DHS COMPARATIVE

REPORTS 48

ABSOLUTE POVERTY, FERTILITY

PREFERENCES, AND FAMILY PLANNING 

USE IN FP2020 FOCUS COUNTRIES

AUGUST 2018

This publication was produced for review by the United States Agency for International Development (USAID). The report was prepared by 

Sarah Staveteig, Tesfayi Gebreselassie, and Kathryn T. Kampa.

DHS Comparative Reports No. 48 

Absolute Poverty, Fertility Preferences, and Family 

Planning Use in FP2020 Focus Countries 

Sarah Staveteig1,2 

Tesfayi Gebreselassie3 

Kathryn T. Kampa3 

ICF 

Rockville, Maryland, USA 

August 2018 

1

 Avenir Health 

2

 The DHS Program 

3

 ICF 

Corresponding author: Sarah Staveteig, The DHS Program, 530 Gaither Road, Suite 500, Rockville, MD 

20850, USA; phone: +1-301-572-0577; fax: 301-572-0999; email: Sarah.Staveteig@icf.com 
Acknowledgments: We thank Shea Rutstein for a valuable review of our draft manuscript; Tom Pullum, 

Courtney Allen, and Trevor Croft for assistance with variable harmonization; Anne Morse for assistance 

compiling regression result tables; and Chris Gramer for assistance with chart legends. 

Editor: Bryant Robey 

Document Production: Natalie Shattuck 

Cover Design: Chris Gramer 

Map: Tom Fish 

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. Additional information about The DHS Program can be obtained 

from ICF, 530 Gaither Road, Suite 500, Rockville, MD 20850 USA; telephone: +1 301-572-0200, fax: 

+1 301-572-0999, email: info@DHSprogram.com, internet: www.DHSprogram.com. 

Recommended citation: 

Staveteig, Sarah, Tesfayi Gebreselassie, and Kathryn T. Kampa. 2018. Absolute Poverty, Fertility 

Preferences, and Family Planning Use in FP2020 Focus Countries. DHS Comparative Reports No. 48. 

Rockville, Maryland, USA: ICF. 
iii 

CONTENTS 

TABLES ........................................................................................................................................................ v

FIGURES .................................................................................................................................................... vii 

PREFACE .................................................................................................................................................... ix 

ABSTRACT.................................................................................................................................................. xi 

1 INTRODUCTION .............................................................................................................................. 1 

1.1 Study Objectives ................................................................................................................. 1 

1.2 Background ......................................................................................................................... 2 

1.2.1 Fertility preferences ............................................................................................... 2 

1.2.2 Family planning: Levels and trends ....................................................................... 3 

1.2.3 Global poverty: Levels and trends ......................................................................... 5 

1.2.4 Family planning outcomes and poverty ................................................................. 6 

2 DATA AND METHODS ................................................................................................................... 9 

2.1 Country and Survey Selection ............................................................................................ 9 

2.2 Key Outcome Indicators .................................................................................................... 11 

2.3 Absolute Poverty Measurement ........................................................................................ 12 

2.3.1 Rationale .............................................................................................................. 12 

2.3.2 Previous approaches ........................................................................................... 12 

2.3.3 Our approach ....................................................................................................... 13 

2.4 Analysis ............................................................................................................................. 15 

3 RESULTS ...................................................................................................................................... 17 

3.1 Absolute Poverty among Married Women: Levels and Trends ........................................ 17 

3.2 Fertility Preferences .......................................................................................................... 21 

3.2.1 Mean ideal family size .......................................................................................... 21 

3.2.2 Non-numeric ideal fertility preferences ................................................................ 27 

3.3 Modern Contraceptive Prevalence .................................................................................... 34 

3.4 Contraceptive Method Mix ................................................................................................ 40 

3.4.1 Levels ................................................................................................................... 40 

3.4.2 Trends .................................................................................................................. 41 

3.4.3 Decadal changes ................................................................................................. 47 

3.5 Demand Satisfied for Modern Methods ............................................................................ 55 

4 DISCUSSION AND CONCLUSIONS ............................................................................................ 63 

4.1 Absolute Poverty ............................................................................................................... 63 

4.2 Fertility Preferences .......................................................................................................... 64 

4.3 Contraceptive Method Mix ................................................................................................ 65 

4.4 Modern Contraceptive Prevalence and Demand Satisfied by Modern Methods .............. 65 

4.5 Overall Conclusions .......................................................................................................... 66 

REFERENCES ............................................................................................................................................ 69 

APPENDIX A ABSOLUTE POVERTY COMPONENTS AMONG THE STUDY POPULATION ............ 75 

APPENDIX B DISTRIBUTIONS AND ABSOLUTE DECADAL CHANGES IN OUTCOME 

MEASURES ...................................................................................................................... 77 

v 

TABLES 

Table 2.1 Surveys included in the analysis ................................................................................ 10

Table 3.1 Average relative decadal changes in poverty composition, by country ..................... 20 

Table 3.2 Average relative decadal changes in ideal number of children, by absolute 

poverty level and country ............................................................................................ 26 

Table 3.3 Average relative decadal changes in non-numeric fertility preferences, 

by absolute poverty level and country ........................................................................ 33 

Table 3.4 Average relative decadal changes in modern contraceptive prevalence, 

by absolute poverty level and country ........................................................................ 39 

Table 3.5a Average relative decadal changes in long-term modern methods as a 

percentage of family planning use, by absolute poverty level and country ................ 49 

Table 3.5b Average relative decadal changes in short-term modern methods as a 

percentage of family planning use, by absolute poverty level and country ................ 51 

Table 3.5c Average relative decadal changes in traditional methods as a percentage 

of family planning use, by absolute poverty level and country ................................... 53 

Table 3.5d Average relative decadal changes in folkloric methods as a percentage 

of all family planning use, by absolute poverty level and country .............................. 54 

Table 3.6 Average relative decadal changes in demand satisfied for modern methods, 

by absolute poverty level and country ........................................................................ 60 

Table A.1 Levels of four deprivations and of asset poverty, married women ............................. 75 

Table B.1 Absolute poverty levels among married women, by survey ....................................... 77 

Table B.2 Average absolute decadal changes in poverty composition, by country ................... 79 

Table B.3 Mean ideal number of children by absolute poverty level and survey ....................... 80 

Table B.4 Average absolute decadal changes in ideal number of children, by absolute 

poverty level and country ............................................................................................ 82 

Table B.5 Percentage of respondents reporting a non-numeric ideal number of children, 

by absolute poverty level and survey ......................................................................... 83 

Table B.6 Average absolute decadal changes in non-numeric fertility preferences, 

by absolute poverty level and country ........................................................................ 85 

Table B.7 Modern contraceptive prevalence by absolute poverty level and survey ................... 86 

Table B.8 Average absolute decadal changes in modern contraceptive prevalence, 

by absolute poverty level and country ........................................................................ 88 

Table B.9 Method mix by absolute poverty level and survey ..................................................... 89 

Table B.10 Average absolute decadal changes in long-term modern methods as a 

percentage of family planning use, by absolute poverty level and country ................ 91 

Table B.11 Average absolute decadal changes in short-term modern methods as a 

percentage of family planning use, by absolute poverty level and country ................ 92 

Table B.12 Average absolute decadal changes in traditional methods as a percentage 

of family planning use, by absolute poverty level and country ................................... 93 

Table B.13 Average absolute decadal changes in folkloric methods as a percentage 

of family planning use, by absolute poverty level and country ................................... 94 

Table B.14 Demand satisfied for modern methods by absolute poverty level and survey ........... 95 

Table B.15 Average absolute decadal changes in demand satisfied for modern methods, 

by absolute poverty level and country ........................................................................ 97 

vii 

FIGURES 

Figure 2.1 Study countries ............................................................................................................. 9 

Figure 2.2 Definitions of absolute poverty groups used in the study ........................................... 15 

Figure 3.1a Absolute poverty composition of women currently in union, Central and 

Western Africa ............................................................................................................ 17

Figure 3.1b Absolute poverty composition of women currently in union, Eastern and 

Southern Africa ........................................................................................................... 18

Figure 3.1c Absolute poverty composition of women currently in union, South Asia, 

Southeast Asia, and Other Areas ............................................................................... 19 

Figure 3.2a Mean ideal number of children by absolute poverty level and survey year, 

Central and Western Africa ........................................................................................ 22 

Figure 3.2b Mean ideal number of children by absolute poverty level, Eastern and 

Southern Africa ........................................................................................................... 23

Figure 3.2c Mean ideal number of children by absolute poverty level, South Asia, 

Southeast Asia, and Other Areas ............................................................................... 25 

Figure 3.3a Percentage of respondents reporting a non-numeric ideal fertility preference 

by absolute poverty level, Central and Western Africa .............................................. 28 

Figure 3.3b Percentage of respondents reporting a non-numeric ideal fertility preference 

by absolute poverty level, Eastern and Southern Africa ............................................ 30 

Figure 3.3c Percentage of respondents reporting a non-numeric ideal fertility preference 

by absolute poverty level, South Asia, Southeast Asia, and Other Areas ................. 32 

Figure 3.4a Modern contraceptive prevalence by absolute poverty level, Central and 

Western Africa ............................................................................................................ 35

Figure 3.4b Modern contraceptive prevalence by absolute poverty level, Eastern and 

Southern Africa ........................................................................................................... 36

Figure 3.4c Modern contraceptive prevalence by absolute poverty level, South Asia, 

Southeast Asia, and Other Areas ............................................................................... 37 

Figure 3.5a Contraceptive method mix by absolute poverty level, Central and 

Western Africa ............................................................................................................ 42

Figure 3.5b Contraceptive method mix by absolute poverty level, Eastern and 

Southern Africa ........................................................................................................... 43

Figure 3.5c Contraceptive method mix by absolute poverty level, South Asia, 

Southeast Asia, and Other Areas ............................................................................... 46 

Figure 3.6a Demand satisfied for modern methods by absolute poverty level, Central 

and Western Africa ..................................................................................................... 57 

Figure 3.6b Demand satisfied for modern methods by absolute poverty level, Eastern 

and Southern Africa .................................................................................................... 58 

Figure 3.6c Demand satisfied for modern methods by absolute poverty level, South Asia, 

Southeast Asia, and Other Areas ............................................................................... 59 

ix 

PREFACE 

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

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

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

One of the objectives of The DHS Program is to provide policymakers and program managers in low- and 

middle-income countries with easily accessible data on levels and trends for a wide range of health and 

demographic indicators. DHS Comparative Reports provide such information, usually for a large number 

of countries in each report. These reports are largely descriptive, without multivariate methods, but when 

possible, they include confidence intervals and/or statistical tests. 

The topics in this series are selected by The DHS Program in consultation with the U.S. Agency for 

International Development. 

It is hoped that the DHS Comparative Reports will be useful to researchers, policymakers, and survey 

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

Sunita Kishor 

Director, The DHS Program 

xi 

ABSTRACT 

Broad gains in contraceptive access and use have been made in low-income countries over the past decade 

while poverty has declined, but the trends have been uneven. In light of the Family Planning 2020 (FP2020) 

goals to improve modern contraceptive uptake, and the Sustainable Development Goals’ emphasis on 

equitable progress, there is renewed interest in monitoring fertility preferences and family planning 

outcomes by poverty level. However, studies of this topic are typically constrained by the fact that standard 

poverty measurements are relative within surveys and cannot be compared across countries or over time. 

This study develops and uses a measure of absolute poverty in 31 of the 69 FP2020 focus countries, 

employing both an unsatisfied basic needs approach and an asset index to help differentiate among the 

levels of the extremely poor. The measure of absolute poverty enables us to compare and test outcomes 

among comparable poverty groups both within and across countries. 

The study classifies married women into one of four absolute poverty groups based on their housing 

characteristics, household level of education, and assets. We compare results from the most recent 

Demographic and Health Survey in each of the 31 selected countries with results from an earlier survey in 

each country, conducted on average 10 years earlier. The study found a statistically significant—and in 

many cases substantial—decline in absolute poverty among married women in all 31 countries. There was 

wide variation in all key indicators across countries. On average, the ideal number of children declined 

most substantially among the poorest group of women, both in absolute terms and relatively across the 

decade. In the majority of countries there were statistically significant increases in modern contraceptive 

prevalence, demand satisfied for modern methods, and use of long-term versus short-term modern methods. 

Increases in all three indicators were greatest and most statistically significant among the poorest women. 

On average, inequalities between the non-poor and the poorest women declined, but substantial disparities 

by absolute poverty group remain both within and across countries. To address these disparities, we 

recommend further analysis incorporating background characteristics and programmatic case studies from 

countries that have largely achieved a high level of demand satisfied for modern methods while also 

increasing equity among poverty groups. 

KEYWORDS: poverty, modern contraceptive use, demand satisfied for modern methods, ideal family size, 

FP2020 

1 

1 INTRODUCTION 

1.1 Study Objectives 

Recognized as a highly cost-effective development intervention, family planning empowers women and 

couples to shape their own lives, supports healthier families, and helps to reduce poverty by increasing 

opportunities for economic growth (Alkema et al. 2013; Bongaarts et al. 2012; Carr et al. 2012; FP2020 

2017b; UNFPA 2017; United Nations, Department of Economic and Social Affairs, and Population Division 

2017b). If all unmet need for modern contraception in developing countries were fulfilled, the number of 

unintended pregnancies, unplanned births, and induced abortions would decline by about 75%, and the 

resulting health benefits would be substantial, including far fewer maternal deaths (Guttmacher Institute 

2017). In most developing countries, however, women in the bottom 20% of households by wealth, and 

particularly women in rural areas, are far less likely to have access to contraceptives than wealthier women 

and urban residents (UNFPA 2017). Too often, the poor are being left behind and losing out in access to 

quality health care and other essential services (United Nations 2018). 

Despite tremendous progress worldwide in boosting overall prosperity and in reducing extreme poverty, 

gaps in wealth have grown, and stark economic disparities remain. According to the most recent 

comprehensive data on global poverty, in 2013, an estimated 767 million people are living below the 

international poverty line of US$1.90 per person per day. By this standard, nearly 11% of the global 

population is poor, over half of whom are in sub-Saharan Africa and another third in South Asia (World 

Bank 2016). 

The Sustainable Development Goals (SDGs) have placed emphasis on reductions in inequality, and in 

disaggregating outcomes by several categories including income (United Nations 2017). These goals, along 

with those of FP2020, have spurned interest in measuring and monitoring inequality in family planning 

outcomes. However, to date, research on fertility preferences, family planning, and poverty has been 

broadly constrained by the fact that most nationally representative surveys that produce these indicators, 

including the Demographic and Health Surveys (DHS), measure poverty in relative terms. The DHS Wealth 

Index (Rutstein and Johnson 2004) is widely used to compare relative economic status, based on household 

assets, construction materials, and services. The index was based on a methodology developed by Filmer 

and Pritchett (2001) to measure relative economic standing within a country at a given point in time in the 

absence of data on income. Using principal components analysis, households are scored relative to each 

other and the household population is divided into quintiles from richest to poorest. These scores and 

quintile rankings enable researchers to measure relative inequality in health outcomes at different points in 

time, but respondents cannot be compared in their economic status across countries or over time. The 

wealthiest 20% of the household population in a poor country may not be anywhere near what would be 

considered wealthy in their actual standard of living; conversely, in an affluent country the poorest may not 

be extremely poor by global living standards. 

Absolute measures of poverty and their relationship with family planning outcomes are the focus of this 

report. The study seeks to answer a few key questions. First, how do fertility intentions differ by levels of 

absolute poverty, and how has this changed over time? Second, are recent gains in modern contraceptive 

prevalence similar among the extremely poor, the poor, and the non-poor? Third, in what countries are 
2 

women in extreme poverty faring best and worst in terms of the percentage of demand for family planning 

satisfied by modern contraceptive methods? Having developed a measure of absolute poverty using DHS 

data for this report, we also examine levels and trends in poverty composition over time and across 

countries. 

1.2 Background 

Sexual and reproductive health is an internationally agreed human right, endorsed by 179 governments in 

the 1994 Program of Action of the International Conference on Population and Development (Barot 2014; 

UNCESCR 2000). Since then, access to services for sexual and reproductive health has increased 

worldwide, and, more recently, global support for these rights has expanded significantly through the 

Family Planning 2020 initiative (FP2020) (United Nations, Department of Economic and Social Affairs, 

and Population Division 2015). FP2020, an outcome of the 2012 London Summit on Family Planning, is a 

global movement with an overall goal of reaching 120 million additional users of modern contraceptive 

methods in the world’s poorest countries by 2020 (FP2020 2017a). Substantial financial commitments to 

support this ambitious effort have been made by many FP2020 countries, donors, civil society 

organizations, and private-sector partners, and over 20 national governments have made commitments to 

address the policy, financing, delivery, and sociocultural barriers that prevent many women from accessing 

contraceptive information, services, and supplies. In 2017, it was estimated that in the 69 FP2020 focus 

countries more than 309 million women and girls were using a modern method of contraception; this is 

38.8 million more than were using contraception in 2012, when FP2020 was launched—an increase that is 

approximately 30% above the historical trend (FP2020 2017b). Uptake of modern contraception has been 

improving in a majority of countries, across urban and rural areas, and at all levels of household wealth 

(UNFPA 2017). This growth in contraceptive use has led not only to improvements in health-related 

outcomes, such as reduced maternal mortality and infant mortality, but also to improvements in schooling 

and economic outcomes, especially for girls and women (United Nations, Department of Economic and 

Social Affairs, and Population Division 2017b). 

1.2.1 Fertility preferences 

Fertility preferences are an important precursor to the decision to use family planning; as desired family 

size declines, demand for contraception rises. Historically, over the course of the transition from high to 

low fertility, and as fertility entered the ‘realm of conscious choice’, desired family size has declined. 

During the demographic transition, economic and social changes such as industrialization, urbanization, 

and increased opportunities for education have led to lower levels of mortality and fertility (Bongaarts et 

al. 2012). The rising costs of raising children and their declining economic value—for labor and old-age 

security—are thought to have been central to the historic decline in desired family size, which in turn has 

led to growing demand for contraception (Becker 1960; Schultz 1973). Moreover, as countries develop, 

parents seek better health care, education, and opportunities for their children. Providing the means to do 

so increases the cost of raising many children, contributing to a desire for smaller but “higher-quality” 

families (Becker 1981). 

As might be expected, in countries with high levels of desired fertility, actual fertility also tends to be high, 

with few births averted by contraception; in contrast, where desired family size is low, many births are 

averted by contraception and actual fertility is lower (Bongaarts et al. 2012). While composite family 
3 

planning measures such as unmet need for family planning and the percentage of demand satisfied for 

contraception rely on women’s immediate or retrospective fertility intentions, an historic relationship exists 

between modern contraceptive prevalence (mCPR) and the mean ideal number of children, as reported by 

women in surveys (FP2020 2017b). 

Many social and cultural norms may affect the stated ideal number of children. For example, cultural 

pronatalism, widespread messaging about child limitation, or religious beliefs can influence perceptions of 

ideal numbers. Additionally, ex-post rationalization may play a role in the response to this question: while 

DHS surveys ask men and women if they could go back to the beginning of their reproductive lives, there 

may be a tendency to state an ideal number of children that is equal to or greater than one’s current number 

of children. Moreover, summarizing one’s ideal number of children with a single number can be a complex 

process; in many cultures, particularly South and Southeast Asia, numeric preference often depends on the 

number of sons and daughters. And for some women and men, childbearing is not under the ‘calculus of 

conscious choice’, while for others it is simply difficult to provide a numeric response when contingencies 

and complexities exist. DHS surveys allow for non-numeric responses; previous studies have shown that 

women who give non-numeric responses are likely to want more children than those who provide numeric 

responses, and are less likely to adopt behaviors that result in smaller families (Olaleye 1993; Riley, 

Hermalin, and Rosero-Bixby 1993; Upadhyay and Karasek 2012). Non-numeric responses have become 

increasingly rare in recent DHS surveys in sub-Saharan Africa and elsewhere (Frye and Bachan 2017). We 

examine both ideal number of children among those who provided a numeric response to this question, and 

the percentage of non-numeric responses. 

1.2.2 Family planning: levels and trends 

In 2017, 58% of married or in-union women of reproductive age were using a modern method of family 

planning worldwide. In sub-Saharan Africa the level was much lower, at 32%, but higher in Asia, at 61% 

(United Nations, Department of Economic and Social Affairs, and Population Division 2017b). In 2017, 

the percentage of demand for family planning satisfied by modern contraceptive methods was 78% 

worldwide, but with wide regional variations (United Nations, Department of Economic and Social Affairs, 

and Population Division 2017b). Progress has been especially rapid in Africa, where the proportion of the 

demand for family planning satisfied with modern contraceptive methods increased from 41% in 2000 to 

56% in 2017 (United Nations, Department of Economic and Social Affairs, and Population Division 2017a). 

Some countries with particularly rapid progress since 2000 in both stimulating and meeting demand for 

family planning, including Ethiopia, Malawi and Rwanda, serve as informative cases for the potential pace 

of change in other countries if investment and attention to family planning could be intensified. Most 

increases in contraceptive prevalence between 1990 and 2010 were attributable to rising use of modern 

methods, while the proportion of married or in-union women using traditional methods declined, from 6% 

in 2000 to 5% in 2017 (Alkema et al. 2013; United Nations, Department of Economic and Social Affairs, 

and Population Division 2017b). 

Method-specific contraceptive prevalence varies widely across the world. Overall, in Africa and Europe 

short-term and reversible methods, such as the pill, injectable, and male condom, are more common than 

other methods, whereas long-acting or permanent methods, such as sterilization, implants, and the IUD, are 

more common in Asia and Northern America (United Nations, Department of Economic and Social Affairs, 

and Population Division 2015). In 2015, over half of all contraceptive users worldwide relied on either 
4 

female sterilization (30%) or the IUD (21%), in large part due to patterns of long-term contraceptive use in 

China and India (United Nations, Department of Economic and Social Affairs, and Population Division 

2015). Since 1994, the worldwide method mix has shifted away from female and male sterilization and 

toward injectables and male condoms. These shifts in part reflect the changing geographic composition of 

users over the past two decades, as contraceptive use has taken off in sub-Saharan African countries where 

injectables are a common method (Bertrand et al. 2014). For the world as a whole, the share of total 

contraceptive use by the pill, implants, IUD, vaginal barrier methods, rhythm, and withdrawal has remained 

relatively stable over the past 20 years (United Nations, Department of Economic and Social Affairs, and 

Population Division 2015). 

The FP2020 initiative focuses solely on modern contraceptive methods, which have higher efficacy than 

traditional methods (Polis et al. 2016; Staveteig, Mallick, and Winter 2015). While family planning 

advocates have applauded the initiatives to increase access to modern contraceptive methods, they have 

also expressed concern that in the rush to meet the FP2020 goals, issues of voluntary use, reproductive 

choice, quality of care, and client-centered service delivery could be compromised (Bertrand et al. 2014; 

Hardee et al. 2013). 

In this paper we focus on levels of modern contraceptive use, method mix, and demand for family planning 

satisfied with modern methods. In addition to contraceptive prevalence, monitoring changes in method mix 

is important; providing access to a wide variety of modern methods makes it more likely that women can 

choose a contraceptive method that best suits their needs and preferences, thereby increasing consistency 

in use and minimizing discontinuation rates (Jain 2016; Jain et al. 2013). Contraceptive method mix 

highlights which methods are driving contraceptive use and can assist in identifying potentially underused 

methods (Bertrand et al. 2014). However, it is a complex indicator, as the choice of a contraceptive method 

reflects individual preferences, societal and cultural norms, and local and regional issues affecting 

contraceptive availability and accessibility (FP2020 2017b). The dominance of a single method in a country 

may signal deficiencies in access to a full range of contraceptive methods (Bertrand et al. 2014; FP2020 

2017b; Ross, Keesbury, and Hardee 2015). Additionally, low rates of use among longer-term methods such 

as implants, injectable contraceptives, and intrauterine devices (IUDs) may be due to a shortage in human 

resources rather than the actual product itself. Of the 57 countries that have chronic shortages of human 

resources for health care, 36 are in sub-Saharan Africa (WHO 2012). Expanding access to contraception 

requires increasing the supplies of quality contraceptive methods available and providing information about 

their safe use, as well as eliminating geographic, social, and economic barriers to contraceptive use (UNFPA 

2017). In an attempt to offset the lack of trained health-care workers, several low- and middle-income 

countries have begun task-shifting, which can be defined as “a more rational distribution of tasks and 

responsibilities among cadres of health workers,” as community health workers (CHWs) have been 

recognized as an effective option for the delivery of more complex family planning services (Scott et al. 

2015). 

The measure of demand for family planning met with modern contraceptive methods represents the 

percentage of women currently using a modern method among all women who are using or who have an 

unmet need for modern family planning (Bradley et al. 2012). This measure reflects voluntarism and 

informed choice—it sets neither contraceptive prevalence nor fertility targets, but rather highlights the 

imperative to fulfill individuals’ and couples’ own choices with regard to number and timing of children 

(United Nations, Department of Economic and Social Affairs, and Population Division 2017b). It reflects 
5 

the existence of substantial levels of unmet need for family planning—women who say they want to avoid 

childbearing but are not using contraception—as well as women already using contraception to avoid 

pregnancy. Furthermore, the measure’s focus on modern contraceptives reflects prioritization of these more 

effective methods. Modern contraceptive use, which results in fewer unintended pregnancies compared 

with traditional methods, can help individuals and couples achieve their reproductive intentions (Fabic et 

al. 2014). Across low-income countries, women in the top wealth quintile have 50% more demand for 

family planning met with modern contraceptive methods, on average, compared with all wealth quintiles 

combined (Fabic et al. 2014). Nevertheless, the historical experiences of formerly low-income countries, 

such as South Korea and Thailand, indicate that with focused attention and widespread support, the demand 

for family planning met with modern contraceptives can increase from low levels to as high as 75% in 20 

years or fewer (Fabic et al. 2014; Robinson and Ross 2007). In the same group of countries, the percentage 

of demand for family planning satisfied with modern contraceptive methods increased from 59% in 2000 

to 68% in 2017 (United Nations, Department of Economic and Social Affairs, and Population Division 

2017a). 

1.2.3 Global poverty: levels and trends 

In 2013, an estimated 11% of the world’s population, about 767 million people, were living under the 

international poverty line of US$1.90 a day, down from an estimated 12% in 2012 (World Bank 2016). 

When measured in all of its dimensions, progress in poverty reduction and shared prosperity over the past 

three decades has been significant (Cruz et al. 2015). Since 1990, nearly 1.1 billion people have moved out 

of extreme poverty (UNDP 2016). Much of this reduction has been driven by remarkable progress in the 

East Asia and Pacific region with 71 million fewer poor people, notably in China and Indonesia, and in 

South Asia with 37 million fewer poor, notably in India (World Bank 2018). A significant change in the 

geography of poverty has meant that in 2013 sub-Saharan Africa contained more than half the world’s poor. 

This is despite the fact that the African subcontinent experienced progress in lowering both the percentage 

of the population that are poor (by 1.6 percentage points) and the number of poor (by 4 million in 2012–

13) (World Bank 2016). These achievements are modest, however, compared with East Asia and Pacific 

and South Asia. Other regions with lower poverty rates and total numbers—notably Eastern Europe and 

Central Asia, as well as Latin America and the Caribbean—saw marginal declines in poverty in 2012–13 

(World Bank 2016). 

The Multidimensional Poverty Index (MPI) measures non-income dimensions of poverty, and aims to 

capture severe deprivations that are faced by individuals with respect to education, health, and living 

standards (UNDP 2016; United Nations 2016). While MPI has declined significantly, it remains 

unacceptably high in some areas. The continued persistence of geographically concentrated pockets of deep 

multidimensional poverty within many countries has led to conflicting views about the extent and pace of 

progress in poverty reduction (Cruz et al. 2015). 

Poverty is a major cause of ill health and is a barrier to accessing health services for many people. Inequality 

in access to health services is widespread in some countries and is associated with higher income inequality. 

Particularly, maternal health and adolescent fertility are closely related to income inequality and the 

incidence of poverty (Gonzales et al. 2015). Globally, the poor are predominantly rural, young, poorly 

educated, mostly employed in the agricultural sector, and live in larger households with more children 
6 

compared with the non-poor (Castañeda et al. 2016). Developing countries tend to exhibit wider withincountry inequality relative to developed countries (World Bank 2016). 

1.2.4 Family planning outcomes and poverty 

Many developing countries have improved their capacity to provide modern contraception and to reduce 

wealth-based inequality in satisfying the demand for family planning. In less-developed countries, however, 

there tends to be a wide gap in contraceptive use between households in the highest and lowest wealth 

quintiles (52% versus 35%) (UNFPA 2017). This gap has persisted despite general improvements in 

socioeconomic status and the expansion of family planning services worldwide (Creanga et al. 2011). In 

the majority of developing countries, contraceptive prevalence is lower among women who are poorer, 

rural, and less educated compared with richer, urban, and more educated women (UNFPA 2017, 2013). 

Low-income countries themselves vary substantially in modern contraceptive prevalence. In 2017 the range 

was from below 10% in Chad, Guinea, and South Sudan to 67% in Zimbabwe and 71% in Democratic 

People’s Republic of Korea (United Nations, Department of Economic and Social Affairs, and Population 

Division 2017b). 

