WPS6529
Policy Research Working Paper 6529
Labor Market Returns to Early Childhood
Stimulation
A 20-year Followup to an Experimental Intervention
in Jamaica
Paul Gertler
James Heckman
Rodrigo Pinto
Arianna Zanolini
Christel Vermeersch
Susan Walker
Susan Chang-Lopez
Sally Grantham-McGregor
The World Bank
Latin America and the Caribbean Region
Education Sector
July 2013
Policy Research Working Paper 6529
Abstract
This paper finds large effects on the earnings of re-interviewed the study participants 20 years after the
participants from a randomized intervention that gave intervention. Stimulation increased the average earnings
psychosocial stimulation to stunted Jamaican toddlers of participants by 42 percent. Treatment group earnings
living in poverty. The intervention consisted of one- caught up to the earnings of a matched non-stunted
hour weekly visits from community Jamaican health comparison group. These findings show that psychosocial
workers over a 2-year period that taught parenting stimulation early in childhood in disadvantaged settings
skills and encouraged mothers to interact and play can have substantial effects on labor market outcomes
with their children in ways that would develop their and reduce later life inequality.
children’s cognitive and personality skills. The authors
This paper is a product of the Education Sector, Latin America and the Caribbean Region. It is part of a larger effort by
the World Bank to provide open access to its research and make a contribution to development policy discussions around
the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be
contacted at cvermeersch@worldbank.org and gertler@haas.berkeley.edu.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
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its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Labor Market Returns to Early Childhood Stimulation:
a 20-year Followup to an Experimental Intervention in Jamaica
Paul Gertlera
James Heckmanb,c
Rodrigo Pintob
Arianna Zanolinib
Christel Vermeersche
Susan Walkerd
Susan Chang-Lopezd
Sally Grantham-McGregorf
JEL classifications: 015, I20, I10, I25
Key words: early childhood development, stunting, randomized trial
Sector board: EDU and HNP
Acknowledgements: The authors gratefully acknowledge research support from the World Bank Strategic
Impact Evaluation Fund (SIEF), the American Bar Foundation, The Pritzker Children's Initiative, NICHD
R37HD065072, R01HD54702, the Human Capital and Economic Opportunity Global Working Group -
an initiative of the Becker Friedman Institute for Research in Economics funded by the Institute for New
Economic Thinking (INET), a European Research Council grant hosted by University College Dublin,
DEVHEALTH 269874, and an anonymous funder. We have benefitted from comments of participants in
seminars at the University of Chicago, UC Berkeley, MIT, the 2011 LACEA Meetings in Santiago Chile
and the 2013 AEA Meetings. We thank the study participants for their continued cooperation and
willingness to participate, and to Sydonnie Pellington for conducting the interviews.
Author Affiliations: aUniversity of California Berkeley, bUniversity of Chicago, cAmerican Bar
Foundation, dThe University of The West Indies, eThe World Bank, fUniversity of London
Contents
1 Introduction 1
2 The Jamaican Study 3
2.1 The Intervention and Experimental Design . . . . . . . . . . . . . . . . . . . 3
2.2 External Comparison Group . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Previous Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 The New Survey 7
3.1 The Experimental Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Non-Stunted Comparison Sample . . . . . . . . . . . . . . . . . . . . . . . . 8
4 Methods 9
4.1 Treatment Eﬀect Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.1.1 Randomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.1.2 Permutation Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.1.3 Baseline Imbalance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.1.4 Accounting for Multiple Outcomes . . . . . . . . . . . . . . . . . . . 13
4.2 Catch-Up Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5 Migration 14
6 Earnings Results 16
6.1 Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
6.2 Earnings Densities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6.3 Point Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6.4 Attrition of Migrants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.5 Employment and Labor Force Participation . . . . . . . . . . . . . . . . . . 19
6.6 Catch-up in Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
7 Pathways to Earnings 20
7.1 Parental Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
7.2 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
7.3 Cognitive and Psychosocial Skills . . . . . . . . . . . . . . . . . . . . . . . . 22
7.4 Catch-up in Education and Skills . . . . . . . . . . . . . . . . . . . . . . . . 23
8 Gender Diﬀerences 24
9 Conclusions 25
References 27
Tables and Figures 32
Appendices 49
i
A Appendix: Supplemental Tables 49
ii
1 Introduction
Early childhood, when brain plasticity and neurogenesis are very high, is an important period
for cognitive and psychosocial skill development.1 Investments during this period create the
foundations for the evolution of the cognitive and psychosocial skills that are key determi-
nants of lifetime earnings.2 Young children who experience negative shocks such as economic
downturns, extreme weather, and infectious diseases suﬀer long lasting consequences in terms
of their educational and labor market outcomes.3 The seeds of inequality are planted in early
life with remediation being less eﬀective and more expensive later in life.4
Today more than 200 million children under the age of 5 living in developing countries
are at risk of not reaching their full developmental potential. The vast majority of these
children live in extreme poverty.5 These children start disadvantaged, receive lower lev-
els of parental investments, and are likely to continue to fall further behind without help
than are children from more aﬄuent environments.6 Based on a growing body of evidence
demonstrating positive impacts, early childhood development (ECD) interventions aimed
at skill development are being promoted as cost-eﬀective remediation policies to help these
children.7 While these ECD interventions are estimated to have substantially higher rates
of return than investments in the human capital of the disadvantaged later in life,8 there
is little rigorous evidence on the long-term eﬀects of ECD on earnings and inequality for
developing countries.
1
See Huttenlocher (1979, 2002) and Thompson and Nelson (2001).
2
See e.g. Knudsen et al. (2006), Borghans et al. (2008), and Almlund et al. (2011a).
3
See van den Berg et al. (2006), Almond et al. (2007), Bleakley (2007), Maccini and Yang (2009) and
Almond and Currie (2011).
4
See Carneiro and Heckman (2003), Cunha et al. (2006), Heckman (2008) and Cunha et al. (2010).
5
See Grantham-McGregor et al. (2007) and Walker et al. (2007).
6
See Paxson and Schady (2007), Fernald et al. (2011), Fernald et al. (2012) and Engle et al. (2011).
7
See e.g. Engle et al. (2007), Heckman (2008) and Engle et al. (2011).
8
See Heckman (2000, 2008), Cunha et al. (2006), Almond and Currie (2011).
1
This paper reports estimates of the labor market returns to an intervention that gave
psychosocial stimulation and nutritional supplementation to growth-retarded toddlers liv-
ing in poverty in Jamaica (Grantham-McGregor et al., 1991). Enrollment in the study was
conditioned on stunting because it is an easily and accurately observed indicator of mal-
nutrition that is strongly associated with poor cognitive development (Walker et al., 2007).
The randomized treatment group assigned to stimulation received weekly visits for a period
of two years from community health workers who actively encouraged mothers to interact
and play with their children in ways designed to develop cognitive and psychosocial skills.
Unlike the eﬀects of many other early childhood interventions that fade out over time,9 the
Jamaican stimulation intervention proved to have large impacts on cognitive development 20
years later (Walker et al., 2011). In contrast, the nutritional intervention had no long-term
impact on any outcome.
We use labor market information collected 20 years after the intervention when the
participants were 22 years old. We show that stimulation increased average earnings by
42%. The magnitude of the estimated impact on earnings is put into perspective when
compared to a non-stunted comparison group identiﬁed at baseline. In fact, the earnings
of the treated stunted group completely caught up with the earnings of the matched non-
stunted comparison group. These results provide evidence that stimulation interventions
very early in life can compensate for developmental delays and thereby reduce inequality
later in life.
We also examine pathways through which the intervention likely aﬀected earnings. First,
we ﬁnd that the intervention increased maternal investment in children during the inter-
vention period. Second, there are large eﬀects on key determinants of earnings including
schooling, cognitive development, and psychosocial development. Finally, we show that the
treatment group was more likely to migrate to the U.S. or U.K., and thereby gained access
to higher quality schools and better labor markets.
9
See Cunha et al. (2006), Almond and Currie (2011) and Engle et al. (2011) for reviews.
2
To our knowledge, our study is the ﬁrst experimental evaluation of the impact of an ECD
stimulation intervention on long-term economic outcomes and inequality in a developing
country.10 This study contributes to a small literature on labor market returns to ECD
programs including Perry Preschool, the Chicago Parent Child program, Abecedarian and
Head Start, all of which are located in the U.S.11 We ﬁnd that the Jamaica stimulation
program had substantially larger eﬀects on earnings than any of the U.S. programs.
2 The Jamaican Study
2.1 The Intervention and Experimental Design
In 1986-1987, the Jamaican Study enrolled 129 stunted children age 9-24 months that lived
in poor disadvantaged neighborhoods of Kingston, Jamaica (Walker et al., 1990). Stunting
was deﬁned as having a standardized height for age z -score less than -2. The children were
stratiﬁed by age (above and below 16 months) and sex. Within each stratum, children
were sequentially assigned to one of four groups using a randomly generated seed to begin
the assignment. The four groups were (1) psychosocial stimulation (N=32), (2) nutritional
supplementation (N=32), (3) both psychosocial stimulation and nutritional supplementation
(N=32), and (4) a control group that received neither intervention (N=33). All children were
given access to free health care regardless of the group to which they were assigned.
The stimulation intervention (comprising groups 1 and 3) consisted of two years of weekly
one-hour play sessions at home with trained community health aides.12 The curriculum for
the cognitive stimulation was based on Piagetian concepts (Powell and Grantham-McGregor,
10
While ours is the ﬁrst to study labor market returns to ECD psychosocial stimulation in a developing
county, there are labor market follow-ups to nutritional interventions. See, for example, Hoddinott et al.
(2008), Maluccio et al. (2009).
11
See Heckman et al. (2010a), Heckman et al. (2010b), Reynolds et al. (2004), Reynolds et al. (2007),
Reynolds et al. (2011), Campbell et al. (2002), Campbell et al. (2012), Campbell, Conti, Heckman, Moon,
and Pinto (2012); Aughinbaugh (2001), and Garces et al. (2002).
12
The aides received 8 weeks of training in nutrition and primary health care and another 8 weeks of
training in child development, teaching techniques and toy making.
3
1989). Mothers were encouraged to converse with their children, to label things and actions in
their environments and to play educational games with their children (Grantham-McGregor
et al., 1987). Particular emphasis was placed on language development, the use of praise,
and on improving the self-esteem of both the child and of the mother. At age 24 months, the
curriculum was enriched to include concepts such as size, shape, position, quantity, color,
etc based on the curriculum in Palmer (1971).
The focus of the weekly play sessions was on improving the quality of the interaction
between mother and child. Mothers were encouraged to continue practicing the activities
and games learned during the visits on a continuing basis beyond the home visitation time.
At every visit, homemade toys were brought to the home and left for the mother and child
to use until the next visit when they were replaced with new ones. The intervention was
innovative both for its focus on activities to promote cognitive and language development
and for its emphasis on direct mother-child interactions.
The nutritional intervention (comprising groups 2 and 3) was aimed at compensating for
the nutritional deﬁciencies that may have caused stunting. The nutritional supplements were
provided weekly for a two-year period. The supplements consisted of one kilogram of formula
containing 66% of daily-recommended energy (calories), and 100% of daily-recommended
protein (Walker et al., 1992). In addition, in an attempt to minimize sharing of the formula
with other family members, the family also received 0.9 kilograms of cornmeal and skimmed
milk powder. Despite this, sharing was common and uptake of the supplement decreased
signiﬁcantly during the intervention (Walker et al., 1991).
Of the 129 study participants, two of the participants dropped out before completion of
the two-year program. The remaining 127 participants were surveyed at baseline, resurveyed
immediately following the the end of the two-year intervention, and again at ages 7, 11, and
18. Our analysis is based on a re-interview of the sample in 2007-08 when the participants
were approximately 22 years old, some 20 years after the original intervention.
4
2.2 External Comparison Group
For comparison purposes, the study also enrolled a sample of non-stunted children from
the same neighborhoods, where non-stunted was deﬁned as having a height for age z -score
greater than -1 standard deviations. At baseline, every fourth stunted child in the study
was matched with one non-stunted child who lived nearby and was the same age (plus or
minus 3 months) and sex. At age 7, this sample of 32 was supplemented with another 52
children who had been identiﬁed in the initial survey as being non-stunted and fulﬁlled all
other inclusion criteria.
While the non-stunted group was better oﬀ than the stunted group in terms of their per-
sonal development and their socioeconomic status, the non-stunted children were still living
in the same economically and socially disadvantaged Kingston neighborhoods. Members of
the non-stunted comparison group did not receive any of the interventions, but did receive
the same free health care as those in the stunted experimental group. From age 7 onwards,
this group was surveyed at the same time as the participants in the experiment.
This sample is used to investigate the extent to which the early childhood stimulation
intervention helped to compensate for initial disadvantage by comparing the stunted treat-
ment group with the non-stunted external comparison group. We deﬁne complete catch-up
as no diﬀerence between the treated stunted group and the non-stunted comparison group.
In order to better understand the external validity of the catch-up analysis we compare
the non-stunted group to the general population using data from two surveys that are rep-
resentative of urban Jamaica: (1) the 1992 Jamaican Survey of Living Conditions (JSLC)
that was collected when the children were 7 years old and when most of the non-stunted
sample was ﬁrst surveyed, and (2) the 2008 Jamaica Labor Force (JLF) survey that was col-
lected in the same year as the last follow-up. Unfortunately the labor supply and earnings
questions in the JLF and in our survey were asked in diﬀerent ways, and there was a 50%
non-response rate in the JLF to the earnings questions among those who were employed.
Only the education variables are directly comparable.
