southern African countries (Garenne and Zwang, 2004 and 2005). When premarital fertility is 

widespread, the relationship between marriage and fertility becomes weaker, as births occur before or 

after first marriage, within or outside wedlock. 
5 

2.4 Contraception and Fertility 

In contrast, the relationship between contraceptive use and fertility is simple and direct, and this applies to 

any period, before or after marriage, as soon as first sexual intercourse has occurred. If contraception is 

effective, periods of sexual activity from first intercourse to menopause protected by contraception have 

nil fertility (in the absence of contraceptive failure), while unprotected periods of sexual activity have a 

natural fertility. So the person-years spent under effective contraception allow one to predict the level of 

fertility, given the underlying level of natural fertility. Unfortunately, the precise periods of contraceptive 

protection are not known for African countries, since available surveys provide only ever-use and currentuse, without the date of onset of contraceptive use. 

Since the approach of this study is long-term and longitudinal, the focus is on ever-use of contraception. 

In fact, women born early in the 20th century, say before 1920, were unlikely to have ever used modern 

contraception, since they were already age 40 or older when family planning programs and modern 

contraception emerged in Africa. So, ever-use of modern contraception is considered here as a critical 

factor for fertility control. Women who did not have access to family planning because they were born too 

early were under a natural fertility regime, while women age 15 in 2005 (born in 1990) were likely to 

have access to family planning services from an early age, and thus were likely to be able to use 

contraceptive methods unless they did not to do so for personal reasons. 

The relationship between contraceptive use and fertility depends upon the modalities of contraceptive use. 

Women may use contraception to avoid a premarital or an extramarital birth, to space two marital births, 

or to limit their family size. They may start early, as soon as first intercourse, or much later when they 

have reached their fertility goal. The impact of contraceptive use on fertility is likely to be different in 

each case, and is expected to be larger in the case of limiting family size when desired family size is low. 

In a perfect contraceptive society with a target of 2 children per woman, one expects ever-use by age 40 

to be close to 100% and the ratio of current-use to ever-use to be close to 80% (assuming 25 years of 

reproductive life span from first birth to last birth, and 5 years taken by two births intervals of 30 months). 

2.5 Period and Cohort Effects 

Period and cohort effects interact in the relationship between marriage, contraceptive use, and fertility. 

This longitudinal study differs from cross-sectional studies because of its emphasis on long-term trends. 

The main issue here is that age at marriage is primarily a cohort phenomenon, as well as ever-use of 

contraception, whereas fertility trends are a period phenomenon, measured as the fertility of a synthetic 

cohort. So, it was necessary for the study to devise a way of relating period and cohort effects. This was 

done by computing period estimates of age at marriage and of ever-use of contraception from cohort data, 

reconstructing the characteristics of the synthetic cohort used for fertility trends. The details of the 

computations are displayed in the methods section of Chapter 3. 

2.6 Longitudinal versus Cross-Sectional Approach 

The approach of this study is longitudinal, so that fertility trends are related with trends in marriage and 

trends in contraceptive use at the aggregate population level. This approach differs markedly from crosssectional approach, which focuses on differentials between individuals or between social groups at a 

given point in time. For instance, age at marriage could be a strong factor in fertility differences among 

individuals, and yet have no effect on fertility trends if it does not change over time while contraceptive 

use increases, as was the case in ancient Europe. Another discrepancy could occur if age at marriage 

changes but premarital fertility occurs, so that changes in age at marriage do not translate into changes in 

fertility. 
6 

Similarly, universal contraceptive use after reaching desired family size could lead to a very low TFR 

(reflecting the full impact of cohort contraceptive prevalence on fertility control). But the years of 

exposure to fertility between first marriage and reaching desired family size would not be protected by 

contraceptive use, leading to only partial protection in terms of period contraceptive prevalence. Here 

again, cross-sectional estimates might lead to misleading results, unless precise periods of exposure to 

fertility are specified. 

2.7 Socioeconomic Correlates of Marriage and Contraception 

Beyond the strict demographic analysis of period and cohort trends, the study also attempts to relate these 

trends with trends in socioeconomic correlates. An earlier study investigated the relationship between 

level of education and fertility in a longitudinal perspective (Garenne, 2012). The current study 

investigates the relationship between women’s age at marriage and women’s level of education the same 

way, in a longitudinal perspective. The situation is the same as that of marriage and fertility. One could 

find large differentials in cross-sectional studies (women with more education having a higher age at 

marriage) while trends in education play no role in trends in age at marriage (either because education 

levels remain unchanged, or because marriage trends are determined by other causes). Furthermore, in a 

number of instances, marriage trends were related with trends in income per capita, and with economic or 

political crises. 

2.8 Objectives of the Study 

This study analyzes the relationships between trends in fertility, trends in marriage, and trends in 

contraceptive use in a long-term longitudinal perspective. The study covers, as much as possible, the 

1950-2005 period and the corresponding cohorts of women age 15-49 (1901-1990 cohorts). The analysis 

is conducted for urban and rural areas separately, because urban and rural dynamics differ for fertility, for 

marriage, and for contraception. The study relates some of the changes with changes in level of education 

and with economic development. 

2.9 Open Questions 

The study addresses several questions of general interest for the demography of sub-Saharan Africa: 

• What was the magnitude of the changes in age at marriage since 1950 in sub-Saharan Africa? 

• Were the changes in age at marriage steady or erratic? 

• Did the changes in age at marriage have an impact of fertility trends? 

• Were the long-term changes in age at marriage attributable to changes in level of education? 

• What were the other causes of short term changes in age at marriage? 

• What was the extent of modern contraception by year 2005? 

• Could this level of contraceptive use explain the fertility decline in urban and rural areas? If not, 

why not? 

• What is the role of marriage and contraception on fertility trends in a multivariate analysis? 

Of course, the answers to these questions are limited by the available quantitative data, and by their 

accuracy. In particular, DHS surveys do not cover all targeted periods and cohorts, and do not include the 

age at first use of modern contraception. 
7 

3. Data and Methods 

This section describes the datasets used for the empirical study, the variables, and the methods followed 

for the longitudinal analysis. 

3.1 Survey Data 

Data were drawn from DHS and related surveys conducted in 33 countries of continental sub-Saharan 

Africa. In addition to DHS surveys, data from the earlier World Fertility Survey (WFS) were included 

because they were based on the same data-collection methods and provided a longer longitudinal 

perspective, covering earlier cohorts. The list of countries and surveys is displayed in Appendix (Table A1). The countries studied covered about 88% of the total population of sub-Saharan Africa. The two 

African islands included in the DHS program (Comoros, Sao Tome and Principe) were excluded, as was 

Northern Sudan. 

A total of 102 surveys were used for analysis of fertility trends and marriage trends. The analysis of 

trends in contraceptive use excluded the most recent surveys (DHS VI round) because they did not 

include the information on ever-use of contraception. 

3.2 Available Variables 

The longitudinal analysis is based on a few variables available in DHS surveys: date of birth (cohort), age 

at first marriage, ever-use and current-use of contraception, urban residence, and level of education, all 

taken from the women’s files (Individual Recode file or ‘IR’ file). 

Date of birth 

The date of birth is provided by the ‘V011’ variable (‘V008’ in WFS surveys). These dates are recorded 

in month and year, following the CMC (century-month calendar) method of DHS surveys. 

Date of first marriage 

The date of first marriage is provided by the ‘V509’ variable in CMC, (‘M012’ in WFS surveys), which 

allows the computation of the age at first marriage in months. 

Cohort median at marriage 

The cohort median age at marriage (labeled here simply median age at marriage) is defined as the age at 

which 50% of a cohort was ever-married. This definition differs somewhat from the classic median age at 

marriage, because it includes women who will never marry. The main advantage to using this definition is 

to allow computing the indicator for the younger generations, since the information on the proportion of 

women who never marry is not necessary. Another feature of this indicator is that it is more sensitive to 

rapid changes, because it includes at the same time the mean age at marriage for those who will ever 

marry and the proportion of terminal celibacy. When the proportion of women who never marry 

increases, the cohort median age at marriage increases too because it takes longer for 50% of the cohort to 

be ever-married. The cohort median age at marriage was calculated in months, and was transformed later 

into decimal values of years by dividing by 12 (a median age of 222 months is reported here as 18.5 

years). 

A single indicator of age at marriage was preferred over more complex indicators, such as the agespecific proportions ever-married. Indeed, there are monotonic relationships between these proportions 
8 

and the median age at marriage as computed in this study. These relationships were verified by computing 

the empirical quadratic relationship between selected values of the proportions ever-married at age 15, 20, 

25, 30, 35 and the median age at marriage in all DHS surveys used in this study. Results of these 

empirical relationships appear in Figure 1. They clearly show a close correspondence. 

Figure 1. Empirical relationships between proportions ever-married and cohort median age at first 

marriage in African DHS surveys 

Premarital fertility 

The effect of age at marriage on fertility may be reduced by premarital fertility, because women may 

marry later but have more premarital births. A simple indicator of premarital fertility was used: the 

proportion of premarital births at last survey (the endpoint). 

Cohort ever-use of modern contraception 

Cohort contraceptive use at age (x) is measured by the proportion of women who ever used modern 

contraception by age (x). The corresponding variable in DHS surveys is ‘V302’ (and ‘V633’ in WFS 

surveys). The coding of this variable changed between the DHS-I round (V302= 2) and DHS-II to DHS-V 

rounds (V302= 3). Unfortunately, as mentioned, this variable was no longer used in the DHS-VI round, 

so that the information is now missing for the younger cohorts. 

DHS surveys distinguish between “modern methods”, “traditional methods”, and “folk methods” of 

contraception. The list of methods recorded in African DHS surveys is provided in Table 2, along with 

the number of surveys in which the method has appeared. In this report we focus on “modern methods”, 

because they clearly have the most influence on fertility trends. The definition and list of modern methods 

varies somewhat from country to country, and sometimes from survey to survey in the same country, 

depending on the availability of new methods and on coding procedures (some methods are lumped 
9 

together as “other modern methods”). The coding of some methods was sometimes inconsistent. This is 

particularly the case for “breastfeeding / lactational amenorrhea”, which can be classified either as a 

modern method or as a traditional method. In any case this analysis is based on the “modern method” 

recoding in the standard recode files based on variable “V302”. 

Table 2. List of contraceptive methods recorded in African DHS surveys 

Category Contraceptive method Number of DHS surveys 

Modern Pill 95 

IUD 95 

Injections 95 

Male condom 95 

Female condom 36 

Female sterilization 94 

Male sterilization 32 

Norplant / Implants 52 

Diaphragm 47 

Foam, jelly, spermicide 47 

Monthly pill 0 

Emergency contraception 0 

Other modern methods 42 

Traditional Long term abstinence 15 

Periodic abstinence / Rhythm 95 

Withdrawal 95 

Breastfeeding / Lactational amenorrhea 36 

Other traditional methods 8 

Folk method Any folk method 91 

Note: The list varies from country to country. Breastfeeding / Lactational amenorrhea is sometimes classified 

among the modern methods. 

The analysis of contraceptive use by age and cohort in Africa is complex. The first feature is a marked 

age pattern to ever-use of contraception, and the second feature is major cohort trends. In a majority of 

countries only a small proportion of women used contraception before the first marriage (for avoiding a 

premarital birth), then the proportion of ever-users increases with age, following the desire first for 

spacing births and later for limiting family size. In this study the age and cohort patterns of ever-use of 

modern contraception are presented in detail for each country in Appendix B. A model converting period 

data into cohort estimates was developed for this study (see below). 

Extent of contraceptive use 

Since precise intervals of contraceptive use are not available in DHS surveys, a simple indicator of the 

extent of contraception was used: the ratio of current-use to ever-use at last survey. The higher this ratio, 

the more extensive is the use of contraception, and therefore the higher the proportion of the reproductive 

life span protected by modern contraception. This variable is a supplement to “ever-use,” which is the key 

factor for understanding fertility trends in Africa. 
10 

Urban residence 

Area of residence (urban / rural) is taken as defined by the surveys (“V102” in DHS surveys, and “V702” 

in WFS surveys) for the calculations of urban and rural trends in marriage and contraceptive use. 

Level of education 

Level of education is measured by the mean number of years of schooling (“V133” in DHS surveys). For 

WFS surveys, the number of years of schooling is calculated from the level of education attained. Details 

on this variable are provided in DHS Analytic Studies 33 published in 2012 (Garenne, 2012). 

3.3 Data Issues 

There were no major data issues of age at marriage and contraceptive use found during this study. Various 

inconsistences are noted in the country studies, however, without much importance for the main results. 

3.4 Trends in Cohort Age at Marriage 

Cohort median age at marriage was calculated for each yearly cohort available, for each country and for 

urban and rural areas separately. Results are shown in Appendix B, country by country. First, a graphic 

visual analysis of data points was conducted. In case of no trend, the average was kept. In case of a linear 

trend, a linear regression line was fitted. In case of more complex changes, a 2nd degree polynomial was 

fitted, or several linear trends were fitted depending on the country situation. For earlier cohorts, trends 

were prolonged backwards when a significant trend was visible. Most often, however, the median age at 

marriage was taken as constant for the earlier cohorts (e.g. < 1930). Urban and rural areas were analyzed 

separately in all countries investigated, since fertility trends differ markedly by urban or rural residence. 

3.5 Converting Period Estimates of Contraceptive Use into Cohort Estimates 

DHS surveys provide data on ever-use of contraception by age at survey and cohort of birth, but without 

the age at which respondents first used contraception, an omission which seriously limits the trend 

analysis. In a survey taken at time (t), cohort effects and age effects are mixed, usually producing a bellshaped pattern. In all countries with two or more surveys, however, the cohort effects as well as the age 

effects were found to be regular, as shown in Appendix B. Several models were tried to convert age and 

period estimates into cohort estimates. After numerous trials, a simple conversion model was selected, 

linking in a cohort the proportion of women who ever used contraception by age (x) to the same 

proportion at age 40 and above. This relationship was obtained by matching the same cohorts in different 

surveys of the same country. The final model was: 

( ) () PLogit )40( − xPLogit β −×= x)40()(

In this equation, x is age below 40, and P(x) the proportion of women who ever-used contraception by age 

x. The β coefficient was estimated from empirical data (β= 0.10403858). 

