Capstone Research Paper. Fall 2015. Huang, Nguyen & Zhang

The Impact of Early Childhood Education
on Children's Non-Cognitive Outcomes
Zijian Huang, Uyen (Sophie) Nguyen, & Jiarui Zhang
Capstone Project Advisor: Professor Meryle Weinstein*
Fall 2015
* We are grateful for having received so much guidance and tireless help from Professor
Weinstein. We also would like to thank Professors Ziol-Guest and Corcoran for invaluable
advice. All EDSP faculty has been wonderful resources throughout the entire course of the
program. Thank you all!

Huang, Nguyen & Zhang 2
Introduction
In the early 1960s, the very first pre-kindergarten (pre-K) programs in the Unites States
(US) served only children from well-educated and middle- or high-income families. Privileged
children who received pre-K education were better off, leaving disadvantaged children far
behind. Although both groups of children differed in many characteristics, attending pre-K was
one of the most important factors that narrow the socio-economic and academic gaps between
those children. The federally funded nationwide pre-K program, Head Start, began in 1965 to
break the vicious circle of poverty that widened such gaps. Since then, policy makers have been
trying to improve the quality of Head Start as many studies have found that the impact of the
program on cognitive outcomes fade out when children enter grade 3. Cognitive outcomes
cannot predict one’s future success accurately, but they have attracted more attention than the
non-cognitive skill component of Head Start. Other research also shows that Head Start
participants do have higher salary, better health status, and lower crime rate when they grow up.
Therefore, this study looks into the effects of Head Start on children’s non-cognitive outcomes to
provide more explanations why those in Head have better life outcomes as adults despite the
fade-out of cognitive impacts. The study design is a combination of multiple regression and
difference in difference analysis to capture changes in children’s non-cognitive skills over time.
The results indicate that Head Start children improved in both internalizing and externalizing
behaviors—the key aspects of non-cognitive outcomes—between grade 3 and grade 8 compared
to children who attended other pre-K programs or did not attend pre-K at all. Though, the
magnitude of the improvement was not the same for internalizing and externalizing behaviors.
For Head Start children from low-income families, they had better non-cognitive skills than
those without pre-K, but they did not fare as well as children in other pre-K programs in terms of

externalizing behaviors. These findings provide important guidelines for Head Start’s future
development, highlighting the need to improve low-income children’s externalizing behaviors
through enhancing Head Start quality.
Background and Significance
Landscape of Pre-K Education in the US
The nation’s interest in pre-K has grown considerably since 1960s—during which only
10 percent of the US’s three-and-four-year-olds attended preschools. By 2005, 69 percent of all
four year-old children nationwide participated in some type of early childhood program,
including Head Start, state-funded pre-K, government-funded special education programs and
private nursery or preschool centers. In 2013 and 2014, state-funded pre-K served about
1,347,272 children. State funding for pre-K has risen by $363.6 million to a total of $5.6 billion
in this fiscal year. Annual funding levels vary dramatically across the country, with an average
ranging from $4,000 to $10,000 for students without disabilities and from $10,000 to $20,000 for
students with disabilities.
Federal Head Start Program and Non-cognitive Outcomes
Founded in 1965, Head Start is a federally funded early childhood education program for
low-income children under the age of mandatory school attendance (which is usually five). It
was designed to provide those preschoolers with a comprehensive program to meet their
emotional, social, health, nutritional and psychological needs. Head Start has served over 32
million children since 1965, growing from an eight-week demonstration project to regular, full-
day services.
Regarding non-cognitive skills, they include a wide range of characteristics such as
motivation, confidence, trustworthiness, perseverance, and communication skills. There is a long

