early-life exposure to an indonesian midwife … fileabstract this paper investigates links between...

EARLY-LIFE EXPOSURE TO AN INDONESIAN MIDWIFE PROGRAM AND

ADOLESCENT COGNITIVE SKILL

Draft version, 18 March 2012 – please do not quote

Sven Neelsen

ifo Institute – Leibniz Institute for Economic Research at the University of Munich

Poschingerstr. 5

81679 Munich, Germany

Phone: +49(0)89/9224-1392

Fax: +49(0)89/92241462

Email: [email protected]

ABSTRACT

This paper investigates links between early-life exposure to a large-scale community

midwife program in Indonesia and the formation of cognitive skills. My empirical ap-

proach exploits both the program’s timing and geographical variation to identify mid-

wife effects. Using data from the 1993, 1997, 2000, and 2007 waves of the Indonesian

Family Life Survey, I find statistically significant positive correlations of cognitive skill

test scores at age 11-14 and early-life midwife exposure. The increases in cognitive skill

scores that are associated with early-life midwife exposure range up to 8.8 percent and

are robust to controlling for different sets of later-life household- and community-level

characteristics. The results from different sample splits indicate that the program low-

ered both geographical and financial access barriers to better early-life living conditions

and that it was effective in improving early-life living conditions for underprivileged

children in villages, but not in towns.

1

1. INTRODUCTION

Recent estimates by Grantham-McGregor et al. (2007) suggest that 200 million

of today’s children will fail to reach their full developmental potential because of poor

living conditions in their formative years. Such poor conditions include early-life expo-

sure to malnutrition, infectious disease, inadequate cognitive stimulation, and violence

(Walker et al., 2007). By impairing health and the development of cognitive abilities,

these early-life insults not only harm individual well-being and socioeconomic pro-

spects. They also bear substantial societal costs that arise in the form of human capital

losses, forgone productivity and income, higher healthcare costs, and rising crime rates

(Naudeau et al., 2011a). Conversely, improving early-life conditions has been shown to

have high societal payoffs (Heckman and Masterov, 2007). To improve child health in

the short run and achieve long-run improvements in individual and societal develop-

ment, national governments and donors are thus reprioritizing their policies towards the

youngest (Naudeau et al., 2011b). To be efficient, this process requires comprehensive

appraisals of the benefits of such policies – including those that accrue in the mid- and

long-runs.

However, while a large number of studies have investigated mid- and long-run

effects of early-life interventions in developed countries (Almond and Currie, 2011)

similar work for developing countries remains scarce and findings are incoherent

(Walker et al., 2007). This study aims to contribute to the closing of this knowledge gap

by examining impacts of early-life exposure to a large-scale Indonesian midwife pro-

gram on adolescent cognitive skill. Using data from the longitudinal Indonesian Family

Life Survey (IFLS) this study is the first to investigate the program’s mid-run effects.

Also, it is one of the first studies on later-life impacts of an early-life community-level

2

intervention in a developing country (see Walker et al., 2005; and Hoddinott et al., 2008

for exceptions).

Primarily aiming to reduce high maternal mortality rates, the program dispatched

trained midwives to over 50,000 Indonesian communities in the early- to mid-1990s.

The midwives’ responsibilities went beyond birth attendance and included basic cura-

tive and preventive care for pregnant women and young children, the distribution of

basic medicines and nutritional supplements, vaccinations, and public health education

measures (Frankenberg et al., 2005). With this broad set of services, the program bore

potential for not only reducing maternal mortality but also for improving early-life liv-

ing conditions, and, thereby, the health and cognitive development of individuals ex-

posed to the program at young age.

Investigating program impacts with data from the 1993, 1997, 2000, and 2007

waves of the Indonesian Family Life Survey, I find statistically significant positive cor-

relations of cognitive skill test scores at age 11-14 and early-life midwife exposure. The

increases in cognitive skill scores that are associated with early-life midwife exposure

range up to 8.8 percent and are robust to controlling for different sets of later-life

household- and community-level characteristics. Furthermore, the results from different

sample splits indicate that the program lowered both geographical and financial access

barriers to better early-life living conditions and that it was effective in improving early-

life living conditions for underprivileged children in villages, but not in towns.

The rest of this paper is organized as follows. Section 2 describes the midwife

program in more detail and Section 3 reviews prior evaluation studies. Section 4 intro-

duces the dataset and Section 5 my empirical approach. Section 6 presents estimation

results and Section 7 concludes and provides an outlook on potential follow-up studies.