The main advantage of this model is to allow a simple and consistent conversion of period estimates into 

cohort estimates. The main limitation is that it assumes the same age pattern in all countries, and therefore 

requires some adjustment to match observed and reconstructed period estimates. This adjustment was 

done by applying a multiplicative coefficient, matching the reconstructed period estimates with the 

observed period estimates at the first survey available. 
11 

Once cohort estimates were calculated for each cohort, each country, and urban and rural areas separately, 

cohort trends were smoothed by a five-year moving average. Earlier cohorts not covered by DHS surveys 

were obtained by prolonging trends backwards, and usually taken as zero for cohorts born before 1930. 

3.6 Estimating the Average Age Pattern of Contraceptive Use 

The average underlying age pattern of contraceptive use was calculated by linear-logistic regression, after 

controlling for cohort effects, using all surveys available. The equation was: 

( ) 10)( 2 ×+×+×+= AgeBAgeBCohortBBxPLogit 23

The regression model was calculated for urban and rural areas separately. This age pattern was smoothed 

with a Picrate Model in order to estimate the mean age at onset of contraceptive use (for details on the 

procedure, see Matthews et al., 2010). 

3.7 Period Estimates Reconstructed from Cohort Data 

This analysis includes formal cohort analysis and longitudinal analysis from reconstructed periods 

(synthetic cohorts). The method for calculating period data from reconstructed cohorts has been explained 

in DHS Analytical Studies 33 for level of education (Garenne, 2012). In brief, the period age at marriage 

to be matched with period fertility is recalculated from the cohort data by applying an age pattern of 

fertility to women of the corresponding age. This gives a measure of the median age at marriage for the 

synthetic cohort of women who gave birth in a given year. 

In formula this gives: 

)( )(),(

49

15

AgeMarriag te aftaAgem a

=

= ×

Where f(a) is the age pattern of fertile women, defined by age-specific fertility rates, with ∑f(a)= 1, and 

Agem (a,t) is the median age at marriage of cohort age (a) at time (t). Synthetic cohort estimates of 

median age at marriage were calculated for each year between 1950 and 2005, separately for urban and 

rural areas in each country. 

The age patterns of fertility used for this reconstruction were calculated for each country and for urban 

and rural areas separately from DHS survey data, and fitted with splines. Details have been presented 

previously (Garenne, 2012). 

The same method was applied for contraceptive use. So, the “Age-Standardized-Contraceptive Use’ 

(ASCU) for period (t) was defined the same way: 

)( )(),(

49

15

tASCU Contracept aftaiveUse a

=

= ×

Where f(a) is the age pattern of fertile women, defined by age-specific fertility rates, with ∑f(a)= 1, and 

Contraceptive Use (a,t) is the proportion of women age (a) at time (t) who ever used contraception. This 

could be computed from reconstructed cohort data, or for the years in which a survey was taken, directly 

from survey data. Period estimates of ASCU were calculated for each year between 1950 and 2005, 

separately for urban and rural areas in each country. 
12 

3.8 Other Data Used 

While most of this analysis was based on DHS data, the analysis also used other sources. Data on 

urbanization were taken from the United Nations Population Division database in its latest revision 

(2012). They were used for computing country estimates (urban and rural areas together) from 

proportions living in urban and rural areas in each year from 1950 to 2005. Data on income per capita 

were taken from the database assembled by Angus Maddison and colleagues, in its latest version 

(Maddison, 2008). This database provides estimates of Gross Domestic Product in Parity Purchasing 

Power (GDP-PPP) expressed in constant US dollars. The data are available for all African countries 

investigated, and for the 1950-2008 period. In this database, Ethiopia and Eritrea were merged together, 

so that the GDP for Ethiopia and Eritrea was considered that of Ethiopia, which is taken as a separate 

country in DHS surveys. 

 
13 

4. Trends in Age at Marriage 

4.1 Long-Term Trends in Cohort Age at Marriage 

Overall, for the 33 countries investigated, the cohort median age at first marriage increased markedly in 

urban areas, and somewhat less in rural areas. In urban areas, it increased by about 5 years, from an 

estimated value of 17.6 years for cohorts born in 1930 to 22.6 years for cohorts born in 1990, although 

this value was based on extrapolations for some Southern African countries. In rural areas the increase 

was about a third of the urban value (+1.8 years), from an estimated value of 17.3 years for the 1930 

cohort to an estimated 19.1 years for the 1990 cohort. As a result, the gap in age at marriage between 

urban and rural areas increased markedly, which has been noted by many analysts in DHS country reports 

(Figure 2). 

Figure 2. Reconstructed trends in cohort median age at marriage, 33 African countries 

4.2 Country Variations in Age at Marriage since 1940 

Changes since 1940 in age at marriage varied substantially among the 33 countries. Changes were 

computed as the estimate for the last cohort available minus the estimate for the 1940 cohort, and 

expressed in years per decade. Some countries had hardly any change in age at marriage, whereas in 

others changes were dramatic (Figure 3). The countries where age at marriage increased the most were in 

Southern Africa (South Africa, Namibia, and Swaziland), while changes were less marked in Lesotho, 

selected countries in Eastern and Central Africa (Tanzania, Gabon, Rwanda, Burundi, Ethiopia, and to a 

lesser extent Zambia and Kenya), and selected countries in West Africa (Senegal, Nigeria, urban Niger, 

and rural Liberia). 

 
14 

Figure 3. Average change in median age at marriage since cohort 1940 (years per decade) 

In contrast, some countries had only very small or no increases in the median age at marriage: Central 

African Republic, Chad, Togo, Benin, Mali, Guinea (urban), Madagascar (urban), Burkina Faso (rural), 

and Malawi (rural). The case of Mozambique is peculiar, since age at marriage decreased markedly from 

cohorts 1940-1970 in both urban and rural areas before increasing again. In Madagascar, age at marriage 

first increased, then decreased (see Figures in Appendix B). 

Some countries had divergent trends in urban and rural areas, in particular countries with rapid increases 

or erratic changes in urban areas but no change in rural areas (Benin, Burkina Faso, Burundi, 

Côte d’Ivoire, Sierra Leone, and Zimbabwe). In one country (Namibia) median age at marriage became 

higher in rural areas than in urban areas among recent cohorts. As a result, Namibia is now the country 

with the highest level of premarital fertility (Garenne and Zwang, 2005). 

4.3 Reversals in Age at Marriage Trends 

Trends in age at marriage varied by country. While an increase in age at marriage occurred at some point 

in urban areas of all 33 countries investigated, there was no increase in rural areas of 15 of the 33 

countries studied. Some 12 countries showed a mild decline for the earlier cohorts (e.g., those born before 

1950), and some 7 countries showed stagnation in age at marriage or even a reversal after age at marriage 

had first increased. (Details are shown in Appendix B.) 

The most striking long-term reversals in age at marriage were found in the following countries: CongoBrazzaville, in both urban and rural areas for cohorts born after 1975; Côte d’Ivoire, in urban areas for 

cohorts born after 1975; Madagascar, in both urban and rural areas for cohorts born after 1970; Mali, in 

urban areas for cohorts born after 1980; and Tanzania, in rural areas, for cohorts born after 1980. In rural 

Mali and rural Tanzania the decline in age at marriage was associated with a decline in the level of 

education. In urban Madagascar and in urban Mali the decline in age at marriage occurred despite an 

increasing level of education. In Congo-Brazzaville and in Côte d’Ivoire decline in age at marriage 

occurred while the level of education remained unchanged. 
15 

In addition to changes in long-term trends, trends in age at marriage were seriously disrupted in two 

countries affected by civil wars: Rwanda and Sierra Leone. In Rwanda age at marriage increased 

markedly for cohorts reaching marriage age during the early civil war years (1990-1994), but decreased 

shortly afterwards, probably because many women who did not marry during the crisis years did so when 

the situation became more stable. These changes could be seen by calculating period first marriage rates 

from DHS data, which showed a major increase in marriage rates in the two years following the April 

1994 genocide (see Appendix B, Figure B-24.4). Similar trends in cohort age at marriage could be seen in 

Sierra Leone, although they affected only urban women, and not rural women. However, in Sierra Leone 

there was no evidence of change in period first marriage rates. 

4.4 Period Trends in Age at Marriage 

Period trends in age at marriage represent the average median age at marriage for the synthetic cohort 

who gave birth in each year. Results indicated a similar rise in age at marriage in both urban and rural 

areas, with a similar difference between both areas, and a smaller magnitude of change compared with 

cohort trends, due to the smoothing effect of merging different cohorts in a same time period (Figure 4). 

Figure 4. Reconstructed trends in median age at marriage of synthetic cohorts, 33 African 

countries 

In urban areas, age at marriage was low and similar to that in rural areas in 1950 (17.6 years in urban and 

17.3 years in rural areas). Thereafter, age at marriage increased more in urban than in rural areas. In urban 

areas, it increased especially after 1965, reaching 20.9 years in 2005; in rural areas, it increased especially 

after 1975, reaching 18.4 years in 2005. These rather minor changes were unlikely to induce any major 

change in fertility in rural areas, and were expected to have only a small effect in urban areas. Note that 

the onset of fertility decline occurred at a low level of age at marriage in both urban and rural areas. 
16 

4.5 Trends in Fertility and in Age at Marriage 

Fertility trends for urban and rural areas were presented in detail in an earlier study (Garenne, 2008). In 

this study, they are analyzed in correlation with trends in age at marriage. Here again, the approach was 

longitudinal, so that changes in age at marriage were matched with changes in fertility over the same 

period of time. The age at marriage in a given year for women who gave birth was computed as the 

weighted average of the age at marriage for the same cohorts, the weights being the age pattern of 

fertility, as explained above (see § 3.8). 

Changes in age at marriage were computed as the difference between median age at marriage at the last 

point available (e.g. 2005 or last survey available) and the median age at marriage at the date of onset of 

fertility decline, defined for each country, and for urban and rural areas separately. Changes in age at 

marriage were correlated with changes in fertility over the same period of time. Both changes in age at 

marriage and in fertility were calculated as the average change per decade (number of children per decade 

for fertility; number of years per decade for age at marriage). 

Results showed no correlation between changes in age at marriage and speed of fertility decline, and this 

was true both in urban areas (ρ = −0.15), and in rural areas (ρ = +0.16). One could find fast and slow 

fertility declines whether or not age at marriage had changed, and independently from the speed of the 

change (Figures 5 and 6). 

Figure 5. Correlation between change in age at marriage and fertility decline, urban areas 
17 

Figure 6. Correlation between change in age at marriage and fertility decline, rural areas 

4.6 Reversal in Age at Marriage and Fertility Trends 

Cases of reversal in cohort age at marriage, documented above, were analyzed for their possible effect on 

fertility trends. In Congo-Brazzaville the decline in age at marriage for cohorts born after 1975 translated 

into a decline in period age at marriage after 2000 in urban areas, and after 1997 in rural areas, but this 

did not alter fertility trends, which continued to decline in urban areas, and to increase in rural areas. In 

urban Côte d’Ivoire the decline in age at marriage for cohorts born after 1975 translated into stagnation in 

period age at marriage after 2002, but did not seem to have an effect on fertility trends which continued to 

decline in urban areas up to 2005. In Madagascar the decline in age at marriage for cohorts born after 

1970 translated into a decline in period age at marriage after 1995 in urban areas, and stagnation after 

year 2000 in rural areas; this did not change the declining fertility trends in either urban or rural areas. In 

urban Mali the decline in age at marriage for cohorts born after 1980 translated into stagnation in period 

age at marriage after 2000, but had no effect on fertility trends which continued to decline. In rural 

Tanzania, the decline in age at marriage for cohorts born after 1980 translated into stagnation in period 

age at marriage after 2000, but did not seem to affect fertility trends which showed a small negative slope. 

Overall, this lack of effect of marriage trends on fertility trends was largely expected: a change in cohort 

trends, even when impressive such as that in Madagascar, translated into a minor change for the situation 

of fertile women (the period estimate), and was unlikely to have any large effect on period fertility, 

mostly because the period changes were small and gradual. 

4.7 Effect of Severe Crises on Age at Marriage and Fertility 

The two cases of severe civil unrest were different, and showed different phenomena, which were 

primarily period effects. In Rwanda, the troubled 1990s had an effect on both marriage and fertility. In the 

two years following the genocide (1994-1995), first marriage rates increased by 73% (158 first marriages 
18 

per 1,000 person-years at age 14-29, compared with 93 per 1,000 expected from trends (P value < 10-10). 

(See also Figure B-24.4 in Appendix B.) This short-term increase in first marriage had an impact on birth 

rates, which also increased during the same years. 

In contrast, in Sierra Leone, no effect of the civil war (1991-2000) was visible on first marriage rates, as 

noted above. With respect to fertility, trends were hardly changed during the war years, but fertility was 

27% higher at the end of the civil unrest (2000-2002) compared with the average of the years before 

(1994-1999) and after (2003-2008), and this was true in both urban and rural areas. This rise in fertility, 

however, occurred without any evidence of an increase in marriage rates (167 first marriage per 1,000 

person-years before 2000, and 172 per 1,000 in 2000-2002). This could be due to spousal separation 

during the crisis years followed by reunion thereafter, or simply by deliberate attempts to postpone births 

during the crisis years. 

4.8 Overall Trends in Age at Marriage and in Education 

The relationship between trends in age at marriage and trends in level of education was analyzed by 

matching the cohort median age at marriage with the cohort average years of schooling in a dynamic way. 

The country dynamics are presented in Appendix B. 

