Learning but Not Earning? the Impact of Job Corps Training on Hispanic Youth

I. INTRODUCTION Job Corps (JC) is a federally funded training program for disadvantaged youth aged 16-24 yr that provides educational and vocational training, life skills, and job placement services. With some exceptions, JC participants reside at the corresponding JC facility. Through this comprehensive approach, the goal of JC was to tackle the adverse effects of being raised under low socioeconomic conditions to enable disadvantaged youths obtain and keep well-paying jobs. From the middle to late 1990s, the National Job Corps Study (NJCS) employed a randomized social experiment to evaluate the effectiveness of JC across various dimensions including employment and earnings, educational achievement, criminal behavior, and health outcomes. 1 The main finding of the NJCS 48 mo after randomization reported in Schochet, Burghardt, and Glazerman (2001) was that the treatment group earned, on average, a statistically significant 12% more per week than the control group. However, this effect was not homogeneous, varying from $46 per week for whites and $23 for blacks to $15 for Hispanics (this last estimate was not statistically significant; see their table D. 14). (2) These differential effects are especially puzzling given similar gains in measurable human capital across the three groups. (3) Since Hispanics represent a significant and growing proportion of the U.S. population and disproportionately exhibit disadvantaged characteristics, it is important to understand the heterogeneous labor market impacts of JC. We trace these heterogeneous treatment effects to different local labor market conditions. First, we note that a higher and statistically significant level of labor market experience is gained by Hispanics in the control group relative to the treatment group during the time of the NJCS (which does not happen among whites or blacks). After adjusting for this labor market experience, the estimated negative effects of JC for Hispanics decrease considerably. Subsequently, we show that the differential labor market outcomes of the three groups is in part due to the distinct local labor market conditions faced on average by each group and, importantly, due to the differential impact that those labor market conditions have on each group's earnings by randomization status. Specifically, we find that (a) blacks and Hispanics face higher local labor market unemployment rates (LUR) that mitigate the potential gains from JC for them and (b) JC is able to "shield" white participants from adverse LUR, but not blacks and Hispanics. This study contributes to different strands of literature. Most labor market program evaluations in the literature generally focus on individuals that have been in the labor market for some time, frequently avoiding the inclusion of youths such as in, for example, Heckman et al. (1998) and Mueser, Troske, and Gorislavsky (2007); or doing separate analyses for adults and youths as in, for example, Heckman et al. (2000) and Heckman and Smith (1999). We add to this literature by focusing on young persons and stressing the importance of "lock-in" effects as defined by van Ours (2004), local labor market conditions, and different early labor market dynamics across racial and ethnic groups. Recent work in Lechner and Wunsch (2006), Lechner, Miquel, and Wunsch (2007), Johansson (2001), Kluve (2007), and others emphasize that the effects of active labor market programs in Europe depend on the state of the economy, typically finding a direct relationship between their effectiveness and the unemployment rate. For the United States, Hotz, Imbens, and Klerman (2006) present evidence for California pointing to an inverse relationship between the labor market programs and the unemployment rate. While most of the previous articles rely heavily on aggregate time series variation of economic conditions (e.g., the unemployment rate), we present new evidence for the United States exploiting large cross-sectional variation of county-specific local unemployment rates. …