The Effects of Two Influential Early Childhood Interventions on Health and Healthy Behaviour - PMC

The Effects of Two Influential Early Childhood Interventions on Health and Healthy Behaviour - PMC
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. Author manuscript; available in PMC: 2017 Apr 1.
Published in final edited form as:
Econ J (London). 2016 Dec 7;126(596):F28–F65. doi:
10.1111/ecoj.12420
The Effects of Two Influential Early Childhood Interventions on Health and Healthy Behaviour
Gabriella Conti
Gabriella Conti
Senior Lecturer in Health Economics at the Department of Applied Health Research at University College London; and a Research Fellow at the Institute for Fiscal Studies, London
Find articles by
Gabriella Conti
James Heckman
James Heckman
Henry Schultz Distinguished Service Professor of Economics at the University of Chicago; Director, Center for the Economics of Human Development, University of Chicago; Co-Director of the Human Capital and Economic Opportunity Global Working Group; a Research Fellow at the American Bar Foundation; and an affiliate of the Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California
Find articles by
James Heckman
Rodrigo Pinto
Rodrigo Pinto
Assistant Professor in the Department of Economics at UCLA
Find articles by
Rodrigo Pinto
Senior Lecturer in Health Economics at the Department of Applied Health Research at University College London; and a Research Fellow at the Institute for Fiscal Studies, London
Henry Schultz Distinguished Service Professor of Economics at the University of Chicago; Director, Center for the Economics of Human Development, University of Chicago; Co-Director of the Human Capital and Economic Opportunity Global Working Group; a Research Fellow at the American Bar Foundation; and an affiliate of the Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California
Assistant Professor in the Department of Economics at UCLA
Issue date 2016 Oct.
PMC Copyright notice
PMCID: PMC5331750  NIHMSID: NIHMS747459  PMID:
28260805
The publisher's version of this article is available at
Econ J (London)
Abstract
This paper examines the long-term impacts on health and healthy behaviors
of two of the oldest and most widely cited U.S. early childhood interventions
evaluated by the method of randomization with long-term follow-up: the Perry
Preschool Project (PPP) and the Carolina Abecedarian Project (ABC). There are
pronounced gender effects strongly favoring boys, although there are also
effects for girls. Dynamic mediation analyses show a significant role played by
improved childhood traits, above and beyond the effects of experimentally
enhanced adult socioeconomic status. These results show the potential of early
life interventions for promoting health.
Keywords:
Health, early childhood intervention, social experiment, randomized trial, Abecedarian Project, Perry Preschool Project
1 Introduction
Discussions of ways to control the soaring costs of the health care system in
the US and elsewhere largely focus on the provision of health care (see, e.g.,
Emanuel, 2012
Jamison
et al.
, 2013
). However, treatment of disease is
only part of the story. Prevention has a substantial role to play. Most medical care
costs in developed countries like the United States arise from a minority of
individuals with multiple chronic conditions, like cardiovascular and metabolic
diseases, and cancer (see
Cohen and Yu,
2012
).
Such conditions are the main causes of
premature death, and managing them effectively requires that patients make lifestyle
changes by adhering to healthy behaviors (
Ford
et al.
, 2012
Kontis
et al.
, 2014
Mokdad
et al.
, 2004
). While prevention holds the key for
lifelong health, changing behavior in adulthood is challenging (
Marteau
et al.
, 2012
).
A substantial body of evidence shows that adult illnesses are more prevalent
and more problematic among those who have experienced adverse early life conditions
Danese
et al.
, 2007
Galobardes
et al.
2008
). At present, the exact pathways through which early life experiences
translate into health over the life cycle are not fully known, although there is
increasing understanding of the role that might be played by biological embedding of
social and economic adversity.
The
evidence on the social determinants of health (
Marmot and Wilkinson, 2006
) suggests that a strategy of prevention
rather than later life treatment may be more effective. Such an approach recognizes
the dynamic nature of health capital formation, and views policies that shape early
life environments as effective tools for promoting health (
Conti and Heckman, 2014
). Following this path, a recent
interdisciplinary literature points to the role that might be played by early
childhood interventions targeted to disadvantaged children in promoting adult health
Black and Hurley, 2014
Campbell
et al.
, 2014
Di Cesare
et al.
, 2013
).
This paper contributes to this literature by examining the effects on health
and healthy behaviors of the two most influential, high-quality, U.S.-based early
childhood interventions – the Perry Preschool Project (PPP) and the
Abecedarian Project (ABC). Both interventions used the method of randomization to
assign enriched environments to disadvantaged children. Participants are followed
into adulthood. PPP was conducted in Ypsilanti, Michigan, starting in 1962; ABC in
Chapel Hill, North Carolina, starting in 1972. PPP provided preschool education at
ages 3–5 and home-based parenting guidance; ABC also included a health care
and a nutritional component, and lasted from birth until age 8.
Data from PPP and ABC enable analysts to learn
about the health benefits of early life interventions for disadvantaged populations.
Since children are generally in good health, and reliable early life biomarkers
predictive of later disease have yet to be discovered, it is challenging to
demonstrate health effects of early life interventions in the absence of long-term
follow-ups.
The PPP data have rich information on behavior but not health. ABC has a
survey of health at age 34 in addition to measures of healthy behaviors. For both
studies, we perform analyses by gender and find substantial differences in the
effects of treatment between males and females. We present evidence that both the
Perry and the Abecedarian interventions have statistically and substantively
significant effects on the health and healthy behaviors of their participants. The
specific outcomes affected vary across studies, although for both interventions,
treatment effects are much stronger and more precisely determined for males. The
Perry male participants have significantly fewer behavioral risk factors (in
particular smoking) by the time they have reached age 40, while the Abecedarian male
participants are in better physical health by their mid 30s. We document the
important role played by enhancements in childhood traits, above and beyond
educational attainment and adult socioeconomic status, as mechanisms producing
treatment effects.
We use robust statistical methods and apply the frameworks developed in
Heckman
et al.
(2010)
and
Campbell
et al.
(2014)
to
systematically account for small sample sizes of the experiments, the effects of
multiple hypothesis testing, and non-random panel attrition to analyze these
studies. We adjust for departures from randomization protocols when appropriate. We
show that accounting for small sample sizes and multiple hypotheses affects
inference from these studies.
Rather than using arbitrarily constructed aggregates of health indicators as
employed in previous analyses of these experiments, we use more interpretable
disaggregated measures. We examine the mechanisms through which treatment effects
arise using dynamic mediation analyses. We use as mediators both early child
developmental traits and adult socioeconomic outcomes.
We address the challenges that analysts face when comparing results across
experiments. The baseline characteristics of the populations treated differ.
Treatments vary. Follow-up periods and questions asked are not strictly comparable.
Many treatment effects across programs are not comparable because different outcomes
are measured, different survey instruments are used, and different ages are sampled.
Where outcome measures are comparable, estimated treatment effects are stronger for
ABC males compared to PPP males. The imprecise estimates for women found in each
program translate into imprecise estimates of differences in female program effects.
Our analysis suggests that simple comparisons of treatment effects across programs
as featured in commonly reported meta-analyses (see, e.g.,
Camilli
et al.
, 2010
Karoly
et al.
, 2005
) are potentially
very misleading guides to policy.
The paper proceeds as follows. Section 2 describes the ABC and PPP
interventions. Section 3 discusses the statistical challenges addressed in this
paper and presents our econometric procedures. Section 4 presents and discusses our
estimates of treatment effects and the results of our mediation analyses. Section 5
concludes.
2 The ABC and PPP Interventions
Both the ABC and the PPP interventions were center-based small-scale
programs designed to enrich the early environments of disadvantaged children. The
main characteristics of both interventions are displayed in
Table 1
. The Perry Preschool Project (PPP) took place in
the mid-1960s in the district of the Perry Elementary School, a public school in
Ypsilanti, Michigan (a small city near Detroit). The Carolina Abecedarian Project
(ABC) took place one decade later at the Frank Porter Graham Child Development
Institute at the University of North Carolina’s Chapel Hill campus.
Eligibility was based on weighted scales which included multiple indicators of
socioeconomic disadvantage, although the specific items and weights differed across
the two interventions.
ABC enrolled
children soon after birth
until 5
years of age
for a very intensive
6.5 to 10 hours per day program. PPP enrolled children at 3 years of age for 2
years
for a less intensive
2.5–3 hours per day program.
10
Details of the randomization protocol are presented in
Section 1 of the Web
Appendix
. In this section we report: (a) the background characteristics of
the two populations (subsection 2.1); (b) the interventions administered (subsection
2.2); and (c) the data collections carried out (subsection 2.3).
Table 1.
ABC and PPP: Main Characteristics and Eligibility Criteria
Abecedarian
Perry
Main
Characteristics
Location:
Chapel Hill,
NC
Location:
Ypsilanti, MI
Racial Composition:
98% African American
Racial Composition:
All
African American
Age of Child:
0–5
Age of Child:
3–5
Sample Size:
111 (57T,
54C)
Sample Size:
123 (58T,
65C)
Intervention Year:
1972
– 1982
Intervention Year:
1962
– 1967
Follow-up:
Through Mid 30s
(2010–2012)
Follow-up:
Through Age 40
(2000–2002)
Intensity:
40 hrs/week (8
hrs/day for 5 days/week) for 50 weeks/year
Intensity:
12.5 to 15
hrs/week (2.5 to 3 hrs/day for 5 days/week) for 30 weeks/year (mid-Oct.
through May)
+ 1.5 hrs/week of home
visits
+ 1 monthly parent group meeting
Number of years:
5 years at
ages 0–5
Number of years:
2 yrs at
ages 3–5 for cohorts 1–4; 1 yr for first cohort
Cost per
child
year:
12,955 (2010$)
Cost per
child
year:
9,604 (2010$)
Eligibility
Criteria
Requirement
: No apparent
biological conditions
Requirement:
Child IQ<85
(“educably mentally retarded”)
Weighted Scale:
High Risk Index
mother’s educational level (last grade completed)
father’s educational level (last grade completed)
family income (dollars per year)
father absent for reasons other than health or death
absence of maternal relatives in local area
siblings of school age one or more grades behind
age-appropriate level or with equivalently low scores on
school-administered achievement tests
payments received from welfare agencies in past 3 yrs
record of father’s work indicates unstable or
unskilled semiskilled labor
mother’s or father’s IQ ≤90
sibling’s IQ ≤90
relevant social agencies in the community indicate the family
is in need of assistance
one or more members of the family has sought counsel-ling or
professional help the past 3 yrs
special circumstances not included in any of the above likely
contributors to cultural or social disadvantage
Weighted Scale:
Cultural Deprivation Scale
parents’ average
years of schooling at entry/2 +
father’s
occupational status at entry
2 +
(rooms/persons in home at
entry)
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Notes:
This figure is inclusive of the health care costs (the figure reported in
Barnett and Masse (2007
) is not).
Estimated from cost-benefit analysis conducted on both PPP and ABC
projects.
See
Ramey
et al.
(2000)
See
Weikart
et al.