5
Comparing childhood conditions in 1992 we ﬁnd that the non-stunted comparison group
grew up in more disadvantaged settings than the general population living in the urban
Jamaica.13 The non-stunted sample was less likely to live in houses with piped water, their
mothers were less likely to have completed grade 9 at school, and they were less likely to
have the father present in the house. Despite this, by age 22, the non-stunted group attained
comparable levels of human capital as those of the same age and living in the Kingston Area
interviewed in the Labor Force Survey. The two samples are equally likely to still be in
school and achieved the same level of educational attainment in terms of years of schooling
and passing national comprehensive matriculation exams.14
2.3 Previous Studies
The stimulation and the combined stimulation-nutrition arms of the Jamaica Study proved
to have a large long-term impact on cognitive development. At age 22 the order of magnitude
of the impacts of stimulation were large at 0.6 standard deviations on a WAIS test (Walker
et al., 2011). While the treatment groups’ cognitive scores improved relative to those of the
control group and caught up with the non-stunted sample in performance IQ , they did not
completely catch-up in all cognitive function domains (Walker et al., 2005, 2000). Moreover,
both stimulation arms had positive impacts on psychosocial skills, schooling attainment
and crime reduction Walker et al. (2011). However, there was no long-term impact on
anthropometric measures (Walker et al., 1996).
While the stimulation arms had strong and lasting eﬀects, the nutrition-only arm had
no long-term eﬀect on any outcome (Walker et al., 2005, 2000).15 Hence, we combine the
13
See Table 14 Panel A in the Appendix.
14
See Table 14 Panel B in the Appendix.
15
This is in contrast to the Guatemala Study in which nutritional supplementation did aﬀect both long-
term health status and earnings (Hoddinott et al., 2008 and Maluccio et al., 2009). This may be due to
the fact that the Guatemala study started supplementing children earlier, in utero and right at birth, while
when the Jamaica program started children were already undernourished. Since there is no study showing
sustained beneﬁts from supplementation in children who were malnourished before beginning supplementa-
tion, supplementation in Jamaica may have begun too late to have an impact. Other possible reasons for
the diﬀerence include the fact that the supplement was oﬀered for less time in Jamaica, the supplement
6
two psychosocial stimulation arms into a single treatment group (N=64) and combine the
nutritional supplementation only group with the pure control group into a single control
group (N=65). Henceforward we use the term stimulation eﬀects of stunted participants to
designate the analysis that compares groups 1 and 3 against groups 2 and 4.
3 The New Survey
We resurveyed both stunted (experimental) and non-stunted (comparison) study popula-
tions in 2007 and 2008 some 20 years after the original intervention when the participants
were approximately 22 years old.16 We attempted to ﬁnd all of the study participants re-
gardless of current location and followed migrants to the the US, Canada, the UK and the
Caribbean. When we could not ﬁnd a participant in Jamaica, we contacted relatives for
further information to ﬁnd the participants.
3.1 The Experimental Sample
We were able to ﬁnd and interview 105 out of the original 127 (83%) stunted participants
who completed the program. For this sample, Table 1 reports the baseline means for the
treatment and control groups, the diﬀerence in the means of the two groups, and p-values for
two sided permutation tests of equality of means. We observe signiﬁcant diﬀerences in 3 out
of 19 variables. Mothers of children in the treatment group were more likely to be employed
and have completed less schooling than mothers of children in the control group, and children
in treatment group had lower weight for height than children in the control group. These
imbalances are already present in the full baseline sample of 127, which suggests that they
was more intensively shared with other family members in Jamaica, the formula provided in the Jamaica
intervention had fewer micronutrients, and the supplement was a smaller share of the total food budget in
Jamaica (Hoddinott et al., 2008; Walker et al., 1992, 1990.)
16
The survey received ethical clearance from the IRB of the University of the West Indies in Kingston
Jamaica.
7
were the result of sampling variation in the original randomization rather than diﬀerential
sample attrition.17
The attrition rate from the experiment is 17%. Of 22 participants that dropped out
of the sample, 10 were not found, 9 died, and 3 of those who were found refused to be
interviewed.18 In addition, treatment status is not a signiﬁcant predictor of the overall
probability of attrition or for any of the reasons for attrition. And, with just 4 exceptions out
of 57, the means of individual variables are not signiﬁcantly diﬀerent between the group that
dropped out and the group that stayed in the sample, even when we stratify by treatment and
control.19 Hence, in terms of measured variables, there appears to be no selective attrition
and the remaining sample is representative of the original sample.
3.2 Non-Stunted Comparison Sample
We found and interviewed 65 children out of the 84 children originally surveyed with an
implied attrition rate of 23%, which is slightly higher than that for the experimental sample.
In the baseline samples, 9 out of 19 characteristics are statistically signiﬁcantly diﬀerent
between stunted and non-stunted. As expected, the non-stunted were less disadvantaged.20
Non-stunted children have taller mothers with higher Picture Peabody Vocabulary Test
(PPVT) scores and have higher birth weight, larger head circumference, and higher initial
developmental scores.
Attrition from the non-stunted sample appears to be selective.21 Mothers in the attrition
group are older, better educated, and perform better on the PPVT than mothers who do
not attrite. In addition, children in the attrition group lived in homes with more verbal
17
See Table 15.
18
See Table 16.
19
See Table 17.
20
Table 19 in the Appendix shows the diﬀerences in baseline and 7 years characteristics for the stunted
and non-stunted samples. The top panel only includes the baseline variables, where the non-stunted group
only consists of 32 people, while the bottom panel also includes variables at 7 years, when the additional 52
non-stunted children were added.
21
See Table 18.
8
stimulation and better housing infrastructure. This suggests that the remaining sample is
not representative of the original sample.
4 Methods
We investigate two questions. First, what are the impacts of treatment on earnings and
associated outcomes? We identify the impacts by comparing the randomized treatment
and control groups from the stunted sample. Second, did stimulation enable the stunted
treatment group to catch-up with the non-stunted group? We identify catch-up by comparing
the stunted treatment group with the non-stunted comparison group. This comparison
examines whether the intervention eﬀectively remediated the initial disadvantage of stunted
children.
4.1 Treatment Eﬀect Analysis
Our analysis uses random assignment to identify treatment eﬀects. Perfectly implemented
randomized trials allow us to assess causal eﬀects and are often called the “gold standard”
for causal inference. However, most randomized trials are compromised in some way. In our
case, we need to address 3 issues: (1) our small sample size, (2) imbalance of a few potentially
key baseline variables between treatment and controls, and (3) a large number of outcomes
and associated treatment eﬀects. In what follows we will ﬁrst describe our framework for
causal inference and then describe the approaches used to address these three issues.
4.1.1 Randomization
The standard program evaluation model describes the observed outcome Yi of participant i
by Yi = Di Yi (1) + (1 − Di )Yi (0), where Di denotes treatment eﬀect assignment (Di = 1 if
treated, Di = 0 otherwise) and (Yi (0), Yi (1)) are potential outcomes for individual i. Our
objective is to estimate the average treatment eﬀect, E (Yi (1) − Yi (0)). However, we are
9
unable to calculate the average treatment eﬀect from ordinary observational data as we only
observe either (Yi (1)|Di = 1) or (Yi (0)|Di = 0) for participant i.
We can estimate the simple diﬀerence-in-means, E (Yi (1)|Di = 1) − E (Yi (0)|Di = 0).
In observational samples, however, this diﬀerence is usually not a consistent estimator of
the average treatment eﬀect due to participants self-selecting into treatment. Selection
bias occurs when the resulting distributions of participant characteristics diﬀer between the
treatment and control groups, and these diﬀerences are correlated with outcomes. Here, any
diﬀerence in outcomes reﬂects a combination of both the underlying diﬀerence of unobserved
characteristics and the treatment eﬀect. Conditioning on observed characteristics may not
fully control for all relevant sources of diﬀerences between treatment and controls.
Perfectly implemented random assignment solves the selection bias problem by induc-
ing independence between the distribution of counter-factual outcomes (Yi (0), Yi (1)) and
treatment status Di conditional on the variables used in the randomization protocol. In
the Jamaica Study, the randomization protocol ﬁrst stratiﬁed the sample by age and sex,
denoted by X , and then randomly assigned children within strata to each treatment group.
4.1.2 Permutation Tests
Our aim is to test the null hypothesis of no treatment eﬀect. The small sample size of the
Jamaican Study suggests that classical statistical inference methods that rely on large sample
asymptotic theory to deﬁne the distribution of test statistics may be misleading. We address
this problem by using non-parametric permutation tests as implemented in Heckman et al.
(2010b). Permutation tests are valid in small samples because they are distribution free and
do not rely on assumptions about the parametric sampling distribution.
Permutation tests are based on exchangeability properties generated directly from the
randomization protocol. Exchangeability exploits the invariance of the joint distribution
of (Y, D) under the null hypothesis. Random assignment guarantees that the vector of
treatment assignments D is exchangeable within blocks of participants that share the same
10
values of X . Any swap of treatment status among participants who belong to the same
pre-randomization strata of gender and age are just as likely to occur as the realized vector.
Hence, under exchangeability we can permute treatment status between individuals with
the same pre-program variables X and the joint distribution of (Y, D) will remain the same
under the null hypothesis of no treatment eﬀects.
Under exchangeability we can generate the exact distribution of a conditional test statistic
T (Y, D|X ). Speciﬁcally, we generate the conditional distribution of the statistic given by all
of the values that T (Y, D) takes as we fully permute the elements of the vector of treatment
status D within strata formed by X . The distribution generated in this way does not
depend on any distributional or asymptotic assumption. We use this generated distribution
for inference to test the null hypothesis that there is no diﬀerence between the treated and
the untreated population means. We report a p-value that is simply the proportion of the
test statistic values that are bigger than the ones computed using the actual data.
4.1.3 Baseline Imbalance
While randomization guarantees that any baseline variable Z is independent of the vector
of treatment status D conditional on variables X used in the randomization protocol, the
realization of baseline variables can turn out to be imbalanced across treatment groups. In
the case of the Jamaica Study, three potentially important characteristics were not balanced
at baseline. In order to control for potential bias, we estimate treatment eﬀects by linear
regression controlling for these variables when relevant for explaining outcomes.
The conditioning variables may also be used to increase the power of the statistical
inference. Heckman et al. (2010b) address this problem by assuming partial linearity as
suggested in Freedman and Lane (1983). Essentially, this involves permutation using the
residuals from a multivariate linear regression. However, we would like to avoid linearity as
this assumption is unlikely to hold for categorical outcomes and imposes a speciﬁc functional
form.
11
Instead we employ a fully non-parametric technique that avoids invoking linearity as-
sumptions by including the variables not balanced at baseline in X . This expands the
number of strata blocks for the permutation tests described above. This is straightforward
for discrete variables, but requires us to discretize continuous variables.
Increasing the number of blocks, however, comes at a cost as it may reduce the number
of valid block permutations. This happens because it reduces the number of participants
that share the same values of the conditioning variables. One can end up with blocks in
which there are only treatments or only controls, rendering the observations in those blocks
lost to the analysis.
Our conditioning set always includes the variables used in the randomization protocol
plus the baseline variables that are imbalanced when their impact is statistically signiﬁcant.
Child age and sex as well as maternal employment and maternal education were constructed
as discrete indicators. Weight-for-height is the only variable that we had to discretize.
We chose the highest possible number of divisions that maximize the minimum number of
observations in a block. This led to dividing the sample in three categories, those with a
z -score higher than -1, those less than -1 but greater than -2, and those less than -2 in the
standardized weight for height distribution. We lost no observations for permutation by
following this rule.
Our method of inference is fully non-parametric and does not require any linearity as-
sumption. It is theoretically exact. While the Freedman and Lane (1983) procedure is
approximate, it often generates reasonably accurate inferences (Anderson and Robinson,
2001). Both the Freedman and Lane procedure and our procedure have drawbacks. The
ﬁrst imposes linearity, while the one we use requires us to discretize continuous variables
when conditioning. While the results of our hypotheses do not change from what is obtained
using the Freedman Lane procedure, our approach produces more precise estimates.
12
4.1.4 Accounting for Multiple Outcomes
The presence of multiple outcomes leads to the danger of arbitrarily selecting “statistically
signiﬁcant” outcomes where high values of test statistics arise by chance. Testing each
hypothesis one at a time with a ﬁxed signiﬁcance increases the probability of a type-I error
exponentially as the number of outcomes tested grows. We correct for this source of bias in
inference by performing multiple hypothesis testing based on the Family-Wise Error Rate
(FWER), which is the probability of rejecting at least one true null Hypothesis. We use
the Stepdown algorithm proposed in Romano and Wolf (2005), which generates inference
exhibiting strong FWER control. Associated with each outcome is a single null hypothesis
of no treatment eﬀect. We implement the Step-Down procedure for conceptually similar
blocks of outcomes.
4.2 Catch-Up Analysis
Our catch-up analysis compares the non-stunted comparison group with the stunted treat-
ment group. Despite being non-randomized, this analysis will employ inference using per-
mutation tests. The intuition is that, under the null hypothesis, being non-stunted has no
advantage with respect to the treatment stunted group. Exchanging stunted status within
blocks should not change the distribution of outcomes. While inference in the treatment
eﬀect analysis tests whether the causal eﬀect of the intervention is statistically signiﬁcant
compared to the control group, inference for the catch-up group tests if the distribution
of outcomes is statistically diﬀerent between treatment and comparison groups. While the
exchangeability criteria for inference between treatment and control groups comes from ran-
domization, the exchangeability criteria for inference between non-stunted and treatment
groups comes from the assumption of equality of outcome distributions under the null hy-
pothesis.
One diﬀerence between the treatment and catch-up analyses is attrition. As previously
noted, attrition in the stunted sample is not a problem. However, attrition in the non-stunted
13
group appears to be selective. When attrition is not random, the observed sample may diﬀer
from the initial sample that was representative of the non-stunted population from poor
urban areas. Hence, the catch-up analysis would be a biased estimate of catch-up to the
non-stunted population.
We correct for attrition by using predicted probabilities of attrition to re-weight observed
data. The predictions come from a logit model of attrition as a function of the baseline
characteristics whose means are signiﬁcantly diﬀerence between attrited and non-attrited.