3

2. THE COMMUNITY MIDWIFE PROGRAM1

Aiming to reduce maternal mortality and make basic maternal and child care

more accessible to underserved populations, the Indonesian government paired with

international donors to initiate a large scale community midwife program (bidan di de-

sa) in 1989. With 13,000 midwives dispatched until 1991, the goal was to place a mid-

wife in all 54,000 communities in need by the mid-1990s (World Bank, 1991).

The program recruited nursing academy graduates to one-year midwifery train-

ings to subsequently place them in selected communities. The placement process initial-

ly prioritized poor and remote areas with little access to healthcare services. To guaran-

tee midwives a steady income despite offering their services at subsidized rates or free

of charge, they received a government salary in the first three to six years of their ser-

vice. To top up this income, they were allowed to practice privately after hours on a fee-

for-service basis.

Besides their primary task of reducing maternal mortality through skilled birth

attendance, the midwives provide a broad set of preventive and curative services to im-

prove child health. These include the provision of pre- and neo-natal care, the admin-

istration of essential medicines and micronutrients such as iron and vitamin A, vaccina-

tions, and the education of mothers and the community on topics like family planning,

child nutrition, hygiene and sanitation.

By 1997, the program had placed over 52,000 midwives across the country,

achieving coverage of 96 percent of targeted communities and raising midwife density

from 0.2 per 10,000 inhabitants in 1986 to 2.6 in 1996 (Ministry of Health, 1997, 2000).

1 For detailed descriptions of the program and of Indonesia’s healthcare infrastructure see Sweet et al.

(1995) and Frankenberg and Thomas (2001).

4

3. EARLIER EVALUATION STUDIES

Earlier evaluation studies indicate high uptake of midwife services and short-run

impacts on maternal and child health.

The share of births attended by skilled midwives doubled to 55 percent between

1990 and 2003 (Shankar et al., 2008) while the socioeconomic divide in skilled birth

attendance dropped sharply (Hatt et al., 2007).2,3

Frankenberg et al. (2009) show that presence of a midwife increased pregnant

women’s intake of iron tablets and their uses of antenatal care and skilled as opposed to

traditional birth attendance. Their results also indicate that these effects extended across

the entire distribution of women’s education. Further, Frankenberg et al. (2005) show

that midwife presence greatly improved access to maternal and child care both through

village midwives being the first healthcare provider to practice in a community, and, in

communities where other providers were already present, by offering care at cheaper

prices.

Against this background, various studies have investigated possible effects of

midwife presence on the health of mothers and young children. Frankenberg and Thom-

as (2001) use data from the 1993 and 1997 ILFS waves to examine associations with the

Body-Mass-Index (BMI) of women of reproductive age as a proxy for maternal health.

Their identification strategy compares the BMI-trend differences between women of

reproductive age and men and older women in communities with midwives to the BMI-

2 In contrast to making access to skilled birth attendance more equitable, Hatt et al. (2007) show that the

program coincided with a widening of the wealth and education gap between users and non-users of cesarean sections.

3 The results by Achadi et al. (2007), Makowiecka et al. (2008), and Ensor et al. (2009) indicate that after the phasing out of government placement of midwives in the late 1990s, underservicing of remote communities and the socioeconomics gap in access to skilled birth attendance again increased during the 2000s.

5

trend differences in communities without. The results indicate that midwife presence

leads to statistically significant BMI-increases for women of reproductive age.

The same study provides estimates of midwife effects on birth-weight. Control-

ling for community-level, time-invariant birth-weight determinants and a set of maternal

characteristics, the authors find positive and statistically significant correlations. They,

however, concede that their estimates may be upward biased because the subsample of

Indonesian babies that is weighted at birth likely constitutes a positively selected group.

With data from four waves of the Indonesian Demographic and Health Survey

(IDHS) between 1991 and 2002, Hatt et al. (2009) model time-trends in first-day and

neonatal mortality, controlling for individual characteristics, including birth context

variables like the type of birth attendance and the location of birth (e.g. at home or in a

public health facility). They find no decrease in first-day mortality over the 1986-2002

period but a decrease in neonatal mortality by an annual average of 3.2 percent. This

trend, however, appears to be independent of the midwife program’s timing of imple-

mentation. Contrary to Hatt et al. (2009), Shrestha (2010) finds evidence for a midwife

effect on child mortality. Exploiting variation in the timing of midwife placement across

communities and controlling for district-level unobserved heterogeneity, she shows that

high midwife density in a district is associated with lower neonatal but not lower post-

neonatal mortality.

