Women’s Health and Pregnancy Outcomes: Do Services Make a Difference?* pptx

E-ABSTRACT We use data from the Indonesia Family Life Survey to investigate the impact of a major expansion in access to midwifery services on health and pregnancy outcomes for women of

Women’s Health and Pregnancy Outcomes:

Do Services Make a Difference?*

March 2001

Elizabeth Frankenberg

Duncan Thomas

*Elizabeth Frankenberg, RAND, 1700 Main Street, Santa Monica, CA 90407; E-mail:

efranken@rand.org Duncan Thomas, RAND and University of California at Los Angeles; mail: dt@ucla.edu This work was supported by NICHD grants P50HD12639, R29HD32627, and P01HD28372, by NIA grant P30AG12815, and by the POLICY Project We gratefully acknowledge the comments of Bondan Sikoki, Wayan Suriastini, and participants at seminars at the University of California at Los Angeles, the Gadjah Mada University, the University of Maryland, the University of Michigan, the University of Pennsylvania, and the University of Washington

E-ABSTRACT

We use data from the Indonesia Family Life Survey to investigate the impact of a major expansion in access to midwifery services on health and pregnancy outcomes for women of reproductive age Between 1990 and 1998 Indonesia trained some 50,000 midwives Between

1993 and 1997 these midwives tended to be placed in relatively poor communities that were relatively distant from health centers We show that additions of village midwives to

communities between 1993 and 1997 are associated with a significant increase in body mass index in 1997 relative to 1993 for women of reproductive age, but not for men or for older women The presence of a village midwife during pregnancy is also associated with increased birthweight Both results are robust to the inclusion of community-level fixed effects, a strategy that addresses many of the concerns about biases because of nonrandom program placement

Decline in mortality is among the most fundamental demographic changes experienced

by developing countries over the past half-century Today, individuals are leading longer and healthier lives than did their parents and grandparents In part these changes reflect investments

in human resources by both individuals and governments In virtually every developing country, governments have built, stocked, and staffed schools, health facilities, and family planning clinics, albeit with varying degrees of success

Although clinical studies have demonstrated that some health interventions in fact

improve health, researchers have long debated about the contribution of public health

investments to health improvements and mortality decline Most macro-level studies conclude that the effect of public spending on health is small (Filmer and Pritchett 1999; Musgrove 1996)

At the micro level, some studies have concluded that investments in providing public health services have a positive causal effect on health outcomes (Caldwell 1986; Jamison et al 1993) The majority of studies, however, indicate that increases in public spending have little or no impact on health; in some cases, public-sector investments are even associated with poorer health outcomes (For a discussion, see Strauss and Thomas 1995.)

At least two critical problems have plagued this literature The first, and perhaps the more difficult to address, is that public health investments are not likely to be located at random with respect to health outcomes For example, if programs are carefully targeted they will be placed where health outcomes are poor and/or utilization of services is low If all program placement decisions are based on observable characteristics that are controlled in an evaluation of the program, such targeting poses no conceptual difficulty Yet insofar as program placement is associated with characteristics that are not observed, failure to take account of nonrandom

placement will generally lead to biased estimates of the impact of the investment (Angeles, Guilkey, and Mroz 1998)

Rosenzweig and Wolpin (1986), for example, show that in a cross-section regression, children’s nutritional status is negatively associated with exposure to public health programs in Laguna, The Philippines In contrast, these authors find a positive and significant effect when they examine how changes in nutritional status respond to changes in exposure to public health

et al 1997)

Detailed community-level data linked to individual-level data are not always sufficient: the application of methods that control community- or individual-specific unobservables requires repeated observations on health outcomes, and very few longitudinal surveys contain that

information on respondents as well as on the health services and other services to which they have access

We use data from a new, extremely rich longitudinal survey from Indonesia to evaluate whether government efforts to provide health care have an impact on the populations targeted by the programs Specifically, we consider the Village Midwife program, which was initiated in the 1990s and is estimated to have posted some 50,000 midwives throughout the country (Gani 1996; Kosen and Gunawan 1996; Sweet, Tickner, and Maclean 1995) Our goal is to provide evidence on the effectiveness of this large and important community-based public health service intervention that is targeted explicitly to reproductive-age women in underserved communities Our results are of general interest because these types of programs have been implemented in many developing countries