Overall for the 33 countries combined, for the 1930-1985 cohorts, the dynamics of age at marriage and 

level of education appeared correlated: both increased in parallel, in urban as well as rural areas. In urban 

areas, when level of education increased from 1.3 to 7.9 years, median age at first marriage increased 

from 17.6 to 22.5 years; in rural areas changes were less striking: when level of education increased from 

0.5 to 4.5 years, age at marriage increased from 17.3 to 19.1 years. However, the relationship was not 

linear, and in particular the increase in age at marriage was faster in the later years than before, and in 

urban areas it seemed to continue despite stagnation in the level of education, which indicates that other 

mechanisms were operating (Figure 7). 

 
19 

Figure 7. Dynamics of cohort age at marriage and cohort level of education, 33 African countries, 

cohorts 1930-1985 

4.9 Diversity in Dynamics of Age at Marriage and Level of Education 

Beyond the overall pattern, the dynamics of age at marriage and level of education were complex and not 

monotonic in many of the countries studied. Changes were monotonic or quasi-monotonic in 10 countries 

for urban areas, and in 19 countries for rural areas. The most common outstanding pattern was an increase 

in age at marriage despite stagnation or a decrease in level of education (15 cases in urban areas, 8 cases 

in rural areas). One could also find cases of a decline in age at marriage despite an increase in level of 

education (6 cases in urban areas, 4 cases in rural areas). The last two cases of serious disruption due to 

civil wars (Rwanda and Sierra Leone) were described above. Note that, as was the case for fertility trends, 

the dynamics of age at marriage and level of education were often different in urban and rural areas (16 

out of 33 countries) (Table 3).

 
20 

Table 3. Distribution of African countries according to the type of correlation between dynamics of 

level of education and dynamics of age at marriage 

Type of change Urban Rural Different dynamics 

Monotonic 10 19 

Reversed 15 8 

Inversed 6 4 

Complex 2 2 

Total 33 33 16 

Note: (1) Monotonic: both level of education and age at marriage increase at the same time; (2) Reversed: age at 

marriage increases despite stagnation or decline in level of education; (3) Inversed: age at marriage declines while 

level of education increases; (4) Complex: complex changes due to wars of civil unrest; (5) Different dynamics: 

correlation differed between urban and rural areas. 

4.10 Correlation between Changes in Age at Marriage and Changes in GDP 

Changes in income per capita (measured by GDP-PPP in constant US Dollars) contrasted among African 

countries, with cases of steady increase (as in Lesotho), cases of stagnation (as in Senegal), and numerous 

cases of major recessions. To determine the economic conditions prevailing at time of marriage, income 

per capita at time (t) was matched with median age at marriage for cohort (t-20). Changes in income per 

capita between 1970 and 2005 (many of them negative) were matched with changes in median age at 

marriage for cohorts born between 1950 and 1985. Results showed virtually no correlation, meaning that 

changes in age at marriage were largely independent of economic development for these cohorts. One 

could find large or small increase in age at marriage at all levels of economic development, and whatever 

the income dynamics. The large increases in age at marriage were all associated with positive economic 

growth (all countries from Southern Africa), but the largest increases in GDP were not associated with the 

largest increases in age at marriage, and the largest decreases in GDP were not necessarily associated with 

stagnation or declines in age at marriage (Figure 8). 

 
21 

Figure 8. Correlation between changes in income per capita and change in age at marriage 

The two cases of serious decline in cohort age at marriage were associated with serious economic 

downturn during the marriage years of the corresponding women. In Congo Brazzaville median age at 

marriage declined from 22.2 years (cohort 1972) to 20.4 years (cohort 1986), while GDP declined by 

28%, from US$ 3,008 (year 1984) to US$ 2,159 (year 2008). In Madagascar median age at marriage 

declined from 19.1 years (cohort 1967) to 18.0 years (cohort 1988), while GDP declined by 53%, from 

US$ 1,246 in 1971 to US$ 664 in 1996. 

Some of the peculiar patterns of the relationship between education and age at marriage (see above § 4.9) 

were analyzed with respect to income dynamics. Overall the patterns across countries were mixed. 

However, some cases of unexpected change in age at marriage could be related to socio-political events. 

For instance, in Ghana the fast increase in age at marriage for urban cohorts 1960-1971, given the level of 

education, seemed associated with the economic crisis of 1978-1983. In Liberia the increase in age at 

marriage for urban cohorts 1971-1976, given the level of education, seemed associated with the civil war. 

In Madagascar the decrease in age at marriage for cohorts born after 1965, given the level of education, 

seemed associated with the economic recession that started around 1975 and lasted for some 20 years. 

Outside severe crises, the relationship between level of education and age at marriage could go either way 

with respect to economic growth. Cases of increasing age at marriage despite no change in level of 

education could occur in cases of economic recession (6 cases), economic growth (4 cases), or economic 

stagnation (4 cases). Cases of declining age at marriage despite increasing level of education could occur 

in situations of economic growth (2 cases) or economic recession (2 cases). The analysis of these 

surprising cases requires further investigation and detailed case studies. 
22 

4.11 Conclusions on Trends in Age at Marriage 

In conclusion, trends in age at marriage and their consequences for fertility appeared far more complex 

than anticipated, and different from what is usually found in cross-sectional studies. Numerous cases were 

found of increase in age at marriage without evidence of increase in level of education, and conversely, 

cases of stable age at marriage despite increases in level of education. As shown earlier for the dynamic 

relationship between level of education and fertility, the dynamic relationship between age at marriage 

and fertility appeared very weak at the aggregate level, in fact virtually non-existent, contrary to the 

findings of most cross-sectional studies based on individual data. 

 

 
23 

5. Trends in Contraception 

This section is devoted to the reconstruction of trends in contraceptive use and their relationship with 

fertility trends. Contraceptive use is defined as the proportion of women who ever-used contraception by 

age 40, and is standardized by the age pattern of fertility. Fertility is defined as the cumulated fertility by 

age 40, as explained in another document (Garenne 2008). 

5.1 Age Pattern of Ever-Use of Contraception 

The regression model linking age and cohort estimates provided an average age pattern of entry into 

modern contraceptive use, after controlling for cohort effects. Results are displayed in Figure 9. For a 

given level of ever-use of contraception by age 40 and above, entry into contraceptive use was gradual, in 

both urban and rural areas. In urban areas, for an ultimate value of 64.8% ever-used of contraception at 

age 40 and above, the proportion of women who ever-used contraception was 20.9% at age 20, and 58.0% 

at age 30, so that the great majority of women (90%) who ever-used contraception began use before age 

30. 

Figure 9. Underlying average age pattern of ever-use of modern contraception, 33 African 

countries (from linear logistic regression, after controlling for cohort) 

In contrast, in rural areas corresponding values were 42.5% ever-use at age 40 and above, for 7.1% at age 

20 and 29.1% at age 30, so that only 69% of rural women who ever used modern contraception did so 

before age 30. This late entry into contraceptive use obviously is likely to have an effect on fertility 

trends. The average age at onset of contraception was estimated using a Picrate model, a simple 

parametric model used for fitting proportions of ever-married women (for details on the model, see: 

Matthews et al. 2008; Garenne et al., 2011). Results gave an average age at first use of contraception of 

24.5 years in urban areas (standard deviation= 7.9 years) and 30.9 years in rural areas (standard 

deviation= 11.3 years). 
24 

5.2 Reconstructed Cohort Trends in Ever-Use of Contraception 

Cohort trends in ever-use of modern contraception were reconstructed from period observed values and 

the age pattern, for each country and for urban and rural areas separately. They provide an estimate of the 

proportion of women who had ever-used a modern method (for the older cohorts) or will eventually everuse one (for the younger cohorts) by age 40. For urban areas, this proportion increased from 17.4% for 

cohorts born in 1930 to 72.4% predicted for cohorts born in 1980. For rural areas, the proportion 

increased from only 3.4% for cohorts born in 1930 to a 62.1% predicted in 1980. Contraceptive use 

started later in rural areas, then appeared to catch up. However, the most recent estimate for the 1980 

cohort is net of age effects. With the age effect presented in the previous paragraph, the real level of everuse of contraception is much lower (Figure 10). 

Figure 10. Underlying cohort trends of ever-use of modern contraception, net of age effect, 33 

African countries (from linear logistic regression, after controlling for age) 

5.3 Trends in Period Ever-Use of Contraception 

After smoothing cohort trends country by country, period trends were calculated for the older cohorts, and 

transformed into period trends for the earlier years, as explained in the methods section (see Chapter 3). 

They were matched with real period estimates drawn directly from the DHS surveys. A few 

inconsistencies were corrected in order to match values of age-standardized proportions of women who 

ever-used contraception calculated from cohort trends with the direct period estimate of ever-use from the 

earliest survey available in each country. 

Results confirmed that overall use of modern contraception was close to zero in 1950. In urban areas it 

started to increase around 1965 and rose to reach about half of women (49.3%) in 2005. In rural areas, the 

increase started some 10 years later and rose to 28.4% in 2005. These levels of ever-use of modern 

contraception are realistic, and are all validated by the most recent survey available in each country 

(Figure 11). 
25 

Figure 11. Reconstructed trends in age-standardized ever-use of modern contraception of 

synthetic cohorts, 33 African countries 

5.4 Trends in Fertility and Contraception 

Period trends in ever-use of modern contraception were matched with period trends in fertility, already 

estimated from previous work (Garenne, 2008). Here the level of fertility is estimated by cumulated 

fertility by age 40, which in Africa is close to 90% of the TFR (cumulated fertility by age 50). Results 

allowed reconstructing the path of the fertility transition according to levels of contraceptive use (Figure 

12). In the first years after 1950, fertility continued to increase despite a small increase in modern 

contraceptive use, simply because of increasing natural fertility. By 1975, fertility stabilized in urban 

areas at about 5.4 children per woman, when contraceptive use was limited (11%), then started to decline. 

By 2005, cumulated fertility by age 40 was 3.7 children per woman, when contraceptive use was 49.2%. 

This means that half of the fertility transition (from 5.4 to 2.0 children by age 40) was achieved with 

about half of women using modern contraception, and that contraceptive use has the potential of 

explaining the fertility decline. This result, which was obtained by two independent calculations, is 

remarkable, and confirms the high consistency between fertility and contraception data in DHS surveys, 

and the apparent high efficacy of contraceptive use in urban areas. However, this effect was reduced by 

the relatively low ratio of current-use to ever-use, 45.2% on average. Only about half of the fertility 

transition in urban areas can be explained by modern contraceptive use. 

In rural areas the results were less consistent. The fertility decline was only 14% (from 6.3 to 5.7 children 

per woman) while some 28% of women ever-used modern contraception by year 2005. This discrepancy 

could be due to the later age at first contraceptive use, as noted above. Again, contraceptive use had the 

potential to explain most of the fertility decline, but the effect was reduced by a relatively low ratio of 

current-use to ever-use, 40.6%. A large share of the early fertility transition in rural areas can be 

explained by modern contraceptive use. 
26 

Figure 12. Reconstructed path in ever-use of contraception and fertility of synthetic cohorts, 33 

African countries, 1950-2005 

5.5 Stagnation of Contraceptive Use and Fertility Stalls 

Instead of a steady increase in modern contraceptive use, a number of countries exhibited some kind of 

stagnation or even a decrease in contraceptive use from one survey to the next (details are provided in 

Appendix B). For age-standardized ever-use of contraception measured at time of survey, significant 

declines were found in the following countries: urban Kenya (1998-2003); urban Malawi (2000-2004), 

urban and rural Mali (2001-2006), urban Niger (1998-2006), and urban and rural Rwanda (1992-2000). In 

urban Kenya (1995-2001) and in Rwanda (1991-1995), the reversals in ever-use of contraception were 

associated with fertility stalls, but this was not the case in the other countries. Of course, fertility stalls 

were more likely to be determined by current use of contraception rather than by ever-use (Table 4).

 
27 

Table 4. Cases of reversal in age-standardized contraceptive use between successive surveys 

Country Residence 

Previous survey Next survey 

T-test P-value 

Impact on 

Year ASCU (/100) Year ASCU (/100) fertility 

Kenya Urban 1998 55.9 2003 52.0 -2.47 0.0134 Stall 

Malawi Urban 2000 53.4 2004 46.8 -4.27 0.0000 None 

Mali Urban 2001 36.3 2006 29.8 -6.39 0.0000 None 

Rural 2001 14.4 2006 12.0 -4.81 0.0000 None 

Niger Urban 1998 34.0 2006 29.9 -2.53 0.0114 None 

Rwanda Urban 1992 32.3 2000 27.8 -1.77 0.0770 Stall 

Rural 1992 19.4 2000 14.1 -8.44 0.0000 Stall 

NB: ASCU = Age standardized ever-use of contraception. 

5.6 Conclusions on Contraceptive Use 

The increase in contraceptive use was impressive in Africa, although the increase was not as fast as 

desired, and was slower than in other developing countries, where virtually universal contraceptive use 

could be achieved in less than a generation. In sub-Saharan Africa, after some 40 years of family planning 

efforts, only half of urban women and hardly more than one-fourth of rural women had ever-used modern 

contraception. This has already been shown using cross-sectional data (Cleland et al., 2010). 

 

29 

6. Synthesis on the Longitudinal Relationships of Age at Marriage 

and Contraception on Fertility 

This section presents an attempt to synthesize the effects of age at marriage and contraceptive use on 

fertility trends. Several linear regression models were explored linking fertility changes with changes in 

age at marriage and contraceptive use at country level. All models showed a small effect of age at 

marriage, and a large effect of contraception. The final model presented here produced the most 

consistent results in both urban and rural areas. It links the level of fertility at the endpoint with 

contraception and age at marriage after controlling for the level of fertility at the onset. 

