DHS ANALYTICAL 

STUDIES 45

Health Insurance Coverage and Its 

Impact on Maternal Health Care

Utilization in Low- and Middle-Income

Countries

SEPTEMBER 2014

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

Wenjuan Wang, Gheda Temsah, and Lindsay Mallick.
 
DHS Analytical Studies No. 45 

Health Insurance Coverage and Its Impact 

on Maternal Health Care Utilization 

in Low- and Middle-Income Countries 

Wenjuan Wang 

Gheda Temsah 

Lindsay Mallick 

ICF International 

Rockville, Maryland, USA 

September 2014 

Corresponding author: Wenjuan Wang, International Health and Development, ICF International, 530 

Gaither Road, Suite 500, Rockville, Maryland, USA; phone: 301–572–0398; fax: 301–572–0950; 

email: wenjuan.wang@icfi.com 
Acknowledgment: The authors would like to thank Ha Nguyen for her review and invaluable comments. 

Editor: Bryant Robey 

Document Production: Yuan Cheng 

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

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

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

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

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

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 301-

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

Recommended citation: 

Wang, Wenjuan, Gheda Temsah, and Lindsay Mallick. 2014. Health Insurance Coverage and Its Impact 

on Maternal Health Care Utilization in Low- and Middle-Income Countries. DHS Analytical Studies No. 

45. Rockville, Maryland, USA: ICF International. 
iii 

Contents 

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

Figures ......................................................................................................................................................... vi 

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

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

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

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

1.1. Major Health Insurance Schemes in LMICs .................................................................................. 1 

1.2. Impact of Health Insurance ........................................................................................................... 2 

2. Data and Methods ................................................................................................................................... 5 

2.1. Data Used in the Analysis .............................................................................................................. 5 

2.2. Definitions of Variables ................................................................................................................. 5 

2.3. Statistical Methods ......................................................................................................................... 7 

3. Results .................................................................................................................................................... 11 

3.1. Health Insurance Coverage, Types, and Differentials ................................................................. 11 

3.2. Propensity Score Estimation and the Quality of Matching .......................................................... 17 

3.3. Effects of Health Insurance Using Propensity Score Matching (PSM) ....................................... 21 

4. Discussion and Conclusion ................................................................................................................... 25 

References .................................................................................................................................................. 29 

Appendix .................................................................................................................................................... 35 

v 

Tables 

Table 1. Percentage of women and men covered by specific types of health insurance in selected 

countries ....................................................................................................................................... 13

Table 2. Percentage of women with health insurance coverage, according to background 

characteristics ............................................................................................................................... 15

Table 3. Percentage of men with health insurance coverage, according to background characteristics ..... 16

Table 4. Propensity score matching performance: results of the mean and median absolute bias, 

pseudo-R2

 and Likelihood ratio (LR) tests ................................................................................... 19

Table 5. Number of cases off the common support in propensity score matching ..................................... 21

Table 6. The average treatment effect on the treated (ATT) of health insurance on utilization of 

selected maternal health services .................................................................................................. 22

Appendix Table A. Characteristics of health insurance schemes in eight study countries ........................ 35

Appendix Table B. Estimates of the (logit) propensity score models, Albania ......................................... 38

Appendix Table C. Estimates of the (logit) propensity score models, Burundi ........................................ 39

Appendix Table D. Estimates of the (logit) propensity score models, Cambodia ..................................... 40

Appendix Table E. Estimates of the (logit) propensity score models, Gabon ........................................... 41

Appendix Table F. Estimates of the (logit) propensity score models, Ghana ........................................... 42

Appendix Table G. Estimates of the (logit) propensity score models, Indonesia ...................................... 43

Appendix Table H. Estimates of the (logit) propensity score models, Namibia ........................................ 44

Appendix Table I. Estimates of the (logit) propensity score models, Rwanda ........................................ 45

Appendix Table J. Mean biases of covariates before and after matching, Albania .................................. 46

Appendix Table K. Mean biases of covariates before and after matching, Burundi ................................. 48

Appendix Table L. Mean biases of covariates before and after matching, Cambodia .............................. 50

Appendix Table M. Mean biases of covariates before and after matching, Gabon .................................... 52

Appendix Table N. Mean biases of covariates before and after matching, Ghana .................................... 55

Appendix Table O. Mean biases of covariates before and after matching, Indonesia ............................... 58

Appendix Table P. Mean biases of covariates before and after matching, Namibia ................................. 61

Appendix Table Q. Mean biases of covariates before and after matching, Rwanda ................................. 64
vi 

Figures 

Figure 1. Percentage of women and men covered by health insurance in Africa ....................................... 11

Figure 2. Percentage of women and men covered by health insurance in Asia .......................................... 12

 
vii 

Preface 

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

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

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

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

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

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

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

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

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

U.S. Agency for International Development. 

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

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

Sunita Kishor 

Director, The DHS Program 

ix 

Abstract 

This study examined levels of health insurance coverage in 30 low- and middle-income countries (LMICs), 

using nationally representative data from the Demographic and Health Surveys (DHS). In eight countries 

with health insurance coverage exceeding 10 percent, we used propensity score matching and estimated the 

impact of health insurance status on the use of antenatal care and facility-based delivery care. 

Health insurance coverage rates were less than 5 percent in most countries. In a few countries (Rwanda, 

Gabon, Ghana, and Indonesia), more than one-third of interviewed women and men reported coverage of 

health insurance, with the highest rate found in Rwanda. Educational attainment was associated with a 

higher likelihood of enrolling in health insurance. Pro-wealthy disparities in health insurance coverage 

existed in the majority of countries. In Cambodia and Gabon, however, poor women were more likely than 

the rich to be covered by health insurance, suggesting that in these countries policies focusing on providing 

insurance for the poor have been effective. 

Our analysis found significant positive effects of health insurance coverage on at least one measure of 

maternal health care use in seven of the eight countries evaluated. Indonesia stands out for the most 

systematic effect of health insurance across all measures, followed by Cambodia, Rwanda, and Ghana. The 

positive impact of health insurance appeared more consistent on the use of facility-based delivery than use 

of antenatal care. The analysis provides clear evidence that health insurance has contributed to the increased 

use of maternal health care services. 

Keywords: health insurance, maternal healthcare, impact evaluation, low- and middle-income countries 

xi 

Executive Summary 

With health insurance on the rise in low- and middle-income countries (LMICs), a growing body of research 

literature documents the impact of health insurance on access and use of general health care. However, 

there is limited empirical evidence on whether health insurance coverage has contributed to the improved 

use of maternal health services. Using nationally representative data from the Demographic and Health 

Surveys (DHS), this report assessed levels of health insurance coverage in 30 LMICs and examined the 

impact of health insurance status on use of maternal health care use in eight countries spanning sub-Saharan 

Africa (Burundi, Gabon, Ghana, Namibia, and Rwanda), West Asia (Albania), and South and Southeast 

Asia (Cambodia and Indonesia). 

Methods 

Data on all interviewed women age 15-49 and men age 15-59 were used to describe levels of health 

insurance coverage for all 30 countries based on the most recent DHS survey. For evaluating the effects of 

health insurance on use of maternal health services, we focused on women who reported a live birth in the 

five years preceding the survey in eight countries where the health insurance coverage rate exceeded 10 

percent. 

Use of maternal health services was measured by four indicators: making at least one antenatal care visit; 

making four or more antenatal care visits; initiating antenatal care within the first trimester; and giving birth 

in a health facility. The main independent variable of interest was a dichotomous measure of health 

insurance coverage. We evaluated the impact of health insurance on antenatal care and facility-based 

delivery care using propensity score matching to determine the differences in care seeking behavior that 

can be attributable to health insurance. 

Results 

Levels and differentials of health insurance coverage. Most study countries had fairly low levels of 

coverage—below 5 percent. In a few countries (Rwanda, Gabon, Ghana, and Indonesia), more than onethird of interviewed women and men reported coverage of health insurance, with the highest rate found in 

Rwanda, at 71 percent for women and 67 percent for men. In all 30 countries the gender gap in health 

insurance coverage favored men, with the exceptions of Cambodia, Gabon, Ghana, and Rwanda. The 

gender gap was small in magnitude given low coverage rates among both women and men. 

In most countries educational attainment was associated with a greater likelihood of participating in health 

insurance even after adjusting for other covariates. Our results also indicated that the education of the head 

of the household matters, in addition to the individual’s level of education. Household wealth status was 

another important determinant of participating in health insurance. Disparities in health insurance coverage 

that favor the rich were evident in five countries. In Cambodia and Gabon, however, poor women were 

more likely to be covered by health insurance than the rich, suggesting that policies targeting the poor have 

been effective. 

Effects of health insurance on use of maternal health care. After propensity score matching, health 

insurance status was significantly associated with an increased likelihood of making at least one antenatal 

care visit in Indonesia and Rwanda. Among women who reported at least one antenatal visit, the raw 

differences between insured and uninsured women in the prevalence of four or more antenatal care visits 

ranged from 4 to 21 percentage points and were statistically significant in all countries. However, after 

matching on covariates that could potentially introduce bias, the positive effect of health insurance coverage 

only remained in Ghana and Indonesia. Health insurance coverage contributed to an increase of 8 
xii 

percentage points in access to four or more antenatal care visits in Ghana and an increase of 3 percentage 

points in Indonesia. 

Concerning the timing of the first antenatal care visit, in the adjusted effect health insurance coverage was 

found to increase the use of antenatal care within the first trimester of pregnancy in Namibia, Burundi, and 

Indonesia by 15, 8, and 2 percentage points, respectively. 

In all study countries at least one-half of women delivered their most recent birth in a healthcare facility. 

After matching, the effect of health insurance on delivery in a healthcare facility was positive and 

statistically significant in four of the eight countries—Cambodia, Ghana, Indonesia, and Rwanda. In these 

countries, health insurance coverage contributed to an increase of 5-11 percentage points in the receipt of 

facility-based delivery care. In Gabon, however, health insurance status had a significant negative effect on 

the use of facility-based delivery care. 

In summary, our impact evaluation found statistically significant positive effects of health insurance 

coverage on at least one measure of maternal health care use in seven of the eight countries evaluated. 

Indonesia stands out for the most systematic effect of health insurance across all measures, followed by 

Cambodia, Rwanda, and Ghana. The positive impact of health insurance appeared more consistent on the 

use of facility-based delivery than on antenatal care services. 

Conclusions 

Health insurance programs in LMICs are still in the early stages. Despite countries’ efforts in targeting the 

poor by reducing or removing premiums of health insurance, disparities that favor the more affluent are 

evident in most countries studied. Health insurance schemes in Cambodia and Gabon are effective in 

increasing coverage among the poor. Overall, there is clear evidence that health insurance has contributed 

to the increased use of maternal health care. 
1 

1. Introduction 

With health insurance on the rise in low- and middle-income countries (LMICs), a growing body of 

literature documents the impact of health insurance on access and use of health care, financial protection, 

and health status in these countries (Chen et al. 2003; Dixon et al. 2014; Dong 2012; El-Shazly et al. 2000; 

Escobar et al. 2010; Hong et al. 2011; Jütting 2005; Kozhimannil et al. 2009; Mensah et al. 2010; Smith 

and Sulzbach 2008; Wang et al. 2009). While a number of rigorous studies have evaluated the impact of 

health insurance on the use of general health care (i.e., outpatient and inpatient care) (Giedion et al. 2013), 

there is limited empirical evidence of its impact on the use of maternal health care. In the context of global 

maternal and child health priorities, as echoed by the 1987 Safe Motherhood Initiative, 1994 International 

Conference on Population and Development, 1995 Fourth World Conference for Women, and the 

Millennium Development Goals (AbouZahr 2003), there is an increased need to evaluate whether health 

insurance has contributed to improved levels of use of maternal health care services. 

Using nationally representative data from the Demographic and Health Surveys (DHS), this report assesses 

levels of health insurance coverage in 30 LMICs and examines the impact of health insurance status on use 

of maternal health care in eight countries spanning sub-Saharan Africa (Burundi, Gabon, Ghana, Namibia, 

and Rwanda), West Asia (Albania), and South and Southeast Asia (Cambodia and Indonesia). We evaluate 

the impact of health insurance on antenatal care and facility-based delivery care using propensity score 

matching to determine the differences in care seeking behavior that can be attributable to health insurance. 

The results of this report provide up-to-date information on levels of health insurance coverage in LMICs. 

The findings help to expand understanding of the effect of health insurance on the use of maternal health 

services in a range of areas poorly covered in the existing literature. 

1.1.Major Health Insurance Schemes in LMICs 

The types of health insurance available can greatly influence the use of health care, the financial burden 

related to health care, and individual health status (Cheng and Chiang 1998). Financial mechanisms impact 

patterns of delivery and use of maternal care services (Ensor and Ronoh 2005). Various aspects of an 

insurance scheme, such as its cost, benefits, location of services provided, and to whom the services are 

targeted affect enrollment (Escobar et al. 2010; Robyn et al. 2013). It is important to consider these 

variations when examining the effect of health insurance on a particular population. Two types of insurance 

schemes are commonly implemented in LMICs—namely, Social Health Insurance (SHI) and CommunityBased Health Insurance (CBHI)—and individual variances within these programs exist as well. These 

schemes differ on enrollment requirements, funding, size of the risk pool, and associated fees, including 

entry fees, out-of-pocket fees, and reimbursement mechanisms. 

1.1.1. Social health insurance 

In countries with SHI, insurance coverage is typically mandated, particularly for those who are employed 

in the public sector, and is funded by employer/employee contributions along with government funding, 

usually through taxation. It is managed and regulated by the government, either at the national or district 

levels, and usually requires employer/employee contributions among the working population (Acharya et 

al. 2013; Wagstaff 2010). While universal health care may be a goal of SHI programs, these programs run 

the risk of financial mismanagement or underfunding and underrepresentation of the poor, due to targeting 

wealthier individuals who can afford to join the programs (Hsiao and Shaw 2007). SHI has become popular 

in several Asian and African countries including the Philippines, Thailand, Vietnam, Indonesia, Ghana, and 

Gabon (Hsiao and Shaw 2007; Humphreys 2013; Spaan et al. 2012; Sparrow et al. 2010). 

1.1.2 Community-based health insurance 
2 

In contrast to SHI, CBHI is managed at the community level by non-governmental organizations (NGOs) 

or providers (Hsiao and Shaw 2007), with subsidies provided by the government or an NGO (Acharya et 

al. 2013; Wang et al. 2009). Although CBHI programs can vary greatly within and across countries, they 

tend to target those who are not covered by formal schemes and who are at risk for impoverishment from 

catastrophic healthcare costs (Hsiao and Shaw 2007). Enrollment is typically voluntary (Aggarwal 2010), 

which in turn affects the characteristics of the risk pool through adverse selection, whereby the less healthy 

may be overrepresented in the risk pool (Hsiao and Shaw 2007). These consequences, as well as lack of 

financial sustainability (Robyn et al. 2013), are inherent to CBHI programs that attempt to avoid potentially 

inadequate government administration. Some countries, including Rwanda, are moving toward universal 

coverage by mandating insurance through a decentralized national system that provides regulation and 

oversight for private and CBHI schemes (Saksena et al. 2011). CBHI has gained in popularity in many 

African countries, including the Democratic Republic of the Congo and Senegal, as well as Rwanda (Spaan 

et al. 2012). 

1.2.Impact of Health Insurance 

1.2.1. Use of health care 

The impact of health insurance is usually assessed on use of healthcare services, financial protection, and 

health status. Among these three outcomes, more research has been done on the use of healthcare services, 

especially general health care, than on the other two outcomes (Giedion et al. 2013; Giedion et al. 2007; 

King et al. 2009; Nguyen et al. 2012; Thornton et al. 2010; Wagner et al. 2011; Wagstaff 2007; Wang et al. 

2009). In one study, cooperative members enrolled in health insurance had a significantly higher number 

of outpatient health care visits as well as surgeries, compared with those who were part of uninsured 

cooperatives (Aggarwal 2010). In another rigorous study using propensity score matching, it was found that 

in Vietnam health insurance increased use of services, particularly inpatient care (Wagstaff 2007). While 

this program targets Vietnam’s poor and requires members to enroll in Vietnam’s SHI program, which 

provides services at hospitals and essential prescription medication, coverage in 2004 was only around 15 

percent among the sample population; these enrollees were disproportionately poor compared with the 

sample. An evaluation of a CBHI program in Rural China, called RMHC, which included both reduced 

prices of health care as well as regulations for village doctors that aim to improve quality of care, showed 

that enrollment in this insurance scheme increased the probability of outpatient care by 70 percent (Wang 

et al. 2009). Using data from over 50 countries surveyed by the World Health Organization (WHO), a 

regression analysis showed that insured families (some or all members) were more likely to have had access 

to care or adult chronic care when last needed, compared with those without insurance (Wagner et al. 2011). 

Maternal and child health services are typically covered in benefit packages of health insurance. However, 

few studies, especially those using rigorous methodology, have assessed the impact of health insurance on 

use of maternal and child health care. After employing propensity score matching to balance demographic 

characteristics such as region, age, marital status, and wealth assets, Mensah, Oppong and Schmidt (2010) 

found that in Ghana women with insurance under the National Health Insurance Scheme (NHIS), compared 

with women without insurance, had significantly fewer birth complications (1.4 versus. 7.5 percent), had 

more births at a hospital (75 versus 53 percent), received professional assistance more commonly during 

birth (65 versus 47 percent), and had at least three prenatal check-ups (86 versus 72 percent) (Mensah et al. 

2010). Postnatal women with insurance had check-ups and vaccinations for their children on average more 

than uninsured women (86 versus 71 percent). In assessing a pilot voucher program in Bangladesh, the 

authors compared the intervention and comparison areas with difference-in-differences methods and found 

that women in intervention areas had significantly higher probability of using antenatal care, institutional 

delivery, and postnatal care (Nguyen et al. 2012). Confirmed with both propensity score matching and 

difference-in-differences methods, Giedian and coauthors suggested that in Colombia the subsidized health 

insurance program increased the use of professionally attended delivery care as well as complete 
3 

immunization coverage among children, even though immunization was provided free in the public sector 

(Giedion et al. 2007). Several other studies have also demonstrated a positive association between use of 

maternal health care and health insurance coverage, but with less rigorous methodology (Kozhimannil et 

al. 2009; Smith and Sulzbach 2008). 

Some research, however, has reported mixed findings regarding the impact of health insurance on use of 

health care. An evaluation of CBHI in one province in India employed a propensity score matching 

technique and did not find differences in maternal health care by health insurance coverage, either in use 

of prenatal services or delivery in private facilities (Aggarwal 2010). The author suggests that this was most 

likely because, at the time of the study, coverage of normal deliveries in private settings had been only 

recently added to the insurance scheme so that there was not enough time to measure meaningful change. 

Additionally, in this province in India maternal health fees were already negligible in government facilities. 

1.2.2. Financial protection 

Individuals with health insurance are more likely to use healthcare services and to use them more frequently 

than those without insurance; additionally, having insurance reduces out-of-pocket expenditures, although 

results are more pronounced in wealthier households (Aggarwal 2010; Acharya 2013). This can be seen in 

the reduction of borrowing or selling of assets in order to pay for care (Aggarwal 2010). While health 

insurance may not influence out-of-pocket spending on health care within the poorest households, it can 

help to avoid catastrophic costs (Wagstaff 2007). Likewise, Thornton and colleagues concluded from a oneyear experimental study of pre-and post-insurance implementation in Nicaragua that insurance does not 

provide cost savings, as out-of-pocket cost reductions are offset by the cost of the insurance premiums 

(Thornton et al. 2010). However, several rigorous studies confirm that both CBHI and SHI protect enrollees 

by reducing out-of-pocket spending and guard against catastrophic expenditures (Bauhoff et al. 2011; 

Nguyen and Wang 2013; Nguyen et al. 2011). 

Studies on SHI in China and CBHI in Senegal and Ghana have demonstrated that out-of-pocket 

expenditures for facility-based delivery decrease with insurance coverage (Long et al. 2010; Smith and 

Sulzbach 2008). Both studies employed linear regression models of a logarithmic scale of out-of-pocket 

expenditures on facility-based delivery and found that the insurance significantly reduced expenditures on 

delivery care, even after controlling for covariates. 

1.2.3. Health outcomes 

Although it is generally expected that health insurance enrollment can improve health outcomes by reducing 

financial barriers to use of health services, research has not clearly demonstrated a causal link between 

health status and health insurance in developing countries (Escobar et al. 2010). Measuring changes in 

health status through indicators of mortality is not only methodologically difficult, but may not be sensitive 

enough to provide evidence of the impact of health insurance, particularly given that health insurance and 

thus research on it are relatively recent in developing countries(Escobar et al. 2010; Long et al. 2010). 

5 

2. Data and Methods 

This study uses a propensity score matching method to assess the impact of health insurance status on 

antenatal and delivery care in eight countries spanning sub-Saharan Africa (Burundi, Gabon, Ghana, 

Namibia, and Rwanda), West Asia (Albania), and South and Southeast Asia (Cambodia and Indonesia). 

2.1.Data Used in the Analysis 

The data used in this study come from Demographic and Health Surveys (DHS). The DHS Program has 

been providing technical assistance in the implementation of more than 300 surveys spanning more than 90 

developing countries. DHS surveys are a key source of nationally representative and comparative data on 

population and health indicators, including maternal health and health insurance coverage. We use data 

from DHS surveys that collected information on health insurance coverage of women and men. We focus 

on countries in Africa and Asia due to the lack of empirical data demonstrating the effects of health 

insurance on the use of healthcare services in these regions. 

The study uses data on all interviewed women age 15-49 and men age 15-591

 to describe levels of health 

insurance coverage for 30 countries based on the most recent survey. To ensure adequate sample size, only 

countries in which levels of health insurance coverage among women exceed 10 percent are analyzed for 

the effects of health insurance on use of maternal health care. Eight countries are included in the evaluation 

of the effects of health insurance, with surveys conducted between 2008 and 2012. Data on women’s sexual 

and reproductive health behavior and outcomes are obtained by interviewing women of reproductive age 

(15-49). Information on socioeconomic characteristics of the women and their households is also collected. 

