Write Your Research Analysis Essay will address th

Write

Your Research Analysis Essay will address the following ideas:

Introduction

? Present a topic or issue relevant to your academic or professional field.
o This will be presented in your introduction. Explain the overall background and background of this issue and include a well-constructed thesis

statement that indicates your topic and the purpose you will develop.

Article 1

? Discuss your first source article.
o Identify the article, and then

? Describe the?perspective presented in the article.?
o Summarize the article?s findings on your identified topic.?
o Show how the article?s perspective contributes to your overall thesis.

Article 2

? Discuss your second source article.
o Identify the article, and then

? Describe the?perspective presented in the article.?
o Summarize the article?s findings on your identified topic.?
o Show how the article?s perspective contributes to your overall thesis.

Article 3

? Discuss your third source article.
o Identify the article, and then

? Describe the?perspective presented in the article.?
o Summarize the article?s findings on your identified topic.?
o Show how the article?s perspective contributes to your overall thesis.

Conclusion

? Analyze the significance of the evidence cited and the connections you have made.
o This will be presented in your conclusion. Connect the ideas presented to show why the topic is an important one, while highlighting your

major takeaways from the articles and relating them to your thesis.

The Research Analysis Essay

? Must be six to eight double-spaced pages in length (not including title and
references pages) and formatted according to APA Style (Links to an external
site.) as outlined in the Writing Center?s APA Formatting for Microsoft
Word (Links to an external site.)

? Must include a separate title page with the following:
o Title of paper in bold font

? Space should appear between the title and the rest of the
information on the title page.

o Student?s name
o Name of institution (The University of Arizona Global Campus)
o Course name and number
o Instructor?s name
o Due date

? Must utilize academic voice. See the Academic Voice (Links to an external
site.) resource for additional guidance.

? Must include an introduction and conclusion paragraph. Your introduction
paragraph needs to end with a clear thesis statement that indicates the
purpose of your paper.

o For assistance on writing Introductions & Conclusions (Links to an
external site.) and Writing a Thesis Statement (Links to an external
site.), refer to the Writing Center resources.

? Must use at least three credible sources in addition to the course text.
o The Scholarly, Peer-Reviewed, and Other Credible Sources (Links to an

external site.) table offers additional guidance on appropriate source
types. If you have questions about whether a specific source is
appropriate for this assignment, please contact your instructor.
Your instructor has the final say about the appropriateness of a
specific source.

o To assist you in completing the research required for this
assignment, view Quick and Easy Library Research (Links to an
external site.) tutorial, which introduces the University of Arizona
Global Campus Library and the research process, and provides some
library search tips.

? Must document any information used from sources in APA Style as outlined in
the Writing Center?s APA: Citing Within Your Paper (Links to an external site.)

? Must include a separate references pages that is formatted according to APA
Style as outlined in the Writing Center. See the APA: Formatting Your
References List (Links to an external site.) resource in the Writing Center for
specifications.

Head Start?s Long-Run Impact: Evidence from the
Program?s Introduction

Owen Thompson

Journal of Human Resources, Volume 53, Number 4, Fall 2018, pp. 1100-1139
(Article)

Published by University of Wisconsin Press

For additional information about this article

Access provided by Ebsco Publishing (27 Oct 2018 09:42 GMT)

https://muse.jhu.edu/article/706377

Head Start?s Long-Run Impact
Evidence from the Program?s Introduction

Owen Thompson

ABSTRACT

This paper estimates the effect of Head Start on health, education, and labor
market outcomes observed through age 48. I combine outcome data from the
NLSY79 with archival records on early Head Start funding levels and for
identification exploit differences across counties in the introduction timing
and size of local Head Start programs. This allows me to compare the long-
term outcomes of children who were too old for Head Start when the program
was introduced in their county with the outcomes of children who were
sufficiently young to be eligible. I find that individuals from counties that had
an average-sized program when they were in Head Start?s target age range
experienced a $2,199 increase in annual adult earnings, completed 0.125
additional years of education, were 4.6 percentage points less likely to have
a health limitation at age 40, and overall experienced a 0.081 standard
deviation improvement in a summary index of these and other outcome
measures. Funding levels at ages outside of Head Start?s target range are
not significantly correlated with long-term outcomes. Estimated treatment
effects are largest among blacks, the children of lower-education parents,
and children exposed to better funded Head Start programs?heterogeneity
that is consistent with a causal program impact.