Poverty, of course, is not the only factor in access to family planning. Local and national service and policy 

environments, levels of education, age structure, and programmatic initiatives can increase family planning 

use even among the very poor. Fertility has declined rapidly in a few countries with unfavorable 

development conditions (e.g., Bangladesh, Indonesia, Nepal, and Sri Lanka). These are traditional, poor, 

rural, and agricultural societies, yet fertility has declined to low levels. The main explanation for these 

unexpected trends is the priority their governments have given to social development (e.g., schooling and 

women’s empowerment) and the implementation of effective family planning and health programs. No 

fertility decline has been observed in a poor and largely illiterate country in the absence of a strong family 

planning program (Bongaarts et al. 2012). 

In 2017, in 76 out of 185 countries for which data are available, 75% or more of the total demand for family 

planning was met with modern contraceptive methods. These countries include 14 in Africa, 13 in Asia, 25 

in Latin America and the Caribbean, and 24 in other regions. In contrast, in another 45 countries less than 

half of the total demand for family planning was met with modern methods. Among these countries, 32 

were in Africa, 8 in Asia, 5 in Europe, and 3 in Oceania (United Nations, Department of Economic and 

Social Affairs, and Population Division 2017b). Among the FP2020 focus countries, the percentage of 

demand for family planning satisfied with modern methods is lowest in the four sub-regions of Africa, at 

24% in Central Africa, 37% in Western Africa, 62% in Eastern and Southern Africa, and 66% in Middle 

East and Northern Africa, followed by Latin America and the Caribbean (66%), South Asia (72%), 

Southeast Asia and Oceania (75%), and Eastern and Central Asia (78%) (FP2020 2017b). 

An analysis of data on the proportion of demand for family planning satisfied with modern contraception 

among women who are married or in a union shows that women in the least developed countries have less 

access than women in other developing countries. It also shows that regardless of a country’s income 

grouping, the richest 20% of households on average have the most access, and the poorest 20% have the 

least access. There are exceptions, however, where use of family planning is generally more equitable. In 

Bangladesh, Bhutan, Cambodia, and Thailand, contraceptive prevalence is higher among the poorest 20% 

of households than the richest 20% (UNFPA 2017). In these and several other countries, concerted efforts 

to expand family planning coverage have led to almost universal access to modern contraception, and near-
7 

equitable prevalence of contraceptive use across the wealth spectrum (UNFPA 2017). A recent study that 

standardized poverty measures in DHS surveys to assess the contributions of family planning programming 

versus changing living standards found that in a majority of study countries, expanding family planning 

services contributed more to an increase in contraceptive use than improvements in living conditions, across 

all deciles of wealth (Emmart, Winfrey, and Davis 2017). 

While some countries have made exceptional progress in reducing inequality of access to contraception, 

others have made great progress in expanding coverage of contraceptive services. Lesotho, Rwanda, and 

Sierra Leone are examples of countries that have made exceptional progress in both areas over about a 10-

year period. For example, analysis by relative household wealth quintiles in Rwanda shows that a previous 

wide gap in demand for family planning satisfied by modern methods has been effectively closed—at an 

access proportion of close to 70% among all five wealth quintiles in the later survey; Lesotho experienced 

similarly strong progress (UNFPA 2017). 

9 

2 DATA AND METHODS 

2.1 Country and Survey Selection 

This study employed data from countries with nationally representative Demographic and Health Surveys 

(DHS) based on the following criteria: (1) The country was among the current list of FP2020 focus countries 

(FP2020 2018); (2) A standard DHS survey was conducted in 2012 or later that was available by May 1, 

2018; (3) A DHS survey was available that was at least five years older than the more recent survey and 

was conducted after 1995; and (4) Both surveys included all variables necessary for the analysis of absolute 

poverty. If more than one older survey met the criteria for year and variables, we gave preference to the 

survey that was closest to a 10-year difference from the most recent survey. If two older surveys were 

equally close to 10 years, we gave preference to the earlier of the two. 

This strategy resulted in selection of 62 surveys in 31 countries, shown in Figure 2.1. Twenty-eight study 

countries were classified into one of three major world regions: Central and Western Africa, Eastern and 

Southern Africa, or South and Southeast Asia. Three additional countries that qualified for inclusion—

Egypt, Haiti, and Kyrgyz Republic—do not share a common region with any other study countries and are 

henceforth referred to as belonging to an “Other Areas” category. 

Figure 2.1 Study countries 
10 

Our analysis focused solely on currently married women of reproductive age (15-49). Per standard DHS 

definitions, the term ‘currently married’ means that the woman is married or living with a man as if married. 

The 62 surveys we study are shown in Table 2.1 along with the corresponding weighted sample sizes of 

married women. Surveys included in the study were fielded as early as 1996 and as late as 2016; intracountry gaps ranged from 5 years (Sierra Leone) to 16 years (Comoros). On average there was a 10-year 

difference between survey rounds. 

Table 2.1 Surveys included in the analysis 

Survey 1 Survey 2 Number of 

years between 

survey rounds Year Sample size Year Sample size 1

Central and Western Africa

Chad 2004 4,663 2014-15 13,263 10.5 

DR Congo 2007 6,622 2013-14 12,096 6.5 

Ghana 2003 3,549 2014 5,322 11.0 

Guinea 2005 6,292 2012 6,726 7.0 

Liberia 2007 4,540 2013 5,386 6.0 

Mali 2001 10,723 2012-13 8,820 11.5 

Niger 1998 6,382 2012 9,881 14.0 

Nigeria 2003 5,336 2013 27,830 10.0 

Senegal 2005 9,866 2016 5,883 11.0 

Sierra Leone 2008 5,525 2013 10,903 5.0 

Eastern and Southern Africa

Burundi 2010 5,421 2016-17 9,782 6.5 

Comoros 1996 1,634 2012 3,261 16.0 

Ethiopia 2005 9,066 2016 10,223 11.0 

Kenya 2003 4,919 2014 8,710 11.0 

Lesotho 2004 3,709 2014 3,612 10.0 

Malawi 2004 8,312 2015-16 16,130 11.5 

Rwanda 2005 5,510 2014-15 6,982 9.5 

Tanzania 2010 6,412 2015-16 8,210 5.5 

Uganda 2006 5,337 2016 11,223 10.0 

Zambia 2001-02 4,694 2013-14 9,859 12.0 

Zimbabwe 2005-06 5,143 2015 6,151 9.5 

South and Southeast Asia

Cambodia 2005 10,087 2014 11,899 9.0 

India 2005-06 93,089 2015-16 511,373 10.0 

Indonesia 2002-03 27,857 2012 33,465 9.5 

Nepal 2006 8,257 2016 9,875 10.0 

Pakistan 2006-07 9,556 2012-13 12,937 6.0 

Philippines 2003 8,671 2013 9,729 10.0 

Timor-Leste 2009-10 7,906 2016 7,697 6.5 

Other Areas 

 

Egypt 2005 18,187 2014 20,460 9.0 

Haiti 2000 5,958 2012 7,808 12.0 

Kyrgyz Republic 1997 2,675 2012 5,256 15.0 

1 If survey fieldwork spans two years, it is assumed to have been fielded at the midpoint between those 

years, e.g., 2014.5 for a 2014-15 survey. 
11 

2.2 Key Outcome Indicators 

We assess five key outcome indicators in relationship to absolute poverty, as follows:

Mean ideal number of children and non-numeric fertility preferences 

Toward the end of the DHS interview, women who have living children are asked, “If you could go back to 

the time you did not have any children and could choose exactly the number of children to have in your 

whole life, how many would that be?” Women who do not have living children are asked, “If you could 

choose exactly the number of children to have in your whole life, how many would that be?” Possible 

response options are none, a specific number, or an “other” response. If a non-numeric response is given, 

interviewers are instructed to probe for a numeric response. The mean ideal number of children refers to 

the mean of the number of children women specified in response to this question, among those who gave a 

numeric response. 

As the term implies, a non-numeric response means that, in spite of the numeric probe, the woman gave a 

qualitative response to this question, such as “it depends,” “up to god,” “as many as possible,” “uncertain,” 

and so forth. Typically, these responses are interpreted as fatalistic, indicative that fertility is outside the 

realm of conscious choice, although they may also suggest substantial uncertainty in some circumstances 

(Hayford and Agadjanian 2011). 

Contraceptive method mix 

Method mix refers to the composition of family planning method types among sampled women who report 

currently using a means to delay or avoid pregnancy. If women report more than one method, the method 

that is most effective is considered their primary method. For our purposes, we classified reported method 

types into four major groups: long-term modern methods, short-term modern methods, traditional methods, 

and folkloric methods, as follows: 

1. Long-term modern methods: IUDs, implants/Norplant; sterilization (male or female).

2. Short-term modern methods: Pill, injectables, condom (male or female), emergency 

contraception, basal body temperature, Billings ovulation, the sympto-thermal method, standard 

days methods, and other modern methods1

 such as diaphragms, cervical caps, foam, jelly, and 

suppositories.

3. Traditional methods: Periodic abstinence (the rhythm method), withdrawal (coitus interruptus), 

prolonged breastfeeding, douche.

4. Folkloric: Herbs, massage, other folkloric methods, and any other method not named above and 

not specifically classified as modern. 

 

1

 “Other modern methods” is a category available for interviewers to select in most DHS surveys. Prior to the survey, 

interviewers are instructed on which methods may be included in the category; however, the full list may not be 

available to analysts. 
12 

Modern contraceptive prevalence 

Modern contraceptive prevalence (mCPR) among married women is defined as the percentage of women 

who report that they or their sexual partners are currently using a modern method of family planning. 

Eligible methods include all long-term and short-term modern methods described above. 

Demand for family planning satisfied by modern methods 

Demand satisfied by modern methods (DSMM) is defined as the number of women who are currently using, 

or whose sexual partner is currently using, at least one modern contraceptive method as a proportion of the 

number of women of reproductive age who use any method of family planning or who have an unmet need 

for family planning (FP2020 2017b; United Nations, Department of Economic and Social Affairs, and 

Population Division 2017b). 

2.3 Absolute Poverty Measurement 

2.3.1 Rationale 

In the absence of income and expenditure data, researchers at the DHS Program have developed an 

economic status measure, the DHS Wealth Index (Rutstein and Johnson 2004), based on an earlier index 

developed using DHS data (Filmer and Pritchett 2001). The DHS Wealth Index is computed based on assets, 

services, and household characteristics within each survey. Other variables such as the presence of a 

domestic servant are added if the survey allows. This composite wealth index has proven enormously useful 

to the study of inequalities in health behaviors and the effect of relative economic status on health outcomes. 

Although commonly misunderstood as a proxy for income, which tends to be volatile in many low-income 

settings, the DHS Wealth Index is designed to capture a more stable measure of economic status than 

income alone, akin to Milton Friedman’s concept of ‘permanent income’ (1957). 

Despite the enduring value of the DHS Wealth Index, its key limitation is that measurement is relative for 

any given country at a point in time, based on the specific assets, services, and construction materials asked 

about in that survey and their distribution within the population. The principal components analysis used in 

computations assigns scores to assets based in part on their prevalence; as asset ownership becomes more 

widespread and as construction materials and access to household services such as electricity and running 

water improve, the scores assigned to these assets and services by the principal components index shift. For 

example, having a cellphone in an early survey might be an important indicator of wealth, but in a later 

survey, if cellphones have become nearly ubiquitous, the wealth score gained by owning a cellphone might 

be near zero. Hence, a household with a stable bundle of assets, services, and construction materials might 

be scored as wealthy in one survey and poor in another. Thus, while the DHS Wealth Index is enormously 

useful within countries, it is constrained by its specificity to a given country and time period. 

2.3.2 Previous approaches 

Efforts to standardize the DHS Wealth Index across countries and over time are made challenging by the 

fact that earlier surveys asked relatively few questions that could be used to measure economic status. 

Before the late 1990s, DHS surveys typically only asked about assets directly related to a key health 

outcome, for example ownership of radios and televisions in relation to family planning messaging and use, 

or dirt floors and inadequate toilets in relationship to diarrhea among young children. As the DHS Wealth 
13 

Index progressed, the number of questions about household assets and services grew, and then in turn 

expanded further as countries became aware of the wealth index and wanted to capture information about 

salient assets. These developments have allowed the index to better differentiate households by economic 

status, particularly at the upper ends of the spectrum, but they make retrospective comparisons difficult. 

Researchers at The DHS Program have worked to standardize the wealth index, both across countries and 

time periods (Rutstein and Staveteig 2014) via the Comparative Wealth Index, and within countries over 

time via the Harmonized Wealth Index (Staveteig and Mallick 2014). The Comparative Wealth Index, which 

uses an anchoring approach, enables greater cross-survey comparability but occasionally suffers from 

undesirable distortions induced by linear displacement (Staveteig and Mallick 2014). The Harmonized 

Wealth Index produces a truly comparable wealth score between surveys that is unaffected by 

displacements. By making use of assets that are salient and sometimes specific to a given country, such as 

a wardrobe in Bangladesh or a water heater in Egypt, the Harmonized Wealth Index is more accurate than 

a cross-country common-denominator approach; but its corollary drawback is that such specific assets and 

within-country distributions inhibit cross-country comparisons. Ultimately, any common-denominator 

approach to remaking a standard wealth index has difficulty differentiating between households at the upper 

ends of the wealth scale, due to the limited number of asset questions in early surveys. Instead of trying to 

create a comparable wealth index, this paper, with its focus on poverty, instead creates a standardized 

poverty measure aimed at differentiating from among the lowest levels of the wealth index; it groups the 

non-poor into a single reference group. 

2.3.3 Our approach 

Inspired by Amartya Sen’s seminal work on measuring poverty in terms of absolute, not relative 

deprivations (1976, 1982), we developed for this paper a ‘direct method’ of poverty measurement: we 

measure a household’s achievement of basic needs to assess what standard of living a household actually 

affords. This approach is in some ways preferable to using monetary income or wealth as an intermediary 

variable, as market prices for basic necessities can vary widely by country. Consider, for example, the 

amount of monetary income required for a household located in an area with accessible electrical lines and 

plentiful piped drinking water to access those services, versus a second household in a rural area of a 

developing country with virtually no public infrastructure beyond roads and schools. In the latter scenario 

clean water and electricity could be obtained, perhaps through generators and bottled water shipments, but 

only at a very high price. As such, simple income or monetary wealth comparisons between these two 

households would be insufficient to gauge deprivations of basic needs. 

Our approach follows a line of earlier work on multidimensional poverty measurement using an index of 

unsatisfied basic needs (UBN). This framework, often referred to in the literature by its Spanish name Indice 

de Necesidades Básicas Insatisfechas, was formalized by the U.N. Economic Commission for Latin 

America (ECLAC) and the Census Institute in Argentina in the 1980s (Feres and Mancero 2001; Instituto 

Nacional de Estadística y Censos [INDEC] 1984). The UBN was designed to capture dimensions of poverty 

that could be determined from census data and that would be difficult to observe from income alone. It 

originally aimed to measure human deprivations, but over time other nonmonetary aspects of poverty—

such as household crowding and children’s non-enrollment in school, which were associated with 

poverty—were added to the measure. The index is now widely used across Latin America (Feres and 

Mancero 2001). Although there is no single definition of unsatisfied basic needs, the index typically 
14 

involves setting a threshold cutpoint for several measures of deprivation or poverty—for example, 

overcrowding, inadequate sanitation, inadequate water, lack of schooling—and summing them to produce 

a poverty index (Hicks 1998). 

The multidimensional poverty index (Alkire and Foster 2011; Alkire and Santos 2011), which is a popular 

extension of the UBN methodology, includes measures of health, education, and living standards to assess 

poverty with DHS and other household surveys. As previously discussed in Rutstein and Staveteig (2014), 

this measure is useful in an aggregate sense, but is not intended to be used for direct household and 

individual comparisons of the type we pursue here. This is largely owing to measurement criteria that are 

either not uniformly applicable to every household (such as children’s school enrollment) or that include 

health outcome measures that would introduce undesirable endogeneity into a study such as ours. 

Therefore, drawing in part from our own previous work on comparable poverty measures in Rutstein et al. 

(2016), we developed a measure of absolute poverty using DHS data for the purposes of this analysis. It 

relies on a definition of UBN and, to distinguish among the poorest households, also relies on an index of 

asset poverty similar to that used by the multidimensional poverty index (Alkire and Kanagaratnam 2018). 

Note that asset variables sometimes have a small number of missing cases. As is standard with the DHS 

Wealth Index, definitions are affirmative—for example in order to not be counted as lacking a radio or 

electricity, the respondent to the household survey must affirm that the household has a radio or electricity. 

We define the four unsatisfied basic needs as follows: 

• Inadequate water or sanitation: The household’s time to reach their source of drinking water is 

30 minutes or more or, as per the WHO/UNICEF Joint Monitoring Programme for Water Supply, 

Sanitation, and Hygiene (JMP) guidelines (WHO and UNICEF 2017), either the household does 

not have improved sanitation,2

 or the household does not have access to improved drinking 

water.3

• Inadequate floors: The household has earth, dirt, mud, dung, or clay floors. 

• Insufficient schooling: No working-age adult de jure member of the household (age 15-64) has 

at least five years of education, or there are no adult de jure members of the household. 

• No electricity: The household does not have electricity. 

Our measurement of absolute poverty is unique in that we consider deprivations differently from ownership 

of certain consumer durable goods (assets): while assets typically signal wealth, one can live a healthy and 

 

2

 Per JMP, improved sanitation means one of the following: networked flush and pour flush toilets connected to 

sewers, on-site flush and pour flush toilets or latrines connected to septic tanks or pits, on-site ventilated improved pit 

latrines, on-site pit latrines with slabs, or on-site composting toilets, including twin pit latrines and container-based 

systems. All households that did not affirmatively have one of these types of toilets were considered to have 

unimproved sanitation. 

3

 Per JMP, improved drinking water means one of the following: piped supplies (tap water in the dwelling, yard, or 

plot; public standposts), boreholes/tubewells, protected wells and springs, rainwater, packaged water (including 

bottled water and sachet water), or delivered water (including tanker trucks and small carts). All households that did 

not affirmatively have one of these types of drinking water were considered to have unimproved drinking water. 
15 

productive life even without specific consumer goods. However, in combination with deprivation, the 

absence of consumer goods may signal an even more extreme type of poverty. We therefore use asset 

poverty to differentiate among the extremely poor. 

For the purposes of our study, household asset poverty is defined as: not having a car or truck; and not 

having more than one of the following small assets: bicycle, radio, telephone (landline or mobile),4

television, refrigerator, or motorcycle/scooter. Note that asset poverty proved to be a useful means of 

differentiation among the extremely poor but not among the poor and non-poor: less than 6% of the poor 

and 1% of the non-poor were also asset poor. 

Based on the above criteria, we classified households into one of four absolute poverty groups—non-poor, 

poor, extremely poor but not asset poor, and extremely poor and asset poor—using the definitions shown 

in Figure 2.2. Non-poor should not be interpreted synonymously with wealthy or well-off. As our paper is 

focused on gradations of poverty, we have grouped together households that do not have any of the four 

UBNs; hence, non-poor households likely span a wide range of actual incomes. 

Figure 2.2 Definitions of absolute poverty groups used in the study 

2.4 Analysis 

We used Stata 15 to compute absolute poverty and to tabulate the outcome indicators using standard DHS 

definitions and weights. Graphical displays were created in part by using equiplot commands in Stata.5

Average relative decadal changes were calculated as: 

ܫ

10 = ݎ

ሺܻ2 − ܻ1ሻ ×

ሻ1ܫ − 2ܫሺ

1ܫ

where Ir is the relative decadal change of indicator I, Y is the calendar year of the survey, I is the specific 

indicator, the subscript 1 denotes the earlier survey of the pair, and the subscript 2 the latter survey. Surveys 

that overlapped two calendar years were assumed to have been fielded at the midpoint between the years. 

Absolute decadal changes, which are shown in appendix tables, are computed in the same way but not 

divided by I1. 

Significance testing for decadal changes was conducted via regression analysis for pooled data from each 

country. For changes in absolute poverty composition, as the categories are ordinal, we ran ordered logit 

 

4

 If the survey asked about both types of telephones, both were included. 5

 See http://www.equidade.org/equiplot. 

1. Non-poor: the household does not have any of the four unsatisfied basic needs (UBNs) 

2. Poor: the household has one UBN 

3. Extremely poor but not asset poor: the household has two or more UBNs but is not asset poor 

4. Extremely poor and asset poor: the household has two or more UBNs and is also asset poor 
16 

regressions to test the significance of changes in the absolute poverty distribution over time. Changes in 

ideal fertility preferences among those respondents expressing a numeric preference—a count variable—

were assessed using Poisson regression. Changes in the contraceptive method mix were assessed using 

multinomial logit regression. Finally, changes in binary indicators—non-numeric fertility preferences, 

mCPR, and DSMM—were assessed using logit regression. All regression results were computed using 

complex sampling weights. Given the large number of surveys and indicators, coefficients have been 

suppressed for ease of interpretation; regression results are shown by direction (positive or negative) and 

statistical significance. 
17 

3 RESULTS 

3.1 Absolute Poverty among Married Women: Levels and Trends 

Figures 3.1a, 3.1b, and 3.1c show the composition of absolute poverty among married women for each of 

the three regional groupings. Across all surveys combined, the percentage of married women who are both 

extremely poor and asset poor ranged widely, from less than 1% in Kyrgyz Republic 2012 and both Egypt 

surveys, to over 90% in Chad 2004, Ethiopia 2005, Niger 1998, and Rwanda 2005. In over half of all 

surveys, the majority of married women were in the poorest group: extremely poor and asset poor. At the 

opposite end of the spectrum, the percentage of married women who were non-poor ranged from less than 

1% in Niger 1998 and Chad 2004, to over 75% in Kyrgyz Republic 2012 and Egypt 2014. In the median 

survey, less than one-tenth of women were non-poor, while around one-sixth were poor or extremely poor 

but not asset poor; half were both extremely poor and asset poor. Components of absolute poverty 

classifications are shown in Appendix Table A.1, while Appendix Table B.1 contains absolute poverty 

distributions underlying Figures 3.1a, 3.1b, and 3.1c. 

Figure 3.1a shows the composition of absolute poverty among married women in Central and Western 

Africa. With the exception of Nigeria during the first round of surveys, the majority of married women were 

in the poorest category (extremely poor and asset poor), from 53% in Senegal 2005 to 94% in Chad 2004. 

Nigeria was close to this level, with 49% of married women in extreme and asset poverty during the first 

survey. Extreme poverty and asset poverty had diminished substantially by the time of the most recent 

survey in Ghana, Guinea, Mali, and Senegal. In the second Guinea and Mali surveys, the majority of married 

women were still extremely poor to some degree, but fewer were asset poor. In Ghana and Senegal, extreme 

poverty in any form was no longer the majority category by the second survey, and over one-fourth of 

married women were classified as non-poor. 

Figure 3.1a Absolute poverty composition of women currently in union, Central and Western Africa 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2004

20014-15

2007

2013-14

2003201420052012200720132001

2012-13

19982012200320132005201620082013

Chad D.R. Congo Ghana Guinea Liberia Mali Niger Nigeria Senegal Sierra Leone

Middle & West Africa

Extremely Poor and Asset Poor Extremely Poor but Not Asset Poor Poor Not Poor
18 

Figure 3.1b shows levels of absolute poverty among married women in Eastern and Southern Africa. Similar 

to Central and Western Africa, all 11 countries in this region also had very high levels of extreme poverty 

and asset poverty in the first survey, from 53% in Zimbabwe to 94% in Rwanda. By the second survey only 

4 of the 11 countries still had a majority of women in extreme and asset poverty: Burundi Ethiopia, Malawi, 

and Rwanda. Even so, the majority of married women remained in some form of extreme poverty in every 

country except Comoros. In four countries—Comoros, Lesotho, Zambia, and Zimbabwe—more than 10% 

of married women in the latest survey were non-poor. These levels of non-poor were relatively high but 

still far short of the levels of non-poor achieved in Western Africa by Ghana and Senegal, at over 25% of 

women. 

Figure 3.1b Absolute poverty composition of women currently in union, Eastern and Southern Africa 

Figure 3.1c shows corresponding compositions of absolute poverty for married women in South Asia, 

Southeast Asia, and the Other Areas category of Egypt, Haiti, and Kyrgyz Republic. Here, the picture is 

substantially different: only Nepal 2006, Timor-Leste 2009-10, and Haiti 2000 had a majority of married 

women in extreme and asset poverty in the first survey; Cambodia also had a majority of married women 

in extreme poverty but not necessarily asset poverty. At the time of the first survey, the majority of married 

women in the Philippines and Egypt were non-poor; these percentages increased over the two survey 

rounds. While the levels of absolute poverty were diverse across countries, outside of these extremes there 

was no clear majority group in these regions in any survey round. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010

2016-17

199620122005201620032014200420142004

2015-16

2005

 2014-15

2010

2015-16

20062016

2001-022013-142005-06

2015

Burundi Comoros Ethiopia Kenya Lesotho Malawi Rwanda Tanzania Uganda Zambia Zimbabwe

Eastern and Southern Africa

Extremely Poor and Asset Poor Extremely Poor but Not Asset Poor Poor Not Poor
19 

Figure 3.1c Absolute poverty composition of women currently in union, South Asia, Southeast Asia, and 

Other Areas 

Table 3.1 shows the relative changes in absolute poverty composition standardized in decadal terms by 

country, and the corresponding absolute decadal changes are shown in Appendix Table B.2. The numbers 

in Table 3.1 represent a decadal-standardized percentage change in the proportion of women in a given 

absolute poverty level. In every country the percentage of women in the poorest category (extremely poor 

and asset poor) declined, with a range from 12% in Sierra Leone to slightly over 100% in Timor-Leste, 

meaning that if the linear trend had continued for the full decade, extreme poverty and asset poverty would 

have been eliminated entirely. Decadal declines in extreme poverty averaged around 38% in both Central 

and Western Africa and Eastern and Southern Africa, and more than double that in South and Southeast 

Asia. In Other Regions, Kyrgyz Republic and Egypt witnessed decadal-averaged substantial declines in 

extreme poverty and asset poverty, while Haiti had a relatively small decline of 25%. 

In all other absolute poverty groups, changes over the decade were generally in a positive direction, but not 

always. Gains in the third poverty group, extremely poor but not asset poor, were substantial in much of 

Eastern and Southern Africa, where Ethiopia had a nearly 12-fold decadal gain and Rwanda a nearly 43-

fold gain in the percentage of married women in extreme poverty but not asset poverty, largely owing to 

very small proportions of women in these groups in the first survey. Lesotho was an exception, with a small 

decline in the third poverty group, as married women shifted out of extreme poverty. Countries in Central 

and Western Africa uniformly experienced gains in the third category of poverty, reflecting the shift out of 

extreme poverty. With the exception of the Philippines and Timor-Leste, countries in South and Southeast 

Asia generally had moderate gains or even small declines in the third poverty group. 

Table 3.1 also shows that the percentage of women classified as non-poor increased in nearly all countries 

over the decade. Average decadal gains were especially strong in areas where the level had been lowest—

Central and Western Africa, notably Niger, Ghana, and Senegal, as well as in Eastern and Southern Africa, 

particularly Lesotho, Uganda, and Rwanda. All six of these countries experienced in decadal terms a more 

than two-and-a-half-fold increase in the non-poor over the decade. In South and Southeast Asia and in Other 

Areas, gains in the non-poor ranged from nearly zero in the Philippines to over 100% in Cambodia, Nepal, 

and Kyrgyz Republic. However, in three countries the percentage of women in the non-poor group declined 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

20052014

2005-062015-162002-03

201220062016

2006-072012-13

20032013

2009-10

2016200520142000201219972012

Cambodia India Indonesia Nepal Pakistan Philippines Timor-Leste Egypt Haiti Kyrgyz

Republic

South and Southeast Asia Other Areas

Extremely Poor and Asset Poor Extremely Poor but Not Asset Poor Poor Not Poor
20 

between surveys—by 13% in Haiti, 24% in the Democratic Republic of Congo (herein DR Congo), and 

37% in Zimbabwe. 

Table 3.1 Average relative decadal changes in poverty composition, by country 

Average relative decadal change (%) 

Statistical 

significance of 

change 

Not 

Poor Poor 

Extremely 

Poor but not 

Asset Poor 

Extremely 

Poor and 

Asset Poor 

Central and Western Africa

Chad 60 154 608 -31 -- 

DR Congo -24 -23 184 -14 - 

Ghana 463 25 142 -74 -- 

Guinea 105 43 461 -57 -- 

Liberia 218 110 238 -27 -- 

Mali 134 98 152 -59 -- 

Niger 536 28 489 -14 -- 

Nigeria 125 -5 122 -45 -- 

Senegal 384 -10 42 -47 -- 

Sierra Leone 57 26 82 -12 -- 

Eastern and Southern Africa

Burundi 100 138 173 -25 -- 

Comoros 51 78 448 -41 -- 

Ethiopia 45 93 1,195 -15 -- 

Kenya 80 140 140 -44 -- 

Lesotho 470 155 -48 -32 -- 

Malawi 149 139 604 -35 -- 

Rwanda 263 262 4,263 -35 -- 

Tanzania 186 83 74 -64 -- 

Uganda 293 162 315 -50 -- 

Zambia 4 71 801 -43 -- 

Zimbabwe -37 39 475 -36 -- 

South and Southeast Asia

Cambodia 147 170 -5 -90 -- 

India 70 14 59 -85 -- 

Indonesia 52 -12 -19 -82 -- 

Nepal 176 143 63 -88 -- 

Pakistan 44 -1 23 -99 -- 

Philippines 9 9 123 -54 -- 

Timor-Leste 98 91 278 -103 -- 

Other Areas

Egypt 14 -30 -73 -99 -- 

Haiti -13 41 228 -25 -- 

Kyrgyz Republic 244 -49 -41 -64 -- 

Notes: 

-- Indicates there was a statistically significant decrease in absolute poverty at p<.01 from earlier to later survey. 