Our target population for assessing the effects of health insurance is women who reported a live birth in the 

five years preceding the survey. 

2.2.Definitions of Variables 

2.2.1. Dependent variables 

The study explores four outcomes of use of maternal health care for the most recent birth: whether a woman 

made at least one antennal care visit (ANC1); whether a woman made at least four antenatal care visits 

(ANC4); whether the first antenatal care visit occurred within the first three months (ANCMONTH); and 

whether the woman gave birth in a healthcare facility (FACBIRTH). The selection of these outcomes is 

based on standards of prenatal and delivery care recommended by the World Health Organization (WHO 

2004). 

The DHS asked women about the number of antenatal care visits during pregnancy of the most recent birth, 

the timing of the first antenatal care visit, and the place of delivery. We constructed two measures of the 

number of visits: whether the woman made at least one visit, and whether the woman made at least four 

antenatal care visits. Because receiving good-quality care during pregnancy promotes better health 

outcomes for mothers and their children throughout the life course (AbouZahr and Wardlaw 2003), the 

study distinguishes between women who made at least one visit and those who made the standard of a 

minimum of four visits recommended by WHO. 

WHO (2004) recommends that in order to detect and effectively treat underlying problems the first antenatal 

care visit occur as early as possible, and preferably within the first trimester. We constructed a measure of 

 

1 In Albania, Cambodia, and Namibia, men age 15-49 were interviewed; in Indonesia, men age 15-54 were 

interviewed. 
6 

whether the woman made her first antenatal care visit during the first three months of her pregnancy based 

on the question on timing of the first antenatal care visit. Additionally, we constructed an indicator of the 

quality of delivery care based on women’s response to where the most recent delivery occurred. 

All of the use measures are dichotomous, coded as 0 or 1. ANC1 and FACBIRTH include all women age 

15-49 who had a live birth in the five years preceding the survey. However, ANC4 and ANCMONTH are 

specific to women who reported at least one antenatal care visit. 

2.2.2. Independent variable 

The main independent variable of interest is health insurance coverage. The DHS asked respondents 

whether they are covered by health insurance and the type of health insurance by which they are covered. 

We constructed a dichotomous measure of health insurance coverage. A variety of health insurance schemes 

exist and may differ, for example, in the range of services offered and reimbursement, which may have a 

different effect on use of health care. Data limitations did not enable us to distinguish between different 

types of health insurance. However, results are interpreted based on each country’s health insurance 

mechanisms. Appendix A provides a summary of characteristics of health insurance schemes in the study 

countries. 

2.2.3. Covariates 

We controlled for a host of background characteristics of women and their households that can have a 

confounding effect on the use of pregnancy-related care seeking behavior (Acharya et al. 2013; Mensah et 

al. 2010). Use of maternal health care is shaped by a myriad of additional factors such as financial, 

geographic, and cultural barriers (Borghi et al. 2006). Because this is a secondary data analysis, our 

selection of covariates was limited to those variables available in the DHS. 

Variables controlled for included: maternal age at the most recent birth, marital status, and employment 

status; mother’s education, education of household head, and household wealth; mother’s exposure to mass 

media; and child’s birth order. Because place of residence can shape both access to health insurance and to 

healthcare services, we included regional dummies and a measure of whether the household is located in 

an urban area (coded 0 or 1) based on country–specific definitions. Similar variables have been controlled 

for in other analyses of the association between health insurance and use of health care (Dixon et al. 2014; 

Ettenger et al. 2014; Hong et al. 2011; Jütting 2004). 

Maternal age at the time of the most recent birth was included as a continuous variable. Because the 

association between age and health insurance and use of health care may be non-linear, we also included a 

quadratic term of the variable. Mother’s education is a self-reported measure reflecting the highest 

education level at the time of the survey and grade within that level. Our analysis used a recoded variable 

of mother’s education and education of household head categorized into three groups: no education, primary 

education, and secondary education or higher. Marital status at the time of the survey was coded as never 

married, currently married, and formerly married. Employment status was coded as a dichotomous variable 

and includes both paid and unpaid work on family farms and businesses. The DHS collects data on various 

household assets and characteristics from which a country-specific wealth quintile index is then calculated 

based on a principal components analysis (Rutstein and Johnson 2004). Birth order was categorized in four 

groups: eldest, second, third, and fourth or higher. 
7 

2.3.Statistical Methods 

We applied a propensity scoring matching (PSM) approach to evaluate the effect of health insurance 

coverage on women’s use of antenatal and delivery care. The propensity to seek health services is likely to 

be correlated with factors that influence the propensity to enroll in health insurance, thereby introducing 

bias both due to observed and unobserved heterogeneity. Multivariate regression does not address the issue 

of selection bias. PSM methods are non-parametric estimation methods that address selection bias due to 

observed heterogeneity by matching a pool of treatment cases to control cases that are identical in their 

propensity to receive treatment whereby the set of observable characteristics X are independent of 

assignment to treatment. 

Developed by Rosenbum and Rubin (1983), this method has been used increasingly in a variety of research 

fields. Unlike other methods that match on a set of covariates X, which can lead to ‘the curse of 

dimensionality’ when there are many covariates, PSM methods match on the propensity to receive treatment 

(propensity score), since observations with the same propensity score share similar distributions of the 

covariates (Mocan and Tekin 2006). The propensity score is defined as a function of a vector of covariates 

X such the covariates are independent of the assignment to treatment (Di) (Rosenbaum and Rubin 1983). 

The average effect of treatment (ATE) on an outcome variable Y can then be estimated as the difference in 

outcomes between treatment and control cases after the control cases are reweighted by the propensity score 

of the distribution of treatment cases (Caliendo and Kopeinig 2008):

߬஺்ா = ܧ	ሺ߬ሻ = ܧ]	ܻሺ1ሻ − ܻሺ0ሻ]

The average treatment effect on the treated (ATT) is a more popular metric since it estimates the effect of 

treatment for individuals for whom the intervention was intended, in contrast to ATE which provides an 

estimate of the population average treatment effect (Caliendo and Kopeinig 2008): 

[1 = ܦ| ሻ0ሺܻ[ܧ − [1 = ܦ| ሻ1ሺܻ	[ܧ = ሻ1 = ܦ	|	߬ሺ	ܧ = ்்஺߬

The ATT is thus the difference between the expected outcome values with and without treatment for cases 

that received treatment (Caliendo and Kopeinig 2008). Since the counterfactual for cases that received 

treatment without treatment is not observed, it is estimated based on assumption that after adjusting for 

observed characteristics it is the same for D=1 and D=0 (Aggarwal 2010). 

2.3.1. Estimating the propensity score 

The first step in the PSM method was estimating the propensity score using a logit regression. This involved 

determining the covariates to be included in the specification of the propensity score. The selection of 

covariates can bias the estimate. Only variables unaffected by treatment (e.g. fixed over time or measured 

before treatment) should be included (Caliendo and Kopeinig 2008). A variable should be excluded if it is 

not correlated with the outcome, or if it is weakly correlated because it does not address confounding 

(Brookhart et al. 2006; Brooks and Ohsfeldt 2013) and can potentially add bias (Brookhart et al. 2006; 

Garrido et al. 2014; Ho et al. 2007; Imbens 2004). The inclusion of unrelated variables may make it harder 

to fulfill the common support condition—namely, ensuring that every treated case is matched to a control 

case (Bryson et al. 2002). In small samples, over-parameterized models may increase the variance of the 

propensity scores without necessarily increasing their bias (Augurzky and Schmidt 2001; Bryson et al. 

2002). 
8 

Our selection of variables was guided by theory and consensus within the literature (Caliendo and Kopeinig 

2008; Rubin and Thomas 1996), as well as data available in the DHS. A variable was dropped only if it was 

not simultaneously correlated with both the treatment and outcome. Because the analytical sample differed 

by outcome, for every country the propensity score was estimated for two samples: all women who had a 

live birth in the last five years (ANC1 and FACBIRTH) and women who had at least one antenatal care 

visit (ANC4 and ANCMONTH). Propensity scores were generated using STATA’s pscore command. 

2.3.2. Balancing test 

The recoding of the covariates is determined by satisfying the balancing property—that is, that the average 

propensity score of treatment and control units do not differ within each group (Becker and Ichino 2002). 

We implemented several iterations of the estimation of the propensity score in which we recoded variables 

in order to satisfy the balancing property. The recoded variables are reflected in the results tables. 

We imposed the common support as it may improve the quality of the match (Heckman et al. 1997). 

Imposing the common support condition ensures that each treated unit (women with health insurance) is 

matched with a corresponding control unit (women with no health insurance). However, this can result in 

loss of sample size (Lechner 2001) due to the exclusion of cases whose propensity score is greater than the 

maximum or less than the minimum score in the comparison group (Aggarwal 2010), possibly producing 

misleading results (Caliendo and Kopeinig 2008). We reported the number of off-support cases for each 

estimation of the propensity score. 

2.3.3. Algorithm for matching and estimation of the effects of health insurance 

Various methods of matching are available to create a comparison group that can be used to construct 

counterfactual outcomes for estimating treatment effects. No method is superior but each has a different 

tradeoff between quantity and quality of results (Becker and Ichino 2002) because of the different ways in 

which the method defines the neighborhood for matching and assigns weights (Caliendo and Kopeinig 

2008). We used STATA’s teffects psmatch command to estimate ATT using several different algorithms 

and selected the one that yielded the best match. The following matching algorithms were tested: nearest 

neighbor with and without replacement and radius matching within various calipers.2

 The estimation of the 

variance of treatment effects includes variation due to the estimation of the propensity score and imputation 

of the common support (Aggarwal 2010). Unlike other STATA commands, teffects psmatch accounts for 

additional variance due to the estimation of the propensity score. 3 

2.3.4. Quality of matching 

Several measures can be used to assess the quality of matching to ensure that the distribution of the 

covariates between the treatment and control group are the same. STATA’s pstest command summarizes 

the balance of covariates between treatment and control groups both before and after matching. It also 

provides the standardized bias, pseudo-R2

, likelihood ratio test for joint insignificance, and two-sample ttest results, which can serve as indicators of the quality of matching. Standardized bias ranging between 3-

5 percent post-matching is deemed sufficient (Caliendo and Kopeinig 2008). Pseudo-R2

 indicates the extent 

to which the covariates explain the probability of receiving treatment; lower R2

 indicates reasonably good 

 

2

 Kernel matching is not available in STATA’s teffects psmatch package. 

3

 Several commands can be used to estimate the average treatment effect in STATA and these include att*, psmatch2

and teffects psmatch. Psmatch2 does not provide as much detail as att*, but provides more matching options, and it 

conveniently illustrates side-by-side comparisons of unmatched and matched cases. However, psmatch2 does not take 

into consideration that the propensity score is estimated when calculating standard errors. Teffects psmatch produces 

more robust standard errors because it accounts for additional variance due to the estimation of the propensity score. 
9 

matches. For logit models, to indicate a good match the likelihood ratio test of joint insignificance should 

be insignificant after matching (Caliendo and Kopeinig 2008). We selected the matching method that 

produced the best quality matching and reported its outcomes as well as the standardized bias, pseudo-R2

, 

likelihood ratio test for joint insignificance, and two-sample t-test. 

11 

3. Results 

3.1. Health Insurance Coverage, Types, and Differentials 

This section presents levels of health insurance coverage among adult women and men in 30 countries. In 

eight countries, Albania, Burundi, Cambodia, Gabon, Ghana, Indonesia, Namibia, and Rwanda, where the 

overall level of coverage was at 10 percent or higher, the section analyzes prevalence rates of specific types 

of insurance and differentials in coverage by respondents’ background characteristics. 

3.1.1. Levels of health insurance coverage 

Figure 1 presents the percentage of interviewed women and men with any type of health insurance in 25 

African countries. Most countries had fairly low levels of coverage. Women in 14 countries and men in 10 

countries reported a coverage rate below 5 percent. In three countries—Rwanda, Gabon, and Ghana—over 

30 percent of women and men had health insurance at the time of the survey. The highest level of coverage 

was found in Rwanda, at 71percent for women and 67 percent for men. 

Figure 1. Percentage of women and men covered by health insurance in Africa 

66.7

71.5

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0

Burkina Faso 2010

Ethiopia 2011

Uganda 2011

Sao Tome and Principe...

Benin 2011-12

Sierra Leone 2008

Nigeria 2008

Niger 2012

Mozambique 2011

Congo (Brazzaville)...

Cote D'Ivoire 2011-12

Cameroon 2011

Madagascar 2008-09

Swaziland 2006-07

Senegal 2010-11

Tanzania 2010

Kenya 2008-09

Zimbabwe 2010-11

Zambia 2007

Lesotho 2009

Burundi 2010

Namibia 2009

Ghana 2008

Gabon 2012

Rwanda 2010

Women Men
12 

Figure 2 shows levels of coverage in five Asian countries for which the most recent DHS collected data on 

health insurance. Indonesia had the highest levels of health insurance coverage, at 37 percent for women 

and 41 percent for men in 2012. The Albania 2008-09 DHS showed a coverage rate of 22 percent for women 

and 29 percent for men. In Armenia and Azerbaijan the level of health insurance coverage was very low, 

especially among women. 

In all 30 countries the gender gap in health insurance coverage favored men, with the exception of 

Cambodia, Gabon, Ghana, and Rwanda. Among the four countries where women had higher levels of 

coverage than men, the largest gap was found in Ghana, at an 8 percentage-point difference in 2008. 

However, in most countries the gender gap was small, with low coverage rates among both women and 

men. 

Figure 2. Percentage of women and men covered by health insurance in Asia 

3.1.2. Types of health insurance 

Table 1 presents the percentage of women and men with specific types of health insurance in seven 

countries with relatively high levels of coverage. Gabon is not included in this table because the 2012 Gabon 

DHS did not collect data on types of insurance. Respondents could report more than one type of health 

insurance. Several major types of insurance schemes were observed in these countries. 

40.9

37.1

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0

Azerbaijan 2008-09

Armenia 2010

Cambodia 2010

Albania 2008-09

Indonesia 2012

Women Men
13 

Table 1. Percentage of women and men covered by specific types of health insurance in selected 

countries 

Country Type of insurance Women Men 

Albania State health insurance 15.0 21.3 

State social insurance 10.9 12.6 

 Private/commercial purchased 2.2 1.7 

 Other 2.5 4.5 

 Total 21.5 28.9 

 

Burundi Mutual/community organization 4.8 4.7 

Provided by employer 4.4 5.5 

 Private/commercially purchased 0.9 0.0 

 Other 0.4 2.0 

 Total 10.4 12.1 

 

Cambodia Health equity fund 8.5 6.4 

Provided by employer 0.2 0.4 

 Private 0.1 0.2 

 Other 1.9 1.0 

 Total 10.7 8.0 

 

Ghana National/ district (nhis) 38.8 29.7 

Provided by employer 0.1 0.2 

 Private/commercially purchased 0.1 0.0 

 Other 1.1 1.2 

Total 40.1 31.0 

 

Indonesia Social security 25.7 25.9 

Provided by employer 6.9 10.5 

 Private/commercially purchased 2.9 3.9 

 Other 2.6 2.5 

 Total 37.1 40.9 

 

Namibia Provided by employer 8.9 11.4 

 Social security 4.5 5.7 

 Mutual/community organization 3.6 4.5 

 Private 2.5 4.6 

 Other 0.6 0.3 

 Total 18.4 21.8 

 

Rwanda Mutual/community based health insurance 68.0 63.9 

 Rama 2.1 1.8 

 Privately purchased/commercial health 0.3 0.3 

 other 0.9 0.7 

 Total 71.4 66.7 

Note: In all the countries except Rwanda, respondents were allowed to report multiple types of insurance; so the 

sum of the percentages may exceed the total prevalence. 

Gabon is not included in this table due to unavailability of data on types of insurance. 
14 

Social health insurance was the primary type of coverage in five countries (Albania, Cambodia, Ghana, 

Indonesia, and Namibia). Almost all Ghanaian women and men with health insurance were enrolled in the 

National Health Insurance Scheme (NHIS). In Indonesia about a fourth (26 percent) of women and men 

were covered by social security. 

Community-based health insurance was reported in a few countries. In Rwanda the vast majority of people 

who reported health insurance coverage were covered by Mutual Health Insurance, a community-based 

health insurance scheme. Community-based health insurance was also reported in Burundi and Namibia, 

although at much lower levels compared with Rwanda. 

Employer-based health insurance was rarely reported except in Namibia, where it was the most common 

type of insurance, reported by 9 percent of women and 11 percent of men in 2009. Private or commercially 

purchased health insurance was uncommon in the study countries. The highest level of private insurance 

coverage was observed in Namibia, at less than 5 percent for both women and men. 

3.1.3. Differentials in health insurance coverage 

Table 2 and Table 3 report the percentage of women and men with any health insurance coverage at the 

time of the interview by background characteristics including respondents’ age, marital status, education, 

employment status, household wealth status, and urban-rural residence. 

Overall, women age 15-24 were less likely than older women to be covered by health insurance. The age 

differences were not substantial among women age 25-49 with the exception of Gabon, where coverage 

was considerably higher among women age 40-49 compared with all other age groups. A similar age 

difference in health insurance coverage was observed among men, with the lowest rates reported among 

men age 15-24. In five countries (Albania, Burundi, Gabon, Ghana, and Namibia) the oldest group(s) 

reported the highest level of health insurance coverage. In Namibia, for example, 40 percent of men age 

40-49 were covered by health insurance, more than double the rate among men under age 30. 

Across all eight countries studied, there was no clear pattern in health insurance coverage by marital status 

for both women and men. In four countries (Burundi, Ghana, Namibia, and Rwanda) currently married 

women and currently married men reported the highest levels of coverage. In three countries (Albania, 

Cambodia, and Gabon) never-married women had the lowest coverage. 

Generally among women and men in the eight countries studied, health insurance coverage was positively 

associated with educational attainment. In Cambodia, however, health insurance rates were highest among 

women and men with no education, and lowest among those with a secondary education or higher. 

In terms of employment status, in all of the countries except Rwanda employed women had higher coverage 

rates than unemployed women. The greatest disparity in health insurance coverage by employment status 

was found in Albania. One-half of employed women reported health insurance coverage compared with 

less than one-tenth of women unemployed at the time of the interview. The difference in coverage between 

employed and unemployed women was also notable in Namibia, at 31 percent among employed women 

compared with 8 percent among women who were unemployed. In Rwanda, conversely, a slightly higher 

percentage of unemployed women reported health insurance coverage compared with employed women. 

Aside from Albania and Namibia, employment-related differences in health insurance coverage in all 

countries were not as prominent among men as among women. 
Table 2. Percentage of women with health insurance coverage, according to background characteristics 

Albania Burundi Cambodia Gabon Ghana Indonesia Namibia Rwanda 

% N % N % N % N % N % N % N % N 

Age 15-19 11.6 1,478 7.5 2,359 10.0 3,734 35.9 1,784 38.5 1,025 32.8 6,927 10.4 2,245 64.4 2,94520-24 12.6 976 8.9 1,832 10.2 3,155 37.1 1,637 34.7 878 34.5 6,305 11.6 1,854 73.1 2,68325-29 26.0 848 11.8 1,608 11.1 3,262 37.4 1,485 41.6 832 33.9 6,959 18.7 1,622 75.3 2,49430-34 22.3 866 14.1 1,064 10.7 2,167 47.2 1,211 43.0 644 36.8 6,876 22.1 1,416 75.0 1,82235-39 25.3 1,097 11.5 1,067 10.3 2,044 46.9 986 42.5 638 42.1 6,882 24.1 1,045 73.2 1,44740-44 27.5 1,232 13.2 745 12.2 2,300 56.8 746 45.0 470 39.3 6,252 29.9 928 70.2 1,168

45-49 28.3 1,088 10.6 714 10.6 2,093 57.8 574 39.4 429 41.4 5,407 29.6 688 70.2 1,112

Marital status 

Never married 17.2 2,357 8.2 3,121 8.6 5,783 39.5 3,047 37.3 1,593 36.3 9,919 14.0 5,671 68.1 5,285Currently married 23.1 4,910 14.9 3,760 10.8 11,515 41.2 1,597 44.4 2,232 37.4 33,291 37.8 1,949 80.3 4,799Living together 26.1 91 5.2 1,661 32.7 112 45.4 2,878 36.5 644 39.7 174 10.1 1,500 65.6 2,098Widowed 34.2 116 11.6 411 20.6 564 46.3 131 30.3 101 42.5 935 15.2 250 66.5 743Divorced/separated 27.1 109 5.8 436 14.3 781 47.1 769 35.2 345 31.4 1,288 18.5 425 58.7 746Education 

None 0.0 26 5.4 4,211 17.4 2,973 19.0 373 32.6 1,042 31.6 1,500 3.7 650 66.2 2,119Primary 9.2 3,813 10.8 4,042 12.6 9,265 47.0 1,786 31.2 988 31.8 15,125 6.1 2,433 70.5 9,337

Secondary and higher 34.2 3,745 27.5 1,136 4.9 6,516 42.8 6,263 45.9 2,886 40.2 28,982 24.2 6,716 80.1 2,216Employment status 

Not currently employed 9.2 5,308 9.4 2,494 9.5 5,592 39.7 4,742 39.2 1,240 35.0 20,348 8.4 5,445 73.4 3,761Currently employed 50.3 2,276 10.7 6,895 11.2 13,162 46.4 3,680 40.4 3,676 38.8 25,259 30.8 4,354 70.6 9,910Wealth quintile 

Lowest 8.0 1,513 4.8 1,898 24.4 3,388 61.6 1,222 29.9 783 38.4 7,767 2.0 1,621 59.8 2,622

Second 11.6 1,486 4.8 1,910 15.0 3,516 39.0 1,621 32.4 900 34.6 8,784 3.9 1,667 68.8 2,661