history of theory and measurement, and competing definitions of what is being discussed and
measured. In this case, it is necessary to keep a broad view and consider all non-cognitive skills
as a whole. Non-cognitive skills develop before and throughout children’s school years. They
play a key role in mediating early childhood effects on longer-term outcomes. The development
of these skills is dependent on family and societal characteristics and on school and teacher
factors. Also, non-cognitive and cognitive skills are interdependent and influence each other.
Some evidence suggests that non-cognitive skills are associated with higher productivity and
earnings. In the labor market, employers increasingly value non-cognitive skills as well (García,
2014, p.4).
On the one hand, previous studies on pre-K programs like Head Start claim that quality
early education transform lives of children and their families. Preschool provides students with
both short-term and long-term benefits—which range from cognitive skills to economic
opportunities. On the other hand, pre-K critics point to a recent evaluation of Tennessee’s
voluntary prekindergarten program and question the efficiency of public investments in early
education. This study finds fade-out effects of children’s cognitive outcomes. On average, 4-
year-olds who attended Tennessee’s pre-K program improved on math, language, and reading
skills, but such gains had vanished by the end of kindergarten. Also, 2012 Head Start Impact
Study (2012) reports that positive cognitive outcomes fade out as children enter their third grade.
Although there are clear findings about Head Start’s impacts on cognitive outcomes, for non-
cognitive outcomes, findings are mixed or there are not many papers that have looked into non-
cognitive outcomes. Giorgio Brunello (2011) argues that high cognitive test scores are likely to
result from not only high cognitive skills but also non-cognitive ones such as high motivation.
Chloe Gibbs (2012) claims that policy proposals to make Head Start “more academic” and

overlook non-cognitive outcomes could exacerbate the underlying problems that contribute to
the test score fade-out. According to the Final Report of Third Grade Follow-up to the Head Start
Impact Study (2012), as to children’s social-emotional development for children in the 4-year-
old cohort, there were no observed impacts through the end of kindergarten while there were
favorable impacts reported by parents and unfavorable impacts reported by teachers at the end of
1st and 3rd grades. In the paper of Vogel (2010), early Head Start children display impacts on
social-emotional functioning that last through grade 5.
Very few studies focus on or develop good measures for non-cognitive outcomes. This
knowledge gap motivates the following questions for our study: What is the impact of attending
preschool on students' non-cognitive outcomes, specifically, Head Start vs. other pre-K programs
vs. no pre-K attendance? Do non-cognitive outcomes fade out as in the case of cognitive
outcomes? If so, when does the fade-out effect begin? A better understanding of the preschool
impact on non-cognitive outcomes will help guide future efforts to revamp pre-K programs like
Head Start and mitigate undesirable fade-out effects.
Research Design and Methods
Data
The dataset used in this study is the Early Childhood Longitudinal Study, Kindergarten
Class of 1998-99 (ECLS-K: 98/99) that focuses on children’s early school experience from
kindergarten to 8th
grade. Besides a wide range of family, school, community, and individual
information, the dataset also includes children’s status at the time just after the age of finishing
pre-K programs. In order to find out the nationwide impact of pre-K education programs,
especially Head Start, on children’s non-cognitive outcomes and the change of the impact during
time, it is necessary to analyze the dataset from a nationally representative longitudinal sample

including children’s non-cognitive outcomes from different years, children’s pre-K education
status, and children’s individual, family, and neighborhood information. The ECLS-K public
dataset is one of the best datasets achieving the goal of the study.
Sample
The sampling frame includes the children from both public and private schools in the US
and their parents, teachers, and school administrators. The sample size for the dataset is over
20,000, but because of the missing values in children’s non-cognitive outcomes and their pre-K
education status, the valid sample size is smaller than 20,000. Also, as the research is about the
impact of Head start and other pre-K programs, it is necessary to ensure the quality and
identifiability of the treatment. Children who attended any pre-K education programs less than 5
hours per week and children who attended both Head Start and other pre-K programs are
excluded in this study. Hence, the final sample size for this study is 13,117. Because this sample
is a nationwide sample, by assuming that all children attending pre-K programs less than 5 hours
per week and attending more than 1 types of pre-K programs are random behaviors, this sample
could represent all children from kindergarten to middle school in the US.
Measurements and Procedure
The sample has been selected from across the U. S. in 7 rounds of surveys and interviews
for children, teachers, parents, and school administrators (ECLS). The information used in this
study is from children’s 5th
round, 6th
round, and 7th
round assessments and questionnaires,
parents’ 1st
to 7th
round interviews, and school administrators’ 5th
to 7th
round questionnaires.
The data have been collected by using a variety of methods, including one-on-one assessments,
computer-assisted telephone interviews (CATI), and self-administered paper questionnaires. The
reliability for ECLS-K dataset has been ensured by with well-trained assessors, interviewers, and

