Two-Sided Matching with (Almost) One-Sided Preferences
By Guillaume Haeringer and Vincent Iehlé
American Economic Journal: Microeconomics 2019, 11(3): 155–190.
Abstract: "In a two-sided matching context we show how we can predict stable matchings by considering only one side’s preferences and the mutually acceptable pairs of agents. Our methodology consists of identifying impossible matches, i.e., pairs of agents that can never be matched together in a stable matching of any problem consistent with the partial data. We analyze data from the French academic job market for mathematicians and show that the match of about 45 percent of positions (and about 60 percent of candidates) does not depend on the preferences of the hired candidates, unobserved and submitted at the final stage of the market."
Haeringer and Iehlé present new theory and explore an interesting data set, described as follows:
"Market for Mathematicians
In 1998, a small group of young mathematicians set up a website, Opération Postes, inviting recruiting committees to announce the lists of candidates to be interviewed as well as the rankings of candidates that will be submitted to the clearinghouse (the ministry), as soon as these would be decided.19 The community of mathematicians was very responsive and the website quickly became a central tool in the job market.20 The data for each position (interviewees list and rank-ings) is usually uploaded by the the chairs of the recruiting committees themselves (and if not, by a member of the committee). On average, about 90–95 percent of the job openings’ interview lists and rankings are available.21 The data of Opération Postes is public, although not in a format that makes it immediately usable for any analysis. There are many misspellings, and we sometimes found confusions between the married and maiden names of some female candidates. By cross-referencing the data with other sources we were able to compose a clean dataset.22We also collected for each year the assignment of candidates to departments. This assignment is computed by the Ministry of Higher Education by using candidate’s submitted preference lists over the departments and the rankings of candidates established by the recruiting committees."
By Guillaume Haeringer and Vincent Iehlé
American Economic Journal: Microeconomics 2019, 11(3): 155–190.
Abstract: "In a two-sided matching context we show how we can predict stable matchings by considering only one side’s preferences and the mutually acceptable pairs of agents. Our methodology consists of identifying impossible matches, i.e., pairs of agents that can never be matched together in a stable matching of any problem consistent with the partial data. We analyze data from the French academic job market for mathematicians and show that the match of about 45 percent of positions (and about 60 percent of candidates) does not depend on the preferences of the hired candidates, unobserved and submitted at the final stage of the market."
Haeringer and Iehlé present new theory and explore an interesting data set, described as follows:
"Market for Mathematicians
In 1998, a small group of young mathematicians set up a website, Opération Postes, inviting recruiting committees to announce the lists of candidates to be interviewed as well as the rankings of candidates that will be submitted to the clearinghouse (the ministry), as soon as these would be decided.19 The community of mathematicians was very responsive and the website quickly became a central tool in the job market.20 The data for each position (interviewees list and rank-ings) is usually uploaded by the the chairs of the recruiting committees themselves (and if not, by a member of the committee). On average, about 90–95 percent of the job openings’ interview lists and rankings are available.21 The data of Opération Postes is public, although not in a format that makes it immediately usable for any analysis. There are many misspellings, and we sometimes found confusions between the married and maiden names of some female candidates. By cross-referencing the data with other sources we were able to compose a clean dataset.22We also collected for each year the assignment of candidates to departments. This assignment is computed by the Ministry of Higher Education by using candidate’s submitted preference lists over the departments and the rankings of candidates established by the recruiting committees."
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