This procedure is termed Inverse Propensity Weighting (IPW).22 This method gives more
weight to those observations in the sample with a low propensity score for attrition correcting
for the censoring eﬀect of non-random attrition. We re-weight the data using variables
measured at the onset of the intervention to correct for the potential bias of non-random
attrition.
5 Migration
We begin by reporting the results of the impact of stimulation on migration to the U.S. or
U.K. (Table 2). As discussed in greater detail below, migration has important implications
for the earnings analysis. Migration is, itself, also an interesting outcome. The stimula-
tion treatment may have improved skills enough so that beneﬁciaries or their families were
encouraged to move overseas to take advantage of better education and labor market oppor-
tunities. Hence, migration might be an important pathway through which the intervention
could have improved human capital and earnings outcomes.
We obtained migration status for the full baseline sample of both stunted and non-stunted
children by ﬁlling missing values with information from relatives of study participants who
dropped out of the sample. For the full stunted sample, 23 participants migrated and the
treatment group was 10 percentage points (83%) more likely to migrate than the control
22
See Robins et al. (1994).
14
group (p-value .08). Migration may have been a pathway to better education and earnings
opportunities.
There is evidence of selective attrition of the migrants. We were able to locate and
interview 14 out of the 23 (60%) migrants, a substantially lower share than the share of
non-migrants that we were able to ﬁnd and interview. Of the 14 migrants who were found
and interviewed, 11 were in the treatment group and 3 were in the control group. This means
that we found a much larger share of the treatment migrants than of the control migrants.
This is apparent from comparing Row 1 of Table 2, which reports the results for the full
baseline sample to Row 2 of Table 2, which reports the impact of treatment on migration
using only the sample found in the follow-up. The third column of Table 2 indicates that
the average migration rate for the observed control group is 6% compared to 12% for the
full sample. The third column represents the conditional diﬀerence in average migration
between the treatment and the control group. The treatment group is 15% more likely to
be a migrant than the control group in the sample found at follow-up (p-value 0.02), but it
is only 10% more likely to be a migrant in the full baseline sample (p-value 0.08).
This ﬁnding causes concern as it could lead to an overestimate of the impact of treat-
ment on earnings. Migrants to the US and UK earn substantially more than those who
remained in Jamaica. Due to the diﬀerential follow-up of migrants, control migrants are
under-represented in the sample of 22 year-olds. Hence, the mean earnings of treatments
might be higher than the mean earnings of controls even when the treatment eﬀect is zero.
We will address this concern with an additional set of analyses that (1) impute the earnings
of the lost migrants, and (2) checks robustness by dropping migrants completely from the
analysis. The later analysis produces a low bound estimate of the treatment eﬀect as it does
not allow migration to be a pathway to improved education and earnings.
15
6 Earnings Results
6.1 Measurement
We examine the impact of treatment on earnings histories as represented by earnings in the
ﬁrst job, in the last job and in the current job, as well as average monthly earnings over the
lifetime. The current job is equal to the last job if the person is currently employed. We
include the last job in order to reduce concerns over censoring as all but two of the study
participants have had some labor market experience. Average monthly lifetime earnings are
calculated as the ratio of total lifetime earnings divided by the number of months worked. All
variables are expressed in terms of monthly earnings and are deﬂated to 2005 dollars using
the CPI and then transformed into logs. Migrants’ earnings are ﬁrst deﬂated to 2005 using
the local CPI, then converted to Jamaican dollars using PPP adjusted exchange rates.23
One issue is that there is a signiﬁcant portion of the sample that is both working and
in school full time. Working, full-time students are likely to have lower earnings than non-
students with the same education, and there are signiﬁcantly more full time students working
in the treatment group than in the control group. Hence, we likely underestimate the long-
run earnings of those who are still in school. As a result, observed average earnings likely
understate the long run earnings of the treatment more than the control group. This would
imply that we are underestimating the long-run eﬀects of treatment on earnings.
In order to assess the extent to which including working full time students in the sample
underestimates the eﬀect of treatment on long run earnings, we additionally analyze the
impact of the program on samples restricted to workers in full time jobs and further restricted
to workers in non-temporary permanent jobs. Restricting the sample to full time workers
partially controls for this source of selection as many of the participants had part-time jobs
while primarily attending school. We deﬁne full time as working at least 20 days per month.
23
The PPP deﬂators are from the University of Pennsylvania, Center for International Comparison of
Production, Income and Prices. As robustness checks, we also estimated the models by converting monetary
amounts using the 2005 currency exchange rates and using the World Bank PPP exchange rates. Results in
both cases are close to the estimates reported in the paper and are available upon request.
16
The sample of workers in non-temporary permanent jobs further omits students working in
summer jobs that may have been full time. Non-temporary is deﬁned as having a full time
job working for 8 months a year or more.
6.2 Earnings Densities
We begin by examining the impact of the intervention on densities of diﬀerent measures of
log earnings. The panels of Figure 1 present the kernel density estimates of diﬀerent earnings
measures for the treatment and control group.24 We display them separately for the ﬁrst job,
last job, current job, and average lifetime earnings. We also present them separately for all
workers, full time workers, and non-temporary workers. The ﬁgures also report Komogorov-
Smirnov test statistics for the null hypothesis that there is no diﬀerence in the treatment
and control distributions.
The ﬁgures show that the densities of log earnings for the treatment group are shifted
everywhere to the right of the control group densities for all comparisons with the single ex-
ception of the density of earnings on the ﬁrst job for all workers. The Kolmogorov-Smirnov
tests reveal that distributions of log earnings for the treatment group are signiﬁcantly dif-
ferent than the distributions for the control group for almost all cases. The diﬀerences are
greater when we restrict the sample to full time workers and even greater when we restrict
the sample further to non-temporary workers.
6.3 Point Estimates
The estimated impacts on log earnings for the observed sample are reported in Table 3,
in Panel I. The table reports the treatment eﬀect, the conditional p-value for the hypothesis
of no treatment eﬀect taken in isolation, and the p-value obtained from the Step-Down
24
The kernels of Figure 1 display the distribution of log earnings for the overall sample. We evaluate
Epanechnikov kernels using a bandwidth that minimizes mean integrated squared error for Gaussian data.
17
procedure. In doing the Step-Down procedure, we group together the outcomes in each
block of rows in each panel separately.
The results show that the treatment group had signiﬁcantly higher earnings over the entire
tenure in labor market and for all job types including part-time, full time and permanent
jobs, as well as for log of average earnings. Average monthly lifetime earnings are 49% higher
for all jobs and 60% higher for the treatment than for the control group when considering
only non-temporary full time jobs.25 , 26 Diﬀerences in earnings in the ﬁrst, last and current
jobs show similar magnitudes. However, the impact is substantially larger for full-time and
even larger for full-time permanent (non-temporary) jobs.
6.4 Attrition of Migrants
We address potential bias from diﬀerential attrition of migrants by imputing earnings for
the missing observations. Imputing the missing observations re-weights the data so that
the treatment and control groups of migrants are no longer under- or over-represented in
the sample. In order to minimize the amount of data imputed, we impute missing earnings
only for migrants who were lost. As a result, we impute earnings for only 9 observations.
We replace missing earnings values with predicted log earnings from an OLS regression on
treatment, gender and migration status.
The results, reported in Panel II of Table 3, show that the impact on earnings remains
large and statistically signiﬁcant for the sample with imputed earnings. Not surprisingly,
however, the point estimates are slightly lower. In this case, the estimated impact on the
average monthly lifetime earnings for all workers is 42%, and for non-temporary workers is
49%. Again, we ﬁnd similar eﬀects of the adjustment on the magnitude of impact in the ﬁrst
job, last job and current job. The estimated impact increases when we restrict the sample
25
We convert the estimated treatment eﬀects on log earnings from Table 3 into percent change with the
following transformation exp(β ) − 1, where β denotes the treatment eﬀect estimate.
26
The kernels of Figure 1 suggest that the results are not driven by outliers. We also examine the inﬂuence
of outliers by excluding values that are more extreme than the 5th and the 95th percentile (see Appendix
Table 22). Trimming leads to slightly smaller but still statistically signiﬁcant point estimates.
18
to full time and non-temporary workers.
As a robustness check, we re-estimate the models excluding the migrants (Table 3,
Panel III). Completely excluding all of the migrants is a very conservative approach because
it rules out migration as a mechanism for obtaining higher earnings and it also signiﬁcantly
reduces sample size. Excluding migrants provides a lower-bound estimate of the impact on
earnings. When we exclude migrants, we ﬁnd that estimated eﬀect sizes fall slightly, but still
remain highly signiﬁcant especially for full-time and non-temporary workers. The estimates
excluding migrants show 38% higher earnings for the treatment group for all jobs and 45%
for full time non-temporary jobs.
6.5 Employment and Labor Force Participation
Censoring is a concern with our estimates of the impact of treatment on current earnings
for all workers because we only observe the earnings of those employed who are in the labor
force. However, treatment does not appear to aﬀect employment or labor force participation
(Table 4), implying negligible bias from censoring in our results for current earnings.
6.6 Catch-up in Earnings
The results reported thus far indicate a substantial and signiﬁcant impact of treatment on
earnings. One important question, however, is whether the stimulation intervention was
strong enough for the earnings of the treatment group to catch-up to a population that
was not stunted in childhood. This question is at the heart of the remediation issue: can
early childhood intervention remediate initial disadvantage? We answer this question by
comparing the earnings of the stunted population with the earnings of the non-stunted
comparison group.
Overall, we ﬁnd that the treatment group caught up with the comparison group on all
measures of earnings, while the control group remained behind. Table 5 compares the non-
stunted comparison group with the stunted treatment group using IPW weights to correct
19
for the higher attrition among the non-stunted group. Table 5 presents results in the same
fashion as Table 3: Panel I examines the observed sample, Panel II uses imputed values for
missing data, and Panel III focus on data for non-migrants only. The p-values represent
one-sided tests for the hypothesis that the diﬀerences between the two groups are null versus
the alternative hypothesis of the non-stunted group has higher earnings. The conditional
diﬀerences in log-earnings between the non-stunted group and the stunted treatment group
are never statistically signiﬁcant and average around zero.
The panels of Figure 2 displays kernel density estimates of the densities of earnings for
the non-stunted comparison group and the stunted treatment group. The ﬁgures generally
show little separation between the earnings densities for the two groups, as conﬁrmed the
Kolmogorov-Smirnov tests. These results are consistent with the ﬁndings reported in Table 5.
In contrast, the stunted control group remains behind. Table 6 presents the mean diﬀer-
ences between the non-stunted comparison group and the stunted control group. The table
shows that the non-stunted comparison group consistently earns more than the stunted
control group, with most diﬀerences in mean earnings being statistically signiﬁcant.
7 Pathways to Earnings
7.1 Parental Investment
The stimulation intervention was designed to improve the maternal-child interaction, i.e.
the quality of parenting. We begin by examining the extent to which treatment resulted in
more maternal investment in stimulation at home during the experimental period when the
children are very young. Although we cannot attribute a causal link between the increase in
the measures of the quality of the home environment and the outcomes, these results suggest
a possible mechanism.
We analyze the eﬀects of treatment on a modiﬁed version of the Caldwell index of stimula-
tion of the home, the infant toddler HOME inventory (Caldwell, 1967; Caldwell and Bradley,
20
1984). The HOME score captures the quality of parental interaction and investment in the
children through the observation of the home environment.27 It includes six main domains:
emotional and verbal responsivity of the caregiver, avoidance of restriction and punishment,
organization of the environment, provision of play materials, parental involvement with the
child and opportunities for variety in daily stimulation.
The results show that the intervention did indeed increase the HOME score (see Table
7). At baseline there was no diﬀerence between treatment and control groups, but the
HOME score of the stunted group was signiﬁcantly lower than the HOME score of the non-
stunted group. At the end of trial, however, the HOME score of the treated is signiﬁcantly
higher than that of the control group, and the HOME score of the treatment caught up
to the HOME score of the non-stunted group.28 In results available from the authors on
request, we ﬁnd that stimulation in the home was not diﬀerent between the treated and
control groups at 7 and 11 years old. These ﬁndings are consistent with the hypothesis that
treatment improved the quality of maternal-child interaction in early life and this diﬀerence
dissipated later. Hence, any impacts on schooling and earnings later in life are likely due to
these investments made early in life.
7.2 Education
A key determinant of labor market success is education. Schooling in Jamaica comprises
primary school, grades 1-6; junior secondary, grades 7-9; and senior secondary grades 10-13.
At the end of grade 11, students take exams called the CXC exams which are similar to the
British O Levels. Most students leave school after grade 11. At the end of grade 13 students
take advanced level exams (CAPE) for college entry.
Overall the results show that treatment is associated with substantially more education
(Table 8, Panel A). The treatment group has completed signiﬁcantly more schooling than the
27
Previous studies have found HOME to be highly correlated with cognitive, social and motor skills. For
example, see Bradley (1993), Bradley et al. (1989), Grantham-McGregor et al. (1997).
28
Moreover, there is no diﬀerence in the impact of HOME for boys versus girls.
21
control group for all indicators of educational attainment. They are three times more likely
to have had some college education than the control group. Members of the treatment group
have passed more CXC and CAPE exams than the control group (Table 8, Panel B). The
treatment group also has about 0.61 year more of schooling than the control group. However,
this is clearly a lower bound estimate as the impact of treatment on school participation
is strongly positive. Persons in the treatment group are twice as likely to be in school
and almost three times more likely to be in full time school (Table 8, Panel A). Since the
earnings of those in school will likely increase as they fully enter that labor force, we likely
underestimate the impact of treatment on earnings.