Finally, Frankenberg et al. (2005) use 1993 and 1997 IFLS data to analyze the

midwife program’s effect on young children’s height-for-age. Their identification strat-

egy bases on both spatial and over-time variation in community midwife presence and

rules out omitted variables bias from time-invariant community-level characteristics

through the inclusion of community effects. Midwife presence in the first years of life,

6

they find, is associated with increases in height-for-age that are particularly large among

children of less-well educated mothers.

4. DATA

My analysis uses data from the 1993/4, 1997, 2000 and 2007/8 IFLS waves

(Frankenberg and Karoly, 1995; Frankenberg and Thomas, 2000; Strauss et al., 2004;

Strauss et al., 2009). IFLS is a longitudinal survey of over 30,000 individuals from ini-

tially 311 communities in 13 of the Indonesia’s 27 provinces. The sample is representa-

tive of 83 percent of the country’s population. In addition to detailed individual- and

household-level information, IFLS provides information on the physical and social en-

vironment in the communities in which IFLS households reside, including the presence

of a program midwife.

In the 2000 and 2007/8 IFLS waves, children aged 7-14 take a cognitive skill test

that comprises of 12 shape-matching tasks of the type shown in Figure 1A. Hereafter, I

refer to the number of correct responses in the shape-matching exercise as the cognitive

test score (CSS).

My sample consists of individuals born 1986-89 and 1993-96 that completed all 12

tasks of the cognitive skill test and for whom data on all independent variables in the

fully specified model I introduce in the following section are available. The CSS for the

1986-89 cohorts come from the 2000 wave and CSS for the 1993-96 cohorts from the

2007/8 wave. Hence, I measure CSS at age 11-14.

As the cognitive skill test was taken by children aged 7-14, CSS are also available

for the 1990-92 and 1997-2000 cohorts. The rationale for limiting my sample to 11-14

7

year olds, i.e. the 1986-89 and 1993-96 cohort is the following. In the full specification

of my empirical model, I control for living conditions at ages 4-7 and 11-14 to reduce

the risk of omitted variables bias. With the timing of IFLS waves, this is possible for the

1986-89 cohorts (age 4-7 in 1993/4, age 11-14 in 2000) and the 1993-96 cohorts (age 4-

7 in 2000, age 11-14 in 2007/8). In contrast, it is not possible for the 1990-92 and 1997-

2000 cohorts.4 Figure 2 depicts the years and ages at which I observe the control varia-

bles and CSS for the 1986-89 and 1993-96 cohorts in the sample.

5. EMPIRICAL APPROACH

The biological and medical literatures suggest that the role of nutrition and health in

shaping cognitive abilities is particularly crucial in the period from conception to the 24th month

after birth (for a review see Victoria et al., 2008).5 If the midwife program was effective, it

improved developmental conditions in this crucial period. With program roll-out start-

ing in the early 1990s and being almost complete by 1997, early-life living conditions

would have improved for individuals born 1993-96 in participating communities. These

individuals form the treatment group.

Because less than 12 percent of communities that report a program midwife in

the 1997 already had a midwife by 1991, individuals from participating communities

born 1986-89 had a far lesser chance of program exposure during the crucial formative

period. They hence form part of the control group, the rest of which is made up of indi-

viduals born in non-participating communities 1986-89 and 1993-96, none of whom had

4 This would require raising additional control variable data from 2003/4 and 2011. 5 Also, in one of the first studies on long-run effects of an early-life nutritional intervention, Hoddinott et

al. (2008) show that a Guatemalan child feeding program was effective in increasing hourly wages for males with program exposure between the first and 24th month of life, but not for males exposed at lat-er ages.

8

program exposure until 1997. Table 1 shows this separation of treatment and control

groups.

I estimate the community-level correlation of CSS and early-life midwife expo-

sure by the poisson model (1)6

log

1

The outcome variable log is the log of individual i’s number of

correctly solved tasks in complete IFLS’s shape-matching exercises. is a constant

and the error term. The treatment variable is , an indicator equaling 1 for

individuals born 1993-97 in participating communities and 0 otherwise.∑ repre-

sents seven birth year indicators that control for secular, country-wide cohort effects in

CSS: the birth year indicators for instance absorb confounders like the age at which an

individual experienced the peak of the Asian Economic Crisis of 1998.

In addition to the cohort effects with I include the age in days on the date

of the cognitive skill test. This variable controls for a linear effect of age on CSS and

accounts for possible age imbalances between the treatment and control groups.

is an indicator equaling 1 for males and 0 for females. Its inclusion pre-

vents a false attribution CSS differences to early-life midwife exposure that are in reali-

ty due to differences in the gender distribution between the treatment and control

groups.