To measure the effect on health status of the introduction of a new health worker in a community, we draw on the “quasi-experiment” that occurred in Indonesia by comparing

changes in health status in communities that gained a health worker with such changes in

communities that did not We recognize that unobserved factors may influence the introduction

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of a health worker to a community, which would cause bias in these “fixed-effects” estimates of the impact of health workers on health outcomes; thus we take an additional step in the analysis Because the health workers are midwives who were trained primarily to serve women of

reproductive age, we contrast the impact on the health of these women (the “treated”) with that

of other adults (the “controls”) who live in the same community into which the midwife was introduced

Our main results focus on the effects of introducing a village midwife on a general

measure of adults’ health, the body mass index (BMI) After controlling community-level

heterogeneity, we find that among reproductive-age women, BMI increases significantly in communities that gained a village midwife and that the increase is substantively important In contrast, men and older women (our “control” groups) do not experience as large an increase in BMI For women of reproductive age, the benefits of access to midwives extend to pregnancy outcomes: we also find that the introduction of a midwife is associated with increases in

birthweight We conclude that the expansion of the Village Midwife program has yielded

significant improvements in health, particularly for women of reproductive age

BACKGROUND

Notwithstanding the economic crisis of the late 1990s, socioeconomic development in Indonesia has improved substantially over the past three decades From 1967 to 1997 Indonesia’s per capita gross domestic product (GDP) increased by almost 5% per year At the same time, Indonesia achieved nearly universal enrollment in primary school and substantial increases in secondary-school enrollment Since the early 1960s, several indicators of health status in Indonesia also have shown major improvements The infant mortality rate has declined steadily, and by the mid-1990s life expectancy surpassed 60 years

Maternal mortality, however, had not shown such impressive gains as of the early 1990s, and the Indonesian government expressed considerable concern about this dimension of health outcomes At 390 to 650 deaths per 100,000 live births, this rate was the highest in any of the ASEAN nations (Handayani et al 1997; Mukti 1996; UNICEF 2000a, 2000b) In fact, for much

of the 1990s Indonesia’s statistics for maternal mortality were on a par with those in India and

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Bangladesh, even though the per capita GDP in Indonesia was about 50% higher than in India and about twice as high as in Bangladesh (Sarwono, Mundiharno, and Fortney 1997)

To address poor maternal health, the Ministry of Health (MOH) embarked on an

ambitious program to make midwifery services more widely available by training midwives and posting them to villages throughout Indonesia (Handayani et al 1997; Kosen and Gunawan 1996; MOH 1994) Between 1990 and 1996 the Government of Indonesia planned to provide a midwife in every nonmetropolitan village or township (MOH 1994) Midwives typically were recruited from three-year nursing academies and received one additional year of midwifery training (Sweet et al 1995) By 1998, 54,000 midwives had been trained; between 1986 and

1996 the number of midwives per 10,000 population increased more than tenfold from 0.2 to 2.6 (Hull et al 1998; MOH 2000; Reproductive Health Focus 2000)

Once assigned to a community, the midwives are paid a salary by the Government of Indonesia for three to six years (Hull et al 1998) They maintain a public practice during normal working hours and are allowed to practice privately after hours It is expected that midwives will build up a client base while working for the government; thus, when their contract ends, they can maintain their practice in the village without a government salary (Gani 1996; MOH 1994) The role of the village midwife, as described by the Indonesian MOH, suggests that she will affect health status, particularly of reproductive-age women Her duties include promoting community participation in health, providing health and family planning services, working with traditional birth attendants, and referring complicated obstetric cases to health centers and

hospitals She is to serve as a health resource in her community, actively seeking out patients and visiting them in their homes rather than waiting passively until they come to her (MOH 1994) These activities bring a village midwife into contact with a wide array of community residents in

a variety of settings, and provide her with opportunities to advise clients on nutrition, food preparation, sanitation, and other health-promoting behaviors

