DHS ANALYTICAL 

STUDIES 58

Long Term Trajectories of

Fertility and Contraceptive Use

August 2016

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

Thomas Pullum and Shireen Assaf.

DHS Analytical Studies No. 58 

Long Term Trajectories of 

Fertility and Contraceptive Use 

Thomas Pullum 

Shireen Assaf 

ICF International 

Rockville, Maryland, USA 

August 2016 

Corresponding author: Shireen Assaf, International Health and Development, ICF International, 

530 Gaither Road, Suite 500, Rockville, MD 20850, USA; telephone: +1 301–407–6500; fax: 

+1 301–407–6501; email: Shireen.Assaf@icfi.com 
Acknowledgment: The authors appreciate Stan Becker’s very helpful comments on a draft of this report.

Editor: Lynne Jennrich 

Document Production: Natalie La Roche 

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

Development (USAID) through The DHS Program (#AIDOAA-C-13-00095). 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. For additional information about the DHS Program contact: The DHS Program, 

ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA. Phone: +1 301-407-6500; fax: 

+1 301-407-6501; email: reports@dhsprogram.com; Internet: www.dhsprogram.com. 

Recommended citation: 

Thomas Pullum and Shireen Assaf. 2016. Long Term Trajectories of Fertility and Contraceptive Use. DHS 

Analytical Studies No. 58. Rockville, Maryland, USA: ICF International. 
iii 

Contents 

Tables ................................................................................................................................................................... v 

Figures ................................................................................................................................................................. v 

Preface ................................................................................................................................................................ ix 

Abstract .............................................................................................................................................................. xi 

Executive Summary ......................................................................................................................................... xiii 

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

Part 1: Fertility Trajectories ............................................................................................................................. 7 

2. Data and Methods ................................................................................................................................. 9 

2.1. DHS Data................................................................................................................................... 9 

2.2. Measures .................................................................................................................................. 10 

2.3. Methods ................................................................................................................................... 12 

3. Results .................................................................................................................................................. 15 

3.1. Bangladesh .............................................................................................................................. 15 

3.2. Colombia ................................................................................................................................. 16 

3.3. Dominican Republic ................................................................................................................ 17 

3.4. Egypt ....................................................................................................................................... 18 

3.5. Ghana ....................................................................................................................................... 19 

3.6. Indonesia.................................................................................................................................. 20 

3.7. Jordan ...................................................................................................................................... 21 

3.8. Kenya ....................................................................................................................................... 22 

3.9. Mali ......................................................................................................................................... 23 

3.10. Peru .......................................................................................................................................... 24 

3.11. Philippines ............................................................................................................................... 25 

3.12. Senegal .................................................................................................................................... 26 

3.13. Tanzania .................................................................................................................................. 27 

3.14. Uganda ..................................................................................................................................... 28 

3.15. Zambia ..................................................................................................................................... 29 

3.16. Zimbabwe ................................................................................................................................ 30 

3.17. Summary of Results ................................................................................................................ 31 

Part 2: Contraceptive Trajectories ................................................................................................................. 43 

4. Data and Methods ............................................................................................................................... 45 

4.1. DHS Data................................................................................................................................. 45 

4.2. Measures .................................................................................................................................. 45 

4.3. Methods ................................................................................................................................... 46 

5. Results .................................................................................................................................................. 49 

5.1. Ghana ....................................................................................................................................... 49 

5.2. Indonesia.................................................................................................................................. 52 
iv 

5.3. Kenya ....................................................................................................................................... 57 

5.4. Senegal .................................................................................................................................... 61 

5.5. Summary.................................................................................................................................. 66 

6. Discussion and Conclusions ................................................................................................................ 67 

References .......................................................................................................................................................... 71 

Appendices ......................................................................................................................................................... 77 

Appendix 1: R code used to fit the Varying Coefficient Models (VCMs) ............................................ 77 

Appendices 2–5: Description of Sample for Ghana, Indonesia, Kenya, and Senegal ........................... 78 
v 

Tables 

Table 2.1. Countries included in the fertility analysis ................................................................................ 9 

Table 3.1. Smoothed values of the Total Fertility Rate in the 16 countries. ............................................. 32 

Table 3.2. Changes in the smoothed values of the Total Fertility Rate in the 16 countries ...................... 33 

Table 3.3. Smoothed values of the General Fertility Rate in the 16 countries. ........................................ 35 

Table 3.4. Changes in the smoothed values of the General Fertility Rate in the 16 countries. ................ 35 

Table 3.5. Smoothed values of the mean age at childbearing in the 16 countries .................................... 37 

Table 3.6. Changes in the smoothed values of the mean age at childbearing in the 16 countries ............ 37 

Table 3.7. Smoothed values of the standard deviation of the age at childbearing in the 16 countries. .... 39 

Table 3.8. Changes in the smoothed values of the standard deviation of the age at childbearing in 

the 16 countries ........................................................................................................................ 39 

Table 4.1. The survey year and number of women age 15-49 in each survey (N) for Ghana, 

Indonesia, Kenya, and Senegal ................................................................................................ 45 

Table 5.1. Odds ratios for the Ghana logit regressions of modern contraceptive use for first and last 

survey and the varying coefficient model for the combined surveys ...................................... 50 

Table 5.2. Odds ratios for the Indonesia logit regressions of modern contraceptive use for first and 

last survey and the varying coefficient model for the combined surveys ................................ 53 

Table 5.3. Odds ratios for the Kenya logit regressions of modern contraceptive use for first and last 

survey and the varying coefficient model for the combined surveys ...................................... 58 

Table 5.4. Odds ratios for the Senegal logit regressions of modern contraceptive use for first and last 

survey and the varying coefficient model for the combined surveys ...................................... 62 

Appendix 2. Description of the sample for each Ghana survey for total sample and for women 15-49 

in a union using a modern contraceptive method (mCPR) ...................................................... 78 

Appendix 3. Description of the sample for each Indonesia survey for total sample and for women 

15-49 in a union using a modern contraceptive method (mCPR) ............................................ 78 

Appendix 4. Description of the sample for each Kenya survey for total sample and for women 15-49 

in a union using a modern contraceptive method (mCPR) ...................................................... 79 

Appendix 5. Description of the sample for each Senegal survey for total sample and for women 15-49 

in a union using a modern contraceptive method (mCPR) ...................................................... 79 

Figures 

Figure 3.1. Fertility trajectory for Bangladesh estimated from 7 DHS surveys for calendar years 

from 1984 to 2013 ................................................................................................................... 15 

Figure 3.2. Fertility trajectory for Colombia estimated from 6 DHS surveys for calendar years 

from 1976 to 2009 ................................................................................................................... 16 

Figure 3.3. Fertility trajectory for Dominican Republic estimated from 7 DHS surveys for calendar 

years from 1976 to 2012 .......................................................................................................... 17 

Figure 3.4. Fertility trajectory for Egypt estimated from 7 DHS surveys for calendar years from 

1979 to 2013 ............................................................................................................................ 18 

Figure 3.5. Fertility trajectory for Ghana estimated from 6 DHS surveys for calendar years from 

1978 to 2013. ........................................................................................................................... 19 

Figure 3.6. Fertility trajectory for Indonesia estimated from 7 DHS surveys for calendar years from 

1977 to 2011 ............................................................................................................................ 20 

Figure 3.7. Fertility trajectory for Jordan estimated from 5 DHS surveys for calendar years from 

1980 to 2011 ............................................................................................................................ 21 
vi 

Figure 3.8. Fertility trajectory for Kenya estimated from 6 DHS surveys for calendar years from 

1979 to 2013 ............................................................................................................................ 22 

Figure 3.9. Fertility trajectory for Mali estimated from 5 DHS surveys for calendar years from 

1977 to 2012 ............................................................................................................................ 23 

Figure 3.10. Fertility trajectory for Peru estimated from 9 DHS surveys for calendar years from 

1976 to 2011. ........................................................................................................................... 24 

Figure 3.11. Fertility trajectory for Philippines estimated from 5 DHS surveys for calendar years 

from 1983 to 2012 ................................................................................................................... 25 

Figure 3.12. Fertility trajectory for Senegal estimated from 7 DHS surveys for calendar years from 

1976 to 2013. ........................................................................................................................... 26 

Figure 3.13. Fertility trajectory for Tanzania estimated from 6 DHS surveys for calendar years from 

1982 to 2009 ............................................................................................................................ 27 

Figure 3.14. Fertility trajectory for Uganda estimated from 5 DHS surveys for calendar years from 

1979 to 2010 ............................................................................................................................ 28 

Figure 3.15. Fertility trajectory for Zambia estimated from 5 DHS surveys for calendar years from 

1982 to 2013 ............................................................................................................................ 29 

Figure 3.16. Fertility trajectory for Zimbabwe estimated from 5 DHS surveys for calendar years from 

1979 to 2010 ............................................................................................................................ 30 

Figure 3.17. Trajectory of the Total Fertility Rate for all 16 countries, using pooled data from 98 DHS 

surveys for the 10 calendar years prior to each survey ............................................................ 31 

Figure 3.18. Trajectory of the General Fertility Rate for all 16 countries, using pooled data from 98 

DHS surveys for the 10 calendar years prior to each survey ................................................... 34 

Figure 3.19. Trajectory of the mean age at childbearing for all 16 countries, using pooled data from 

98 DHS surveys for the 10 calendar years prior to each survey .............................................. 36 

Figure 3.20. Trajectory of the standard deviation of the age at childbearing for all 16 countries, using 

pooled data from 98 DHS surveys for the 10 calendar years prior to each survey .................. 38 

Figure 3.21. Scatterplot of mean age of women when achieving specified parities, at the date of the 

first estimate (about 1980) and the date of the last estimate (about 2010). ............................. 40 

Figure 5.1. Modern contraceptive use for women 15-49 in a union in Ghana from 1988 to 2014 ............ 49 

Figure 5.2a. Odds ratio plots for Ghana comparing primary education, and secondary or higher, with 

no education. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). ...... 51 

Figure 5.2b. Odds ratio plot for Ghana comparing urban areas with rural. The plots extend from June 

1987 (CMC 1050) to July 2014 (CMC 1375).......................................................................... 51 

Figure 5.3. Modern contraceptive use for women 15-49 in a union in Indonesia from 1987 to 2012 ....... 52 

Figure 5.4a. Odds ratio plots for Indonesia comparing parities two, three, and four or more with parities 

zero and one. The plots extend from June 1987 (CMC 1050) to June 2012 (CMC 1350). ..... 54 

Figure 5.4b. Odds ratio plots for Indonesia comparing primary education, and secondary or higher, 

with no education. The plots extend from June 1987 (CMC 1050) to June 2012 (CMC 

1350). ....................................................................................................................................... 55 

Figure 5.4c. Odds ratio plots for Indonesia comparing the ideal number of children categories of three 

and above with ideal number of children of zero to two. The plots extend from June 1987 

(CMC 1050) to June 2012 (CMC 1350). ................................................................................. 55 

Figure 5.4d. Odds ratio plot for Indonesia comparing urban areas with rural. The plots extend from 

June 1987 (CMC 1050) to June 2012 (CMC 1350). ................................................................ 56 

Figure 5.4e. Odds ratio plot for Java Bali region category compared to the outer Java Bali reference 

category. The plots extend from June 1987 (CMC 1050) to June 2012 (CMC 1350). ............ 56 

Figure 5.5. Modern contraceptive use for women 15-49 in a union in Kenya from 1988 to 2014 ............ 57 
vii 

Figure 5.6a. Odds ratio plots for Kenya comparing parities two, three, and four or more with parities 

zero and one. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). ...... 59 

Figure 5.6b. Odds ratio plots for Kenya comparing primary education, and secondary or higher, with 

no education. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). ...... 60 

Figure 5.6c. Odds ratio plots for Kenya comparing regions of Kenya with Nairobi/Central. The plots 

extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). ............................................ 60 

Figure 5.7. Modern contraceptive use for women 15-49 in a union in Senegal from 1986 to 2014 .......... 61 

Figure 5.8a. Odds ratio plots for Senegal comparing parities two, three, and four or more with parities 

zero and one. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). ...... 63 

Figure 5.8b. Odds ratio plots for Senegal comparing primary education, and secondary or higher, 

with no education. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 

1375). ....................................................................................................................................... 64 

Figure 5.8c. Odds ratio plot for Sengal comparing urban areas with rural. The plots extend from June 

1987 (CMC 1050) to July 2014 (CMC 1375).......................................................................... 64 

Figure 5.8d. Odds ratio plots for Senegal showing the three Senegal regions compared to the West 

region reference category. The plots extend from June 1987 (CMC 1050) to July 2014 

(CMC 1375). ............................................................................................................................ 65 

Figure 6.1. Scatterplot showing the most recent TFR and current mCPR for each survey from Ghana, 

Kenya, Indonesia, and Senegal ................................................................................................ 69 

ix 

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to analyze DHS data and provide findings that will be useful 

to policymakers and program managers in low- and middle-income countries. DHS Analytical Studies serve 

this objective by providing in-depth research on a wide range of topics, typically including several 

countries, and applying multivariate statistical tools and models. These reports are also intended to illustrate 

research methods and applications of DHS data that may build the capacity of other researchers. 

The topics in the DHS Analytical Studies series are selected by The DHS Program in consultation with the 

U.S. Agency for International Development. 

It is hoped that the DHS Analytical Studies will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries. 

Sunita Kishor 

Director, The DHS Program

xi 

Abstract 

DHS estimates of current fertility and contraceptive use have immediate interest after the release of each 

survey. This report takes a long-term perspective, examining trajectories of fertility and contraception by 

piecing together the data from the countries that have had the most surveys. It includes 16 countries that 

have had five or more surveys—Bangladesh, Colombia, the Dominican Republic, Egypt, Ghana, Indonesia, 

Jordan, Kenya, Mali, Peru, the Philippines, Senegal, Tanzania, Uganda, Zambia, and Zimbabwe, with a 

total of 98 surveys. The fertility trajectories span an interval from about 1980 to about 2010. All of these 

countries have experienced declines in their TFR, by amounts ranging from one child in Tanzania to about 

four children in Jordan. The median TFR declined from 6.4 to 3.8, a reduction of 42% in about 30 years. 

There was a strong correlation, 0.72, between the first and last values of the TFR. In most countries the 

mean age at childbearing did not change but there was a greater concentration around that mean. Changes 

in the use of modern contraception were tracked in a subset of four of the countries that had six or seven 

surveys—Ghana, Indonesia, Kenya, and Senegal, with a total of 26 surveys—using time-varying coefficient 

models (VCMs). The interest is in whether odds ratios are moving toward one, indicating similar levels of 

contraceptive prevalence across sub-populations. In most countries there has been a gradual reduction in 

the differences between sub-populations, indicating that access to contraception has broadened as overall 

use has increased. 

KEYWORDS: Fertility, Total Fertility Rate, mean age at childbearing, modern contraceptive prevalence, 

varying coefficient models, odds ratio trends 

xiii 

Executive Summary 

One of the most important functions of Demographic and Health Surveys is to provide national estimates 

of the Total Fertility Rate (TFR), usually for the three years before the survey, and of the percentage of 

women in union who are using modern methods of contraception (the modern contraceptive prevalence rate 

or mCPR) at the time of the survey. Most of the countries that conduct DHS surveys have programs to 

increase the use of modern contraception in order to bring fertility down to replacement level. Some 

countries have had several DHS surveys, typically about five years apart. By pooling the birth histories 

from successive surveys in such countries it is possible to construct a continuous record of fertility spanning 

approximately 30 years, from about 1980 to about 2010. By connecting the successive snapshots of 

contraceptive use at the time of each survey, it is possible to produce an overview of how contraceptive use 

has changed and how its relationship to important determinants has changed. 

The first part of this report describes changes in fertility in 16 countries that have had 5 or more DHS 

surveys—a total of 98 surveys. Eight of these countries are in sub-Saharan Africa (SSA). The other eight 

are scattered across Latin America (2), the Caribbean (1), South Asia (1), Southeast Asia (2), the Middle 

East (1), and North Africa (1). Long-term trajectories are derived from a pooling and smoothing of the birth 

histories for the ten calendar years before each survey. Except for the first five years and the final five years, 

each annual estimate is generally based on a pooling of the overlap in birth histories of two successive 

surveys. Trajectories are presented for two measures of the quantum or volume of fertility—the Total 

Fertility Rate and the General Fertility Rates—and for three measures of the tempo or timing of fertility—

the mean age at childbearing, the standard deviation of the age at childbearing, and the ages at reaching 

successive integer values of the Cumulative Fertility Rate (CFR). 

In these 16 countries, the median TFR fell from 6.4 around 1980 to 3.8 around 2010, a reduction of 42%. 

Initially, all eight SSA countries were above the median, except for Zimbabwe, which was slightly below. 

The highest eight were Ghana, Jordan, Kenya, Mali, Senegal, Tanzania, Uganda, and Zambia (listed in 

alphabetical order), all with a TFR of 7.0 or more. At the end, the eight countries above the median included 

all of the SSA countries, and only those countries. Jordan had a dramatic decline in fertility. Zimbabwe had 

only a small decline, compared to the decline in the median, and therefore was above the median at the end. 

The countries with the lowest fertility at the end were Colombia, Bangladesh, the Dominican Republic, 

Peru, Indonesia, and the Philippines (listed from lowest to highest), all with a TFR of 3.0 or less. The highest 

fertility countries at the end were Zambia, Tanzania, Mali, and Uganda (listed from lowest to highest), all 

with a TFR above 5.0. The remaining countries—Jordan, Egypt, Kenya, Ghana, Zimbabwe, and Senegal 

(listed from lowest to highest)—were greater than 3.0 and less than 5.0. Some countries show evidence of 

a recent increase in fertility, especially Tanzania and Zimbabwe. The amount of decline in the TFR ranges 

from about one child in Tanzania to about four children in Jordan. 

For most countries, the mean age at childbearing changed very little as fertility declined. Bangladesh, the 

Dominican Republic, and Colombia—the three countries with the lowest fertility at the end of the series—

were the only ones that had a substantial change, and for all three it was a reduction in the mean age. Their 

fertility reductions were mainly achieved by the elimination of births at later ages, rather than by delaying 

the first birth, and greater spacing between births. The standard deviation of the age at childbearing had a 

net decline of about half a year in most countries. Egypt, Jordan, Bangladesh, and the Dominican Republic 

were the only ones that had a conspicuously larger drop, that is, more concentration of age at childbearing. 

The SSA countries, at both the beginning and the end, have the largest standard deviations, that is, the 

greatest dispersion in age at childbearing. The changes in the ages at reaching successive parities were also 

in the direction of somewhat more concentrated distributions. 
xiv 

The second part of the report focuses on four of the countries that had 6 or 7 surveys—Ghana, Indonesia, 

Kenya, and Senegal—a total of 26 surveys. Variations in the use of modern contraception are tracked 

according to the woman’s parity, education, work status, ideal number of children, urban/rural residence, 

and region. Differences in use are represented with odds ratios comparing each category with a reference 

category for each covariate. An innovative method in this context, varying coefficient models (VCM), is 

able to link the six or seven surveys together with a continuous function of time. The trajectories of the 

odds ratios that describe sub-population tend to show substantial variation early in a transition because early 

users tend to be urban women with more education who desire a smaller family size. With time, as fertility 

declines and overall prevalence of contraception increases, it is desirable that the odds ratios move toward 

1, reflecting greater equality in contraceptive use across sub-populations. 

In general, the VCM trajectories do move toward odds ratios that are closer to 1, in some cases not being 

significantly different from 1, and in some cases even reversing, such that higher prevalence in urban areas 

is replaced by higher prevalence in rural areas, for example. The finding that most subgroups are becoming 

more similar in their contraceptive use over time implies that family planning programs and interventions 

are successful in reaching various sub-populations. Further analysis when more surveys become available 

will determine whether these trends continue. 
1 

1. Introduction 

This report describes the long-term trajectories of fertility and contraceptive use for the countries that have 

had the most surveys and the longest continuous intervals of DHS data. The goal is to provide an integrated 

overview of long-term patterns of change. 

The Demographic and Health Surveys Program (DHS) has been conducting surveys since the mid-1980s. 