- Indicates there was a statistically significant decrease in absolute poverty at p<.05 from earlier to later survey. 
21 

Ordinal logit regressions were run to test the significance of changes in absolute poverty between the two 

survey rounds. Table 3.1 indicates that all countries had a statistically significant decline in absolute 

poverty; in all countries except DR Congo the decline was statistically significant at p<.01. In DR Congo—

which experienced substantial declines in the top two groups (not poor and poor) and only a modest decline 

of 14% in the poorest group—the statistical significance of changes in absolute poverty was weaker, though 

still significant at p<.05. 

3.2 Fertility Preferences 

3.2.1 Mean ideal family size 

This section reports the mean ideal number of children by absolute poverty level and survey year in each 

of the three regions. As expected, poverty tends to be inversely related to ideal number of children: in 49 of 

62 surveys, the poorest women had a higher ideal number of children than any other group. Countries in 

the Central and Western Africa region had the highest mean ideal number of children overall, ranging from 

4.7 in Ghana to 9.5 in Niger in the most recent surveys. As Figure 3.2a shows, three of the ten countries 

saw an increase in the overall mean ideal number of children. Niger had the largest increase, 1.0, followed 

by Guinea, 0.3, and Sierra Leone, 0.1. The mean ideal number of children also increased among the 

extremely poor and asset poor in these three countries, as well as in Ghana and Nigeria. Conversely, Mali 

and Chad had the largest decreases in mean ideal number of children among the poorest respondents, at 0.5 

and 0.4 respectively. Changes in disparities between the non-poor and poorest groups varied greatly in the 

region. Chad, Mali, Niger, and Senegal all experienced decreases in disparity between these absolute 

poverty levels, from a decline of 0.7 in Chad to 0.3 in Senegal. In contrast, the disparity grew by 0.2 in 

Liberia, Nigeria, and Sierra Leone. Three countries, DR Congo, Ghana, and Guinea, had no change in 

disparity between the non-poor and poorest groups. 
22 

Figure 3.2a Mean ideal number of children by absolute poverty level and survey year, Central and Western 

Africa 

Figure 3.2b shows the mean ideal number of children by absolute poverty level and survey year for countries 

in the Eastern and Southern Africa region. Nine of the 11 countries experienced a decline between surveys 

in overall mean ideal number of children; in the two other countries, the number remained constant in 

Zambia and increased slightly in Zimbabwe, from 4.1 to 4.3. Rwanda and Lesotho had the greatest declines, 

at 0.9 and 0.6 respectively. In the most recent survey, the mean ideal number of children in this region 

ranged from 2.9 in Lesotho to 5.6 in Comoros. Changes in disparity between non-poor and poorest were 

2004

2014

0 1 2 3 4 5 6 7 8 9 10

Chad

2007

2013

0 1 2 3 4 5 6 7 8 9 10

DR Congo

2003

2014

0 1 2 3 4 5 6 7 8 9 10

Ghana

2005

2012

0 1 2 3 4 5 6 7 8 9 10

Guinea

2007

2013

0 1 2 3 4 5 6 7 8 9 10

Liberia

2001

2012

0 1 2 3 4 5 6 7 8 9 10

Mali

1998

2012

0 1 2 3 4 5 6 7 8 9 10

Niger

2003

2013

0 1 2 3 4 5 6 7 8 9 10

Nigeria

2005

2016

0 1 2 3 4 5 6 7 8 9 10

Senegal

2008

2013

0 1 2 3 4 5 6 7 8 9 10

Sierra Leone

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
23 

not as widespread; three countries experienced increases and two remained constant between survey years. 

Of the six countries with declines in disparity between non-poor and poorest respondents, Uganda and 

Rwanda had the largest decreases, at 0.9 and 0.7 respectively. In Rwanda, disparity between these absolute 

levels of poverty was effectively eliminated between survey years, with the mean ideal number of children 

falling from 3.8 among non-poor women and 4.5 among poorest women in 2005 to 3.6 among women in 

both poverty groups in 2014. 

Figure 3.2b Mean ideal number of children by absolute poverty level, Eastern and Southern Africa 

2010

2016

0 1 2 3 4 5 6 7 8 9 10

Burundi

1996

2012

0 1 2 3 4 5 6 7 8 9 10

Comoros

2005

2016

0 1 2 3 4 5 6 7 8 9 10

Ethiopia

2003

2014

0 1 2 3 4 5 6 7 8 9 10

Kenya

2004

2014

0 1 2 3 4 5 6 7 8 9 10

Lesotho

2004

2015

0 1 2 3 4 5 6 7 8 9 10

Malawi

2005

2014

0 1 2 3 4 5 6 7 8 9 10

Rwanda

2010

2015

0 1 2 3 4 5 6 7 8 9 10

Tanzania

2006

2016

0 1 2 3 4 5 6 7 8 9 10

Uganda

2001

2013

0 1 2 3 4 5 6 7 8 9 10

Zambia

2005

2015

0 1 2 3 4 5 6 7 8 9 10

Zimbabwe

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
24 

As Figure 3.2c shows for South Asia, Southeast Asia, and Other Areas, moderate decreases in mean ideal 

number of children occurred between survey years in the majority of countries. However, the number 

remained constant at 4.1 between surveys in Pakistan, and increased by 0.3 in Kyrgyz Republic and 0.1 in 

Egypt. Timor-Leste experienced more substantial changes. Although Timor-Leste had the highest mean 

ideal number of children of all countries in the region, it also experienced the greatest overall decrease 

between surveys, from 5.7 in 2009 to 4.4 in 2016. Changes in disparity between the non-poor and poorest 

groups varied, with half of the countries experiencing declines in disparity. Timor-Leste had the greatest 

change, with disparity levels between the non-poor and poorest groups decreasing by 0.8 between surveys. 

No changes to disparity among absolute poverty levels took place in India or Nepal, and in Indonesia, 

Pakistan, and the Philippines disparity increased. The distributions used to chart Figures 3.2a, 3.2b, and 

3.2c are shown in Appendix Table B.3. 
25 

Figure 3.2c Mean ideal number of children by absolute poverty level, South Asia, Southeast Asia, and Other 

Areas 

Table 3.2 shows the average decadal changes in ideal number of children by absolute poverty level and 

country; corresponding average absolute changes are shown in Appendix Table B.4. Overall, decreases 

occurred in 24 of the 31 countries. Regionally, South and Southeast Asia had the highest proportion of 

countries (six of the seven) with a statistically significant decline in mean ideal family size. Of the 16 

countries with statistically significant decreases, Timor-Leste had the largest, with a decline of 35%. 

Moreover, decreases within poverty levels were also greatest in Timor-Leste, with extremely poor and asset 

2005

2014

0 1 2 3 4 5 6 7 8 9 10

Cambodia

2005

2015

0 1 2 3 4 5 6 7 8 9 10

India

2002

2012

0 1 2 3 4 5 6 7 8 9 10

Indonesia

2006

2016

0 1 2 3 4 5 6 7 8 9 10

Nepal

2006

2012

0 1 2 3 4 5 6 7 8 9 10

Pakistan

2003

2013

0 1 2 3 4 5 6 7 8 9 10

Philippines

2009

2016

0 1 2 3 4 5 6 7 8 9 10

Timor-Leste

2005

2014

0 1 2 3 4 5 6 7 8 9 10

Egypt

2000

2012

0 1 2 3 4 5 6 7 8 9 10

Haiti

1997

2012

0 1 2 3 4 5 6 7 8 9 10

Kyrgyz Republic

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
26 

poor women having the greatest decline, 36%. Sixteen other countries—half of them in Eastern and 

Southern Africa—had declines among respondents at the poorest level. Conversely, seven countries had an 

overall increase in average relative decadal changes in the ideal number of children, four of them 

statistically significant—Niger (8%), Guinea (6%), Kyrgyz Republic (6%), and Egypt (5%). Among nonpoor respondents, increases occurred in 16 of the 31 countries, but only six were statistically significant. 

Among these six countries, increases ranged from 5% in Zimbabwe to 27% in Kyrgyz Republic. 

Table 3.2 Average relative decadal changes in ideal number of children, by absolute poverty level and country 

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change 

NonPoor Poor 

Extremely 

Poor but 

Not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

Not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

Central and 

Western Africa

Chad 3 8 -3 -5 -6 10.5 -- -- 

DR Congo -7 -13 0 -3 -4 6.5 -- 

Ghana 12 7 5 6 -3 11.0 ++ ++ + 

Guinea 10 3 11 7 5 7.0 ++ ++ ++ 

Liberia -11 12 20 -2 -3 6.0 ++ 

Mali 3 7 -2 -6 -6 11.5 + -- -- 

Niger (18) 17 25 9 8 14.0 (+) ++ ++ ++ ++ 

Nigeria 2 -8 -1 4 -2 10.0 -- + 

Senegal 4 6 3 -1 -3 11.0 + - 

Sierra Leone -3 -4 13 5 4 5.0 

Eastern and 

Southern Africa

Burundi 0 -4 1 -13 -10 6.5 -- -- 

Comoros 1 2 (8) 2 0 16.0 

Ethiopia 7 6 8 -2 -3 11.0 

Kenya 3 -1 0 -1 -8 11.0 -- 

Lesotho -8 -13 -10 -16 -16 10.0 -- -- -- -- 

Malawi -5 -1 -1 -8 -8 11.5 -- -- 

Rwanda -6 -7 * -20 -19 9.5 -- * -- -- 

Tanzania -9 1 3 0 -4 5.5 

Uganda 15 8 5 -5 -5 10.0 + ++ + -- -- 

Zambia -3 8 10 2 1 12.0 ++ ++ 

Zimbabwe 5 7 6 2 4 9.5 + ++ 

South and 

Southeast Asia

Cambodia -1 -6 -10 -10 -10 9.0 -- -- -- -- 

India -1 -2 2 0 -6 10.0 - -- ++ -- 

Indonesia -7 -8 1 4 -8 9.5 -- -- -- 

Nepal -3 1 -5 -5 -9 10.0 - -- 

Pakistan -2 6 8 4 -1 6.0 + ++ 

Philippines -7 -5 -5 8 -6 10.0 -- -- ++ -- 

Timor-Leste -23 -25 -32 -36 -34 6.5 -- -- -- -- -- 

Continued
27 

Table 3.2—Continued

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change 

NonPoor Poor 

Extremely 

Poor but 

Not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

Not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

Other Areas

Egypt 6 7 6 * 5 9.0 ++ ++ * ++ 

Haiti 4 -2 -2 -9 -8 12.0 -- -- 

Kyrgyz Republic 27 5 1 * 6 15.0 ++ ++ * ++ 

Notes: 

++ indicates there was a statistically significant increase at p<.01 from earlier to later survey. 

+ indicates there was a statistically significant increase at p<.05 from earlier to later survey. 

-- indicates there was a statistically significant decrease at p<.01 from earlier to later survey. 

- indicates there was a statistically significant decrease at p<.05 from earlier to later survey. 

Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted 

cases and has been suppressed. 

3.2.2 Non-numeric ideal fertility preferences 

Figures 3.3a through 3.3c show the percentage of respondents reporting a non-numeric ideal fertility 

preference by absolute poverty level for each of the three regions; corresponding numbers are in Appendix 

Table B.5. Overall, percentages in the most recent surveys ranged from less than 0.5% to 24%. The Central 

and Western Africa region had the highest overall percentages of non-numeric ideal fertility preferences 

reported, and the South Asia, Southeast Asia, and Other Areas region had the lowest. Generally, women’s 

stated ideal number of children rises as the level of poverty becomes more extreme. 

The poverty level with the highest percentage of non-numeric responses concerning ideal number of 

children varied greatly among countries and between survey years. However, in nearly half of all surveys 

(30 of 62), extremely poor and asset poor women accounted for the largest percentage of such respondents. 

Figure 3.3a shows large variation among countries in overall percentage of respondents reporting a nonnumeric fertility preference, from 2% in Ghana to 24% in Chad in the most recent survey year. Decreases 

between surveys are seen in 6 of the 10 countries in this region. The largest changes were in Mali, at 23%, 

and Niger, at 17%. Three of the four countries with an increase in the total percentage of respondents 

reporting a non-numeric fertility preference experienced moderate increases of less than 1%. However, the 

change was much more substantial in Chad, with an increase of 11%. Interestingly, the highest percentages 

of respondents reporting a non-numeric fertility preference are found throughout all the poverty levels, and 

often change from one poverty level to another between survey years. For example, the non-poor in Chad 

made up the largest share of respondents reporting a non-numeric fertility preference, at 20% in the first 

survey, but in the second survey the poorest respondents accounted for the largest percentage, at 25%. In 

Mali, the poorest respondents had the greatest percentage of non-numeric responses in the first survey, at 

29%, and then the lowest percentage in the second survey, at 3%. It is important to note that changes 

between surveys may, in part, be due to alterations to the interviewer training process, probing 

inconsistencies, and potential protocol changes. 
28 

Figure 3.3a Percentage of respondents reporting a non-numeric ideal fertility preference by absolute 

poverty level, Central and Western Africa 

Figure 3.3b shows the percentage of respondents reporting a non-numeric fertility preference by absolute 

poverty level for countries in the Eastern and Southern Africa region. Overall, non-numeric responses 

ranged from 0.3% in Lesotho 2014 and Zimbabwe 2015 to 11% in Comoros 2012. In 11 of the 22 surveys, 

extremely poor and asset poor women had the highest percentage of respondents with non-numeric fertility 

preferences. In Malawi, however, non-poor women made up the largest percentage of such respondents in 

both survey years, at 4% and 2% respectively. Similarly, in Rwanda non-poor women made up the largest 

2004

2014

0 5 10 15 20 25 30

Chad

2007

2013

0 5 10 15 20 25 30

DR Congo

2003

2014

0 5 10 15 20 25 30

Ghana

2005

2012

0 5 10 15 20 25 30

Guinea

2007

2013

0 5 10 15 20 25 30

Liberia

2001

2012

0 5 10 15 20 25 30

Mali

1998

2012

0 5 10 15 20 25 30

Niger

2003

2013

0 5 10 15 20 25 30

Nigeria

2005

2016

0 5 10 15 20 25 30

Senegal

2008

2013

0 5 10 15 20 25 30

Sierra Leone

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
29 

percentage of respondents with non-numeric fertility preferences in the first survey year, at 4.4%, but then 

dropped to the lowest percentage, at just 0.8% in the second survey year. Kenya had the greatest overall 

decrease in percentage of respondents reporting a non-numeric fertility preference between surveys, with a 

decrease from 5.8% in 2003 to 2.3% in 2014. In three countries, Comoros, Lesotho, and Tanzania, the total 

percentage of respondents reporting a non-numeric fertility preference increased between survey years. The 

largest overall increase, 3%, occurred in Comoros, but with a significant decline in disparity between nonpoor and poorest women due to an increase in the percentage of non-poor women reporting a non-numeric 

ideal fertility preference. Additionally, disparities were nearly nonexistent in the more recent surveys in 

Burundi, Lesotho, Rwanda, and Zimbabwe. 
30 

Figure 3.3b Percentage of respondents reporting a non-numeric ideal fertility preference by absolute 

poverty level, Eastern and Southern Africa 

Figure 3.3c shows the percentage of respondents reporting a non-numeric ideal fertility preference by 

absolute poverty level for each country in South Asia, Southeast Asia, and Other Areas. Overall, the region 

has the lowest percentage of respondents reporting a non-numeric fertility preference. In the second survey, 

percentages ranged from 0.3% in the Philippines to 12% in Timor-Leste. Eight of the ten countries had a 

decrease in the overall percentage of respondents giving a non-numeric response, and in three-quarters of 

these countries the percentage within each poverty level also fell between survey years. Indonesia had the 

2010

2016

0 5 10 15 20 25 30

Burundi

1996

2012

0 5 10 15 20 25 30

Comoros

2005

2016

0 5 10 15 20 25 30

Ethiopia

2003

2014

0 5 10 15 20 25 30

Kenya

2004

2014

0 5 10 15 20 25 30

Lesotho

2004

2015

0 5 10 15 20 25 30

Malawi

2005

2014

0 5 10 15 20 25 30

Rwanda

2010

2015

0 5 10 15 20 25 30

Tanzania

2006

2016

0 5 10 15 20 25 30

Uganda

2001

2013

0 5 10 15 20 25 30

Zambia

2005

2015

0 5 10 15 20 25 30

Zimbabwe

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
31 

greatest decline, from 14% in 2002 to 9% in 2012. Although Egypt and Kyrgyz Republic also had overall 

decreases, declines within poverty levels were not as widespread. While in Kyrgyz Republic the total 

percentage was relatively low and fluctuations among poverty levels were moderate, Egypt experienced a 

more notable change, with percentages dropping nearly in half among the non-poor, from 7% to 4%, and 

the poor, from 10% to 6%, while increasing among the two poorest absolute poverty levels. Two countries, 

Nepal and Timor-Leste, had overall increases in percentage of respondents reporting a non-numeric ideal 

fertility preference. Unlike the moderate increase of 0.2% in Nepal, Timor-Leste had a much more 

significant increase of 9%. Additionally, Timor-Leste was one of five countries with an increase in disparity 

between non-poor and poorest respondents. Egypt had the largest growth in disparity, with an increase of 

5%, followed by Timor-Leste, 3%, Haiti, 0.8%, and Nepal and Pakistan, each 0.2%. 
32 

Figure 3.3c Percentage of respondents reporting a non-numeric ideal fertility preference by absolute 

poverty level, South Asia, Southeast Asia, and Other Areas 

Table 3.3 presents the average relative decadal changes in non-numeric fertility preferences by absolute 

poverty level and country, with corresponding absolute average decadal changes shown in Appendix Table 

B.6. Overall, percentages decreased in 22 of the 31 countries, of which 15 decreases were statistically 

significant. Cambodia had the biggest decrease in non-numeric fertility preferences, at 96%, and also the 

greatest decreases within each individual poverty level. Of the nine countries experiencing an increase in 

the total percentage of non-numeric responses about fertility preferences, four were in the Central and 

2005

2014

0 5 10 15 20 25 30

Cambodia

2005

2015

0 5 10 15 20 25 30

India

2002

2012

0 5 10 15 20 25 30

Indonesia

2006

2016

0 5 10 15 20 25 30

Nepal

2006

2012

0 5 10 15 20 25 30

Pakistan

2003

2013

0 5 10 15 20 25 30

Philippines

2009

2016

0 5 10 15 20 25 30

Timor-Leste

2005

2014

0 5 10 15 20 25 30

Egypt

2000

2012

0 5 10 15 20 25 30

Haiti

1997

2012

0 5 10 15 20 25 30

Kyrgyz Republic

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
33 

Western Africa region, three in the Eastern and Southern Africa region, and two in South and Southeast 

Asia. Two of the three total increases found to be of statistical significance were also the largest increases—

Timor-Leste, 423%, Tanzania, 237%, and Chad, 86%. The non-poor had the most statistically significant 

decreases, with 17 occurrences, compared with just 7 occurrences within the extremely poor and asset poor 

levels. 

Table 3.3 Average relative decadal changes in non-numeric fertility preferences, by absolute poverty level and country 

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

Central and 

Western Africa 

Chad -31 10 122 96 86 10.5 ++ ++ ++ 

DR Congo 89 8 27 -35 -30 6.5 

Ghana -11 166 88 -43 7 11.0 ++ 

Guinea 238 19 -22 25 10 7.0 + 

Liberia -97 128 2 -21 -17 6.0 

Mali -67 -64 -72 -80 -76 11.5 -- -- -- -- -- 

Niger (26) -28 -53 -51 -50 14 (--) - -- -- 

Nigeria -18 -21 -40 -36 -34 10.0 -- - -- 

Senegal 29 -5 -14 2 -4 11.0 

Sierra Leone 82 -67 7 18 9 5.0 

Eastern and 

Southern Africa 

Burundi -91 -78 -59 -97 -92 6.5 -- - -- 

Comoros 447 47 NA 9 24 16.0 NA 

Ethiopia -6 9 1297 1 -1 11.0 ++ 

Kenya -44 -66 -51 -48 -55 11.0 -- -- -- -- 

Lesotho NA NA NA 18 127 10.0 NA NA NA 

Malawi -44 -29 -43 -55 -53 11.5 -- -- 

Rwanda -86 -51 * -62 -64 9.5 -- -- * -- 

Tanzania 47 213 456 169 237 5.5 ++ ++ ++ 

Uganda -14 18 -19 -32 -41 10.0 -- - 

Zambia -49 -47 81 -18 -29 12.0 -- - - 

Zimbabwe -65 -83 -76 -82 -80 9.5 -- - -- 

South and 

Southeast Asia 

Cambodia -96 -104 -92 -100 -96 9.0 -- -- -- -- -- 

India -84 -80 -72 -75 -82 10.0 -- -- -- -- -- 

Indonesia -36 -43 -15 -28 -40 9.5 -- -- -- - 

Nepal -62 280 750 213 147 10.0 + + 

Pakistan -91 -57 -85 -96 -81 6.0 -- -- - -- -- 

Philippines -65 -66 -74 -68 -70 10.0 -- -- -- - 

Timor-Leste 314 303 511 383 423 6.5 ++ ++ ++ ++ ++ 

Continued 
34 

Table 3.3—Continued

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

Other Areas 

Egypt -53 -51 9 * -53 9.0 -- -- -- * 

Haiti -83 -53 -26 -10 -33 12.0 NA -- 

Kyrgyz Republic -44 -54 NA * -45 15.0 -- - NA * -- 

Notes: 

++ indicates there was a statistically significant increase at p<.01 from earlier to later survey. 

+ indicates there was a statistically significant increase at p<.05 from earlier to later survey. 

-- indicates there was a statistically significant decrease at p<.01 from earlier to later survey. 

- indicates there was a statistically significant decrease at p<.05 from earlier to later survey. 

NA indicates there were an insufficient number of cases in the numerator to test the difference between survey years. 

Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and 

has been suppressed. 

3.3 Modern Contraceptive Prevalence 

Figures 3.4a to 3.4c show mCPR among married women by absolute poverty level and country for each of 

the three regional groups; numbers underlying these charts are given in Appendix Table B.7. On average 

across surveys, 28% of married women were using a modern method at the time of the survey, while withinpoverty averages ranged from 22% among the poorest group to 37% among the non-poor. As with other 

indicators studied, strong regional variations exist. Modern contraceptive prevalence, both nationwide and 

among both categories of the extremely poor, was typically more than three times as high in Eastern and 

Southern Africa, in South Asia, Southeast Asia, and in Other Areas as in Central and Western Africa. 

Figure 3.4a shows mCPR by absolute poverty level in Central and Western Africa. Ghana, Liberia, and 

Senegal were the only countries to have started with at least 10% modern method use; by the later survey 

round the only other countries with at least 10% modern method use were Niger and Sierra Leone. Central 

and Western Africa has, on average, the greatest relative inequality in mCPR of any region studied, typically 

more than twice as high among the non-poor as among the poorest. There was a reduction in relative 

inequalities in modern contraceptive use between married women in the highest and lowest absolute poverty 

levels in every country except Chad, Niger, and Nigeria. Convergence on mCPR between absolute poverty 

groups was particularly strong in Ghana, Guinea, and Sierra Leone. In Ghana and Sierra Leone this 

convergence was largely driven by an apparent increase in use of modern methods among married women 

in extreme poverty and asset poverty, and partly driven by a decline in mCPR among women in the nonpoor group. In Guinea, however, the convergence was driven entirely by declines in use of modern 

contraception among the non-poor. Overall, average mCPR was 9% in the first survey round and 13% in 

the second round, largely owing to increases among the poor as well as the extremely poor and asset poor. 

Figure 3.4b shows modern contraceptive prevalence by absolute poverty levels in Eastern and Southern 

Africa. On average in this region, prevalence was 25% in the first survey, rising to 43% in the second 

survey—the highest of any region studied. Between the two survey rounds, disparities in modern 

contraceptive use between the poorest and the non-poor declined in all 11 countries in this region. In the 
35 

first survey, in the average country non-poor married women were more than twice as likely to use a modern 

method compared with non-poor women. By the second survey round in each country, that difference 

declined to around 20%. Rwanda, Uganda, and Malawi achieved the greatest declines in disparities in 

modern contraceptive use between the poorest and the non-poor. 

Figure 3.4a Modern contraceptive prevalence by absolute poverty level, Central and Western Africa 

2004

2014

0 10 20 30 40 50 60 70 80 90 100

Chad

2007

2013

0 10 20 30 40 50 60 70 80 90 100

DR Congo

2003

2014

0 10 20 30 40 50 60 70 80 90 100

Ghana

2005

2012

0 10 20 30 40 50 60 70 80 90 100

Guinea

2007

2013

0 10 20 30 40 50 60 70 80 90 100

Liberia

2001

2012

0 10 20 30 40 50 60 70 80 90 100

Mali

1998

2012

0 10 20 30 40 50 60 70 80 90 100

Niger

2003

2013

0 10 20 30 40 50 60 70 80 90 100

Nigeria

2005

2016

0 10 20 30 40 50 60 70 80 90 100

Senegal

2008

2013

0 10 20 30 40 50 60 70 80 90 100

Sierra Leone

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
36 

Figure 3.4b Modern contraceptive prevalence by absolute poverty level, Eastern and Southern Africa 

Figure 3.4c shows mCPR by absolute poverty level for South Asia, Southeast Asia, and Other Areas. Here 

the trends are quite divergent. Countries in this group had the highest mCPR of any region in the first round 

of surveys, at 38%, and slightly higher in the second round, at 40%. However, there was enormous variation 

by country, from 21% in Timor-Leste 2009-10 to 58% in Indonesia 2012. Three countries—India, Nepal, 

and Kyrgyz Republic—experienced a decline in mCPR between survey rounds. In the average country, 

mCPR increased among the poorest group (extremely poor and asset poor), but declined among the non2010

2016

0 10 20 30 40 50 60 70 80 90 100

Burundi

1996

2012

0 10 20 30 40 50 60 70 80 90 100

Comoros

2005

2016

0 10 20 30 40 50 60 70 80 90 100

Ethiopia

2003

2014

0 10 20 30 40 50 60 70 80 90 100

Kenya

2004

2014

0 10 20 30 40 50 60 70 80 90 100

Lesotho

2004

2015

0 10 20 30 40 50 60 70 80 90 100

Malawi

2005

2014

0 10 20 30 40 50 60 70 80 90 100

Rwanda

2010

2015

0 10 20 30 40 50 60 70 80 90 100

Tanzania

2006

2016

0 10 20 30 40 50 60 70 80 90 100

Uganda

2001

2013

0 10 20 30 40 50 60 70 80 90 100

Zambia

2005

2015

0 10 20 30 40 50 60 70 80 90 100

Zimbabwe

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
37 

poor and the third absolute poverty group (extremely poor but not asset poor). The greatest relative 

disparities in mCPR between the non-poor and the poorest were in Egypt 2005, both India surveys, both 

Pakistan surveys, and Timor-Leste 2009-10. At the time of the most recent survey, Cambodia and Haiti had 

inverse disparities: modern contraceptive prevalence was higher among the poorest than the non-poor. 

Figure 3.4c Modern contraceptive prevalence by absolute poverty level, South Asia, Southeast Asia, and 

Other Areas 

2005

2014

0 10 20 30 40 50 60 70 80 90 100

Cambodia

2005

2015

0 10 20 30 40 50 60 70 80 90 100

India

2002

2012

0 10 20 30 40 50 60 70 80 90 100

Indonesia

2006

2016

0 10 20 30 40 50 60 70 80 90 100

Nepal

2006

2012

0 10 20 30 40 50 60 70 80 90 100

Pakistan

2003

2013

0 10 20 30 40 50 60 70 80 90 100

Philippines

2009

2016

0 10 20 30 40 50 60 70 80 90 100

Timor-Leste

2005

2014

0 10 20 30 40 50 60 70 80 90 100

Egypt

2000

2012

0 10 20 30 40 50 60 70 80 90 100

Haiti

1997

2012

0 10 20 30 40 50 60 70 80 90 100

Kyrgyz Republic

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
38 

Table 3.4 presents the relative average decadal changes in modern contraceptive prevalence by absolute 

poverty levels and country; corresponding absolute decadal changes are given in Appendix Table B.8. While 

24 of 31 study countries experienced a statistically significant decadal increase in mCPR, three countries—

Chad, India, and Kyrgyz Republic—had statistically significant declines. The decline was numerically 

greatest in Chad, where where the lactational amenorrheic method (LAM) comprised a substantial amount 

of modern method use in the first survey but declined by the time of the second survey. In India the decline 

was small (2%), but statistically significant due to very large sample sizes. Guinea and Nepal also 

experienced decreases in modern method use overall, but these were not statistically significant. 

Turning to changes within absolute poverty groups, we found that the poorest group (extremely poor and 

asset poor) experienced the largest increase in mCPR, an average of 82%. It was also the poverty group for 

which the largest number of increases were statistically significant: 19 of the 29 countries with sufficiently 

large sample sizes of extremely poor and asset poor married women experienced a statistically significant 

increase in modern contraceptive use between surveys. Notably seven countries in sub-Saharan Africa—

Rwanda, Sierra Leone, Senegal, Liberia, Niger, Ethiopia, and Uganda—more than doubled the prevalence 

of modern method use among the poorest over the decade, and the increase was statistically significant. 

