MEASURE DHS+ assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Funded by the U.S. Agency for International Development 

(USAID), MEASURE DHS+ is implemented by ORC Macro in Calverton, Maryland.

The main objectives of the MEASURE DHS+ project are: 

1) to provide decisionmakers in survey countries with information useful for informed policy choices, 

2) to expand the international population and health database, 

3) to advance survey methodology, and 

4) to develop in participating countries the skills and resources necessary to conduct high-quality 

demographic and health surveys.

Information about the MEASURE DHS+ project or the status of MEASURE DHS+ surveys is available on 

the Internet at http://www.measuredhs.com or by contacting: 

ORC Macro

11785 Beltsville Drive, 

Suite 300

Calverton, MD 20705 USA

Telephone: 301-572-0200

Fax: 301-572-0999

E-mail: reports@orcmacro.com
DHS Comparative Reports No. 3

Fertility Levels, Trends, and

Differentials 1995-1999

Shea Oscar Rutstein

December 2002

ORC Macro

Calverton, Maryland USA
ii

Recommended citation:

Rutstein, Shea O. 2002. Fertility Levels, Trends, and Differentials 1995-1999. DHS Comparative Reports 

No. 3. Calverton, Maryland: ORC Macro.
iii

Table of Contents

Preface...........................................................................................................v

Acknowledgments.........................................................................................vi

Executive Summary......................................................................................vii

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

2 Data and Definitions................................................................................2

2.1 Fertility Data ................................................................................2

2.2 Fertility Measures.........................................................................4

2.2 Socioeconomic Variables..............................................................6

3 Fertility Rates.........................................................................................8

3.1 Overall Fertility............................................................................8

3.2 Socioeconomic Differences in Fertility ........................................14

3.3 Fertility Trends...........................................................................22

4 Other Fertility Components....................................................................26

4.1 Onset of Childbearing: Age at First Birth .....................................26

4.1.1 Socioeconomic Differences in Age at First Birth...............28

4.1.2 First Births to Younger Adolescents.................................30

4.1.3 Postponement of Childbearing to Age 30 or Above ...........30

4.2 Parity Progression and Birth Intervals ..........................................33

4.2.1 Parity Progression...........................................................33

4.2.2 Socioeconomic Differences in Parity Progression Ratios...37

4.2.3 Birth Intervals ................................................................41

5 Proximate Determinants Model..............................................................50

5.1 Bongaarts’ Proximate Determinants Model..................................50

5.2 Delayed Entry into Sexual Union.................................................50

5.3 Contraception.............................................................................52

5.4 Postpartum Infecundity ...............................................................53

5.5 Education...................................................................................53

5.6 Induced Abortion........................................................................53

6 Conclusions ..........................................................................................56

Group 1 countries.......................................................................56

Group 2 countries.......................................................................56

Group 3 countries.......................................................................57

Group 4 countries.......................................................................58

Group 5 countries.......................................................................59

References...................................................................................................61

Appendix A: Life Table Estimates (Trimeans) of Birth Intervals by Parity .......65
v

Preface

One of the most significant contributions of the MEASURE DHS+ program is the creation of an 

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

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

comparative framework. The DHS Analytical Studies series focuses on specific topics. The principal 

objectives of both series are to provide information for policy formulation at the international level and to 

examine individual country results in an international context. Whereas Comparative Reports are primarily descriptive, Analytical Studies take a more analytical approach. 

The Comparative Reports series covers a variable number of countries, depending on the

availability of data sets. Where possible, data from previous DHS surveys are used to evaluate trends over 

time. Each report provides detailed tables and graphs organized by region. Survey-related issues such as 

questionnaire comparability, survey procedures, data quality, and methodological approaches are

addressed as needed.

The topics covered in Comparative Reports are selected by MEASURE DHS+ staff in

conjunction with the MEASURE DHS+ Scientific Advisory Committee and USAID. Some reports are 

updates and expansions of reports published previously.

It is anticipated that the availability of comparable information for a large number of developing 

countries will enhance the understanding of important issues in the fields of international population and 

health by analysts and policymakers.

Martin Vaessen

Project Director
vi

Acknowledgments

The author wishes to thank Fred Arnold and Sunita Kishor for their careful review of this report and 

George Bicego and Charlie Westoff for their suggestions and Sidney Moore for her polished editing. 
vii

Executive Summary

Fertility continues to fall around the world. Based on DHS surveys conducted between 1990 and 1999, 

this report shows fertility rates, trends, and differentials for 43 countries. Eight types of measures are presented 

including total and age-specific fertility rates, general and crude birth rates, the mean number of children born 

to women, total marital fertility rates and marital duration-specific fertility rates, age at first birth, parity 

progression ratios, and birth intervals. These indicators are presented for the total country and by women’s 

residence, education, work status, and migration status, and by the education and occupation of their husband. 

Trends are examined for all countries using birth history data and for 22 countries by comparison with a 

previous DHS survey in each country. The Bongaarts proximate determinants indexes are calculated to assess 

the impact of these trends on fertility.

The total fertility rate (TFR) for the three years preceding the survey in each country ranges from a 

high of 7.2 children per woman in Niger to 2.3 children per woman in Vietnam. Ten countries, Yemen and 

nine countries in sub-Saharan Africa, have TFRs of 6 children or more per woman. The high fertility in these 

countries is due mainly to early entry into motherhood and little behavior aimed at limiting births. Birth 

intervals for these countries are not long; however, short birth intervals do not account for the high fertility 

levels. Likewise, contraception has little impact on fertility in these countries. Although relatively few in 

number, women with secondary education in these countries have substantially lower fertility levels, with an 

average of 3.7 births per woman for the ten countries.

Seven countries in the regions surveyed report TFRs below 3 children per woman. Fertility limiting 

behavior including relatively high levels of contraceptive use and long intervals between births, is responsible 

for most of the low fertility levels in these countries. Delayed entry into motherhood contributes to reduced 

fertility levels in four of the countries. Although information on induced abortion is available for three of the 

countries in this group, it plays a very small role in the level of fertility.

There are substantial differences in fertility levels by socioeconomic characteristics. In all countries, 

fertility is higher in rural than in urban areas. In all but a few countries outside of sub-Saharan Africa, urban 

fertility rates are less than 3 children per woman. Fertility is lowest for women born in urban areas. 

By education, women with at least a secondary education have the lowest fertility levels. The largest 

differences by education are found in the Latin America/Caribbean region, with an average difference between 

women with no schooling and women with secondary or more schooling of 3.1 children. Husband’s education 

has a mixed effect on fertility. A curvilinear relationship exists in about half of the sub-Saharan countries 

where fertility is higher for women whose husbands have primary education than for those whose husbands 

have either no education or secondary or higher education.

Women’s work status is important for lowering marital fertility in all but six countries; in these it 

makes no substantial difference. In all but two countries, women whose husbands work in agriculture have the 

highest marital fertility rates. In all but five countries, fertility is most depressed among women whose

husbands have professional or clerical occupations.

Fertility has declined in all countries except Colombia. The average decline per year for the 22

countries with more than one survey was 1.3 percent. Declines of one or more births over a period of four 

years occurred most commonly in sub-Saharan Africa, where more than half of the surveyed countries had a 

decline of 20 percent or more. A word of caution is needed here because the decline may have been 

exaggerated as a result of omission or displacement of births. For example, the direct calculated current TFR 

for India based on women’s reports is estimated to be 0.5 children too low because of the omission of recent 

births.
1

1 Introduction

Many studies indicate that the effects of socioeconomic and cultural factors on fertility vary from 

one region to another. Moreover, the existence of substantial variations in fertility behavior across

socioeconomic categories—place of residence, level of education, and occupation—is a pervasive finding 

of social demography (Cleland, 1985; Freedman and Blanc, 1992; Rodriguez and Aravena, 1991; Singh 

and Casterline, 1985; United Nations, 1987). A pervasive decline in fertility in less developed countries is 

occurring in all regions of the world. This decline is a result of decreasing desire for large families due to 

improved infant and child mortality, increasing modernization, and the increased ease of controlling 

fertility (Rutstein, 1998). Confirming the scenario of fertility decline by Rutstein (1998), the economic 

crises of the 1980s have spurred changes in fertility even in the most resistant region, sub-Saharan Africa 

(Capo-chichi and Juarez, 2001; Eloundou-Enyegue et al., 2000). Declines in fertility have lead to reduced 

gender inequality and greater female empowerment (Dyson, 2001; Sanderson, 2001). 

The objectives of this study are to examine current fertility levels, assess fertility trends, and 

analyze socioeconomic differentials in fertility in developing countries of sub-Saharan Africa, Asia, the 

Near East and North Africa, and Latin America and the Caribbean. The study provides an update to 

previous comparative studies on fertility based on data from Demographic and Health Surveys (DHS) 

(Arnold and Blanc, 1990; Muhuri et al., 1994; Mboup and Saha, 1998).

The report includes data from 43 countries in which DHS surveys were conducted between 1995 

and 1999 in the third and fourth phases of the DHS program. Several measures of fertility are presented 

according to women’s residence, migration status, level of education, current work status, and husband’s 

education and occupation. The report examines trends in fertility for all 43 countries, using birth history 

data. In addition, for the 22 countries for which data are available from at least two DHS surveys, the 

study examines fertility trends. The study also examines age at first birth, parity progression ratios, and 

information on the length of birth intervals to evaluate changes in the onset of reproduction, spacing of 

births, and termination of childbearing. Finally, the study uses the Bongaarts proximate determinants 

framework to assess the levels of fertility inhibition due to later marriage and cohabitation, lactational 

amenorrhea and postpartum abstinence, and contraception.
2

2 Data and Definitions

The data analyzed here come from 43 countries in which DHS surveys were conducted (Table 

2.1). Twenty-two of these countries are in sub-Saharan Africa, four countries are in the Near East/North 

Africa, nine countries are in Asia, and eight countries are in Latin America/Caribbean.

2.1 Fertility Data

The fertility rates presented in this report are based on data collected in the reproduction section 

of the DHS questionnaire. In this section, each woman was first asked about the number of sons and 

daughters living with her, the number living elsewhere, and the number who had died. Next, her complete 

birth history was collected, including the child’s sex, date of birth, and survival status. 

The quality of DHS data on fertility has been evaluated for surveys between 1986 and 1989 by 

Arnold (1990) and between 1990 and 1993 by Gage (1995). A comparison of changes in accuracy

between repeat surveys is given in Marckwardt and Rutstein (1996). There are two principal problems 

with data for the calculation of fertility rates: misreporting of date of birth and omission of children. 

Transference

A number of surveys have shown evidence of some misreporting resulting in a transfer of birth 

dates across the boundary for inclusion of children in questions on maternal and child health. In most 

surveys where this transfer has occurred, the maternal and child health section was applied to children 

under age five. In these surveys, the transfer results in an apparent dearth of children born four years 

before survey and an apparent excess of children born five and six years before the survey. Given that the 

current fertility rates are calculated over the period up to two years before the survey, they will be 

unaffected by misreporting date of birth in these surveys. 

It is believed that some interviewers misreport the date of birth of children in order to avoid 

asking the questions in the maternal and child health section. An effort was made in the design of the 

DHS Phase III model questionnaire to lighten the interviewer’s burden by restricting the maternal and 

health survey questions to only children born in the three years before the survey. This change meant that 

when the maternal and child health section was used, the questions would usually be applied to one child 

rather than to the two to three children previously eligible. Not all DHS Phase III surveys followed the 

model in this regard; many continued to use eligibility for children born in the five preceding years, and a 

few used the four preceding years. 

An evaluation by Rutste in (1999) of the effect on transfer of births by this change in the 

eligibility criterion found that in some surveys, transference of birth dates across this new boundary still 

occurred. In the case of countries that used the new boundary, current fertilit y rates could be biased 

downward by transfer of age at birth. This bias can be corrected by adjusting the period on which the 

fertility rates are based to include the years showing an excess of births.

Omission

Omission of children is a much more serious problem for the estimation of fertility rates than 

transference is because there is no easy correction. Omission of children has been thought to occur more 

frequently for births that took place long before the survey, especially for children who died at a very 

young age. Although there is evidence that omission of nonsurviving children does occur in some surveys 

(especially for children who died in the neonatal period), this has only a moderate effect on current
3

Table 2.1 DHS surveys included in this report, Demographic and Health 

Surveys, 1986-1999

Region and

country DHS-I DHS-II DHS-III

Second DHSIII or DHS-IV

Sub-Saharan Africa

 Benin 1996

 Burkina Faso 1993 1999

 Cameroon 1991 1998

 Central African Republic 1994-1995

 Comoros 1996

 Côte d'Ivoire 1994

 Eritrea 1995

 Ghana 1988 1993 1998

 Guinea 1999

 Kenya 1989 1993 1998

 Madagascar 1992 1997

 Mali 1987 1996

 Mozambique 1997

 Niger 1992 1998

 Senegal 1986 1993 1997

 South Africa 1998

 Tanzania 1992 1996 1999

 Tchad 1997

 Togo 1988 1998

 Uganda 1995

 Zambia 1992 1996

 Zimbabwe 1988-1989 1994 1999

Near East/North Africa

 Egypt 1992 1995 1997

 Jordan 1990 1997

 Turkey 1993 1998

 Yemen 1997

Asia

 Bangladesh 1994 1997

 India 1992-1993 1998-1999

 Indonesia 1987 1991 1994 1997

 Kazakhstan 1995 1999

 Kyrgyz Republic 1997

 Nepal 1996

 Philippines 1993 1998

 Uzbekistan 1996

 Vietnam 1997

Latin America/Caribbean

 Bolivia 1989 1993-1994 1998

 Brazil 1986 1991a1996

 Colombia 1986 1990 1995

 Dominican Republic 1986 1991 1996

 Guatemala 1987 1995 1999

 Haiti 1994

 Nicaragua 1997

 Peru 1986 1991-1992 1996

a Northeast region only

DHS-I: Demographic and Health Surveys, Phase I

DHS-II: Demographic and Health Surveys, Phase II

DHS-III: Demographic and Health Surveys, Phase III

DHS-IV: Demographic and Health Surveys, MEASURE DHS+
4

fertility rates.1 Of greater importance is omission of recently born living children. This omission is 

common in many censuses and surveys without a birth history. The evaluations of the quality of DHS 

surveys have shown that surveys with birth histories may also be subject to omission of recent births. 

Although the interviewer may be partly responsible for this omission, respondents may also be reluctant 

to disclose the presence of young children in order to protect them.

2.2 Fertility Measures

Eight measures of fertility are presented because no single measure is ideal for all purposes:

1. The total fertility rate (TFR) and its component age-specific fertility rates (ASFRs) 

2. The general fertility rate (GFR)

3. The crude birth rate (CBR)

4. The mean number of children ever born (mean CEB) for women age 40-49

5. The total marital fertility rate (TMFR) and its component duration-specific marital fertility rates 

(DSMFRs)

6. The median age at first birth (MAFB)

7. The parity progression ratios (PPRs)

8. The birth intervals (BIs).

Each measure has different strengths and weaknesses. The level of fertility is given by the total 

fertility rate, the general fertility rate, the crude birth rate, and the total marital fertility rate. The pattern of 

fertility is given by the age-and duration-specific fertility rates, the median age at first birth, the parity 

progression ratios, and the length of birth intervals. 

The total fertility rate is a widely used measure that adjusts for differences due to age

distributions. However, its relative sampling error is large when some age groups include only a small 

number of women. The advantage of the general fertility rate is that its relative sampling error is smaller 

than that of the TFR, but it is not age standardized. Both the TFR and GFR are synthetic cohort measures 

representing the current situation. In contrast, the third measure—the mean number of children ever born 

to women age 40-49—represents the childbearing experience of a real age cohort and reflects mainly past 

fertility behavior. The median age at first birth, mean birth intervals, and parity progression ratios are 

used to evaluate changes in fertility in terms of the onset of reproduction, spacing of births, and

termination of childbearing, respectively. Although over time the TFR can provide information on change 

in the average number of children per woman, it cannot give insight into the nature of this change, which 

is provided by period parity progression ratios, that measure the proportion of women moving from one 

parity to the next.

 

1

 As an example, suppose that instead of the measured neonatal mortality rate of 50 neonatal deaths per 

1,000 births, the real rate was 100 per 1,000. If the measured total fertility rate were 4.0, then the real fertility rate 

including the omitted births would be 4.2, an increase of 5 percent.
5

Age-specific fertility rates are calculated from birth histories by dividing the number of births to 

women in a specific age group, during a specific period, by the number of woman-years of exposure 

during the same period. 2 The formula used is

ASFR(a,t) = b(a,t) / e(a,t),

where a is five-year age group, b(a,t) is births to women in age group a during period t (here three years 

before the survey), and e(a,t) is woman-years of exposure among women in age group a during period t. 

In surveys that included only ever-married women—Bangladesh, Egypt, Indonesia, Jordan, Nepal, 

Turkey, and Yemen—the denominator of the rate is inflated to include all women, using the proportion 

ever-married from the household schedule.3

The total fertility rate at time t is calculated by summing the age-specific fertility rates for fiveyear- age groups and multiplying by 5. The TFR may be interpreted as the number of births that a woman 

would have if she survived all her reproductive years and experienced the age-specific fertility rate 

prevailing in a given period.

The general fertility rate is the ratio of the number of births among women age 15-44 during the 

three years preceding the survey to the sum of the woman-years of exposure during the same period.

Duration-specific marital fertility rates are calculated as follows:

DSMFR(j,t) = b(j,t) / e(j,t),

where j is five-year marital duration group, b(j,t) is all births to women at marital duration j during period 

t (here the three years before the survey), and e(j,t) is all woman-years of exposure among women at 

marital duration j during the period t. Marital duration is measured from the time of first marriage or first 

cohabitation. Because DHS surveys do not include a full marriage history, it is not possible to distinguish 

only births that occurred within marriage or cohabitation and exposure times only for married or 

cohabiting women. Instead, all births and all exposure since first marriage or cohabitation are taken for 

the numerators and denominators, respectively. (In the rest of this report, the shortened term marriage is 

used to represent both formal marriage and cohabitation.)

The total marital fertility rate represents the average number of children born in the first 24 or 29 

years of marriage to a hypothetical woman experiencing the fertility rates of a specific period. The TMFR 

is calculated by summing the DSMFRs for five-year marital duration groups and multiplying by 5. The 

TMFR as used here includes all births and exposure since first marriage.

Depending on the size of the survey and the distribution of women according to background 

variables, fertility rates may be based on relatively small numbers of women. In this report, the discussion 

of fertility rates by socioeconomic variables for a three-year reference period is based on women age 15-

39 and for up to 14 years of marital duration.4 The choice of women age 15-39 instead of women 15-44 or 

15-49 is because there are often few cases in some subgroups after age 39 for some socioeconomic 

categories (e.g., women with secondary education). Age-specific fertility rates and duration-specific 

marital fertility rates are in parentheses if the estimate is based on 125 to 249 unweighted woman-years of 

exposure in the age subgroup or marital-duration subgroup. The estimate is replaced with an asterisk 

 

2

 The month of interview is excluded.

3

 The inflation factors are the number of ever-married women of each single age 15-49 (from the household 

sample) divided by the number of all women of each single age 15-49 (ever-married and never-married women). It 

is assumed that never-married women have had no births.

4

 Duration-specific marital fertility rates for up to 29 years are presented in Appendix A.
6

when there are fewer than 125 woman-years of exposure. TFRs and TMFRs are in parentheses or 

replaced with an asterisk if at least one of the ASFRs or DSMFRs, respectively, is based on 125 to 249 

woman-years of exposure or fewer than 50 woman-years of exposure. The same guidelines apply in 

presenting general fertility rates, which are based on exposure in the 15-44 age group. For the mean 

number of children born to women age 40-49, the estimate is replaced with an asterisk if there are fewer 

than 25 women age 40-49. Parentheses are used if there are between 25 and 49 women who are age 40-

49.

Finally, life table methodology5 is used to estimate parity progression ratios by period (the 

probability of progressing to the next parity within five years of the previous birth, called the quintum) 

and by the length of the interval between births (tempo). The analysis of these two components of family 

formation is relatively simple if birth histories of women who have completed their reproductive years are 

examined. The ratios can be measured directly from the distribution of family size, and the length (or 

timing) for each birth can be calculated. The survey situation is more complicated because complete 

longitudinal data are not available. Except for older women, most survey respondents are not at the end of 

their reproductive years. Consequently, information on birth histories is incomplete, and parity

progression ratios cannot be calculated directly. It is important to distinguish two problems posed by the 

incomplete nature of the data: selectivity and censoring (Rodriguez and Hobcraft, 1980; Ryder, 1983).

Selectivity refers to the fact that the transition from parity n to parity n+1 can only be calculated 

for women who have reached parity n or higher at the time of the survey. For example, the transition from 

parity 2 to parity 3 can only be analyzed for women having two or more children at the time of the survey. 

Such women may be selected for early marriage, may be more fecund, and may be less educated than 

those of the same age who have not yet reached parity 2.

Censoring refers to the fact that some women, having reached parity n at the time of the survey, 

are included in the analysis although they have not yet reached the next parity, n+1. For these women, 

two outcomes are possible: either they will never reach parity n+1 or they will reach it after an unknown 

interval beyond the survey date. Censoring renders the definition of PPR and BI ambiguous.

Censoring can be overcome by using life table techniques, which are designed for dealing with 

this kind of problem. Selectivity can be accounted for by introducing appropriate controls into the 

analysis and by studying the process of family formation as a function of the women’s sociodemographic 

characteristics (Rodriguez and Hobcraft, 1980). Hence, in this study, life table methods are used to

examine parity progression ratios and birth intervals.

2.2 Socioeconomic Variables

The DHS model questionnaire contains a limited number of questions about the socioeconomic 

characteristics of women. For most countries, information on education and urban-rural residence is 

available, and many surveys also collect some information on women’s employment and migration status. 

The DHS model questionnaire includes a few questions on the socioeconomic characteristics of husbands 

of ever-married women, including their education and occupation (Muhuri et al., 1994).

The TFR, mean CEB, and TMFR are presented by urban-rural residence, migration status, 

education, women’s current work status, and husband’s occupation. For countries where the sample is 

ever-married women, however, fertility measures are not estimated for migration status and current work 

status because data for never-married women are not available.

 

5

 The trimean will be used here to estimate length of birth interval. The trimean = (Q1 + 2*Q2 + Q3) / 4, 

where Q1 indicates the first quartile, that is, the 25th percentile; Q2 the second quartile, that is, the median; and Q3 

indicates the third quartile, that is, the 75th percentile.
7

Urban-Rural Residence

Urban residence or rural residence is not reported by the woman herself but instead is determined 

by the location of the sample point or cluster in which she is interviewed (which is classified as urban or 

rural in the sampling frame). Because most surveys were based on a de facto sample that included 

overnight visitors to the household, the location for visitors may be different from the type of place in 

which they usually live.

Migration Status

To determine migration status, a woman’s childhood place of residence is compared with the 

place where she was interviewed. The woman is categorized as an urban native if both places were urban, 

as a rural-to-urban migrant if the childhood place of residence was rural and the place of the interview 

was urban, as an urban-to-rural migrant if the earlier place was urban and the later place was rural, and as 

a rural native if both places were rural.

Unlike the current place of residence, the childhood pla ce of residence is determined by

information supplied by the respondent. In the DHS questionnaire, the respondent is asked whether she 

spent most of the time until she was 12 years old “in the city, in a town, or in the countryside.” For all 

countries, city and town are considered urban.

Education

The categories used for women’s and husband’s levels of education are no schooling, primary, 

and secondary or higher. This analysis considers only the information on level of education and does not 

take account of the number of years spent at this level. Here it may be noted that a given level of 

education does not necessarily correspond to the same number of years of education in every country 

because educational systems differ among countries. For example, primary school is five years in

Bangladesh, Colombia, and Peru; six years in Bolivia, Egypt, Indonesia, Jordan, and Senegal; seven years 

in Cameroon, Kenya, Zambia, and Zimbabwe; and eight years in Brazil, the Dominican Republic, and 

Ghana.

Current Work Status

Women’s work categories include working for cash or kind (paid worker), working on the family 

farm or in the family business (unpaid family worker), and not working.

Husband’s Occupation

Occupations were grouped into five categories: 1) agriculture (whether self-employed or labor), 

2) skilled and unskilled manual labor, 3) sales and services, 4) professional and clerical positions, and 

5) other occupations. Fertility rates for women whose husband was in the other occupations category and 

for women whose husband has never worked are not shown because there were too few women in these 

groups.
8

3 Fertility Rates

3.1 Overall Fertility

Table 3.1 shows four summary measures of overall fertility. As noted earlier, all period rates 

(TFRs and GFRs) pertain to the three-year period preceding the survey. The TFR for women age 15-49 

ranges from 2.3 in Vietnam to 7.2 in Niger, with substantial variation within and among regions. In subSaharan Africa, the TFR ranges from 2.9 in South Africa to 7.2 in Niger. Most of the countries in subSaharan Africa show a TFR of more than five children per woman. The fertility for the Near East/North 

Africa region varies greatly from 2.6 in Turkey to 6.5 in Yemen. Of the countries surveyed in Asia, only 

Nepal has a TFR higher than four (4.6).6 In the Latin America/Caribbean region, the TFR ranges from 

2.5 in Brazil to 5.0 in Guatemala.

