DHS ANALYTICAL 

STUDIES 48

Religion and Reproductive 

Behavior in Sub-Saharan 

Africa

FEBRUARY 2015

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

Charles F. Westoff and Kristin Bietsch of Princeton University.

 

DHS Analytical Studies No. 48 

Religion and Reproductive Behavior 

in Sub-Saharan Africa 

Charles F. Westoff1

Kristin Bietsch1

ICF International 

Rockville, Maryland, USA 

February 2015 

1 Office of Population Research, Princeton University 

Corresponding author: Charles F. Westoff, Office of Population Research, Princeton University, Princeton, 

NJ 08544-2901; phone 609-258-5867; email: westoff@princeton.edu 
Acknowledgments: The authors would like to thank Sanyukta Mathur for insightful comments on an 

earlier draft of this report. In addition, we thank Kerry MacQuarrie and Tim Heaton for helpful reviews, 

German Rodriguez for statistical advice, Judi Miller for preparation of tables and figures, Bryant Robey for 

editing the report, and Yuan Cheng for formatting it. 

Editor: Bryant Robey 

Document Production: Yuan Cheng 

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

Development (USAID) through The DHS Program (#AID-OAA-C-13-00095). The views expressed are 

those of the authors and do not necessarily reflect the views of USAID or the United States Government. 

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

population, health, and nutrition programs. For additional information about The DHS Program, contact 

The DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 

301-407-6500; fax: 301-407-6501; email: reports@dhsprogram.com; Internet: www.dhsprogram.com. 

Recommended citation: 

Westoff, Charles F., and Kristin Bietsch. 2015. Religion and Reproductive Behavior in Sub-Saharan Africa. 

DHS Analytical Studies No. 48. Rockville, Maryland, USA: ICF International. 
iii 

Contents 

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

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

Preface ........................................................................................................................................... vii

Abstract .......................................................................................................................................... ix

Executive Summary ....................................................................................................................... xi

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

2. Population Composition...................................................................................................... 1

3. Sources of Data ................................................................................................................... 1

4. Fertility ................................................................................................................................ 3

5. Analysis............................................................................................................................. 11

5.1 Nuptiality............................................................................................................... 11

5.2 Number of Children Desired ................................................................................. 14

5.3 Contraceptive Practice ......................................................................................... 20

5.4 Child Mortality ...................................................................................................... 23

6. Conclusions ....................................................................................................................... 23

References ..................................................................................................................................... 25
iii 
v 

Tables 

Table 1. Percentage of women age 15-49 in sub-Saharan Africa, by religion ........................................ 2

Table 2. Mean number of births in past five years to currently married women, by religion ................. 7

Table 3. Marriage indicators for Muslim and non-Muslim women in sub-Saharan African 

countries .................................................................................................................................. 12

Table 4. Education and urban residence comparisons of Muslim and non-Muslim women age 

15-49 in sub-Saharan Africa .................................................................................................... 12

Table 5. Hazard ratios from Cox proportional hazard models for factors associated with age at first 

marriage. Hazard ratios are significantly different from 1, at the .05 level, unless 

indicated by parentheses. ......................................................................................................... 13

Table 6. Measures of reproductive preferences for Muslim and non-Muslim women in subSaharan Africa ......................................................................................................................... 15

Table 7. Multivariate regression of factors associated with the number of children desired by 

currently married women (standardized partial regression coefficients). Coefficients are 

significantly different from 0, at the .05 level, unless indicated by parentheses. .................... 16

Table 8. Multivariate regression of factors associated with the number of children desired by 

women under age 20 (standardized partial regression coefficients). Coefficients are 

significantly different from 0, at the .05 level, unless indicated by parentheses. .................... 18

Table 9. Multivariate logistic regression (odds ratios) of factors associated with wanting no more 

children for currently married women. Coefficients are significantly different from 0, at 

the .05 level, unless indicated by parentheses. ........................................................................ 19

Table 10. Measures of family planning for Muslim and non-Muslim currently married women in 

sub-Saharan Africa .................................................................................................................. 21

Table 11. Multivariate logistic regression (odds ratios) of factors associated with ever-use of 

contraception by currently married women. Coefficients are significantly different from 

0, at the .05 level, unless indicated by parentheses. ................................................................ 22

Figures 

Figure 1a. Total fertility rates for West and Central Africa, by religion, .................................................. 5

Figure 1b. Total fertility rates for East and Southern Africa, by religion ................................................... 6

Figure 2. Recent total fertility rates for Muslim and non-Muslim women in sub-Saharan Africa ........... 8

Figure 3. Recent trends in total fertility rates for Muslim and non-Muslim women ............................... 9
vi 
vii 

Preface 

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

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

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

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

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

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

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

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

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

U.S. Agency for International Development. 

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

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

Sunita Kishor 

Director, The DHS Program 

ix 

Abstract 

This paper reports an analysis of how religion may influence reproductive behavior in 28 countries of subSaharan Africa. In comparisons between Muslim and non-Muslim populations (including Catholics and 

other Christians as well as those with traditional religious beliefs), Muslim populations typically show 

higher fertility accompanied by preference for larger families, less use of contraception, earlier age at 

marriage, and greater prevalence of polygyny. The main objective of the study was to determine whether 

the pattern of higher Muslim fertility remains when important social and economic covariates are taken into 

account. These factors include education, wealth, rural-urban residence, exposure to mass media, child 

mortality, and measures of gender equality. The multivariate analyses reported here do not support the 

expectation that these socioeconomic covariates are responsible for the fertility-related differences between 

Muslim and non-Muslim women, although in some instances they reduce the magnitude of existing 

differences between groups. 

xi 

Executive Summary 

This paper reports an analysis of the association of reproductive behavior with religion in 28 countries of 

sub-Saharan Africa. Although various traditional belief systems exist in some of these countries, the 

dominant religions are Christianity and Islam. Data from The Demographic and Health Survey Program 

shows no specific differences between Catholic and other Christian denominations in reproductive 

behavior. Muslim populations, however, typically show higher fertility, a preference for larger families, 

less use of contraception, earlier age at marriage, and greater prevalence of polygyny. Detailed analyses 

compare Muslim with non-Muslim women. 

The main objective of this study was to determine whether the observed pattern of higher Muslim fertility, 

associated with differences in marital behavior, reproductive preferences, and family planning, would 

persist when important social and economic covariates were taken into account. These covariates include 

education, wealth, rural-urban residence, exposure to mass media, child mortality, and measures of gender 

equality. Multivariate analyses did not support the expectation that these socio-economic covariates would 

be responsible for the fertility-related differences between Muslims and non-Muslims, although in some 

instances they operated to reduce the magnitude of these differences. 

There is also the possibility that unobserved covariates, particularly those connected with the status of 

women, are not well-measured with DHS data. Moreover, the only information on religion available from 

the DHS survey is based entirely on women’s answers to the question: “What is your religion?” 

1 

1. Introduction 

Research on the effects of religion on reproductive behavior has had a long history in the social sciences. 