(1978)
2.1 The background characteristics of the two populations
While both ABC and PPP targeted disadvantaged populations, the
background characteristics of the participants differed. We summarize them in
Table 2
and
Figures 1
and
11
Table 2.
Descriptive Statistics of ABC and PPP Pre-program Variables
IQ at 3 years
Birth Weight
Mother’s Age
Father’s Age
ABC
PPP
ABC
PPP
ABC
PPP
ABC
PPP
Mean
92.65
79.02
3.19
3.10
19.78
25.56
23.21
32.81
Std. Dev.
15.95
6.44
0.61
0.47
4.77
6.53
5.84
6.88
Skewness
0.04
−0.76
−0.59
−0.05
2.16
0.52
1.29
0.52
Mother’s Education
Father’s Education
Number of Siblings
ABC
PPP
ABC
PPP
ABC
PPP
Mean
10.17
9.42
10.89
8.60
0.64
4.28
Std. Dev.
1.84
2.20
1.78
2.40
1.09
2.59
Skewness
−0.28
−0.78
−0.38
−0.32
2.15
0.90
Mother’s Working
Status
Father’s Working
Status
Father Presence
ABC
PPP
ABC
PPP
ABC
PPP
Mean
0.36
0.20
0.73
0.14
0.29
0.53
Std. Dev.
0.48
0.40
0.45
0.35
0.45
0.50
Skewness
0.58
1.47
−1.03
2.09
0.94
−0.11
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Notes:
This table provides some descriptive statistics of the
ten pre-program variables which were collected in both the Abecedarian and
Perry interventions: (1) the Stanford-Binet IQ score at 3 years of age (we
only use data from the control group for the ABC intervention, since it
started at birth); (2) weight at birth in kilograms; (3) mother’s
and father’s age at the time of the participant’s birth; (4)
mother’s and father’s last grade completed; (5) number of
participant’s siblings; (6) mother’s and father’s
working status (this variable takes value 1 if the parent is employed and 0
otherwise); (7) presence of the father (a binary indicator which takes value
1 if the participant’s father is a current resident of the
household). The descriptive statistics reported are the arithmetic mean, the
standard deviation and the skewness. Those are respectively measured by
and
, where
denotes sample
size and
denotes the outcome for participant
Figure 1. Comparison Between Pre-program Variables of ABC and PPP.
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Notes:
These figures present the density estimation of four
pre-program variables collected in both the Perry and Abecedarian interventions.
Panel A plots the Stanford-Binet IQ score at 3 years of age (we only use data
for the control group for the ABC intervention, since it started at birth).
Panel B plots the weight at birth in kilograms. Panel C and D plot the
mother’s and father’s age at the time of the
participant’s birth. These estimates are based on a normal kernel
function with optimal bandwidth for normal densities.
Figure 2. Comparison Between the Baseline Variables of ABC and PPP.
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Notes:
These figures present estimates of the empirical
distributions of three categorical and three binary variables collected in both
the Perry and Abecedarian interventions. All variables were collected at the
onset of each intervention. Panels A and B: mother’s and
father’s last grade completed. Panel C: the number of
participant’s siblings. Panel D and E: mother’s and
father’s working status (a binary indicator which takes value 1 if the
parent is employed and 0 otherwise). Panel F: presence of the father (a binary
indicator which takes value 1 if the participant’s father is a current
resident of the household).
The first substantial difference that emerges is in the IQs of
participants. While the average Stanford-Binet score at 3 years of age is 79
points in PPP, it is 14 points higher at the same age in the control group of
ABC.
12
This difference
is also visible in Panel A of
Figure 1
which shows that the region of common support is limited to the bottom half of
the density of ABC. The partial overlap in the IQ distributions across the two
interventions arises because PPP required an IQ smaller than 85 to be eligible
to participate in the program.
There is no significant difference in average health at birth (
Table 2
). However, more ABC participants
are born at low (< 2, 500 grams) or high birthweight (> 4, 000 grams), as
shown in Panel B of
Figure 1
13
Turning to the parental demographic characteristics, we see that the
parents in PPP are older than those in ABC, with the age difference amounting to
six years for the mothers and to nine years for the fathers (when fathers are
present). The density reported in Panel D of
Figure 1
shows that the region of common support for paternal age
only extends between the ages 20–45. In line with the older parental
age, the participants of the PPP intervention also have, on average, a greater
number of siblings (4, up to a maximum of 12, as shown in Panel C of
Figure 2
), while ABC children are more likely
to be first born. Additionally, ABC participants are more likely to be born to
single mothers, with the father being present almost twice as often in PPP
households than in ABC households (53% vs. 29%,
Table 2
). Finally, the parents of ABC participants
have higher socioeconomic backgrounds, higher levels of education, and are more
likely to be employed (as shown in
Table
and Panels A-B and D-E of
Figure
, respectively).
In sum, while more children in Perry are from two-parent
homes,
14
many other
socioeconomic characteristics are more favorable for ABC participants,
especially for those with fathers present.
15
However, as shown in
Table 1 of the Web Appendix
controlling for these background characteristics does not substantially change
estimated treatment effects.
2.2 The Interventions
16
Intervention Strategies
From 1962 to 1967, the Perry Preschool Project (PPP) recruited
disadvantaged children three to four years of age on the basis of two
selection criteria: “cultural deprivation” and evidence of
being “educably mentally retarded” based on the
Stanford-Binet Intelligence score (mean = 79). Mid-intervention and
follow-up summaries describe a program that operated for 2.5 to 3 hours each
morning, 5 days per week over the course of a school year (
Weikart, 1966
1967
1970
). Except for
the first treatment group that participated for one year only, four
treatment groups experienced two years of the instructional program. In
addition to a monthly parent group meeting hosted by social work staff, PPP
further incorporated a 60–90-minute weekly home visit, designed to
offer individualized instruction as needed, establish teacher-primary
care-giver relationship, and involve the latter in their child’s
development (
Weikart, 1964
1967
1970
).
Weikart’s descriptions of the program change significantly
throughout the course of the intervention, including its length and format
for both children and parents, the intervention method-ologies and learning
activities, the role of the teacher, the role of the child as a learner, and
even his/her understanding of cognitive development (
Weikart, 1964
1967
1970
). This
reflects both experimentation within the program and the changed framing of
it as the literature on child development evolved while the program was
being implemented. What remains consistent, however, are Weikart’s
stated primary goals of cognitive development with an emphasis on language
development, the use of developmental theory in guiding curriculum framework
and intervention methods, and a combined approach of a morning center-based
preschool program and a weekly afternoon home visit by the child’s
teacher (
Weikart, 1964
1967
1970
). The learning program implemented in PPP from 1962 to
early 1965 included unit-based instruction, intentional adult-child
interactive language, and a rich set of learning materials including
Montessori tools, movement/dancing, and an emphasis on caregiver-planned
large- and small-group activities. In the final year of PPP, the learning
program more closely resembled the later developed HighScope curriculum
including “Plan, Do, Review.” Individual instruction was not
a specific feature of the Perry center-based program (see
Weikart
et al.
, 1978
and
Kuperman, 2014a
), whereas in ABC, it
was a key component of the learning program.
Ten years after PPP began, ABC recruited four cohorts of infants
born between 1972 and 1977 at hospitals near Chapel Hill, NC, for an
intensive early childhood intervention designed to prevent retardation for
low-income multi-risk populations. Treated children were transported by
program staff from their homes to the newly built Frank Porter Graham Center
(FPGC) for up to 9 hours each day for 50 weeks/year (
Ramey
et al.
, 1976
).
What is now known as the “Abecedarian Approach”
emerged from a process of distinctive curriculum development. The number of
teaching and learning activities expanded through formal testing and
evaluation with each successive ABC cohort. The
Learningames for the
First Three Years
were designed by both Joseph Sparling and
Isabelle Lewis as play-based adult-child activities for the expressed
purposes of minimizing infants’ maladaptive, high-risk behaviors,
and enhancing adult-infant interactions that support children’s
language, motor, and cognitive development and socio-emotional competence,
including task orientation (
Sparling and
Lewis, 1979
). Influenced by Piaget’s theory of
developmental stages, each individual activity included a stated learning
objective thought to be developmentally appropriate, specification of needed
materials, directions for teacher behavior, and expected child outcome. In
addition to tracking and dating activity assignments, these records enabled
staff to prescribe a specific instructional program every 2 to 3 weeks for
each child by rotating learning activities and to note developmental
progress or its lack thereof from program entry to approximately age 36
months (
Ramey
et al.
1976
). During preschool, ABC supplemented the original
Learningames
with a program for three and four year
olds, thought to be developmentally appropriate and developed together by
staff and caregiving professionals with assistance from outside consultants.
The
Abecedarian Approach to Social Competence
encouraged
cognitive development, sociolinguistic and communicative competence, and
reinforced socially adaptive behaviors involved in task orientation,
peer-peer relations, adult-child relationships, and emotional self-awareness
Ramey
et al.
1976
1982
). Language
intervention remained the critical ABC vehicle for supporting cognition and
social skills (
McGinness and Ramey,
1981
).
The two randomized controlled trials share many features in common,
including an emphasis on language and cognitive development in the
intervention for disadvantaged children, the background influence of
developmental theory on the design of the curriculum but with plenty of room
for individual adaptation, and general similarities such as the use of field
trips as a learning tool, organization of the learning environment during
preschool years, and ongoing professional development for staff. However, a
comparison of reports drafted by the directors of PPP and ABC concurrently
with their own interventions also reveals some key differences.
The programs differed in the way they perceived their treated
children and designed their intervention goals and conceptual approaches.
Perry was motivated by a “deficits” model, and the
intervention was perceived as
remediating
cultural
deprivation and mental retardation. PPP was launched in an era when
cognitive psychology was in ascendance and shaped educational
policy.
17
This
conceptual approach initially led Weikart to prioritize cognitive over
socio-emotional learning in his reporting of the Perry program, which he
described as a key feature of a traditional middle class nursery school.
However, in practice, PPP teachers modified this agenda and intentionally
fostered the child’s socio-emotional development, including
self-regulation and the capacity of making judgments.
18
The middle class teachers who
initiated the program did for the disadvantaged children in Perry what
middle class parents do for their own children (
Heckman
et al.
, 2014b
) and
effectively prevented the program from being focused solely on cognition.
Indeed, in reporting the first findings from the study,
Weikart (1967)
wrote
“Preschool must demonstrate ability to affect the
general development of children in three areas. These are
intellectual growth, academic achievement, and school
behavior.”
In contrast, ABC aspired to
prevent
retardation and
thus recruited their sample from birth. By the time it was launched, the
literature on child development had evolved beyond a sole focus on
cognition. It benefitted from an enhanced understanding of the work of child
development psychologists Piaget and Vygotsky. For ABC, socio-emotional
learning and cognitive development were intertwined and embedded within
adult-child interactions and adult-mediated activities that incorporated an
intentional use of language as a teaching tool to elicit children’s
emerging social competence and ability to reason.