Table 3.1 also presents the TFRs for women age 15-39 and 15-44. For almost all countries not in 

sub-Saharan Africa, the TFRs for age groups 15-39 and 15-49 are close, demonstrating that women have 

most of their births before they reach the age of 40, after which fertility is low. However, most subSaharan Africa countries have differences greater than 0.3 children between the TFRs for women 15-39 

and 15-49. In this region, fertility at ages over 40 is still important.

A comparison of the cumulative measure of childbearing, children ever born (CEB), with the 

TFR gives a rough indication of the trend in fertility over time. Table 3.1 shows that the number of 

children ever born is higher than the TFR (for women 15-49) for all countries. The difference between the 

number of children ever born and the TFR is two or more children in four countries (Bangladesh, 

Comoros, Jordan, and Nicaragua). For nine countries in sub-Saharan Africa, the difference between the 

number of children ever born and the TFR is less than one child. The smallest difference, only 0.1 

children, occurs in Eritrea.

Comparison of total marital fertility rates indicates that in the 30 years following their first 

marriage, women in sub-Saharan Africa gave birth to between 4.2 (Zimbabwe) and 7.7 (Niger) children, 

except in South Africa, where women had only 2.7 children. Marital fertility rates in Haiti, Nepal, 

Guatemala, Jordan, and Yemen are as high as rates for sub-Saharan countries (5.1, 5.2, 5.6, 6.1, and 7.6 

children, respectively). The lowest TMFRs occurred in Kazakhstan, Brazil, South Africa, and Vietnam 

(2.5 to 2.8).

 

6

 There is considerable evidence of omission of recent births in some states in the India National Family 

Health Survey of 1998-1999 (Retherford et al., 2001). The current TFRs for all of India are therefore suspect. Based 

on calculations by the author, the extent of the underestimation is on the order of 0.5 children for the current TFR for 

women 15-49 years of age (TFR between 3.31 and 3.39). A paper by Retherford and Mishra (2001) estimates the 

TFR to be between 3.39 and 3.55. 

The author has evaluated the reporting of births in all the countries included in this report using indexes 

measuring omission and displacement. Only Bangladesh and India indicate high levels of omission from the index 

values. The author estimates that 3.6 is a more likely TFR for Bangladesh than the reported 3.3 children per woman. 

Three countries, Kazakhstan, Uzbekistan, and Vietnam have index values indicating displacement levels that may 

affect the TFR. Visual examination of the births by year prior to the survey indicates that all of these countries show 

declining number of births well before the cut-off date for the child health section of the questionnaire. Therefore, 

the index values are more likely the result of year-to-year fluctuations than of true displacement. These evaluations 

are available from the author.
9

Table 3.1 Fertility rates for the period 0-2 years preceding the survey and mean number of children ever born, Demographic and 

Health Surveys, 1995-1999

Total fertility rate

General

fertility rate

Mean 

number of 

children

ever born Total marital fertility rate

Region and Women Women Women Women Women 0-14 0-19 0-24 0-29

Country 15-39 15-44 15-49 15-44

Crude

Birth

Rate 40-49 years years years years

Sub-Saharan Africa

 Benin 5.4 5.8 6.0 202 40.2 7.1 4.3 5.4 6.1 6.3 

 Burkina Faso 5.8 6.3 6.4 215 42.5 7.4 4.3 5.4 6.2 6.6 

 Cameroon 4.5 4.7 4.8 170 35.2 6.2 3.7 4.6 5.1 5.3 

 Central African Republic 4.7 5.0 5.1 182 38.0 5.7 3.7 4.5 5.0 5.1 

 Comoros 4.2 4.5 4.6 147 31.2 6.7 4.4 5.2 5.7 6.0 

 Côte d'Ivoire 4.8 5.2 5.3 185 38.5 6.7 3.8 4.6 5.2 5.5 

 Eritrea 5.4 5.9 6.1 198 37.5 6.2 4.1 5.2 6.1 6.6 

 Ghana 4.0 4.4 4.4 152 32.3 5.7 3.4 4.1 4.6 4.7 

 Guinea 5.1 5.4 5.5 193 36.9 6.5 3.8 4.9 5.5 5.7 

 Kenya 4.4 4.6 4.7 166 34.6 6.6 3.8 4.6 5.0 5.2 

 Madagascar 5.4 5.8 6.0 208 42.3 6.6 4.3 5.2 5.9 6.3 

 Mali 6.2 6.6 6.7 233 45.1 7.6 4.5 5.7 6.6 7.0 

 Mozambique 4.6 5.0 5.2 182 38.7 5.8 3.6 4.4 4.9 5.2 

 Niger 6.5 7.0 7.2 248 51.2 7.5 4.8 6.2 7.2 7.7 

 Senegal 5.0 5.5 5.7 185 38.7 7.1 4.2 5.3 6.2 6.6 

 South Africa 2.7 2.8 2.9 100 21.9 3.7 2.1 2.5 2.7 2.7 

 Tanzania 5.0 5.4 5.6 195 41.4 6.7 3.9 5.0 5.5 5.8 

 Tchad 6.0 6.3 6.4 228 46.0 6.8 4.7 5.8 6.5 6.7 

 Togo 4.6 5.0 5.2 175 34.4 6.4 3.9 4.8 5.4 5.7 

 Uganda 6.3 6.7 6.9 247 47.9 7.3 4.8 5.9 6.7 7.1 

 Zambia 5.6 6.0 6.1 213 45.2 7.3 4.6 5.6 6.3 6.7 

 Zimbabwe 3.7 3.9 4.0 141 30.8 5.9 3.1 3.7 4.0 4.2 

Near East/North Africa

 Egypt 3.5 3.6 3.6 124 28.0 5.5 3.6 4.1 4.3 4.4 

 Jordan 4.1 4.3 4.4 144 33.1 6.8 4.8 5.5 5.9 6.1 

 Turkey 2.5 2.6 2.6 94 23.4 4.2 2.7 3.0 3.1 3.1 

 Yemen 5.7 6.2 6.5 206 u 8.4 4.9 6.2 7.1 7.6 

Asia

 Bangladesh 3.1 3.2 3.3 129 29.4 6.0 3.2 3.7 4.0 4.1 

 India 2.8 2.8 2.8 110 24.8 4.4 3.0 3.3 3.4 3.4 

 Indonesia 2.6 2.8 2.8 97 23.1 4.3 2.7 3.1 3.3 3.4 

 Kazakhstan 2.5 2.5 2.5 83 18.9 3.1 2.3 2.4 2.5 2.5 

 Kyrgyz Republic 3.3 3.4 3.4 118 25.9 4.5 2.9 3.1 3.2 3.2 

 Nepal 4.4 4.6 4.6 167 36.9 5.7 3.9 4.6 5.0 5.2 

 Philippines 3.5 3.7 3.7 126 28.0 4.4 3.6 4.1 4.4 4.5 

 Uzbekistan 3.3 3.3 3.3 123 26.7 4.6 3.1 3.3 3.3 3.3 

 Vietnam 2.3 2.3 2.3 80 19.0 3.8 2.5 2.7 2.8 2.8 

Latin America/Caribbean

 Bolivia 3.9 4.2 4.2 142 30.4 5.1 3.7 4.2 4.6 4.7 

 Brazil 2.4 2.5 2.5 88 21.5 3.7 2.3 2.5 2.6 2.7 

 Colombia 2.8 3.0 3.0 107 26.4 3.8 2.7 3.0 3.1 3.2 

 Dominican Republic 3.1 3.2 3.2 119 27.7 4.3 3.0 3.3 3.4 3.5 

 Guatemala 4.7 5.0 5.0 176 38.0 5.7 4.1 4.9 5.4 5.6 

 Haiti 4.3 4.7 4.8 155 33.8 5.4 3.8 4.5 5.0 5.1 

 Nicaragua 3.4 3.6 3.6 131 29.3 5.6 3.1 3.6 3.9 4.1 

 Peru 3.3 3.5 3.5 121 27.4 4.9 3.1 3.6 3.9 4.1 

u = Unknown (not available)
Age-Specific Fertility Rates

The age-specific fertility rates presented by region in Figure 3.1 and in Table 3.2 show that the

smallest differences between countries are generally found in the oldest age groups. In the youngest age

group, the fertility rate is largely affected by the age at which women marry. The age-specific fertility rate

for sub-Saharan women age 15-19 ranges from 59 births per 1,000 women for Comoros, where the

median age at marriage is 19, to 218 per 1,000 in Niger, where the median age at first marriage is 15.

Only four of the 21 sub-Saharan countries have age-specific rates for 15-19 below 100: Comoros (59),

South Africa (76), Ghana (88), and Togo (89). At the other end of the reproductive span, all countries

except South Africa, Cameroon, and Zimbabwe have an ASFR for age 40-44 greater than 50.

Figure 3.1 Age-specific fertility rates for the period 0-2 years preceding the

survey, women 15-49, Demographic and Health Surveys, 1995-1999

Sub-Saharan Africa

350 c:

Q)

E 300 0

==

g 250 °.

::: 200 Q)

Co

(/)

..r:;

t::

m

-- - - n - n -- - - -n_nn- n n - - -- - - - --

0

15-19 20-24 25-29 30-34 35-39 40-44 45-49

Age Group

- Benin + Burkina Faso .ok-Cameroon

-e- Central African Republic * Comoros -+-Cote d'ivoire

Eritrea -8- Ghana -+-Guinea

~ Kenya -,$-Madagascar" Mali

+ Mozambique ... Niger *- SouthAfrica

+ Tanzania + Tchad+- Togo

-g.-Ugandac} Zambia".. Zimbabwe

10


13

Table 3.2 Age-specific fertility rates for the period 0-2 years preceding the survey by mother’s age at birth, 

Demographic and Health Surveys, 1995-1999

 Region and Age at birth

 country 15-19 20-24 25-29 30-34 35-39 40-44 45-49

Sub-Saharan Africa

 Benin 119 266 263 244 195 78 27 

 Burkina Faso 131 293 282 247 199 104 31 

 Cameroon 137 226 221 185 123 49 20 

 Central African Republic 155 237 231 192 117 60 23 

 Comoros 59 184 223 226 147 67 25 

 Côte d'Ivoire 140 232 231 210 149 73 25 

 Eritrea 125 245 269 245 189 110 37 

 Ghana 88 197 203 177 136 74 11 

 Guinea 168 238 246 214 144 69 26 

 Kenya 111 248 218 188 109 51 16 

 Madagascar 180 279 254 215 152 88 25 

 Mali 187 299 296 244 205 91 21 

 Mozambique 171 234 233 169 105 94 27 

 Niger 218 311 308 273 195 97 38 

 Senegal 103 219 240 245 186 99 41 

 South Africa 76 139 143 109 74 29 9 

 Tanzania 138 268 240 213 138 78 37 

 Tchad 190 304 300 242 156 67 15 

 Togo 89 214 246 207 166 82 37 

 Uganda 204 319 309 244 177 89 29 

 Zambia 158 280 274 229 175 77 24 

 Zimbabwe 112 199 180 135 108 46 15 

Near East/North Africa

 Egypt 61 200 210 140 81 27 7 

 Jordan 43 172 246 206 144 48 11 

 Turkey 60 163 150 93 42 13 1 

 Yemen 105 279 301 258 196 105 54 

Asia

 Bangladesh 147 192 150 96 44 18 6 

 India 107 210 143 69 28 8 3 

 Indonesia 62 143 149 108 66 24 6 

 Kazakhstan 64 190 136 67 35 7 0 

 Kyrgyz Republic 75 246 179 113 47 13 0 

 Nepal 127 266 229 160 94 37 15 

 Philippines 46 177 210 155 111 40 7 

 Uzbekistan 61 266 176 114 39 9 3 

 Vietnam 32 164 132 81 41 15 2 

Latin America/Caribbean

 Bolivia 84 207 201 165 117 57 15 

 Brazil 86 152 123 81 46 16 3 

 Colombia 89 173 148 101 56 24 2 

 Dominican Republic 112 199 157 111 39 14 1 

 Guatemala 117 276 236 182 131 60 7 

 Haiti 76 179 233 206 166 78 19 

 Nicaragua 130 192 162 122 78 32 9 

 Peru 75 179 162 140 99 44 7 
14

Among the countries of the Near East/North Africa region, the highest level of teenage fertility is found in 

Yemen (105). In Asia, the highest teenage rate is 147 in Bangladesh, and the lowest is 32 in Vietnam. In 

the Latin America/Caribbean region, the rate ranges from 130 in Nicaragua to 75 in Peru.

Outside of sub-Saharan Africa, at ages 40-44, only Yemen has a rate above 100 (105). Three 

countries, all in the Latin America/Caribbean region, have a fertility rate above 40 at age 40-44: Haiti 

(78), Guatemala (60), and Bolivia (57). At age 45-49, only Yemen has an ASFR above 20.

3.2 Socioeconomic Differences in Fertility

It has generally been hypothesized that women who are educated, whose spouses are educated, 

who are working, and who are living in urban areas have lower fertility than women who are not 

educated, who are not working, and who are living in rural areas. Tables 3.3 and 3.4 summarize

differences in fertility by residence and migration status, respectively. Tables 3.5 and 3.6 show education 

and work status differentials, and Table 3.7 presents marital fertility rates by husband’s characteristics.

As mentioned in Section 2.2, the small number of women in some socioeconomic categories 

makes it difficult to estimate TFRs for women age 15-49 and age 15-44 and TMFRs for marriage 

durations up to 29 years and up to 24 years. Consequently, only TFRs for women age 15-39, TMFRs for 

marriage durations up to 14 years, and CEBs for women age 40-49 are used to study the socioeconomic 

differences in fertility. The restriction of the analysis to women age 15-39 is unlikely to bias the

differentials because most childbearing occurs before age 40.

Urban-Rural Residence

Fertility (as measured by the TFR for age 15-39) is higher in rural areas than in urban areas in all 

of the surveyed countries (Table 3.3). South Africa; Egypt; Turkey, all countries of Asia; and all countries 

of the Latin America/Caribbean region except Guatemala, Haiti, and Bolivia have a TFR of less than 

three children in urban areas. Rural TFRs are particularly high—six or more children per woman—in nine 

of the 22 countries in sub-Saharan Africa, but only in Yemen among the other regions. The smallest 

difference (0.3 children) between urban and rural TFRs is found in the Central African Republic; the 

greatest difference is found in Bolivia and Tanzania (both 2.7 children). In sub-Saharan Africa, the 

greatest differences between urban and rural TFRs are found in Tanzania, Togo (both 2.6 children), 

Burkina Faso, and Eritrea (both 2.5 children). In Asia, the smallest difference is in Indonesia (0.5 

children) and the greatest is in Nepal (1.9 children). In the Latin America/Caribbean, region the smallest 

difference between urban and rural TFRs is that of one child in Brazil.

Marital fertility for the 15 years following the first marriage is also higher in rural areas than in 

urban areas in all countries except Indonesia (where there is no difference). A similar pattern of ruralurban difference is observed for the number of children ever born to women age 40-49, except that in the 

Central African Republic and Mozambique, the number of children ever born in urban areas is larger than 

that in rural areas by 0.3 and 0.1 children, respectively.

Migration Status

First-generation migrants often retain the fertility traditions of their group of origin (Goldstein 

and Tirasawat, 1972). In countries where the pace of urbanization is rapid, the study of fertility

differentials based solely on current place of residence may be inadequate. Moreover, data on urban-rural 

residence do not take account of the length of time a woman has lived in her current home. For example, a 

woman classified as urban may have lived in an urban area for only a short time before she was

interviewed.
15

Table 3.3 Fertility rates for the period 0-2 years preceding the survey and mean number of 

children ever born, by urban-rural residence, Demographic and Health Surveys, 1995-1999

 Region and

Total fertility rate 

(Women 15-39)

Total marital

fertility rate

(0-14 years)

Mean number of

children ever born

(Women 40-49)

country Urban Rural Urban Rural Urban Rural

Sub-Saharan Africa

 Benin 4.4 6.1 3.8 4.6 6.5 7.5 

 Burkina Faso 3.7 6.2 3.3 4.4 6.3 7.6 

 Cameroon 3.5 5.0 3.1 4.0 5.7 6.4 

 Central African Republic 4.5 4.8 3.6 3.7 5.9 5.6 

 Comoros 3.4 4.5 (4.0) 4.5 6.3 6.9 

 Côte d'Ivoire 3.9 5.5 3.3 4.0 6.1 7.0 

 Eritrea 3.7 6.2 3.6 4.3 5.4 6.6 

 Ghana 3.5 5.3 3.2 4.1 5.3 6.5 

 Guinea 4.1 5.5 3.3 4.1 6.2 6.6 

 Kenya 3.0 4.8 3.0 4.1 4.6 7.0 

 Madagascar 3.9 6.0 3.5 4.6 5.3 7.2 

 Mali 5.0 6.7 4.1 4.7 7.1 7.8 

 Mozambique 4.3 4.7 3.5 3.7 5.9 5.8 

 Niger 5.2 6.9 4.6 4.9 7.2 7.6 

 Senegal 3.9 5.9 3.8 4.5 6.5 7.5 

 South Africa 2.2 3.6 1.7 2.8 3.2 4.7 

 Tanzania 3.1 5.8 2.7 4.3 5.3 7.1 

 Tchad 5.6 6.1 4.6 4.7 6.4 6.9 

 Togo 3.0 5.6 2.9 4.4 5.2 7.0 

 Uganda 4.5 6.6 4.0 4.9 6.4 7.4 

 Zambia 4.8 6.2 4.2 4.8 7.1 7.5 

 Zimbabwe 3.0 4.4 2.8 3.7 4.7 6.8 

Near East/North Africa

 Egypt 2.9 4.0 3.1 4.0 4.7 6.5 

 Jordan 3.9 4.6 4.7 5.4 6.5 8.3 

 Turkey 2.3 3.0 2.5 3.3 3.8 5.0 

 Yemen 4.7 6.1 4.7 5.0 7.8 8.5 

Asia

 Bangladesh 2.0 3.3 2.5 3.3 4.6 6.1 

 India 2.2 3.0 2.6 3.1 3.8 4.7 

 Indonesia 2.3 2.8 2.7 2.7 4.0 4.5 

 Kazakhstan 2.0 3.0 1.9 2.8 2.5 4.4 

 Kyrgyz Republic 2.3 3.8 2.1 3.3 3.1 5.3 

 Nepal 2.7 4.6 3.1 4.0 4.6 5.8 

 Philippines 2.8 4.4 3.3 4.1 3.7 5.3

 Uzbekistan 2.7 3.6 2.5 3.4 3.6 5.5 

 Vietnam 1.5 2.4 1.9 2.7 2.7 4.2 

Latin America/Caribbean

 Bolivia 3.1 5.8 3.2 4.7 4.3 6.5 

 Brazil 2.3 3.3 2.2 2.8 3.3 5.2 

 Colombia 2.5 4.0 2.4 3.5 3.3 5.1 

 Dominican Republic 2.7 3.9 2.7 3.6 3.8 5.4 

 Guatemala 3.9 5.4 3.6 4.5 4.8 6.4 

 Haiti 3.1 5.2 3.0 4.5 4.0 6.1 

 Nicaragua 2.8 4.6 2.7 3.8 4.8 7.0 

 Peru 2.6 5.1 2.7 4.2 4.1 6.8 

Note: Rates in parentheses are based on 125 to 249 unweighted woman-years of exposure 

and are subject to increased sampling variation.
15

Table 3.3 Fertility rates for the period 0-2 years preceding the survey and mean number of 

children ever born, by urban-rural residence, Demographic and Health Surveys, 1995-1999

 Region and

Total fertility rate 

(Women 15-39)

Total marital

fertility rate

(0-14 years)

Mean number of

children ever born

(Women 40-49)

country Urban Rural Urban Rural Urban Rural

Sub-Saharan Africa

 Benin 4.4 6.1 3.8 4.6 6.5 7.5 

 Burkina Faso 3.7 6.2 3.3 4.4 6.3 7.6 

 Cameroon 3.5 5.0 3.1 4.0 5.7 6.4 

 Central African Republic 4.5 4.8 3.6 3.7 5.9 5.6 

 Comoros 3.4 4.5 (4.0) 4.5 6.3 6.9 

 Côte d'Ivoire 3.9 5.5 3.3 4.0 6.1 7.0 

 Eritrea 3.7 6.2 3.6 4.3 5.4 6.6 

 Ghana 3.5 5.3 3.2 4.1 5.3 6.5 

 Guinea 4.1 5.5 3.3 4.1 6.2 6.6 

 Kenya 3.0 4.8 3.0 4.1 4.6 7.0 

 Madagascar 3.9 6.0 3.5 4.6 5.3 7.2 

 Mali 5.0 6.7 4.1 4.7 7.1 7.8 

 Mozambique 4.3 4.7 3.5 3.7 5.9 5.8 

 Niger 5.2 6.9 4.6 4.9 7.2 7.6 

 Senegal 3.9 5.9 3.8 4.5 6.5 7.5 

 South Africa 2.2 3.6 1.7 2.8 3.2 4.7 

 Tanzania 3.1 5.8 2.7 4.3 5.3 7.1 

 Tchad 5.6 6.1 4.6 4.7 6.4 6.9 

 Togo 3.0 5.6 2.9 4.4 5.2 7.0 

 Uganda 4.5 6.6 4.0 4.9 6.4 7.4 

 Zambia 4.8 6.2 4.2 4.8 7.1 7.5 

 Zimbabwe 3.0 4.4 2.8 3.7 4.7 6.8 

Near East/North Africa

 Egypt 2.9 4.0 3.1 4.0 4.7 6.5 

 Jordan 3.9 4.6 4.7 5.4 6.5 8.3 

 Turkey 2.3 3.0 2.5 3.3 3.8 5.0 

 Yemen 4.7 6.1 4.7 5.0 7.8 8.5 

Asia

 Bangladesh 2.0 3.3 2.5 3.3 4.6 6.1 

 India 2.2 3.0 2.6 3.1 3.8 4.7 

 Indonesia 2.3 2.8 2.7 2.7 4.0 4.5 

 Kazakhstan 2.0 3.0 1.9 2.8 2.5 4.4 

 Kyrgyz Republic 2.3 3.8 2.1 3.3 3.1 5.3 

 Nepal 2.7 4.6 3.1 4.0 4.6 5.8 

 Philippines 2.8 4.4 3.3 4.1 3.7 5.3

 Uzbekistan 2.7 3.6 2.5 3.4 3.6 5.5 

 Vietnam 1.5 2.4 1.9 2.7 2.7 4.2 

Latin America/Caribbean

 Bolivia 3.1 5.8 3.2 4.7 4.3 6.5 

 Brazil 2.3 3.3 2.2 2.8 3.3 5.2 

 Colombia 2.5 4.0 2.4 3.5 3.3 5.1 

 Dominican Republic 2.7 3.9 2.7 3.6 3.8 5.4 

 Guatemala 3.9 5.4 3.6 4.5 4.8 6.4 

 Haiti 3.1 5.2 3.0 4.5 4.0 6.1 

 Nicaragua 2.8 4.6 2.7 3.8 4.8 7.0 

 Peru 2.6 5.1 2.7 4.2 4.1 6.8 

Note: Rates in parentheses are based on 125 to 249 unweighted woman-years of exposure 

and are subject to increased sampling variation.
16

To study the fertility behavior of migrants and nonmigrants, a variable combining childhood 

place of residence and current place of residence has been constructed. The results are presented in Table 

3.4. Overall, fertility is expected to be lowest among urban natives and highest among rural natives, with 

the fertility of the two other groups being in between.

The data show that in all countries, the TFR is lowest for urban natives (tied for lowest in three 

countries). In all but seven countries the highest TFR is for rural natives (tied for highest in three 

countries). Rural-to-urban migrants have the highest TFRs in the Central African Republic and

Mozambique, and urban-to-rural migrants have the highest TFRs in Comoros, Tchad, Uzbekistan, the 

Dominican Republic, and Haiti. The mean number of children ever born, which is a measure of

completed fertility, shows that all but four countries (the Central African Republic, Comoros, Côte 

d’Ivoire, and Mozambique) have the highest number of children born to rural native women age 40-49. In 

Kenya and the Dominican Republic, rural natives are tied with urban-to-rural migrants for the highest 

number of children ever born. Urban native women age 40-49 have the lowest number of children ever 

born in all countries surveyed except Comoros, Niger, and Tchad. In the Central African Republic and 

Côte d’Ivoire, urban natives are tied for the lowest fertility levels.

Women’s Education

It is hypothesized that as education increases, fertility declines. This statement is generally but not 

always true. In eight of the countries surveyed, the pattern does not hold because women with a primary 

education have higher TFRs than women with no schooling (Table 3.5). These countries are the Central 

African Republic, Madagascar, Mozambique, Tchad, and Uganda in sub-Saharan Africa; Jordan in the 

Near East/North Africa region; and Indonesia and the Philippines in Asia. In all countries, women with 

secondary or higher education have the lowest TFRs. 

In sub-Saharan countries, the difference between TFRs for women with no schooling and women 

with secondary or more schooling ranges from 3.2 children in Benin and Burkina Faso to one child in the 

Central African Republic and Mozambique. In the Near East/North Africa region the differences range 

from a high of 2.7 (Yemen) to 0.1 (Jordan). In Asia, the largest difference in the TFR between women 

with no schooling and women with secondary schooling or more is 2.3 children in Nepal, and the smallest 

difference is 0.2 children in Indonesia. In the three central Asian republics—Kazakhstan, the Kyrgyz 

Republic, and Uzbekistan—there are not enough women who have either no schooling or primary

schooling to present differential results for TFR. In the Latin America/Caribbean region, the differences 

by schooling level are the highest of any region, from 2.2 in Colombia to 3.9 in Peru, with an average 

difference of 3.1 children.