Most of that research has so far concentrated on developed countries, though in recent years, the focus has 

included developing countries (Jones 2006; Morgan et al. 2002; Roudi-Fahimi 2004; Boonstra 2001; 

Akafuah 2008; Dharmalingam 2004; Knodel et al. 1999; Karim 1997; Immerman and Ronald 2003; 

Skirbekk et al. 2015). Most recently, sub-Saharan Africa has gained attention (Heaton 2011; Johnson-Hanks 

2006; Kahle 2013). The most relevant publication to result from this research to date is Tim Heaton’s 2011 

article: “Does Religion Influence Fertility in Developing Countries?” It is based on data for 30 countries, 

including 17 in sub-Saharan Africa. His study documents little difference in fertility between Catholics and 

Protestants in that part of the world, but he finds substantially higher fertility among Muslims. His analysis 

concludes that the country’s level of development and various social characteristics do not fully account 

for this fertility among Muslims. In the following analysis, more recent and extensive data show 

similar findings. 

2. Population Composition 

The composition of religions in the sub-Saharan African countries included in this report is summarized in 

Table 1. In many of the countries of West and Central Africa, Muslims are the largest religious subgroup; 

they comprise almost the entire populations of Mali, Niger, and Senegal. The remaining two large 

populations are Catholics and a variety of other Christian denominations, all of which are grouped in this 

paper as “Other Christians.” 

In Eastern and Southern Africa, women classified in this paper as “Other Christians” predominate, with 

Catholics as the next largest group, while Muslims are a minority, typically 10 percent to 30 percent of the 

population. In Ethiopia, 47 percent of the population identifies itself as Orthodox Christian. 

3. Sources of Data 

The 29 countries in Table 1 comprise those in sub-Saharan Africa where Demographic and Health Surveys 

were conducted over the past ten years (2004 to 2014). Religious identification is based on the answer to a 

single question “What is your religion?” Various categories of answers were pre-coded in the interview 

questionnaire based on local knowledge in each country. 

No other information on the subject of religion was obtained. It is particularly unfortunate that there is no 

information on religiosity which would be an important component of the general topic. The available data 

on religion are limited. Our groupings of religion, particularly in the Other Christian category, are crude in 

that they do not differentiate various denominations but such a grouping is necessary to permit inter-country 

comparisons. 
2 

Table 1. Percentage of women age 15-49 in sub-Saharan Africa, by religion 

 Catholic 

Other 

Christian Muslim 

Traditional/ 

Animist Other2 None 

West and Central Africa 

Benin 2012 33 25 22 13 2 5 

Burkina Faso 2010 23 7 62 7 ― 1 

Cameroon 2011 37 36 20 3 1 2 

Chad 2004 22 17 57 1 ― 2 

Congo B 2011-12 31 27 1 10 25 6 

Congo DR 2013-14 30 64 1 1 2 1 

Cote d’Ivoire 2012 19 26 40 2 1 11 

Gabon 2012 42 46 6 1 ― 5 

Ghana 2008 12 65 15 4 ― 3 

Guinea1 2012 ― 9 87 ― ― 4 

Liberia1 2013 ― 86 11 1 ― 2 

Mali1 2012-13 ― 4 92 1 1 2 

Niger1 2006 ― 1 99 ― ― 1 

Nigeria 2013 11 36 52 1 ― ―

Senegal1 2010-11 ― 4 95 1 ― ―

Sierra Leone1 2013 ― 21 78 ― ― 1 

 

Eastern and 

Southern Africa 

Burundi 2010 62 34 2 ― 1 1 

Ethiopia 2011 1 22 28 1 47 ―

Kenya 2008-09 22 68 7 ― 1 2 

Lesotho 2009 43 55 ― ― 1 1 

Madagascar 2008-09 36 36 1 2 6 20 

Malawi 2010 21 65 13 ― ― 1 

Mozambique 2011 29 42 18 ― 2 9 

Namibia 2013 20 70 ― ― 9 1 

Rwanda 2010 43 54 1 ― 1 1 

Swaziland 2006-07 5 73 ― 18 ― 4 

Uganda 2011 41 45 13 ― 1 ―

Zambia 2007 20 78 1 ― 1 ―

Zimbabwe 2010-11 8 84 1 1 ― 6 

1 These countries do not distinguish between Catholic and Other Christian religions. 

2 In Ethiopia, this is the rate for Orthodox Christian women.
3 

4. Fertility 

Figures 1a and 1b show total fertility rates (TFRs) for the three major religious categories of two geographic 

regions of sub-Saharan Africa: (1) West and Central Africa and (2) East and Southern Africa. The 

calculation of the TFRs for religions in each country requires large samples of women to attain reliable 

denominators of women in each of the seven five-year age categories. Thus only the categories of Catholics, 

Other Christians, and Muslims can be used for calculations of the TFR. 

There is little difference between West and Central Africa in the TFRs (Figure 1a) for Catholics and Other 

Christians, while the Muslim fertility rates are typically, although not universally, higher. In the countries 

of East and Southern Africa (Figure 1b), the fertility rates for Catholics and Other Christians are also 

similar, while Muslim fertility is higher, except in Uganda. 

The fertility estimates shown in Table 2 differ from the TFRs in Table 1 (though they highly correlate at 

0.97 across these African countries) in that they are based on currently married women rather than on all 

women and are simple averages of the number of births in the past five years, uncontrolled for age. This 

fertility measure does not require the large samples needed for the calculation of the TFR and therefore can 

include other religions in addition to the three major groups. Estimates of fertility for women who say they 

have no religion tend to be on the high side, which is quite the opposite of the pattern in more developed 

countries. There is no obvious explanation for this difference, although these women are less educated than 

women with specific religious affiliations. The calculations are also based on small numbers of women. 

Considering the similar fertility of Catholic and Other Christians, and the generally higher fertility of 

Muslims in comparison, the focus of the following analyses is confined to Muslim and non-Muslim 

comparisons. This allows women with traditional beliefs or no religious affiliation to be included in further 

analysis. The difference in the TFR between these two groups is summarized in Figure 2 for sub-Saharan 

countries with Muslim populations. Countries with small numbers of Muslims–Congo Brazzaville, 

Democratic Republic of the Congo, Burundi, Madagascar, Rwanda, Zambia, and Zimbabwe–are excluded 

from further analysis. The higher fertility for Muslims is the predominant pattern, but there are several 

exceptions. For example, in Chad the reverse pattern is seen, although the data is 10 years old.1

 Also, in 

Mali in 2012-13, the TFR for Muslims (92 percent of the population) was 6.5, lower than that for nonMuslims at 7.2.2

 

Recent trends in the TFR for Muslims and non-Muslims are shown in Figure 3. The most common pattern 

is for the fertility rates of both populations to move more or less in the same direction. There are exceptions 

to this pattern, however, in Chad, Ghana, and Malawi, where Muslim fertility seems to be rising in contrast 

with the rates for non-Muslims. In Niger and Senegal where Muslims comprise almost the entire population, 

the trends are in opposite directions, with a uniform decline in Senegal and a recent increase in Niger to a 

TFR of 7.6 from 7.1 five years earlier. 

 

1

 An earlier study of the differences in fertility of Muslim and non-Muslim populations of West Africa (JohnsonHanks 2006) showed a generally similar picture compared with fewer and earlier surveys. The more recent data 

reviewed here do not support her conclusions about the differences in fertility related to whether Muslims are a 

minority or a majority of the population. 