6 I find no indication for over-dispersion or excess zeros in the CSS variable that would motivate the use

of negative binominal or zero-inflated models: testing the model form using Stata’s estat gof com-mand, the goodness-of-fit chi-squared test is not statistically significant for any of the models for which I present results in Tables 3-6.

9

Finally, ∑ is a set of 310 birth community indicators for each of the 311

original IFLS communities minus a reference community. The indicators control for the

effects of time-invariant birth community characteristics on CSS.

In model (1), identification of a causal midwife effect depends on the assump-

tion of a parallel outcome trend for individuals from participating and non-participating

communities, holding the control variables in (1) constant. This assumption may, how-

ever, not hold in reality. As discussed in Section 2, the program targeted communities

with poor healthcare access as an indicator of overall underdevelopment. It is likely that

the socioeconomic dynamics that Indonesia experienced 1986-2007 varied between

communities of different initial developmental levels. For instance, economic growth in

the 1980s and 1990s may have been pro-poor in that it improved early-life living condi-

tions in initially underdeveloped communities more. With the correlation between un-

derdevelopment and program participation, model (1) may falsely attribute CSS im-

provements in the treatment group to program exposure that in reality may at least in

part be due to better socioeconomic dynamics in the participating communities.

I address this issue in two ways. First, I add to model (1) a set of controls varia-

bles that model living conditions at ages 4-7 and, at the age of outcome measurement,

11-14. Model (2) shows the specification with the full set of additional control variables

log

∅

2

10

∑ ∅ represents the control variables in model (1). With respect to addition-

al controls, equals 1 for individuals for whom a midwife was present at some

point during age 4-7. For the 1986-89 (1993-96) cohorts, therefore equals one

for individuals from communities for which a midwife is reported in the 1993 (2000)

wave. Correspondingly, equals 1 for individuals for whom a midwife was

present at some point during age 11-14. For the 1986-89 (1993-96) cohorts

therefore equals one for individuals from communities for which a midwife is reported

in the 2000 (2007) wave. As I outline above, I assume that with the program’s primary

target group being pregnant women and very young children, program exposure at older

ages, e.g. 4-7 and 11-14 has no CSS effect. If I nevertheless estimate statistically signif-

icant effects for or this may hence indicate omitted variables bias. In

this case, the coefficients on the midwife exposure variables, including con-

tain effects of other developments that correlate with program participation and causal

interpretations are no longer warranted.

Model (2) moreover controls for an extensive set of household (∑ ,

∑ ) and community (∑ , ∑ ) characteristics at the two age

intervals 4-7 and 11-14. These include parent presence, caretaker education, proxies for

household income and wealth, sanitary conditions at the household and community lev-

els, and a set of proxies for community development. Model (2) also controls for school

enrollment at the time of the cognitive skill test.

My second approach to address spurious correlation issues is to split the full

sample into subsamples that are more homogenous with respect to observable variables.

This method is appropriate if greater homogeneity on observables correlates with great-

11

er unobserved homogeneity, i.e. if the split leads to greater similarity in unobserved

characteristics between individuals from program participating and non-participating

communities. The first sample split is into children of caretakers that at maximum have

primary education and children of caretakers with higher education. The second is into

children born in villages and children born in towns. Finally, I split the subsample of

children from villages by caretaker education to combine the two prior splitting meth-

ods.

6. RESULTS

Descriptive Statistics (Table 2)

Table 2 shows mean values and standard errors for CSS, midwife exposure, and all

control variables in the full specification of model (2). Column 1 is for individuals from

communities participating in the midwife program and column 2 for individuals from

non-participating communities.

The differences in means between column 1 and 2 of Table 2 reflect the Indonesian

government’s aim of targeting disadvantaged populations for the midwife program. CSS

are 6 percent higher for children from non-participating communities. Rather than indi-

cating perverted program effects, this difference likely stems from inferior early- and

late-life living conditions in the participating communities: individuals from participat-

ing communities have less educated caretakers; most household wealth and sanitary

condition proxies at both ages 4-7 and 11-14 show that living conditions in their homes

are inferior to those of individuals from non-participating communities; also both at

ages 4-7 and 11-14, individuals from participating communities are more likely to re-

side in rural communities with inferior socioeconomic infrastructures.

12

The birth community effects in my empirical model rule out effects of time-

invariant birth community characteristics as possible confounders of the midwife ef-

fects. Moreover, the cohort effects account for over-time changes in living environ-

ments that occur at the same pace in both participating and non-participating communi-

ties. Table 2, however, shows that the differences in environments between participating

and non-participating communities are often not the same at age 4-7 and age 11-14.