I. Introduction

On average children from economically disadvantaged backgrounds
experience worse life outcomes than their more affluent peers in the United States, and
how best to improve the life chances of poor children has long been a question of
intense policy and research interest (see Coleman et al. 1966; Almond and Currie
2011). Because many early markers of success already appear worse for poor children

Owen Thompson is Assistant Professor, Department of Economics, Williams College. He thanks Martha Bailey
and Andrew Goodman-Bacon for assembling and making available Head Start funding data and John
Heywood for helpful comments. The data and computer code used in this article are available on the
author?s personal web page (https://sites.google.com/site/othompsonecon/). The author is willing to assist
([email?protected]).
[Submitted February 2016; accepted May 2017]; doi:10.3368/jhr.53.4.0216-7735R1
JEL Classification: I260, J24, and H430
ISSN 0022-166X E-ISSN 1548-8004 ? 2018 by the Board of Regents of the University of Wisconsin System

T H E J O U R N A L O F H U M A N R E S O U R C E S ? 5 3 ? 4

by the time they enter kindergarten, researchers often view preschool-based interven-
tions as the policies with the most potential to promote early human capital development
and social mobility (Bronfenbrenner 1979; Currie 2001). This view has been reinforced
by the success of small-scale model preschool programs like the Abecedarian and Perry
Preschool projects, and by interdisciplinary evidence that early childhood constitutes a
sensitive period with disproportionate influence on long-term outcomes (Shonkoff and
Phillips 2000; Knudsen et al. 2006).
Head Start is by far the largest scale preschool-based intervention in the United States,

with the Department of Health and Human Services reporting that Head Start currently
serves nearly 1 million low-income children nationwide at a cost of approximately
$8 billion annually (DHHS 2014). Since its inception in 1965 as a central component of
the War on Poverty, Head Start?s effectiveness has been a topic of considerable contro-
versy. Evenafter a large-scale randomized evaluation anda numberofquasi-experimental
studies, which are reviewed in greater detail below, skepticism and controversy regarding
Head Start?s causal impact onthe outcomes of participants remain, especially withrespect
to longer-term outcomes (see Haskins 2004; Barnett 2011; Klein 2011).
The present paper assesses the impact of Head Start on a variety of health, education,

and labor market outcomes observed through age 48. My empirical approach uses
archival data on county-level Head Start spending in the early years of the program
to compare the adult outcomes of children with different levels of childhood exposure to
Head Start. Variation in program exposure is primarily due to the fact that some children
in my sample were too old for Head Start when the program was introduced in their
county, while other children from the same county were sufficiently young for Head
Start when it was introduced.
Individual level outcome data are drawn from the 1979 National Longitudinal Survey

of Youth (NLSY79), whose respondents were born between 1957 and 1964. Approxi-
mately 50 percent of individuals from these cohorts were beyond Head Start?s target age
range when the program was launched, while the other 50 percent were sufficiently
young to participate at the time of its introduction, providing a rich source of plausibly
exogenous variation in program exposure within the NLSY79 sample. Respondents
have been closely followed well into middle-age, allowing me to assess Head Start?s
impact further into the life course and for a wider range of outcomes than most previ-
ous research.
The main finding is that exposure to early implementations of Head Start had sta-

tistically and substantively significant effects on a variety of long-term outcomes. My
preferred models use a composite measure of adult socioeconomic well-being as the
dependent variable and restrict the sample to individuals who were between the ages of
two and seven when Head Start was introduced in their county. Estimates from these
models indicate that being exposed to an average-sized Head Start program led to a
0.081 standard deviation improvement in adult socioeconomic well-being.1 With re-
spect to more specific outcomes, I find that exposure to an average-sized Head Start
program increased annual adult earnings by $2,199 (in 2012 dollars), improved final
educational attainment by 0.125 years, and reduced the probability of a health limitation
at age 40 by 4.6 percentage points, among other impacts.