2

 Muslim fertility in Mali is higher than that of the Other Christian group as shown in Figure 1a but lower than for the 

larger category of non-Muslims shown in Figure 2. This is because of the higher fertility for Animists and women in 

other Traditional religions. Also, the three heavily Muslim regions in the north of Mali were not included in the most 

recent survey for reasons of security (Westoff, Bietsch, and Mariko 2014). 
4 

Research on the association of religion with reproductive behavior typically considers both formal belief 

systems as well as differences in the social and economic composition of the members of various religions 

(Voas 2007). Some religions may carry particular views, for example, on the practice of contraception and 

abortion, and on marriage and the family. In the present study, the comparisons of Muslims and nonMuslims is so limiting that the focus will be on the connections with different covariates of this dichotomy 

such as education, wealth, child mortality, and polygyny (Regenerus and Smith 2005; Watkins and Warriner 

2003). There is no belief system in Islam that forbids contraception (Boonstra 2001; Karim 1997; Weeks 

1988; Roudi-Fahmi 2004), though some local religious leaders may believe otherwise, while the nonMuslim category is so broad as to preclude singling out specific denominations. 
Figure 1a. Total fertility rates for West and Central Africa, by religion* 

4.4

5.4

6.2

4.8

6.8

5.1

5.5

6.6

4.6

7.0

5.1

6.1 6.2 6.0 6.0

8.07.06.05.04.03.02.0

Benin 2012 Burkina Faso 2010 Burundi 2010 Cameroon 2011 Chad 2004

4.6 4.6

7.1

4.4

3.8 4.1

5.1

5.9

7.6

6.5

5.0 5.1

8.07.06.05.04.03.02.0

Liberia 2013** Mali 2013** Niger 2012*** Nigeria 2013 Senegal 2010** Sierra Leone 2013**

4.4

6.1

4.1 3.9 3.8 4.4

5.3

6.7

4.6

4.0 3.7

5.2

7.1 7.2

5.0 5.0

5.4

8.07.06.05.04.03.02.0

Guinea 2012** Congo B 2011 Congo DR 2013-14 Cote d' Ivoire 2012 Gabon 2012 Ghana 2008

CatholicOther ChristianMuslim

Catholic Other Christian Muslim

* Based on three years prior to the survey ** Does not distinguish between Catholics and Other Christians 

*** Did not ask religion in 2012 but in 2006 Muslims were 98 percent of the population

5 
Figure 1b. Total fertility rates for East and Southern Africa, by religion

4.3

2.8

6.3

5.9

3.3

4.7

4.1

6.1 6.2

3.5

5.9

8.07.06.05.04.03.02.0

Rwanda 2010 Swaziland 2006 Uganda 2011 Zambia 2007 Zimbabwe 2011

4.2

3.3

4.7

5.3

5.9

3.7

4.8

3.3

4.5

5.6 5.8

3.5

6.1

7.0

6.0

3.9 *

8.07.06.05.04.03.02.0

Ethiopia 2011 Lesotho 2009 Madagascar 2009 Malawi 2010 Mozambique 2011 Namibia 2006

* In Ethiopia, this is the TFR for Orthodox women

Catholic Other Christian Muslim

6 

4.2

4.8

6.1

3.9 **

* In Ethiopia, this is the TFR for Orthodox Christian women 
7 

Table 2. Mean number of births in past five years to currently married women, by religion 

 Catholic 

Other 

Christian Muslim Other2 None Total 

Benin 2012 1.03 1.04 1.08 1.14 1.07 1.15 

Burkina Faso 2010 1.06 1.16 1.10 1.14 1.29 1.17 

Burundi 2010 1.33 1.38 1.38 ― 1.19 1.34 

Cameroon 2011 1.00 1.00 1.20 1.20 1.09 1.06 

Chad 2004 1.28 1.30 1.17 1.38 1.36 1.22 

Congo B 2011 0.92 1.02 1.17 1.06 1.00 1.01 

Congo DR 2013 1.26 1.35 1.39 1.29 1.35 1.33 

Cote d’Ivoire 2012 0.99 0.89 1.02 1.12 1.19 1.01 

Ethiopia 2011 0.89 1.08 1.24 0.90 ― 1.07 

Gabon 2012 0.79 ― 1.00 0.75 1.05 0.83 

Ghana 2008 0.85 0.55 1.09 1.13 1.06 0.90 

Guinea1 2012 ― 0.89 0.99 ― 0.90 0.98 

Lesotho 2009 0.74 0.72 ― ― 0.84 0.73 

Liberia1 2013 ― 0.89 0.97 0.99 1.20 0.91 

Madagascar 2009 0.88 0.87 0.67 0.94 1.03 0.91 

Malawi 2010 1.08 1.10 1.16 ― 1.23 1.10 

Mali1 2012-13 ― 1.18 1.14 1.28 1.26 1.15 

Mozambique 2011 1.07 1.08 1.00 1.06 0.75 1.06 

Namibia 2013 0.81 0.72 ― 0.65 1.06 0.73 

Niger1 2006 ― 0.89 1.22 ― 1.40 1.22 

Nigeria 2013 1.05 1.00 1.15 1.01 ― 1.10 

Rwanda 2010 1.09 1.17 1.07 ― 1.36 1.13 

Senegal1 2010 ― 0.90 1.05 1.23 ― 1.05 

Sierra Leone1 2013 ― 0.91 0.98 ― ― 0.97 

Swaziland 2006 0.54 0.67 ― 0.49 0.80 0.80 

Uganda 2011 1.30 ― 1.25 ― ― 1.29 

Zambia 2007 1.19 1.25 ― ― ― 1.24 

Zimbabwe 2011 0.67 0.86 ― 0.95 ― 0.84 

1 These countries do not distinguish between Catholic and Other Christian religions. 

2 In Ethiopia, “Other” refers to Orthodox Christianity. Otherwise it refers to traditional or animist religions. 
Figure 2. Recent total fertility rates for Muslim and non-Muslim women in sub-Saharan Africa* 

8 
Figure 3. Recent trends in total fertility rates for Muslim and non-Muslim women* 

(Continued...) 

6.4

5.7 6.1

5.1

5.8 5.6 5.5

4.8

8642

1996 2001 2006 2012

TFR Benin

6.0

6.9

6.5 6.8

6.0

4.5

8642

1997 2004 2013

TFR Chad

4.9 5.3 5.4

4.4 4.3 3.8

8642

1998 2003 2008

TFR Ghana

Muslim non-Muslim

6.7 6.3 6.1 6.1

6.2 6.5

5.5 5.7

8642

1993 1999 2003 2010

TFR Burkina Faso

6.0 5.8 5.9 6.0

5.8

4.5 4.7 4.8

8642

1991 1998 2004 2011

TFR Cameroon

6.1

5.0

4.6 4.8

8642

1999 2012

TFR Cote d’ Ivoire

6.6 6.1

5.0

4.3

8642

2005 2011

TFR Ethiopia

5.5 5.8

5.2

4.4

5.2

5.5

8642

1999 2005 2012

TFR Guinea

4.2 4.6

5.8

5.1 5.4 4.7

4.8 4.5

8642

1993 1998 2003 2009

TFR Kenya

9 
Figure 3. – Continued

4.5

5.9 6.0

5.3

5.4 5.9

8642

1997 2003 2011

TFR Mozambique

6.3 6.0 5.7 5.3 5.0

3.9

4.2

8642

1986 1992 1997 2005 2010

TFR Senegal

6.5 6.7 6.9 6.5

5.8

4.5 4.7 4.5

8642

1990 2003 2008 2013

TFR Nigeria

7.1 7.2 7.0 7.6 8

642

1992 1998 2006 2012

TFR Niger**

4.4 4.1

5.2 5.1

8642

2008 2013

TFR Sierra Leone

6.7 6.5

5.9

6.9 6.7

6.2

8642

2001 2006 2011

TFR Uganda

5.2

4.7 5.1

5.1

8642

2008 2013

TFR Liberia

6.7

6.5

5.9

6.6

7.2 6.9 8

642

2001 2006 2013

TFR Mali

*Based on three years prior to the survey **Did not ask religion in 2012, but in 2006 Muslims were 98 percent of the population