In the third absolute poverty group, extremely poor but not asset poor, countries experienced an average 

increase of 15% over a decade. Seven countries—Cambodia, Kenya, Lesotho, Malawi, Pakistan, Senegal, 

and Sierra Leone—had a positive and statistically significant increase, while Chad, India, Indonesia, and 

Nepal had a statistically significant decline. Among the second absolute poverty group, the poor, 10 

countries experienced a statistically significant increase in mCPR, while five countries experienced a 

statistically significant decline. Finally, among the non-poor group, only Rwanda experienced a statistically 

significant increase in mCPR, while seven countries experienced a statistically significant decline. India 

and Kyrgyz Republic were the only countries that experienced a statistically significant decline in mCPR 

among the non-poor group, of 10% and 25% respectively in relative decadal terms, and also experienced a 

statistically significant decline in mCPR nationwide. 

 
39 

Table 3.4 Average relative decadal changes in modern contraceptive prevalence, by absolute poverty level and 

country 

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

Central and Western Africa

Chad -40 -47 -52 -60 -47 10.5 - -- -- -- 

DR Congo -37 46 26 70 54 6.5 + + 

Ghana -31 8 1 24 17 11.0 -- ++ 

Guinea -73 -61 -54 -1 -26 7.0 -- -- 

Liberia 76 3 13 182 144 6.0 ++ ++ 

Mali -1 -1 -13 3 36 11.5 ++ 

Niger (-27) -4 -21 161 118 14.0 (--) ++ ++ 

Nigeria -5 16 17 -20 18 10.0 + 

Senegal 20 38 104 189 113 11.0 ++ ++ ++ ++ 

Sierra Leone -48 94 144 387 263 5.0 ++ ++ ++ ++ 

Eastern and Southern Africa

Burundi -30 -24 39 49 45 6.5 ++ ++ 

Comoros 0 -1 (-16) 13 15 16.0 + 

Ethiopia 3 3 6 160 139 11.0 ++ ++ 

Kenya 5 23 14 73 63 11.0 ++ + ++ ++ 

Lesotho 17 28 21 93 70 10.0 ++ + ++ ++ 

Malawi 11 33 59 98 93 11.5 ++ ++ ++ ++ 

Rwanda 41 34 * 461 381 9.5 ++ ++ * ++ ++ 

Tanzania -12 -3 24 46 31 5.5 ++ ++ 

Uganda -19 11 20 106 95 10.0 ++ ++ 

Zambia 9 8 12 84 64 12.0 ++ ++ 

Zimbabwe 5 6 8 22 13 9.5 ++ ++ 

South and Southeast Asia

Cambodia 3 34 52 81 48 9.0 ++ ++ ++ ++ 

India -10 -10 -17 -6 -2 10.0 -- -- -- -- - 

Indonesia -1 6 -13 -18 2 9.5 + -- -- 

Nepal -20 -19 -20 11 -3 10.0 -- -- - 

Pakistan 9 15 38 85 33 6.0 ++ ++ ++ 

Philippines 4 19 10 13 13 10.0 ++ ++ 

Timor-Leste -36 8 9 36 22 6.5 -- + ++ 

Other Areas 

Egypt -2 2 -2 * 1 9.0 * 

Haiti -9 54 9 28 31 12.0 ++ ++ ++ 

Kyrgyz Republic -25 -24 -15 * -21 15.0 -- -- * -- 

Notes: 

++ indicates there was a statistically significant increase at p<.01 from earlier to later survey. 

+ indicates there was a statistically significant increase at p<.05 from earlier to later survey. 

-- indicates there was a statistically significant decrease at p<.01 from earlier to later survey. 

- indicates there was a statistically significant decrease at p<.05 from earlier to later survey. 

Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted 

cases and has been suppressed. 
40 

3.4 Contraceptive Method Mix 

Figures 3.5a, 3.5b, and 3.5c show the contraceptive method mix—the share of long-term modern, shortterm modern, traditional, and folkloric methods—among users of family planning for each of the four 

absolute poverty groups by regions. Corresponding figures are shown in Appendix Table B.9. Bars were 

suppressed when the unweighted number of women in that group was less than 25. 

3.4.1 Levels 

Overall, short-term modern contraceptives, such as the pill, injectables, and male condoms, are more 

common than other methods in sub-Saharan Africa, whereas long-term modern methods are more common 

in South and Southeast Asia. A majority of current users in India, Nepal, Egypt, and Kyrgyz Republic were 

using long-term modern contraceptives, whereas more women in other FP2020 countries used short-term 

modern methods. The majority of current users in DR Congo, and more than a quarter of current users in 

Nigeria, Comoros, Cambodia, Pakistan, and the Philippines, used traditional methods. The use of folkloric 

methods is low in most countries in this study, except in Guinea at 27% in 2005, Niger at 42% in 1998 and 

11% in 2012, and Sierra Leone at 15% in 2008. 

Examining method mix by absolute poverty level shows that a slightly larger proportion of non-poor women 

used long-term modern methods compared with the other poverty groups. In addition, women who are 

extremely poor and asset poor were typically the least likely to use long-term modern methods. However, 

this disparity does not appear among users of short-term modern methods. On average, a greater proportion 

of women in the second and third poverty groups (poor and extremely poor but not asset poor) used shortterm modern methods compared with the non-poor and the extremely poor and asset poor women. 

The levels of long-term and short-term modern contraceptive use by absolute poverty level vary widely 

across countries. As Figures 3.5a to 3.5c and Appendix Table B.9 indicate, for the earlier DHS surveys in 

our study, among non-poor married women, long-term modern methods use ranged from 5% in Guinea 

2005 to 68% in Egypt 2005, while among the poor it ranged from 4% in DR Congo 2007 to 76% in India 

2005-06. Among the extremely poor but not asset poor, use of long-term modern methods ranged from 2% 

in DR Congo 2007 to 75% in India 2005-06, and among the extremely poor and asset poor it ranged from 

1% in Chad 2004 to 71% in India 2005-06. Similarly, use of short-term modern methods among the nonpoor ranged from 23% in India 2005-06 to 84% in Zimbabwe 2005-06, while among the poor it ranged 

from 12% in Kyrgyz Republic 1996 to 93% in Zimbabwe 2005-06. Among the extremely poor but not asset 

poor, short-term method use ranged from 12% in India 2005-06 to 95% in Ethiopia 2005, and among the 

extremely poor and asset poor it ranged from 11% in India 2005-06 to 95% in Ethiopia 2005. A similar 

pattern is observed for the most recent DHS surveys as well. For example, among non-poor women longterm modern method use ranged from 9% in DR Congo 2013-14 to 69% in India 2015-16. Similarly, shortterm modern method use in the most recent surveys ranged from 21% in India 2015-16 to 87% in Lesotho 

2014. 

The use of traditional methods varies widely by absolute poverty levels in sub-Saharan Africa. In the earliest 

DHS surveys, among women in the non-poor and poor groups, the lowest level of use was in Lesotho, while 

the highest use was in DR Congo, at 59%. For women in the extremely poor categories, the highest use was 

also in DR Congo. On average, a higher percentage of women in Central and Western Africa were using 

traditional methods compared with women in Eastern and Southern Africa, for all absolute poverty levels. 
41 

Moreover, higher percentages of non-poor women used traditional methods compared with the other 

categories of absolute poverty. Similar patterns are observed for traditional method use in the most recent 

DHS surveys. These findings are consistent with our earlier study of traditional method use worldwide, 

where in some countries wealthier women were more likely to report traditional method use, while in other 

countries the poorest women were the most likely to report (Gebreselassie et al. 2017). The findings are 

further complicated by an apparent underreporting of traditional method use in DHS surveys (Staveteig 

2017; Staveteig et al. 2018). 

In, Cambodia, a higher proportion of women in three categories—non-poor, poor, and extremely poor but 

not asset poor—reported using traditional methods compared with the other countries in the South and 

Southeast Asia region. Among women in the extremely poor and asset poor category, use of traditional 

methods was higher in the Philippines. On average in this region, non-poor women were more likely to use 

traditional methods compared with the other poverty groups. 

Use of folkloric methods was reported in some countries in this study. Women in Central and Western Africa 

were more likely to use folkloric methods compared with other regions. On average, use of folkloric 

methods increases as the level of poverty worsens, from non-poor to extremely poor categories. 

3.4.2 Trends 

Figures 3.5a, 3.5b, and 3.5c also show trends in method mix use by absolute poverty levels for the countries 

in this study, and are also presented in Appendix Table B.9. The changes in contraceptive method mix use 

between the earlier and more recent DHS surveys among the absolute poverty levels are mixed. Among 

non-poor women in sub-Saharan Africa, use of long-term modern methods increased in 11 of the 21 

countries. Increases in the use of long-term modern methods occurred among the poor in 16 countries, the 

extremely poor but not asset poor in 17 countries, and the extremely poor and asset poor in 19 countries. 

Fewer than half of the countries in sub-Saharan Africa had increases in the use of short-term modern 

methods between the two surveys among women who were non-poor (eight countries), poor (eight 

countries), and extremely poor but not asset poor (seven countries). Only in 11 sub-Saharan countries did 

women who were extremely poor and asset poor experience an increase in use of short-term modern 

methods. Increase in the use of traditional methods occurred in fewer than five sub-Saharan countries 

among three of the absolute poverty groups—with the exception of non-poor women, among whom 

traditional method use increased in nine countries. 
42 

Figure 3.5a Contraceptive method mix by absolute poverty level, Central and Western Africa 

Chad Congo Democratic Republic 

 

Ghana Guinea 

Liberia Mali 

 

Continued 

0

20

40

60

80

100

2004 2014-15 2004 2014-15 2004 2014-15 2004 2014-15 2004 2014-15

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2007 2013-14 2007 2013-14 2007 2013-14 2007 2013-14 2007 2013-14

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2003 2014 2003 2014 2003 2014 2003 2014 2003 2014

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2005 2012 2005 2012 2005 2012 2005 2012 2005 2012

Non-poor Poor Extremely

Poor but not

Asset Poor

Extremely

Poor and Asset

Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2007 2013 2007 2013 2007 2013 2007 2013 2007 2013

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2001 2012-13 2001 2012-13 2001 2012-13 2001 2012-13 2001 2012-13

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric
43 

Figure 3.5a—Continued

Niger Nigeria 

 

Senegal Sierra Leone 

Note: Bars were suppressed when the unweighted number of women in that group was less than 25. 

Figure 3.5b Contraceptive method mix by absolute poverty level, Eastern and Southern Africa 

Burundi Comoros 

 

Continued 

 

0

20

40

60

80

100

1998 2012 1998 2012 1998 2012 1998 2012 1998 2012

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2003 2013 2003 2013 2003 2013 2003 2013 2003 2013

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2005 2016 2005 2016 2005 2016 2005 2016 2005 2016

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2008 2013 2008 2013 2008 2013 2008 2013 2008 2013

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

10

20

30

40

50

60

70

80

90

100

2010 2016-17 2010 2016-17 2010 2016-17 2010 2016-17 2010 2016-17

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

10

20

30

40

50

60

70

80

90

100

1996 2012 1996 2012 1996 2012 1996 2012 1996 2012

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric
44 

Figure 3.5b—Continued

Ethiopia Kenya 

 

Lesotho Malawi 

 

Rwanda Tanzania 

 

Continued 

 

0

10

20

30

40

50

60

70

80

90

100

2005 2016 2005 2016 2005 2016 2005 2016 2005 2016

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

10

20

30

40

50

60

70

80

90

100

2003 2014 2003 2014 2003 2014 2003 2014 2003 2014

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2004 2014 2004 2014 2004 2014 2004 2014 2004 2014

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2004 2015-16 2004 2015-16 2004 2015-16 2004 2015-16 2004 2015-16

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

10

20

30

40

50

60

70

80

90

100

2005 2014-15 2005 2014-15 2005 2014-15 2005 2014-15 2005 2014-15

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

10

20

30

40

50

60

70

80

90

100

2010 2015-16 2010 2015-16 2010 2015-16 2010 2015-16 2010 2015-16

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric
45 

Figure 3.5b—Continued

Uganda Zambia 

 

Zimbabwe 

Note: Bars were suppressed when the unweighted number of women in that group was less than 25. 

In more than half of the countries in South and Southeast Asia, increases between survey rounds occurred 

among the extremely poor and asset poor groups of women for use of long-term modern methods (in 

Cambodia, India, Pakistan, and Timor-Leste), and for use of short-term modern methods (in India, 

Indonesia, Nepal, Pakistan, and the Philippines). In addition, in four countries increases in short-term 

modern methods occurred among women in the second and third absolute poverty groups—the poor and 

extremely poor but not asset poor. Increases in traditional method use occurred among non-poor women in 

five countries of this region (Indonesia, Nepal, Pakistan, the Philippines, and Timor-Leste). 

In the Other Areas group, increases in traditional method use occurred among women who were extremely 

poor but not asset poor. In Kyrgyz Republic use of long-term modern methods among non-poor women 

increased by 13% between the earlier and more recent DHS surveys, whereas in Haiti use of short-term 

modern methods increased among all of the absolute poverty groups of women except extremely poor but 

not asset poor. On average, the use of folkloric methods decreased between the earlier and more recent 

surveys in all regions. 

0

10

20

30

40

50

60

70

80

90

100

2006 2016 2006 2016 2006 2016 2006 2016 2006 2016

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

10

20

30

40

50

60

70

80

90

100

2001-02 2013-14 2001-02 2013-14 2001-02 2013-14 2001-02 2013-14 2001-02 2013-14

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

10

20

30

40

50

60

70

80

90

100

2005-06 2015 2005-06 2015 2005-06 2015 2005-06 2015 2005-06 2015

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric
46 

Figure 3.5c Contraceptive method mix by absolute poverty level, South Asia, Southeast Asia, and Other 

Areas 

Cambodia India 

 

Indonesia Nepal 

 

Pakistan The Philippines 

 

Continued 

 

0

20

40

60

80

100

2005 2014 2005 2014 2005 2014 2005 2014 2005 2014

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2005-062015-162005-062015-162005-062015-162005-062015-162005-062015-16

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2002-03 2012 2002-03 2012 2002-03 2012 2002-03 2012 2002-03 2012

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2006 2016 2006 2016 2006 2016 2006 2016 2006 2016

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2006-072012-132006-072012-132006-072012-132006-072012-132006-072012-13

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2003 2013 2003 2013 2003 2013 2003 2013 2003 2013

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric
47 

Figure 3.5c—Continued

Timor-Leste Egypt 

 

Haiti Kyrgyz Republic 

 

Note: Bars were suppressed when the unweighted number of women in that group was less than 25. 

3.4.3 Decadal changes 

Tables 3.5a to 3.5d show, for each of the four method groups, average relative decadal changes in method 

mix as a percentage of family planning use, by level of absolute poverty. The corresponding absolute 

decadal changes are shown in Appendix Table B.10. Multinomial logit regressions were run to test the 

significance of changes in method mix among absolute poverty levels between the two survey rounds. 

Short-term modern methods, the only category with sufficiently stable sample sizes in every survey, were 

chosen as the reference group. 

Long-term modern methods 

Table 3.5a shows that in seven countries in sub-Saharan Africa, non-poor married women experienced a 

decline in the use of long-term modern methods. This decline was statistically significant relative to shortterm methods only in Lesotho. In the remaining sub-Saharan African countries with sufficient sample sizes 

of non-poor married women, 11 experienced increases in long-term modern method use among the nonpoor, ranging from 5% in Uganda to 394% in Sierra Leone (note caution in interpretation due to small 

sample size); these increases were statistically significant relative to changes in short-term methods only in 

Mali, Senegal, and Zimbabwe. 

0

20

40

60

80

100

2009-10 2016 2009-10 2016 2009-10 2016 2009-10 2016 2009-10 2016

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2005 2014 2005 2014 2005 2014 2005 2014 2005 2014

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

2000 2012 2000 2012 2000 2012 2000 2012 2000 2012

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric

0

20

40

60

80

100

1997 2012 1997 2012 1997 2012 1997 2012 1997 2012

Non-poor Poor Extremely Poor

but not Asset

Poor

Extremely Poor

and Asset Poor

National

Long-term modern Short-term modern Traditional Folkloric
48 

At the opposite end of the poverty spectrum, in all countries studied in sub-Saharan Africa except Guinea 

and Lesotho, women in both the third and fourth absolute poverty groups (extremely poor but not asset 

poor, and extremely poor and asset poor) experienced gains in the use of long-term modern methods 

between surveys. On average, in Central and Western Africa the extremely poor and asset poor increased 

their use of long-term modern methods by more than sevenfold (the largest decadal gain was approximately 

28-fold in Chad) compared to a threefold average gain in Eastern and Southern Africa. Overall, higher 

average decadal gains in use of long-term modern methods were found among the extremely poor group of 

women. Among the poorest women, these gains were statistically significant in every sub-Saharan African 

country except Comoros, DR Congo, Guinea, Niger, and Lesotho. Among the extremely poor in Lesotho, 

there was a statistically significant decline in long-term method use relative to short-term method use. 

In South and Southeast Asia, only two countries—Cambodia and Timor-Leste—had decadal gains in the 

use of long-term modern methods among women at all levels of absolute poverty. In contrast, in Indonesia, 

Nepal, and the Philippines, married women at every absolute poverty level experienced decadal declines in 

long-term modern method use. In Pakistan, only women in the poorest category experienced decadal gains 

in use of long-term modern methods. Cambodia and Timor-Leste had statistically significant increases in 

long-term modern method use among the two poorest groups (p <. 01) relative to short-term use. Non-poor 

women in India and Timor-Leste had significant increases in long-term modern method use relative to shortterm use. 

In the Other Areas group, women in Haiti at all absolute poverty groups experienced declines in long-term 

modern method use, and in Egypt there were declines in long-term modern method use among women in 

three absolute poverty groups (the poorest category had an insufficient number of women for measurement). 

Among non-poor women in Kyrgyz Republic, there was a significant increase in long-term methods use 

relative to short-term methods use. 
49 

Table 3.5a Average relative decadal changes in long-term modern methods as a percentage of family planning use, by 

absolute poverty level and country 

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change relative to 

changes in short-term modern methods 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

Central and 

Western Africa 

Chad * (-55) 190 2,714 1,053 10.5 ++ ++ 

DR Congo -50 (162) 652 55 98 6.5 

Ghana -8 78 134 162 88 11.0 ++ ++ ++ 

Guinea 196 139 (-30) -7 105 7.0 

Liberia * -29 172 170 118 6.0 + 

Mali 90 209 382 426 309 11.5 + ++ ++ ++ ++ 

Niger * -18 (6) 18 11 14.0 

Nigeria 11 14 122 235 57 10.0 ++ ++ 

Senegal 76 165 480 203 169 11.0 + ++ ++ ++ ++ 

Sierra Leone (396) 393 971 2,836 974 5.0 ++ + ++ ++ 

Eastern and Southern Africa 

Burundi -50 111 67 92 77 6.5 ++ ++ ++ 

Comoros (-3) -24 * 1 -8 16.0 -- - 

Ethiopia -3 221 727 1,568 598 11.0 ++ ++ ++ ++ 

Kenya -2 13 1 49 31 11.0 ++ ++ 

Lesotho (-60) -56 -40 -51 -43 10.0 (-) -- -- -- 

Malawi 31 60 108 83 88 11.5 ++ ++ ++ ++ 

Rwanda 41 156 * 368 249 9.5 ++ ++ ++ 

Tanzania 48 88 76 129 100 5.5 + ++ ++ 

Uganda 5 117 110 135 116 10.0 ++ ++ ++ ++ 

Zambia 13 118 20 367 126 12.0 ++ ++ ++ 

Zimbabwe 72 255 94 608 186 9.5 ++ ++ ++ ++ 

South and Southeast Asia 

Cambodia 2 33 127 340 98 9.0 ++ ++ ++ 

India 10 -4 -8 1 -1 10.0 ++ -- -- -- -- 

Indonesia -32 -31 -43 -29 -31 9.5 -- -- -- -- 

Nepal -11 -5 -4 -17 -12 10.0 

Pakistan -10 -21 -28 19 -19 6.0 - -- -- 

Philippines -29 -27 -16 -36 -27 10.0 -- -- -- -- 

Timor-Leste 176 385 210 266 270 6.5 ++ ++ ++ ++ ++ 

Other Areas 

Egypt -20 -20 -12 * -18 9.0 -- -- -- 

Haiti -33 -37 -3 -33 -37 12.0 -- -- -- 

Kyrgyz Republic 16 -4 (-10) * -2 15.0 + -- * -- 

Notes: 

++ indicates there was a statistically significant increase at p<.01 from earlier to later survey. 

+ indicates there was a statistically significant increase at p<.05 from earlier to later survey. 

-- indicates there was a statistically significant decrease at p<.01 from earlier to later survey. 

- indicates there was a statistically significant decrease at p<.05 from earlier to later survey. 

Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases 

and has been suppressed. 
50 

Short-term modern methods 

Table 3.5b shows relative average decadal changes for short-term contraceptive method use by absolute 

poverty levels; absolute average decadal changes are shown in Appendix Table B.11. Because short-term 

modern methods were the reference group for multinomial regressions, statistical significance cannot be 

shown. In 10 of the 21 countries in sub-Saharan Africa studied, non-poor married women experienced 

decadal increases in use of short-term modern methods, from 1% in Ghana to 34% in Comoros. In contrast, 

declines in short-term modern method use among non-poor women in the other sub-Saharan countries 

ranged from 1% in Zambia to 61% in DR Congo. 

Among poor women, increases in use of short-term modern methods over the decade occurred in Chad, DR 

Congo, Guinea, Liberia, Niger, Comoros, Kenya, and Lesotho. In South and Southeast, non-poor women 

in Cambodia, Indonesia, and the Philippines experienced decadal increases in short-term method use, from 

4% in Cambodia to 21% in the Philippines. In Other Areas, increases in short-term modern method use 

occurred among non-poor women in Egypt and Haiti. 

In eight countries in sub-Saharan Africa, married women who are poor and extremely poor but not asset 

poor experienced increases in short-term modern method use. Average relative decadal gains were small, 

although levels of short-term method use were high. In contrast, in Cambodia, Nepal, and Timor-Leste, 

declines in short-term method use occurred among poor and extremely poor women, while in India, 

Indonesia, Pakistan, and the Philippines, poor and extremely poor women experienced increases in use of 

short-term methods. 
51 

Table 3.5b Average relative decadal changes in short-term modern methods as a percentage of family 

planning use, by absolute poverty level and country 

Average relative decadal change (%) Years of 

difference 

between 

Non-Poor Poor surveys 

Extremely 

Poor but not 

Asset Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa 

Chad * (2) -11 -32 -23 10.5 

DR Congo -61 35 21 78 47 6.5 

Ghana 1 -5 9 -1 -8 11.0 

Guinea 7 11 (60) 65 44 7.0 

Liberia * 14 -1 -1 0 6.0 

Mali -14 -7 -15 -12 -14 11.5 

Niger * 7 (2) 68 42 14.0 

Nigeria -18 -9 -20 2 -9 10.0 

Senegal -15 -25 -41 -23 -24 11.0 

Sierra Leone (-11) -36 -12 1 -9 5.0 

Eastern and Southern Africa 

Burundi 12 -40 -15 -23 -23 6.5 

Comoros (34) 51 * 40 33 16.0 

Ethiopia 19 -15 -25 -24 -21 11.0 

Kenya 17 3 7 10 6 11.0 

Lesotho (23) 15 9 9 9 10.0 

Malawi -16 -20 -16 -7 -11 11.5 

Rwanda 15 -2 * 42 32 9.5 

Tanzania -44 -17 -10 -11 -19 5.5 

Uganda -3 -5 -6 3 -1 10.0 

Zambia -1 -11 7 18 8 12.0 

Zimbabwe -12 -13 -5 -12 -11 9.5 

South and Southeast Asia 

Cambodia 4 -2 -5 -16 -13 9.0 

India -9 24 47 42 24 10.0 

Indonesia 14 9 12 8 10 9.5 

Nepal -18 -16 -13 6 -11 10.0 

Pakistan -2 27 48 47 19 6.0 

Philippines 21 26 13 15 21 10.0 

Timor-Leste -60 -54 -37 -26 -48 6.5 

Other Areas 

Egypt 53 35 28 * 46 9.0 

Haiti 21 33 0 22 24 12.0 

Kyrgyz Republic -6 91 (67) * 56 15.0 

Notes: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on 

fewer than 25 unweighted cases and has been suppressed. 
52 

Traditional Methods 

Table 3.5c shows relative average decadal changes in the use of traditional methods by absolute poverty 

level for each of the countries in this study; corresponding absolute decadal changes are shown in Appendix 

Table B.12. Table 3.5c also shows statistical significance tests for changes in traditional method use relative 

to changes in short-term methods. In most countries in sub-Saharan Africa, the use of traditional methods 

declined over the decade among women in the three poorest groups—particularly the poorest group. The 

declines for this group ranged from 180% in Sierra Leone to 2% in Burundi. In contrast, declines in use of 

traditional methods among non-poor women occurred in only eight sub-Saharan countries studied. Overall, 

average decadal declines in the use of traditional methods were greater in Central and Western Africa than 

in Eastern and Southern Africa. Declines were for the most part statistically significant among extremely 

poor and asset poor women in 15 of the 21 sub-Saharan countries. 

In South and Southeast Asia, decadal changes in the use of traditional methods over the decade varied 

among the absolute poverty groups. In five of the seven countries studied, the use of traditional methods 

declined among women in the poorest category, while it increased in Nepal and the Philippines. In 

Cambodia and India, the use of traditional methods declined only among non-poor women. In Indonesia, 

Nepal, and Timor-Leste, women in the poor and extremely poor but not asset poor categories experienced 

gains in the use of traditional methods over the decade. Overall, average decadal changes in South and 

Southeast Asia show increases in the use of traditional methods in all absolute poverty groups except the 

poorest categories. 

Results of statistical significance tests indicate that significant declines in traditional method use at all 

poverty levels occurred only in India, while in Nepal women at all poverty level groups had significant 

increases. Overall, in Nepal and Timor-Leste both non-poor and poor women experienced significant 

increases in the use of traditional methods over the decade. 

Among countries in the Other Areas group, women at all levels of absolute poverty had decadal declines in 

the use of traditional methods (Egypt and Kyrgyz Republic have insufficient numbers of women for analysis 

in the two extremely poor categories). 
53 

Table 3.5c Average relative decadal changes in traditional methods as a percentage of family planning use, by 

absolute poverty level and country 

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change relative to 

changes in short-term modern methods 

NonPoor Poor 

Extremely 

Poor but not 

Asset Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and 

Western Africa 

Chad * (32) -30 -4 -6 10.5 

DR Congo 58 (-31) -33 -24 -24 6.5 -- -- 

Ghana 3 -41 -70 -69 -29 11.0 - -- -- 

Guinea -59 -35 (143) 10 -8 7.0 

Liberia * -38 -105 -91 -80 6.0 - 

Mali -87 -82 -85 -87 -85 11.5 -- -- -- -- -- 

Niger * -26 NA -64 -45 14.0 ++ -- -- 

Nigeria 26 11 108 -3 17 10.0 + + 

Senegal 20 -18 -40 -56 -18 11.0 

Sierra Leone (-54) -90 -184 -180 -145 5.0 - -- - 

Eastern and Southern Africa 

Burundi 77 74 -23 -2 4 6.5 

Comoros (-22) -24 * -36 -23 16.0 (--) -- -- -- 

Ethiopia -43 -54 -13 -65 -61 11.0 - - - -- 

Kenya -40 -35 -24 -56 -51 11.0 -- -- -- 

Lesotho (400) -57 -100 -82 -76 10.0 -- -- -- 

Malawi -33 -12 -79 -76 -73 11.5 -- -- -- 

Rwanda -54 -64 NA -82 -77 9.5 -- -- ++ -- -- 

Tanzania 52 -10 -69 -75 -29 5.5 -- 

Uganda 8 -39 -45 -64 -52 10.0 -- -- 

Zambia -24 -7 -41 -47 -48 12.0 -- -- 

Zimbabwe 40 -64 -25 -44 -41 9.5 

South and Southeast Asia

Cambodia -5 -10 -20 -17 -3 -5 

India -28 3 6 -23 -18 -28 -- -- -- -- -- 

Indonesia 3 0 201 -19 14 3 + 

Nepal 90 99 132 100 138 90 ++ ++ ++ ++ ++ 

Pakistan 15 -4 -21 -81 0 15 - - 

Philippines 8 -5 -4 5 3 8 -- -- 

Timor-Leste 82 196 372 -74 106 82 ++ ++ ++ 

Other Areas 

Egypt -38 -31 -49 * 42 9.0 -- -- -- -- 

Haiti -29 -54 -5 -53 -47 12.0 -- -- -- 

Kyrgyz Republic -39 -44 (-6) * 40 15.0 -- -- -- 

Notes: 

++ indicates there was a statistically significant increase at p<.01 from earlier to later survey. 

+ indicates there was a statistically significant increase at p<.05 from earlier to later survey. 

-- indicates there was a statistically significant decrease at p<.01 from earlier to later survey. 

- indicates there was a statistically significant decrease at p<.05 from earlier to later survey. 

Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases 

and has been suppressed. 
54 

Folkloric methods 

Table 3.5d shows average relative decadal changes in folkloric method use among the absolute poverty 

groups, alongside their statistical significance relative to short-term modern method use. The corresponding 

absolute average decadal changes in folkloric method use are shown in Appendix Table B.13. Note that 

across the poverty groups and even nationwide in some cases there were insufficient sample sizes or zero 

cases in the numerator during the first survey on which to base relative trends; these are indicated by an 

asterisk if the denominator was too small or by NA if the numerator was zero. In some cases, while a relative 

change cannot be shown (division by 0), the significance of the change can still be measured. 