Many countries have had multiple surveys, typically about five years apart. The DHS data for countries 

with a long series of surveys provide researchers with an opportunity to review and integrate historical 

trends for many different indicators. Micro data files from all the surveys, archived with standard recodes, 

are publicly available. 

From the beginning of DHS, two of the most important topics have been fertility and contraceptive use. 

Information about fertility is provided with retrospective birth histories, and is particularly rich because the 

histories provide a continuous record for intervals before the first survey, between successive surveys, and 

with many years of overlapping surveys. Information about current contraceptive use is detailed, including 

method. In addition to current use, the surveys include a retrospective calendar for the five years before the 

survey; however, this analysis uses the current status information current at the time of the surveys. Our 

interest is partially in trends in the use of modern methods of contraception (the modern contraceptive 

prevalence rate, or mCPR), but primarily in trends in the relationship of use to selected covariates—parity, 

level of education, work status, ideal number of children, urban/rural residence, and region. 

This analysis does not attempt to articulate the correspondence between the two trajectories. Such an effort 

is needed, but it would require within-country and within-survey analysis that is beyond the scope of the 

present report. Contraceptive use and marital exposure are well known to be the principal proximate 

determinants of fertility. Broadly speaking, an increase in contraception will be followed by a decline in 

fertility, but for a variety of reasons, the correspondence is not simple, and it is difficult to observe in a 

single survey because the birth history is chronologically prior to current contraceptive status, the reverse 

of the causal sequence. 

Part 1 of this report concerns the long-term trajectory of fertility in 16 countries that have had at least 5 

surveys each. DHS has previously produced many reports on fertility, generally including many countries. 

The following lists of DHS Analytical Studies, Comparative Reports, and Methodological Reports, in 

chronological order, have focused on levels and trends in fertility, the relationship of fertility to covariates 

and proximate determinants, and methodological considerations: 

Analytical Studies (AS): 

AS4. The Contraception-Fertility Link in Sub-Saharan Africa and in Other Developing Countries. 

(Westoff and Bankole 2001) 

AS21. Birth Spacing and Limiting Connections. (Westoff and Koffman 2010) 

AS23. Changes in the Direct and Indirect Determinants of Fertility in Sub-Saharan Africa. (Johnson, 

Abderrahim, and Rutstein 2011) 

AS34. Indicators of Trends in Fertility in Sub-Saharan Africa. (Westoff, Bietsch, and Koffman 2013) 

AS42. Trends in Marriage and Contraception in Sub-Saharan Africa: A longitudinal perspective on 

factors of fertility decline. (Garenne 2014) 

AS48. Religion and Reproductive Behavior in Sub-Saharan Africa. (Westoff and Bietsch 2015) 

Comparative Reports (CR) and Comparative Studies (CS): 

CS2. Fertility Levels and Trends (Arnold and Blanc 1990) 

CS18. Men’s Fertility, Contraceptive Use, and Reproductive Preferences. (Ezeh, Seroussi, and Raggers 

1996) 
2 

CS28. Fertility Levels, Trends, and Differentials. (Mboup and Saha 1998) 

CR3. Fertility Levels, Trends, and Differentials 1995-1999. (Rutstein 2002) 

CR16. Contraceptive trends in developing countries. (Khan et al. 2007) 

CR18. Fertility Changes in Sub-Saharan Africa. (Garenne 2008) 

CR23. Fertility Transition in Sub-Saharan Africa: A Comparative Analysis of Cohort Trends in 30 

Countries. (Sneeringer 2009) 

CR28. Trends in Birth Spacing. (Mboup and Saha 1998) 

Methodological Reports (MR): 

MR5. An Assessment of Age and Date Reporting in the DHS Surveys 1985-2003. (Pullum 2006) 

MR11. Evidence of Omission and Displacement in DHS Birth Histories. (Pullum and Becker 2014) 

MR12. Quality and Consistency of DHS Fertility Estimates, 1990 to 2012. (Schoumaker 2014) 

In addition to these DHS reports, much literature is available on fertility trends in low- and middle-income 

countries, much of it relying heavily on DHS data. Some analysts have worked with the U.N. Population 

Division’s estimates and projections1

, which are based on a pooling of all available data sources, including 

the DHS. In the literature, trajectories of the TFR are described mainly in terms of a pre-transitional level 

that is very high—usually in the range of 6 to 8—and relatively stable; the initiation of decline, which can 

be abrupt but is almost always identifiable within some interval of ten years or less; and completion, with 

a final value in the vicinity of replacement fertility, with a TFR of 2.1. In many countries, the last phase of 

the fertility decline from a TFR of about 3 to about 2.1 has been gradual. In most developed countries, the 

TFR is currently, or has been, below 2.1, and thus the achievement of a final value is uncertain. The lowest 

observed value of the TFR for the countries in this report is 2.1, seen in only one country, Colombia. 

The path from the onset of decline to replacement fertility can vary considerably. Some counties experience 

intervals of extremely rapid decline, for example, by more than a full child in a five-year interval. Some 

countries experience stalls during which decline is slow. Sometimes such stalls are historical, but in others 

they extend to the latest date for which data are available. These kinds of trajectories—the ones that are 

currently stalled or are declining gradually—are of greatest concern for policy and programs. Lessons can 

be learned from countries that have experienced declines that are rapid and uninterrupted, or that have 

resumed substantial decline following a stall. Another pattern that is occasionally observed—and that is of 

even greater concern—is a recent non-trivial uptick in the TFR. Small reversals are not significantly 

distinguishable from a stall, but if the TFR is clearly increasing, it is important to understand why. 

Juárez and Gayet (2015) provide an overview of long-term trends in Latin America and the Caribbean, 

going back to the early 1950s, well before the earliest DHS estimates, using the U.N. Population Division 

estimates and projections. They distinguish among countries in the Caribbean, Central America including 

Mexico, and South America. This report includes one country from the Caribbean, the Dominican Republic, 

and two from South America, Colombia and Peru. The Central American countries, when pooled, had a 

pre-transitional TFR of about 6.7, with a decline beginning about 1970 and reaching 2.6 by 2005–2010. As 

two pooled groups, the Caribbean and South American countries had lower levels throughout that entire 

interval, with low pre-transitional TFRs of 5.3 in the Caribbean and 5.7 in South America, a decline that 

began about 1965, and 2005–2010 TFRs of 2.4 in the Caribbean and 2.2 in South America. By 2010, the 

three sub-regions were converging and near a complete transition. 

This report includes one country from North Africa, Egypt, and one from the Middle East, Jordan. In an 

overview of Arab countries, Eltigani (2005) described a decline from approximately the late 1970s to the 

late 1990s of about two children per woman, despite “continued desires for large families.” Tabbarah 

(2009), using UN data in the 1998 Revision of World Population Prospects, looked specifically at the 

 

1

 World Population Prospects: https://esa.un.org/unpd/wpp/ 
3 

fertility transition in Jordan, Lebanon, and Syria. Jordan, in particular, appeared to have a pre-transitional 

TFR of 8, a decline that began around 1975, and a 1990–1995 level of 5.58. Among these three countries, 

Jordan had the highest initial level, the latest onset of decline, and the highest TFR in 1990–1995. 

Heuveline and Hirschman (2015), looking at 11 countries in Southeast Asia, again using World Population 

Prospects, but the 2012 revision, describe estimated TFRs in 2010–2015 that were generally close to 

replacement. Our report includes two countries from Southeast Asia, Indonesia and the Philippines. In 

Indonesia, a relatively low pre-transitional level of 5.6—the lowest in the region—began to decline in 1970–

1975, and by 2010–2015 was at 2.1. In the Philippines, the pre-transitional level was 7.42—the highest in 

the region—but the TFR began to decline in 1960–1965 and was estimated to be 3.05 in 2010–2015. The 

TFR in the Philippines was higher than Indonesia’s throughout the entire interval, even though the transition 

began earlier in the Philippines. 

An analysis of fertility decline in Asia, more broadly, using the 2004 Revision of World Population 

Prospects, was prepared by Gubhaju (2007). The present report includes one country in South Asia, 

Bangladesh, which was included in that overview. Gubhaju classified countries in five categories, based on 

the levels of the TFR, as of 1970–1975, 1990–1995, and 2000–2005. The five categories were high, a TFR 

of 5.0 or greater; transitional, 3.0 to 4.9; near-replacement, 2.2 to 2.9; low, 1.6 to 2.1; and critically low, 

1.5 or less. In 1970–1975, Sri Lanka was the only South Asian country that had moved from high to 

transitional. By 1990–1995, Pakistan, Bhutan, and Nepal were the only South Asian countries remaining in 

the high category, and by 2000–2005 all South Asian countries were in the transitional category, except Sri 

Lanka, which was in the low category. With the exception of Sri Lanka, South Asian countries, relative to 

the rest of Asia, have much higher percentages of women age 20-24, and especially age 15-19, who are 

ever-married. 

SSA has probably received more attention than any other region because of its persistent high fertility and 

projections of continued high population growth. This report includes eight countries from SSA: Ghana, 

Kenya, Mali, Senegal, Tanzania, Uganda, Zambia, and Zimbabwe. 

Using DHS and World Fertility Survey data, Bongaarts and Casterline (2013) contrasted SSA with other 

regions. The age distribution of childbearing tends to be wider in SSA than in other regions, implying 

differences in timing and spacing. The second birth interval, in particular, tends to be longer in SSA than 

in other regions. Perhaps the most striking contrast is in the ideal number of children. In SSA, the ideal 

number is declining in nearly all countries, but the mean is usually above 4 in the most recent surveys. 

Lesthaeghe (2014) examined the fertility transition in SSA using primarily DHS data, but without the 

blending approach used here, so his estimates are consistent with DHS main reports, but they will not match 

the numbers in the present analysis. He found that by 2010, the only countries with a TFR below 4 were in 

Southern Africa. Many countries still had a TFR of 6 or more, including Mali and Uganda. Several countries 

have actually seen net increases in fertility, attributed as “often caused by declining infertility levels” and 

by “the eroding of traditional birth-spacing practices (long periods of breastfeeding and postpartum 

abstinence.” Lesthaege suggests that, “After the turn of the century, it seems that the fertility transition is 

picking up momentum again” in some countries, including Senegal, Ghana, and Uganda. 

Shapiro and Gebreselassie (2008) write specifically about the pattern of “falling and stalling” during the 

fertility transition in SSA, also using the TFRs given in DHS reports. They note that virtually all of the 

countries that have had a DHS survey have begun a decline, but of those that have, about a third have 

experienced stalls, which they define as an increase in the TFR from one survey to the next. Of the 23 

countries with two surveys in their study, 7 had an increase in the TFR, 1 had no change, 7 had a decrease 

but of less than 0.05 per year, and only 7 had a decrease of more than 0.05 per year. The authors consider 

only the countries with an increase to be stalled, but it would be possible to extend that label to countries 

that increased but by less than 0.05 per year—that is, by about a quarter of a child during the typical five-
4 

year interval between surveys—and to count as increasing only the seven countries with an increase of 

more than 0.05 per year. All of the SSA countries in the present report are in the range of -0.05 to +0.05, 

net annual change reported by these authors. In a multivariate analysis, the authors show that the only 

significant correlates of fertility decline are a decline in the percentage of women with no schooling, a 

decline in infant and child mortality rates, and an increase in gross domestic product per capita. 

Contraceptive use is the pathway to fertility reduction, allowing these factors to have their effect, but an 

increase in contraceptive prevalence is not significant when these other factors are in the model. 

Moultrie, Sayi, and Timæus (2012) focus on the relationship between birth intervals and fertility decline in 

Africa, using 76 DHS surveys from 24 countries. They find that “birth intervals have lengthened in every 

country examined.” They report, “This analysis uncovered a distinctive and previously undocumented 

pattern of childbearing that is prevalent across sub-Saharan Africa. After allowing for time trends in birth 

interval length, the lengthening of birth intervals in almost every country varies little by women’s age or 

parity.” Timing and spacing are included in the present report, as measured by the mean and standard 

deviation of the age at childbearing, and the ages at reaching integer values of the Cumulative Fertility Rate 

(CFR), rather than birth intervals. 

Part 2 of this report analyzes the trajectory of modern contraceptive use in a subset of four countries—

Ghana, Indonesia, Kenya, and Senegal. Increased use of contraception, especially of modern methods, is 

the main proximate determinant of fertility decline. The following DHS Analytical Studies and 

Comparative Reports have focused on levels, trends, and differentials in contraceptive use: 

Analytical Studies (AS): 

AS4. The Contraception-Fertility Link in Sub-Saharan Africa and in Other Developing Countries. 

(Westoff and Bankole 2001) 

AS8. Recent Trends in Abortion and Contraception in 12 Countries. (Westoff 2005) 

AS14. Contraceptive Use, Breastfeeding, Amenorrhea and Abstinence during the Postpartum Period an 

Analysis of Four Countries. (Gebreselassie, Rutstein, and Mishra 2008) 

AS20. Levels, Trends, and Reasons for Contraceptive Discontinuation. (Bradley, Schwandt, and Khan 

2009) 

AS42. Trends in Marriage and Contraception in Sub-Saharan Africa: A Longitudinal Perspective on 

Factors of Fertility Decline. (Garenne 2014) 

AS49. Men and Contraception: Trends in Attitudes and Use. (MacQuarrie et al. 2015) 

Comparative Reports (CR) and Comparative Studies (CS): 

CR16. Contraceptive Trends in Developing Countries. (Khan et al. 2007) 

CS6. Knowledge and Use of Contraception. (Rutenberg et al. 1991) 

CS18. Men’s Fertility, Contraceptive Use, and Reproductive Preferences. (Ezeh, Seroussi, and Raggers 

1996) 

CS19. Contraceptive Knowledge, Use, and Sources. (Curtis and Neitzel 1996) 

In general, modern contraceptive use in developing countries has increased (Darroch and Singh 2013; 

Garenne 2014; Khan et al. 2007). DHS has found increases in the mCPR in all four countries examined 

(Agence Nationale de la Statistique et de la Démographie and ICF International 2015; Ghana Statistical 

Service, Ghana Health Service, and ICF International 2015; Kenya National Bureau of Statistics and ICF 

International 2015; Statistics Indonesia [Badan Pusat Statistik] et al. 2013). 

Typically, and especially in the early stages of a fertility transition, use of contraceptive methods—and 

specifically modern contraceptive methods, is not equal in all subgroups in a country. Use tends to be 

highest in subgroups that are wealthier, more educated, higher parity, and urban. Higher use among higher 

parity women is related primarily to demand, of course; other kinds of differences may be related to a 
5 

combination of demand and access (Maja 2007). Differentials have previously been identified in Ghana, 

Indonesia, Kenya, and Senegal (Achana et al. 2015; Clements and Madise 2004; Eliason et al. 2014; Leyé 

et al. 2014; Nonvignon and Novignon 2014; Paskaria 2015; Widyastuti and Saikia 2011). 

A high prevalence of contraception at the national level requires that contraceptive use is high in all major 

regions and subgroups. The focus of this analysis is not on the overall increase of modern contraceptive use 

but on the trends, over time, in disparities across specific subgroups. The findings can identify where 

interventions have been successful but also where interventions are needed, to reduce gaps between 

subgroups that may be unchanged or even increased. Nonvignon and Novignon (2014) studied changes in 

gaps, but only by comparing the odds ratios for contraceptive use in two surveys conducted in 2003 and 

2008, and their methods do not allow the testing of whether the change in these odds ratios was significant. 

Since an odds ratio compares one group to a reference group, the approach can be used to examine equality. 

The further the odds ratio is from 1, the less equality exists between the comparison group and the reference 

group. The method used to analyze disparities in this report focuses on whether the odds ratios have changed 

over time and tests whether changes are statistically significant. 

 

7 

Part 1: Fertility Trajectories 

One of the core functions of the surveys conducted by DHS is to estimate recent fertility. Every main report 

presents age-specific fertility rates for the three years before the survey, and the TFR by residence, region, 

education, and wealth quintile, also for the three years before the survey. Trends in age-specific and Total 

Fertility Rates are typically given for three or four five-year intervals before the survey, but they are drawn 

primarily from earlier DHS surveys conducted in the same country. The main report provides little 

description of fertility going back more than five years before the latest survey. 

Part 1 of this report takes a long-term perspective on fertility in countries that have had multiple DHS 

surveys. The goal is to piece together the successive surveys by going back to the original data files, pooling 

and re-calculating rates in an integrated way, and trying to bring out patterns or trajectories that may not be 

clear in the analysis of only the most recent survey or even the most recent pair of surveys. The focus is on 

extracting as much information about fertility as possible to describe the long-term trajectories. 

9 

2. Data and Methods 

2.1. DHS Data 

Countries and surveys 

The analysis describes levels and changes in childbearing for the 16 countries that have had five or more 

DHS surveys during the course of the entire DHS program and had their most recent survey in 2010 or 

later, a total of 98 surveys.2

 

Table 2.1. Countries included in the fertility analysis

Country First survey Last survey Country ID Number of surveys 

Bangladesh 1993–1994 2014 BD 7 

Colombia 1986 2010 CO 6 

Dominican Republic 1986 2013 DR 7 

Egypt 1988 2014 EG 7 

Ghana 1988 2014 GH 6 

Indonesia 1987 2012 ID 7 

Jordan 1990 2012 JO 5 

Kenya 1989 2014 KE 6 

Mali 1987 2012–2013 ML 5 

Peru 1986 2012 PE 9 

Philippines 1993 2013 PH 5 

Senegal 1986 2014 SN 7 

Tanzania 1991–1992 2010 TZ 6 

Uganda 1988–1989 2011 UG 5 

Zambia 1992 2013–2014 ZM 5 

Zimbabwe 1988 2010–2011 ZW 5 

Eight of the countries are in SSA. The other eight represent a wide range of geographic regions—South 

Asia (Bangladesh), Southeast Asia (Indonesia and the Philippines), Latin America and the Caribbean 

(Colombia, the Dominican Republic, and Peru), North Africa (Egypt), and the Middle East (Jordan). 

Generally the results will be presented simply with an alphabetical listing of countries, rather than a 

grouping into regions. 

DHS birth histories 

Although the focus on fertility in most DHS reports is on recent fertility, DHS surveys obtain complete 

birth histories for all eligible women age 15-49 at the time of the survey. Working from the first birth to the 

most recent birth, interviewers ask women about the sex of the child, whether it was part of a multiple birth, 

the month and year of birth, and whether the child is still alive. If the child is alive, the mother is asked the 

current age of the child, in completed years, and whether the child lives with the mother. If the child has 

died, the mother is asked the age at death, with detail in days up to one month, in months up to one year, 

and then only in completed years. 

If the month or year of birth is missing, or if, for a living child, the age and birth date are inconsistent, 

automated reconciliation and imputation during data processing will force the specification of a month and 

year. A code is added to the record to indicate the completeness of reporting of date and age. Also, during 

 

2

 If the criterion were reduced to four surveys, rather than five, an additional set of 14 countries would qualify for 

inclusion. The closing date for inclusion in this report was April 1, 2016. 
10 

fieldwork, unless the child has died or is living elsewhere, the interviewer records the line number of the 

child in the household schedule.3

DHS reporting of fertility rates has virtually always been in terms of “years ago,” rather than in calendar 

years, and refers to a window of time that depends on the date of interview. For example, “the past three 

years” or “0-2 [completed] years before the survey” means that the window is the 36 months before the 

month of interview. For a woman interviewed in March 2014, say, the time interval will be March 2011 

through February 2014, inclusive. Births that occur in the month of interview are never included in the 

fertility rates or the child mortality rates, although they will be recorded in the interview and will be counted 

among the woman’s number of children ever born. The justification for ignoring these births is that there 

is less than a full month of exposure to the month of interview. 

The justification for calculating rates as “years ago” rather than as calendar years is that the number of 

births in the calendar year of interview can be small and statistically unstable if the interviews tend to be 

early in the year. Also, rates calculated for single years tend to be statistically unstable, and rates for groups 

of three or five calendar years can be awkward for presentation. 

In this report, the concern is not with recent fertility, but with long-term trends. Calendar years are used, 

rather than years ago, for easier comparisons across countries and easier blending of successive surveys 

from the same country. Births and exposure in the year of the survey—or, for surveys that spanned two 

years, births and exposure in the last year of field work—are ignored. 