7.1

6.3

7.0

6.2 5.9 5.6

8642

2000 2004 2010

TFR Malawi

Muslim non-Muslim

10 
11 

5. Analysis 

The primary objective of the following analysis is to determine whether the tendency for Muslim women 

in sub-Saharan Africa to have higher fertility than non-Muslim women (Figure 2) reflects differences in 

age at marriage, number of children desired, contraceptive use, and child mortality. We explore the 

socioeconomic covariates of these antecedents of fertility to see whether they account for the Muslim – 

non-Muslim differences. Proportions and rates presented in this study are weighted to account for sampling 

probability and nonresponse. Regression analyses are unweighted. 

5.1 Nuptiality 

An important factor in the higher fertility of Muslim women compared with non-Muslim women is that 

more Muslim women are currently married, and further, they have married at an earlier age. In the Arab 

world, a significant reason for the decline in fertility is changes in nuptiality (Rashad 2000 and 2005). In 

the 17 countries of sub-Saharan Africa (Table 3), every country has higher proportions of Muslims who are 

currently married. An early age at first marriage is characteristic for Muslims, with the exception of 

Mozambique. The average proportions of women married in the other countries are 72 percent for Muslims 

and 64 percent for non-Muslims. The median age at first marriage is slightly more than one year earlier for 

Muslim women (17.7) than for non-Muslim women (19.0). In Nigeria, the difference is 5.5 years. 

Polygyny is also more common for Muslims, affecting an average of 35 percent of Muslim women 

compared with 22 percent of non-Muslim women. According to Islamic law, a man may have more than 

one wife, although in many countries, for example in Turkey and Tunisia, it is forbidden by law. Moreover, 

the practice of polygyny has declined in recent years. 

Two background factors usually associated with both nuptiality and fertility are education and urban-rural 

residence. Their relationship is explored in Table 4. In 15 of the 17 countries, the percentages of Muslim 

women with no schooling are higher than for non-Muslim women. The two exceptions are Mali and 

Uganda. In Kenya, there is a large difference—44 percent of Muslim women compared with only 6 percent 

of non-Muslim women have no schooling. Muslims in Kenya are concentrated in the two least developed 

regions—the Northeast and the Coastal regions. In the Northeast, 97 percent of the population is Muslim 

and in the Coastal region it is 39 percent; these percentages compare with only 7 percent nationally. The 

mean number of years of schooling, again with the exception of Mali and Uganda, is lower for Muslim 

women. Nigeria shows a particularly large difference in education between the two categories, with an 

average of 9.2 years of schooling for non-Muslim women and 3.2 years for Muslim women. 

The differences between the two religious groups in the proportions living in cities are mixed; in about half 

of the countries more Muslims are likely to live in rural areas, and in the other half, more live in urban 

settings. 

A survival analysis of age at first marriage (including cohabitation) with these and several other variables 

is shown using Cox proportional hazard models for each country in Table 5. Survival analysis is ideal for 

examining age at first marriage as it allows all women to contribute information, including those who have 

not yet married or who may never marry. The hazard ratios showing the association of the Muslim – nonMuslim distinction with age at marriage are mostly greater than 1 and significant, indicating a higher 

likelihood of marriage for Muslims in the presence of the other covariates (residence, education, and age). 

In Ethiopia and Ghana, the coefficients for religion are less than 1, though neither is statistically significant. 

The urban-rural variable shows city residence is associated with a lower likelihood of marriage more 

consistently than religion, but with roughly the same influence. Years of schooling are strongly associated 
12 

with later age at marriage without exception. As urbanization and education increase in the future, we can 

expect to see a rise in the age at first marriage, and if levels of education and urban residence become more 

similar among religious groups, we may see the differences in age at marriage between Muslims and nonMuslims lessen. 

Table 3. Marriage indicators for Muslim and non-Muslim women in sub-Saharan African countries 

 

Percentage currently 

married 

Percentage 

polygynous1 

Median age at first 

marriage 

 Muslim Non-Muslim Muslim Non-Muslim Muslim Non-Muslim 

Benin 2012 75 69 44 35 19.2 20.0 

Burkina Faso 2010 82 75 46 36 17.6 18.3 

Cameroon 2011 77 60 42 22 15.8 19.2 

Chad 2004 77 76 40 37 15.4 16.8 

Cote d’Ivoire 2012 69 59 35 23 18.8 20.4 

Ethiopia 2011 69 59 16 7 16.4 16.6 

Ghana 2008 64 57 33 16 19.6 19.8 

Guinea 2012 74 71 50 33 17.0 17.3 

Kenya 2009 65 58 22 13 18.7 20.1 

Liberia 2013 65 57 33 11 18.3 18.9 

Malawi 2010 71 67 21 13 17.2 17.9 

Mali 2012-13 85 82 36 28 18.0 18.3 

Mozambique 2011 72 67 20 21 18.9 18.8 

Nigeria 2013 83 59 44 17 15.9 21.4 

Senegal 2010 67 49 35 19 19.2 22.6 

Sierra Leone 2013 69 54 39 24 17.7 19.0 

Uganda 2011 63 62 42 26 17.2 18.0 

 

All countries 72 64 35 22 17.7 19.0 

1 Based on currently married women 

Table 4. Education and urban residence comparisons of Muslim and non-Muslim women age 15-49 

in sub-Saharan Africa 

 

Percentage with no 

schooling 

Mean years of 

schooling Percentage urban 

 Muslim Non-Muslim Muslim Non-Muslim Muslim Non-Muslim 

Benin 2012 70 57 2.2 3.3 47 46 

Burkina Faso 2010 79 66 1.4 2.4 26 29 

Cameroon 2011 49 13 2.8 7.1 42 57 

Chad 2004 90 54 0.5 2.1 23 19 

Cote d’Ivoire 2012 71 42 2.2 4.2 62 44 

Ethiopia 2011 61 48 2.0 3.3 15 28 

Ghana 2008 68 60 2.5 7.0 58 47 

Guinea 2012 68 60 2.5 2.8 39 20 

(Continued...)
13 

Table 4. – Continued

 

Percentage no 

schooling 

Mean years of 

schooling Percentage urban 

 Muslim Non-Muslim Muslim Non-Muslim Muslim Non-Muslim 

Kenya 2009 44 6 4.4 8.1 46 24 

Liberia 2013 55 33 2.9 4.8 54 62 

Malawi 2010 28 14 4.1 5.5 17 19 

Mali 2012-13 76 78 1.9 1.6 26 12 

Mozambique 2011 42 30 2.7 4.0 32 35 

Nigeria 2013 65 9 3.2 9.2 33 52 

Senegal 2010 59 36 2.8 5.3 49 62 

Sierra Leone 2013 61 39 3.0 5.6 32 49 

Uganda 2011 11 14 6.2 5.7 32 18 

 

All countries 59 39 2.8 4.8 37 37 

Table 5. Hazard ratios from Cox proportional hazard models for factors associated with age at first 

marriage. Hazard ratios are significantly different from 1, at the .05 level, unless indicated by 

parentheses. 