ABC and PPP differ on a number of program elements. In addition to
the difference in intensity and duration, ABC and PPP involved the family in
different ways. PPP incorporated weekly home visits, designed to offer
opportunities for individualized instruction as needed, to establish a
relationship between the child’s center-based teacher and the
mother/primary caregiver, and to involve her in the child’s
education. Weekly home visits lasted approximately 60–90 minutes
Weikart, 1964
1970
). In addition, PPP offered an opportunity
for parents to participate in monthly group meetings hosted by social work
staff (
Weikart, 1964
1967
). In ABC, while there were no
home visits, parents were invited to be actively involved in preschool
classrooms and to participate in parent-teacher conferences to share updates
about the treated child. Both treatment and control groups in ABC received
family support in the form of social work services on a request basis to
obtain family planning and legal help.
Early reports of parental involvement in ABC suggest that each
nursery and classroom staff member was assigned four treatment families to
contact in order to establish individualized and open communication between
parents and the center. Teachers were directed to plan an afternoon for each
family to visit FPGC, observe their child, and to meet other teachers and
medical staff. Families were provided photographs of their child engaging in
program activities that served to further strengthen the connection between
home and school. Reports indicate that family holiday parties were well
attended (
Ramey
et al.
1977
).
The health care and nutritional components
ABC differed significantly from PPP because it also included health
care and nutritional components.
Table
displays the treatments and exams included in the health care
component of the ABC. Free pediatric care was provided to all the treated
children who attended the Frank Porter Graham (FPG) center (
Ramey
et al.
, 1982
). The on-site
medical staff had two pediatricians, a family nurse practitioner, and a
licensed practical nurse.
19
The
well child care
component included assessments at ages
2, 4, 6, 9, 12, 18 and 24 months, and yearly thereafter, in which a complete
physical exam was performed and parents of the treated children were
counseled about child health care, nutrition, growth and
development.
20
The
ill child care
component included daily surveillance of
all the treated children in the FPG center for illness.
21
Table 3.
The Health Care Components of ABC for the Treated Children
Component
Content
Well-Child
Care
Well-Child Visits
Assessments were made at 2, 4, 6, 9, 12, 18,
and 24 months, and yearly thereafter.
A health history and a
social history were obtained and a complete physical examination was
performed.
Immunizations
Appropriate immunizations (diphteria,
pertussis, tetanus, polio, measles, mumps, and rubella) as recommended
by the
American Academy of Pediatrics
were given.
Lab Tests
A sickle cell preparation was obtained at 9
and 12 months from all black children.
A skin test for
tubercolosis was given yearly, and a hematocrit was done at 9 and 18
months and yearly thereafter.
During symptom-free periods, the
children were cultured for bacteria at two-week intervals, and for
viruses and mycoplasmas every four weeks.
Health Education
The parents were present at the child
well-care visits. They were taught and counseled in the areas of:
feeding and nutrition, weaning, cleanliness, skin care, child growth and
development, behavior, toilet training, accident prevention, and dental
hygiene.
They were also encouraged to express their concerns and
to discuss the problems that they were facing.
Vision Hearing
Routine screening for vision was provided
annually.
During symptom-free periods, the children underwent
pneumatic otoscopy and tympanometry once a month.
If any
tympanogram was abnormal, the child was seen for repeat otoscopy and
tympanometry after two weeks.
Ill-Child
Care
(for
Treated Children
Only
after the First Year)
Sick-care
Daily surveillance of all children in the
center for illness: the licensed practical nurse visited the classroom
daily to review the health status of the children and receive reports
from the parents.
Children who were unwell were promptly seen by
a member of the health care staff.
A history was obtained and a
physical examination done; appropriate laboratory tests and cultures
were performed.
Children had their upper respiratory secretions
cultured by throat swab and a saline nasal wash for isolation of viruses
and bacteria.
A computer form was completed each time the child
was examined, listing pertinent history, physical findings, diagnosis,
and culture results.
Parents were informed of the nature of the
child’s ailment, and given prescriptions, but were responsible
for buying medicines.
The family nurse practitioner made sure
that half of the prescriptions were sent home and half to the
center.
The children were followed through the illness to
recovery. They were allowed to attend the center when ill except in case
of chickenpox.
These referrals were made to specialists and
hospitals but specialized visits and hospitalizations were not paid
for.
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Notes:
Sources:
Campbell
(2014)
Ramey
et
al.
(1982)
Sanyal
et al.
(1980)
In the first year of the study, the control children received medical care
from the FPG center. After the first year, they were left on their own.
Free medical care for the control children was offered by FPGC and 2
university-affiliated hospitals to control families, and reports suggest
that this incentive was discontinued after the first year (
Heckman
et al.
, 2014a
Ramey
et al.
1976
).
When ill, children were examined by a member of the health care
staff, laboratory tests were performed, the appropriate treatment was given,
and the child was followed until recovery (
Ramey
et al.
, 1982
). The cost of medicines was
not covered; the parents were responsible for buying them, but the staff
on-site ensured they were taken. If children were referred to a hospital,
hospitalization costs were not covered. Only the treated children received
the free pediatric care. Free medical care for the control children had been
initially offered at the FPG center and two university-affiliated hospitals.
However, this incentive was discontinued after the first year (
Heckman
et al.
, 2014b
Ramey
et al.
1976
), and the control families were left with the other sources
of health care that were available at the time: community clinics for visits
(mostly crowded and with rotating doctors), the local office of the health
department for well-baby checkups and immunizations, and the hospital E.R.
for emergencies.
22
Hence,
an important difference was the continuity of early health care provided to
the treated as compared to the control group.
In addition to primary pediatric care, the treated children also
received breakfast, lunch, and an afternoon snack at the center. Food was
prepared in kitchens approved by the local health department. A nutritionist
who planned the local public school menus consulted with the kitchen service
to plan menus for breakfast, lunch, and daily snacks. On the other hand, PPP
did not provide any form of health care or nutrition. ABC utilized meal
times as educational experiences, complementing the rest of the learning
program for promoting self-help, motor skill development, social cognition
and social behavior, self-regulation, language development, and
specifically, for knowledge of healthy eating behaviors. Not only were meals
and snacks at ABC prepared according to state nutritional guidelines, but a
formal educational structure was in place for meals and eating at FPGC
before ABC started collecting data. In contrast, in Perry, there was no
formal activity supporting healthy nutrition or eating behaviors. The
teachers provided healthy snacks in the form of crackers and juice. Perry
used snack time to support language and social development.
23
Child care experiences of the control group
The PPP was launched before Head Start and the push for early
childhood interventions. The control group was in home care or in
neighborhood home-care settings with neighbors, friends, and relatives.
Things had changed ten years later. Children in the control group of the ABC
intervention attended various types of out-of-home care before age 5, for
periods of time varying between 0 and 60 months (
Pungello
et al.
, 2010
). This
paper does not account for control contamination, which is dealt with
extensively in
García
et
al.
(2014)
. They find that doing so enhances
estimated program effects. Thus, our estimates are conservative.
2.3 The Data Collected
Both the ABC and PPP interventions followed participants over time and
collected a substantial amount of information about their lives. In PPP, data
were collected annually from age 3 (the entry age) until the fourth grade
(measures of intelligence and academic aptitude, achievement tests, assessments
of socio-emotional development and information from school records starting at
kindergarten through secondary education). We know if participants went to
post-secondary education but do not know teacher ratings or performance there,
apart from information on graduation. Four follow-ups with interviews were
conducted at ages 15, 19, 27, and 40. The retention rate has been high
throughout: 91% of the original participants were re-interviewed at age
40.
24
Information on
the health of the subjects was collected only at ages 27 and 40, all based on
self-reports.
25
Richer data were collected for the Abecedarian intervention than for the
Perry intervention. Background characteristics were collected at the beginning
of the program, and include parental attributes, family structure, socioeconomic
status, and the health of the mother and of the baby. Anthropometric measures
were collected and a wide variety of assessments of the cognitive and
socio-emotional development of the child and of both the family and the
classroom environment were conducted, from soon after the start of the preschool
program until the end of the school year. Four follow-ups with interviews were
carried out at ages 12, 15, 21, and 30. A biomedical sweep was conducted when
the participants were in their mid-30s, for the purpose of collecting indicators
of cardiovascular and metabolic disease risk (
Campbell
et al.
, 2014
).
Many measures taken are not strictly comparable across programs.
Section 3 in the Web
Appendix
gives details on the exact survey questions asked and on the
construction of the variables examined.
Table 2 in the Web Appendix
summarizes their comparability. The lack of comparability poses several
challenges for meta-analyses, commonly reported in the literature and child
development.
We focus our empirical analysis on a set of outcomes of public health
relevance according to the following categories: (1) Physical Health; (2) Health
Insurance and Demand for Health Care; (3) Behavioral Risk Factors/Lifestyles
(diet and physical activity, smoking and drinking).
3 Methodology
Randomized Controlled Trials (RCTs) are often touted as the “gold
standard” of program evaluation (see, e.g.,
Ludwig
et al.
, 2011
). A major benefit of randomization
is that, when properly executed, it solves the problem of selection bias for mean
outcomes. RCTs can render treatment assignments statistically independent of
unobserved characteristics that affect the choice of participation in a program and
that might also affect outcomes. As a consequence, a perfectly implemented
randomized experiment enables analysts to evaluate mean treatment effects by using
simple differences-in-means between treatment and control groups.
26
In spite of their potential benefits, RCTs are often plagued by a range of
statistical problems that require careful attention. They often have small sample
sizes and many outcomes. They are often implemented through complex randomization
protocols that depart from an idealized random experiment (see, e.g.,
Heckman
et al.
, 2010
). A
compromised randomization protocol is not an issue for the ABC experiment. It is a
substantial issue in PPP.
Heckman
et
al.
(2010)
discuss this point in detail. We apply their
method in this paper and refer the reader to that paper for details of the procedure
and how it effects estimated treatment effects.
In addition to these challenges, the small sample sizes of the PPP and ABC
interventions suggest that standard applications of large sample statistical
inference procedures, which rely on the asymptotic behavior of test statistics, may
be inappropriate. The large number of outcomes poses the danger of arbitrarily
selecting “statistically significant” treatment effects for which
high values of test statistics arise by chance. Indeed, for any particular treatment
parameter, the probability of rejecting a true null hypothesis of no treatment
effect, i.e., the type-I error, grows exponentially as the number of tested outcomes
increases. This phenomenon leads to “cherry picking” of
“significant” results. Finally, non-random attrition can generate
spurious inferences.
We account for all of these issues in our statistical analysis. We address
the common criticism of analyses of the Perry and Abecedarian data regarding the
validity of large sample inferential procedures. We examine if statistically
significant results survive after accounting for small sample sizes, multiple
hypothesis testing, non-random attrition, and departures from the intended
randomization protocols. For many outcomes, we find a gain in statistical
significance when we analyze the PPP data using permutation tests valid in small
samples. However, for a similar proportion of outcomes, when we analyze the ABC data
with the same methods, we lose statistical significance. Additionally, adjusting for
multiple hypothesis testing affects inference in both PPP and ABC. Hence, our more
careful statistical analyses make a substantial difference in the inference about
the effectiveness of early childhood programs that is often not fully appreciated in
the advocacy-driven early childhood literature. Adjustments for attrition and
compromised randomization are implemented but not discussed in this paper.