Village midwives provide general services in addition to those oriented toward maternal and well-baby care, as supported by research in central Java (Mukti et al 1997) On the basis of interviews, record abstraction, and client observations with 19 village midwives, the study finds

abscesses Almost all village midwives dispense medications such as antibiotics, cough

medicine, vitamins, and supplements of micronutrients such as iron and Vitamin A

The comprehensiveness of services offered by village midwives suggests some of the pathways through which availability of a village midwife may improve health For example, if a village midwife provides curative care, her presence may reduce durations of illness from

diarrheal and respiratory diseases and thus may limit the weight loss associated with such

illnesses Because of the midwife’s years of health training and her ability to offer an array of curative and preventive services, coupled with nutrition education and distribution of vitamins and micronutrients, her arrival in a community may well lead to improvements in her clients’ nutritional status

The Village Midwife program builds on the public health system of clinics and outreach activities established in Indonesia during the 1970s and 1980s The backbone of this system is

the community health center (puskesmas) The health center provides an array of services and is

a basic source of subsidized outpatient care Health centers generally are headed by a doctor, who oversees a midwife and various paramedical workers (MOH 1990) In better-off areas the center’s staff may include several doctors, as well as one or two dentists Each subdistrict

(kecamatan), consisting of 20 to 40 villages or townships, has one or more health centers

Staff members of the health center, in conjunction with family planning fieldworkers, are

responsible for conducting outreach activities, such as supervision of posyandus (neighborhood

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health posts), within the villages and townships in their catchment area The posyandu is held

monthly and is attended by children under five and their mothers It is staffed by neighborhood volunteers and (if possible) by staff members from the health centers or by family planning fieldworkers (The latter also provide contraceptive supplies to workers from the health centers

and to posyandus.) When health workers attend, the posts generally provide prenatal care,

immunization, and contraceptive injections (Kosen and Gunawan 1996) When helath workers

do not attend, services are limited to provision of vitamins and oral rehydration solution,

nutritional screening, and oral contraceptives

Private practitioners also are an important source of health care in Indonesia Private services are more widely available in urban than in rural areas, but because employees of the health center generally offer private services in off-hours, private practitioners are found in rural areas as well (Brotowasisto et al 1988; Gani 1996; World Bank 1990)

In an effort to isolate the role of health services, a number of studies have contrasted spatial variation in program availability or strength with spatial variation in health outcomes Yet

a correlation between access and health outcomes at a point in time does not identify the

direction of causality Services may be provided in a particular location in response to demand for those services, or people who want services may move to places where they are provided (Rosenzweig and Wolpin 1986, 1988) Either scenario yields a spurious correlation between access to services and health outcomes because the relationship is governed by a common

(unobserved) factor

It is also possible that governments target particular types of communities for

interventions Targeting will not bias estimates of the effects of the intervention if it is based on

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characteristics that are observed and controlled in a regression context If targeting is based on unobserved characteristics, however (or if the full set of characteristics used for targeting is not controlled in the regression), and if those unobserved characteristics are correlated with the outcome of interest, estimated effects of the intervention will be biased The direction of that bias

is ambiguous

To illustrate, imagine that government services are provided in communities that are underserved by private providers and that health status in those communities is relatively poor,

everything else held equal Unless all characteristics that underlie the placement of the program

are controlled, the estimated impact of the intervention will be biased negatively, and the bias will be greatest for the interventions targeted to the people who need them most This issue of selective program placement is important in the context of health policies in Indonesia

(Frankenberg 1992; Gertler and Molyneaux 1994; Pitt, Rosenzweig, and Gibbons 1993)

In theory, these complicating issues are sidestepped by social experiments involving random assignment of subjects to treatment and control groups Although such experiments have produced valuable findings regarding some policy questions (see, for example, Berggren,

Ewbank, and Berggren 1981; Dow et al 1999; Faveau et al 1991; Newhouse 1994), they have their own drawbacks They tend to be small in scale and to involve homogeneous populations; thus their generalizability is limited (Ewbank 1994) They are typically expensive, take a long time to complete, and can be difficult to implement In some instances, experiments induce behavioral responses (such as migration to areas that are served in the trial) that substantially complicate evaluation of the intervention