DHS recently produced two reports on the quality of the fertility data. Schoumaker (2014) approached this 

issue by comparing successive surveys in the same country. The fertility data in the five years before a 

survey should be in approximate agreement with the fertility data five to nine years before another survey 

conducted five years later. Schoumaker found this kind of agreement in many countries, but in some other 

countries, the surveys being compared were inconsistent, with a pattern that suggests some omission or 

displacement of births. 

Pullum and Becker (2014) examined the birth histories directly, searching for internal evidence of omission 

and displacement. Over time, the level of completeness of the reporting of dates in the birth histories has 

increased steadily, but evidence is strong that in some surveys, some children who were born inside the 

time interval for the health questions have been shifted to earlier dates, giving spurious evidence of recent 

fertility decline. In some surveys children tend to be omitted rather than displaced, although omission is 

much more difficult to detect. Both omission and displacement are more likely for children who have died. 

In this report, no explicit adjustment is made for possible omission or displacement. 

2.2. Measures 

Every woman in every survey contributes to the calculation of age-period-specific fertility rates for single 

years of age and single calendar years of time, calculated here for the 10 calendar years before the survey. 

Descriptions of these calculations, as well as methods for pooling surveys, imputing rates for ages that are 

censored, and smoothing the rates, appear in section 2.3. 

Five summary measures of fertility are calculated from the long-term arrays of births, exposure, and ageperiod-specific rates for each country. Two measures are used for volume or quantum of fertility: the Total 

Fertility Rate (TFR) and the General Fertility Rate (GFR). Three measures of the timing or tempo of fertility 

 

3

 The sequence of the questions has varied somewhat across time periods and surveys. The household line number of 

the child was not included in early surveys. The most recent surveys include day of birth, as well as month and year, 

but day is not be used in this report. 
11 

are (1) the mean age at childbearing (macb), (2) the standard deviation of the age at childbearing (sdacb), 

and (3) the age when women reach successive parities. 

Total Fertility Rate. The TFR is a synthetic summary of births and exposure in an interval of time or a 

period that can be interpreted as the number of children a woman would be expected to have if she survived 

from exact age 15 to exact age 50 and experienced the observed age-period-specific fertility rates for the 

full age range. It is synthetic because period age-specific rates are used, rather than cohort age-specific rates 

for the births of a real cohort of women. The TFR for a specific calendar year is the sum of the age-specific 

rates estimated for that year.4

 It is not affected by the age distribution in the range from 15 to 49, an 

advantage for the analysis of trends and differentials. 

General Fertility Rate. The GFR is the total number of births to women age 15-49 in an interval of time, 

divided by the total exposure to age 15-49 in that interval of time. In this report the interval of time is one 

year. The numerator of the GFR is the sum of the numerators of the age-specific rates and the denominator 

is the sum of the denominators of the age-specific rates.5

 We multiply this ratio by 1,000 to obtain the usual 

interpretation as the average number of births per year per 1,000 women age 15-49.6

 The GFR can also be 

interpreted as 1,000 times the average of the age-specific rates, when those rates are weighted by the 

exposure to each age, that is, as a weighted sum of the age-specific rates for a calendar year. The GFR is 

thus affected by the age-distribution of the sample, but it is perhaps more interpretable than the TFR because 

it is simpler and not synthetic. 

Mean and standard deviation of the age at childbearing. The second group of indicators describes 

fertility in terms of timing. The mean and standard deviation of the age at childbearing are standard 

statistical measures of central tendency and dispersion. They are calculated with simple formulas in which 

each single year of age x is assigned the value x+1⁄2, which is the midpoint of the continuous range of age 

corresponding with age x at last birthday, and is weighted by the age-specific rate for age x. As the level of 

fertility declines, changes in the mean age of childbearing will be determined by the balance between 

delaying the first birth, with potential repercussions for the timing of all subsequent births, and avoiding 

high order births. It is possible for the mean to remain unchanged, even as fertility falls, if the fertility 

decline is due to both kinds of effects—delaying early births and avoiding later births. The standard 

deviation is more sensitive than the mean to changes in the lengths of birth intervals. If fertility declines 

and the intervals between births remain the same or become even shorter, then the standard deviation will 

decline. If fertility declines, but the birth intervals increase, as programs would usually encourage for the 

health of the children and the mother, then the standard deviation may show little change. 

Trends in the ages at successive parities. A relatively innovative component of the analysis is based on 

simple synthetic estimates of the ages at which women achieve each successive birth order, and how these 

ages change over time. As stated earlier, the TFR is calculated by summing the age-specific fertility rates 

for single years of age 15 through 49. Incomplete sums of rates from 15 to some age before 49, such as 39, 

are referred to as Cumulative Fertility Rates. A CFR is normally keyed to a cutoff age (such as 39, to give 

the number of children born before the 40th birthday), but as an alternative, we can key it to a specific 

value, such as 1, 2, or 3, etc., and estimate the age when the CFR reaches that value. That age is loosely 

described as the age at which surviving women have their first child, second child, third child, and so on. 

The age at which the CFR for a synthetic cohort reaches a specific parity is not strictly the same as the mean 

 

4

 As a sum of age-specific rates, the TFR is a compound rate. If the rates were for five-year intervals of age, which is 

more common, then they would be multiplied by five either before or after the summation, but this multiplier is not 

required when the rates are for single years of age. 5

 In the main reports and in StatCompiler, DHS uses a definition of the GFR that excludes exposure to women age 

45-49 from the denominator (but includes their births in the numerator). Here we follow the standard definition in 

the demographic literature (see Pullum 2004) and include age 45-49 in both the numerator and the denominator. 6

 This description of the TFR and GFR assumes that the age-specific rates do not include a factor of 1,000. 
12 

age at reaching that parity, which would usually be calculated only for women who do reach that parity. 

For parities greater than the TFR, the age when the CFR reaches that age is undefined, and thus, when the 

TFR goes below 5, for example, the age at reaching parity 5 can no longer be tracked with this approach, 

although some women will still have five children, and their mean age at the birth of the fifth child could 

be calculated. 

2.3. Methods 

Arrays of births, exposure, and fertility rates 

In the original definition of an age-specific fertility rate, say for women age 20-24 in calendar year 2000, 

the numerator is the number of births in 2000 to women who were age 20-24 at the time of birth, and the 

denominator is the number of women age 20-24 at the midpoint of the year, that is, on July 1, 2000. The 

numerator comes from a vital statistics system and the denominator is estimated from a completely different 

source, such as censuses or a civil registration system. In developed countries, official statistics on fertility 

are calculated in this way. (See, for example, Pullum 2004.) 

By contrast, when age-specific fertility rates are calculated from retrospective birth histories collected in a 

survey, the denominators are not the number of women in an age group, but rather they are the number of 

woman-years of exposure to the specified age interval in a specified time interval. All women in the survey 

whose lives included any time (“exposure”) in the combination of age and time, not just those who had a 

birth, contribute something to the denominators. Each woman’s month and year of birth are used to calculate 

her age in the time interval. The standard five-year age intervals are 15-19, 20-24...45-49, but in this report 

age is disaggregated into single years 15, 16...49. 

The procedures to calculate fertility rates will not be described here in detail because they are consistent 

with standard DHS procedures, described in Pullum 2004. The only modification when using calendar 

years, rather than “years ago,” is with the specification of the window within which births and exposure are 

calculated. For example, the intervals for a woman interviewed in March of 2014, say, would not be the 

three-year window from March 2011 through February 2014, inclusive; instead, the intervals would be 

January 2012 through December 2012, inclusive, for calendar year 2012. Otherwise, the arbitrary 

demographic decisions that are required because we know the month and year of the mother’s birth and the 

child’s birth, but not the day, are handled in exactly the same way here as for the standard rates in DHS 

reports. 

Conceptually, age-period specific fertility rates for each survey are calculated from two arrays, one with 

numerators (births) and the other with denominators (exposure). In each array or matrix, the rows are the 

35 single years of age from 15 through 49. The columns are calendar years. From each survey, we restrict 

to the 10 calendar years before the survey, with each year treated separately. For example, if the survey was 

done in 2014, or if it was done in 2013–2014, the 10 years would be 2004 through 2013, inclusive. We thus 

have a 35 × 10 array of births and a separately calculated 35 × 10 array of woman-years of exposure. Each 

birth and each contribution of exposure is multiplied by the sampling weight before calculating the totals 

in the cells. If the number in each cell of the births array is divided by the number in the corresponding cell 

of the exposure array, we obtain a third array, the age-period specific rates.7

Some cells in the arrays will be empty or extremely small because of the upper age cutoff for the sample. 

In the example of a survey conducted in 2014, there will be little exposure in 2013 to age 49, none in 2012 

 

7

 The actual calculation of rates uses poisson regression, in which the outcome is the number of births, with an offset 

for the natural logarithm of the exposure. Adjustments for weights are included so that the point estimates are 

unbiased. Adjustments for clusters and strata are made so that robust standard errors can be estimated analytically. 

The description in the text is more conceptual. 
13 

to age 49, and little to age 48, and so on. The women who would have contributed to those combinations 

of years and ages would have been older than 49 at the date of the survey and, therefore, they were not 

interviewed. We fill in missing cells for ages 40+ with an empirically based procedure. Exposure and births 

in single-years of age from 40 through 49 are imputed as needed for the 10 years before the survey, simply 

by maintaining constant rates and proportional exposure as observed in the years immediately prior to the 

surveys. The results will have low sensitivity to how this is done because these are ages in which fertility 

is generally low, relative to earlier ages. 

The surveys from Egypt and Jordan were limited to ever-married women. Unbiased estimates for the 

national population of all women age 15-49 living in households, regardless of marital status, require that 

the exposure for every woman in a survey be inflated with “all-woman factors” (awfactt). DHS calculates 

these factors during the preparation of the main data files, as the inverse of the proportion of all women age 

x in the household survey who are ever-married, with x measured in single years of age. 

The next step, after calculating the births and exposure arrays for each survey, is to combine fertility data 

from the successive surveys in each country. This is done by expanding the number of columns in the arrays 

of births and exposure, and consolidating or adding the contributions from each survey. For example, 

suppose that we have surveys from 2009 and 2014 for a specific country. Each survey produces 35 x 10 

arrays of births and exposure. The 10 columns from the 2014 survey, as described earlier, refer to calendar 

years 2004 through 2013. The 10 columns from the 2009 survey refer to calendar years 1999 through 2008. 

Both surveys provide information about births and exposure in five overlapping years, 2004 through 2008. 

In the consolidated arrays, the births and exposure for 2008 coming from the 2009 survey and the 2014 

survey are added, cell by cell. The combined array has 35 rows, for each age 15 through 49, and 15 columns, 

for each year 1999 through 2013. The most recent columns include data from only the most recent survey. 

The earliest columns include data from only the earliest survey. The middle columns contain a pooling of 

the births and exposure from both surveys. The array of age-period-specific fertility rates is again obtained 

by dividing the births array by the exposure array, cell by cell. 

As earlier surveys are added, the arrays for each country become wider. This study includes all countries 

that have had five or more DHS surveys. Typically, the most recent five years come only from the most 

recent survey; the earliest five years come only from the earliest survey. Otherwise, depending on the 

spacing between surveys, contributions for each year usually come from two successive surveys, but 

sometimes three surveys and sometimes only one. 

In the consolidation of births and exposure from successive surveys, no adjustment is made for the sizes of 

the surveys. The general trend has been toward increasingly large samples over the course of the DHS 

program. When births and exposure from two surveys of unequal size are pooled, the resulting pooled rate 

is intermediate to the separate rates from the two surveys, but it is closer to the one with more exposure, 

and typically, more births. 

It might seem desirable to re-scale the sampling weights in successive surveys, which could give equal 

weight to both surveys or give weight in proportion to the changing population of the country. This analysis, 

however, does not do this. Estimates of the fertility rate for a specific year of age and year of time, coming 

from two successive surveys, are generally unbiased.8

 The optimal way to pool two unbiased rates is to add 

the numerators, add the denominators, and then divide, without any re-weighting. 

 

8

 A statistical association is possible between fertility and survival. For example, women who had more births by 

some specific age may be more likely to have died before the survey. An association is possible between fertility 

and recall. For example, women who had more births may be more likely to have children who died, and those 

children may be more likely to be omitted from the birth history. Either association could lead to downward bias in 

retrospective estimates; otherwise, the estimates from each survey are believed to be unbiased. 
14 

We briefly expand on the description in section 2.2 of how age at successive parities is estimated. In this 

calculation, cumulative fertility at age 20, for example, is interpreted as the number of children born by the 

20th birthday and, therefore, it is the sum of the age-specific rates up to and including age 19, and excluding 

the rate for age 20. Suppose that this sum up to and including age 19 is less than or equal to 1.00, but the 

next sum, for age 21—the previous sum plus the age-specific rate for age 20—is greater than 1.00. The 

mean age when achieving parity 1 is then estimated to be in the interval between age 20.00 and 21.00, but 

not including 21.00. The specific value of age in that one-year age interval is calculated by linear 

interpolation. 

Single-year estimates are shown in the figures for each country. The figures show some year-to-year 

fluctuation or jaggedness, resulting from a combination of sampling error and genuine fluctuation in the 

underlying population values.9

 The observed outcomes—TFR, GFR, mean age at childbearing, standard 

deviation of the age at childbearing, and ages when the CFR reached integer values—have been smoothed 

with 4-year linear splines. The knots are at calendar years that are divisible by 4, such as 2000. The selection 

of 4 is largely arbitrary (alternatives were tried, with little apparent effect), but it is important to avoid 5 

because of the typical 5-year spacing of surveys. Alternatives, such as cubic splines, which is another option 

with Stata, higher-degree polynomials, and lowess smoothing, would give similar results. The estimates 

given in the text and tables for specific calendar years are the smoothed values, not the observed values. 

Substantial sampling variability can occur in the cells of the arrays of births, exposure, and rates. The 

calculation of age-specific rates for single years of age or time, or the calculation of the TFR and GFR for 

single years of time, departs from standard DHS practice. The standard error of a rate based on single years 

of age and time will be approximately four times as large as a rate based on five years of age and three 

years of time given in a DHS report.10 However, this more granular approach, followed by smoothing, 

seems preferable to alternatives, such as arbitrarily wide multiyear intervals of age or time, or both, or 

moving averages, etc. 

Graphs of the TFR and GFR include approximate 95% confidence intervals.11 The figures generally show 

a widening of the intervals for the five years in the beginning of the series and the five years at the end, 

because those estimates are based on only one survey, rather than the overlap of two surveys. Confidence 

intervals are not calculated for the other measures, but it can be seen from the figures that those estimates 

do not change much from one year to the next, indicating statistical stability, and the intervals would be 

narrow. 

The next chapter reports the results of our analyses for the 16 countries covered in this report, organized 

alphabetically. The figures show the survey dates for the Total Fertility Rate, the General Fertility Rate, the 

mean and standard deviations for the age at childbearing, and the age at reaching successive number of 

children. The last section is an overview of all the countries. 

 

9

 There is, undoubtedly, some measurement error, apart from sampling error, but we do not attempt to assess its 

direction or magnitude and we made no adjustments for it. 

10 In the comparison of a rate for 1 year of age and 1 year of time with a rate for 5 years of age and 3 years of time, 

the approximate factor for the standard error is the square root of 5×3, or 3.9. 

11 Robust confidence intervals are much easier to calculate for the General Fertility Rate (GFR) than for the Total 

Fertility Rate (TFR), although they are just re-arrangements of the same births and exposure, because the TFR is a 

compound rate. To arrive at an approximation, our strategy was to calculate the confidence interval for the GFR, 

then express the lower and upper ends as percentages of the GFR, and then apply those percentages to the TFR to 

get lower and upper bounds for the TFR. 
15 

3. Results 

3.1. Bangladesh 

Figure 3.1. Fertility trajectory for Bangladesh, estimated from seven DHS surveys for calendar 

years from 1984 to 2013. Figures show the Total Fertility Rate, the General Fertility Rate, the mean 

and standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

Bangladesh underwent a truly remarkable fertility transition during the time interval, with an estimated 

TFR of 5.9 in 1984, declining to 2.4 in 2013. The decline in the TFR and GFR was especially abrupt in the 

first 10 years, when the TFR dropped by about two children. It is clear that this rapid initial decline began 

before 1984. The most recent survey suggests that the rate of decline is slowing as replacement fertility is 

being approached. 

The decline in the number of children has been accompanied by a continuous and steady decline in both 

the mean and the standard deviation of the age at childbearing. Fewer women have proceeded to parities 

above 2, and the age at having the first and second births has increased substantially; the age at the second 

birth has increased by nearly 10 years during the time interval. The interval between the first and second 

births has steadily increased. The reduction in family size has come about through a combination of not 

progressing to higher parities, having the first child later, and increasing the intervals between births. 
16 

3.2. Colombia 

Figure 3.2. Fertility trajectory for Colombia, estimated from six DHS surveys for calendar years 

from 1976 to 2009. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

Colombia had completed its fertility transition by the end of the time interval, reaching a replacement-level 

TFR of 2.1 in 2009. At the beginning of the interval, in 1976, its TFR was 4.2, twice replacement level, but 

already well into a fertility transition as a consequence of several years of decline before 1976. It appears 

that the TFR trajectory leveled at about three children from the mid-1980s to the mid-1990s, but declined 

steadily since then. 

The mean age at childbearing declined moderately across the interval, and the standard deviation fluctuated, 

but with little net change. The timing of the first birth has been relatively stable, typically being reached in 

the mid 20s, but the second birth has been typically delayed to the late 30s. 
17 

3.3. Dominican Republic 

Figure 3.3. Fertility trajectory for Dominican Republic, estimated from seven DHS surveys for 

calendar years from 1976 to 2012. Figures show the Total Fertility Rate, the General Fertility Rate, 

the mean and standard deviation of the age at childbearing, and the age at reaching successive 

numbers of children. 

The trajectories of the TFR and GFR for the Dominican Republic are similar to those for Colombia, 

although at a slightly higher level. The TFR declined by about half, moving from 5.2 in 1976 to 2.5 in 2012. 

The decline was clearly well underway before 1976. The rates plateaued between the mid-1980s and the 

mid-1990s, but then the decline resumed, with some evidence of another plateau around 2000 and another 

one in the past decade or so. 

The mean and standard deviation of the age at childbearing declined steadily until the late 1990s but have 

been flat or slightly increasing since then. The scales for these indicators, shown in the third graph of Figure 

3.3, are different, given by the left and right vertical axes, and the apparent overlap of the trajectories is a 

coincidence. The ages at reaching parity 1 and parity 2 have increased, but not as dramatically as in 

Colombia, and the most recent age at the second birth is much earlier than in Colombia, despite a difference 

of only 0.3 in the TFR. 
18 

3.4. Egypt 

Figure 3.4. Fertility trajectory for Egypt, estimated from seven DHS surveys for calendar years 

from 1979 to 2013. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

The TFR in Egypt has declined, from an initial value of 5.2 in 1979 to a final value of 3.6 in 2013. Compared 

with many other countries, however, the decline in Egypt has been only moderate. During the final 10 years 

or so, the TFR was essentially flat, and the GFR increased slightly to about the same level as 20 years 

earlier. Because of the lack of change in the first few years after 1979, it appears that the fertility transition 

had barely started, but the initial level of 5.2 is below the typical pre-transition TFR. 

The mean age at childbearing has remained relatively constant for the full interval, around 28.0 years, but 

the degree of concentration increased (that is, the standard deviation decreased) between 1985 and 2000 

and then became flat. The most recent standard deviation, about 6.0 years, is among the lowest in all the 

countries. Finally, the ages at reaching successive parities increased gradually until about 2003, and since 

then have returned to earlier levels. 
19 

3.5. Ghana 

Figure 3.5. Fertility trajectory for Ghana, estimated from six DHS surveys for calendar years from 

1978 to 2013. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

The decline in the TFR in Ghana has been monotonic and steady, from 7.0 in 1978 to 4.0 in 2013, the same 

final value as Kenya. The initial TFR was high, but the immediate decline suggests that the transition had 

already begun, which suggests an inflection point soon after 1990, such that the rapid decline up to that 

point became gradual, up to the present. The decline in recent years has been slow, as measured with the 

TFR and negligible with the GFR. 

The mean age at childbearing was initially high, nearly 31.0. It declined until about 1990, and it has been 

essentially unchanged since then, somewhat below 30.0, but still one of the highest levels of all countries. 

