DHS ANALYTICAL 

STUDIES 41

Contextual influences of modern 

contraceptive use among rural 

women in rwanda and nepal

SEPTEMBER 2013

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

Wenjuan Wang, Soumya Alva, Rebecca Winter, and Clara Burgert of ICF International, Calverton, Maryland, USA.
MEASURE DHS assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Additional information about the MEASURE DHS project can 

be obtained by contacting MEASURE DHS, ICF International, 11785 Beltsville Drive, Suite 300, 

Calverton, MD 20705 (telephone: 301-572-0200; fax: 301-572-0999; e-mail: reports@measuredhs.com; 

Internet: www.measuredhs.com). 

The main objectives of the MEASURE DHS project are: 

• to provide decision-makers in survey countries with information useful for informed policy 

choices; 

• to expand the international population and health database; 

• to advance survey methodology; and 

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

demographic and health surveys. 
DHS Analytical Studies No. 41 

Contextual Influences of Modern Contraceptive Use 

among Rural Women in Rwanda and Nepal 

Wenjuan Wang 

Soumya Alva 

Rebecca Winter 

Clara Burgert 

ICF International 

Calverton, Maryland, USA 

September 2013 

Corresponding author: Wenjuan Wang. International Health and Development, ICF International, 11785 Beltsville 

Drive, Calverton, Maryland 20705, USA; Phone 301-572-0200; Fax 301-572-0999; Email: wenjuan.wang@icfi.com 
Acknowledgments: The authors would like to thank Dr. Tom Pullum who guided the research effort. 

Special thanks go to Dr. Saifuddin Ahmed for his detailed review of the report and invaluable comments. 

In addition, we thank Bryant Robey for editing the report and Yuan Cheng for formatting it. 

Editor: Bryant Robey 

Document Production: Yuan Cheng 

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

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

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

Government. 

Recommended citation: 

Wang, Wenjuan, Soumya Alva, Rebecca Winter, and Clara Burgert. 2013. Contextual Influences of 

Modern Contraceptive Use among Rural Women in Rwanda and Nepal. DHS Analytical Studies No. 41. 

Calverton, Maryland, USA: ICF International. 
iii 

Contents 

List of Tables ............................................................................................................................................... v 

List of Figures .............................................................................................................................................. v 

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

Executive Summary ................................................................................................................................... ix 

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

1.1 Context of Family Planning in Nepal ........................................................................................... 1 

1.2 Context of Family Planning in Rwanda ........................................................................................ 2 

1.3 Research Focus: Community-Level Influences on Modern Contraceptive Use ........................... 3 

1.4 Literature Review ......................................................................................................................... 3 

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

2.1 Data and Measurements ............................................................................................................... 9 

2.2 Analysis Method ......................................................................................................................... 15 

3 Results ................................................................................................................................................ 17 

3.1 Background Characteristics of Individuals and Clusters ........................................................... 17 

3.2 Contraceptive Prevalence and Method Mix ............................................................................... 20

3.3 Modern Contraceptive Use by Women’s Background Characteristics ...................................... 21 

3.4 Multilevel Analysis Results ......................................................................................................... 23 

4 Discussion........................................................................................................................................... 33 

References .................................................................................................................................................. 37 

v 

List of Tables 

Table 1. Operational definitions of individual, household, and community variables ............................. 14 

Table 2. Random-effects regression models included in the analysis ...................................................... 16 

Table 3. Background characteristic of currently married women in rural areas who did not want a 

child within two years ................................................................................................................. 17 

Table 4. Description of community-level variables by percentage of clusters ......................................... 19 

Table 5. Prevalence of modern contraceptive use among currently married women in rural areas 

who do not want a child within two years, according to background characteristics ................. 22 

Table 6. Results of multilevel modeling of modern contraceptive use among currently married rural 

women who do not want a child within two years, Rwanda ...................................................... 24 

Table 7. Results of multilevel modeling of modern contraceptive use among currently married rural 

women who do not want a child within two years, Nepal .......................................................... 29 

List of Figures 

Figure 1. DHS clusters in rural areas in the 2010 Rwanda DHS and population density estimated for 

some clusters ............................................................................................................................... 11 

Figure 2. DHS clusters in rural areas in the 2011 Nepal DHS and population density estimated for 

some clusters ............................................................................................................................... 12 

Figure 3. Percent distribution of women who currently use modern contraception, by type of 

method, Rwanda ......................................................................................................................... 20 

Figure 4. Percent distribution of women who currently use modern contraception, by type of 

method, Nepal ............................................................................................................................. 21 

vii 

Preface 

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

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

developing countries. 

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

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

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

individual country results in an international context. 

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

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

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

multivariate statistical techniques. 

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

selects the topics covered in Analytical Studies. 

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

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

Sunita Kishor 

Project Director 

ix 

Executive Summary 

This study takes a multilevel approach to identify determinants of women’s use of modern contraceptives 

in two diverse settings, Nepal and Rwanda. We explore the extent to which four spheres of communitylevel influence—socioeconomic development, women’s empowerment, fertility norms, and access to 

family planning information and services—shape women’s contraceptive use, after adjusting for women’s 

individual socio-demographic characteristics and for other contextual factors. 

Data for this study come primarily from the 2011 NDHS and the 2010 RDHS, and also draw from spatial 

data sources to measure community levels of socioeconomic development. The study focuses on rural 

women who do not wish to have a child within the next two years, a subset of women of particular 

interest to family planning policymakers. A sequence of random-effects logit regression models are run—

separately for Rwanda and Nepal—to examine the variation in modern contraceptive use explained by 

each of the four selected spheres of community-level influences. A final model includes all four sets of 

community-level variables, as well as individual-level factors. 

In the final model for both Rwanda and Nepal, community-level variables in each of the four spheres 

remain significant predictors of women’s current use of modern contraception. After adjusting for 

women’s individual characteristics, the community’s level of socioeconomic development, the extent to 

which women in the community participate in decisions around family planning, prevailing small-family 

size norms, and the community’s access to modern methods appear to be positively associated with 

women’s contraceptive use. 

Overall, results from this study indicate the relevance for family planning programs of the community 

context in which women live. The demand for family planning services is influenced not only by 

women’s individual and household characteristics, but also by the community’s socioeconomic 

development, its access and exposure to family planning, the gender norms of local community members 

regarding decision-making for family planning and health issues, and community norms regarding 

marriage and childbirth. Among the factors we explored, the analysis suggests that socioeconomic 

development and access to family planning services may play a bigger role in affecting women’s 

contraceptive behaviors than the community’s norms regarding gender and fertility. 

 1 

1 Introduction 

This study takes a multilevel approach to identify determinants of women’s use of modern contraceptives 

in two diverse settings, Nepal and Rwanda, which offer two different contexts of family planning. Nepal 

has a longer history of family planning policy, and modern contraceptive prevalence among married 

women has been relatively high for over 15 years. It was already over 25 percent in 1996, 35 percent in 

2001, and 44 percent in 2006, but appears to have stalled since then, estimated at 43 percent in the 2011 

Nepal Demographic and Health Survey (NDHS) (Ministry of Health and Population (MOHP) [Nepal], 

New Era, & ICF International Inc., 2012). 

In contrast, in Rwanda the family planning policy was implemented more recently. Modern contraceptive 

prevalence was quite low in 2000, at 6 percent. However, the 2010 Rwanda Demographic and Health 

Survey (RDHS) found modern contraceptive prevalence to be 45 percent, exceeding expectations 

(National Institute of Statistics of Rwanda (NISR) [Rwanda], Ministry of Health (MOH) [Rwanda], & 

ICF International Inc., 2012). 

While Nepal and Rwanda each face unique challenges, both governments have invested heavily in 

population and family planning policies. Expanding the reach of family planning resources—so that 

women who do not wish to have a child right away are able to have appropriate contraceptive options—is 

currently a high priority for the governments of Rwanda and Nepal. Overall, the two governments have 

incorporated numerous strategies into their population policies. Both have focused largely on increasing 

the supply of family planning methods, so that women can access the methods of their choice. Both 

policies have recognized the relation between socioeconomic development and contraceptive use and 

have integrated these two goals. Both policies have also focused on altering social norms, to promote 

small family policies and to enhance the acceptability of the use of family planning. Finally, policymakers 

in both countries have understood the importance of women’s empowerment to the success of family 

planning programs, and have included programs to promote women’s education and labor force 

participation, along with efforts to address gender norms. 

The proximate determinants of fertility and the influence of individual characteristics on modern 

contraceptive use have been well documented, but information on contextual influences is limited in 

comparison. Using the 2011 NDHS and the 2010 RDHS, this study will build on previous work that has 

examined contextual determinants of contraceptive use to examine multiple spheres of contextual 

influence and will be useful to inform policy makers in a variety of settings. 

1.1 Context of Family Planning in Nepal 

Family planning initiatives were first introduced in Nepal in the late 1950s by the Family Planning 

Association of Nepal (FPAN), a non-governmental organization. Government-supported family planning 

programs were initiated in 1968 with the implementation of Nepal’s Third Development Plan (1965-

1970), which first stated the need for a national population policy and identified family planning as a 

critical component of this policy (Ministry of Health and Population (MOHP) [Nepal], New ERA, & 

Macro International Inc., 2007). 

Since then, family planning programs have expanded their goals and have increased the availability of 

family planning methods across Nepal. In addition to directing resources toward increasing the supply 

and availability of contraceptives and the reach of trained providers, family planning and population 

policies in Nepal have long focused on promoting a small family norm through education and media 

campaigns. They also have recognized the interconnectedness of women’s status and fertility, promoting 

women’s employment and education (Ministry of Health and Population (MOHP) [Nepal] et al., 2007). 
 2 

Most recently, the Three-Year Interim Plan (2006/07 to 2009-10) put forth the goal to remove all costs of 

family planning services in the public sector. Family planning supplies are now available free of charge at 

public facilities in Nepal (Shrestha, Shrestha, & Ghimire, 2012). Temporary family planning methods can 

be obtained from a wide range of health facilities, including health posts and primary health care outreach 

clinics, and can also be obtained from periphery-level health workers and volunteers. More permanent 

contraceptive methods such as IUDs and implants are available at certain hospitals, primary health care 

centers, and health posts with trained staff. Sterilization services are provided at some health facilities and 

through mobile outreach services (Shrestha et al., 2012). 

Between 1996 and 2011, fertility declined steadily in Nepal, from a total fertility rate (TFR) of 4.6 births 

per woman in 1996 to 4.1 in 2001, 3.1 in 2006, and 2.6 in 2011 (Ministry of Health and Population 

(MOHP) [Nepal] et al., 2012). This reduction in fertility was accompanied by an impressive rise in the 

use of modern contraceptives among currently married women. As mentioned, however, the contraceptive 

prevalence rate (CPR) remained stagnant between 2006 and 2011 (Ministry of Health and Population 

(MOHP) [Nepal] et al., 2012). 