When completed fertility (mean number of children ever born to women age 40-49) is

considered, similar educational differences are observed in many countries. The exceptions are

Cameroon, the Central African Republic, Guinea, Madagascar, Mozambique, Niger, Tchad, and Uganda 

in sub-Saharan Africa and Indonesia and the Philippines in Asia. The exceptions come about because 

women with no schooling have not reported higher CEBs than women with primary schooling. However, 

five of the 11 exceptions have differences of 0.1 children or less. 

In general, the lowest number of children ever born is found among women with secondary or 

more education. The largest differences in children ever born between no schooling and secondary

schooling or more occur in Yemen (4.7children), followed by Peru (4.0), Nic aragua (3.9), Turkey (3.8), 

and Guatemala (3.7). The smallest differences between these two categories occur in the Central African 

Republic (0.2 children), Indonesia (0.7), and Mozambique (0.8).

Table 3.5 also presents the total marital fertility rate for women married up to 14 years, by education 

level. The lowest TMFR is generally found among women with secondary education. The only exceptions
17

Table 3.4 Fertility rates for the period 0-2 years preceding the survey and mean number of children ever born, by migration 

status, Demographic and Health Surveys, 1995-1999

Total fertility rate

(women 15-39)

Total marital fertility rate

(0-14 years)

Mean number of 

children ever born

(women 40-49)

Rural Urban Rural Urban Rural Urban

Region and Urban to to Rural Urban to to Rural Urban to to Rural

country native urban rural native native urban rural native native urban rural native

Sub-Saharan Africa

 Benin 4.1 5.2 5.9 6.2 3.7 (4.1) 4.5 4.6 6.1 7.1 7.4 7.5 

 Burkina Faso u u u u u u u u u u u u

 Cameroon 3.3 4.2 4.3 5.3 2.9 3.6 (3.6) 4.1 5.7 5.8 6.3 6.4 

 Central African Republic 4.4 5.2 4.6 4.8 3.6 (3.7) 3.7 3.7 5.6 6.6 5.7 5.6 

 Comoros 3.3 4.1 4.6 4.5 (3.9) * * 4.5 6.5 5.0 7.8 6.8 

 Côte d'Ivoire 3.8 4.2 5.2 5.6 3.3 3.5 3.9 4.1 6.1 6.1 7.1 7.0 

 Eritrea 3.1 4.8 4.9 6.3 3.4 3.8 * 4.3 5.2 5.5 6.1 6.6 

 Ghana 2.7 3.3 4.7 4.7 2.5 * 3.7 3.7 4.6 4.8 5.8 6.4 

 Guinea u u u u u u u u u u u u

 Kenya 2.9 3.1 4.8 4.8 (2.9) (3.0) (3.9) 4.1 4.0 4.9 7.0 7.0 

 Madagascar 3.4 4.9 5.5 6.1 5.2 (4.1) 4.1 4.7 4.9 6.1 7.1 7.3 

 Mali 4.7 5.4 6.4 6.7 4.1 4.0 (4.3) 4.7 6.8 7.5 7.2 7.8 

 Mozambique 3.9 5.2 5.1 4.6 3.2 4.0 3.9 3.7 5.5 6.3 6.4 5.7 

 Niger 4.9 6.0 6.7 6.9 4.5 4.8 * 4.9 7.2 7.3 6.7 7.6 

 Senegal 3.7 4.6 5.6 5.9 3.7 (4.2) 4.3 4.5 6.3 7.1 7.3 7.5 

 South Africa 2.0 2.5 2.4 3.7 1.6 1.9 (1.6) 3.0 2.9 3.8 3.3 4.8 

 Tanzania 2.9 3.2 4.5 5.9 2.6 (2.9) (3.2) 4.4 4.9 5.5 5.6 7.2 

 Tchad 5.6 5.7 6.2 6.1 4.5 4.7 (4.9) 4.7 6.5 6.2 6.8 6.9 

 Togo 3.0 3.0 5.4 5.6 2.9 3.0 4.3 4.4 5.1 5.6 6.7 7.1 

 Uganda 4.2 4.6 6.2 6.6 (3.9) 4.0 * 5.0 5.3 6.6 7.0 7.4

 Zambia 4.7 4.9 6.0 6.3 4.3 4.2 4.8 4.8 6.8 7.4 7.3 7.5 

 Zimbabwe 2.8 2.8 3.9 4.2 (2.6) 2.6 (3.2) 3.4 4.3 4.6 6.2 6.4 

Near East/North Africa

 Egypt u u u u 3.0 3.8 3.8 4.1 u u u u

 Jordan u u u u 4.6 4.9 (4.7) 5.6 u u u u

 Turkey u u u u u 2.7 (3.2) 3.3 u u u u

 Yemen u u u u 4.5 5.1 * 5.0 u u u u

Asia

 Bangladesh u u u u u u u u u u u u

 India u u u u u u u u u u u u

 Indonesia u u u u 2.6 2.7 2.8 2.7 u u u u

 Kazakhstan 1.8 2.3 2.7 3.1 1.8 2.2 * 2.8 2.3 2.6 4.1 4.4 

 Kyrgyz Republic 2.0 2.6 3.8 3.8 1.9 2.2 * 3.4 2.7 3.8 4.2 5.5 

 Nepal u u u u 3.1 * * 4.0 u u u u

 Philippines u u u u u u u u u u u u

 Uzbekistan 2.5 4.1 4.5 3.6 2.4 (3.2) * 3.4 3.4 4.5 5.1 5.5 

 Vietnam u u u u 2.0 (1.9) (2.6) 2.7 u u u u

Latin America/Caribbean

 Bolivia 2.9 4.3 5.3 6.0 3.0 3.7 4.4 4.9 4.0 5.6 6.0 6.7 

 Brazil 2.2 2.6 2.9 3.4 2.2 2.3 2.2 3.0 3.1 3.8 4.6 5.4 

 Colombia 2.4 2.7 3.8 4.1 2.4 2.4 3.1 3.6 3.2 3.6 4.6 5.3 

 Dominican Republic 2.7 2.7 4.2 3.8 2.8 2.6 3.8 3.6 3.5 4.3 5.4 5.4 

 Guatemala 3.5 5.1 4.3 5.7 3.3 (4.6) 3.8 4.8 4.3 6.0 5.9 6.6 

 Haiti 2.9 3.8 5.3 5.2 2.8 3.3 * 4.5 3.6 4.8 5.5 6.1 

 Nicaragua 2.7 3.5 4.2 4.7 2.6 3.1 3.4 3.9 4.6 5.9 6.6 7.2 

 Peru 2.5 3.6 4.5 5.4 2.6 3.2 3.8 4.5 3.9 5.4 6.4 7.0 

Note: Total fertility rate and number of children ever born are not available for countries with ever-married samples because 

information on migration for never-married women was not collected. Rates in parentheses are based on 125 to 249 

unweighted woman-years of exposure in at least one age group or one marital duration group, respectively. An asterisk 

indicates rate is based on fewer than 125 woman-years of exposure and is not shown.

u = Unknown (not available)
18

Table 3.5 Fertility rates for the period 0-2 years preceding the survey and mean number of children ever born, by 

women's education, Demographic and Health Surveys, 1995-1999

Total fertility rate

(women 15-39)

Total marital fertility rate

(0-14 years)

Mean number of 

children ever born

(women 40-49)

Region and

country

No

schooling Primary

Secondary+No

schooling Primary

Secondary+No

schooling Primary

Secondary+

Sub-Saharan Africa

 Benin 6.0 4.5 2.8 4.5 4.1 (2.9) 7.3 6.4 4.8 

 Burkina Faso 6.1 4.5 2.9 4.4 (3.8) * 7.5 6.6 4.5 

 Cameroon 5.7 4.7 3.4 4.3 3.8 3.0 6.2 6.5 5.2 

 Central African Republic 4.7 4.9 3.7 3.7 3.8 3.3 5.7 5.7 5.5 

 Comoros 4.8 4.3 2.8 4.8 * * 6.9 5.8 4.2 

 Côte d'Ivoire 5.3 4.6 3.0 3.9 3.8 2.8 6.8 6.1 5.3 

 Eritrea 6.1 5.0 3.0 4.2 4.1 (3.4) 6.3 5.8 5.0 

 Ghana 5.0 4.5 3.3 3.9 3.7 2.9 6.5 6.2 4.5 

 Guinea 5.3 4.6 3.3 3.9 3.9 2.7 6.6 6.8 5.5 

 Kenya 5.4 4.7 3.3 (3.8) 4.2 3.2 7.1 6.8 4.9 

 Madagascar 5.9 6.0 3.9 4.3 4.7 3.6 6.8 7.2 4.6 

 Mali 6.5 5.4 3.7 4.6 4.1 (3.6) 7.7 7.4 6.5 

 Mozambique 4.5 4.8 3.5 3.5 3.8 (2.6) 5.8 5.8 5.0 

 Niger 6.8 5.5 4.1 4.9 4.4 (4.3) 7.5 7.7 6.1 

 Senegal 5.5 4.6 2.8 4.3 4.3 (3.4) 7.2 7.1 5.4 

 South Africa 4.1 3.4 2.4 (2.6) 2.5 1.9 4.9 4.2 3.0 

 Tanzania 5.8 4.8 3.5 4.0 3.9 (3.8) 7.0 6.3 4.5 

 Tchad 6.0 6.3 4.7 4.6 4.8 (4.8) 6.8 6.9 5.1 

 Togo 5.6 4.2 2.5 4.3 3.9 2.6 6.9 6.0 3.8 

 Uganda 6.4 6.5 4.7 4.6 5.0 4.1 7.3 7.4 6.3 

 Zambia 6.2 6.1 4.2 4.8 4.8 4.0 7.7 7.5 6.2 

 Zimbabwe 4.8 4.2 3.2 * 3.3 2.8 6.3 6.1 4.3 

Near East/North Africa

 Egypt 4.4 3.4 2.4 4.1 3.4 3.0 6.4 5.5 3.3 

 Jordan 4.1 4.3 4.0 * 5.1 4.7 8.1 7.8 5.6 

 Turkey 3.9 2.7 1.8 3.7 2.7 2.1 5.8 3.8 2.0 

 Yemen 6.1 5.2 3.4 5.0 4.8 4.2 8.5 5.6 3.8 

Asia

 Bangladesh 3.8 3.0 2.1 3.5 3.0 2.6 6.1 6.0 4.6 

 India 3.4 2.8 2.1 3.3 3.0 2.5 5.0 4.2 3.1 

 Indonesia 2.6 2.9 2.4 2.5 2.7 2.7 4.3 4.6 3.6 

 Kazakhstan * * 2.5 * * 2.3 * 4.0 3.1 

 Kyrgyz Republic * * 3.3 * * 2.9 (1.0) * 4.5 

 Nepal 4.8 3.8 2.5 4.0 3.6 2.9 5.8 5.3 3.7 

 Philippines 4.5 4.7 3.1 * 4.2 3.4 5.4 5.5 3.6 

 Uzbekistan * * 3.3 * * 3.1 * * 4.6 

 Vietnam 3.3 2.6 2.1 * 2.9 2.4 5.1 4.5 3.3 

Latin America/Caribbean

 Bolivia 6.5 5.3 2.8 4.7 4.3 3.0 6.4 5.8 3.3 

 Brazil 4.7 3.2 2.0 (3.4) 2.7 2.1 5.8 4.2 2.6 

 Colombia 4.6 3.6 2.4 * 3.0 2.4 5.8 4.3 2.7 

 Dominican Republic 4.9 3.6 2.4 4.1 3.4 2.5 5.8 4.8 2.5 

 Guatemala 6.4 4.8 2.9 4.8 4.3 2.8 7.0 5.1 3.3 

 Haiti 5.5 4.4 2.5 4.3 3.9 (2.5) 5.7 4.9 2.9 

 Nicaragua 5.3 4.1 2.4 4.2 3.5 2.4 7.3 5.4 3.4 

 Peru 6.4 4.7 2.5 4.9 3.9 2.6 7.2 5.7 3.2 

Note: Figures in parentheses are based on 125 to 249 unweighted woman-years of exposure or, for mean number of 

children ever born, 25 to 49 women. An asterisk indicates figure is based on less than 125 unweighted woman-years 

of exposure, or, for mean number of children ever born, fewer than 25 women and is not shown.
19

are Indonesia and Tchad. Like the TFR and the number of children ever born, the relationship between 

TMFR and primary education in not consistent. In six countries, women with primary schooling have 

TMFRs that exceed those of women with no schooling by more than 0.1 children (Kenya, Madagascar, 

Mozambique, Tchad, Uganda, and Indonesia). In all countries with sufficient numbers of women to make 

an assessment, the lowest TMFR occurs for women with secondary or higher education. In Indonesia, 

women with primary schooling and secondary and higher schooling have the same TMFR for up to 14 

years of marriage.

Women’s Employment Status

Table 3.6 shows the TFR for women age 15-39, the number of children ever born for women age 

40-49, and the TMFR for women married up to 14 years by employment status. In sub-Saharan Africa, 

only South Africa shows a difference in total fertility of one child or more between women who are not 

working and women who are working. In ten of the 22 sub-Saharan countries surveyed, the difference in 

TFR between working and nonworking women is 0.2 children or less. In six of the other 12 countries, 

working women have more children than nonworking women; in six of the countries the opposite is true. 

In countries of Asia with data on work status for single women and in the surveyed countries in the Latin 

America/Caribbean region, working women have a lower TFR. 

Total marital fertility rates (up to 14 years) are lower for working women in all but six countries. 

Of these six, three have no difference between working and nonworking women (Cameroon, Niger, and 

Tchad), and two have TMFRs higher for working women by only 0.1 children (Central African Republic 

and Guinea).

Husband’s Education

Table 3.7 presents marital fertility rates by husband’s characteristics (husband’s education and 

occupation). For currently married women, husband refers to the current husband; for formerly married 

women, husband refers to the most recent husband. In all countries surveyed, the lowest marital fertility 

occurs among women whose husband has secondary or more education.

A curvilinear relationship between fertility and husband’s education is observed in about half of 

the countries in sub-Saharan Africa with data for all three education groups. In Madagascar, marital 

fertility among women whose husband has primary education exceeds by 0.7 births the rate among 

women whose husband has no schooling. In the Central African Republic, Tchad, and Uganda, this 

excess is 0.5 births. In the Near East/North Africa region and in Asia, there is no or little difference in 

TMFR among women whose husband has no or primary education.

Husband’s Occupation

In almost all countries surveyed, the highest total marital fertility for duration up to 14 years since 

first marriage occurs among women whose husband works in agriculture; the exceptions are Niger, where 

the rate for women whose husband is a manual worker (skilled/unskilled) is higher by 0.3 births, and 

Indonesia, where the rate for women whose husband is in sales and service is higher by 0.1 births. The 

lowest fertility occurs among women whose husband has a professional or clerical occupation. There are 

five exceptions to this finding: the Central African Republic and Kenya, where wives of manual laborers 

have lower fertility, and Mali, Uzbekistan, and Nicaragua, where wives of sales and service workers have 

lower fertility. In Nepal and Yemen, the wives of professional or clerical workers are tied for lowest 

fertility with the wives of sales and services workers.
20

Table 3.6 Fertility rates for the period 0-2 years preceding the survey and mean 

number of children ever born, by women's employment status, Demographic and 

Health Surveys, 1995-1999

Total fertility rate

Total marital 

fertility rate

Mean number of 

children ever born

(women 15-39) (0-14 years) (women 40-49)

Region and Not Not Not

country Working working Working working Working working 

Sub-Saharan Africa

 Benin 5.6 5.0 4.3 (4.4) 7.1 7.3 

 Burkina Faso 5.7 6.0 4.2 4.5 7.4 7.6 

 Cameroon 4.7 4.3 3.7 3.7 6.2 6.2 

 Central African Republic 4.8 4.6 3.7 3.6 5.7 5.6 

 Comoros 3.9 4.5 4.2 4.5 6.5 7.0 

 Côte d'Ivoire 5.0 4.5 3.7 3.8 6.7 6.4 

 Eritrea 5.0 5.5 3.9 4.2 6.1 6.3 

 Ghana 4.2 3.9 3.3 (3.4) 5.7 4.9 

 Guinea 5.2 4.9 3.9 3.8 6.5 6.4 

 Kenya 4.4 4.6 3.6 4.2 6.6 6.6 

 Madagascar 5.4 5.5 4.2 4.5 6.6 6.6 

 Mali 6.1 6.2 4.5 4.6 7.6 7.6 

 Mozambique 4.5 4.7 3.5 3.9 5.8 5.8 

 Niger 6.5 6.6 4.8 4.8 7.6 7.2 

 Senegal 5.2 4.7 4.3 4.1 7.1 6.9 

 South Africa 2.1 3.1 1.6 2.5 3.4 4.1 

 Tanzania 5.1 4.9 3.9 4.1 6.7 6.4 

 Tchad 5.8 6.1 4.7 4.7 6.8 6.8 

 Togo 4.7 4.8 3.8 4.3 6.4 6.3 

 Uganda 6.3 6.3 4.7 4.9 7.4 7.0 

 Zambia 5.7 5.6 4.5 4.7 7.2 7.4 

 Zimbabwe 3.4 4.2 2.8 3.4 5.7 6.1 

Near East/North Africa

 Egypt u u 2.9 3.8 u u

 Jordan u u 4.0 4.9 u u

 Turkey u u 2.3 2.9 u u

 Yemen u u 4.6 5.0 u u

Asia

 Bangladesh u u 2.9 3.3 u u

 India u u 2.7 3.1 u u

 Indonesia u u u u u u

 Kazakhstan 1.2 3.9 1.2 3.4 2.9 3.9 

 Kyrgyz Republic 1.8 4.5 1.7 3.7 4.3 5.0 

 Nepal u u 3.8 4.1 u u

 Philippines 2.5 4.5 3.0 4.2 4.2 4.8 

 Uzbekistan 2.1 4.4 2.1 3.9 4.4 5.0 

 Vietnam u u 2.5 (3.2) u u

Latin America/Caribbean

 Bolivia 3.3 4.7 3.2 4.2 4.9 5.4 

 Brazil 1.7 3.2 1.7 2.9 3.5 3.9 

 Colombia 2.3 3.5 2.2 3.2 3.4 4.4 

 Dominican Republic 2.4 3.6 2.4 3.5 3.9 4.8 

 Guatemala 3.0 5.7 3.0 4.7 4.6 6.4 

 Haiti 4.3 4.5 3.5 4.1 5.5 5.2 

 Nicaragua 2.8 3.9 2.5 3.5 5.1 6.1 

 Peru 2.8 3.9 2.8 3.5 4.8 5.0 

Note: Total fertility rate and number of children ever born are not available for countries 

with ever-married samples because information on women's work status for nevermarried women was not collected. Rates in parentheses are based on 125 to 249 

unweighted women-years of exposure.

u = Unknown (Not available)
21

Table 3.7 Marital fertility rates for the period 0-2 years preceding the survey, by husband's education and occupation, 

Demographic and Health Surveys, 1995-1999

Total marital fertility rate (0-14 years)

Husband's education Husband's occupation

Region and No Secon- Agricul- Skilled/ Sales/ Professional/

country schooling Primary dary+ ture unskilled service clerical

Sub-Saharan Africa

 Benin 4.6 4.3 3.5 4.7 4.3 (3.6) (3.0)

 Burkina Faso 4.4 (3.7) * 4.4 (4.1) 4.0 (3.4)

 Cameroon 4.2 3.8 3.3 4.1 * 3.5 3.4 

 Central African Republic 3.3 3.8 3.7 3.7 3.5 3.6 (3.7)

 Comoros 4.6 * (3.9) (4.8) 4.7 * *

 Côte d'Ivoire 4.0 4.3 3.1 4.1 3.7 3.7 2.8 

 Eritrea 4.2 4.3 3.6 4.3 4.1 3.7 (3.6)

 Ghana 3.9 (4.1) 3.1 4.1 3.1 2.9 (2.4)

 Guinea 4.0 3.9 3.3 4.1 3.7 3.7 (3.2)

 Kenya * 4.2 3.5 4.2 3.6 3.7 3.7 

 Madagascar 4.1 4.8 3.7 4.6 3.5 3.7 (2.9)

 Mali 4.6 4.4 3.8 4.7 (4.6) 4.2 4.3 

 Mozambique 3.6 3.9 3.0 3.7 3.6 3.7 3.3 

 Niger 4.9 4.8 (4.5) 4.9 (5.2) 4.7 (4.6)

 Senegal 4.4 (4.0) 3.7 4.7 4.4 3.9 3.5 

 South Africa (2.8) 2.6 1.9 * 2.2 2.1 1.8 

 Tanzania u u u u u u u

 Tchad 4.5 5.0 4.9 4.7 4.6 4.6 4.5 

 Togo 4.5 4.0 3.4 4.6 3.3 3.4 3.1 

 Uganda 4.5 5.0 4.4 5.0 4.6 4.7 4.3 

 Zambia (4.7) 4.9 4.3 5.0 4.4 4.4 3.7 

 Zimbabwe * 3.5 2.9 3.5 3.2 3.0 2.3 

Near East/North Africa

 Egypt 3.9 3.8 3.3 4.2 3.6 3.6 3.1 

 Jordan * 4.8 4.8 * 5.1 4.6 4.5 

 Turkey * 3.0 2.4 3.4 2.8 2.6 2.4 

 Yemen 5.0 5.0 4.9 5.4 5.1 5.0 5.0 

Asia

 Bangladesh 3.4 3.4 2.6 3.2 3.2 * 3.1 

 India 3.2 3.0 2.8 3.1 3.1 2.8 2.5 

 Indonesia 2.7 2.7 2.7 2.7 2.7 2.8 2.6 

 Kazakhstan * * 2.3 (2.8) 2.2 (2.2) (2.1)

 Kyrgyz Republic * * 2.9 3.7 2.7 2.5 (2.4)

 Nepal 4.0 4.1 3.6 4.2 3.7 3.5 3.5 

 Philippines * 4.1 3.4 4.1 3.6 3.3 3.1 

 Uzbekistan * * 3.1 3.3 2.9 (2.8) 3.1 

 Vietnam * 3.1 2.4 u u u u

Latin America/Caribbean

 Bolivia * 4.6 3.2 4.8 3.6 3.2 2.7 

 Brazil 3.3 2.5 2.1 3.2 2.3 2.2 2.1 

 Colombia (3.3) 3.0 2.4 3.4 2.7 2.4 2.2 

 Dominican Republic 3.9 3.2 2.7 3.8 3.1 2.8 2.5 

 Guatemala 4.7 4.4 3.2 4.9 3.7 (3.7) (3.3)

 Haiti 4.7 4.3 2.8 4.6 3.1 (3.4) (2.6)

 Nicaragua 4.0 3.4 2.5 3.9 3.0 2.2 2.3 

 Peru (4.5) 4.0 2.8 4.2 3.0 2.8 2.4 

Note: Rates in parentheses are based on 125 to 249 unweighted woman-years of exposure. An asterisk indicates that rate is 

based on fewer than 125 woman-years of exposure and is not shown.

u = Unknown (not available)
22

Between women whose husband works in agriculture and those whose husband works in

professional and clerical occupations, the difference in marital fertility is greatest in the Latin

America/Caribbean region, where it is more than one birth in all surveyed countries and is two births or 

more in Bolivia and Haiti. In sub-Saharan Africa, the difference between agricultural and

professional/clerical is larger than one birth in eight countries: Benin, Côte d’Ivoire, Ghana, Madagascar, 

Senegal, Togo, Zambia and Zimbabwe. The Near East/North Africa region and Asia only have one 

country each in which the difference between agricultural and professional/clerical is larger than one 

birth: Egypt and the Kyrgyz Republic.

3.3 Fertility Trends

Fertility trends are analyzed here in two ways. One way is to use data from the birth history in a 

single survey. A second way is to compare data from two surveys. Table 3.8 shows the change in TFR for 

women 15-44 between periods four to seven and 0-3 years preceding the survey, calculated using the 

birth history of the most recent survey.7

The rates shown in Table 3.8 indicate that recent declines occurred in all surveyed countries, 

except for Colombia, where fertility did not change (Figure 3.2). The average decline for the countries 

was 0.8 births, which represents an average percent decline of 15 percent. Declines of one birth or more 

occurred mostly in sub-Saharan Africa, where half of the surveyed countries had declines of this 

magnitude. Five sub-Saharan countries had a decline of 20 percent or more. In the other regions, only 

three countries had a decline of 20 percent or more (Bangladesh, Nicaragua, and Vietnam) and three had a 

decline of one birth or more (Bangladesh, Vietnam, and Yemen). In general, the larger declines occurred 

in countries where the fertility in the period four to seven years before the survey was higher. A word of 

caution should be added here since some of the declines (as in India) may have been exaggerated due to 

omission and/or displacement of recent births. Indeed, a study in rural Mali indicates that some women 

are fearful of verbalizing numbers of children because of the supernatural risks that would be incurred 

(Castle, 2001).

Small declines—less than 10 percent and less than 0.5 births—occurred in three and four 

countries, respectively, of the eight countries in the Latin America/Caribbean region. Only four countries 

from a different region had a decline of less than 10 percent: Ghana, Tanzania, Tchad, and Zambia. Four 

countries outside of the Latin America/Caribbean region had a decline of less than 0.5 births in total 

fertility: Ghana, Indonesia, Kazakhstan, and Turkey.