The standard deviation also was initially high, near 8.0. It declined until 1990, then was flat for about 10 

years, and has been in decline since about 2000. The timing of each parity has been steadily increasing, 

despite the recent flatness in the TFR and GFR, a pattern that is consistent with the increasing concentration 

of the age at childbearing. The intervals between the first and second, the second and third, and the third 

and fourth births have continued to be about equal to one another, but have gradually increased. As a result, 

the increase in age at reaching successive parities has moved progressively more steeply. The continuing 

decrease in the standard deviation and increases in the ages at successive parities show that the TFR and 

GFR do not completely capture the changing pattern of childbearing. 
20 

3.6. Indonesia 

Figure 3.6. Fertility trajectory for Indonesia, estimated from seven DHS surveys for calendar years 

from 1977 to 2011. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

Indonesia moved from a TFR of 5.2 in 1977 to 2.7 in 2011, so now it is only a little more than half a child 

above replacement level, but most of that decline was before approximately 1990. Since then, the trajectory 

has been relatively flat, and the most recent survey suggests a possible increase, with the TFR and GFR in 

2011 being at approximately the same level as in 2000. 

The mean age at childbearing increased slightly, from an initial value of about 28.0 years, but after a slight 

decline in the past few years, it was back to almost exactly the initial value in 2011. The standard deviation 

in 2011 was also almost exactly at the initial level. The ages at reaching parities 1 and 2 increased steadily 

until about 2008, and the interval between those two ages also increased steadily, but with a possible 

reduction in the years just before the 2012 survey. 

East Timor was included in the data collection for the early surveys of Indonesia, but after Indonesia 

relinquished control in 1999, East Timor was not included. The present analysis omitted East Timor from 

the early surveys, so that the entire trajectory presented here is exclusive of East Timor. 
21 

3.7. Jordan 

Figure 3.7. Fertility trajectory for Jordan, estimated from five DHS surveys for calendar years from 

1980 to 2011. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

In Jordan, the TFR in the starting year, 1980, was calculated to be 8.0. There is other evidence that Jordan’s 

pre-transitional TFR was about 8, but a more plausible estimate for 1980 would be about 7.5. Our estimates 

of the TFR and GFR in the early 1980s are almost certainly too high. As described earlier, the all-woman 

factors from the 1990 survey were used to inflate exposure for the 10 years 1980 through 1989, but during 

the 1980s age at marriage was rising, and it appears that the application of 1990 factors led to an 

exaggeration of the rates for single years of age 15 through 24 in the early 1980s. This distortion is not 

observed for later surveys of Jordan or for Egypt. 

At any rate, Jordan began with one of the highest initial TFRs of all 16 countries, and the highest outside 

of SSA. By 1990 the TFR had declined to 5.3, and by 2000 to 3.7, that is, by about four children in the span 

of 20 years. Since 2000, however, there has been a plateau, such that the estimated TFR in the final year, 

2011, is 3.5. 

The mean age at childbearing has remained relatively steady, about 29.5 years, during most of the interval. 

The standard deviation has dropped steadily by about one year, from 7 to 6 years, but most of that decline 

was before 2000. The age at reaching successive parities has increased steadily, but again, most of that 

increase was before 2000. The data from the 2012 survey, for the years just before that survey, suggests a 

possible resumption of the downward trend in the TFR. 
22 

3.8. Kenya 

Figure 3.8. Fertility trajectory for Kenya, estimated from six DHS surveys for calendar years from 

1979 to 2013. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

Kenya had high initial fertility, with a TFR of 7.7 in 1977, the first year of the series. The trajectory from 

then to about 1995 was steeply downward, with a reduction of about one child every five years. The TFR 

increased slightly from 1995 to 2000, when it was 5.1, and then declined to 4.0 in the most recent year, 

2013, with a rate of decline that was about half what it had been during the earlier phase. The final TFR, 

for the same year, matched that of Ghana. 

Both the mean and the standard deviation of the age at childbearing declined before 1995, and since then 

have been relatively flat. The apparent overlap in the figure is a coincidence resulting from the scales. The 

ages at reaching successive parities have increased slowly, with nearly the same intervals from one birth to 

the next, although those intervals are wider in recent years than they were at the beginning of the interval. 
23 

3.9. Mali 

Figure 3.9. Fertility trajectory for Mali, estimated from five DHS surveys for calendar years from 

1977 to 2012. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

Mali had one of the highest TFRs at the beginning of the series, 7.7 in 1977. The TFR and GFR showed a 

substantial dip, and then a return to previous levels between 1980 and 1990, with consistent alterations to 

the other indicators. It is likely that this evidence of a dip is spurious. Apart from that dip, the decline has 

been gradual, to a TFR of 6.0 in 2012, one of the highest, and the same as Uganda in its last year, 2010. 

The mean and standard deviation of the age at childbearing have also been relatively unchanged since about 

1990. The intervals between successive births have been remarkably uniform, with only slight increases in 

age and corresponding slight increases in the intervals between parities. 

Most of the surveys in Mali have omitted some geographic areas because they are sparsely populated or 

because of difficult field conditions. These areas have differed from one survey to the next. The pooling of 

surveys in this report has not taken those irregularities into account. 
24 

3.10. Peru 

Figure 3.10. Fertility trajectory for Peru, estimated from nine DHS surveys for calendar years from 

1976 to 2011. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

Peru’s TFR declined from 6.0 in 1976 to 2.5 in 2011, similar to the decline in the Dominican Republic but 

somewhat steeper overall. The TFR is approaching replacement level. The reduction in the final 10 years 

has been only about 0.3 of a child, with virtually no reduction in the past 5 years. 

The mean age at childbearing declined gradually from about 29 to about 28, with little change since 2000 

and little change in the standard deviation during the entire interval. The reduction in fertility was clearly 

due to a combination of eliminating high-order births and increasing the age at earlier births, particularly 

the age at the second birth, which rose by about 10 years during the full interval. The age at the first birth 

increased by about four years and the interval to the second birth increased by about six years. 

Peru had its last standard DHS survey in 2000, and since 2004 has had a continuous survey, with data 

collection every year and a rolling sample design. The years of the continuous survey have been grouped 

together (as in the data files DHS makes available) to reach a nominal total of nine surveys. 
25 

3.11. Philippines 

Figure 3.11. Fertility trajectory for the Philippines, estimated from five DHS surveys for calendar 

years from 1983 to 2012. Figures show the Total Fertility Rate, the General Fertility Rate, the mean 

and standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

The trajectory for the Philippines has been close to linear throughout the interval from 1983 to 2012. The 

initial value of the TFR was 4.7 and the final value was 3.0, with an average reduction of 0.06 of a child 

per year. No plateau is indicated between 1983 or 2012, and not at the end, but this gradual pattern would 

need to be projected for another 15 years or so to reach replacement fertility. 

The mean age at childbearing has been steady. The standard deviation has steadily increased since the early 

1990s. The timing of the first birth has been flat, between approximately ages 23 and 25. The ages at the 

second and third births show a faster rise, particularly the third birth, in the most recent years. 

The Philippines had its first National Demographic Survey in 1968, and other surveys at five-year intervals 

between then and the first DHS survey in 1988. It would be possible to extend this series back by 20 years. 
26 

3.12. Senegal 

Figure 3.12. Fertility trajectory for Senegal, estimated from seven DHS surveys for calendar years 

from 1976 to 2013. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

Senegal appears to have begun its fertility transition near the beginning of the series, in 1976, when the 

TFR was high at 7.4, with a fairly steady decline until 1995, and then a plateau, with just a gradual decline 

from 5.2 in 2000 to the final value of 4.9 in 2013. 

The mean age at childbearing increased by about a year, from age 29 in 1976 to age 30 in 1995, and it has 

been increasing since about 2000. The standard deviation went from a higher level to a lower level, with 

most of the change happening in the late 1990s, about the same time as the plateau in the TFR and GFR. 

The ages at reaching successive parities have increased gradually, mostly before 1995, but steadily since 

then as well. The age at the fifth child remains on the figure until just before 2012, because that is when the 

TFR finally decreased below 5.0, but it increased by about 10 years of age during the time interval. 
27 

3.13. Tanzania 

Figure 3.13. Fertility trajectory for Tanzania, estimated from six DHS surveys for calendar years 

from 1982 to 2009. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

Tanzania had a TFR of 6.5 in 1982 and 5.5 in 2009, a decline of one child across a span of 27 years. In 

SSA, a pre-transition level of 6.5 would be relatively low, so it is possible that some decline had already 

occurred, despite the early pattern for the TFR and GFR, which is not compatible with a previous decline. 

The decline to about 4.5 in 2000, and then a plateau, is a familiar pattern from other countries in SSA, but 

the 2010 survey shows an increase of about one child after about 2005, a conspicuous difference from the 

other countries in this report, with the exception of Zimbabwe. 

Both the mean and standard deviation of the age at childbearing had a net decline over the time interval, 

but most of the change was before 2000. The initial standard deviation for Tanzania, about 7.5, was also 

high—as is typical for the initial values of countries in SSA. The initial and final values of the ages at 

reaching successive parities are nearly the same, another manifestation of the apparent recent uptick in the 

TFR and GFR. 

A more recent survey, conducted in 2015, but not available in time to be included here, will clarify whether 

the apparent increase before the 2010 survey was real. 
28 

3.14. Uganda 

Figure 3.14. Fertility trajectory for Uganda, estimated from five DHS surveys for calendar years 

from 1979 to 2010. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

After Tanzania, Uganda shows the least net change in fertility of all the countries. Its TFR in 1982 was 7.4, 

a high pre-transitional level, and in 2010 it had reached was 6.0, which matches the final value in Mali and 

is higher than the initial value in several other countries. Most of the net decline of 1.4 births occurred after 

2000, when the estimated TFR was 7.2. After 2000, the decline has been gradual but steady and may well 

continue. 

Virtually no change in the mean age at childbearing took place during the entire interval. The standard 

deviation moved irregularly before 2000, but it was steady during the period of decline after 2000. The ages 

at reaching successive parities were consistently and uniformly spaced for the entire interval, with gradual 

and parallel increases after about 2000, suggesting a slight delay in the timing of the first birth, but with no 

or only small additional delays in later births apart from that. 
29 

3.15. Zambia 

Figure 3.15. Fertility trajectory for Zambia, estimated from five DHS surveys for calendar years 

from 1982 to 2013. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

Zambia began with a high TFR, 7.6, in 1982, and ended with a TFR of 5.1 in 2013. An initial rapid decline 

is apparent, followed by an interval of about 20 years during which the TFR hovered near 6. The recent 

decline in TFR and GFR suggested in the 2014 survey is encouraging. 

The mean age at childbearing declined during the first few years, and then remained virtually unchanged. 

The standard deviation appears to have had a long-term net decline of half a year, but with fluctuations. 

The ages at reaching successive parities have been steady and equally spaced. The age parity 1, just slightly 

above age 20, is one of the earliest in all the countries. The uptick in age from the 2014 survey is seen for 

all parities, including parity 1. 
30 

3.16. Zimbabwe 

Figure 3.16. Fertility trajectory for Zimbabwe, estimated from five DHS surveys for calendar years 

from 1979 to 2010. Figures show the Total Fertility Rate, the General Fertility Rate, the mean and 

standard deviation of the age at childbearing, and the age at reaching successive numbers of 

children. 

The TFR in Zimbabwe is estimated at 6.4 in the first year, 1979, and 4.6 in the final year, 2010. There was 

an initial rapid decline to 4.9 in 1990 and 4.2 in 2000, but with a levelling in approximately 2003 to 2007. 

The TFR increased abruptly in the years just before the 2011 survey, returning to approximately 1990 

levels. Tanzania is the only other country in this report that shows such an abrupt increase in the most recent 

survey. 

The mean age at childbearing was unchanged during the first 10 years or so, but then it declined gradually. 

The standard deviation was also steady for the initial 10 years, and then it declined even more gradually. 

The overlap of lines for the mean and standard deviation is a coincidence that results from the scales on the 

figure. The ages at successive parities increased throughout most of the time interval, but it declined 

abruptly in the final five years, to the levels of the late 1990s. Another DHS survey of Zimbabwe will be 

released soon. 
31 

3.17. Summary of Results 

This section reviews and summarizes the changes for all 16 countries for the TFR, GFR, mean age at 

childbearing, standard deviation of the age at childbearing, and the ages at reaching successive parities. 

Results for all countries are illustrated in figures, supplemented by a table listing the estimated values by 

years, and a table comparing the changes across selected years. 

Total Fertility Rate 

Figure 3.17. Trajectory of the Total Fertility Rate for all 16 countries, based on pooled data from 98 

DHS surveys for the 10 calendar years before each survey. 

The TFR for the 16 countries covered in this analysis has declined substantially, overall. The median initial 

level was 6.43, and the median final value was 3.76, a reduction of 42%.12 Every country showed a decline. 

Despite the various fluctuations and, for several countries, upticks or downticks at the end, the consistency 

across countries was high. The correlation between the initial and final TFRs was 0.72. Squaring this 

number, fully half of the variation in the final TFR was statistically explained by the initial value. In a 

comparison of the countries above or below the medians at the initial and final values, only two countries 

that did not remain in the same half. Jordan is exceptional for being above the initial median but below the 

final median. Zimbabwe is exceptional for being below the initial median, although only slightly, but above 

the final median. 

 

12 Each median was calculated as the midpoint between the eighth and ninth values of the TFR, when the countries 

are ranked by their initial TFR and then by their final TFR. 
32 

Table 3.1 gives the TFR for all countries in the initial year; in 1980, if the initial year was before 1980; in 

1990; in 2000; in 2010, if the last year was after 2010; and in the last year. The first three columns also 

identify the first year, last year, and the length of the interval.13 TFR values are given with two decimal 

places, but in the text they are usually rounded to the nearest tenth. 

Table 3.1. Smoothed values of the Total Fertility Rate in the 16 countries. Includes smoothed values for the 

first year and last year, 1990, and 2010. If the first year was before 1980, smoothed values for 1980 are also 

included. If the last year was after 2010, smoothed values for 2010 are also included. 

Country First year Last year Interval 

TFR in 

first year 1980 1990 2000 2010 

TFR in 

last year 

Bangladesh 1984 2013 29 5.88 4.42 3.50 2.41 2.35 

Colombia 1976 2009 33 4.24 4.02 2.96 2.56 2.14 

Dominican Republic 1976 2012 36 5.21 4.49 3.41 3.14 2.58 2.50 

Egypt 1979 2013 34 5.17 5.11 4.27 3.57 3.35 3.55 

Ghana 1978 2013 35 7.01 6.87 5.40 4.62 4.09 3.99 

Indonesia 1977 2011 34 5.20 4.85 3.07 2.66 2.61 2.67 

Jordan* 1980 2011 31 7.99 7.99 5.26 3.68 3.61 3.51 

Kenya 1979 2013 34 7.67 8.09 5.75 5.12 4.19 3.96 

Mali 1977 2012 35 7.74 8.46 7.84 6.94 6.34 6.01 

Peru 1976 2011 35 6.02 5.16 3.95 2.79 2.51 2.53 

Philippines 1983 2012 29 4.65 4.14 3.75 3.18 2.97 

Senegal 1976 2013 37 7.42 7.53 6.39 5.21 5.16 4.91 

Tanzania 1982 2009 27 6.49 6.21 4.43 5.46 

Uganda 1979 2010 31 7.50 7.62 7.25 7.17 6.04 6.04 

Zambia 1982 2013 31 7.61 6.39 5.93 5.61 5.10 

Zimbabwe 1979 2010 31 6.37 6.70 4.86 4.19 4.61 4.61 

*As described in the text, the initial TFR for Jordan is distorted; a suggested approximation would be 7.5. 

Colombia is the only country that could be said to have achieved replacement-level fertility by the end of 

the time interval, but five other countries had a final TFR value below 3: Bangladesh, the Dominican 

Republic, Peru, Indonesia, and the Philippines. These six lowest-fertility countries are all in Latin America 

and the Caribbean or in Asia. The next lowest countries are Jordan and Egypt, from the Middle East and 

North Africa, respectively. All of the remaining countries are above the median and are in SSA. Of these 

eight, Kenya and Ghana are just barely below 4 (to the nearest tenth, their TFR is 4.0). Senegal is just below 

5 (at 4.9); Zambia is just above 5 (at 5.1); Tanzania has a TFR of 5.5, Mali and Uganda have a TFR of 6.0. 

 

13 The interval is calculated as the last year minus the first year. To get the total number of calendar years for which 

an estimate is provided, 1 should be added. 
33 

Table 3.2 describes the change between the TFR values given in the columns of Table 3.1. It is important 

to recognize that changes here, as throughout the text, are expressed as arithmetic differences between the 

values at two time points, and not as, say, annual rates of reduction, which are relative changes. Time 1 

refers to 1980 or to the initial value in Table 3.1, if the first time point is after 1980; Time 2 is always 1990; 

Time 3 is always 2000; Time 4 refers to 2010 or to the final value in Table 3.1, if the last time point is 

before 2010. The arithmetic differences in the TFR are divided by the elapsed number of years, to express 

change on an annualized scale.14 The goal is to describe, as nearly as possible, the decadal changes between 

1980 and 2010. The last column of Table 3.2 can be interpreted as the average annual change from 

approximately 1980 to approximately 2010. 

Table 3.2. Changes in the smoothed values of the Total Fertility Rate in the 16 countries 

Country First year Last year Interval 

Change 12 

annual 

Change 23 

annual 

Change 34 

annual Change 14

Change 14 

annual 

Bangladesh 1984 2013 29 −0.2441 −0.0921 −0.0881 −3.5296 −0.1217 

Colombia 1976 2009 33 −0.0913 −0.0402 −0.0469 −2.1026 −0.0637 

Dominican Republic 1976 2012 36 −0.1283 −0.0270 −0.0533 −2.7051 −0.0751 

Egypt 1979 2013 34 −0.0817 −0.0705 −0.0011 −1.6188 −0.0476 

Ghana 1978 2013 35 −0.1347 −0.0775 −0.0486 −3.0227 −0.0864 

Indonesia 1977 2011 34 −0.1637 −0.0411 0.0008 −2.5296 −0.0744 

Jordan* 1980 2011 31 −0.2735 −0.1573 −0.0159 −4.4838 −0.1446 

Kenya 1979 2013 34 −0.1744 −0.0633 −0.0886 −3.7036 −0.1089 

Mali 1977 2012 35 0.0078 −0.0902 −0.0778 −1.7338 −0.0495 

Peru 1976 2011 35 −0.1483 −0.1158 −0.0238 −3.4961 −0.0999 

Philippines 1983 2012 29 −0.0731 −0.0385 −0.0657 −1.6851 −0.0581 

Senegal 1976 2013 37 −0.0739 −0.1179 −0.0229 −2.5112 −0.0679 

Tanzania 1982 2009 27 −0.0345 −0.1778 0.1145 −1.0242 −0.0379 

Uganda 1979 2010 31 −0.0229 −0.0082 −0.1132 −1.4653 −0.0473 

Zambia 1982 2013 31 −0.1532 −0.0454 −0.0637 −2.5086 −0.0809 

Zimbabwe 1979 2010 31 −0.1372 −0.0667 0.0418 −1.7573 −0.0567 

Note: “Change 14” is the change from the first smoothed value to the last smoothed value. “Change 14 annual” is “Change 14” 

divided by the number of years in the full interval, which ranges between 27 and 37 years. “Change 12 annual” is the annualized

change from the first smoothed value to 1990; “Change 23 annual” is the annualized change from 1990 to 2000; “Change 34

annual” is the annualized change from 2000 to the last smoothed value. 

*Using 7.5 as the initial value for Jordan, Change 12 annual is −0.2240, Change 14 is −3.99, and Change 14 annual is −0.1287. 

To assess whether the annual reduction was large or small, it is important to keep in mind the actual levels, 

particularly the initial distance from replacement level. The greatest annual reductions are for Jordan (0.13, 

if the initial value is replaced with 7.5), Bangladesh (0.12), Kenya (.11), and Peru (.10). Even if the initial 

TFR for Jordan is replaced with 7.5, the decline for Jordan remains the largest of all countries. The smallest 

annual reductions are for Tanzania (.04), Uganda, Egypt, and Mali (.05), and Zimbabwe and the Philippines 

(.06). The other eight countries had intermediate changes (.07, .08, or .09). Except for Jordan, the country 

with the greatest change, and Tanzania, the country with the least change, the other 14 countries are in a 

range from 0.05 to 0.12 in their annualized decline in the TFR, from about 1980 to about 2010. 