survey administrators (ECLS). Details of data collection and sample recruitment can be found at
the National Center for Educational Statistics website.
Regression Models
In order to find out the effect of Head Start on children’s non-cognitive outcomes, the
study uses linear regression models. The sample is grouped into three categories: children who
attended Head Start program, those who attended other pre-K programs, and those who did not
attend any pre-K program. The regression models are used to determine the effect of Head Start
by both comparing Head Start children with ‘other pre-K’ children and comparing Head Start
children with ‘no pre-K’ children.
The dependent variables represent children’s non-cognitive outcome—which is a
comprehensive concept for children’s behaviors, attitudes, and mental health. The two dependent
variables are from children’s self-reported externalizing behavior scores (or external scores) in
grade 3 and grade 5 and internalizing behavior scores (or internal scores) in grade 3, grade 5, and
grade 8. Both of the original scores use a four-point Likert scale, but the scale makes it hard to
observe any changes in them. As a result, the regression uses the standardized scores based on
the sample. The scores are also reversed so that higher values reflect better internalizing or
externalizing behaviors. That will allow the interpretation to be more intuitive.
The key independent variable is a dummy variable that indicates whether children
attended Head Start program or not. Although both ‘other pre-K’ children and ‘no pre-K’
children belong to one group in the key independent variable, each regression will only compare
the Head Start children with either of them. Table 1 shows all descriptive statistics for the
dependent variables, independent variable, and covariates. It also reports the p-values of the
different means of non-cognitive scores and covariates in three pre-K groups by using one-way

ANOVA. According to the combination of two different non-cognitive outcomes and two
different comparison groups, there should be four different sets of regressions.
For each set of regression, the regression coefficients of Head Start on children’s non-
cognitive outcomes have been tested through four models to ensure the coefficients are unbiased.
Model (1) is the simple naive regression model, which uses to find the treatment effect of Head
Start by assuming no covariates could impact the relationship between Head Start and children’s
non-cognitive outcomes. Model (1) to model (4) are nested models within the next model. In
other words, children’s individual characteristics, family characteristics, and neighborhood
characteristics are controlled from model (2) to (4) stage by stage. By comparing the coefficients
of Head Start in the four models, potential bias caused by different characteristics could be
detected. Table 2 displays one of the comparison results for internal scores when the model
compares Head Start with ‘other pre-K’ programs.
In order to measure the treatment and fade-out effects of Head Start over time, the
regression model (5) includes both the time points dummy variables and the interactions of time
dummies and the key independent variable to represent the change of the pre-K effect from grade
3 to grade 5 for the external score models and from grade 3 to grade 8 for the internal score
models. Based on the results of the first four models, model (5) includes all individual, family,
and neighborhood characteristics to minimize bias, so the equation of the final model is as
follows:
𝑌!" = 𝛽! + 𝛽! 𝑍! + 𝛽! 𝑇 + 𝛽! 𝑍! 𝑇 + 𝛽! 𝑋!" + 𝜀!"
In the model above, Z represents the Head Start treatment, T represents the time dummy
variables, and X represents individual, family, and neighborhood covariates. The average change
of non-cognitive outcomes for Head Start children from grade 3 to time point T could be defined
as 𝛽! + 𝛽!. And the average change of non-cognitive outcomes for comparison group children