Table 3.9 examines trends in fertility in 22 countries by comparing the results of the most recent 

DHS survey with the results of an earlier DHS survey for rates calculated for the period up to two years 

before each survey. This comparison is appropriate because the methods of data collection and rate 

calculation are identical between the two surveys. A comparison of TFRs calculated for the 1990-1994 

DHS surveys and the 1995-1999 DHS surveys for the ten countries in sub-Saharan Africa shows that 

fertility has declined by an average of 1.3 percent per year during this period. 8 The average masks the 

wide range of declines from a decline of 3.0 percent and 2.8 percent per year in Ghana and Cameroon, 

respectively, to a small increase (0.5 percent) in Niger and no change in Burkina Faso. The other regions 

show similar variation in rates of decline, with small annual average increases in the lower fertility 

countries of Colombia and Turkey and high rates of decline in India and Jordan (2.8 percent and

3.5 percent per year, respectively).

 

7

 It should be noted that due to limiting the age of respondents to 49 years at the time of the survey, the rate 

for the earlier period (four to seven years preceding the survey) is based on data that are partially truncated 

beginning in the sixth year prior to the survey.

8

 To calculate the annual percentage decline, the following formula is used:

r = ln (TFR0 / TFR1) / t x 100

where r is annual growth rate, TFR0 is TFR during 1994-1995, TFR1 is TFR in 1995-1999, and t is years between 

the two surveys.
23

Table 3.8 Total fertility rates for women 15-44 for the periods 0-3 and 4-7 

years preceding the survey, and absolute change and percent change,

Demographic and Health Surveys, 1995-1999

Total fertility rate

(women 15-44)

Region and

country

0-3 years

preceding

survey

4-7 years

preceding

survey

Absolute

change

Percent

change

Sub-Saharan Africa

 Benin 6.0 7.1 -1.1 -16.2

 Burkina Faso 6.5 7.6 -1.1 -14.2

 Cameroon 4.9 6.2 -1.3 -21.3

 Central African Republic 5.0 6.3 -1.3 -20.1

 Comoros 4.7 6.2 -1.5 -23.5

 Côte d'Ivoire 5.3 7.2 -1.9 -26.8

 Eritrea 5.8 6.7 -0.9 -13.0

 Ghana 4.4 4.8 -0.4 -7.8

 Guinea 5.5 7.0 -1.5 -20.6

 Kenya 4.6 5.2 -0.6 -11.0

 Madagascar 5.8 6.7 -0.9 -13.3

 Mali 6.6 8.2 -1.6 -19.4

 Mozambique 5.3 6.3 -1.0 -16.4

 Niger 7.2 8.1 -0.9 -11.4

 Senegal 5.6 6.7 -1.1 -16.4

 South Africa 2.8 3.4 -0.6 -16.6

 Tanzania 5.4 6.0 -0.6 -9.3

 Tchad 6.5 7.2 -0.7 -9.6

 Togo 5.1 6.2 -1.1 -17.4

 Uganda 6.6 7.4 -0.8 -10.7

 Zambia 5.9 6.4 -0.5 -7.1

 Zimbabwe 3.9 4.7 -0.8 -16.5

Near East/North Africa

 Egypt 3.7 4.5 -0.8 -17.8

 Jordan 4.4 5.0 -0.6 -12.3

 Turkey 2.6 2.9 -0.3 -11.7

 Yemen 6.3 7.8 -1.5 -19.2

Asia

 Bangladesh 3.3 4.4 -1.1 -24.2

 India 2.9a3.7 -0.8a-23.8a

 Indonesia 2.8 3.1 -0.3 -10.7

 Kazakhstan 2.5 2.9 -0.4 -12.4

 Kyrgyz Republic 3.4 3.9 -0.5 -12.5

 Nepal 4.6 5.3 -0.7 -11.8

 Philippines 3.7 4.2 -0.5 -10.8

 Uzbekistan 3.4 4.2 -0.8 -19.6

 Vietnam 2.4 3.4 -1.0 -30.0

Latin America/Caribbean

Bolivia 4.2 4.7 -0.5 -10.4

 Brazil 2.5 3.0 -0.5 -14.1

 Colombia 3.0 3.0 0.0 -2.6

 Dominican Republic 3.2 3.6 -0.4 -10.6

 Guatemala 5.0 5.3 -0.3 -4.5

 Haiti 4.8 5.3 -0.5 -9.3

 Nicaragua 3.7 4.6 -0.9 -20.0

 Peru 3.6 4.0 -0.4 -10.7

a After adjustment for omission of recent births, the TFR for 0-3 years is 3.4, 

which results in a change of -0.3 births and -8 percent.
24

Figure 3.2 Change in the total fertility rate between the periods 0-3 and 4-7 years 

before the survey, Demographic and Health Surveys, 1995-1999

 Brazil

 Colombia

 Dominican Republic

 Peru

 Nicaragua

 Bolivia

 Haiti

 Guatemala

 Vietnam

 Kazakhstan

 Indonesia

 India

 Bangladesh

 Kyrgyz Republic

 Uzbekistan

 Philippines

 Nepal

 Turkey

 Egypt

 Jordan

 Yemen

 South Africa

 Zimbabwe

 Ghana

 Kenya

 Comoros

 Nigeria

 Cameroon

 Central African Republic

 Togo

 Côte d’Ivoire

 Mozambique

 Tanzania

 Guinea

 Senegal

 Eritrea

 Madagascar

 Zambia

 Benin

 Burkina Faso

 Tchad

 Mali

 Uganda

 Niger

-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0

Total Fertility Rate

TFR 0-3 years before survey

Change in TFR between 0-3 

and 4-7 years before survey

8.0 6.0 4.0 2.0 0.0 -2.0 -4.0
25

Table 3.9 Total fertility rates for women 15-44 for the period 0-2 years preceding 

survey and percent annual decline, Demographic and Health Surveys, 1990-1999

Year of survey

Total fertility rate

(wom en 15-44)

Region and 

country

Preceding

survey

Latest

survey

Preceding

survey

Latest

survey

Percent

annual

decline

Sub-Saharan Africa

 Burkina Faso 1993 1999 6.3 6.3 0.0

 Cameroon 1991 1998 5.7 4.7 2.8

 Ghana 1993 1998 5.1 4.4 3.0

 Kenya 1993 1998 5.2 4.6 1.3

 Madagascar 1992 1997 6.0 5.8 0.7

 Niger 1992 1998 6.8 7.0 -0.5

 Senegal 1992-1993 1997 5.9 5.5 1.4

 Tanzania 1996 1999 5.6 5.4 1.2

 Zambia 1992 1996 6.3 6.0 1.2

 Zimbabwe 1994 1999 4.2 3.9 1.5

Near East/North Africa

 Egypt 1991 1997 3.9 3.6 1.3

 Jordan 1990 1997 5.5 4.3 3.5

 Turkey 1993 1998 2.5 2.6 -0.8

Asia

 Bangladesh 1993-1994 1997 3.4 3.2 1.7

 India 1992-1993 1998-1999 3.4 2.8 2.9

 Indonesia 1990 1997 3.0 2.8 1.0

 Philippines 1993 1998 4.1 3.7 2.1

Latin America/Caribbean

 Bolivia 1993-1994 1998 4.7 4.2 2.5

 Colombia 1990 1995 2.8 3.0 -1.4

 Dominican Republic 1991 1996 3.3 3.2 0.6

 Peru 1991-1992 1996 3.5 3.5 0.0
26

4 Other Fertility Components

4.1 Onset of Childbearing: Age at First Birth

Median age at first birth is calculated for cohorts using the life table method and is presented here 

to examine when the transition to parity 1 is accomplished from one country to another. The median age 

at first birth is based on retrospective reports by women and represents the exact age by which half of a 

cohort had had a birth. Previous studies have found some reporting problems among older women, who 

appear to displace the exact timing of these events to higher ages (Gage, 1995; Westoff et al., 1994). The 

same result can occur if some of the older women omit reporting their first birth, perhaps births of 

children who died soon after birth.

Of the countries surveyed between 1995 and 1999, the lowest median age at first birth occurred in 

Bangladesh (17.2 years), which is also known for its low ages at first marriage (Table 4.1). Niger is the 

only other country where more than half of the women had had a birth before their 18th birthday (median 

of 17.8 years). Such low ages at first birth increase morbidity and mortality risks for both the child and 

the mother. The countries with the highest ages at first birth are Jordan, the Philippines, and Vietnam, 

with median ages of 23.2, 23.2, and 23.3 years, respectively. Thus, there is about a five-year difference 

between the countries with the highest and lowest median ages at first birth. The reduction of the fertile 

period due to the variation in this component is 18 percent.9 The variation in total fertility due to a fiveyear delay in having the first birth is likely to be greater, however, because of the high level of fecundity 

during the later teen years and the early twenties. The variation in fertility according to age at first birth is 

shown in Figure 4.1.

Most women in sub-Saharan Africa begin their reproductive life earlier than women in the Latin 

America/Caribbean region or Asia, with the median age at first birth varying from 17.8 years in Niger to 

21.4 years in Eritrea (Table 4.1). Except for Comoros, Eritrea, Ghana, South Africa, and Togo, more than 

half of the women had had a birth before age 20. There is a lack of correlation between age at marriage, 

age at first birth, and level of fertility in many of the sub-Saharan countries. This is because, while age at 

first marriage has been rising, so has nonmarital fertility (Westoff et al., 1994; Njogu and Martin, 1991; 

Casterline and Trussel, 1980). Indeed, the total fertility rate exceeds the total marital fertility rate in South 

Africa because there are more never-married women with births than married women with births. 

Although age at first marriage has been rising in several countries, there has been less change in age at 

first birth.

In the North Africa/Near East region, age at first birth ranges from a low of 19.5 in Yemen to a 

high of 23.2 in Jordan. 

In Asia, the variation in age at first birth is the greatest of any region, having both the lowest and 

highest ages (Bangladesh and the Philippines). In the Latin America/Caribbean region, there is little 

variation in median age at first birth. The lowest age is in Nicaragua, which is also the only country to 

have a median age of less than 20 in this region (19.7 years). Most of the other countries have medians 

close to 21 or 22 years. Brazil, Colombia, and Haiti have the highest medians (22.4, 22.1, and 22.1 years, 

respectively), but these countries have very different total fertility rates (2.5, 3.0, and 4.8 births,

respectively). 

 

9

 Assuming that the median age at the end of the fertile period is 45.0 years, the length of the fertile period 

is then 27.8 years when the median age at first birth is lowest (17.2 years) and is 5.1 years shorter (18 percent = 100 

x 5/27.8) when the median age at first birth is highest (23.2 years). 
27

Table 4.1 Median age at first birth among women age 25-49 by urban-rural residence, migration status, and education, 

Demographic Health Surveys, 1995-1999

Migration status

Urban-rural Urban Rural Education

 Region and residence Urban to to Rural No Secon-

 country Urban Rural native rural urban native schooling Primary dary + Total

Sub-Saharan Africa

 Benin 20.4 19.3 20.4 20.3 19.6 19.1 19.3 20.0 22.9 19.6 

 Burkina Faso 19.8 19.2 u u u u 19.2 19.0 23.3 19.3 

 Cameroon 19.4 18.9 19.6 19.1 18.9 18.9 18.7 18.7 19.9 19.0 

 Central African Republic 18.7 19.9 18.7 18.9 19.1 20.1 19.8 18.7 19.1 19.4 

 Comoros 22.2 20.7 22.5 19.8 20.6 20.7 20.3 21.0 a 21.0 

 Côte d'Ivoire 19.0 18.7 19.1 18.8 18.6 18.7 18.8 18.1 20.2 18.8 

 Eritrea 21.9 21.2 21.9 21.8 20.9 21.2 21.2 21.1 23.7 21.4 

 Ghana 21.0 20.0 21.1 20.6 19.8 20.1 19.7 20.0 21.0 20.3 

 Guinea 19.4 18.5 u u u u 18.5 19.0 21.0 18.8 

 Kenya 20.7 19.1 20.6 20.8 19.0 19.2 18.2 18.7 21.6 19.4 

 Madagascar 20.8 19.1 u u u u 18.5 18.8 21.8 19.5 

 Mali 19.4 18.6 19.4 19.3 18.7 18.6 18.7 18.6 21.2 18.8 

 Mozambique 19.0 19.2 19.1 18.8 18.8 19.3 19.0 19.1 21.8 19.2 

 Niger 18.6 17.6 18.8 18.4 18.3 17.6 17.6 18.4 21.4 17.8 

 Senegal 21.0 19.0 21.4 19.5 19.6 18.9 19.2 20.7 23.6 19.8 

 South Africa 21.3 20.1 21.6 20.6 20.3 20.0 19.8 19.7 21.5 20.8 

 Tanzania 19.5 18.9 19.5 19.5 18.9 18.9 18.1 19.2 23.4 19.0 

 Tchad 18.5 18.3 18.5 18.5 18.0 18.3 18.3 18.5 19.7 18.3 

 Togo 20.8 19.7 20.8 20.9 19.7 19.7 19.8 19.8 22.0 20.1 

 Uganda 19.1 18.5 19.2 19.0 18.7 18.5 18.0 18.6 20.0 18.6 

 Zambia 19.0 18.4 19.1 18.8 18.5 18.4 18.2 18.2 20.4 18.6 

 Zimbabwe 20.5 19.6 20.9 20.3 20.0 19.5 18.8 19.2 21.1 19.9 

North Africa/Near East

 Egypt 22.9 20.1 u u u u 19.7 20.5 a 21.4 

 Jordan 23.3 22.2 u u u u 21.3 20.7 23.9 23.2 

 Turkey 21.7 20.7 22.4 20.7 21.3 20.6 19.6 20.8 24.4 21.3 

 Yemen 19.3 19.5 u u u u 19.3 19.1 23.8 19.5 

Asia

 Bangladesh 18.2 17.0 u u u u 16.8 17.0 19.3 17.2 

 India 20.6 19.0 u u u u 18.5 19.0 21.8 19.4 

 Indonesia 22.3 20.2 u u u u 19.6 19.8 23.6 20.8 

 Kazakhstan 22.4 22.1 22.5 22.4 21.5 22.1 * * 22.3 22.3 

 Kyrgyz Republic 22.2 21.5 22.4 22.0 21.9 21.4 * * 21.7 21.7 

 Nepal 19.8 19.8 u u u u 19.6 19.7 21.5 19.8 

 Philippines 24.3 22.2 u u u u 20.5 21.1 24.5 23.3 

 Uzbekistan 22.2 21.2 22.3 22.1 21.1 21.2 * * 21.6 21.6 

 Vietnam 25.0 22.7 u u u u 22.4 21.9 23.5 23.2 

Latin America/Caribbean

 Bolivia 21.8 20.8 22.0 21.0 20.4 20.9 21.0 20.3 22.7 21.5 

 Brazil 22.6 21.4 22.9 21.8 21.2 21.4 20.0 20.9 23.9 22.4 

 Colombia 22.7 20.7 22.9 21.9 20.5 20.7 19.5 20.4 24.0 22.1 

 Dominican Republic 21.9 19.9 22.4 21.0 20.1 19.9 18.6 19.4 24.6 21.1 

Guatemala 21.0 19.7 21.6 20.0 20.9 19.4 19.1 20.0 22.8 20.3 

 Haiti 23.1 21.4 23.3 22.8 21.0 21.4 21.1 22.0 a 22.1 

 Nicaragua 20.1 18.9 20.3 19.4 19.1 18.9 18.0 18.8 21.9 19.7 

 Peru 22.3 20.0 22.6 20.6 20.2 19.9 19.5 19.9 23.5 21.5 

Note: The median age at first birth is calculated only for women 25-49 because, for many countries in Asia, the Near 

East/North Africa region, and the Latin America/Caribbean region, more than half the women in cohorts 15-19 and 20-24 have 

not yet had a birth. Median age at first birth by migration is not available for countries with ever-married samples because 

migration status for never-married women was not collected. An asterisk indicates figure is based on fewer than 25 

unweighted women and is not shown.

u = Unknown (not available)

a

 Median age at first birth is not calculated because more than half of the women have not yet had a birth.

29

Urban-Rural Migration

Median age at first birth is a retrospective indicator. A woman could spend her first years of 

reproductive life in a rural area and yet be interviewed in an urban area (or vice versa). It is interesting to 

compare the median age at first birth for women native to each of the two areas (urban and rural) with that 

for migrants. In sub-Saharan Africa, where urban-rural differences are less marked, the differences in 

median age at first birth for urban natives and for rural natives increases slightly in the context of 

migration (Table 4.1). In all but two countries, urban natives have a higher median age at first birth than 

rural natives. The two exceptions are the Central African Republic and Mozambique. 

In 22 countries of all the regions, the difference between urban natives and rural natives is greater 

than the urban-rural difference. In seven countries, all in sub-Saharan Africa, it is the same.

In most countries, women who moved to urban areas have a lower age at first birth than urban 

natives. The lowest differences between urban natives and migrants occur in sub-Saharan Africa, where 

there is no substantial difference in almost half of the countries with data. On the other hand, migrants to 

rural areas have lower ages at first birth than rural natives in many countries, thus having the lowest 

median ages of all the migration categories.

Women’s Education

Table 4.1 shows age at first birth by level of education in three categories (no schooling, primary 

incomplete or complete, and secondary or higher). For most countries, there is a positive relationship 

between level of education and age at first birth. Age at first birth is usually lowest for women with no 

education and then increases with educational attainment. Although differences between women with 

primary education and women with no schooling are not always pronounced (or in the same direction), 

women with secondary education have the highest age at first birth in all countries studied, except for the 

Central African Republic. The average increase in age at first birth between primary and no schooling is 

only 0.3 years and there are ten countries where women with no schooling have a higher age at first birth. 

In contrast, the average increase in age at first birth between primary and secondary education is 2.7 

years, and there are no countries where women with primary education have a higher median age at first 

birth than women with secondary education. The regions with the largest delay in first births by education 

are Near East/North Africa and Latin America/Caribbean, where women with secondary education have 

their first child on average four years later than women with no education.

In sub-Saharan Africa, the most notable variation in age at first birth by education is found in 

Tanzania, where age at first birth is 5.3 years greater for women with secondary education than for 

women with no education. Contrasting with other countries in sub-Saharan Africa, the difference in age at 

first birth between women with primary education and no education in Tanzania is also the highest (1.1 

years). 

In the Near East/North Africa region, the differences in age at first birth between women with 

primary and women with no education are small: about 0.3 years on average. On the other hand, the 

differences between women with secondary or higher education and women with primary education are 

the highest of any of the regions: 3.8 years on average. Indeed, in Egypt, less than half of the women 25-

49 at the time of the survey with secondary education had their first birth by age 25 so that it is not 

possible to calculate an unbiased median.

In Asia, on average, there is hardly any increase in age at first birth between women with no 

schooling and primary education (0.2 years) and only a modest average increase between women with 

primary and secondary or higher education (2.6 years). In part, these results are due to the fact that three 

of the countries–Kazakhstan, the Kyrgyz Republic and Uzbekistan—have too few women with no 
30

schooling or only primary schooling to present medians for these groups. Where medians can be

presented, the largest difference between women with secondary education and women with no schooling 

occurs in Indonesia and the Philippines (4.0 years), and the smallest is in Vietnam (1.1 years).

Differences in age at first birth by educational level (especially between women with secondary 

or more education and women with no education) are also observed in the Latin America/Caribbean 

region. This difference is more than 3.5 years in all countries except Bolivia, where it is 1.7 years. In the 

Dominican Republic, the difference is about six years, the highest of any country with calculated medians 

for these two education levels.

4.1.2 First Births to Younger Adolescents

A birth to a girl who has not yet finished her own physical, mental, emotional, and social 

development creates problems for herself, her child, her family, and the rest of society. In addition to the 

increased health risks for both the mother and baby, juvenile births also diminish women’s autonomy, 

bargaining power, and appreciation of self-worth. Although the DHS surveys collect information directly 

from women 15 years of age and older, through the birth history, estimates can be made about fertility at 

ages younger than 15.

Table 4.2 presents the percentage of girls 15, 16, and 17 who had had a birth. Overall, the 

percentage varies with age from 3percent to 8percent to 16 percent, respectively. Regionally, subSaharan Africa has the highest levels of births to juveniles. However, there is a large amount of variation 

within the regions. In sub-Saharan Africa, at age 15, 8 percent of girls in Côte d’Ivoire and 7 percent in 

the Central African Republic and Niger have had a birth. At age 16, in more than half of the countries, 

10 percent or more of girls have had a birth. The countries with the highest percentages of girls16 years 

old with a child are Niger (20 percent); Guinea (18 percent); and Cameroon, Côte d’Ivoire, and Mali (all 

16 percent). At age 17, only one country in sub-Saharan Africa has fewer than 10 percent of girls who 

have had a birth (Comoros, 5percent). At this age, the countries with the highest percentages are Niger 

(39 percent), Mali and Uganda (33 percent each), and Tchad and Guinea (32 percent each).

Among the four Near East/North African countries, all have less than 10 percent of girls with a 

birth at any of the three ages. In Asia, there are three countries with levels of juvenile childbearing for 

10 percent or more of girls: Bangladesh, where the percentages for ages 15, 16, and 17 are 9, 24, and 

33 percent, respectively; India, where the percentages are 3, 7, and 15 percent, respectively; and Nepal, 

where the percentages are 1, 6, and 15 percent, respectively.

In the Latin America/Caribbean region, no country has more than 5 percent of girls age 15 who 

have given birth. At age 16, however, 12 percent of girls in Nicaragua have started childbearing. At age 

17, in six of the eight countries, more than 10 percent of girls have had a child. At this age in two 

countries, a fifth or more of girls have had a juvenile birth: 22 percent in Nicaragua and 20 percent in the 

Dominican Republic.