7.3 Cognitive and Psychosocial Skills
Cognitive and psychosocial skills are important determinants of labor market outcomes.29
We examine the impact of treatment on psychosocial skills at age 18, a critical age for
labor market decisions. The survey at age 18 collected multiple psychometric scales of
cognitive and psychosocial skills. The cognitive scales included the WRAT math, WRAT
reading comprehension, Picture Peabody Verbal Test, Verbal Analogies, Raven matrices,
and WAIS full-scale IQ tests.30 For psychosocial skills, available scales include the Conners’
scale for oppositional behavior, inattention, and hyperactivity, as well as a self-esteem scale,
an anxiety scale and a depression scale
We use factor analysis to aggregate the scales above, extracting three factors: one for
cognitive skills, and two for psychosocial skills, which represent externalizing behavior and
internalizing behavior. All three types of skills have proven to have independent eﬀects on
earnings (Almlund et al., 2011a) and have been used as outcomes for the evaluation of early
childhood policies (Heckman, Pinto, and Savelyev, 2013). Externalizing behavior was mea-
sured using the Conners’ scales described above, while Internalizing behavior was measured
29
See Heckman, Pinto, and Savelyev, 2013; Heckman, Stixrud, and Urz´ ua, 2006; Almlund, Duckworth,
Heckman, and Kautz, 2011b, and Heckman and Kautz, 2012.
30
See Walker et al. (2005) for results of impact on the individual scales using large sample inference
methods
22
with self-esteem, anxiety and depression scales. Exploratory factor analysis conﬁrmed that
there were three factors. We then used conﬁrmatory factor analysis to recover the factors
using the Bartlett’s method to extract the factor scores.31 Measurements were dedicated to
a factor, so that each measure only had positive loadings on one factor. However, factors
are allowed to be correlated. The factors are measured in standard deviations and all are
recoded so that a higher level of the transformed measure is more desirable.
The results reported in Table 8, Panel C, present the impact of stimulation treatment on
aggregated factors of cognitive and psychosocial skills. The coeﬃcient on the factors can be
interpreted as the impact of treatment measured on standard deviations of the scale. Overall
we ﬁnd strong and statistically signiﬁcant eﬀects of treatment on all measures of cognition,
as well as on internalizing behavior. Plots of the densities of these factors classiﬁed by
treatment and control status (Figure 3) show a substantial impact of treatment and suggest
that the treatment was particularly eﬀective for those in the upper part of the distribution.
Overall the results are consistent with strong positive eﬀects of treatment on earnings and
are in line with the recent literature on the importance of both cognitive and psychosocial
skills for earnings (see Borghans et al., 2008 and Almlund et al., 2011a).
7.4 Catch-up in Education and Skills
We return to the question of whether the impact of stimulation helped the stunted group
catch-up with the non-stunted comparison group. The catch-up analysis shows that the
treated group did indeed catch-up with the non-stunted group for educational outcomes
(Panel I of Table 9). The results conﬁrm the ﬁndings from Walker et al. (2005) in that
the treatment group did not completely catch-up to the non-stunted group in cognitive
skills. However, the treatment group completely caught up in terms of education and both
31
See Appendix B for details about our factor analysis. Exploratory factor analysis conﬁrmed that a three
factor model was the most suitable to explain the measurements with each measure loading clearly on one
and only one factor (results available upon request). Kaiser and Scree tests also selected a three factor model.
This ﬁnding was conﬁrmed by conﬁrmatory factor analysis ﬁt statistics (Chi square test, RMSEA, CFI, AIC
and BIC) with better performance for a three-factor model compared to a simpler two-factor model.
23
internalizing and externalizing behavior. Figure 4 shows that the treatment and comparison
group densities overlap for the internalizing and externalizing factor, but not for the cognitive
factor. The comparison group density is shifted to the right of the treatment group density
for the cognitive factor. The results reported in Panel II of Table 9 show that the control
group did not catch-up with the comparison group.
8 Gender Diﬀerences
The literature on early childhood interventions shows that ECD treatment eﬀects can diﬀer
substantially by gender (e.g. Heckman et al., 2010a, 2013). In this section we investigate
the gender-speciﬁc eﬀects of the Jamaican intervention. We interpret the results of these
analyses with caution as the study was not originally designed nor powered for this purpose.
Table 10 reports the impact of treatment on log earnings separately for males and fe-
males.32 We ﬁnd statistically signiﬁcant eﬀects of stimulation on earnings for both males
and females. While the point estimates are in general somewhat larger for females than for
males, tests for equality cannot reject the hypothesis that the impact on earnings is equal
for males and females.33
Not only do we ﬁnd signiﬁcant eﬀects on earnings for both stunted males and females,
but we also ﬁnd that both males and females in the stunted treatment group catch-up to the
earnings of the non–stunted comparison group (Table 11, Panel I). The point estimates of the
diﬀerences are generally close to zero and are not signiﬁcantly diﬀerent from zero. However,
the earnings of the stunted control group are also not signiﬁcantly diﬀerent from those of the
non–stunted comparison group. In this case, the point estimates are positive, indicating that
the stunted control group earns consistently less than the non–stunted comparison group,
32
We only display the results for the sample with imputed earnings for lost migrants. However, the results
do not diﬀer substantively for either the observed sample or the non-migrants only sample. These results
are available upon request.
33
See Table 20 in Appendix A. This table reports estimates for female treatment eﬀects and the diﬀerence
of treatment eﬀects for males versus females. The table also presents inference on gender diﬀerence of
treatment eﬀects.
24
but these diﬀerences are only statistically signiﬁcant for earnings in full time last job and
current job for females, and for earnings in ﬁrst jobs for males (Table 11, Panel II).
We also compare gender diﬀerences in the determinants of earnings for treated and control
groups (Table 12). All of the point estimates are generally positive. However, a couple of
important diﬀerences emerge. For males (Table 12, Panel II) there are statistically signiﬁcant
treatment eﬀects on cognitive ability and on the probability of being expelled from school,
while for females (Table 12, Panel I) there are statistically signiﬁcant treatment eﬀects on
passing exams and on reduction on externalizing and internalizing behavior. However, the
hypothesis of equality of the treatment eﬀects for males and females cannot be rejected for
these outcomes in particular and in general for 10 out of 12 of these outcomes.34
The female stunted treatment group caught up with the non–stunted comparison in all of
the educational and skills outcomes. However, males did not catch-up completely in exams
(Table 13, Panel I). While the female stunted control group did not catch-up to the female
comparison group, the male control group appears to have caught up to the male comparison
group in terms of educational outcomes and psychosocial skills (Table 13, Panel II).
9 Conclusions
This is the ﬁrst study to experimentally evaluate the long-term impact of early childhood
stimulation on economic outcomes in a low income country. Twenty years after the in-
tervention was conducted, we ﬁnd that the average earnings of the stimulation group are
approximately 42% higher than those of the control group. These ﬁndings show that simple
psychosocial stimulation in very early childhood in disadvantaged settings can have a sub-
stantial eﬀect on labor market outcomes. The magnitude of the estimated treatment eﬀects
can be put into perspective when the outcomes for the treated are compared to those for
a non-stunted comparison group. The stunted children who received the stimulation inter-
vention caught up to the earnings of a non-stunted comparison group. These results imply
34
See Table 20.
25
that stimulation interventions very early in life can compensate for developmental delays
and thereby reduce inequality later in life. The estimated impacts found for Jamaica are
substantially larger than the impacts reported for the US–based interventions. Early Child-
hood Development may be an especially eﬀective strategy for improving long-term outcomes
of disadvantaged children in developing countries.
26
References
Almlund, M., A. Duckworth, J. J. Heckman, and T. Kautz (2011a). Personality psychology
oßmann (Eds.), Handbook of the
and economics. In E. A. Hanushek, S. Machin, and L. W¨
Economics of Education, Volume 4, pp. 1–181. Amsterdam: Elsevier.
Almlund, M., A. Duckworth, J. J. Heckman, and T. Kautz (2011b, February). Personality
psychology and economics. IZA Discussion Paper (No. 5500). http://ftp.iza.org/
dp5500.pdf.
Almond, D. and J. Currie (2011). Human capital development before age ﬁve. In O. Ashen-
felter and D. Card (Eds.), Handbook of Labor Economics, Volume 4B, Chapter 15, pp.
1315–1486. North Holland: Elsevier.
Almond, D., L. Edlund, H. Li, and J. Zhang (2007, September). Long-term eﬀects of the
1959-1961 China famine: Mainland China and Hong Kong. Working Paper 13384, National
Bureau of Economic Research.
Anderson, M. J. and J. Robinson (2001, March). Permutation tests for linear models. The
Australian and New Zealand Journal of Statistics 43 (1), 75–88.
Aughinbaugh, A. (2001). Does Head Start yield long-term beneﬁts? Journal of Human
Resources 36 (4), 641–665.
Bleakley, H. (2007, February). Disease and development: Evidence from hookworm eradica-
tion in the American South. Quarterly Journal of Economics 122 (1), 73–117.
Borghans, L., A. L. Duckworth, J. J. Heckman, and B. ter Weel (2008, Feburary). The
economics and psychology of personality traits. IZA Discussion Paper (3333). http:
//ftp.iza.org/dp3333.pdf.
Bradley, R. H. (1993). Children’s home environments, health, behavior, and intervention
eﬀorts: a review using the HOME inventory as a marker measure. Genetic Social and
General Psychology Monographs 119 (4), 437–490.
Bradley, R. H., B. M. Caldwell, S. L. Rock, C. T. Ramey, K. E. Barnard, C. Gray, M. A.
Hammond, S. Mitchell, A. W. Gottfried, L. Siegel, and D. Johnson (1989). Home environ-
ment and cognitive development in the ﬁrst 3 years of life: A collaborative study involving
six sites and three ethnic groups in North America. Developmental Psychology 25 (2),
217–235.
Caldwell, B. M. (1967). Descriptive evaluations of child development and of developmental
settings. Pediatrics 40 (1), 46–54.
Caldwell, B. M. and R. H. Bradley (1984). HOME observation for measurement of the
environment. Little Rock, AR: University of Arkansas at Little Rock.
Campbell, F., G. Conti, J. Heckman, S. Moon, and R. Pinto (2012). The long-term health
eﬀects of early childhood interventions. Under review, Economic Journal.
27
Campbell, F. A., E. P. Pungello, M. Burchinal, K. Kainz, Y. Pan, B. H. Wasik, O. A.
Barbarin, J. J. Sparling, and C. T. Ramey (2012). Adult outcomes as a function of an
early childhood educational program: An Abecedarian Project follow-up. Developmental
Psychology 48 (4), 1033–1043.
Campbell, F. A., C. T. Ramey, E. Pungello, J. Sparling, and S. Miller-Johnson (2002).
Early childhood education: Young adult outcomes from the abecedarian project. Applied
Developmental Science 6 (1), 42–57.
Carneiro, P. and J. J. Heckman (2003). Human capital policy. In J. J. Heckman, A. B.
Krueger, and B. M. Friedman (Eds.), Inequality in America: What Role for Human Capital
Policies?, pp. 77–239. Cambridge, MA: MIT Press.
Cunha, F., J. J. Heckman, L. J. Lochner, and D. V. Masterov (2006). Interpreting the
evidence on life cycle skill formation. In E. A. Hanushek and F. Welch (Eds.), Handbook
of the Economics of Education, Chapter 12, pp. 697–812. Amsterdam: North-Holland.
Cunha, F., J. J. Heckman, and S. M. Schennach (2010, May). Estimating the technology of
cognitive and noncognitive skill formation. Econometrica 78 (3), 883–931.
Engle, P. L., M. M. Black, J. R. Behrman, M. Cabral de Mello, P. J. Gertler, L. Kapiriri,
R. Martorell, M. Eming Young, and The International Child Development Steering Group
(2007, January). Strategies to avoid the loss of developmental potential in more than 200
million children in the developing world. The Lancet 369 (9557), 229–242.
Engle, P. L., L. C. H. Fernald, H. Alderman, J. Behrman, C. O’Gara, A. Yousafzai,
M. Cabral de Mello, M. Hidrobo, N. Ulkuer, I. Ertem, and S. Iltus (2011, October).
Strategies for reducing inequalities and improving developmental outcomes for young chil-
dren in low-income and middle-income countries. The Lancet 378 (9799), 1339–1353.
Fernald, L., P. Kariger, M. Hidrobo, and P. Gertler (2012, October). Socio-economic gradi-
ents in child development in very young children: Evidence from india, indonesia, peru and
senegal. Proceedings of the National Academy of Sciences (Suplement 2) 109, 17273–17280.
Fernald, L. C., A. Weber, E. Galasso, and L. Ratsifandrihamanana (2011). Socioeconomic
gradients and child development in a very low income population: Evidence from Mada-
gascar. Developmental Science 14 (4), 832–847.
Freedman, D. and D. Lane (1983, October). A nonstochastic interpretation of reported
signiﬁcance levels. Journal of Business and Economic Statistics 1 (4), 292–298.
Garces, E., D. Thomas, and J. Currie (2002, September). Longer-term eﬀects of Head Start.
American Economic Review 92 (4), 999–1012.
Grantham-McGregor, S., Y. B. Cheung, S. Cueto, P. Glewwe, L. Richter, and B. Strupp
(2007). Developmental potential in the ﬁrst 5 years for children in developing countries.
The Lancet 369 (9555), 60–70.
28
Grantham-McGregor, S., W. Schoﬁeld, and C. Powell (1987). Development of severely
malnourished children who received psychosocial stimulation: Six-year follow-up. Pe-
diatrics 79 (2), 247–254.
Grantham-McGregor, S., S. Walker, S. Chang, and C. Powell (1997). Eﬀects of early child-
hood supplementation with and without stimulation on later development in stunted Ja-
maican children. American Journal of Clinical Nutrition 66 (2), 247–253.
Grantham-McGregor, S. M., C. A. Powell, S. P. Walker, and J. H. Himes (1991). Nutritional
supplementation, psychosocial stimulation, and mental development of stunted children:
The Jamaican study. The Lancet 338 (8758), 1–5.
Heckman, J. J. (2000, March). Policies to foster human capital. Research in Eco-
nomics 54 (1), 3–56.