27
The rest of this section is organized as follows. We discuss our method for
inference in sub-section 3.1. Subsection 3.2 explains how we address the problem of
multiple-hypothesis testing. Subsection 3.3 describes our correction for attrition.
Subsection 3.4 describes our method for decomposing statistically significant adult
treatment effects into interpretable components associated with inputs that are
enhanced by the treatment.
28
A more
detailed description of our methodology is presented in
Section 3 of the Web Appendix
3.1 Small Sample Inference
We address the problem of small sample size by using exact permutation
tests which are tailored to the randomization protocol implemented in each
intervention, following the analysis of
Heckman
et al.
(2010)
. Permutation tests are
distribution free. They are valid in small samples since they do not rely on the
asymptotic behavior of the test statistics. Permutation-based inference gives
accurate
-values even when the sampling distribution is skewed
(see, e.g.,
Lehmann and Romano, 2005
). It
is often used when sample sizes are small and sample statistics are unlikely to
be normal. In order to discuss our methodology more formally, we first introduce
some notation.
Let
= (
∈ ℐ) denote the vector of outcomes
for participant
in
sample ℐ. Let
∈ ℐ) be
the binary vector of treatment assignments,
= 1 if participant
is assigned to the treatment
group, and
= 0 otherwise. We use
= (
∈ ℐ) for the set of covariates used in
the randomization protocol. Our method exploits the invariance of the joint
distribution (
) under permutations that
swap the elements of the vector of treatment status
The invariance of the joint distribution (
) stems from two statistical properties. First, randomized
trials guarantee that
is exchangeable for the set of
permutations that swap elements in
within the strata formed
by the values taken by
(see
Heckman
et al.
, 2010
for a discussion). This
exchangeability property
comes from the fact that under the
null hypothesis of no treatment effect, scrambling the treatment status of the
participants sharing the same values of
does not change the
underlying distribution of the vector of treatment assignments
D.
Second, the hypothesis of no treatment effect implies
that the
joint
distribution of (
) is invariant under these selected permutations of the
vector
D.
As a consequence, a statistic based on assignments
and outcomes
is distribution-invariant
under reassignments based on the class of admissible permutations.
Lehmann and Romano (2005)
show that under
the null hypothesis and conditional on the data, the exact distribution of such
statistics is given by the collection of its values generated by all admissible
permutations.
An important feature of the exchangeability property is that it relies
on limited information on the randomization protocol. It does not require a full
specification of the distribution
nor of the assignment
mechanism, but only the knowledge of which variables are used as covariates
in implementing the randomization protocol. Moreover, the
exchangeability property remains valid under compromises of the randomization
protocol that are based on the information contained in observed variables
X.
In PPP, the assignment variables
used
in the randomization protocol are cohort, gender, child IQ, socio-economic
status (SES, as measured by the cultural deprivation scale) and maternal
employment status. Treatment assignment was randomized for each family on the
basis of strata defined by these variables. In the ABC study, the assignment
variables
are cohort, gender, maternal IQ, High Risk Index
and number of siblings. The participants were matched in pairs on the basis of
strata defined by the
variables.
3.2 Correcting for Multiple Hypothesis Testing
The presence of multiple outcomes in these studies creates the potential
problem of
cherry picking
by analysts who report
“significant” estimates. This generates a downward-biased
inference with
-values smaller than the true ones. To see why,
suppose that a single-hypothesis test statistic rejects a true null hypothesis
at significance level
. Thus, the probability of
rejecting a single null hypothesis out of
null hypotheses is
1 − (1 −
even if there are
no significant treatment effects. As the number of outcomes
increases without bound, the likelihood of rejecting a null hypothesis becomes
1.
One approach that avoids these problems is to form arbitrarily equally
weighted indices of outcomes (see, e.g.,
Muennig
et al.
, 2011
2009
). Doing so, however, produces estimates that are difficult to
interpret. Instead, we analyze disaggregated outcomes. We correct for the
possibility of arbitrarily selecting statistically significant treatment effects
by conducting tests of multiple hypotheses. We adopt the
familywise
error rate
(FWER) as the Type-I error. FWER is the probability of
rejecting any true null hypothesis in a joint test of a set of hypotheses. The
stepdown algorithm of
Lehmann and Romano
(2005)
exhibits
strong FWER control
, that is to say
that FWER is held at or below a specified level regardless of which individual
hypotheses are true within a set of hypotheses.
The
Lehmann and Romano (2005)
stepdown method achieves better statistical properties than traditional
Bonferroni and Holm methods by exploiting the statistical dependence of the
distributions of test statistics. By accounting for the correlation among single
hypothesis
-values, we are able to create less conservative
multiple hypothesis tests. In addition, the stepdown method generates as many
adjusted
-values as there are hypotheses, which facilitates
examination of which sets of hypotheses are rejected. There is some
arbitrariness in defining the blocks of hypotheses that are jointly tested in a
multiple-hypothesis testing procedure. In an effort to avoid this arbitrariness,
we define blocks of independent interest that are selected on interpretable
a priori
grounds (for example, unhealthy lifestyles such as
smoking and drinking). We also report the
-values obtained
with the traditional Bonferroni method to compare it with the stepdown
results.
3.3 Correcting for Attrition
Non-random attrition is also a potential source of bias in the
estimation and inference of treatment effects. While the treatment status
and preprogram variables
are observed
for all participants, outcomes
are not observed for some
participants due to panel attrition. As a consequence, this may induce
correlation between the treatment status and the unobserved characteristics that
affect sample retention.
We address this issue by implementing an Inverse Probability Weighting
(IPW) procedure that identifies features of the full outcome distribution by
reweighting non-missing observations by their probability of being non-attrited,
which is modelled as function of observed covariates.
29
The IPW method relies on matching on
observed variables to generate weights that are used to adjust the treatment
effects for the probability of retention. These probability weights are
estimated using a logit model, following the approach used in
Campbell
et al.
(2014)
30
Small sample IPW inference is
performed by recalculating these probabilities for each draw used to construct
permutations. In PPP, attrition rates are below 10% at age 30 follow-up.
For ABC, attrition rates are lower –roughly 6%. However, for the
health component, there was substantial attrition, and we replicate the analysis
of
Campbell
et al.
(2014)
to correct for it.
3.4 Mediation Analysis
We also conduct a dynamic mediation analysis to decompose the effects of
the treatment into components associated with the experimentally induced
enhancement of inputs at different ages in the production of health.
31
Recall that the observed
outcome is:
(1)
where
denotes treatment
assignment (
= 1 if treated and
= 0 otherwise), and
and
are the counterfactual outcomes when
is fixed at 1 and 0, respectively. Our analysis is based
on the following linear health production function:
(2)
where
is
an intercept;
and
are
vectors of parameters;
are pre-program variables
assumed not to be affected by the treatment;
ε̃
is a zero-mean error
term;
are inputs in
the production of health that can be changed by the intervention, so that
(1 −
. Let
𝒥 be the index set of all inputs 𝒥
= {1
,...
} and
𝒥\𝒥
. Following
Heckman
et al.
(2013)
, we decompose the term
in
equation (2)
into components
due to the 𝒥
inputs we measure and the
𝒥\𝒥
inputs we do not:
(3)
(4)
where
and
Our aim is to decompose treatment effects into components attributable
to changes in measurable inputs. The decomposition is as follows:
(5)
where
is a change in the
parameters, and Δ
is a change in the
inputs. Clearly, unobserved inputs may also be changed by the experiment. Those
changes may be correlated with the observed input changes.
Heckman
et al.
(2013)
discuss these
issues and propose and implement methods for addressing this potential
endogeneity problem. Under assumptions specified in that paper, they test and do
not reject the null hypothesis that increments in unobservables are independent
of increments of observables. We apply their test for both interventions and we
also fail to reject this null hypothesis.
32
Thus, we can safely simplify the notation and write
equation (4)
as:
(6)
Equation (1)
can thus be
rewritten as:
(7)
where
is the contribution of unmeasured
inputs to mean treatment effects,
Dε
+ (1 −
is a zero-mean
error term, and
(1 −
are
the measured inputs. On the basis of
equation (7)
, we can decompose the effects of the intervention on
health as:
(8)
where the second term on the right hand side is
the contribution of measured inputs to the treatment effect.
We next expand this framework to consider two sets of inputs: childhood
(indexed by
) and adulthood (indexed by
inputs, so that the vector
can be partitioned
into two subvectors
], and
equation (7)
can be rewritten as:
(9)
The adult inputs are produced according to the following linear
production function:
(10)
where
Dη
+ (1 −
, and
. On the basis of
equations (9)
and
(10)
, the effect of the intervention on
health can be then decomposed as:
treatment
effect
due
to
unmeasured
inputs
treatment
effect
due
to
early
inputs
direct
effect
(11)
treatment
effect
due
to
late
inputs
treatment
effect
due
to
early
inputs
through
late
inputs
indirect
effect
(12)
We denote this mediation analysis as “dynamic,” since we
consider inputs at different ages, where the early inputs can have both direct
effects on the health outcomes, and indirect effects operating through the late
stage inputs. In our empirical application, we also compare it with the results
obtained from two “static” mediation analyses, i.e., a first one
based on the following health production function in which only early inputs are
included:
(13)
as done for example in
Heckman
et al.
(2013)
- and a second
one based on the following health production function in which only late inputs
are included:
(14)
as done for example in
Muennig
et al.
(2009)
33
As we will see, accounting for
both early and late inputs and for the dynamics in the process of formation of
human capital makes a substantial difference. Excluding early inputs leads to an
overestimation of the role played by late ones in explaining the mechanisms
through which the ABC and PPP interventions produced health impacts.
4 Empirical Results
This section presents the results of our empirical analysis. We discuss the
mean treatment effects in subsection 4.1, and the dynamic mediation analysis results
in subsection 4.2.
Departing from the previous literature in child development,
34
we conduct our analysis by gender.
The rationale for this choice is based on both biological and behavioral
considerations. It is well-established in both animal and human studies that males
are more greatly affected by stressful environments (
Kudielka and Kirschbaum, 2005
). Gender differences in growth, health,
and mortality have been reported in the medical literature, starting
in
utero
(see, e.g.,
Case and Paxson,
2005
Eriksson
et al.
2009
). In addition, differences between men and women in the propensity
to engage in unhealthy behaviors and in developing cardiovascular disease in the
presence of common risk factors have been well documented. These behavioral
differences have led some scholars to propose gender-based interventions (see, e.g.,
Courtenay
et al.
, 2002
Juutilainen
et al.
2004
Marino
et al.
2011
Wardle
et al.
2004
). Despite the large body of interdisciplinary evidence, substantial
gaps remain in our understanding of the sources of gender differences, especially in
relation to the interconnections between social and biological processes (
Rieker and Bird, 2005
Short
et al.