6.1 Basic Regression Model 

The basic model is an application of standard regression models for longitudinal data, such as that 

described as “analysis of covariance model” by Winship and Morgan (1999). The model links fertility 

level at endpoint with modern contraceptive use at endpoint and with changes in age at marriage since 

onset, controlling for level of fertility at onset of the transition. The last control simply says that the effect 

of a given level of contraceptive use and of change in age at marriage depends on the level of natural 

fertility preexisting before the transition, which varies from less that 6 to more than 8 children per women 

in Africa. The endpoint was set as year 2005, or the date of the latest DHS survey available before 2005. 

The regression was based on 32 countries for urban areas and 28 countries for rural areas (CongoBrazzaville, Congo-Kinshasa, Niger, and Uganda were excluded because fertility transition had not yet 

started in their rural areas). 

Results confirmed that the effect of age at marriage was small and not even significant when urban and 

rural areas are considered separately. The net effect of contraceptive use at endpoint was strong and 

largely significant in both urban (P= 0.002) and in rural (P= 0.008) areas. This calculation assumes that 

the level of contraceptive use at onset was nil. The effect of the level of natural fertility at onset of fertility 

decline was not significant in urban areas, but was significant in rural areas. The net effect of 

contraceptive use was stronger in urban than rural areas, and this was true both in absolute and relative 

value, as was expected because of the later age at first use of contraception in rural areas. Overall, the net 

effect of ever-use of modern contraception explained some 50.9% of the change in fertility in urban areas, 

and only 41.4 % in rural areas. The net effect of change in age at marriage could only be small, with only 

a small contribution in both urban and rural areas compared with the overall change in fertility (Table 5). 

 
30 

Table 5. Results from the regression analysis linking fertility with ever-use of modern 

contraception and change in age at marriage (aggregate data) 

Mean value B coefficient P-Value Signif. Net effect Percent explained 

Urban areas (32 countries) 

Fertility at end 4.09 

Fertility at onset 6.91 +0.3021 0.107 NS 

Ever-use at end 0.510 −2.8169 0.002 * −1.437 50.9% 

Change in age at marriage +2.74 −0.0892 0.180 NS −0.244 

Rural areas (28 countries) 

Fertility at end 5.95 

Fertility at onset 7.46 +0.5315 0.006 * 

Ever-use at end 0.318 −1.9658 0.008 * −0.625 41.4% 

Change in age at marriage +1.09 −0.1365 0.151 NS −0.149 

NB: (*) significant at P< 0.05; NS: Not significant 

Model: Fertility(t) = B0 + B1×Fertility(onset) + B2×Contraception(t) + B3×Change in age at marriage(t)+ε

6.2 Country Heterogeneity 

The model was further developed to take into account the heterogeneity of the relationship by country, 

explored in the Appendix and documented above. This heterogeneity comes from the confounding factors 

of contraceptive use and age at marriage: the extent of contraceptive use, that is, the periods of use during 

the reproductive life span, including age at first use and the ratio of current-use to ever-use, and the level 

of premarital fertility. The basic model was extended and applied while adding a dummy variable for 

urban and rural areas and dummy variables for each country. In the regression models only few country 

coefficients were statistically significant. Among the consistent negative residuals, that is, the countries 

with lower fertility than predicted by the model, were Lesotho (residual= -1.09), Togo (-0.99), Zimbabwe 

(-0.98), Kenya (-0.96) and Ethiopia (-0.87). These countries achieved lower levels of fertility primarily 

because of higher use of contraception (more current-use per ever-use). In contrast, three counties 

exhibited positive residuals, that is, higher fertility than expected by the model: Congo-Brazzaville, 

Congo-Kinshasa, and Zambia. These are three countries in which the fertility transition had not yet started 

in rural areas. The addition of country dummy variables did not change the magnitude of the overall 

effects of age at marriage and contraceptive use on fertility decline. 

6.3 Full model with Extent of Contraception and Premarital Fertility 

The basic model was also extended by including the two confounding factors: one for contraception (the 

extent of contraception measured by the ratio of current-use to ever-use at the endpoint), and one for 

marriage (the extent of premarital fertility measured by the proportion of premarital births at the 

endpoint). The results confirm that a large share of the fertility decline can be explained by contraceptive 

use, and that age at marriage played a minor role (Table 6). 

 
31 

Table 6. Results from the regression analysis linking fertility with contraceptive use, age at 

marriage and confounding factors (aggregate data, urban and rural combined) 

Mean value B coefficient P-Value Signif. Net effect Percent explained 

Fertility change −2.210 

Contraception 

Ever-use at end 0.421 −2.270 0.0005 * −0.955 43.2% 

Extent of contraception at end 0.430 −1.833 0.0240 * ±0.231 ±10.5% 

Marriage 

Change in age at marriage +1.971 −0.104 0.131 NS −0.204 

Premarital fertility at end 0.132 +1.038 0.398 NS +0.137 

Controls 

Natural fertility at onset 7.165 +0.510 0.0003 * 

Urban dummy 0.533 −0.935 0.0001 * 

NB: (*) significant at P< 0.05, (NS) not significant. Linear regression model after controlling for level of fertility at 

onset of the transition and urbanization. Net effect of ever-use of contraception at endpoint. The “net effect” of 

contraception at the endpoint is the +/− effect of one standard deviation (+/-0.126). 

The effect of ever-use of contraception remained stable, whatever control was added, explaining about 

half of the fertility decline (53.7% in urban, 36.4% in rural areas). Contraception played a moderating role 

around the mean. One standard deviation (+/−12.6%) adds or subtracts an additional +/− 10.5%, so that 

an average ever-use of contraception combined with extensive use (say 70% ever-use/current-use) can 

explain about two-thirds of the fertility decline (43.2% + 2×10.5%), whereas when combined with low 

use (say 20%), it can explain only a tiny fraction (about 22%). 

When premarital fertility is added, the net effect of age at marriage is hardly changed. The effect of 

marriage remains statistically insignificant, in urban areas, in rural areas, and in both combined, whereas 

all other variables and controls remain highly statistically significant. 

6.4 Univariate versus Multivariate Analysis 

The aggregate regression model presented here shows only a limited impact of ever-use of contraception 

on fertility trends. This is in part due to heterogeneity and confounding factors. When combined with the 

extent of contraception, the proportion of fertility decline explained by the model increases. This 

observation matches with a previous observation in the univariate analysis: ever-use of contraception has 

the potential to explain all the fertility decline, since the proportion of women who ever-used modern 

contraception is similar (in urban) or exceeds (in rural) the percent of fertility decline. However, the net 

effect is reduced by the pattern of use of contraception over the life course, in particular by the age at first 

use and the continuity of use. If one takes the average empirical value of the extent of contraceptive use 

(45.2% in urban areas and 40.6% in rural areas) one finds again that about half of fertility decline can be 

explained by contraceptive use, given that a prevalence of about 80% is necessary for achieving a desired 

family size of two children. 
32 

The aggregate models also show consistently a minor impact of age at marriage on fertility trends. This 

can be compared with previous observations in the univariate analysis, and seems to be due to the small 

changes in marriage characteristics of the synthetic cohorts documented above, itself due to the mixing of 

cohorts in period data, and to increases in premarital fertility. 
33 

7. Discussion 

7.1 Contraceptive Use 

This study described trends in contraceptive use and their probable impact on fertility trends. Broadly 

speaking, the consistency between both trends was remarkable in urban areas: when half of women were 

using modern contraception half of the fertility transition was achieved. In rural areas, the relationship 

was weaker, probably because of later age at onset of contraceptive use. The full analysis of the 

relationship between contraception and fertility was limited by the lack of data, starting with missing 

information on age at first use, and ending with the dropping of the question on ever-use in the DHS-VI 

round. 

In sub-Saharan Africa, the study of trends in contraceptive use was further complicated by marked age, 

period, and cohort effects. Entry into contraception appeared very gradual, and not as quasi-universal 

since first intercourse as in developed countries. Family planning programs were installed in sub-Saharan 

African countries at various points in time, leading to strong cohort effects. Women born early in the 20th 

century had virtually no access to modern contraception, whereas women born late in the 20th century 

generally had ample access. Large differences were found between urban and rural areas, leading to 

different dynamics, and different age patterns. 

Beyond the spread of modern contraception among African women, the extent of contraceptive use, 

measured by the ratio of current-use to ever-use, appeared to play an important role on fertility decline. 

Some women probably used modern contraception for limited periods of time over their reproductive life 

span, while others probably used efficiently modern contraception when needed, achieving therefore low 

levels of fertility, as exemplified in numerous cases where the TFR is below three children per woman in 

urban areas. These periods of contraception deserve more investigation in the future, whether for avoiding 

a premarital birth, for spacing births, or for limiting family size. 

7.2 Missing Information on Abortion 

Although this study found consistent trends in contraception and fertility, ever-use and current-use of 

modern contraception did not explain all fertility declines in regression analysis. Several reasons could 

explain this discrepancy. First, data on contraception were limited, and the reconstruction of trends had 

problems, even though the beginning and the end were properly calibrated. Second, information on 

induced abortion was missing, and this could be an important element of fertility control in some 

countries, in particular in urban areas. 

7.3 Other Proximate Determinants 

This study deliberately ignored other factors of natural fertility, in order to focus on marriage and 

contraception. It should be recalled that, in many African countries, fertility was increasing in the 1950s, 

and probably even before then, for a variety of reasons. Little research has been conducted on trends in 

factors of natural fertility such as age at puberty, age at menopause and secondary sterility. If trends in 

these biological factors were favorable to fertility (earlier puberty, later menopause, less sterility), one 

could underestimate the effect of contraception by ignoring them. 

Furthermore, other biological and behavioral factors of natural fertility could be interacting with fertility 

trends in the opposite direction. Little work has been conducted on recent trends in primary and secondary 

sterility due to emerging diseases. In particular HIV/AIDS, which is highly prevalent in many African 

countries, was found repeatedly to reduce fecundity, and could have had an effect on fertility trends. 
34 

Finally, changing behaviors could also interact. In particular, marital disruption or spousal separation 

associated with HIV/AIDS, with migration or with new forms of occupation for men and women could 

have an effect on coital frequency and therefore on fertility. These effects could also account for parts of 

the unexplained fertility decline identified in this study. 

7.4 Marriage Trends 

Trends in age at first marriage were found to be more complicated than expected with all possible 

permutations: little changes (no trend), regular increase, increase followed by decrease, and decrease 

followed by increase. Some of the unexpected trends could be explained by economic or political crises, 

while others remain to be further analyzed. 

The relationship of age at marriage with female modern education, universally described in crosssectional analysis, was found to be complex when viewed in a longitudinal perspective. Here again all 

possible interactions were found: regular increase in both education and age at marriage; no increase in 

age at marriage despite rising level of education: increase in age at marriage without changes in level of 

education; and various loops and reversals in either or both variables. This analysis showed one more 

time how misleading it can be to extrapolate results from cross-sectional surveys to longitudinal analysis. 

This study considered only the first marriage, because information on marriage history was lacking in 

DHS surveys. Of course, the first marriage is only one element of marital life, and in some African 

countries divorce has become quite frequent, with long delays to remarriage, not counting widowhood 

associated with HIV/AIDS. 

Several other factors could be interacting with fertility, particularly migration and female employment. 

Numerous cases of spousal separation have been reported throughout Africa, due to temporary or circular 

migration of husbands or wives, which may occur with new economic opportunities for both males and 

females. In these cases, spouses may appear as married in demographic surveys, but may be living apart 

and therefore unlikely to conceive. 

7.5 Relation with Individual Data Analysis 

An attempt was made to compare the results of the longitudinal analysis of aggregate data with a more 

conventional analysis of individual data. It was necessary to select cohorts of women already 40 years of 

age or older, but that seriously limited the analysis because it selected older cohorts who had barely 

entered the fertility transition. In a multivariate analysis linking children ever born with age at marriage 

and ever-use of modern contraception, the net effect of age at marriage was always significant. This 

reveals the effect of individual heterogeneity (women who marry later have fewer children), but not the 

effect of changing age at marriage on fertility trends, as shown in the aggregate data analysis. 

7.6 Conclusion 

This study provided a broad overview of the relationship between increases in use of modern 

contraception and fertility trends over a long period of time—more than half a century. This analysis was 

possible thanks to the demographic surveys conducted in most sub-Saharan African countries, despite the 

absence of reliable vital registration of live births, and despite lack of precise information on 

contraceptive use. This broad overview hides many details that need to be further analyzed at the country 

level, and possibly also for regions or ethnic groups within countries. 
35 

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41 

Appendix A: Surveys Included in the Study 

Table A-1. List of DHS and related surveys included in the study 

Country WFS DHS-I DHS-II DHS-III DHS-IV DHS-V DHS-VI 

Benin 1982 1996 2001 2006 

Burkina Faso 1993 1999 2003 2010 

Burundi 1987 2010 

Cameroon 1978 1991 1998 2004 2011 

Central African Rep. 1994 

Chad 1997 2004 

Congo-Brazzaville 2005 

Congo-Kinshasa 2007 2009 

Côte d'Ivoire 1980 1994 1999 2005 

Ethiopia 2000 2005 2011 

Gabon 2000 2012 

Ghana 1979 1988 1993 1999 2003 2008 

Guinea 1999 2005 

Kenya 1978 1988 1993 1998 2003 2008 

Lesotho 1977 2004 2009 

Liberia 1986 2007 

Madagascar 1992 1997 2003 2008 

Malawi 1992 1996 2000 2004 2010 

Mali 1987 1995 2001 2006 

Mozambique 1997 2003 2011 

Namibia 1992 2000 2007 

Niger 1992 1998 2006 

Nigeria 1982 1990 1999 2003 2008 

Rwanda 1983 1992 2000 2005 2011 

Senegal 1978 1986 1993 1997 2005 2011 

Sierra Leone 2008 

South Africa 1998 

Swaziland 2006 

Tanzania 1991 1996 1999 2004 2010 

Togo 1988 1998 

Uganda 1988 1995 2001 2006 2011 

Zambia 1992 1996 2001 2007 

Zimbabwe 1988 1994 1999 2005 2010 

 

43 

Appendix B: Country Studies 

This appendix details, country by country, the results of the procedures for fitting trends in age at 

marriage, their relationship with trends in level of education, and the data on age and cohort effects on 

contraceptive use. Selection of data and methods used for the calculations are presented in Chapter 3 of 

the report. 

