🤖 AI Summary
This paper investigates the efficiency of regulatory policies in two-sided matching markets subject to distributional disparities, focusing on fairness-efficiency trade-offs under regional constraints—exemplified by ensuring physician allocation to rural areas in Japan’s residency match. We propose a data-driven tax mechanism design framework that integrates a structured discrete choice model, counterfactual utility estimation, and convex optimization with upper- and lower-bound regional constraints to automatically derive welfare-maximizing interventions. We establish theoretical feasibility and tractability of the formulation. Numerical experiments demonstrate that the resulting tax policy strictly satisfies all regional constraints while significantly improving aggregate social welfare—outperforming existing quota- and subsidy-based approaches.
📝 Abstract
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.
