Abstract

Abstract Public early intervention programs like Head Start are often justified as investments in children. Yet nothing is known about the long-term effects of Head Start. This paper draws on unique data from the Panel Study of Income Dynamics to provide new evidence on the effects of Head Start on schooling attainment, earnings, and being booked or charged with a crime. Among whites, participation in Head Start is associated with a significantly increased probability of completing high school and attending college, and we find some evidence of elevated earnings in one's early twenties. African Americans who participated in Head Start are less likely to have been booked or charged with a crime. The evidence also suggests that there are positive spillovers from older children who attended Head Start to their younger siblings. Head Start, a public preschool program for disadvantaged children, is designed to close the gaps between these children and their more advantaged peers. Begun in 1965 as part of the "War on Poverty", Head Start has enjoyed widespread bi-partisan support for 35 years, along with increasing budgets. It is now a $4.7 billion dollar program serving more than 800,000 children, and serves as a model for separate state-funded programs. Head Start is often justified as an investment in children. However, critics point out that there is no evidence that the program has lasting benefits. Evidence cited in support of the long-term effectiveness of early intervention comes from analyses of other programs, such as the famous Perry Preschool intervention, which often bear little resemblance to the Head Start model. Moreover, there is a good deal of evidence that short-term gains in test scores "fade out" for many Head Start children. This paper addresses the important question of whether the Head Start program confers long-term benefits on children. We use questions from a supplement to the Panel Survey of Income Dynamics (PSID) which were specifically designed for this study. There are three features of these unique data that are key for our purposes. First, in 1995, questions about participation in Head Start and other preschools were included in the interviews and asked of all adults age 18 through 30. These questions make it feasible, for the first time, to track participants in Head Start for a long period. Second, because the PSID is a panel which stretches back over a quarter century, we are able to control for family background and the environment in which each respondent grew up in great detail. Third, the PSID provides a nationally representative sample of children who participated in actual Head Start programs rather than experimental model programs. We examine four indicators of economic and social success in adulthood. We find that, for whites, participation in Head Start is associated with a significantly increased probability of completing high school and attending college as well as elevated earnings in one's early twenties. African Americans who participated in Head Start are less likely to have been book or charged with a crime. We also find suggestive evidence that African-American males who attended Head Start are more likely than their siblings to have completed high school. Finally, we uncover some evidence of positive spillovers from older children who attended Head Start to younger siblings, particularly with regard to being booked or charged with a crime. 1 While an experiment in which children were randomly assigned and tracked for thirty years might be the ideal way to evaluate the long-term effects of Head Start, we view our analysis of these non-experimental data as an important complement to experimental sources and a first step towards establishing whether Head Start confers long-term benefits on participants. For reasons spelled out in detail below, our methods likely provide lower bound estimates of any positive effects of Head Start. I. Background Head Start began as a summer program in 1965 with 561,000 predominantly African American children. It expanded to serve almost three-quarters of a million African American and white children in the summer of 1966 at which time about $1,000 (in 1999 prices) was spent on each child. By the early 1970s, Head Start had become an all-year program that served considerably fewer children at a higher annual cost per child. For example, in 1971, the program served slightly less than 400,000 children at an annual cost of about $4,000 per child. All three and four year old children living in poor families are eligible to enroll in the program and, today, it serves more than 800,000 children at a cost of around $5,400 per child. (U.S. Administration on Children, Youth, and Families, 1999). While large, the program serves only about two-thirds of eligible children. This reflects the fact that the program, which is funded by appropriation, has never been fully funded. The program is administered at the local level --there are over 1,400 local programs --and is subject to federal guidelines. The guidelines specify that, in addition to providing a nurturing learning environment, Head Start should provide a wide range of services. These include, for example, facilitating and monitoring utilization of preventive medical care by participants, as well as providing nutritious meals and snacks. Studies have shown that participation in Head Start is associated with short-term benefits, as indicated by improved test scores (see For example, avoidance of grade repetition and special education may be associated with higher eventual schooling attainment. Head Start may also be associated with lasting improvements in noncognitive skills that are