 Benin 

Burkina 

Faso Cameroon Chad 

Cote 

d’Ivoire Ethiopia Ghana Guinea Kenya 

 2012 2010 2011 2004 2012 2011 2008 2012 2009 

Muslim 1.110 1.219 1.538 1.154 1.361 (0.967) (0.994) (1.070) (1.032) 

Rural 1.171 1.372 1.119 (0.923) 1.538 1.310 1.377 1.432 1.341 

Years of 

schooling 0.910 0.900 0.898 0.923 0.926 0.907 0.930 0.905 0.916 

Age 0.987 0.997 1.006 1.012 1.007 1.022 1.020 1.003 (1.002) 

 

Number of 

women 16,599 17,073 15,404 6,068 10,039 16,503 4,890 9,135 8,443 

 Liberia Malawi Mali Mozambique Nigeria Senegal 

Sierra 

Leone Uganda

 2013 2010 2012-13 2011 2013 2010 2013 2011 

Muslim 1.163 1.061 1.160 (1.042) 1.800 1.463 1.134 1.285 

Rural 1.171 1.155 1.245 1.239 1.291 1.461 1.272 1.245 

Years of schooling 0.939 0.920 0.924 0.938 0.912 0.891 0.911 0.921 

Age 1.012 0.995 0.972 0.977 1.005 1.003 1.004 1.007 

 

Number of women 9,218 22,987 10,424 13,745 38,802 15,686 16,593 8,670 

Note: ( ) Not significant at the .05 level 
14 

5.2 Number of Children Desired

Another important determinant of fertility is the desired number of children. The number desired is 

measured here by the number considered ideal3

 and by the intent to cease childbearing. These two measures 

of reproductive preference are presented for Muslim and non-Muslim women in Table 6. The mean number 

of children desired by Muslim women is higher in every country except in Mali in 2012-13. A similar 

pattern is evident for the proportion of (married) women who want no more children, which is lower for 

Muslims in all countries studied except Guinea. 

Several multivariate analyses are presented in connection with reproductive preferences to determine 

whether the difference between Muslim and non-Muslim women can be explained by interrelations among 

other covariates. In addition to education, urban-rural residence, polygyny and an age control, we included 

the number of living children, the number of child deaths, wealth, exposure to radio and television, and two 

indices intended to assess attitudes toward the status of women (which partner makes various decisions in 

the home and attitude toward husbands’ beating of wives, both coded with a positive direction indicating 

the more egalitarian position). 

The multivariate regression results in Table 7 indicate that even with all of these controls, Muslim women 

still have a desire for larger families in all 17 countries. This positive relationship is maintained at 

statistically significant levels for 15 of the countries. Other variables that relate directly to the number of 

children desired in this multivariate analysis include actual number of children (which would be expected), 

later age at marriage, and number of child deaths. The latter association strengthens the implication that a 

reduction in child mortality will ultimately lead to a decline in fertility. Education and wealth are also 

universally connected with a desire for fewer children (with the exception of Chad). Urban residence is for 

the most part related to wanting fewer children, although there is less consistency. With few exceptions, 

polygyny is associated with a desire for more children. Exposure to radio and television shows some 

evidence of relating to a desire for fewer children, although there are numerous exceptions. And finally, the 

measures of gender equality also tend to indicate a preference for smaller families. This latter association 

with the two gender equality measures, which is above the association with the Muslim – non-Muslim 

difference is not in accord with another study of four Asian countries. That study concluded that the general 

pronatalist attitudes of Muslim women “cannot be accounted for by any general tendency of Muslim women 

to have less autonomy than non-Muslim women” (Morgan et al. 2002). A simple comparison of the 

averages of these two measures for Muslim and non-Muslim women in 16 countries in our analysis 

indicates consistently more egalitarian attitudes on the “decision” measure for non-Muslim women but less 

difference on the “beating” indicator (not shown). 

The unstandardized partial regression coefficients for the Muslim – non-Muslim variable, which include 

the effects of all of the same listed covariates, indicate its importance for the desired number of children 

across all of the countries (at the bottom of Table 7). This comparison shows the strongest associations for 

Kenya, Ethiopia, Nigeria, and Chad. 

These results are for currently married women, most of whom already have children which, of course, can 

be expected to influence the number of children desired. A clearer picture can be seen by confining the 

analysis to young women under age 20. The mean number of children desired by young Muslim women is 

greater in 14 of the 17 countries, averaging 5.1 compared with 4.4 desired by non-Muslim teenagers. A 

multivariate regression analysis with a reduced number of covariates relevant for younger women is shown 

in Table 8. This analysis again shows a direct, statistically significant effect of being Muslim in most of the 

 

3

 Non-numeric responses such as “It’s up to God” have been assigned a value one standard deviation above the average 

number desired by those responding with a number. 
15 

countries. Education is strongly inversely related to the number of children desired, and viewing television 

also shows a negative impact. Rural residence generally shows a positive effect. The statistical importance 

of the religion variable across countries (the unstandardized partial regression coefficient) indicates the 

strongest associations for mostly the same countries (Kenya, Ethiopia, Ghana, Nigeria, and Chad), as noted 

for Table 7. 

The other measure of reproductive preferences included in this analysis is the proportion of married women 

who say they want no more children. In all countries except Guinea, non-Muslim women are more inclined 

to want to cease childbearing (Table 6). 

An additional multivariate logistic regression looks at whether women want more children or no more 

children, with a control for the number of existing children (Table 9). Also included are the covariates seen 

before. Significant odds ratios for the Muslim – non-Muslim variable are evident in most of the countries 

showing lower proportions of Muslims wanting no more children. The countries with the strongest 

association are the same as listed above in connection with the desired number of children (plus Cameroon). 

Other covariates of intention to terminate childbearing, besides the obvious connections with age and 

number of children, are years of schooling, child deaths, and one of the measures of gender equality. These 

variables indicate that the intention to have no more children increases with education and with a greater 

participation of women in making various decisions in the household. 

Table 6. Measures of reproductive preferences for Muslim and non-Muslim women in sub-Saharan 

Africa 

 Mean number of children desired1 

Percentage wanting no more 

children2

 Muslim Non-Muslim Muslim Non-Muslim 

Benin 2012 5.1 4.4 22 29 

Burkina Faso 2010 5.7 5.4 21 28 

Cameroon 2011 7.1 5.3 19 29 

Chad 2004 9.7 8.6 6 12 

Cote d’Ivoire 2012 5.7 5.2 19 21 

Ethiopia 2011 5.5 4.4 31 40 

Ghana 2008 5.6 4.1 21 40 

Guinea 2012 6.1 5.5 21 14 

Kenya 2009 5.6 3.7 26 58 

Liberia 2013 5.6 4.9 27 30 

Malawi 2010 4.4 4.0 41 48 

Mali 2012-13 5.9 6.1 21 24 

Mozambique 2011 5.3 4.7 21 30 

Nigeria 2013 8.1 5.2 11 31 

Senegal 2010 5.8 4.9 21 28 

Sierra Leone 2013 5.2 4.4 25 30 

Uganda 2011 5.0 4.9 38 43 

 

All countries 6.0 5.0 23 32 

1 Non-numeric responses such as: “It’s up to God” have been assigned a value one standard deviation above the 

average number desired by those responding with a number. 