from grade 3 to time point T could be defined as 𝛽!. Therefore, the difference of the change from
grade 3 to time point T between Head Start and comparison children, or the difference in
difference, could be defined as 𝛽! + 𝛽! − 𝛽! = 𝛽!. By detecting the difference of outcomes over
multiple time points, the model could infer the treatment effect over and the existence of fade-out
effect.
Main Findings
Internalizing behavior scores
Internalizing behavior scores of children that attended Head Start improved as they
progressed from grade 3 to grade 8. At the baseline that is grade 3, children in Head Start had
more internalizing behavior problems than their peers who either attended other pre-K programs
or were not enrolled in pre-K. Compared to the ‘other pre-K’ group, Head Start children scored
consistently higher in their internal scores across the five models as Table 2 shows. That
difference shrunk when the models controlled for more covariates in models 1 through 4, though
it still remained statistically significant at the 99% level of confidence. In the final model with
interaction terms, the difference became bigger again: the internal scores of Head Start children
were 0.320 standard deviation lower than those of the ‘other pre-K’ group, holding other
variables constant. As all children grew up, their internalizing behaviors improved, so the
internal scores rose. The magnitudes of the score changes were quite stable across the five
models, but most of the progress happened between grade 3 and grade 5. Most importantly,
compared to the ‘other pre-K’ group, Head Start children’s internal scores increased by 0.121
standard deviation more in grade 5, and by 0.494 standard deviation more in grade 8. Those
improvements were reflected in the interaction terms, and the difference of 0.494 in grade 8 was
statistically significant at the 1% level of significance.

The findings remained similar when the models compared Head Start children with those
children without any formal pre-K education. At the baseline, the internal scores of the Head
Start group were 0.213 standard deviation lower than those of the ‘no pre-K’ group, and the
difference was statistically significant as summarized in Table 3. On average, fifth and eighth
graders also had fewer their internalizing behavior problems than third graders. The progress was
statistically significant at the 99% level of confidence as the coefficients on “Grade 5” and
“Grade 8” indicated. The internal scores of Head Start children went up by 0.309 standard
deviation more in grade 8, compared to the outcomes of children without preschool. Overall, for
the Head Start group, their internal scores improved from grade 3 to grade 8, and the
improvement is considerably bigger than that of children in other pre-K programs or without pre-
K. As the coefficients on both grades were consistently positive, the models suggested that the
fade-out effects did not exist between those grades for internalizing behaviors.
Externalizing behavior scores
The results for external scores were less straightforward. Although the externalizing
behavior problems of Head Start children declined between grade 3 and grade 5, the progress
those children made was smaller than that by their peers in other pre-K programs or without pre-
K. In grade 3, Head Start children had worse external scores than the ‘other pre-K’ group, but the
difference was not statistically significant. As the children moved onto grade 5, their external
scores rose by 0.198 standard deviation on average, and this change was statistically significant.
Yet, children enrolled in other pre-K did better as they scored about 0.045 standard deviation
more than Head Start children.
Similarly, when compared with ‘no pre-K’ children, the Head Start group in grade 3
scored about 0.092 standard deviation less on average, holding other factors constant. The

positive coefficient of ‘Grade 5’ implied that all children in grade 5 improved their external
scores by 0.223 standard deviation. The score change was statistically significant at the 99%
level of confidence. However, children who did not attend pre-K, surprisingly, had fewer
externalizing behavior problems in grade 5 than those who were in Head Start. That difference
was approximately 0.061 standard deviation, though it was not statistically significant. All
children displayed fewer externalizing behavior problems as they grew up, but Head Start
children did not improve their external scores as much as children in other pre-K or without pre-
K.
Subgroup Analyses: Children from Low-Income Families
Compared with children from middle-income families, children growing up in poverty
had far more behavior problems, while children with affluent parents had similar or better
behavior scores. The coefficients on ‘Low Income’ in Table 3 were all negative and statistically
significant at the 1% level of significance. Therefore, the subgroup analysis focuses on the low-
income group—which is also the population Head Start aims to serve.
Internalizing Behavior Scores
The outcomes of low-income children in Head Start were similar to the overall findings
for internal scores. Although Head Start children had more internalizing behavior problems in
grade 3, they exhibited better behaviors in grade 5 and grade 8. Compared to the ‘other pre-K’
children, children in Head Start scored about 0.078 standard deviation more in grade 5 and 0.190
standard deviation more in grade 8. Compared to the ‘no pre-K’ group, they scored much higher
in internal scores: 0.147 and 0.260 standard deviation more in grade 5 and grade 8, respectively.
All children made progress on their internalizing behaviors, but the Head Start group showed
greater improvement.