4.1.3 Postponement of Childbearing to Age 30 or Above

Late childbearing is becoming increasingly popular in the more developed countries as women 

desire to complete their education and establish their career before beginning a family. This pattern of 

delaying the first child until late in the reproductive period may become established in the middle classes 

of the more developed of the less developed countries (LDCs), principally those in Asia and the Latin 

America/Caribbean region. Table 4.3 examines late initial childbearing by presenting the percentage of 

women 35 years of age and over who had their first child at age 30 years or above. The five-year 

difference in current age versus age at first birth is used to reduce the effects of truncation on the 

summary measures.
31

Table 4.2 Percentage of teenagers 15-17 years old who have 

had a birth, by age, Demographic and Health Surveys , 1995-

1999

Region and Age

country 15 16 17

Sub-Saharan Africa

Benin 1.4 4.3 15.3

Burkina Faso 2.5 3.8 14.3

Cameroon 2.9 15.6 26.2

Central African Republic 7.0 12.0 24.0

Comoros 2.6 3.1 5.2

Côte d'Ivoire 8.0 16.0 28.0

Eritrea 1.5 10.4 12.5

Ghana 0.6 5.0 11.6

Guinea 5.7 17.9 31.7

Kenya 1.7 4.3 14.1

Madagascar 6.0 11.6 29.3

Mali 5.2 16.4 33.2

Mozambique 3.6 15.2 28.0

Niger 7.4 20.3 39.1

Senegal 2.9 7.9 16.1

South Africa 2.0 5.0 11.0

Tanzania 0.9 9.0 9.7

Tchad 4.4 9.9 31.7

Togo 1.1 5.9 14.6

Uganda 4.6 12.9 33.1

Zambia 1.9 10.7 20.3

Zimbabwe 3.0 4.0 13.0

Average 3.5 10.1 21.0

Near East/North Africa

Egypt 0.6 2.0 6.3

Jordan 0.5 1.7 2.4

Turkey 1.3 1.6 4.9

Yemen 1.0 3.2 9.3

Average 0.9 2.1 5.7

Asia

Bangladesh 8.5 23.5 32.6

India 3.4 7.4 14.6

Indonesia 1.2 2.3 6.6

Kazakhstan 0.0 0.0 3.3

Kyrgyz Republic 0.0 0.0 1.7

Nepal 1.1 6.4 15.0

Philippines 0.3 0.8 3.6

Uzbekistan 0.0 0.0 0.3

Vietnam 0.0 0.6 1.4

Average 1.6 4.6 8.8

Latin America & Caribbean

Bolivia 2.6 2.7 10.7

Brazil 3.1 7.5 15.4

Colombia 3.1 7.1 9.3

Dominican Republic 3.1 8.6 19.7

Guatemala 2.6 5.9 18.0

Haiti 2.0 4.0 11.0

Nicaragua 5.1 12.4 22.4

Peru 1.6 5.9 9.1

Average 2.9 6.8 14.5

Grand Average 2.7 7.6 15.8
32

Table 4.3 Percentage of women age 35 or older who had a birth at age 30 or older, by residence and education, 

Demographic and Health Surveys, 1995-1999

Education

Region and Residence No Secondary

country Urban Rural schooling Primary or more Total

Sub-Saharan Africa

Benin 2.9 1.9 1.9 1.3 11.3 2.2

Burkina Faso 2.4 1.7 1.8 0.0 5.6 1.8

Cameroon 2.3 1.8 3.1 0.9 1.5 1.9

Central African Republic 3.5 4.7 5.0 3.0 0.0 4.2

Comoros 7.9 4.4 4.5 5.7 17.8 5.3

Cote d'Ivoire 2.8 2.2 2.7 0.2 4.4 2.4

Eritrea 7.8 7.9 8.1 6.1 8.7 7.9

Ghana 4.7 3.6 4.2 3.3 4.0 3.9

Guinea 3.1 1.9 2.0 0.9 5.7 2.2

Kenya 5.0 1.4 1.7 1.6 3.5 2.0

Madagascar 7.0 3.2 4.6 2.6 8.3 4.3

Mali 2.1 2.4 2.2 1.7 6.7 2.3

Mozambique 1.7 4.4 3.8 4.1 0.6 3.9

Niger 0.9 1.9 1.8 0.0 0.0 1.7

Senegal 2.0 1.8 1.4 0.7 7.1 1.9

South Africa 5.0 4.4 4.6 3.7 5.6 4.8

Tanzania 0.6 1.6 1.5 1.0 7.2 1.4

Tchad 1.2 1.5 1.5 1.0 1.7 1.5

Togo 4.4 2.5 2.5 2.8 7.0 3.1

Uganda 3.0 1.4 1.5 1.4 2.5 1.5

Zambia 1.3 1.5 2.1 0.7 2.8 1.4

Zimbabwe 1.2 2.1 2.7 1.1 3.4 1.8

Average 3 3 3 2 5 3

Near East/North Africa

Egypt 7.2 2.6 2.6 3.7 12.3 5.0

Jordan 6.2 4.5 2.6 4.8 7.4 6.0

Turkey 4.1 2.7 2.0 2.6 9.1 3.6

Yemen 2.2 2.9 2.7 0.0 6.5 2.7

Average 5 3 2 3 9 4

Asia

Bangladesh 0.6 0.6 0.5 0.8 0.7 0.6

India 3.1 1.3 0.9 1.5 4.2 1.8

Indonesia 3.9 3.5 3.9 2.6 6.5 3.7

Kazakhstan 5.0 2.6 * * 4.1 4.1

Kyrgyz Republic 5.9 3.1 * * 3.9 4.1

Nepal 4.4 2.2 2.2 3.2 4.7 2.4

Philippines 10.6 8.2 6.1 5.5 12.3 9.5

Uzbekistan 3.8 2.1 * * 2.8 2.8

Vietnam 11.1 5.2 4.7 4.2 7.8 6.5

Average 5 3 3 3 5 4

Latin America/Caribbean

Bolivia 6.2 5.9 5.6 3.7 8.8 6.1

Brazil 7.7 4.4 4.0 5.0 9.6 7.1

Colombia 8.3 6.0 2.6 6.5 9.8 7.7

Dominican Republic 8.4 4.3 2.5 3.2 15.4 7.0

Guatemala 4.7 2.9 3.4 2.7 6.9 3.7

Haiti 10.5 6.3 6.6 7.9 17.5 7.8

Nicaragua 3.2 3.3 2.1 1.8 6.6 3.3

Peru 8.3 3.6 2.4 2.8 11.9 7.0

Average 7 5 4 4 11 6

Grand Average 4 3 3 3 7 4

Note: An asterisk indicates percentage is based on fewer than 25 unweighted women and is not shown.
33

Overall, about 4 percent of women 35 years of age or older began childbearing at age 30 years or 

older. There is some variation by region, with late initial childbearing lowest in sub-Saharan Africa 

(3 percent) and highest in the Latin America/Caribbean region (6 percent). Bangladesh has the

lowest percentage of women with a first birth at age 30 or above (0.6 percent). In nine of 22 sub-Saharan 

countries, less than 2 percent of women started childbearing at age 30 or above. The Philippines is the 

country with the highest percentage of women with late entry into childbearing, where more than

9 percent of women began at age 30 or above. In the Latin America/Caribbean region, five of eight 

countries had about 7 percent or more women who began childbearing late. Although there are somewhat 

higher percentages of women with late initial births in urban areas than in rural areas, the highest 

percentages starting childbearing late are women with secondary education in the Near East/North Africa 

and in the Latin America/Caribbean regions. For this education group, the countries with the highest 

percentage are Comoros and Haiti (nearly 18 percent each) and the Dominican Republic (15 percent).

4.2 Parity Progression and Birth Intervals

In the preceding sections, it has been shown that the fertility transition has advanced in the 

countries of the Latin America/Caribbean region and Asia, as well as in some countries of sub-Saharan 

Africa. One objective of this section is to examine two aspects of family formation. The first is the 

proportion of women at a particular parity who attain the next parity, or the parity progression ratio, 

which is related to the quantity or quantum of fertility. The other is the time elapsed from one parity to the 

next, or the distribution of birth intervals, which is related to timing or the tempo of fertility (Ryder, 

1980).

4.2.1 Parity Progression

Table 4.4 indicates that in sub-Saharan Africa, when the transition from parity 1 to 2 is examined, 

the lowest PPRs are observed in South Africa and Zimbabwe (48 percent and 69 percent, respectively). 

The low proportion of women progressing to parity 2 in these two countries is probably related to the use 

of contraceptive methods for limiting or spacing births. 

PPRs of 90 percent or higher are found in the high-fertility countries, Burkina Faso, Mali, Niger, 

Tchad, and Uganda, with PPRs up to 94 percent. 

An earlier comparative analysis (Mboup and Saha, 1998) found that the PPR in Zimbabwe was 

78. The decline may be due to an extension of early contraceptive use. In nine other sub-Saharan 

countries, it is possible to compare PPRs with those of the survey (Table 4.5). Among these nine, the 1 to 

2 PPR dropped by 10 percent and 12 percent in Ghana and Cameroon, respectively. The percentage 

progressing from parity 1 to 2 dropped by 3percent in Zambia and Madagascar, and by 4 percent in 

Niger. Only in Burkina Faso did the 1 to 2 PPR increase (3 percent).

In most countries in sub-Saharan Africa, the probability of progression from parity 5 to 6 is fairly 

high, that is, greater than 70 percent. Of the countries included in this report, only Ghana, South Africa, 

and Zimbabwe have PPRs for parity 5 to 6 of less than 70 percent. In the latter two countries, the PPR for 

parity 5 to 6 is somewhat higher than that for parity 4 to 5. This increase may be because women who 

attain parity 5 are selected for high fertility; their fertility level essentially depends on their natural 

reproductive capacity. However, this increase may also be because there are sampling variations that are 

more pronounced with the small numbers of cases at the higher parities. The rest of the sub-Saharan 

countries have PPRs for parity 5 to 6 that are equally divided between 70 to 79 percent and 80 to 

89 percent. The countries with the highest PPRs for parity 5 to 6 are again those with the highest PPRs for 

parity 1 to 2 and the highest levels of fertility (Burkina Faso, Mali, and Niger).
34

Table 4.4 Parity progression ratios (quintums) for the six-year period preceding the survey, 

Demographic and Health Surveys, 1995-1999

Parity progression ratio

Region and Parity Parity Parity Parity Parity

country 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6

Percentage

reaching

parity 6

Sub-Saharan Africa

 Benin 899 885 845 843 791 45 

 Burkina Faso 937 933 896 886 850 59 

 Cameroon 781 783 786 738 735 26 

 Central African Republic 799 773 824 799 804 33 

 Comoros 853 890 795 757 763 35 

 Côte d'Ivoire 726 779 807 796 728 26 

 Eritrea 855 840 854 830 846 43 

 Ghana 768 701 746 667 620 17 

 Guinea 820 864 813 791 779 35 

 Kenya 831 752 701 676 757 22 

 Madagascar 854 877 837 829 792 41 

 Mali 900 890 881 876 859 53 

 Mozambique 812 852 726 745 721 27 

 Niger 918 920 933 909 879 63 

 Senegal 882 800 841 849 791 40 

 South Africa 484 415 402 388 444 1 

 Tanzania 891 773 818 737 770 32 

 Tchad 925 922 919 938 807 59 

 Togo 794 802 827 814 815 35 

 Uganda 918 915 905 866 828 55 

 Zambia 844 862 857 838 822 43 

 Zimbabwe 692 598 566 427 568 6 

Near East/North Africa

 Egypt 887 711 613 569 559 12 

 Jordan 953 887 821 721 679 34 

 Turkey 708 432 414 447 561 3 

 Yemen 922 928 913 897 860 60 

Asia

 Bangladesh 737 639 561 499 567 7 

 India 835 644 567 526 564 9 

 Indonesia 525 407 403 406 462 2 

 Kazakhstan 603 340 412 342 293 1 

 Kyrgyz Republic 757 584 531 375 386 3 

 Nepal 911 783 719 697 685 24 

 Philippines 782 705 598 615 584 12 

 Uzbekistan 861 672 521 342 307 3 

 Vietnam 636 356 363 355 279 1 

Latin America/Caribbean

 Bolivia 744 644 621 644 680 13 

 Brazil 600 422 368 468 461 2 

 Colombia 584 533 466 452 508 3 

 Dominican Republic 716 700 410 423 544 5 

 Guatemala 800 739 677 817 800 26 

 Haiti 773 824 806 797 740 30 

 Nicaragua 668 594 584 533 581 7 

 Peru 644 579 536 545 615 7 

Note: Parity progression ratio is the proportion of women at each parity who attain the next parity 

within five years.
35

Table 4.5 Trends in parity progression ratios (quintums) for the six-year period preceding the 

survey, Demographic and Health Surveys, 1995-1999

Parity progression ratio

Parity 1 to 2 Parity 5 to 6

Region and 

country

Previous 

survey

Latest 

survey Change

Previous 

survey

Latest 

survey Change

Sub-Saharan Africa

 Burkina Faso 909 937 28 871 850 -21

 Cameroon 899 781 -118 801 735 -66

 Central African Republic 793 799 6 851 804 -47

 Ghana 872 768 -104 708 620 -88

 Kenya 844 831 -13 712 757 45

 Madagascar 885 854 -31 898 792 -106

 Niger 954 918 -36 906 879 -27

 Senegal 882 882 0 893 791 -102

 Zambia 876 844 -32 896 822 -74

 Zimbabwe 782 692 -90 684 568 -116

Near East/North Africa

 Egypt 918 887 -31 627 559 -68

 Jordan 964 953 -11 842 679 -164

 Turkey 813 708 -105 571 561 -10

Asia

 Bangladesh 809 737 -72 560 567 7

 India 836 835 -1 604 564 -40

 Indonesia 669 525 -144 486 462 -24

 Philippines 843 782 -62 719 584 -135

Latin America/Caribbean

 Bolivia 780 744 -36 727 680 -47

 Brazil 769 600 -169 562 461 -101

 Colombia 625 584 -41 520 508 -12

 Dominican Republic 797 716 -82 572 544 -28

 Peru 793 644 -149 596 615 19

Note: Parity progression ratio is the proportion of women at each parity who attain the next 

parity within five years.
36

Mboup and Saha (1998) found that most countries in sub-Saharan Africa had parity progression 

ratios for parity 5 to 6 that were above 80 percent. In the countries in this report, less than half have such 

high ratios. Indeed, the PPRs have decreased in all countries with comparable trend data, except Kenya, 

which shows a 5 percent rise.

One way of summarizing the parity progression ratios is the percentage of women who reach 

parity 6 among women who have had at least one birth. In sub-Saharan Africa, the percentage who reach 

parity 6 from parity 1 varies from 1 percent in South Africa and 6 percent in Zimbabwe, to 63 percent in 

Niger. Other countries with percentages over 50 percent are Burkina Faso, Mali, Tchad, and Uganda.

The PPRs in Asia and the Near East/North Africa region vary considerably. In Indonesia, 

Kazakhstan, and Vietnam in Asia and in Turkey in the Near East/North Africa region, where the fertility 

transition is well advanced, the PPR from parity 1 to 2 is already low (71 percent or less). By contrast, the 

PPRs at higher parities are greater than 80 percent in Yemen, where the proportion progressing to parity 6 

is 86 percent. Overall, the PPRs observed in Yemen and in the early parities in Jordan are as high as those 

observed in some sub-Saharan countries such as Niger.

There is wide variation in the levels of the PPRs in Asia and the Near East/North Africa region as 

women progress in parity. The steepest decline occurs in Uzbekistan, from 86 percent for the parity 1 to 2

progression ratio to 31 percent for the parity 5 to 6 progression ratio. Yemen and Indonesia show the least 

decline in PPR (about 6 percent) but the PPR levels at each parity are very different.

In Turkey, there is a steep fall in the PPR between parities 1 and 2 and 2 and 3, but then there is a 

substantial rise between parities 4 and 5 and 5 and 6.

As measured by the parity 1 to 6 transition, fertility in Asia and the Near East/North Africa region 

is generally low with the exception of Yemen. In eight of the 13 countries, fewer than 10 percent of 

mothers reach parity 6. In Yemen, 60 percent reach parity 6. The country with the next highest percentage 

is Jordan, where 34 percent reach parity 6 from parity 1.

Six of the seven countries with earlier surveys in Asia and the Near East/North Africa region have 

had declines in PPRs for parity 1 to 2. The declines vary from basically no change in India to 14 percent 

in Indonesia. For parity 5 to 6, the PPRs declined in six countries, from the largest decline of 16 percent 

in Jordan to 1 percent in Turkey. In Bangladesh the PPR rose by less than 1 percent between parity 5 and 6.

In the Latin America/Caribbean region, where the fertility transition is well advanced, PPRs from 

1 to 2 are lower on average than those of any other region, varying from 58 percent in Colombia to 

80 percent in Guatemala. After parity 1, Haiti and Guatemala have the highest PPRs, that is, greater than 

70 percent. For all the remaining countries of the Latin America/Caribbean region, the PPRs are

70 percent or less after parity 1. The lowest PPRs are generally found in Brazil, the Dominican Republic, 

and Colombia, with PPRs reaching 37 percent, 47 percent, and 47 percent, respectively, for the transition 

between parity 3 and 4. 

The summary measure shows that in five of the eight Latin American/Caribbean countries, less 

than 10 percent of mothers reached parity 6. In the countries with the highest percentages, Guatemala and 

Haiti, 26 and 30 percent of mothers had a sixth birth.

Decreases over time in parity progression ratios are also evident in the Latin America/Caribbean 

region. For PPRs for parity 1 to 2, the declines range from 4percent in Bolivia and Colombia to 

17 percent in Brazil. For the transition between parity 5 and 6, the PPRs declined in four countries but 

rose in Peru by 2 percent.
37

4.2.2 Socioeconomic Differences in Parity Progression Ratios

This section examines the differentials in PPRs according to urban-rural residence and women’s 

education.

Urban-Rural Residence

To study the relationship between residence and fertility, separate PPRs for urban women and for 

rural women have been calculated (Table 4.6). Because it is difficult to discuss this large mass of ratios, 

the discussion is limited to a summary indicator; the percentage of women who progress from parity 1 to 

parity 6 (PPR 1 to 6) is used for each area of each country. 

There is no country in which the percentage of women who progress from parity 1 to 6 in urban 

areas is equal to or greater than that in rural areas. On average, the greatest urban-rural difference occurs 

in sub-Saharan Africa (average difference of 23 percent). The smallest average difference between urban 

and rural areas in PPR 1 to 6 occurs in Asia (8 percent), and the Near East/North Africa and Latin 

America/Caribbean regions fall in between (differences of 17 and 18 percent, respectively). Sub-Saharan 

Africa also has the countries with the largest differences between urban and rural areas, Burkina Faso, 

with a difference of 58 percent, and Tanzania, with a difference of 45 percent. The smallest differences 

occur in the countries with low fertility: Indonesia (1 percent); Brazil (2 percent); the Kyrgyz Republic 

(3 percent); and South Africa, Turkey, and Uzbekistan (all 4percent). It is likely that Kazakhstan and 

Vietnam also have very small urban-rural differences in the percentage attaining parity 6, but because of 

small numbers of women at parity 5, the PPR for 5 to 6 is unstable and not shown.

By parity level, the differences between urban and rural areas in PPR increase in the Near 

East/North Africa region and after parity 3 in sub-Saharan Africa (Figure 4.2). The differences decrease 

with parity, except for parity 5, in Asia. In the Latin America/Caribbean region, the urban-rural 

differences in PPR are about constant until parity 5, when the difference decreases. A lot of these 

differences between areas by parity are related to rural-urban migration, since women at lower parities are 

more likely to be natives than women at higher parities. If a woman is a migrant, then she brings with her 

a history of fertility behavior that occurred in an area different from that in which she is currently living. 

Part of the variation in the urban-rural differences by parity may be due to differing levels of migration 

between the regions.

Women’s Education

The effect of education on the PPR is observed by calculating life tables by birth order separately 

for three broad educational groups: no education, primary education, and secondary or higher education 

(Table 4.7). Overall, women with a secondary education show much lower PPRs than women with no 

education or some primary education. This differential emerges in the transition from parity 1 to 2 and 

becomes fully fledged in the transition from parity 2 to higher parities.

Only four countries, all in sub-Saharan Africa, have PPRs from parity 1 to 6 for women with 

primary education that are larger than those for women with no education. For women with secondary or 

higher education (where there are a sufficient number of cases), the 1 to 6 PPR is 20 percent or less. 

Three notable exceptions are Uganda (42 percent), Jordan (33 percent), and Yemen (30 percent), 

but even in these countries, the PPR 1 to 6 is lower for women with secondary or higher education than 

for women with lower levels of education. Only four other countries have 1 to 6 PPRs above 20 percent: 

Mali (26 percent), Zambia (25 percent), the Central African Republic (22 percent), and Madagascar

(21 percent). In Asia and the Latin/America Caribbean region, none of the 1 to 6 PPRs for women with 

secondary education exceeds 8 percent. 
38

Table 4.6 Parity progression ratios (quintums) for the six-year period preceding the survey, by urban-rural residence, Demographic and Health Surveys, 

1995-1999

Parity progression ratio Parity progression ratio

Region and Parity Parity Parity Parity Parity Parity Region and Parity Parity Parity Parity Parity

country Residence 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 1 to 6 country Residence 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6

Sub-Saharan Africa Near East/North Africa

 Benin Urban 808 831 756 738 727 273 Egypt Urban 875 601 478 403 359

 Benin Rural 949 916 900 887 818 567 Egypt Rural 897 801 699 664 651

 Burkina Faso Urban 697 776 647 559 569 111 Jordan Urban 958 883 811 690 642

 Burkina Faso Rural 982 946 921 927 872 692 Jordan Rural 935 915 868 836 856

 Cameroon Urban 703 662 710 615 624 127 Turkey Urban 658 378 344 402 621

 Cameroon Rural 815 828 813 785 783 337 Turkey Rural 809 559 512 516 527

 Central African Republic Urban 757 816 848 728 773 295 Yemen Urban 887 914 854 839 735

 Central African Republic Rural 829 751 811 838 829 350 Yemen Rural 937 935 935 916 889

 Comoros Urban 814 842 771 533 (676) (190)

 Comoros Rural 872 902 809 813 796 412 Asia

 Côte d'Ivoire Urban 639 689 755 736 687 168 Bangladesh Urban 613 433 334 423 305

 Côte d'Ivoire Rural 778 838 835 823 759 340 Bangladesh Rural 754 667 586 507 581

 Eritrea Urban 739 741 742 873 817 290 India Urban 779 513 451 472 453

 Eritrea Rural 887 867 881 824 858 479 India Rural 853 683 597 538 585

 Ghana Urban 654 594 518 388 275 21 Indonesia Urban 568 387 314 375 358

 Ghana Rural 837 741 848 744 715 280 Indonesia Rural 511 414 431 418 514

 Guinea Urban 694 852 694 738 712 216 Kazakhstan Urban 465 251 424 (277) *

 Guinea Rural 872 869 856 809 800 420 Kazakhstan Rural 741 468 409 367 346

 Kenya Urban 762 575 486 479 (535) (55) Kyrgyz Republic Urban 561 476 320 500 (338)

 Kenya Rural 857 811 758 698 775 285 Kyrgyz Republic Rural 835 631 622 331 398

 Madagascar Urban 778 745 706 768 737 232 Nepal Urban 826 529 572 446 (478)

 Madagascar Rural 878 921 870 845 809 480 Nepal Rural 920 808 733 713 698

 Mali Urban 855 823 825 850 839 414 Philippines Urban 720 656 551 530 490

 Mali Rural 919 916 905 886 864 584 Philippines Rural 849 756 643 673 645

 Mozambique Urban 731 712 775 650 666 174 Uzbekistan Urban 797 535 441 219 (219)

 Mozambique Rural 838 884 719 758 744 300 Uzbekistan Rural 898 759 560 415 324

 Niger Urban 847 804 894 798 826 401 Vietnam Urban 409 201 188 (119) *

 Niger Rural 933 943 943 929 889 686 Vietnam Rural 693 395 381 368 286

 South Africa Urban 467 291 309 230 281 3

 South Africa Rural 505 588 517 551 551 47 Latin America/Caribbean

 Senegal Urban 822 677 709 826 694 226 Bolivia Urban 678 528 507 488 583

 Senegal Rural 917 880 896 869 845 530 Bolivia Rural 912 866 785 823 767

 Tanzania Urban 796 548 584 170 381 17 Brazil Urban 585 400 340 446 442

 Tanzania Rural 932 838 864 850 817 469 Brazil Rural 670 519 464 510 484

 Tchad Urban 826 914 922 877 860 526 Colombia Urban 539 474 410 380 394

 Tchad Rural 957 924 919 956 794 617 Colombia Rural 708 673 565 537 604

 Togo Urban 679 629 696 695 696 144 Dominican Rep. Urban 643 665 335 370 536

 Togo Rural 849 872 872 851 843 463 Dominican Rep. Rural 854 777 513 480 551

 Uganda Urban 842 814 812 688 739 283 Guatemala Urban 762 677 552 802 778

 Uganda Rural 928 931 919 887 837 589 Guatemala Rural 836 793 756 832 811

 Zambia Urban 760 782 820 777 783 297 Haiti Urban 652 705 714 550 622

 Zambia Rural 915 918 888 879 857 561 Haiti Rural 881 890 855 918 788

 Zimbabwe Urban 565 502 464 250 (267) (9) Nicaragua Urban 586 513 503 410 440

 Zimbabwe Rural 778 646 618 508 639 101 Nicaragua Rural 806 723 686 685 701

 Peru Urban 556 471 420 421 473

 Peru Rural 849 797 727 691 750

Note: Parity progression ratio is the proportion of women at each parity who attain the next parity within five years. Parentheses indicate ratio is based 

on 25 to 49 unweighted women reaching lower parity. An asterisk indicates fewer than 25 unweighted women reached lower parity and the ratio is not 

shown.