Presentation of the Country Studies 

The country studies are all presented on the same model. Each country section presents the data used, 

with a table containing some basic statistics: the number of women interviewed, the estimate of period 

median age at marriage (as published in DHS reports), the estimate of period ever-use of contraception 

among women age 15-49 (recalculated), and the estimate of period level of education, measured by the 

mean number of years schooling among women age 20-49 (recalculated). 

Three figures are then presented, each with a brief comment. The first figure presents trends in cohort 

median age at first marriage, for urban and rural areas separately, with the fitted trend in solid lines and 

the observed values in five-year cohorts as dots. The reading of this figure is straightforward. 

The second figure presents the path followed by the relationship between cohort level of education (Xaxis) and cohort age at first marriage (Y-axis), each dot representing a yearly cohort. The reading of this 

figure is more complex, since trends appear in the path, not on the X-axis. The main objective of these 

figures is to show the complexity of the relationship in a longitudinal perspective, which is far more 

complex that the classic linear relationship found in the cross-sectional studies. 

The third figure presents the age and cohort effects of ever-use of modern contraception. This figure aims 

at displaying the linearity of the age and cohort effects. The dots are the surveys conducted in the country. 

The lines display the cohort effects (X-axis). The age effects can be deduced by comparing dots on 

vertical lines (for the same cohorts). Two situations may occur. In case of several surveys in the same 

country, the cohort effects appear as the slopes of each line. Cohort trends are usually monotonic and 

parallel for each age group, except for age 15-19. In case of a single survey in a country, the pattern 

presented by the age groups is always a bell-shaped (e.g. inversed U-shape) pattern, the first part 

(increasing) describing the cohort effect, the second part (decreasing) describing the age effect. 

Providing a full explanation for all patterns displayed in the following figures is beyond the scope of this 

study. Readers are encouraged to go back to the original data and to analyze the country data further. 
44 

1. Benin

Four surveys were available in Benin: three DHS surveys conducted in 1996, 2001, and 2006, plus a WFS 

survey conducted in 1982. They were compatible for age at marriage, level of education, and ever-use of 

contraception. 

Table B-1.1. Survey characteristics, Benin 

Survey Year 

Sample size 

Median age at first 

marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent) 

WFS 1982 4,018 18.3 1.14 3.0 

DHS 1996 5,416 18.4 1.53 11.7 

DHS 2001 6,277 18.8 2.09 21.6 

DHS 2006 18,455 18.6 2.31 22.1 

Trends in age at marriage (Figure B-1.1) 

Median age at first marriage was average in Benin for cohorts born in the 1930s, with a small difference 

between urban and rural areas, and a small declining trend. It increased in urban areas for cohorts born 

after 1960, while remaining roughly constant in rural areas. 

Figure B-1.1. Reconstructed trends in median age at first marriage, Benin 
45 

Relationship with education (Figure B-1.2) 

The relationship between trends in level of education and in age at marriage was not linear. The change in 

trends in urban areas was due to a decline in level of education for cohorts 1965-1975 while age at 

marriage continued to increase. 

Figure B-1.2. Relationship between level of education and age at first marriage, Benin 

 
46 

Trends in contraceptive use (Figure B-1.3) 

Contraceptive use increased steadily for cohorts born before the 2006 survey. However, in the 2006 

survey the proportion of women who ever-used contraception stagnated for women age 30-49, meaning 

that there were few new acceptors between 2001 and 2006. This was not true for younger women age 15-

29. 

Figure B-1.3. Cohort trends in age-specific ever-use of contraception, Benin 
47 

2. Burkina Faso 

Four surveys were available in Burkina Faso, conducted in 1993, 1999, 2003, and 2010. The 2010 survey 

did not have information on ever-use of contraception. Otherwise, the surveys were compatible with 

respect to the three variables, with a minor discrepancy for the mean level of education between the 1993 

and the 1999 surveys. 

Table B-2.1. Survey characteristics, Burkina Faso 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1993 6,354 17.5 1.09 9.9 

DHS 1999 6,445 17.6 0.93 13.1 

DHS 2003 12,477 17.7 1.38 20.4 

DHS 2010 17,087 17.8 1.77 

Trends in age at marriage (Figure B-2.1) 

Median age at first marriage was average in Burkina Faso for cohorts born in the 1930s, with no 

difference between urban and rural areas. Age at marriage increased markedly in urban areas for cohorts 

born after 1960, while remaining roughly constant in rural areas. 

Figure B-2.1. Reconstructed trends in median age at first marriage, Burkina Faso 
48 

Relationship with education (Figure B-2.2) 

Relationship with education was hardly existent in rural areas because of the low level of education, and 

complex in urban areas, where age at marriage continued to increase while level of education was 

declining. 

Figure B-2.2. Relationship between level of education and age at first marriage, Burkina Faso 

 
49 

Trends in contraceptive use (Figure B-2.3) 

Trends in cohort use of contraception were regular, and very marked (strong slope) for cohorts born after 

1950, however with a rather low level (< 30%). 

Figure B-2.3. Trends in age-specific ever-use of contraception, Burkina Faso 
50 

3. Burundi 

Two surveys were available in Burundi, conducted in 1987 and 2010. The 2010 survey did not provide 

information on ever-use of contraception.

Table B-3.1. Survey characteristics, Burundi 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1987 3,970 19.5 0.89 1.9 

DHS 2010 9,389 20.3 3.07 

Trends in age at marriage (Figure B-3.1) 

Median age at first marriage was rather high in Burundi by African standards (as in nearby Rwanda), and 

appeared somewhat lower in urban areas than in rural areas for cohorts born before 1960. Age at marriage 

increased markedly in urban areas since cohort 1950, and reached relatively high values for the cohorts 

born after 1980, while it remained approximately constant in rural areas. 

Figure B-3.1. Reconstructed trends in median age at first marriage, Burundi 
51 

Relationship with education (Figure B-3.2) 

The relationship between trends in level of education and in age at marriage was straightforward in rural 

areas, but not linear in urban areas. The change in trends in urban areas was due to a decline in level of 

education, a first time for cohorts 1962-1964, and a second time for cohorts 1983-1990, while age at 

marriage continued to increase. 

Figure B-3.2. Relationship between level of education and age at first marriage, Burundi 

 
52 

Trends in contraceptive use (Figure B-3.3) 

Knowledge of trends in contraceptive use is limited in Burundi, because of a lack of data since the 1987 

survey. If the age effect is clearly visible for the 15-34 age group, the cohort effect is not as expected for 

women age 35-49. This case requires further research. 

Figure B-3.3. Trends in age-specific ever-use of contraception, Burundi 

 
53 

4. Cameroon 

Four DHS surveys and one WFS survey were available for Cameroon. The 2011 survey had no 

information on ever-use of contraception. The surveys were compatible for all three variables considered. 

Table B-4.1. Survey characteristics, Cameroon 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

WFS 1978 8,219 17.0 2.12 2.6 

DHS 1991 3,871 16.5 3.97 15.7 

DHS 1998 5,501 17.4 5.30 29.5 

DHS 2004 10,656 17.6 5.62 38.1 

DHS 2011 15,426 18.5 6.20 

Trends in age at marriage (Figure B-4.1) 

Median age at first marriage was average in Cameroon for cohorts born before 1950, with a small 

difference between urban and rural areas, and a small declining trend. It increased markedly in urban 

areas for cohorts born after 1960, and increased only marginally in rural areas. 

Figure B-4.1. Reconstructed trends in median age at first marriage, Cameroon 
54 

Relationship with education (Figure B-4.2) 

The relationship between trends in level of education and in age at marriage was not linear, in both urban 

and rural areas. The change in trends was due to a decline in level of education for cohorts 1970-1980 in 

urban areas, and for cohorts 1975-1980 in rural areas, while age at marriage continued to increase. 

Figure B-4.2. Relationship between level of education and age at first marriage, Cameroon 

 
55 

Trends in contraceptive use (Figure B-4.3) 

Ever-use of contraception increased steadily for all cohorts considered, starting from virtually no 

contraception for the oldest cohorts (1930s) to relatively high levels (above 50%) for cohorts born in the 

1970s. Even the younger age group (15-19) now has a relatively high level of contraceptive use. 

Figure B-4.3. Trends in age-specific ever-use of contraception, Cameroon 
56 

5. Central African Republic 

Only one survey (1994) was available in the Central African Republic, which seriously limits the trend 

analysis.

Table B-5.1. Survey characteristics, Central African Republic 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1994 5,884 17.3 2.18 12.2 

Trends in age at marriage (Figure B-5.1) 

Median age at first marriage was average in the Central African Republic, with a small difference 

between urban and rural areas, and virtually no trend. Only a very small increse counld be noticed in the 

most recent cohort in urban areas, 

Figure B-5.1. Reconstructed trends in median age at first marriage, Central African Republic 

 
57 

Relationship with education (Figure B-5.2) 

The relationship between age at marriage and level of education was not monotonic in urban areas. For 

the earlier cohorts, age at marriage tended to decline while level of education increased slightly. Then it 

stayed constant for a long time, to increase markedly for cohorts born between 1962 and 1975, despite 

only a minor increase in level of education. In rural areas, it stayed roughly constant, despite a minor 

increase in level of education. 

Figure B-5.2. Relationship between level of education and age at first marriage, Central African 

Republic 

 
58 

Trends in contraceptive use (Figure B-5.3) 

Knowledge about ever-use of contraception is limited in the Central African Republic. However, one 

could notice a strong cohort effect by comparing the proportion of ever-use between the 25-29 and the 45-

49 age groups, and a large age effect between the 15-19 and the 25-29 age groups, as expected. 

Figure B-5.3. Trends in age-specific ever-use of contraception, Central African Republic 

 
59 

6. Chad 

Two surveys were available in Chad, conducted in 1996 and 2004. Both were compatible with respect to 

the three variables considered. Note the low values for age at marriage, level of education, and ever-use of 

contraception. 

Table B-6.1. Survey characteristics, Chad 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1996 7,454 15.8 0.80 2.8 

DHS 2004 6,085 15.9 1.21 4.8 

Trends in age at marriage (Figure B-6.1) 

Median age at first marriage was very low in Chad (as in nearby Niger), with a small slope in urban areas, 

and a tiny difference between urban and rural areas. 

Figure B-6.1. Reconstructed trends in median age at first marriage, Chad 

 
60 

Relationship with education (Figure B-6.2) 

The relationship between trends in level of education and in age at marriage was linear, as expected, but 

at low levels and with a low slope. 

Figure B-6.2. Relationship between level of education and age at first marriage, Chad 

 
61 

Trends in contraceptive use (Figure B-6.3) 

Contraceptive use had the expected age and cohort patterns, but remained at a low level (< 10% for any 

age group considered) and without significant differences for age 30-34. 

Figure B-6.3. Trends in age-specific ever-use of contraception, Chad 

 
62 

7. Congo-Kinshasa (Democratic Republic of Congo) 

Only one survey was available in Congo-Kinshasa, a country with low level of income despite relatively 

high level of education. 

Table B-7.1. Survey characteristics, Congo-Kinshasa 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 2007 9,995 18.6 5.32 19.4 

Trends in age at marriage (Figure B-7.1) 

Median age at first marriage was average in Congo-Kinshasa for cohorts born before the 1960s, with no 

difference between urban and rural areas. Among the recent cohorts age at marriage increased in urban 

areas but increased only slightly in rural areas. 

Figure B-7.1. Reconstructed trends in median age at first marriage, Congo-Kinshasa 

 
63 

Relationship with education (Figure B-7.2) 

The relationship between trends in level of education and in age at marriage was not linear in urban areas. 

The change in trends in urban areas was due to a decline in level of education for cohorts 1970-1985, 

while age at marriage continued to increase. In rural areas, the increase in level of education had virtually 

no effect on age at marriage. 

Figure B-7.2. Relationship between level of education and age at first marriage, Congo-Kinshasa 

 
64 

Trends in contraceptive use (Figure B-7.3) 

The pattern of contraceptive use by age and cohort was as expected, with a strong cohort effect visible in 

the 25-29 to 45-49 age groups, and a strong age effect in the 15-19 to 25-29 age groups. 

Figure B-7.3. Trends in age-specific ever-use of contraception, Congo-Kinshasa 

 
65 

8. Congo-Brazzaville (Republic of Congo) 

Two surveys were available in Congo-Brazzaville (Republic of Congo). The second survey, an AIDS 

Indicator survey, had no information on contraceptive use. 

Table B-8.1. Survey characteristics, Congo-Brazzaville 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 2005 7,051 20.4 7.14 59.0 

AIS 2009 12,413 20.2 7.95 

Trends in age at marriage (Figure B-8.1) 

Median age at first marriage appeared to be relatively high in Congo-Brazzaville, with a unique pattern of 

ups (cohorts born before 1975) and downs (cohorts born after 1975). The changing pattern prevailed in 

both urban and rural areas, but was more marked in urban areas. 

Figure B-8.1. Reconstructed trends in median age at first marriage, Congo-Brazzaville 

 
66 

Relationship with education (Figure B-8.2) 

The relationship between trends in level of education and in age at marriage was complex, due to 

unexpected declining trends in level of education and in age at marriage. As a result, in urban areas the 

relationship changed from steady, to increasing, then to decreasing, and likewise in rural areas. 

Figure B-8.2. Relationship between level of education and age at first marriage, Congo-Brazzaville 

 
67 

Trends in contraceptive use (Figure B-8.3) 

Contraceptive use showed regular cohort and age patterns, with increasing cohort effect (age groups 25-

29 to 45-49) and increasing age effect (age groups 15-19 to 25-29). Note the high levels for mid-aged 

cohorts, above 70%. 