, 2013
). The magnitude of,
and explanations for, gender differences likely vary depending on the specific stage
of the life cycle and the particular health measure considered (
Matthews
et al.
, 1999
). The existing
literature does not provide a definitive answer as to why men and women have
differential responses to environments. Nonetheless, our analysis confirms the
importance of taking the gender dimension into account when analyzing the impacts of
interventions. For the outcomes we study, we find much stronger effects of these
programs for boys than for girls.
4.1 Estimates and Inference
Our main results are displayed in
Tables
(for PPP) and 5 (for ABC). A complete set of results is displayed
in
Web Appendix Section
. The general pattern reported there is that for most blocks of
outcomes, there are few statistically significant health and/or health lifestyle
outcomes for girls, although there are numerous statistically significant health
and/or health lifestyle outcomes for boys. For each table, we present simple
differences in means between the treatment and control groups, and different
-values. These range from the traditional large-sample
-value for the one-sided single hypothesis that treatment
had a positive effect to the constrained permutation
-value
based on the Inverse Probability Weighting (IPW)
-statistic
associated with the difference in means between the treatment groups, and its
corresponding multiple hypothesis testing (stepdown)
-value.
Column (11) of each table reports
-values which account for
all the statistical challenges addressed in this paper. Finally, column (13)
reports conservative Bonferroni
-values that adjust for
multiple hypothesis testing for comparison. We find statistically significant
health effects for males in both interventions. PPP promoted healthy behaviors.
ABC improved biomarkers for cardiovascular and metabolic health.
Table 4.
Inference Results: Perry Preschool Intervention
Variable
# C
# T
Ctr. M.
Treat. M.
Diff. Ms.
Asy.
-val.
Naive
-val.
Blk.
-val.
Per. S.D.
Blk.
-val.
IPW P. S.D.
Bonf.
-val.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
Lifestyles:
Diet and Physical Activity at 40 y.o. -
Males
Physical activity
35
30
0.457
0.367
0.090
0.766
0.779
0.584
0.584
0.545
0.545
1.000
Healthy Diet
35
29
0.229
0.379
0.151
0.097
0.113
0.015
0.033
0.020
0.072
0.040
Lifestyles:
Smoking at 27 y.o. - Males
Not a daily smoker
39
31
0.462
0.581
0.119
0.164
0.160
0.092
0.092
0.089
0.089
0.267
Not a heavy smoker
39
31
0.615
0.903
0.288
0.003
0.002
0.004
0.005
0.004
0.005
0.012
No. of cigarettes
39
31
8.744
4.291
4.453
0.011
0.010
0.008
0.009
0.006
0.011
0.018
Lifestyles:
Smoking at 40 y.o. - Males
Never smoker
36
30
0.444
0.600
0.156
0.107
0.109
0.042
0.042
0.040
0.040
0.160
Not a daily smoker
36
30
0.472
0.667
0.194
0.058
0.063
0.014
0.042
0.010
0.035
0.040
Not a heavy smoker
35
28
0.743
0.929
0.186
0.027
0.027
0.013
0.023
0.011
0.021
0.044
No. of cigarettes
35
28
6.543
3.714
2.829
0.080
0.082
0.043
0.057
0.035
0.049
0.140
Lifestyles: Diet
and Physical Activity at 40 y.o. - Females
Physical activity
22
24
0.045
0.375
0.330
0.003
0.003
0.002
0.005
0.002
0.012
0.004
Healthy Diet
22
24
0.227
0.375
0.148
0.143
0.144
0.238
0.238
0.283
0.283
0.566
Lifestyles:
Drinking at 27 y.o. - Females
Not a frequent drinker
22
25
0.773
0.880
0.107
0.169
0.193
0.004
0.019
0.015
0.028
0.030
Alcohol consumption
22
25
3.818
3.200
0.618
0.314
0.320
0.085
0.085
0.094
0.094
0.188
Lifestyles:
Drinking at 40 y.o. - Females
Not a frequent drinker
22
23
0.909
0.870
0.040
0.659
0.663
0.600
0.600
0.698
0.698
1.000
Alcohol consumption
22
23
4.227
2.826
1.401
0.248
0.256
0.406
0.406
0.467
0.469
0.920
Open in a new tab
Notes:
This table presents the inference results for selected
outcomes of the Perry Intervention. The columns present the following
information: (1) describes the variable of interest; (2) displays the sample
size for the control group; (3) displays the sample size for the treatment
group; (4) displays the control mean; (5) displays the treatment mean; (6)
displays the unconditional difference in means between treatment and control
groups (absolute value); (7) displays the asymptotic
-value for the one-sided single hypothesis based on the
-statistic associated with the unconditional
difference in means. The remaining columns present permutation
-values based on 30,000 draws. (8) displays the single
hypothesis one-sided naive permutation
-value (by naive we
mean based on an unconstrained permutation scheme); (9) displays the
one-sided single hypothesis constrained permutation
-value
based on the
-statistic associated with the difference in
means between treatment groups (by constrained permutation we mean that
permutations are done within strata defined by the pre-program variables
used in the randomization protocol: gender, cohort indicator, the median of
the cultural deprivation scale, child IQ at entry and mother employment
status. More specifically, we simulate the pairwise matching defined in the
randomization protocol using these variables and permute the treatment
status within matched participants). (10) displays the multiple hypothesis
testing (step-down)
-values associated with (9). The
multiple hypothesis testing is applied to blocks of outcomes indicated by
horizontal lines. (11) displays the one-sided single hypothesis constrained
permutation
-value based on the IPW (Inverse Probability
Weighting)
-statistic associated with the difference in
means between treatment groups. Probabilities of IPW are estimated using the
following variables: gender, presence of the father in the home at entry,
cultural deprivation scale, child IQ at entry (Stanford-Binet), number of
siblings and maternal employment status. (12) displays the multiple
hypothesis testing (stepdown)
-values associated with
(11). The multiple hypothesis testing is applied to block of outcomes
indicated by horizontal lines. (13) displays the Bonferroni
-value=
m × p
IP
, where
IP W
is the
unadjusted
-value in col. (11) and
is
the number of hypotheses to test in the block.
Ctr. or C=Control; Treat. or T=Treatment; M.=Mean;
Ms.=Means; Diff.=Difference; Asy.=Asymptotic;
Blk.=Block; Per.=Permutation;
-val.=
-value;
S.D.=Stepdown; y.o.=years old; IPW=Inverse
Probability Weighting; Bonf.=Bonferroni.
We first examine the treatment effects for the PPP. It is evident from
Table 4
that there is a substantial
and significant reduction in both smoking prevalence and intensity among the
males in the treatment group, with effects already present at age 27 and
sustained through age 40.
Muennig
et
al.
(2009)
also examine the impact of the intervention
on smoking, but were unable to detect any impact, since they pool male and
female samples. A separate analysis by gender is justified on a priori grounds,
on the basis of the interdisciplinary literature documenting differences in both
determinants of smoking behavior (
Hamilton
et al.
, 2006
Waldron, 1991
) and responses to interventions (
Bjornson
et al.
, 1995
McKee
et al.
, 2005
).
Males in the treatment group have a lower lifetime prevalence (0.40
versus 0.56 in the control group). They also have significantly lower rates of
daily smoking than the controls, with the proportion of daily smokers declining
from 0.42 to 0.33 between age 27 and the age 40 follow-up for the treated, while
remaining stable at just above 50% for the controls, so that the
difference between the treated and the controls doubles in a decade. This
difference - 20 percentage points (p.p.) - amounts to the gap in smoking
prevalence between men with an undergraduate degree (11.9%) and those
with low education (29.5%) in the US in 2005 (CDC,
U.S. Department of Health and Human Services, 2010
).
Additionally, while the smoking prevalence among the treated aligns with US-wide
figures for men below the poverty level in 2005 (34.3%, CDC), the one
among the controls is 20 p.p. higher. Another finding is that the biggest
difference between the two groups emerges in relation to the intensity of
smoking, which is only partly reduced between the ages 27 and 40 due to a
decline in intensity among the controls: the average number of cigarettes smoked
per day falls from 8.7 at age 27 to 6.5 at age 40.
35
This is consistent with the decreasing
trend in smoking behavior which has been experienced in US after the release of
the Surgeon’s General Report in 1964, as documented in the literature
(see, e.g.,
Fiore
et al.
1989
) – an opposite to the trend documented for obesity.
These estimates have substantial relevance for public health. Tobacco
use is considered the leading preventable cause of early death in the United
States, and about half of all long-term smokers are expected to die from a
smoking-related illness (
U.S. Department of
Health and Human Services, 2010
). In two major studies carried out
for the U.S., one estimated that lifetime male smokers have a reduced life
expectancy of 11 years as compared to nonsmokers, and that, although male
smokers who quit at younger ages have greater gains in life expectancy (by 6.9
to 8.5 years for those who quit by age 35), even those who quit much later in
life gain some benefits (
Taylor
et
al.
, 2002
). Typical male smokers at age 24 have a
reduced lifetime expectancy of up to 6 years as compared to nonsmokers (
Sloan
et al.
, 2004
); this
includes those who subsequently quit. Hence, we would expect this reduction in
smoking to translate into improved health among the treated participants
relative to the controls as they age.
Additionally, the treated males at age 40 in the PPP are more likely
than the controls to report having made dietary changes in the last 15 years for
health reasons (38% versus 23%, see
Table 4
): most of these changes are related to
reductions in the amount of fat and salt in the diet, and in the intake of junk
food. Hence, we would expect these changes in dietary habits to also translate
into substantial health improvements (see, e.g.,
Sacks
et al.
, 2001
for the effects of diet on blood
pressure).
Finally, the PPP intervention also substantially improved the healthy
habits of the women who were randomized to the treatment group: by age 40, they
are 33 percentage points more likely to engage in regular physical activity than
those randomly assigned to the control group (
Table 4
); they also report to drink significantly less frequently in
the age 27 sweep, although this difference is no longer significant by the time
they reach age 40.
We next turn to analyze the impacts of the Abecedarian intervention,
where anthropometric and cardiovascular biomarkers have been collected during a
physician’s visit when the subjects were in their mid-30s. We first
examine three outcomes not previously reported: weight, height and BMI. For each
of them, the treated males perform better than the controls: they are on average
7 kilograms lighter, 5 cm taller, and with a BMI 4 points lower - just below the
obesity threshold. However, the statistical significance of these differences
vanishes once we account for multiple hypothesis testing. A comparison with
nation-wide figures for 2011–2012 (
Ogden
et al.
, 2014
) reveals that ABC male participants
are more likely to be both overweight and obese than 20–39 year old
African-Americans: the prevalence of being overweight is 72% for the
treated and 75% for the controls, against a nationwide figure of
63%, while that of obesity is 56% for the treated and
62.5% for the controls, against a US average of 35%.
Substantial differences are also found for all the reported outcomes
related to blood pressure. Treated males have on average lower values of both
systolic and diastolic blood pressure, and are less likely to fall into the
Stage I hypertension category, according to the definition of the American Heart
Association.