Among poverty groups with sufficient sample size to measure trends in folkloric method use, they are 

inconsistent. In sub-Saharan Africa, for example, three countries—Burundi, Chad, and Liberia—

experienced statistically significant gains nationwide in use of folkloric methods relative to short-term 

modern methods, while 11 countries experienced statistically significant relative declines in folkloric 

method use. A number of other countries experienced nationwide declines in folkloric method use, but these 

were only statistically significant relative to short-term method use in Egypt, India, Indonesia, the 

Philippines, and Kyrgyz Republic. 

Significant increases in the use of folkloric methods are observed among the non-poor in Ghana and 

Comoros within sub-Saharan Africa; in Nepal within South and Southeast Asia; and Haiti and Kyrgyz 

Republic within Other Areas. Meanwhile, among the extremely poor categories of women, use of folkloric 

methods significantly declined in Ghana, Guinea, Niger, and Sierra Leone, within Middle and West Africa, 

and in Comoros, Kenya, Malawi, Uganda, Zambia, and Zimbabwe, within Eastern and Southern Africa.

Table 3.5d Average relative decadal changes in folkloric methods as a percentage of all family planning use, by 

absolute poverty level and country 

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change relative to 

changes in short-term modern methods 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

Central and Western Africa 

Chad NA NA -42 NA 1,238 10.5 * NA ++ + 

DR Congo 115 7 NA 21 20 6.5 ++ 

Ghana NA 45 -80 -65 -58 11.0 ++ - - 

Guinea -31 -86 (-122) -105 -108 7.0 (--) -- -- 

Liberia NA NA NA NA NA 6.0 NA NA NA ++ ++ 

Mali 357 -54 -32 -14 -34 11.5 

Niger * -34 (-18) -53 -52 14.0 -- -- 

Nigeria 25 19 -48 -48 -43 10.0 - 

Senegal -71 -55 56 -52 -46 11.0 

Sierra Leone (-105) 124 -142 -140 -128 5.0 -- -- 

Continued 

 
55 

Table 3.5d—Continued

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change relative to 

changes in short-term modern methods 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

Eastern and Southern Africa 

Burundi NA NA NA NA NA 6.5 NA ++ ++ ++ ++ 

Comoros NA -45 NA -59 -55 16.0 (++) NA -- -- 

Ethiopia NA NA NA NA NA 11.0 NA NA NA NA NA 

Kenya -68 -50 -82 -73 -74 11.0 -- -- -- 

Lesotho NA NA NA NA NA 10.0 NA NA NA NA NA 

Malawi -62 -75 -57 -78 -79 11.5 -- -- 

Rwanda NA NA NA NA NA 9.5 NA NA NA NA NA 

Tanzania -80 -65 -41 -82 -63 5.5 

Uganda -71 14 -79 -81 -80 10.0 -- -- -- 

Zambia -56 NA -50 -75 -74 12.0 ++ -- -- 

Zimbabwe -105 -105 -105 -95 -105 9.5 -- -- -- -- 

South and Southeast Asia 

Cambodia -56 -111 0 -74 -56 9.0 

India -100 -100 -100 -100 -100 10.0 -- -- -- -- -- 

Indonesia -18 -39 19 203 -26 9.5 ++ - 

Nepal NA NA NA 700 NA 10.0 ++ NA 

Pakistan -42 -100 417 -167 -67 6.0 -- 

Philippines -57 -71 -50 -31 -67 10.0 - -- -- 

Timor-Leste NA -68 -123 -58 -107 6.5 NA 

Other Areas 

Egypt NA -111 -111 NA -111 9.0 -- -- -- NA -- 

Haiti NA 125 NA 125 188 12.0 ++ ++ 

Kyrgyz Republic NA NA NA NA NA 15.0 ++ NA NA NA -- 

Notes: 

++ indicates there was a statistically significant increase at p<.01 from earlier to later survey. 

+ indicates there was a statistically significant increase at p<.05 from earlier to later survey. 

-- indicates there was a statistically significant decrease at p<.01 from earlier to later survey. 

- indicates there was a statistically significant decrease at p<.05 from earlier to later survey. 

NA indicates there were an insufficient number of cases in the numerator to test the difference between survey years. Figures in parentheses 

are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. 

3.5 Demand Satisfied for Modern Methods (DSMM) 

Figures 3.6a, 3.6b, and 3.6c show the percentage of demand satisfied for modern contraceptive methods by 

absolute poverty level in the three regions; corresponding distributions are given in Appendix Table B.14. 

Across all surveys, DSMM ranged from 12% in DR Congo 2007 to 85% in Zimbabwe 2015. As is also true 

of modern contraceptive prevalence, South Asia, Southeast Asia, and Other Areas had the highest level of 

DSMM in the first round of surveys, while Eastern and Southern Africa had the highest level in the most 

recent surveys. 
56 

While the percentage of demand satisfied is closely linked to contraceptive prevalence—the two indicators 

share the same numerator, married women using modern methods—the denominator for DSMM is 

restricted to married women with a demand for family planning, including married women with an unmet 

need (unsatisfied demand), as well as women using a modern method (satisfied demand) or a 

traditional/folkloric method (unsatisfied demand). In contrast, the denominator for contraceptive prevalence 

is all married women of reproductive age, including women who want to have more children and therefore 

are not using family planning (no demand). Thus, because the denominator for DSMM is smaller than for 

mCPR by definition, the level of demand satisfied for modern methods is always higher than modern 

contraceptive prevalence in a country, often almost twice as high. 

Figure 3.6a shows that in Central and Western Africa the trends and disparities in DSMM by absolute 

poverty level broadly resemble those in mCPR in the same region. Generally, levels of DSMM increased 

over the decade and disparities by poverty level declined; Chad and Guinea, however, experienced declines 

in DSMM, and relative disparities widened in Liberia and Niger.
57 

Figure 3.6a Demand satisfied for modern methods by absolute poverty level, Central and Western Africa 

Figure 3.6b shows demand satisfied for modern contraception among married women in Eastern and 

Southern Africa. With the exception of Burundi and Comoros, in every country of the region the demand 

for modern methods was satisfied among a majority of married women nationwide by the time of the most 

recent survey. Disparities between non-poor and extremely poor and asset poor groups narrowed in every 

country except Comoros, and even inverted in the second survey in Tanzania, whereby non-poor married 

women had a lower percentage of demand satisfied than all other groups. 

2004

2014

0 10 20 30 40 50 60 70 80 90 100

Chad

2007

2013

0 10 20 30 40 50 60 70 80 90 100

DR Congo

2003

2014

0 10 20 30 40 50 60 70 80 90 100

Ghana

2005

2012

0 10 20 30 40 50 60 70 80 90 100

Guinea

2007

2013

0 10 20 30 40 50 60 70 80 90 100

Liberia

2001

2012

0 10 20 30 40 50 60 70 80 90 100

Mali

1998

2012

0 10 20 30 40 50 60 70 80 90 100

Niger

2003

2013

0 10 20 30 40 50 60 70 80 90 100

Nigeria

2005

2016

0 10 20 30 40 50 60 70 80 90 100

Senegal

2008

2013

0 10 20 30 40 50 60 70 80 90 100

Sierra Leone

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
58 

Figure 3.6b Demand satisfied for modern methods by absolute poverty level, Eastern and Southern Africa 

Figure 3.6c shows the percentage of demand for modern methods satisfied in South Asia, Southeast Asia, 

and Other Areas by absolute poverty levels. With the exception of Haiti, Pakistan, and Timor-Leste, demand 

for modern contraception was satisfied for a majority of women in all countries of the region by the time 

of the second survey. Disparities between the poorest and the non-poor typically declined between survey 

rounds, and even inverted in Cambodia and Haiti; Indonesia, where DSMM fell among the poorest group, 

was an exception to this pattern. 

2010

2016

0 10 20 30 40 50 60 70 80 90 100

Burundi

1996

2012

0 10 20 30 40 50 60 70 80 90 100

Comoros

2005

2016

0 10 20 30 40 50 60 70 80 90 100

Ethiopia

2003

2014

0 10 20 30 40 50 60 70 80 90 100

Kenya

2004

2014

0 10 20 30 40 50 60 70 80 90 100

Lesotho

2004

2015

0 10 20 30 40 50 60 70 80 90 100

Malawi

2005

2014

0 10 20 30 40 50 60 70 80 90 100

Rwanda

2010

2015

0 10 20 30 40 50 60 70 80 90 100

Tanzania

2006

2016

0 10 20 30 40 50 60 70 80 90 100

Uganda

2001

2013

0 10 20 30 40 50 60 70 80 90 100

Zambia

2005

2015

0 10 20 30 40 50 60 70 80 90 100

Zimbabwe

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
59 

Figure 3.6c Demand satisfied for modern methods by absolute poverty level, South Asia, Southeast Asia, 

and Other Areas 

Table 3.6 shows relative changes in DSMM over the decade by absolute poverty levels and country; 

corresponding absolute decadal changes are given in Appendix Table B.15. Of the 31 countries studied, 27 

experienced a statistically significant increase in DSMM over the decade, while Chad, Kyrgyz Republic, 

and Nepal experienced statistically significant declines. Egypt had no significant change in DSMM. India, 

which had a statistically significant decline in mCPR over the decade, experienced a small but statistically 

significant relative decadal increase in DSMM of 4%. Ethiopia and Liberia had decadal-standardized 

2005

2014

0 10 20 30 40 50 60 70 80 90 100

Cambodia

2005

2015

0 10 20 30 40 50 60 70 80 90 100

India

2002

2012

0 10 20 30 40 50 60 70 80 90 100

Indonesia

2006

2016

0 10 20 30 40 50 60 70 80 90 100

Nepal

2006

2012

0 10 20 30 40 50 60 70 80 90 100

Pakistan

2003

2013

0 10 20 30 40 50 60 70 80 90 100

Philippines

2009

2016

0 10 20 30 40 50 60 70 80 90 100

Timor-Leste

2005

2014

0 10 20 30 40 50 60 70 80 90 100

Egypt

2000

2012

0 10 20 30 40 50 60 70 80 90 100

Haiti

1997

2012

0 10 20 30 40 50 60 70 80 90 100

Kyrgyz Republic

Not Poor

Poor

Extremely poor but not asset poor

Nationwide

Extremely poor and asset poor

Legend
60 

increases in DSMM that would have meant a doubling, and Rwanda and Sierra Leone had increases that 

would have meant a tripling nationwide over the course of a decade. 

Improvements in DSMM were relatively and statistically significant most often among the poorest group. 

Of 29 countries with sufficient sample sizes of married women in extreme and asset poverty, 22 experienced 

an increase in the percentage of demand for modern methods satisfied with modern contraceptive use 

between the two surveys. Only in Chad did the percentage of demand satisfied decline among the poorest 

group. By comparison, non-poor married women had a statistically significant increase in the percentage 

of demand satisfied in only four countries, all in sub-Saharan Africa—Rwanda, Senegal, Zambia, and 

Zimbabwe. Across all regions, average improvements in demand satisfied for modern methods were over 

four times as high in the poorest group as in any other absolute poverty group. 

Table 3.6 Average relative decadal changes in demand satisfied for modern methods, by absolute poverty level 

and country 

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

Central and Western Africa 

Chad -26 -36 -35 -53 -42 10.5 -- -- -- 

DR Congo -33 45 36 67 52 6.5 + ++ 

Ghana -24 10 12 38 23 11.0 -- ++ ++ 

Guinea -39 -53 -48 11 -19 7.0 -- 

Liberia 170 18 11 143 118 6.0 ++ ++ 

Mali 17 24 -3 15 41 11.5 + ++ 

Niger (-4) 3 -2 138 93 14.0 ++ ++ 

Nigeria 10 6 12 -6 14 10.0 + 

Senegal 22 46 104 143 92 11.0 + ++ ++ ++ ++ 

Sierra Leone -23 54 118 313 207 5.0 + ++ ++ ++ 

Eastern and Southern Africa 

Burundi -27 -15 38 34 32 6.5 + ++ ++ 

Comoros 10 6 (-20) 17 23 16.0 ++ 

Ethiopia 5 18 -9 127 110 11.0 ++ ++ ++ 

Kenya 5 16 6 55 45 11.0 ++ ++ ++ 

Lesotho 9 15 22 63 48 10.0 ++ ++ ++ ++ 

Malawi 6 14 36 62 58 11.5 ++ ++ ++ ++ 

Rwanda 45 39 * 325 271 9.5 ++ ++ * ++ ++ 

Tanzania -15 -5 14 29 17 5.5 ++ ++ 

Uganda -10 15 20 91 78 10.0 + ++ ++ 

Zambia 15 7 15 63 46 12.0 ++ ++ ++ 

Zimbabwe 5 9 4 15 11 9.5 + ++ ++ ++ 

Continued

 
61 

Table 3.6—Continued

Average relative decadal change (%) 

Years of 

difference 

between 

surveys 

Statistical significance of change 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

NonPoor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset 

Poor Total 

South and Southeast Asia 

Cambodia 10 28 45 63 40 9.0 ++ ++ ++ ++ 

India 0 -3 -5 4 4 10.0 -- -- ++ ++ 

Indonesia 1 5 -4 -6 3 9.5 ++ + 

Nepal -18 -15 -17 2 -8 10.0 -- -- -- - 

Pakistan 9 21 36 93 31 6.0 + ++ ++ ++ 

Philippines 2 18 7 16 11 10.0 ++ + ++ 

Timor-Leste -15 16 25 46 30 6.5 ++ ++ 

Other Areas 

Egypt 0 2 4 * 1 9.0 * 

Haiti -8 52 8 23 27 12.0 ++ ++ ++ 

Kyrgyz Republic -7 -9 -15 * -6 15.0 - -- * -- 

Notes: 

++ indicates there was a statistically significant increase at p<.01 from earlier to later survey. 

+ indicates there was a statistically significant increase at p<.05 from earlier to later survey. 

-- indicates there was a statistically significant decrease at p<.01 from earlier to later survey. 

- indicates there was a statistically significant decrease at p<.05 from earlier to later survey. 

Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted 

cases and has been suppressed. 

63 

4 DISCUSSION AND CONCLUSIONS 

Modern contraception can help empower women and couples to shape their own lives, reduce poverty, and 

improve maternal and child health. FP2020, a global initiative designed to reach 120 million new users of 

modern methods in 69 of the world’s poorest countries, has mobilized enormous funding and policy 

commitments to increase uptake of modern contraception. Even so, substantial disparities in access and use 

remain both within and across countries. Family planning research has typically been constrained by the 

fact that most usable poverty indices are computed within each country at a single point in time. Because 

the landscape of poverty differs across countries, these measures of poverty are not comparable, and only 

relative differentials can be compared. This paper, however, takes a step forward by creating and using a 

comparable measure of absolute poverty across low-income countries. The poverty measure employs the 

unsatisfied basic needs approach and also includes an asset index to help differentiate among the levels of 

the extremely poor. 

In this paper we studied the relationship between fertility preferences, family planning outcomes, and 

absolute poverty in 31 FP2020 countries from 1996 to 2016, using nationally representative data on married 

women from the DHS surveys. Our data allowed us to observe levels and differentials and to test the 

significance of trends. 

4.1 Absolute Poverty 

The extent of extreme absolute poverty was substantial in most countries studied, particularly in subSaharan Africa. In the first survey in all 21 African countries except Nigeria, the majority of married women 

were in the poorest category—extremely poor and asset poor. In Nigeria, when the extremely poor 

categories were combined—asset poor and not asset poor—the majority of married women in the first 

survey were still extremely poor (two or more unsatisfied basic needs). By the second survey, the poorest 

category was no longer the majority in Guinea, Kenya, Lesotho, Mali, Tanzania, Zambia, and Zimbabwe. 

However, as in Nigeria, the majority of married women remained in extreme poverty of either type. In 

Comoros, Ghana, and Senegal, extremely poor (whether combined or not) was no longer the majority 

category by the time of the second survey. 

In South and Southeast Asia and in the Other Areas category, patterns in absolute poverty were more 

diverse. Of the seven Asian countries, only two—Nepal and Timor-Leste—had a majority of married 

women in extreme poverty and asset poverty in the first survey round. When extreme poverty categories 

are combined, Cambodia and India also had a majority of married women in extreme poverty of some form 

in the first survey. In Other Areas, the majority of married women in Haiti were also in extreme and asset 

poverty in the first survey. By the time of the most recent survey, no country studied in Asia and only one 

in Other Areas (Haiti) had a majority of married women in any form of extreme poverty. In Egypt and the 

Philippines, a majority of married women were non-poor in both surveys, and in Kyrgyz Republic the 

majority were non-poor in the second survey. 

All 31 countries in our study experienced a decline in extreme and asset poverty, sometimes substantially 

so: in standardized decadal terms, 14 study countries experienced a more than 50% relative decline in 

extreme poverty and asset poverty. Most other categories saw a relative increase, particularly the non-poor, 
64 

but in DR Congo, Haiti, and Zimbabwe the percentage of married women who were non-poor declined 

between survey rounds. Nonetheless, ordinal logit regression tests of the absolute poverty classifications 

showed significant declines in absolute poverty between the survey rounds in all 31 countries. 

4.2 Fertility Preferences 

Concerning women’s fertility preferences, we found that women’s stated ideal number of children generally 

increases as the level of poverty becomes more extreme. Overall, the mean ideal number of children among 

the 31 countries included in the study ranged from 2.2 in Nepal to 9.5 in Niger in the most recent survey. A 

slight decrease in mean ideal number of children occurred between survey years, from a cross-country 

average of 4.9 children in the first survey to 4.7 children in the second survey. The poorest group of women 

(extremely poor and asset poor) did not experience similar declines; the average among women at this 

poverty level remained constant at 5.1 children in both survey years. In over half of the countries (16 of 31) 

studied, there was an overall statistically significant decrease in the ideal number of children between 

survey years; in four countries there was a statistically significant increase in ideal number of children. 

Among poverty levels, women in the poorest group were more likely to experience a statistically significant 

decline in mean ideal number of children compared with non-poor women (extremely poor and asset poor 

women in 11 countries, non-poor women in 4 countries). 

Regionally, the mean ideal number of children was substantially higher in Central and Western Africa, at 

6.5, and remained unchanged between survey years. Among poverty levels in the region, averages increased 

between survey years. Extremely poor and asset poor women had the smallest increase, at 0.1—from 6.8 

to 6.9 children—while all other absolute poverty levels had a similar increase of 0.3 in mean ideal number 

of children—from 5.0 to 5.3 among non-poor, 5.3 to 5.6 among poor, and 6.3 to 6.6 among extremely poor 

but not asset poor. In the Eastern and Southern Africa region, results were mixed among the poverty levels. 

While the overall average declined by 0.3 between survey years—from 4.7 to 4.4 children—a more 

moderate decrease of 0.2 was found among the poorest level respondents—from 4.8 to 4.6 children—and 

all other absolute poverty levels increased slightly or remained constant. 

More substantial declines in mean ideal number of children were found in the South and Southeast Asia 

region, where all absolute poverty levels experienced a decline. Conversely, in the three countries in Other 

Areas, the mean ideal number of children increased slightly, from an average of 3.3 children in the first 

survey to 3.4 children in the second survey. Changes within absolute poverty levels varied, with an increase 

among extremely poor and asset poor women from 3.7 to 3.8 children, and among poor women from 3.3 to 

3.4 children. The increase was greatest among the non-poor, from 2.8 to 3.3 children, and no change in 

desired fertility occurred among the extremely poor but not asset poor group of women, remaining constant 

at a mean ideal 3.6 children. 

Twenty-two of the 31 countries experienced an overall decline in non-numeric fertility preferences; in all 

but five of these countries (17 of 22), the decline was statistically significant. Only three countries 

experienced a statistically significant increase in non-numeric fertility preferences. Among poverty levels, 

extremely poor and asset poor women in 7 of the 31 countries studied had a statistically significant decline 

in non-numeric responses concerning the ideal number of children, while non-poor women in more than 

twice as many countries (17) experienced statistically significant declines in non-numeric responses 

between survey years. 
65 

4.3 Contraceptive Method Mix 

In almost all of the 21 countries studied in sub-Saharan Africa, in both survey rounds, a majority of married 

women using family planning reported using short-term modern methods. The main exception was DR 

Congo, where a majority of women in both survey rounds were using traditional methods. In the first survey 

in Comoros there was no majority method, while traditional methods were the modal method. By the time 

of the most recent survey, the majority of contraceptive users in Comoros were using short-term modern 

methods. In three of seven study countries in South and Southeast Asia—Cambodia, Indonesia, and TimorLeste—a majority of women were using short-term modern methods, which was also the case in Haiti 

within Other Areas. This was true for all four of these countries in both survey rounds. Of the 31 study 

countries overall, only in Egypt, India, and Kyrgyz Republic were a majority of married women using longterm modern methods in both survey rounds, as well as in Nepal in the first survey round. 

In most countries in this study, a slightly larger proportion of non-poor women used long-term modern 

methods compared with any absolute poverty group. Moreover, in 27 of 55 surveys with adequate sample 

sizes of all absolute poverty groups in both rounds, a smaller proportion of extremely poor and asset poor 

women were using long-term modern methods compared with any other absolute poverty group. 

A greater proportion of women in the poor or extremely poor but not asset poor groups were using shortterm modern methods compared with non-poor women and extremely poor and asset poor women. 

Traditional method use, on average, was more common among non-poor women compared with all absolute 

poverty groups in both sub-Saharan Africa and South and Southeast Asia. 

Among the 21 countries studied in sub-Saharan Africa, statistically significant increases in the use of longterm modern methods relative to short-term methods occurred in 3 countries among non-poor women, in 

11 countries among poor women, in 7 countries among the extremely poor but not asset poor, and in 16 

countries among the extremely poor and asset poor women. On the other hand, significant declines in 

traditional methods relative to short-term methods were found in 12 of the 21 sub-Saharan Africa countries 

in this study. 

Increases in use of long-term and short-term modern methods were found among women who are extremely 

poor and asset poor in more than half of the countries in South and Southeast Asia. In addition, non-poor 

women in two countries of the region had significant increases in traditional method use relative to shortterm modern methods between survey rounds. 

4.4 Modern Contraceptive Prevalence and Demand Satisfied by Modern 

Methods 

As mentioned, the percentage of demand satisfied by modern methods is always higher than modern 

contraceptive prevalence because demand satisfied by modern methods has a smaller denominator, while 

the numerator for both measures is same—married women currently using modern contraception. In our 

study, demand satisfied by modern methods is typically one-and-a-half times higher than modern 

contraceptive prevalence, but the proportional relationship varies widely among countries, reflecting both 

differences in traditional method use and in the extent of unmet need for family planning among women 

who are not using modern methods. On average across surveys, 28% of all married women of reproductive 

age were using modern contraception at the time of the survey, while 45% of the demand for family 
66 

planning was satisfied by the use of modern contraceptive methods; however, variation was substantial 

across countries and within poverty groups. 

Relative disparities by absolute poverty and trends in both modern contraceptive prevalence and demand 

satisfied by modern methods were generally similar across the 31 study countries. As expected, the level of 

poverty generally had an inverse relationship with both modern contraceptive prevalence and demand 

satisfied by modern methods. Differentials in demand satisfied by modern methods between the poorest 

group of women and the non-poor ranged from 43 percentage points in Uganda 2006 to a reverse differential 

of 7 percentage points in Cambodia 2014, where a higher percentage of women in the extremely poor and 

asset poor group compared with the non-poor group had their demand for family planning satisfied with 

modern contraceptive use. 

While South and Southeast Asia and the Other Areas group of countries had both the highest level of 

demand satisfied by modern methods and the highest modern contraceptive prevalence in the first survey, 

by the time of the most recent survey the average country in Eastern and Southern Africa was slightly higher 

in both measures—despite substantial differences in absolute poverty between the regions. By the time of 

the second survey, Burundi and Comoros were the only countries in Eastern and Southern Africa in which 

the majority of married women did not have their demand for modern methods satisfied. In contrast, in 

Haiti, Pakistan, Timor-Leste, and none of the 10 study countries in Central and Western Africa was demand 

for modern methods over 50%. 

In 24 of the 31 countries studied, there were statistically significant increases in modern contraceptive 

prevalence, and in 27 of the 31 countries there were statistically significant increases in demand satisfied 

by modern methods. Increases in both indicators were most significant among the extremely poor and asset 

poor, both numerically and statistically. Even so, overall levels of demand satisfied continue to be 

unacceptably low in many countries and significant disparities by absolute poverty group remain. In Chad, 

Mali, Niger, Nigeria, Senegal, and Zambia in the most recent survey, a disparity of more than a 20 

percentage points in the level of demand satisfied still exists between the non-poor group and the extremely 

poor and asset poor group. An additional 10 countries have a disparity of between 10 and 20 percentage 

points in demand satisfied by modern methods between the poorest and wealthiest groups. 

4.5 Overall Conclusions 

Overall, the encouraging news from our study is that in all 31 countries there were statistically significant 

declines in poverty among married women over the decade. Also, while the surveys are cross-sectional, 

shifts in absolute poverty levels and resulting movement to a higher-prevalence modern contraceptive use 

group lead us to speculate that declines in poverty helped improve modern contraceptive use. Moreover, 

married women in the poorest category (extremely poor and asset poor) typically experienced the greatest 

and often the most statistically significant improvements in modern method use and in demand satisfied for 

modern methods. 

Differentials in modern contraceptive prevalence and the percent of demand satisfied by modern methods 

across countries were substantial within every absolute poverty group—for example, demand satisfied by 

modern methods ranged from 9% in DR Congo 2007 to 82% in Zimbabwe 2015 among the poorest group. 

It is important to keep in mind that an absolute poverty level is a singular designation—women in a given 

poverty group are likely to have differences in education, residence, access to health care, levels of 
67 

empowerment, and so forth. Although disparities tended to decline while levels of demand satisfied 

increased, more work remains to be done. The extreme diversity in outcomes even among women at the 

same absolute poverty level suggest the presence of substantial disparities in local policy environments and 

in access to family planning among the countries. 

Our assessments and significance testing of key fertility preferences and contraceptive outcomes by 

absolute poverty levels in 31 FP2020 countries provide valuable findings, but more work remains to be 

done. Assessments of the changing relationship between absolute poverty and use of modern contraception 

within and across countries would benefit from further disaggregation by women’s characteristics, such as 

education, residence, and parity. Country case studies on the local policy environment in countries with 

substantial improvements in contraceptive outcomes, both overall and in equity, could demonstrate best 

practices and would help show the kinds of policies and programs most likely to succeed. 

69 

REFERENCES 

Alkema, L., V. Kantorova, C. Menozzi, and A. Biddlecom. 2013. “National, Regional, and Global Rates 

and Trends in Contraceptive Prevalence and Unmet Need for Family Planning between 1990 and 2015: A 

Systematic and Comprehensive Analysis.” The Lancet 381: 1642-1652. http://dx.doi.org/10.1016/S0140-

6736(12)62204-1. 

Alkire, S., and J. Foster. 2011. “Counting and Multidimensional Poverty Measurement.” Journal of 

Public Economics 95 (7): 476-487. https://doi.org/10.1016/j.jpubeco.2010.11.006. 

Alkire, S., and U. Kanagaratnam. 2018. Multidimensional Poverty Index Winter 2017-18: Brief 

Methodological Note and Results. MPI Methodological Notes 45. Oxford, UK: University of Oxford. 

https://ophi.org.uk/wp-content/uploads/OPHIMethNote_45_Winter_2017-18_FINAL.pdf. 

Alkire, S., and M. E. Santos. 2011. Acute Multidimensional Poverty: A New Index for Developing 

Countries. German Development Economics Conference. Berlin, Germany. 

http://hdl.handle.net/10419/48297. 

Barot, S. 2014. “Looking Back While Moving Forward: Marking 20 Years since the International 

Conference on Population and Development.” Guttmacher Policy Review 17 (3). Guttmacher Institute. 

https://www.guttmacher.org/sites/default/files/article_files/gpr170322.pdf. 

Becker, G. 1981. A Treatise on the Family. Cambridge, MA: Harvard University Press. 

Becker, G. S. 1960. “An Economic Analysis of Fertility.” In Demographic and Economic Change in 

Developed Countries, 209-240. Columbia University Press. 

Bertrand, J. T., T. M. Sullivan, E. A. Knowles, M. F. Zeeshan, and J. D. Shelton. 2014. “Contraceptive 

Method Skew and Shifts in Method Mix in Low- and Middle-Income Countries.” International 

Perspectives on Sexual and Reproductive Health 40 (3): 144-153. https://doi.org/10.1363/4014414. 

Bongaarts, J., J. Cleland, J. W. Townsend, J. T. Bertrand, and M. D. Gupta. 2012. Family Planning 

Programs for the 21st Century : Rationale and Design. New York: Population Council. 

http://www.popcouncil.org/research/family-planning-programs-for-the-21st-century-rationale-and-design. 

Bradley, S. E. K., T. N. Croft, J. D. Fishel, and C. F. Westoff. 2012. Revising Unmet Need for Family 

Planning. DHS Analytical Studies No. 25. Calverton, Maryland, USA. https://dhsprogram.com/ 

pubs/pdf/AS25/AS25%5B12June2012%5D.pdf. 

Carr, B., M. F. Gates, A. Mitchell, and R. Shah. 2012. “Giving Women the Power to Plan Their 

Families.” The Lancet 380: 80-82. http://dx.doi.org/10.1016/S0140-6736(12)60905-2. 

Castañeda, A., D. Doan, D. Newhouse, M. C. Nguyen, H. Uematsu, J. P. Azevedo, and Data for Goals 

Group. 2016. Who Are the Poor in the Developing World?” Policy Research Working Paper 7844. 