These changes in differences can be due to both different socioeconomic dynamics or to

individuals migrating to communities with different living conditions during later child-

hood as do somewhat less than 10 percent of the sample. For instance, the likelihood of

having a toilet with a septic tank in the home is 54 percent higher for individuals from

non-participating communities at age 4-7 while it decreases to 30 percent at age 11-14.

At the same time, the difference between individuals from participating and non-

participating communities in piped water being the primary drinking water source in

their current community of residence increases from 57 percent at age 4-7 to 73 percent

at age 11-14. As outlined in Section 5, if both access to septic toilets and piped water

correlate with the formation of cognitive skills, this requires controlling for them as is

done in model (2).

Regression Results: Full Sample (Table 3)

Table 3, columns (1) show poisson coefficient estimates for model (1). The esti-

mates in column (2)-(5) are obtained for specifications that include subsets of the full

set of control variable in model (2) and column (6) shows coefficients for the full speci-

fication of model (2).

13

For all specifications there is a statistically significant positive correlation between

early-life midwife exposure and CSS. The effect ranges from 5.7 percent higher CSS for

individuals with early-life midwife exposure in the specification in column (2) to 3.6

percent higher CSS in the full specification in column (6) 7.

The reduction in coefficient sizes when including environmental controls indicates

a somewhat better dynamic with regards to the controls in participating communities.

The addition of the two later-life midwife exposure variables in column (2) does

not cause a reduction in size and statistical significance of the coefficient of early-life

midwife exposure compared to the specification without later-life midwife exposure

controls in column (1). This findings somewhat alleviates concerns that the positive

correlation between early-life midwife exposure and CSS is spurious: if it was not mid-

wife presence but overall improvements in living conditions in participating communi-

ties that caused the positive correlations with CSS, the later-life midwife exposure coef-

ficients would be of similar size and statistical significance as that of early-life expo-

sure. However, in line with the midwife program primarily targeting young children, the

later-life midwife exposure coefficients are small and not statistically significant in all

specifications in Table 3.

Regression Results: Sample-Split by Caretaker Education (Table 4)

7 The poisson coefficients are the log of the ratio of expected CSS for individuals with early-life midwife

exposure and the expected CSS for individuals without. I transform the coefficients into percentage differences in expected CSS between individuals with early-life midwife exposure and individuals without by exponentiation using the incidence rate ratio option for Stata’s poisson command.

14

Table 4 shows coefficient estimates for the subsample of children whose caretakers

have at maximum primary education in panel 1 and for the subsample of children whose

caretakers have higher than primary education in panel 2.

In panel 1, CSS are between 8.8 (column 2) and 7.3 (column 6) percent higher for

children with early-life midwife exposure than for children without. Comparison with

the results in Table 3 show that the relative reduction in coefficient size when including

additional control variables is smaller in panel 1 of Table 4 than in Table 3. This finding

is in line with living conditions being more homogenous across children with early-life

midwife exposure and children without in the subsample of children with less-educated

caretakers. Within, the subsample, early-life midwife exposure may therefore be more

of a “random” treatment than in the full sample, providing greater credibility to a causal

interpretation of the positive association with CSS.

In contrast to the results in panel 1, the negative and statistically insignificant coef-

ficients in panel 2 show that children with better educated caretakers did not benefit

from the program. Because better caretaker education correlates with better early-life

environments, this finding may be due to the subsample of children with better educated

caretakers having had adequate access to early-life care and nutrition independent from

the program.

Regression Results: Sample-Split by Type of Birth Community (Table 5)

Table 5 shows coefficient estimates for the subsample of children who spent their

first life-years in villages in panel 1 and for the subsample of children who spent their

first life-years in towns in panel 2.

15

In panel 1, CSS are between 6.3 (column 2) and 5.5 (column 3) percent higher for

children with early-life midwife exposure than for children without. Just like in the

comparison of Tables 3 and 4, the relative reduction in coefficient size when including

additional control variables is smaller in panel 1 of Table 5 than in Table 3. This again

indicates that the sample split led to a more homogenous distribution of environmental

characteristics between children with early-life midwife exposure and children without

in the village-born subsample compared to the full sample.

In contrast to the results in panel 1, the negative and statistically insignificant coef-

ficients in panel 2 indicate that children who spent their first-life years in towns with

midwives on average did not benefit from the program.