Figure B-8.3. Trends in age-specific ever-use of contraception, Congo-Brazzaville 

 
68 

9. Côte d’Ivoire 

Four surveys were available in Côte d’Ivoire, conducted in 1980 (WFS), 1994, and 1999 (DHS), and 

2005 (AIS). The 2005 AIS survey had no information on contraception. 

Table B-9.1. Survey characteristics, Côte d’Ivoire 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

WFS 1980 5,764 17.3 1.30 3.0 

DHS 1994 8,099 18.1 2.36 23.5 

DHS 1999 3,040 18.7 2.90 32.6 

AIS 2005 5,183 19.3 3.04 

Trends in age at marriage (Figure B-9.1) 

Trends in age at first marriage were irregular in Côte d’Ivoire. In urban areas, after a long period of 

stagnation, age at marriage increased suddenly (cohorts 1962-1974), then declined as rapidly. In rural 

areas, after the same period of stagnation at a similar level as in urban areas, it increased slowly, to reach 

the same level as urban areas around 1985. 

Figure B-9.1. Reconstructed trends in median age at first marriage, Côte d’Ivoire 
69 

Relationship with education (Figure B-9.2) 

The relationship between trends in level of education and in age at marriage was not linear. The change in 

trends in urban areas was due to the rise and fall of age at marriage, in a time during which level of 

education changed little. In rural areas, the increase in age at marriage occurred when level of education 

remained constant, while age at marriage remained constant when level of education increased. 

Figure B-9.2. Relationship between level of education and age at first marriage, Côte d’Ivoire 

 
70 

Trends in contraceptive use (Figure B-9.3) 

Cohort and age effects of contraceptive use were straightforward in Côte d’Ivoire, with only a small 

discrepancy for the 45-49 age group (for the same cohorts, the proportion of women who ever-used 

contraception appeared somewhat lower than in the 40-44 age group). This minor discrepancy seems to 

be due to the small sample size of this group. 

Figure B-9.3. Trends in age-specific ever-use of contraception, Côte d’Ivoire 
71 

10. Ethiopia

Three surveys were available in Ethiopia, conducted in 2000, 2005, and 2011. They were compatible for 

the three variables investigated. The 2011 survey had no information on ever-use of contraception. 

Table B-10.1. Survey characteristics, Ethiopia 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 2000 15,367 16.0 1.30 11.0 

DHS 2005 14,070 16.1 1.88 17.4 

DHS 2011 16,515 16.5 2.92 

Trends in age at marriage (Figure B-10.1) 

Age at marriage was increasing steadily in both urban and rural areas for cohorts born after 1955, and was 

probably at low levels before. 

Figure B-10.1. Reconstructed trends in median age at first marriage, Ethiopia 

 
72 

Relationship with education (Figure B-10.2) 

The relationship between trends in level of education and in age at marriage was quite regular up to the 

most recent cohorts in urban areas. This changing pattern in urban areas was due to stagnation in level of 

education for cohorts 1965-1975 while age at marriage continued to increase. 

Figure B-10.2. Relationship between level of education and age at first marriage, Ethiopia 

 
73 

Trends in contraceptive use (Figure B-10.3) 

Contraceptive use increased steadily for all cohorts available, reaching moderate values for the mid-age 

groups. 

Figure B-10.3. Trends in age-specific ever-use of contraception, Ethiopia 

 
74 

11. Gabon 

Two surveys were available in Gabon, conducted in 2000 and 2012. Both were compatible for the same 

variables, but the second survey had no information on ever-use of contraception. 

Table B-11.1. Survey characteristics, Gabon 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 2000 6,183 19.7 6.91 60.9 

DHS 2012 8,422 22.0 8.30 

Trends in age at marriage (Figure B-11.1) 

The increase in median age at first marriage was remarkable and steady in urban areas, which account for 

the majority of the population. In rural areas age at marriage stagnated for cohorts born after 1975, after 

rising for earlier cohorts. 

Figure B-11.1. Reconstructed trends in median age at first marriage, Gabon 

 
75 

Relationship with education (Figure B-11.2) 

The relationship between trends in level of education and in age at marriage was unexpected. In urban 

areas, after a steady increase for both, the relationship changed because of a severe decline in level of 

education (cohorts born after 1955). In rural areas, where the decline in level of education occurred later 

(cohorts born after 1970), the relationship also changed because of the stagnation in the age at marriage. 

Figure B-11.2. Relationship between level of education and age at first marriage, Gabon 

 
76 

Trends in contraceptive use (Figure B-11.3) 

Contraceptive use had the expected pattern of cohort and age effects. Note the highly values, above 70% 

for the mid-aged cohorts. 

Figure B-11.3. Trends in age-specific ever-use of contraception, Gabon 
77 

12. Ghana

Six surveys were available in Ghana, starting with the 1979 WFS, and followed by the 1988, 1993, 1999, 

2003, and 2008 DHS. All six surveys were compatible for the three variables investigated. 

Table B-12.1. Survey characteristics, Ghana 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

WFS 1979 6,125 18.1 4.14 17.7 

DHS 1988 4,488 18.1 4.92 20.5 

DHS 1993 4,562 18.8 5.34 29.5 

DHS 1999 4,843 19.1 5.97 32.9 

DHS 2003 5,691 19.4 5.90 38.9 

DHS 2008 4,916 19.8 6.58 42.3 

Trends in age at marriage (Figure B-12.1) 

Median age at first marriage was average in Ghana for cohorts born before 1950, somewhat higher but 

declining in urban areas, and more stable in rural areas. Age at marriage increased markedly for women 

born after 1950 in urban areas, and increased only marginally in rural areas. 

Figure B-12.1. Reconstructed trends in median age at first marriage, Ghana 
78 

Relationship with education (Figure B-12.2) 

The relationship between trends in level of education and in age at marriage was not linear. In urban 

areas, age at marriage first declined, then stayed stable, then increased rapidly and continued to increase 

even when the level of education stagnated. In rural areas, it increased only marginally while the level of 

education increased rapidly. 

Figure B-12.2. Relationship between level of education and age at first marriage, Ghana 

 
79 

Trends in contraceptive use (Figure B-12.3) 

Contraceptive use increased steadily for all cohorts considered, reaching levels above 50% for the midage groups. The age pattern was quite regular above age 20. However, the 15-19 age group stayed at a 

very low level for the cohorts born between 1960 and 1990. 

Figure B-12.3. Trends in age-specific ever-use of contraception, Ghana 

 
80 

13. Guinea 

Two surveys were available in Guinea, conducted in 1999 and 2005. They were compatible for the three 

variables analyzed. 

Table B-13.1. Survey characteristics, Guinea 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1999 6,753 16.4 1.40 10.3 

DHS 2005 7,954 16.2 1.50 16.9 

Trends in age at marriage (Figure B-13.1) 

Median age at first marriage was low and stable in rural areas, and changing in urban areas. In the first 

phase, it was declining, then rising for cohorts born after 1965. 

Figure B-13.1. Reconstructed trends in median age at first marriage, Guinea 

 
81 

Relationship with education (Figure B-13.2) 

The relationship between trends in level of education and in age at marriage was not linear in urban areas. 

In the first phase, age at marriage was declining while level of education was increasing, then it continued 

to decline despite a decline in level of education, then both increased simultaneously. In rural areas, the 

level of education remained very low. 

Figure B-13.2. Relationship between level of education and age at first marriage, Guinea 

 
82 

Trends in contraceptive use (Figure B-13.3) 

Contraceptive use increased steadily for all cohorts, and the age pattern was as expected. However, the 

overall proportion of women who ever-used modern contraception remained low, even for the recent 

cohorts. 

Figure B-13.3. Trends in age-specific ever-use of contraception, Guinea 

 
83 

14. Kenya 

Kenya has a wealth of high quality data, with six surveys conducted regularly, starting with the 1978 

WFS, up to the 2008 DHS. All surveys were compatible with respect to the three variables investigated. 

Table B-14.1. Survey characteristics, Kenya 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

 (number of women) (years) (years of schooling) (percent)

WFS 1978 8,100 17.8 3.34 11.1 

DHS 1989 7,150 18.1 5.24 24.1 

DHS 1993 7,540 18.8 6.01 34.0 

DHS 1998 7,881 19.2 6.90 42.2 

DHS 2003 8,195 19.7 7.12 43.5 

DHS 2008 8,444 20.0 7.85 53.6 

Trends in age at marriage (Figure B-14.1) 

Median age at first marriage appeared to have been lower in urban areas than in rural areas for the early 

cohorts, born before 1940. Then age at marriage increased steadily in urban areas, whereas in rural areas 

it first declined somewhat, to increase later, and then to stagnate for cohorts born after 1975. 

Figure B-14.1. Reconstructed trends in median age at first marriage, Kenya 
84 

Relationship with education (Figure B-14.2) 

The relationship between trends in level of education and in age at marriage was straightforward, with the 

exception of the early cohorts in urban areas and the later cohorts in rural areas. Note the high level of 

education, and the similar relationships in both urban and rural areas. 

Figure B-14.2. Relationship between level of education and age at first marriage, Kenya 

 
85 

Trends in contraceptive use (Figure B-14.3) 

Cohort trends were marked, and very early, since even cohorts born in the 1930s were already using 

modern contraception, at least in the old age groups. The age patterns were also quite regular, with the 

exception of the low values for women age 15-19. Overall, the proportion of women who ever-used 

contraception was high (above 70% for the mid-age range). 

Figure B-14.3. Trends in age-specific ever-use of contraception, Kenya 
86 

15. Lesotho 

Three surveys were available in Lesotho, starting with the 1977 WFS, and followed by the 2004 and 2009 

DHS. The surveys were compatible in the three variables investigated, however the 2009 DHS survey had 

no information on ever-use of contraception. 

Table B-15.1. Survey characteristics, Lesotho 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

WFS 1977 3,603 18.5 5.02 6.3 

DHS 2004 7,095 19.1 7.29 58.7 

DHS 2009 7,624 19.9 8.10 

The 1977 sample includes only ever-married women age 15-49. This leads to a bias in the estimation of 

the median age at marriage, but the bias is believed to be small because almost all women marry by age 

40+, both in 2004 (96.0%) and in 2009 (94.4%). The bias was estimated by assuming that 98% of women 

age 40-49 were ever married in 1977. This would lead to a small change in the median age at marriage: 

from 18.43 years for the ever married sample to 18.51 years, which is a negligible when compared with 

an increase of +5.0 years in urban areas and +2.0 years in rural areas. Furthermore, the cohort trends were 

consistent in the three surveys. 

 
87 

Trends in age at marriage (Figure B-15.1) 

Median age at first marriage was average in Lesotho for cohorts born in the 1930, with hardly any 

difference between urban and rural areas. Age at marriage increased steadily in urban areas, while it first 

slightly decreased and then increased in rural areas. 

Figure B-15.1. Reconstructed trends in median age at first marriage, Lesotho 

 
88 

Relationship with education (Figure B-15.2) 

The relationship between trends in level of education and in age at marriage was quite monotonic in urban 

areas until the most recent cohorts, where it continued to increase despite a stagnating level of education. 

In rural areas the changes in age at marriage were not associated with level of education at first, but then 

both increased rapidly for cohorts born after 1967. 

Figure B-15.2. Relationship between level of education and age at first marriage, Lesotho 

 
89 

Trends in contraceptive use (Figure B-15.3) 

Contraceptive use increased steadily for all cohorts available, with a regular age pattern except for women 

age 15-19. Note the high proportion of women who ever-used contraception (> 80% for mid-aged 

women). 

Figure B-15.3. Trends in age-specific ever-use of contraception, Lesotho 
90 

16. Liberia

Two surveys were conducted in Liberia in 1986 and 2007. Both were compatible with respect to the three 

variables analyzed. 

Table B-16.1. Survey characteristics, Liberia 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1986 5,239 17.2 2.59 18.6 

DHS 2007 7,092 18.4 3.61 32.7 

Trends in age at marriage (Figure B-16.1) 

Median age at first marriage was average in Liberia for cohorts born in the 1930s and 1940s, with a small 

difference between urban and rural areas. In urban areas, the age at marriage first stagnated, then incread 

for cohorts born after 1955. In rural areas the increase was regular, but slow. 

Figure B-16.1. Reconstructed trends in median age at first marriage, Liberia 

 
91 

Relationship with education (Figure B-16.2) 

The relationship between trends in level of education and in age at marriage was not linear. In urban areas 

the relation was at first non-existent, then age at marriage increased slowly while the level of education 

was increasing, and then it continued to increase despite a decline followed by stagnation in level of 

education (cohorts born after 1962). In rural areas the relationship was straightforward, but at low levels 

of both education and age at marriage. 

Figure B-16.2. Relationship between level of education and age at first marriage, Liberia 

 
92 

Trends in contraceptive use (Figure B-16.3) 

Contraceptive use increased steadily for all cohorts considered, and the age patterns were consistent, with 

the exception of the 45-49 age group, which was probably due to small sample size. 

Figure B-16.3. Trends in age-specific ever-use of contraception, Liberia 

 
93 

17. Madagascar 

Four surveys were available in Madagascar, conducted in 1992, 1997, 2003, and 2008. They were 

compatible for the three variables analyzed. Note that the lower age at marriage and lower level of 

education in 2008 survey compared with the 2003 survey are real effects, as will be seen below. 

Table B-17.1. Survey characteristics, Madagascar 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1992 6,260 18.2 3.71 8.3 

DHS 1997 7,060 18.5 3.74 13.9 

DHS 2003 7,949 19.1 4.49 31.3 

DHS 2008 17,375 18.9 4.28 40.8 

Trends in age at marriage (Figure B-17.1) 

Median age at first marriage was average in Madagascar for cohorts born in the 1940s, with a small 

difference between urban and rural areas. In both urban and rural areas, age at marriage first increased, 

then declined, a pattern markedly different from most other countries. 