Heckman, J. J. (2008, July). Schools, skills and synapses. Economic Inquiry 46 (3), 289–324.
Heckman, J. J. and T. Kautz (2012, August). Hard evidence on soft skills. Labour Eco-
nomics 19 (4), 451–464. Adam Smith Lecture.
Heckman, J. J., S. H. Moon, R. Pinto, P. A. Savelyev, and A. Q. Yavitz (2010a, August).
Analyzing social experiments as implemented: A reexamination of the evidence from the
HighScope Perry Preschool Program. Quantitative Economics 1 (1), 1–46.
Heckman, J. J., S. H. Moon, R. Pinto, P. A. Savelyev, and A. Q. Yavitz (2010b, Febru-
ary). The rate of return to the HighScope Perry Preschool Program. Journal of Public
Economics 94 (1-2), 114–128.
Heckman, J. J., R. Pinto, and P. A. Savelyev (2013). Understanding the mechanisms
through which an inﬂuential early childhood program boosted adult outcomes. Unpub-
lished manuscript, University of Chicago, Department of Economics (ﬁrst draft, 2008).
Forthcoming, American Economic Review.
ua (2006, July). The eﬀects of cognitive and noncog-
Heckman, J. J., J. Stixrud, and S. Urz´
nitive abilities on labor market outcomes and social behavior. Journal of Labor Eco-
nomics 24 (3), 411–482.
Hoddinott, J., J. A. Maluccio, J. R. Behrman, R. Flores, and R. Martorell (2008). Eﬀect of
a nutrition intervention during early childhood on economic productivity in Guatemalan
adults. The Lancet 371 (9610), 411–416.
Huttenlocher, P. (1979). Synaptic density in human frontal cortexdevelopmental changes
and eﬀects of aging. Brain research 163 (2).
Huttenlocher, P. R. (2002). Neural plasticity: The eﬀects of environment on the development
of the cerebral cortex. Cambridge, MA: Harvard University Press.
Knudsen, E. I., J. J. Heckman, J. Cameron, and J. P. Shonkoﬀ (2006, July). Economic,
neurobiological, and behavioral perspectives on building America’s future workforce. Pro-
ceedings of the National Academy of Sciences 103 (27), 10155–10162.
29
Maccini, S. L. and D. Yang (2009). Under the weather: Health, schooling, and economic
consequences of early-life rainfall. American Economic Review 99 (3), 1006–1026.
Maluccio, J. A., J. Hoddinott, J. R. Behrman, R. Martorell, A. R. Quisumbing, and A. D.
Stein (2009). The impact of improving nutrition during early childhood on education
among Guatemalan adults. Economic Journal 119 (537), 734–763.
Palmer, F. H. (1971). Concept training curriculum for children ages two to ﬁve. Stony Brook,
NY: State University of New York at Stony Brook.
Paxson, C. and N. Schady (2007, Summer). Cognitive development among young children in
Ecuador: The roles of wealth, health, and parenting. Journal of Human Resources 42 (1),
49–84.
Powell, C. and S. Grantham-McGregor (1989). Home visiting of varying frequency and child
development. Pediatrics 84 (1), 157–164.
Reynolds, A. J., S.-R. Ou, and J. W. Topitzes (2004, September–October). Paths of eﬀects of
early childhood interventions on educational attainment and deliquency: A conﬁrmatory
analysis of the Chicago Parent-Child Centers. Child Development 75 (5), 1299–1328.
Reynolds, A. J., J. A. Temple, S.-R. Ou, I. A. Arteaga, and B. A. B. White (2011, July).
School-based early childhood education and age-28 well-being: Eﬀects by timing, dosage,
and subgroups. Science 333 (6040), 360–364.
Reynolds, A. J., J. A. Temple, S.-R. Ou, D. L. Robertson, J. P. Mersky, J. W. Topitzes,
and M. D. Niles (2007, August). Eﬀects of a school-based, early childhood intervention
on adult health and well-being: A 19-year follow-up of low-income families. Archives of
Pediatrics and Adolescent Medicine 161 (8), 730–739.
Robins, J. M., A. Rotnitzky, and L. P. Zhao (1994). Estimation of regression coeﬃcients
when some regressors are not always observed. Journal of the American Statistical Asso-
ciation 89 (427), 846–866.
Romano, J. P. and M. Wolf (2005). Stepwise multiple testing as formalized data snooping.
Econometrica 73 (4), 1237–1282.
Thompson, R. A. and C. A. Nelson (2001, January). Developmental science and the media:
Early brain development. American Psychologist 56 (1), 5–15.
van den Berg, G. J., M. Lindeboom, and F. Portrait (2006, March). Economic conditions
early in life and individual mortality. American Economic Review 96 (1), 290–302.
Walker, S., S. Grantham-McGregor, C. Powell, J. Himes, and D. Simeon (1992). Morbidity
and the growth of stunted and nonstunted children, and the eﬀect of supplementation.
American Journal of Clinical Nutrition 56 (3), 504–510.
Walker, S., C. Powell, and S. Grantham-McGregor (1990). Dietary intakes and activity
levels of stunted and non-stunted children in Kingston, Jamaica. Part 1. Dietary intakes.
European Journal of Clinical Nutrition 44 (7), 527–534.
30
Walker, S. P., S. M. Chang, C. A. Powell, and S. M. Grantham-McGregor (2005). Eﬀects
of early childhood psychosocial stimulation and nutritional supplementation on cogni-
tion and education in growth-stunted Jamaican children: prospective cohort study. The
Lancet 366 (9499), 1804–1807.
andez, and S. Grantham-McGregor (2011). Early
Walker, S. P., S. M. Chang, M. Vera-Hern´
childhood stimulation beneﬁts adult competence and reduces violent behavior. Pedi-
atrics 127 (5), 849–857.
Walker, S. P., S. M. Grantham-McGregor, J. H. Himes, C. A. Powell, and S. M. Chang
(1996). Early childhood supplementation does not beneﬁt the long-term growth of stunted
children in Jamaica. Journal of Nutrition 126 (12), 3017–3024.
Walker, S. P., S. M. Grantham-McGregor, C. A. Powell, and S. M. Chang (2000, July).
Eﬀects of growth restriction in early childhood on growth, IQ, and cognition at age 11
to 12 years and the beneﬁts of nutritional supplementation and psychosocial stimulation.
Journal of Pediatrics 137 (1), 36–41.
Walker, S. P., C. A. Powell, S. M. Grantham-McGregor, J. H. Himes, and S. M. Chang
(1991). Nutritional supplementation, psychosocial stimulation, and mental development
of stunted children: the Jamaican study. American Journal of Clinical Nutrition 54 (4),
642–648.
Walker, S. P., T. D. Wachs, J. M. Gardner, B. Lozoﬀ, G. A. Wasserman, E. Pollitt,
J. A. Carter, and The International Child Development Steering Group (2007, Jan-
uary). Child development: Risk factors for adverse outcomes in developing countries.
The Lancet 369 (9556), 145–157.
31
Tables and Figures
32
Table 1: Baseline (1986) Descriptive Statistics for Stunted Experimental Sample
Sample Control Treatment Diﬀerence Single
Size Mean Mean in Means p-value
A. Parental Characteristics
Mother present 105 0.96 0.94 -0.02 1.00
Mother/guardian’s age (years) 105 24.4 25.8 1.41 0.28
Mother /guardian employed 105 0.15 0.32 0.17 0.05
Mother/guardian school >= 9th grade 105 0.21 0.05 -0.16 0.02
Mother/guardian’s PPVT 105 84.9 86.8 1.91 0.64
Mothers/guardian’s height (cm) 103 159.3 159.4 0.06 0.96
Father present 105 0.46 0.45 -0.01 1.00
HOME score on enrolment 105 17.1 16.02 -1.08 0.22
Housing index 105 7.56 7.17 -0.39 0.20
B. Child Characteristics
Age (years) 105 1.55 1.55 0.00 1.00
Male 105 0.56 0.53 -0.03 0.85
Child’s birth order 105 2.98 3.38 0.40 0.38
Birth Weight < 2500 grams 104 0.19 0.25 0.06 0.58
Head Circumference (cm) 105 46.2 45.9 -0.27 0.37
Daily Calories Consumed 105 1006 912.9 -93.11 0.31
Daily Protein Consumed (grams) 105 27.0 26.96 -0.04 1.00
Griﬃth Developmental Quotient 105 97.1 99.3 2.21 0.21
Height for Age z -Score 105 -2.87 -3.00 -0.13 0.28
Weight for Height z -Score 105 -0.87 -1.18 -0.31 0.02
Notes: This table reports and compares arithmetic means of variables of interest for the stunted treatment
and control groups at baseline (1986) for the sample found in 2008. The p-values reported in the last
column are for two-sided block permutation tests of the null hypotheses that the diﬀerence in means between
treatment and control groups are zero. The permutation blocks are child’s age and sex. Variable deﬁnitions
include: PPVT denotes the raw score from Peabody Picture Vocabulary Test (Dunn and Dunn, 1981),
HOME denotes the raw score from the HOME environment test (Caldwell, 1967), and Griﬃth Development
Quotient reports the raw score for this test (Griﬃths, 1954; Griﬃths 1970).
33
Table 2: Impact of Stimulation Treatment on Migration and Catch-up in Migration.
A. Treatment Eﬀect Sample Control Treatment Single
Size Group Eﬀect p-value
Mean
Full baseline sample 127 0.12 0.10 0.08
Sample found at follow-up 105 0.06 0.15 0.02
B. Treatment Group Catch-Up Sample Treatment Diﬀerence Single
Size Group Comparison vs. Treated p-value
Mean
34
Full baseline sample 141 0.19 0.01 0.47
Sample found at follow-up 115 0.16 -0.06 0.83
Notes: Panel A reports the estimated impact of treatment on the probability of migration out of Jamaica to another country. The ﬁrst row presents
the results for the full sample enrolled in the experiment at baseline in 1986. The second row reports the results for the sample found at follow-up in
2008. The treatment eﬀects are estimated by linear regression and are interpreted as the diﬀerences in the migration rates of the stunted treatment and
stunted control groups conditional on baseline values of child age, gender, weight-for-height z -score, maternal employment, and maternal education.
Our p-values are for one-sided block permutation tests of the null hypothesis of no treatment eﬀect (Single p-value). Permutation blocks are based
on the conditioning variables used in the treatment eﬀect regressions. Panel B reports the diﬀerence in the probability of migration of the weighted
non-stunted comparison group and the stunted treatment group. Again, the ﬁrst row reports the results for the full sample and the second row for the
sample found at follow-up in 2008. The comparison group observations are weighted using Inverse Probability Weights (IPW) to correct for attrition.
Our p-values are for one-sided block permutation tests of the null hypothesis of complete catch-up (Single p-value). Permutation blocks are based on
gender.
Table 3: Impact of Stimulation Treatment on Log Earnings
I. Observed Sample II. Imputed Missing Values III. Non-Migrants Only
Treatment Single Stepdown Treatment Single Stepdown Treatment Single Stepdown
Eﬀect p-value p-value Eﬀect p-value p-value Eﬀect p-value p-value
A. First Job
All 0.27 0.11 0.11 0.21 0.16 0.16 0.25 0.05 0.05
Full Time 0.35 0.04 0.06 0.27 0.07 0.10 0.28 0.04 0.06
Non-Temporary 0.53 0.01 0.03 0.45 0.02 0.04 0.46 0.01 0.03
B. Last Job
All 0.27 0.06 0.06 0.23 0.09 0.09 0.15 0.19 0.19
Full Time 0.40 0.00 0.01 0.36 0.01 0.01 0.29 0.02 0.03
Non-Temporary 0.50 0.00 0.00 0.45 0.00 0.00 0.40 0.01 0.02
C. Current Job
35
All 0.27 0.09 0.09 0.21 0.13 0.13 0.12 0.26 0.26
Full Time 0.43 0.01 0.02 0.37 0.01 0.03 0.35 0.02 0.04
Non-Temporary 0.44 0.01 0.02 0.34 0.02 0.04 0.40 0.01 0.03
D. Average Earnings
All 0.40 0.01 0.01 0.35 0.00 0.01 0.32 0.01 0.02
Full Time 0.34 0.01 0.01 0.28 0.01 0.01 0.22 0.03 0.03
Non-Temporary 0.47 0.00 0.01 0.40 0.01 0.01 0.37 0.01 0.02
Notes: This table reports the estimated impact of treatment on log monthly earnings for 3 samples as indicated by the following column panels: (I)
the observed sample, (II) the observed sample with imputations for the earnings of missing migrants, and (III) the observed sample without migrants.
In each sample, treatment eﬀects are reported for the following jobs as indicated by the row blocks: (A) First Job, (B) Last Job, (C) Current Job, and
(D) Average Lifetime Earning over all jobs. Within each type of job, results are reported for the following types of workers as indicated by the rows:
All workers, Full Time Workers, and Full Time Non-Temporary workers. The treatment eﬀects are estimated by linear regression and are interpreted
as the diﬀerences in the means of log earnings between the stunted treatment and stunted control groups conditional on baseline values of child age,
gender, weight-for-height z -score, maternal employment, and maternal education. Our p-values are for one-sided block permutation tests of the null
hypothesis of no treatment eﬀect (Single p-value) and multiple hypotheses (Stepdown p-value) of no treatment. Permutation blocks are based on the
conditioning variables used in the treatment eﬀect regressions.
Table 4: Impact of Stimulation Treatment on Employment and Labor Force Participation
Sample Control Treatment Single Stepdown
Size Mean Eﬀect p-value p-value
Current Employment Status
Employed 105 0.65 0.12 0.08 0.16
Employed Full Time 105 0.58 0.03 0.31 0.31
Employed in Non-Temporary Job 105 0.34 0.07 0.18 0.26
Looking For Work 99 0.27 -0.09 0.17 0.34
Notes: This table reports the estimated impact of treatment on measures of employment status including
currently employed, employed in a full time job, employed in a non-temporary job and looking for work.