Externalizing Behavior Scores
The external outcomes of Head Start children from low-income families were mixed.
Children also had fewer externalizing behavior problems in grade 5 than grade 3. Compared to
low-income children in other pre-K programs, those enrolled in Head Start did not improve their
external behaviors as much: their external scores were about 0.097 standard deviation lower than
those of the other group (as in Table 4). Compared to low-income children without pre-K, the
Head Start group did better: their external scores were 0.052 standard deviation higher than those
of the ‘no pre-K’ group. For children from low-income families, Head Start preschoolers had
better external outcomes than no pre-K children, but they did worse than those in other pre-K
programs.
Conclusion
Limitations
The limitation of this research includes two parts: the restriction of variables and the
assumptions of models. First, children’s reports on their internalizing behavioral status are
available only from grade 3 to grade 8 and those on their externalizing behavioral status are only
from grade 3 to grade 5. Ideally, if the data had both pre-treatment and post-treatment reports,
the difference in difference analysis method could be used to detect the treatment effect of Head
Start. Also, if more pre-treatment covariates were available, propensity score matching analysis
could be used to infer the causal relationship between Head Start and children’s non-cognitive
outcomes, which would effectively minimize potential biases caused by the impacts of Head
Start on other covariates. However, the start point of the ECLS-K study is from kindergarten, so
there is no pre-treatment report or pre-treatment covariate. In addition, children were too young
to report their emotional status before grade 3, so non-cognitive outcomes from earlier years are

not available in the dataset. The treatment variables are restricted by children’s school entry ages,
weekly hours, and single attendance, but there is no variable indicating the total length of the
treatments and the quality of the treatments that could strongly impact the efficiency of treatment
effect.
Second, in order to establish the analysis models to identify the causal effects, it is also
necessary to assume that the baseline covariates have changed little from pre-K to grade 8. The
baseline covariates like parent’s highest education level, frequency of parents-kids activities, and
special education status were observed when children were in kindergarten. Yet, they are
controlled variables when the regression calculates the Head Start coefficient—which means that
these characteristics have to be valid and consistent during those years, or else the coefficient
will be biased. Besides, because the non-cognitive outcomes in kindergarten or the first few
years following pre-K are not available, the study assumes that all the covariates which might
impact children’s non-cognitive outcomes between kindergarten and grade 3 are independent
from children’s pre-K treatment, and those covariates are randomly assigned to the treatment
group and comparison groups. The validity of those assumptions affects the accuracy of the
results.
Policy Implications
Despite the aforementioned limitations, the findings provide more evidence to support
Head Start. The debate over this federal pre-K program should move from whether to keep Head
Start to how Head Start could improve to better support preschoolers. Some studies suggest that
making Head Start more academic would mitigate the fade-out effects of cognitive outcomes.
However, one should be careful about such cognitive-focus recommendations as non-cognitive
skills mediate the effects of early childhood education like Head Start on long-term outcomes.

High test scores are likely to result from both strong cognitive skills and non-cognitive skills like
high motivation. When revamping Head Start, policy makers and educators cannot ignore the
non-cognitive skill component of children’s development. They need to integrate and incentivize
practices that enhance non-cognitive skills of preschoolers. The long-term impact of Head Start
is contingent on both cognitive and non-cognitive skills development in children.