Figure B-17.1. Reconstructed trends in median age at first marriage, Madagascar 
94 

Relationship with education (Figure B-17.2) 

The relationship between trends in level of education and in age at marriage was not linear, partly because 

of the trends in age at marriage, and partly because of irregular trends in level of education. In urban areas 

both changes were roughly concomitant for a while, and then the declining age at marriage prevailed, 

despite an increasing level of education. In rural areas, the pattern was more complex, with stagnation of 

the age at marriage when the level of education declined, and decline in age at marriage when level of 

education remained unchanged. 

Figure B-17.2. Relationship between level of education and age at first marriage, Madagascar 

 
95 

Trends in contraceptive use (Figure B-17.3) 

In contrast with the complex changes noted above, contraceptive use increased steadily for all cohorts 

considered, reaching relatively high levels for the mid-age groups. The age pattern was regular, with the 

exception of the 15-19 age group, which remained at low levels, although increasing steadily in the recent 

cohorts. 

Figure B-17.3. Trends in age-specific ever-use of contraception, Madagascar 
96 

18. Malawi 

Four surveys were conducted in Malawi, in 1992, 2000, 2004, and 2010. They were compatible in the 

three variables investigated. 

Table B-18.1. Survey characteristics, Malawi 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1992 4,849 17.8 2.31 16.9 

DHS 2000 13,220 17.8 3.99 38.6 

DHS 2004 11,698 17.9 4.71 46.3 

DHS 2010 23,020 17.8 5.35 61.8 

Trends in age at marriage (Figure B-18.1) 

Median age at first marriage was average in Malawi for cohorts born in the 1950s, with hardly any 

difference between urban and rural areas. Age at marriage increased soemwhat in urban areas for cohorts 

born after 1960, while remaining roughly constant in rural areas. 

Figure B-18.1. Reconstructed trends in median age at first marriage, Malawi 
97 

Relationship with education (Figure B-18.2) 

The relationship between trends in level of education and in age at marriage was hardly visible, with a 

very small change in age at marriage associated with a major increase in level of education. The 

relationship was basically the same in urban and rural areas. 

Figure B-18.2. Relationship between level of education and age at first marriage, Malawi 

 
98 

Trends in contraceptive use (Figure B-18.3) 

Contraceptive use increased steadily for all cohorts considered. The age pattern was also consistent, with 

the 15-19 age group at much lower levels. Otherwise, the proportion of women who ever-used 

contraception reached high values (> 80%) for mid-age groups. 

Figure B-18.3. Trends in age-specific ever-use of contraception, Malawi 

 
99 

19. Mali 

Four surveys were available in Mali, conducted in 1987, 1996, 2001, and 2006.They were compatible in 

the three variables investigated, each with low values, with the exception of contraceptive use in the last 

survey found lower than expected from previous trends. 

Table B-19.1. Survey characteristics, Mali 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1987 3,200 15.7 0.86 3.2 

DHS 1996 9,704 16.0 1.01 12.4 

DHS 2001 12,849 16.5 1.28 20.1 

DHS 2006 14,583 16.6 1.42 17.5 

Trends in age at marriage (Figure B-19.1) 

Median age at first marriage was very low in Mali for cohorts born before the 1950s, with hardly any 

difference between urban and rural areas. Age at marriage increased somewhat in urban areas for cohorts 

1950-1975, then stagnated and even declined slightly for the most recent cohorts. There was no change in 

age at marriage in rural areas. 

Figure B-19.1. Reconstructed trends in median age at first marriage, Mali 
100 

Relationship with education (Figure B-19.2) 

The relationship between trends in level of education and in age at marriage was limited due to the small 

magnitude of change in both variables. The relationship was roughly linear in both urban and rural areas, 

with a small loop in urban areas due to stagnation or mild decline in level of education, and a slightly 

inversed relationship in the recent cohorts. 

Figure B-19.2. Relationship between level of education and age at first marriage, Mali 

 
101 

Trends in contraceptive use (Figure B-19.3) 

In contrast, trends in contraceptive use were steady for cohorts before the 2006 survey. In this survey the 

proportion of women who ever-used contraception stagnated or declined for all age groups, which is 

surprising and might be due to an over-estimation in the 2001 survey followed by an under-estimation in 

the 2006 survey. This point would require further investigation. 

Figure B-19.3. Trends in age-specific ever-use of contraception, Mali 
102 

20. Mozambique 

Three surveys were available in Mozambique, conducted in 1997, 2003, and 2011. They were compatible 

in the three variables investigated. However, the 2011 survey did not include information on ever-use of 

contraception. 

Table B-20.1. Survey characteristics, Mozambique 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

 (number of women) (years) (years of schooling) (percent)

DHS 1997 8,779 17.1 2.07 13.0 

DHS 2003 12,418 17.5 2.48 47.4 

DHS 2011 13,745 18.8 3.82 

Trends in age at marriage (Figure B-20.1) 

Median age at first marriage was originally rather high in Mozambique but declined for cohorts born in 

1940-1965. Then age at marriage increased in urban areas after 1965, and was little changed in rural 

areas. 

Figure B-20.1. Reconstructed trends in median age at first marriage, Mozambique 
103 

Relationship with education (Figure B-20.2) 

As a result of the trends in age at marriage mentioned above, the relationship between trends in level of 

education and in age at marriage was inverse for the earlier cohorts, and as expected for the more recent 

cohorts. The pattern was further complicated by the rapid increase in education for the 1950-1960 

cohorts, followed by stagnation later on. 

Figure B-20.2. Relationship between level of education and age at first marriage, Mozambique 

 
104 

Trends in contraceptive use (Figure B-20.3) 

Trends in contraceptive use were straightforward and very marked in Mozambique, with a rapid increase 

between 1997 and 2003 affecting all cohorts and all ages. 

Figure B-20.3. Trends in age-specific ever-use of contraception, Mozambique 

 
105 

21. Namibia 

Three surveys were available in Namibia, conducted in 1992, 2000, and 2007. They were compatible for 

age at marriage, level of education, and ever-use of contraception. 

Table B-21.1. Survey characteristics, Namibia 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1992 5,421 24.8 5.89 37.1 

DHS 2000 6,755 26.2 7.54 61.0 

DHS 2007 9,804 29.1 8.41 70.5 

Trends in age at marriage (Figure B-21.1) 

Median age at first marriage was already high in Namibia for cohorts born in the 1940s, with a gap 

between urban and rural areas. Age at marriage continued to increase in urban areas, and was not 

available for the most recent cohorts because less than 50% were ever-married in 2007. In rural areas, 

median age at marriage increased rapidly, surpassed that of urban areas, and continued to increase up to 

the most recent cohort available. This pattern is unque in Africa, and it has led to a huge prevalence of 

premarital fertility documented elsewhere (Garenne and Zwang, 2006). 

Figure B-21.1. Reconstructed trends in median age at first marriage, Namibia 
106 

Relationship with education (Figure B-21.2) 

The relationship between trends in level of education and in age at marriage was straightforward in urban 

areas, and marked by the rapid increase in rural areas, documented above. 

Figure B-21.2. Relationship between level of education and age at first marriage, Namibia 

 
107 

Trends in contraceptive use (Figure B-21.3) 

Contraceptive use was already high for the older cohorts, and continued to increase steadily for all cohorts 

considered, reaching high values (> 80%) for the mid-age cohorts. Only the 15-19 age group stands out 

with a lower proportion of women who ever-used contraception. 

Figure B-21.3. Trends in age-specific ever-use of contraception, Namibia 
108 

22. Niger 

Three surveys were available in Niger, conducted in 1992, 1997, and 2006. They were all compatible with 

respect to the three variables investigated. Age at marriage, level of education, and ever-use of 

contraception all were at low levels, with stagnation in ever-use of contraception in the 2006 survey 

compared with the 1997 survey. 

Table B-22.1. Survey characteristics, Niger 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1992 6,503 14.9 0.57 4.4 

DHS 1997 7,577 15.1 0.97 10.9 

DHS 2006 9,223 15.5 0.99 10.9 

Trends in age at marriage (Figure B-22.1) 

Median age at first marriage was very low in Niger for cohorts born in the 1940s, with hardly any 

difference between urban and rural areas. In urban areas, it increased steadily over the years, while 

remaining low in rural areas, although with a significant positive slope. 

Figure B-22.1. Reconstructed trends in median age at first marriage, Niger 
109 

Relationship with education (Figure B-22.2) 

The relationship between trends in level of education and in age at marriage was hardly visible in rural 

areas, because of the low level of education. In urban areas it followed a regular pattern up to cohorts 

born in 1973; then age at marriage continued to increase despite stagnation, and even a regression, in the 

level of education. 

Figure B-22.2. Relationship between level of education and age at first marriage, Niger 

 
110 

Trends in contraceptive use (Figure B-22.3) 

Contraceptive use increased steadily for all cohorts until the 2006 survey. In the 2006 survey, however, 

for women under age 35, the proportion who ever-used contraception stagnated or even regressed, 

suggesting a problem with data. This was not the case for older women, age 35-49, among whom ever-use 

continued to increase. 

Figure B-22.3. Trends in age-specific ever-use of contraception, Niger 
111 

23. Nigeria 

Five surveys were available in Nigeria, starting with the 1982 WFS, followed by DHS surveys conducted 

in 1990, 1999, 2003, and 2008. Overall, the surveys were compatible with respect to the three variables 

investigated, although the 1999 survey, known to have some data problems, seems to have over-estimated 

age at marriage, while the 2003 survey seems to have under-estimated age at marriage. 

Table B-23.1. Survey characteristics, Nigeria 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

WFS 1982 9,727 16.3 2.48 2.6 

DHS 1990 8,781 16.9 3.13 9.0 

DHS 1999 9,810 17.9 4.97 17.8 

DHS 2003 7,620 16.6 5.10 22.7 

DHS 2008 33,385 18.3 6.10 24.0 

Trends in age at marriage (Figure B-23.1) 

Median age at first marriage was average in Nigeria for cohorts born in the 1930s, with a small difference 

between urban and rural areas, and a small declining trend. It increased markedly in urban areas for 

cohorts born after 1960, while increasing only slowly in rural areas. 

Figure B-23.1. Reconstructed trends in median age at first marriage, Nigeria 
112 

Relationship with education (Figure B-23.2) 

The relationship between trends in level of education and in age at marriage was not linear. For the older 

cohorts, it was reversed (declining age at marriage despite increasing level of education), then it was flat 

for a long time, to increase rapidly thereafter, and becoming almost vertical for the recent cohorts, when 

age at marriage continued to rise while level of education stagnated. 

Figure B-23.2. Relationship between level of education and age at first marriage, Nigeria 

 
113 

Trends in contraceptive use (Figure B-23.3) 

Contraceptive use increased steadily for cohorts until the 2008 survey, when the proportion of women 

who ever-used contraception appeared to stagnate or decline for women age 45-49, 25-39, and 30-34, 

indicating a mild problem with data. The 15-19 age groups showed a much lower level of ever-use of 

contraception. 

Figure B-23.3. Trends in age-specific ever-use of contraception, Nigeria 
114 

24. Rwanda 

Five surveys were available in Rwanda, starting with the 1983 WFS, followed by the 1992, 2000, 2005 

and 2011 DHS surveys. Overall, the surveys were compatible for age at marriage and level of education, 

while apparently inconsistent for ever-use of contraception. However, the 2011 survey did not provide 

information on ever-use of contraception. Rwanda underwent an extremely difficult period during the 

civil war in the 1990s. 

Table B-24.1. Survey characteristics, Rwanda 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

WFS 1983 5,738 19.1 1.92 1.5 

DHS 1992 6,551 20.0 3.40 16.8 

DHS 2000 10,421 20.7 3.71 15.5 

DHS 2005 11,321 20.7 3.75 15.2 

DHS 2011 13,671 21.4 4.40 

 
115 

Trends in age at marriage (Figure B-24.1) 

Median age at first marriage was average in Rwanda for cohorts born in the 1940s, with harldy any 

difference between urban and rural areas. Both urban and rural areas experienced a rise in age at marriage 

for cohorts 1940-1965, followed by a drop for cohorts reaching adulthood in the 1990s, then followed by 

a new rise for cohorts born after 1975. 

Figure B-24.1. Reconstructed trends in median age at first marriage, Rwanda 

 
116 

Relationship with education (Figure B-24.2) 

The relationship between trends in level of education and in age at marriage was particularly complex in 

Rwanda, due to changes in both level of education and age at marriage associated with the difficult years 

of the civil war. Both urban and rural areas followed the same pattern of showing, first, a positive 

relationship, then a loop during the crisis years, and finally a negative relationship corresponding to the 

declining level of education. 

Figure B-24.2. Relationship between level of education and age at first marriage, Rwanda 

 
117 

Trends in contraceptive use (Figure B-24.3) 

Contraceptive use showed a strange pattern, with an abnormal rise and fall. The second survey (1992) 

might have over-estimated the level of contraceptive use. The later stagnation makes sense, since it 

occurred mostly during the crisis years. At last survey however, the proportion of women who ever-used 

contraception increased markedly, and seems to have recovered from earlier trends. Population dynamics 

were so complex over this period, with mass killing and large international migration flows, that 

empirical findings are difficult to interpret due to numerous possible selection biases. 

Figure B-24.3. Trends in age-specific ever-use of contraception, Rwanda 

 
118 

Short term changes during the crisis years (1994-1995) 

During the two years following the genocide, marriage rates computed as the number of first marriages by 

the person-years lived by never-married women age 14-29, had a major increase compared with trends 

before and after. This effect deserves more research. (Figure 24.4) 

Figure B-24.4. Trends in period first marriage rates, women age 14-29, Rwanda 
119 

25. Senegal 

Six surveys were available in Senegal, starting with the 1978 WFS, followed by five DHS surveys 

conducted in 1986, 1993, 1997, 2005 and 2011. Overall, the surveys were compatible for the three 

variables investigated, with some mild inconstancy for age at marriage in 1993. The 2011 DHS did not 

provide information on ever-use of contraception. 

