Abstract

We analyze the impact of an experimental maternal and child health and family planning program that was established in Matlab, Bangladesh, in 1977. Village data from 1974, 1982, and 1996 suggest that program villages experienced a decline in fertility of about 17 %. Household data from 1996 confirm that this decline in “surviving fertility” persisted for nearly two decades. Women in program villages also experienced other benefits: increased birth spacing, lower child mortality, improved health status, and greater use of preventive health inputs. Some benefits also diffused beyond the boundaries of the program villages into neighboring comparison villages. These effects are robust to the inclusion of individual, household, and community characteristics. We conclude that the benefits of this reproductive and child health program in rural Bangladesh have many dimensions extending well beyond fertility reduction, which do not appear to dissipate rapidly after two decades.

Introduction

In this article, we estimate the impact of a reproductive health intervention in Matlab, Bangladesh, on broad measures of well-being of women and their families. The Maternal and Child Health and Family Planning (MCH-FP) program, launched in 1977, provided married women in designated “treatment” villages with home delivery of contraceptive supplies, follow-up services, and general advice (Phillips et al. 1982). Additional maternal and child health services were added over time (Fauveau 1994; Muhuri 1995; Phillips et al. 1988). Women in neighboring “comparison” villages were served mainly by clinics run by the Bangladesh government. Women in both areas were tracked carefully and continually. The Matlab experiment has been shown to have significantly reduced fertility as well as maternal, infant, and child mortality (Fauveau 1994; Koenig et al. 1990, 1991; Phillips et al. 1982, 1988; Rahman et al. 2009).

This study uses census data from 1974 and 1982 together with the Matlab Health and Socioeconomic Survey (MHSS) of 1996 to make three contributions to the literature. First, new methods confirm that treatment and comparison areas in Matlab had similar pre-program characteristics, a fact that has been implicitly assumed but rarely demonstrated in much of the Matlab literature. This increases confidence in the quasi-random design of this intervention and proposes that some studies of the Matlab program be treated as an example of the growing literature on randomized evaluations (e.g., Bertrand et al. 2004; Duflo et al. 2008). Second, unlike most existing studies, which have focused on the initial demographic impacts of the program, particularly between 1976 and 1985, this study illustrates that the program had long-term impacts on not only fertility but also child mortality and maternal and child health more broadly. Third, this study illustrates that the program may have had informational “spillovers” that lowered fertility in comparison-area villages that lie adjacent to the treatment villages. This has implications for estimating impact and cost-effectiveness of the program. Overall, the results suggest that policies along the lines of the Matlab experiment may be effective not only in lowering fertility but also in improving the long-term health of mothers and their children. Traditional cost-benefit calculations of such policies tend to neglect such multifaceted effects.

This article also contributes to the broader literature on the effectiveness of family planning and reproductive health programs (Schultz 2008). Some research now corroborates the assertion that such programs reduce fertility and have cross effects on variables such as infant mortality and female employment. Most studies however, are based on cross-sectional or panel data and face the challenge of omitted variables and/or nonrandom program placement: if programs are placed in areas with different demand for children and health, estimates of impact may be biased and lead to spurious estimates (Rosenzweig and Wolpin 1982; Schultz 1994). The Matlab experiment’s quasi-random design permits researchers to compare individuals who did and did not have the opportunity to benefit from the program and to make stronger causal inferences. The finding that its effects persisted over nearly two decades and had numerous “spillovers” is thus noteworthy.

The remainder of this article is structured as follows: first, we briefly review the literature on Matlab; we then contrast village outcomes from 1974 to 1996. After we define the variables from the 1996 survey for the subsequent regression analysis at the individual level, we summarize relevant studies of program costs and benefits and then draw our conclusions.

Background

Matlab thana (subdistrict) lies about 55 km south of Dhaka, Bangladesh’s capital. It is a flat and low-lying deltaic plain that is crisscrossed by rivers. The region is entirely rural, lacks major towns or cities (except for the Matlab bazaar), and has limited intervillage trade and commerce. The dominant occupations are subsistence farming and fishing. The society is quite traditional and religiously conservative, particularly with regard to the status of women (Abdullah and Zeidenstein 1979; Chen et al. 1983; Fauveau 1994; Menken and Phillips 1990). The total fertility rate has declined from more than 6 in 1976 to 3.2 by 1995 (Fauveau 1994; ICDDR,B 2004). Infant mortality has fallen from 110 per thousand live births in 1983 to 75 in 1989 and 65 in 1995.

In 1966, the International Center for Diarrhoeal Disease Research, Bangladesh (ICDDR,B) established a Demographic Surveillance System (DSS) to record monthly births, deaths, marriages, and migrations within 149 villages. In October 1977, it launched a maternal and child health and family planning program. Villages in contiguous areas (blocks A, B, C, and D), including about half of the 180,000 people of Matlab, received the services of a family planning outreach program (hereafter referred to as “program”), while the remainder (blocks E and F) continued to receive only usual health and family planning services delivered through local government clinics or private providers (hereafter “comparison”).

The program recruited relatively well-educated and married women from the surrounding area who used contraception themselves to provide home delivery of health services to married women of childbearing age every two weeks. These Community Health Workers (CHWs) advised women on the use of birth control, provided supplies (including the pill and injectable) as well as follow-up services, and referred women to local clinics or hospital when necessary (Phillips et al. 1982, 1988). After 1981, additional maternal and child health services were added to the program, such as tetanus toxoid immunization for women, measles immunization for children 9 months to 5 years old, other Expanded Programme on Immunization (EPI) childhood vaccinations, oral rehydration therapy (ORT) for diarrhea, vitamin A supplements, and antenatal care (Fauveau 1994; Phillips et al. 1988). By the 1990s, the Government of Bangladesh provided tetanus toxoid immunization, measles, and other EPI child vaccinations at its own clinics. These were presumably available in the clinics in the comparison areas, but we could find “essentially no information on their use in the comparison areas” (LeGrand and Phillip 1996:58). Later in this article, we show that by 1996, women in treatment villages reported more frequent use of these maternal and child health inputs for children born in the past five years than did women in the comparison villages (Joshi and Schultz 2007: Tables 8 and A).1 The MCH-FP program is noteworthy for its quasi-experimental design, accurate measurement of demographic indicators for vital rates in the program and comparison populations (largely because of the preexisting DSS), and the low-income rural living conditions of the population.