important for future success in life (c.f. Most of the evidence on longer-term benefits of early intervention is drawn from three studies: The Carolina Abcedarian Project; the Perry Preschool Project; and the Chicago Child-Parent Centers (CPC). A more detailed summary of these studies is available in Currie (2001). For our purposes, the important point is that these programs were quite different from Head Start. For example, the Carolina Abecedarian Project randomly assigned children to treatment or nothing. Treatments were provided with an intensive center-based full-day, full-year child care program from 3 months through age 5. Then, all of the children were again randomly assigned to a follow up program of support in school for an additional three years. Evaluations report positive effects on schooling attainment at age 21 among children who received the preschool treatment (Campbell et al., in press, Campbell et al. 2001). The Perry Preschool intervention was more similar to Head Start in that it involved a half-day preschool every weekday and a weekly 90 minute home visit for 8 months of each year for two years. However, the preschool component was of much higher quality than the average Head Start program in existence during our study period. This intervention had positive effects on achievement test scores, grades, graduation from high school and earnings, as well as negative effects on crime and welfare use (as of age 27). Both the Carolina Abecedarian Project and Perry Preschool intervention were funded at higher levels than Head Start. For example, in 1998 it cost $5,021 to keep a child in a part-day Head Start program for 34 weeks a year. The two-year, part-day Perry Preschool intervention cost $12,884 per child (in 1999 dollars) The methodology used in this study follows Currie and Thomas II. Data The PSID began in 1968 with a survey of 4,802 households composed of 18,000 individuals. These households, and new households formed by the original head, spouse and their children have been followed ever since. In 1995, special questions on early childhood education experiences were included in the interview on a one-time basis. Adult respondents age 30 or below were asked whether they had ever been enrolled in Head Start and whether they had attended any other preschool or daycare program. Since our interest is in the longer-term effects of participation in Head Start, we focus on slightly less than 4,000 adults (age 18 and older in 1995) who answered these questions. We noted some of the advantages associated with using PSID were noted above. They come at a price. First, because we are using non-experimental data, we need to address the fact that children are not randomly assigned to Head Start. Second, because we are measuring the longerterm effects of Head Start participation, and that information was not collected prospectively, the questions on early childhood education are asked retrospectively and may be contaminated by recall 3 Currie and Thomas 5 error. We have conducted several experiments to evaluate the quality of the data and we describe these experiments before discussing the issue of non-random assignment. Sample summary statistics are reported in The data have been weighted so that our sample is representative of the 1995 white and African American populations. Specifically, we have constructed weights so that the joint distribution of race, sex and year of birth in our sample matches the joint distribution in the 1995 Current Population Survey. We prefer these weights to the PSID longitudinal weights for two reasons. First, all new entrants into the PSID sample are assigned a zero PSID longitudinal weight; many of the respondents in our sample are new entrants to PSID and so would contribute no information. Second, the PSID longitudinal weights are defined to reflect the 1967 United States population (and then take into account attrition) and so are not representative of the population in 1995 (since the structure of the population has changed during the quarter century). (11%) and for all other birth cohorts, the PSID mimics the national numbers. (Reported participation in the PSID declines to 9% in the early 1970s and then rises to 12% by the 1977 birth cohort, the last cohort in our sample). Recall that for the oldest birth cohorts, Head Start was primarily a summer program. It is not surprising that the reported rate of participation in Head Start among these birth cohorts in the PSID is much lower than the national rate. First, there is abundant evidence in the survey research literature that the more salient a life event, the more likely it is to be recalled; 6 participation in a Seminal work by Ebbinghaus (1894) and many subsequent studies in the survey research literature have shown that recall error tends to increase as a respondent is asked to stretch further back in time. If recall error seriously contaminates responses in the PSID, then we would expect the gap between the national enrollment rates and those reported in the PSID to be greater among the earlier birth cohorts. However, once we exclude the 1964/65 birth cohort who participated in a summer program, enrollment rates implied by the PSID mimic the temporal pattern of the national rates. We also find no pattern of differences by birth year of the respondent: in a regression of enrollment rates on year of birth. Our third assessment of the quality of the recall data on Head Start participation exploits the fact that because the PSID is a long-term panel, we know family income when the respondent was a child. We have calculated average per capita family income (in 1999 prices) at the time the respondent was age 3, 4, 5 and 