Replication data for: Designing Matching Mechanisms under General Distributional Constraints

Principal Investigator(s): View help for Principal Investigator(s) Masahiro Goto; Fuhito Kojima; Ryoji Kurata; Akihisa Tamura; Makoto Yokoo

Version: View help for Version V1

Name	File Type	Size	Last Modified
Program			10/25/2021 03:56:PM
LICENSE.txt	text/plain	14.6 KB	10/12/2019 06:53:PM

Project Citation:

Goto, Masahiro, Kojima, Fuhito, Kurata, Ryoji, Tamura, Akihisa, and Yokoo, Makoto. Replication data for: Designing Matching Mechanisms under General Distributional Constraints. Nashville, TN: American Economic Association [publisher], 2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E114354V1

Project Description

Summary: To handle various applications, we study matching under constraints. The only requirement on the constraints is heredity; given a feasible matching, any matching with fewer students at each school is also feasible. Heredity subsumes existing constraints such as regional maximum quotas and diversity constraints. With constraints, there may not exist a matching that satisfies fairness and nonwastefulness (i.e., stability). We demonstrate our new mechanism, the Adaptive Deferred Acceptance mechanism (ADA), satisfies strategy-proofness for students, nonwastefulness, and a weaker fairness property. We also offer a technique to apply ADA even if heredity is violated (e.g., minimum quotas).

Scope of Project

Subject Terms: two-sided matching; school-choice

JEL Classification:
      C78 Bargaining Theory; Matching Theory
      D47 Market Design
      D63 Equity, Justice, Inequality, and Other Normative Criteria and Measurement
      D82 Asymmetric and Private Information; Mechanism Design
      I20 Education and Research Institutions: General

Universe: preferences of students/schools in two-sided matching

Data Type(s): other:; program source code

Collection Notes: Randomly generated data set

Methodology

Data Source: Randomly generated based on statistical model

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Published Versions

V1 [2019-10-12]

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