"When Anything Can Happen”: Anticipated Adversity and Postsecondary Decision-Making
We examine how disadvantaged students make postsecondary education decisions, focusing on why they often opt for short, ﬂexible programs that tend to have low returns in the labor market. Prior literature emphasizes information deﬁcits and ﬁnancial constraints. We draw upon qualitative data collected via open-ended interviews conducted with a sample of economically disadvantaged Black youth in Baltimore. We use these data to develop and explore a complementary narrative: students who have faced instability or hardship in the form of disruptive events, or “adverse shocks” (e.g., violence, eviction or incarceration of a family member), anticipate future shocks that could derail their educational plans. In response, they opt for shorter, more ﬂexible educational programs that they expect they can complete despite anticipated shocks. When possible, we corroborate this narrative using publicly available, large-N data sets such as the National Longitudinal Survey of Youth (NLSY). Finally, we formalize this narrative as a simple dynamic structural model calibrated using data on education choices and returns. The model clariﬁes that it is impossible to identify costs of schooling without data on beliefs about the probability of non-completion, thus providing guidance on future data collection priorities. More broadly, our approach demonstrates a novel application of mixed methods research: using qualitative data to aid in the speciﬁcation of a structural model. This approach could be applied in other contexts where behavior is poorly understood and extant data do not contain all of the information needed to generate and test plausible hypotheses.
We thank Kathryn Edin and Susan Clampet-Lundquist, who were the Co-PIs with DeLuca for the MTO Q10 Transition to Adulthood Study in Baltimore, which provided the qualitative data we use in this paper. We are grateful for the generous support of the Russell Sage Foundation and the William T. Grant Foundation. We also acknowledge excellent research support from Paige Ackman, Olivia Cigarroa, Jamie Chan, Courtney Colwell, Olivia Morse, Lauren Ricci and Oscar Volpe. The usual caveats apply. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.