36
The
magnitude of these impacts is both statistically and medically significant.
These estimated reductions in blood pressure are at least twice as large as
those obtained from the most successful multiple behaviors change risk factors
randomized controlled trials (
Ebrahim
et
al.
, 2011
). For example, the greatest reduction reported
in their meta-analysis is −8.5 and −10 for diastolic and
systolic blood pressure, respectively (
Cakir and
Pinar, 2006
), against the −13.5 and −17.5 reported in
the ABC.
The superior health status of the males in the ABC treatment group is
confirmed when we analyze the use of health care (
Table 5
). The treated are significantly less likely
to have ever been hospitalized (21% versus 56% in the control
group), and also to have had a scheduled treatment or exam in the past 12 months
(22% versus 48% in the control group). They also enjoy higher
health insurance coverage than those in the control group, especially if
provided by the employer.
Table 5.
Inference Results: Abecedarian Intervention
Variable
# C
# T
Ctr. M.
Treat. M.
Diff. Ms.
Asy.
-val.
Naive
-val.
Blk.
-val.
Per. S.D.
Blk.
-val.
IPW P. S.D.
Bonf.
-val.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
Physical Health
in the 30s - Males
Measured weight
18
100.6
93.80
6.850
0.242
0.274
0.320
0.320
0.154
0.303
0.462
Measured height
18
1.739
1.790
0.050
0.044
0.061
0.083
0.187
0.215
0.215
0.645
BMI
18
33.29
29.22
4.075
0.076
0.108
0.141
0.175
0.093
0.204
0.279
Overweight (BMI≥25)
18
0.750
0.722
0.028
0.444
0.455
0.391
0.466
0.234
0.234
0.468
Obese (BMI≥30)
18
0.625
0.556
0.069
0.376
0.378
0.448
0.448
0.227
0.335
0.454
Diastolic blood pressure
19
92.00
78.53
13.47
0.017
0.046
0.075
0.075
0.025
0.025
0.050
Systolic blood pressure
19
143.3
125.8
17.54
0.022
0.059
0.057
0.085
0.019
0.031
0.038
Hypertension I
19
0.444
0.105
0.339
0.019
0.043
0.063
0.063
0.010
0.018
0.020
Hypertension II
19
0.556
0.211
0.345
0.033
0.049
0.061
0.095
0.037
0.037
0.074
Health Insurance
at 30 y.o. - Males
Health care coverage
21
27
0.476
0.704
0.228
0.057
0.062
0.080
0.080
0.040
0.040
0.080
Employer-provided/bought
21
27
0.333
0.444
0.296
0.021
0.018
0.034
0.048
0.035
0.055
0.070
Demand for Health
Care in the 30s - Males
Hospitalized
19
0.556
0.211
0.345
0.033
0.039
0.042
0.042
0.100
0.100
0.200
Scheduled treatment/exam
21
27
0.476
0.222
0.254
0.033
0.040
0.026
0.051
0.043
0.080
0.086
Lifestyles: Diet
and Physical Activity at 21 y.o. - Females
Physical activity
28
25
0.071
0.320
0.249
0.010
0.013
0.009
0.009
0.004
0.004
0.008
# Fruit servings
28
25
0.286
0.800
0.514
0.005
0.009
0.002
0.004
0.003
0.006
0.006
Lifestyles:
Drinking at 30 y.o. - Females
Not a frequent drinker
28
25
0.857
0.880
0.023
0.405
0.414
0.493
0.586
0.547
0.547
1.000
Alcohol consumption
28
25
3.536
3.180
0.356
0.422
0.430
0.536
0.536
0.516
0.586
1.000
Age of onset < 17
28
25
0.571
0.280
0.291
0.016
0.018
0.023
0.061
0.009
0.023
0.018
Open in a new tab
Notes:
This table presents the inference results for selected
outcomes of the Abecedarian Intervention. The columns present the following
information: (1) describes the variable of interest; (2) displays the sample
size for the control group; (3) displays the sample size for the treatment
group; (4) displays the control mean; (5) displays the treatment mean; (6)
displays the unconditional difference in means between treatment and control
groups (absolute value); (7) displays the asymptotic
-value for the one-sided single hypothesis based on the
-statistic associated with the unconditional
difference in means. The remaining columns present permutation
-values based on 30,000 draws. (8) displays the single
hypothesis one-sided naive permutation
-value (by naive we
mean based on an unconstrained permutation scheme); (9) displays the
one-sided single hypothesis constrained permutation
-value
based on the
-statistic associated with the difference in
means between treatment groups (by constrained permutation we mean that
permutations are done within strata defined by the pre-program variables
used in the randomization protocol: gender, cohort indicator, number of
siblings, high risk index at birth, and mother WAIS full IQ score. More
specifically, we simulate the pairwise matching defined in the randomization
protocol using these variables and permute the treatment status within
matched participants). (10) displays the multiple hypothesis testing
(stepdown)
-values associated with (9). The multiple
hypothesis testing is applied to blocks of outcomes indicated by horizontal
lines. (11) displays the one-sided single hypothesis constrained permutation
-value based on the IPW (Inverse Probability
Weighting)
-statistic associated with the difference in
means between treatment groups. Probabilities of IPW are estimated using
gender- and wave-specific covariates. See
Campbell
et al.
(2014)
for details. (12)
displays the multiple hypothesis testing (stepdown)
-values associated with (11). The multiple hypothesis
testing is applied to block of outcomes indicated by horizontal lines. (13)
displays the Bonferroni
-value=
m ×
IP W
, where
IP W
is the unadjusted
-value in col. (11) and
is the number of hypotheses to test in the block.
Ctr. or C=Control; Treat. or T=Treatment; M.=Mean;
Ms.=Means; Diff.=Difference; Asy.=Asymptotic;
Blk.=Block; Per.=Permutation;
-val.=
-value;
S.D.=Stepdown; y.o.=years old; IPW=Inverse
Probability Weighting; Bonf.=Bonferroni.
Finally, although they do not appear to be in better health than the
controls, the females in ABC benefit from the treatment in terms of improved
healthy habits. Interestingly, we find an improvement in healthy behavior for
PPP and ABC. For example, the treated females in ABC and PPP are more likely to
engage in physical activity, although the measures are not strictly comparable.
ABC treatment women are more likely to eat fresh fruit than controls. They start
drinking alcohol later. Perry treatment women are less likely to drink than
controls.
For the outcomes with high comparability we find significant differences
in the effects of the treatment between the ABC and PPP interventions for males.
For females, reflecting the imprecision of estimates for them within each
program, there are no sharp differences in treatment effect across
programs.
37
Table 7
reports tests
of equality of comparably measured treatment effects by gender across the two
studies. The relatively stronger effects found in ABC are consistent with (but
do not definitively establish) the efficacy of the early health care given to
participants in that program.
Methodological Issues
As noted in Section 3, both the ABC and PPP studies are plagued by
several problems. We deal with these problems using methods tailored to the
characteristics of each intervention. They make a substantial difference in
inference, especially in case of the PPP. For many outcomes in that
intervention, statistical significance is gained (e.g., for the probability
of never being a smoker by age 40) or increases as we move from a
large-sample analysis to a permutation-based analysis. In contrast is the
effect of applying more refined methods to the Abecedarian sample. In that
sample, no outcome is a gain seen in statistical significance. For a few
outcomes the treatment effects do not survive the multiple hypothesis
testing correction (height and BMI). This suggests that using large-sample
methods does no harm in analyzing the Abecedarian sample. However,
accounting for multiple hypotheses makes a difference. This is evident when
we compare the stepdown
-values with the more conservative
ones obtained using the Bonferroni method (column 13). The analysis of the
Perry intervention requires more sophisticated methods to obtain reliable
inference due, in part, to the greater complexity – and compromise
– in its randomization protocol.
38
As reported in
Campbell
et al.
(2014)
, adjusting for attrition
from ABC makes a difference.
4.2 Mechanisms Producing the Treatment Effects
We next investigate the mechanisms through which estimated treatment
effects arise using the mediation analysis described in Section 3.4. The
literature suggests both direct and indirect mechanisms through which early
childhood experiences might affect later health. Inadequate levels of
stimulation and nutrition, the lack of a nurturing environment and of a secure
attachment relationship, are all inputs which have been shown to play important
roles in retarding development, by altering the stress response and metabolic
systems, and leading to changes in brain architecture (
Taylor, 2010
).
39
On the one hand, child development might directly
affect adult health, both because early health conditions are quite persistent
throughout the lifecycle (as for example in the case of obesity, see
Millimet and Tchernis, 2013
), and because
early traits are determinants of lifestyles (
Conti and Heckman, 2010
).
40
On the other hand, child development might also affect
adult health indirectly, by improving socioeconomic determinants such as
education, employment and income (
Heckman
et al.
, 2010
) - factors which might also have an
independent effect on health, as documented in a large interdisciplinary
literature (
Deaton, 2001
Heckman
et al.
, 2014a
Lochner, 2011
Marmot, 2002
Smith,
1999
).
As described in Section 3.4, we use a dynamic mediation analysis to
examine the role of childhood and adult inputs in explaining treatment effects.
We allow early childhood developmental traits to have both a direct impact on
outcomes, and an indirect one through educational attainment and adult
socioeconomic status. We then compare the results obtained from a dynamic
mediation analysis with those obtained by performing two static mediation
analyses, where only childhood and adulthood inputs in turn are included in the
health capital production function. The rationale for this exercise is to show
the bias researchers might encounter by not considering a dynamic model of human
capital formation.
Differences in both the timing and the content of the data collected do
not allow us to use exactly the same childhood mediators. Nonetheless, we can
analyze the role played by cognitive and behavioral traits for both
interventions. Additionally, we include comparable mediators for educational
attainment and adult socioeconomic status. In particular, for PPP, as early
childhood mediators we consider (following
Heckman
et al.
, 2013
): IQ (the Stanford-Binet
scale), reduced externalizing behavior (reduced aggression) and academic
motivation (constructed from selected items of the Pupil Behavior Inventory
available in Perry). All are measured at ages 7–9.
Heckman
et al.
(2013)
show the
powerful role of reduced externalizing behavior in producing a variety of
beneficial behaviors in PPP. For adult inputs, we use high school graduation as
a measure of educational attainment, unemployment (number of months unemployed
in the last two years) and monthly income at age 27 as measures of socioeconomic
status.
Heckman
et al.
(2010)
show that these measures were significantly affected by
treatment. For the ABC, the childhood mediators represent the three different
domains of development of the child: the Bayley Mental Development and the
Stanford-Binet Scales for cognition, the Infant Behavior Record (IBR) Task
Orientation Scale for behavioral development,
41
and the Body Mass Index of the child for physical
health. All are averages of standardized measurements taken at ages 1–2.
All of these measures have been shown in previous work to be significantly
affected by the treatment (
Burchinal
et
al.
, 1997
Campbell
et al.
, 2014
). For adult inputs, we use college
graduation as a measure of educational attainment, and employment status and
earnings at age 30 as measures of socioeconomic status.