Table 1: Description of Variables
Head Start Other Pre-K No Pre-K
One-way ANOVA
P-value
Children’s Non-cognitive Outcomes
(Standardized Mean)
3rd grade internal score -0.61 0.09 -0.24 0.00
3rd grade external score -0.45 0.08 -0.14 0.00
5th grade internal score -0.29 0.28 0.02 0.00
5th grade external score -0.28 0.27 0.08 0.00
8th grade internal score -0.05 0.16 0.06 0.00
Individual Characteristics (%)
White 27.33 70.92 48.91 0.00
Black 35.57 8.04 9.28 0.00
Hispanic 22.20 11.14 28.28 0.00
Asian 5.38 5.66 6.78 0.00
Other race(s) 9.47 4.13 6.59 0.00
Female 51.65 48.99 49.23 0.99
Only speaking English at home 76.02 87.99 70.92 0.00
Special education 13.62 8.66 10.36 0.00
Low birthweight 12.73 7.27 8.78 0.00
Family Characteristics (%)
Parent’s college degree 24.44 67.49 35.33 0.00
Children doing chores 95.36 97.47 93.67 0.00
Parental involvement 54.86 74.77 62.47 0.00
Married 56.54 82.64 80.02 0.00
Low income 64.29 12.65 33.02 0.00
Middle income 34.42 61.12 57.88 0.08
High income 1.29 26.23 9.10 0.00
College expectation 63.00 82.62 71.06 0.00
Siblings 80.37 80.59 85.28 0.00
Receiving AFDC aid 31.34 4.76 13.11 0.00
Spanking children 32.14 23.29 28.07 0.00
Having computer(s) 28.58 69.18 45.37 0.00
Family Characteristics (Mean)
The number of books and tapes 51.78 106.87 71.52 0.00
The number of important skills 5.87 5.78 5.81 0.00
Parent’s non-cognitive outcomes 0.22 -0.07 0.04 0.00
Neighborhood Characteristics (%)
City 70.25 81.68 77.40 0.00
Northeast 15.74 20.58 18.55 0.00
Midwest 25.70 30.32 20.74 0.00
South 37.40 25.72 29.88 0.00
West 21.16 23.38 30.82 0.00
Crime is a serious problem 52.89 26.77 40.92 0.00
Empty house is a serious problem 29.59 11.64 20.03 0.00
Drug is a serious problem 20.37 6.63 12.63 0.00
Not very safe to play outside 38.82 15.63 26.75 0.00
Number of Observations 2,027.00 6,434.00 4,247.00
Note: The percentages and scores represent the distribution within the Head Start, Other Pre-K, and No Pre-K groups.

Table 2: Regression Results for Internal Scores Head Start vs. Other Pre-K
VARIABLES (1) (2) (3) (4) (5)
Head Start -0.484*** -0.290*** -0.136*** -0.117*** -0.320***
(0.038) (0.043) (0.044) (0.044) (0.073)
Grade 5 0.195*** 0.220*** 0.222*** 0.227*** 0.193***
(0.039) (0.040) (0.039) (0.039) (0.043)
Grade 8 0.232*** 0.258*** 0.253*** 0.269*** 0.131***
(0.038) (0.039) (0.038) (0.039) (0.042)
Head Start Grade 5 0.121
(0.092)
Head Start Grade 8 0.494***
(0.089)
Individual Characteristics
Age 0.046*** 0.054*** 0.060*** 0.060***
(0.016) (0.015) (0.015) (0.015)
Black -0.328*** -0.198*** -0.203*** -0.201***
(0.052) (0.056) (0.058) (0.057)
Hispanic -0.147*** -0.093* -0.108* -0.108*
(0.053) (0.053) (0.056) (0.055)
Asian 0.079 0.047 0.027 0.026
(0.060) (0.061) (0.062) (0.061)
Other races -0.054 0.008 0.017 0.017
(0.069) (0.070) (0.071) (0.070)
Female -0.092*** -0.090*** -0.087*** -0.088***
(0.031) (0.030) (0.029) (0.029)
Only speaking English 0.303*** 0.212*** 0.238*** 0.236***
(0.056) (0.056) (0.057) (0.057)
Special education -0.148*** -0.127** -0.127** -0.126**
(0.057) (0.055) (0.055) (0.055)
Low birthweight -0.085 -0.050 -0.061 -0.062
(0.055) (0.054) (0.055) (0.054)
Family Characteristics
Parent’s college degree 0.065* 0.060* 0.059*
(0.034) (0.034) (0.034)
Parental involvement 0.042 0.039 0.038
(0.037) (0.037) (0.037)
Children doing chores -0.137 -0.176 -0.174
(0.103) (0.107) (0.107)
The number of important skills -0.007 -0.014 -0.013
(0.026) (0.026) (0.026)
Parent’s non-cognitive outcomes -0.111*** -0.112*** -0.111***
(0.029) (0.028) (0.029)
Married 0.026 0.033 0.030
(0.043) (0.042) (0.042)
Low income -0.144*** -0.134*** -0.135***
(0.049) (0.050) (0.049)
High income -0.019 -0.031 -0.021
(0.038) (0.039) (0.039)
College expectation 0.104*** 0.103*** 0.102***
(0.039) (0.039) (0.039)
Siblings 0.031 0.032 0.032
(0.041) (0.040) (0.040)
Receiving AFDC aid -0.152** -0.157*** -0.156***
(0.060) (0.059) (0.059)
Books and tapes 0.001*** 0.001*** 0.001***