An influential literature in public health and demography has examined these reliable and detailed registration data, interpreting observational regularities and experimental programs in Matlab. Although the literature is too large to review comprehensively, we note a few important studies whose results are related to ours. The prevalence of modern methods of contraception among married women of reproductive ages increased sharply from 7 % to 33 % after 18 months of program operation, a trend that was sustained for two years and then continued to increase after 1982 (Koenig et al. 1992; Phillips et al. 1988). DSS quarterly general fertility rates from 1976 to 1981 showed that the treatment areas that provided family planning services to women in their homes starting in October 1977 experienced a 22 % to 25 % lower fertility than did the comparison areas, controlling for a short-lived prior program called the Contraceptive Distribution Program (Phillips et al. 1982). From 1978 onward, neonatal mortality rates were slightly lower in treatment than comparison areas, but the slope of the subsequent declines in these rates do not seem to differ (Fauveau 1994:144). The impact of measles vaccination on child mortality in the treatment areas is well documented and contributed to the adoption of this preventive health intervention by the Bangladesh Government Health program and elsewhere (Koenig et al. 1990, 1991; LeGrand and Phillips 1996; Menken and Phillips 1990). But the persistence of these early CMH-FP intervention effects on long-term family outcomes of fertility and maternal and child health are rarely assessed. Several other studies are discussed in subsequent sections of this article.

This article extends the literature on Matlab by analyzing the impact of the program using the 1996 MHSS. This is a multistage cluster random sample of approximately one-third (2,687) of the baris (residential compounds typically of linked kin) in the Matlab DSS, covering 4,364 households (Rahman et al. 1999).2 One household in each bari is randomly selected, and one additional household is purposely selected to favor close kin. All married women who are 50 years or older and those whose spouses are 50 years or older are sampled. From among women aged 15–49, one is randomly interviewed from the household as strata 1 if she is the head or spouse of head of household. A second woman aged 15–49 is systematically sampled as a respondent in strata 2 if her spouse is present in the household. The survey is designed to be representative when these two strata of women are suitably weighted to account for the lesser representation in the sample of people living in baris with many households or households with many women aged 15 to 49 years.3 Our sample focuses on married women because they were eligible for the CMH-FP treatment. In the group of women aged 15–24 in 1996, 41 % are ever married. For women aged 25–29, this proportion is 89 %; and among those aged 30–54, it is 99 %. Census data from 1974 and 1982 are also used to analyze pre-program population characteristics and to estimate the program’s initial impact.

Unconditional Estimates of Program Impact

The first step of our analysis is to compare the fertility of married women older than 14 between the program and comparison areas using the 1996 MHSS.4 We regress total fertility (children ever born) on age dummy variables as well as the interaction of the age dummy variables and a dummy variable for residence in a program village. Figure 1 plots the resulting expected values of fertility for women by age groups in the treatment and comparison areas. The lower panel shows the difference between the treatment and comparison fertility coefficients and their 95 % confidence interval (i.e., 1.96 × standard errors). Fertility among married women over the age of 55 in 1996 is indistinguishable between the program and comparison villages, consistent with the hypothesis that these older women had virtually completed their childbearing when the program began. Conversely, the unconditional fertility of younger women in the treatment villages is lower than in comparison areas. This corroborates the persistence of early evidence by Phillips et al. (1982, 1988) and others who found that post-program general fertility rates were 25 % lower in the treatment areas than in comparison areas by 1978–1979.

A second way to assess the program effect on fertility between the treatment and control villages is to analyze changes over time in aggregate measures of fertility at the village level. We perform this comparison using census data from 1974 and 1982. Since the number of children ever born to a woman is not reported, we use age and sex of residents to construct the ratio (C / W) of the number of children aged 0–4 to the number of women of childbearing age, 15 to 49, as an aggregate measure of “surviving fertility” in the last five years. Aggregate difference-in-difference estimates of the program’s effect can be derived from a regression across the 141 villages constructed from the census of 1974 before the program, and either the 1982 census or the weighted 1996 MHSS:
formula
formula

where C / Wjt is the child-woman ratio in village j in time period t; Pj takes value 1 if village j is in the program treatment area, and 0 otherwise; Tt takes value 1 if the observation is for a year after the program has started (i.e., 1982, 1996), and 0 for the pre-program year 1974; Pj × Tt is the interaction of the two variables; and ejt is the error. The pre-program fertility differences between treatment and comparison villages is estimated by β1, change over time in all areas is estimated by β2, and the post-program treatment effect on those residing in a program village in a subsequent census or survey is estimated by β3. Because the impact of the treatment is assumed homogeneous in all villages, including village-level fixed effects would yield the same estimates of the program treatment effect.

OLS estimates of the above equation generate the program’s local average treatment effect (LATE) after the program started in 1977, or β3, holding constant for any preexisting persistent differences in fertility between the treatment and comparison villages as measured in 1974 that are represented by β1. Because the variance in the observations on villages’ surviving fertility, ejt, are expected to be heteroskedastic and greater for smaller villages, the regressions are therefore estimated using generalized least squares (GLS), where the weights (i.e., STATA aweights) are the inverse of the square root of the number of women aged 15–49 observed in each village observation. The sample size is 282 from combining two cross sections of villages, and the GLS estimates are reported in the two columns of Table 1 for the two different post-program census or survey years, 1982 and 1996.