Regression Results: Sample-Split by Caretaker Education in Village-born Subsample

(Table 6)

Table 6 combines the two prior sample splits by splitting the subsample of children

who spent their first life-years in villages by caretaker education. The results in panel 1

are for the subsample of children whose caretakers have at maximum primary education

and the results in panel 2 for the subsample of children whose caretakers have higher

than primary education. The split investigates if socioeconomic differences in program

benefits prevail in a more homogenous sample than the full sample in Table 3.

The coefficients in panel 1 indicate between 8.3 (column 1) and 6.7 (column 3, not

statistically significant on conventional levels) percent higher CSS for children with

early-life midwife exposure. Just like in Table 3, panel 1 of Table 4, and panel 1 of Ta-

ble 5, the coefficients for the later-life midwife exposure variables are small and not

statistically significant.

16

The coefficients for the early-life midwife exposure variable in panel 2 are small,

not statistically significant, and show a high degree of variation across the different

specifications in columns (1)-(6). Also, while the early-life midwife exposure coeffi-

cients are of the expected sign, there is no clear order of magnitude between early-life

and later-life midwife exposure coefficients. With just 609 observations in the subsam-

ple underlying the estimates in panel 2, these results may in part be due to a lack of

power. Comparing them with those in panel 1, however, indicates that socioeconomic

differences in program benefits in fact exist even among the more homogenous subsam-

ple of village-born individuals.

7. CONCLUSION AND OUTLOOK

This paper investigates effects of a early-life exposure to a large-scale community

midwife program in Indonesia on the cognitive skill of adolescents age 11-14. The em-

pirical approach exploits both the program’s timing and geographical variation to identi-

fy midwife effects. To address that the choice of program-participating communities

was not random, I control for early-life community of residence effects, and test for the

robustness of results against the inclusion of a broad set of controls for later-life living

conditions. I also examine the association of early-life midwife exposure in different

split-samples that arguably are more homogenous with respect to unobserved determi-

nants of both program participation and CSS.

I find statistically significant positive correlations of CSS and early-life midwife

exposure in the full sample, for children with less-well educated caretakers, for children

who were exposed to the program in villages, and for children with exposure in villages

17

who have less-educated caretakers. The CSS increases associated with early-life mid-

wife exposure range from 3.6 to 8.8 percent.

The effects are largest in the subsamples of children with less-well educated care-

takers. As caretaker education correlates with other indicators of a family’s socioeco-

nomic status that determine early-life living conditions, the results indicate that the pro-

gram contributed to the narrowing of the socioeconomic divide in early-life conditions

that are crucial to the formation of cognitive skills.

Furthermore, the results indicate that the access barriers the program lowered were

both financial and geographical. Program benefits accrued to children with less-well

educated caretakers in villages but not in towns8, suggesting that the program improved

geographical access to care for rural children. In fact, as Frankenberg et al. (2005) show

that in rural communities, the program midwives were often one of the first or the only

current health worker. In addition, the finding of benefits for children with less-well

educated caretakers but not for children with better educated caretakers in villages sug-

gests that the program also improved early-life living conditions by lowering financial

access barriers. With no statistically significant correlations of the program with CSS in

towns regardless of caretaker education, the evidence for a lowering of financial access

barriers is, however, limited to rural areas.

In summary, while the results indicate that the program was effective in improving

early-life living conditions for underprivileged children in villages, it appears to not

have had a significant impact in towns. This finding may be explained by lower mid-

wife density in towns compared to villages: in the 1997 IFLS wave, median midwife

density is 1.8 per 10,000 inhabitants in towns and 3.2 in villages. Against this back-

8 Results not shown here for space reasons can be obtained from the author on request.

18

ground, program effects may accrue to a smaller number of individuals in towns, hence

muting any long-run effects in the overall town-born sample. Alternatively, the lack of

benefits for individuals from towns may in fact stem from poor service delivery. Be-

cause large socioeconomic differences in early-life living conditions persist in urban

Indonesia, further investigation is needed to better understand the absence of benefits

for town-dwellers.

Frankenberg et al. (2005) examine channels by which early-life exposure to the

program may have improved height-for-age z-scores of young children. They find that

the program lead to longer exclusive breastfeeding and higher uptake of prenatal care.

Because both have been shown to affect cognitive development these channels may also

apply for the CSS effects in this paper (see the review by Walker et al., 2007).

In addition to the estimates I present above, I conducted a series of tests for corre-

lations of early-life program exposure with other adolescent outcome measures.

9 Like for the CSS, I find statistically significant positive correlations with scores in

IFLS’s mathematical skill test that comprises of five mathematical problems of ascend-

ing difficulty. In a number of specifications, the results indicate positive correlations not

only of early-life midwife exposure but also of exposure at age 4-7. The formation of

mathematical skill can be assumed to be more responsive to later-life conditions like

access to education infrastructure than that of cognitive skill. Therefore, the positive

correlation of program exposure at age 4-7 and mathematical skill may be explained by

better unobserved infrastructural dynamics in program participating as compared to

non-participating communities.