(0.000) (0.000) (0.000)
Spanking children -0.095*** -0.083** -0.083**
(0.036) (0.036) (0.036)
Having computer(s) -0.075** -0.077** -0.077**
(0.034) (0.034) (0.034)
Neighborhood Characteristics
City 0.122*** 0.123***
(0.035) (0.035)
Northeast 0.021 0.021
(0.041) (0.041)
South -0.027 -0.028
(0.041) (0.041)
West 0.030 0.029
(0.046) (0.046)
Crime -0.128*** -0.127***
(0.044) (0.044)
Empty house 0.138*** 0.134***
(0.052) (0.052)
Drug 0.011 0.014
(0.057) (0.057)
Safe play 0.014 0.010
(0.042) (0.041)
Constant 0.011 -0.316*** -0.326 -0.381* -0.322
(0.030) (0.083) (0.199) (0.203) (0.205)
Observations 11,538 11,538 11,538 11,538 11,538
R-squared 0.060 0.093 0.128 0.134 0.143
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: Individual characteristics include children’s age, Black, Hispanic, Asian, other races, female, English, special
education, and low birth weight. Family characteristics include college, parental involvement, chores, important skills,
parental emotion, married, low income, high income, college expectation, siblings, AFDC aid, books and audios, spank,
and computer. Neighbor characteristics include city and town, Northeast, South, West, crime, empty house, drug, and
safe play.

Table 3: Head Start Impact on Children’s Internal and External Scores
Internal Score External Score
VARIABLES
HS vs.
Other Pre-K
HS vs.
No Pre-K
HS vs.
Other Pre-K
HS vs.
No Pre-K
Head Start -0.320*** -0.213*** -0.128 -0.092
(0.073) (0.082) (0.078) (0.080)
Grade 5 0.193*** 0.246*** 0.198*** 0.223***
(0.043) (0.057) (0.045) (0.055)
Grade 8 0.131*** 0.298***
(0.042) (0.055)
Head Start Grade 5 0.121 0.037 -0.045 -0.061
(0.092) (0.104) (0.096) (0.104)
Head Start Grade 8 0.494*** 0.309***
(0.089) (0.100)
Individual Characteristics Y Y Y Y
Family Characteristics Y Y Y Y
Low income -0.135*** -0.172*** -0.219*** -0.182***
(0.049) (0.053) (0.063) (0.063)
High income -0.021 0.166** 0.015 0.176**
(0.039) (0.074) (0.050) (0.071)
Neighborhood
Characteristics Y Y Y Y
Constant -0.322 -1.003*** -0.403* -1.257***
(0.205) (0.252) (0.231) (0.315)
Observations 11,538 7,008 7,713 4,679
R-squared 0.143 0.147 0.181 0.197
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
education, and low birthweight. Family characteristics include parent’s college degree, children doing chores,
parental involvement, married, income category, college expectation, siblings, receiving AFDC aid, spanking,
resources at home, and parental non-cognitive outcomes. Neighborhood characteristics include city, region, crime,
empty house, drug, and safe play.