The values of the child-woman ratio for the treatment villages are on average slightly larger than in the comparison villages, β1 = .022 (Table 1, column 1). In 1982, five years after the launch of the program, the C / W ratio is, holding constant the initial village levels in 1974, 17 % lower in program villages than in comparison villages (β3 = –.143, or –.14 / .81 = –.17). In 1996, this difference-in-difference estimator of the program effect (Table 1 column 2) is –.127, or 16 % lower than in 1974, despite the fact that the child-woman ratio declined in the comparison villages by 39 % by 1996 (β2 / β0 or –.31 / .81 = –.39). This difference in child-woman ratios is one approximation for the program’s impact on individual surviving family size or community natural rates of population growth.

The 1974 census also provides indicators of education, housing, and religion. Population-weighted differences between the averages for program and comparison villages are summarized in the last two columns of Table 2.5 Note that there is no statistically significant difference in levels of formal schooling in the two areas for adults over the age of 14 and children aged 6–14 in 1974. Seventy percent of adults and 41 % of the children aged 6–14 had not attended any secular or religious school, and these shares do not differ significantly between program and comparison villages. However, Muslims are more dominant in 1974 in the comparison than in the program areas, 88 % versus 79 %, which is statistically significant (t = 2.01) between the groups of village means. This religious difference also increases over time, and by 1996 (Table 2, panel B) it is 93 % and 80 % in the two areas, respectively. Because Muslims engage in different occupations than the minority Hindus, and their livelihoods might affect their desired family size and economic behavior, a control for Muslim religion of the household head is included in the subsequent multivariate analysis of the 1996 MHSS. Moreover, the program average treatment effects are allowed to differ for Muslim and Hindu households to assess possible heterogeneity in their responses to the program (Fauveau 1994; Munshi and Myaux 2006).

Between 1974 and 1996, years of schooling for adults roughly doubled in Matlab (Table 2). In 1996, they were greater in program than in comparison areas (3.73 vs. 3.60 years), but the difference is not significant. The village average years of schooling of children aged 6–14 are also substantially higher in the program villages by 1996, 2.26 versus 1.84 years, consistent with the hypothesis that parents with program assistance in controlling their fertility traded off child quantity for child quality (e.g., Becker and Lewis 1974). Sample weighted statistics from the 1996 MHSS subsequently analyzed in the regressions are shown in Table 3. The last column for the dependent variables reports the unconditional differences between program and comparison areas.

Conditional Estimates of Program Impact

Assuming that the program and comparison areas had similar characteristics in 1977, the partial associations of the program in 1996 with long-term family consequences are estimated at the individual level of the married woman, first as the treatment-comparison differences unconditional on any control variables (Table 3), and then conditional on a common set of arguably predetermined control variables. The following indicators of well-being are used as dependent variables:

  1. Fertility/child mortality: (a) number of children ever born (Total Children); (b) number of children alive (Total Alive); (c) age (in years) at which a woman had her first birth (Age First Birth); (d) years between the birth of the first and second child (Second Birth Interval); (e) years between the birth of the second and third child (Third Birth Interval); and (f) a binary variable that takes value 1 if the child died before the age of 5 (Died 5), and 0 otherwise.

  2. Women’s health: (a) a subjective measure of current health (Currently Healthy) that takes the value of 1 if a woman’s self-assessment of her health status is “Healthy,” and 0 otherwise; (b) the self-reported capacity to perform five activities of daily living (ADLs) that is normalized to 1 (no functional limitations) or 0 (maximum limitations) (Activity Index, ADLEq0)6; (c) the woman’s weight in kg (Weight); (d) the woman’s body mass index in kg/m2 (BMI); and (e) a binary variable that takes value 1 if a woman’s BMI is greater than 18, and 0 otherwise (BMI > 18).

  3. Use of preventive health inputs: (a) average number of antenatal visits per pregnancy for all of a woman’s pregnancies (Num Antenatal); (b–d) binary variables that take value 1 if the most recent child born in the past five years received a vaccination against, respectively, polio (Polio Vac), measles (Measles Vac), and DPT (DPT Vac).

We examine the program’s impact using a reduced-form framework. The key independent variable is whether a particular woman resides in a program area. The program’s impacts are, however, expected to vary by the woman’s birth cohort or age in 1996. As already noted, the program effect should be negligible among women over the age of 60 in 1996 unless there are intergenerational spillovers within the household or bari. But the program effect may not increase monotonically among younger women who had more years at risk of childbearing after the program started but fewer years to bear children. Rather than assume a specific structural model that prescribes how family size goals are determined and how birth control is used to achieve these goals, the program’s impact on fertility is allowed to vary freely by women’s five-year age groups.

A second independent variable that is of interest to us is whether a woman resides in a comparison-area village that shares a common boundary with a program village. The program’s impact in these communities can provide insights into the mechanisms through which it achieved behavioral change. For example, if the program’s main contribution was to reduce the costs of using contraceptives and health-care services, we can expect women in boundary areas to remain largely unaffected by the program’s presence and show outcomes similar to other comparison villages. If, however, the program’s main contribution was to change social norms and provide new information about improved health technologies, we should see these women in boundary villages resemble their counterparts in program villages. In this case, we could infer that knowledge may have diffused geographically through social networks and influenced behavior in neighborhoods where women shared their knowledge and experiences (Munshi and Myaux 2006). Understanding such spillover effects is important for two additional reasons: first, positive (or negative) spillovers can lead to an understatement (or overstatement) of the program’s effects estimated only by local average treatment effects (LATE). Second, if spillovers are small relative to program direct effects, such evidence of weak diffusion could help justify the continuing costly program component of the fortnightly visits to each woman’s home. The strength of social networks may also differ by the age of the women, suggesting an additional reason to allow the spillover effect to differ by the woman’s age. We assume the effect of the Matlab MCH-FP program in “boundary” comparison villages is a constant share of the direct program effect if it shares at least one boundary with a treatment village, and is otherwise negligible.7