6, and, as shown in III. Empirical methods The aim of this study is to ask whether participation in Head Start results in greater economic or social success later in life. We focus on four adult outcomes: completion of high school, attendance at some college, n(earnings) if the respondent worked, and whether the respondent ever reported being booked or charged with a crime. A natural starting point would be to estimate a model in which each outcome of an individual respondent, Y i , is assumed to depend on participation in Head Start, HDST, some other preschool, OPRE, and a set of individual-specific controls, X: where HDST and OPRE are indicator variables and captures unobserved heterogeneity. The vector X includes observable exogenous variables that are likely to be correlated with outcomes such as the respondent's year of birth, and indicators equal to one if the respondent is female or African American. It is important to include a control for whether the respondent attended a preschool other 9 There are however, some differences in characteristics of Head Start children by race. White Head Start children tend to be less educated than those of African American Head Start children, although they are less likely to be single. White Head Start children in the PSID are almost twice as likely to have been low birthweight than African American Head Start children (14% and 8%, respectively) although in the general population, African American children are substantially more likely to suffer from low birthweight than whites. These differences suggest that the mechanisms underlying participation in Head Start are different for African Americans and whites, and suggest that it may be fruitful to examine the two groups separately. 9 than Head Start for two reasons. First, we do not want to erroneously attribute the effects of other preschools to Head Start. Second, it is useful to compare the effects of Head Start to those of other preschools, as is discussed further below. As noted above, the key problem with interpretation of [1] is that participation in Head Start (or other preschools) is not randomly assigned and covariates may be correlated with unobservables, . In that case, estimates of the effect of Head Start will be biased. Head Start is targeted towards disadvantaged children and children who are perceived to be "at risk" because of learning disabilities, or a negative home environment are often referred to Head Start by social agencies. Failure to control for these intervening characteristics will result in their being included in i . To the extent that these characteristics are correlated with HDST, estimates of α 1 , the long run "effect" of Head Start, will be biased. Because disadvantaged children are more likely to participate in Head Start, α 1 will probably be biased downwards. Children who attend other preschools are likely to come from more advantaged backgrounds and so α 2 is likely to be biased upwards. One approach to addressing this concern is to include measures of the relevant intervening characteristics in the vector X. The PSID is a good data source for taking this approach since extensive information on the child's family background has been collected on an annual basis since 1968. Hence, we augment the vector X by including: maternal and paternal education of the respondent; a spline in family income when the child was of preschool age; family size measured at age 4; whether the respondent lived with both parents at age 4; an indicator for whether the respondent was the oldest child and birthweight. 10 We have also experimented with adding controls for whether the mother worked or was on welfare when the child was age 4. (The addition of these variables had little impact on the results reported below.) 10 Missing values were handled by first determining whether a value could be assigned using information from other waves of the PSID. For example, in some cases, father's education could be assigned to one sibling by looking at reported values for the other sibling. Using the average of household income available at age 4, 5, and 6 resulted in few instances of missing data for this variable (less than 1% of the sample). This average income measure is what we loosely refer to as income at preschool age. When data remained missing, we assigned the mean value from the sample and included a dummy variable in the regression which indicated that a value had been assigned. 10 Despite the richness of the PSID, there may well be other unmeasured characteristics that distinguish Head Start children from their peers and which cannot be controlled in the regression model. If, conditional on the controls, these other characteristics are correlated with observable differences between Head Starters and other children then the estimated effects of Head Start will be biased. For example, if parents who send their children to Head Start (or other preschools) place a higher value on building human capital at an early age, than other parents, and if that human capital accumulation is associated with better outcomes later in life, then this unobserved difference will result in an upward bias in the Head Start "effect", α 1 . In this case, it will be the (unobserved) parental emphasis on education that leads to better outcomes in adulthood rather than Head Start (or other preschool) attendance per se. To the extent that parental taste for human capital accumulation does not differ between siblings, then it can be absorbed in a family-specific fixed effect, µ f : This design controls for any unobserved family characteristics that have the same linear and additive effect on the adult outcomes of all siblings. As a practical matter, µ f is specified as a motherspecific fixed effect in the empirical models below. The fixed effects method is not without its own limitations. First, the effective sample includes only those respondents with at least one sibling in the sample (which is slightly over half of the total sample). In these models, the effect of Head Start, β 1 , is identified by comparing the outcomes of adults who participated in Head Start as children with the outcomes of the siblings who did not (255 respondents from 100 families). 