Middle 16.0 1,533 6.3 1,854 8.9 3,594 35.9 1,784 38.5 979 31.7 9,243 8.9 1,882 73.4 2,736Fourth 25.9 1,480 9.4 1,811 5.9 3,827 35.2 1,879 45.7 1,119 34.0 9,743 18.4 2,291 77.6 2,677Highest 45.2 1,573 26.4 1,916 2.4 4,428 47.2 1,915 49.4 1,135 46.3 10,071 47.6 2,338 76.6 2,976Residence 

Rural 11.5 4,204 8.3 8,387 12.2 14,818 59.7 957 36.9 2,533 32.3 21,802 8.3 5,028 71.4 11,614Urban 34.0 3,380 27.7 1,002 5.2 3,936 40.4 7,465 43.6 2,383 41.5 23,805 29.0 4,771 71.4 2,057

Total 21.5 7,584 10.4 9,389 10.7 18,754 42.6 8,422 40.1 4,916 37.1 45,607 18.4 9,799 71.4 13,671

15 
Table 3. Percentage of men with health insurance coverage, according to background characteristics 

Albania Burundi Cambodia Gabon Ghana Indonesia Namibia Rwanda 

% N % N % N % N % N % N % N % N 

Age 15-19 17.1 670 9.3 932 7.5 1,863 40.3 1,012 34.6 911 41.3 28 12.4 910 62.1 1,449

20-24 13.0 393 5.9 732 7.0 1,402 33.9 805 23.4 704 33.3 345 11.5 749 61.7 1,159

25-29 29.0 269 10.4 584 8.4 1,377 29.4 813 20.8 624 31.9 1,127 18.5 702 70.2 1038

30-34 35.5 273 15.0 442 8.6 1,014 35.2 776 36.0 533 37.8 1,674 28.8 586 73.5 710

35-39 33.4 372 17.2 388 9.1 835 36.1 715 32.4 528 42.4 1,775 32.2 398 67.3 490

40-44 37.3 501 15.2 349 8.0 956 40.6 534 31.7 394 46.8 1,693 40.1 331 70.3 430

45-49 41.0 536 15.0 331 8.2 792 44.5 453 30.0 364 45.4 1,371 40.0 235 67.0 412

50+ na na 17.2 520 na na 49.8 546 41.0 510 40.4 1,292 na na 69.7 642Marital status Never married 19.1 1,291 9.6 1,653 6.9 3,181 34.4 2,346 29.4 1,942 na na 15.1 2,544 62.3 2,879Currently married 36.0 1,671 16.4 1,945 8.5 4,815 37.2 1,423 35.0 2,163 41.0 9,286 44.9 705 76.1 2,433Living together 51.2 32 5.4 604 9.0 37 44.8 1,469 19.8 241 15.5 20 27.4 498 59.6 854Widowed 58.9 4 10.4 31 16.4 54 25.6 29 29.4 26 na na 24.4 12 43.0 54Divorced/separated 38.9 15 4.7 47 10.4 152 36.3 387 16.8 195 na na 7.6 151 40.0 108Education None 15.7 18 6.7 1,348 13.5 641 9.8 378 18.7 639 29.2 265 6.1 360 60.2 757

Primary 16.7 1,219 10.2 2,089 9.8 3,394 32.9 864 22.3 665 32.1 3,489 11.2 1,108 65.5 4,323

Secondary and higher 37.5 1,775 25.4 843 5.7 4,205 41.3 4,412 35.2 3,264 47.0 5,552 28.9 2,443 74.5 1,249

Employment status Not currently employed 14.5 1,026 14.2 540 7.3 1,556 41.4 1,748 33.6 928 37.2 155 10.3 1,471 65.6 593Currently employed 36.4 1,987 11.8 3,740 8.1 6,683 36.3 3,906 30.3 3,640 41.0 9,151 28.7 2,441 66.8 5,736Wealth quintile Lowest 14.5 475 5.0 686 16.4 1,454 48.3 830 17.6 809 41.3 1,596 1.8 560 53.9 937

Second 20.6 600 6.3 789 11.8 1,544 28.1 1,183 23.0 815 36.3 1,866 7.6 605 64.2 1,108

Middle 26.3 661 7.6 818 6.6 1,637 27.0 1,246 27.9 784 32.8 2,008 12.6 875 66.4 1,306

Fourth 32.5 625 11.8 907 4.4 1,696 36.0 1,204 37.7 1,079 38.7 1,962 24.9 963 73.0 1,391

Highest 46.3 652 24.3 1,080 2.8 1,908 53.5 1,191 42.6 1,081 56.3 1,875 49.2 909 70.6 1,586

Residence Rural 21.0 1,622 9.7 3,649 8.8 6,542 48.9 739 26.1 2,443 34.5 4,567 10.0 1,951 66.7 5,324

Urban 38.1 1,391 25.5 631 4.7 1,697 36.2 4,915 36.6 2,125 47.2 4,739 33.5 1,960 66.4 1,005

Total 28.9 3,013 12.1 4,280 8.0 8,239 37.9 5,654 31.0 4,568 40.9 9,306 21.8 3,911 66.7 6,329

16 
17 

Health insurance coverage for women was positively associated with household wealth in five countries 

(Albania, Burundi, Ghana, Namibia, and Rwanda). Coverage was highest among women in the richest 

households and lowest among women in the poorest households. The most striking disparity between rich 

and poor was found in Albania and Namibia. In Namibia 48 percent of women in the richest households 

were covered by a health insurance compared with 2 percent of women in the poorest households. In 

Albania the difference between women in the highest and lowest wealth quintiles was 37 percentage points. 

An opposite relationship between household wealth and health insurance coverage was observed in 

Cambodia. While the coverage rate at the national level was only 11 percent for women, almost one in 

every four women in the poorest households reported health insurance coverage; a higher level than in any 

other wealth quintile. In Gabon and Indonesia the relationship between household wealth and insurance 

coverage was somewhat non-linear; coverage rates were highest among the poorest and the richest groups. 

It is important to note that in Gabon 62 percent of the poorest women were covered by health insurance; 

this represents the highest level of health insurance coverage among this wealth group in any of the eight 

countries. A similar pattern of wealth disparities in health insurance coverage was also observed among 

men. 

With regard to the urban-rural differences, in five countries (Albania, Burundi, Ghana, Indonesia, and 

Namibia) both women and men in urban areas were more likely to report health insurance than their rural 

counterparts. In contrast, in Cambodia and Gabon coverage rates were higher in rural than urban areas for 

both women and men. The absolute urban-rural difference ranged from 7 to 20 percentage points for 

women, and from 4 to 10 percentage points for men. Rwandans in urban and rural areas had similar 

coverage rates, at 71 percent for women and 66-67 percent for men. 

3.2. Propensity Score Estimation and the Quality of Matching 

3.2.1. Propensity score estimation 

As described in the section on methods, we assessed the effects of health insurance status on maternal health 

care use in eight countries in which health insurance rates exceeded 10 percent in order to ensure adequate 

sample size. We took three steps to implement the assessment. First, we ran logit models to estimate the 

propensity score, which is the predicted probability of being insured given a set of covariates. Second, the 

estimated propensity scores were used to match a group of individuals who were not insured but had 

comparable scores to those who were insured. Finally, we compared the outcomes of the insured and the 

uninsured to obtain the effects of health insurance on the insured (ATT). 

This section describes the estimation results of the logit models in each of the eight countries. As the four 

outcomes of interest were based on two different samples, in each country there were two models for 

estimating propensity scores—the full-sample model and the sub-sample model. Appendices B-I report the 

coefficients and standard errors of covariates that were included in the propensity score estimation in 

individual countries. 

Across all eight countries, household wealth status was a significant determinant of participation in health 

insurance. However, the direction of the relationship varied among countries. Net of the effects of other 

background characteristics, and consistent with the results of the bivariate analyses, wealth status was 

positively associated with women’s participation in health insurance in five countries (Albania, Burundi, 

Ghana, Namibia, and Rwanda). In Cambodia and Gabon, however, the relationship was the inverse; women 

in poorer households were more likely to have health insurance coverage than women in wealthier 

households. 

Women’s education and the education of the head of household were important predictors of women’s 

enrollment in health insurance. In most countries, women with higher levels of education were more likely 
18 

to report health insurance coverage, with the exception of Namibia, where the association was statistically 

non-significant. Since it is common for individuals to enroll in health insurance as part of households, the 

education of the household head was expected to affect women’s participation in health insurance. A 

positive association between education of the household head and women’s health insurance status was 

observed in Burundi, Ghana, Indonesia, and Namibia. Results from Cambodia illustrate a counterintuitive 

relationship: both women’s education and education of household head were negatively associated with 

health insurance coverage. 

Women’s employment status was excluded from several models because it was neither associated with 

health insurance status nor associated with the outcomes of interest. When included, current employment 

was positively associated with health insurance status in most countries except Rwanda, where the 

relationship was reversed. 

Birth order (which can be interpreted as the number of children a woman had) was negatively associated 

with women’s health insurance status in most models. Higher parity was associated with lower chances of 

being covered by health insurance. In Cambodia, however, women with more children were more likely to 

be enrolled in health insurance. 

The urban-rural gap in insurance status that was observed in the bivariate analysis was largely diminished 

after controlling for other covariates. As expected, in all eight countries regional differences in health 

coverage remained statistically significant even after controlling for background characteristics. The 

magnitude and statistical significance of age, marital status, and mass media exposure on health insurance 

enrollment differed by country, with no consistent pattern. 

3.2.2. The quality of propensity score matching 

After the propensity score was estimated, common support between the control and treatment groups was 

examined by assessing the range of propensity scores for both groups on a histogram graph. The distribution 

of the propensity scores illustrated a satisfactory overlap between the insured and the uninsured in all 

countries (graphs are available upon request from the authors). 

As discussed previously, we experimented with various propensity score matching algorithms. The final 

approach was chosen according to the quality of matching, which was assessed based on several model 

parameters including the mean and median of absolute biases of covariates, pseudo-R2

, and standard 

Likelihood ratio test X2

. The pre- and post-matching comparisons on means and percent of absolute bias 

reduced for individual covariates were also taken into consideration in assessing the quality of matching. 

Table 4 presents the results of the best quality matching method as well as quality measurements before 

and after matching for full and sub-samples in each country. 

Radius matching generally resulted in the best quality of matching in most countries with caliper width 

ranging from 0.01 to 0.05. It is expected that smaller calipers result in better quality of matching but also 

entail a greater possibility of losing treated cases that do not have a matched control (Grilli and Rampichini 

2011). Therefore, to achieve a good-quality matching and maximize the use of data from treated cases, the 

choice of caliper was determined by two criteria: the quality of matching and the least number of unmatched 

treated cases. The nearest neighbor matching was chosen for both samples in Burundi for its best quality of 

matching over other algorithms. 
Table 4. Propensity score matching performance: results of the mean and median absolute bias, pseudo-R2 and Likelihood ratio (LR) tests 

Country Matching approach Sample Mean Median Std. dev. Pseudo-R2 LR χ2 p> χ2Albania Full sample Radius matching 

(caliper=0.025) 

Unmatched 36.9 22.1 34.6 0.312 420.24 0.000 

Matched 7.0 4.9 7.4 0.022 16.01 0.523 

Subsample Radius matching 

(caliper=0.025) 

Unmatched 42.4 22.4 36.3 0.305 411.28 0.000 Matched 6.6 5.0 5.8 0.016 11.04 0.683 

Burundi Full sample Nearest neighbor Unmatched 33.3 17.1 32.2 0.271 1,080.66 0.000 

Matched 5.3 4.2 3.9 0.016 30.52 0.106 

Subsample Nearest neighbor Unmatched 37.8 33.6 34.9 0.255 1,016.15 0.000 

Matched 3.8 1.7 4.6 0.013 24.78 0.100 

Cambodia Full sample Radius matching 

(caliper=0.01) 

Unmatched 20.5 13.7 19.0 0.111 626.33 0.000 Matched 1.2 1.1 0.6 0.001 2.39 1.000 

Subsample Radius matching 

(caliper=0.01) 

Unmatched 20.5 13.7 19.0 0.111 626.33 0.000 Matched 1.6 1.4 1.1 0.001 3.15 1.000 

Gabon Full sample Radius matching 

(caliper=0.01) 

Unmatched 20.0 18.6 12.1 0.137 736.87 0.000 Matched 2.1 1.4 1.8 0.003 16.13 0.950 

Subsample Radius matching 

(caliper=0.05) 

Unmatched 21.4 20.4 12.0 0.135 727.22 0.000 Matched 2.0 1.5 1.9 0.002 11.57 0.984 

Ghana Full sample Radius matching 

(caliper=0.012) 

Unmatched 16.4 14.6 12.0 0.158 403.83 0.000 Matched 1.8 1.7 1.2 0.002 4.47 1.000 

Subsample Radius matching 

(caliper=0.011) 

Unmatched 17.0 16.4 11.9 0.158 403.64 0.000 Matched 2.0 1.7 1.2 0.002 4.33 1.000 

Indonesia Full sample Radius matching 

(caliper=0.020) 

Unmatched 8.4 7.3 5.7 0.038 756.85 0.000 Matched 0.6 0.5 0.5 0.000 4.96 1.000 

Subsample Radius matching 

(caliper=0.01) 

Unmatched 8.9 8.1 5.7 0.038 756.66 0.000 Matched 0.5 0.4 0.4 0.000 2.83 1.000 

Namibia Full sample Radius matching 

(caliper=0.05) 

Unmatched 36.0 28.1 32.9 0.383 1,082.65 0.000 Matched 3.2 2.8 2.1 0.008 8.75 1.000 

Subsample Radius matching 

(caliper=0.02) 

Unmatched 36.8 28.1 33.5 0.366 1,033.89 0.000 Matched 3.6 3.9 2.6 0.009 9.57 0.999 

(Continued...)

Table 4. – Continued

19 
Country Matching approach Sample Mean Median Std. dev. Pseudo-R2 LR χ2 p> χ2

Rwanda Full sample Radius matching 

(caliper=0.01) 

Unmatched 11.1 9.2 8.9 0.066 472.45 0.000 

Matched 3.1 2.3 2.4 0.006 75.87 0.000 

Subsample Radius matching 

(caliper=0.01) 

Unmatched 11.1 9.2 8.9 0.066 472.45 0.000 Matched 1.1 1.0 1.0 0.001 14.08 0.899 
21 

Overall, in all countries matching substantially reduced the mean and median biases between the insured 

and the uninsured with respect to the observed covariates included in the models. The mean absolute bias 

was less than 5 percent in the majority of models—the threshold for decent quality matches (Rosenbaum 

and Rubin 1983). In 9 of the 16 models, mean absolute bias was 2 percent or less. Despite a significant 

reduction in bias after matching, mean absolute bias was the highest in the models for Albania 

(approximately 7 percent), but statistically non-significant. 

Comparisons on individual covariates are reported in Appendices J-Q. In four countries (Cambodia, Ghana, 

Indonesia, and Rwanda), the post-matching standardized biases of all covariates across all models were less 

than 5 percent. In Albania and Namibia several covariates showed post-matching bias greater than 5 

percent, but not statistically significant. Post-matching standardized bias of individual covariates in Burundi 

and Gabon was less satisfactory, although mean bias was 5 percent or less. In Gabon significant bias 

between the insured and uninsured persisted after matching for two education categories in the full-sample 

model and the fifth wealth quintile in the sub-sample model. In Burundi, six variables in the full-sample 

model and one variable in the sub-sample model showed significant bias after matching. 

Pseudo-R2

 comes from the regressions of the propensity score on all covariates used in matching on both 

matched and unmatched samples. It should be substantially reduced after matching if the covariates are 

well matched between the insured and uninsured groups (Aggarwal 2010). Table 4 indicates fairly low 

Pseudo-R2 in all models. In all models except the full-sample model for Rwanda, the Likelihood-ratio test 

of joint insignificance of all covariates was insignificant after matching, indicating similar distributions of 

observed covariates after matching. 

Due to imposing the common support, some women (mostly from the uninsured group) were excluded from 

the analysis after the matching process. Table 5 presents the number of women off the common support. 

Table 5. Number of cases off the common support in propensity score matching 

 Full sample Sub-sample 

Albania 8 12 

Burundi 18 4 

Cambodia 21 17 

Gabon 3 0 

Ghana 37 43 

Indonesia 4 1 

Namibia* 339 141 

Rwanda 28 25 

* Among those off the common support, only one case is from the insured group for both samples 

3.3. Effects of Health Insurance Using Propensity Score Matching (PSM) 

This section reports the results of the PSM used to estimate the effects of health insurance on four outcomes: 

having at least one antenatal visit (ANC1), four or more antenatal visits (ANC4), first antenatal visit in first 

three months of the pregnancy (ANCMONTH), and delivery in a healthcare facility (FACBIRTH). ANC1 

and FACBIRTH are based on the full sample of all women who had a live birth in the five years preceding 

the survey. ANC4 and ANCMONTH are based on a sub-sample of women who had at least one antenatal 
22 

care visit. Table 6 presents the differences in outcomes between the insured and uninsured before matching 

as well as the effects of health insurance (ATT) estimated based on the matched samples. 

Table 6. The average treatment effect on the treated (ATT) of health insurance on utilization of 

selected maternal health services 

Outcomes 

Means before matching 

Average treatment 

effect on the treated Number 

of cases 

on 

Insured Uninsured Difference support 

pvalue ATT SE pvalue 

Albania 

ANC 11 0.964 0.964 0.000 0.989 

ANC 4 0.830 0.624 0.207 0.000 0.064 0.056 0.247 1,265 

First ANC visit in first 

3 months 0.894 0.746 0.148 0.000 0.092 0.053 0.084 1,265 

Facility delivery 0.985 0.956 0.029 0.023 0.014 0.022 0.519 1,317 

Burundi 

ANC 11 0.990 0.990 0.000 0.967 

ANC 4 0.402 0.332 0.070 0.000 -0.019 0.039 0.622 4,771 

First ANC visit in first 

3 months 0.348 0.196 0.153 0.000 0.075 0.036 0.039 4,771 

Facility delivery 0.827 0.624 0.203 0.000 0.014 0.030 0.642 4,806 

Cambodia 

ANC 1 0.824 0.884 -0.059 0.000 0.0408 0.0176 0.020 5,978 

ANC 4 0.563 0.683 -0.119 0.000 -0.0104 0.0252 0.680 5,211 

First ANC visit in first 

3 months 0.595 0.686 -0.091 0.000 -0.0029 0.02618 0.913 5,211 

Facility delivery 0.479 0.586 -0.107 0.000 0.073 0.0226 0.001 5,978 

Gabon 

ANC 1 0.909 0.938 -0.030 0.000 -0.023 0.014 0.105 3,932 

ANC 4 0.728 0.781 -0.053 0.000 -0.003 0.024 0.914 3,626 

First ANC visit in first 

3 months1 0.574 0.602 -0.028 0.086 

Facility delivery 0.818 0.882 -0.065 0.000 -0.043 0.019 0.025 3,932 

Ghana 

ANC 1 0.986 0.946 0.039 0.000 -0.0036 0.0085 0.672 1,837 

ANC 4 0.895 0.770 0.126 0.000 0.0771 0.0257 0.003 1,753 

First ANC visit in first 

3 months 0.619 0.545 0.074 0.000 0.0184 0.0365 0.614 1,753 

Facility delivery 0.740 0.471 0.269 0.000 0.1058 0.0319 0.001 1,837 

Indonesia 

ANC 1 0.975 0.945 0.030 0.000 0.016 0.004 0.000 14,954 

ANC 4 0.911 0.870 0.041 0.000 0.026 0.006 0.000 14,318 

First ANC visit in first 

3 months 0.810 0.775 0.036 0.000 0.017 0.008 0.033 14,318 

Facility delivery 0.625 0.529 0.096 0.000 0.049 0.009 0.000 14,954 

(Continued...)

 

Table 6. – Continued
23 

Outcomes 

Means before matching 

Average treatment 

effect on the treated Number 

of cases 

on 

Insured Uninsured Difference support 

pvalue ATT SE pvalue 

Namibia 

ANC 1 0.986 0.957 0.029 0.000 -0.0012 0.0114 0.916 2,950 

ANC 4 0.909 0.800 0.110 0.000 0.0209 0.03 0.486 3,005 

First ANC visit in first 

3 months 0.538 0.292 0.246 0.000 0.1585 0.0509 0.002 3,005 

Facility delivery 0.969 0.798 0.171 0.000 0.0325 0.0206 0.115 2,950 

Rwanda 

ANC 1 0.990 0.965 0.025 0.000 0.0154 0.0056 0.006 6,122 

ANC 4 0.379 0.318 0.061 0.000 0.0195 0.0203 0.337 6,016 

First ANC visit in first 

3 months 0.411 0.332 0.080 0.000 0.017 0.0206 0.410 6,016 

Facility delivery 0.765 0.613 0.152 0.000 0.0745 0.0186 0.000 6,122 

1 ATT was not estimated because the raw difference in the outcome between the insured and uninsured was 

statistically non-significant. 

3.3.1. Effect of health insurance on number of ANC visits 

Irrespective of health insurance status, most women in all eight countries made at least one antenatal care 

visit. In four countries (Ghana, Indonesia, Namibia, and Rwanda) the proportion of women reporting at 

least one antenatal care visit was higher among insured women than uninsured women. In Cambodia and 

Gabon health insurance coverage seemed to have a negative impact on ANC access. In Albania and Burundi 

the difference between the two groups was statistically non-significant; therefore this outcome was not 

analyzed using PSM.

After matching, health insurance status no longer significantly influenced women’s probability of making 

one antenatal care visit in three countries (Gabon, Ghana, and Namibia), which means the differences seen 

in the unmatched sample were due to the biases in covariates that were both related to participation in health 

insurance and the outcome. After the bias was eliminated or reduced, the differences in the outcomes were 

no longer significant. However, in Indonesia and Rwanda after matching, the impact of health insurance on 

increasing women’s chance of accessing antenatal care remained significant, although the magnitude of the 

impact decreased compared with before matching. It is noteworthy that in Cambodia the impact of health 

insurance was negative before matching but positive and statistically significant after matching. This could 

be because some of the covariates on which we matched, such as education and household wealth, were 

negatively associated with women’s health insurance status, as described earlier, but positively associated 

with access to antenatal care. 