Because the behavior of women living in a village on the boundary of the program may differ from women in other comparison villages and may differ by the woman's age, we estimated auxiliary regressions within the restricted sample of women in comparison areas to represent the unconditional differences between women in boundary and nonboundary comparison villages. To conserve space, we only summarize those results here. Estimates of the unconditional differences between boundary and nonboundary villages (both in the comparison area) are interesting because they provide one measure of local program spillovers, assuming that these two populations were otherwise the same from 1977 to 1996. We find that 12 % of the women in boundary villages had 0.35 (t = 2.55) fewer children ever born in 1996, which is significant, whereas the proportion of their children born before 1991 who had died by age 5 is surprisingly larger, at .022 (t = 1.86, not reported). Women’s health indicators are mixed: better for weight, BMI, BMI greater than 18, and ADLs, whereas fewer women report themselves as currently healthy in the boundary villages (–.061; t = 2.46). The receipt of childhood vaccinations for polio and DPT are significantly less common in the boundary than in the other comparison villages, whereas the frequency of measles shots and antenatal care of the women do not differ (not reported). These simple geographic differences with no controls suggest that fertility changes may have partially diffused from the program villages without the benefit of supplies and services delivered in the home, but this spillover was not evident uniformly for indicators of child or maternal health or the use of preventive health inputs emphasized in the MCH-FP program.

Because women’s reproductive behaviors may be influenced by additional variables that are not themselves attributable to family choices and could differ across treatment and comparison areas, we also include a variety of control variables in our analysis. Female years of schooling (Years of Schooling) is included to control for the higher opportunity costs of the time of more-educated women to have an additional child that may be partially offset by their higher income opportunities (Mincer 1963; Schultz 1981, 2002), and schooling may improve their skills to evaluate health inputs or contraceptives. Female schooling is also interacted with the program (Treatment × Years of Schooling) to assess the possibility that a woman’s schooling and access to the treatment are substitutes in the use of effective new forms of birth control. Some previous studies of family planning in Colombia in 1964, Taiwan in the late 1960s, and Thailand in the late 1970s (Schultz 1980, 1992) found that both schooling and local family planning programs are associated with lower levels of fertility, but their interaction is partially associated with higher fertility. Muhuri (1995) also reported that the MCH-FP program is associated with a larger decline in child mortality among the less-schooled women.8

As explained previously, we include a dummy variable for whether the household head is Muslim and interact it with the treatment area dummy variable (Treatment × Muslim). If family planning knowledge is less likely to be shared informally between Muslims and Hindus than within these groups, the minority Hindus (12 % in 1996: Table 3) might be at a disadvantage in social learning processes and thus have more to gain from the program’s outreach informational efforts (Munshi and Myaux 2006).

We also include a variety of controls for husband’s characteristics, household composition, and village infrastructure. A control is included for the husband’s education (Husband Years Schooling) as a measure of household income/wealth that is fixed at the start of the adult life cycle; it is not expected to reduce fertility as much as the wife’s education because children customarily occupy women’s time (Schultz 1981). Husband’s age is also included in quadratic form (Husband Age and Husband Age Squared) as an indicator of household life-cycle income and wealth; a dummy variable is set to 1 if the husband’s education or age is missing, allowing us to retain these women in the sample.

Finally, four infrastructure features of the 141 villages in the 1996 MHSS are included as controls, which could influence the economic, health, and environmental conditions of families in the village. In particular, controls are included for (1) the presence of a paved/pucca road in the village (Village Has Pucca Road); (2) our map-estimated distance between the village and a subcenter hospital where contraceptives are believed to be provided by regular government programs (Distance to Subcenter Hospital or Clinic); (3) the presence of a secondary school in either the same village or a neighboring village (Secondary School Nearby); and (4) a village’s access to motorboat (Village Has Motorboat), presuming that the village is located along one of the canals or tributaries of the rivers in Matlab. Village infrastructure could not be matched for one village and is omitted here.

Impact on Fertility and Mortality

The weighted fertility (Total Children) regression estimates in Table 4, column 1, indicate that the program reduces fertility by 1.54 children for women aged 45–49. The estimate is at least 1.0 child lower for women aged 30–54. As expected, there is no significant partial impact on the fertility of women older than 54. The individual weighted regressions in Table 4 (as well as all subsequent tables) include all discussed control variables, and standard errors are adjusted for clustering at the bari level.

An additional year of schooling on average reduces fertility by 0.064 children, but the interaction of schooling and treatment is not statistically significant, suggesting that female education and program treatment are neither substitutes nor complements. Muslims have 0.10 more children than do Hindus in the comparison areas; in the treatment area, Muslims have 0.48 more children (i.e., 0.10 + 0.38). The program appears to be associated with a reduction in the relative fertility of the minority group, the Hindus, compared with the Muslims.

Women in boundary villages report lower fertility. This reduction of –0.34 births is statistically significant for women aged 15–34 and is 43 % of the impact seen for women this age in program villages. This confirms a diffusion of family planning knowledge and application beyond the treatment area among younger women, although this spillover effect does not statistically extend further to affect fertility in next neighboring villages, or to diminish systematically as a linear or a polynomial function of distance to the nearest program village (not shown), perhaps because women’s social networks are quite circumscribed under purdah (Abdullah and Zeidenstein 1979).9

Joint F tests for the 12 treatment variables, the two Muslim variables, the three boundary-area variables, and the five infrastructure variables are provided at the bottom of Table 4. All of the F tests except those for the four village infrastructure variables are significant at least at the 5 % level. The sample size is 5,273 married women, and the R2 is .59. Although the heterogeneity in fertility response to the program is not confirmed individually with respect to the mother’s schooling or Muslim beliefs, these interaction variables are occasionally significant and informative in the subsequent regressions.

