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 The Journal of Human Resources


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enrollments, the presence of full-year programs, and higher program quality, larger treatment effects would be expected from better funded programs. The final panel of Table 5 estimates models that simultaneously include a binary

indicator of Head Start presence at ages three to six and the continuous funding mea- sure that has been used throughout the analysis. The binary indicator is very close to zero, with a coefficient of -0.012. More importantly, the coefficient on the contin- uous measure is of the same magnitude as previous estimates and is highly statistically significant. This indicates that even among children with a Head Start program in their county, those exposed to programs with higher funding levels per child ages three to six experienced systematically better outcomes, which is consistent with my empirical ap- proach capturing causal program impacts. In addition to treatment effect heterogeneity along the dimensions explored in Table

5, it is noteworthy that recent analyses of data from the Head Start Impact Study have found that, in its modern form, Head Start’s effectiveness varies considerably across different centers (Walters 2015; Bloom and Weiland 2015; Kline and Walters 2016). The evidence in these studies suggests that much of the variability in treatment effects is attributable to program characteristics, such as hours of care and the use of home visits, as well as to the types of alternative care arrangements available. While data limita- tions prevent me from analyzing how the characteristics of Head Start centers from the period studied here impacted their effectiveness, substantial heterogeneity seems likely given the low standards for receiving initial Head Start grants and the lack of uniform performance standards and monitoring in this period. It is therefore likely that the average effects reported in Table 3 mask substantial heterogeneity across higher and lower quality programs.

B. Balancing Tests

The validity of the baseline estimates from Table 3 rests on the assumption that indi- viduals who had low Head Start exposure because they were beyond the program’s target age when it was locally introduced were similar to individuals who were suffi- ciently young for the program at the time of its introduction and therefore had higher exposure, such that the expected long-term outcomes of these two groups were the same in the absence of program exposure. While this assumption cannot be tested directly, a partial assessment of its validity can be made by comparing the pretreatment character- istics of the individuals within the estimation sample who did and did not have positive levels of Head Start exposure. This exercise is conceptually similar to the “balancing tests” that are standard practice for regression discontinuity research designs (see Lee and Lemieux 2010) and is useful in the current context as well even though the main specifications are difference-in-difference models. Panel A of Table 6 compares the simple means of predetermined characteristics for

individuals who had positive Head Start exposure versus those who had no Head Start exposure. Specifically, I report the race and gender composition of the two groups, parental education levels, birth order, and number of siblings. Reassuringly, the levels of these predetermined characteristics are very similar across the two groups, and t-tests (not shown) indicate that none of the differences are statistically significant. Although none of the individual characteristics from Table 6 vary significantly with

Head Start exposure, there may be some concern that they are jointly related to Head Start

Thompson 1123



T ab

le 6

B a la n ci n g Te st s

M at er n al

E d u ca ti o n

P at er n al

E d u ca ti o n

B la ck

W h it e

F em

al e

B ir th

O rd er

N u m b er

o f S ib li n g s

S u m m ar y

o f P re d et er m in ed

C h ar ac te ri st ic s

(1 )

(2 )

(3 )

(4 )

(5 )

(6 )

(7 )

(8 )

P an

el A : M ea n s b y E xp

os u re

S ta tu s

P o si ti ve

H ea d S ta rt ex p o su re

1 0 .8 4 8

1 0 .2 2 6

0 .1 4 4

0 .8 5 6

0 .4 7 3

2 .9 5 1

3 .3 7 5

-0 .0 4 3

N o H ea d S ta rt ex p o su re

1 0 .7 5 0

1 0 .3 1 6

0 .1 5 4

0 .8 4 6

0 .5 2 4

3 .1 0 0

3 .3 7 8

-0 .0 4 6

P an

el B : R eg re ss io n R es u lt s

H ea d S ta rt ex p o su re

-0 .0 3 4 9

-0 .0 0 2 6

0 .0 0 4 7

-0 .0 0 4 7

0 .0 1 3 7

-0 .0 8 3 6

-0 .0 8 6 4

-0 .0 0 0 7

(0 .0 4 8 5 )

(0 .0 6 9 9 )

(0 .0 0 5 5 )

(0 .0 0 5 5 )

(0 .0 1 5 7 )

(0 .0 5 8 2 )

(0 .0 6 9 7 )

(0 .0 0 5 9 )

O b se rv at io n s

2 ,6 8 5

2 ,6 8 5

2 ,6 8 5

2 ,6 8 5

2 ,6 8 5

2 ,6 8 5

2 ,6 8 5

2 ,6 8 5

N o te s: P an el A re p o rt s si m p le m ea n s o f th e va ri ab le s in d ic at ed

in th e co lu m n ti tl es

w it h in th e es ti m at io n sa m p le ,s p li tb y in d iv id u al s w h o h ad

p o si ti ve

H ea d S ta rt ex p o su re an d

th o se

w h o h ad

n o H ea d S ta rt ex p o su re .P

an el B re p o rt s es ti m at es

fr o m

m o d el s id en ti ca l to

th o se

in T ab le 3 bu t th at u se

th e ch ar ac te ri st ic s in d ic at ed

in th e co lu m n ti tl es

as th e

d ep en d en t va ri ab le .S

ta n d ar d er ro rs cl u st er ed

at th e co u n ty le ve la re in p ar en th es es .T

h e su m m ar y o f p re d et er m in ed

ch ar ac te ri st ic s is a va ri ab le ta k in g o n th e p re d ic te d va lu es

fr o m a re g re ss io n w it h th e o u tc o m e in d ex

as th e d ep en d en t va ri ab le an d th e li st ed

p re d et er m in ed

ch ar ac te ri st ic s as

in d ep en d en t va ri ab le s. C u st o m N L S Y 7 9 sa m p li n g w ei g h ts

ar e ap p li ed . S ig n if ic an ce

le ve ls : * p < 0 .1 0 , * * p < 0 .0 5 , * * * p < 0 .0 1 .

1124 The Journal of Human Resources



exposure.To addressthis possibilitythe final column ofTable6 PanelA reports means for a summary measure of the listed characteristics. This index consists of the predicted values from a regression with the outcome index used in the main specifications as the dependent variable and the listed predetermined characteristics as independent variables, an approach similar to the one used by Almond et al. (2010). The means of this summary measure also indicate no statistically or substantively significant differences in the characteristics of individuals with and without positive Head Start exposure. Panel B of Table 6 implements more formal balancing tests by reestimating the

preferred specification from Table 3 above but replacing long-term outcomes with predetermined covariates as the dependent variable. None of the the predetermined characteristics display a practically or statistically significant association with Head Start eligibility, and the summary measure of predetermined characteristics is also uncorrelated with treatment. These results suggest that the observed increases in long- term outcomes are not an artifact of structural differences in the characteristics of the treatment and control groups

C. Endogenous Program Adoption

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