 

14 For example, the change from Time 1 to Time 2 for Bangladesh, with the first estimate for 1984, is expressed as 

the 1990 estimate, minus the 1984 estimate, divided by 6. Calculations were done with more decimal places than are 

shown in Table 3.1. Table 3.2 gives four digits to the right of the decimal place because often the first digit is 0, and 

sometimes also the second and even the third. Small differences from one date or country to another should not be 

over-interpreted. 
34 

General Fertility Rate 

Figure 3.18. Trajectory of the General Fertility Rate for all 16 countries, based on pooled data from 

98 DHS surveys for the 10 calendar years before each survey. 

Figure 3.18 and Tables 4.3 and 4.4, are analogous to Figure 3.17 and Tables 4.1 and 4.2, for the GFR for 

all 16 countries. 

Although the TFR and GFR show similar patterns, they are conceptually different, and that is the reason 

for including both of them throughout this analysis. The TFR is a synthetic measure, with an interpretation 

that depends on an obviously false assumption that there is no mortality for women between the exact ages 

15 and 50. The interpretation of the GFR requires no such assumption, and it can be interpreted simply as 

the annual number of births per 1,000 women of childbearing age. 

The early period covered by this report had few years and countries for which the GFR exceeded 250. By 

the end of the observations, no countries exceeded a GFR of 250, and six countries had a GFR below 100: 

Colombia (68), Peru (79), Indonesia and the Dominican Republic (both 83), Bangladesh (86), and the 

Philippines (92). 

Targets are generally specified in terms of the TFR because it is not affected by the age distribution and 

can be considered a pure measure of fertility. As is well known, a TFR of 2.1 can be interpreted as 

replacement fertility, although the exact replacement value depends on the level of mortality. The GFR 

does not have a specific value that corresponds with replacement fertility, but the value of 68 for Colombia, 

which has a final TFR of 2.14, is close to replacement. The estimated initial TFR for Jordan is too high, by 

5% to 10%, but will not be adjusted downward. 

The remainder of the figures and tables for the GFR will not be discussed because of the similarity to 

the TFR. 
35 

Table 3.3. Smoothed values of the General Fertility Rate in the 16 countries. Includes smoothed values for the 

first year and last year, 1990, and 2010. If the first year was before 1980, smoothed values for 1980 are also 

included. If the last year was after 2010, smoothed values for 2010 are also included. 

Country First year Last year Interval 

GFR in 

first year 1980 1990 2000 2010 

GFR in 

last year 

Bangladesh 1984 2013 29 209.94 165.50 126.69 88.60 86.37 

Colombia 1976 2009 33 143.82 136.86 101.31 81.98 68.03 

Dominican Republic 1976 2012 36 176.82 153.50 123.54 107.57 85.89 83.38 

Egypt 1979 2013 34 174.65 169.58 140.05 113.47 116.33 121.95 

Ghana 1978 2013 35 211.53 212.08 175.99 147.39 133.33 131.20 

Indonesia 1977 2011 34 169.55 161.66 104.54 85.20 81.20 82.71 

Jordan 1980 2011 31 248.83 248.83 163.20 121.14 110.00 105.98 

Kenya 1979 2013 34 255.80 271.47 192.47 171.70 145.99 139.51 

Mali 1977 2012 35 245.07 281.13 263.96 231.33 217.25 204.17 

Peru 1976 2011 35 196.08 169.34 130.12 88.05 78.54 78.79 

Philippines 1983 2012 29 152.16 135.41 120.21 98.72 91.78 

Senegal 1976 2013 37 243.81 253.18 206.37 167.87 164.31 157.03 

Tanzania 1982 2009 27 207.02 200.58 148.07 180.94 

Uganda 1979 2010 31 248.64 254.97 247.17 239.28 201.54 201.54 

Zambia 1982 2013 31 249.46 210.45 200.52 188.56 169.66 

Zimbabwe 1979 2010 31 210.82 221.18 159.34 142.45 160.09 160.09 

Table 3.4. Changes in the smoothed values of the General Fertility Rate in the 16 countries. 

Country First year Last year Interval 

Change 12 

annual 

Change 23 

annual 

Change 34 

annual Change 14

Change 14 

annual 

Bangladesh 1984 2013 29 −7.4069 −3.8807 −3.1014 −123.5670 −4.2609 

Colombia 1976 2009 33 −3.0364 −1.9330 −1.5498 −75.7876 −2.2966 

Dominican Republic 1976 2012 36 −3.8060 −1.5966 −2.0162 −93.4437 −2.5957 

Egypt 1979 2013 34 −3.1454 −2.6577 0.6522 −52.6973 −1.5499 

Ghana 1978 2013 35 −2.9623 −2.8593 −1.2458 −80.3360 −2.2953 

Indonesia 1977 2011 34 −5.0008 −1.9345 −0.2258 −86.8396 −2.5541 

Jordan 1980 2011 31 −8.5632 −4.2058 −1.3787 −142.8563 −4.6083 

Kenya 1979 2013 34 −5.7570 −2.0764 −2.4764 −116.2837 −3.4201 

Mali 1977 2012 35 1.4531 −3.2630 −2.2636 −40.9020 −1.1686 

Peru 1976 2011 35 −4.7114 −4.2071 −0.8423 −117.2948 −3.3513 

Philippines 1983 2012 29 −2.3925 −1.5199 −2.3687 −60.3706 −2.0817 

Senegal 1976 2013 37 −2.6749 −3.8495 −0.8343 −86.7883 −2.3456 

Tanzania 1982 2009 27 −0.8043 −5.2511 3.6526 −26.0716 −0.9656 

Uganda 1979 2010 31 −0.1329 −0.7891 −3.7745 −47.0983 −1.5193 

Zambia 1982 2013 31 −4.8765 −0.9931 −2.3741 −79.8065 −2.5744 

Zimbabwe 1979 2010 31 −4.6802 −1.6887 1.7636 −50.7323 −1.6365 

Note: “Change 14” is the change from the first smoothed value to the last smoothed value. “Change 14 annual” is “Change 14” 

divided by the number of years in the full interval, which ranges between 27 and 37 years. “Change 12 annual” is the annualized

change from the first smoothed value to 1990; “Change 23 annual” is the annualized change from 1990 to 2000; “Change 34

annual” is the annualized change from 2000 to the last smoothed value. 

 
36 

Mean age at childbearing 

Figure 3.19. Trajectory of the mean age at childbearing for all 16 countries, using pooled data from 

98 DHS surveys for the 10 calendar years before each survey 

The trajectories of the mean age at childbearing are described in Figure 3.19 and Tables 3.5 and 3.6, which 

are discussed together. Virtually all of the initial values of the mean age at childbearing were in the range 

of 28.0 to 30.0 years. Ghana was a little higher and Egypt was a little lower. The initial value for Bangladesh, 

in 1984, was nearly a full year below that range, but possibly would have been in that range in 1980 if there 

had been an earlier survey. For most countries, the mean age at the end of the series was also in that range, 

and if the range is widened to, say, 27.5 to 30.1, in the final year only three countries—Bangladesh (24.7) 

and Colombia and the Dominican Republic (26.3 for both)—would be outside that range, and they would 

be substantially below it. These three countries had a steadily declining mean age at the same time that the 

TFR and GFR were dropping rapidly; otherwise, the countries with declining fertility did not show nearly 

such large declines in their mean age at childbearing. Four countries, however, did show a change of 

approximately a year, up or down, in their mean age: Zimbabwe declined by 1.14 years, Kenya by 1.04 

years, and Peru by 0.95 of a year; Senegal increased by 1.11 years. 

A relationship that is not reflected in the figures or tables in this report is that the TFR and the mean age at 

childbearing across all countries are positively and significantly correlated. At the initial year, if the mean 

age is regressed on the TFR, the regression coefficient is 0.35, and at the final year it is 0.62. In general, 

countries with a lower TFR also have a lower mean age at childbearing. That is, a reduction of about 1.6 in 

the TFR corresponds with a reduction of about one full year in the mean age at childbearing. This pattern 

probably reflects the dominant role of the omission of high parities, rather than the delay of the early 

parities, during the transition to lower fertility. 

No adjustment is made to the estimates of the mean and standard deviation of the age at childbearing in 

Jordan in the early 1980s. The mistiming of the all-woman factors, described earlier, slightly exaggerated 

the rates for ages 15-24, probably producing a slight downward bias in the mean and upward bias in the 

standard deviation, but only for 1980. 
37 

Table 3.5. Smoothed values of the mean age at childbearing in the 16 countries. Includes smoothed values for 

the first year and last year, 1990, and 2010. If the first year was before 1980, smoothed values for 1980 are also 

included. If the last year was after 2010, smoothed values for 2010 are also included. 

Country First year Last year Interval 

Mean age in 

first year 1980 1990 2000 2010 

Mean age in 

last year 

Bangladesh 1984 2013 29 27.14 26.13 25.54 24.90 24.69 

Colombia 1976 2009 33 28.12 27.81 27.03 26.30 26.28 

Dominican Republic 1976 2012 36 28.29 27.90 26.29 25.42 26.07 26.29 

Egypt 1979 2013 34 27.84 28.15 28.01 27.76 27.43 27.64 

Ghana 1978 2013 35 30.20 30.60 29.47 29.84 29.57 29.54 

Indonesia 1977 2011 34 28.07 27.75 27.85 27.99 28.32 28.19 

Jordan 1980 2011 31 29.47 29.47 29.83 29.51 29.59 29.70 

Kenya 1979 2013 34 28.87 29.01 28.69 28.18 28.07 27.82 

Mali 1977 2012 35 29.48 28.78 28.64 28.71 28.71 28.87 

Peru 1976 2011 35 29.07 28.95 28.64 27.91 28.12 28.12 

Philippines 1983 2012 29 29.16 29.07 28.73 28.72 28.87 

Senegal 1976 2013 37 28.99 29.02 29.73 29.36 29.92 30.10 

Tanzania 1982 2009 27 29.24 29.39 28.54 28.71 

Uganda 1979 2010 31 28.57 28.61 28.73 28.82 28.84 28.84 

Zambia 1982 2013 31 29.32 28.87 28.65 28.51 28.59 

Zimbabwe 1979 2010 31 29.03 28.95 28.90 27.90 27.88 27.88 

Table 3.6. Changes in the smoothed values of the mean age at childbearing in the 16 countries 

Country First year Last year Interval 

Change 12 

annual 

Change 23 

annual 

Change 34 

annual Change 14

Change 14 

annual 

Bangladesh 1984 2013 29 −0.1692 −0.0589 −0.0654 −2.4547 −0.0846 

Colombia 1976 2009 33 −0.0781 −0.0724 −0.0019 −1.8335 −0.0556 

Dominican Republic 1976 2012 36 −0.1433 −0.0872 0.0728 −2.0050 −0.0557 

Egypt 1979 2013 34 0.0153 −0.0251 −0.0092 −0.2024 −0.0060 

Ghana 1978 2013 35 −0.0602 0.0367 −0.0232 −0.6565 −0.0188 

Indonesia 1977 2011 34 −0.0165 0.0138 0.0180 0.1204 0.0035 

Jordan 1980 2011 31 0.0354 −0.0318 0.0172 0.2251 0.0073 

Kenya 1979 2013 34 −0.0158 −0.0510 −0.0274 −1.0412 −0.0306 

Mali 1977 2012 35 −0.0644 0.0071 0.0133 −0.6065 −0.0173 

Peru 1976 2011 35 −0.0309 −0.0723 0.0189 −0.9483 −0.0271 

Philippines 1983 2012 29 −0.0137 −0.0337 0.0117 −0.2927 −0.0101 

Senegal 1976 2013 37 0.0527 −0.0369 0.0571 1.1116 0.0300 

Tanzania 1982 2009 27 0.0186 −0.0857 0.0189 −0.5377 −0.0199 

Uganda 1979 2010 31 0.0146 0.0090 0.0021 0.2714 0.0088 

Zambia 1982 2013 31 −0.0563 −0.0221 −0.0043 −0.7284 −0.0235 

Zimbabwe 1979 2010 31 −0.0116 −0.0999 −0.0014 −1.1410 −0.0368 

Note: “Change 14” is the change from the first smoothed value to the last smoothed value. “Change 14 annual” is “Change 14” divided 

by the number of years in the full interval, which ranges between 27 and 37 years. “Change 12 annual” is the annualized change from 

the first smoothed value to 1990; “Change 23 annual” is the annualized change from 1990 to 2000; “Change 34 annual” is the annualized 

change from 2000 to the last smoothed value. 

 
38 

Standard deviation of the age at childbearing 

Figure 3.20. Trajectory of the standard deviation of the age at childbearing for all 16 countries, 

using pooled data from 98 DHS surveys for the 10 calendar years before each survey. 

The trajectories of the standard deviation of the age at childbearing, shown in Figure 3.20 and listed in 

Tables 3.7 and 3.8, are discussed together. The scale of Figure 3.20 is compressed because the variation in 

the standard deviation is relatively narrow. It is almost never outside a range of six to eight years. 

The distribution of age at childbearing (not shown, but summarized with the mean and standard deviation) 

is well-known to be asymmetric, with more births in, say the 10 years before the mean age than in the 10 

years after the mean age. Nevertheless, as with a normal distribution, nearly all births will be in the range 

from the mean age minus two standard deviations to the mean plus two standard deviations, and the full 

range in age at childbearing, with few exceptions, is 35 years. 

In the initial year, the standard deviation is almost always in a range from 6.5 years to 8.0 years. In the final 

year, it is almost always in a range from 6.0 years to 7.5. Four countries had the most concentrated values, 

around 6.0, in the final year: Egypt (5.93), Bangladesh (6.12), Jordan (6.13), and the Dominican Republic 

(6.29). These countries were also among the ones with the lowest initial values of the standard deviation. 

The standard deviation tends to decline as the TFR declines; the two are positively and significantly 

correlated. When the standard deviation in the initial year is regressed on the TFR in that year, the regression 

coefficient is 0.27. In the final year, it is almost unchanged at 0.29. Thus, if one country has a TFR that is 

1.74 less than another country, its standard deviation will tend to be half a year less than the other country, 

but the relationship between the TFR and the standard deviation is not as strong as the relationship between 

the TFR and the mean of the age at childbearing. 
39 

Table 3.7. Smoothed values of the standard deviation of the age at childbearing in the 16 countries. Includes 

smoothed values for the first year and last year, 1990, and 2010. If the first year was before 1980, smoothed 

values for 1980 are also included. If the last year was after 2010, smoothed values for 2010 are also included. 

Country First year Last year Interval 

SD of age 

in first 

year 1980 1990 2000 2010 

SD of age 

in last 

year 

Bangladesh 1984 2013 29 7.02 6.88 6.59 6.25 6.12 

Colombia 1976 2009 33 6.86 6.68 6.66 6.59 6.68 

Dominican Republic 1976 2012 36 6.87 6.85 6.35 6.09 6.18 6.29 

Egypt 1979 2013 34 6.67 6.59 6.43 6.01 5.96 5.93 

Ghana 1978 2013 35 8.01 8.06 7.38 7.44 7.05 7.06 

Indonesia 1977 2011 34 6.70 6.79 6.79 6.58 6.74 6.72 

Jordan 1980 2011 31 6.90 6.90 6.47 6.16 6.08 6.13 

Kenya 1979 2013 34 7.35 7.51 7.36 7.07 6.94 6.90 

Mali 1977 2012 35 7.88 7.86 7.42 7.63 7.68 7.62 

Peru 1976 2011 35 7.04 7.18 6.97 6.91 6.90 6.86 

Philippines 1983 2012 29 6.61 6.60 6.65 6.89 6.96 

Senegal 1976 2013 37 7.53 7.56 7.56 7.21 7.36 7.47 

Tanzania 1982 2009 27 7.65 7.79 7.45 7.22 

Uganda 1979 2010 31 7.60 7.71 7.58 7.62 7.41 7.41 

Zambia 1982 2013 31 7.65 7.54 7.59 7.35 7.30 

Zimbabwe 1979 2010 31 7.61 7.59 7.32 7.16 6.96 6.96 

Table 3.8. Changes in the smoothed values of the standard deviation of the age at childbearing in the 16 

countries 

Country First year Last year Interval 

Change 12 

annual 

Change 23 

annual 

Change 34 

annual Change 14 

Change 14 

annual 

Bangladesh 1984 2013 29 −0.0242 −0.0284 −0.0360 −0.8966 −0.0309 

Colombia 1976 2009 33 −0.0147 −0.0066 0.0094 −0.1872 −0.0057 

Dominican Republic 1976 2012 36 −0.0373 −0.0261 0.0166 −0.5857 −0.0163 

Egypt 1979 2013 34 −0.0218 −0.0420 −0.0065 −0.7438 −0.0219 

Ghana 1978 2013 35 −0.0523 0.0064 −0.0296 −0.9479 −0.0271 

Indonesia 1977 2011 34 0.0068 −0.0209 0.0126 0.0187 0.0006 

Jordan 1980 2011 31 −0.0437 −0.0303 −0.0035 −0.7786 −0.0251 

Kenya 1979 2013 34 0.0003 −0.0281 −0.0132 −0.4507 −0.0133 

Mali 1977 2012 35 −0.0350 0.0203 −0.0009 −0.2627 −0.0075 

Peru 1976 2011 35 −0.0054 −0.0059 −0.0049 −0.1893 −0.0054 

Philippines 1983 2012 29 −0.0019 0.0056 0.0258 0.3525 0.0122 

Senegal 1976 2013 37 0.0026 −0.0349 0.0197 −0.0562 −0.0015 

Tanzania 1982 2009 27 0.0174 −0.0345 −0.0256 −0.4358 −0.0161 

Uganda 1979 2010 31 −0.0024 0.0041 −0.0208 −0.1935 −0.0062 

Zambia 1982 2013 31 −0.0132 0.0044 −0.0225 −0.3528 −0.0114 

Zimbabwe 1979 2010 31 −0.0264 −0.0163 −0.0203 −0.6564 −0.0212 

Note: “Change 14” is the change from the first smoothed value to the last smoothed value. “Change 14 annual” is “Change 14” 

divided by the number of years in the full interval, which ranges between 27 and 37 years. “Change 12 annual” is the annualized

change from the first smoothed value to 1990; “Change 23 annual” is the annualized change from 1990 to 2000; “Change 34 annual”

is the annualized change from 2000 to the last smoothed value. 

 
40 

Age at reaching successive parities 

Figure 3.21. Scatterplot of mean age of women when achieving specified parities, at the date of 

the first estimate, about 1980, and the date of the last estimate, about 2010. Each parity is 

indicated with a different color. Line segments show the trend in age for each parity. The 

estimates use the first and the most recent DHS surveys for each of the 16 countries. 

The pattern of change in the ages at reaching successive parities is summarized in Figure 3.21, a scatterplot 

with a dot for each parity in each country that is observed at both the initial and final years. The dot color 

indicates the parity. The vertical axis gives the age at reaching the parity in the initial year, and the 

horizontal axis gives the corresponding age in the final year. The 45-degree line indicates no change 

between the initial and final years. With only one exception, the age at reaching a specific parity is higher 

in the final year than in the initial year, a shift that corresponds with delays in childbearing. 

A straight line for each parity, with the same color as the dots for that parity, describes the results of a 

regression of the age in the final year on the age in the initial year. If the line is approximately parallel to 

the line of no change, then then the tendency is toward a fixed delay that is approximately the same for all 

initial ages at that parity. This pattern particularly characterizes parity 1. That is, all countries had a net 

delay in the age at the first birth, and the amount of the delay was about the same for all countries, regardless 

of what was the initial age at first birth. 