Because the MCH-FP Program was designed to reduce both fertility and mortality, the woman’s number of surviving children is also a dependent variable (Table 4, column 2). This approximates the net program effect on final family size for older women, or the effect on population growth.10 The estimated coefficient on the program treatment is a smaller absolute value than the coefficient with children ever born, signaling that the program is associated with a larger fraction of children surviving. But the program-induced reduction in fertility exceeds the magnitude of the increased child survival, confirming that the program has reduced the surviving size of families and, at least in this first two decades, slowed population growth among the remaining residents. For example, among women 45 to 49 years old, the program impact on child survival “offsets” almost half of the decrease in fertility (i.e., (1.55 – .81) / 1.55 = .48), and the increase in child survival offsets 30 % of the fertility decline among younger women aged 25 to 29 ((.84 – .59) / .84 = .29).

Column 3 in Table 4 shows that the program is not associated consistently with the age at first birth, but the program increases significantly the spacing between the second and third birth at ages 25–34 (column 5). This is consistent with previous studies that have found that the MCH-FP program contributed to women adopting contraception not only to avoid unwanted births at the end of their reproductive period but also to space their later births farther apart (DeGraff 1991; Koenig et al. 1992; Phillips et al. 1988).

The effect of the woman’s schooling on her number of surviving children is one-third smaller than the effect on fertility because her schooling is associated with lower child mortality. Her husband’s schooling is associated with having a larger number of surviving children, shown in column 2 of Table 4, although this effect is not significant. During the last 22 years, the educational attainment of adults has increased in Matlab, as shown in Table 2, but our regression estimates of fertility in 1996, shown in Table 4, suggest that the increased schooling of women by about 2.1 years between the cohorts aged 50–54 and 25–29 (not reported) could account for only a small reduction in fertility of –.13 = .064 × 2.1, or in surviving fertility of –.09 = .043 × 2.1. Conversely, controlling for the same variables in these regressions, the partial local average age effects of the MCH-FP program intervention on fertility and surviving fertility for women aged 30–54 are about seven times larger than those associated with the remarkable advance in women’s schooling.

The MHSS survey-sample weighted mean of child mortality for births before 1991 is 0.160, shown in Table 3, and is higher for males than for females. The unconditional difference between the program and comparison areas (i.e., local average treatment effect), shown in the last column of Table 3, is negative and significant for both sexes, and the program effect is larger for males than for females, although the gender difference is insignificant (not reported). Previous studies based on the larger Matlab DSS noted the sensitivity of child mortality rates by gender to the number and gender of the child’s siblings. However, as earlier noted, these family composition and birth timing variables are excluded here as controls because they may be affected by family choices and hence are potentially correlated with mortality and fertility for other reasons or endogenous to these family outcomes (Koenig et al. 1990, 1991; Muhuri 1995; Muhuri and Menken 1997; Muhuri and Preston 1991).

The MCH-FP program expanded its objectives in about 1982 to include reduction of child mortality and improvement in maternal health (Fauveau 1994:91). ICDDR,B (2004) reports from the full DSS vital event registry suggest that year-to-year variation in infant and child death rates in all of Matlab was substantial, but child mortality in treatment areas may have been lower than in the comparison areas by the late 1980s, and the two areas may have begun to reconverge by 2000. Table 5 reports weighted logistic maximum likelihood estimates of the probability that a child died before age 5, represented first in column 1 by odds ratios and associated z statistics and then in columns 3 and 5 for boys and girls estimated separately. With a nonlinear model, especially when interactions between variables such as the mother’s age and program treatment are included, it may be preferable to evaluate the derivative of the probability of child mortality with respect to program treatment, evaluating the conditional marginal effect at the sample means (Ai and Norton 2003; STATA 11, User Guide 20.25.1). These are reported for comparison in columns 2, 4, and 6. Our observations on births extend from about 1960 to 1991, and it is not surprising that treatment-comparison differences are not significant for women older than 54 in 1996, whose children for the most part would have been born before 1982, when the program expanded to focus on child health. For younger women, the conditional marginal effects of the program are of similar magnitude across four age groups (i.e., 0.9), indicating a reduction in child mortality, although they are not significantly different from 1.0 for women between ages 25 and 34. The estimated odds ratios in column 1 are less than 1.0 and are statistically significant for women aged 35–44 and 45–54. When the boys and girls are estimated separately, the odds ratios and the conditional marginal effects at sample means are not significant for boys; for girls, however, the odds ratio for women aged 35–44 are significant, and the conditional marginal effects are significant among women less than 25, 35–44, and 45–54.11

The effect of one more year of schooling for women is a 0.04 lower odds ratio of child mortality, whereas the schooling of husbands is not significantly related to overall child mortality. Mother’s schooling is more closely related to boy’s mortality, and father’s schooling to girl’s mortality. Inclusion of time trends for the child’s date of birth are significantly different from zero only after 1970, and inclusion of these time trends did not change the sign or significance of the program treatment variables, as reported in Table 5.

There is no discernible program spillover effect on child mortality in comparison villages adjacent to treated villages. We interpret this lack of relationship as suggesting the provision of child health services to women directly in their homes by the MCH-FP may have been an important feature enhancing the program impact reducing child mortality, and this interpretation is consistent with the subsequent analysis of the use of preventive health inputs for children, such as vaccinations.