40

Table 4.7 Parity progression ratios (quintums) for the six-year period preceding the survey, by education, Demographic and Health Surveys, 1995-1999

Parity progression ratio Parity progression ratio

Region and Parity Parity Parity Parity Parity Parity Region and Parity Parity Parity Parity Parity Parity

country Education 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 1 to 6 country Education 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 1 to 6

Sub-Saharan Africa Near East/North Africa

Benin No schooling 933 919 850 875 820 522 Egypt No schooling 898 807 733 685 648 235

Benin Primary 793 872 932 828 639 341 Egypt Primary 859 690 598 502 454 81

Benin Secondary + 858 688 653 (486) * * Egypt Secondary + 894 633 422 360 223 19

Burkina Faso No schooling 968 943 912 900 853 639 Jordan No schooling * 947 918 830 796 *

Burkina Faso Primary 762 822 801 803 (6) (2) Jordan Primary 935 960 804 822 636 377

Burkina Faso Secondary + 714 754 (409) * * * Jordan Secondary + 953 879 820 699 686 329

Cameroon No schooling 889 902 857 821 855 482 Turkey No schooling 818 769 722 631 654 187

Cameroon Primary 841 773 769 717 693 248 Turkey Primary 763 441 343 342 502 20

Cameroon Secondary + 637 652 732 672 682 139 Turkey Secondary + 556 225 272 (81) * *

Central African 

Republic

No schooling 805 749 820 797 785 309 Yemen No schooling 926 937 938 903 875 643

Central African 

Republic

Primary 847 839 847 842 833 422 Yemen Primary 963 903 874 958 782 569

Central African 

Republic

Secondary + 679 726 802 723 774 221 Yemen Secondary + 870 933 756 726 669 298

Comoros No schooling 910 916 853 780 738 409

Comoros Primary 824 943 * * (910) * Asia

Comoros Secondary + 786 739 600 (337) * * Bangladesh No schooling 783 702 645 576 594 121

Côte d'Ivoire No schooling 797 796 831 828 750 327 Bangladesh Primary 751 619 465 335 509 37

Côte d'Ivoire Primary 679 795 783 704 723 215 Bangladesh Secondary + 617 460 393 489 (464) (25)

Côte d'Ivoire Secondary + 530 617 676 740 516 85 India No schooling 861 757 655 574 597 146

Eritrea No schooling 874 862 868 827 857 463 India Primary 864 679 527 440 504 69

Eritrea Primary 813 814 836 805 845 376 India Secondary + 791 439 344 320 337 13

Eritrea Secondary + 843 631 653 * * * Indonesia No schooling 636 462 513 391 551 32

Ghana No schooling 899 854 859 747 677 333 Indonesia Primary 482 409 416 438 460 16

Ghana Primary 856 729 794 645 808 258 Indonesia Secondary + 574 395 329 326 371 9

Ghana Secondary + 657 574 623 577 490 66 Kazakhstan No schooling * * * * * *

Guinea No schooling 857 857 822 807 787 383 Kazakhstan Primary * * * * * *

Guinea Primary 732 * 872 830 760 * Kazakhstan Secondary + 603 340 408 343 310 9

Guinea Secondary + 592 810 674 531 (656) (112) Kyrgyz Republic No schooling * * * * * *

Kenya No schooling 996 819 732 657 874 343 Kyrgyz Republic Primary * * * * * *

Kenya Primary 859 840 741 703 760 285 Kyrgyz Republic Secondary + 756 584 531 375 386 34

Kenya Secondary + 748 586 574 639 686 110 Nepal No schooling 909 817 757 710 694 277

Madagascar No schooling 864 898 865 941 785 496 Nepal Primary 967 799 599 485 (499) (112)

Madagascar Primary 903 923 879 858 843 530 Nepal Secondary + 899 513 466 (714) * *

Madagascar Secondary + 751 779 740 669 724 209 Philippines No schooling (858) (861) (624) (448) * *

Mali No schooling 918 909 892 884 864 568 Philippines Primary 858 800 672 721 703 233

Mali Primary 881 858 826 864 830 448 Philippines Secondary + 755 667 565 542 474 73

Mali Secondary + 717 697 775 758 (869) (255) Uzbekistan No schooling * * * * * *

Mozambique No schooling 841 830 576 785 748 236 Uzbekistan Primary * * * * * *

Mozambique Primary 800 848 872 742 701 308 Uzbekistan Secondary + 861 673 521 342 307 32

Mozambique Secondary + 845 275 421 (460) * * Vietnam No schooling 783 794 (574) * * *

Niger No schooling 940 931 939 913 883 661 Vietnam Primary 734 561 468 306 262 15

Niger Primary 785 910 925 946 886 554 Vietnam Secondary + 600 271 301 374 255 5

Niger Secondary + 815 777 879 (828) * *

Senegal No schooling 897 846 871 873 814 470 Latin America/Caribbean

Senegal Primary 901 796 811 882 698 358 Bolivia No schooling 930 924 736 783 778 385

Senegal Secondary + 685 561 689 555 (811) (119) Bolivia Primary 839 780 733 739 712 253

South Africa No schooling 609 585 577 503 497 51 Bolivia Secondary + 673 490 461 434 521 34

South Africa Primary 627 541 510 452 469 37 Brazil No schooling 983 708 771 594 427 136

South Africa Secondary + 446 350 314 284 373 5 Brazil Primary 639 542 440 476 425 31

Tanzania No schooling 901 801 787 785 800 356 Brazil Secondary + 572 326 254 411 583 11

Tanzania Primary 910 760 817 742 764 320 Colombia No schooling * 903 (598) * (708) *

Tanzania Secondary + 528 813 332 (277) (553) 22 Colombia Primary 670 631 523 479 514 54

Tchad No schooling 935 923 921 943 806 603 Colombia Secondary + 530 434 387 325 343 10

Tchad Primary 918 910 913 926 818 578 Dominican RepublicNo schooling 932 880 715 598 637 223

Tchad Secondary + 780 * 948 (939) (937) * Dominican RepublicPrimary 801 761 488 410 551 67

Togo No schooling 821 871 881 843 862 458 Dominican RepublicSecondary + 613 599 227 372 (413) (13)

Togo Primary 791 752 863 862 761 337 Guatemala No schooling 871 914 757 920 846 469

Togo Secondary + 673 643 498 385 (629) (52) Guatemala Primary 794 739 700 807 826 274

Uganda No schooling 936 942 878 859 875 582 Guatemala Secondary + 810 593 505 (495) * *

Uganda Primary 926 919 934 886 807 569 Haiti No schooling 853 904 946 821 789 472

Uganda Secondary + 859 843 846 762 908 423 Haiti Primary 788 853 766 816 698 293

Zambia No schooling 883 905 813 827 829 446 Haiti Secondary + 611 522 431 (578) * *

Zambia Primary 886 912 879 853 859 520 Nicaragua No schooling 846 755 796 699 689 245

Zambia Secondary + 747 727 820 795 698 247 Nicaragua Primary 772 708 670 565 553 114

Zimbabwe No schooling (747) * (588) (556) (605) * Nicaragua Secondary + 525 409 366 292 418 10

Zimbabwe Primary 807 639 672 428 575 85 Peru No schooling 949 901 871 789 750 441

Zimbabwe Secondary + 630 561 450 364 557 32 Peru Primary 851 733 641 602 689 166

Peru Secondary + 545 462 392 360 379 13

Note: Parity progression ratio is the proportion of women at each parity who attain the next parity within five years. Parentheses indicates the ratio is based on 25 to 49 

unweighted women reaching lower parity. An asterisk indicates fewer than 25 unweighted women reached lower parity and the ratio is not shown.
41

4.2.3 Birth Intervals

The average birth interval 

as calculated by the median duration of closed intervals between

births is 33 months (Table 4.8).

Overall, there is practically no variation by parity (Table 4.9). The

Latin America/Caribbean and North 

Africa/Near East regions have the

shortest averages of interval length 

(31 months), and Asia and subSaharan Africa have the longest

averages (34 months) (Figure 4.3). 

There is some varia tion within the 

regions, however. 

In sub-Saharan Africa,

Comoros, Madagascar, and Uganda 

have the shortest intervals (all 29 

months), and South Africa and Zimbabwe have the longest (47 and 40 

months, respectively). It is interesting to note that whereas in the

Comoros, intervals beginning with 

first or second order births last 27 

months on average, in South Africa, 

they last 50 months, a difference of 

23 months (Table 4.9). In Comoros, 

first and second order intervals are 

the shortest of the intervals by

parity, and in South Africa, they are 

the longest.

Birth intervals of less than

30 months on average occur in two 

of the four countries of the Near

East/North Africa region (Jordan and 

Yemen), in the Philippines in Asia, 

and in Guatemala and the Dominican Republic in the Latin America/

Caribbean region. Jordan has the

shortest average interval (26 months) 

and the shortest interval by birth

order (23 months for intervals beginning with birth order 1 or 2).

Table 4.8 Preceding birth intervals (median), by residence and

education, Demographic and Health Surveys, 1995-1999

Education

Residence

Region and

country Urban Rural

No

schoolingPrimary

Secondary

or more Total

Sub-Saharan Africa

Benin 35 34 34 34 37 34

Burkina Faso 38 34 35 35 41 35

Cameroon 33 31 30 33 33 32

Central African 

Republic 31 30 31 30 32 31

Comoros 30 28 29 29 31 29

Côte d'Ivoire 34 33 33 33 36 33

Eritrea 32 31 31 32 30 31

Ghana 43 37 38 38 39 38

Guinea 37 35 35 35 40 35

Kenya 35 33 34 33 34 33

Madagascar 31 29 29 29 30 29

Mali 31 30 30 31 32 30

Mozambique 34 35 35 34 38 35

Niger 32 30 31 30 35 31

Senegal 34 33 33 32 35 33

South Africa 54 43 44 47 49 47

Tanzania 37 33 35 33 36 34

Tchad 30 32 31 31 32 31

Togo 40 35 35 38 40 36

Uganda 28 29 31 29 28 29

Zambia 32 32 33 32 33 32

Zimbabwe 43 39 39 40 40 40

Near East/North Africa

Egypt 37 30 31 35 33 32

Jordan 26 25 28 26 25 26

Turkey 41 32 31 38 44 37

Yemen 29 28 29 26 28 28

Asia

Bangladesh 43 36 36 37 41 37

India 31 31 31 30 31 31

Indonesia 48 44 44 48 42 45

Kazakhstan 39 29 * * 32 32

Kyrgyz Republic 34 31 * * 32 32

Nepal 33 32 33 30 29 32

Philippines 29 28 26 28 29 28

Uzbekistan 32 30 * * 31 31

Vietnam 48 35 30 36 36 36

Latin America/Caribbean

Bolivia 33 29 31 29 35 31

Brazil 38 30 31 33 40 36

Colombia 38 29 30 31 38 34

Dominican Republic 31 27 28 28 32 29

Guatemala 32 28 28 29 34 29

Haiti 32 30 31 30 30 30

Nicaragua 33 29 28 30 38 30

Peru 37 30 31 31 37 33

Note: An asterisk indicates fewer than 25 unweighted women reached 

lower parity and the ratio is not shown.
42

Table 4.9 Preceding birth intervals (median) by sex, survival, and birth order of preceding child, 

Demographic and Health Surveys, 1995-1999

Sex of

preceding child

Survival of 

preceding child Birth order of preceding child

Region and

country Male Female Living Dead 1-2 3-5

6 or

higher

Sub-Saharan Africa

Benin 34.0 33.6 34.8 27.7 33.9 33.9 33.4

Burkina Faso 34.6 34.9 36.1 28.5 33.9 35.2 35.4

Cameroon 32.0 31.1 32.5 25.2 31.5 31.6 31.5

Central African Republic 30.6 30.7 31.4 26.1 30.4 31.4 29.6

Comoros 28.6 29.1 29.7 23.5 26.9 30.6 29.9

Côte d'Ivoire 33.0 33.4 34.1 26.7 34.4 32.8 31.9

Eritrea 31.2 31.3 31.7 27.7 31.7 32.2 29.1

Ghana 38.9 37.4 39.1 28.8 38.2 38.8 37.5

Guinea 35.2 35.6 36.4 26.5 35.3 35.6 34.9

Kenya 32.9 32.9 33.7 25.0 32.5 33.1 33.3

Madagascar 29.5 29.2 30.0 25.8 28.9 29.5 29.8

Mali 30.3 30.3 31.7 26.1 29.9 30.7 30.2

Mozambique 35.2 33.9 35.8 29.1 34.7 34.8 33.4

Niger 30.9 30.4 32.3 26.2 29.9 31.4 30.7

Senegal 33.0 33.0 33.5 29.3 33.0 33.2 32.8

South Africa 47.1 47.0 47.9 30.6 49.7 45.6 36.1

Tanzania 33.6 33.7 34.2 28.1 33.4 33.8 33.9

Tchad 31.0 31.2 32.1 27.0 30.5 31.8 31.1

Togo 35.6 36.5 36.7 30.6 36.5 36.2 34.5

Uganda 29.4 29.2 29.7 26.5 29.0 29.3 29.7

Zambia 32.3 31.6 32.7 26.7 31.3 31.7 33.9

Zimbabwe 40.1 39.8 41.3 28.5 39.5 41.6 39.4

Near East/North Africa

Egypt 33.6 30.8 33.0 25.4 30.5 34.2 34.2

Jordan 26.1 24.9 25.8 18.8 22.6 27.8 29.9

Turkey 38.0 36.1 38.4 21.6 37.6 37.5 33.6

Yemen 28.9 27.8 28.8 23.4 26.1 28.3 30.0

Asia

Bangladesh 37.1 36.2 37.9 26.8 37.8 36.1 33.4

India 31.1 30.6 31.6 25.2 30.7 31.1 29.9

Indonesia 44.9 45.8 46.9 31.7 47.3 46.2 35.8

Kazakhstan 31.0 32.2 32.1 23.9 30.7 33.2 *

Kyrgyz Republic 32.3 31.6 32.9 19.6 29.2 37.5 38.2

Nepal 33.0 31.1 32.7 26.4 31.4 32.4 32.9

Philippines 28.7 27.8 28.5 24.7 26.7 30.2 28.6

Uzbekistan 32.4 29.4 31.1 22.3 28.1 35.0 38.7

Vietnam 36.3 35.1 36.3 24.9 35.7 35.9 33.1

Latin America/Caribbean

Bolivia 30.2 30.8 31.3 24.3 31.0 30.4 30.1

Brazil 35.5 35.9 36.9 22.9 38.6 30.0 31.0

Colombia 33.4 34.2 34.4 23.0 35.3 32.2 30.0

Dominican Republic 28.9 29.1 29.7 24.8 29.1 29.1 28.0

Guatemala 29.5 28.3 29.2 24.0 28.8 29.4 28.3

Haiti 30.4 30.5 30.9 26.6 29.4 31.2 30.9

Nicaragua 30.8 30.1 30.8 24.1 31.3 31.5 27.6

Peru 32.9 33.1 33.6 27.1 33.7 33.3 31.3

Note: An asterisk indicates fewer than 25 unweighted women reached lower parity and the ratio is not 

shown.
43

Figure 4.3 Median length of preceding birth interval by region,

Demographic and Health Surveys, 1995-1999

34

31

34

31

33

Sub-Saharan

Africa

Near East/

North Africa

Asia Latin America/

Caribbean

Grand

Average

15

20

25

30

35

40

Months

Urban-Rural Residence

In sub-Saharan Africa, urban areas have an average length of birth interval that is two months 

longer than rural areas (Figure 4.4). The countries with the greatest urban-rural differences in birth 

interval in sub-Saharan Africa are South Africa and Ghana, where urban areas have average intervals of 

11 and six months longer than rural areas, respectively.

In both the Near East/North Africa region and Asia, urban areas have average lengths of intervals 

that are four months longer than in rural areas. In the Latin America/Caribbean region, the urban

Figure 4.4 Median length of preceding birth interval by residence and region,

Demographic and Health Surveys, 1995-1999

35

33

37

34

35

33

29

33

29

32

Sub-Saharan

Africa

Near East/

North Africa

Asia Latin America/

Caribbean

Grand

Average

15

20

25

30

35

40

Months

Urban Rural
44

advantage is five months on average. However, in Brazil, Colombia, and Peru, this advantage rises to 

seven months or more. 

Overall, the longer intervals in urban areas are likely due to higher rates of contraceptive use, 

enough to offset a decrease in interval length due to shortened postpartum amenorrhea and/or abstinence 

that may also be found in urban areas.

Women’s Education

One would expect that as education of women increases, changes in fertility would begin by 

reductions in the length of breastfeeding and postpartum abstinence, followed by contraceptive practice 

that is mainly for limiting the number of births. These changes would lead to shorter birth intervals 

among more educated women. In none of the regions is this hypothesis confirmed. Indeed, in sub-Saharan 

Africa, the duration of birth intervals for women with secondary or more education, averaged across 

parities and countries, is two months longer than for women with no education or for women with 

primary education (Figure 4.5). In fact, in only two of the 22 sub-Saharan countries does the birth interval 

for women with no education exceed that for women with secondary or more education, and that is only 

by one month in Eritrea. In Uganda, the excess is three months. At the other extreme, in Burkina Faso, 

Guinea, Niger, South Africa, and Togo, the women with secondary or more education have intervals four 

or more months longer than women with no education. 

In the Latin America/Caribbean region, women with secondary or more education have birth 

intervals that are six months longer, on average, than women with no education. In the Near East/North 

Africa region, the average of the difference is three months, and in Asia, this difference is one month on 

average.

Figure 4.5 Median length of preceding birth interval by mother’s education and 

region, Demographic and Health Surveys, 1995-1999

33

30

33

30

32

33

31

35

30

33

35

32

34

35 35

Sub-Saharan

Africa

Near East/

North Africa

Asia Latin America/

Caribbean

Grand

Average

15

20

25

30

35

40

Months

No schooling Primary Secondary
45

Survival of the Preceding Child

The average duration of birth intervals varies substantially according to the survival of the 

preceding child. There are three mechanisms that can explain this difference. The first is the cessation of 

breastfeeding, which leads to a cessation of postpartum amenorrhea. The second is a desire to replace the 

child, which can lead to a reduction in postpartum abstinence and less use of contraception. A third 

mechanism is that short birth intervals increase the preceding child’s chance of dying. 

Overall, the average duration is eight months shorter if the preceding child did not survive until 

the date of the survey. The smallest reduction in birth interval occurred in Uganda (three months), and the 

greatest occurred in South Africa and Turkey (both 17 months). Nine other countries had reductions of 10 

or more months in the birth interval if the preceding child died: Bangladesh (11), Brazil (14), Ghana (10), 

Guinea (10), Indonesia (15), the Kyrgyz Republic (13), Vietnam (11), and Zimbabwe (13).

By region, the greatest reductions in the duration of birth intervals occurred in the Near

East/North Africa region and Asia (nine months), and the least occurred in sub-Saharan Africa (seven 

months).

Sex of the Child

Overall, there is almost no difference in the duration of birth intervals according to the sex of the 

preceding child. In only five countries does the difference between intervals by sex vary by more than one 

month. In each of those five countries, birth intervals are longer if the preceding birth was of a male child. 

Egypt and Uzbekistan had intervals three months longer following a male birth, and Ghana, Nepal, and 

Turkey had intervals two months longer. 

Given that male children generally have higher rates of mortality, it could be expected on that 

basis that birth intervals following a boy’s birth would be shorter than those following a girl’s due to 

cessation of breastfeeding and therefore a reduction in the duration of postpartum amenorrhea. Therefore, 

it is unclear whether the longer intervals following a male birth are due to longer postpartum amenorrhea 

(due to longer breastfeeding or later introduction of complementary foods), longer postpartum abstinence, 

or greater use of contraception for spacing births.

Short Birth Intervals

Birth intervals have been shown to have a substantial effect on the mortality of children (Hobcraft 

et al., 1983). Initial studies of intervals classified into two-year groups indicated that birth intervals 

shorter than 24 months are associated with elevated mortality risks. A subsequent study using intervals 

classified into six-month groups revealed that births occurring after an interval of 24 to 35 months also 

have elevated mortality risks compared with intervals of 36 months or more (Rutstein, 2000).

Tables 4.10 and 4.11 show the percentages of births after intervals of less than 24 months and less 

than 36 months, respectively, for all births and by residence and education. The discussion here will 

concentrate on intervals of less than 36 months to avoid unnecessary repetition.

Overall, 58 percent of births of second order or higher occur after an interval of 35 months or 

less. The Near East/North Africa region has the highest average percentage of short-interval births 

(62 percent), and Asia has the lowest average percentage (56 percent), with the Latin America/Caribbean 

region and sub-Saharan Africa in between (60 percent and 58 percent, respectively).

By country, there is wide variation in the percentages of births after a short interval. In South 

Africa and Indonesia, 33 and 36 percent of children, respectively, are born after a short interval. At the 

other extreme, in Jordan and Uganda, 74 and 70 percent, respectively, of births have short intervals.
46

Table 4.10 Percentage of birth intervals less than 24 months, by residence and education, Demographic 

and Health Surveys, 1995-1999

Education

Region and Residence

country Urban Rural

No

schooling Primary

Secondary

or more Total

Sub-Saharan Africa

Benin 15 17 17 15 13 17

Burkina Faso 12 18 17 15 9 17

Cameroon 23 26 29 23 21 25

Central African Republic 24 27 26 27 22 26

Comoros 32 35 35 35 29 34

Côte d'Ivoire 17 22 21 20 16 20

Eritrea 28 25 25 27 31 26

Ghana 12 14 14 13 13 13

Guinea 14 18 17 16 11 17

Kenya 24 23 21 23 24 23

Madagascar 29 31 35 30 28 31

Mali 22 27 27 23 18 26

Mozambique 19 18 20 18 11 19

Niger 21 25 24 27 21 25

Senegal 18 18 17 20 18 18 

South Africa 11 16 14 13 14 14

Tanzania 13 18 18 18 15 18

Tchad 26 23 24 24 24 24

Togo 10 16 16 11 9 14

Uganda 35 27 25 29 34 28

Zambia 20 19 18 20 19 19

Zimbabwe 12 11 11 11 11 11

Near East/North Africa

Egypt 22 28 27 22 28 26

Jordan 43 48 35 40 46 44

Turkey 22 32 30 26 19 26

Yemen 35 37 36 43 37 37

Asia

Bangladesh 17 18 18 18 16 18

India 29 28 28 28 30 28

Indonesia 16 15 14 14 19 15

Kazakhstan 26 39 * * 34 34

Kyrgyz Republic 28 30 * * 30 30

Nepal 25 24 24 26 30 24

Philippines 35 36 41 34 36 36

Uzbekistan 30 30 * * 30 30

Vietnam 18 19 23 20 18 19

Latin America/Caribbean

Bolivia 27 30 26 30 27 28

Brazil 27 35 32 33 25 29

Colombia 26 35 33 32 26 30

Dominican Republic 33 38 33 37 33 35

Guatemala 32 32 30 34 28 32

Haiti 26 25 24 27 30 25

Nicaragua 30 34 35 34 26 32

Peru 23 26 25 25 24 24

Note: An asterisk indicates fewer than 25 unweighted women reached lower parity and the ratio is not shown.
47

Table 4.11 Percentage of birth intervals less than 36 months, by residence and education, 

Demographic and Health Surveys, 1995-1999

Education

Region and Residence No Secondary

country Urban Rural schooling Primary or more Total

Sub-Saharan Africa

 Benin 55 60 59 57 46 58

 Burkina Faso 42 55 54 56 36 54

 Cameroon 60 64 69 60 58

 Central African Republic 65 67 66 68 61

 Comoros 62 70 68 70 63

63

66

68

 Côte d'Ivoire 55 61 60 59 50 6859

 Eritrea 61 66 65 64 61 65

 Ghana 35 46 46 44 41 44

 Guinea 48 54 53 55 42 53

 Kenya 53 58 55 59 56 58

 Madagascar 64 68 68 68 65 67

 Mali 62 68 67 65 59 66

 Mozambique 55 53 52 55 47 54

 Niger 62 69 69 66 52 68

 Senegal 57 62 61 60 56 60

 South Africa 26 39 37 33 32 33

 Tanzania 47 60 55 59 50 58

 Tchad 69 65 65 69 64 66

 Togo 40 53 53 45 40 50

 Uganda 69 70 64 72 74 70

 Zambia 64 64 61 66 60 64

 Zimbabwe 33 43 42 40 39 40

Near East/North Africa

 Egypt 48 64 62 52 57 58

 Jordan 72 81 70 71 75 74

 Turkey 42 57 59 46 35 48

 Yemen 66 69 68 72 68 68

Asia

 Bangladesh 37 49 50 47 40 48

 India 61 63 62 64 62 63

 Indonesia 35 37 37 34 41 36

 Kazakhstan 49 63 * * 57 57

 Kyrgyz Republic 52 60 * * 58 58

 Nepal 58 61 59 68 70 61

 Philippines 62 69 68 69 64 66

 Uzbekistan 59 64 * * 63 63

 Vietnam 37 53 64 50 50 51

Latin America/Caribbean

 Bolivia 54 69 62 68 51 62

 Brazil 47 60 60 54 45 51

 Colombia 47 64 59 59 47 54

 Dominican Republic 58 68 70 64 57 62

 Guatemala 60 72 72 68 54 68

 Haiti 61 66 63 67 62 65

 Nicaragua 55 66 68 63 48 60

 Peru 48 65 63 60 48 56

Note: An asterisk indicates fewer than 25 unweighted women reached lower parity and the 

ratio is not shown.
48

Children of mothers residing in urban areas and children of mothers with secondary education or 

more are less likely to be born after short birth intervals than children of mothers in rural areas or of 

mothers with lower than secondary education. The overall difference between urban and rural areas is 

8 percent. Figure 4.6 shows the average values. The excess of short-interval births in rural sub-Saharan 

Africa is 10 percent or more than that of urban areas in six of 22 countries. A 10 percent or greater 

difference also occurs in two of four Near East/North Africa countries, three of nine Asian countries, and 

all but one of the eight Latin American/Caribbean countries. 

The overall difference between women with no schooling and secondary or more education is 

7 percent.10 Average values for the region by education are shown in Figure 4.7. The Latin America/

Caribbean region has the most countries with a 10 percent or more difference (seven of eight countries) 

and an average difference for the region of 13 percent. Seven countries in sub-Saharan Africa have 

10 percent or more difference, as well as one in Near East/North Africa and two in Asia. In only four 

countries—Jordan, Indonesia, Nepal, and Uganda—are there more children of women with secondary or 

more education with short birth intervals than children of women with no schooling.

Figure 4.6 Percentage of preceding birth intervals less than 36 months in length 

by residence and region, Demographic and Health Surveys, 1995-1999

54

57

50

54 53

60

68

57

66

61

Sub-Saharan

Africa

Near East/

North Africa

Asia Latin America/

Caribbean

Grand

Average

15

25

35

45

55

65

75

Percentage

Urban Rural

 

10 In three countries in Asia, former Soviet republics, there are too few births to women of less than 

secondary education to show the percentage of children born after short birth intervals. These countries are not 

included in the regional or overall averages.
49

Figure 4.7 Percentage of preceding birth intervals less than 36 months in length 

by mother’s education and region, Demographic and Health Surveys, 1995-1999

59

65

57

65

60 59 60

55

63

59

52

59

56

51

54

Sub-Saharan

Africa

Near East/

North Africa

Asia Latin America/

Caribbean

Grand

Average

15

25

35

45

55

65

75

Percentage

No schooling Primary Secondary
50

5 Proximate Determinants Model

5.1 Bongaarts’ Proximate Determinants Model

One useful way to summarize the proximate determinants of fertility is through the use of a 

model. John Bongaarts (1978) developed the best known of these models. Although this model has 

received substantial criticism, primarily due to a lack of consideration of demand factors, this criticism 

rightly applies to a misuse of the model: use of the model to project fertility levels given changes in the 

model’s parameters. As a descriptive tool, the model is useful and does not have the pitfalls involved 

when it is used for projection. The use here is purely descriptive.

The Bongaarts proximate determinants model summarizes the effects of four principal factors on 

fertility: entry into regular sexual relations (Cm, the index of marriage), prevalence and efficacy of 

contraception (Cc), postpartum infecundity due to postpartum lactational amenorrhea and postpartum 

abstinence (Ci), and incidence of induced abortion (Ca). Other proximate determinants, such as frequency 

of sexual relations, fecundability, secondary sterility, and fetal death and still birth rates are considered to 

have minor impacts on differences in fertility due either to reduced variation among countries or small 

effects on fertility; therefore they are essentially held constant in the model. Each of the four parameters 

represents a proportionate decrease in actual fertility from potential fertility (TF, total fecundity).