9 Estimation results can be obtained from the author on request.

19

Moreover, while I find positive correlations of early-life midwife exposure and

adolescent height-for-age z-scores as a proxy for long-run physical development, the

correlations are not statistically significant in any of the specifications for which I pre-

sent CSS estimates. This result, however, does not necessarily conflict with earlier work

that finds higher height-for-age z-scores for young children with program exposure

(Frankenberg et al., 2005). For instance, during adolescence, the timing of growth spurts

differs between children. This additional volatility in the outcome variable increases the

requirements for precise estimation. Data from future IFLS waves can be used to test if

the correlations with young children’s height prevail if individuals have reached their

adult height.

Also with future IFLS data it can be examined if the correlation of early-life mid-

wife exposure with adolescent cognitive skill extends into better adult socioeconomic

outcomes like higher educational attainment, and greater wealth and income. The cur-

rently available data are indicative of such links: for the 1986-89 cohorts born before the

program started, an additional correctly solved task in IFLS’s cognitive skill test at age

11-14 (2000 wave) is associated with a 3 percent gain in labor income at age 18-21

(2007/8 wave).

20

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Figure 1: Sample task in shape-matching exercise in 2000 and 2007/8 IFLS

Source: RAND (2011)

24

Figure 2: Age at time of control variable and CSS measurement for 1986-89 and 1993-96 cohorts

25

Table 1: Treatment and control groups

From participating com-munities

From non-participating communities

Born 1986-89 – older at time of program implementation

Control Control

Born 1993-96 – young at time of program implementation

Treatment Control

26

Table 2: Variable means for individuals from participating and non-participating communities From participating

communities (n = 3142)


(n = 1417) Mean S.E. Mean S.E. Dependent variable

Cognitive skill score 8.86 2.64 9.38 2.39

Independent variables

Midwife exposure Midwife young .47 .50 0 0

Midwife age 4-7 .58 .49 .18 .38 Midwife age 11-14 .80 .40 .42 .50

Individual controls

Age in days at test date 4685.52 429.75 4676.53 426.69 Birth year 1990.83 3.71 1990.74 3.70

Male .51 .50 .51 .50

Household controls 4-7 Household size 5.66 2.03 5.78 2.23

Birth father present .88 .33 .91 .29 Birth mother present .95 .22 .96 .20

Caretaker > primary education .29 .50 .42 .50 Cement walling .52 .50 .56 .50

Electricity .74 .44 .82 .38 Piped drinking water .14 .34 .26 .44

Toilet with septic tank .28 .45 .43 .50 Staple food / total expenditure .17 .13 .12 .10 Staple food / food expenditure .26 .17 .20 .15

Community controls 4-7

% of households electrified .65 .33 .78 .31 Cottage industries .75 .43 .74 .44

Factories .26 .44 .36 .48 Urban area .29 .45 .68 .47

Farming primary income source .75 .43 .40 .50 Main road asphalt / cement .67 .47 .79 .41 Piped main drinking water .19 .39 .42 .50

Table continued on next page

27

Table 2 (continued): Variable means for individuals from participating and non-

participating communities

From participating communities

(n = 3142)


(n = 1417) Mean S.E. Mean S.E.

Individual controls 11-14

Child in school .90 .30 .93 .26

Household controls 11-14

Household size 5.28 1.94 5.43 2.03

Birth father present .86 .34 .89 .31

Birth mother present .93 .25 .94 .23

Cement walling .65 .48 .69 .46

Electricity .92 .28 .94 .23

Piped drinking water .19 .39 .33 .47

Toilet with septic tank .46 .50 .60 .49

Fridge .28 .45 .44 .50

Gas or electric stove .10 .30 .18 .39

Television .64 .48 .78 .41

In health fund (dana sehat) .22 .41 .20 .40

Used underprivileged family letter .11 .31 .10 .30

Staple food / total expenditure .16 .11 .12 .10

Staple food / food expenditure .26 .16 .21 .15

Community controls 11-14

% of households electrified .82 .23 .89 .20

Factories .36 .48 .45 .50

Farming primary income source .75 .43 .43 .50

Main road asphalt / cement .83 .38 .81 .40

Piped main drinking water .22 .42 .43 .50

1-3 on 1-6 asc. comm. dev. scale .71 .46 .61 .49

28

Table 3: Regression results - Midwife exposure and adolescent cognitive skill; full sample

(1) (2) (3) (4) (5) (6)

Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E.