1. As described in detail below, an average Head Start program is defined here as onewith expenditures of $170
per child ages three to six living in the county (in 2012 dollars).

Thompson 1101

To validate my research design I present a set of balancing tests that show that the
baseline characteristics of children in my sample with and without positive Head Start
exposure are very similar. I also demonstrate that Head Start funding at ages outside of the
program?s target range is not significantly associated with improved long-term outcomes
and that my main findings are largely robust to the inclusion of controls for exposure to
other War on Poverty programs or county-specific time trends, and to a variety of al-
ternative sample restrictions and specifications. Analyses of treatment effect heterogeneity
indicate that program exposure has the largest effects among blacks, the children of lower-
education parents, and children exposed to better funded Head Start programs. Given
Head Start eligibility criteria and participation rates, these heterogeneous effects are
generally consistent with causal program impacts. Finally, I present analyses that compare
the outcomes of siblings with different levels of exposure to Head Start during childhood,
and the results similarly suggest substantial long-term program effects, but these sibling-
based estimates are imprecise and not statistically significant at conventional levels.
Relative to the existing Head Start literature, the present study examines a broader

range of outcomes further into the life cycle than most previous studies and also im-
plements an identification strategy that relies on cross-county variation in the timing and
intensity of Head Start?s initial introduction, which complements the sibling and policy-
discontinuity based approaches of previous studies. I additionally note that both the
nature of Head Start programing and the counterfactual environments of Head Start
participants have changed substantially since its original implementation, and as a result,
the current findings are not directly comparable to evaluations of more recent imple-
mentations of Head Start, which have been the focus of most previous studies.

II. Background

A. The Head Start Program

Head Start was introduced as an eight-week summer program in 1965, when 560,000
children were enrolled and the program received $96 million in federal funding. Federal
Head Start funding increased to approximately $200 million in 1966 and to over $300
million in subsequent years as new centers were added, enrollment at existing centers
increased, and many programs transitioned from summer-only to full-year programing
(DHHS 2014).
Initial Head Start programs were funded through the War on Poverty?s Community

Action Programs (CAP) and administered by the Office of Economic Opportunity
(OEO). In keeping with the general approach of all CAP programs, Head Start grants
were issued directly to thousands of local organizations, rather than via the states.
Bypassing state governments was designed to encourage the ?maximum feasible partic-
ipation? of beneficiaries while also limiting the ability of southern states to direct funds
away from low-income African American communities. As a result of this approach, the
Head Start rollout across the country was characterized by the sporadically timed intro-
duction of local programs, which were also of widely varying sizes and quality (Levitan
1969; Vinovskis 2008). These geographic differences in the timing and intensity of local
program introduction form the basis of my identification strategy below.
One important difference between the early implementations of Head Start studied

here and the modern program is the age profile of participating children. Detailed data

1102 The Journal of Human Resources

on the ages of Head Start participants in both summer and full-year programs from
1965?1968 are reported in Table 1, using data from representative surveys of Head Start
centers conducted by the Census Bureau.2 The table shows that in full-year Head Start
programs in 1966, 1967, and 1968, three-year-old children comprised between 10 percent
and 18 percent of participants, while approximately 45 percent of participants were age
four, just over 30 percent were age five, and less than 10 percent were age six or older.
Participants insummerprograms from this periodtendedto beolder, with30to40percent
of participants age six or older and very few three-year-olds.3

In contrast, DHHS (2014) reports that in contemporary Head Start programs there
are significant numbers of three-year-old participants and that fewer than 5 percent of
participants are older than five. The unique participant age profiles of early versions of
Head Start are reflected in the construction of the program exposure measures described
in Section III below.
Other than being of an age served by the program, the main eligibility requirement for

both historical and current Head Start programs is a family income below the federal
poverty line, although up to 10 percent of a local program?s enrollees can have incomes
above this level.
With respect to Head Start program content in the period under study, most early