The stall in CPR has been attributed to several possible factors, including increased levels of spousal 

separation, failure to reach remote populations and special sub-groups with family planning supplies and 

services, and increased use of traditional family planning methods, abortion (following the introduction of 

a safe abortion policy in 2003), and emergency contraception (Shrestha et al., 2012). According to the 

2011 NDHS, female sterilization remains the most prevalent form of contraception currently used by 15 

percent of currently married women, while 9 percent of women use injectables, 8 percent rely on male 

sterilization by their partners, and 7 percent use traditional methods (Ministry of Health and Population 

(MOHP) [Nepal] et al., 2012). 

Due to Nepal’s mountainous terrain, some areas of the country are very remote. These rural areas tend to 

have fewer or inadequate reproductive health care facilities, fewer supplies and method choices, and 

insufficient health care professional staff, while the staff at health posts may not be trained to counsel 

women regarding family planning options. Spousal separation has also become a critical component of 

the family planning context in Nepal today; nearly one-third of married women age 15-49 had absent 

husbands at the time of the 2011 NDHS, and 11 percent of husbands had been away for at least a year 

(Khanal, Shrestha, Pant, & Mehata, 2013). 

1.2 Context of Family Planning in Rwanda 

Rwanda implemented its first family planning program in the early 1980s (May, Mukamanzi, & 

Vekemans, 1990). Since then the government has invested ample resources in making contraceptive 

methods available, affordable, and acceptable across the country (Wang, Wang, Pullum, & Ametepi, 

2012). While family planning activities were suspended in the 1990s due to the Rwandan genocide, 

reproductive health and family planning services have received renewed attention since the early 2000s. 

In 2007 the Government of Rwanda prioritized family planning as part of its effort to reduce the country’s 

high population growth rate, address poverty, and achieve development goals (Muhoza, Rutayisire, & 

Umubyeyi, 2013). 

The government has taken a comprehensive approach to increasing family planning uptake, with an 

ambitious expansion of services, including the construction of new hospitals, health centers, and health 

posts in previously under-served areas; an increase in the range of methods available at existing facilities; 

expansion of trainings for medical staff in family planning service provision; and a decentralization of 

family planning service delivery in order to gain community support and participation (Muhoza et al., 

2013). Between 2000 and 2013, Rwanda’s family planning efforts have emphasized transforming 

community norms concerning family planning. The Government of Rwanda has led a widespread national 
 3 

public education campaign to promote a small family size norm and proper birth spacing, to gain 

acceptance of family planning, and to frame population policy and family planning as an important means 

to achieving national development goals and reducing poverty (Muhoza et al., 2013). 

Finally, Rwanda’s family planning efforts have long acknowledged the interconnectedness of family 

planning uptake, socioeconomic development, and women’s empowerment. To create an environment 

favorable to behavioral change that would in turn result in lower fertility rates, population and family 

planning policies have promoted women’s education and school attendance, and the employment and 

advancement of women (National Institute of Statistics of Rwanda (NISR) [Rwanda] et al., 2012). 

Rwanda’s fertility rate remained at roughly six births per woman between 1992 and 2005, but the TFR 

declined rapidly between 2005 and 2010, from 6.1 to 4.6 births per woman (National Institute of Statistics 

of Rwanda (NISR) [Rwanda] et al., 2012). During this same period there has been a dramatic rise in 

modern contraceptive use among currently married women, from 6 percent in 2000 to 10 percent in 2005, 

27 percent in 2007/08, and 45 percent in 2010 (MEASURE DHS STATcompiler). Injectables are the 

most prevalent contraceptive method among currently married women (26 percent), followed by the pill 

(7 percent), and implants (6 percent) (National Institute of Statistics of Rwanda (NISR) [Rwanda] et al., 

2012). 

1.3 Research Focus: Community-Level Influences on Modern Contraceptive Use 

This study will focus on a subset of women of particular interest to family planning policymakers: rural 

women who do not wish to have a child within the next two years. We limit the study to rural women 

because they may face unique barriers to contraceptive use, such as geographic remoteness and lack of 

contraceptive options. We limit the study to women who do not wish to have a child right away because 

these are the women family planning programs hope to reach; thus, better understanding what influences 

their contraceptive behavior is of highest interest. 

This study examines four realms of community-level influence—socioeconomic development, women’s 

empowerment, access to family planning information and services, and fertility norms. We explore the 

influence of each these community realms on women’s current use of modern contraception, after 

accounting for the influence of women’s individual and household socioeconomic characteristics. Each 

realm of community influence could affect a woman’s decision to use modern contraception, either by 

impacting her access to contraceptive supplies and services, or her decision to use contraceptives by 

influencing her desire for more children or her sense of the acceptability of family planning itself. These 

four realms are examined in more detail below. 

1.4 Literature Review 

Socioeconomic development 

The community’s overall level of economic development could affect women’s contraceptive use through 

several pathways. Better roads and higher population density associated with development relate closely 

to women’s geographic access to family planning. At the same time, living in a community with higher 

levels of socioeconomic development, better access to education, expanded employment opportunity, and 

more exposure to modern ideas could affect women’s ideal family size and attitudes toward using family 

planning (Giusti & Vignoli, 2006). 

Among the studies that have examined community-level determinants of women’s contraceptive use, the 

majority have included measures of the community’s socioeconomic development, with mixed findings 

(DeGraff, Bilsborrow, & Guilkey, 1997; Giusti & Vignoli, 2006; Stephenson, Baschieri, Clements, 
 4 

Hennink, & Madise, 2007). In a study in Ethiopia, Giusti and Vignoli (2006) used the community’s mean 

household wealth index score as a measure of the community’s degree of modernization, and found it to 

be positively associated with women’s level of contraceptive use. However, using a similar measure of 

community wealth, Stephenson and colleagues (2007) found that among six sub-Saharan African 

countries studied, in only two (Tanzania and Burkina Faso) was the community’s mean score positively 

associated with women’s modern contraceptive use. 

Other community-level measures of socioeconomic development have been included in studies modeling 

women’s contraceptive use, including the percentage of women with access to piped water (Kaggwa, 

Diop, & Storey, 2008), the presence of asphalt or concrete roads in a community, the distance to the 

nearest population center, and the presence of electricity, irrigation, and secondary schools (DeGraff et 

al., 1997), and the type of habitat (Stephenson et al., 2007). However, these indicators were not found to 

be determinants of contraceptive use. 

Women’s empowerment 

There is ample evidence that women’s personal level of empowerment affects their contraceptive use 

(Barbieri & Hertrich, 2005; Kaye, Mirembe, Bantebya, Johansson, & Ekstrom, 2006). In a study using 

DHS data from 18 sub-Saharan African countries, for example, Barbieri and Hertrich (2005) examined 

spousal age difference as a proxy for gender inequality within the couple and found that being at least 15 

years younger than one’s husband was associated with lower levels of contraceptive use. Women’s 

educational attainment, employment status, and discussion of family planning with one’s partner have 

also been correlated with women’s use of contraceptives (Kimuna & Adamchak, 2001). 

A smaller body of research has examined community-level measures of women’s empowerment as 

determinants of contraceptive use (DeGraff et al., 1997; DeRose & Ezeh, 2010; Moursund & Kravdal, 

2003; Speizer et al., 2009; Stephenson, Beke, & Tshibangu, 2008). The rationale behind this research is 

that a woman’s contraceptive behavior is likely to be influenced not only by her own level of autonomy 

but also by the level of autonomy of other women in her community, and by prevailing gender norms. 

Many of these studies have found a positive relationship between contraceptive use and community-level 

measures of gender attitudes and women’s empowerment. DeGraff and colleagues (1997) found that the 

community-level average wage for women—interpreted as a measure of women’s status and 

independence—had a positive effect on women’s contraceptive use. Building on this finding, Stephenson 

and colleagues (2008) found that among sexually active women currently using a method of contraception 

in South Africa, the proportion of women in a community with control over their own earnings was 

positively associated with women’s use of the pill or another more permanent method of contraception, 

rather than injectables. They also found that the proportion of women in a community with only a primary 

education—another measure of women’s autonomy—was inversely associated with women’s use of a 

more permanent method of contraception, rather than injectables. 

DeRose and Ezeh (2010), using data from the 1995-96 Negotiating Reproductive Outcomes Study, 

conducted in Uganda, examined the influence of both individual and community-level measures of 

women’s control over household decision-making on the adoption of modern contraceptive methods. The 

study found that women in communities where women more commonly have control over household 

decisions were 29 percent more likely to use modern contraception compared with women in 

communities where women have less decision-making power, independent of individual and community 

educational attainment. 

Finally, women’s experience of domestic violence has been found to be a risk factor for unwanted 

pregnancy and for induced abortion (Kaye et al., 2006). Community-level measures of spousal violence 
 5 

have also been correlated with contraceptive use. In a sample of sexually-experienced unmarried youth in 

South Africa, Speizer and colleagues (2009) found that youth from communities with higher levels of 

sexual violence were less likely to have used a condom at last sex, were more likely to have had a 

pregnancy during adolescence, and were more likely to be HIV-positive compared with youth from 

communities with lower levels of sexual violence. 

Fertility norms 

In general, an individual’s decisions are shaped by the perceived attitudes and behaviors of others in the 

community (Rimal & Real, 2003). Community norms regarding family size and family planning, then, 

are likely to influence women’s own attitudes, and ultimately influence their use of family planning. 

While family planning programs both in Nepal and Rwanda have sought to transform norms concerning 

family size ideals and fertility behavior, few studies in these two settings have examined the extent to 

which community norms around family planning may explain geographic variation in contraceptive use. 

Studies in other settings have had mixed findings regarding the extent to which community-level norms 

around family planning and family size affect women’s use of modern contraceptives (Kaggwa et al., 

2008; Stephenson et al., 2007). In Mali, Kaggwa and colleagues (2008) found that after controlling for 

individual-level factors, there was no evidence that women’s use of modern contraceptives was affected 

either by community norms about desired family size (measured by the proportion of women desiring 

four or fewer children and the mean number of births per woman), or by attitudes toward family planning 

(measured by the proportion of women in the community who approved of family planning). 

In their study of six sub-Saharan African countries, however, Stephenson and colleagues (2007) found 

that in four of the countries (Kenya, Malawi, Tanzania, and Ghana), after adjusting for individual and 

other community-level factors, the percentage of women in the community approving of family planning 

was positively associated with individual women’s current use of modern contraceptives. In Kenya, 

men’s community-level approval of family planning was a significant positive predictor of women’s 

contraceptive use; this association held even after adjusting for the partner’s approval of family planning 

at the individual level, indicating a community influence above and beyond that of the nuclear family. 

