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Regulating Matching Markets with Constraints: Data-driven Taxation

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Listed:
  • Akira Matsushita
  • Kei Ikegami
  • Kyohei Okumura
  • Yoji Tomita
  • Atsushi Iwasaki

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.

Suggested Citation

  • Akira Matsushita & Kei Ikegami & Kyohei Okumura & Yoji Tomita & Atsushi Iwasaki, 2022. "Regulating Matching Markets with Constraints: Data-driven Taxation," Papers 2205.14387, arXiv.org, revised Mar 2023.
    • Handle: RePEc:arx:papers:2205.14387
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    References listed on IDEAS

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    6. Yuichiro Kamada & Fuhito Kojima, 2015. "Efficient Matching under Distributional Constraints: Theory and Applications," American Economic Review, American Economic Association, vol. 105(1), pages 67-99, January.
    7. Alfred Galichon & Bernard Salani'e, 2021. "Cupid's Invisible Hand: Social Surplus and Identification in Matching Models," Papers 2106.02371, arXiv.org, revised Jan 2023.
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