Head Start programs were designed as holistic child development interventions and
placed particular emphasis on health, self-esteem, noncognitive skills, and parental en-
gagement rather than purely academic objectives such as learning the alphabet or counting
(Vinovskis 2008). Common health-relevant program activities included the provision of
nutritious meals and snacks, immunizations, and screenings for common health condi-
tions, such as tuberculosis and dental problems. Most of the initial Head Start programs

Table 1
Age Distributions of Head Start Participants, 1965?1968

Summer
1965

Full
Year
1966

Summer
1966

Full
Year
1967

Summer
1967

Full
Year
1968

Summer
1968

Younger than 3 0% 1% 1% 1% 0% 3% 1%
3 years?3 years
and 11 months

1% 10% 2% 12% 1% 18% 3%

4 years?4 years
and 11 months

13% 45% 18% 44% 20% 43% 20%

5 years?5 years
and 11 months

42% 31% 44% 34% 45% 31% 40%

6 years or older 39% 10% 35% 5% 31% 3% 34%
Not reported 6% 3% 2% 3% 3% 2% 2%

Notes: Table reports the percentage of Head Start participants in each age category for the indicated time
period. Data are drawn from Bureau of Census (1968, 1970, 1972).

2. See Bureau of Census (1968, 1970, 1972).
3. Similar age profiles are reported in Levitan (1969) Table 4-4.

Thompson 1103

included home visits and frequent formal parent?teacher meetings, and parental
volunteering and paid parental classroom employment were also widespread (Zigler
and Valentine 1979; Bureau of Census 1968). The recruitment of adequate numbers of
qualified professional staff was problematic for early Head Start programs, leading to
the widespread use of paraprofessionals with minimal training (Levitan 1969).
While program content in modern implementations of Head Start has substantial

overlap with early programs, current programs have a more professionalized staff and a
more uniform curriculum that places greater emphasis on cognitive development and
academic preparation, among other important differences. The implications of these
programing changes for interpreting the present study?s main findings, as well as the
effect of differences in the likely counterfactual environments of early versus contem-
porary Head Start participants, are discussed in Section VII.

B. Existing Research

Head Start?s impacts are the topic of a large interdisciplinary literature, with excellent
reviews provided by Gibbs, Ludwig, and Miller (2013) and Duncan and Magnuson
(2013). Early evaluations of Head Start, most prominently Westinghouse Learning
Corporation (1969), typically found modest short-term effects on cognitive test score
outcomes that faded by second or third grade. Early evaluations did not addressselection
into program participation in a rigorous manner and could not evaluate outcomes other
than short-term test scores.
In part to address the ambiguity of early observational studies, in 2002 the federal

government sponsored a randomized experiment known as the National Head Start
Impact Study (NHSIS), with the official findings reported in Puma et al. (2010). The
NHSIS included 4,667 children who had applied to wait-listed Head Start programs
across the country, with approximately 50 percent of participants then randomly
assigned admission to the program for which they had applied. Around 86 percent of
children assigned to the treatment group enrolled, while children who were not ran-
domly selected for admission were free to enroll in other Head Start programs in their
area, and 18 percent did so (a much larger percentage of control observations enrolled in
non?Head Start preschool programs). Various cognitive and social?emotional devel-
opment measures were recorded through spring of each participant?s first grade year,
with a limited followup in third grade.
Ludwig and Phillips (2007) calculated treatment effects on the treated in the NHSIS,

which account for the described imperfect compliance patterns, and found statistically
significant program impacts of approximately 0.2 to 0.4 standard deviations for most
language and literacy related test scores, with smaller effects on math scores and social?
emotional outcomes. However, these effects fade almost entirely by the end offirst grade
(Duncan and Magnuson 2013), leading some observers to conclude that the program is
ineffective (for example, Barnett 2011). No outcome measures beyond third grade were
recorded.
Various quasi-experimental evaluations of Head Start have looked beyond impacts on

short-term test scores. One strand of this literature uses cross-sibling variation in Head
Start participation to account for unobserved family level characteristics, typically uti-
lizing longitudinal survey data to measure outcomes. Prominent examples include Currie
and Thomas (1995), who use data from the NSLY79 Mother-Child Supplement, also

