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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.

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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.

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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.

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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 occurred in each NLSY79 respondent’s county of birth during the three calendar years that they were ages three to four, four to five, and five to six. For instance, respondents born in calendar year 1961 are assigned the mean of the Head Start spending that occurred in their county of birth during calendar years 1965 (when they were ages three to four), 1966 (when they were ages four to five), and 1967 (when they were ages five to six). One important feature of measuring exposure as the mean of local funding levels

during the three calendar years when each respondent was ages three to six is that greater weight is given to funding levels occurring at ages four and five than at ages three and six. This occurs because both of the calendar years in which an individual was ages four and five are included in this measure, but only one of the two calendar years in which they wereagesthreeand six.IbelievethisisappropriategiventhedatainTable1 indicating that 60–80 percent of participants in early Head Start implementations were ages four or five, with smaller numbers of three- and six-year-olds participating. In Section VI below, I also present results that estimate the effects of funding levels at each age separately, and the strongest effects are found for ages four and five. Anotherimportantconsequence ofconstructing theHeadStart exposurevariableinthis

way is that it results in a continuous treatment measure, as opposed to a binary indicator of whether a program existed in a given county–year. This is especially important given that I do not reliably observe actual Head Start enrollments in the NLSY79 because higher per-capita funding levels are likely indicative of higher enrollment rates, making it more

8. The electronic NARA archives can be accessed at http://aad.archives.gov/aad/series-description.jsp?s=536& cat=TS16&bc=,sl (accessed June 21, 2017). 9. County population totals are drawn from the decennial censuses with linear interpolations for noncensus years.

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likely that children from counties with higher per-capita funding actually participated. Lower per-capita funding levels are also likely indicative of summer-only programs rather than more expensive full-year programs, and the current measure allows for this variable treatment intensity to be taken into account. Results using binary treatment measures are presented below and result in much less precise estimates.10

It is essential for the validity of the analysis that the utilized NACAP Head Start funding data be accurate. Given this, I have cross-validated the NACAP data using two additional, independent sources of information on early Head Start funding levels. First are county level data for 1968 and 1972 from the Federal Outlays System Files, as assembled by Ludwig and Miller (2007), which report federal expenditures on various programs, including Head Start.11 There is a high level of agreement between the Federal Outlays data and the NACAP grant records used here, with a simple correlation between the two measures of 0.893 for 1968 and 0.875 for 1972. An additional cross-validation of the NACAP grant data was performed using transcriptions of state-level Head Start expenditure totals reported in the OEO’s first, second, and fourth annual reports to Congress (OEO 1965, 1966, 1968).12 The state aggregates in these reports for 1966 and 1968 correspond quite closely to those generated from the NARA grant records, with simple correlation coefficients of 0.896 for 1966 and 0.962 for 1968.13

While these strong correlations between independently collected funding measures for 1966 and 1968 are reassuring, there are large discrepancies in 1965 funding levels between the NACAP grant records and the state aggregates from the 1965 OEO annual report, with much lower levels in the NACAP records. Bailey and Duquette (2014) also note this discrepancy, and they suggest it is potentially related to the fact that in 1965 Head Start existed only as a summer program, and the NACAP grant record dating (which was initially performed in fiscal years) may have charged these summer expenditures to 1966. This explanation is especially plausible given that in this period the federal fiscal year began on July 1, making it ambiguous which fiscal year summer Head Start expendi- tures should be assigned to. Regardless of the root cause of these discrepancies, since no reliable 1965 Head Start funding data are available, 1965 funding levels are set to zero in

10. The use of a three-year average to define Head Start exposure also helps account for ambiguity that arises from a lack of information on the exact birthday cutoffs used to determine age-based eligibility for local Head Start programs. For instance, a child born in June of 1964 would have been age two (and presumably not eligible for Head Start) for approximately six months of 1966, but would then be age three (and potentially eligible) for the other six months of 1966. The evolving mix of summer and full-year programs during the study period introduces additional imprecision in the assignment of funding data to individual NLSY79 respondents, since some summer participants may have attended a full-year program as well, while for others no full-year program was available, which also makes the more flexible three-year average exposure definition attractive. 11. As discussed in greater detail in Ludwig and Miller (2007), the authors determined that the Federal Outlays data for Head Start were unreliable for years other than 1968 and 1972. 12. The OEO’s third annual report to Congress, OEO (1967), did not disaggregate Head Start expenditures from expenditures on other CAP activities. Also note that the online data appendix for Bailey and Duquette (2014) reports a similar validation of the NACAP grant figures using OEO annual reports, but does so for total CAP spending rather than for Head Start specifically and reports high levels of agreement. 13. A related data quality concern is that some counties with active Head Start programs may have been recorded as not receiving any Head Start funding in the NACAP grant data because of incompleteness in how the NACAP data treated recipients providing services in multiple counties. However, there is a very high level of agreement between the NACAP data and the FederalOutlays datawithrespect tothe number of counties receivingHead Start funding in each state–year, with simple correlations of 0.99 in 1968 and 0.98 in 1972. It should be cautioned, however, that Ludwig and Miller (2007) indicate that the Federal Outlays data may also be flawed in accounting for agencies providing Head Start services in multiple counties, making this exercise less than conclusive.

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the working data set. While not ideal, the practical consequences of this are likely to be minimal given how the utilized Head Start exposure measure is constructed. In par- ticular, most children who attended a 1965 summer program, but are not coded as such due to missing summer 1965 data, will still be assigned positive exposure due to positive funding levels in years 1966 and beyond. Figure 1 uses the NACAP grant data to map the timing of Head Start program

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