2 Based on currently married women
Table 7. Multivariate regression of factors associated with the number of children desired by currently married women (standardized 

partial regression coefficients). Coefficients are significantly different from 0, at the .05 level, unless indicated by parentheses. 

Benin 

Burkina 

Faso Cameroon Chad 

Cote 

d’Ivoire Ethiopia Ghana Guinea Kenya 

2012 2010 2011 2004 2012 2011 2008 2012 2009 

Muslim .066 .043 .100 .168 .040 .200 .218 .079 .233 Number of children .261 .291 .120 .175 .239 .214 .160 .157 .171 

Age -.056 -.093 .113 .042 (.004) (.010) .068 (.008) .034 

Rural .023 .035 (-.015) .132 .060 -.107 (-.018) (-.013) -.041 

Age at marriage (-.009) -.040 -.045 (-.009) -.028 .020 -.026 -.028 (-.014) 

Years of schooling -.065 -.107 -.156 -.149 -.130 -.055 -.127 -.112 -.237 

Wealth1 -.161 -.154 -.155 .100 -.035 -.137 -.210 -.134 -.100 

Listens to radio -.018 .023 -.025 -.050 -.021 (-.006) -.031 .051 -.090 

Watch television (.016) -.043 -.026 (-.006) -.040 -.047 (.034) -.081 (-.016) 

Polygynous .046 (.007) .018 (-.003) .034 .050 (.015) .032 .046 

Number of child deaths .114 .184 .073 .103 .107 .102 .152 .144 (.020) 

Decision making2 -.043 -.038 -.046 NA (-.001) -.067 (.005) (.003) -.049 

Beating3 .018 (-.008) -.033 NA -.071 -.063 -.090 (-.005) (-.010) 

Muslim unstandardized .356 .179 .692 1.21 .182 1.45 1.29 .583 1.50 Number married women 11,200 13,226 8,940 4,380 6,107 9,659 2,809 6,710 4,656 R2 .170 .296 .253 .154 .185 .213 .328 .170 .366 

(Continued...)

16 
Table 7. – Continued

 Liberia Malawi Mali Mozambique Nigeria Senegal Sierra Leone Uganda 

2013 2010 2012-13 2011 2013 2010 2013 2011 

Muslim .048 .039 (.003) .068 .214 .025 .058 (.019) Number of children .231 .294 .173 .271 .210 .054 .268 .182 Age .102 .111 (.011) .086 -.019 .073 .034 .029 

Rural (-.021) .049 .033 .050 .012 .085 .073 .073 

Age at marriage (-.025) -.027 (-.009) -.026 -.046 -.052 -.025 (.013) 

Years of schooling -.056 -.110 -.071 -.108 -.117 -.129 -.068 -.233 

Wealth1 -.138 -.030 -.057 -.134 -.122 -.031 -.070 -.076 

Listens to radio -.035 (-.006) -.024 .036 -.016 (-.014) (-.011) -.056 

Watch television (.004) -.029 -.046 -.062 -.022 (-.018) -.018 .106 Polygynous .032 (.008) (.014) (.012) .014 -.032 .032 .063 

Number of child deaths .105 .092 .094 .131 .135 .014 .173 .108 Decision making2 (.004) (-.004) -.028 -.044 -.067 (-.005) .019 .035 

Beating3 -.051 -.030 -.059 -.026 -.032 (-.077) -.045 -.024 

Muslim unstandardized .368 .199 (.025) .421 1.35 .327 .331 (.125) 

Number married women 5,805 15,064 8,723 7,837 26,163 10,754 10,702 4,998 

R2 .186 .253 .102 .304 .358 .089 .218 .224 

Note: ( ) Not significant at the .05 level 

1 Wealth index as calculated by the DHS, a composite measure of household’s cumulative living standard (http://dhsprogram.com/topics/Wealth-Index.cfm) 2 Who makes various decisions in the home, coded with a positive direction indicating a more egalitarian position 3Attitudes towards husbands’ beating of wives, coded with a positive direction indicating a more egalitarian position 

17 
Table 8. Multivariate regression of factors associated with the number of children desired by women under age 20 (standardized partial 

regression coefficients). Coefficients are significantly different from 0, at the .05 level, unless indicated by parentheses.

Benin 

Burkina 

Faso Cameroon Chad 

Cote 

d’Ivoire Ethiopia Ghana Guinea Kenya 

2012 2010 2011 2004 2012 2011 2008 2012 2009 

Muslim .082 .049 .147 .139 .053 .248 .168 .115 .328 Rural .113 .110 .016 .083 .141 (-.015) .130 .050 .061 Years of education -.196 -.274 -.306 -.131 -.240 -.162 -.197 -.189 -.249 Listens to radio .046 .013 (.003) (-.029) (-.018) -.050 (-.050) (.022) -.042 

Watch television -.059 -.072 -.103 -.093 (-.043) -.073 -.077 -.193 -.094 Muslim unstandardized .339 .180 .870 .988 .208 1.39 .707 .719 1.90 Number of women 2,922 3,338 3,587 1,450 1,967 3,802 1,033 1,988 1,767 R2 .082 .151 .208 .096 .127 .137 .142 .121 .291 

Liberia Malawi Mali Mozambique Nigeria Senegal Sierra Leone Uganda 

2013 2010 2012-13 2011 2013 2010 2013 2011 

Muslim .080 .044 (-.014) .080 .251 .042 .067 (.023) Rural .102 .097 .097 .124 .057 .136 .179 .145 Years of education (-.038) -.098 -.138 -.149 -.222 -.208 -.131 -.289 Listens to radio (-.036) (.011) -.050 .049 -.026 (-.019) (-.045) -.076 

Watch television -.065 -.040 (-.017) -.108 -.045 -.045 -.035 (.019) 

Muslim unstandardized .491 .189 (-.120) .446 1.35 .447 .226 (.113) Number of women 1,915 5,040 1,918 3,065 7,902 3,604 4,051 1,394 

R2 .032 .033 .053 .110 1.35 .106 .089 .139 

Note: ( ) Not significant at the .05 level 

18 
19 

Table 9. Multivariate logistic regression (odds ratios) of factors associated with wanting no more 

children for currently married women. Coefficients are significantly different from 0, at the .05 level, 

unless indicated by parentheses. 