The normative context in South Asia is likely to be different than the context in sub-Saharan Africa. In 

contrast to Rwanda, a preference for sons appears to influence fertility and family planning norms in 

Nepal. An ethnographic study among Hindu women in one semi-urban village of Nepal found that 

women had internalized the expectations of their society, community, and family and felt a strong 

pressure to produce sons (Brunson, 2010). Nepal is a patriarchal society; particularly in rural areas there is 

a persistent culture of obedience to one’s husband (Stash, 1999). In Nepal, husband’s approval of family 

planning has been shown to be a pivotal determinant of women’s contraceptive use (Kamal & Lim, 2010). 

Family planning information and services 

There is widespread agreement that expanding access to family planning services and supplies leads to 

increased use of contraceptives. In addition to helping meet a community’s current demand for family 

planning, increasing the availability of family planning information and services can enhance the 

desirability and acceptability of using family planning among members of the community, and thus 

stimulate more demand for contraception. 

The majority of studies examining the effect of family planning service provision on contraceptive use 

has relied on data collected at the health-facility level to assess the availability and quality of family 

planning services, and then has linked these data with women’s individual survey data to assess their 

relationship with women’s contraceptive use. Such linked studies have been undertaken since the 1970s, 
 6 

when countries surveyed as part of the World Fertility Survey (WFS) often included a community-level 

module with information on service availability. Some DHS surveys before 2000 also included a service 

availability module. 

Studies using these earlier survey data provided empirical evidence that women’s contraceptive use is 

shaped at least to some extent by the availability of services, and also by the perceived quality of those 

services (Magnani, Hotchkiss, Florence, & Shafer, 1999; Mensch, Arends-Kuenning, & Jain, 1996; T 

Pullum, 1991; Steele, Curtis, & Choe, 1999). For example, Steele and colleagues (1999) linked data from 

the 1995 Morocco DHS with data from the 1992 Morocco DHS service availability module to study the 

impact of family planning service provision on women’s contraceptive use. Using multilevel models that 

adjusted for individual women’s characteristics, they found that the presence of a nearby public health 

center was associated with higher rates of adoption of a modern method after giving birth, and with lower 

contraceptive method failure rates. 

Similarly, in a study linking DHS data from Peru with data from a situation analysis, Mensch and 

colleagues (1996) found that the quality of family planning care available in a community had an effect 

on women’s contraceptive use, independent of women’s individual and household characteristics. The 

authors estimated that contraceptive prevalence would have been 16 to 23 percent higher if all women 

lived in a community with the highest quality of care compared with the lowest. 

While important, these studies have methodological issues. Some studies used cluster-level health facility 

data in the individual-level analysis without specifying in their statistical models that cluster-level data 

could only explain variation between clusters in contraceptive use. Additionally, the DHS service 

availability modules were based on interviews with key informants rather than on information obtained 

directly from health facilities, and were not necessarily representative of all health facilities in the 

country. Finally, while many of these studies adjusted for women’s characteristics and for household 

characteristics, few adjusted for other spheres of community influence, which would provide useful 

information on the relative importance of different aspects of women’s physical and social environments. 

Since the late 1990s, MEASURE DHS has conducted the Service Provision Assessment (SPA) surveys to 

collect information on health facilities. SPA data provide nationally representative detailed information 

about the health service environment. Several studies have used GIS coordinates of health facilities and of 

DHS clusters to link SPA and DHS data. Using data from the 2002 Egypt Service Provision Assessment 

(ESPA) survey and the 2003 Egypt Interim Demographic and Health Survey (EIDHS), Hong and 

colleagues (2006) found that the quality of available family planning services was positively associated 

with women’s use of the IUD, independent of the distance to the facility, the facility type, and women’s 

individual demographic characteristics (Hong, Montana, & Mishra, 2006). 

However, there are important methodological challenges concerning analysis linking DHS and SPA data. 

Most often, SPA surveys use a sample of all private and public facilities, rather than a complete census, 

and this sample is not typically designed to be representative beyond the regional level. Linking these 

data at lower levels of geographic disaggregation than the sample was designed to permit can produce 

large misclassification errors (Skiles, Burgert, Curtis, & Spencer, 2013). Furthermore, there is often a gap 

between when the facility data and the women’s data were collected, so the study must make the 

assumption that the service environment did not change during this time. Finally, while this type of 

linkage also makes the assumption that cluster-level summaries accurately describe women’s individual 

service environment and access, some portion of women are likely to travel outside their cluster for 

family planning services. 

A less common method of assessing the family planning service environment is to aggregate women’s 

individual responses on their contraceptive use to the cluster level, in order to make inferences about the 
 7 

local service environment. This strategy to our knowledge has not been used in studies examining the 

effect of access to family planning services on contraceptive use, but has been used in other analyses of 

reproductive health service utilization. Using DHS data from Malawi and Kenya, Pullum and colleagues 

(2006) examined women’s survey responses regarding respondents’ source of supplies for family 

planning, place of childbirth, and place to obtain an AIDS test, in order to draw inferences about the 

acceptability of integrating those services. A key component of the study was to control for the 

availability of services, so that limited availability would not be misinterpreted as preference. In order to 

assess the cluster-level availability of services, the study counted the total number of different types of 

facilities that women reported using within a cluster, and used this count as a proxy for the total number 

of facilities available in that cluster (Thomas Pullum, Cleland, & Shah, 2006). 

Our study will use a similar strategy to assess health service availability at the cluster level. While 

imperfect, aggregate measures provide a useful proxy for the availability and reach of family planning 

services within a cluster, and this approach avoids the many methodological challenges associated with 

facility data. 

9 

2 Data and Methods 

2.1 Data and Measurements 

This analysis primarily uses data from the most recent DHS surveys in Rwanda (2010) and Nepal (2011). 

DHS surveys typically adopt a two-stage sample design in countries after ensuring representativeness at 

the level of the region, and in urban and rural areas. The first stage involves selecting clusters (usually 

neighborhoods in urban areas and villages in rural areas) with probability proportional to size from a 

national master sample frame. During this stage, 492 clusters were selected in the Rwanda survey and 289 

clusters were selected in the Nepal survey. 

At the second stage, a random systematic sample of households is drawn from a listing of households in 

each of the DHS clusters. All women age 15-49 in the selected households are eligible for the individual 

interview. For the purpose of this analysis, we focus on currently married women in rural areas in both 

countries. We further limit the analysis to women who did not want (or were not sure if they wanted) a 

birth in the next two years from the date of the survey. In the end, 5,460 women from 411 clusters in 

Rwanda and 7,502 women from 194 clusters in Nepal who met the criteria (currently married, living in 

rural areas, and wanting to avoid childbearing for at least two years) were included in our analysis. 

Outcome variable 

The outcome variable for this analysis is women’s current use of any modern contraceptive methods 

including female sterilization, male sterilization, IUD, injectables, pill, implants, male condom, lactational 

amenorrhoea method, and standard days method. In the survey women could give multiple methods, but 

this study considers the most effective method as their current contraceptive method. 

Contextual variables 

The main focus of the analysis is on community-level influences, measured at the DHS cluster level, on 

women’s modern contraceptive use. The indicators of community-level influences, which were selected 

based on the research literature and data availability, are listed below: 

1) Community’s socioeconomic development 

a. Region/Province 

b. Distance to a main road 

c. Distance to a large city 

d. Low population density 

e. Female educational attainment 

2) Women’s empowerment 

a. Women’s experience of domestic violence 

b. Women’s participation in household decision-making on own health care 

c. Women’s participation in decision-making on family planning use 

3) Community fertility norms 

a. Average age at first marriage 

b. Average interval between marriage and first birth 

c. Average number of children desired 

4) Community access to family planning information and services 

a. Access to modern contraception 

b. Exposure to family planning messages 

c. Contacts with family planning providers 
10 

Table 1 presents the operational definitions of the variables. Most of the variables were obtained by 

aggregating individual-level responses to the cluster level, with the exception of three socioeconomic 

development measurements, as described below: 

Measurements of socioeconomic development 

Three geographic variables were merged with the DHS clusters to create the measures of socioeconomic 

development: distance to main road, distance to the nearest city of 50,000 people, and the cluster’s 

population density. The distances in meters to the nearest main road and nearest city of 50,000 were 

calculated using ArcGIS Desktop (ESRI, Redlands, CA) tools and extensions. Data were obtained from 

the CIESIN website including the Global Roads Open Access Data Set (gRoads) and for the cities the 

Global Rural Urban Mapping Project. A list of cities with populations of 50,000 or more (according to the 

2002 census in Rwanda and 2011 census in Nepal) was taken from the City Population website 

(http://www.citypopulation.de/). 

In DHS, GPS locations are recorded at an estimated center of the sampled cluster. These locations are 

then displaced to maintain the confidentiality of respondents. Rural clusters are displaced up to 5 

kilometers with 1% of rural locations are displaced up to 10 kilometers (Perez-Heydrich, Burgert, & 

Emch, 2013). The analysis took into account the displacement by using categories instead of exact 

distances in the measurements. 

The variable related to population density was categorized into groups as indicated in Table 1. The 

calculation of the population density for each cluster used the AfriPop and AsiaPop data layers, which are 

population density estimates for approximately 100 square meters from 2010 adjusted to match UN 

population estimates. The mean population density for the buffers of 10km around the DHS rural cluster 

points was calculated. Finally, a variable indicating low density was created with two categories: 1-

bottom 20% of clusters according to population density, 0-other clusters. The maps (Figures 1 and 2) 

below display the DHS clusters in rural areas included in the Rwanda and Nepal surveys, as well as the 

variable of population density estimated for some clusters. 

Female education attainment measured by women’s average years of schooling in the cluster is also 

included as a socioeconomic development indicator. This variable was calculated based on the responses 

from all interviewed women in the cluster. 

 
11 

Figure 1. DHS clusters in rural areas in the 2010 Rwanda DHS and population density estimated 

for some clusters 
12 

Figure 2. DHS clusters in rural areas in the 2011 Nepal DHS and population density estimated for 

some clusters 
13 

Measurements of women’s empowerment 

All of the women’s empowerment variables were obtained by aggregating individual-level data to the 

cluster level. Women’s experience of domestic violence was measured based on women’s responses to a 

set of questions related to physical or sexual violence perpetrated by their husbands/partners. In both 

Rwanda and Nepal, the domestic violence module covered a subsample of households included in the 

DHS survey. In our study the cluster-level variable—percentage of women in the cluster who reported 

spousal physical or sexual violence—was calculated among the households selected for the domestic 

violence module. 