Midwife young .055*** 0.19 .056*** .019 .049*** .019 .044** .018 .043** .018 .036** .018

Midwife age 4-7 - .007 .016 .005 .016 .006 .016 .007 .016 .006 .015

Midwife age 11-14 - .011 .015 .010 .015 .011 .014 .016 .014 .015 .014

Community controls 4-7 N N Y Y Y Y

Household controls 4-7 N N N Y Y Y

Community controls 11-14 N N N N Y Y

Household controls 11-14 N N N N N Y

Notes: Dependent variable number of correctly answered tasks in IFLS cognitive skill test; all coefficients estimated with poisson models; all specifications con-trol for sex, birth year, age in days at the time of cognitive skill test and birth community effects; standard errors clustered at birth community level; n=4559; *** and ** indicate statistical significance on the 1 and 5 percent levels, respectively.

29

Table 4: Regression results - Midwife exposure and adolescent cognitive skill; by caretaker’s highest education level

(1) (2) (3) (4) (5) (6)


Panel 1: Primary or less

Midwife young .083*** .028 .085*** .028 .081*** .028 .074*** .028 .076*** .028 .071*** .026

Midwife age 4-7 - .007 .024 .005 .024 .010 .023 .013 .024 .010 .023

Midwife age 11-14 - .015 0.24 .010 .025 .015 .025 .030 .026 .027 .025





n = 3063

Panel 2: > primary

Midwife young -.015 .025 -.015 .025 -.024 .025 -.028 .025 -.030 .025 -.029 .025

Midwife age 4-7 - -.007 .019 -.005 .020 -.003 .020 -.006 .020 -.006 .020

Midwife age 11-14 - .004 .018 .005 .017 .007 .017 -.004 .017 .002 .017





n = 1496

Notes: Dependent variable is number of correctly answered tasks in IFLS cognitive skill test; all coefficients estimated with poisson models; all specifications control for sex, birth year, age in days at the time of cognitive skill test and birth community effects; standard errors clustered at birth community level. *** indicates statistical significance on the 1 percent level.

30

Table 5: Regression results - Midwife exposure and adolescent cognitive skill; by birth community type

(1) (2) (3) (4) (5) (6)


Panel 1: Village

Midwife young .059* .030 .061** .030 .053* .032 .057* .032 .056* .031 .059** .030

Midwife age 4-7 - -.006 .024 -.008 .024 -.004 .023 -.001 .022 -.001 .021

Midwife age 11-14 - .026 .025 .023 .024 .024 .024 .032 .024 .026 .023





n = 2683

Panel 2: Town

Midwife young .003 .023 .004 .024 .002 .024 -.002 .023 -.006 .023 -.014 .022

Midwife age 4-7 - .009 .020 .009 .021 .008 .020 .015 .021 .012 .020

Midwife age 11-14 - .002 .017 .004 .017 .007 .016 .016 .017 .016 .017





n = 1876

Notes: Dependent variable is number of correctly answered tasks in IFLS cognitive skill test; all coefficients estimated with poisson models; all specifications control for sex, birth year, age in days at the time of cognitive skill test and birth community effects; standard errors clustered at birth community level.; ** and * indicate statistical significance on the 5 and 10 percent levels, respectively.

31

Table 6: Regression results - Midwife exposure and adolescent cognitive skill; village subsample, by caretaker’s highest level of education

(1) (2) (3) (4) (5) (6)


Panel 1: Primary or less

Midwife young .080* .042 .080* .041 .067 .042 .068 .042 .071* .042 .075* .040

Midwife age 4-7 - -.008 .032 -.012 .031 -.005 .030 -.006 .030 -.007 .028

Midwife age 11-14 - .014 .034 .005 .034 .011 .034 .023 .035 .020 .032





n = 2080

Panel 2: > primary

Midwife young .023 .053 .035 .054 .020 .054 .019 .052 .032 .056 .061 .054

Midwife age 4-7 - -.013 .029 -.019 .029 -.020 .031 -.022 .032 -.020 .034

Midwife age 11-14 - .032 .036 .032 .038 .032 .040 .022 .044 .030 .040





n = 603

Notes: Dependent variable is log of correctly answered tasks in IFLS cognitive skill test; all coefficients estimated with poisson models; all specifications control for sex, birth year, age in days at the time of cognitive skill test and birth community effects; standard errors clustered at birth community level. * indicates statistical significance on the 10 percent level.

early-life exposure to an indonesian midwife … fileabstract this paper investigates links between...

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