Washington, DC: Poverty and Equity Global Practice, World Bank Group. 

http://documents.worldbank.org/curated/en/187011475416542282/pdf/WPS7844.pdf. 
70 

Creanga, A., D. Gillespie, S. Karklins, and A. Tsui. 2011. Low Use of Contraception among Poor Women 

in Africa: An Equity Issue. Bulletin of the World Health Organization. https://doi.org/10.2471/ 

BLT.10.083329. 

Cruz, M., J. Foster, B. Quillin, and P. Schellekens. 2015. Ending Extreme Poverty and Sharing 

Prosperity: Progress and Policies. Policy Research Note. http://pubdocs.worldbank.org/en/ 

109701443800596288/PRN03Oct2015TwinGoals.pdf. 

Emmart, P., W. Winfrey, and J. Davis. 2017. “If You Build It, They Will Come? Interpreting the 

Contribution of Family Planning Program to Rising Contraceptive Prevalence in a Period of Rising 

Living Standards.” Mimeograph. Glastonbury, Connecticut, USA: Avenir Health. 

Fabic, M. S., Y. Choi, J. Bongaarts, J. E. Darroch, J. A. Ross, J. Stover, A. O. Tsui, J. Upadhyay, and E. 

Starbird. 2014. “Meeting Demand for Family Planning within a Generation: The Post-2015 Agenda.” The

Lancet 385 (9981): 1928-1931. https://doi.org/10.1016/S0140-6736(14)61055-2. 

Feres, J. C., and X. Mancero. 2001. El Método de las Necesidades Básicas Insatisfechas (NBI) y sus 

Aplicaciones en América Latina. CEPAL-Serie Estudios Estadísticos y Prospectivos. Santiago, Chile. 

https://repositorio.cepal.org/bitstream/handle/11362/4784/S0102117_es.pdf?sequence=1. 

Filmer, D., and L. Pritchett. 2001. “Estimating Wealth Effects without Expenditure Data - or Tears: An 

Application to Educational Enrollments in States of India.” Demography 38 (1): 115-132. 

https://doi.org/10.2307/3088292. 

FP2020. 2017a. “The Family Planning Summit for Safer, Healthier and Empowered Futures.” http://ec2-

54-210-230-186.compute-1.amazonaws.com/wp-content/uploads/2017/10/FP2020_Summit_Outcome_ 

Document_V10_Clean.pdf. 

FP2020. 2017b. FP2020: The Way Ahead 2016–2017. Washington, DC, USA. 

http://www.track20.org/download/pdf/FP2020%20Progress%20Report%202016-2017.pdf. 

FP2020. 2018. FP2020 Focus Countries. http://www.familyplanning2020.org/entities/map. 

Friedman, M. 1957. “The Permanent Income Hypothesis.” In A Theory of the Consumption Function. 

Princeton, NJ, USA: Princeton University Press. 

Frye, M., and L. Bachan. 2017. “The Demography of Words: The Global Decline in Non-Numeric 

Fertility Preferences, 1996-2011.” Population Studies 71 (2): 187-209. https://www.ncbi.nlm.nih.gov/ 

pmc/articles/PMC5525551/pdf/nihms865758.pdf. 

Gebreselassie, T., K. Bietsch, S. Staveteig, and T. Pullum. 2017. Trends, Determinants, and Dynamics of 

Traditional Contraceptive Method Use. DHS Analysis Studies No. 63. Rockville, Maryland, USA: ICF. 

http://dhsprogram.com/pubs/pdf/AS63/AS63.pdf. 

Gonzales, C., S. Jain-Chandra, K. Kochhar, M. Newiak, and T. Zeinullayev. 2015. Catalyst for Change: 

Empowering Women and Tackling Income Inequality. Washington, D.C.: International Monetary Fund. 

https://www.imf.org/external/pubs/ft/sdn/2015/sdn1520.pdf. 
71 

Guttmacher Institute. 2017. Adding It Up: Investing in Contraception and Maternal and Newborn Health, 

2017. New York: Guttmacher Institute. https://www.guttmacher.org/fact-sheet/adding-it-upcontraception-mnh-2017. 

Hardee, K., K. Newman, L. Bakamjian, J. Kumar, S. Harris, M. Rodriguez, and K. Willson. 2013.

Voluntary Family Planning Programs that Respect, Protect, and Fulfill Human Rights: A Conceptual 

Framework. Washington, DC, USA. https://www.engenderhealth.org/files/pubs/familyplanning/Voluntary_Family_Planning_Programs_A_Conceptual_Framework. 

Hayford, S. R., and V. Agadjanian. 2011. “Uncertain Future, Non-Numeric Preferences, and the Fertility 

Transition: A Case Study of Rural Mozambique.” African Population Studies 25 (2): 419-439. 

https://doi.org/10.11564/25-2-239. 

Hicks, N. 1998. An Analysis of the Index of Unsatisfied Basic Needs (NBI) of Argentina, with Suggestions 

for Improvements. Quinto Taller Regional sobre la Medición de la Pobreza: Métodos y Aplicaciones: 

Documentos Presentados - LC/R.2026. Aguascalientes, Mexico: Comisión Económica para América 

Latina y el Caribe (CEPAL). https://repositorio.cepal.org/handle/11362/31528. 

Instituto Nacional de Estadística y Censos [INDEC]. 1984. La Pobreza en Argentina: Indicadores de 

Necesidades Básicas Insatisfechas a Partir de los Datos del Censo Nacional de Población y Vivienda. 

Mimeo. Buenos Aires: INDEC. 

Jain, A. K. 2016. “Examining Progress and Equity in Information Received by Women Using a Modern 

Method in 25 Developing Countries.” International Perspectives on Sexual and Reproductive Health 42 

(3). https://doi.org/10.1363/42eXX16. 

Jain, A. K., F. Obare, S. RamaRao, and I. Askew. 2013. “Reducing Unmet Need by Supporting Women 

with Met Need.” International Perspectives on Sexual and Reproductive Health 39 (3): 133-141. 

https://doi.org/10.1363/3913313. 

Olaleye, D. 1993. Ideal Family Size: A Comparative Study of Numerical and Non-Numerical Fertility 

Desires of Women in Two Sub-Saharan African Countries. Calverton, MD, USA. 

http://www.measuredhs.com/publications/publication-wp7-working-papers.cfm. 

Polis, C. B., S. E. K. Bradley, A. Bankolea, T. Ondaa, T. Croft, and S. Singh. 2016. “Typical-Use 

Contraceptive Failure Rates in 43 Countries with Demographic and Health Survey Data: Summary of a 

Detailed Report.” Contraception 94 (1): 11-17. http://dx.doi.org/10.1016/j.contraception.2016.03.011. 

Riley, A., A. Hermalin, and L. Rosero-Bixby. 1993. “A New Look at the Determinants of Nonnumeric 

Response to Desired Family Size: The Case of Costa Rica.” Demography 30 (2): 159-174. 

https://doi.org/10.2307/2061835. 

Robinson, W. C., and J. A. Ross. 2007. The Global Family Planning Revolution: Three Decades of 

Population Policies and Programs. Washington, DC: World Bank. https://doi.org/10.1596/978-0-8213-

6951-7. 
72 

Ross, J., J. Keesbury, and K. Hardee. 2015. “Trends in the Contraceptive Method Mix in Low- and 

Middle-Income Countries: Analysis Using a New ‘Average Deviation’ Measure.” Global Health: Science 

and Practice 3 (1): 34-55. https://doi.org/10.9745/GHSP-D-14-00199. 

Rutstein, S. O., and K. Johnson. 2004. The DHS Wealth Index. DHS Comparative Reports No. 6. 

Calverton, Maryland, USA: ORC Macro. https://dhsprogram.com/publications/publication-cr6-

comparative-reports.cfm 

Rutstein, S. O., and S. Staveteig. 2014. Making the Demographic and Health Surveys Wealth Index 

Comparable. DHS Methodological Reports No. 9. Rockville, Maryland, USA. 

https://www.dhsprogram.com/pubs/pdf/MR9/MR9.pdf. 

Rutstein, S. O., S. Staveteig, R. Winter, and J. Yourkavitch. 2016. Urban Child Poverty, Health, and 

Survival in Low- and Middle-Income Countries. DHS Comparative Reports No. 40. Rockville, Maryland, 

USA. https://dhsprogram.com/pubs/pdf/CR40/CR40.pdf. 

Schultz, T. W. 1973. “The Value of Children: An Economic Perspective.” Journal of Political Economy

81 (2, Part 2): S2-S13. https://doi.org/10.1086/260151. 

Scott, V. K., L. B. Gottschalk, K. Q. Wright, C. Twose, M. A. Bohren, M. E. Schmitt, and N. Ortayli. 

2015. “Community Health Workers’ Provision of Family Planning Services in Low‐ and Middle‐Income 

Countries: A Systematic Review of Effectiveness.” Studies in Family Planning 46 (3): 241-261. 

https://doi.org/10.1111/j.1728-4465.2015.00028.x. 

Sen, A. 1976. “Poverty: An Ordinal Approach to Measurement.” Econometrica: Journal of the 

Econometric Society 44 (2): 219-231. https://doi.org/10.2307/1912718. 

Sen, A. 1982. Poverty and Famines: An Essay on Entitlement and Deprivation. New York: Oxford 

University Press. 

Staveteig, S. 2017. “Fear, Opposition, Ambivalence, and Omission: Results from a Follow-up Study on 

Unmet Need for Family Planning in Ghana.” PLOS ONE 12 (7): e0182076. 

https://doi.org/10.1371/journal.pone.0182076. 

Staveteig, S., and L. Mallick. 2014. Intertemporal Comparisons of Poverty and Wealth with DHS Data: A 

Harmonized Asset Index Approach. DHS Methodological Reports No. 15. Rockville, Maryland, USA: 

ICF International. http://dhsprogram.com/pubs/pdf/MR15/MR15.pdf. 

Staveteig, S., L. Mallick, and R. Winter. 2015. Uptake and Discontinuation of Long-Acting Reversible 

Contraceptives (LARCs) in Low-Income Countries. DHS Analytical Studies No. 54. Rockville, Maryland, 

USA: ICF International. http://dhsprogram.com/pubs/pdf/AS54/AS54.pdf. 

Staveteig, S., N. Shrestha, S. Gurung, and K. T. Kampa. 2018. Barriers to Family Planning Use in 

Eastern Nepal: Results from a Mixed Methods Study. DHS Qualitative Research Studies No. 21. 

Rockville, Maryland, USA: ICF. http://dhsprogram.com/pubs/pdf/QRS21/QRS21.pdf. 
73 

UNCESCR. 2000. The Right to the Highest Attainable Standard of Health. (E/C.12/2000/4). Geneva, 

Switzerland. http://www.refworld.org/pdfid/4538838d0.pdf. 

UNDP. 2016. Human Development Report 2016. New York, NY: The United Nations Development 

Programme. http://hdr.undp.org/sites/default/files/2016_human_development_report.pdf. 

UNFPA. 2013. Adolescent Pregnancy: A Review of the Evidence. New York, NY: United Nations 

Population Fund. https://www.unfpa.org/sites/default/files/pub-pdf/ADOLESCENT%20PREGNANCY_ 

UNFPA.pdf. 

UNFPA. 2017. The State of World Population 2017. United Nations Population Fund. 

https://www.unfpa.org/sites/default/files/sowp/downloads/UNFPA_PUB_2017_EN_SWOP.pdf. 

United Nations. 2016. Leaving No One Behind: The Imperative of Inclusive Development. Report on the 

World Social Situation 2016. New York: United Nations Department of Economic and Social Affairs. 

http://www.un.org/esa/socdev/rwss/2016/full-report.pdf. 

United Nations. 2017. Revised List of Global Sustainable Development Goal Indicators. New York: 

United Nations. https://unstats.un.org/sdgs/indicators/official%20revised%20list%20of%20global%20sdg 

%20indicators.pdf. 

United Nations. 2018. Promoting Inclusion through Social Protection: Report on the World Social 

Situation 2018. New York. https://www.un.org/development/desa/dspd/wp-content/uploads/sites/ 

22/2018/06/rwss2018-full-advanced-copy.pdf. 

United Nations, Department of Economic and Social Affairs, and Population Division. 2015. Trends in 

Contraceptive Use Worldwide. New York. http://www.un.org/en/development/desa/population/ 

publications/pdf/family/trendsContraceptiveUse2015Report.pdf. 

United Nations, Department of Economic and Social Affairs, and Population Division. 2017a. “Tracking 

Global Progress in Family Planning.” Population Facts. http://www.un.org/en/development/desa/ 

population/publications/pdf/popfacts/PopFacts_2017-11.pdf. 

United Nations, Department of Economic and Social Affairs, and Population Division. 2017b. World 

Family Planning 2017 – Highlights (ST/ESA/SER.A/414). http://www.un.org/en/development/desa/ 

population/publications/pdf/family/WFP2017_Highlights.pdf. 

Upadhyay, U. D., and D. Karasek. 2012. “Women's Empowerment and Ideal Family Size: An 

Examination of DHS Empowerment Measures in Sub-Saharan Africa.” International Perspectives on 

Sexual and Reproductive Health 38 (2): 78-89. https://www.jstor.org/stable/41585362. 

WHO. 2012. WHO Recommendations: Optimizing Health Worker Roles to Improve Access to Key 

Maternal and Newborn Health Interventions through Task Shifting. Geneva, Switzerland: World Health 

Organization. www.who.int/reproductivehealth/publications/maternal_perinatal_health/ 

978924504843/en/. 
74 

WHO, and UNICEF. 2017. Progress on Drinking Water, Sanitation and Hygiene: 2017 Update and SDG 

Baselines. Geneva, Switzerland: World Health Organization and the United Nations Children’s Fund. 

http://apps.who.int/iris/bitstream/handle/10665/258617/9789241512893-eng.pdf?sequence=1. 

World Bank. 2016. Poverty and Shared Prosperity 2016: Taking on Inequality. Washington, DC: The 

World Bank. https://doi.org/10.1596/978-1-4648-0958-3. 

World Bank. 2018. Poverty Overview. The World Bank. http://www.worldbank.org/en/topic/poverty/ 

overview. 
75 

APPENDIX A ABSOLUTE POVERTY COMPONENTS AMONG 

THE STUDY POPULATION 

Table A.1 Levels of four deprivations and of asset poverty, married women 

Inadequate 

sanitation 

Inadequate 

flooring 

No 

electricity 

Insufficient 

household 

education 

Number of deprivations 

Total 

Small 

asset 

poverty 

Sample 

0 1 2 3 4 size 

Central and Western Africa

Chad 2004 98.2 95.4 96.5 72.9 0.6 1.3 3.1 24.3 70.6 100 94.9 4,663 

Chad 2014-15 94.6 89.1 92.3 59.9 1.5 3.3 6.2 36.0 53.0 100 69.0 13,263 

D.R. Congo 2007 91.9 77.8 84.8 22.3 2.7 11.7 11.6 54.1 19.9 100 80.8 6,622 

D.R. Congo 2013-14 92.5 81.9 86.5 20.0 2.5 9.7 10.7 58.7 18.4 100 73.5 12,096 

Ghana 2003 94.2 12.4 51.7 28.4 5.2 35.1 33.2 20.9 5.6 100 67.3 3,549 

Ghana 2014 59.1 6.0 21.7 19.3 30.5 43.3 17.3 7.2 1.6 100 18.5 5,322 

Guinea 2005 90.8 56.0 79.8 64.7 4.2 11.7 16.1 24.8 43.3 100 82.3 6,292 

Guinea 2012 85.0 45.7 73.8 52.9 7.4 16.9 19.0 24.2 32.5 100 50.0 6,726 

Liberia 2007 91.1 55.0 97.0 38.3 1.5 7.3 28.3 34.1 28.8 100 90.0 4,540 

Liberia 2013 87.9 46.6 90.2 30.7 3.5 12.3 30.8 32.0 21.4 100 76.2 5,386 

Mali 2001 95.6 81.7 89.2 78.4 2.0 4.7 7.4 18.2 67.7 100 75.5 10,723 

Mali 2012-13 82.2 71.6 74.4 66.0 5.4 10.2 14.6 24.4 45.4 100 27.0 8,820 

Niger 1998 99.2 85.1 93.3 76.6 0.5 4.7 5.6 18.7 70.5 100 94.1 6,382 

Niger 2012 92.5 80.9 85.6 73.0 3.3 6.4 7.2 21.1 62.0 100 78.4 9,881 

Nigeria 2003 93.1 33.9 47.8 31.6 6.2 34.7 22.5 19.8 16.9 100 55.3 5,336 

Nigeria 2013 79.2 34.5 44.4 29.6 12.7 33.7 21.0 18.5 14.2 100 29.0 27,830 

Senegal 2005 90.0 34.3 52.9 48.4 6.8 27.6 21.3 21.8 22.5 100 58.6 9,866 

Senegal 2016 61.3 20.8 35.5 37.2 27.1 27.9 19.2 14.7 11.1 100 27.1 5,883 

Sierra Leone 2008 92.7 60.7 87.9 54.7 2.8 9.5 18.3 27.8 41.7 100 82.9 5,525 

Sierra Leone 2013 92.8 59.3 86.5 42.4 3.4 10.5 19.9 34.2 32.1 100 76.9 10,903 

Eastern & Southern Africa

Burundi 2010 82.4 88.9 94.7 47.2 2.1 4.0 12.3 42.0 39.6 100 88.7 5,421 

Burundi 2016-17 72.9 83.5 91.3 40.5 3.4 7.2 18.4 39.7 31.3 100 77.9 9,782 

Comoros 1996 80.1 49.8 71.1 46.6 8.7 17.3 19.8 26.2 28.0 100 84.9 1,634 

Comoros 2012 75.4 29.6 30.7 26.8 15.0 38.0 24.2 15.0 7.8 100 30.9 3,261 

Ethiopia 2005 95.8 90.5 86.0 67.1 2.0 5.8 5.9 23.5 62.9 100 93.7 9,066 

Ethiopia 2016 95.4 81.2 74.4 50.5 2.7 11.2 11.4 31.5 43.3 100 75.8 10,223 

Kenya 2003 92.2 62.1 84.0 18.8 5.2 11.3 20.4 47.2 15.9 100 78.9 4,919 

Kenya 2014 83.4 47.3 64.0 12.6 9.9 26.7 18.9 35.3 9.2 100 44.4 8,710 

Lesotho 2004 88.4 40.5 93.2 23.8 2.9 9.9 41.5 29.8 15.9 100 78.6 3,709 

Lesotho 2014 64.1 33.2 72.2 20.7 12.7 25.5 30.2 22.1 9.5 100 62.3 3,612 

Malawi 2004 96.8 79.3 93.1 37.3 2.5 3.9 12.6 46.6 34.4 100 91.7 8,312 

Malawi 2015-16 66.8 74.3 89.2 25.0 5.5 9.9 25.0 43.1 16.5 100 62.6 16,130 

Rwanda 2005 92.6 87.0 95.2 44.4 2.1 3.6 8.5 44.4 41.3 100 96.7 5,510 

Rwanda 2014-15 72.1 75.5 77.2 32.4 6.7 13.0 19.7 37.7 22.9 100 69.3 6,982 

Tanzania 2010 92.0 67.4 85.2 20.3 4.4 11.4 16.8 49.6 17.7 100 65.9 6,412 

Tanzania 2015-16 85.3 56.9 77.5 16.6 8.5 17.2 17.5 43.4 13.5 100 46.8 8,210 

Continued 

 
76 

Table A.1—Continued

Inadequate 

sanitation 

Inadequate 

flooring 

No 

electricity 

Insufficient 

household 

education 

Number of deprivations 

Total 

Small 

asset 

poverty 

Sample 

0 1 2 3 4 size 

Uganda 2006 95.9 76.6 91.0 29.4 1.8 7.6 12.9 51.2 26.5 100 85.2 5,337 

Uganda 2016 88.0 61.2 71.4 20.2 6.2 18.4 19.2 40.5 15.6 100 48.8 11,223 

Zambia 2001-02 88.8 63.0 82.6 21.5 9.4 9.3 17.1 44.5 19.7 100 82.0 4,694 

Zambia 2013-14 81.1 54.8 72.1 15.6 12.1 17.5 17.5 40.4 12.5 100 44.8 9,859 

Zimbabwe 2005-06 67.4 31.3 62.8 10.3 21.0 23.4 23.6 26.7 5.3 100 65.6 5,143 

Zimbabwe 2015 71.9 25.2 66.3 8.6 14.2 29.3 30.1 22.9 3.5 100 43.8 6,151 

South and Southeast Asia

Cambodia 2005 84.8 8.5 79.5 32.7 10.5 10.5 44.4 32.1 2.5 100 43.0 10,087 

Cambodia 2014 64.5 8.3 43.9 26.3 24.5 27.1 30.5 16.6 1.3 100 10.7 11,899 

India 2005-06 73.3 46.8 32.1 28.6 21.1 25.0 19.9 19.8 14.1 100 42.7 93,089 

India 2015-16 54.3 34.8 11.8 17.4 36.2 28.3 20.4 11.1 4.0 100 8.6 511,373 

Indonesia 2002-03 65.4 14.0 9.3 13.6 31.3 43.0 18.5 6.4 0.7 100 26.5 27,857 

Indonesia 2012 49.5 8.2 4.0 10.8 45.8 39.3 11.6 3.0 0.3 100 7.6 33,465 

Nepal 2006 79.7 73.5 48.8 41.0 8.6 17.0 21.6 28.2 24.5 100 64.9 8,257 

Nepal 2016 41.6 59.9 9.5 27.4 21.9 37.4 24.3 13.2 3.3 100 10.7 9,875 

Pakistan 2006-07 57.9 49.2 10.8 27.9 29.7 23.9 23.1 17.4 5.9 100 24.8 9,556 

Pakistan 2012-13 45.1 43.4 6.4 24.7 38.9 23.2 20.8 13.8 3.3 100 10.1 12,937 

Philippines 2003 33.9 12.7 23.4 8.2 51.5 26.7 14.9 6.1 0.8 100 32.4 8,671 

Philippines 2013 33.5 9.1 12.5 7.1 56.3 29.5 10.4 3.3 0.5 100 13.9 9,729 

Timor-Leste 2009-10 68.9 60.9 62.0 26.8 15.9 15.0 20.5 31.9 16.8 100 66.3 7,906 

Timor-Leste 2016 58.1 51.9 26.7 25.8 24.0 24.6 24.9 17.8 8.6 100 28.2 7,697 

Other Areas

Egypt 2005 8.7 10.8 0.6 14.5 73.2 20.3 5.1 1.2 0.1 100 3.7 18,187 

Egypt 2014 5.1 4.7 0.2 15.0 78.0 19.3 2.6 0.2 0.0 100 0.5 20,460 

Haiti 2000 86.4 46.4 66.3 48.7 8.1 20.4 17.3 24.0 30.2 100 74.9 5,958 

Haiti 2012 88.6 38.3 62.1 31.0 6.0 28.2 24.0 23.6 18.3 100 54.4 7,808 

Kyrgyz Republic 1997 77.5 4.8 0.2 5.4 20.9 70.5 8.4 0.3 0.0 100 9.5 2,675 

Kyrgyz Republic 2012 17.0 3.6 0.2 4.8 76.2 22.1 1.6 0.1 0.0 100 0.2 5,256 

Note: See text for detailed descriptions of these metrics. 
77 

APPENDIX B DISTRIBUTIONS AND ABSOLUTE DECADAL 

CHANGES IN OUTCOME MEASURES 

Table B.1 Absolute poverty levels among married women, by survey 

Not Poor Poor 

Extremely 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Sample 

Size 

Central and Western Africa

Chad 2004 0.8 1.3 4.4 93.5 100 4,663 

Chad 2014-15 1.3 3.4 32.5 62.8 100 13,263 

D.R. Congo 2007 2.6 11.4 7.7 78.3 100 6,622 

D.R. Congo 2013-14 2.2 9.7 16.9 71.2 100 12,096 

Ghana 2003 5.2 32.8 6.2 55.8 100 3,549 

Ghana 2014 31.7 41.9 15.9 10.5 100 5,322 

Guinea 2005 4.2 11.5 7.4 76.9 100 6,292 

Guinea 2012 7.3 15.0 31.3 46.4 100 6,726 

Liberia 2007 1.3 6.8 5.6 86.3 100 4,540 

Liberia 2013 3.0 11.3 13.6 72.1 100 5,386 

Mali 2001 2.2 4.9 22.3 70.6 100 10,723 

Mali 2012-13 5.6 10.4 61.4 22.6 100 8,820 

Niger 1998 0.4 4.4 1.9 93.3 100 6,382 

Niger 2012 3.4 6.1 14.9 75.6 100 9,881 

Nigeria 2003 5.3 31.2 14.1 49.4 100 5,336 

Nigeria 2013 11.9 29.5 31.3 27.4 100 27,830 

Senegal 2005 5.7 28.3 13.3 52.7 100 9,866 

Senegal 2016 29.8 25.3 19.4 25.4 100 5,883 

Sierra Leone 2008 2.1 7.8 8.3 81.8 100 5,525 

Sierra Leone 2013 2.7 8.8 11.7 76.8 100 10,903 

Eastern and Southern Africa

Burundi 2010 2.3 3.8 7.9 86.0 100 5,421 

Burundi 2016-17 3.8 7.2 16.8 72.2 100 9,782 

Comoros 1996 8.5 18.2 2.3 71.0 100 1,634 

Comoros 2012 15.5 40.9 18.8 24.9 100 3,261 

Ethiopia 2005 1.6 4.8 0.7 92.9 100 9,066 

Ethiopia 2016 2.4 9.7 9.9 78.0 100 10,223 

Kenya 2003 5.2 10.2 10.1 74.5 100 4,919 

Kenya 2014 9.8 25.9 25.6 38.7 100 8,710 

Lesotho 2004 2.7 11.3 17.0 69.0 100 3,709 

Lesotho 2014 15.4 28.8 8.8 47.1 100 3,612 

Malawi 2004 2.1 4.2 3.8 89.9 100 8,312 

Malawi 2015-16 5.7 10.9 30.2 53.2 100 16,130 

Rwanda 2005 2.2 3.7 0.4 93.7 100 5,510 

Rwanda 2014-15 7.7 12.9 16.6 62.8 100 6,982 

Tanzania 2010 4.4 11.2 26.7 57.6 100 6,412 

Tanzania 2015-16 8.9 16.3 37.6 37.2 100 8,210 

Continued 

 
78 

Table B.1—Continued

Not Poor Poor 

Extremely 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Sample 

Size 

Uganda 2006 1.5 7.1 8.2 83.2 100 5,337 

Uganda 2016 5.9 18.6 34.0 41.5 100 11,223 

Zambia 2001-02 10.6 10.1 3.1 76.2 100 4,694 

Zambia 2013-14 11.1 18.7 32.9 37.3 100 9,859 

Zimbabwe 2005-06 21.3 21.4 3.9 53.4 100 5,143 

Zimbabwe 2015 13.9 29.3 21.5 35.2 100 6,151 

South and Southeast Asia

Cambodia 2005 11.7 11.1 39.2 38.0 100 10,087 

Cambodia 2014 27.2 28.1 37.5 7.2 100 11,899 

India 2005-06 22.4 25.4 17.6 34.6 100 93,089 

India 2015-16 38.1 28.9 28.0 5.1 100 511,373 

Indonesia 2002-03 33.1 46.1 8.8 12.0 100 27,857 

Indonesia 2012 49.4 40.7 7.2 2.7 100 33,465 

Nepal 2006 8.8 15.7 18.8 56.7 100 8,257 

Nepal 2016 24.3 38.2 30.6 6.9 100 9,875 

Pakistan 2006-07 31.0 24.1 25.5 19.4 100 9,556 

Pakistan 2012-13 39.1 24.0 29.0 7.9 100 12,937 

Philippines 2003 52.7 27.3 2.2 17.8 100 8,671 

Philippines 2013 57.2 29.8 4.9 8.2 100 9,729 

Timor-Leste 2009-10 18.1 17.0 8.9 56.0 100 7,906 

Timor-Leste 2016 29.6 27.1 25.0 18.4 100 7,697 

Other Areas

Egypt 2005 74.7 19.0 5.3 0.9 100 18,187 

Egypt 2014 84.4 13.8 1.8 0.1 100 20,460 

Haiti 2000 7.8 21.9 3.8 66.6 100 5,958 

Haiti 2012 6.6 32.8 14.2 46.4 100 7,808 

Kyrgyz Republic 1997 16.8 77.5 3.6 2.2 100 2,675 

Kyrgyz Republic 2012 78.2 20.3 1.4 0.1 100 5,256 
79 

Table B.2 Average absolute decadal changes in poverty composition, by country 

Average absolute decadal change 

(percentage points) 