In our view, observational data are an important complement to evaluations of

interventions based on randomized trials Of course, studies based on observational data cannot ignore the complicating issues discussed above

We adopt a quasi-experimental approach to evaluate the effects of an expansion in access

to midwifery services and health outcomes in Indonesia Using longitudinal household survey data, we compare an individual’s health before the introduction of a midwife in a community with the same individual’s health after the intervention In doing so, we sweep out of the model

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all factors that are fixed at the individual and community level and enter the model additively, including any fixed characteristics that are correlated with the placement of midwives This

“fixed-effects” model has been used extensively in the program evaluation literature (for a

discussion, see Heckman and Robb 1985) We are contrasting changes in health of the “treated” with changes in health of a control group, namely respondents in communities where midwives

were not introduced: ∆θi = α +βM c + εic , where ∆θi is the change in health of individual i and M c

is an indicator variable for whether or not a village midwife was introduced in community c

Time-varying unobserved heterogeneity that affects changes in health is captured in εic The intercept, α, reflects changes in health of the population between the two waves of the survey that are not related to the introduction of a midwife β measures the difference in changes in health status of those living in communities where a midwife was introduced relative to other communities This is an “average treatment effect,” calculated over all people living in the

where I i pf is an indicator variable for prime-age females and I i pm is defined analogously for

prime-age males The coefficient on the interaction between the prime-age female and midwife indicator variables, β1, is an estimate of the change in the health of a prime-age woman in a

“treated” community relative to the change in health of a similar woman in a community where a midwife was not introduced

If the introduction of a midwife in a village is uncorrelated with time-varying unobserved heterogeneity, εic , then this model will provide an unbiased estimate of the effect of the program Below, however, we show that midwives are more likely to be introduced in poorer communities with little infrastructure If changes in health differ between poorer and better-off communities,

“midwife” effect

It may be that midwives do in fact influence males’ health—directly (through providing services to men, for example) or indirectly (through spillovers such as nutrition education to women, which in turn affects men’s health) In this case, the “difference-in-difference” will be a biased estimate of the impact of introducing a midwife The empirical importance of this concern

can be probed by expanding the control groups to include older females, I of , and older males, I om:

∆θi = α1I i pf + α2I i pm + α3I i of + α4I i om + β1M c *I i pf + β2M c *I i pm (1)

+ β3M c *I i of + β4M c *I i om + εic

Older men are the least likely to benefit directly from the introduction of a midwife If we

assume that midwives are not detrimental to older men’s health, the difference-in-difference, β1– β4, provides a lower-bound estimate of the effect of a midwife.1 Older women’s health, on the other hand, has more in common with that of prime-age women; thus older women may well benefit from the introduction of a midwife Therefore we expect that β1 – β3 is likely to

understate the effect of a midwife

If the survey measures all the correlates of changes in health status that affect the

allocation of midwives, it is possible to directly estimate the effect of a midwife by controlling those characteristics in the regression We will experiment with this approach by drawing on the rich array of community-level information contained in our data source In addition, the

inclusion of individual- and community-level observables will increase the efficiency of the regression estimates

1Midwives might encourage families to reduce their investments in older men’s health, which would bias upward the difference-in-difference results This strikes us as unlikely, however

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It is possible, however, that even with controls for observed differences across

communities, the introduction of a midwife is correlated with unobserved heterogeneity, εic, which would bias estimates of the program’s effect Thus we include a community-specific fixed effect, µc; this effect, in a regression of changes in health, ∆θ, serves as a community-specific time trend and sweeps out all changes that are common across adults in each community that gained a midwife The conceptual experiment that we have in mind is to contrast changes in

health of reproductive-age women with changes in health of other adults living in the same community Bias due to program placement will be absorbed in the community effect, and we

can estimate the effect of the midwife program Clearly, in this case, we can estimate only the difference-in-differences We exclude the term for prime-age males from the regressions,