 Benin 

Burkina 

Faso Cameroon Chad 

Cote 

d’Ivoire Ethiopia Ghana Guinea Kenya 

 2012 2010 2011 2004 2012 2011 2008 2012 2009 

Muslim 0.61 0.72 0.48 0.47 (0.88) 0.52 0.36 (1.05) 0.12 

Number of children 1.50 1.57 1.72 1.37 1.58 1.41 2.12 1.65 1.86 

Age 1.11 1.15 1.07 1.07 1.10 1.04 1.06 1.13 1.08 

Rural 0.76 0.69 (0.95) 0.64 0.77 (0.87) (0.93) (0.90) 1.27 

Years of education 1.04 1.04 (1.02) 1.06 1.05 0.98 1.05 (0.84) 1.07 

Wealth1 1.17 1.14 1.29 (1.01) (1.07) 1.18 1.22 (1.00) 1.22 

Listens to radio 0.91 0.90 (1.00) (1.01) (0.97) 0.92 (1.00) 0.88 1.14 

Watch television (1.00) 1.21 (0.99) (1.11) (1.07) 1.09 (1.04) 1.13 (0.97) 

Polygynous (0.93) (1.07) (1.05) (1.16) (1.06) (0.90) (0.88) (1.15) (0.97) 

Number of child 

deaths 

1.08 1.05 1.09 1.11 1.09 1.07 (1.01) 1.15 (0.98) 

Decision making2 1.03 1.10 1.04 NA (0.95) 1.04 (1.05) 0.94 1.09 

Beating3 (1.03) 0.95 (1.01) NA 1.08 1.03 1.07 1.13 (1.04) 

 

Married women 11,200 13,226 8,940 4,382 6,107 9,659 2,809 6,710 4,508 

R2 .247 .371 .317 .213 .303 .179 .331 .354 .338 

 Liberia Malawi Mali Mozambique Nigeria Senegal Sierra Leone Uganda 

 2013 2010 2012-13 2011 2013 2010 2013 2011 

Muslim (0.84) 0.77 0.89 0.61 0.36 0.72 0.63 0.81 

Number of children 1.69 1.76 1.44 1.53 1.47 1.68 1.75 1.65 

Age 1.05 1.08 1.12 1.06 1.14 1.10 1.11 1.08 

Rural (1.11) (0.62) (0.92) 0.65 0.88 0.75 0.73 0.58 

Years of education (1.01) 1.03 (1.01) 1.02 1.02 1.05 (1.01) 1.03 

Wealth1 (1.05) 1.09 1.06 1.29 1.29 (0.99) (1.02) (1.04) 

Listens to radio (0.97) (0.97) (0.72) 0.75 (1.04) (1.03) 0.90 1.08 

Watch television (1.01) (1.02) (1.00) 1.15 (1.05) (1.07) (0.92) 0.88 

Polygynous (0.91) (0.98) (0.97) (0.94) (1.04) 1.15 1.13 0.81 

Number of child 

deaths 

1.06 1.19 1.09 (1.05) (1.02) 1.19 1.07 (1.03) 

Decision making2 (1.00) (1.02) 1.13 1.06 1.18 1.05 1.07 (0.96) 

Beating3 (1.03) (1.06) (1.02) (1.02) (1.01) 0.98 1.04 (1.01) 

 

Married women 5,790 15,064 8,723 7,837 26,181 10,574 10,642 4,998 

R2 .252 .292 .279 .226 .360 .363 .321 .297 

Note: ( ) Not significant at the .05 level 

1 Wealth index as calculated by the DHS, a composite measure of household’s cumulative living standard 

(http://dhsprogram.com/topics/Wealth-Index.cfm) 

2 Who makes various decisions in the home, coded with a positive direction indicating a more egalitarian position 

3 Attitudes towards husbands’ beating of wives, coded with a positive direction indicating a more egalitarian position 
20 

5.3 Contraceptive Practice 

In the Muslim world, there is no universal formal opposition to family planning. Two reviews of this subject 

both concluded that Islam is permissive of family planning (Boonstra 2001) and that Islam is not a barrier 

to programs aimed at reducing unplanned pregnancies (Roudi-Fahimi 2004). This does not imply that local 

religious leaders are necessarily in accord with this view. 

In most of the 17 countries studied, the percentage of married Muslim women currently using a 

contraceptive method is lower than for non-Muslim women. Exceptions include Benin, Mali, and Uganda 

(Table 10). Unmet need for family planning tends to be similar in many countries and does not show a 

dominant difference by religion. This is probably because Muslims are more likely to desire a birth soon 

and are classified in the “no demand for contraception” category. The total demand for family planning, 

defined as the sum of contraceptive prevalence and unmet need, shows lower proportions of Muslim women 

in every country except for Mali. This exception of Mali is probably related to the exclusion in the 2012-

13 DHS of three northern regions for reasons of security; these regions are 99 percent Muslim (Westoff, 

Bietsch, and Mariko 2014). In the earlier 2006 DHS survey in Mali, the total demand for family planning 

showed lower proportions among Muslims. 

A multivariate analysis of the factors affecting ever use of contraception is shown in Table 11. The “ever 

use” measure is preferred to current use because of the typically low prevalence in sub-Saharan countries. 

The odds ratios indicate that being Muslim is associated with lower levels of ever use of contraception 

controlling for all of the other covariates in all of the countries with the exceptions of Benin and Uganda 

where there is no difference. Strong associations appear in Cameroon, Chad, Ethiopia, Guinea, Kenya, 

Malawi, and Nigeria. A higher number of children desired is consistently associated with less contraceptive 

use. Years of schooling and wealth show consistent positive odds ratios with the use of contraception. 

Exposure to mass media shows a positive relationship, as does one of the gender measures (which partner 

makes household decisions). Polygyny is negatively related, and child mortality shows little association 

with contraceptive use. 

We also examined the relationship of religion with the length of birth intervals, which is related to 

contraceptive use. Muslim women had shorter birth intervals than non-Muslim women in many of the 

countries (especially in Cameroon, Ethiopia, and Kenya), but in only a few did this connection remain in 

the multivariate context (not shown). 
21 

Table 10. Measures of family planning for Muslim and non-Muslim currently married women in subSaharan Africa 

 

Percentage currently 

using contraception 

Percentage with 

unmet need 

Total demand for 

family planning 

 Muslim Non-Muslim Muslim Non-Muslim Muslim Non-Muslim 

Benin 2012 14 13 28 34 42 47 

Burkina Faso 2010 15 19 25 24 39 43 

Cameroon 2011 7 29 24 24 31 52 

Chad 2004 1 5 20 22 21 27 

Cote d’Ivoire 2012 14 21 28 27 42 48 

Ethiopia 2011 20 33 31 24 50 57 

Ghana 2008 12 26 34 36 47 62 

Guinea 2012 5 12 24 21 29 33 

Kenya 2009 20 48 24 26 43 74 

Liberia 2013 11 22 31 31 42 55 

Malawi 2010 32 48 32 25 64 73 

Mali 2012-13 10 9 26 23 37 32 

Mozambique 2011 7 13 23 30 29 43 

Nigeria 2013 6 29 15 18 21 47 

Senegal 2010 13 27 30 23 43 50 

Sierra Leone 2013 16 21 25 26 41 47 

Uganda 2011 32 30 31 35 63 65 

 

All countries 14 24 26 26 40 50 

Note: Demand is the sum of the percentage of women using a contraceptive method and the percentage with an 

unmet need for family planning. 
22 

Table 11. Multivariate logistic regression (odds ratios) of factors associated with ever-use of 

contraception by currently married women. Coefficients are significantly different from 0, at the .05 

level, unless indicated by parentheses. 