Empirical studies by Bongaarts led him to adopt 15.3 children as the average level of fertility that women 

would have if they had none of the fertility restrictions imposed by the proximate determinants, that is, if 

they married at age 15, used no contraception, did not breastfeed or abstain from sex after a birth, and had 

no induced abortions.

Cm, the index of entry into marriage, is calculated as the ratio of the total fertility rate to the total 

marital fertility rate.11 

Cc, the index of the effect of contraception, combines method-specific rates of contraceptive use 

with method-specific effectiveness rates estimated from empirical studies. In our application of the 

model, we use the effectiveness rates given by Bongaarts as constants that do not vary over countries or 

within countries.

Ci, the index of the effect of postpartum infecundity, is calculated as the ratio of the waiting time 

from a birth to conception in the absence of lactational amenorrhea or extended postpartum abstinence to 

the increased time due to either or both of the postpartum effects. The duration of postpartum infecundity 

is measured directly in the applications given here.

For most countries, the true incidence of induced abortion is impossible to measure. Due to this 

fact, Ca is not estimated and is assumed to be 1. For a few countries, notably those of central Asia, 

Turkey, and Vietnam, abortion rates can be calculated from the DHS data, although their completeness 

may be questionable because of a reluctance to disclose abortions even in countries where abortion is 

legal. For these countries, the complete Bongaarts model is presented separately.

5.2 Delayed Entry into Sexual Union

Table 5.1 presents the Bongaarts model parameters and total fecundity for countries as a whole 

and for urban and rural areas. The reduction in fertility due to delayed entry into regular sexual relations 

 

11 In the DHS surveys, marital rates are for women who have ever been married (ever-married rates). 

Women are considered ever married if they were married to or lived with a man at any time. In South Africa, 

because of the low proportion of women in cohabiting consensual unions and the high proportion of births outside of 

marriage or a consensual union, women who had a regular sexual partner at the time of the survey were included in 

the ever-married category for the sake of the marital fertility rate in the calculation of the Cm index.
51

Table 5.1 Bongaarts’ proximate determinants of fertility model parameters, by urban-rural residence, Demographic and Health 

Surveys, 1995-1999

Cm Cc Ci Total fecundity

Region and

country Urban Rural Total Urban Rural Total Urban Rural Total Urban Rural Total

Sub-Saharan Africa

Benin 0.70 0.87 0.80 0.87 0.91 0.89 0.56 0.53 0.54 13.8 15.9 15.2 

Burkina Faso 0.67 0.91 0.87 0.77 0.95 0.93 0.57 0.48 0.48 * 16.7 16.3 

Cameroon 0.60 0.77 0.71 0.74 0.91 0.86 0.64 0.57 0.58 (12.1) 12.9 12.7 

Central African Republic 0.67 0.81 0.75 0.87 0.96 0.93 0.61 0.54 0.57 12.3 11.9 12.0 

Comoros 0.50 0.61 0.57 0.80 0.86 0.85 (0.77) 0.66 0.68 * 14.5 13.9 

Côte d'Ivoire 0.63 0.76 0.71 0.84 0.95 0.91 0.59 0.55 0.56 12.5 13.7 13.3 

Eritrea 0.53 0.82 0.73 0.84 0.99 0.95 0.63 0.56 0.57 15.1 15.4 15.4 

Ghana 0.57 0.74 0.68 0.76 0.86 0.83 (0.62) 0.57 0.58 (10.7) 14.2 13.2 

Guinea 0.75 0.95 0.88 0.89 0.98 0.95 0.49 0.47 0.48 (12.8) 13.8 13.6 

Kenya 0.58 0.67 0.65 0.60 0.71 0.68 (0.71) 0.65 0.66 (11.2) 15.1 14.5 

Madagascar 0.60 0.69 0.67 0.74 0.89 0.85 0.70 0.62 0.64 (12.5) 16.0 15.2 

Mali 0.75 0.94 0.88 0.87 0.98 0.95 0.63 0.58 0.59 12.4 13.5 13.3 

Mozambique 0.71 0.83 0.80 0.85 0.98 0.95 0.64 0.52 0.54 11.0 12.0 11.8 

Niger 0.68 0.95 0.89 0.82 0.97 0.95 0.63 0.57 0.58 * 14.5 14.7 

Senegal 0.56 0.84 0.71 0.82 0.96 0.91 0.62 0.56 0.58 14.1 14.7 14.6 

South Africa10.33 0.44 0.38 0.40 0.62 0.51 * 0.59 0.59 * 16.0 15.3 

Tanzania 0.52 0.77 0.70 0.69 0.86 0.82 0.68 0.59 0.60 (11.3) 15.9 15.0 

Tchad 0.80 0.88 0.86 0.94 0.99 0.98 0.60 0.53 0.55 (12.8) 14.0 13.8 

Togo 0.55 0.79 0.70 0.82 0.88 0.86 0.58 0.53 0.54 11.6 16.7 15.4 

Uganda (0.63) 0.80 0.78 (0.69) 0.91 0.89 0.71 0.62 0.63 * 15.3 15.3 

Zambia 0.61 0.73 0.68 0.75 0.89 0.83 0.69 0.60 0.63 15.1 16.8 16.2 

Zimbabwe 0.60 0.72 0.67 0.50 0.63 0.58 0.62 0.61 0.61 (14.6) 15.6 15.5 

Average 0.63 0.80 0.75 0.77 0.89 0.86 0.63 0.57 0.58 12.8 14.8 14.4 

North Africa/Near East

Egypt 0.56 0.69 0.63 0.54 0.65 0.60 0.79 0.70 0.73 12.7 13.3 13.0 

Jordan 0.52 0.54 0.52 0.57 0.63 0.58 0.86 (0.83) 0.85 16.6 (17.6) 16.8 

Turkey 0.65 0.62 0.64 0.48 0.57 0.51 0.79 0.78 0.79 9.7 11.1 10.2 

Yemen 0.66 0.78 0.75 0.73 0.90 0.86 0.76 0.71 0.72 13.5 14.0 14.0 

Average 0.60 0.66 0.64 0.58 0.69 0.64 0.80 0.76 0.77 13.1 14.0 13.5 

Asia

Bangladesh 0.67 0.87 0.85 0.50 0.56 0.55 0.72 0.66 0.67 * 10.6 10.4 

India 0.61 0.79 0.75 0.60 0.64 0.63 0.73 0.65 0.66 8.6 9.3 9.1 

Indonesia 0.53 0.77 0.69 0.48 0.49 0.49 0.72 0.64 0.66 13.1 12.3 12.5 

Kazakhstan 0.47 0.54 0.50 0.47 0.56 0.52 (0.76) * 0.76 (8.8) * 10.3 

Kyrgyz Republic 0.60 0.70 0.67 0.49 0.57 0.55 * 0.71 0.72 * 13.8 12.8 

Nepal 0.69 0.83 0.82 0.61 0.76 0.74 0.70 0.63 0.64 * 12.1 12.0 

Philippines 0.45 0.64 0.53 0.61 0.68 0.65 0.79 0.72 0.75 13.6 14.8 14.2 

Uzbekistan 0.64 0.69 0.67 0.57 0.56 0.57 0.82 0.76 0.78 9.1 12.7 11.3 

Vietnam 0.38 0.56 0.53 0.42 0.39 0.40 * 0.72 0.73 * 16.1 15.1 

Average 0.56 0.71 0.67 0.53 0.58 0.57 0.75 0.69 0.71 10.6 12.7 12.0 

Latin America/Caribbean

Bolivia 0.53 0.72 0.59 0.55 0.76 0.62 0.70 0.64 0.67 15.3 17.2 16.2 

Brazil 0.51 0.66 0.54 0.19 0.37 0.29 0.79 0.84 0.80 28.0 16.1 18.7 

Colombia 0.49 0.66 0.53 0.34 0.38 0.36 0.83 0.80 0.82 16.4 19.9 17.5 

Dominican Republic 0.50 0.72 0.57 0.44 0.44 0.44 0.84 0.82 0.83 14.7 14.9 14.8 

Guatemala 0.60 0.72 0.67 0.55 0.77 0.67 0.72 0.64 0.67 16.5 16.1 16.3 

Haiti 0.56 0.70 0.64 0.79 0.90 0.86 0.72 0.60 0.63 10.3 15.3 13.7 

Nicaragua 0.59 0.74 0.65 0.39 0.50 0.41 0.77 0.72 0.75 16.1 18.3 17.9 

Peru 0.51 0.71 0.57 0.43 0.58 0.48 0.73 0.65 0.69 16.6 19.8 18.0 

Average 0.54 0.70 0.60 0.46 0.59 0.52 0.76 0.71 0.73 16.7 17.2 16.6 

Note: Parentheses indicate the ratio is based on 25 to 49 unweighted women reaching lower parity. An asterisk indicates fewer than 

25 unweighted women reached lower parity and the ratio is not shown.

1

 Cm also based on women with regular partners (see footnote 11, page 51).
52

is greatest in the Latin America/Caribbean region (40 percent, with a Cm index of 0.60) to a low of 

25 percent in sub-Saharan Africa (Cm of 0.75).12 South Africa has the lowest Cm index of all the 

countries. However, this country’s value is misleading since more births occur outside of cohabitation 

than within. 

The lowest values of Cm—indicating the greatest reductions in fertility due to delayed entry into 

sexual union—occur in Comoros (0.57) in sub-Saharan Africa; Jordan (0.52) in the Near East/North

Africa region; the Philippines (0.53), Vietnam (0.53) and Kazakhstan (0.50) in Asia; and Colombia 

(0.53), Brazil (0.54), the Dominican Republic (0.57), Peru (0.57), and Bolivia (0.59) in the Latin

America/Caribbean region. Seven of the 22 countries in sub-Saharan Africa have values of 0.80 or above, 

as do Nepal and Bangladesh in Asia. In the Latin America/Caribbean region, no countries have a Cm of 

0.70 or more, and only Yemen (0.75) does in the Near East/North Africa region.

Urban areas have lower values of Cm than rural areas. By region the urban areas are 0.15 to 0.17 

lower in sub-Saharan Africa, Asia, and the Latin America/Caribbean region. Only in the Near East/North 

Africa region is the difference less (0.06), in part due to a lower Cm for rural areas.

By country, the lowest urban values of Cm occur in Vietnam (0.38), the Philippines (0.45), 

Kazakhstan (0.47), and Colombia (0.49). In the Latin America/Caribbean region, all countries have an 

urban Cm of 0.60 or lower. Regarding the countries of other regions, five of nine countries in Asia have 

urban Cms of 0.60 or lower, as do ten of 22 countries in sub-Saharan Africa and two of four countries in 

the Near East/North Africa region. The highest Cm values for urban areas are in Tchad (0.80), Guinea, and 

Mali (both 0.75).

In rural areas, the highest levels of Cm, indicating little reduction in fertility due to delayed entry 

into sexual union, occur in sub-Saharan Africa, where four countries have values that exceed 0.90 

(Burkina Faso, 0.93; Mali, 0.94; Guinea, 0.95; and Niger, 0.95). Also in sub-Saharan Africa, seven

additional countries have rural Cm values between 0.80 and 0.89. In the other regions, the Cm in the rural 

areas in only two countries equals or exceeds 0.80 (Nepal, 0.83 and Bangladesh, 0.87). Aside from South 

Africa, in these regions, the rural areas of only two countries have Cm values of less than 0.60: Jordan at 

0.54 and Vietnam at 0.56.

5.3 Contraception

The index of the effect of contraception on fertility, Cc, is based on the use of contraceptive 

methods among women in union by type of method and on the efficacy of the methods. Sub-Saharan 

Africa is the region with the least effect of contraception, with a value of Cc of 0.86 on average, which is 

less than the effects of either delayed entry into a union or postpartum infecundity. The Latin

America/Caribbean region has the greatest reduction in fertility due to contraception with an average 

value for Cc of 0.52. Asia, with 0.57, and the Near East/North Africa region, with 0.64, follow. 

By country, no countries in Sub-Saharan Africa, except South Africa, or in the Near East/North 

Africa region have values of Cc less than 0.50. Two of the nine countries in Asia have such low values, 

but a majority (five of eight) in the Latin America/Caribbean region do. Indeed the values of Cc for Brazil 

and Colombia are 0.29 and 0.36, respectively. At the other extreme, nine of 22 sub-Saharan countries 

have Cc values of 0.90 or more, and ten have values between 0.80 and 0.89. Only two other countries 

have values that exceed 0.80: Haiti and Yemen, both with 0.86.

Urban areas have lower values of Cc, indicating a greater effect of contraception, than rural areas. 

The differential is greatest in sub-Saharan Africa and in the Latin America/Caribbean region (0.12 and 

 

12 The values for regions and all countries together are unweighted averages of the model parameters.
53

0.13 points of difference, respectively). The region with the smallest differential is Asia (difference of 

0.05).

In two countries, Vietnam and Uzbekistan, the value of Cc for rural areas is lower than that for 

urban areas (by 0.03 and 0.01, respectively). Other countries with small differences between urban and 

rural areas in the effect of contraception on the level of fertility are the Dominican Republic (no

difference in Cc), India, Indonesia, and Colombia (urban areas lower by 0.01, 0.04, 0.04, and 0.04, 

respectively). Large differences, 0.20 or more, occur in Uganda in sub-Saharan Africa and Bolivia and 

Guatemala in the Latin America/Caribbean region.

5.4 Postpartum Infecundity

Ci measures the effect on fertility of postpartum infecundity due either to lactational amenorrhea 

or extended postpartum abstinence. The duration of the postpartum infecundity period has been measured 

directly in the results presented here.

As a regional value, only Ci for sub-Saharan Africa (0.58) is lower than either Cm (0.75) or Cc

(0.86), indicating that postpartum effects are more important in this region in restricting fertility than 

either delayed entry in sexual union or contraception. The value of Ci indicates that fertility is lower than 

potential fertility by 42 percent because of lactational amenorrhea and extended postpartum abstinence. 

The other regions have similar values for Ci, from 0.71 to 0.77, indicating a reduction in fertility of about 

25 percent.

The variation within regions is less for postpartum infecundity than for the other two proximate 

determinants in three of the four regions. Only in the Latin America/Caribbean region does Cm vary less 

than Ci. In sub-Saharan Africa, 15 of the 22 countries have values of Ci less than 0.60. In the other 

regions, no countries have values less than 0.60. At the other extreme, postpartum infecundity has a 

reduced effect (less than 20 percent, Ci of 0.80 or more) in a handful of countries: Brazil, Colombia, and 

the Dominican Republic in the Latin America/Caribbean region; Kazakhstan in Asia; and Jordan in the 

Near East/North Africa region.

There is not much regional urban-rural difference in Ci, the largest being a value of Ci in rural 

sub-Saharan Africa that is lower by 0.06 than that of urban areas. The largest country differences between 

urban and rural areas are in Haiti, Comoros, and Mozambique, 0.12, 0.12, and 0.11, respectively. Only in 

Brazil does the urban area have a lower value of Ci than the rural area.

5.5 Education

The Bongaarts model was applied to women according to three levels of education: none, 

primary, and secondary or more. The results are shown in Table 5.2. However, due to small numbers in 

many of the education groups, the results are not discussed in detail here. Overall, Cm varies from 0.82 to 

0.68 to 0.51 for no education, primary, and secondary or more education, respectively. Surprisingly, the 

values for Cc are just about the same as those for Cm: 0.82, 0.64, and 0.49, respectively. Ci varies from 

0.55 to 0.63 to 0.74, respectively, indicating that some of the reduction in fertility due to delayed entry 

into marriage and increased use of contraception with higher levels of education is being offset by

reduced levels of postpartum infecundity.

5.6 Induced Abortion

Five countries—Kazakhstan, the Kyrgyz Republic, Uzbekistan, Turkey, and Vietnam—have data 

that allow the calculation of induced rates of abortion and hence Bongaarts’ model Ca values (Table 5.3). 

The Ca values are all over 0.90, indicating a small effect in reducing fertility in these countries. The
54

Table 5.2 Bongaarts’ proximate determinants of fertility model parameters, by education, Demographic and Health Surveys, 1995-1999

Cm Cc Ci Total fecundity

Region and

country

No 

schooling Primary

SecondaryNo 

schooling Primary

SecondaryNo 

schooling Primary

SecondaryNo 

schooling Primary

Secondary

Sub-Saharan Africa

Benin 0.87 (0.70) * 0.92 (0.84) * 0.52 (0.59) * 15.7 * *

Burkina Faso 0.90 * * 0.95 * * 0.48 * * 16.4 * *

Cameroon 0.92 0.73 (0.57) 0.98 0.85 (0.69) 0.56 0.57 (0.65) 12.1 (13.3) *

Central African 

Republic

0.81 0.74 * 0.97 0.91 * 0.53 (0.58) * 11.8 * *

Comoros 0.61 * * 0.89 * * (0.66) * * (14.5) * *

Côte d'Ivoire 0.79 (0.63) * 0.96 (0.84) * 0.55 * * 13.2 * *

Eritrea 0.83 (0.65) * 0.98 (0.89) * 0.56 (0.57) * 14.8 * *

Ghana 0.77 0.68 0.64 0.91 0.84 0.78 (0.53) (0.60) 0.63 (15.1) * 11.1 

Guinea 0.93 * (0.59) 0.97 * (0.81) 0.47 * * 13.5 * *

Kenya 0.78 0.69 0.54 0.83 0.72 0.54 (0.63) 0.66 (0.69) (13.5) 14.0 *

Madagascar 0.75 0.67 0.59 0.98 0.88 0.68 0.59 0.64 0.71 14.9 15.9 *

Mali 0.93 * * 0.97 * * 0.58 * * 13.2 * *

Mozambique 0.89 0.77 * 0.98 0.93 * 0.50 0.56 * 11.2 12.5 *

Niger 0.93 * * 0.96 * * 0.57 * * 14.6 * *

Senegal 0.82 (0.58) (0.44) 0.95 (0.83) (0.68) 0.56 (0.60) * 14.1 16.5 *

South Africa * 0.43 0.35 * 0.63 0.45 * (0.59) 0.60 * (14.6) 14.7 

Tanzania 0.78 0.69 * 0.91 0.78 * 0.55 0.63 * 16.1 (14.2) *

Tchad 0.89 (0.83) * 0.99 (0.96) * 0.54 (0.56) * 13.4 * *

Togo 0.82 0.67 (0.47) 0.89 0.85 (0.77) 0.53 0.55 * 16.0 14.0 *

Uganda 0.86 0.77 * 0.96 0.88 * 0.59 0.64 * 14.3 15.9 *

Zambia 0.78 0.73 (0.53) 0.91 0.86 (0.72) 0.61 0.62 * 15.3 16.4 *

Zimbabwe * 0.77 0.60 * 0.62 0.52 * 0.61 (0.62) * 14.7 *

Average 0.83 0.64 0.48 0.94 0.73 0.52 0.56 0.52 0.65 14.2 14.7 12.9

North Africa/Near 

East Egypt 0.77 0.68 0.37 0.66 0.58 0.56 0.69 0.73 0.80 13.0 12.4 14.8 

Jordan * 0.57 0.52 * 0.63 0.57 * (0.85) 0.86 * (15.0) 16.8 

Turkey 0.77 0.71 0.48 0.66 0.51 0.44 (0.70) 0.81 0.83 (11.4) 9.2 (10.5)

Yemen 0.79 * * 0.89 * * 0.71 * * 13.8 * *

Average 0.78 0.65 0.46 0.74 0.57 0.52 0.23 0.80 0.83 5.1 12.2 7.0

Asia

Bangladesh 0.92 0.92 (0.71) 0.58 0.53 (0.53) 0.65 0.68 (0.70) 11.3 9.6 *

India 0.89 0.80 0.59 0.57 0.56 0.61 0.63 0.68 0.73 11.0 9.3 8.3 

Indonesia 0.74 0.81 0.56 0.59 0.48 0.46 (0.62) 0.64 0.72 (9.7) 12.3 13.5 

Kazakhstan * * 0.50 * * 0.52 * * 0.76 * * 10.3 

Kyrgyz Republic * * 0.67 * * 0.54 * * 0.72 * * 12.8 

Nepal 0.87 (0.82) * 0.77 (0.69) * 0.63 (0.64) * 12.0 * *

Philippines * 0.68 0.49 * 0.69 0.62 * 0.73 0.76 * 14.6 14.0 

Uzbekistan * * 0.67 * * 0.57 * * 0.78 * * 11.3 

Vietnam (0.60) 0.59 0.49 (0.54) 0.42 0.34 * (0.70) 0.76 * (15.5) 16.6 

Average 0.80 0.77 0.59 0.61 0.56 0.52 0.63 0.68 0.74 11.0 12.3 12.4

Latin America/Caribbean

Bolivia (0.79) 0.73 0.49 (0.85) 0.70 0.50 * 0.65 0.72 * 16.0 15.2 

Brazil (0.91) 0.67 0.47 (0.03) 0.34 0.30 * 0.81 0.79 * 16.0 17.0 

Colombia * 0.65 0.47 * 0.38 0.19 * 0.80 0.84 * 17.0 29.6 

Dominican Republic 0.85 0.70 0.44 0.56 0.41 0.45 0.76 0.83 0.86 (13.7) 15.2 (14.0)

Guatemala 0.81 0.69 0.50 0.84 0.65 0.51 0.63 0.66 (0.81) 15.7 17.2 (13.5)

Haiti 0.76 0.65 * 0.92 0.84 * (0.60) 0.65 * (14.6) (13.3) *

Nicaragua 0.76 0.75 0.53 0.56 0.44 0.36 0.71 0.74 0.79 18.7 17.1 15.8 

Peru 0.74 0.73 0.49 0.69 0.53 0.42 (0.60) 0.67 0.73 (21.8) 18.2 16.3 

Average 0.80 0.70 0.48 0.64 0.54 0.39 0.66 0.73 0.79 16.9 16.3 17.3

Grand Average 0.82 0.68 0.51 0.82 0.64 0.49 0.55 0.63 0.74 13.4 14.4 13.4 

Note: Parentheses indicate the ratio is based on 25 to 49 unweighted women reaching lower parity. An asterisk indicates fewer than 25 

unweighted women reached lower parity and the ratio is not shown.
55

greatest effect of abortion is found in the Kyrgyz Republic, with a value of 0.93. There is little or no 

difference between urban and rural areas in the five countries. The greatest difference again occurs in the 

Kyrgyz Republic, where urban areas have a Ca of 0.89 and rural areas have a Ca of 0.94.

Table 5.3 Bongaarts’ proximate determinants of fertility model parameters, by urban-rural residence, five selected countries, Demographic and Health Surveys, 1996-1999

Cm Cc Ci Ca Total fecundity

Region and

country Urban Rural Total Urban Rural Total Urban Rural Total Urban Rural Total Urban Rural Total

Turkey 0.65 0.62 0.64 0.48 0.57 0.51 0.79 0.78 0.79 0.97 0.97 0.97 10.0 11.4 10.5 

Kazakhstan 0.48 0.54 0.50 0.46 0.56 0.52 (0.76) (0.76) 0.76 0.94 0.96 0.95 (9.6) (12.1) 11.1 

Kyrgyz Republic 0.60 0.70 0.67 0.49 0.57 0.55 * 0.71 0.72 0.89 0.94 0.93 * 14.7 13.8 

Uzbekistan 0.64 0.69 0.67 0.57 0.56 0.57 0.82 0.76 0.78 0.94 0.98 0.96 9.1 13.0 11.8 

Vietnam 0.38 0.56 0.53 0.42 0.39 0.40 * 0.72 0.73 1.00 0.99 0.99 * 16.3 15.3 

Note: Parentheses indicate parameter is based on 25 to 49 unweighted women. An asterisk indicates paramenter is based on fewer than 25 unweighted women and is 

not shown.
56

6 Conclusions

This study examines countries at different stages of the fertility transition. In the Latin

America/Caribbean region, Asia, and the Near East/North Afric a region, the DHS results depict the 

magnitude of the continuing transition. The DHS results also assess the signs of the onset of the fertility 

transition in many countries in sub-Saharan Africa. Among the countries covered, Niger has the highest 

fertility level, while Vietnam has the lowest. The total fertility rates for women age 15-49 range from 2.3 

to 7.2 children per woman (Figure 6.1). The countries can be classified into five groups by TFR level.

Group 1 countries

The first group includes ten countries with TFRs of six children and above. All but one of these 

countries are located in sub-Saharan Africa. They are Benin, Burkina Faso, Eritrea, Madagascar, Mali, 

Niger, Tchad, Uganda, and Zambia. Yemen is the only country from another region in this group. With 

the exception of women in Eritrea, most women in these countries have their first birth by age 20. After 

the first birth, the parity progression ratio is more than 79 percent for parity 5 to 6 in these countries. It is 

interesting to note, however, that the high level of fertility in these countries is not mainly due to shorter 

birth intervals but, rather, is due to early entry into motherhood and to little practice of limiting behavior. 

Only in Madagascar, Uganda, and Yemen are median birth intervals shorter than 30 months. 