1104 The Journal of Human Resources

known as the CNLSY; Garces, Thomas, and Currie (2002), who use the Panel Study of
Income Dynamics (PSID) sample with additional data collection by the authors; and
Deming (2009), who also uses outcome data from the CNLSY. These sibling-based
studies have typicallyfoundshort-term test scoregains thatfadeas childrenmove through
school, but they have also found large impacts on a variety of medium-term socioeco-
nomic outcomes, including grade-repetition, high school graduation and college enroll-
ment rates, arrest rates, and self-rated health in early adulthood. A potential issue with the
sibling fixed-effects approach is that unobserved child-varying characteristics may in-
fluence the decision to enroll one sibling in Head Start but not the other, and spillovers
from participating to nonparticipating siblings are also possible. These threats to identi-
fication are discussed at length in the cited studies, which include axillary tests for within-
family selection and spillovers that on balance support the validity of the approach.
Additional quasi-experimental studies have used discontinuities in Head Start funding

levels or eligibility rules to identify program effects. For instance, Ludwig and Miller
(2007) exploited Head Start grant-writing assistance that the OEO provided to the 300
highest poverty counties as of 1960, which resulted in a large and lasting discontinuity in
Head Start funding levels. Using vital statistics data, the authors found that counties just
below the 300-poorest threshold experienced large reductions in age and cause specific
mortality rates compared to counties just above the threshold. Improvements in edu-
cational attainment were also observed using data from the Census and the National
Educational Longitudinal Survey, though various data limitations made these estimates
less than conclusive, and no lasting effects on standardized test scores were found.
Additionally, Carneiro and Ginja (2014) compared the outcomes of children in the
CNLSY sample from families with income-to-needs ratios on either side of state-
specific Head Start eligibility thresholds.4 They found that Head Start participation
had significant positive effects on the health and behavioral outcomes of males ob-
served at ages 12?21 and substantive but statistically insignificant effects on inter-
mediate educational outcomes, such as grade repetition and special education usage,
with no significant effects on cognitive test scores.
Finally, a number of recent papers use Head Start to study specific issues in early

childhood education policy. For instance, Gelber and Isen (2013) find that Head Start
participation increases the amount of time parents spend engaging in educational ac-
tivities with children, even after Head Start participation is completed. Walters (2015)
uses programing differences across Head Start centers to identify which preschool char-
acteristics most influence test scores. Kline and Walters (2016) evaluate how substitution
between Head Start, other preschool programs, and home-based care affects estimates
of Head Start?s test score effects and fiscal impacts.5

Overall, the current state of the literature can be summarized as finding short-term test
score effects that quickly fade, coupled with substantial effects on a broader set of
socioeconomic outcomes though the early 20s. The present paper extends this lit-
erature in two important ways. First, it examines a broader range of outcomes sub-
stantially further into the life cycle than most existing work. Second, it implements a
new approach to identifying causal effects in observational data, which complements
the existing set of quasi-experimental methods.

4. While Head Start eligibility determination is not typically state-specific, children determined to be AFDC or
TANF eligible are usually automatically Head Start eligible, and AFDC/TANF requirements vary by state.
5. All of these studies use data from the NHSIS.