Two variables on women’s participation in household decision-making were analyzed—decisions on 

women’s own health care and decisions on family planning. Women were considered to participate if they 

make these decisions either by themselves or jointly with their husbands. While data on the percentage of 

women participating in decision-making for their own health care were based on all interviewed women 

in the cluster, data on decision-making on family planning use were limited to women who currently use a 

family planning method (modern or traditional). 

Measurements of fertility norms 

Women’s average age at first marriage and mean number of children desired by women in the cluster 

were based on all married women interviewed in the cluster. For the variable of longer interval between 

marriage to first birth, we first calculated the interval (in months) between marriage and the first birth, 

then further dichotomized it by comparing it with the overall average among all women: 0-equal to or less 

than overall average, 1- greater than overall average. 

Measurements of access to family planning information and services 

We measured access to modern contraception by the total number of modern contraceptive methods 

reported by women in the cluster—the greater number of methods reported, the better the access. It is a 

proxy measurement based on an assumption that a specific contraceptive method is accessible to the 

cluster when at last one woman in this cluster reported use of this method. This assumption may not be 

true if some women obtained certain methods through special channels not available to the majority of 

women. It is also possible that some methods are available to the community but are not reported by any 

women. 

We also measured the cluster-level exposure to family planning messages based on the percentages of 

women in the cluster who heard about family planning messages through television, radio, or newspapers. 

Lastly, we measured an aggregate level of women’s contacts with family planning providers in the last 12 

months. 
14 

Table 1. Operational definitions of individual, household, and community variables 

Variables Operational definition 

Individual and household variables 

Age of woman Self-reported age at time of survey: 15-24, 25-34, 35-44, 45-49 years 

Woman's educational level Highest level of education attained: none, primary, secondary or higher 

Occupation Current occupation: not-employed, agricultural or manual, 

professional/technical/managerial/clerical, sales and services 

Religion Rwanda-Catholic, Protestant and Adventist, Muslim and other; 

Nepal - Hindu, Buddhist, and Muslim/Kirat/Christian/Other 

Parity Number of living children the woman had: 0, 1-2, 3-4, 5+ 

Having a son Having at least one son 

Husband's education Reported by wife: none, primary, secondary or higher 

Husband's residency status Lives in house, lives elsewhere for less than 1 year, and lives elsewhere 

for a year or longer 

Household wealth quintile Lowest, second, middle, fourth, and fifth 

 

Community variables 

Socioeconomic development 

Region/province Rwanda- Provinces: Kigali, South, West, North and East 

Nepal- development regions: Eastern, Central, Western, Mid-Western, 

Far-Western 

Distance to a main road Distance of cluster site to the nearest main road: 5km or less and >5km 

Distance to a large city Distance of DHS cluster point from its nearest populated settlement of 

50,000 people or more: <10km, 10-19, 20-29, 30-39, 40-49km, 50+km 

Low population density Bottom 20% of clusters according to population density(people per 

square kilometers) 

Female educational attainment Average years of schooling among women in the cluster 

Women's empowerment 

Women's experience of domestic 

violence 

Percentage of women in the cluster who reported spousal physical or 

sexual violence 

Women's participation in household 

decision-making 

Percentage of women in the cluster who participated in household 

decision-making on woman's own health care 

Women's participation in decisionmaking on family planning Percentage of women in the cluster who participated in decision-making 

on using contraception 

Fertility norms 

Average age at first marriage Mean age at first marriage among women in the cluster 

Longer interval between marriage and 

first birth 

Mean interval between marriage and first birth is greater than the overall 

average 

Fertility desire Mean number of children desired by women in the cluster 

 

Access to family planning 

information and services 

Access to modern contraception Total number of modern contraceptive methods reported by women in 

the cluster 

Exposure to family planning messages Percentage of women in the cluster who are exposed to family planning 

messages through mass media (i.e., TV, radio, or newspaper) 

Contacts with family planning providers Percentage of women in the cluster who visited health facility for family 

planning or were visited by family planning providers in last 12 months 
15 

Individual variables 

The multivariate analysis controls for the individual variables that may influence women’s contraceptive 

use. These include the woman’s age, education level, occupation, household wealth status, number of 

living children, religion, and husband’s education, which all have been shown in the literature to be 

associated with use of family planning. All of these variables are specified as categorical variables in the 

models. 

While the variables and reference groups are typically the same in both Rwanda and Nepal, the 

definitions of region/province and religion vary for each country. The analysis for Nepal includes two 

additional variables—whether the husband lives at home or has been away from home for less than one 

year, or for more than one year; and whether the respondent has at least one son, which is included to 

account for the effect of son preference. 

2.2 Analysis Method 

We use random-effects logit regressions for the multivariate analysis presented in this paper. DHS data 

follow a hierarchical structure—that is, individuals are nested within clusters, and clusters are nested 

within regions. Respondents who live in the same cluster or region may not be independent of one 

another. Compared with regular individual-level regression analyses that assume that all individuals are 

independent, the multilevel modeling approach accounts for the fact that people who live in the same area 

may have some characteristics in common. It should be noted women living in the same household are 

not independent. However this clustering effect is ignored in this analysis given it is not common that 

multiple women from a same household were interviewed. 

While the outcome variable of this study—modern contraceptive use—is measured at the individual level, 

the predictors of most interest (community-level influences) are measured at the cluster level. Using the 

standard statistical approach, questions arise about the appropriate unit of analysis. Individual-level 

analysis ignores the nesting of people within clusters, which results in underestimating the standard errors 

and increasing the chance of incorrectly rejecting null hypotheses (Raudenbush & Bryk 2002). 

Alternatively, if the unit of analysis is the cluster, it becomes difficult to include individual-level variables 

in the analysis. These problems are addressed with multilevel modeling that allows for simultaneous 

investigation of the effects of the group-level and individual-level predictors on individual-level 

outcomes. 

Another important feature of the random-effects model is that it gives information on the proportion of 

total variation that is explained by the cluster-level predictors. Random-effects models typically include a 

random intercept and/or random slopes. This analysis allows for random intercepts across clusters and 

assumes fixed effects of covariates across clusters. 

The analysis is performed separately for Rwanda and Nepal. The model for each country can be 

expressed with two equations: one at the individual level and one at the cluster level. 

At the individual level: 
16 

At the cluster level: 

Where pij is the fitted probability of using a modern contraceptive method for the ith individual in the jth 

cluster, Xij is a column vector of individual-level variables, and R j represents contextual variables at 

the cluster level. β oj is a random intercept, which varies across clusters, and β1 is a row vector of fixed 

coefficients for the predictors. μ oj is the random component of the intercept and is assumed to be 

normally distributed with mean zero and variance 00 τ . Significance of the random effect for cluster will 

indicate that the cluster-level predictors play a role after adjusting for individual-level variables. 

In order to examine the variation in outcomes explained by different sets of variables, particularly the 

contribution of the four types of community-level influences in explaining the variation in women’s 

contraceptive use, we examine six random-effects models sequentially, for each country, with different 

groups of variables included in each model. Model 1 includes only individual-level variables. Models 2-5 

all have the individual-level variables, plus each of the four sets of community-level influences, in turn. 

Model 2 includes predictors for community socioeconomic development; Model 3 includes communitylevel variables for women’s empowerment; Model 4 includes variables for community fertility norms; 

and Model 5 includes variables for community access to family planning information and services. 

Model 6 is the complete model including all the individual-level and cluster-level variables considered. 

Given the high levels of sterilization in Nepal, one additional model is fitted for Nepal—Model 7, which 

is the same as Model 6 except that it is restricted to women whose current contraceptive method is not 

female sterilization or male sterilization. The sample size drops from 7,502 in Models 1-6 to 5,548 in 

Model 7, while the number of clusters remains the same. Table 2 gives a summary of the models for both 

countries. 

Sampling weight is applied in both descriptive analysis and multivariate regressions. 

Table 2. Random-effects regression models included in the analysis 

Variables in regression model 

Model 1 Individual-level variables only 

Model 2 Individual-level variables + Community socioeconomic development 

Model 3 Individual-level variables + Community variables for women’s empowerment 

Model 4 Individual-level variables + Community fertility norms 

Model 5 Individual-level variables + Community access to family planning information and services 

Model 6 Individual-level variables + All community variables 

Model 7 

(only for Nepal) 

All individual-level variables + All community variables (restricted to women who do not 

use sterilization as their current contraceptive method) 
17 

3 Results 

3.1 Background Characteristics of Individuals and Clusters 

Table 3 shows selected background characteristics of women included in the sample in each country 

(currently married women in rural areas who do not want, or are not sure if they want, a birth in the next 

two years). Slightly more than 60 percent of women are under age 35, in both countries. However, Nepal 

has a younger population, with one-quarter of women under age 25 compared with 15 percent in Rwanda. 

A large majority of the women (72 percent) in Rwanda have a primary education, while only 7 percent 

have a secondary education, and 21 percent are not educated. In Nepal most women are not educated (51 

percent), while 20 percent have a primary education, and 30 percent are educated at the secondary level or 

beyond. In both countries, agriculture or manual labor are the most common occupations among women, 

while 20 percent of women in Nepal are unemployed compared with only 8 percent in Rwanda. 

Most women in Rwanda are either Catholic or other Christian, while Hinduism is the predominant 

religion in Nepal. Given that the analysis is restricted to women in rural areas, only a small percentage of 

women in either country live in households in the highest wealth quintile. Women in Rwanda are almost 

equally divided between those with 1-2, 3-4, or 5 or more living children, while few women have no 

children. In Nepal a smaller proportion of women have 5 or more children and a larger proportion have 1-

2 children, compared with women in Rwanda. 

Table 3. Background characteristic of currently married women in rural areas who did not want a 

child within two years 

 Rwanda Nepal 

 % Number of women % Number of women 

Age 

15-24 15.1 825 25.6 1,924 

25-34 46.8 2,557 36.8 2,762 

35-44 27.8 1,518 29.1 2,181 

45-49 10.3 560 8.5 635 

Education level 

No education 21.4 1,168 50.7 3,801 

Primary 71.9 3,926 19.5 1,459 

Secondary or higher 6.7 366 29.9 2,241 

Occupation 

Unemployed 7.9 431 20.0 1,498 

Agriculture/manual 87.1 4,755 69.3 5,202 

Professional 4.7 257 2.7 205 

Sales and service 0.3 17 8.0 597 

Religion1

Catholic (Hindu) 43.2 2,356 84.8 6,360 

Christian (Buddhist) 53.8 2,939 8.4 631 

Muslim/other (Muslim/kirat/Chris/other) 3.0 164 6.8 511 

(Continued...)
18 

Table 3. – Continued 

 Rwanda Nepal 

 % Number of women % Number of women 

Wealth quintile 

Lowest 21.0 1,146 19.9 1,490 

Second 23.0 1,255 21.3 1,594 

Middle 22.5 1,231 22.9 1,720 

Fourth 22.0 1,200 21.0 1,578 

Highest 11.5 629 14.9 1,120 

Number of living children 

5+ 30.4 1,660 12.0 904 

3-4 32.5 1,775 35.4 2,652 

1-2 33.8 1,844 45.0 3,374 

none 3.3 181 7.6 571 

Total 100.0 5,460 100.0 7,502 

1

 Categories in parentheses are the main religions in Nepal 

Table 4 describes background characteristics of the clusters included in the analysis sample, which 

includes 411 clusters in Rwanda and 194 clusters in Nepal all in rural areas. Most clusters in both 

countries are not far from a main road. In Rwanda 98 percent of the clusters are located within 5 km from 

a main road. Given such minimal variation in distance to a main road among the clusters, this variable is 

excluded from the multivariate analysis for Rwanda presented later. In Nepal overall access to main road 

is more difficult than in Rwanda, and 18 percent of the sampled clusters are located more than 5 km from 

a main road. 

