Identification and Estimation in Many‐to‐One Two‐Sided Matching Without Transfers
研究了在无转移支付的多对一双边匹配（如大学招生）中，如何利用单一市场的匹配数据识别双方偏好，并通过蒙特卡洛模拟和智利学校录取数据验证了贝叶斯估计方法的效果。
In a setting of many‐to‐one two‐sided matching with nontransferable utilities, for example, college admissions, we study conditions under which preferences of both sides are identified with data on one single market. Regardless of whether the market is centralized or decentralized, assuming that the observed matching is stable, we show nonparametric identification of preferences of both sides under certain exclusion restrictions. To take our results to the data, we use Monte Carlo simulations to evaluate different estimators, including the ones that are directly constructed from the identification. We find that a parametric Bayesian approach with a Gibbs sampler works well in realistically sized problems. Finally, we illustrate our methodology in decentralized admissions to public and private schools in Chile and conduct a counterfactual analysis of an affirmative action policy.