Impact on Women’s Health

The provision of family planning as well as health services may impact long-term female health through improved reproductive health, reduced morbidity and/or improved nutrition, and longer intervals between later births. Such impacts of policy interventions, however, are rarely confirmed because of the lack of social experiments and long-term follow-up evaluation studies of reproductive health programs (an exception is Frankenberg and Thomas 2001). Moreover, there is limited agreement on how to measure adult health status at reasonable cost in a household survey (Kuhn et al. 2004; Rahman et al. 2004; Strauss and Thomas 2008). The 1996 MHSS asked women whether they were “healthy” (GH01), and three-fourths responded positively (Table 3), but the proportion does not differ significantly between the program and comparison areas (unconditional weighted difference in Table 3, last column), nor when controls are added in Table 6, column 1. This self-reported subjective indicator of health is weakly related to female schooling, and the only significant pattern other than age is that women aged 35–54 in boundary comparison villages reported lower self-assessed health status than in other comparison villages.

An index of ADLs is a second survey indicator of adult health status, used primarily among the elderly to assess the onset of chronic illness and disabilities that limit physical functioning. However, ADLs have not been extensively validated at younger ages or in low-income countries as a reliable measure of health status (Stewart and Ware 1992; Strauss et al. 1995), though they have been related to mortality among persons over age 50 in Matlab (Kuhn et al. 2004). The unconditional difference in our ADL index between the program and comparison areas is not significant, and it is not substantial (+.003) in relation to the weighted mean of 0.88 (1 = no limitations in five activities and 0 for the largest number of limitations in the sample; see footnote 6). The weighted regression estimates of the program’s impact on a woman’s ADL index by her age are all insignificant (Table 6, column 2).

A third class of survey indicators of health and nutritional status relies on weight and height. Women in the treatment area weigh 0.79 kg more than their counterparts in the comparison area (Table 3), where the mean is 41.6 kg; this difference is statistically significant. In the weighted regression with controls, significant differences are shown for women aged 30–54 (Table 6, column 3). We also consider women’s body mass index (BMI = kg/m2) as an indicator of health because BMI is often consulted as a risk factor for various causes of death (Fogel 2004; Waaler 1984). We find that women’s BMI is unconditionally 0.47 units larger in the program areas than in comparison areas, and this difference is significant. When the controls are added and the program effects are disaggregated by age, however, the point estimates are larger for women aged 30–54 but are no longer significant.

We also explore whether a woman’s BMI exceeds a critical healthy threshold. Values below 18.5 are thought to be driven by deficits in calorie consumption, combined with physically demanding work, and poor health (such as diarrhea and inflammatory disease), and are also known to cause mortality risks for women (WHO 1995, 2006). The average BMI in Matlab is close to this threshold in 1996, at about 18.4 kg/m2 in the comparison areas (Joshi and Schultz 2007). Menken et al. (2003) estimated the hazard of dying for women in the Matlab comparison areas between 1975 and 1986 and found that, for women aged 16–65, a one-point increase in BMI lowers the prospective hazard of death by 17 %.12 To overcome the nonmonotonic nature of the relationship between BMI and health, we focus on the upward shift in the distribution of women’s BMI to values greater than 18. The unconditional difference between program and comparison areas is .060 with the mean of .590, which is significant (Table 3). The effect is statistically significant in the regression with controls in Table 6, column 5, for women aged 35–40 and 50–59.

We regard BMI in excess of 18 as the most reliable and objective health indicator of those available for adult women in the MHSS. BMI gains in program areas are shown elsewhere to be concentrated among women aged 25–54, who are likely to have benefited most directly from the program’s provision of new methods of birth control and reproductive health initiatives, corroborating the hypothesis that the program effect is not operating through general health improvements for the entire population or gains in economic well-being shared by all family members. These treatment-comparison differences in BMI are not significant for men aged 25–54 or for younger men or women aged 15–24 in the MHSS (Schultz 2010).

Impact on Use of Preventive Health Inputs

Many specific interventions promoted by the MCH-FP program might be responsible for the program-associated improvements in maternal and child health. One way to investigate these mechanisms is to estimate the unconditional impact of the program on use of preventive health inputs (Table 3) as well as the conditional impact with controls (Table 7). The program effects on the use of curative health inputs, such as oral rehydration therapy (ORT), are more difficult to interpret because the program may reduce the incidence of diarrhea while increasing the use of ORT only for those who are ill. The unconditional weighted differences in preventive health input used for all women in the program and comparison areas are significant (Table 3). The average number of antenatal medical visits a woman reports for all of her pregnancies (Table 7, column 1) is significantly larger in program areas than in the comparison areas (0.95, with mean of 1.39), including controls for women under age 25, 25–29, 30–34, 35–39, and 40–44.13

Because the MHSS reports childhood vaccinations only for the last child born after 1991, the number of mothers reporting whether their children received these health inputs is relatively small, as shown in the regressions in Table 7, columns 2–4, and describes input use at a later stage in the program, when government clinics may have also made them available. Nonetheless, all age groups of recent mothers (younger than age 55) reported obtaining these childhood vaccinations more frequently in the program villages than those in the comparison villages. The public health priorities stressed in the program involving use of prenatal care and childhood vaccinations appear to have been effectively implemented, and the inputs were differentially adopted, contributing in all likelihood to the noted declines in child mortality in program villages. The distance from the village to the nearest subcenter hospital (clinic) is significantly associated with a reduction in prenatal care, as might be anticipated, although this distance to clinic is positively related to childhood vaccinations for polio and DPT. Women residing in boundary villages next to the program villages reported no difference in their childhood vaccinations.14

Group Differences in Individual Responses to Program Access

The village analysis and the unconditional individual difference estimates of the MCH-FP program assume that the program has uniform effects on all villages and individuals (Bertrand et al. 2004). Documenting group differences in responsiveness to the program could clarify program mechanisms and help improve program design (Schultz 1992).