García
et al.
(2014)
document a significant impact of the intervention on these outcomes.
Complete results on mediation analyses are reported in the
Web Appendix, Section 6
The main results for the PPP are displayed in
Figure 3
. We decompose the treatment effects for the outcomes which
survive the multiple hypothesis testing correction, and display the results for
those for which we find that the mediators explain statistically significant
shares of the treatment effects. Consistent with the evidence in
Heckman
et al.
(2013)
, we find that
externalizing behavior is the main mediator of the effect of the intervention on
smoking for males. Its mediating role survives even when later educational
attainment or socioeconomic status is entered, and its effects on adult
behaviors are accounted for. It accounts for shares of the treatment effects
ranging between 17% and 48%. For example, it explains almost
half of the treatment effect on the probability of not being a daily smoker at
27 years (
=0.084), and 43% on the number of
cigarettes smoked per day at age 40 (
=0.052). The
contribution of later life mediators is much smaller and fails to reach
statistical significance. The role played by childhood behavioral traits is
consistent with evidence reported in
Conti and
Heckman (2010)
, who show that improvements in child self-regulation
are associated with a significantly lower probability of being a daily smoker at
age 30, above and beyond its effect on education and the effect of boosts in
education attainment on outcomes. This finding also contributes to the recent
but flourishing literature on the importance of personality and preferences for
healthy behaviors (
Cobb-Clark
et
al.
, 2014
Conti and
Hansman, 2013
Heckman
et
al.
, 2014a
Moffitt
et al.
, 2011
). For females, we find that
enhancements in cognition are the main mediators of the effect of the
intervention on physical activity. This is in line with the evidence reported in
Conti and Heckman (2010)
, who show
that improvements in cognition are associated with better health for women but
not for men.
Figure 3. PPP Dynamic Mediation Analysis of Treatment Effects on Male Outcomes.
Open in a new tab
Notes:
This graph provides a simplified representation of the
results of the dynamic mediation analysis of the statistically significant
outcomes for the PPP intervention. Each bar represents the total treatment
effect normalized to 100%. One-sided
-values that test
if the share is statistically significantly different from zero are shown above
each component of the decomposition. The mediators displayed are: externalizing
behavior, as in
Heckman
et al.
(2013)
among the early childhood inputs; and income as in
Heckman
et al.
(2010)
among the adult inputs. The complete mediation results are reported in
Tables 2 and 3 in the Web
Appendix
. The definition of each outcome is reported in
Section 3 of the Web
Appendix
. The sample the outcomes refer to (M = males; F
= females) and the age at which they have been measured (y.o. =
years old) are shown in parentheses to the left of each bar, after the
description of the variable of interest. ***:
significant at the 1 percent level; **: significant at the 5
percent level; *: significant at the 10 percent level.
Figure 4
compares the results from
the dynamic mediation analysis with those obtained from the two static mediation
analyses, including, respectively, those using only childhood mediators (panel
(a)) and those using only adult mediators (panel (b)). They show that the
decomposition components for the childhood mediators are unchanged in the static
and dynamic mediation analysis (both in case of externalizing behaviors for
males, and of cognition for females). However, only including adult
socioeconomic factors as inputs overestimates their importance. Indeed, while
the shares explained by income are large and statistically significant in the
static model, they are substantially reduced in magnitude and driven to
insignificance when childhood factors are accounted for. Childhood factors have
an impact on health behaviors
above and beyond
their effects on
socioeconomic status in adulthood.
Figure 4. PPP: Static versus Dynamic Mediation Analysis of Treatment Effects on
Statistically Significant Male and Female Outcomes.
Open in a new tab
Notes:
This figure consists of two panels. Each panel compares the
decomposition obtained from using the childhood (a) or adult (b) mediators alone
(static) and the effects when both are used together (dynamic) for the results
of the statistically significant outcomes for the PPP intervention. For each
outcome and mediator, the lighter-colored bars display the static mediation
analysis results, while the darker-colored bars display the dynamic mediation
analysis results (as shown in
Figure 3
).
Complete mediation results are reported in
Tables 2, 3 and 4 in the Web
Appendix
. The definition of each outcome is reported in
Section 3 of the Web
Appendix
. The sample the outcomes refer to (M = males; F
= females) and the age at which they have been measured (y.o. =
years old) are shown in parentheses to the left of each bar, after the
description of the variable of interest. S=static mediation analysis;
D=dynamic mediation analysis.
We now turn to the results for the Abecedarian Program, which are
displayed in
Figure 5
42
We only report the results for men.
Analysis of the female data from ABC shows few treatment effects. The mediators
are clearly not comparable with those used in the analysis of Perry. We confirm
the PPP results that early childhood traits mediate the health effects of the
treatment, above and beyond any induced improvement in adult socioeconomic
status. The shares explained by task orientation and the body mass index of the
child range between 17% and 28% for blood pressure, and between
20% and 31% for hypertension. Together, they explain half of the
treatment effect. This is consistent with existing evidence on both the role of
child temperament
43
and that of
physical development in the early years as key predictors for the risk of later
obesity.
44
Interventions to fight the obesity epidemic starting in the childhood years are
increasingly being advocated, both to promote healthy dietary and exercise
patterns (
Deckelbaum and Williams, 2001
),
and to improve parental knowledge of proper nutrition and recognition of the
child being overweight.
45
As
described in Section 2, the Abecedarian intervention included all these
components. Treated children enjoyed better nutrition and time for exercise
while they were in the childcare center. These features of the intervention
could have had both a direct effect on their fat mass composition, and an
indirect effect through a change in their preferences and behaviors.
Additionally, participants were not allowed to eat outside meals and had to
clean up the table once they were finished. This feature might have further
contributed to the development of their self-regulatory skills. Finally, the
counseling provided to the parents during the child well-care visits might have
also improved the eating environment at home. Unfortunately, the data at our
disposal do not allow us to disentangle the roles of these different
channels.
Figure 5. ABC Dynamic Mediation Analysis of Treatment Effects on Outcomes for
Males.
Open in a new tab
Notes:
This graph provides a simplified representation of the
results of the dynamic mediation analysis of the statistically significant
outcomes for the ABC intervention. Each bar represents the total treatment
effect normalized to 100%. One-sided
-values that test
if the share is statistically significantly different from zero are shown above
each component of the decomposition. The mediators displayed are: task
orientation as in
Burchinal
et
al.
(1997)
and BMI as in
Campbell
et al.
(2014)
among the early childhood
inputs; and employment as in
García
et al.
(2014)
among the adult inputs. The
complete mediation results are reported in
Table 5 in the Web Appendix
. The
definition of each outcome is reported in
Section 3 of the Web Appendix
. The
sample refers to males and the age at which they have been measured (y.o.
= years old) are shown in parentheses to the left of each bar, after the
description of the variable of interest (HI=Health Insurance).
BMI-Employment is the share of the treatment effect which can be attributed to
the indirect effect of experimentally induced changes in BMI affecting health
insurance coverage through its impact on employment (see
equation 12
). ***:
significant at the 1 percent level; **: significant at the 5
percent level; *: significant at the 10 percent level.
On the other hand, the role of childhood traits in explaining the effect
of the treatment on the greater availability of health insurance is much reduced
when adult mediators are introduced. Consistent with the fact that the provision
of health insurance is tied to a job, we find that employment status is the main
mediator of the effect of treatment, with explained shares of 39% in
case of health care coverage and 26% in case of employment-provided
health insurance, respectively. Additionally, we also uncover evidence of a
dynamic interaction between child and adult factors, with 20% and
13% of the effect of the treatment on the health insurance outcomes
being mediated by the indirect effect of child BMI on adult
employment.
46
We also compare the dynamic mediation analysis results with those
obtained from the two static mediation analyses (
Figure 6
). As for the PPP, we find that the shares explained by the
childhood mediators are comparable in the static and in the dynamic model for
the physical health outcomes. However, for health insurance outcomes they are
substantially reduced in the dynamic model (from 25% to 0% in
the case of BMI) and driven to insignificance. In other words, the effects of
early traits on health care coverage work entirely through their impact on adult
socioeconomic status. Conversely, the small and insignificant shares of the
treatment effects on the physical health outcomes explained by employment in the
static model are reduced to zero in the dynamic model. Employment status still
explains a significant share of the treatment effect on the health insurance
outcomes in the dynamic model (Panel (b) of
Figure
).
47
For females,
income appears to explain half of the treatment effect on alcohol consumption in
the static mediation model. This share is reduced to 12% and driven to
insignificance in the dynamic model (as shown in Panel (b) of
Figure 4
).
Figure 6. ABC: Static (S) versus Dynamic (D) Mediation Analysis of Treatment Effects on
Outcomes for Males.
Open in a new tab
Notes:
This figure is comprised of two panels. Each panel provides a
simplified representation of the results of the static and of the dynamic
mediation analyses of the statistically significant outcomes for the ABC
intervention, respectively by comparing the results for the early child
development mediators task orientation and BMI (panel (a)) and for the adult
socioeconomic input employment (panel (b)). For each outcome and mediator, the
lighter-colored bars display the static mediation analysis results, while the
darker-colored bars display the dynamic mediation analysis results (as shown in
Figure 5
). The complete mediation
results are reported in
Tables
5 and 6 in the Web Appendix
. The definition of each outcome is
reported in
Section 3 of the
Web Appendix
. The sample is for males and the age at which outcomes
have been measured (y.o. = years old) are shown in parentheses, to the
left of each bar, after the description of the variable of interest. The 40 term
BMI-Employment in
Figure 5
does not appear
here since the static mediation analyses do not account for the indirect effects
of early inputs affecting health outcomes through their impacts on late inputs.
S=static mediation analysis; D=dynamic mediation analysis.
In sum, our analysis shows the powerful role of enhanced early childhood
traits in explaining the effect of the treatment on adult health and health
behaviors,
above and beyond
any effects of adult socioeconomic
status. This is consistent with the framework of
Cunha and Heckman (2009)
and
Cunha
et al.
(2010)
, as reviewed and extended by
Heckman and Mosso (2014)
, in which early
investments promote later life skills by boosting the base of capabilities that
shape performance on a variety of tasks. Our analysis shows the importance of
developing the child in her entirety, going beyond purely cognitive traits,
within an integrated approach which also promotes behavioral and health
development.
5 Conclusions
This paper analyzes the long-term impacts on healthy behaviors and health of
two of the oldest and most cited U.S. early childhood interventions: the Ypsilanti
Perry Preschool Program and the Carolina Abecedarian Project. We address some of the
major limitations of previous work analyzing these data. That research does not
account for the variety of statistical challenges that arise in analyzing these
studies.
48
For many
outcomes, these corrections make a substantial difference.
49
We also demonstrate differences across
interventions in: (a) characteristics of the treated populations; (b) the nature of
the treatment; and (c) the data collected. These differences create serious
challenges for the meta-analyses routinely conducted in the literature on child
development.
There are strong differences in the impact of the interventions by gender.