The prevalence of four or more antenatal care visits varied by country. Among women who reported at least 

one antenatal visit, over 70 percent in Gabon, Ghana, Indonesia, and Namibia had four or more visits, 

regardless of health insurance status. In contrast, the prevalence of four or more antenatal care visits was 

relatively low in the other four countries, especially Burundi and Rwanda. The raw differences in the 

prevalence of four or more antenatal care visits between insured and uninsured women ranged from 4 

percentage points in Indonesia to 21 percentage points in Albania and were statistically significant in all 

countries. In Cambodia health insurance status appeared to have a negative impact on making four or more 

antenatal care visits. 
24 

After matching on covariates that could potentially introduce bias, the positive effect of health insurance 

coverage was eliminated in all countries except Ghana and Indonesia. Health insurance coverage 

contributed to an 8 percentage-point increase in access to four or more antenatal care visits in Ghana and a 

3 percentage-point increase in Indonesia. 

3.3.2. Effect of health insurance on timing of the first ANC visit 

In terms of the timing of the first antenatal care visit, Table 6 shows that more than one-half of women in 

Albania, Cambodia, Gabon, Ghana, and Indonesia started antenatal care in the first trimester regardless of 

insurance status. Most women in the remaining three countries waited until the second or third trimester 

before making the first visit. Before matching, the difference between insured and uninsured women was 

significant in all countries except Gabon. Health insurance seemed to increase the likelihood of women 

starting antenatal care early, with the exception of Cambodia, where insured women seemed less likely to 

make the first visit in the first trimester. 

After matching, a similar effect was observed as with the above two outcomes—the effect of health 

insurance on the timing of the first antenatal care visit disappeared in several countries including Cambodia, 

Ghana, and Rwanda. Its effect remained significant in Namibia, Burundi, and Indonesia. In these three 

countries, health insurance coverage increased the use of antenatal care within the first trimester of 

pregnancy by 15, 8, and 2 percentage points, respectively. 

3.3.3. Effect of health insurance on facility delivery 

At least one-half of women in all study countries delivered their most recent birth in a healthcare facility. 

The raw differences between insured and uninsured women were significant in all countries. Health 

insurance seemed to be associated with a greater likelihood of delivering in a healthcare facility, with the 

exception of Cambodia and Gabon, where insured women appeared less likely to do so. 

After matching, the effect of health insurance on delivery in a healthcare facility was positive and 

statistically significant in four of the eight countries (Cambodia, Ghana, Indonesia, and Rwanda). In these 

countries, health insurance coverage contributed to a 5-11 percentage-point increase in the receipt of 

facility-based delivery care. In Albania, Burundi, and Namibia health insurance coverage had a small 

positive but statistically non-significant effect, at a 1-3 percentage-point increase. Even after adjusting for 

possible bias due to observed characteristics, health insurance status had a negative and statistically 

significant effect in Gabon. 

 
25 

4. Discussion and Conclusion 

This study examined levels of health insurance coverage in 30 low- and middle-income countries using 

nationally representative DHS data. We also assessed the impact of health insurance status on the use of 

maternal health care in eight countries. 

Health insurance coverage is generally low across all countries. Many countries have coverage rates under 

5 percent for both women and men. The observation of low enrollment is not surprising. Health insurance 

programs in the developing world are still in the early stages, although they are attracting increasing 

attention as countries seek the means to address financial barriers that limit access to health care and try to 

find ways to protect families from catastrophic out-of-pocket expenditures (Spaan et al. 2012). 

A few countries included in this study already have widespread health insurance coverage—Rwanda, 

Gabon, Ghana, and Indonesia. Rwanda has the highest rate of coverage of any study country, with more 

than two-thirds of women and men reporting health insurance coverage in 2010. The community-based 

health insurance in Rwanda, known as Mutuelle de Santé, was initiated in 2004 and has been heavily 

promoted and subsidized by the government. The reach of the Mutuelle has steadily increased, bringing 

more Rwandans under the umbrella of health insurance due to effective promotion campaigns and low 

premiums, including a zero premium for the extremely poor, as well as the broad range of coverage of 

preventive and curative care services provided at various levels of health facilities in Rwanda (Saksena et 

al. 2011). Enrollment in the Mutuelle has risen further after passage of the mutual health insurance law in 

2007(Ministry of Health of Rwanda 2010). 

While in most countries men have slightly higher coverage rates than women, it is important to note that 

women’s rates are higher than men’s in several countries with relatively high levels of coverage. This 

finding could be because women, especially women of reproductive age, have greater demand for maternal 

health services related to childbearing, which are usually covered by health insurance schemes. 

It would be expected that social health insurance and community-based health insurance are more common 

than employer-based health insurance and commercial health insurance in low- and middle-income 

countries. Commercial health insurance is the least common scheme in these countries due to the nature of 

its financing and the complexity of administration and regulation (Drechsleri and Jüttingii 2005). 

In all countries except Cambodia, educational attainment is associated with a greater likelihood of 

participating in health insurance, even after adjusting for other factors that affect participation. This finding 

is consistent with previous studies in Rwanda and elsewhere (Adebayo et al. 2014; Cofie et al. 2013). Our 

results also indicate that the education of the head of the household matters, in addition to the individual’s 

education. In many societies household heads are often the primary decision-makers on important family 

matters, including seeking health care and enrolling in health insurance (Vallieres et al. 2013). 

Household wealth is strongly correlated with participation in health insurance. In five of the eight countries 

studied, we found a positive association between wealth and insurance status after adjusting for other 

factors. In some countries the relationship is reversed, however, with women in poorer households more 

likely to have health insurance coverage than women in more affluent households. In several countries 

health insurance mechanisms primarily target the poor and heavily subsidize or even remove premiums for 

them. In Rwanda, for example, the extremely poor are not required to pay a premium for the Mutuelle

program (Saksena et al. 2011). In Indonesia full subsidization is provided to low-income households 

(Sparrow et al. 2010)). Nonetheless, reduced premiums may not equalize access to health insurance. Despite 

the provisions of the Mutuelle program meant to target the poor, Rwandan women in the poorest households 

remain much less likely to be covered by health insurance than women in the richest households. Health 
26 

insurance premiums are just one factor influencing enrollment; many other factors also play a role—for 

example, perceived need for enrollment, knowledge of health insurance, and cultural factors, as well as an 

individual’s health condition (Acharya et al. 2013; Cofie et al. 2013; Thornton et al. 2010). Additionally, 

women in poorer households may not be knowledgeable of their health insurance status if membership is 

household based, thereby leading to underreporting from this segment of the population. 

In Cambodia and Gabon poor women are more likely than the rich to have health insurance coverage, 

suggesting that the targeting of the poor in these countries has been effective. In Cambodia, Health Equity 

Funds, the main insurance mechanisms, are intended to improve financial access to health care for the poor 

(Annear et al. 2008). Although national coverage is still low and some areas suffer from program 

discontinuation, our results confirm that Health Equity Funds are accomplishing their objective of reaching 

the poor. We were not able to examine specific health insurance types in Gabon due to lack of data. The 

literature shows that the main scheme in Gabon is a social health insurance known as CNAMGS. Launched 

in 2008, CNAMGS primarily targets the poorest people in the country (Humphreys 2013). 

The urban-rural gap in health insurance coverage disappears in most countries after controlling for 

background characteristics of women and their households, but there is regional variation in insurance 

coverage in all of the study countries. 

Our results indicate that, even after matching on a number of observed background characteristics that can 

otherwise bias estimates of the effects of health insurance on use of maternal care, health insurance coverage 

has a positive impact on women’s access to at least one antenatal care visit in Indonesia, Rwanda and 

Cambodia, and in Ghana and Indonesia insurance coverage has a positive impact on making at least four 

antenatal visits, as recommended by WHO. The characteristics of health insurance schemes in these 

countries may partially explain the higher frequency of antenatal visits. 

The primary health system in Indonesia consists of three schemes: Askes, Jamsostek, and Askeskin. Akes

and Jamsostek have limited coverage of the population and are for the formal sector. Askeskin targets the 

informal sector and has the largest share of beneficiaries, especially those with low income. Askeskin covers 

most types of care with no cost sharing. The positive effect of health insurance status both on making one 

antenatal visit and on making four antenatal visits is consistent with previous evaluations of these programs, 

in increasing the use of public health services (Aji et al. 2013; Sparrow et al. 2010). Our results are also 

consistent with findings from Taiwan, where the provision of 10 free antenatal care visits under the national 

health insurance scheme resulted in an increase in the frequency of antenatal care visits with the rollout of 

the program (Chen et al. 2003). 

Rwanda’s community-based health insurance aims to increase use of health care, especially for the poor. 

Our analysis showed that, although the great majority of Rwandan women receive antenatal care, only about 

one-third meet the recommended standard of at least four antenatal visits. A possible reason might be that 

many women lack knowledge about the extent of health services covered by insurance. 

Similarly, in Cambodia health insurance enrollment has positive effect on women making one antenatal 

visit but not the recommended four visits. This finding may be partially explained by the overall low 

coverage of health insurance in Cambodia and the limited coverage of antenatal care in Health Equity 

Funds, the primary insurance mechanism. 

The finding of a positive effect of health insurance on making four antenatal care visits in Ghana is 

consistent with another study using the same methodology but different data sources (Long et al. 2010; 

Mensah et al. 2009). 
27 

In all countries the number of antenatal care visits can also be influenced by the facility and provider, if 

health insurance schemes increase financial incentives for clinics and doctors to provide more antenatal 

care visits compared with hospitals and medical assistants (Chen et al. 2003). Other factors such as the cost 

of transportation can introduce prohibitive costs not covered by health insurance (Borghi et al. 2006). Our 

reliance on secondary data did not enable us to account for these factors in our analyses. 

We also assessed the effect of health insurance on the timing of the first antenatal visit, which has been 

largely neglected by existing research that has mostly focused on the frequency of antenatal care visits. 

Health insurance status shows positive effects on initiating antenatal care in the first trimester in three of 

the eight countries (Namibia, Burundi, and Indonesia) even after matching on covariates that can bias the 

estimate of the difference in the timing of the first antenatal care visit that can be attributed to health 

insurance coverage. Similar findings were observed in a study of community-based health insurance in 

three West African countries, in which health insurance membership was associated with greater likelihood 

of starting antenatal care within the first trimester in Senegal, although not in Mali and Ghana (Smith and 

Sulzbach 2008). While our results indicate that in Ghana health insurance affects frequency of antenatal 

care, the effect is not statistically significant for the timing of antenatal care. A similar finding was observed 

in a regression analysis of timing of antennal care in Ghana (Dixon et al. 2014). The authors argue that it is 

possible for health insurance status to have a different effect on these two outcomes of using maternal health 

care because the early use of antenatal care requires that women know that they are pregnant (Dixon et al. 

2014). 

When assessing the impact of health insurance on the use of facility-based delivery care, we found strong 

evidence of positive effects of health insurance in Cambodia, Ghana, Indonesia, and Rwanda, where 

delivery care is fully covered by health insurance. These findings are consistent with other studies (Hong 

et al. 2011; Mensah et al. 2009; Smith and Sulzbach 2008). Unexpectedly, in Gabon we found that health 

insurance status contributes to a decrease in facility delivery care. Unintended effects of health insurance 

status on use of healthcare services have been documented in other studies. In a systematic review of the 

impact of health insurance in Africa and Asia, the authors identified four studies showing negative effects 

of social health insurance on resource mobilization (Spaan et al. 2012). Another study in Columbia found 

that women who were covered by health insurance were less likely to be offered and less likely to receive 

HIV testing during antenatal visits compared with women without insurance (Ettenger et al. 2014). 

A growing number of studies have documented the impact of health insurance on the use of health services 

in developing countries, but very few have used rigorous methods, such as propensity score matching or a 

quasi-experimental approach, to assess its impact on use of maternal care across a range of countries. The 

matching technique eliminates bias due to selection on observable characteristics, but bias can still result 

from variable omission and unobserved heterogeneity. Because our assessment relied on secondary data, 

we could not include other factors such as provider type and health insurance characteristics that could 

influence both enrollment in health insurance and use of maternal care. The matching was carefully done 

but the results are not free of bias, and the estimates could be improved by including other important 

confounders, such as the outcome of a previous pregnancy. 

Our adjusted estimates of the effects of health insurance show positive and statistically significant effects 

for at least one measure of the use of maternal health care in seven of the eight countries studied. Indonesia 

stands out for the most systematic effect of health insurance across all measures of use of maternal health 

services, followed by Cambodia, Rwanda, and Ghana. Only in Albania is there no significant impact. 

Sample sizes in Albania were smaller relative to other study countries, making results more sensitive to 

bias because of model specification. 

Overall, our results point to a significant increase in the uptake of recommended standards of adequate 

maternal health care attributable to health insurance coverage. The results contribute to the body of evidence 
28 

available for health insurance policymaking, by using rigorous methods to demonstrate the impact of health 

insurance. By revealing the positive impact of health insurance on the use of antenatal care and facilitybased delivery care, our findings suggest that in some contexts enrollment in health insurance can reduce 

inequality in access to maternal health care. Additional research is needed to identify what particular aspects 

of health insurance design can improve these impacts. 

 
29 

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Appendix 

Appendix Table A. Characteristics of health insurance schemes in eight study countries 

Albania Burundi Cambodia Gabon Ghana Indonesia Namibia Rwanda 

Date established 1995 (1) 1984 (4) 1998 (7); 

2005; 2000 (7) 

2008 (9) 2003 (12) 2004; 

2008 (15) 

Information not available 

1999 (24) - 2003 

Type of insurance scheme 

SHI: Bismark model, National Health, administered by the Health Insurance Institute (1) 

La Carte D’Assurance Maladie (CAM); CBHI - 29 schemes in 2009: MSAG w/ 14 and MUSCABU w/15 schemes (6) 

CBHI: SKY and CAAFW; SHI for formal sector workers; HEF for extreme poor 

National Health Insurance and Social Welfare Fund (CNAMGS) 

District Mutual, Private Mutual and Private Commercial Schemes (12) 

Askeskin, Jamkesmas SHI, Askes (civil servants), Asabri (police/military) Jamsostek (formal private) (15, 17) 

Gov’t (Public Service Employee Med Aid Scheme PSEMAS)(19) and private “medical aid funds” (21) 

Mutual (CBHI), RAMA (Formal), Military (MMI, formal), Private 

Percent of target or total population covered 

40% of total population (2) 

10-25% (6) SKY- 3-14% 

(2008); CAAFW 31% (2009) (7); 

40% (11) 33 percent of the 

Ghanaian population (13) 

40% of 100% goal (16) 

5% of population has private, 10% has PSEMAS, rest depend on public health care (flat fees) (19,20) 

91% of total in 2010 (25) 

Participating providers 

Public and private, in 2011: 610 personnel, 690 pharmacies, 160 agencies, 80 drugstores, 421 PHC centers, 39 secondary and tertiary facilities (1) 

Public, private, NGO 

Information not available 

Public and private health structures and pharmacies (11) 

Local health compounds, health centres, clinics, hospitals (14) 

Public (926) and private (220) providers, 1146 facilities (15) 

Information not available 

Local health centers (dispensaries, health posts, centers, district and national hospitals), public only (26) 

(Continued...)

35 
Appendix Table A. – Continued

Albania Burundi Cambodia Gabon Ghana Indonesia Namibia Rwanda

Enrollment requirements 

Compulsory for all employed 

Information not available 

CBHI- voluntary, informal sector ; HEF- extreme poor; SHIcompulsory, formal sector (7) 

Currently: Poor Gabonese people and government workers; goal is all 1.5 million citizens by the end of 2014 (10) 

Every Ghanaian except Armed Forces and Police (12) 

Mandatory for civil servants, formal private sector, police and military (17) 

PSEMAS- civil servant, Private - voluntary 

Every Rwandan (24) 

Premium payments 

3-7% for workers “active” persons, (50/50 between employer/employee( state covers (1) 

varies Free for extreme 

poor through Health Equity Fund HEF (7) 

2.5-4.1% of pay, low income 100% subsidized (11) 

GH¢7.2 (minimum) to GH¢48.00 (maximum), 0¢ for indigent, pregnant women, children <18 and adults >70 yrs old, annually (12) 

Askeskin- fully subsidized, (@ Rp5000/44c per month) (17) 

In PSEMAS, employers pay 20% of premium, private plans vary 

~ $6 per person per year + 10% copay at health center/hospital (26), sliding scale 

Coverage of maternal health services 

Prenatal care is free, but poor quality (3) 

100% (4) Vouchers for 

women at some facilities starting in 2007: 3-4 ANC visits, delivery, 1 postnatal visit, fees are waived (8) 

Maternal health care- 100% (9) 

Prenatal – 100% Antenatal and 

normal or complicated delivery – 100% (18) 

Information not available 

ANC- 100% 

Antenatal care Delivery normal – 100% Delivery- normal Delivery 

complicatedcertain procedures 100% (13) 

Deliverycomplicated Sources: 

1. http://www.rsijournal.eu/ARTICLES/December_2013/7.pdf 2. http://qshr.org/wp-content/uploads/2013/07/4.17-Enkelejda.pdf 3. http://hrp.law.harvard.edu/wp-content/uploads/2013/03/women-of-the-world.pdf 4. http://heapol.oxfordjournals.org/content/26/suppl_2/ii63.full 5. http://www.equitablehealthfinancing.org/countries/burundi 6. http://www.who.int/healthsystems/topics/financing/healthreport/48_CHI.pdf 

(Continued...)

36 
Appendix Table A. – Continued

7. http://www.who.int/healthsystems/topics/financing/healthreport/48_CHI.pdf 

8. http://www.rbfhealth.org/system/files/Can%20vouchers%20deliver%20An%20evaluation%20of%20subsidies%20for%20maternal%20health%20care%20in%20Cambodia.pdf 9. http://www.who.int/bulletin/volumes/91/5/13-020513/en/ 10. http://www.gemalto.com/brochures/download/gabon.pdf 11. http://www.oxfordbusinessgroup.com/news/universal-coverage-preparing-public-insurance-system-launch-2014 

12. http://www.nhis.gov.gh/ 13. http://www.ifpri.org/sites/default/files/publications/ifpridp01309.pdf 14. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3645172/ 

15. http://healthmarketinnovations.org/program/jamkesmas-scheme16. https://openknowledge.worldbank.org/bitstream/handle/10986/13304/74996.pdf?sequence=1 17. http://www.smeru.or.id/report/workpaper/askeskin/askeskin_eng.pdf 18. http://www-wds.worldbank.org/servlet/WDSContentServer/IW3P/IB/2011/03/08/000333037_20110308235022/Rendered/PDF/600380BRI0Heal10BOX358307B01PUBLIC1.pdf 19. http://www.economist.com.na/special-focus/1499-health-insurance-affordability-still-a-challenge20. http://www.brookings.edu/~/media/press/books/2011/theimpactofhealthinsuranceonlowandmiddleincomecountries/theimpactofhealthinsurance_fulltext.pdf#page=48 21. http://sun.com.na/health/medical-aids-get-grading.61843 22. http://www.iss.nl/fileadmin/ASSETS/iss/Documents/DevISSues/DevISSues_Volume_10__number_2__November_2008_01.pdf 23. http://www.shopsproject.org/sites/default/files/resources/Final%20Namibia%20PSA%204-19-11.pdf 24. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2649549/ 25. http://www.globalhealthcheck.org/?p=324 26. http://www.gov.rw/Health-System 

37 
38 

Appendix Table B. Estimates of the (logit) propensity score models, Albania 

 Full sample Sub-sample 

Variable Coefficient S.E. Coefficient S.E. 

Maternal age at most recent birth (continuous)1 0.191 0.148 

Maternal age at most recent birth squared1 -0.002 0.002 

Education (ref.: none) 

Primary 16.914 0.000 15.823 0.000 

Secondary or higher 17.459*** 0.214 16.490*** 0.217 

Marital status (ref.: never married)1 

Currently married -0.653 2.256 

Formerly married -0.142 2.329 

Birth order (ref.: first child) 

Second -0.571* 0.233 -0.300 0.212 

Third -0.923** 0.295 -0.524* 0.256 

Fourth or more -1.170** 0.360 -0.652* 0.313 

Employment status (ref.: not currently employed) 

Currently employed 2.198*** 0.180 2.373*** 0.183 

Education of head of household (ref.: none) 

Primary -0.056 0.527 0.230 0.588 

Secondary or higher 0.251 0.537 0.497 0.598 

Wealth terciles (ref.: first tercile) 

Second 0.269 0.296 0.389 0.305 

Third 1.151** 0.367 1.260*** 0.377 

Mass media exposure2 

Exposed to media at least once a week 0.335 0.829 0.183 0.833 

Residence (ref.: rural) 

Urban 0.436 0.284 0.430 0.291 

Region (ref.: Coastal) 

Central 0.185 0.257 0.118 0.260 

Mountain 0.895*** 0.249 0.876*** 0.253 

Urban Tirana -0.139 0.298 -0.152 0.298 

 

Constant -22.948 0.000 -19.591*** 1.008 

N 1,325 1,277 

log pseudolikelihood -460 -437 

* p<0.05. ** p< 0.01, *** p< 0.001 

1 Not included in the estimation of the propensity score for the sub-sample 

2 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 

 
39 

Appendix Table C. Estimates of the (logit) propensity score models, Burundi 

 Full sample Sub-sample 

Variable Coefficient S.E. Coefficient S.E. 