Table 4: Subgroup Analyses
Internal Score
HS vs. Other Pre-K HS vs. No Pre-K
VARIABLES
Low
Income
Middle
Income
High
Income
Low
Income
Middle
Income
High
Income
Head Start -0.133 -0.350*** -0.050 -0.338** -0.124 -0.252
(0.138) (0.116) (0.274) (0.135) (0.131) (0.251)
Grade 5 0.297** 0.210*** 0.159* 0.181* 0.312*** -0.169
(0.129) (0.059) (0.085) (0.107) (0.079) (0.171)
Grade 8 0.416*** 0.117** 0.001 0.370*** 0.278*** -0.127
(0.141) (0.056) (0.084) (0.114) (0.079) (0.112)
Head Start Grade 5 0.078 0.193 -0.532 0.147 0.086 -0.471
(0.168) (0.157) (0.344) (0.163) (0.176) (0.390)
Head Start Grade 8 0.190 0.550*** -0.162 0.260 0.347** -0.404
(0.179) (0.141) (0.446) (0.166) (0.160) (0.471)
Individual
Characteristics Y Y Y Y Y Y
Family Characteristics Y Y Y Y Y Y
Neighborhood
Constant -0.621 0.025 -0.373 -1.234** -0.991*** 0.116
(0.484) (0.317) (0.450) (0.581) (0.366) (0.654)
Observations 2,092 5,695 2,315 2,239 3,124 448
R-squared 0.163 0.087 0.073 0.119 0.089 0.158
External Score
HS vs. Other Pre-K HS vs. No Pre-K
VARIABLES
Low
Income
Middle
Income
High
Income
Low
Income
Middle
Income
High
Income
Head Start 0.004 -0.105 0.666** -0.195 0.034 0.602***
(0.138) (0.130) (0.323) (0.128) (0.134) (0.204)
Grade 5 0.315** 0.188*** 0.210** 0.171 0.256*** 0.063
(0.130) (0.061) (0.092) (0.110) (0.073) (0.102)
Grade 8
Head Start Grade 5 -0.097 -0.052 -1.063* 0.052 -0.112 -1.719***
(0.175) (0.170) (0.595) (0.163) (0.177) (0.381)
Head Start Grade 8
Individual
Family Characteristics Y Y Y Y Y Y
Neighborhood
Constant 1.500** -0.235 -0.768* -0.733 -1.440*** -0.537
(0.700) (0.335) (0.421) (0.773) (0.407) (0.598)
Observations 1,488 3,878 1,442 1,580 2,075 274
R-squared 0.149 0.141 0.184 0.164 0.187 0.441
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

education, and low birthweight. Family characteristics include parent’s college degree, children doing chores,
parental involvement, married, income category, college expectation, siblings, receiving AFDC aid, spanking,
resources at home, and parental non-cognitive outcomes. Neighborhood characteristics include city, region, crime,
empty house, drug, and safe play.

Reference
Barnett, W. S., & Hustedt, J., Robin, K., & Schulman, K. (2003). The State of Preschool
Yearbook. New Brunswick, NJ: Rutgers University, National Institute for Early
Education Research.
Brunello, B., & Schlotter, M. (2011). Non-Cognitive Skills and Personality Traits: Labour
Market Relevance and Their Development in Education & Training Systems. IZA
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Early Childhood Longitudinal Program (ECLS) - Data Collection Procedures. (n.d.). Retrieved
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https://nces.ed.gov/ecls/kinderdatainformation.asp
García, E. (2014) The Need to Address Non-cognitive Skills in the Education Policy Agenda.
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in-the-education-policy-agenda/
Gibbs, C., Ludwig, J., & Miller, D.L. (2011). Does Head Start Do Any Lasting Good?. National
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Kirp, D.L. (October 3rd, 2015). Does Pre-K Make Any Difference?. The New York Times.
http://www.nytimes.com/2015/10/04/opinion/sunday/does-pre-k-make-any-
difference.html?_r=0
Morrison, L., & Schoon, G.I. (2013). The impact of non-cognitive skills on outcomes for young
people. Institute of Education. 21
National Head Start Association. (2014). Benefits of Head Start and Early Head Start Programs.
Retrieved from http://www.nhsa.org/research/head_start_benefits.

Pre-kindergarten: What the research shows. Center for Public Education. Retrieved from
http://www.centerforpubliceducation.org/Main-Menu/Pre-kindergarten/Pre-
Kindergarten/Pre-kindergarten-What-the-research-shows.html
The American Speech-Language-Hearing Association (ASHA). Retrieved from
http://www.asha.org/Advocacy/schoolfundadv/Overview-of-Funding-For-Pre-K-12-
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Workman, E., Griffith, M., & Atchison, B. (2014). State Pre-K Funding – 2013-14 Fiscal Year

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