This paper develops a framework to conduct a counterfactual analysis to regulate matching markets with regional constraints that impose lower and upper bounds on the number of matches in each region. Our work is motivated by the Japan Residency Matching Program, in which the policymaker wants to guarantee the least number of doctors working in rural regions to achieve the minimum standard of service. Among the multiple possible policies that satisfy such constraints, a policymaker wants to choose the best. To this end, we develop a discrete choice model approach that estimates the utility functions of agents from observed data and predicts agents' behavior under different counterfactual policies. Our framework also allows the policymaker to design the welfare-maximizing tax scheme, which outperforms the policy currently used in practice. Furthermore, a numerical experiment illustrates how our method works.

翻译：本文提出一个基于反事实分析的研究框架，用于调控带有区域约束条件的匹配市场，其中约束条件规定了每个区域匹配数量的上下限。本研究的动因源于日本住院医师匹配计划，政策制定者需确保农村地区至少有最低数量的医生工作，以满足基本医疗服务标准。在众多满足此类约束条件的可行政策中，政策制定者期望选择最优方案。为此，我们开发了一种离散选择模型方法，通过观测数据估计参与者的效用函数，并预测不同反事实政策下参与者的行为。该框架还允许政策制定者设计福利最大化的税收方案，其实践表现优于当前使用的政策。此外，数值实验验证了该方法的具体有效性。