Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Accounting for sample selection is a challenge not only for empirical researchers, but also for the agents populating our models. Yet most models abstract from these issues and assume that agents successfully tackle selection problems. We design an experiment where a person who understands selection observes all the data required to account for it. Subjects make choices under uncertainty and their choices reveal valuable information that is biased due to the presence of unobservables. We find that almost no subjects optimally account for endogenous selection. On the other hand, behavior is far from random, but actually quite amenable to analysis: Subjects follow simple heuristics that result in a partial accounting of selection and mitigate mistakes.