Moving to parity 2, and then to parity 3, the regression lines become steeper, a pattern that suggests 

progressively greater delays or postponements in countries that had a higher initial age. For parity 3, the 

line is particularly steep. That is, the postponement of the third birth, in the final year, compared with the 

first year, was especially strong if the third birth was already relatively late in the initial year. For parities 

4 and 5, the regression lines are less steep than the line of no change. That is, although these births strongly 

tend to occur later in the final year than in the first year, the postponement tends to be less for countries in 

which that birth was relatively late in the initial year. The combined effect is greater compression of the 

age distribution of childbearing, a pattern that is consistent with a reduction in the standard deviation. 

A final caveat for the interpretation of these fertility trajectories is a reminder that the data have been structured by 

age and calendar year. An alternative approach would construct birth cohorts, and then express the rates as a function 

of age and cohort, or even as a function of age, period, and cohort together, as was done by Pullum in an analysis of 

long-term changes in U.S. fertility (Pullum 1980). In that analysis, age and period were found with a statistical model 
41 

to be the primary determinants of the rates, and birth cohort added little. It is possible that in some countries in this 

analysis, cohort would be relatively more important, perhaps because successive cohorts may be sharply 

differentiated by their completed years of schooling, typically an important determinant of fertility. 

Cohort identification was not included here because even with about 30 years of data we do not encompass the full 

span of childbearing—35 years, and the modelling required would have greatly expanded the scope of this report. 

Recently, Li and Tsui (2016) proposed an innovative approach to describing cohorts with a long series of DHS 

surveys from the same country. A future analysis may be able to re-analyze the data in this report with a cohort 

perspective. Certainly, actual family formation proceeds during the lifetime of individual women, moving from one 

parity to the next as they get older, and the period approach is artificially removed from that process. 

Part 1 of this report describes the fertility trajectories of 16 countries from about 1980 to about 2010, using 

a minimum of five DHS surveys for each country. In general, the most important proximate determinant 

behind changes in fertility is known to be contraceptive use (Garenne 2014; Cleland et al. 2016). The second 

most important proximate determinant is generally changes in marital status, which is indicated in most 

countries by postponement of the first birth. Part 2 of the report uses a reduced set of four countries—

Ghana, Indonesia, Kenya, and Senegal—to describe the use of modern contraception and how it is related 

to a standard set of covariates in those four countries. 

43 

Part 2: Contraceptive Trajectories 

Part 1 of this report describes the fertility trajectories of 16 countries from about 1980 to about 2010, using 

a minimum of five DHS surveys for each country. Part 2 of the report uses a reduced set of four countries—

Ghana, Indonesia, Kenya, and Senegal—to describe the use of modern contraception and how it is related 

to a standard set of covariates. The selected countries have at least six DHS surveys that span more than 20 

years. The interest is partly in trends in modern contraceptive use and primarily in whether the use of 

contraceptives is becoming more equitable across subgroups over time. This analysis includes six 

background variables to examine these trends: parity, education, work status, ideal number of children, 

place of residence, and region. The results show whether the subgroups identified by these variables are 

becoming more similar in their contraceptive use. The analysis was performed by fitting logit models to 

allow the odds ratio to vary with time, where time is the date of the survey. The results are shown as odds 

ratio plots over time to clearly demonstrate their trends. 

45 

4. Data and Methods 

4.1. DHS Data 

Selection of the countries for the analysis was determined primarily by having completed at least six DHS 

surveys in the time interval from the 1980s to the 2010s. Ten countries met this criterion, and four were 

selected for this in-depth analysis: Ghana, Indonesia, Kenya, and Senegal (see Table 4.1). The three African 

countries selected are also USAID family-planning priority countries.15 

The data collection for Indonesia, up to the survey in 1997, included East Timor. A few years later, East 

Timor gained independence, and it was no longer included in the Indonesia DHS; therefore, observations 

from interviews conducted in East Timor were dropped from the survey data for 1987−1997. Table 4.1 

shows that of the four countries selected for the in-depth analysis, Indonesia has by far the largest sample 

sizes. Senegal and Indonesia completed seven DHS surveys; Ghana and Kenya completed six DHS surveys. 

Table 4.1. The survey year and number of women age 15-49 in each survey (N) for Ghana, Indonesia, Kenya,

and Senegal 

 year (N) year (N) year (N) year (N) year (N) year (N) year (N) Total N 

Ghana 1988 

(4488) 

1993–94 

(4562) 

1998–99 

(4843) 

2003 

(5691) 

2008 

(4916) 

2014 

(9396) 

33896 

Indonesia* 1987 

(9451) 

1991 

(22813) 

1994 

 (28044) 

1997 

(28690) 

2002–03 

(29483) 

2007 

(32895) 

2012 

(45607) 

196983 

Kenya 1988–89 

(7150) 

1993 

(7540) 

1998 

(7881) 

2003 

(8195) 

2008–09 

(8444) 

2014 

(31079) 

70289 

Senegal 1986 

(4415) 

1992–1993 

(6310) 

1997 

(8593) 

2005 

(14602) 

2010–11 

(15688) 

2012–13 

(8636) 

2014 

(8488) 

66732 

Note: * East Timor was removed from the Indonesia data of the earlier surveys. 

4.2. Measures 

The outcome of interest is the use of modern contraception by women age 15-49 who are currently in a 

union. This is a binary variable categorized as non-users and users of a traditional contraceptive method 

versus users of a modern method such as pills, IUD, injections, implants, diaphragm, female and male 

condoms, female and male sterilizations, foam or jelly, and the lactational amenorrhea method (LAM). 

The analysis included six independent variables: parity (0 or 1, 2, 3, 4 or more), education (none, primary, 

secondary or more), work status (currently working, not working), ideal number of children (0-2, 3 or 4, 5 

or 6, 7 or more), place of residence (urban, rural), and region. The earlier surveys in Indonesia had no 

question on work status and, therefore, this variable was removed from the analysis of Indonesia. Both 

women’s age and parity are relevant to contraceptive use, but they are highly correlated, and it would be 

redundant to include both of them. Parity was chosen over age because it is more directly related to fertility 

preferences. 

Variations across regions within a country are important, but of course the regions are defined differently 

in every country. The four countries covered by this in-depth analysis have the following regions: 

Ghana, five main regions: (1) Greater Accra, (2) West and Central, (3) East and Volta, (4) BrongAhafo and Ashanti, and (5) North, including Upper West and Upper East 

 

15 https://www.usaid.gov/what-we-do/global-health/family-planning/countries
46 

Senegal, four main regions: (1) West, (2) Central, (3) South, and (4) Northeast 

Kenya, categorized four main regions: (1) Nairobi and Central, (2) Coast and East and Northeast, 

(3) West and Nyanza, and (4) Rift Valley 

Indonesia, several regions and provinces, with some changes over time, but grouped by provinces 

into two major regions: (1) Java Bali and (2) Outer Java Bali 

There is great interest in variation by the wealth index, but the wealth index was not included as a covariate 

because it is a relative measure that is specific to the country and time of the survey. It is, therefore, difficult 

to interpret differences between countries or changes over time. Moreover, the earliest surveys for each 

country preceded the development of the wealth index, and it is not included in the full series. 

Time is obviously a central component of the analysis. It is measured with the mean century month code, 

or CMC, of the dates of the interviews in each survey. The CMC is the ordinal number of each month 

beginning with the start of the 20th century: January 1900 has CMC=1. If M is the ordinal number of the 

months within a year (1 through 12) and Y is year, with four digits, then CMC=M+12*(Y−1900). The 

following formulae can be used to convert a CMC to year and month, where the function “int” gives the 

integer part of a number: 

Y = 1900 + int (CMC/12) 

M = CMC − 12* (Y−1900) 

For example, CMC=1350 is June 2012 and CMC=1375 is July 2014. If a continuous version of year is 

desired, the formula is y = 1900 + (CMC/12) − (1/24). Thus CMC=1375, or July 2014, on a continuous 

scale would be converted to 2014.542, which is the middle of July. Unless the correction of −1/24 is made, 

on a continuous scale y would be half a month too far into the calendar year. 

4.3. Methods 

Logit regressions and varying coefficient models (VCM) are applied to the combined data described earlier 

to model trends in the mCPR. Varying coefficient models are essentially models that allow the regression 

coefficients themselves to vary as flexible functions of other variables (Hastie and Tibshirani 1993). In this 

analysis, the coefficients are allowed to vary with time though flexible spline functions. Therefore, instead 

of the usual logit regression model, 

log ቀ ௣

௜ܺ	௜ߚ ∑	 + ଴ߚ=ቁ௣ିଵ

௡

௜ୀଵ , 

where p is the fitted probability of modern contraceptive use, β0 is the intercept, βi represents the usual 

fixed coefficients, and Xi represents the independent variables, we have the varying coefficient model: 

log ቀ ௣

	௜ܺ	௜ߚ ∑	 + ଴ߚ=ቁ௣ିଵ

௡

௝ߙ ∑	 + ሻݐሺ଴ߙ	+ ୀଵ௜

௠

௝ୀଵ ሺݐሻܺ௝. 

This equation includes the fixed coefficients β that are in the usual logit regression model, but it also 

includes α0(t), a time-varying intercept, and time-varying coefficients α(t) for the Xj variables, where t

represents time. A VCM variable, therefore, such as education, may have both a fixed coefficient β and a 

time-varying coefficient α(t). A null hypothesis to be tested is whether the time-varying coefficient is equal 

to zero. If that hypothesis is retained, then the time-varying coefficient can be removed from the model. 

The VCM can be fitted with spline functions having a specified number of knots (Eilers and Marx 2002; 

Hastie and Tibshirani 1993; Marx 2010). 
47 

VCM are discussed extensively in the statistics literature (Cai, Fan, and Li 1999, 2000; Chiang, Rice, and 

Wu 2001; Fan and Zhang 2008; Hastie and Tibshirani 1993; Hoover et al. 1998; Huang, Wu, and Zhou 

2002, 2004; Marx 2010). These models have been applied to the study of trends in health-related outcomes 

using longitudinal data (Cai, Fan, and Li 1999; Chiang, Rice, and Wu 2001; Hoover et al. 1998; Huang, 

Wu, and Zhou 2002; Lynch et al. 2011), as well as using cross-sectional health surveillance data (Assaf and 

Campostrini 2015; Assaf et al. 2016; Young et al. 2008). 

Part 2 of this report uses models fitted with R software, using the mgcv and survey packages (Lumley 2014; 

Wood 2012), with additional detail in Appendix 1. Other analyses were performed with Stata version 14. 

Beginning with a model that included time-varying coefficients for all the independent variables, a 

backward model selection compared the values of the Akaike Information Criterion (AIC). A comparison 

between two models led to the selection of the more complex model with more time-varying coefficients 

only if it produced a large drop in the AIC (a cutoff of more than 20 was chosen). No correction for the 

sample size was needed because the datasets for all the countries in the analysis are large (Anderson 2002). 

In addition to fitting the VCM, logit regressions of the first and most recent survey for each country were 

also fitted for comparison. The analysis accounts for sample weights and the survey stratification. 

The results are shown as plots of coefficients compared to time for all time-varying coefficients in the final 

model for each country. For easier interpretability, the coefficient plots are transformed to odds ratio plots 

for each category other than the reference category. These odds ratios are produced by adding the constant 

coefficient β for the category and exponentiating. The plots include 95% confidence bands produced by the 

mgcv R package (Marra and Wood 2012; Wood 2012, 2006). The constant odds ratios for each category, 

produced with standard logit regressions, are superimposed on the VCM odds ratio plots to show how the 

VCM captures the odds ratio trend for the period of observation. The odds ratio plots are produced on 

different scales, across countries and covariates. It would have been difficult to find a common odds ratio 

scale for the different models. 

The next chapter lists the results of our analyses for the four countries covered in Part 2 of this report, 

organized alphabetically. The figures show the current modern contraceptive use for women age 15-49 and 

in a union. The tables show logit regressions for the first and last surveys, as well as the varying coefficient 

model for the surveys combined. 

49 

5. Results 

5.1. Ghana 

Figure 5.1 shows that current modern contraceptive use for women age 15-49 and in a union in Ghana 

increased from 5% in 1988 to 22% in 2014 (also see Appendix 2). Although the increase was fairly steady, 

the average annual increase was only about two-thirds of one percentage point. The TFR in 2013 was 

estimated in Part 1 to be 4.0. The goal of the analysis is to show the changing relationship of contraceptive 

prevalence to the covariates, including the use of the VCM approach to show trajectories of these 

relationships across the full time interval. 

Table 5.1 summarizes the results of the logit regressions for the first and last surveys, as well as the VCM 

for all six Ghana surveys combined. In the logit regressions, and for both the first and last survey, the odds 

ratios are significant for women at parity 4 or more, for women with primary or secondary or higher levels 

of education, for working women, and for women who said their ideal number of children was seven or 

more. Between the first and last survey, these odds ratios all move toward a value of 1. 

Urban and rural women are not significantly different in the first survey, but the difference becomes 

significant and reverses direction in the last survey; an odds ratio of 1.4 in the first survey changes to an 

odds ratio of 0.7 in the last survey. Without observing the odds ratio plots produced from the VCM, it would 

be difficult to understand how this change occurred or whether it was significant. 

Figure 5.1. Modern contraceptive use by women 15-49 in a union in Ghana from 1988 to 2014. 

 
50 

Table 5.1. Odds ratios for the Ghana logit regressions of modern 

contraceptive use for first and last survey and the varying coefficient 

model for the combined surveys 

 1st survey last survey VCM 

Parity (ref.= 0 or 1) 

2 0.95 1.42 1.34** 

3 1.52 1.67** 1.66*** 

4+ 3.17*** 1.77** 2.18*** 

Education (ref.=none) 

primary 1.73** 1.58*** 1.79*** 

secondary+ 2.45* 1.44*** 2.2*** 

s(time):edu none . 

s(time):edu primary . 

s(time):edu secondary+ ** 

Work status (ref.=not working) 

working 1.78** 1.36* 1.41*** 

Ideal number of children (ref.=0-2) 

3 or 4 0.35** 0.83 0.92 

5 or 6 0.26** 0.70 0.77** 

7+ 0.30** 0.58** 0.53*** 

Place of residence (ref.=rural) 

urban 1.41 0.72** 1.2** 

s(time):rural ** 

s(time):urban *** 

Ghana regions (ref.=Accra) 

West/Central 0.55 1.23 0.85 

East/Volta 0.68 1.32 1.01 

Brong-Ahafo/Ashanti 0.84 1.09 0.97 

North/Upper West/Upper East 0.13** 0.87 0.76* 

Observations 3,154 5,452 21651 

s(time): coefficient spline function of time; ref.: reference category 

. not significant, * p<0.05, ** p<0.001, *** p<0.001 

The VCM selected for Ghana, using backward selection based on AIC values, contains time-varying 

coefficients for the categories of education and place of residence, shown in the following model: 

log ൬ ݌݊݋݁݃݅ݎ଺ߚ + ݅݀݁݊ܿ݁ݏ݁ݎହߚ + ݈݅݀݁ܽସߚ + ݇ݎ݋ݓଷߚ + ݑ݁݀ଶߚ + ݕݐ݅ݎܽ݌ଵߚ + ଴ߚ = ൰݌1−

	݅݀݁݊ܿ݁,ݏ݁ݎሻݐଶሺߙ + ݑ݁݀ሻݐଵሺߙ + ሻݐሺ଴ߙ +

where p is the fitted probability of modern contraceptive use, the βs are the constant or fixed coefficients, 

and α1(t) and α2(t) are time-varying coefficients for education and place of residence, respectively. 

As shown in Table 5.1, the spline functions used to model the time-varying coefficients of these categories 

are significant for the secondary or more education category and for the rural and urban residence category. 

The lack of significance for the primary education category is also apparent in the odds ratio plot (see Figure 

5.2a). Despite a relatively higher odds ratio in the second survey, compared to the remaining surveys, as 

shown by the second odds ratio point, the plot shows a relatively steady trend over time in the odds of 

modern contraceptive use for women with primary education, compared to women with no education. The 

odds ratio plot for women with secondary or more education shows an initial increase in the odds ratio, 

after the first survey, before decreasing again toward an odds ratio of 1 (see Figure 5.2a). As stated in the 

methods, the odds ratio scales in the plots for the two education categories are different. The trend for the 

urban category in Figure 5.2b shows a clearly decreasing linear odds ratio trend. The trends for the 

secondary or more education category and the urban category also indicate a closing of the gap between 
51 

these categories and their reference categories in women’s modern contraceptive use; in both odds ratio 

plots, the trend is toward an odds ratio of 1. 

Figure 5.2a. Odds ratio plots for Ghana comparing primary education and secondary or higher, 

with no education. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). 

 Primary Secondary or higher 

 

Figure 5.2b. Odds ratio plot for Ghana comparing urban areas with rural. The plots extend from 

June 1987 (CMC 1050) to July 2014 (CMC 1375). 

 Urban 
5.2. Indonesia 

Modern contraceptive use in Indonesia was already relatively high at the time of the first survey, with 43% 

of women age 15-49 in a union currently using a modern contraceptive method in 1987. This increased to 

58% in 2012, as shown in Figure 5.3 (see also Appendix 3). The average net increase in contraceptive use 

over the 25-year span is about three-fifths of one percentage point. This is a small annual increase, but 

because of the high initial value, the final value is consistent with the low estimated TFR of 2.7 in 2011 

seen in Part 1. 

Table 5.2 summarizes the regressions, which show changes between the first and last survey. Almost all of 

the variables are significant and follow the expected relationship with modern contraceptive use. In the first 

survey, however, the women with urban and rural residence were not significantly different, and in the last 

survey urban women had significantly lower odds of using modern contraception than rural women. 

Figure 5.3. Modern contraceptive use for women 15-49 in a union in Indonesia from 1987 to 2012. 

 
53 

Table 5.2. Odds ratios for the Indonesia logit regressions of modern 

contraceptive use for first and last survey and the varying coefficient 

model for the combined surveys 

 1st survey last survey VCM 

Parity (ref.= 0 or 1) 

2 3.20*** 2.81*** 2.74*** 

3 4.07*** 2.96*** 3.03*** 

4+ 3.57*** 2.33*** 2.36*** 

s(time):parity 0 or 1 * 

s(time):parity 2 . 

s(time):parity 3 *** 

s(time):parity 4 or more *** 

Education (ref.=none) 

primary 1.75*** 2.04*** 1.84*** 

secondary+ 2.82*** 2.21*** 2.2*** 

s(time):edu none . 

s(time):edu primary . 

s(time):edu secondary+ * 

Ideal number of children (ref.=0-2)

3 or 4 0.73*** 0.61*** 0.68*** 

5 or 6 0.46*** 0.46*** 0.44*** 

7+ 0.35*** 0.51*** 0.43*** 

s(time):ideal 0-2 . 

s(time):ideal 3 or 4 ** 

s(time):ideal 5 or 6 . 

s(time):ideal7 or more * 

Place of residence (ref.=rural)

urban 0.98 0.79*** 0.87*** 

s(time):rural *** 

s(time):urban ** 

Region (ref.= Outer Java Bali) 

Java Bali 1.58*** 1.37*** 1.44*** 

s(time):Outer Java Bali . 

s(time):Java Bali * 

Observations 9,495 32,689 172805 

s(time): coefficient spline function of time; ref.: reference category 

. not significant, * p<0.05, ** p<0.001, *** p<0.001

The following VCM for modern contraceptive use selected for Indonesia includes time-varying coefficients 

for all the independent variables: 

log ൬ ݌

ሻݐሺ଴ߙ + ݊݋݁݃݅ݎହߚ + ݅݀݁݊ܿ݁ݏ݁ݎସߚ + ݈݅݀݁ܽଷߚ + ݑ݁݀ଶߚ + ݕݐ݅ݎܽ݌ଵߚ	+ ଴ߚ = ൰݌1−

	.	݊݋݁݃݅ݎሻݐହሺߙ	+	݅݀݁݊ܿ݁ݏ݁ݎሻݐସሺߙ + ݈݅݀݁ܽሻݐଷሺߙ + ݑ݁݀ሻݐଶሺߙ + ݕݐ݅ݎܽ݌ሻݐଵሺߙ +

Work status was not available in the earlier Indonesian surveys and, therefore, it is omitted from the model. 