Treatment effects are particularly strong for men. Both the Perry and the
Abecedarian interventions have statistically significant effects on the healthy
behavior and health of their participants. The specific health outcomes affected
vary by intervention. The Perry participants have significantly fewer behavioral
risk factors (in particular smoking) by the time they reach age 40. The Abecedarian
participants are in better physical health in their mid-30s. When strictly
comparable outcomes are compared across program, including people of the same
gender, estimated treatment effects are stronger for male ABC participants. This is
broadly consistent with the emphasis on early health found for ABC. We find no
statistically significant differences across program for women.
In an attempt to shed light on the mechanisms through which these treatment
effects emerge, we conduct dynamic mediation analyses. Despite the lack of overlap
in the measurements taken in the two interventions, the outcomes significantly
affected by them, and the imperfect comparability of the mediators, we have
uncovered an important role of enhanced early childhood traits as sources of adult
treatment effects, above and beyond adult enhancements in socioeconomic status. This
evidence is broadly consistent with the models of dynamic capability formation
reviewed in
Heckman and Mosso (2014)
. Skills
developed early in life enhance the capabilities of persons to effectively perform a
variety of lifetime tasks.
As the cohorts we have studied age and diseases start becoming more
prevalent and manifest, it will be valuable to assess the contribution of behavioral
risk factors and health insurance as additional mechanisms explaining the health
effects of early childhood interventions. Our results contribute to an emerging body
of evidence that shows the potential of early life interventions for preventing
disease and promoting health.
Supplementary Material
Appendix
NIHMS747459-supplement-Appendix.pdf
(484.7KB, pdf)
Acknowledgments
This research was supported in part by the American Bar Foundation, the JB & MK
Pritzker Family Foundation, Susan Thompson Buffett Foundation, NICHD R37HD065072,
R01HD54702, a grant from 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), and an anonymous funder.
The views expressed in this paper are those of the authors and not necessarily those
of the funders or persons named here. We thank the editor and two anonymous
referees, Chase Corbin and Sylvi Kuperman Rothkopf, as well as seminar participants
at University of Chicago, Duke University, London School of Economics (CEP),
Northwestern University, Princeton University, University College London, University
of Essex (ISER), University of Sussex, University of Southern California (CESR) and
University of Wisconsin for numerous valuable comments. The
Web Appendix
for this paper can be
found at
Footnotes
In the United States, in 2008, 1% of the population accounted for
20% of total health care expenditures. These are older patients with
cancer, diabetes, heart disease, and other multiple chronic conditions. In
contrast, the bottom half of the expenditure distribution accounted for
3.1% of spending.
The United Nations in 2011 has set a goal of reducing the probability of
premature mortality due to these diseases by 25% by the year 2025.
One potentially promising approach uses insights from behavioral economics to
design effective programs implemented by employers, insurers, and health care
providers, to increase patient engagement and to encourage individuals to take
better care of themselves (
Loewenstein
et al.
, 2013
2007
). These chronic conditions can indeed be prevented, or, at
least, their onset can be substantially delayed (
Ezzati and Riboli, 2012
Sherwin
et al.
, 2004
).
Committee on Psychosocial Aspects of Child and
Family Health
et al.
(2011)
Entringer
et al.
(2012)
Gluckman
et al.
(2009)
Heijmans
et al.
(2008)
Hertzman (1999)
Knudsen
et al.
(2006)
The Abecedarian Project had a second-stage intervention at ages 5–8 via
another randomized experimental design.
Campbell
et al.
(2008)
show that the early educational
intervention had far stronger effects than the school-age treatment on the
majority of the outcomes studied.
Campbell
et al.
(2014)
also show that the second-stage
intervention had no effects on health. Hence, in this paper we only analyze the
first-stage intervention.
The specific ABC and PPP items and the PPP weights are reported in
Table 1
; the weights used for the ABC scale are
reported in Table 1 of
Ramey
et
al.
(2000)
The average age at entry for the treated was 8.8 weeks, and it ranged between 6
and 21 weeks.
As mentioned, the intervention consisted of a two-stage treatment: a preschool
stage (0–5) and a school-age stage (5–8). In this paper we only
study the effects of the preschool treatment, both for comparability with PPP,
and because previous work has reported negligible or no effects from the
second-stage treatment.
The first cohort experienced only one year of treatment, starting at age 4.
10
Note that, if we compute the hourly cost per child, the PPP intervention was more
expensive than the ABC.
11
See
Hojman
et al.
(2013)
for a comparison of the background characteristics of the ABC, PPP, CARE
(Carolina Approach to Responsive Education), IHDP (the Infant Health and
Development Program) and ETP (Early Training Project).
12
We only use data from the control group for ABC, since it started at birth, hence
by age 3 the treatment group would have already received three years of the
program.
13
Parenthetically, the median birthweight for PPP was 3.14 kg, compared to a
national population average of 3.29 kg in 1964. For ABC, the median birthweight
was 3.24, compared to a national population average of 3.34.
14
See, e.g.,
Lopoo and DeLeire (2014)
for a
recent study on the long-term outcomes of children born to single mothers.
15
See, e.g.,
Carneiro
et al.
(2013)
and
Dickson
et
al.
(2015)
on the intergenerational effects of maternal
education on cognitive and behavioural outcomes for a sample of children from US
and UK, respectively.
16
See
Heckman
et al.
(2014b)
for a more detailed description of the ABC and PPP
interventions.
17
See
Heckman and Kautz (2014)
18
Source: Meeting held at the University of Chicago in date 26 July 2013 with the
former Perry teachers Louise Derman-Sparks, Constance Kamii and Evelyn Moore
Heckman
et al.
2014b
).
19
Active research on respiratory tract infections in children was also ongoing
Roberts
et al.
1986
Sanyal
et al.
1980
).
20
Apart from this health counseling, there was no parenting component in the ABC
intervention.
21
The licensed practical nurse visited the classroom daily to review the health
status of the children and receive reports from the parents (
Sanyal
et al.
, 1980
).
22
Source:
Campbell (2014)
23
See
Hall and Holmberg (1974)
Kuperman (2014a
);
Moore
et
al.
(1965)
Ramey
et al.
(1977)
24
Among those lost at follow-up, 5 controls and 2 treated were dead, 2 controls and
2 treated had gone missing.
25
An age 50 follow-up has almost been completed, which includes collection of an
extensive set of biomarkers.
26
As noted by
Heckman (1992)
, experiments
only identify means and not distributions and so do not directly address many
important policy questions without making assumptions beyond the validity of
randomization. See also
Heckman
et
al.
(1997)
27
We refer the reader to
Campbell
et
al.
(2014)
for a discussion of attrition in the health
wave of ABC and to
Heckman
et
al.
(2010)
for compromised randomization in PPP.
Attrition is not an issue for PPP, nor is compromised randomization an issue for
ABC.
28
This approach is called “mediation analysis” in the applied
statistics literature.
29
For a recent review, see
Huber (2012)
30
We use a logit specification that models attrition as function of pre-program
variables for PPP and for ABC at ages 21 and 30, and also as function of
variables collected in the previous sweep for ABC at mid 30s, given the severity
of attrition in the biomedical sweep. We follow the procedure applied in
Campbell
et al.
(2014)
which is described in greater detail there.
31
We thank an anonymous referee for suggesting this analysis. A full comparable
mediation analysis for both the ABC sample and the PPP sample is difficult.
Different measurements have been collected in the two interventions (for
example, the Pupil Behavior Inventory has only been used in PPP, while height
and weight have only been measured in ABC), and the data collection was carried
out at different ages.
32
The results are displayed in
Tables 8 and 11 of the Web Appendix
33
However, they do not control for omitted inputs.
34
Heckman
et al.
(2010)
and
Campbell
et al.
(2014)
are exceptions.
35
Instead, the ABC intervention seems not to have affected smoking behavior to the
same extent. The only statistically significant impact is a delay in the age of
onset of smoking by approximately three years, from 17 years old for the
controls to 20 years old for the treated males (
Table 5 in the Web Appendix
).
However, this effect loses statistical significance once we account for multiple
hypothesis testing. One plausible explanation for the lack of impact of the ABC
on smoking could be the much lower smoking prevalence experienced by the two
cohorts, who lived at two different phases of the smoking epidemics.
36
A more extensive set of health outcomes from the biomedical sweep is analyzed in
Campbell
et al.
(2014)
37
Table 2 of the Web
Appendix
summarizes the comparability of the measures available in
PPP and ABC.
38
See
Heckman
et al.
(2010)
, where this is discussed in depth.
39
Given the lack of brain scans and measures of cortisol, we use proxies related to
the underlying biological systems, such as cognitive and behavioral test
scores.
40
See also
D’Onise
et al.
(2010)
for a review of the literature on the health effects of
ECIs.
41
As seen in subsection 2.2, task orientation was one of the adaptive behaviors
emphasized in the Abecedarian curriculum.
42
We only report mediation results for the males in case of the ABC, since the
dynamic mediation analysis and the static mediation analysis with late inputs
cannot be performed for females, since the only statistically significant
outcomes for this sample are those at age 21, and the late mediators are
measured at age 30. The results for the static mediation analysis with early
inputs for the ABC are shown in the lower panel of
Table 14 in the Web Appendix
Differently from the case for males, no mediator appears to explain a
statistically significant share of the treatment effect. IQ explains 42%
of the effect of the treatment on physical activity — a mechanisms
similar to the one uncovered for the PPP — although it fails to achieve
statistical significance at conventional levels
43
Specifically, task orientation has been associated with increased physical
activity (
Boyd
et al.
2002
); this seems a plausible mechanism through which this trait
might have by itself affected obesity, although data limitations prevent us from
testing this formally.
44
Conti and Heckman (2010)
Park
et al.
(2012)
Pulkki-R°aback
et al.
(2005)
45
Etelson
et al.
(2003)
46
As expected, higher child BMI at ages 1–2 is associated with a lower
probability of being employed at age 30.
47
It should also be noticed that in the case of the static mediation analysis we do
not pass the specification test we apply following
Heckman
et al.
(2013)
. See
Table 11 in the Web
Appendix
48
Compromised randomization is not an issue with the ABC program. For Perry, where
it is an issue, we apply the methods discussed in
Heckman
et al.
(2010)
, where they
make a difference in the reported estimates.
49
Heckman
et al.
(2010)
show that correcting for compromised randomization in Perry as we do in this
paper makes a difference. Correcting for attrition from the medical wave of ABC
has substantial impacts on estimates. (See
Campbell
et al.
, 2014
.)
Contributor Information
Gabriella Conti, Senior Lecturer in Health Economics at the Department of Applied Health Research at University College London; and a Research Fellow at the Institute for Fiscal Studies, London.
James Heckman, Henry Schultz Distinguished Service Professor of Economics at the University of Chicago; Director, Center for the Economics of Human Development, University of Chicago; Co-Director of the Human Capital and Economic Opportunity Global Working Group; a Research Fellow at the American Bar Foundation; and an affiliate of the Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California.
Rodrigo Pinto, Assistant Professor in the Department of Economics at UCLA.
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