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Preprints, Working Papers, ... Year : 2024

Correlation of Rankings in Matching Markets


We study the role of correlation in matching, where multiple decision-makers simultaneously face selection problems from the same pool of candidates. We propose a model in which decision-makers have varying information on candidates from different sociodemographic groups when evaluating and ranking them, thus leading to varying correlations among candidates’ priority scores. Such differential correlation arises, for example, when the cost of information acquisition, decision-maker preferences, or the prevalence of selection criteria vary across sociodemographic groups. We show that a lower correlation for one of the groups worsens the outcome for all groups, thus leading to efficiency loss. Moreover, the proportion of students from a given group who remain unmatched is increasing in its own correlation level. This implies that it is advantageous to belong to the low-correlation group. Finally, we extend the extent tie-breaking literature to multiple priority classes and intermediate levels of correlation. Overall, our results point to a previously overlooked systemic source of group inequalities in school, university, and job admissions.
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Dates and versions

hal-03672270 , version 1 (19-05-2022)
hal-03672270 , version 2 (20-04-2023)
hal-03672270 , version 3 (15-06-2023)
hal-03672270 , version 4 (13-09-2023)
hal-03672270 , version 5 (05-03-2024)
hal-03672270 , version 6 (16-05-2024)



  • HAL Id : hal-03672270 , version 6


Rémi Castera, Patrick Loiseau, Bary Pradelski. Correlation of Rankings in Matching Markets. 2024. ⟨hal-03672270v6⟩
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