The results of the VCM shown in Table 5.2 indicate that many of the coefficients changed significantly 

over time. Parities 3 and 4 or more show a significant and decreasing trend toward an odds ratio of 1. As 

Figure 5.4a shows, these categories are becoming more similar to the reference category of one child or 

none, in terms of modern contraceptive use. Parity 2 does not show a significant change, which is also 

apparent in the odds ratio plot in Figure 5.4a.16 

 

16 This plot also shows a confidence interval with a width that reaches zero at one point. When the trend is estimated 

with a straight line, Bayesian confidence intervals can reach a width of zero (Marra and Wood 2012; Wood 2006). 
54 

No significant trend is apparent for the primary education category, as indicated in Table 5.2 and Figure 

5.4b. The odds ratio for the secondary or higher education category decreases initially toward 1, but then 

appears to plateau in the recent surveys (see Figure 5.4b). 

As shown in Figure 5.4c, women reporting three or four as the ideal number of children slightly decreases 

their odds of contraceptive use, compared to women reporting zero to two children as the ideal number. 

Table 5.2 indicates that this trend is significant. The decrease, however, moves away from an odds ratio of 

1 and, therefore, increases the gap between the two groups. On the other hand, the odds for women reporting 

seven or more children as the ideal number increases toward an odds ratio of 1 and, therefore, the gap 

between these women and those who reported zero to two as the ideal number of children declines. 

Although Table 5.2 indicates that the time-varying coefficient for the urban category is significant, the odds 

ratio trend in Figure 5.4d appears to be more or less constant after the first two surveys. The Java Bali odds 

ratio plot in Figure 5.4e indicates a slightly decreasing but significant trend toward an odds ratio of 1. 

Figure 5.4a. Odds ratio plots for Indonesia comparing parities 2, 3, and 4 or more with parities 0 

and 1. The plots extend from June 1987 (CMC 1050) to June 2012 (CMC 1350). 

 Two Three 

 

 Four or more 

 
55 

Figure 5.4b. Odds ratio plots for Indonesia comparing primary education and secondary or higher, 

with no education. The plots extend from June 1987 (CMC 1050) to June 2012 (CMC 1350). 

 Primary Secondary or higher 

 

Figure 5.4c. Odds ratio plots for Indonesia comparing the ideal number of children categories of 

three and above with ideal number of children of zero to two. The plots extend from June 1987 

(CMC 1050) to June 2012 (CMC 1350). 

 Three or four Five or six 

 

 Seven or more 

 
56 

Figure 5.4d. Odds ratio plot for Indonesia comparing urban areas with rural. The plots extend from 

June 1987 (CMC 1050) to June 2012 (CMC 1350). 

Urban 

Figure 5.4e. Odds ratio plot for Java Bali region category, compared to the outer Java Bali 

reference category. The plots extend from June 1987 (CMC 1050) to June 2012 (CMC 1350). 

Java Bali 
5.3. Kenya 

As shown in Figure 4.4, modern contraceptive use for Kenyan women age 15-49 in a union increased 

steadily from 18% in 1988–89 to 53% in 2014 (see also Appendix 4). This is the largest increase of all four 

countries in Part 2, and the final value is the highest of the three SSA countries in Part 2. The net annual 

increase is one and one-third of a percentage point. In Part 1, the TFR in 2013 is estimated to be 4.0. 

Table 5.3 indicates that almost all the variables are significantly related to modern contraceptive use in both 

the first and last surveys. There were some changes in the odds ratios between the two surveys, including a 

large drop in the odds ratio for parity 4 or more, from 7.8 in the first survey to 2.7 in the last survey, 

compared to the reference parity of 0 or 1. 

Figure 5.5. Modern contraceptive use for women 15-49 in a union in Kenya from 1988 to 2014 

 
58 

Table 5.3. Odds ratios for the Kenya logit regressions of modern 

contraceptive use for first and last survey and the varying coefficient 

model for the combined surveys 

 1st survey last survey VCM 

Parity (ref.= 0 or 1) 

2 2.94*** 2.48*** 2.53*** 

3 4.67*** 2.69*** 3.07*** 

4+ 7.80*** 2.65*** 3.9*** 

s(time):parity 0 or 1 . 

s(time):parity 2 . 

s(time):parity 3 . 

s(time):parity 4 or more *** 

Education (ref.=none) 

primary 2.35*** 3.25*** 2.61*** 

secondary+ 3.92*** 3.80*** 4.41*** 

s(time):edu none . 

s(time):edu primary . 

s(time):edu secondary+ ** 

Work status (ref.=not working) 

working 1.58*** 1.24** 1.35*** 

Ideal number of children (ref.=0-2) 

3 or 4 0.68* 0.60*** 0.62*** 

5 or 6 0.44*** 0.40*** 0.39*** 

7+ 0.43** 0.20*** 0.25*** 

Place of residence (ref.=rural) 

urban 1.38* 1.16* 1.14** 

Region (ref.=Nairobi/Central) 

Coast/East/Northwest 0.74 0.77* 0.62*** 

West/Nyanza 0.36*** 0.73** 0.43*** 

Rift Valley 0.79 0.58*** 0.50*** 

s(time):Nairobi/Central . 

s(time):Coast/East/Northeast ** 

s(time): West/Nyanza *** 

s(time):Rift Valley ** 

Observations 4,746 9,001 33049 

s(time): coefficient spline function of time; ref.: reference category 

. not significant, * p<0.05, ** p<0.001, *** p<0.001

The following VCM for modern contraceptive use selected for Kenya includes time-varying coefficients 

for parity, education, and region: 

log ൬ ݌	݊݋݁݃݅ݎ଺ߚ + ݅݀݁݊ܿ݁ݏ݁ݎହߚ + ݈݅݀݁ܽସߚ + ݇ݎ݋ݓଷߚ + ݑ݁݀ଶߚ + ݕݐ݅ݎܽ݌ଵߚ + ଴ߚ = ൰݌1−

		݊݋݁݃݅ݎሻݐଷሺߙ	+ݑ݁݀ሻݐଶሺߙ + ݕݐ݅ݎܽ݌ሻݐଵሺߙ + ሻݐሺ଴ߙ +

As shown in Table 5.3, only one category of the parity and education variables has a significant timevarying coefficient. As Figure 5.6a shows, the odds ratio plots for parities 2 and 3 show a relatively constant 

trend, and women with parity 4 or more show a significantly reduced odds ratio of using modern 

contraceptive use, compared to women with parities 0 or 1. The trend for parity 4 or more also decreases 

toward an odds ratio of 1, a trend that indicates a closing of the gap between this category and the reference 

category. 
59 

This decreasing gap between categories is also found in the odds ratio plot of the secondary or higher 

education category, but only for the last three surveys (see Figure 5.6b). While the primary education 

category appears to have a slightly increasing odds ratio trend, this was not statistically significant, as 

indicated in Table 5.3. 

All the coefficients for regions are significantly time-varying, relative to the reference region. As seen in 

Figure 5.6c, after the second survey all the odds ratio plots for region have a trend that is increasing and 

moving toward 1. The regions are becoming similar in their modern contraceptive use. 

Figure 5.6a. Odds ratio plots for Kenya comparing parities 2, 3, and 4 or more with parities 0 and 

1. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). 

 Two Three 

 Four or more 

 
60 

Figure 5.6b. Odds ratio plots for Kenya comparing primary education and secondary or higher, 

with no education. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). 

 Primary Secondary or higher 

 

Figure 5.6c. Odds ratio plots for Kenya comparing regions of Kenya with Nairobi/Central. The 

plots extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). 

 Coast, East, and Northeast Rift Valley 

 West Nyanza 
5.4. Senegal 

In Senegal, modern contraceptive use for women age 15-49 in a union increased from only 2% in 1986 to 

20% in 2014 (see Figure 5.7 and Appendix 5). The net annual increase in prevalence was about two-thirds 

of one percentage point, close to the net annual increases in Ghana and Indonesia over approximately the 

same time interval. The final prevalence is close to the final prevalence in Ghana, 20%, but in Part 1, the 

TFR for Senegal in 2013 is estimated to be 4.9, almost a full child greater than the final TFR in Ghana, 4.0. 

Table 5.4 shows that all the parity and education categories, except for parity 2 in the first survey, as well 

as the category for urban women, have significant odds ratios for modern contraceptive use, compared to 

their reference categories. Several regions and categories of the ideal number of children, however, have 

non-significant odds ratios. These odds ratios decrease between the first and last survey for the categories 

of secondary and more education, urban, and the northeast region. 

Figure 5.7. Modern contraceptive use for women 15-49 in a union in Senegal from 1986 to 2014. 

 
62 

Table 5.4. Odds ratios for the Senegal logit regressions of modern 

contraceptive use for first and last survey and the varying coefficient 

model for the combined surveys 

 1st survey last survey VCM 

Parity (ref.= 0 or 1) 

2 2.75 3.38*** 2.03*** 

3 3.83** 2.86*** 2.91*** 

4+ 3.55** 4.85*** 3.75*** 

s(time):parity 0 or 1 . 

s(time):parity 2 ** 

s(time):parity 3 . 

s(time):parity 4 or more . 

Education (ref.=none) 

primary 2.61* 2.07*** 2.5*** 

secondary+ 7.27*** 2.70*** 3.98*** 

s(time):edu none . 

s(time):edu primary * 

s(time):edu secondary+ *** 

Work status (ref.=not working)

working 1.84* 0.96 1.1 

Ideal number of children (ref.=0-2)

3 or 4 1.03 0.81 1.05 

5 or 6 0.45 0.78 0.8 

7+ 0.31* 0.38* 0.5*** 

Place of residence (ref.=rural)

urban 6.34** 1.48** 2.6*** 

s(time):rural * 

s(time):urban ** 

Senegal regions (ref.=West)

Central 2.57 1.51 0.62*** 

South 1.13 0.78 0.73*** 

Northeast 3.56* 1.00 0.77** 

s(time):region west * 

s(time):region central . 

s(time):region south *** 

s(time):region northeast . 

Observations 3,358 5,820 46383 

s(time): coefficient spline function of time; ref.: reference category 

. not significant, * p<0.05, ** p<0.001, *** p<0.001

The following Senegal VCM for modern contraceptive use includes time-varying coefficients for parity, 

education, place of residence and region: 

log ൬ ݌	݊݋݁݃݅ݎ଺ߚ + ݅݀݁݊ܿ݁ݏ݁ݎହߚ + ݈݅݀݁ܽସߚ + ݇ݎ݋ݓଷߚ + ݑ݁݀ଶߚ + ݕݐ݅ݎܽ݌ଵߚ + ଴ߚ = ൰݌1−

		݊݋݁݃݅ݎሻݐସሺߙ	+	݅݀݁݊ܿ݁ݏ݁ݎሻݐଷሺߙ	+	ݑ݁݀ሻݐଶሺߙ + ݕݐ݅ݎܽ݌ሻݐଵሺߙ + ሻݐሺ଴ߙ +

Table 5.4 shows that, of the parities, only parity 2 has significant time-varying coefficients. This pattern is 

also indicated in the odds ratio plots in Figure 5.8a. Parities 3 and 4 show a constant odds ratio trend over 

time, but the odds ratio for parity 2 appears to be increasing for the last three surveys, away from a value 

of 1. 
63 

As shown in Figure 5.8b, the odds ratios for the education categories, compared to the no education 

reference category, have been decreasing significantly. The trends imply that gaps between the education 

categories are decreasing, in terms of modern contraceptive use. 

Figure 5.8c shows that urban women have a significantly decreasing odds ratio, compared to rural women, 

moving toward 1 since the third survey. Table 5.4 indicates that only the south region has significant timevarying coefficients, with an odds ratio trend that increases toward an odds ratio of 1, but then decreases 

again after the fourth survey (see Figure 5.8d). 

Figure 5.8a. Odds ratio plots for Senegal comparing parities 2, 3, and 4 or more with parities 0 and 

1. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). 

 Two Three 

 Four or more 

 
64 

Figure 5.8b. Odds ratio plots for Senegal comparing primary education and secondary or higher, 

with no education. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 1375). 

 Primary Secondary or higher 

 

Figure 5.8c. Odds ratio plot for Senegal comparing urban areas with rural. The plots extend from 

June 1987 (CMC 1050) to July 2014 (CMC 1375). 

 Urban 
65 

Figure 5.8d. Odds ratio plots for Senegal showing the three Senegal regions, compared to the 

west region reference category. The plots extend from June 1987 (CMC 1050) to July 2014 (CMC 

1375). 

 Central South 

 Northeast 
66 

5.5. Summary 

The application of varying coefficient models to a long series of DHS surveys from Ghana, Indonesia, 

Kenya, and Senegal has shown that the odds ratios of most of the covariates included in the model have 

been significantly changing over time. For all four countries, education has significantly varying 

coefficients for one or more of its categories, usually for the secondary or more education category. Varying 

coefficients are also found for parity (except for Ghana), ideal number of children (except for Ghana and 

Kenya), place of residence (except for Kenya), and region (except for Ghana). The work status variable 

does not have significant time-varying coefficients in any of the countries. The odds ratio plots for almost 

all the significant varying coefficients show a clear trend moving toward an odds ratio of 1. Inequalities in 

modern contraceptive use for women age 15-49 and in a union appear to be decreasing for these subgroups. 

Some patterns that appear in the fertility trends in Part I may be explained by findings in the VCMs. In 

Indonesia, although the VCMs were significant—most likely due to the large sample size, compared to the 

other countries in the analysis—the odds ratio trends do not appear to be pronounced. Weak trends were 

found for the categories of secondary or more education, seven or more ideal number of children, urban 

women, and the Java Bali region. This plateauing of the odds ratio trends may be related to the plateau in 

the TFR, as shown in Figure 3.6. Indonesia has the highest mCPR, compared to the remaining countries in 

the analysis, which could also be a reason for less prominent trends than the other countries in the analysis. 

The analysis includes only three African countries and one Asian country, so generalizations to other 

countries are limited; however, extension to other countries with six or more surveys, or perhaps even with 

five surveys, which would include all the countries in Part 1, would be possible. 

Some limitations of VCMs can be noted. First, these models were fitted with relatively few time points. In 

this analysis, two countries had six surveys and two countries had seven surveys, providing only six or 

seven distinct time points per country. More time points would improve the ability to capture trends in the 

coefficients/odds ratios. The VCM fit for all the countries use third degree splines and two knots. Using a 

different degree for the splines or more knots may produce slightly different results, but it was beyond the 

scope of this report to review the possible different combinations. In general, the odds ratio plots do appear 

to track the data points well for most of the subgroups examined. 

A second limitation is that fitting VCMs requires long computation times, especially with adjustments for 

the complex survey design (sampling weights, clusters, and strata). Computation time would increase 

further with larger samples or more surveys or the addition of more covariates. Only six covariates were 

included here, but future studies could include other covariates, such as partner’s education or indicators of 

women’s empowerment. Future analysis using this method with more surveys or variables could select 

models with a subsample of the data before using the complete data to fit the final selected model. 

Another limitation is the inability of the method to project trends, mainly because the method is nonparametric and uses spline functions. The method can be used to describe trends in the range of the available 

data and to test whether the trajectory is statistically significant from a constant line, but we cannot project 

the trend seen in the odds ratio plots. Many statistical procedures do allow for extrapolation or projection, 

but VCM does not. 

Despite these limitations, the method can show how the odds ratios for the selected covariates and countries 

are, in fact, changing significantly over time, for the most part moving toward a reduction in disparities. 

The analysis can inform policy makers of the timing when past interventions appear to have been successful 

and for which groups. 
67 

6. Discussion and Conclusions 

Part 1 of this report (Chapters 2 and 3) describes trajectories of fertility in 16 countries that have had at least 

five DHS surveys. Using a total of 98 surveys, and the birth history data for the 10 calendar years before 

the surveys, we describe levels and trends over approximately the 30-year time interval from 1980 to 2010, 

with some variation from one country to another in the first year, last year, and interval. The analysis blends 

the surveys together by aggregating births and exposure into arrays for single years of age and time—35 

years of age and about 30 years of time. 

Five trajectories were constructed from these arrays. The quantum or volume of fertility is measured by the 

Total Fertility Rate and the General Fertility Rate. The tempo or timing of fertility is measured by the mean 

age at childbearing, the standard deviation of the age at childbearing, and, derived from the Cumulative 

Fertility Rate, the ages at which women reached parities 1, 2, 3, and so on. The estimates are adjusted for 

sampling weights. The numbers were smoothed with a simple procedure (four-year splines with knots at 

calendar years divisible by four) and with no adjustments for possible displacement or omission of births. 

All of the specific numbers given in the report are smoothed. 

The surveys from Egypt and Jordan were limited to ever-married women, and were also adjusted with allwomen factors to provide unbiased estimates for the national population of all women age 15-49 living in 

households, regardless of marital status. When factors from the 1990 survey of Jordan were extended 

backward through the 1980s, a period in which age at marriage was increasing, the result was a spuriously 

high estimate of 8.0 for the TFR. Based on information in the DHS report on the 1990 survey, a more 

plausible estimate of 7.5 is suggested for 1980. Otherwise, the estimates for Egypt and Jordan are plausible 

and consistent with other sources. 

The 16 countries included 8 from SSA and 8 from other regions. At the beginning of the series, all of the 

SSA countries other than Zimbabwe were above the median. The only non-SSA country above the median 

was Jordan. At the end of the series, all of the SSA countries were above the median, and all of the countries 

above the median were in SSA. The level of consistency is high between the initial and final levels of the 

TFR. Zimbabwe had a slow rate of decline and moved from an initial level that was below the median to a 

final level that was above the median. Jordan had a large rate of decline and moved from well above the 

median to slightly below the median. About half of the statistical variation in the TFR at the end of the 

series could be explained by the TFR at the beginning of the series. 

The TFR and GFR declined in all countries. The median TFR declined by 42%, from 6.43 to 3.76. This is 

an average annual decline in the median of almost a tenth of a child per year. Briefly, the following 

paragraphs review the patterns by grouping the 16 countries into three groups based on the final TFR, as 

given in Table 3.1. The values, given to two decimal places, overstate the accuracy of the estimates but 

make it easier for readers to manipulate and confirm the estimates. 

Five countries ended the series in a half-child range from 2.14 to 2.67 that is close to replacement: 

Colombia, Bangladesh, the Dominican Republic, Peru, and Indonesia (from lowest to highest). The 

trajectories for all five are characterized by a steep early decline, including an interval in which the TFR 

dropped by a full child in an interval of about five years, followed by a levelling off as the TFR approached 

replacement. In some cases, the most recent part of the trajectory is so flat that it cannot readily be described 

as still converging to replacement, but at least the levels are low. 

The Philippines is the only other country with a TFR below 3.00, and just barely, at 2.97. Its trajectory has 

been steady, but much more gradual than the other five countries. Together, these six countries include all 

of the countries from Latin America and the Caribbean, South Asia, and Southeast Asia that are included 

in this report. 
68 

At their most recent estimate of the TFR, five countries have a level of 4.91 or more—say, between 5 and 

6: Senegal, Zambia, Tanzania, Mali, and Uganda (from lowest to highest). Senegal, Mali, and Uganda 

appear to have fallen by about one child since about 2000, and their latest surveys suggest continuing 

decline. By contrast, Zambia has been flat since 2000 and Tanzania has had an increase of about one full 

child. 

Five countries are intermediate to these two groups, with their final smoothed TFR in a range from 3.51 to 

4.61: Jordan, Egypt, Kenya, Ghana, and Zimbabwe (from lowest to highest). It is fair to classify their 

trajectories as stalled from about 2000 to 2010. Kenya shows the most evidence of a net decline since 2000. 

Zimbabwe, by contrast, appears to have had a net increase since then. 

In most countries, the mean age at childbearing has fluctuated somewhat, but with little net change across 

the span of about 30 years. Generally, it is in the range of 28 to 30 years of age. Countries with an initially 

younger childbearing age have remained younger, and countries with an initially older childbearing age 

have remained older. This pattern has three principal exceptions. Bangladesh, Colombia, and the Dominican 

Republic can be traced back to values of around age 28 in the late 1970s, and as they progressed to being 

the three countries with the lowest final TFR, their mean age at childbearing also fell substantially. The 

mean age in Bangladesh at the final date is estimated to be age 24.7, an extremely low level. It is clear that 

the main pathway to low fertility in these three countries was a reduction in higher-order births. With time 

this may be balanced with the delay of early births, pushing the mean age up again. This pattern is suggested 

by the Dominican Republic, in which the mean age dropped rapidly until 2000, and since then has been 

rising, returning to the same level as in 1990. 