Not 

Poor Poor 

Extremely 

Poor 

Extremely Poor 

and Asset Poor 

Central and Western Africa 

Chad 0.5 2.0 26.8 -29.2 

DR Congo -0.6 -2.6 14.2 -10.9 

Ghana 24.1 8.3 8.8 -41.2 

Guinea 4.4 5.0 34.1 -43.6 

Liberia 2.8 7.5 13.3 -23.7 

Mali 3.0 4.8 34.0 -41.7 

Niger 2.1 1.2 9.3 -12.6 

Nigeria 6.6 -1.7 17.2 -22.0 

Senegal 21.9 -2.7 5.5 -24.8 

Sierra Leone 1.2 2.0 6.8 -10.0 

Eastern and Southern Africa 

Burundi 2.3 5.2 13.7 -21.2 

Comoros 4.4 14.2 10.3 -28.8 

Ethiopia 0.7 4.5 8.4 -13.5 

Kenya 4.2 14.3 14.1 -32.5 

Lesotho 12.7 17.5 -8.2 -21.9 

Malawi 3.1 5.8 23.0 -31.9 

Rwanda 5.8 9.7 17.1 -32.5 

Tanzania 8.2 9.3 19.8 -37.1 

Uganda 4.4 11.5 25.8 -41.7 

Zambia 0.4 7.2 24.8 -32.4 

Zimbabwe -7.8 8.3 18.5 -19.2 

South and Southeast Asia 

Cambodia 17.2 18.9 -1.9 -34.2 

India 15.7 3.5 10.4 -29.5 

Indonesia 17.2 -5.7 -1.7 -9.8 

Nepal 15.5 22.5 11.8 -49.8 

Pakistan 13.5 -0.2 5.8 -19.2 

Philippines 4.5 2.5 2.7 -9.6 

Timor-Leste 17.7 15.5 24.8 -57.8 

Other Areas 

Egypt 10.8 -5.8 -3.9 -0.9 

Haiti -1.0 9.1 8.7 -16.8 

Kyrgyz Republic 40.9 -38.1 -1.5 -1.4 

Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a 

figure is based on fewer than 25 unweighted cases and has been suppressed. 
80 

Table B.3 Mean ideal number of children by absolute poverty level and survey 

Not 

Poor Poor 

Extremely 

Poor 

Extremely Poor 

and Asset Poor Total 

Central and Western Africa

Chad 2004 7.0 7.1 8.6 9.2 9.2 

Chad 2014-15 7.3 7.8 8.3 8.8 8.6 

DR Congo 2007 5.3 5.5 6.4 7.1 6.8 

DR Congo 2013-14 5.1 5.1 6.4 6.9 6.6 

Ghana 2003 3.7 4.2 5.3 5.2 4.8 

Ghana 2014 4.1 4.5 5.6 5.6 4.7 

Guinea 2005 4.7 5.2 6.0 6.1 5.9 

Guinea 2012 5.0 5.2 6.5 6.4 6.2 

Liberia 2007 4.4 4.3 4.5 5.6 5.4 

Liberia 2013 4.1 4.6 5.1 5.5 5.3 

Mali 2001 5.0 4.9 6.3 6.7 6.5 

Mali 2012-13 5.1 5.3 6.1 6.2 6.0 

Niger 1998 (5.7) 6.3 6.9 8.7 8.5 

Niger 2012 7.2 7.8 9.3 9.7 9.5 

Nigeria 2003 5.7 6.5 7.9 7.9 7.3 

Nigeria 2013 5.8 5.9 7.9 8.2 7.1 

Senegal 2005 4.9 4.9 5.8 6.3 5.7 

Senegal 2016 5.1 5.3 6.0 6.2 5.6 

Sierra Leone 2008 3.9 4.3 5.0 5.5 5.3 

Sierra Leone 2013 3.8 4.3 5.3 5.6 5.4 

Eastern and Southern Africa

Burundi 2010 4.0 4.1 4.2 4.3 4.3 

Burundi 2016-17 4.0 4.0 4.2 3.9 4.0 

Comoros 1996 5.0 5.3 (5.2) 5.9 5.7 

Comoros 2012 5.0 5.5 5.9 6.1 5.6 

Ethiopia 2005 4.2 3.9 4.3 5.2 5.1 

Ethiopia 2016 4.5 4.2 4.6 5.1 4.9 

Kenya 2003 3.2 3.3 3.9 4.5 4.3 

Kenya 2014 3.3 3.3 3.9 4.5 3.9 

Lesotho 2004 3.0 3.2 3.2 3.6 3.5 

Lesotho 2014 2.8 2.8 2.9 3.0 2.9 

Malawi 2004 3.6 3.5 4.0 4.3 4.3 

Malawi 2015-16 3.4 3.4 4.0 3.9 3.9 

Rwanda 2005 3.8 3.8 * 4.5 4.5 

Rwanda 2014-15 3.6 3.6 3.7 3.6 3.6 

Tanzania 2010 4.3 4.1 5.5 5.5 5.3 

Tanzania 2015-16 4.1 4.2 5.6 5.5 5.2 

Uganda 2006 4.1 4.3 5.0 5.5 5.3 

Uganda 2016 4.7 4.6 5.3 5.2 5.1 

Zambia 2001-02 4.4 4.1 4.9 5.3 5.1 

Zambia 2013-14 4.2 4.5 5.4 5.4 5.1 

Zimbabwe 2005-06 3.4 3.6 4.4 4.6 4.1 

Zimbabwe 2015 3.5 3.8 4.7 4.7 4.3 

Continued
81 

Table B.3—Continued

Not 

Poor Poor 

Extremely 

Poor 

Extremely Poor 

and Asset Poor Total 

South and Southeast Asia

Cambodia 2005 3.1 3.4 3.7 3.7 3.6 

Cambodia 2014 3.1 3.2 3.4 3.3 3.3 

India 2005-06 2.1 2.2 2.5 2.7 2.4 

India 2015-16 2.1 2.2 2.5 2.7 2.3 

Indonesia 2002-03 2.8 2.9 2.7 3.2 2.9 

Indonesia 2012 2.6 2.7 2.8 3.4 2.7 

Nepal 2006 2.1 2.1 2.5 2.5 2.4 

Nepal 2016 2.0 2.1 2.4 2.4 2.2 

Pakistan 2006-07 3.6 3.9 4.4 4.9 4.1 

Pakistan 2012-13 3.6 4.0 4.6 5.0 4.1 

Philippines 2003 3.1 3.2 3.5 3.7 3.2 

Philippines 2013 2.9 3.1 3.3 3.9 3.0 

Timor-Leste 2009-10 4.8 5.3 5.7 6.1 5.7 

Timor-Leste 2016 4.1 4.5 4.5 4.6 4.4 

Other Areas

Egypt 2005 2.8 3.0 3.2 3.2 2.9 

Egypt 2014 3.0 3.2 3.4 * 3.0 

Haiti 2000 2.8 2.8 3.0 3.5 3.3 

Haiti 2012 2.9 2.7 2.9 3.1 3.0 

Kyrgyz Republic 1997 3.0 4.0 4.6 (4.4) 3.9 

Kyrgyz Republic 2012 4.2 4.4 4.6 * 4.2 

Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on 

fewer than 25 unweighted cases and has been suppressed. 
82 

Table B.4 Average absolute decadal changes in ideal number of children, by absolute poverty level and 

country 

Average absolute decadal change 

(percentage points) 

Non-Poor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa

Chad 0.2 0.6 -0.2 -0.4 -0.6 

DR Congo -0.4 -0.7 0.0 -0.2 -0.3 

Ghana 0.4 0.3 0.3 0.3 -0.1 

Guinea 0.5 0.1 0.7 0.4 0.3 

Liberia -0.5 0.5 0.9 -0.1 -0.2 

Mali 0.1 0.3 -0.1 -0.4 -0.4 

Niger (1.0) 1.1 1.7 0.7 0.7 

Nigeria 0.1 -0.5 -0.1 0.4 -0.2 

Senegal 0.2 0.3 0.2 -0.1 -0.1 

Sierra Leone -0.1 -0.2 0.7 0.3 0.2 

Eastern and Southern Africa

Burundi 0.0 -0.2 0.0 -0.6 -0.4 

Comoros 0.0 0.1 (0.4) 0.1 0.0 

Ethiopia 0.3 0.3 0.3 -0.1 -0.1 

Kenya 0.1 0.0 0.0 -0.1 -0.3 

Lesotho -0.2 -0.4 -0.3 -0.6 -0.6 

Malawi -0.2 0.0 0.0 -0.3 -0.3 

Rwanda -0.2 -0.3 * -0.9 -0.9 

Tanzania -0.4 0.0 0.2 0.0 -0.2 

Uganda 0.6 0.3 0.3 -0.2 -0.2 

Zambia -0.2 0.3 0.5 0.1 0.0 

Zimbabwe 0.2 0.3 0.3 0.1 0.2 

South and Southeast Asia

Cambodia 0.0 -0.2 -0.4 -0.4 -0.4 

India 0.0 0.0 0.1 0.0 -0.1 

Indonesia -0.2 -0.2 0.0 0.1 -0.2 

Nepal -0.1 0.0 -0.1 -0.1 -0.2 

Pakistan -0.1 0.2 0.4 0.2 0.0 

Philippines -0.2 -0.2 -0.2 0.3 -0.2 

Timor-Leste -1.1 -1.4 -1.9 -2.2 -2.0 

Other Areas

Egypt 0.2 0.2 0.2 * 0.1 

Haiti 0.1 -0.1 0.0 -0.3 -0.3 

Kyrgyz Republic 0.8 0.2 0.0 * 0.2 

Note: An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been 

suppressed. 
83 

Table B.5 Percentage of respondents reporting a non-numeric ideal number of children, by absolute 

poverty level and survey 

Not Poor Poor 

Extremely 

Poor 

Extremely Poor 

and Asset Poor Total 

Central and Western Africa

Chad 2004 19.5 16.6 9.6 12.4 12.4 

Chad 2014-15 13.3 18.4 21.9 24.8 23.5 

DR Congo 2007 2.4 3.2 5.7 10.3 8.9 

DR Congo 2013-14 3.9 3.3 6.7 8.0 7.2 

Ghana 2003 2.5 1.0 1.1 3.0 2.2 

Ghana 2014 2.2 2.7 2.1 1.6 2.3 

Guinea 2005 3.7 8.7 12.6 11.9 11.2 

Guinea 2012 9.9 9.8 10.6 14.0 12.0 

Liberia 2007 5.7 3.1 5.8 6.4 6.1 

Liberia 2013 2.4 5.5 5.8 5.6 5.5 

Mali 2001 11.2 12.4 21.6 29.0 26.1 

Mali 2012-13 2.6 3.2 3.8 2.5 3.4 

Niger 1998 (8.6) 20.4 22.9 25.4 25.1 

Niger 2012 11.8 12.5 5.9 7.4 7.6 

Nigeria 2003 7.3 9.1 16.0 14.8 12.8 

Nigeria 2013 6.0 7.2 9.6 9.4 8.4 

Senegal 2005 15.6 19.3 25.8 23.2 22.0 

Senegal 2016 20.5 18.2 21.9 23.7 21.0 

Sierra Leone 2008 4.0 6.5 5.5 5.5 5.6 

Sierra Leone 2013 5.7 4.3 5.7 6.0 5.8 

Eastern and Southern Africa

Burundi 2010 5.3 2.9 3.9 4.6 4.5 

Burundi 2016-17 2.2 1.4 2.4 1.7 1.8 

Comoros 1996 1.4 6.7 (0.0) 9.4 8.0 

Comoros 2012 11.8 11.8 9.8 10.7 11.1 

Ethiopia 2005 5.7 4.3 0.8 11.3 10.8 

Ethiopia 2016 5.4 4.8 11.9 11.4 10.6 

Kenya 2003 5.3 4.6 5.6 6.0 5.8 

Kenya 2014 2.7 1.3 2.5 2.8 2.3 

Lesotho 2004 0.0 0.0 0.0 0.2 0.2 

Lesotho 2014 0.4 0.3 0.8 0.3 0.3 

Malawi 2004 4.2 1.5 2.8 3.5 3.4 

Malawi 2015-16 2.0 1.0 1.4 1.3 1.3 

Rwanda 2005 4.4 2.7 * 3.4 3.4 

Rwanda 2014-15 0.8 1.4 1.2 1.4 1.3 

Tanzania 2010 2.5 1.4 1.7 2.2 2.0 

Tanzania 2015-16 3.1 2.9 6.0 4.3 4.6 

Uganda 2006 1.9 1.1 2.9 4.4 4.0 

Uganda 2016 1.7 1.4 2.3 3.0 2.4 

Zambia 2001-02 3.1 2.7 2.3 6.8 5.9 

Zambia 2013-14 1.3 1.2 4.5 5.3 3.8 

Zimbabwe 2005-06 0.8 1.1 1.5 1.6 1.3 

Zimbabwe 2015 0.3 0.2 0.4 0.4 0.3 

Continued
84 

Table B.5—Continued

Not Poor Poor 

Extremely 

Poor 

Extremely Poor 

and Asset Poor Total 

South and Southeast Asia

Cambodia 2005 4.0 3.0 2.4 2.6 2.7 

Cambodia 2014 0.5 0.2 0.4 0.3 0.4 

India 2005-06 1.5 1.7 1.9 2.6 2.0 

India 2015-16 0.3 0.3 0.5 0.7 0.4 

Indonesia 2002-03 12.0 15.3 11.6 16.2 14.0 

Indonesia 2012 7.9 9.1 10.0 11.8 8.6 

Nepal 2006 0.4 0.1 0.1 0.2 0.2 

Nepal 2016 0.1 0.4 0.5 0.5 0.4 

Pakistan 2006-07 7.3 7.1 8.5 7.2 7.5 

Pakistan 2012-13 3.3 4.7 4.2 3.0 3.9 

Philippines 2003 0.6 1.1 0.6 1.7 0.9 

Philippines 2013 0.2 0.4 0.2 0.6 0.3 

Timor-Leste 2009-10 4.4 4.5 2.5 2.5 3.2 

Timor-Leste 2016 13.3 13.3 10.9 8.6 11.9 

Other Areas 

Egypt 2005 6.8 10.1 9.9 8.5 7.6 

Egypt 2014 3.6 5.5 10.8 * 4.0 

Haiti 2000 3.1 1.2 2.0 2.1 2.0 

Haiti 2012 0.0 0.4 1.4 1.8 1.2 

Kyrgyz Republic 1997 1.3 1.5 0.0 (0.0) 1.4 

Kyrgyz Republic 2012 0.5 0.3 0.9 * 0.4 

Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based 

on fewer than 25 unweighted cases and has been suppressed. 
85 

Table B.6 Average absolute decadal changes in non-numeric fertility preferences, by absolute poverty 

level and country 

Average absolute decadal change 

(percentage points) 

Non-Poor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa 

Chad -6.0 1.7 11.7 11.9 10.6 

DR Congo 2.2 0.2 1.5 -3.6 -2.7 

Ghana -0.3 1.6 0.9 -1.3 0.2 

Guinea 8.9 1.7 -2.8 3.0 1.1 

Liberia -5.5 4.0 0.1 -1.3 -1.0 

Mali -7.5 -8.0 -15.4 -23.1 -19.8 

Niger (2.3) -5.6 -12.1 -12.9 -12.5 

Nigeria -1.3 -1.9 -6.5 -5.3 -4.4 

Senegal 4.4 -1.0 -3.5 0.4 -0.9 

Sierra Leone 3.3 -4.4 0.4 1.0 0.5 

Eastern and Southern Africa 

Burundi -4.8 -2.3 -2.3 -4.4 -4.1 

Comoros 6.4 3.2 (6.1) 0.8 1.9 

Ethiopia -0.3 0.4 10.1 0.1 -0.1 

Kenya -2.3 -3.0 -2.9 -2.9 -3.2 

Lesotho 0.4 0.3 0.8 0.0 0.2 

Malawi -1.9 -0.4 -1.2 -1.9 -1.8 

Rwanda -3.8 -1.4 * -2.1 -2.2 

Tanzania 1.1 2.9 7.8 3.8 4.8 

Uganda -0.3 0.2 -0.5 -1.4 -1.6 

Zambia -1.5 -1.3 1.8 -1.2 -1.7 

Zimbabwe -0.5 -0.9 -1.1 -1.3 -1.1 

South and Southeast Asia 

Cambodia -3.9 -3.1 -2.2 -2.6 -2.6 

India -1.3 -1.3 -1.4 -2.0 -1.6 

Indonesia -4.3 -6.6 -1.7 -4.6 -5.7 

Nepal -0.2 0.3 0.5 0.3 0.2 

Pakistan -6.7 -4.0 -7.3 -6.9 -6.1 

Philippines -0.4 -0.7 -0.5 -1.2 -0.6 

Timor-Leste 13.8 13.5 12.9 9.5 13.4 

Other Areas 

Egypt -3.6 -5.2 0.9 * -4.0 

Haiti -2.5 -0.6 -0.5 -0.2 -0.6 

Kyrgyz Republic -0.6 -0.8 0.6 * -0.6 

Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a 

figure is based on fewer than 25 unweighted cases and has been suppressed. 
86 

Table B.7 Modern contraceptive prevalence by absolute poverty level and survey 

Not poor Poor 

Extremely 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa 

Chad 2004 22.0 19.7 15.8 9.4 9.9 

Chad 2014-15 12.8 10.0 7.2 3.5 5.0 

DR Congo 2007 18.8 14.8 9.0 3.7 5.8 

DR Congo 2013-14 14.3 19.3 10.4 5.4 7.8 

Ghana 2003 31.9 22.5 20.3 15.0 18.7 

Ghana 2014 21.0 24.5 20.6 19.0 22.2 

Guinea 2005 17.4 12.8 5.5 4.0 5.7 

Guinea 2012 8.5 7.4 3.4 4.0 4.6 

Liberia 2007 14.3 18.7 19.4 9.0 10.3 

Liberia 2013 20.7 19.0 21.0 18.7 19.1 

Mali 2001 25.0 21.7 10.0 4.4 7.0 

Mali 2012-13 24.8 21.5 8.5 4.6 9.9 

Niger 1998 (52.2) 29.4 22.6 2.9 4.6 

Niger 2012 32.6 27.7 15.9 9.3 12.2 

Nigeria 2003 18.0 14.6 5.0 4.1 8.3 

Nigeria 2013 17.1 17.0 5.9 3.3 9.8 

Senegal 2005 24.8 18.6 8.8 4.7 10.3 

Senegal 2016 30.4 26.4 19.0 14.4 23.1 

Sierra Leone 2008 29.6 18.4 10.3 4.7 6.7 

Sierra Leone 2013 22.5 27.0 17.7 13.7 15.6 

Eastern and Southern Africa 

Burundi 2010 35.7 33.7 20.4 16.3 17.7 

Burundi 2016-17 28.7 28.5 25.6 21.5 22.9 

Comoros 1996 17.3 14.8 18.4 9.6 11.4 

Comoros 2012 17.3 14.7 (13.8) 11.6 14.2 

Ethiopia 2005 37.8 48.1 45.5 11.5 13.9 

Ethiopia 2016 39.1 49.6 48.5 31.7 35.3 

Kenya 2003 57.7 47.3 49.5 25.1 31.5 

Kenya 2014 60.6 59.3 56.9 45.2 53.4 

Lesotho 2004 53.7 50.7 46.0 29.3 35.2 

Lesotho 2014 63.0 64.7 55.7 56.5 59.8 

Malawi 2004 51.3 44.5 35.9 26.5 28.2 

Malawi 2015-16 58.0 61.3 60.2 56.3 58.1 

Rwanda 2005 35.5 37.8 * 8.6 10.3 

Rwanda 2014-15 49.4 50.1 49.6 46.2 47.5 

Tanzania 2010 35.8 38.3 26.3 25.1 27.4 

Tanzania 2015-16 33.5 37.6 29.8 31.4 32.0 

Uganda 2006 55.1 37.3 29.7 14.4 17.9 

Uganda 2016 44.6 41.3 35.7 29.7 34.8 

Zambia 2001-02 51.1 50.8 39.0 17.8 25.3 

Zambia 2013-14 56.8 55.7 44.7 35.7 44.8 

Zimbabwe 2005-06 68.2 66.2 57.9 51.5 58.4 

Zimbabwe 2015 71.6 70.0 62.1 62.2 65.8 

Continued
87 

Table B.7—Continued

Not poor Poor 

Extremely 

Poor 

Extremely 

Poor and 

Asset Poor Total 

South and Southeast Asia 

Cambodia 2005 34.9 29.4 28.0 23.2 27.2 

Cambodia 2014 35.7 38.2 41.3 40.2 38.8 

India 2005-06 58.0 56.6 48.7 36.4 48.5 

India 2015-16 52.4 51.0 40.5 34.2 47.8 

Indonesia 2002-03 57.9 56.6 65.5 47.4 56.7 

Indonesia 2012 57.5 59.6 57.4 39.4 57.9 

Nepal 2006 53.3 53.5 52.7 37.4 44.2 

Nepal 2016 42.6 43.5 42.4 41.6 42.8 

Pakistan 2006-07 29.4 25.4 16.9 11.3 21.8 

Pakistan 2012-13 31.0 27.6 20.8 17.1 26.1 

Philippines 2003 36.5 33.1 37.1 24.1 33.4 

Philippines 2013 37.9 39.4 40.9 27.3 37.6 

Timor-Leste 2009-10 31.4 26.6 21.9 16.0 21.1 

Timor-Leste 2016 24.0 27.9 23.2 19.8 24.1 

Other Areas 

Egypt 2005 59.3 51.3 39.1 34.6 56.5 

Egypt 2014 58.1 52.1 38.4 * 56.9 

Haiti 2000 28.6 19.9 30.8 22.7 22.8 

Haiti 2012 25.5 32.6 34.1 30.3 31.3 

Kyrgyz Republic 1997 55.5 48.0 45.0 (35.67) 48.9 

Kyrgyz Republic 2012 34.5 30.5 34.9 * 33.7 

Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a 

figure is based on fewer than 25 unweighted cases and has been suppressed. 
88 

Table B.8 Average absolute decadal changes in modern contraceptive prevalence, by absolute poverty 

level and country 

Average absolute decadal change 

(percentage points) 

Non-Poor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa 

Chad -8.8 -9.2 -8.2 -5.6 -4.7 

DR Congo -6.9 6.9 2.3 2.6 3.1 

Ghana -9.9 1.9 0.2 3.6 3.2 

Guinea -12.8 -7.8 -3.0 0.0 -1.5 

Liberia 10.8 0.6 2.6 16.3 14.8 

Mali -0.1 -0.2 -1.3 0.1 2.5 

Niger (-14) -1.2 -4.8 4.6 5.4 

Nigeria -0.9 2.4 0.8 -0.8 1.5 

Senegal 5.1 7.1 9.2 8.8 11.6 

Sierra Leone -14.2 17.2 14.8 18.1 17.7 

Eastern and Southern Africa 

Burundi -10.9 -8.1 8.0 8.0 8.0 

Comoros 0.0 -0.1 (-2.9) 1.3 1.7 

Ethiopia 1.2 1.3 2.8 18.3 19.4 

Kenya 2.7 10.9 6.7 18.3 19.8 

Lesotho 9.4 14.1 9.6 27.2 24.6 

Malawi 5.8 14.6 21.1 25.9 26.1 

Rwanda 14.6 13.0 * 39.6 39.2 

Tanzania -4.1 -1.3 6.4 11.5 8.4 

Uganda -10.6 4.0 6.0 15.3 16.9 

Zambia 4.8 4.0 4.8 14.9 16.2 

Zimbabwe 3.6 4.0 4.4 11.3 7.7 

South and Southeast Asia 

Cambodia 0.9 9.9 14.7 18.8 12.9 

India -5.6 -5.6 -8.2 -2.3 -0.8 

Indonesia -0.4 3.2 -8.5 -8.3 1.2 

Nepal -10.7 -10.0 -10.3 4.2 -1.4 

Pakistan 2.6 3.7 6.5 9.6 7.3 

Philippines 1.4 6.4 3.9 3.2 4.3 

Timor-Leste -11.4 2.0 2.0 5.8 4.5 

Other Areas 

Egypt -1.4 0.8 -0.8 * 0.4 

Haiti -2.6 10.7 2.8 6.3 7.0 

Kyrgyz Republic -14.0 -11.7 -6.7 * -10.1 

Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure 

is based on fewer than 25 unweighted cases and has been suppressed. 
89 

Table B.9 Method mix by absolute poverty level and survey 

Non-Poor Poor Extremely poor but not asset poor Extremely poor and asset poor Total

Longterm 

modern 

Shortterm 

modern 

Traditional Folk- loric Total N 

Longterm 

modern 

Shortterm 

modern 

Traditional Folk- loric Total N 

Longterm 

modern 

Shortterm 

modern 

Traditional Folk- loric Total N 

Longterm 

modern 

Shortterm 

modern 

Traditional Folk- loric Total N 

Longterm 

modern 

Shortterm 

modern 

Traditional Folk- loric Total N 

Central and Western Africa

Chad 2004 * * * * * 10 (10.6) (75.4) (14.0) (0.0) 100 14 6.8 78.8 12.5 1.8 100 38 1.0 89.4 9.6 0.0 100 454 1.9 87.8 10.1 0.1 100 516 Chad 2014-15 * * * * * 25 (4.5) (76.8) (18.7) (0.0) 100 55 20.4 70.1 8.5 1.0 100 344 29.5 59.7 9.2 1.6 100 325 22.9 66.2 9.5 1.4 100 749 

DR Congo 2007 12.6 38.3 46.2 2.8 100 63 3.9 32.9 58.7 4.5 100 303 2.1 28.8 69.2 0.0 100 149 5.3 17.5 72.8 4.5 100 853 5.0 23.1 68.0 3.9 100 1,367 

DR Congo 2013-14 8.5 23.1 63.5 4.9 100 123 8.0 40.4 46.9 4.7 100 466 11.0 32.8 54.2 2.0 100 488 7.2 26.4 61.3 5.1 100 1,395 8.2 30.1 57.3 4.4 100 2,472 

Ghana 2003 28.2 47.4 24.5 0.0 100 78 16.8 57.9 24.9 0.4 100 350 12.8 56.2 25.0 6.1 100 65 11.1 63.1 21.6 4.2 100 401 14.9 59.2 23.4 2.5 100 893 

Ghana 2014 25.6 47.8 25.4 1.1 100 481 31.3 54.4 13.7 0.6 100 639 31.6 61.9 5.8 0.7 100 186 30.9 62.7 5.1 1.2 100 113 29.4 53.8 16.0 0.9 100 1,419 

Guinea 2005 5.4 66.6 23.8 4.2 100 64 6.6 66.6 13.3 13.5 100 126 (1.9) (57.6) (5.8) (34.7) 100 43 2.0 54.8 8.5 34.7 100 339 3.4 58.9 11.1 26.6 100 572 

Guinea 2012 12.8 69.9 13.9 3.3 100 50 13.0 71.7 10.0 5.4 100 88 1.5 81.8 11.6 5.1 100 87 1.9 79.9 9.1 9.1 100 151 5.9 77.1 10.5 6.5 100 376 

Liberia 2007 * * * * * 9 12.7 71.3 16.1 0.0 100 68 8.9 77.5 13.6 0.0 100 57 5.1 85.9 9.0 0.0 100 385 6.8 82.8 10.4 0.0 100 520

Liberia 2013 * * * * * 37 10.5 77.1 12.4 0.0 100 133 18.1 76.9 5.0 0.0 100 162 10.3 85.4 4.1 0.2 100 759 11.6 82.9 5.4 0.1 100 1,090 

Mali 2001 19.0 67.8 12.2 0.9 100 69 9.2 72.2 14.6 4.0 100 139 4.8 85.9 3.8 5.5 100 264 4.0 81.3 7.2 7.5 100 394 6.3 80.2 7.8 5.8 100 866 

Mali 2012-13 38.7 56.6 0.0 4.6 100 130 31.3 66.4 0.8 1.5 100 203 25.9 70.6 0.1 3.5 100 479 23.6 70.1 0.0 6.3 100 100 28.7 67.6 0.2 3.5 100 912 

Niger 1998 * * * * * 15 8.1 78.3 3.3 10.2 100 96 (2.5) (83.0) (0.0) (14.5) 100 32 2.0 42.7 1.9 53.4 100 380 3.2 52.9 2.2 41.7 100 524 

Niger 2012 9.9 82.1 3.4 4.6 100 120 6.1 86.4 2.1 5.4 100 181 2.7 85.9 0.6 10.8 100 264 2.5 83.4 0.2 13.8 100 809 3.7 84.2 0.8 11.4 100 1,374 

Nigeria 2003 15.0 54.1 29.0 2.0 100 74 8.6 59.5 28.3 3.6 100 358 5.5 71.4 12.1 11.0 100 49 3.4 52.9 25.9 17.8 100 191 7.6 57.9 26.5 8.0 100 672 

Nigeria 2013 16.6 44.5 36.4 2.5 100 924 9.8 54.4 31.5 4.3 100 2,175 12.2 57.0 25.2 5.7 100 738 11.4 54.1 25.2 9.3 100 379 11.9 52.6 30.9 4.6 100 4,216 

Senegal 2005 18.4 70.6 5.1 5.9 100 158 13.2 77.1 6.9 2.8 100 575 6.9 80.9 6.8 5.4 100 132 12.7 67.6 4.7 15.0 100 302 13.1 74.2 6.1 6.7 100 1,166 

Senegal 2016 33.7 58.8 6.2 1.3 100 577 37.2 56.3 5.5 1.1 100 419 43.3 44.1 3.8 8.7 100 248 41.1 50.6 1.8 6.5 100 234 37.5 54.3 4.9 3.3 100 1,479 

Sierra Leone 2008 (4.8) (81.0) (10.0) (4.2) 100 41 8.2 84.7 4.9 2.1 100 85 3.4 81.4 3.8 11.4 100 56 1.1 76.6 1.0 21.2 100 272 3.1 79.1 2.9 14.9 100 453 

Sierra Leone 2013 14.3 76.5 7.3 2.0 100 73 24.3 69.6 2.7 3.4 100 274 19.9 76.4 0.3 3.3 100 234 16.7 76.8 0.1 6.4 100 1,232 18.2 75.6 0.8 5.4 100 1,813 

Eastern and Southern Africa

Burundi 2010 40.0 40.0 20.0 0.0 100 55 16.9 70.5 12.6 0.0 100 80 19.4 58.2 22.5 0.0 100 112 16.3 64.7 18.9 0.0 100 938 17.8 63.3 18.9 0.0 100 1,185 

