Career technical education (CTE) programs at community colleges are increasingly seen as an attractive alternative to four-year colleges, yet little systematic evidence exists on the returns to specific certificates and degrees. We estimate returns to CTE programs using administrative data from the California Community College system linked to earnings records. We employ estimation approaches including individual fixed effects and individual-specific trends, and find average returns to CTE certificate and degrees that range from 14 to 45 percent. The largest returns are for programs in the healthcare sector; estimated returns in non-health related programs range from 15 to 23 percent.

Many papers use fixed effects (FE) to identify causal impacts of an intervention. In this paper we show that when the treatment status only varies within some groups, this design can induce nonrandom selection of groups into the identifying sample, which we term selection into identification (SI). We begin by illustrating SI in the context of several family fixed effects (FFE) applications with a binary treatment variable. We document that the FFE identifying sample differs from the overall sample along many dimensions, including having larger families. Further, when treatment effects are heterogeneous, the FFE estimate is biased relative to the average treatment effect (ATE). For the general FE model, we then develop a reweighting-on-observables estimator to recover the unbiased ATE from the FE estimate for policy-relevant populations. We apply these insights to examine the long-term effects of Head Start in the PSID and the CNLSY. Using our reweighting methods, we estimate that Head Start leads to a 2.6 percentage point (p.p.) increase (s.e. = 6.2 p.p.) in the likelihood of attending some college for white Head Start participants in the PSID. This ATE is 78% smaller than the traditional FFE estimate (12 p.p). Reweighting the CNLSY FE estimates to obtain the ATE produces similar attenuation in the estimated impacts of Head Start.

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This paper estimates the labor market returns to the associate’s degree in nursing (ADN), which is one of the most popular community college programs. I use student-level academic and earnings records across two decades for all community college students in California. I leverage random variation from admissions lotteries to produce causal estimates of the effect of the ADN on earnings and employment at a single large ADN program. Enrolling in the program increases earnings by 44 percent and the probability of working in the health care industry by 19 percentage points. These estimates are similar to ones in models that do not use the lottery variation but do control for individual fixed effects and individual-specific linear time trends, which I also estimate in a wider set of institutions where lottery estimates are not possible. In light of concerns about nursing shortages, I estimate that the economic benefit of expanding an ADN program by one seat far outweighs the costs. (JEL D44, I11, I23, I26, J24, J31)