A hypothesis proposed earlier was that less-educated women may benefit more from the program, since they would be less likely to have evaluated and adopted modern birth control or to use effective preventive health inputs. However, the fertility estimates in Table 4 do not show evidence of this “substitution” in the form of a significant positive effect of the interaction of female schooling and program treatment on fertility. Some preventive health inputs, on the other hand, are more frequently used by better-educated mothers, although not significantly in the case of polio vaccinations. The polio and DPT vaccinations encouraged by the program appear to be a “substitute” for the woman’s schooling; in other words, the estimated effect of the interaction of schooling and program treatment is negative and significant. Thus, the program is associated with an equalizing convergence in the use of some preventive health inputs across mothers with different levels of schooling but is not associated with a convergence in fertility across such women.

How Cost-Effective Is the Program?

This study affirms that the MCH-FP program contributed to longer-term declines in cohort fertility and surviving fertility, as well as improvements in women’s BMI and child health. Although aggregation of these benefits into a single program outcome is beyond the scope of this study, our results nevertheless contribute to the discussion over the program’s cost-effectiveness. An often-cited estimate of program costs per prevented birth in Matlab is about $180, with a range of $150–$220.15 Simmons et al. (1991) concluded that between 1978 and 1985, 60 % of costs were attributable to personnel and transportation, 8 % for contraceptives, and 12 % for other service-related supplies. Although this exceeds the cost of most family planning programs implemented at this time, it has been argued that the program was actually more cost-effective than the Bangladesh Government program operating at this time in the comparison areas (Simmons et al. 1991).16

Our study suggests that these estimates may have understated program benefits for three reasons. First, the benefits were experienced over a longer time horizon than is typically considered as families adjust their allocation of resources over a lifetime. Weighting the program effects by population shares in the respective age groups suggests that the local program effects on the average number of children born in treatment areas is a 0.78-child reduction by 1996, which is 16 % of the average in comparison areas (4.98; Tables 3 and 4). Second, the program’s information benefits may diffuse into villages that border the program villages. The population-weighted spillover effects of the program are estimated to represent a further decline in overall fertility of 0.9 % for women under 35 and 0.6 % for women aged 35–54. Third, the program’s benefits exceed its original objectives of reducing fertility and mortality. It also seems likely the program had persisting effects on the physical and mental development of children through improvements in health (Table 5), schooling (e.g., Table 2), and potentially their cognitive functioning (e.g., Barham 2009; Schultz 2008). The Matlab quasi-experiment has created an environment in which families substitute their life-cycle resources toward greater market-oriented human-capital investments invested in women and in their children (Foster and Roy 2000; Joshi and Schultz 2007; Schultz 2010).

Conclusions

This article illustrates that the well-known MCH-FP program launched in Matlab, Bangladesh, in 1977 had long-term impacts on family well-being. By 1982, surviving fertility (child-to-woman ratio) was significantly 17 % lower in the treatment villages than in the comparison villages. In 1996, surviving fertility remained 16 % lower despite the fact that this measure of fertility has fallen rapidly by 39 % in the comparison areas. Regression analysis of fertility with individual, household, and community controls suggests that the fertility of women under age 55 in 1996 was reduced by 0.78 children, or 16 % of that in comparison areas. The program also decreased child mortality and improved women’s health as measured by their weights and BMI. The program also led women in the program area to more frequently use preventive health inputs for themselves and their children.

Because the program offered a combination of family planning, reproductive health, and child health interventions, it is not possible to attribute any particular share of the estimated consequences of the program to one or another of the program’s components (Joshi and Schultz 2007). The contrast between women in the program villages and those in the boundary villages, however, underscores the value of health workers visiting women directly in their homes. Women in boundary villages are affected by informational spillovers of the program and report lower fertility than other women in comparison areas, but this spillover effect is nonetheless only two-fifths of the direct effect of the program in treatment villages. They do not, however, experience reductions in child mortality or more frequent use of preventive health inputs in the boundary villages. These side effects of the program suggest that investments in family planning, reproductive health, and child health may generate broad improvements in the well-being of women and their children in poor agrarian environments. Such benefits may accrue slowly, but conventional cost-benefit estimates per averted births may overlook the poverty-alleviating effects of such programs that enable families to reallocate resources within a smaller family over its life cycle.

Acknowledgments

This research was funded by the MacArthur Foundation. T. Paul Schultz was also supported in part by a grant from the Hewlett Foundation. We appreciate the helpful comments from participants at various workshops and conferences at which earlier versions of this paper were presented, as well as from Kenneth Land and three anonymous referees. The programming assistance of Paul McGuire has been valuable. Errors and omissions are our own.

Notes

1

Maternal and child health activities were introduced in blocks A and C between 1981 and 1985. After 1985, they were also extended into blocks B and D. We were unable to find significant differences in infant or under-5 child mortality in A and C versus B and D villages in these years. Other research has found that measles declined more rapidly in the entire MCH-FP program areas than in the comparison areas, although perinatal mortality did not decline in the early period of 1979–1982 (Fauveau et al. 1990; Koenig et al. 1990; LeGrand and Phillips 1996). There may also have been a more rapid decline in maternal mortality in treatment villages, although significant variation in this more rare event was difficult to estimate until recently (Fauveau 1994; Koenig et al. 1988; Rahman et al. 2009).

2

The MHSS is a collaborative effort distributed by the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan and Rand (http://rand.org/labor/FLS/MHSS.html).

3

We use sample weights that correspond to an individual’s probability of selection from Matlab into the MHSS. They are from the Rand public use data file called MHDWGTS (variable name is pr_ind12) and are intended to adjust observations for within-household selection as well as the selection of the household. We cap very low probabilities of selection at .1. All values below this are recoded as .1, as suggested by the MHSS codebook (p. 34). Our sample of 5,307 omits 34 women for whom sample weights could not be found in the public release data file and community infrastructure data could not be matched to one village. Standard errors of estimated coefficients are corrected for clustering of the sample at the bari level. Differences between program and comparison individuals and estimates of reduced-form relationships with predetermined control variables discussed in the article are weighted to be representative. Unweighted regressions were also estimated and are available from the authors. See further discussion of weights in footnote 5.