Poor access in Nepal is also reflected in the distance from a large city. In Nepal 41 percent of the clusters 

are located more than 40 km from a city with at least 50,000 people, versus 11 percent in Rwanda. In both 

countries the average level of women’s education in the clusters is four years of schooling. 

Among measures of women’s empowerment, women’s level of participation in decision-making both on 

their own health care and on family planning is high in both countries (see Table 4). Spousal violence is 

more prevalent in Rwanda than in Nepal. On average, 46 percent of women in a cluster in Rwanda 

compared with 15 percent in Nepal reported physical or sexual spousal violence. 

For the community fertility norms, in Nepal women on average marry at age 17 compared with age 20 in 

Rwanda. Forty percent of the clusters studied in Rwanda and 38 percent in Nepal have a mean interval 

between marriage and first birth greater than the average among all clusters in the country. The average 

number of children desired among women in a cluster is higher in Rwanda, at 3.3 children, than in Nepal, 

at 2.2 children. 

Although in both countries, on average, more than 60 percent of women in a cluster have access to family 

planning messages, fewer women have access to family planning services. In Rwanda 39 percent of 

women in a cluster reported visiting a health facility for family planning or being visited by a family 

planning provider. In Nepal, this percentage is much lower, at 19 percent, possibly because many women 

may have been sterilized. 
19 

In terms of the number of family planning methods used by women, in both countries, on average, 

women within any given sampled cluster reported having used three or four modern contraceptive 

methods (3.7 in Rwanda, and 4.2 in Nepal). Looking at the distribution of the clusters (data not shown), 

we find that Rwandan women in about 90 percent of the clusters have access to three or four modern 

methods. Women in 20 percent of the clusters reported access to five or more methods but very few (4 

percent) reported six or seven methods. Women in Nepal have access to more methods. In 28 percent of 

the clusters included in the analysis in Nepal, women reported having access to five methods, and in 15 

percent they reported six or seven modern methods. 

Table 4. Description of community-level variables by percentage of clusters 

Community-level variables Rwanda (%) Nepal (%) 

Socioeconomic development 

Province/Development region1

Kigali city (Eastern) 2.4 24.7 

South (Central) 27.5 24.2 

West (Western) 25.6 17.0 

North (Mid-western) 18.5 19.6 

East (Far-western) 26.0 14.4 

Distance to a main road 

5km or less 98.3 72.0 

more than 5km 1.7 18.0 

Distance to a large city 

<10km 12.9 9.8 

10-19km 32.9 20.1 

20-29km 28.7 12.4 

30-39km 14.1 17.0 

40+ km 11.4 40.7 

Low population density 20.0 20.0 

Female educational attainment (mean) 4.0 3.9 

Women's empowerment 

Women's experience of spousal physical or sexual violence 45.9 14.5 

Women's participation in household decision-making 72.8 63.5 

women's participation in decision-making on family planning 87.8 86.8 

Fertility norms 

Average age at first marriage (mean) 20.5 17.3 

Longer marriage to first birth interval 40.4 37.6 

Average number of children desired by women (mean) 3.3 2.2 

Access to family planning information and services 

Number of modern contraceptive methods reported by women (mean) 3.7 4.2 

Percentage of women exposed to family planning messages 65.8 64.0 

Percentage of women who had contacts with family planning providers 39.1 19.4 

Total number of clusters 411 194 

1 Categories in parentheses are the development regions in Nepal 
20 

3.2 Contraceptive Prevalence and Method Mix 

The contraceptive prevalence rate (CPR) is similar among women studied in Rwanda and Nepal. Among 

currently married women in rural areas who do not want a child within two years, 47 percent in Rwanda 

and 46 percent in Nepal are currently using modern contraception (Table 5). The method mix however 

differs substantially between the two countries. In Rwanda among modern method users, injectables are 

the most popular method (Figure 3), at 60 percent. Other methods used include the pill and implants (15 

percent each), and male condom (6 percent). Use of permanent methods such as male and female 

sterilization is very low. 

In contrast, in Nepal long-acting and permanent methods are popular (Figure 4). Female sterilization and 

male sterilization together account for 56 percent of all modern method use. Other methods used include 

injectables (21 percent), the pill (9 percent), and male condom (8 percent). 

Figure 3. Percent distribution of women who currently use modern contraception, by type of 

method, Rwanda* 

1.5

1.3

6.0

14.7

59.6

15.4

1.5

Male sterilization/IUD/Diaphragm/SDM

LAM

Male condom

Implant

Injectables

Pill

Female sterilization

*This is the method mix among currently married women in rural areas who do not want a child within 

two years.
21 

Figure 4. Percent distribution of women who currently use modern contraception, by type of 

method, Nepal* 

2.9

8.1

2.7

21.2

9.0

19.2

37.0

IUD

Male condom

Implant

Injectables

Pill

Male sterilization

Female sterilization

*This is the method mix among currently married women in rural areas who do not want a child within 

two years.

3.3 Modern Contraceptive Use by Women’s Background Characteristics 

Table 5 presents the prevalence of modern contraceptive use by women’s characteristics, among currently 

married rural women who do not want a child within two years. In Nepal, 63 percent of women age 35-44 

and 53 percent of women age 45-49 use a modern method, reflecting the high levels of sterilization 

among women in Nepal. In Rwanda women’s education is associated with contraceptive use, with 59 

percent of women with secondary or higher education using contraception. In contrast, in Nepal the level 

of contraceptive use is highest among women with no education (53 percent) and lowest among women 

with secondary or higher education (37 percent). The popularity of sterilization among uneducated and 

less educated women in Nepal may contribute to this pattern. 

In both countries women who are not employed have the lowest levels of contraceptive use. Women in 

professional occupations in Rwanda and women in sales and service occupations in Nepal have the 

highest levels of contraceptive use. Differences in contraceptive use by religion are small in both 

countries, except that in Nepal women belonging to religions other than Hindu/Buddhist are least likely to 

use contraception. 

Overall, a positive relationship between wealth status and contraceptive use is evident in both countries. 

Also, in both countries women with three or four living children are most likely to be using modern 

methods. In Nepal, where son preference is the norm, more than half of the married women who have a 

son use a modern method compared with only 20 percent among women without a son. Not surprisingly, 

woman’s husband being away from home is also associated with lower levels of modern contraceptive 

use. 
22 

Table 5. Prevalence of modern contraceptive use among currently married women in rural areas 

who do not want a child within two years, according to background characteristics 

 Rwanda Nepal 

 % Number of women % Number of women 

Age 

15-24 43.7 825 22.5 1,924 

25-34 52.5 2,557 48.7 2,762 

35-44 50.1 1,518 62.5 2,181 

45-49 22.5 560 53.3 635 

Education level 

No education 39.2 1,168 53.2 3,801 

Primary 48.8 3,926 42.3 1,459 

Secondary or higher 58.8 366 37.4 2,241 

Occupation 

Unemployed 37.1 431 35.5 1,498 

Agri/manual 48.1 4,755 48.7 5,202 

Professional 51.5 257 41.4 205 

Sales and service 47.4 17 55.5 597 

Religion 

Catholic (Hindu) 50.8 2,356 47.5 6,360 

Christian (Buddhist) 44.5 2,939 42.7 631 

Muslim/other (Muslim/kirat/Chris/other) 50.1 164 36.6 511 

Wealth quintile 

Lowest 40.2 1,146 38.5 1,490 

Second 43.1 1,255 45.9 1,594 

Middle 50.3 1,231 47.5 1,720 

Fourth 52.1 1,200 49.2 1,578 

Highest 54.7 629 51.8 1,120 

Number of living children 

5+ 43.0 1,660 49.3 904 

3-4 52.7 1,775 61.4 2,652 

1-2 50.8 1,844 39.7 3,374 

None 1.8 181 11.3 571 

Having a son 

No 20.3 1,584 

Yes 53.4 5,918 

Husband's residence status 

Lives in house 57.4 4,965 

Lives elsewhere for less than one year 26.0 1,664 

Lives elsewhere for one year or more 22.2 872 

Total 47.4 5,460 46.4 7,502 
23 

3.4 Multilevel Analysis Results 

Tables 6 and 7 present the results of multilevel analyses for Rwanda (Table 6) and Nepal (Table 7). As 

described earlier, in each table, Models I-5 present results of the individual-level variables and the 

different community-level predictors. Model 6 shows the effect of all variables included in the analysis. 

The analysis sample shown in these tables is currently married women in rural areas who do not want (or 

are not sure if they want) a birth in the two years after the survey. Given the high rates of sterilization in 

Nepal, Table 7 presents an additional model (Model 7) which is based on a smaller sample restricted to 

women in the analysis sample who are not sterilized and whose husbands are not sterilized. 

In both tables, as we examine results presented across models, we look at the variation explained by 

different sets of variables, especially the contribution of cluster-level variables in explaining betweencluster variability in contraceptive use among sampled women. The intra-class correlation coefficient, 

rho, measures the proportion of variation in contraceptive use that is between clusters. The total variation 

can be partitioned into within-cluster variation and between-cluster variation. While individual-level 

variables explain within-cluster and between-cluster variation, cluster-level variables can explain only 

variation at the cluster level. As more cluster predictors are added to the model, rho is expected to move 

toward zero. 

Rwanda 

Individual and Household Characteristics 

Multilevel analysis results in Rwanda indicate that, among currently married rural women who do not 

want a child in the next two years, women’s age is negatively associated with modern contraceptive use, 

while women’s education and household socioeconomic status have a positive association. These 

relationships remain consistent across all models even after taking into account the role of contextual 

effects. Women age 35 and older are less likely than women age 15-24 to use modern contraceptive 

methods. At the same time, women with secondary or higher education have one-and-a-half times the 

odds of using contraception compared with uneducated women (Model 6). 