 Benin 

Burkina 

Faso Cameroon Chad 

Cote 

d’Ivoire Ethiopia Ghana Guinea Kenya

 2012 2010 2011 2004 2012 2011 2008 2012 2009 

Muslim (1.01) 0.81 0.43 0.50 0.67 0.44 0.54 0.43 0.46 

Number of children 1.04 1.24 1.19 1.10 1.20 1.14 1.26 1.22 1.15 

Number desired (0.99) 0.84 0.91 0.90 0.90 0.90 0.91 0.90 0.82 

Age 1.02 0.99 0.99 (0.99) 0.98 0.97 0.96 (0.99) 1.02 

Age at marriage (0.99) 0.98 (1.00) (1.03) 1.01 0.97 (0.99) (1.02) 0.98 

Urban-rural (1.01) 0.66 1.24 0.73 (0.97) 0.77 (0.98) (0.87) 1.24 

Years of schooling 1.06 1.11 1.23 1.21 1.08 1.04 1.10 1.05 1.15 

Wealth1 1.11 1.31 1.41 1.36 1.22 1.44 1.21 1.22 1.26 

Listen to radio 1.09 1.11 1.08 1.16 1.16 1.09 1.16 1.23 1.20 

Watch television 1.17 1.33 1.17 1.15 1.12 1.39 (1.07) (1.05) 1.12 

Polygynous (0.94) 0.73 (0.95) (1.09) 0.78 0.44 (0.97) 0.80 0.78 

Number of child deaths 1.05 (1.00) (1.04) (1.02) (0.94) 0.92 (1.10) (1.04) (0.95) 

Decision making2 (0.98) 1.05 1.07 NA 1.15 1.09 1.07 1.07 1.10 

Beating3 (0.98) (1.00) (0.97) NA (0.98) 1.07 (0.99) 1.18 (0.98) 

 

Number of women 11,200 13,226 8,940 4,182 6,107 9,659 2,809 6,710 4,656 

R2 .033 .159 .281 .255 .104 .228 .112 .096 .264 

 Liberia Malawi Mali Mozambique Nigeria Senegal Sierra Leone Uganda 

 2013 2010 2012-13 2011 2013 2010 2013 2011 

Muslim 0.72 0.49 0.73 0.69 0.49 0.69 (0.92) (1.00) 

Number of children 1.27 1.64 1.24 1.34 1.31 1.31 1.27 1.25 

Number desired 0.89 0.88 0.91 0.93 0.84 0.88 0.92 0.87 

Age 0.95 0.96 0.97 0.96 0.98 (0.99) 0.98 (0.99) 

Age at marriage 1.02 (1.00) 1.02 (1.01) (1.01) (1.00) (1.01) 0.97 

Urban-rural 0.87 0.78 0.67 0.83 0.79 0.60 0.59 0.80 

Years of schooling 1.08 1.12 1.08 1.13 1.08 1.13 1.06 1.14 

Wealth1 1.10 1.06 1.33 1.40 1.35 1.24 1.21 1.43 

Listen to radio 1.33 1.13 1.07 1.10 1.09 (1.05) 1.30 1.12 

Watch television (1.04) 1.06 1.15 1.20 1.12 1.23 (0.95) 0.91 

Polygynous (1.13) (0.92) (0.95) (0.95) 0.91 (0.90) 0.86 (0.94) 

Number of child deaths 1.16 1.10 1.12 (1.06) 0.92 (1.04) 1.09 0.93 

Decision making2 1.13 1.04 1.15 1.14 1.19 1.04 1.10 (0.96) 

Beating3 1.04 0.95 1.03 (1.05) (0.97) (1.01) 1.06 (1.01) 

 

Number of women 5,790 15,064 8,723 7,837 26,163 10,574 10,642 4,998 

R2 .078 .110 .124 .159 .269 .141 .101 .166 

Note: ( ) Not significant at the .05 level 

1 Wealth index as calculated by the DHS, a composite measure of household’s cumulative living standard 

(http://dhsprogram.com/topics/Wealth-Index.cfm) 

2 Who makes various decisions in the home, coded with a positive direction indicating a more egalitarian position 

3 Attitudes towards husbands’ beating wives, coded with a positive direction indicating a more egalitarian position 
23 

5.4 Child Mortality 

Child mortality rates are declining rapidly in sub-Saharan Africa, but they remain relevant to reproductive 

behavior. Because this study focuses on covariations with religion, we have examined the possibility of 

child mortality differences between Muslims and non-Muslims. In only 8 of the 17 countries are there 

significant differences in child mortality, all with higher rates for Muslims. The greatest difference in child 

mortality exists in Nigeria where, among women with at least one live birth, 43 percent of Muslim women 

report having had at least one child death compared with 29 percent of non-Muslims. Other countries with 

significantly higher child mortality among Muslim women are Cameroon, Ethiopia, Ghana, Guinea, 

Liberia, and Sierra Leone. In most countries, with the exception of Nigeria, Cameroon, and Ghana, 

controlling for the number of births, education, and the use of contraception erases any religious difference 

in child mortality. 

6. Conclusions 

This study has focused on an analysis of the association of religion and fertility in sub-Saharan Africa. It is 

based on data from a DHS survey that includes only a single question on religion (respondents are asked if 

they identify with a particular religion). Because there is little difference in fertility between Catholics and 

Protestants, but fertility is typically higher for Muslims, the analysis concentrates on Muslim – non-Muslim 

differences in 17 countries in the region. The primary objective was to determine whether the observed 

higher fertility among Muslim women in sub-Saharan Africa compared with non-Muslim women reflects 

differences in age at marriage, number of children desired, contraceptive use, and child mortality as well as 

their social and economic covariates. 

There are strong differences in nuptiality. Higher proportions of Muslim women than non-Muslim women 

are currently married, and they marry at consistently earlier ages. Polygyny is more common among 

Muslims. Reproductive preferences, measured by the standard survey question on the ideal number of 

children desired, and also by the proportion of married women who want no more children, are both strongly 

related to the Muslim – non-Muslim dichotomy. This association with indicators of higher fertility was 

subjected to multivariate analyses of existing number of children, rural-urban residence, education, wealth, 

child mortality, polygyny, mass media exposure, and two measures of gender equality; however, the 

connections with religion were not eliminated. 

A multivariate analysis of the association of contraceptive use with the Muslim – non-Muslim difference 

yields similar results. Unmet need for family planning shows a mixed picture across countries, but the total 

demand for family planning estimated (by the sum of modern contraceptive prevalence and unmet need) 

clearly indicates a greater demand for family planning among non-Muslim women. 

The persistent association of the Muslim – non-Muslim effect on these reproductive measures when 

numerous covariates are controlled may be explained by some aspects of the religion itself or by local 

religious practices. More likely, the association may result from other unobserved covariates or from 

inadequate measurement of those included, such as measures of the status of women. If one considers the 

declines in Muslim fertility in other countries such as Bangladesh or Indonesia, the conclusions of Gavin 

Jones (2006) seem appropriate—namely, that there is nothing intrinsic to Islam that would suggest in our 

study the eventual disappearance of the Muslim effect on fertility in sub-Saharan Africa. However, the 

evidence assembled in our analysis does not suggest a rapid disappearance of the Muslim effect in subSaharan Africa. 
24 
25 

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