We provide the first evidence that the employers of network contacts from college can influence job search success. In contrast to the peer effects literature that emphasizes peer characteristics, we examine how the hiring rate of in-network firms affects reemployment probabilities. To avoid endogeneity from voluntary transitions, we focus on individuals who experience mass layoffs. Using administrative data and a cross-cohort design, we find that network connections with actively hiring employers increase the reemployment rate. This result is driven by reemployment at contacts’ firms. These results suggest that college can improve employment outcomes beyond improved human capital and signaling.

We estimate the returns to college using administrative data on both college enrollment and earnings. Exploiting that colleges dismiss low-performing students on the basis of exact GPA cutoffs, we use a regression discontinuity design to estimate the earnings impacts of college. Dismissal leads to a short-run increase in earnings and tuition savings, but the future fall in earnings is sufficiently large that 8 years after dismissal, persisting students have already recouped their up-front investment with an internal rate of return of 4.1%. We provide a variety of evidence that manipulation of the running variable does not drive our results.

We highlight that analyses using interaction terms to study treatment effect heterogeneity are susceptible to a form of omitted variable bias that is often overlooked in economics. Unlike most instances of omitted variable bias, the omitted variables in this case are available to the researcher but were not included in the model. We demonstrate that this exclusion matters based on a replication of 205 estimates across seventeen papers published in the American Economic Review over a five-year period. For approximately 60% of these papers, failing to account for the omitted variables changes the majority of estimates by more than 100%.