Likewise, women in the richest households are more likely to use modern methods compared with the 

poorest women. Women’s employment in agriculture or as manual labor is also positively associated with 

contraceptive use as compared to unemployed women. The odds of Catholics using modern contraception 

are also higher compared with other Christians. Compared with women with 5 or more children, women 

with no children or 1-2 children are less likely to use contraception. However, there is little difference 

between women who have 3-4 children or 5 or more children. A rho of 0.07 for Model 1 indicates that the 

between-cluster variation accounts for 7 percent of the total variation in modern contraceptive prevalence. 
Table 6. Results of multilevel modeling of modern contraceptive use among currently married rural women who do not want a child

within two years, Rwanda 

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 

OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI 

Individual and household variables Age (ref.=15-24) 

25-34 0.94 0.77 - 1.14 0.92 0.76 - 1.11 0.94 0.77 - 1.14 0.91 0.75 - 1.11 0.93 0.77 - 1.13 0.91 0.75 - 1.1135-44 0.76* 0.60 - 0.98 0.72** 0.56 - 0.92 0.77* 0.60 - 0.98 0.72** 0.56 - 0.92 0.75* 0.59 - 0.95 0.70** 0.55 - 0.9045-49 0.20*** 0.15 - 0.28 0.19*** 0.14 - 0.27 0.20*** 0.15 - 0.28 0.19*** 0.14 - 0.26 0.21*** 0.15 - 0.28 0.19*** 0.14 - 0.27

Education level (ref.=none) 

Primary 1.13 0.97 - 1.32 1.08 0.93 - 1.26 1.13 0.97 - 1.32 1.11 0.95 - 1.29 1.11 0.95 - 1.29 1.08 0.93 - 1.26Secondary or higher 1.54** 1.14 - 2.07 1.44* 1.07 - 1.93 1.55** 1.15 - 2.08 1.50** 1.12 - 2.02 1.51** 1.13 - 2.03 1.48** 1.10 - 1.99

Occupation (ref.=not employed) 

Agriculture/manual 1.44** 1.13 - 1.83 1.35* 1.06 - 1.71 1.43** 1.13 - 1.83 1.41** 1.11 - 1.79 1.36* 1.07 - 1.73 1.27* 1.01 - 1.60Professional 1.36 0.95 - 1.96 1.29 0.89 - 1.85 1.37 0.95 - 1.98 1.38 0.96 - 1.98 1.30 0.90 - 1.86 1.31 0.91 - 1.88Sales and service 1.14 0.39 - 3.32 1.09 0.38 - 3.14 1.15 0.40 - 3.35 1.15 0.40 - 3.30 1.08 0.37 - 3.14 1.13 0.40 - 3.19

Wealth (ref.=Poorest) 

Second 1.08 0.90 - 1.29 1.10 0.92 - 1.31 1.09 0.91 - 1.30 1.08 0.90 - 1.29 1.07 0.89 - 1.27 1.10 0.92 - 1.32Middle 1.52*** 1.26 - 1.82 1.53*** 1.27 - 1.83 1.51*** 1.26 - 1.82 1.49*** 1.25 - 1.79 1.48*** 1.24 - 1.78 1.50*** 1.25 - 1.80Fourth 1.65*** 1.37 - 2.00 1.65*** 1.36 - 1.99 1.66*** 1.38 - 2.01 1.64*** 1.36 - 1.97 1.57*** 1.30 - 1.89 1.61*** 1.33 - 1.94Highest 1.72*** 1.34 - 2.20 1.66*** 1.29 - 2.13 1.74*** 1.36 - 2.23 1.70*** 1.33 - 2.17 1.55*** 1.21 - 1.97 1.63*** 1.27 - 2.09

Religion (ref.=Catholic) 

Christian 0.78*** 0.68 - 0.88 0.81** 0.72 - 0.92 0.77*** 0.68 - 0.87 0.79*** 0.69 - 0.89 0.78*** 0.69 - 0.89 0.81*** 0.72 - 0.91Muslim/other 0.95 0.67 - 1.35 0.98 0.69 - 1.38 0.94 0.66 - 1.33 0.94 0.66 - 1.33 0.94 0.66 - 1.32 0.93 0.66 - 1.31

Number of living children (ref.=5+) 

3-4 1.06 0.89 - 1.25 1.03 0.87 - 1.22 1.06 0.90 - 1.25 1.03 0.87 - 1.21 1.05 0.89 - 1.24 1.01 0.86 - 1.201-2 0.86 0.71 - 1.05 0.83 0.68 - 1.01 0.86 0.71 - 1.05 0.80* 0.66 - 0.98 0.85 0.70 - 1.04 0.80* 0.66 - 0.97None 0.01*** 0.00 - 0.04 0.01*** 0.00 - 0.04 0.01*** 0.00 - 0.04 0.01*** 0.00 - 0.04 0.01*** 0.00 - 0.04 0.01*** 0.00 - 0.04

Husband's education (ref.=None) 

Primary 1.16 1.00 - 1.35 1.15 0.99 - 1.34 1.16 1.00 - 1.35 1.14 0.98 - 1.33 1.16 1.00 - 1.35 1.14 0.98 - 1.33Secondary or higher 1.06 0.82 - 1.37 1.04 0.80 - 1.35 1.06 0.82 - 1.38 1.03 0.80 - 1.34 1.07 0.83 - 1.39 1.05 0.81 - 1.36

(Continued...)

24 
Table 6. – Continued

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 

OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI 

Contextual variables Socioeconomic development Provinces (ref.=Kigali City) 

South 1.28 0.80 - 2.05 1.34 0.87 - 2.04West 0.70 0.43 - 1.13 0.84 0.55 - 1.30North 1.27 0.79 - 2.05 1.34 0.88 - 2.06East 1.11 0.68 - 1.81 1.16 0.75 - 1.79

Distance to large city (ref.=<10km) 

10-19km 0.98 0.77 - 1.25 1.05 0.85 - 1.3020-29km 1.15 0.89 - 1.48 1.20 0.96 - 1.5130-39km 1.16 0.86 - 1.57 1.16 0.88 - 1.5340+km 0.98 0.68 - 1.41 1.19 0.85 - 1.68

Low population density 0.67*** 0.54 - 0.84 0.71** 0.58- 0.86 Women's average years of education 1.08* 1.02 - 1.16 0.99 0.92 - 1.06Women's empowerment Women's participation in household decisionmaking on health care 0.79 0.52 - 1.19 0.81 0.57 - 1.17Women's participation in decision-making on family planning use 2.19** 1.26 - 3.78 2.34*** 1.46 - 3.75Women's experience of spousal violence 0.86 0.62 - 1.20 0.94 0.71 - 1.25Fertility norms Average ideal number of children 0.60*** 0.52 - 0.70 0.73*** 0.63 - 0.84Average age at first marriage 1.03 0.98 - 1.09 1.01 0.95 - 1.07Longer marriage to first birth interval 1.24** 1.07 - 1.44 1.17* 1.03 - 1.34

(Continued...)

25 
Table 6. – Continued

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 

OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI 

Access to family planning information and services Exposed to family planning messages 1.40 0.89 - 2.21 1.11 0.70 - 1.75Contact with family planning providers 1.69 0.95 - 2.99 1.61 0.95 - 2.73Number of family planning methods reported 1.35*** 1.26 - 1.45 1.32*** 1.24 - 1.41rho 0.070 0.045 0.066 0.052 0.042 0.012 *p<0.05 **p<0.01 *** p<0.001 

26 
27 

Contextual effects 

Among the contextual effects in Rwanda, the level of development in the community where women 

reside affects their use of contraception. Before incorporating other contextual effects, women in clusters 

with low population density are less likely than others to use modern methods, while women in clusters 

with a higher level of women’s education are more likely to do so (Model 2).While the population density 

remains significant, the effect of education disappears when all contextual factors are taken into 

consideration (Model 6). One key aspect defining women’s empowerment—the level of women’s ability 

to make decisions regarding family planning in the cluster (Model 3)—has a statistically positive 

association with contraceptive use. After controlling for all other individual and community-level 

characteristics, we find that residence in a community with a higher percentage of women making 

decisions on family planning is a key characteristic that remains statistically significant with a strong 

positive effect (Model 6). 

With regard to the influence of community fertility norms, while the mean age of women at first marriage 

in the community of residence in Rwanda does not influence contraceptive use among our sample of 

women, the mean number of children desired by women in the community does have an influence. 

Women studied who live in communities with a greater average number of children desired are less likely 

to use modern methods (Model 4). Moreover, residing in a community with a longer gap between 

marriage and first birth also increases the likelihood of contraceptive use for women in our sample. These 

relationships hold strong even after taking into account all other effects in Model 6. 

Model 5 examines the relationship between the community’s access to modern contraception, family 

planning messages, and contacts with family planning providers and women’s contraceptive use. In 

Rwanda contraceptive use is higher among women in communities with a greater contraceptive method 

mix compared with other communities. The role of the indicators of family planning information and 

contact with family planning providers is not statistically significant. 

Based on the rho for each model, we also measure the contributions of contextual factors to betweencluster variations in contraceptive use. In Model I, with only individual-level variables, about 7 percent of 

the total variation in contraceptive use is between clusters. This proportion decreases in Models 2-5 as 

various groups of contextual factors are added to the analysis. When all the contextual variables are 

included (Model 6), the between-cluster variation only accounts for 1 percent of the total variation in 

contraceptive use. This means about 83 percent (1-.012/0.7) of the unobserved heterogeneity in 

contraceptive use at the cluster level is explained by the contextual variables analyzed. The reduction in 

rho is greater in Model 2 and Model 5, which indicates the variables on socioeconomic development and 

access to family planning information and services explain more between-cluster variation than the other 

two groups of variables (Models 3 and 4). 

Overall in Rwanda, our analysis finds that contextual effects influence use of contraception among 

women in the study sample. As indicated above, specific characteristics of communities where Rwandan 

women reside affect their use of modern methods. The decline in rho in successive models demonstrates 

the increase in between-cluster variation that is explained by the addition of these variables to the 

regression models. Relatively, socioeconomic development and access to family planning services play a 

bigger role than the community gender and fertility norms. 
28 

Nepal 

Individual and household characteristics 

As in Rwanda, the multilevel analysis results for Nepal in Table 7 indicate that the odds of modern 

contraceptive use vary according to women’s age (Model 1 and Model 6). The odds of using modern 

contraceptives are lowest among women age 15-24 and are highest among women age 35-44. The picture 

is somewhat different when the analysis is restricted to women who did not report using male or female 

sterilization (Model 7). We find that women age 45-49 are the least likely to use modern contraception, 

while women age 25-34 are more likely to use contraception than women age 15-24. The uptake of 

contraceptive use among women age 35-44 is not statistically different from women age 15-24. 

