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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Draft version, 18 March 2012 – please do not quote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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23
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)
From non-participating communities
(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
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Table 2 (continued): Variable means for individuals from participating and non-
participating communities
From participating communities
(n = 3142)
From non-participating communities
(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
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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)
Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E.
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
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
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
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
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)
Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E.
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
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
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
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
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)
Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E.
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
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
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
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
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.
32