Table B-25.1. Survey characteristics, Senegal 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

WFS 1978 3,985 15.8 0.95 1.3 

DHS 1986 4,415 16.4 1.78 6.2 

DHS 1993 6,310 16.2 1.81 10.2 

DHS 1997 8,593 17.4 2.31 14.8 

DHS 2005 14,602 18.3 2.69 19.9 

DHS 2011 15,688 19.3 2.94 

 
120 

Trends in age at marriage (Figure B-25.1) 

Median age at first marriage was low in Senegal for cohorts born in the 1930s, with a small difference 

between urban and rural areas, and a small declining trend. It increased in urban areas for cohorts born 

after 1945, and somewhat later in rural areas, but stagnated in urban areas for the more recent cohorts. 

Figure B-25.1. Reconstructed trends in median age at first marriage, Senegal 

 
121 

Relationship with education (Figure B-25.2) 

The relationship between trends in level of education and in age at marriage was not linear. The change in 

trends in urban areas was due to stagnation in level of education for cohorts 1955-1965, and cohorts 1975-

1985, while age at marriage continued to increase. In the most recent cohorts, both age at marriage and 

level of education stagnated. In rural areas level of education even reversed, while age at marriage 

increased somewhat. 

Figure B-25.2. Relationship between level of education and age at first marriage, Senegal 

 
122 

Trends in contraceptive use (Figure B-25.3) 

In contrast, ever-use of contraception increased steadily for all cohorts considered. The minor erratic 

patterns seen at age 45-49 and 40-44 seem due to small sample size. The mid-age women reached a 

medium level (around 35%), while contraceptive use remained low for women age 15-19. 

Figure B-25.3. Trends in age-specific ever-use of contraception, Senegal 
123 

26. Sierra Leone 

Only one survey is available in Sierra Leone. The country underwent a troubled period during the civil 

war in the late 1980s and early 1990s. 

Table B-26.1. Survey characteristics, Sierra Leone 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 2008 7,374 17.0 2.56 20.5 

Trends in age at marriage (Figure B-26.1) 

Median age at first marriage was low in Sierra Leone for cohorts born in the late 1950s, with a small 

difference between urban and rural areas. Age at marriage dropped, then increased in urban areas, 

corresponding to the civil war period, while it remained constant at a low level in rural areas. 

Figure B-26.1. Reconstructed trends in median age at first marriage, Sierra Leone 

 
124 

Relationship with education (Figure B-26.2) 

The relationship between trends in level of education and in age at marriage would be linear except for a 

loop during the civil war years. Age at marriage remained constant in rural areas despite some mild rise in 

level of education. 

Figure B-26.2. Relationship between level of education and age at first marriage, Sierra Leone 

 
125 

Trends in contraceptive use (Figure B-26.3) 

Contraceptive use at the 2008 survey showed a rising cohort trend and the expected trend by age. Everuse increased with some irregularities in the 40-44 age group (cohort trend) and in the 20-24 age group 

(age pattern). 

Figure B-26.3. Trends in age-specific ever-use of contraception, Sierra Leone 
126 

27. South Africa 

Only one DHS survey, conducted in 1998, was available in South Africa, which seriously limits the 

analysis. Compared with other African countries, South Africa is more advanced, with higher levels of 

income, levels of education, age at marriage, and contraceptive prevalence. 

Table B-27.1. Survey characteristics, South Africa 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1998 11,735 24.2 8.65 73.9 

Trends in age at marriage (Figure B-27.1) 

Median age at first marriage was high in South Africa, and steadily increasing in both urban and rural 

areas for the available cohorts. 

Figure B-27.1. Reconstructed trends in median age at first marriage, South Africa 

 
127 

Relationship with education (Figure B-27.2) 

Because level of education increased steadily for cohorts born after 1925, the relationship between trends 

in level of education and in age at marriage was monotonic, and almost linear, with some stagnation in the 

older cohorts and some acceleration in the younger cohorts. 

Figure B-27.2. Relationship between level of education and age at first marriage, South Africa 

 
128 

Trends in contraceptive use (Figure B-27.3) 

Ever-use of contraception was very high for the 1950-1975 cohorts, reaching almost 90% for women age 

30-34, with a mild cohort trend. The age effect was small, with only a relatively small level of ever-use 

for women age 15-19, although still above the levels in most other African countries. 

Figure B-27.3. Trends in age-specific ever-use of contraception, South Africa 
129 

28. Swaziland 

Only one survey was available in Swaziland, conducted in 2006. Like South Africa, Swaziland is also 

very advanced in the marriage, education, contraception, and fertility transitions. 

Table B-28.1. Survey characteristics, Swaziland 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 2006 4,987 25.6 8.09 69.8 

Trends in age at marriage (Figure B-28.1) 

As in South Africa, median age at first marriage was high and rising rapidly in both urban and rural areas. 

Median age could not be computed for the most recent cohorts in urban areas, since less than 50% of 

women had ever-married. 

Figure B-28.1. Reconstructed trends in median age at first marriage, Swaziland 

 
130 

Relationship with education (Figure B-28.2) 

The relationship between trends in level of education and in age at marriage was linear in urban areas, but 

not in rural areas, where level of education declined while age at marriage continued to increase. 

Figure B-28.2. Relationship between level of education and age at first marriage, Swaziland 

 
131 

Trends in contraceptive use (Figure B-28.3) 

Ever-use of contraception was very high for the 1950-1975 cohorts, reaching 90% for women age 30-34, 

with a mild cohort trend. The age effect was small, however, with a smaller prevalence for the 15-19 age 

group, a situation similar to that of South Africa. 

Figure B-28.3. Trends in age-specific ever-use of contraception, Swaziland 
132 

29. Tanzania 

Five surveys were available in Tanzania, conducted in 1991, 1996, 1999, 2004, and 2010. Overall, they 

were compatible for the three variables investigated, with a low value of age at marriage in 1999. The 

2010 survey had no information on ever-use of contraception. 

Table B-29.1. Survey characteristics, Tanzania 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1991 9,238 17.9 4.22 14.1 

DHS 1996 8,120 18.2 4.56 22.6 

DHS 1999 4,029 18.1 4.70 29.9 

DHS 2004 10,329 18.6 5.18 37.1 

DHS 2010 10,139 18.8 5.74 

Trends in age at marriage (Figure B-29.1) 

Median age at first marriage was low in Tanzania for cohorts born in the 1940s, with hardly any 

difference between urban and rural areas. Age at marriage increased steadily in urban areas, faster for 

cohorts born between 1950 and 1970, and slower for later cohorts. The pattern was similar for rural areas, 

although age at marriage stropped increasing for cohorts born after 1975. 

Figure B-29.1. Reconstructed trends in median age at first marriage, Tanzania 
133 

Relationship with education (Figure B-29.2) 

The relationship between trends in level of education and in age at marriage was quite monotonic, with 

only some minor variations in the recent cohorts. In urban areas age at marriage continued to increase 

despite a decline in level of education, while in rural areas both stagnated for the recent cohorts. 

Figure B-29.2. Relationship between level of education and age at first marriage, Tanzania 

 
134 

Trends in contraceptive use (Figure B-29.3) 

Contraceptive use increased steadily for all cohorts considered, reaching medium levels for the mid-aged 

women. However, levels of ever-use remained low for women age 15-19. 

Figure B-29.3. Trends in age-specific ever-use of contraception, Tanzania 
135 

30. Togo 

Two surveys were available in Togo, conducted in 1988 and 1998. They were compatible with respect to 

the three variables investigated. 

Table B-30.1. Survey characteristics, Togo 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1988 3,360 18.3 2.32 10.7 

DHS 1998 8,569 18.8 2.59 26.0 

Trends in age at marriage (Figure B-30.1) 

Median age at first marriage was average in Togo, for cohorts born in the 1930s and 1940s, with a small 

gap between urban and rural areas. It increased in urban areas for cohorts born after 1950, while 

remaining almost constant in rural areas. 

Figure B-30.1. Reconstructed trends in median age at first marriage, Togo 

 
136 

Relationship with education (Figure B-30.2) 

The relationship between trends in level of education and in age at marriage was not linear. For a long 

time, there was hardly any change in age at marriage in urban areas, despite an increasing level of 

education. Then age at marriage increased while level of education did not change. In rural areas the 

relationship was more straightforward, although at low levels of education and with minor changes in age 

at marriage. 

Figure B-30.2. Relationship between level of education and age at first marriage, Togo 

 
137 

Trends in contraceptive use (Figure B-30.3) 

Contraceptive use increased steadily for cohorts considered, reaching average values for the mid-aged 

women, and relatively small age effects, underlying a strong period effect. Compared with other 

countries, the proportion of women who ever-used contraception was particularly high for women age 15-

19. 

Figure B-30.3. Trends in age-specific ever-use of contraception, Togo 
138 

31. Uganda 

Five surveys were available in Uganda, conducted in 1988, 1995, 2001, 2006 and 2011. They were 

compatible for the three variables investigated, with minor changes for period age at marriage. The 2011 

survey did not provide data on ever-use of contraception. 

Table B-31.1. Survey characteristics, Uganda 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1988 4,730 17.0 3.29 7.4 

DHS 1995 7,070 17.4 3.78 15.7 

DHS 2001 7,246 17.8 4.54 34.9 

DHS 2006 8,531 17.6 4.96 38.1 

DHS 2011 8,674 17.9 5.74 

Trends in age at marriage (Figure B-31.1) 

Median age at first marriage was rather low in Uganda for cohorts born in the mid 1930s, with virtually 

no difference between urban and rural areas. Age at marriage increased almost steadily in urban areas, as 

well as in rural areas but at a slower speed. 

Figure B-31.1. Reconstructed trends in median age at first marriage, Uganda 
139 

Relationship with education (Figure B-31.2) 

The relationships between trends in level of education and in age at marriage were basically monotonic 

and linear in both urban and rural areas, with some acceleration in urban areas due in part to faster 

increase in level of education. 

Figure B-31.2. Relationship between level of education and age at first marriage, Uganda 

 
140 

Trends in contraceptive use (Figure B-31.3) 

Ever-use of contraception increased steadily for cohorts considered, with only a minor stagnation for the 

45-49 and the 15-19 age groups. The level reached an average value (almost 50%) for mid-aged women, 

and remained low for women age 15-19. 

Figure B-31.3. Trends in age-specific ever-use of contraception, Uganda 
141 

32. Zambia 

Four surveys were available in Zambia, conducted in 1992, 1996, 2001, and 2007. They were compatible 

with respect to the three variables investigated. 

Table B-32.1. Survey characteristics, Zambia 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1992 7,060 17.4 5.25 22.9 

DHS 1996 8,021 17.7 5.68 32.6 

DHS 2001 7,658 17.8 5.87 45.7 

DHS 2007 7,146 18.2 6.40 56.3 

Trends in age at marriage (Figure B-32.1) 

Median age at first marriage was low in Zambia for cohorts born in the 1940, with hardly any difference 

between urban and rural areas. It increased steadily in both urban and rural areas for cohorts born after 

1950, faster in urban than rural areas. 

Figure B-32.1. Reconstructed trends in median age at first marriage, Zambia 
142 

Relationship with education (Figure B-32.2) 

The relationship between trends in level of education and in age at marriage was monotonic, although 

increasing for the more recent cohorts. This was due to a faster increase in level of education for cohorts 

born after 1945. 

Figure B-32.2. Relationship between level of education and age at first marriage, Zambia 

 
143 

Trends in contraceptive use (Figure B-32.3) 

Contraceptive use increased steadily for all cohorts considered, reaching high levels (above 70%) for the 

mid-aged women. Only women age 15-19 showed a low level of ever-use of contraception. 

Figure B-32.3. Trends in age-specific ever-use of contraception, Zambia 
144 

33. Zimbabwe 

Five DHS surveys were available in Zimbabwe, conducted in 1988, 1994, 1999, 2005 and 2010. They 

were compatible with respect to the three variables investigated. The 2010 survey, however, did not 

provide information on ever-use of contraception. 

Table B-33.1. Survey characteristics, Zimbabwe 

Survey Year 

Sample size 

Median age at 

first marriage 

Mean level of 

education 

Ever-use of 

contraception 

(number of women) (years) (years of schooling) (percent)

DHS 1988 4,201 18.6 6.05 48.4 

DHS 1994 6,128 18.9 6.74 56.1 

DHS 1999 5,907 19.3 7.81 60.8 

DHS 2005 8,907 19.3 7.89 65.6 

DHS 2010 9,171 19.7 9.02 

Trends in age at marriage (Figure B-33.1) 

Median age at first marriage was average in Zimbabwe for cohorts born in the 1940s, with vitually no 

difference between urban and rural areas. Age at marriage increased steadily in urban areas, but hardly 

changed in rural areas. 

Figure B-33.1. Reconstructed trends in median age at first marriage, Zimbabwe 
145 

Relationship with education (Figure B-33.2) 

The relationship between trends in level of education and in age at marriage remained flat for a long time, 

and the strong increase in level of education for cohorts born between 1930 and 1964 hardly translated 

into any increase in age at marriage. Conversely, in urban areas, age at marriage increased for the recent 

cohorts (1965-1985), while level of education stagnated. In rural areas age at marriage remained constant 

despite fluctuations in level of education. 

Figure B-33.2. Relationship between level of education and age at first marriage, Zimbabwe 

 
146 

Trends in contraceptive use (Figure B-33.3) 

Ever-use of contraception increased steadily for all cohorts considered, and was high even for the early 

cohorts (born in the 1940s), reaching values greater than 90% for the mid-aged women. Ever-use of 

contraception was low only for women age 15-19, and increased slowly. 

Figure B-33.3. Trends in age-specific ever-use of contraception, Zimbabwe