Tournaments with and without Private Information: A Nonparametric Approach

Speaker: Dr Jun Zhang

Affiliation: University of Technology Sydney

Location: Room 215, Chamberlain Building (#35), St Lucia Campus

Zoom: https://uqz.zoom.us/j/82603079317

Abstract: We establish nonparametric identification and estimation of the distribution of random shocks for two tournament models with and without private information. We then apply the proposed estimation procedure to analyze a broiler production tournament data set. Our estimation results show that the model without private information fits the data much better than the one with. Our counterfactual results show that switching from the observed prize structure to an optimal one would lead to a large gain in the principal's profit but only a modest gain in social welfare, while that based on the poorly fit model predicts a misleading result.