The treatment eﬀects are estimated by linear regression and are interpreted as the diﬀerences in the means
of employment outcomes between the stunted treatment and stunted control groups conditional on baseline
values of child age, gender, weight-for-height z -score, maternal employment, and maternal education. Our
p-values are for one-sided block permutation tests of the null hypothesis of no treatment eﬀect (Single p-
value) and multiple hypotheses (Stepdown p-value) of no treatment. Permutation blocks are based on the
conditioning variables used in the treatment eﬀect regressions.
36
Table 5: Treatment Group Catch-up in Log Earnings
I. Observed Sample II. Imputed Missing Values III. Non-Migrants Only
Diﬀerence Single Stepdown Diﬀerence Single Stepdown Diﬀerence Single Stepdown
Comp. vs. Treated p-value p-value Comp. vs. Treated p-value p-value Comp. vs. Treated p-value p-value
A. First Job
All 0.11 0.16 0.24 0.15 0.09 0.14 0.11 0.17 0.26
Full Time 0.16 0.11 0.19 0.19 0.07 0.12 0.18 0.07 0.14
Non-Temporary -0.12 0.78 0.78 -0.11 0.79 0.79 -0.02 0.56 0.56
B. Last Job
All 0.12 0.26 0.26 0.14 0.21 0.34 0.23 0.12 0.21
Full Time -0.05 0.62 0.70 0.00 0.49 0.57 -0.01 0.50 0.59
Non-Temporary -0.23 0.93 0.93 -0.17 0.88 0.88 -0.14 0.82 0.82
C. Current Job
All -0.04 0.59 0.74 0.05 0.40 0.54 0.08 0.36 0.51
37
Full Time -0.25 0.88 0.92 -0.15 0.78 0.84 -0.20 0.82 0.88
Non-Temporary -0.35 0.94 0.94 -0.26 0.91 0.91 -0.32 0.92 0.92
D. Average
Earnings
All -0.04 0.64 0.74 0.00 0.52 0.63 0.03 0.43 0.55
Full Time -0.07 0.69 0.74 -0.03 0.60 0.65 0.00 0.49 0.55
Non-Temporary -0.21 0.91 0.91 -0.16 0.87 0.87 -0.11 0.77 0.77
Notes: This table compares the earnings of the stunted treatment group to the earnings of the non-stunted comparison group in terms of log monthly
earnings for 3 samples as indicated by the following column panels: (I) the observed sample, (II) the observed sample with imputations for the
earnings of missing migrants, and (III) the observed sample without migrants. In each sample, catch-up is reported for the following jobs as indicated
by the row blocks: (A) First Job, (B) Last Job, (C) Current Job, and (D) Average Lifetime Earning over all jobs. Within each type of job category,
results are reported for the following types of workers as indicated by the rows: All workers, Full Time Workers, and Full Time Non-Temporary
workers. Catch-up is estimated as the diﬀerence in the means of log earnings between the weighted non-stunted comparison group and the stunted
treatment group. The comparison group observations are weighted using Inverse Probability Weights (IPW) to correct for attrition. Our p-values
are for one-sided block permutation tests of the null hypothesis of complete catch-up (Single p-value) and multiple hypotheses (Stepdown p-value) of
complete catch-up. Permutation blocks are based on gender.
Table 6: Control Group Catch-up in Log Earnings
I. Observed Sample II. Imputed Missing Values III. Non-Migrants Only
Diﬀerence Single Stepdown Diﬀerence Single Stepdown Diﬀerence Single Stepdown
Comp. vs. Control p-value p-value Comp. vs. Control p-value p-value Comp. vs. Control p-value p-value
A. First Job
All 0.23 0.04 0.04 0.24 0.02 0.04 0.25 0.03 0.03
Full Time 0.31 0.01 0.02 0.31 0.01 0.01 0.32 0.01 0.02
Non-Temporary 0.26 0.04 0.06 0.23 0.05 0.05 0.28 0.02 0.04
Last Job
All 0.29 0.03 0.07 0.28 0.03 0.05 0.26 0.05 0.10
Full Time 0.26 0.03 0.05 0.28 0.02 0.04 0.22 0.06 0.09
Non-Temporary 0.16 0.19 0.19 0.18 0.14 0.14 0.12 0.25 0.25
B. Current Job
All 0.18 0.18 0.30 0.21 0.12 0.21 0.12 0.27 0.42
38
Full Time 0.14 0.21 0.28 0.17 0.16 0.22 0.06 0.36 0.47
Non-Temporary 0.02 0.50 0.50 0.03 0.47 0.47 -0.07 0.65 0.65
D. Average
Earnings
All 0.25 0.03 0.05 0.27 0.02 0.03 0.23 0.04 0.07
Full Time 0.17 0.08 0.11 0.18 0.08 0.09 0.15 0.11 0.14
Non-Temporary 0.16 0.16 0.16 0.17 0.13 0.13 0.14 0.18 0.18
Notes: This table compares the earnings of the stunted control group catch up to the earnings of the non-stunted comparison group in terms of log
monthly earnings for 3 samples as indicated by the following column panels: (I) the observed sample, (II) the observed sample with imputations
for the earnings of missing migrants, and (III) the observed sample without migrants. In each sample, catch-up is reported for the following jobs
as indicated by the row blocks: (A) First Job, (B) Last Job, (C) Current Job, and (D) Average Lifetime Earning over all jobs. Within each type
of job category, results are reported for the following types of workers as indicated by the rows: All workers, Full Time Workers, and Full Time
Non-Temporary workers. Catch-up is estimated as the diﬀerence in the means of log earnings between the weighted non-stunted comparison group
and the stunted treatment group. The comparison group observations are weighted using Inverse Probability Weights (IPW) to correct for attrition.
Our p-values are for one-sided block permutation tests of the null hypothesis of complete catch-up (Single p-value) and multiple hypotheses (Stepdown
p-value) of complete catch-up. Permutation blocks are based on gender.
Table 7: Impact of Treatment on HOME Scores: Treatment vs. Control (A) and Comparison Group vs. Treatment (B)
A. Treatment Eﬀect Sample Control Treatment Single
Size Group Eﬀect p-value
Mean
HOME at enrollment 127 16.64 -0.51 0.51
HOME at end of trial 127 15.98 2.53 0.01
B. Treatment Group Catch-Up Sample Treatment Diﬀerence Single
Size Group Comparison vs. Treated p-value
Mean
39
HOME at enrollment 94 16.14 1.28 0.13
HOME at end of trial 94 18.53 0.91 0.39
Notes: Panel A reports the estimated impact of treatment on HOME Scores compared to controls. The ﬁrst row reports the results at baseline
(1986) before the intervention began and the second row reports the results at end of the two-year intervention period (1988). The treatment eﬀects
are estimated by linear regression and are interpreted as the diﬀerences in the means of the HOME Scores of the stunted treatment and stunted
control groups conditional on baseline values of child age, gender, weight-for-height z -score, maternal employment, and maternal education. Reported
p-values are for one-sided block permutation tests of the null hypothesis of no treatment eﬀect (Single p-value). Permutation blocks are based on
the conditioning variables used in the treatment eﬀect regressions. Panel B reports the diﬀerence in means of the HOME Scores of the weighted
non-stunted comparison group compared to the stunted treatment group. Again, the ﬁrst row reports the results at baseline and the second row at
the end of the intervention period. The comparison group observations are weighted using Inverse Probability Weights (IPW) to correct for attrition.
Our p-values are for one-sided block permutation tests of the null hypothesis of complete catch-up (Single p-value). Permutation blocks are based on
gender.
Table 8: Impact of Treatment on Education and Skills
Sample Control Treatment Single Stepdown
Size Mean Eﬀect p-value p-value
A. Schooling
School years completed 105 10.96 0.61 0.07 0.16
Any vocational training 105 0.56 0.12 0.16 0.16
Any college 104 0.04 0.11 0.07 0.14
In school 97 0.15 0.17 0.01 0.04
In school full time 97 0.07 0.18 0.01 0.01
B. Exams
Passed at least one CXC exam 94 0.22 0.15 0.09 0.15
Passed 4 or more CXC exams 94 0.1 0.16 0.12 0.12
Passed at least one CAPE 94 0.00 0.09 0.03 0.08
C. Skills
Cognitive factor 102 -0.46 0.59 0.00 0.01
Externalizing Behavior factor 102 -0.23 0.22 0.17 0.30
Internalizing Behavior factor 102 -0.32 0.39 0.02 0.05
Ever expelled from school 105 0.17 -0.12 0.02 0.02
Notes: This table reports the estimated impact of treatment on educational and skill outcomes. Treatment
eﬀects are reported for the following sets of outcomes indicated by the row blocks: (A) Schooling, (B) Exam,
(C) Skills. The treatment eﬀects are estimated by linear regression and are interpreted as the diﬀerences in
the means of the outcomes between the stunted treatment and stunted control groups conditional on baseline
values of child age, gender, weight-for-height z -score, maternal employment, and maternal education. Our
p-values are for one-sided block permutation tests of the null hypothesis of no treatment eﬀect (Single p-
value) and multiple hypotheses (Stepdown p-value) of no treatment. Permutation blocks are based on the
conditioning variables used in the treatment eﬀect regressions.
40
Table 9: Treatment Group and Control Group Catch-up in Education and Skills
I. Non-Stunted Comparison Group vs. Treatment II. Non-Stunted Comparison Group vs. Control
Sample Treatment Diﬀerence Single Stepdown Sample Control Diﬀerence Single Stepdown
Size Mean in Means p-value p-value Size Mean in Means p-value p-value
A. Schooling
Total years of education 115 11.50 -0.12 0.60 0.84 108 11.08 0.47 0.05 0.16
Any vocational training 115 0.66 -0.09 0.81 0.93 116 0.52 0.03 0.37 0.37
Any college 110 0.13 0.01 0.38 0.73 113 0.08 0.11 0.04 0.14
In school 113 0.25 -0.03 0.58 0.86 113 0.20 0.10 0.08 0.19
In school full time 113 0.18 -0.08 0.84 0.84 113 0.09 0.06 0.14 0.25
B. Exams
Passed at least one CXC exam 106 0.30 0.18 0.02 0.04 110 0.27 0.29 0.00 0.00
41
Passed 4 or more CXC exams 106 0.21 0.13 0.06 0.10 110 0.12 0.25 0.00 0.00
Passed at least one CAPE 106 0.08 0.01 0.35 0.35 110 0.04 0.10 0.01 0.01
C. Skills
Cognitive factor 112 -0.07 0.38 0.01 0.04 118 -0.32 0.81 0.00 0.00
Externalizing Behavior factor 112 0.01 0.06 0.27 0.43 118 -0.18 0.37 0.02 0.04
Internalizing Behavior factor 112 0.07 0.06 0.38 0.41 118 -0.41 0.48 0.00 0.01
Ever expelled from school 115 -0.02 -0.09 0.98 0.98 116 -0.10 0.06 0.15 0.15
Notes: This table reports the results of the analyses of treatment and control groups catch-up to the comparison group in educational and skill
outcomes. Column panel I reports the results of the analyses of stunted treatment group catch-up to the non-stunted comparison group while Column
panel II reports the control group catch-up. Catch-up is reported for the following sets of outcomes indicated by the row blocks: (A) Schooling, (B)
Exam, (C) Skills. Catch-up is estimated as the diﬀerence in the means of the outcomes between the weighted non-stunted comparison group and the
designated stunted group. The comparison group observations are weighted using Inverse Probability Weights (IPW) to correct for attrition. Our
p-values are for one-sided block permutation tests of the null hypothesis of complete catch-up (Single p-value) and multiple hypotheses (Stepdown
p-value) of complete catch-up. Permutation blocks are based on gender.
Table 10: Impact of Stimulation Treatment on Log Earnings by Gender
I. Females II. Males
Treatment Single Stepdown Treatment Single Stepdown
Eﬀect p-value p-value Eﬀect p-value p-value
A. First Job
All 0.22 0.30 0.30 0.11 0.11 0.16
Full Time 0.34 0.09 0.12 0.11 0.13 0.13
Non-Temp 0.45 0.04 0.09 0.37 0.05 0.09
B. Last Job
All 0.18 0.18 0.18 0.29 0.15 0.15
Full Time 0.51 0.01 0.01 0.26 0.12 0.17
Non-Temp 0.53 0.01 0.02 0.40 0.03 0.07
C. Current Job
All 0.28 0.13 0.13 0.15 0.29 0.29
Full Time 0.66 0.00 0.01 0.16 0.26 0.36
Non-Temp 0.56 0.02 0.04 0.24 0.15 0.28
D. Average Earnings
All 0.30 0.19 0.19 0.44 0.01 0.02
Full Time 0.39 0.07 0.11 0.24 0.09 0.09
Non-Temp 0.49 0.04 0.09 0.37 0.05 0.06
Notes: This table reports the estimated impact of treatment on log monthly earnings using the sample
with imputations for the earnings of missing migrants. Column panel I reports the results for Females
while Column panel II reports the results for Males. In each sample, treatment eﬀects are reported for
the following jobs as indicated by the row blocks: (A) First Job, (B) Last Job, (C) Current Job, and (D)
Average Lifetime Earning over all jobs. Within each type of job, results are reported for the following types
of workers as indicated by the rows: All workers, Full Time Workers, and Full Time Non-Temporary workers.
The treatment eﬀects are estimated by separate linear regressions for each gender. The treatment eﬀects
are interpreted as the diﬀerences in the means of log earnings between the stunted treatment and stunted
control groups conditional on baseline values of child age, weight-for-height z -score, maternal employment,
and maternal education. Our p-values are for one-sided block permutation tests of the null hypothesis of no
treatment eﬀect (Single p-value) and multiple hypotheses (Stepdown p-value) of no treatment. Permutation
blocks are based on the conditioning variables used in the treatment eﬀect regressions.