∆θi = α1I i pf + α3I i of + α4I i om + β1M c ∗ I i pf + β3M c ∗ I i of + β4M c ∗ I i om

but include individual characteristics, X i, to improve efficiency

The difference-in-differences will be biased if program placement is based on the health

of reproductive-age women relative to the health of other adults in a particular community We

will explore the evidence for this sort of targeting in the analyses below

DATA

The data we use for this study come from two rounds of the IFLS, an ongoing panel survey of individuals, households, communities, and facilities The first round of data (IFLS1, collected in 1993) included interviews with 7,224 households (Frankenberg and Karoly 1995) The IFLS conducted interviews in 321 enumeration areas in 13 of Indonesia’s 26 provinces, and represents about 83% of the Indonesian population.2

In 1997 we constructed a resurvey (IFLS2) in which we sought to reinterview all IFLS1 households (and all members of these households in 1997), as well as a set of target members of IFLS1 households in 1993 who had migrated out by 1997 (Frankenberg and Thomas 2000) IFLS2 succeeded in reinterviewing 94.5% of IFLS1 households and 92% of the individuals who

2The 321 IFLS enumeration areas are small survey-defined clusters of households located in 312 administrative

areas known as desa (village) or keluruhan (township), of which there are more than 62,000 in Indonesia We refer

to desa and keluruhan collectively as “villages.” For the remainder of this paper we use the term community to

designate both an IFLS enumeration area and the larger administrative area (“village”) in which it is located

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were age-eligible for this study When we condition on observable characteristics (measured in

1993), recontact is slightly higher (0.7%, t = 1.3) in communities that gained a village midwife

than in those that did not We conclude that attrition is not likely to be a source of contamination

in our results

The IFLS questionnaire covers a broad array of topics A trained anthropometrist

recorded the height and weight of each household member in both IFLS1 and IFLS2—a central consideration for this study Our primary indicator of adults’ health will be body mass index (BMI), which is weight (in kilograms) divided by height (in meters) squared BMI is more directly interpretable than weight (which varies systematically with height); extreme values of BMI are associated with elevated risk of morbidity, difficulties in activities of daily living, and mortality (Fogel 1998; Strauss and Thomas 1998; Waaler 1984) BMI also is associated with physical capacity as indicated by maximal oxygen uptake (Spurr 1983) and labor productivity (Thomas and Strauss 1997)

Table 2 presents summary statistics of BMI levels for four groups: reproductive-age women (age 20 to 45 in 1993), men of the same age, older women, and older men On average, BMI has increased for prime-age men and women but has remained constant for older

respondents The table also reports the fraction of each group whose BMI is below 18.5, a cutoff below which elevated risks of morbidity and mortality are well documented About 10% of prime-age adults fall below this cutoff; this percentage declined between 1993 and 1997 Some 30% of older adults are below the cutoff; the fraction has increased for older men In a tiny fraction of Indonesians, the BMI is high enough to suggest that they are at risk of health

problems from being overweight.3 The regression models are specified in terms of change in BMI for each respondent; this can be regarded as change in weight for prime-age adults (for whom height is fixed) We interpret change in BMI as indicating a change in general health status Because increases in BMI in the normal range do not have the same implications for

3In 1993 only 4.5% of the sample had a BMI of 28 or higher, the level above which morbidity and mortality have been shown to rise (Fogel 1998; Waaler 1984) Rates are low for each of the demographic groups as well Among women of reproductive age, 6.7% had a BMI of 28 or higher, as did 6.4% of women 46 and older Among men, rates were 2% for younger men and 1.9% for older men In 1997 a total of 6% of respondents had a BMI of 28 or higher

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health as do increases among those with low BMI, we also present results that focus on

respondents of the latter type

In part, the changes in BMI reflect changes over the life course and changes in diet or energy expenditure due to changes in availability of household resources The regressions

control each respondent’s age and education (which are displayed in Table 2) along with

household per capita expenditure (PCE) at the time of the survey PCE is considered to be a reliable measure of resource availability in the household

In this paper we focus on the impact of expanding the Village Midwife program As clarified in the discussion above, it is important to control for community-level characteristics that might be correlated both with changes in health and with the introduction of a midwife The IFLS is a particularly rich resource in this regard Each wave of the survey contains a detailed set

of community questionnaires administered in the IFLS enumeration areas Extensive interviews are conducted with the head of the village or township (or a designated staff member), with the head of the community women’s group (typically the wife of the head of the village), and with knowledgeable informants in a sample of up to 12 health providers and up to eight schools in the community Drawing on those data, we construct measures of other dimensions of the health service environment and of levels of infrastructure for each wave of the survey