4

An important caveat here is that the original resident population in 1977 may not be represented in the 1996 MHSS. Female migration and mortality could change the composition of the population observed in 1996 in treatment and control villages. When dummy variables are added to the fertility or other outcome regressions that are equal to 1 only if the woman moved after marriage into the DSS area, or moved from program to comparison areas, or vice versa, these dummy variables are never statistically significant as explanatory variables in the fertility or family outcomes studied here. More people, however, migrated out of the comparison areas than the program areas after 1996, possibly because of the higher fertility in comparison areas (ICDDR,B 2004).

5

Many individuals in the 1996 MHSS in the representative strata 1 and 2 cannot be matched to a weight in the Rand data file: roughly one-fourth of the adults aged 15 and older and a somewhat larger share of the children aged 6–14. To see if the characteristics of those matched to a weight differed, we assigned the unweighted individuals the average weight in the matched sample, which was .53. The population means for the villages in the treatment and comparison areas did not change appreciably, and the differences were very similar to those reported in Table 2, panel B. In other words, of the variables examined in Table 2, only the child schooling and Muslim variables were significantly different between the treatment and comparison areas in 1996, whether the full “representative” sample or the sample matched to a Rand sample weight is included.

6

ADLEq0 = (1.0 – ADLscore). A woman’s self-reported ability to perform five activities of daily living, drawn from section GH2 of the MHSS, are aggregated into a score: (a) walk for 1 mile; (b) carry a heavy load for 20 m; (c) draw a pail of water from a tube well; (d) stand up from a sitting position without help; (e) use a ladder to climb to a storage place that is at least 5 feet high. Responses were coded either as “can perform the task easily” (a value of 1), “can do it with difficulty,” (a value of 2) and “unable to perform” (a value of 3). This ADL index is normalized following Stewart and Ware (1992).

7

The functional form that the diffusion of health knowledge takes is not well established in the empirical literature. Alternative specifications of this spillover effect were explored, but none we tried provided a better fit to the data on children ever born, child mortality to age 5, or other family outcomes or use of preventive health inputs. For example, the spillover effect might be an inverse function of the estimated distance between the control village and the nearest program village, a quadratic function of this intervillage distance, or a step function determined by the number of program villages adjacent to a specific control village, which ranges in Matlab from 0 to 4. One randomized study of spillovers (externalities resulting from reducing intestinal worms in a school cohort) estimated the logarithmic impacts on a school cohort as a function of the log of number of treated persons in that cohort in a geographic area and the log of the total cohort in that geographic area (Miguel and Kremer 2004). It is unclear why this specification was adopted, although it provides a basis to decompose the effects of the intervention operating through population density and the density of treatment in the area.

8

If program services complement or reinforce the fertility-reducing effect of women’s education, we would expect, other things being equal, fertility differences by women’s education to increase in program areas. Where program services substitute for women’s education, fertility differences by education are expected to diminish.

9

In an earlier version of this article, we included whether the village had a BRAC office, a microfinance NGO that also encourages family planning and female self-employment, in 1996. It was not significantly partially associated with fertility in these weighted regressions, but these institutions entered Matlab only toward the end of the study period and may have purposefully started offices in villages where the status of women was relatively low, violating our assumption that the village infrastructure was initially independent of household behavior.

10

The lower level of child mortality among women over age 65 could not have credibly been caused by the program. Other family interactions involving gender, birth spacing, mortality, and child schooling are explored in other studies of Matlab (e.g., Fauveau et al. 1991; Foster and Roy 2000; Joshi and Schultz 2007; Muhuri and Menken 1997; Muhuri and Preston 1991; Rahman et al. 2004; Sinha 2005). Their potential endogeneity at the family level makes their inclusion here as control variables more complicated.

11

When the samples of boys and girls are stacked, allowing all estimated coefficients to be sex-specific, the estimated program effects are not statistically significantly different for boys and girls when either the odds ratio or conditional marginal effects are used (not reported) because the estimated effects for boys are often of a similar magnitude to those of girls and are estimated less precisely than for girls.

12

Anthropometric data are undoubtedly reported with error (WHO 1995, 2006). In an effort to trim outliers, which are likely to embody errors, we initially dropped from the estimation sample women with BMI less than 10 or greater than 30. Adding back these 35 women with outlier values (to 5,273) has only a slight effect on the estimates of the program impact on BMI and no notable effect on the likelihood that BMI exceeds 18.

13

In previous work, we examined the difference in inoculations for neonatal tetanus, which was a serious health risk in Matlab at this time (Joshi and Schultz 2007). We omit this indicator here, however, because programs other than the MCH-FP prescribed tetanus toxoid inoculations, particularly as a placebo in cholera vaccine trials in the 1970s and later in government clinics in the 1990s (Fauveau 1994; LeGrand and Phillips 1996).

14

Barham (2009) explored the consequences of the program’s promotion of maternal and child health inputs in various blocks of villages from 1982 to 1986 for cognitive functioning of adolescents in the 1996 MHSS. We did not find significant differences in fertility or child mortality from 1981 to 1985 between these blocks (A and C; and B and D) when there were differences in program priorities in child and maternal health.

15

The estimate assumes that the differences in general fertility rates between the program and comparison areas are attributable to program expenditures (Simmons et al. 1991).

16

The cost per averted birth of the government program in the comparison areas was estimated to be $298 (Simmons et al. 1991). Obtaining this estimate is complicated by the lack of an obvious control population. Moreover, many features of the MCH-FP and the Bangladesh Government program differed, including not only the outreach design, but the systems of oversight and personnel tenure and compensation. It is possible that the government program was withdrawn from Matlab program villages (LeGrand and Phillips 1996). Fauveau (1994) revised the cost accounting of the program for the period 1986–1989 and concluded that the program expenditure per prevented birth was $60, substantially lower than previously estimated.

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