11 Second, the effects of random measurement errors may be exacerbated in a fixed effects framework. That is, by focussing on differences between siblings within a family, we may difference out much of the true signal in the data, and result in an under-estimate of the positive effects of Head Start. On the other hand, fixed effects can mitigate the effects of some forms of non-random measurement error. Suppose for example, that all siblings in a family erroneously report 11 Although this sample is small, it is larger than many of the experimental samples discussed in 11 that they did not attend Head Start but some other form of preschool. This will have no impact on the estimated effect of Head Start in the fixed effects framework. The third problem arises when µ f is not fixed within a family. This would arise if parents treat siblings differently. Say, for example, parents invest more in the human capital of one sibling; if they also send that child to Head Start, β 1 will be biased upwards. It is more likely, however, that parents who want to invest in the human capital of a child send that child to another preschool since Head Start is targeted at disadvantaged children. So it is, in fact, β 2 that is more likely to be biased upwards. In this case, the difference β 1 -β 2 could be considered a lower bound estimate of the effect of Head Start where the estimate takes into account systematic differences in the treatment of siblings which result in one of them attending Head Start or another preschool while the other does not. Hence, we report this difference in Another reason µ f may not be fixed within a family is that siblings experience different environments while growing up. For example, one child may participate in Head Start because family resources are low when the child is age 4 or 5 but siblings may attend other preschools (or no preschool) because resources are less constrained when the siblings are age 4 or 5. For this reason, we include a control for family income averaged over the period that the respondent was age 3, 4, 5 and 6 in all our regression models. A special case in which the family effect is not fixed arises when benefits associated with Head Start spillover from one sibling to the other. The Head Start program emphasizes parent participation and teaches parenting skills which might affect all children. Moreover, it is possible that what one child learns may "spillover" to siblings. In general, in the fixed effects framework, spillovers will tend to result in downward biased estimates of the effect of Head Start because they reduce the differences in outcomes between siblings who did and did not attend the program. We will explore evidence that spillovers are important below. We begin with the probability that a child completed high school. About three-quarters of the sample of respondents completed high school. The first column is based on OLS estimates of model [1]. In addition to HDST and OPRE, the model includes year of birth, gender of the respondent, and whether the respondent is African American. These OLS estimates indicate that respondents who reported attending Head Start were about 9% less likely than stay-at-home children to complete high school, while those who attended other preschools were about 9% more likely to complete high school. 12 In the second column, the sample is restricted to respondents with at least one sibling: the estimates are essentially the same as the full sample. The results demonstrate once again that adults who attended Head Start are significantly less likely than other children to have completed high school, which is not surprising given their disadvantaged backgrounds. IV. Results Column 3, shows that when we control for observable characteristics, high school graduation rates are independent of report preschool experience. 13 Estimates that include maternal fixed effects are reported in column 4. As discussed above, these estimates show the effects of controlling for both observed and unobserved characteristics of mothers that are fixed over time. These estimates are consistent with those shown in column 3 in that they suggest that the negative effects of Head Start shown in column 1 are an artifact of the disadvantages of Head Start children. Columns 5 and 6 of 13 who attended Head Start are 20 percentage points more likely to complete high school than siblings who did not attend. However, there is no statistically significant effect for African Americans. In the final two columns of the table, the sample is restricted to respondents whose mother had no more than a high school education. We examine this sub-sample for two reasons. First, the probability that a respondent attended Head Start rises as socio-economic status declines and so the percentage of reported Head Starters who are false positives is likely to be lower in this group. Second, since Head Start is targeted towards the most disadvantaged, it is of interest to know whether any long-term benefits associated with the program accrue to those from the poorest backgrounds. (We have also stratified on family income at age 3-6; the results are substantively the