Table 3 summarizes aspects of the health service environment and the physical

infrastructure environment, as measured by the IFLS1 and IFLS2 community-facility surveys Access to the Village Midwife program is measured with an indicator of whether a village

midwife was present in the community in each of the two survey years Access to health services

is measured as the distance to the health center and to the private practitioner that are closest to the village leader’s office With respect to outreach efforts by health centers, we construct a

variable indicating whether or not the community’s posyandus receive monthly visits from health

center staff members Physical infrastructure is measured by whether a public phone is located in the community and whether the community’s main roads are paved

The IFLS reflects the dramatic expansion of the Village Midwife program documented in the literature on the Indonesian health system In 1993 just under 10% of IFLS communities had

fraction of communities reporting that health center staff members visited posyandus in the

community monthly decreased from 96% in 1993 to 88% in 1997 Only about 3% of

communities gained monthly visits to posyandus from health center staff members, while 11% of

communities lost such visits Possibly in these communities village midwives now attend the

posyandu, rendering supervisory visits from health center staff less necessary

The basic measures of access to public and to private services—distances to the closest public and private facilities as reported by the village leader—changed little between 1993 and

1997 In 1993 the mean distances to public and to private facilities were 1.0 and 0.6 kilometers respectively In 1997 the mean distances were 1.1 and 0.5 kilometers Neither change is

statistically significant The distance to a health center probably did not change because most of the expansion in fixed-site government health facilities took place before the 1990s This fact is helpful in identifying the effect of an expansion in the midwife program

With respect to physical infrastructure, about half the communities had a public phone in

1997, up from 44% in 1993 Between 1993 and 1997 the fraction of communities in which most roads are paved increased by 14 percentage points, bringing the total percentage to 84%

The descriptive statistics indicate a substantial increase in access to village midwives between 1993 and 1997 In examining how these midwives were allocated across communities,

we use the IFLS data from 1993 to explore how aspects of socioeconomic development and health status, measured at the community level in 1993, are associated with expansion in access

to midwives between 1993 and 1997 The dependent variable in the regressions is a dichotomous indicator of whether the community gained a village midwife between 1993 and 1997 The results are presented in Table 4

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In the first model, we include only average per capita expenditure levels of households in the community (measured in 1993) This model tests whether gaining a village midwife varies with the community’s wealth Expenditure is specified as a spline with a knot at the 25th

percentile For communities in the lowest quartile of the expenditure distribution, higher

household expenditure does not affect the probability that a village midwife will be assigned to the community between 1993 and 1997 In contrast, for mean expenditure level in communities with expenditures in the top three quartiles of the distribution, the coefficient is large, negative, and statistically significant The results provide strong evidence that among the IFLS

communities, the poorest as of 1993 were most likely to gain a village midwife by 1997

In the second specification, we introduce controls for province (coefficients not shown) and for other aspects of community infrastructure The introduction of these additional controls

produces almost a threefold increase in the R2 of the model, from 0.08 to 0.22 Moreover, the results reveal that the greater a community’s distance from a health center in 1993, the more likely that community was to gain a village midwife by 1997 Distance from a private

practitioner also has a positive but only marginally significant effect In addition, communities with a public phone in 1993 were significantly less likely to gain a village midwife by 1997

In the third specification we add controls for per capita expenditure levels in 1997 and for

whether the community’s posyandus received monthly visits from health center staff members in

1997 Because we control simultaneously for these characteristics in 1993, the 1997

characteristics can be regarded as reflecting change since 1993 On the basis of the coefficients for the 1997 characteristics, it does not appear that the communities that were becoming poorer over time were more likely to gain a midwife, or that health centers reduced their outreach activities in communities that gained a midwife

In the fourth specification, we introduce a control for the average body mass index of adults in the community in 1993, as a means of assessing whether health status in the community

is correlated with subsequent introduction of a midwife The coefficient on this variable is not statistically significant Possibly the BMI of certain demographic groups (rather than of all