Maternal age at most recent birth (continuous)1 0.150* 0.067 

Maternal age at most recent birth (squared)1 -0.002 0.001 

Education (ref.: none) 

Primary 0.611*** 0.125 0.531*** 0.122 

Secondary or higher 1.520*** 0.188 1.489*** 0.181 

Marital status (ref.: never married)1 

Currently married 0.792** 0.288 

Formerly married 0.140 0.352 

Birth order (ref.: first child) 

Second -0.072 0.172 0.279 0.161 

Third -0.071 0.189 0.497** 0.164 

Fourth or more -0.488* 0.199 0.455** 0.139 

Employment status (ref.: not currently employed)1 

Currently working 0.463*** 0.129 

Education of Household head (ref.: none) 

Primary 0.148 0.126 0.150 0.125 

Secondary or higher 1.064*** 0.178 1.188*** 0.173 

Wealth quintiles (ref.: poorest) 

Second quintile 0.193 0.224 0.190 0.223 

Third quintile 0.289 0.222 0.303 0.221 

Fourth quintile 0.948*** 0.206 0.928*** 0.206 

Richest 1.630*** 0.210 1.638*** 0.209 

Mass media exposure2 

Exposed to media at least once a week 0.205 0.119 0.225 0.118 

Residence (ref.: rural) 

Urban 0.723*** 0.151 0.612*** 0.149 

Region (ref.: Bujumbura) 

North 1.123*** 0.196 1.182*** 0.190 

Centre-east 1.521*** 0.193 1.568*** 0.189 

West 0.482* 0.222 0.494* 0.217 

South 1.078*** 0.196 1.172*** 0.190 

 

Constant -8.652*** 1.000 -5.178*** 0.279 

N 4,824 4,775 

log pseudolikelihood -1,451 -1,470 

* p<0.05. ** p< 0.01, *** p< 0.001 

1 Not included in the estimation of the propensity score for the sub-sample 

2 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 

 
40 

Appendix Table D. Estimates of the (logit) propensity score models, Cambodia 

 Full sample Sub-sample 

Variable Coefficient S.E. Coefficient S.E. 

Maternal age at birth -0.107* 0.048 -0.087 0.054 

Maternal age at birth squared 0.002* 0.001 0.001 0.001 

Education (ref.: no education) 

Primary education -0.203* 0.087 -0.153 0.101 

Secondary or higher -0.855*** 0.142 -0.760*** 0.151 

Marital status (ref.: never married)1 

Currently married -1.129 1.457 

Formerly married -1.006 1.466 

Birth order (ref.: first child) 

2 0.291** 0.108 0.230* 0.114 

3 0.280* 0.130 0.214 0.140 

4 or higher 0.380** 0.141 0.412** 0.153 

Employment status (ref.: not currently employed) 

Currently employed 0.104 0.079 0.065 0.086 

Education of head of the household (ref.: no education) 

Primary education -0.082 0.090 -0.177 0.101 

Secondary or higher -0.316** 0.119 -0.423*** 0.127 

Mass media exposure (ref.: not exposed)2 

Exposed to media at least once a week -0.009 0.077 -0.066 0.085 

Wealth tercile (ref.: first tercile) 

Second -0.453*** 0.085 -0.532*** 0.093 

Third -1.286*** 0.129 -1.343*** 0.136 

Residence (ref.: rural) 

Urban 0.130 0.110 0.096 0.118 

Provinces (ref.: Northwestern provinces)3 

Southern provinces -0.110 0.099 -0.257* 0.112 

Southwestern provinces 0.535*** 0.091 0.451*** 0.103 

Siem Reap, Takeo, and Kandal -0.702*** 0.135 -0.835*** 0.146 

Phnom Penh -1.410*** 0.428 -1.454*** 0.430 

 

Constant 1.684 1.612 0.576 0.762 

N 5,999 5,228 

log pseudolikelihood -2,489 -2,066 

* p<0.05. ** p< 0.01, *** p< 0.001 

1 Not included in the estimation of the propensity score for the sub-sample 

2 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 

3 Northwestern provinces include Kampong Thom, Kratie, Preah Vihear & Steung Treng, Mondol Kiri/Rattanak Kiri 

Southern provinces include Kampong Cham, Svay Rieng, Kampong Speu, Pursat, and Kampot/Kep 

Southwestern provinces include Kampong Chhang, Preah Sihanouk/Daoh Kong, Battambang/Pailin, Banteay 

Mean Chey, and Otdar Mean Chey 

 
41 

Appendix Table E. Estimates of the (logit) propensity score models, Gabon 

 Full sample Sub-sample 

Variable Coefficient S.E. Coefficient S.E. 

Maternal age at most recent birth (continuous) 0.101* 0.042 0.075 0.043 

Maternal age at most recent birth (squared) -0.001 0.001 -0.001 0.001 

Education (ref.: none) 

Primary 1.345*** 0.183 1.480*** 0.208 

Secondary or higher 1.433*** 0.186 1.538*** 0.208 

Marital status (ref.: never married)1 

Currently married -0.089 0.100 

Formerly married -0.316* 0.149 

Birth order (ref.: first child) 

Second 0.189 0.116 0.175 0.116 

Third 0.319* 0.138 0.304* 0.140 

Fourth or more 0.484** 0.148 0.416** 0.149 

Employment status (ref.: not currently employed)1 

Currently employed 0.178* 0.077 

Education of household head (ref.: none) 

Primary -0.015 0.133 0.021 0.140 

Secondary -0.022 0.132 0.047 0.137 

Wealth quintiles (ref.: poorest) 

Second quintile -0.259* 0.106 -0.249* 0.108 

Third quintile -0.348** 0.127 -0.361** 0.128 

Fourth quintile -0.520*** 0.144 -0.533*** 0.145 

Richest -0.198 0.158 -0.227 0.159 

Mass media exposure2 

Exposed to media at least once a week -0.172 0.111 -0.146 0.118 

Residence (ref.: rural) 

Urban -0.055 0.092 0.009 0.095 

Region (ref.: Libreville-Port-Gentil) 

Estuaire 0.458** 0.157 0.463** 0.159 

Haut-Ogooué 1.725*** 0.151 1.678*** 0.151 

Moyen-Ogooué 1.206*** 0.167 1.221*** 0.169 

Ngounié 1.522*** 0.155 1.480*** 0.158 

Nyanga 0.691*** 0.161 0.666*** 0.165 

Ogooué Maritime 0.347* 0.165 0.338* 0.168 

Ogooué-Ivindo 1.801*** 0.156 1.707*** 0.161 

Ogooué-Lolo 1.413*** 0.163 1.384*** 0.165 

Woleu-Ntem 0.234 0.166 0.303 0.172 

 

Constant -3.768*** 0.590 -3.667*** 0.616 

N 3,935 3,626 

log pseudolikelihood -2,320 -2,173 

* p<0.05. ** p< 0.01, *** p< 0.001 

1 Not included in the estimation of the propensity score for the sub-sample 

2 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 
42 

Appendix Table F. Estimates of the (logit) propensity score models, Ghana 

 Full sample Sub-sample 

Variable Coefficient S.E. Coefficient S.E. 

Maternal age at birth 0.326*** 0.071 0.320*** 0.072 

Maternal age at birth squared -0.005*** 0.001 -0.005*** 0.001 

Education (ref.: no education) 

Primary education -0.077 0.165 -0.100 0.167 

Secondary or higher 0.459** 0.167 0.444** 0.168 

Marital status (ref.: never married) 

Currently married 0.682* 0.270 0.664* 0.277 

Formerly married 0.484 0.339 0.506 0.346 

Birth order (ref.: first child) 

2 -0.689*** 0.183 -0.720*** 0.186 

3 -0.965*** 0.206 -0.943*** 0.208 

4 or higher -0.875*** 0.221 -0.842*** 0.222 

Employment status (ref.: not currently employed)1 

Currently employed 0.052 0.161 

Education of head of the household (ref.: no education) 

Primary education -0.101 0.186 -0.079 0.188 

Secondary or higher 0.360* 0.156 0.331* 0.157 

Mass media exposure (ref.: not exposed)2 

Exposed to media at least once a week 0.106 0.141 0.129 0.144 

Wealth quintile (ref.: first quintile) 

Second 0.786*** 0.168 0.678*** 0.170 

Middle 0.948*** 0.197 0.867*** 0.199 

Fourth 1.552*** 0.221 1.455*** 0.222 

Highest 2.009*** 0.275 1.901*** 0.276 

Residence (ref.: rural) 

Urban 0.002 0.147 0.004 0.148 

Region (ref.: Western) 

Central -0.897*** 0.271 -0.858** 0.274 

Greater Accra -1.484*** 0.264 -1.443*** 0.265 

Volta -0.012 0.245 0.031 0.250 

Eastern 0.817*** 0.239 0.818*** 0.243 

Ashanti 0.020 0.212 0.023 0.214 

Brong Ahafo 1.236*** 0.239 1.247*** 0.243 

Northern 0.580* 0.244 0.528* 0.246 

Upper West 1.336*** 0.245 1.251*** 0.248 

 

Constant -7.225*** 1.011 -7.002*** 1.017 

N 1,874 1,796 

log pseudolikelihood -1,064 -1,036 

* p<0.05. ** p< 0.01, *** p< 0.001 

1 Not included in the estimation of the propensity score for the sub-sample 

2 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 
43 

Appendix Table G. Estimates of the (logit) propensity score models, Indonesia 

 Full sample Sub-sample

Variable Coefficient S.E. Coefficient S.E.

Maternal age at most recent birth (ref.: 15-19) 

20-24 -0.012 0.104 0.019 0.107 

25-29 0.182 0.104 0.194 0.107

30-34 0.344** 0.109 0.353** 0.111

35-39 0.477*** 0.114 0.504*** 0.115

40-44 0.432*** 0.126 0.453*** 0.125

45-49 0.342 0.181 0.382* 0.184 

Education (ref.: none) 

Primary 0.408** 0.126 0.090 0.143

Secondary or higher 0.707*** 0.127 0.375** 0.144

Marital status (ref.: never married) 

Currently married 0.796 0.679 0.347 0.725

Formerly married 0.697 0.687 0.261 0.732

Birth order (ref.: first child)1

Second 0.031 0.047 

Third 0.015 0.060 

Fourth or more 0.041 0.068 

Birth order 2 

Second or higher 0.040 0.046

Employment status (ref.: not currently employed)

Currently employed 0.244*** 0.035 0.264*** 0.036

Education of household head 

Primary education or higher 0.329*** 0.090 0.225* 0.094

Wealth quintiles (ref.: poorest) 

Second quintile -0.067 0.053 -0.100 0.054

Third quintile -0.133* 0.058 -0.162** 0.058 

Fourth quintile 0.015 0.061 -0.015 0.062

Richest 0.386*** 0.067 0.363*** 0.068

Mass media exposure3 

Exposed to media at least once a week 0.124* 0.054 0.054 0.055

Residence (ref.: rural) 

Urban 0.250*** 0.039 0.241*** 0.040 

Region (ref.: Sumatera) 

Java -0.144** 0.050 -0.151** 0.050

Bali and Nusa Tenggara 0.747*** 0.066 0.701*** 0.067

Kalimantan 0.331*** 0.061 0.339*** 0.062 

Sulawesi 0.322*** 0.053 0.332*** 0.054

Maluku and Papua 0.558*** 0.064 0.630*** 0.068

 

Constant -2.931*** 0.699 -1.989** 0.746

N 14,958 14,319 

log pseudolikelihood -9,690 -9,329

* p<0.05. ** p< 0.01, *** p< 0.001 

1 Not included in the estimation of the propensity score for the sub-sample

2 Not included in the estimation of the propensity score for the full sample

3 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 
44 

Appendix Table H. Estimates of the (logit) propensity score models, Namibia 

 Full sample Sub-sample 

Variable Coefficient S.E. Coefficient S.E. 

Maternal age at birth 0.203* 0.096 0.255** 0.096 

Maternal age at birth squared -0.002 0.002 -0.002 0.002 

Education (ref.: no education) 

Primary education -0.298 0.411 -0.505 0.416 

Secondary or higher 0.728 0.384 0.451 0.386 

Marital status (ref.: never married)1 

Currently married 0.894*** 0.158 

Formerly married 0.456 0.308 

Birth order (ref.: first child) 

2 -0.560** 0.195 -0.395* 0.191 

3 -0.669** 0.229 -0.498* 0.225 

4 or higher -0.990*** 0.262 -0.763** 0.260 

Employment status (ref.: not currently employed) 

Currently employed 1.230*** 0.150 1.262*** 0.151 

Education of head of the household (ref.: no education) 

Primary education -0.246 0.277 -0.190 0.278 

Secondary or higher 0.654** 0.254 0.750** 0.252 

Mass media exposure (ref.: not exposed)2 

Exposed to media at least once a week 0.884* 0.360 1.003** 0.376 

Wealth quintile (ref.: first quintile) 

Second 0.245 0.380 0.197 0.381 

Middle 1.021** 0.349 0.993** 0.349 

Fourth 1.783*** 0.363 1.779*** 0.360 

Highest 3.389*** 0.389 3.452*** 0.388 

Residence (ref.: rural) 

Urban -0.405* 0.176 -0.507** 0.175 

Region (ref.: Caprivi) 

Erongo 0.148 0.385 0.166 0.388 

Hardap 0.161 0.430 -0.028 0.438 

Karas 0.741 0.400 0.650 0.400 

Kavango 0.812* 0.408 0.933* 0.410 

Khomas 0.565 0.362 0.458 0.364 

Kunene 0.537 0.457 0.624 0.456 

Ohangwena 1.849*** 0.398 1.610*** 0.398 

Omusati 1.298*** 0.391 1.102** 0.388 

Oshana 0.483 0.397 0.329 0.397 

Oshikoto 0.979* 0.392 0.888* 0.394 

Otjozondjupa 0.695 0.384 0.595 0.386 

 

Constant -10.931*** 1.453 -11.252*** 1.471 

N 3,289 3,146 

log pseudolikelihood -773 -769 

* p<0.05. ** p< 0.01, *** p< 0.001 

1 Not included in the prediction of the propensity score for the sub-sample 

2 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 

 
45 

Appendix Table I. Estimates of the (logit) propensity score models, Rwanda 

 Full sample Sub-sample 

Variable Coefficient S.E. Coefficient S.E. 

Maternal age at birth 0.169*** 0.042 0.157*** 0.043 

Maternal age at birth squared -0.002*** 0.001 -0.002** 0.001 

Education (ref.: no education) 

Primary education 0.194* 0.080 0.174* 0.081 

Secondary or higher 0.531*** 0.150 0.554*** 0.153 

Marital status (ref.: never married) 

Currently married 0.494*** 0.119 0.526*** 0.121 

Formerly married -0.142 0.141 -0.102 0.144 

Birth order (ref.: first child) 

2 -0.480*** 0.102 -0.470*** 0.103 

3 -0.576*** 0.118 -0.559*** 0.119 

4 or higher -0.896*** 0.123 -0.868*** 0.124 

Employment status (ref.: not currently employed) 

Currently employed -0.162* 0.079 -0.155 0.080 

Education of head of the household (ref.: no education) 

Primary education 0.026 0.076 0.024 0.077 

Secondary or higher 0.146 0.139 0.134 0.140 

Mass media exposure (ref.: not exposed)1 

Exposed to media at least once a week 0.330*** 0.068 0.331*** 0.070 

Wealth quintile (ref.: first quintile) 

Second 0.297*** 0.087 0.276** 0.089 

Middle 0.536*** 0.096 0.515*** 0.097 

Fourth 0.750*** 0.104 0.723*** 0.105 

Highest 0.962*** 0.135 0.923*** 0.136 

Residence (ref.: rural) 

Urban 0.187 0.126 0.185 0.127 

Region (ref.: Kigali city) 

South 1.107*** 0.149 1.090*** 0.150 

West 1.397*** 0.154 1.383*** 0.155 

North 1.534*** 0.163 1.506*** 0.164 

East 0.806*** 0.148 0.791*** 0.149 

 

Constant -3.456*** 0.624 -3.264*** 0.634 

N 6,150 6,041 

log pseudolikelihood -3,352 -3,273 

* p<0.05. ** p< 0.01, *** p< 0.001 

1 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 
Appendix Table J. Mean biases of covariates before and after matching, Albania 

Variable 

Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

 Mean % bias 

reduction t-statistic Sample Insured Uninsured % bias p>|t| Insured Uninsured % bias p>|t| 

Maternal age at most recent birth (continuous)1

Unmatched 28.869 27.227 30.1 4.36 0.000 

Matched 28.835 29.630 -14.6 51.6 -1.64 0.101 

Maternal age at most recent birth squared1

Unmatched 860.880 773.100 27.8 4.06 0.000 

Matched 859.210 912.260 -16.8 39.6 -1.85 0.064 

Marital status1

Never married Unmatched 0.000 0.001 -4.4 -0.51 0.609 

Matched 0.000 0.000 -0.1 98.8 -0.06 0.956 

Currently married Unmatched 0.982 0.989 -5.6 -0.89 0.376 

Matched 0.981 0.951 24.8 -344.6 1.92 0.055 

Formerly married Unmatched 0.018 0.010 6.5 1.04 0.298 

Matched 0.019 0.049 -25.2 -289.5 -1.92 0.055 

Education 

None Unmatched 0.000 0.014 -17.0 -1.99 0.046 0.000 0.014 -17.0 -1.99 0.046 

Matched 0.000 0.000 -0.2 98.8 -0.21 0.830 0.000 0.000 -0.3 98.1 -0.26 0.795 

Primary Unmatched 0.273 0.689 -91.4 -13.35 0.000 0.273 0.689 -91.4 -13.35 0.000 

Matched 0.281 0.289 -1.9 97.9 -0.22 0.827 0.273 0.293 -4.5 95.1 -0.51 0.612 

Secondary or higher Unmatched 0.727 0.297 95.2 13.96 0.000 0.727 0.297 95.2 13.96 0.000 

Matched 0.719 0.710 1.9 98.0 0.22 0.824 0.727 0.707 4.6 95.2 0.51 0.607 

Employment status 

Currently not employed Unmatched 0.364 0.878 -124.9 -20.82 0.000 0.364 0.878 -124.9 -20.82 0.000 

Matched 0.375 0.372 0.7 99.5 0.06 0.949 0.364 0.357 1.7 98.6 0.16 0.871 

Currently employed Unmatched 0.636 0.122 124.9 20.82 0.000 0.636 0.122 124.9 20.82 0.000 

Matched 0.625 0.628 -0.7 99.5 -0.06 0.949 0.636 0.643 -1.7 98.6 -0.16 0.871 

Mass media exposure1

No exposure to media Unmatched 0.007 0.024 -13.4 -1.73 0.084 0.007 0.024 -13.4 -1.73 0.084 

Matched 0.007 0.006 0.9 93.5 0.15 0.881 0.008 0.006 1.9 86.0 0.32 0.752 

Exposure to media at least once a week 

Unmatched 0.993 0.976 13.4 1.73 0.084 0.993 0.976 13.4 1.73 0.084 

Matched 0.993 0.994 -0.9 93.5 -0.15 0.881 0.992 0.994 -1.9 86.0 -0.32 0.752 

Education of household head 

None Unmatched 0.018 0.050 -17.4 -2.28 0.023 0.018 0.050 -17.4 -2.28 0.023 

Matched 0.019 0.019 0.0 99.9 0.00 0.999 0.016 0.016 -0.3 98.2 -0.05 0.960 

Primary Unmatched 0.349 0.631 -58.8 -8.65 0.000 0.349 0.631 -58.8 -8.65 0.000 

Matched 0.360 0.383 -4.9 91.7 -0.56 0.578 0.356 0.398 -8.9 84.9 -0.99 0.323 

Secondary or higher Unmatched 0.633 0.319 66.1 9.86 0.000 0.633 0.319 66.1 9.86 0.000 

Matched 0.622 0.598 4.9 92.6 0.55 0.581 0.628 0.585 9.1 86.2 0.99 0.320 

(Continued...)