42
Table 11: Treatment and Control Group Catch-up in Log Earnings by Gender
I. Non-Stunted Comparison Group vs. Treatment II. Non-Stunted Comparison Group vs. Control
Females Males Females Males
Diﬀerence Single Stepdown Diﬀerence Single Stepdown Diﬀerence Single Stepdown Diﬀerence Single Stepdown
in Means p-value p-value in Means p-value p-value in Means p-value p-value in Means p-value p-value
A. First Job
All -0.12 0.79 0.90 0.37 0.01 0.02 0.00 0.49 0.49 0.46 0.01 0.01
Full Time -0.14 0.82 0.90 0.46 0.01 0.01 0.03 0.41 0.57 0.56 0.00 0.00
Non-Temp -0.35 0.95 0.95 0.04 0.42 0.42 0.02 0.46 0.61 0.39 0.02 0.02
B. Last Job
All 0.08 0.35 0.51 0.19 0.24 0.38 0.25 0.09 0.14 0.31 0.08 0.14
Full Time 0.02 0.46 0.56 -0.01 0.52 0.60 0.40 0.02 0.03 0.18 0.19 0.19
Non-Temp -0.27 0.90 0.90 -0.10 0.68 0.68 0.13 0.28 0.28 0.22 0.15 0.21
C. Current Job
All -0.09 0.63 0.77 0.16 0.26 0.37 0.18 0.23 0.31 0.24 0.18 0.29
43
Full Time -0.30 0.90 0.90 -0.06 0.60 0.69 0.39 0.06 0.10 0.00 0.50 0.61
Non-Temp -0.30 0.88 0.92 -0.23 0.79 0.79 0.19 0.26 0.26 -0.08 0.61 0.61
D. Average
Earnings
All 0.03 0.43 0.55 -0.02 0.52 0.60 0.23 0.09 0.15 0.30 0.05 0.07
Full Time -0.09 0.69 0.75 0.02 0.46 0.54 0.15 0.18 0.22 0.21 0.13 0.13
Non-Temp -0.33 0.93 0.93 -0.06 0.61 0.60 0.06 0.39 0.39 0.26 0.11 0.12
Notes: This table reports the results of treatment and control groups catch-up in log earnings by gender. Column panel I reports the results for
stunted treatment group catch-up to the non-stunted comparison group for Females and Males. Column panel II reports the results for stunted
control group catch-up also by gender. In each sample, catch-up is reported for the following jobs as indicated by the row blocks: (A) First Job, (B)
Last Job, (C) Current Job, and (D) Average Lifetime Earning over all jobs. Within each type of job, results are reported for the following types of
workers as indicated by the rows: All workers, Full Time Workers, and Full Time Non-Temporary workers. Catch-up is estimated as the diﬀerence
in the means of log earnings between the weighted non-stunted comparison group and the stunted group for each gender. The comparison group
observations are weighted using Inverse Probability Weights (IPW) to correct for attrition. Our p-values are for one-sided block permutation tests of
the null hypothesis of complete catch-up (Single p-value) and multiple hypotheses (Stepdown p-value) of complete catch-up. Permutation blocks are
based on gender.
Table 12: Impact of Stimulation Treatment on Education and Skills by Gender
I. Females II. Males
Treatment Single Stepdown Treatment Single Step Down
Eﬀect p-value p-value Eﬀect p-value p-value
A. Schooling
Total years of education 0.77 0.03 0.08 0.41 0.38 0.53
Any training 0.07 0.34 0.51 0.15 0.10 0.23
Any college 0.14 0.04 0.15 0.08 0.40 0.40
Currently in school 0.06 0.36 0.36 0.25 0.01 0.05
Currently in school full time 0.13 0.13 0.28 0.14 0.05 0.16
B. Exams
Passed at least 1 CXC exams 0.28 0.03 0.06 -0.01 0.56 0.56
Passed 4 or more CXC exams 0.2 0.05 0.05 0.09 0.39 0.53
Passed at least 1 CAPE exams 0.15 0.02 0.06 0.04 0.30 0.56
C. Skills
Cognitive factor 0.36 0.13 0.32 0.57 0.01 0.04
Externalizing Behavior factor 0.58 0.05 0.10 -0.07 0.6 0.71
Internalizing Behavior factor 0.76 0.01 0.02 0.08 0.37 0.53
Ever expelled from school -0.09 0.09 0.09 -0.15 0.04 0.04
Notes: This table reports the estimated impact of treatment on educational and skill outcomes by gender.
Column panel I reports the results for Females while Column panel II reports the results for Males. The
treatment eﬀects are reported for the following sets of outcomes indicated by the row blocks: (A) Schooling,
(B) Exam, (C) Skills. The treatment eﬀects are estimated by separate linear regressions for each gender and
are interpreted as the diﬀerences in the means of the outcomes between the stunted treatment and stunted
control groups conditional on baseline values of child age, weight-for-height z -score, maternal employment,
and maternal education. Our p-values are for one-sided block permutation tests of the null hypothesis of no
treatment eﬀect (Single p-value) and multiple hypotheses (Stepdown p-value) of no treatment. Permutation
blocks are based on the conditioning variables used in the treatment eﬀect regressions.
44
Table 13: Treatment and Control Group Catch-up in Education and Skills by Gender
I. Non-Stunted Comparison Group vs. Treatment II. Non-Stunted Comparison Group vs. Control
Females Males Females Males
Diﬀ. Single Stepdown Diﬀ. Single Stepdown Diﬀ. Single Stepdown Diﬀ. Single Stepdown
Means p-value p-value Means p-value p-value Means p-value p-value Means p-value p-value
A. Schooling
Total years of education 0.01 0.52 0.74 -0.22 0.73 0.95 1.10 0.00 0.02 -0.14 0.64 0.64
Any training -0.13 0.83 0.83 -0.05 0.63 0.96 0.06 0.33 0.33 0.01 0.47 0.74
Any college 0.05 0.33 0.63 -0.03 0.65 0.94 0.25 0.01 0.02 -0.02 0.63 0.77
Currently in school 0.02 0.42 0.68 -0.07 0.76 0.85 0.10 0.21 0.31 0.09 0.11 0.37
Currently in school full time -0.04 0.62 0.79 -0.11 0.91 0.91 0.10 0.18 0.35 0.02 0.29 0.65
Passed ≥ 1 CXC exams 0.09 0.25 0.45 0.26 0.02 0.04 0.42 0.00 0.00 0.16 0.11 0.11
B. Exams
45
Passed ≥ 4 CXC exams 0.05 0.35 0.49 0.20 0.04 0.07 0.32 0.00 0.01 0.17 0.07 0.17
Passed ≥ 1 CAPE exams 0.03 0.40 0.40 0.00 0.44 0.44 0.17 0.03 0.03 0.03 0.10 0.18
Cognitive factor 0.34 0.11 0.24 0.42 0.04 0.13 0.88 0.00 0.00 0.75 0.00 0.01
C. Skills
Externalizing Behavior factor 0.08 0.37 0.52 0.04 0.44 0.61 0.82 0.00 0.00 -0.02 0.52 0.72
Internalizing Behavior factor 0.04 0.43 0.61 0.08 0.42 0.5 0.90 0.00 0.00 0.10 0.33 0.40
Ever expelled from school -0.03 0.43 0.43 -0.14 0.97 0.97 -0.09 0.09 0.09 -0.03 0.39 0.39
Notes: This table reports the results of treatment and control group catch-up in educational and skills outcomes by gender. Column panel I reports
the results for stunted treatment group catch-up to the non-stunted comparison group for Females and Males. Column panel II reports the results for
stunted control group catch-up also by gender. In each sample, catch-up is reported for the following outcomes as indicated by the row blocks: (A)
Schooling, (B) Exam, (C) Skills. Catch-up is estimated as the diﬀerence in the means of the outcomes between the weighted non-stunted comparison
group and the stunted group for each gender. The comparison group observations are weighted using Inverse Probability Weights (IPW) to correct for
attrition. Our p-values are for one-sided block permutation tests of the null hypothesis of complete catch-up (Single p-value) and multiple hypotheses
(Stepdown p-value) of complete catch-up. Permutation blocks are based on gender.
Figure 1: Impact of Stimulation Treatment on the Densities of Log Earnings
A. Treatment (solid line) and Control (dotted line) Densities for First Job
K-S test p-value 0.29 K-S test p-value 0.11 K-S test p-value 0.04
B. Treatment (solid line) and Control (dotted line) Densities for Current Job
K-S test p-value 0.09 K-S test p-value 0.04 K-S test p-value 0.08
C. Treatment (solid line) and Control (dotted line) Densities for Last Job
K-S test p-value 0.17 K-S test p-value 0.03 K-S test p-value 0.02
D. Treatment (solid line) and Control (dotted line) Densities for Average Earnings
K-S test p-value 0.04 K-S test p-value 0.04 K-S test p-value 0.02
Notes: These ﬁgures present the log earnings densities for the treatment and control groups. The control
density is the dotted line and the treatment density the solid one. Separate densities are presented for
earnings in the ﬁrst, last and current jobs as well as average lifetime earnings by all workers, full-time
workers, and full-time non-temporary workers. The densities are estimated using Epanechnikov kernels. The
treatment densities were estimated with an optimal bandwidth deﬁned as the width that would minimize
the mean integrated squared error under the assumption that the data are Gaussian. For comparability
purposes, the same bandwidth used was used for the corresponding control group. The p-values are for
Kolmogorov-Smirnov tests of the equality of treatment and control densities.
46
Figure 2: Catch-up of Treatment Group Earnings to Comparison Group Earnings
A. Comparison (dotted line) and Treated (solid line) Densities for First Job
K-S test p-value 0.66 K-S test p-value 0.48 K-S test p-value 0.55
B. Comparison (dotted line) and Treated (solid line) Densities for Current Job
K-S test p-value 0.10 K-S test p-value 0.04 K-S test p-value 0.06
C. Comparison (dotted line) and Treated (solid line) Densities for Last Job
K-S test p-value 0.28 K-S test p-value 0.18 K-S test p-value 0.10
D. Comparison (dotted line) and Treated (solid line) Densities for Average Earnings
K-S test p-value 0.86 K-S test p-value 0.93 K-S test p-value 0.35
Notes: These ﬁgures present the log earnings densities for the non-stunted comparison and stunted treatment
Groups. The comparison group density is the dotted line and the treatment group density the solid one.
Separate densities are presented for earnings in the ﬁrst, last and current jobs as well as average lifetime
earnings by all workers, full-time workers, and full-time non-temporary workers. The densities are estimated
using Epanechnikov kernels. The treatment densities were estimated with an optimal bandwidth deﬁned
as the width that would minimize the mean integrated squared error under the assumption that the data
are Gaussian. For comparability purposes, the same bandwidth used was used for the corresponding control
group. The p-values are for Kolmogorov-Smirnov tests of the equality of comparison and treatment densities.
47
Figure 3: Impact of Stimulation Treatment on Skills
Stunted Control (dotted line) and Stunted Treated (solid one) Densities
K-S test p-value 0.01 K-S test p-value 0.17 K-S test p-value 0.00
Notes: These ﬁgures present the cognitive, internalizing and externalizing factor densities for the treatment
and control groups. The control density is the dotted line and the treatment density the solid one. The
densities are estimated using Epanechnikov kernels. The treatment densities were estimated with an optimal
bandwidth deﬁned as the width that would minimize the mean integrated squared error under the assump-
tion that the data are Gaussian. For comparability purposes, the same bandwidth used was used for the
corresponding Control Group. The p-values are for Kolmogorov-Smirnov tests of the equality of treatment
and control densities.
Figure 4: Catch-up of Treatment Group Skills to Comparison Group Earnings
Non-stunted (dotted line) and Stunted Treated (solid line) Densities
K-S test p-value 0.07 K-S test p-value 0.43 K-S test p-value 0.72
Notes: These ﬁgures present the cognitive, internalizing and externalizing factor densities for the non-stunted
comparison and treatment groups. The density for the comparison group is the dotted line and the density for
the treated is the solid one. The densities are estimated using Epanechnikov kernels. The treatment densities
were estimated with an optimal bandwidth deﬁned as the width that would minimize the mean integrated
squared error under the assumption that the data are Gaussian. For comparability purposes, the same
bandwidth used was used for the corresponding control Group. The p-values are for Kolmogorov-Smirnov
tests of the equality of treatment and control densities.
48
A Appendix: Supplemental Tables
49
Table 14: External Validity of Non-stunted Comparison Group
A. Comparison with JSLC 1992 JSLC Comparison Diﬀerence Single
Mean Group Means p-value
Mean
Mother completed 9th grade 0.57 0.83 0.26 0.00
Father present in the house 0.61 0.73 0.12 0.09
Poor sanitation 0.16 0.13 -0.03 0.36
Piped water in the house 0.66 1.12 0.46 0.00
B. Comparison with JLFS 2008 JLFS Comparison Diﬀ. Single
Mean Group in Means p-value
Mean
Studying full time 0.09 0.06 -0.03 0.46
Highest Grade Completed 10.83 10.87 0.04 0.76
Passed at least one CXC exam 0.44 0.36 -0.08 0.22
Passed 4 or more CXC exams 0.28 0.32 0.04 0.33
Passed at least one CAPE 0.13 0.20 0.07 0.02
Notes: This table compares the non-stunted comparison group with the Jamaican Survey of Living Condi-
tions (JSLC) in Panel A and with the Jamaican Labor Force Survey 2008 (JLFS) in Panel B. The JSLC
sample is restricted to households with children between the ages of 9 and 24 months from the Kingston
Metropolitan Area. The JLFS sample includes individuals of ages 22 and 23 years old living in Kingston
Metropolitan Area. The p-values reported in the last column are for two-sided permutation tests of the null
hypotheses that the diﬀerence in means between the two samples is zero.