Although in the past, birth intervals for high fertility countries were long due to prolonged 

durations of lactational amenorrhea and postpartum abstinence linked to traditional practices considered 

the norm, the countries with high fertility in the late 1990s do not have exceptionally long birth intervals, 

which range from a high of 35 months to a low of 28 months. Contraception, however, still has little 

effect on fertility in these countries. The Bongaarts model parameter for contraception Cc is above 0.80 

for all of these countries, with half of them having values above 0.90, indicating less than 20 and 

10 percent reductions in potential fertility, respectively. However, women in this group of countries who 

have secondary education have lower fertility rates and also have their first birth relatively late. The 

average TFR for women with secondary education of these countries is 3.7 births versus 6.2 for all 

women. In many of the countries in this group, birth intervals tend to be somewhat longer for women 

with secondary or higher education than for women who have no schooling.

Group 2 countries

In the second group, eight countries with TFRs ranging from 5.0 to 5.9 children per woman are 

identified. Six countries are in sub-Saharan Africa (the Central African Republic, Côte d’Ivoire, Guinea, 

Mozambique, Senegal, Tanzania, and Togo) and one country is in the Latin America/Caribbean region 

(Guatemala). Fertility transition for this group is mixed. All but two countries have TFRs in the lower half 

of the range. 

Fertility has started declining, mostly in the urban areas and among women with primary or more 

education, in countries such as Côte d’Ivoire, Guinea, Senegal, Tanzania, Togo, and Guatemala. On the 

other hand, the lower fertility levels observed in the Central African Republic and Mozambique may be 

associated with high levels of sterility (Larsen, 2001; Larsen and Raggers, 2001). In both these countries, 

women who have a primary education have higher fertility rates than women who are uneducated. 

Women in the Central African Republic, Côte d’Ivoire, Guinea, Mozambique, Senegal, and Tanzania 

enter into motherhood as early as women in the first group of countries (i.e., median age at first birth 

before age 20). The majority of women in Togo and Guatemala have their first birth slightly after age 20. 

PPRs are high; that is, they exceed or are equal to 70 percent for all parities in all the countries of the 

group except Guatemala, where the proportion progressing from parity 3 to 4 is less than 70 percent. 
57

Figure 6.1 Fertility levels in selected DHS countries,

Demographic and Health Surveys, 1995-1999

2.0

3.0

4.0

5.0

6.0

7.0

Niger

Uganda

Mali

Yemen

Burkina Faso, Tchad

Eritrea, Zambia

Benin, Madagascar

Senegal

Tanzania

Guinea

Côte d'Ivoire

Mozambique, Togo

Central African Republic

Guatemala

Cameroon, Haiti

Kenya, Nigeria

Comoros, Nepal

Ghana, Jordan

Bolivia

Zimbabwe

Philippines

Egypt, Nicaragua

Peru

Kyrgyz Republic

Bangladesh, Uzbekistan

Dominican Republic

Colombia

South Africa

Indonesia, India

Turkey

Kazakhstan, Brazil

Vietnam
58

Group 3 countries

The third group includes countries with TFRs of 4.0 to 4.9 children per woman. These countries 

come from each of the regions, five from sub-Saharan Africa (Cameroon, Comoros, Ghana, Kenya, and 

Zimbabwe), one from Asia (Nepal), one from the Near East/North Africa region (Jordan), and two from 

the Latin America/Caribbean region (Boliva and Haiti).

In the preceding comparative report on fertility, covering the first half of the 1990s, only 

Zimbabwe from sub-Saharan Africa was in this category. All these countries have already experienced 

substantial fertility declines, although the declines did not occur at the same time. From the medians for 

age at first birth, it would appear that the timing of the onset of reproduction has contributed little to the 

lowered fertility levels in Cameroon and Kenya, where age at first birth is about as early as that observed

in the first group of countries. However, the Cm parameter (for delayed entry into regular sexual union) 

from the Bongaarts model indicates that in both countries, there has been an effect of delayed exposure to 

the risk of pregnancy. The contradiction may be explained by high levels of primary sterility in Cameroon 

and substantial out-of-wedlock births in Kenya. 

Limiting births plays a role in all of the countries in this group, where the cumulative effect of the 

parity progression ratios varies from 35 percent of women reaching parity 6 from parity 1 in Comoros to 

6 percent in Zimbabwe. The countries of the first group have cumulative 1 to 6 ratios of at least 

40 percent and reaching to more than 60 percent. Long birth intervals play a role in the lowered fertility 

levels for only two countries, Ghana and Zimbabwe, which have median lengths of preceding intervals of 

38 and 40 months, respectively. 

Lowered fertility in both urban and rural areas is characteristic of this group as well as substantial 

differences between the areas. Bolivia and Haiti have the largest differences in TFR between urban and 

rural areas, mostly because fertility in rural areas remains high at 5.8 and 5.2 births per woman,

respectively.

Women’s education has approximately the same effect on fertility as residence, partly because 

most women with secondary or higher education reside in urban areas. Women with no education in the 

countries in this group still have a high fertility regime, which is offset by much lower fertility by women 

with secondary or more education. The one exception to this pattern is Jordan, where the TFR is 

approximately the same in all of the three education groups.

Group 4 countries

The fourth group includes nine countries with TFRs from 3.0 to 3.9 children per woman. They 

are located mostly in Asia (Bangladesh, the Kyrgyz Republic, the Philippines, and Uzbekistan) and the 

Latin America/Caribbean region (Colombia, the Dominican Republic, Nicaragua, and Peru), with one 

(Egypt) in the Near East/North Africa region. As indicated by their TFR levels, these countries have 

advanced far in the fertility transition. 

For seven countries, entry into motherhood is delayed, with median ages at first birth over 21 

years. Only in Bangladesh is entry into motherhood very early (median age of 17.2 years). This country 

has the earliest entry, which is substantially younger than the countries of the first group, those with TFRs 

of 6.0 or higher. The lowered fertility level in Bangladesh is due much more to birth limitation and 

spacing than to a delay in the entry into reproduction. The combined effects of the PPRs indicate that only 

7 percent of women with a birth will reach parity 6. The birth interval is long as well, at a median of 37 

months. 

The Bongaarts parameters for Bangladesh also reflect these differences with the other countries 

of the group. The Cm parameter for Bangladesh is only 0.85, while that of the countries in this group with 
59

the next highest value is 0.75 (Kyrgyz Republic and Uzbekistan). On the other hand the parameter for 

contraception, Cc, is 0.55 for Bangladesh, and the value for the postpartum infecundity parameter, Ci, is 

0.67, the lowest of the group.

The low fertility levels of the other countries in this group are due to the combined effects of 

delay in the onset of reproduction and the use of contraception. Lower fertility levels (4.0 or below) are 

observed in rural areas in seven of these countries, with slightly higher rural fertility for Nicaragua and 

the Philippines. At 5.1, Peru has the highest rural TFR of the group. Urban-area fertility of the countries 

in this group is less than 3.0 children per woman by age 39. Except for Bangladesh, the countries in this 

group are predominantly urban, thus bringing down the country TFRs even in those countries with 

somewhat higher rural fertility.

Fertility differences by education and other socioeconomic variables are greater in the Latin 

American/Caribbean region than in the Near East/North Africa or Asia regions.

Group 5 countries

The fifth group of countries—Brazil, India, Indonesia, Kazakhstan, South Africa, Turkey, and 

Vietnam—have the lowest fertility levels among all the countries, with TFRs from 2.3 to 2.9.13 Delayed 

entry into motherhood contributes a reduced amount to the low fertility levels in India, Indonesia and 

South Africa, where the median age at first birth is less than 21 years. In the latter two of these countries, 

fertility limiting is important as shown by the low PPRs even at low parities. 

Birth spacing is important in reducing fertility in all countries except for India, and Kazakhstan, 

as indicated by medians for duration of birth intervals of three or more years (up to 47 months in South 

Africa). In India and Kazakhstan, the median birth interval is somewhat shorter at 31 and 32 months, 

respectively. 

As indicated by the Cc parameter of the Bongaarts model, contraceptive use is important in all 

these countries, with values ranging from 0.52 in Kazakhstan to 0.29 in Brazil. The exception again is 

India, with a Cc value of 0.63. Postpartum infecundity plays a substantial role in three of the countries: 

South Africa (Ci of 0.59), India (0.66), and Indonesia (0.66). Information on induced abortion is available 

in three of these countries; however, induced abortion plays very little role in lowering fertility, as 

evidenced by the Bongaarts Ca parameter of at least 0.95.

Residence still plays a part in level of fertility, although all the countries in the fifth group but 

India are predominantly urban. Rural areas of Brazil and South Africa have TFRs for women under age 

40 of more than three children per woman. Women with no education, while a smaller percentage of the 

population than for countries in the other groups, have relatively high levels of fertility in Brazil, South 

Africa, and Turkey, with TFRs for women under 40 of 3.9 children or more.

This publication has presented evidence of countries covered by DHS surveys in the latter half of 

the 1990s and indicates the variation by country along the fertility transition. Compared with the previous 

 

13 The TFR estimated by Retherford and Mishra (2001) would put India into the fourth group of countries, 

where its values for age at first birth, birth spacing, use of contraception, postpartum infecundity and the percentage 

urban fit better than with the other countries of group five. Indeed, the total potential fecundity (TF) calculated by 

the Bongaarts model, 9.1 births, is the lowest of all countries. If India had the average TF value of the other Asia 

countries, 12.3, then its predicted TFR would be 3.8, one child higher than the reported TFR. However, induced 

abortion, an unmeasured determinant, may account for some of the discrepancy between the reported and calculated 

TFRs. Induced abortion is estimated to range as high as 8 to 11 times the reported number of 600,000. At these 

levels, between 4.1 and 5.6 million births would have been averted through induced abortion. Without this amount 

of induced abortion, the TFR would increase to 3.3 to 3.5 births per woman, near the levels estimated by Retherford 

and Mishra and the author.
60

report on fertility, a smaller proportion of countries is in the first group and a larger proportion is in the 

fifth group. Indeed, several countries, which had been covered by the previous report and preceding 

surveys, have shifted to lower fertility groups: Cameroon, Ghana, Indonesia, Jordan, Kenya, the

Philippines, and Senegal. Colombia is the only country to change to a higher group, although with a TFR 

of 3.0 children per woman, it is just at the lower limit of the group.
61

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Studies No. 10. Calverton, Maryland: Macro International Inc.

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Columbia, Maryland: Department of Statistics and IRD/Macro International Inc.
65

Appendix A

Life Table Estimates (Trimeans)

of Birth Intervals by Parity
66

Table A.1 Birth Intervals (trimean) in months for births in the 6-year period preceding the survey, 

Demographic and Health Surveys, 1995-1999

Birth Interval (trimean) in months

Region and Parity Parity Parity Parity Parity

country 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 Average

Sub-Saharan Africa

 Benin 33 35 34 34 34 34

 Burkina Faso 35 36 34 36 36 35

 Cameroon 31 31 32 29 31 31

 Central African Republic 30 32 31 32 33 31

 Comoros 25 28 29 27 30 28

 Côte d'Ivoire 35 33 32 34 33 33

 Eritrea 31 31 32 31 34 32

 Ghana 35 36 36 35 34 35

 Guinea 34 34 35 35 35 35

 Kenya 31 32 31 31 32 31

 Madagascar 28 30 30 30 30 30

 Mali 31 32 31 32 32 31

 Mozambique 32 33 32 36 34 33

 Niger 29 31 30 31 31 30

 Senegal 33 31 33 32 33 32

 South Africa 38 36 35 32 38 36

 Tanzania 34 30 33 31 33 32

 Tchad 31 31 32 33 32 32

 Togo 35 34 35 35 35 35

 Uganda 29 30 29 29 29 29

 Zambia 31 30 31 30 33 31

 Zimbabwe 38 38 38 32 34 36

Average 32 32 32 32 33 32

Near East/North Africa

 Egypt 28 33 31 31 30 31

 Jordan 21 26 27 28 27 26

 Turkey 30 33 30 31 32 31

 Yemen 25 27 28 28 27 27

Average 26 30 29 29 29 29 

Asia

 Bangladesh 34 35 34 33 33 34

India 30 31 31 31 31 31

 Indonesia 35 35 34 35 33 34

 Kazakstan 24 29 27 26 31 27

 Kyrgyz Republic 25 29 32 28 28 29

 Nepal 30 32 32 32 32 31

 Philippines 24 27 28 28 28 27

 Uzbekistan 26 33 32 34 33 31

 Vietnam 31 33 31 33 29 31

Average 29 31 31 31 31 31 

Latin America/Caribbean

 Bolivia 28 27 29 28 29 28

 Brazil 32 30 25 24 25 27

 Colombia 30 30 31 28 29 30

 Dominican Republic 27 28 28 26 29 28

 Guatemala 26 28 29 28 28 28

 Haiti 28 30 31 32 31 30

 Nicaragua 28 28 32 27 27 28

 Peru 28 30 29 29 31 29

Average 28 29 29 28 29 29 

Grand Average 30 31 31 31 31 31
67

Table A.2 Birth intervals (trimean) for the 6-year period preceding the survey, by urban-rural residence, Demographic and Health Surveys, 1995-1999

Birth interval (trimean) in months Birth interval (trimean) in months

Region and Parity Parity Parity Parity Parity Region and Parity Parity Parity Parity Parity

country Residence 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 Average country Residence 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 Average

Sub-Saharan Africa Near East/North Africa

 Benin Urban 34 35 33 31 35 34 Egypt Urban 30 34 35 34 32 33 

 Benin Rural 33 34 33 34 34 33 Egypt Rural 27 30 30 30 30 29 

 Burkina Faso Urban 38 36 39 37 37 37 Jordan Urban 21 26 27 27 29 26 

 Burkina Faso Rural 34 35 35 36 35 35 Jordan Rural 20 24 25 26 25 24 

 Cameroon Urban 28 32 32 30 30 30 Turkey Urban 33 35 30 35 36 34 

 Cameroon Rural 32 32 32 28 31 31 Turkey Rural 28 28 29 25 27 27 

 Central African Republic Urban 29 32 32 32 29 31 Yemen Urban 24 30 30 29 28 28 

 Central African Republic Rural 30 31 31 31 35 32 Yemen Rural 25 27 28 27 27 27 

 Comoros Urban 24 28 29 25 (39) 29 Average Urban 27 31 30 31 31 30 

 Comoros Rural 25 28 30 27 29 28 Average Rural 25 27 28 27 27 27 

 Côte d'Ivoire Urban 33 33 31 34 36 33 

 Côte d'Ivoire Rural 36 33 33 34 31 33 Asia

 Eritrea Urban 27 31 31 31 33 31 Bangladesh Urban 39 37 33 37 27 35 

 Eritrea Rural 32 31 32 30 34 32 Bangladesh Rural 34 36 34 33 33 34 

 Ghana Urban 35 36 37 36 34 36 India Urban 30 31 30 29 30 30 

 Ghana Rural 36 36 36 35 34 35 India Rural 30 31 32 31 31 31 

 Guinea Urban 34 35 37 37 38 36 Indonesia Urban 35 35 35 37 28 34 

 Guinea Rural 35 33 35 35 34 34 Indonesia Rural 34 35 34 34 36 34 

 Kenya Urban 32 32 30 26 (28) 30 Kazakhstan Urban 25 28 35 (30) * 30 

 Kenya Rural 31 32 30 32 32 31 Kazakhstan Rural 24 29 25 26 31 27 

 Madagascar Urban 30 31 30 31 30 30 Kyrgyz Republic Urban 28 28 32 37 (23) 30 

 Madagascar Rural 28 29 30 30 31 30 Kyrgyz Republic Rural 24 30 33 28 29 29 

 Mali Urban 32 32 31 33 33 32 Nepal Urban 32 35 30 31 (31) 32 

 Mali Rural 31 31 32 30 31 31 Nepal Rural 30 31 32 32 32 31 

 Mozambique Urban 31 26 32 30 32 30 Philippines Urban 25 28 28 28 27 27 

 Mozambique Rural 32 34 31 36 36 34 Philippines Rural 25 27 28 28 29 27 

 Niger Urban 28 31 32 32 35 32 Uzbekistan Urban 26 32 36 33 (26) 30 

 Niger Rural 29 31 30 31 32 30 Uzbekistan Rural 27 32 31 35 33 32 

 Senegal Urban 34 33 31 32 34 33 Vietnam Urban 36 35 25 (15) * 28 

 Senegal Rural 33 32 33 32 33 32 Vietnam Rural 30 32 32 32 30 31 

 South Africa Urban 38 35 36 31 41 36 Average Urban 31 32 32 31 27 31 

 South Africa Rural 34 37 34 33 36 35 Average Rural 29 31 31 31 31 31 

 Tanzania Urban 44 30 46 38 31 38 

 Tanzania Rural 32 30 31 32 33 32 Latin America/Caribbean

 Tchad Urban 29 30 31 31 30 30 Bolivia Urban 29 27 30 28 31 29 

 Tchad Rural 31 31 32 34 32 32 Bolivia Rural 27 27 29 28 28 28 

 Togo Urban 39 36 37 37 43 38 Brazil Urban 34 32 27 24 25 28 

 Togo Rural 35 34 35 35 33 34 Brazil Rural 28 27 24 24 25 25 

 Uganda Urban 28 30 27 27 26 27 Colombia Urban 32 32 34 25 32 31 

 Uganda Rural 29 30 29 29 30 29 Colombia Rural 25 26 28 28 28 27 

 Zambia Urban 30 30 31 31 34 31 Dominican Republic Urban 29 29 27 29 37 30 

 Zambia Rural 31 31 31 31 31 31 Dominican Republic Rural 27 28 27 23 27 26 

 Zimbabwe Urban 38 38 40 27 (27) 34 Guatemala Urban 30 31 32 29 27 30 

 Zimbabwe Rural 37 36 36 33 35 36 Guatemala Rural 25 27 28 29 28 27 

Average Urban 32 32 33 32 33 33 Haiti Urban 28 31 31 32 27 30 

Average Rural 32 32 32 32 33 32 Haiti Rural 29 30 30 32 31 30 

 Nicaragua Urban 28 30 33 27 27 29 

 Nicaragua Rural 28 28 30 28 29 29 

 Peru Urban 30 31 30 29 34 31 

 Peru Rural 28 28 28 30 31 29 

Average Urban 30 30 31 28 30 30 

Average Rural 27 28 28 28 28 28 

Note: Parentheses indicate figure is based on 25 to 49 women. An asterisk indicates figure is based on fewer than 25 women and is not shown.
68

Table A.3 Birth intervals (trimeans) for the six-year period preceding the survey, by women's education, Demographic and Health Surveys, 1995-1999

Birth interval (trimean) in months Birth interval (trimean) in months

Region and Parity Parity Parity Parity Parity Region and Parity Parity Parity Parity Parity

country Education 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 country Education 1 to 2 2 to 3 3 to 4 4 to 5 5 to 6

Sub-Saharan Africa Near East/North Africa

 Benin No schooling 33 35 33 33 34 Egypt No schooling 27 29 30 30 30

 Benin Primary 31 34 35 35 34 Egypt Primary 28 31 30 32 31

 Benin Secondary + 36 35 37 (38) * Egypt Secondary + 30 37 33 37 29

 Burkina Faso No schooling 34 36 35 36 35 Jordan No schooling * 26 27 28 30

 Burkina Faso Primary 33 34 34 41 (43) Jordan Primary 21 24 25 25 23

 Burkina Faso Secondary + 41 37 (50) * * Jordan Secondary + 21 26 27 27 27

 Cameroon No schooling 28 30 32 29 35 Turkey No schooling 25 28 29 30 29

 Cameroon Primary 33 32 31 28 27 Turkey Primary 30 33 31 33 36

 Cameroon Secondary + 30 32 33 30 37 Turkey Secondary + 36 38 27 (24) *

 Central African Republic No schooling 30 31 31 32 33 Yemen No schooling 25 27 28 27 28

 Central African Republic Primary 30 31 30 32 32 Yemen Primary 25 27 28 32 26

 Central African Republic Secondary + 29 34 32 30 26 Yemen Secondary + 25 34 31 40 29

 Comoros No schooling 23 26 29 26 29

 Comoros Primary 26 29 * * (34) Asia

 Comoros Secondary + 30 30 28 (21) * Bangladesh No schooling 33 36 34 33 32

 Côte d'Ivoire No schooling 34 33 33 34 33 Bangladesh Primary 34 35 32 33 34

 Côte d'Ivoire Primary 34 33 34 32 30 Bangladesh Secondary + 38 38 39 48 (26)

 Côte d'Ivoire Secondary + 35 32 29 42 30 India No schooling 30 31 32 31 32

 Eritrea No schooling 31 31 32 30 34 India Primary 29 31 31 30 29

 Eritrea Primary 30 31 29 30 32 India Secondary + 30 30 30 27 29

 Eritrea Secondary + 28 32 30 * * Indonesia No schooling 38 34 32 35 37

 Ghana No schooling 36 36 36 36 32 Indonesia Primary 35 36 36 36 34

 Ghana Primary 37 35 36 34 38 Indonesia Secondary + 34 35 31 33 28

 Ghana Secondary + 34 36 36 35 37 Kazakhstan No schooling * * * * *

 Guinea No schooling 35 34 35 35 34 Kazakhstan Primary * * * * *

 Guinea Primary 35 * 38 34 32 Kazakhstan Secondary + 24 29 28 26 31

 Guinea Secondary + 35 39 36 43 (33) Kyrgyz Republic No schooling * * * * *

 Kenya No schooling 30 32 29 30 34 Kyrgyz Republic Primary * * * * *

 Kenya Primary 32 31 31 32 32 Kyrgyz Republic Secondary + 25 29 32 28 28

 Kenya Secondary + 30 33 30 32 33 Nepal No schooling 29 32 32 32 32

 Madagascar No schooling 28 30 31 30 29 Nepal Primary 31 31 29 27 (28)

 Madagascar Primary 29 30 29 30 32 Nepal Secondary + 32 32 33 (33) *

 Madagascar Secondary + 29 31 32 30 30 Philippines No schooling (26) (26) (25) (25) *

 Mali No schooling 31 32 31 31 31 Philippines Primary 23 27 28 29 30

 Mali Primary 35 30 30 29 36 Philippines Secondary + 25 28 27 28 26

 Mali Secondary + 28 32 34 30 (37) Uzbekistan No schooling * * * * *

 Mozambique No schooling 32 33 31 35 33 Uzbekistan Primary * * * * *

 Mozambique Primary 32 33 32 36 35 Uzbekistan Secondary + 26 33 32 34 33

 Mozambique Secondary + 28 48 38 (50) * Vietnam No schooling 28 35 (32) * *

 Niger No schooling 29 30 30 31 32 Vietnam Primary 29 38 33 30 34

 Niger Primary 29 30 31 33 32 Vietnam Secondary + 32 32 27 32 28

 Niger Secondary + 28 32 38 (41) *

 Senegal No schooling 33 32 33 32 34 Latin America/Caribbean

 Senegal Primary 35 32 32 32 34 Bolivia No schooling 27 27 28 27 29

 Senegal Secondary + 29 30 29 28 (43) Bolivia Primary 26 28 29 28 28

 South Africa No schooling 33 38 30 30 39 Bolivia Secondary + 32 30 31 29 31

 South Africa Primary 35 35 39 35 36 Brazil No schooling 26 32 28 24 23

 South Africa Secondary + 38 36 33 31 37 Brazil Primary 28 29 24 24 23

 Tanzania No schooling 33 30 32 28 33 Brazil Secondary + 35 31 28 24 27

 Tanzania Primary 35 30 33 33 34 Colombia No schooling * 30 (24) * (33)

 Tanzania Secondary + 35 33 47 (30) (31) Colombia Primary 25 28 31 28 28

 Tchad No schooling 31 32 32 34 32 Colombia Secondary + 33 33 33 24 31

 Tchad Primary 30 31 29 32 30 Dominican Republic No schooling 27 26 29 24 31

 Tchad Secondary + 29 * 41 (29) (36) Dominican Republic Primary 27 26 27 27 32

 Togo No schooling 35 34 36 34 35 Dominican Republic Secondary + 29 30 31 25 (25)

 Togo Primary 37 37 36 38 36 Guatemala No schooling 25 29 26 28 28

 Togo Secondary + 41 35 36 35 (38) Guatemala Primary 26 27 29 31 27

 Uganda No schooling 30 31 31 31 29 Guatemala Secondary + 34 33 35 (24) *

 Uganda Primary 29 29 28 28 28 Haiti No schooling 29 30 33 30 32

 Uganda Secondary + 26 28 26 31 37 Haiti Primary 28 30 29 34 28

 Zambia No schooling 30 33 30 32 32 Haiti Secondary + 30 29 29 (38) *

 Zambia Primary 30 30 31 30 33 Nicaragua No schooling 26 26 28 26 28

 Zambia Secondary + 32 32 33 32 36 Nicaragua Primary 27 29 32 29 26

 Zimbabwe No schooling (34) * (30) (46) (34) Nicaragua Secondary + 30 30 31 25 27

 Zimbabwe Primary 36 36 38 31 35 Peru No schooling 26 29 29 29 29

 Zimbabwe Secondary + 38 38 37 29 34 Peru Primary 29 29 28 30 33

Peru Secondary + 29 31 30 29 35

Note: Parentheses indicate figure is based on 25 to 49 women. An asterisk indicates figure is based on fewer than 25 women and is not shown.
69

DHS Comparative Reports Series

1. Westoff, Charles F. 2001. Unmet Need at the End of the Century.

2. Westoff, Charles F. and Akinrinola Bankole . 2002. Reproductive Preferences in Developing 

Countries at the Turn of the Century.

3. Rutstein, Shea O. 2002. Fertility Levels, Trends, and Differentials 1995-1999.