Model 6 also indicates that the effect of women’s education is negative, while the role of household 

wealth is positive. The odds of using a modern method are closely related to the number of living children 

a woman has. Relative to women with 5 or more children, women with 1-2 or 3-4 living children are 

more likely to use a modern method; in fact women with 3-4 living children have almost twice the odds 

of using modern contraception compared with women with 5 or more children (Model 6). Women with 

no living children are least likely to use contraception. But when the analysis is based on the sample of 

women without male or female sterilization (Model 7), there is no difference by education or household 

wealth. Also, the difference between women with 1-2, 3-4, and 5 or more children is eliminated. These 

findings are possibly because of the popularity of sterilization for contraception among less educated 

women and women in households of lower socioeconomic status. 

Occupation has some effect on contraceptive use in Nepal; women who are not employed are least likely 

to use modern methods. The odds of modern method use are greatest among women in agricultural or 

manual occupations. The odds also are greater among women belonging to the dominant religion, 

Hinduism, compared with other religions. 

While husband’s education has little effect on women’s use of contraception, with Nepal’s high levels of 

spousal separation the fact that husbands live at home matters. The odds of contraceptive use among 

women with husbands who have lived away from home for more than one year are only 17 percent the 

odds of contraceptive use among women with husbands who live at home. Son preference is also 

reflected in the study results. Having a son increases the likelihood of contraceptive use considerably 

(odds ratio is 2.6 in Model 6). Among women who did not report sterilization, the effect is weaker but the 

odds of using a modern method are still 1.7 times those of women without a son (Model 7). 
Table 7. Results of multilevel modeling of modern contraceptive use among currently married rural women who do not want a child

within two years, Nepal 

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 

OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI 

Individual and household variables Age (ref.=15-24) 

25-34 1.92*** 1.62 - 2.26 1.97*** 1.66 - 2.32 1.93*** 1.63 - 2.28 1.93*** 1.63 - 2.28 1.92*** 1.62 - 2.26 2.00*** 1.70 - 2.37 1.26* 1.04 - 1.54 35-44 2.67*** 2.19 - 3.25 2.78*** 2.28 - 3.38 2.71*** 2.23 - 3.31 2.66*** 2.18 - 3.24 2.67*** 2.19 - 3.25 2.80*** 2.30 - 3.41 1.26 0.98 - 1.62 

45-49 1.42** 1.09 - 1.83 1.50** 1.15 - 1.94 1.44** 1.11 - 1.87 1.41** 1.09 - 1.83 1.41** 1.09 - 1.83 1.51** 1.17 - 1.96 0.36*** 0.25 - 0.52 

Education level (ref.=none) 

Primary 0.81* 0.69 - 0.95 0.83* 0.71 - 0.98 0.82* 0.70 - 0.96 0.80** 0.68 - 0.94 0.81** 0.69 - 0.95 0.81* 0.69 - 0.95 0.84 0.68 - 1.04 

Secondary or higher 0.75** 0.62 - 0.89 0.78** 0.65 - 0.93 0.75** 0.63 - 0.90 0.74*** 0.62 - 0.88 0.74*** 0.62 - 0.88 0.76** 0.64 - 0.91 1.12 0.90 - 1.40 

Occupation (ref.=not employed) 

Agriculture/manual 1.90*** 1.61 - 2.24 1.93*** 1.64 - 2.28 1.91*** 1.62 - 2.25 1.83*** 1.55 - 2.16 1.87*** 1.58 - 2.20 1.82*** 1.55 - 2.15 1.90*** 1.53 - 2.35 

Professional 1.31 0.90 - 1.89 1.30 0.90 - 1.88 1.32 0.91 - 1.90 1.29 0.90 - 1.87 1.30 0.90 - 1.89 1.33 0.92 - 1.92 1.56* 1.02 - 2.39 

Sales and service 1.65*** 1.30 - 2.09 1.69*** 1.33 - 2.14 1.65*** 1.30 - 2.09 1.64*** 1.30 - 2.08 1.64*** 1.29 - 2.07 1.68*** 1.33 - 2.13 1.66*** 1.24 - 2.21 

Wealth (ref.=Poorest) 

Second 1.43*** 1.19 - 1.72 1.38*** 1.15 - 1.67 1.46*** 1.21 - 1.76 1.43*** 1.19 - 1.72 1.43*** 1.19 - 1.72 1.36** 1.13 - 1.63 1.05 0.84 - 1.32 Middle 1.75*** 1.43 - 2.15 1.65*** 1.34 - 2.02 1.83*** 1.49 - 2.24 1.77*** 1.45 - 2.16 1.76*** 1.44 - 2.16 1.69*** 1.39 - 2.07 1.14 0.89 - 1.46 Fourth 2.08*** 1.66 - 2.61 1.86*** 1.47 - 2.35 2.18*** 1.73 - 2.74 2.08*** 1.66 - 2.61 2.08*** 1.66 - 2.61 1.89*** 1.50 - 2.38 1.16 0.87 - 1.54 

Highest 2.18*** 1.66 - 2.88 1.82*** 1.36 - 2.43 2.31*** 1.74 - 3.06 2.24*** 1.70 - 2.95 2.19*** 1.65 - 2.89 1.96*** 1.47 - 2.60 1.38 0.98 - 1.94 

Religion (ref.=Hindu) 

Buddhist 0.82 0.65 - 1.03 0.79* 0.63 - 1.00 0.82 0.66 - 1.04 0.85 0.68 - 1.07 0.81 0.65 - 1.02 0.79* 0.63 - 0.99 1.26 0.98 - 1.64 

Muslim/kirat/Chris/other 0.68** 0.53 - 0.88 0.72** 0.56 - 0.92 0.67** 0.52 - 0.86 0.75* 0.58 - 0.96 0.69** 0.53 - 0.88 0.74* 0.58 - 0.94 1.55** 1.16 - 2.09 

Number of living children (ref.=5+) 

3-4 1.90*** 1.59 - 2.28 1.93*** 1.61 - 2.31 1.92*** 1.60 - 2.30 1.90*** 1.58 - 2.28 1.90*** 1.59 - 2.28 1.94*** 1.62 - 2.32 1.00 0.79 - 1.28 

1-2 1.26* 1.03 - 1.55 1.28* 1.04 - 1.57 1.28* 1.04 - 1.57 1.25* 1.02 - 1.55 1.26* 1.02 - 1.55 1.27* 1.03 - 1.56 0.93 0.70 - 1.22None 0.58** 0.40 - 0.86 0.59** 0.40 - 0.87 0.60** 0.40 - 0.88 0.59** 0.40 - 0.87 0.58** 0.40 - 0.86 0.59** 0.40 - 0.88 0.28*** 0.18 - 0.44 

Husband's education (ref.=None) 

Primary 0.87 0.74 - 1.02 0.90 0.76 - 1.06 0.87 0.74 - 1.03 0.85 0.72 - 1.00 0.86 0.73 - 1.01 0.87 0.73 - 1.02 0.85 0.68 - 1.06 

Secondary or higher 1.11 0.94 - 1.32 1.17 0.99 - 1.39 1.11 0.94 - 1.32 1.08 0.91 - 1.28 1.09 0.92 - 1.30 1.14 0.96 - 1.35 1.14 0.91 - 1.43 

Husband 's residency status (ref.=living in home) 

Away for <1 year 0.25*** 0.22 - 0.29 0.25*** 0.22 - 0.29 0.25*** 0.22 - 0.29 0.25*** 0.21 - 0.29 0.25*** 0.22 - 0.29 0.26*** 0.22 - 0.30 0.17*** 0.14 - 0.20 

Away for 1+ year 0.16*** 0.13 - 0.20 0.17*** 0.14 - 0.20 0.16*** 0.14 - 0.20 0.17*** 0.14 - 0.20 0.17*** 0.14 - 0.20 0.17*** 0.14 - 0.21 0.03*** 0.02 - 0.04 

 Having a son 2.63*** 2.20 - 3.14 2.61*** 2.18 - 3.12 2.62*** 2.19 - 3.13 2.62*** 2.19 - 3.13 2.62*** 2.19 - 3.14 2.59*** 2.17 - 3.10 1.67*** 1.37 - 2.03 

(Continued...)

29 
Table 7. – Continued

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 

OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI 

Contextual variables Socioeconomic development Development region (ref.= Eastern) 

Central 2.75*** 2.14- 3.53 1.91*** 1.50 - 2.44 0.97 0.74 - 1.28 Western 1.29* 1.00 - 1.67 0.85 0.66 - 1.10 0.58*** 0.44 - 0.77 Mid-western 1.67*** 1.24 - 2.25 0.93 0.70 - 1.26 0.73 0.54 - 1.02 Far-Western 2.04*** 1.47 - 2.86 0.94 0.67 - 1.33 0.84 0.59 - 1.23 

Distance to main road (ref.= <5km) 

5+ km 1.08 0.85 - 1.38 1.03 0.83 - 1.28 1.29 0.93 - 1.61 

Distance to large city (ref.=<10km) 

10-19km 0.97 0.73 - 1.29 0.88 0.769 - 1.13 0.64** 0.49 - 0.83 

20-29km 1.15 0.82 - 1.62 1.11 0.863- 1.49 0.57** 0.41 - 0.78 30-39km 0.67* 0.48 - 0.94 0.61** 0.45 - 0.81 0.44*** 0.32 - 0.61 40+km 0.64** 0.47 - 0.88 0.63** 0.48-0.83 0.38*** 0.28 - 0.52 

Low population density 1.29 0.94 - 1.77 1.25 0.95 - 1.66 1.24 0.92 - 1.69 Women's average years of education 1.07* 1.00 - 1.13 1.04 0.96 - 1.12 1.01 0.92 - 1.10 Women's empowerment Women's participation in household decision-making on health care 0.76 0.40 - 1.45 0.30*** 0.16 - 0.57 0.24*** 0.12 - 0.49 Women's participation in decisionmaking on family planning use 2.40 0.99 - 5.84 2.12* 1.05 - 4.25 3.29** 1.50 - 7.22 Women's experience of spousal violence 2.14 0.94 - 4.86 1.47 0.75 - 2.90 1.02 0.49 - 2.10 Fertility norms Average ideal number of children 0.45*** 0.32 - 0.61 0.34*** 0.23 - 0.51 0.47** 0.30 - 0.74 Average age at first marriage 0.74*** 0.67 - 0.82 0.78*** 0.71 - 0.87 0.90 0.81 - 1.01 Longer marriage to first birth interval 0.81 0.65 - 1.02 0.98 0.80 - 1.19 1.06 0.85 - 1.32 

Access to family planning information and services 

Exposed to family planning message 0.66 0.41 - 1.07 0.64 0.38 - 1.08 2.13* 1.19 - 3.83 