50
Table 15: Baseline (1986) Descriptive Statistics for Stunted Sample Enrolled in the Study
Sample Control Treatment Diﬀerence Single
Size Mean Mean in Means p-value
A. Parental Characteristics
Mother present 127 0.97 0.96 -0.03 0.48
Mother/guardian’s age (years) 127 23.9 25.4 1.53 0.19
Mother/guardian employed 127 0.15 0.29 0.14 0.09
Mothers/guardian education 127 0.20 0.06 -0.14 0.02
Mother/guardian’s education, any training 127 0.20 0.27 0.07 0.42
Mother/guardian’s PPVT 127 84.5 86.1 1.65 0.63
Mothers/guardian’s height (cm) 125 159.5 159.1 -0.36 0.73
Father presence 127 7.55 7.21 -0.34 0.28
HOME score on enrolment 127 -0.21 0.07 0.14 0.65
Housing index 127 7.55 7.21 -0.34 0.28
B. Child Characteristics
Child age (years) 127 1.53 1.54 0.01 0.9
Male 127 0.59 0.55 -0.04 0.69
Child’s birth order 127 2.89 3.32 0.43 0.28
Birth Weight < 2500 grams 126 0.19 0.22 0.03 0.77
Head Circumference (cm) 127 46.1 45.9 -0.21 0.47
Daily Calories Consumed 127 970.4 939.0 -31.4 0.71
Daily Proteins Consumed (grams) 127 25.6 27.6 2.04 0.55
Griﬃth Developmental Quotient 127 97.2 98.9 1.7 0.3
Stunting 127 -2.91 -3.01 -0.1 0.33
Wasting 127 -0.94 -1.17 -0.23 0.05
Notes: This table presents baseline means of variables of interest for the stunted treatment and control
groups at baseline (1986) for the sample enrolled at baseline. The p-values reported in the last column are
for two-sided block permutation tests of the null hypotheses that the diﬀerence in means between treatment
and control groups are zero. Variable deﬁnitions include: PPVT denotes the raw score from Peabody Picture
Vocabulary Test (Dunn and Dunn, 1981), HOME denotes the raw score from the HOME environment test
(Caldwell, 1967), and Griﬃth Development Quotient reports the raw score for this test (Griﬃths, 1954;
Griﬃths 1970).
51
Table 16: Attriton From the Stunted Sample
Total Control Diﬀerence Single
Sample Size Sample Size in Attrition Rates p-value
A. Attrition
Baseline Sample Size 127 65 – –
Sample lost in 2008 resurvey 22 13 -0.05 0.4
B. Reason lost
Could not locate 10 6 -0.03 0.57
Located but refused interview 3 1 0.02 0.55
Died 9 6 -0.04 0.37
Notes: This table presents baseline sample sizes and attrition in panel A. The diﬀerence in attrition rates is
the diﬀerence between the treatment and control groups. The p-values reported in the last 2 columns are for
two-sided block permutation tests of the null hypotheses that the diﬀerence in treatment and control means
are zero. The permutation blocks are child’s age and sex.
52
Table 17: p-Values for Tests of Attrition Bias in the Stunted Sample
Full Treatment Control
Sample Group Group
A. Parental Characteristics
Mother present 1.00 0.68 1.00
Mother/guardian’s age (years) 0.02 0.12 0.18
Mother has > 9 years of Schooling 0.31 1.00 0.30
Mothers education 1.00 0.72 1.00
Mother has any job training 0.56 0.20 0.69
Mother/guardian’s PPVT 0.47 0.74 0.57
Mother/guardian’s height (cm) 0.88 0.60 0.37
Father present 0.80 0.75 1.00
HOME score on enrolment 0.30 0.12 0.71
Housing index 0.68 0.95 0.53
B. Child Characteristics
Child age (years) 0.18 0.28 0.47
Male 0.26 0.56 0.50
Child’s birth order 0.26 0.41 0.56
Birth Weight < 2500 grams 0.12 1.00 0.09
Head Circumference (cm) 0.21 0.25 0.52
Daily Calories Consumed 0.73 0.05 0.20
Daily Protein Consumed (grams) 0.47 0.02 0.49
Griﬃth Developmental Quotient 0.59 0.87 0.34
Stunting 0.30 0.24 0.80
Wasting 0.27 0.12 0.81
Notes: This table presents p-values for two-sided permutation tests of the null hypotheses that the diﬀerence
in baseline means of the sample found in the 2008 and the sample not found in 2008 are equal. The ﬁrst
column reports that results for the full sample and the next two columns report the results separately for
the treatment and control samples.
53
Table 18: Attrition From the Non-Stunted Comparison Group
Non-Attrited Attrited Diﬀerence Single
Group Group in Means p-value
Mean Mean
Maternal age 32.38 37.45 5.07 0.05
Mother Present 0.86 0.66 -0.20 0.13
Maternal employment 0.66 0.56 -0.10 0.47
Maternal education 0.36 0.17 -0.19 0.10
Maternal PPVT Score 94.78 84.35 -10.43 0.09
Home stimulation: books +paper 0.46 0.20 -0.26 0.30
Home stimulation: games and trips 0.03 -0.01 -0.04 0.89
Home stimulation: verbal stimulation 0.12 -0.30 -0.42 0.05
Home stimulation: writing material 0.09 -0.06 -0.15 0.44
Housing score 8.83 9.56 0.73 0.09
Child misses school because of money 0.33 0.28 -0.05 0.77
Weight for Age z -Score 0.19 0.16 -0.03 0.88
Height for Age z -Score 0.81 0.90 0.09 0.76
Stanford Binet 82.23 80.74 -1.49 0.48
Ravens 13.86 12.84 -1.02 0.24
Notes: This table presents baseline descriptive statistics for the sample of non-stunted comparison group
member found (Non-Attrited) in the 2008 survey and the group lost (Attrited) in the 2008 survey. The
p-values reported in the last 2 column are for two-sided permutation tests of the null hypotheses that the
diﬀerence in non-attrited and attrited group means are zero.
54
Table 19: Baseline (1986) Descriptive Statistics for the Non-Stunted versus Stunted
Samples
Non-
Stunted Diﬀerence Single
stunted
Group Group in Means p-value
Mean Mean
A. Parental Characteristics
Mother present 1.0 1.0 0 0.3
Mother/guardian’s age (years) 25.1 25.0 -0.1 1
Mother /guardian employed 0.2 0.2 0.0 0.9
Mother/guardian school 9th grade 0.1 0.1 0.0 0.7
Mother/guardians PPVT 85.8 98.2 12.4 0
Mothers/guardians height (cm) 159.3 163.8 4.5 0
Father present 0.5 0.6 0.1 0.5
HOME score on enrolment 16.6 17.9 1.3 0.2
Housing index 7.4 8.7 1.3 0
B. Child Characteristics
Age (years) 1.6 1.6 0.0 1
Male 0.5 0.4 -0.1 0.5
Child’s birth order 3.2 2.1 -1 0.1
Birth Weight < 2500 grams 0.2 0.0 -0.2 0
Head Circumference (cm) 46.1 47.8 1.7 0
Daily Calories Consumed 959.8 909.0 -50.8 0.6
Daily Protein Consumed (grams) 27.0 25.9 -1.1 0.8
Griﬃth Developmental Quotient 98.2 106.6 8.4 0
Height for Age z -Score -2.9 0.1 3.0 0
Weight for Height z -Score -1 0.1 1.1 0
C. Variables at 7 years
Mother present 0.9 0.9 0.0 0.9
Mother/guardian’s age (years) 32.8 32.4 -0.4 0.7
Mother /guardian employed 0.4 0.6 0.2 0
Mother/guardian school 9th grade 0.3 0.4 0.1 0.5
Mother/guardians PPVT 86.6 94.7 8.1 0
Father present 0.3 0.5 0.2 0
Housing index 8.4 8.9 0.5 0.1
Missed school due to lack of money 0.5 0.3 -0.2 0
Notes: This table presents baseline descriptive statistics for the non-stunted comparison group vs. Full
Stunted Sample (Treatment and Control Groups). The p-values reported in the last 2 column are for two-
sided permutation tests of the null hypotheses that the diﬀerence in non-stunted and stunted group means
are zero.
55
Table 20: Testing for Gender Diﬀerences in the Impact of Treatment and Catch Up in Log Earnings
I. Treatment vs. Control II. Comparison vs. Treatment
Treatment Diﬀerential Eﬀect Single p-Value Treatment Diﬀerential Eﬀect Single p-Value
Eﬀect for Women Eﬀect for Women
A. First Job
All 0.23 -0.15 0.58 -0.14 0.47 0.07
Full time 0.30 -0.20 0.50 -0.15 0.59 0.03
Non temp 0.46 -0.08 0.81 -0.35 0.38 0.24
B. Last Job
All 0.25 0.03 0.94 0.06 0.11 0.77
Full time 0.54 -0.28 0.33 -0.02 -0.06 0.85
Non temp 0.52 -0.13 0.69 -0.31 0.13 0.71
C. Current Job
All 0.37 -0.23 0.59 -0.18 0.24 0.55
Full time 0.81 -0.65 0.08 -0.38 0.56 0.60
56
Non temp 0.72 -0.46 0.29 -0.39 0.07 0.89
D. Average Earnings
All 0.27 0.14 0.63 0.00 -0.08 0.76
Full time 0.38 -0.14 0.60 -0.11 0.08 0.75
Non temp 0.50 -0.14 0.67 -0.36 0.25 0.45
Notes: This table reports the results of tests for gender diﬀerences in the treatment eﬀect and catch-up of the treatment group in log earnings.
The columns in Panel I present the results for the treatment eﬀects. The columns in Panel II present the analysis of treatment group catch-up to
the comparison group. The treatment eﬀects and treatment eﬀects interacted with a female dummy variable to estimate the Diﬀerential Eﬀect for
Women are estimated by linear regression. The treatment eﬀect is interpreted as the diﬀerence in the means of log earnings for males between the
stunted treatment and stunted control groups conditional on baseline values of child age, gender, weight-for-height z -score, maternal employment,
and maternal education. The treatment eﬀect interacted with the female dummy is interpreted as the diﬀerence between the treatment eﬀect for
females versus males conditional on the same variables. Our p-values are for two-sided block permutation tests of the null hypothesis of no diﬀerential
eﬀect for women (Single p-value). Permutation blocks are based on the conditioning variables used in the treatment eﬀect regressions. Catch-up for
males is estimated as the diﬀerence in the means of the outcomes between the weighted non-stunted comparison group and the stunted group. The
comparison group observations are weighted using Inverse Probability Weights (IPW) to correct for attrition. The interaction of the diﬀerence in
means and females is interpreted as the diﬀerence in catch-up for females versus males.
Table 21: Testing for Gender Diﬀerences in the Impact of Treatment on Education and
Skills
Treatment Diﬀerential Treatment Single p-Value
Eﬀect Eﬀect for Women
A.Schooling
Total years of education 0.95 -0.90 0.11
Any vocational training 0.16 -0.07 0.73
Any college 0.16 -0.12 0.27
In school 0.07 0.12 0.40
In school full time 0.12 0.03 0.80
B.Exams
Passed at least one CXC exam 0.31 -0.37 0.04
Passed 4 or more CXC exams 0.25 -0.24 0.12
Passed at least one CAPE 0.14 -0.10 0.21
C.Skills
Cognitive factor 0.49 -0.11 0.76
Externalizing Behavior factor 0.60 -0.59 0.13
Internalizing Behavior factor 0.88 -0.71 0.06
Ever expelled from school 0.09 0.08 0.51
Notes: This table reports the results of tests for gender diﬀerences in the treatment eﬀect on schooling
and skills. The treatment eﬀects and the diﬀerential treatment eﬀect for Wwomen are estimated by linear
regression. The treatment eﬀect is interpreted as the diﬀerences in the means of the outcome variables for
males between the stunted treatment and stunted control groups conditional on baseline values of child age,
gender, weight-for-height z -score, maternal employment, and maternal education. The diﬀerential treatment
eﬀect is interpreted as the diﬀerence between the treatment eﬀect for females versus males conditional on
the same variables. Our p-values are for two-sided block permutation tests of the null hypothesis that the
interactions of the treatment eﬀect and female are zero (Single p-value). Permutation blocks are based on
the conditioning variables used in the treatment eﬀect regressions.
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Table 22: Outliers Robustness Analysis of Stimulation Treatment on Log Earnings
I. Original Sample II. Trimmed Sample
Treatment Single Treatment Single
Eﬀect p-value Eﬀect p-value
A. First Job
First Job 0.27 0.11 0.24 0.03
First Full Time 0.35 0.04 0.24 0.03
First Non Temp 0.53 0.01 0.23 0.07
B. Last Job
All 0.27 0.06 0.18 0.10
Full Time 0.40 0.00 0.31 0.00
Non-Temp 0.50 0.00 0.33 0.01
C. Current Job
All 0.27 0.09 0.26 0.06
Full Time 0.43 0.10 0.44 0.00
Non-Temp 0.44 0.10 0.40 0.02
D. Average Earnings
All 0.40 0.01 0.28 0.01
Full Time 0.34 0.01 0.13 0.11
Non-Temp 0.47 0.00 0.18 0.10
Notes: This table reports the estimated impact of treatment on log monthly earnings using the original
sample. The results from Table 3 are presented in column panel I and the results for the sample trimmed of
the lowest and highest 5% values is presented in panel II. In each sample, treatment eﬀects are reported for
the following jobs as indicated by the rows blocks: (A) First Job, (B) Last Job, (C) Current Job, and (D)
Average Lifetime Earning over all jobs. Within each type of job, results are reported for the following types
of workers as indicated by the rows: All workers, Full Time Workers, and Full Time Non-Temporary workers.
The treatment eﬀects are estimated by linear regression and are interpreted as the diﬀerences in the means of
log earnings between the stunted treatment and stunted control groups conditional on baseline values of child
age, gender, weight-for-height z -score, maternal employment, and maternal education. Our p-values are for
one-sided block permutation tests of the null hypothesis of no treatment eﬀect (Single p-value). Permutation
blocks are based on the conditioning variables used in the treatment eﬀect regressions.
58