Burundi 2016-17 26.9 43.1 30.0 0.0 100 154 29.1 52.0 18.7 0.2 100 246 27.9 52.5 19.2 0.4 100 523 26.0 55.1 18.7 0.2 100 1,869 26.7 53.7 19.4 0.2 100 2,792 

Comoros 1996 (16.7) (33.3) (50.0) (0.0) 100 48 18.8 32.9 44.7 3.5 100 85 * * * * * 10 12.5 43.0 38.5 6.0 100 200 14.6 39.7 41.4 4.4 100 343 

Comoros 2012 16.0 51.4 32.3 0.2 100 129 11.6 59.7 27.7 1.0 100 275 11.5 62.6 25.9 0.0 100 115 12.7 70.4 16.5 0.3 100 113 12.7 60.4 26.3 0.5 100 631 

Ethiopia 2005 20.7 56.0 23.2 0.0 100 70 8.2 81.3 10.6 0.0 100 234 3.3 95.4 1.4 0.0 100 30 1.6 95.2 3.2 0.0 100 1,000 3.8 90.7 5.5 0.0 100 1,334 

Ethiopia 2016 20.1 67.8 12.1 0.0 100 108 28.1 67.6 4.3 0.0 100 515 29.7 69.1 1.2 0.0 100 497 29.2 69.9 0.9 0.0 100 2,548 28.8 69.4 1.8 0.0 100 3,669 

Kenya 2003 42.3 42.7 14.2 0.8 100 173 24.7 62.0 12.2 1.1 100 275 29.3 58.5 10.1 2.1 100 280 15.8 60.5 21.2 2.5 100 1,207 21.4 58.9 17.7 2.1 100 1,934 

Kenya 2014 41.2 50.7 7.9 0.2 100 565 28.3 63.8 7.5 0.5 100 1,455 29.6 62.9 7.4 0.2 100 1,369 24.4 67.0 8.1 0.5 100 1,665 28.8 63.1 7.7 0.4 100 5,054 

Lesotho 2004 (28.7) (71.0) (0.3) (0.0) 100 55 19.9 79.4 0.7 0.0 100 217 13.0 84.8 2.3 0.0 100 308 9.8 86.9 3.3 0.0 100 804 12.9 84.6 2.5 0.0 100 1,383 

Lesotho 2014 11.5 87.0 1.5 0.0 100 355 8.7 91.0 0.3 0.0 100 676 7.8 92.2 0.0 0.0 100 176 4.8 94.6 0.6 0.0 100 966 7.4 92.0 0.6 0.0 100 2,173 

Malawi 2004 37.4 55.8 6.1 0.7 100 94 25.3 71.1 2.9 0.7 100 161 18.8 68.5 10.4 2.3 100 130 18.8 66.9 8.3 6.0 100 2,314 19.9 66.8 8.0 5.3 100 2,699 

Malawi 2015-16 50.7 45.3 3.8 0.2 100 559 42.9 54.4 2.5 0.1 100 1,104 42.2 56.1 1.0 0.8 100 2,983 36.7 61.8 1.0 0.6 100 4,907 40.0 58.2 1.3 0.5 100 9,552 

Rwanda 2005 22.3 47.4 30.3 0.0 100 61 9.8 68.6 21.7 0.0 100 98 * * * * * 3 3.8 52.0 44.2 0.0 100 796 5.7 53.5 40.9 0.0 100 958 

Rwanda 2014-15 30.9 54.2 14.8 0.0 100 313 24.3 67.1 8.5 0.0 100 493 17.1 69.3 13.7 0.0 100 665 17.1 72.9 9.9 0.0 100 2,247 19.2 69.9 10.8 0.0 100 3,718 

Tanzania 2010 20.7 51.2 26.5 1.6 100 141 15.0 60.5 23.1 1.4 100 366 23.6 58.0 15.3 3.1 100 551 17.0 64.0 16.0 2.9 100 1,144 18.6 61.1 17.7 2.6 100 2,202 

Tanzania 2015-16 26.2 38.8 34.1 0.9 100 375 22.3 54.9 21.8 0.9 100 652 33.4 54.7 9.5 2.4 100 1,044 29.1 60.0 9.4 1.6 100 1,079 28.8 54.6 14.9 1.7 100 3,149 

Uganda 2006 31.0 55.4 12.1 1.4 100 50 10.4 66.8 22.1 0.7 100 183 13.5 65.5 16.8 4.2 100 165 11.7 62.1 21.4 4.8 100 866 12.5 63.0 20.5 4.0 100 1,263 

Uganda 2016 32.7 53.8 13.1 0.4 100 341 22.6 63.2 13.4 0.8 100 1,004 28.3 61.6 9.2 0.9 100 1,515 27.5 64.0 7.6 0.9 100 1,513 27.0 62.2 9.9 0.8 100 4,373 

Zambia 2001-02 19.9 71.9 7.6 0.6 100 278 7.7 85.9 6.4 0.0 100 257 14.1 68.3 14.7 3.0 100 69 2.7 60.8 24.4 12.1 100 1,002 7.0 67.1 18.2 7.8 100 1,605 

Zambia 2013-14 23.0 71.4 5.4 0.2 100 657 18.6 75.0 5.9 0.5 100 1,097 17.5 73.8 7.5 1.2 100 1,587 14.6 73.6 10.6 1.2 100 1,491 17.6 73.7 7.8 0.9 100 4,832 

Zimbabwe 2005-06 14.1 84.2 1.3 0.5 100 759 5.2 92.8 1.8 0.2 100 745 6.4 90.8 2.1 0.7 100 118 2.2 93.7 3.1 1.0 100 1,476 6.0 91.0 2.3 0.7 100 3,098 

Zimbabwe 2015 23.8 74.4 1.8 0.0 100 625 17.8 81.5 0.7 0.0 100 1,272 12.1 86.4 1.6 0.0 100 834 14.9 83.1 1.8 0.1 100 1,376 16.6 81.9 1.4 0.0 100 4,107 

Continued 
90 

Table B.9—Continued

Non-Poor Poor Extremely poor but not asset poor Extremely poor and asset poor Total

Longterm 

modern 

Shortterm 

modern 

Traditional Folk- loric Total N 

Longterm 

modern 

Shortterm 

modern 

Traditional Folk- loric Total N 

Longterm 

modern 

Shortterm 

modern 

Traditional Folk- loric Total N 

Longterm 

modern 

Shortterm 

modern 

Traditional Folk- loric Total N 

Longterm 

modern 

Shortterm 

modern 

Traditional Folk- loric Total N 

South and Southeast Asia

Cambodia 2005 19.1 39.5 41.2 0.2 100 703 13.3 51.5 35.1 0.1 100 505 7.1 62.6 30.1 0.2 100 1,590 4.7 67.4 27.6 0.3 100 1,237 9.2 58.7 31.9 0.2 100 4,035 Cambodia 2014 19.4 40.9 39.5 0.1 100 1,913 17.3 50.6 32.0 0.0 100 1,881 15.2 59.7 24.8 0.2 100 2,456 19.1 57.4 23.4 0.1 100 451 17.3 51.7 30.9 0.1 100 6,701 

India 2005-06 63.3 22.8 13.7 0.2 100 14,033 76.2 14.0 9.5 0.3 100 14,842 75.3 11.7 12.5 0.5 100 9,151 70.9 10.6 17.2 1.4 100 14,412 71.1 15.0 13.2 0.6 100 52,438 

India 2015-16 69.4 20.8 9.8 0.0 100 113,240 72.9 17.3 9.8 0.0 100 83,530 69.5 17.2 13.3 0.0 100 66,824 71.6 15.1 13.3 0.0 100 10,196 70.6 18.6 10.8 0.0 100 273,790 

Indonesia 2002-03 27.3 64.7 7.4 0.6 100 5,800 21.6 73.4 4.3 0.8 100 7,663 25.9 71.9 1.1 1.1 100 1,648 24.3 68.8 5.4 1.4 100 1,695 24.3 69.8 5.2 0.8 100 16,806 

Indonesia 2012 18.9 73.1 7.6 0.5 100 10,336 15.2 79.9 4.3 0.5 100 8,524 15.4 80.1 3.2 1.3 100 1,447 17.5 73.9 4.4 4.1 100 396 17.1 76.4 5.9 0.6 100 20,704 

Nepal 2006 47.2 39.3 13.5 0.0 100 450 45.2 45.0 9.8 0.0 100 768 61.2 33.8 5.0 0.0 100 861 55.5 37.5 6.8 0.1 100 1,881 53.8 38.4 7.8 0.0 100 3,960 

Nepal 2016 42.1 32.2 25.7 0.0 100 1,375 42.9 37.6 19.5 0.0 100 2,038 59.0 29.3 11.6 0.1 100 1,451 45.8 39.7 13.6 0.8 100 331 47.4 34.0 18.6 0.1 100 5,195 

Pakistan 2006-07 30.3 42.0 27.3 0.4 100 1,205 37.6 35.4 26.5 0.5 100 801 42.7 33.8 23.3 0.2 100 539 41.8 31.8 24.7 1.6 100 284 35.9 37.6 26.0 0.5 100 2,829 

Pakistan 2012-13 28.5 41.5 29.7 0.3 100 2,235 32.8 41.1 25.8 0.2 100 1,159 35.4 43.6 20.3 0.7 100 988 46.5 40.7 12.7 0.0 100 199 31.9 41.8 26.0 0.3 100 4,581 

Philippines 2003 33.4 36.1 29.8 0.7 100 2,399 28.7 39.3 30.6 1.4 100 1,150 25.2 44.8 29.2 0.8 100 101 20.1 43.1 33.6 3.2 100 588 30.1 38.1 30.5 1.2 100 4,238 

Philippines 2013 23.8 43.8 32.2 0.3 100 3,120 21.0 49.4 29.2 0.4 100 1,622 21.2 50.4 28.0 0.4 100 270 12.9 49.6 35.4 2.2 100 349 22.1 46.2 31.3 0.4 100 5,362 

Timor-Leste 2009-10 20.6 71.3 8.1 0.0 100 489 10.8 85.3 2.9 0.9 100 370 13.2 84.7 1.2 1.0 100 157 9.7 85.0 2.9 2.4 100 749 13.3 81.2 4.2 1.3 100 1,765 

Timor-Leste 2016 44.2 43.4 12.4 0.0 100 623 37.8 55.1 6.6 0.5 100 625 31.2 64.5 4.1 0.2 100 466 26.5 70.6 1.5 1.5 100 288 36.6 55.9 7.1 0.4 100 2,001 

Other Areas

Egypt 2005 67.7 28.2 4.1 0.0 100 8,411 57.5 37.6 4.6 0.3 100 1,870 49.9 39.9 10.0 0.2 100 423 49.0 39.0 12.0 0.0 100 65 65.1 30.3 4.5 0.1 100 10,770 

Egypt 2014 55.7 41.7 2.7 0.0 100 10,301 47.0 49.6 3.3 0.0 100 1,521 44.4 50.0 5.6 0.0 100 147 * * * * * 6 54.4 42.8 2.8 0.0 100 11,974 

Haiti 2000 17.5 56.5 26.0 0.0 100 179 13.4 58.0 28.0 0.6 100 362 10.1 80.3 9.6 0.0 100 76 21.5 63.9 14.2 0.4 100 1,055 18.8 62.6 18.2 0.4 100 1,672 

Haiti 2012 10.5 70.9 16.8 1.7 100 161 7.4 81.3 9.8 1.5 100 944 9.7 79.9 9.0 1.4 100 421 13.0 80.8 5.1 1.0 100 1,168 10.4 80.3 8.0 1.3 100 2,694 

Kyrgyz Republic 1997 52.2 30.5 17.3 0.0 100 301 71.0 11.5 17.6 0.0 100 1,206 (70.7) (12.4) (16.9) (0.0) 100 52 (61.6) (0.8) {37.6) (0.0) 100 33 67.2 14.9 17.9 0.0 100 1,593 

Kyrgyz Republic 2012 65.1 27.6 7.2 0.2 100 1,530 66.8 27.2 6.0 0.0 100 347 (59.8) (24.9) (15.3) (0.0) 100 30 * * * * * 1 65.3 27.5 7.1 0.1 100 1,907 

Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. 
91 

Table B.10 Average absolute decadal changes in long-term modern methods as a percentage of family 

planning use, by absolute poverty level and country 

Average absolute decadal change 

(percentage points) 

Non-Poor Poor 

Extremely 

Poor but not 

Asset Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa 

Chad * (-5.8) 13.0 27.1 20.0 

DR Congo -6.3 (6.3) 13.7 2.9 4.9 

Ghana -2.4 13.2 17.1 18.0 13.2 

Guinea 10.6 9.1 (-0.6) -0.1 3.6 

Liberia -19.0 -3.7 15.3 8.7 8.0 

Mali 17.1 19.2 18.3 17.0 19.5 

Niger 4.1 -1.4 (0.1) 0.4 0.4 

Nigeria 1.6 1.2 6.7 8.0 4.3 

Senegal 13.9 21.8 33.1 25.8 22.2 

Sierra Leone (19.0) 32.2 33.0 31.2 30.2 

Eastern and Southern Africa 

Burundi -20.2 18.8 13.1 14.9 13.7 

Comoros (-0.4) -4.5 * 0.1 -1.2 

Ethiopia -0.5 18.1 24.0 25.1 22.7 

Kenya -1.0 3.3 0.3 7.8 6.7 

Lesotho -17.2 -11.2 -5.2 -5.0 -5.5 

Malawi 11.6 15.3 20.3 15.6 17.5 

Rwanda 9.1 15.3 * 14.0 14.2 

Tanzania 10.0 13.3 17.8 22.0 18.5 

Uganda 1.7 12.2 14.8 15.8 14.5 

Zambia 2.6 9.1 2.8 9.9 8.8 

Zimbabwe 10.2 13.3 6.0 13.4 11.2 

South and Southeast Asia 

Cambodia 0.3 4.4 9.0 16.0 9.0 

India 6.1 -3.3 -5.8 0.7 -0.5 

Indonesia -8.8 -6.7 -11.1 -7.2 -7.6 

Nepal -5.1 -2.3 -2.2 -9.7 -6.4 

Pakistan -3.0 -8.0 -12.2 7.8 -6.7 

Philippines -9.6 -7.7 -4.0 -7.2 -8.0 

Timor-Leste 36.3 41.5 27.7 25.8 35.8 

Other Areas 

Egypt -13.3 -11.7 -6.1 * -11.9 

Haiti -5.8 -5.0 -0.3 -7.1 -7.0 

Kyrgyz Republic 8.6 -2.8 (-7.3) * -1.3 

Notes: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure 

is based on fewer than 25 unweighted cases and has been suppressed. 

 
92 

Table B.11 Average absolute decadal changes in short-term modern methods as a percentage of family 

planning use, by absolute poverty level and country 

Average absolute decadal change 

(percentage points) 

Non-Poor Poor 

Extremely 

Poor but not 

Asset Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa

Chad * (1.3) -8.3 -28.3 -20.6 

DR Congo -23.4 (11.5) 6.2 13.7 10.8 

Ghana 0.4 -3.2 5.2 -0.4 -4.9 

Guinea 4.7 7.3 (34.6) 35.9 26.0 

Liberia 13.5 9.7 -1.0 -0.8 0.2 

Mali -9.7 -5.0 -13.3 -9.7 -11.0 

Niger -1.1 5.8 (2.1) 29.1 22.4 

Nigeria -9.6 -5.1 -14.4 1.2 -5.3 

Senegal -10.7 -18.9 -33.5 -15.5 -18.1 

Sierra Leone (-9.0) -30.2 -10.0 0.4 -7.0 

Eastern and Southern Africa

Burundi 4.8 -28.5 -8.8 -14.8 -14.8 

Comoros (11.3) 16.8 * 17.1 12.9 

Ethiopia 10.7 -12.5 -23.9 -23.0 -19.4 

Kenya 7.3 1.6 4.0 5.9 3.8 

Lesotho 16.0 11.6 7.4 7.7 7.4 

Malawi -9.1 -14.5 -10.8 -4.4 -7.5 

Rwanda 7.2 -1.6 * 22.0 17.3 

Tanzania -22.5 -10.2 -6.0 -7.3 -11.8 

Uganda -1.6 -3.6 -3.9 1.9 -0.8 

Zambia -0.4 -9.1 4.6 10.7 5.5 

Zimbabwe -10.3 -11.9 -4.6 -11.2 -9.6 

South and Southeast Asia

Cambodia 1.6 -1.0 -3.2 -11.1 -7.8 

India -2.0 3.3 5.5 4.5 3.6 

Indonesia 8.8 6.8 8.6 5.4 6.9 

Nepal -7.1 -7.4 -4.5 2.2 -4.4 

Pakistan -0.8 9.5 16.3 14.8 7.0 

Philippines 7.7 10.1 5.6 6.5 8.1 

Timor-Leste -42.9 -46.5 -31.1 -22.2 -38.9 

Other Areas 

Egypt 15.0 13.3 11.2 * 13.9 

Haiti 12.0 19.4 -0.3 14.1 14.8 

Kyrgyz Republic -1.9 10.5 (8.3) * 8.4 

Notes: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure 

is based on fewer than 25 unweighted cases and has been suppressed. 
93 

Table B.12 Average absolute decadal changes in traditional methods as a percentage of family planning 

use, by absolute poverty level and country 

Average absolute decadal change 

(percentage points) 

Non-Poor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa

Chad * (4.5) -3.8 -0.4 -0.6 

DR Congo 26.6 (-18.2) -23.1 -17.7 -16.5 

Ghana 0.8 -10.2 -17.5 -15.0 -6.7 

Guinea -14.1 -4.7 (8.3) 0.9 -0.9 

Liberia * -6.2 -14.3 -8.2 -8.3 

Mali -10.6 -12.0 -3.2 -6.3 -6.6 

Niger * -0.9 (0.4) -1.2 -1.0 

Nigeria 7.4 3.2 13.1 -0.7 4.4 

Senegal 1.0 -1.3 -2.7 -2.6 -1.1 

Sierra Leone (-5.4) -4.4 -7.0 -1.8 -4.2 

Eastern and Southern Africa

Burundi 15.4 9.4 -5.1 -0.3 0.8 

Comoros (-11.1) -10.6 * -13.8 -9.4 

Ethiopia -10.1 -5.7 -0.2 -2.1 -3.4 

Kenya -5.7 -4.3 -2.5 -11.9 -9.1 

Lesotho (1.2) -0.4 -2.3 -2.7 -1.9 

Malawi -2.0 -0.3 -8.2 -6.3 -5.8 

Rwanda -16.3 -13.9 14.4 -36.1 -31.7 

Tanzania 13.8 -2.4 -10.5 -12.0 -5.1 

Uganda 1.0 -8.7 -7.6 -13.8 -10.6 

Zambia -1.8 -0.4 -6.0 -11.5 -8.7 

Zimbabwe 0.5 -1.2 -0.5 -1.4 -0.9 

South and Southeast Asia

Cambodia -1.9 -3.4 -5.9 -4.7 -1.1 

India -3.9 0.3 0.8 -3.9 -2.4 

Indonesia 0.2 0.0 2.2 -1.1 0.7 

Nepal 12.2 9.7 6.6 6.8 10.8 

Pakistan 4.0 -1.2 -5.0 -20.0 0.0 

Philippines 2.4 -1.4 -1.2 1.8 0.8 

Timor-Leste 6.6 5.7 4.5 -2.2 4.5 

Other Areas

Egypt -1.6 -1.4 -4.9 -13.3 -1.9 

Haiti -7.7 -15.2 -0.5 -7.6 -8.5 

Kyrgyz Republic -6.7 -7.7 -1.1 -25.1 -7.2 

Notes: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a 

figure is based on fewer than 25 unweighted cases and has been suppressed. 
94 

Table B.13 Average absolute decadal changes in folkloric methods as a percentage of family planning use, 

by absolute poverty level and country 

Average absolute decadal change 

(percentage points) 

Non-Poor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa

Chad * (0.0) -0.8 1.5 1.2 

DR Congo 3.2 (0.3) 3.1 0.9 0.8 

Ghana 1.0 0.2 -4.9 -2.7 -1.5 

Guinea -1.3 -11.6 (-42.3) -36.6 -28.7 

Liberia * 0.0 0.0 0.3 0.2 

Mali 3.2 -2.2 -1.7 -1.0 -2.0 

Niger * -3.4 (-2.6) -28.3 -21.6 

Nigeria 0.5 0.7 -5.3 -8.5 -3.4 

Senegal -4.2 -1.5 3.0 -7.7 -3.1 

Sierra Leone (-4.4) 2.6 -16.2 -29.6 -19.0 

Eastern and Southern Africa

Burundi 0.0 0.3 0.6 0.3 0.3 

Comoros (0.1) -1.6 * -3.6 -2.4 

Ethiopia 0.0 0.0 0.0 0.0 0.0 

Kenya -0.5 -0.5 -1.7 -1.8 -1.5 

Lesotho 0.0 0.0 0.0 0.0 0.0 

Malawi -0.4 -0.5 -1.3 -4.7 -4.2 

Rwanda 0.0 0.0 * 0.0 0.0 

Tanzania -1.3 -0.9 -1.3 -2.4 -1.6 

Uganda -1.0 0.1 -3.3 -3.9 -3.2 

Zambia -0.3 0.4 -1.5 -9.1 -5.8 

Zimbabwe -0.5 -0.2 -0.7 -0.9 -0.7 

South and Southeast Asia

Cambodia -0.1 -0.1 0.0 -0.2 -0.1 

India -0.2 -0.3 -0.5 -1.4 -0.6 

Indonesia -0.1 -0.3 0.2 2.8 -0.2 

Nepal 0.0 0.0 0.1 0.7 0.1 

Pakistan -0.2 -0.5 0.8 -2.7 -0.3 

Philippines -0.4 -1.0 -0.4 -1.0 -0.8 

Timor-Leste 0.0 -0.6 -1.2 -1.4 -1.4 

Other Areas

Egypt 0.0 -0.3 -0.2 * -0.1 

Haiti 1.4 0.8 1.2 0.5 0.8 

Kyrgyz Republic 0.1 0.0 (0.0) * 0.1 

Notes: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a 

figure is based on fewer than 25 unweighted cases and has been suppressed. 
95 

Table B.14 Demand satisfied for modern methods by absolute poverty level and survey 

 Not poor Poor 

Extremely 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa 

Chad 2004 47.9 39.7 34.5 30.8 31.4 

Chad 2014-15 34.9 24.9 21.8 13.7 17.6 

DR Congo 2007 28.9 22.5 16.0 8.6 12.2 

DR Congo 2013-14 22.6 29.0 19.7 12.4 16.3 

Ghana 2003 50.3 37.6 33.7 25.4 31.3 

Ghana 2014 37.3 41.7 38.3 36.0 39.2 

Guinea 2005 36.4 31.7 17.9 13.9 18.3 

Guinea 2012 26.4 19.9 11.8 14.9 15.8 

Liberia 2007 24.6 35.4 37.3 19.4 21.8 

Liberia 2013 49.8 39.3 39.8 36.0 37.2 

Mali 2001 46.0 36.6 24.4 12.8 18.5 

Mali 2012-13 54.7 46.7 23.6 15.0 27.2 

Niger 1998 (70.1) 55.8 46.6 11.9 17.8 

Niger 2012 66.2 58.3 45.1 35.0 40.8 

Nigeria 2003 36.4 37.4 20.5 17.1 27.4 

Nigeria 2013 40.1 39.5 23.1 16.1 31.3 

Senegal 2005 44.7 35.3 20.4 12.3 23.5 

Senegal 2016 55.7 53.2 43.8 31.6 47.3 

Sierra Leone 2008 48.6 39.7 25.7 13.5 18.4 

Sierra Leone 2013 42.9 50.4 40.8 34.7 37.5 

Eastern and Southern Africa 

Burundi 2010 56.8 54.4 32.8 30.8 32.7 

Burundi 2016-17 46.8 49.0 40.9 37.6 39.4 

Comoros 1996 30.8 25.6 (41.2) 16.9 20.1 

Comoros 2012 35.9 27.9 27.7 21.6 27.4 

Ethiopia 2005 61.5 66.6 83.3 23.2 27.4 

Ethiopia 2016 64.8 79.5 75.3 55.7 60.6 

Kenya 2003 73.0 67.2 67.6 39.0 47.3 

Kenya 2014 77.2 78.8 71.7 62.4 70.7 

Lesotho 2004 72.9 69.8 62.4 44.3 51.5 

Lesotho 2014 79.7 80.6 76.1 72.1 76.1 

Malawi 2004 70.0 66.7 53.9 42.7 44.8 

Malawi 2015-16 75.0 77.4 76.3 73.0 74.6 

Rwanda 2005 48.8 50.6 * 15.7 18.4 

Rwanda 2014-15 69.7 69.5 67.1 64.2 65.8 

Tanzania 2010 54.4 58.0 48.5 45.4 48.3 

Tanzania 2015-16 49.9 56.4 52.3 52.6 52.9 

Uganda 2006 66.9 51.8 43.3 24.1 29.0 

Uganda 2016 60.4 59.8 52.1 45.9 51.6 

Zambia 2001-02 66.2 68.7 52.6 31.0 41.0 

Zambia 2013-14 77.8 74.6 62.2 54.5 63.8 

Zimbabwe 2005-06 85.6 81.5 79.8 71.3 77.2 

Zimbabwe 2015 89.9 88.5 83.0 81.8 85.2 

Continued
96 

Table B.14—Continued

 Not poor Poor 

Extremely 

Poor 

Extremely 

Poor and 

Asset Poor Total 

South and Southeast Asia 

Cambodia 2005 46.8 44.6 43.1 37.2 41.6 

Cambodia 2014 51.0 56.0 60.4 58.4 56.4 

India 2005-06 75.3 76.8 69.3 57.5 69.1 

India 2015-16 75.3 74.8 65.6 59.6 71.9 

Indonesia 2002-03 77.4 77.4 83.8 69.3 77.1 

Indonesia 2012 77.8 80.9 81.0 65.2 79.0 

Nepal 2006 65.9 66.0 69.8 55.0 60.9 

Nepal 2016 53.7 56.2 57.8 56.2 56.1 

Pakistan 2006-07 48.4 42.6 34.5 24.3 39.7 

Pakistan 2012-13 51.1 47.9 41.8 37.9 47.0 

Philippines 2003 50.7 45.7 50.6 35.4 46.7 

Philippines 2013 51.9 54.0 53.9 41.1 51.8 

Timor-Leste 2009-10 50.9 46.0 40.6 31.9 39.2 

Timor-Leste 2016 45.9 50.8 47.3 41.5 46.9 

Other Areas 

Egypt 2005 81.0 74.9 64.0 65.1 79.0 

Egypt 2014 80.8 76.5 66.5 * 80.0 

Haiti 2000 43.4 29.1 43.8 33.6 33.8 

Haiti 2012 39.0 47.1 47.8 43.0 44.8 

Kyrgyz Republic 1997 70.1 68.3 68.2 (58.6) 68.5 

Kyrgyz Republic 2012 62.8 59.5 53.1 * 62.0 

Notes: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that 

a figure is based on fewer than 25 unweighted cases and has been suppressed. 

 
97 

Table B.15 Average absolute decadal changes in demand satisfied for modern methods, by absolute 

poverty level and country 

Average absolute decadal change 

(percentage points) 

Non-Poor Poor 

Extremely 

Poor but 

not Asset 

Poor 

Extremely 

Poor and 

Asset Poor Total 

Central and Western Africa

Chad -12.4 -14.1 -12.0 -16.3 -13.1 

DR Congo -9.6 10.1 5.8 5.8 6.3 

Ghana -11.8 3.8 4.2 9.6 7.2 

Guinea -14.2 -16.8 -8.6 1.5 -3.5 

Liberia 41.9 6.5 4.2 27.8 25.8 

Mali 7.6 8.8 -0.6 1.9 7.6 

Niger -2.8 1.8 -1.1 16.5 16.5 

Nigeria (3.7) 2.1 2.6 -1.0 3.9 

Senegal 10.0 16.3 21.3 17.6 21.6 

Sierra Leone -11.4 21.5 30.3 42.3 38.1 

Eastern and Southern Africa

Burundi -15.4 -8.4 12.4 10.5 10.3 

Comoros 3.2 1.4 (-8.4) 2.9 4.5 

Ethiopia 3.1 11.7 -7.2 29.6 30.2 

Kenya 3.9 10.6 3.8 21.3 21.3 

Lesotho 6.8 10.8 13.7 27.8 24.6 

Malawi 4.4 9.2 19.5 26.4 25.9 

Rwanda 22.0 19.9 * 51.0 49.9 

Tanzania -8.2 -2.9 6.9 13.1 8.4 

Uganda -6.4 8.0 8.8 21.8 22.7 

Zambia 9.6 5.0 8.0 19.6 19.0 

Zimbabwe 4.6 7.3 3.4 11.1 8.5 

South and Southeast Asia

Cambodia 4.7 12.7 19.3 23.5 16.5 

India -0.1 -1.9 -3.8 2.1 2.9 

Indonesia 0.5 3.6 -2.9 -4.3 2.0 

Nepal -12.2 -9.8 -11.9 1.3 -4.8 

Pakistan 4.5 8.8 12.3 22.7 12.2 

Philippines 1.2 8.3 3.3 5.7 5.1 

Timor-Leste -7.7 7.3 10.3 14.8 11.8 

Other Areas 

Egypt -0.3 1.8 2.8 * 1.1 

Haiti -3.6 15.0 3.4 7.8 9.2 

Kyrgyz Republic -4.9 -5.9 -10.1 * -4.3 

Notes: Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a 

figure is based on fewer than 25 unweighted cases and has been suppressed.