46 
Appendix Table J. – Continued

Variable 

Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

 Mean % bias 

reduction t-statistic Sample Insured Uninsured % bias p>|t| Insured Uninsured % bias p>|t| 

Birth order Eldest Unmatched 0.342 0.259 18.1 2.74 0.006 0.342 0.259 18.1 2.74 0.006 

Matched 0.337 0.334 0.7 96.2 0.08 0.939 0.324 0.345 -4.6 74.7 -0.50 0.619 

Second Unmatched 0.378 0.336 8.8 1.30 0.192 0.378 0.336 8.8 1.30 0.192 

Matched 0.378 0.338 8.5 3.4 0.98 0.329 0.403 0.377 5.4 38.0 0.60 0.549 

Third Unmatched 0.182 0.229 -11.6 -1.67 0.095 0.182 0.229 -11.6 -1.67 0.095 

Matched 0.184 0.218 -8.6 26.1 -1.00 0.320 0.182 0.182 0.1 99.6 0.01 0.995 

Fourth or more Unmatched 0.098 0.176 -22.8 -3.15 0.002 0.098 0.176 -22.8 -3.15 0.002 

Matched 0.101 0.110 -2.7 88.3 -0.34 0.731 0.091 0.096 -1.6 93.2 -0.21 0.837 

Wealth terciles 

First Unmatched 0.200 0.490 -63.9 -8.88 0.000 0.200 0.490 -63.9 -8.88 0.000 

Matched 0.206 0.226 -4.4 93.2 -0.56 0.579 0.186 0.223 -8.2 87.2 -1.04 0.301 

Second Unmatched 0.364 0.409 -9.2 -1.35 0.176 0.364 0.409 -9.2 -1.35 0.176 

Matched 0.375 0.418 -8.9 3.3 -1.03 0.306 0.391 0.437 -9.4 -1.8 -1.04 0.297 

Third Unmatched 0.436 0.102 81.3 14.03 0.000 0.436 0.102 81.3 14.03 0.000 

Matched 0.419 0.356 15.4 81.1 1.50 0.134 0.423 0.340 20.2 75.2 1.92 0.055 

Residence 

Rural Unmatched 0.273 0.615 -73.4 -10.56 0.000 0.273 0.615 -73.4 -10.56 0.000 

Matched 0.281 0.308 -5.9 91.9 -0.70 0.485 0.265 0.299 -7.2 90.2 -0.84 0.400 

Urban Unmatched 0.727 0.385 73.4 10.56 0.000 0.727 0.385 73.4 10.56 0.000 

Matched 0.719 0.692 5.9 91.9 0.70 0.485 0.735 0.701 7.2 90.2 0.84 0.400 

Region Coastal Unmatched 0.167 0.257 -22.1 -3.12 0.002 0.167 0.257 -22.1 -3.12 0.002 

Matched 0.172 0.166 1.5 93.0 0.19 0.846 0.182 0.138 10.8 51.0 1.35 0.177 

Central Unmatched 0.233 0.291 -13.4 -1.93 0.053 0.233 0.291 -13.4 -1.93 0.053 

Matched 0.240 0.200 9.1 32.1 1.11 0.267 0.213 0.173 9.2 31.3 1.15 0.252 

Mountain Unmatched 0.349 0.344 1.1 0.16 0.870 0.349 0.344 1.1 0.16 0.870 

Matched 0.333 0.384 -10.7 -867.9 -1.23 0.219 0.332 0.431 -20.9 -1783.2 -2.31 0.021 

Urban Tirana Unmatched 0.251 0.108 38.0 6.23 0.000 0.251 0.108 38.0 6.23 0.000 

Matched 0.255 0.250 1.3 96.6 0.13 0.896 0.273 0.258 4.0 89.5 0.38 0.701 

1 Not included in the estimation of the propensity score for the sub-sample 2 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 

47 
Appendix Table K. Mean biases of covariates before and after matching, Burundi 

Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

 Mean % bias 

reduction t-statistic Variable Sample Insured Uninsured % bias p>|t| Insured Uninsured % bias p>|t| 

Maternal age at most recent birth (continuous)1

Unmatched 29.446 28.296 17.1 4.04 0.000 

Matched 29.394 30.206 -12.1 29.4 -2.28 0.023 

Maternal age at most recent birth squared1

Unmatched 907.900 850.520 14.1 3.35 0.001 

Matched 905.460 957.270 -12.7 9.7 -2.38 0.017 

Marital status1

Never married Unmatched 0.030 0.034 -2.1 -0.50 0.618 

Matched 0.031 0.023 4.2 -100.9 0.83 0.405 

Currently married Unmatched 0.927 0.877 17.0 3.85 0.000 

Matched 0.925 0.954 -9.9 41.8 -2.28 0.023 

Formerly married Unmatched 0.043 0.090 -18.9 -4.15 0.000 

Matched 0.044 0.022 8.9 52.9 2.28 0.023 

Education None Unmatched 0.196 0.538 -75.9 -17.24 0.000 0.196 0.538 -75.9 -17.24 0.000 

Matched 0.201 0.238 -8.1 89.3 -1.64 0.102 0.198 0.202 -1.0 98.7 -0.20 0.840 

Primary Unmatched 0.402 0.407 -1.1 -0.26 0.793 0.402 0.407 -1.1 -0.26 0.793 

Matched 0.413 0.404 1.8 -67.2 0.33 0.741 0.406 0.403 0.6 44.8 0.11 0.913 

Secondary or higher Unmatched 0.402 0.055 90.9 30.23 0.000 0.402 0.055 90.9 30.23 0.000 

Matched 0.386 0.358 7.3 92.0 1.06 0.287 0.397 0.395 0.4 99.6 0.06 0.956 

Employment status1

Not currently employed Unmatched 0.167 0.210 -10.8 -2.57 0.010 

Matched 0.172 0.164 1.9 82.6 0.36 0.717 

Currently employed Unmatched 0.833 0.790 10.8 2.57 0.010 

Matched 0.828 0.836 -1.9 82.6 -0.36 0.717 

Mass media exposure No access to any media Unmatched 0.197 0.443 -54.6 -12.43 0.000 0.197 0.443 -54.6 -12.43 0.000 

Matched 0.203 0.217 -3.3 94.0 -0.66 0.506 0.199 0.193 1.3 97.6 0.27 0.786 

Exposure to media at least once a week 

Unmatched 0.803 0.557 54.6 12.43 0.000 0.803 0.557 54.6 12.43 0.000 Matched 0.797 0.783 3.3 94.0 0.66 0.506 0.801 0.807 -1.3 97.6 -0.27 0.786 

Education of household head None Unmatched 0.193 0.478 -63.3 -14.34 0.000 0.193 0.478 -63.3 -14.34 0.000 

Matched 0.198 0.188 2.3 96.4 0.48 0.631 0.192 0.179 2.9 95.4 0.62 0.533 

Primary Unmatched 0.378 0.462 -17.1 -4.14 0.000 0.378 0.462 -17.1 -4.14 0.000 

Matched 0.388 0.392 -0.9 94.8 -0.17 0.868 0.382 0.385 -0.6 96.5 -0.11 0.912 

Secondary or higher Unmatched 0.429 0.060 95.0 31.16 0.000 0.429 0.060 95.0 31.16 0.000 

Matched 0.414 0.420 -1.5 98.4 -0.22 0.826 0.426 0.436 -2.6 97.2 -0.38 0.703 

(Continued...)

48 
Appendix Table K. – Continued

 Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

 Mean % bias 

reduction t-statistic Variable Sample Insured Uninsured % bias p>|t| Insured Uninsured % bias p>|t| 

Birth order Eldest Unmatched 0.185 0.185 -0.2 -0.06 0.955 0.185 0.185 -0.2 -0.06 0.955 

Matched 0.184 0.132 13.2 -5577.8 2.61 0.009 0.183 0.180 0.7 -221.1 0.14 0.889 

Second Unmatched 0.193 0.177 4.2 1.05 0.296 0.193 0.177 4.2 1.05 0.296 

Matched 0.189 0.185 1.1 73.1 0.21 0.835 0.196 0.163 8.6 -103.8 1.62 0.106 

Third Unmatched 0.182 0.151 8.4 2.10 0.035 0.182 0.151 8.4 2.10 0.035 

Matched 0.181 0.201 -5.5 34.0 -0.96 0.335 0.179 0.157 5.9 30.0 1.08 0.280 

Fourth or higher Unmatched 0.441 0.487 -9.4 -2.28 0.022 0.441 0.487 -9.4 -2.28 0.022 

Matched 0.446 0.482 -7.1 24.4 -1.30 0.193 0.442 0.500 -11.7 -24.6 -2.16 0.031 

Wealth quintile Poorest Unmatched 0.056 0.222 -49.5 -10.31 0.000 0.056 0.222 -49.5 -10.31 0.000 

Matched 0.057 0.065 -2.2 95.6 -0.57 0.571 0.057 0.057 0.0 100.0 0.00 1.000 

2nd quintile Unmatched 0.072 0.220 -43.1 -9.20 0.000 0.072 0.220 -43.1 -9.20 0.000 

Matched 0.073 0.084 -3.0 93.1 -0.70 0.481 0.073 0.073 0.0 100.0 0.00 1.000 

3rd quintile Unmatched 0.080 0.200 -35.2 -7.65 0.000 0.080 0.200 -35.2 -7.65 0.000 

Matched 0.082 0.079 0.9 97.6 0.20 0.842 0.081 0.076 1.7 95.2 0.40 0.689 

4th quintile Unmatched 0.149 0.181 -8.6 -2.06 0.040 0.149 0.181 -8.6 -2.06 0.040 

Matched 0.153 0.159 -1.6 81.7 -0.30 0.765 0.147 0.154 -2.0 77.3 -0.38 0.706 

Richest Unmatched 0.644 0.176 108.0 28.81 0.000 0.644 0.176 108.0 28.81 0.000 

Matched 0.634 0.614 4.7 95.6 0.78 0.434 0.642 0.641 0.3 99.7 0.06 0.955 

Residence Rural Unmatched 0.519 0.864 -80.3 -22.76 0.000 0.519 0.864 -80.3 -22.76 0.000 

Matched 0.533 0.545 -2.7 96.6 -0.43 0.664 0.523 0.560 -8.5 89.5 -1.35 0.176 

Urban Unmatched 0.481 0.136 80.3 22.76 0.000 0.481 0.136 80.3 22.76 0.000 

Matched 0.467 0.455 2.7 96.6 0.43 0.664 0.477 0.440 8.5 89.5 1.35 0.176 

Region Bujumbura Unmatched 0.213 0.074 40.5 11.82 0.000 0.213 0.074 40.5 11.82 0.000 

Matched 0.219 0.201 5.1 87.3 0.80 0.425 0.214 0.218 -1.3 96.9 -0.20 0.844 

North Unmatched 0.202 0.243 -9.9 -2.37 0.018 0.202 0.243 -9.9 -2.37 0.018 

Matched 0.206 0.160 11.0 -10.5 2.18 0.030 0.205 0.132 17.5 -76.4 3.61 0.000 

Center East Unmatched 0.296 0.283 3.0 0.73 0.466 0.296 0.283 3.0 0.73 0.466 

Matched 0.279 0.308 -6.5 -118.0 -1.19 0.234 0.294 0.321 -6.1 -105.0 -1.11 0.267 

West Unmatched 0.083 0.192 -32.1 -7.04 0.000 0.083 0.192 -32.1 -7.04 0.000 

Matched 0.085 0.082 0.9 97.3 0.20 0.845 0.084 0.090 -1.7 94.7 -0.38 0.703 

South Unmatched 0.206 0.208 -0.6 -0.13 0.893 0.206 0.208 -0.6 -0.13 0.893 

Matched 0.211 0.248 -9.1 -1543.4 -1.61 0.108 0.203 0.238 -8.6 -1461.6 -1.56 0.119 

1 Not included in the estimation of the propensity score for the sub-sample 2 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 

49 
Appendix Table L. Mean biases of covariates before and after matching, Cambodia 

Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Maternal age at birth Unmatched 27.636 26.938 10.8 3.29 0.001 27.636 26.938 10.8 3.29 0.001 

Matched 27.627 27.498 2.0 81.4 0.45 0.656 27.105 27.006 1.5 85.8 0.32 0.747 

Maternal age at birth squared 

Unmatched 809.390 763.760 11.9 3.67 0.000 809.390 763.760 11.9 3.67 0.000 

Matched 808.990 800.340 2.3 81.0 0.50 0.618 776.650 769.550 1.9 84.4 0.39 0.695 

Education 

No education Unmatched 0.331 0.174 36.9 11.78 0.000 0.331 0.174 36.9 11.78 0.000 

Matched 0.330 0.323 1.7 95.4 0.35 0.723 0.274 0.266 1.9 95.0 0.37 0.709 

Primary education Unmatched 0.573 0.512 12.2 3.61 0.000 0.573 0.512 12.2 3.61 0.000 

Matched 0.574 0.584 -2.0 83.6 -0.47 0.642 0.611 0.623 -2.4 80.4 -0.51 0.609 

Secondary or higher Unmatched 0.096 0.314 -56.2 -14.74 0.000 0.096 0.314 -56.2 -14.74 0.000 

Matched 0.096 0.093 0.7 98.7 0.22 0.829 0.114 0.110 1.0 98.2 0.26 0.792 

Marital status1

Never married Unmatched 0.001 0.000 3.1 1.20 0.231 

Matched 0.000 0.001 -2.9 5.4 -0.86 0.389 

Currently married Unmatched 0.949 0.952 -1.4 -0.41 0.684 

Matched 0.950 0.952 -0.7 51.4 -0.15 0.878 

Formerly married Unmatched 0.050 0.048 1.0 0.31 0.759 

Matched 0.050 0.048 1.0 3.6 0.23 0.820 

Birth order 

1 Unmatched 0.226 0.315 -20.0 -5.72 0.000 0.226 0.315 -20.0 -5.72 0.000 

Matched 0.227 0.233 -1.3 93.2 -0.33 0.744 0.259 0.263 -0.7 96.5 -0.15 0.884 

2 Unmatched 0.269 0.281 -2.8 -0.83 0.408 0.269 0.281 -2.8 -0.83 0.408 

Matched 0.269 0.268 0.3 88.3 0.08 0.939 0.279 0.280 -0.2 93.5 -0.04 0.970 

3 Unmatched 0.177 0.178 0.0 0.00 0.997 0.177 0.178 0.0 0.00 0.997 

Matched 0.178 0.183 -1.2 -9541.0 -0.28 0.778 0.174 0.179 -1.5 -11409.6 -0.31 0.758 

4 or higher Unmatched 0.328 0.227 22.7 6.97 0.000 0.328 0.227 22.7 6.97 0.000 

Matched 0.326 0.317 2.1 90.9 0.45 0.650 0.288 0.278 2.1 90.6 0.44 0.659 

Employment status 

Not currently employed Unmatched 0.294 0.308 -3.1 -0.91 0.362 0.294 0.308 -3.1 -0.91 0.362 

Matched 0.294 0.302 -1.9 38.6 -0.44 0.661 0.306 0.312 -1.3 59.0 -0.26 0.792 

Currently employed Unmatched 0.706 0.692 3.1 0.91 0.362 0.706 0.692 3.1 0.91 0.362 

Matched 0.706 0.698 1.9 38.6 0.44 0.661 0.694 0.688 1.3 59.0 0.26 0.792 

Education of head of the household 

No education Unmatched 0.274 0.171 25.0 7.83 0.000 0.274 0.171 25.0 7.83 0.000 

Matched 0.273 0.269 0.9 96.2 0.20 0.840 0.262 0.257 1.2 95.3 0.23 0.818 

Primary education Unmatched 0.550 0.474 15.3 4.52 0.000 0.550 0.474 15.3 4.52 0.000 

Matched 0.551 0.556 -1.0 93.6 -0.23 0.820 0.543 0.551 -1.7 89.0 -0.35 0.725 

Secondary or higher Unmatched 0.176 0.355 -41.5 -11.47 0.000 0.176 0.355 -41.5 -11.47 0.000 

Matched 0.176 0.175 0.2 99.4 0.06 0.952 0.195 0.192 0.8 98.0 0.19 0.850 

(Continued...)

50 
Appendix Table L. – Continued

Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Mass media exposure2Not exposed to media Unmatched 0.522 0.364 32.2 9.64 0.000 0.522 0.364 32.2 9.64 0.000 

Matched 0.523 0.525 -0.4 98.6 -0.10 0.919 0.499 0.501 0.3 99.2 -0.05 0.957 

Exposed to media at least once a week 

Unmatched 0.478 0.636 -32.2 -9.64 0.000 0.478 0.636 -32.2 -9.64 0.000 Matched 0.477 0.475 0.4 98.6 0.10 0.919 0.501 0.499 0.3 99.2 0.05 0.957 

Wealth tercile 

First Unmatched 0.571 0.307 55.1 16.71 0.000 0.571 0.307 55.1 16.71 0.000 

Matched 0.570 0.578 -1.6 97.1 -0.35 0.725 0.539 0.543 -0.8 98.5 -0.17 0.868 

Second Unmatched 0.292 0.281 2.3 0.69 0.489 0.292 0.281 2.3 0.69 0.489 

Matched 0.293 0.291 0.3 86.2 0.07 0.941 0.305 0.308 -0.7 72.1 -0.13 0.894 

Third Unmatched 0.137 0.411 -64.6 -17.31 0.000 0.137 0.411 -64.6 -17.31 0.000 

Matched 0.137 0.131 1.4 97.8 0.41 0.680 0.155 0.149 1.6 97.5 0.40 0.688 

Residence 

Rural Unmatched 0.842 0.698 34.7 9.60 0.000 0.842 0.698 34.7 9.60 0.000 

Matched 0.842 0.848 -1.5 95.7 -0.39 0.693 0.831 0.836 -1.3 96.2 -0.31 0.756 

Urban Unmatched 0.158 0.302 -34.7 -9.60 0.000 0.158 0.302 -34.7 -9.60 0.000 

Matched 0.158 0.152 1.5 95.7 0.39 0.693 0.169 0.164 1.3 96.2 0.31 0.756 

Provinces3

Northwestern provinces Unmatched 0.298 0.247 11.4 3.45 0.001 0.298 0.247 11.4 3.45 0.001 

Matched 0.296 0.299 -0.7 93.8 -0.16 0.873 0.243 0.246 -0.5 95.3 -0.12 0.907 

Southern provinces Unmatched 0.232 0.260 -6.6 -1.93 0.054 0.232 0.260 -6.6 -1.93 0.054 

Matched 0.232 0.236 -0.8 87.4 -0.19 0.846 0.240 0.242 -0.5 93.1 -0.10 0.923 

Southwestern provinces Unmatched 0.385 0.252 28.9 8.88 0.000 0.385 0.252 28.9 8.88 0.000 

Matched 0.386 0.380 1.3 95.4 0.29 0.769 0.422 0.416 1.3 95.6 0.25 0.805 

Siem Reap, Takeo, and Kandal 

Unmatched 0.080 0.172 -28.1 -7.57 0.000 0.080 0.172 -28.1 -7.57 0.000 

Matched 0.080 0.076 1.1 96.2 0.30 0.765 0.088 0.085 0.9 96.9 0.21 0.830 

Phnom Penh Unmatched 0.006 0.069 -33.9 -8.08 0.000 0.006 0.069 -33.9 -8.08 0.000 

Matched 0.006 0.009 -1.6 95.4 -0.81 0.420 0.007 0.011 -2.3 93.2 -0.96 0.338 

1 Not included in the estimation of the propensity score for the sub-sample 2 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 3 Northwestern provinces include Kampong Thom, Kratie, Preah Vihear & Steung Treng, Mondol Kiri/Rattanak Kiri 

Southern provinces include Kampong Cham, Svay Rieng, Kampong Speu, Pursat, and Kampot/Kep Southwestern provinces include Kampong Chhang, Preah Sihanouk/Daoh Kong, Battambang/Pailin, Banteay Mean Chey, and Otdar Mean Chey 

51 
Appendix Table M. Mean biases of covariates before and after matching, Gabon 

Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

 Mean % bias 

reduction t-statistic Variable Sample Insured Uninsured % bias p>|t| Insured Uninsured % bias p>|t| 

Maternal age at most recent birth (continuous) 

Unmatched 27.618 24.831 38.1 11.75 0.000 27.618 24.831 38.1 11.75 0.000 

Matched 27.598 27.883 -3.9 89.8 -1.25 0.212 27.358 27.251 1.5 96.1 0.46 0.648 

Maternal age at most recent birth squared 

Unmatched 820.640 665.800 37.4 11.51 0.000 820.640 665.800 37.4 11.51 0.000 

Matched 819.310 836.860 -4.2 88.7 -1.32 0.186 805.920 798.090 1.9 94.9 0.58 0.563 

Marital status1

Never married Unmatched 0.195 0.237 -10.1 -3.16 0.002 

Matched 0.196 0.189 1.6 84.5 0.55 0.585 

Currently married Unmatched 0.712 0.675 8.0 2.48 0.013 

Matched 0.712 0.721 -2.0 74.7 -0.69 0.491 

Formerly married Unmatched 0.093 0.088 1.7 0.53 0.597 

Matched 0.093 0.090 1.0 41.9 0.33 0.741 

Education None Unmatched 0.030 0.081 -22.3 -7.16 0.000 0.030 0.081 -22.3 -7.16 0.000 

Matched 0.030 0.026 2.0 91.0 0.93 0.351 0.022 0.020 0.8 96.4 0.41 0.682 

Primary Unmatched 0.439 0.298 29.6 9.14 0.000 0.439 0.298 29.6 9.14 0.000 

Matched 0.439 0.422 3.5 88.1 1.13 0.257 0.409 0.387 4.8 83.8 1.49 0.135 

Secondary or higher Unmatched 0.530 0.621 -18.4 -5.69 0.000 0.530 0.621 -18.4 -5.69 0.000 

Matched 0.531 0.553 -4.3 76.3 -1.44 0.151 0.569 0.593 -5.0 72.6 -1.60 0.109 

Employment status1

Not currently employed Unmatched 0.542 0.627 -17.4 -5.38 0.000 

Matched 0.543 0.553 -2.0 88.6 -0.66 0.512 

Currently employed Unmatched 0.458 0.373 17.4 5.38 0.000 

Matched 0.457 0.447 2.0 88.6 0.66 0.512 

Mass media exposure No access to any media Unmatched 0.234 0.133 26.4 8.08 0.000 0.234 0.133 26.4 8.08 0.000 

Matched 0.233 0.234 -0.2 99.3 -0.05 0.958 0.193 0.187 1.6 94.0 0.49 0.623 

Exposure to media at least once a week 

Unmatched 0.766 0.867 -26.4 -8.08 0.000 0.766 0.867 -26.4 -8.08 0.000 Matched 0.767 0.766 0.2 99.3 0.05 0.958 0.807 0.813 -1.6 94.0 -0.49 0.623 

Education of household head None Unmatched 0.102 0.108 -1.9 -0.60 0.549 0.102 0.108 -1.9 -0.60 0.549 

Matched 0.102 0.100 0.7 61.5 0.25 0.800 0.094 0.094 0.2 91.0 0.06 0.954 

Primary Unmatched 0.368 0.290 16.6 5.14 0.000 0.368 0.290 16.6 5.14 0.000 

Matched 0.367 0.337 6.3 61.9 2.08 0.037 0.344 0.325 4.1 75.5 1.29 0.197 

Secondary or higher Unmatched 0.530 0.602 -14.6 -4.52 0.000 0.530 0.602 -14.6 -4.52 0.000 

Matched 0.531 0.563 -6.5 55.6 -2.15 0.032 0.562 0.581 -4.0 72.8 -1.27 0.206 

(Continued...)