The standard deviation of age at childbearing was generally in the range of 6.5 to 8.0 years around 1980 

and in the range from 6.0 to 7.5 years in 2010, a decline of about half a year, implying a greater 

concentration in the age of the mother at the births of her children as the number of children fell. Jordan 

and Egypt had the smallest standard deviations at the end, and were among the smallest standard deviations 

at the beginning of the series. Bangladesh and the Dominican Republic had the next lowest values at the 

end. The eight countries of SSA had the eight largest standard deviations at the beginning, ranging from 

7.35 to 8.01. By the end, all had fallen, and Kenya—the country with the lowest TFR in SSA at that time—

no longer had one of the eight most highly dispersed distributions of age at birth. The standard deviation 

for Kenya had fallen to 6.9, slightly less than the Philippines. The only evidence that the standard deviation 

may tend to return to earlier levels comes from the Dominican Republic. Its standard deviation, like its 

mean, as noted above, by the end had returned to approximately the 1990 level. 

The calculation of the ages when the Cumulative Fertility Rate reaches successive integer values is 

somewhat innovative. The numbers must not be interpreted as mean ages, but they do convey information 

about the prevailing timing and spacing of births and how they have changed over time. The main 

generalization, implied in Figure 3.21, is that, with one exception, all parities were achieved later at the end 

of the series than at the beginning. For the first parity, the delay was about the same amount across countries, 

regardless of the initial level; for parities 2 and 3, but especially for parity 3, the delay was greater if the 

initial age was later; for parities 4 and 5, but especially for parity 5, the delay was less if the initial age was 

later. We summarize this pattern as another manifestation of a slightly greater concentration of childbearing 

around a relatively stable mean age. 
69 

Figure 6.1. Scatterplot showing the most recent TFR and current mCPR for each survey from 

Ghana, Kenya, Indonesia, and Senegal. 

Figure 6.1 is, a scatterplot showing the most recent TFR and current mCPR for the 4 countries and 26 

surveys included in both Parts 1 and 2 of this report. “Recent” and “current” apply to the dates of the 

surveys. The TFR values in this figure are extracted from Stat Compiler for the three years before each 

survey. That is, the TFR values in the figure are not the smoothed single-calendar year TFRs calculated and 

described in Part 1. The mCPR values are also drawn from Stat Compiler. The TFR and mCPR from Stat 

Compiler generally match with the numbers in the main survey report. Each country—Ghana, Kenya, 

Indonesia, and Senegal—is shown in a different color. 

The figure illustrates the general negative relationship between fertility and contraceptive prevalence. The 

causal relationship between fertility and contraceptive prevalence is predominantly that higher prevalence 

is followed by lower fertility, the reverse of the sequence in the data from a single survey, so the timing is 

reversed in the figure, but there is enough continuity in each country to produce strong linear relationships 

between the two indicators. If a line were fitted through the points for each country, one line per country, 

all four lines would have negative slopes, although the slopes and intercepts would be different for each 

line. Each country follows a sequence from the upper left point to the lower right point, a kind of joint 

trajectory of fertility and contraception that follows the chronology of the surveys. 

The analysis in Part 2 does not focus on trends in contraceptive prevalence, but on trends in the association 

between contraceptive prevalence and parity, ideal number of children, level of education, work status, 

urban-rural residence, and region of the country. The main interest is in the logit regression of modern 

contraceptive use on the covariates in the first survey, in the last survey, and in a smoothed trajectory going 

from the first survey to the last survey. The trajectory of odds ratios indicates whether there is a general 

movement toward odds ratios that are not significantly different from 1, a pattern that would imply a 

convergence of prevalence levels in the comparison and reference categories. The statistical method is 

VCM, or varying coefficient models. The use of VCM for a sequence of DHS surveys is innovative. 

Not only is modern contraceptive use increasing steadily in Ghana, Indonesia, Kenya, and Senegal, but the 

disparities between specific groups in their use of modern contraceptive methods are also decreasing. 
70 

Family-planning programs and interventions appear to be successful in reaching major sub-populations. A 

number of review articles have found successful interventions in increasing contraceptive use that target 

both the demand and supply side (Cleland et al. 2006; Lopez et al. 2015; Mwaikambo et al. 2011; Phiri, 

King, and Newell 2015). 

The analysis uses the six covariates to estimate the VCMs. Depending on the country, some covariates have 

significant time-varying coefficients, that is, the odds ratios were changing, and some did not. Education 

had time-varying coefficients in all four countries, all showing a decrease in disparity, especially for the 

comparison of secondary or higher education with no education. Disparities also decreased between certain 

regions and urban and rural residence. The wealth index was not included, but in an analysis of recent trends 

in inequalities in 11 outcomes, including use of modern contraception, Assaf and Pullum (2016) found that 

disparities significantly decreased between the last two surveys for the countries included here. In addition, 

two of the countries, Ghana and Indonesia, were found to have achieved equality in modern contraceptive 

use across wealth quintiles in the most recent survey, according to the concentration index (Assaf and 

Pullum 2016). The absence of significant time-varying coefficients for work status in any of the countries 

indicates that disparities between working and non-working women in their contraceptive use still remain. 

The VCM and odds ratio plots highlight not only where interventions are successful in decreasing the gaps 

between the subgroups but also where interventions are needed due to increasing or unchanged disparities. 
71 

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77 

Appendices 

Appendix 1: R code used to fit the Varying Coefficient Models (VCMs) 

The mgcv R package (Wood 2012, 2006) is used to fit the varying coefficient models, using the gam 

function. The gam function is used to fit generalized additive models and can also be used to fit varying 

coefficient models. Using Ghana as an example, we have the following VCM, as describe in section 5.1: 

log ൬ ݌݊݋݁݃݅ݎ଺ߚ + ݅݀݁݊ܿ݁ݏ݁ݎହߚ + ݈݅݀݁ܽସߚ + ݇ݎ݋ݓଷߚ + ݑ݁݀ଶߚ + ݕݐ݅ݎܽ݌ଵߚ + ଴ߚ = ൰݌1−

	݅݀݁݊ܿ݁ݏ݁ݎሻݐଶሺߙ + ݑ݁݀ሻݐଵሺߙ + ሻݐሺ଴ߙ +

where p is the probability of modern contraceptive use, the βs are the constant coefficients, α0(t) is the timevarying intercept, and α1(t) and α2(t) are the time-varying coefficients for the education and place of 

residence variables, respectively. 

The following R code can be used to fit this VCM: 

############################################################################### 

Loading the mgcv package 

library(mgcv) 

 

# Attach the Ghana data (GHall) which appended data from six surveys 

attach(GHall) 

# The data contains the coded variables mcpr (modern contraceptive use), time (CMC of the survey), prty 

# (parity), edu (education), work (work status), ideal (ideal number of children), locl (place of residence) # 

and region. 

# Fit the VCM model defined above and store it in an object with the name modelghana

modelghana <- gam(mcpr ~ 1 + s(time, bs="ps", k=12, m=c(3,2)) + prty + edu 

+ s(time, bs="ps", k=12, m=c(3,2), by=edu) + work + ideal + locl 

+ s(time, bs="ps", k=12, m=c(3,2), by=locl) + region, 

family=binomial("logit")) 

############################################################################## 

In the code above, gam is the function from the mgcv package, s is used to define the spline functions, and 

ps selects for P-splines, k are the number of knots, and m=c(3,2) indicates the use of a third-degree B-spline 

basis with a second-order difference penalty, as recommended by (Eilers and Marx 2002). The number of 

knots provided is 12, or two for each survey; for Indonesia and Senegal, we used 14 knots based on seven 

surveys. To indicate that we would like to have varying coefficients for the education and the place of 

residence categories, we use the by option as shown. The model also specifies that a binomial family with 

a logit link is to be used. Please see Assaf and Campostrini (2015) for a more detailed explanation of using 

VCMs for repeated surveys. 

The odds ratio plots are produced by the plot.gam function of the mgcv package. We used the AIC of the 

models, using the AIC basic function in R. 

The models fit for this analysis also uses the survey package (Lumley 2014) to account for the stratification 

design and the sampling weights. We combined the mgcv and survey packages to define the svygam

function, which can be used as the gam function above. 
78 

Appendices 2–5: Description of Sample for Ghana, Indonesia, Kenya, and Senegal 

Appendix 2. Description of the sample for each Ghana survey for total sample and for women 15-49 in a union 

using a modern contraceptive method (mCPR) 

 1988 1993–1994 1998–1999 2003 2008 2014 

variable category % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR

 5.2 10.1 13.3 18.7 16.6 22.2 

parity 

0 or 1 37.3 3.1 38.0 7.1 43.4 10.9 45.4 12.9 47.4 12.3 45.4 16.7

2 12.7 3.0 13.9 9.5 13.3 8.7 12.2 19.6 13.2 15.5 14.0 21.8

3 11.0 4.1 11.9 12.0 10.9 14.5 11.3 18.5 11.0 17.4 12.0 24.6

4+ 39.0 7.0 36.2 11.2 32.3 16.1 31.1 21.5 28.3 19.1 28.6 24.1

education 

none 39.7 3.2 35.0 3.6 29.1 8.9 28.2 11.0 21.2 10.8 19.1 17.4

primary 52.8 6.5 54.7 13.4 18.0 12.9 20.0 20.7 20.1 18.0 17.8 26.8

secondary or more 7.5 10.1 10.3 25.0 52.8 16.8 51.8 24.7 58.7 19.5 63.1 23.1

work status not working 49.3 3.1 25.5 8.4 26.3 8.7 24.8 14.7 24.7 9.7 26.5 17.6 

working 50.7 6.7 74.5 10.5 73.7 14.1 75.2 19.2 75.3 17.3 73.5 23.0

ideal number of 

children 

0-2 3.6 15.1 11.0 15.7 11.5 15.5 9.6 19.5 9.8 16.8 9 (8 25.0

3 or 4 39.7 6.2 52.0 12.1 54.6 14.7 56.3 22.4 59.3 18.4 57.2 23.4

5 or 6 30.8 4.7 21.0 8.0 19.4 13.2 22.5 17.7 21.5 17.3 23.8 21.9

7 or more & don't know 25.9 3.5 16.0 5.5 14.5 7.9 11.5 7.5 9.4 7.6 10.0 17.0

place of residence rural 66.1 3.9 62.3 7.4 64.1 11.4 51.6 14.9 51.5 15.1 46.2 24.6 

urban 33.9 8.1 37.7 15.8 35.9 17.4 48.4 24.2 48.5 18.6 53.8 19.8

region 

Greater Accra 13.3 10.6 13.5 18.0 16.7 17.4 16.6 26.0 17.3 22.2 20.2 19.4

West/Central 19.1 4.1 18.3 10.7 23.6 10.9 17.3 15.6 17.7 15.1 21.0 25.4

East/Volta 26.8 5.0 22.1 10.3 24.0 16.3 19.2 20.5 18.6 18.9 17.0 27.3

Brong-Ahafo/ 

Ashanti 29.5 6.0 26.7 10.1 22.4 14.3 30.1 22.4 29.2 17.6 27.3 22.5 

North/Upper West/ 

Upper East 11.3 0.7 19.3 5.7 13.3 7.1 16.9 10.0 17.1 10.2 14.5 16.0 

Total observations 4488 3156 4562 3204 4843 3131 5691 3549 4916 2876 9396 5321

Appendix 3. Description of the sample for each Indonesia survey for total sample and for women 15-49 in a union 

using a modern contraceptive method (mCPR) 

 1987 1991 1994 1997 2002–2003 2007 2012 

variable category % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR 

 43.0 47.2 52.2 54.9 56.7 57.4 57.9 

parity 

0 or 1 24.5 27.6 28.3 34.1 28.1 40.7 30.6 45.1 30.9 46.0 32.5 46.6 48.6 45.2

2 17.8 51.4 19.5 56.2 21.1 60.7 22.5 64.9 25.3 68.7 28.1 69.8 23.8 68.3

3 15.1 54.1 15.8 55.6 17.3 63.5 17.1 63.5 18.0 65.8 18.0 65.1 14.2 65.6

4+ 42.6 44.1 36.4 48.4 33.5 50.3 29.8 52.1 25.7 51.0 21.4 50.8 13.4 56.1

education 

none 21.5 30.9 19.0 35.9 15.7 38.6 13.0 43.1 7.9 44.8 6.9 40.1 3.3 41.8

primary 61.2 43.9 61.0 48.3 60.2 53.3 58.8 56.1 53.9 57.5 47.6 57.7 33.2 59.6

secondary or 

more 17.3 54.5 20.0 53.8 24.1 57.5 28.1 57.5 38.2 58.0 45.5 59.4 63.5 57.7 

ideal number of 

children 

0-2 30.2 48.3 36.2 52.8 38.1 57.5 39.1 61.7 42.2 63.3 48.8 63.1 57.6 62.7

3 or 4 42.5 46.8 38.2 49.6 33.3 55.1 33.7 57.2 37.2 56.6 33.2 55.9 29.3 53.5

5 or 6 11.3 35.4 8.3 36.6 5.9 41.5 5.7 44.9 5.4 42.8 5.7 43.8 3.9 47.3

7 or more & don't 

know 16.0 27.9 17.3 34.5 22.6 41.1 21.4 40.8 15.2 43.4 12.3 44.6 9.2 49.9 

place of 

residence 

rural 71.4 40.8 70.7 45.6 70.8 50.7 72.0 54.7 54.2 56.5 58.2 57.5 47.8 58.7 

urban 28.6 48.7 29.3 51.1 29.2 55.8 28.0 55.2 45.8 57.0 41.8 57.1 52.2 57.0

region outer Javi Bali 41.5 35.4 35.8 40.2 36.0 44.7 37.1 48.7 36.7 50.5 36.9 52.5 39.9 53.3

Java Bali 58.5 48.5 64.2 51.1 64.0 56.4 62.9 58.5 63.3 60.3 63.1 60.2 60.1 60.8 

Total observations 9451 8724 22813 21022 28044 26071 28690 26770 29483 27857 32895 30931 45607 33465
79 

Appendix 4. Description of the sample for each Kenya survey for total sample and for women 15-49 in a union 

using a modern contraceptive method (mCPR) 

 1988–1989 1993 1998 2003 2008–2009 2014 

 % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR

 17.9 27.3 31.5 31.5 39.4 53.2 

parity 

0 or 1 34.1 6.2 40.4 13.7 41.7 19.5 42.9 21.3 41.3 28.2 42.2 41.8 

2 10.1 14.7 11.4 26.8 13.1 35.2 12.7 35.9 14.5 49.0 16.3 60.4

3 9.6 19.8 9.3 29.4 10.2 33.3 11.5 39.9 12.5 46.0 13.3 62.1

4+ 46.1 20.8 38.9 31.3 35.1 34.4 33.0 31.6 31.7 37.6 28.1 51.4

education 

none 25.2 9.7 17.9 15.3 11.5 16.1 12.7 8 7 8.9 12.0 7 2 15.3 

primary 54.4 19.2 57.6 25.6 59.3 28.2 58.0 28.8 56.8 38.3 50.3 55.7

secondary or more 20.4 29.3 24.5 44.9 29.2 46.3 29.3 51.7 34.3 52.1 42.7 59.0

work status not working 88.4 16.1 51.0 23.0 48.1 28.3 41.5 23.3 43.2 31.2 38.5 44.7 

working 11.6 33.9 49.0 30.8 51.9 33.6 58.5 36.0 56.8 44.1 61.5 57.2

ideal number of 

children 

0-2 11.7 27.9 25.0 40.9 22.9 44.7 23.7 46.9 24.5 53.4 27.4 67.1 

3 or 4 52.7 20.7 51.7 28.6 51.2 34.0 49.2 35.6 51.7 42.9 52.7 56.2

5 or 6 23.9 14.0 13.0 17.1 16.2 21.4 14.4 23.8 14.3 27.8 12.1 43.8

7 or more & don't 

know 11.7 11.3 10.3 11.9 9.7 16.7 12.7 9.7 9.4 16.4 7.8 21.0 

place of residence rural 82.7 16.4 82.2 25.4 76.8 29.0 74.9 29.2 74.6 37.2 59.2 50.9 

urban 17.3 25.5 17.8 37.9 23.2 41.0 25.1 39.9 25.4 46.6 40.8 56.9

region 

Nairobi/Central 23.4 29.9 21.2 46.0 20.4 51.3 24.6 52.6 19.3 57.1 25.0 62.8 

Coast/East/ 

Northeast 24.7 18.0 28.2 25.8 25.3 31.0 26.3 28.7 26.5 35.8 26.1 48.9

West/Nyanza 30.6 10.1 29.9 21.6 32.9 23.9 26.2 23.6 27.4 36.0 23.4 55.2

Rift valley 21.2 18.1 20.7 21.0 21.5 26.4 22.8 24.5 26.8 34.7 25.6 46.8

Total observations 7150 4765 7540 4629 7881 4834 8195 4919 8444 4828 3107 18549

Appendix 5. Description of the sample for each Senegal survey for total sample and for women 15-49 in a union 

using a modern contraceptive method (mCPR) 

 1986 1992–1993 1997 2005 2010–2011 2012–2013 2014 

variable category % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR % 

% 

mCPR

 2.4 4.8 8.1 10.3 12.1 16.1 20.3 

parity 

0 or 1 38.1 0.9 39.3 2.4 42.7 3.7 48.0 7.6 48.7 7.4 49.3 7.5 49.0 10.6 

2 11.3 3.4 10.8 3.3 10.6 8.9 10.7 8.5 11.2 12.8 11.0 19.6 11.3 26.8

3 10.0 3.4 8.6 5.2 8.6 10.2 9 1 12.6 9.4 15.0 10.0 22.3 10.4 21.3

4+ 40.6 2.6 41.3 5.9 38.1 9.3 32.4 12.0 30.7 13.9 29.6 18.1 29.3 24.0

education 

none 77.2 1 2 73.0 2.2 66.6 3.9 59.6 5.4 57.9 7.9 54.8 11.6 51.0 14.3 

primary 13.5 5.9 17.1 13.1 20.9 17.1 25.2 18.3 21.8 20.5 22.5 24.5 23.3 29.6

secondary or more 9.2 22.1 9.9 29.4 12.5 34.5 15.2 29.7 20.4 25.6 22.8 29.2 25.7 33.4

work status not working 73.5 1.8 55.3 4.8 43.9 8.9 62.0 8.5 60.4 10.1 56.0 13.4 54.3 18.3 

working 26.5 4 9 44.7 4.8 56.1 7.5 38.0 13.1 39.6 14.9 44.0 19.0 45.7 22.3

ideal number of 

children 

0-2 3.4 8 8 4.3 12.0 4.5 20.4 5 7 18.5 4 6 15.6 3.3 25.6 2.9 27.8 

3 or 4 17.3 8.4 24.3 9.7 30.1 15.2 25.4 16.8 29.8 19.1 30.5 23.1 32.5 27.0

5 or 6 26.3 2.5 28.7 5.2 29.2 7.3 30.2 11.2 30.5 12.3 35.6 17.6 32.8 23.9

7 or more & don't 

know 53.0 0.7 42.7 2.3 36.3 3.8 39.3 5.9 35.8 7.7 30.7 9.7 31.8 12.2 

place of 

residence 

rural 59.0 0.3 58.2 1.4 55.8 2.1 51.3 5 5 50.7 6.5 54.2 9.2 46.3 13.0 

urban 41.0 6.7 41.8 11.9 44.2 19.3 48.7 18.0 49.3 20.2 45.8 27.3 53.7 28.8

region 

West 37.9 5.5 36.9 10.1 38.1 16.4 40.0 17.7 38.9 19.5 38.0 26.9 41.1 31.6 

Central 34.6 0.5 36.1 2.2 34.1 3.9 33.0 4.9 34.7 7.4 33.8 10.2 30.2 12.3

South 14.5 0.6 11.0 1.7 12.0 4.1 11.0 12.0 10.6 10.9 10.2 11.7 9.8 17.9

Northeast 13.0 2.4 16.0 3.2 15.8 5.1 16.0 6.1 15.7 8.5 18.0 11.3 18.9 15.5

Total observations 4415 3364 6310 4450 8593 5851 14602 9866 15688 10347 8636 5554 8488 5500