No. 949: Identification in Discrete Choice Models with Imperfect Information
Cristina Gualdani Queen Mary University of London Shruti Sinha Toulouse School of Economics, University of Toulouse Capitole, Toulouse, France.
June 21, 2023
Abstract
We study identification of preferences in static single-agent discrete choice models where decision makers may be imperfectly informed about the state of the world. We leverage the notion of one-player Bayes Correlated Equilibrium by Bergemann and Morris (2016) to provide a tractable characterization of the sharp identified set. We develop a procedure to practically construct the sharp identified set when the state of the world is continuous following a sieve approach, and provide sharp bounds on counterfactual outcomes of interest. We use our methodology and data on the 2017 UK general election to estimate a spatial voting model under weak assumptions on agents’ information about the returns to voting. Counterfactual exercises quantify the consequences of imperfect information on the well-being of voters and parties.
J.E.L classification codes: C01, C25, D72, D80
Keywords: Discrete choice model, Bayesian Persuasion, Bayes Correlated Equilibrium, Incomplete Information, Partial Identification, Moment Inequalities, Spatial Model of Voting.