52 
Appendix Table M. – Continued

 Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

 Mean % bias 

reduction t-statistic Variable Sample Insured Uninsured % bias p>|t| Insured Uninsured % bias p>|t| 

Birth order Eldest Unmatched 0.181 0.315 -31.4 -9.88 0.000 0.181 0.315 -31.4 -9.88 0.000 

Matched 0.181 0.185 -1.0 96.8 -0.37 0.709 0.192 0.200 -1.8 94.3 -0.61 0.542 

Second Unmatched 0.179 0.227 -12.1 -3.78 0.000 0.179 0.227 -12.1 -3.78 0.000 

Matched 0.179 0.164 3.7 69.5 1.32 0.188 0.189 0.178 2.7 77.4 0.91 0.365 

Third Unmatched 0.153 0.155 -0.7 -0.22 0.825 0.153 0.155 -0.7 -0.22 0.825 

Matched 0.153 0.157 -1.2 -72.2 -0.41 0.683 0.155 0.160 -1.4 -100.3 -0.45 0.653 

Fourth or higher Unmatched 0.488 0.303 38.5 11.89 0.000 0.488 0.303 38.5 11.89 0.000 

Matched 0.487 0.493 -1.3 96.7 -0.41 0.684 0.463 0.462 0.4 99.1 0.11 0.911 

Wealth quintiles Poorest Unmatched 0.530 0.324 42.5 13.13 0.000 0.530 0.324 42.5 13.13 0.000 

Matched 0.529 0.508 4.3 89.8 1.40 0.162 0.488 0.459 5.9 86.0 1.84 0.066 

2nd quintile Unmatched 0.206 0.238 -7.6 -2.36 0.018 0.206 0.238 -7.6 -2.36 0.018 

Matched 0.206 0.203 0.8 89.8 0.27 0.790 0.224 0.224 -0.1 98.8 -0.03 0.977 

3rd quintile Unmatched 0.113 0.185 -20.6 -6.48 0.000 0.113 0.185 -20.6 -6.48 0.000 

Matched 0.113 0.120 -1.9 90.7 -0.71 0.478 0.122 0.129 -1.9 90.9 -0.64 0.523 

4th quintile Unmatched 0.076 0.150 -23.6 -7.49 0.000 0.076 0.150 -23.6 -7.49 0.000 

Matched 0.076 0.076 -0.2 99.3 -0.06 0.950 0.083 0.086 -0.8 96.6 -0.29 0.771 

Richest Unmatched 0.076 0.103 -9.5 -2.96 0.003 0.076 0.103 -9.5 -2.96 0.003 

Matched 0.076 0.093 -5.9 37.7 -2.02 0.043 0.083 0.102 -6.7 28.9 -2.12 0.034 

Residence Rural Unmatched 0.420 0.293 26.8 8.29 0.000 0.420 0.293 26.8 8.29 0.000 

Matched 0.419 0.405 3.0 89.0 0.96 0.339 0.379 0.360 4.0 85.0 1.26 0.207 

Urban Unmatched 0.580 0.707 -26.8 -8.29 0.000 0.580 0.707 -26.8 -8.29 0.000 

Matched 0.581 0.595 -3.0 89.0 -0.96 0.339 0.621 0.640 -4.0 85.0 -1.26 0.207 

Region Libreville-Port-Gentil Unmatched 0.079 0.242 -45.4 -14.54 0.000 0.079 0.242 -45.4 -14.54 0.000 

Matched 0.079 0.084 -1.3 97.1 -0.57 0.569 0.086 0.092 -1.6 96.6 -0.63 0.531 

Estuaire Unmatched 0.065 0.111 -16.2 -5.13 0.000 0.065 0.111 -16.2 -5.13 0.000 

Matched 0.066 0.063 1.0 94.1 0.37 0.710 0.070 0.068 0.7 95.6 0.26 0.797 

Haut-Ogooué Unmatched 0.140 0.073 21.9 6.68 0.000 0.140 0.073 21.9 6.68 0.000 

Matched 0.140 0.137 1.2 94.7 0.35 0.730 0.151 0.150 0.3 98.7 0.08 0.938 

Moyen-Ogooué Unmatched 0.081 0.064 6.4 1.97 0.049 0.081 0.064 6.4 1.97 0.049 

Matched 0.081 0.088 -2.9 55.4 -0.89 0.373 0.085 0.087 -0.9 85.4 -0.28 0.782 

Ngounié Unmatched 0.146 0.082 20.2 6.16 0.000 0.146 0.082 20.2 6.16 0.000 

Matched 0.146 0.144 0.6 97.0 0.18 0.857 0.144 0.147 -1.0 95.1 -0.28 0.779 

Nyanga Unmatched 0.079 0.092 -4.7 -1.47 0.142 0.079 0.092 -4.7 -1.47 0.142 

Matched 0.079 0.079 0.2 95.1 0.08 0.935 0.078 0.077 0.2 95.8 0.07 0.947 

Ogooué Maritime Unmatched 0.051 0.089 -14.8 -4.68 0.000 0.051 0.089 -14.8 -4.68 0.000 

Matched 0.051 0.053 -0.7 95.4 -0.26 0.792 0.054 0.056 -0.7 95.4 -0.24 0.808 

(Continued...)

53 
Appendix Table M. – Continued

 Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

 Mean % bias 

reduction t-statistic Variable Sample Insured Uninsured % bias p>|t| Insured Uninsured % bias p>|t| 

Ogooué-Ivindo Unmatched 0.197 0.079 34.7 10.51 0.000 0.197 0.079 34.7 10.51 0.000 

Matched 0.196 0.189 1.8 94.8 0.52 0.601 0.168 0.156 3.6 89.7 1.05 0.294 

Ogooué-Lolo Unmatched 0.110 0.067 15.3 4.68 0.000 0.110 0.067 15.3 4.68 0.000 

Matched 0.111 0.109 0.6 96.1 0.18 0.856 0.114 0.114 -0.1 99.2 -0.03 0.972 

Woleu-Ntem Unmatched 0.052 0.101 -18.8 -5.96 0.000 0.052 0.101 -18.8 -5.96 0.000 

Matched 0.052 0.055 -1.1 94.2 -0.43 0.667 0.050 0.053 -0.9 95.2 -0.35 0.730 

1 Not included in the estimation of the propensity score for the sub-sample 2 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 

54 
Appendix Table N. Mean biases of covariates before and after matching, Ghana 

Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Maternal age at birth Unmatched 28.386 27.715 9.9 2.09 0.037 28.386 27.715 9.9 2.09 0.037 

Matched 28.380 28.293 1.3 87.1 0.26 0.793 28.394 28.278 1.7 82.7 0.34 0.730 

Maternal age at birth squared 

Unmatched 846.760 819.430 6.8 1.44 0.149 846.760 819.430 6.8 1.44 0.149 

Matched 846.720 840.050 1.7 75.6 0.34 0.730 847.810 839.860 2 70.9 0.4 0.686 

Education 

No education Unmatched 0.260 0.373 -24.4 -5.16 0.000 0.260 0.373 -24.4 -5.16 0.000 

Matched 0.264 0.249 3.2 86.8 0.65 0.513 0.268 0.248 4.3 82.2 0.87 0.385 

Primary education Unmatched 0.180 0.281 -24.1 -5.07 0.000 0.180 0.281 -24.1 -5.07 0.000 

Matched 0.183 0.182 0.2 99.2 0.04 0.967 0.181 0.187 -1.3 94.5 -0.27 0.785 

Secondary or higher Unmatched 0.559 0.346 43.8 9.41 0.000 0.559 0.346 43.8 9.41 0.000 

Matched 0.553 0.569 -3.2 92.7 -0.61 0.544 0.551 0.566 -3 93.2 -0.55 0.581 

Marital status 

Never married Unmatched 0.038 0.071 -14.8 -3.07 0.002 0.038 0.071 -14.8 -3.07 0.002 

Matched 0.039 0.036 1.5 90.1 0.34 0.737 0.039 0.036 1.1 92.7 0.25 0.806 

Currently married Unmatched 0.906 0.865 12.8 2.70 0.007 0.906 0.865 12.8 2.7 0.007 

Matched 0.907 0.906 0.2 98.1 0.05 0.959 0.906 0.903 0.9 92.9 0.19 0.851 

Formerly married Unmatched 0.056 0.063 -3.0 -0.65 0.517 0.056 0.063 -3 -0.65 0.517 

Matched 0.054 0.058 -1.7 43.5 -0.34 0.732 0.055 0.061 -2.3 26.1 -0.43 0.664 

Birth order 

1 Unmatched 0.256 0.201 13.2 2.85 0.004 0.256 0.201 13.2 2.85 0.004 

Matched 0.249 0.249 0 99.7 0.01 0.994 0.251 0.258 -1.5 88.6 -0.27 0.784 

2 Unmatched 0.216 0.206 2.3 0.50 0.618 0.216 0.206 2.3 0.5 0.618 

Matched 0.215 0.225 -2.3 -0.4 -0.44 0.657 0.210 0.216 -1.4 38.6 -0.27 0.786 

3 Unmatched 0.157 0.172 -4.0 -0.86 0.391 0.157 0.172 -4 -0.86 0.391 

Matched 0.159 0.154 1.3 67.5 0.26 0.797 0.157 0.159 -0.5 87.2 -0.1 0.921 

4 or higher Unmatched 0.371 0.421 -10.2 -2.18 0.030 0.371 0.421 -10.2 -2.18 0.030 

Matched 0.377 0.372 0.9 90.9 0.18 0.857 0.381 0.367 2.9 71.9 0.55 0.581 

Employment status1

Not currently employed Unmatched 0.132 0.137 -1.6 -0.34 0.735 

Matched 0.132 0.142 -2.8 -77.4 -0.54 0.590 

Currently employed Unmatched 0.868 0.863 1.6 0.34 0.735 

Matched 0.868 0.858 2.8 -77.4 0.54 0.590 

(Continued...)

55 
Appendix Table N. – Continued

 Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Education of head of the household No education Unmatched 0.229 0.374 -31.9 -6.74 0.000 0.229 0.374 -31.9 -6.74 0.000 

Matched 0.230 0.218 2.6 91.9 0.54 0.589 0.232 0.217 3.2 89.9 0.67 0.504 

Primary education Unmatched 0.109 0.182 -21.1 -4.41 0.000 0.109 0.182 -21.1 -4.41 0.000 

Matched 0.110 0.104 1.8 91.7 0.38 0.702 0.112 0.108 1.1 94.7 0.24 0.810 

Secondary or higher Unmatched 0.663 0.444 45.1 9.59 0.000 0.663 0.444 45.1 9.59 0.000 

Matched 0.659 0.677 -3.7 91.8 -0.73 0.465 0.656 0.675 -3.8 91.5 -0.75 0.453 

Mass media exposure 

Not exposed to media Unmatched 0.157 0.253 -23.9 -5.03 0.000 0.157 0.253 -23.9 -5.03 0.000 

Matched 0.160 2.1 91.2 0.45 0.653 0.156 0.150 1.5 93.7 0.32 0.751 

Exposed to media Unmatched 0.843 0.747 23.9 5.03 0.000 0.843 0.747 23.9 5.03 0.000 

Matched 0.840 0.848 -2.1 91.2 -0.45 0.653 0.844 0.850 -1.5 93.7 -0.32 0.751 

Wealth quintile 

First Unmatched 0.150 0.321 -41 -8.54 0.000 0.150 0.321 -41 -8.54 0.000 

Matched 0.153 0.152 0.4 99 0.1 0.924 0.156 0.151 1.3 96.9 0.28 0.778 

Second Unmatched 0.212 0.242 -7.3 -1.55 0.121 0.212 0.242 -7.3 -1.55 0.121 

Matched 0.218 0.228 -2.3 68.4 -0.45 0.656 0.214 0.225 -2.5 65 -0.49 0.624 

Middle Unmatched 0.190 0.188 0.5 0.10 0.918 0.190 0.188 0.5 0.1 0.918 

Matched 0.192 0.201 -2.2 -360.7 -0.42 0.675 0.196 0.210 -3.6 -651.9 -0.67 0.505 

Fourth Unmatched 0.254 0.154 24.9 5.41 0.000 0.254 0.154 24.9 5.41 0.000 

Matched 0.253 0.238 3.8 84.9 0.67 0.500 0.253 0.235 4.5 82 0.79 0.428 

Highest Unmatched 0.195 0.095 28.6 6.29 0.000 0.195 0.095 28.6 6.29 0.000 

Matched 0.183 0.182 0.4 98.5 0.07 0.942 0.181 0.179 0.4 98.5 0.08 0.940 

Residence 

Rural Unmatched 0.539 0.539 -28.7 -6.18 0.000 0.539 0.678 -28.7 -6.18 0.000 

Matched 0.548 0.548 -2.2 92.4 -0.41 0.682 0.546 0.560 -3.1 89.4 -0.57 0.572 

Urban Unmatched 0.461 0.322 28.7 6.18 0.000 0.461 0.322 28.7 6.18 0.000 

Matched 0.452 0.442 2.2 92.4 0.41 0.682 0.454 0.440 3.1 89.4 0.57 0.572 

(Continued...)

56 
Appendix Table N. – Continued

 Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Region Western Unmatched 0.094 0.099 -1.5 -0.33 0.742 0.094 0.099 -1.5 -0.33 0.742 

Matched 0.096 0.088 2.7 -73.9 0.53 0.596 0.094 0.090 1.2 21.7 0.23 0.814 

Central Unmatched 0.044 0.106 -23.4 -4.83 0.000 0.044 0.106 -23.4 -4.83 0.000 

Matched 0.046 0.046 -0.2 99.1 -0.05 0.961 0.047 0.049 -0.9 96.1 -0.21 0.833 

Greater Accra Unmatched 0.077 0.127 -16.5 -3.45 0.001 0.077 0.127 -16.5 -3.45 0.001 

Matched 0.077 0.075 0.4 97.3 0.10 0.923 0.079 0.075 1.1 93.4 0.23 0.815 

Volta Unmatched 0.077 0.101 -8.5 -1.79 0.073 0.077 0.101 -8.5 -1.79 0.073 

Matched 0.079 0.084 -1.7 80.6 -0.33 0.741 0.080 0.085 -1.8 78.4 -0.36 0.718 

Eastern Unmatched 0.123 0.070 17.8 3.90 0.000 0.123 0.070 17.8 3.9 0.000 

Matched 0.124 0.127 -1.1 93.8 -0.19 0.851 0.124 0.122 0.8 95.7 0.13 0.896 

Ashanti Unmatched 0.183 0.153 7.9 1.71 0.087 0.183 0.153 7.9 1.71 0.087 

Matched 0.187 0.201 -3.8 52.5 -0.69 0.492 0.189 0.202 -3.3 58.3 -0.59 0.552 

Brong Ahafo Unmatched 0.153 0.076 24.4 5.38 0.000 0.153 0.076 24.4 5.38 0.000 

Matched 0.145 0.146 -0.2 99.1 -0.04 0.970 0.141 0.144 -1.1 95.5 -0.19 0.851 

Northern Unmatched 0.119 0.177 -16.4 -3.46 0.001 0.119 0.177 -16.4 -3.46 0.001 

Matched 0.121 0.120 0.4 97.4 0.09 0.930 0.120 0.117 0.9 94.4 0.19 0.848 

Upper West Unmatched 0.129 0.091 12.4 2.69 0.007 0.129 0.091 12.4 2.69 0.007 

Matched 0.125 0.113 4.0 67.5 0.75 0.455 0.126 0.115 3.5 71.3 0.65 0.517 

1 Not included in the estimation of the propensity score for the sub-sample 

57 
Appendix Table O. Mean biases of covariates before and after matching, Indonesia 

Full sample Sub-sample 

Mean % bias 

reduction t-statistic 

 Mean % bias 

reduction t-statistic Variable Sample Insured Uninsured % bias p>|t| Insured Uninsured % bias p>|t| 

Maternal age at most recent birth 15-19 years Unmatched 0.028 0.041 -7.0 -4.10 0.000 0.028 0.041 -7.0 -4.10 0.000 

Matched 0.028 0.029 -0.3 95.6 -0.19 0.853 0.027 0.028 -0.4 94.3 -0.24 0.813 

20-24 years Unmatched 0.157 0.217 -15.6 -9.22 0.000 0.157 0.217 -15.6 -9.22 0.000 

Matched 0.157 0.161 -1.1 92.7 -0.66 0.510 0.156 0.160 -1.0 93.3 -0.60 0.549 

25-29 years Unmatched 0.266 0.283 -3.8 -2.27 0.023 0.266 0.283 -3.8 -2.27 0.023 

Matched 0.267 0.267 -0.2 96.0 -0.08 0.933 0.268 0.267 0.0 99.0 0.02 0.984 

30-34 years Unmatched 0.261 0.228 7.6 4.55 0.000 0.261 0.228 7.6 4.55 0.000 

Matched 0.261 0.258 0.5 93.2 0.28 0.783 0.261 0.258 0.7 90.9 0.36 0.717 

35-39 years Unmatched 0.192 0.150 11.1 6.71 0.000 0.192 0.150 11.1 6.71 0.000 

Matched 0.191 0.186 1.3 88.1 0.69 0.488 0.192 0.188 1.1 90.3 0.56 0.579 

40-44 years Unmatched 0.082 0.066 5.9 3.57 0.000 0.082 0.066 5.9 3.57 0.000 

Matched 0.082 0.083 -0.5 92.2 -0.24 0.810 0.081 0.083 -0.8 86.9 -0.40 0.689 

45-49 years Unmatched 0.015 0.015 0.4 0.26 0.795 0.015 0.015 0.4 0.26 0.795 

Matched 0.015 0.015 -0.3 39.6 -0.14 0.888 0.014 0.015 -0.3 34.7 -0.15 0.878 

Marital status 

Never married Unmatched 0.001 0.001 -1.8 -1.06 0.287 0.001 0.001 -1.8 -1.06 0.287 

Matched 0.001 0.001 -0.2 86.7 -0.16 0.874 0.001 0.001 0.0 99.9 0.00 0.999 

Currently married Unmatched 0.974 0.971 1.7 1.03 0.301 0.974 0.971 1.7 1.03 0.301 

Matched 0.974 0.973 0.5 73.9 0.25 0.801 0.974 0.974 0.2 90.1 0.10 0.923 

Formerly married Unmatched 0.026 0.028 -1.4 -0.86 0.387 0.026 0.028 -1.4 -0.86 0.387 

Matched 0.026 0.027 -0.4 71.2 -0.23 0.817 0.025 0.026 -0.2 87.9 -0.10 0.922 

Education 

None Unmatched 0.018 0.031 -9.0 -5.23 0.000 0.018 0.031 -9.0 -5.23 0.000 

Matched 0.018 0.019 -0.6 92.9 -0.40 0.687 0.015 0.015 0.0 99.8 0.01 0.989 

Primary Unmatched 0.261 0.338 -16.9 -10.04 0.000 0.261 0.338 -16.9 -10.04 0.000 

Matched 0.261 0.265 -0.8 95.1 -0.47 0.640 0.256 0.259 -0.7 96.0 -0.38 0.706 

Secondary or higher Unmatched 0.722 0.631 19.5 11.60 0.000 0.722 0.631 19.5 11.60 0.000 

Matched 0.721 0.717 1.0 94.8 0.58 0.563 0.730 0.727 0.6 96.7 0.37 0.713 

Employment status 

Not currently employed Unmatched 0.462 0.539 -15.6 -9.33 0.000 0.462 0.539 -15.6 -9.33 0.000 

Matched 0.462 0.469 -1.4 91.0 -0.77 0.443 0.462 0.467 -1.0 93.7 -0.53 0.598 

Currently employed Unmatched 0.538 0.461 15.6 9.33 0.000 0.538 0.461 15.6 9.33 0.000 

Matched 0.538 0.531 1.4 91.0 0.77 0.443 0.538 0.533 1.0 93.7 0.53 0.598 

(Continued...)

58 
Appendix Table O. – Continued

 Full sample Sub-sample 

Mean % bias 

reduction t-statistic 

 Mean % bias 

reduction t-statistic Variable Sample Insured Uninsured % bias p>|t| Insured Uninsured % bias p>|t| 

Mass media exposure1No access to any media Unmatched 0.138 0.157 -5.3 -3.18 0.001 0.138 0.157 -5.3 -3.18 0.001 

Matched 0.138 0.139 -0.1 98.0 -0.06 0.952 0.134 0.134 -0.1 98.6 -0.04 0.967 

Exposure to media at least once a week 

Unmatched 0.862 0.843 5.3 3.18 0.001 0.862 0.843 5.3 3.18 0.001 Matched 0.862 0.861 0.1 98.0 0.06 0.952 0.866 0.866 0.1 98.6 0.04 0.967 

Education of household head 

No education Unmatched 0.034 0.058 -11.7 -6.83 0.000 0.034 0.058 -11.7 -6.83 0.000 

Matched 0.034 0.036 -1.1 90.5 -0.69 0.488 0.032 0.034 -0.8 93.3 -0.49 0.621 

Primary education or higher Unmatched 0.966 0.942 11.7 6.83 0.000 0.966 0.942 11.7 6.83 0.000 

Matched 0.966 0.964 1.1 90.5 0.69 0.488 0.968 0.966 0.8 93.3 0.49 0.621 

Birth order2

Eldest Unmatched 0.317 0.368 -10.6 -6.35 0.000 

Matched 0.317 0.319 -0.3 96.9 -0.18 0.855 

Second Unmatched 0.310 0.299 2.3 1.41 0.160 

Matched 0.309 0.307 0.6 76.1 0.31 0.760 

Third Unmatched 0.188 0.168 5.3 3.17 0.002 

Matched 0.188 0.186 0.4 92.0 0.23 0.822 

Fourth or higher Unmatched 0.185 0.166 5.1 3.08 0.002 

Matched 0.185 0.188 -0.7 86.5 -0.37 0.712 

Birth order - recode3

Eldest Unmatched 0.317 0.368 -10.6 -6.35 0.000 

Matched 0.320 0.321 -0.3 96.9 -0.18 0.858 

Second or more Unmatched 0.683 0.632 10.6 6.35 0.000 

Matched 0.680 0.679 0.3 96.9 0.18 0.858 

Wealth quintiles Poorest Unmatched 0.261 0.298 -8.2 -4.87 0.000 0.261 0.298 -8.2 -4.87 0.000 

Matched 0.261 0.261 0.2 98.1 0.09 0.932 0.251 0.252 -0.1 98.9 -0.05 0.959 

2nd quintile Unmatched 0.189 0.213 -6.1 -3.63 0.000 0.189 0.213 -6.1 -3.63 0.000 

Matched 0.189 0.193 -1.0 83.5 -0.56 0.576 0.189 0.191 -0.6 90.3 -0.33 0.744 

3rd quintile Unmatched 0.171 0.195 -6.2 -3.68 0.000 0.171 0.195 -6.2 -3.68 0.000 

Matched 0.171 0.172 -0.3 95.2 -0.17 0.867 0.173 0.173 -0.1 98.9 -0.04 0.970 

4th quintile Unmatched 0.179 0.171 2.1 1.23 0.217 0.179 0.171 2.1 1.23 0.217 

Matched 0.179 0.175 1.0 51.8 0.54 0.589 0.182 0.180 0.7 67.2 0.36 0.719 

Richest Unmatched 0.200 0.123 21.0 12.85 0.000 0.200 0.123 21.0 12.85 0.000 

Matched 0.200 0.199 0.2 99.1 0.10 0.922 0.205 0.204 0.1 99.4 0.07 0.948 

(Continued...)

