Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper shows point-identification in first-price auctions with risk aversion and unobserved auction heterogeneity, by exploiting multiple bids per auction and variation in the number of bidders. If the exclusion restriction required for point-identification is violated, the recovered primitives are still valid bounds under weaker restrictions. We propose a Sieve Maximum Likelihood Estimator (SMLE). Monte Carlo experiments illustrate that the estimator performs well and that ignoring unobserved auction heterogeneity can bias risk aversion estimates. In an application to timber auctions we find that the bidders are risk-neutral, but we would reject risk-neutrality without accounting for unobserved auction heterogeneity.