Thompson 1105

III. Data

A. 1979 National Longitudinal Survey of Youth (NLSY79)

My primary individual-level data source is the 1979 National Longitudinal Survey of
Youth (NLSY79), which follows a sample of 12,686 individuals who were ages 14?21
when the survey began in 1979. Participants were eligible to be interviewed annually
until 1994 and biannually thereafter, with the most recent wave available at the time of
writing occurring in 2012, when respondents were ages 48?55.6 The extensive NLSY
survey instrument includes detailed information on labor market outcomes, educational
attainment, and a variety of health measures. The utilized outcome measures are de-
scribed in greater detail below.
Central to my empirical approach is the fact that NLSY79 respondents are members

of the 1957?1964 birth cohorts. Since Head Start was rolled out beginning in the
summer of 1965, approximately half of the NLSY79 sample was over the program?s
target age by the time of its launch, while the other half was sufficiently young to be
potentially eligible for Head Start. The NLSY79 also contains data on state and county
of birth,which allow me to link respondents to local Head Start funding levels when they
were in the program?s target age range.7

Because NLSY79 surveying did not begin until respondents were ages 14?21, con-
temporaneous reports of actual Head Start participation are not available. A retrospective
question asking whether respondents had attended Head Start as children was included
in the 1994 wave of the survey, when respondents were ages 30?37. While these retro-
spective self-reports of Head Start attendance are in general positively correlated with
the Head Start funding measures I use in the main analysis below, these correlations are
generally quiteweak, which prevents me from directly analyzing the effects of actual Head
Start participation rather than exposure to Head Start funding. Estimates of the relationship
between Head Start funding levels and self-reported enrollment, as well as results using an
alternative enrollment data source, are reported and discussed in Section V.
As discussed above, children are typically eligible for Head Start only if they come

from families with incomes below the federal poverty level, and since most respondents
in the full NLSY79 sample did not grow up in poor households, it will be difficult to
detect any impacts of Head Start in the full sample. Given this, most of the analysis
below focuses on the approximately 70 percent of NLSY79 respondents whose own
parent(s) had 12 years of education or less, since we would expect Head Start partici-
pation rates to be very low among the children of higher-education parents. Indeed,
records indicate that only 5 to 10 percent of Head Start enrollees in this period had
a parent with any post-secondary education (see Bureau of Census 1968; Bureau of
Census 1970, 1972; Westinghouse Learning Corporation 1969 Appendix A). As a
falsification test, I also report results that use the subsample of NLSY79 respondents
who have one or more college-educated parents and find effects close to zero, as
would be expected given the low Head Start participation rates in this subpopulation.

6. The NLSY79 survey design included oversamples of minorities, economically disadvantaged whites, and
military members. The economically disadvantaged white and military oversamples were dropped between
1984 and 1990 for budgetary reasons and are excluded from the current analysis.
7. State and county of birth are available in a restricted access NLSY-geocode supplement. See http://www.bls
.gov/nls/nlsgeo.htm (accessed January 9, 2018) for application procedures.

1106 The Journal of Human Resources

B. Head Start Funding Data

Head Start funding data are drawn from the National Archives and Records Adminis-
tration Community Action Program (NACAP) electronic files (Community Services
Administration 1981).8 The NACAP files consist of two record types. First are records
for all 4,769 organizations receiving any Community Action Program grant between
1965 and 1981, and among other items these grantee-level records contain the recipi-
ent organization?s county. Second is a record for each specific grant action, such as a
disbursement, extension, renewal, or termination. This grant action-level data contain
information on total federal grant dollars, the service delivery county (which in a limited
number of cases differs from the grant recipient?s county), and the year of disbursement.
The grant action-level records also contain a brief project description that indicates
whether the grant was for a Head Start program.
The information in these two sets of NACAP records is used to calculate aggregate

federal Head Start grant dollars at the county?year level. Most of the utilized county?
year Head Start funding data were assembled and generously shared by Bailey and
Goodman-Bacon (2015), with some supplemental data collection by the author from the
primary NACAP records. I then divide the annual federal Head Start grant totals for each
county by the number of children in the county who were ages three to six in each year
(which as noted above was the age range of Head Start participants in this period) and
express these grant amounts per child aged three to six in 2012 dollars.9

To construct a measure of Head Start exposure for individual NLSY79 respondents, I
calculate the average level of Head Start funding per child aged three to six that oc