Contact with family planning providers 1.19 0.46 - 3.10 2.42* 1.08 - 5.41 6.33*** 2.63 - 15.24 Number of family planning methods reported 1.17*** 1.08 - 1.27 1.15*** 1.07 - 1.23 1.28*** 1.18 - 1.38 rho 0.102 0.064 0.095 0.08 0.093 0.033 0.019 

*p<0.05 **p<0.01 *** p<0.001 

The analysis sample for model 1-6 is currently married women in rural who do not want (or are not sure if they want) a child within 2 years In model 7, women who currently use male and female sterilization are excluded 

30 
31 

Contextual effects 

Selected contextual factors show an influence on women’s use of modern methods in Nepal. The level of 

development of the community where women reside in Nepal has a bearing on their contraceptive use 

shown in both Model 2 and Model 6. Controlling for all variables in Model 6, the odds of using 

contraception are higher among women in the Central region compared with the Eastern region. This is in 

line with the patterns reported among all women in the 2011 Nepal DHS report that indicate that the use 

of a modern method is highest in the Central region, a region with the highest socioeconomic status in the 

country. Modern method use among the sampled women is lowest in the Eastern region in Nepal, where 

socioeconomic status of the population is relatively lower. While residence in a cluster with a high level 

of women’s education is significant in Model 1, its role is eliminated when we take into account the role 

of all individual and community-level characteristics in Model 6. Unlike the case of Rwanda, population 

density has little role to play in use of modern contraception, but proximity to a large city is relevant 

(Model 6). Removing all women who use sterilization from the analysis sample, the main indicator that 

remains relevant is distance from the city (Model 7); the odds of contraceptive use steadily diminish with 

an increase in proximity to a large city. 

The two key aspects defining women’s empowerment that influence contraceptive use in Nepal among 

the women sampled are the community level of women’s decision-making about health and family 

planning (Model 6). After controlling for all other characteristics, results indicate that residence in a 

community with a high level of women’s decision-making on health care has a negative effect on 

contraceptive use among the study sample, while the effect of residence in a cluster with a higher 

percentage of women’s decision-making on family planning is positive. Similar effects are evident among 

women who reported a non-sterilization method (Model 7). 

With regard to fertility norms in Nepal, a strong negative relationship between the mean number of 

children desired among women in the community and contraceptive use among the study sample is 

evident (Model 4). Living in a community where there is a greater desire to have more children is 

associated with lower use of modern contraceptive methods. A similar negative relationship with 

community mean age at first marriage and contraceptive use among women in the study sample is also 

observed. In communities where the mean age at first marriage is higher, the likelihood of women using 

modern contraception tends to be lower. Both relationships remain consistent even after controlling for all 

individual and contextual characteristics (Model 6). However, when we restrict the analysis to women 

who reported that they did not use sterilization for contraception, only the role of desired number of 

children remains significant (Model 7). 

In Nepal, contraceptive use is higher among women in communities with a greater method mix of 

contraceptives compared with women in other communities (Models 5-7). Community access to family 

planning services measured in terms of contact with family planning providers also bears a positive 

relationship to use of modern methods in the complete models (Models 6 and 7). Among women in the 

study sample who do not use a permanent contraceptive method, such as male or female sterilization, the 

role of community access to family planning services is significantly higher (6.33). Furthermore, 

residence in communities with greater exposure to family planning messages also has a positive effect on 

contraceptive use in Model 7. 

Overall, after taking into account contextual variables, the between-cluster variation drops in all 

successive models. Controlling for all contextual effects brings the rho down to 3 percent in Model 6, 

which means 69% (1-.032/ .102) of the variation between clusters is explained by the contextual 

variables. Comparing Models 2-5, socioeconomic development variables and access to family planning 

services variables explain more cluster-variation than the other two groups of variables. 

33 

4 Discussion 

This study analyzed contextual determinants of modern contraceptive use in two culturally different 

settings, Rwanda and Nepal, with a primary interest in four spheres of community-level influences—

socioeconomic development, women’s empowerment, fertility norms, and access to family planning 

information and services. 

The focus of the study was on a subset of women of particular interest to family planning programs: rural 

women who do not wish to have a child within the next two years. We limited the study to rural women 

because they may face unique barriers to contraceptive use, such as geographic remoteness and lack of 

contraceptive options. The study was also limited to women who do not wish to have a child right away 

because these are women who need effective contraception and who are of great interest to family 

planning programs. 

In both Nepal and Rwanda our study found that socioeconomic development significantly affects use of 

modern contraception among rural women who do not want to have a child in the next two years. Among 

the measures of the community’s socioeconomic development, in Nepal we found evidence that the 

distance from a large city, a measure of geographic remoteness, is inversely associated with women’s 

contraceptive use. This pattern is particularly strong among rural women who are not using female or 

male sterilization and who do not want a child in the next two years, even after adjusting for other 

individual and contextual factors (Model 7 in Table 7). This finding highlights the particular challenges of 

Nepal’s mountainous topography and points to the need for family planning programs to continue to seek 

new ways to make contraception available and accessible in the most remote parts of the country. 

Similarly, in both countries community-level indicators of women’s access to family planning 

information and services are associated with contraceptive use among women in the study sample. In both 

countries women living in communities with better access to modern contraceptive methods (measured by 

the total number of methods reported by women in the cluster) are more likely to use modern methods of 

contraception. This finding is in line with what the literature has shown—that easy access to a wide range 

of contraceptive methods results in a more balanced method mix and higher levels of contraceptive 

prevalence (Roser, 2002). A study based on the linked DHS and SPA data in four African countries 

(Rwanda, Kenya, Uganda, and Tanzania) found that regional variation in contraceptive use is 

significantly associated with regional differences in family planning service availability and the service 

environment at health facilities (Wang et al., 2012). 

Contact with family planning providers is important for women to receive family planning information 

and counseling about adopting a contraceptive method. We found that the community-level contact with 

family planning providers is significantly associated with modern contraceptive use in Nepal, but not in 

Rwanda. 

In Nepal the level of contact with family planning providers is low; in an average cluster less than 20 

percent of the women in the cluster visited or were visited by a family planning provider in the last 12 

months. This is partially due to the common use of female sterilization and male sterilization, which 

typically do not require women to visit health facilities. Despite the low level of contact with family 

planning providers in the communities in Nepal, such contact is strongly associated with use of modern 

contraception. The effect is even stronger for non-sterilization users. In Nepal female community health 

workers and reproductive health volunteers are the two types of health providers visiting women in the 

field. The findings from the analysis in Nepal highlight the importance of contact with family planning 

providers for women in rural Nepal to adopt reversible modern methods. 
34 

Women’s empowerment is usually believed to be associated with their ability and behavior to seek health 

services. Among the indicators of women’s empowerment, community norms about women’s 

involvement in family planning decisions appear to have an important positive effect on individual 

women’s contraceptive use, in both Rwanda and Nepal. Enabling women to make decisions about family 

planning and contraceptive use is critical to the use of contraception. 

We also found evidence that community-level fertility norms are important determinants of women’s 

decision to use contraception. Previous research has shown that the number of children desired by women 

is associated with their contraceptive use. Our analysis shows that the number of children desired among 

others in the community also affects use of contraception among women in the study sample. In both 

countries studied, women living in communities where most people want to have more children are 

significantly less likely to be currently using modern contraception. Family planning programs in Nepal 

and Rwanda both have sought to encourage people to have small families, and these efforts have shown 

an impact. In Rwanda in particular, women’s desired number of children has decreased substantially over 

the past decade, from 5.0 in 2000 to 3.6 children in the 2010 DHS. 

Given the methodological challenges to analyzing community-level factors, it is worth commenting on 

the analysis used in this study, which is based on data from the most recent DHS surveys linked to spatial 

data from a variety of sources in Rwanda and Nepal. Aggregating responses of individuals living in the 

same area is one way to obtain measurements for small geographic units such as communities. Since DHS 

surveys randomly select clusters and then households within the selected clusters, and finally all women 

age 15-49 in selected households, the aggregation of indicators for individuals in the cluster acts as a good 

proxy for the cluster’s characteristics. For example, it is fairly safe to say that a community has an 

educated population if the average years of schooling of the interviewed individuals in that community 

are high. In this analysis, aggregated cluster-level indicators provide meaningful information regarding 

the context in which the surveyed women live. We also took advantage of the availability of spatial 

variables from a variety of sources and linked them to the DHS cluster locations with GIS software. 

Nevertheless, limitations associated with using some proxy variables in the analysis need to be 

acknowledged. For example, the community access to modern contraception is measured by the total 

number of modern methods reported by women in the cluster. It is a proxy measurement based on an 

assumption that a specific contraceptive method is accessible to the cluster when at least one woman in 

that cluster reported use of the method. Access to contraceptive methods would be overestimated if some 

women obtained certain methods through special channels that are not available to the majority of 

women. Considering our focus on the clusters in rural areas, this may not happen that often. Access could 

be underestimated if some methods are available to the community but are not used by any women, for 

one reason or another, and therefore not reported. 

Another contextual variable—the percentage of women in the cluster who visited a health facility for 

family planning or were visited by family planning providers in the last 12 months—may not precisely 

measure access to services, because method choice could affect women’s visits to health facilities. 

Women who use long-term method (IUDs, implants, and sterilization) need less-frequent visits to 

providers compared with women who use short-term methods (pills, injectables, and condoms). The 

effect of access would be underestimated if many women rely on long-term methods, which is the case in 

Nepal where more than half the modern contraceptive users reported using long-term methods. Indeed, 

after we exclude the women who reported female or male sterilization as their method (Model 7), the 

effect of this variable becomes stronger, as reflected by an increase in the odds ratio from 2.4 to 6.3. We 

expect the underestimation to be minimal in Rwanda, where women predominantly reported using shortterm contraceptive methods. 
35 

Overall, results from this study indicate the relevance for family planning programs of the community 

context in which women live. The demand for family planning services is influenced not only by 

women’s individual and household socioeconomic characteristics, but also by the community’s 

socioeconomic development, its access and exposure to family planning, the gender norms of local 

community members regarding decision-making for family planning and health issues, and community 

norms regarding marriage and childbirth. As this study has shown, based on an analysis of Rwanda and 

Nepal, the role of these contextual characteristics also is not necessarily consistent across countries. In 

general, among the factors we explored, those relevant to socioeconomic development and access to 

family planning services seem to play a bigger role in affecting women’s contraceptive behaviors than do 

community gender and fertility norms. Furthermore, in countries such as Nepal with high levels of male 

migration and son preference, there is a greater need for family planning programs to take into account 

the nature of the demand for contraceptive services among women, based on their circumstances. 

 

37 

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