59 
Appendix Table O. – Continued

 Full sample Sub-sample 

Mean % bias 

reduction t-statistic 

 Mean % bias 

reduction t-statistic Variable Sample Insured Uninsured % bias p>|t| Insured Uninsured % bias p>|t| 

Residence Rural Unmatched 0.485 0.575 -18.1 -10.89 0.000 0.485 0.485 -18.1 -10.89 0.000 

Matched 0.485 0.483 0.5 97.1 0.29 0.773 0.478 0.475 0.5 97.4 0.25 0.803 

Urban Unmatched 0.515 0.425 18.1 10.89 0.000 0.515 0.425 18.1 10.89 0.000 

Matched 0.515 0.517 -0.5 97.1 -0.29 0.773 0.522 0.525 -0.5 97.4 -0.25 0.803 

Region 

Sumatera Unmatched 0.267 0.315 -10.5 -6.29 0.000 0.267 0.315 -10.5 -6.29 0.000 

Matched 0.267 0.264 0.6 94.0 0.36 0.722 0.270 0.266 0.9 91.4 0.50 0.617 

Java Unmatched 0.217 0.259 -9.7 -5.80 0.000 0.217 0.259 -9.7 -5.80 0.000 

Matched 0.218 0.216 0.3 96.6 0.19 0.851 0.221 0.221 0.0 100.0 0.00 0.999 

Bali and Nusa Tenggara Unmatched 0.116 0.070 15.7 9.65 0.000 0.116 0.070 15.7 9.65 0.000 

Matched 0.116 0.123 -2.5 83.9 -1.24 0.216 0.117 0.122 -1.7 89.5 -0.80 0.425 

Kalimantan Unmatched 0.114 0.105 2.9 1.74 0.082 0.114 0.105 2.9 1.74 0.082 

Matched 0.114 0.113 0.4 87.3 0.20 0.844 0.113 0.113 0.0 100.0 0.00 1.000 

Sulawesi Unmatched 0.172 0.162 2.8 1.70 0.089 0.172 0.162 2.8 1.70 0.089 

Matched 0.173 0.170 0.7 76.8 0.36 0.722 0.173 0.170 0.9 69.8 0.46 0.648 

Maluku and Papua Unmatched 0.113 0.089 8.0 4.82 0.000 0.113 0.089 8.0 4.82 0.000 

Matched 0.113 0.114 -0.2 97.7 -0.09 0.925 0.106 0.108 -0.8 89.5 -0.44 0.661 

1 Mass media exposure is defined as read newspaper, listen to radio, or watch TV at least once a week 2 Not included in the estimation of the propensity score for the sub-sample 3 Not included in the estimation of the propensity score for the full-sample 

60 
Appendix Table P. Mean biases of covariates before and after matching, Namibia 

Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Maternal age at birth Unmatched 29.683 26.209 53.2 10.53 0.000 29.683 26.209 53.2 10.53 0.000 

Matched 29.425 29.117 4.7 91.1 0.71 0.477 29.387 29.051 5.1 90.3 0.76 0.446 

Maternal age at birth squared 

Unmatched 920.570 732.720 49.3 10.01 0.000 920.570 732.720 49.3 10.01 0.000 

Matched 904.580 886.820 4.7 90.5 0.68 0.497 902.640 883.380 5.1 89.7 0.73 0.468 

Education 

No education Unmatched 0.021 0.111 -36.8 -6.15 0.000 0.021 0.111 -36.8 -6.15 0.000 

Matched 0.024 0.027 -1.1 97.1 -0.24 0.810 0.025 0.023 0.6 98.4 0.14 0.890 

Primary education Unmatched 0.069 0.334 -69.9 -11.99 0.000 0.069 0.334 -69.9 -11.99 0.000 

Matched 0.067 0.072 -1.2 98.2 -0.26 0.792 0.067 0.068 -0.4 99.4 -0.09 0.929 

Secondary or higher Unmatched 0.910 0.555 87.4 15.19 0.000 0.910 0.555 87.4 15.19 0.000 

Matched 0.909 0.901 1.8 97.9 0.36 0.720 0.909 0.909 0.0 100.0 0.00 0.997 

Marital status1

Never married Unmatched 0.254 0.453 -42.5 -8.26 0.000 

Matched 0.274 0.323 -10.5 75.4 -1.54 0.123 

Currently married Unmatched 0.695 0.485 43.8 8.65 0.000 

Matched 0.673 0.620 11.0 74.9 1.60 0.111 

Formerly married Unmatched 0.050 0.062 -4.9 -0.97 0.334 

Matched 0.053 0.057 -1.7 65.4 -0.25 0.805 

Birth order 

1 Unmatched 0.294 0.312 -4.0 -0.81 0.420 0.294 0.312 -4.0 -0.81 0.420 

Matched 0.317 0.339 -4.8 -19.9 -0.67 0.500 0.320 0.343 -5.0 -26.2 -0.70 0.485 

2 Unmatched 0.284 0.253 7.0 1.44 0.149 0.284 0.253 7.0 1.44 0.149 

Matched 0.276 0.282 -1.3 82.1 -0.18 0.859 0.276 0.289 -2.9 58.4 -0.41 0.683 

3 Unmatched 0.212 0.157 14.3 3.04 0.002 0.212 0.157 14.3 3.04 0.002 

Matched 0.214 0.203 2.9 79.4 0.40 0.686 0.214 0.196 4.8 66.1 0.66 0.509 

4 or higher Unmatched 0.210 0.278 -15.9 -3.12 0.002 0.210 0.278 -15.9 -3.12 0.002 

Matched 0.192 0.176 3.8 76.3 0.60 0.549 0.190 0.172 4.0 74.6 0.64 0.523 

Employment status 

Not currently employed Unmatched 0.214 0.599 -85.0 -16.27 0.000 0.214 0.599 -85.0 -16.27 0.000 

Matched 0.212 0.222 -2.4 97.2 -0.38 0.707 0.207 0.221 -3.2 96.3 -0.50 0.617 

Currently employed Unmatched 0.786 0.401 85.0 16.27 0.000 0.786 0.401 85.0 16.27 0.000 

Matched 0.788 0.778 2.4 97.2 0.38 0.707 0.793 0.779 3.2 96.3 0.50 0.617 

(Continued...)

61 
Appendix Table P. – Continued

 Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Education of head of the household No education Unmatched 0.061 0.244 -52.6 -9.08 0.000 0.061 0.244 -52.6 -9.08 0.000 

Matched 0.067 0.079 -3.3 93.6 -0.64 0.520 0.069 0.078 -2.6 95.2 -0.48 0.629 

Primary education Unmatched 0.116 0.390 -66.5 -11.89 0.000 0.116 0.390 -66.5 -11.89 0.000 

Matched 0.120 0.127 -1.6 97.6 -0.29 0.770 0.121 0.132 -2.8 95.8 -0.50 0.618 

Secondary or higher Unmatched 0.824 0.366 105.2 19.79 0.000 0.824 0.366 105.2 19.79 0.000 

Matched 0.813 0.794 4.2 96.0 0.66 0.507 0.810 0.790 4.7 95.5 0.73 0.466 

Mass media exposure 

Not exposed Unmatched 0.029 0.177 -49.9 -8.3 0.000 0.029 0.177 -49.9 -8.30 0.000 

Matched 0.024 0.029 -1.7 96.5 -0.46 0.648 0.022 0.023 -0.3 99.3 -0.10 0.922 

Exposed to media at least once a week 

Unmatched 0.971 0.823 49.9 8.30 0.000 0.971 0.823 49.9 8.30 0.000 Matched 0.976 0.971 1.7 96.5 0.46 0.648 0.978 0.977 0.3 99.3 0.10 0.922 

Wealth quintile 

First Unmatched 0.032 0.229 -61.3 -10.11 0.000 0.032 0.229 -61.3 -10.11 0.000 

Matched 0.031 0.038 -2.2 96.4 -0.56 0.578 0.032 0.034 -0.5 99.2 -0.13 0.894 

Second Unmatched 0.050 0.206 -47.8 -8.21 0.000 0.050 0.206 -47.8 -8.21 0.000 

Matched 0.053 0.057 -1.3 97.3 -0.26 0.794 0.052 0.053 -0.3 99.4 -0.06 0.956 

Middle Unmatched 0.118 0.260 -37.1 -6.83 0.000 0.118 0.260 -37.1 -6.83 0.000 

Matched 0.118 0.109 2.3 93.8 0.40 0.688 0.118 0.104 3.7 90.0 0.65 0.518 

Fourth Unmatched 0.277 0.223 12.5 2.61 0.009 0.277 0.223 12.5 2.61 0.009 

Matched 0.281 0.263 4.1 66.8 0.58 0.562 0.283 0.265 4.2 66.6 0.58 0.565 

Highest Unmatched 0.523 0.082 109.5 28.74 0.000 0.523 0.082 109.5 28.74 0.000 

Matched 0.517 0.532 -3.9 96.5 -0.45 0.654 0.515 0.545 -7.4 93.2 -0.85 0.396 

Residence 

Rural Unmatched 0.330 0.631 -63.2 -12.73 0.000 0.330 0.631 -63.2 -12.73 0.000 

Matched 0.332 0.350 -3.9 93.8 -0.57 0.571 0.337 0.319 3.9 93.8 0.57 0.570 

Urban Unmatched 0.670 0.369 63.2 12.73 0.000 0.670 0.369 63.2 12.73 0.000 

Matched 0.668 0.650 3.9 93.8 0.57 0.571 0.663 0.681 -3.9 93.8 -0.57 0.570 

(Continued...)

62 
Appendix Table P. – Continued

 Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Region Caprivi Unmatched 0.032 0.083 -22.2 -3.94 0.000 0.032 0.083 -22.2 -3.94 0.000 

Matched 0.036 0.026 4.4 80.1 0.85 0.396 0.037 0.028 3.8 83.1 0.70 0.487 

Erongo Unmatched 0.084 0.060 9.5 2.06 0.040 0.084 0.060 9.5 2.06 0.040 

Matched 0.096 0.100 -1.7 82.5 -0.21 0.836 0.096 0.111 -5.8 39.3 -0.69 0.487 

Hardap Unmatched 0.057 0.056 0.3 0.06 0.954 0.057 0.056 0.3 0.06 0.954 

Matched 0.050 0.042 3.7 -1209.8 0.59 0.553 0.047 0.035 5.3 -1750.1 0.88 0.379 

Karas Unmatched 0.095 0.042 20.8 4.95 0.000 0.095 0.042 20.8 4.95 0.000 

Matched 0.094 0.079 5.9 71.5 0.77 0.444 0.094 0.078 6.0 71.1 0.77 0.443 

Kavango Unmatched 0.061 0.146 -28.1 -5.06 0.000 0.061 0.146 -28.1 -5.06 0.000 

Matched 0.060 0.053 2.3 91.6 0.44 0.659 0.062 0.046 5.1 81.7 0.98 0.329 

Khomas Unmatched 0.197 0.087 31.9 7.48 0.000 0.197 0.087 31.9 7.48 0.000 

Matched 0.192 0.188 1.2 96.1 0.16 0.875 0.187 0.183 1.2 96.2 0.15 0.877 

Kunene Unmatched 0.034 0.060 -12.3 -2.29 0.022 0.034 0.060 -12.3 -2.29 0.022 

Matched 0.036 0.042 -2.8 76.9 -0.44 0.656 0.037 0.048 -5.3 57.0 -0.79 0.432 

Ohangwena Unmatched 0.082 0.100 -6.4 -1.27 0.205 0.082 0.100 -6.4 -1.27 0.205 

Matched 0.084 0.077 2.4 62.7 0.37 0.715 0.084 0.083 0.2 97.1 0.03 0.978 

Omusati Unmatched 0.099 0.092 2.4 0.50 0.619 0.099 0.092 2.4 0.50 0.619 

Matched 0.101 0.114 -4.6 -90.1 -0.63 0.532 0.103 0.109 -1.8 26.1 -0.24 0.809 

Oshana Unmatched 0.080 0.091 -4.0 -0.80 0.425 0.080 0.091 -4.0 -0.80 0.425 

Matched 0.070 0.072 -1.0 76.0 -0.15 0.880 0.069 0.064 1.7 57.9 0.27 0.788 

Oshikoto Unmatched 0.086 0.095 -3.2 -0.65 0.517 0.086 0.095 -3.2 -0.65 0.517 

Matched 0.079 0.096 -5.9 -81.0 -0.86 0.391 0.081 0.077 1.4 56.3 0.21 0.831 

Otjozondjupa Unmatched 0.095 0.089 2.1 0.43 0.667 0.095 0.089 2.1 0.43 0.667 

Matched 0.101 0.110 -3.1 -46.2 -0.41 0.679 0.103 0.137 -11.7 -459.3 -1.48 0.140 

1 Not included in the estimation of the propensity score for the sub-sample 

63 
Appendix Table Q. Mean biases of covariates before and after matching, Rwanda 

Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Maternal age at birth Unmatched 28.415 28.541 -1.9 -0.67 0.503 28.415 28.541 -1.9 -0.7 0.50 

Matched 28.410 28.367 0.6 66.2 0.31 0.760 28.413 28.354 0.9 53.6 0.42 0.676 

Maternal age at birth squared 

Unmatched 849.290 860.610 -2.8 -0.99 0.322 849.290 860.610 -2.8 -0.99 0.322 Matched 849.220 848.910 0.1 97.2 0.04 0.970 849.270 848.410 0.2 92.4 0.10 0.920 

Education 

No education Unmatched 0.162 0.231 -17.4 -6.26 0.000 0.162 0.231 -17.4 -6.26 0.000 

Matched 0.163 0.154 2.1 88.2 1.05 0.294 0.162 0.154 2.0 88.7 1.00 0.316 

Primary education Unmatched 0.718 0.703 3.4 1.18 0.239 0.718 0.703 3.4 1.18 0.239 

Matched 0.723 0.732 -1.9 43.3 -0.92 0.358 0.722 0.729 -1.5 55.7 -0.71 0.476 

Secondary or higher Unmatched 0.120 0.066 18.5 6.11 0.000 0.120 0.066 18.5 6.11 0.000 

Matched 0.115 0.114 0.2 99.0 0.08 0.936 0.116 0.117 -0.4 98.1 -0.15 0.882 

Marital status 

Never married Unmatched 0.072 0.094 -7.9 -2.85 0.004 0.072 0.094 -7.9 -2.85 0.004 

Matched 0.073 0.085 -4.2 47.6 -2.02 0.044 0.071 0.083 -4.4 45.0 -2.13 0.033 

Currently married Unmatched 0.852 0.751 25.6 9.36 0.000 0.852 0.751 25.6 9.36 0.000 

Matched 0.852 0.836 3.9 84.8 2.00 0.045 0.855 0.840 4.0 84.6 2.04 0.041 

Formerly married Unmatched 0.075 0.155 -25.1 -9.44 0.000 0.075 0.155 -25.1 -9.44 0.000 

Matched 0.075 0.079 -1.2 95.1 -0.69 0.491 0.074 0.078 -1.1 95.5 -0.63 0.529 

Birth order 

1 Unmatched 0.245 0.191 13.0 4.45 0.000 0.245 0.191 13.0 4.45 0.000 

Matched 0.242 0.257 -3.5 73.0 -1.58 0.114 0.242 0.257 -3.6 72.5 -1.60 0.110 

2 Unmatched 0.195 0.195 -0.1 -0.05 0.962 0.195 0.195 -0.1 -0.05 0.962 

Matched 0.195 0.196 -0.1 57.0 -0.03 0.978 0.196 0.196 0.1 61.9 0.02 0.981 

3 Unmatched 0.155 0.150 1.4 0.48 0.629 0.155 0.150 1.4 0.48 0.629 

Matched 0.155 0.147 2.4 -75.1 1.16 0.248 0.155 0.146 2.7 -91.1 1.26 0.209 

4 or higher Unmatched 0.405 0.463 -11.7 -4.11 0.000 0.405 0.463 -11.7 -4.11 0.000 

Matched 0.407 0.401 1.2 89.7 0.57 0.566 0.406 0.401 1.0 91.5 0.48 0.634 

Employment status 

Not currently employed Unmatched 0.214 0.177 9.3 3.19 0.001 0.214 0.177 9.3 3.19 0.001 

Matched 0.212 0.218 -1.4 84.6 -0.65 0.515 0.213 0.219 -1.5 83.6 -0.69 0.491 

Currently employed Unmatched 0.786 0.823 -9.3 -3.19 0.001 0.786 0.823 -9.3 -3.19 0.001 

Matched 0.788 0.782 1.4 84.6 0.65 0.515 0.787 0.781 1.5 83.6 0.69 0.491 

(Continued...)

64 
Appendix Table Q. – Continued

 Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Education of head of the household No education Unmatched 0.199 0.269 -16.6 -5.95 0.000 0.199 0.269 -16.6 -5.95 0.000 

Matched 0.200 0.203 -0.7 96.1 -0.32 0.746 0.199 0.203 -0.9 94.3 -0.47 0.640 

Primary education Unmatched 0.670 0.648 4.5 1.59 0.112 0.670 0.648 4.5 1.59 0.112 

Matched 0.674 0.670 0.6 85.7 0.31 0.757 0.672 0.668 1.0 78.7 0.46 0.647 

Secondary or higher Unmatched 0.131 0.082 15.8 5.28 0.000 0.131 0.082 15.8 5.28 0.000 

Matched 0.127 0.127 -0.1 99.3 -0.05 0.963 0.128 0.129 -0.2 98.8 -0.08 0.935 

Mass media exposure 

Not exposed to media Unmatched 0.297 0.446 -31.2 -11.09 0.000 0.297 0.446 -31.2 -11.09 0.000 

Matched 0.298 0.308 -2.1 93.2 -1.04 0.300 0.296 0.307 -2.2 93.1 -1.06 0.291 

Exposed to media at least once a week 

Unmatched 0.703 0.554 31.2 11.09 0.000 0.703 0.554 31.2 11.09 0.000 Matched 0.702 0.692 2.1 93.2 1.04 0.300 0.704 0.693 2.2 93.1 1.06 0.291 

Wealth quintile 

First Unmatched 0.195 0.319 -28.8 -10.41 0.000 0.195 0.319 -28.8 -10.41 0.000 

Matched 0.196 0.200 -0.9 96.8 -0.47 0.640 0.195 0.198 -0.8 97.3 -0.39 0.693 

Second Unmatched 0.203 0.227 -5.7 -2.00 0.045 0.203 0.227 -5.7 -2.00 0.045 

Matched 0.205 0.205 0.1 98.9 0.03 0.977 0.204 0.202 0.4 93.5 0.18 0.859 

Middle Unmatched 0.198 0.171 7.1 2.45 0.014 0.198 0.171 7.1 2.45 0.014 

Matched 0.200 0.198 0.4 94.3 0.19 0.851 0.200 0.197 0.7 89.9 0.33 0.742 

Fourth Unmatched 0.198 0.138 15.9 5.37 0.000 0.198 0.138 15.9 5.37 0.000 

Matched 0.198 0.201 -0.7 95.5 -0.31 0.753 0.199 0.202 -0.8 94.7 -0.37 0.712 

Highest Unmatched 0.206 0.145 16.1 5.42 0.000 0.206 0.145 16.1 5.42 0.000 

Matched 0.201 0.197 1.3 92.2 0.56 0.573 0.203 0.200 0.6 96.4 0.26 0.798 

Residence 

Rural Unmatched 0.850 0.858 -2.3 -0.79 0.427 0.850 0.858 -2.3 -0.79 0.427 

Matched 0.850 0.850 0.2 92.7 0.08 0.938 0.850 0.849 0.4 80.8 0.20 0.838 

Urban Unmatched 0.150 0.142 2.3 0.79 0.427 0.150 0.142 2.3 0.79 0.427 

Matched 0.150 0.150 -0.2 92.7 -0.08 0.938 0.150 0.151 -0.4 80.8 -0.20 0.838 

(Continued...)

65 
Appendix Table Q. – Continued

 Full sample Sub-sample 

 Mean % bias 

reduction t-statistic 

pvalue 

 Mean % bias 

reduction t-statistic 

pvalue Variable Sample Insured Uninsured % bias Insured Uninsured % bias 

Region Kigali city Unmatched 0.111 0.134 -7.2 -2.54 0.011 0.111 0.134 -7.2 -2.54 0.011 

Matched 0.111 0.109 0.7 90.6 0.33 0.739 0.112 0.111 0.4 94.5 0.19 0.847 

South Unmatched 0.247 0.266 -4.4 -1.53 0.126 0.247 0.266 -4.4 -1.53 0.126 

Matched 0.247 0.253 -1.3 69.8 -0.63 0.530 0.247 0.251 -1.1 75.7 -0.50 0.614 

West Unmatched 0.241 0.203 9.2 3.15 0.002 0.241 0.203 9.2 3.15 0.002 

Matched 0.241 0.239 0.6 93.2 0.28 0.776 0.242 0.241 0.2 97.5 0.11 0.916 

North Unmatched 0.172 0.108 18.5 6.18 0.000 0.172 0.108 18.5 6.18 0.000 

Matched 0.170 0.177 -2.0 89.1 -0.87 0.384 0.169 0.175 -1.9 89.7 -0.82 0.412 

East Unmatched 0.229 0.289 -13.6 -4.83 0.000 0.229 0.289 -13.6 -4.83 0.000 

Matched 0.230 0.223 1.8 86.6 0.90 0.370 0.231 0.222 2.1 84.9 1.01 0.314 

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