Matching in the Large: An Experimental Study

Yan Chen, Ming Jiang (), Onur Kesten (), Stéphane Robin () and Min Zhu ()
Additional contact information
Ming Jiang: Antai College of Economics and Management, Shanghai Jiao Tong University. 1954 Huashan Road, Shanghai 200434, China
Onur Kesten: Tepper School of Business, Carnegie Mellon University, PA 15213
Stéphane Robin: Univ Lyon, CNRS, GATE L-SE UMR 5824, F-69131 Ecully, France
Min Zhu: School of Business, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China

No 1702, Working Papers from Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon

Abstract: Market size has been predicted to play an influential role in a broad class of economic environments. We study the performance of the Boston and the Deferred Acceptance (DA) mechanism in a laboratory where we increase the market size. Our results show that increasing the market size from 4 to 40 students per match increases participant truth-telling under the DA but decreases it under the Boston mechanism, leading to a decrease in efficiency for both mechanisms but no change in the large stability advantage of the DA over the Boston mechanism. We then further increase the market size to 4,000 by introducing robots. When humans play truthful robots (without strategic uncertainty), we find that scale has no effect on best response behavior. However, when humans play empirical robots (with strategic uncertainty), scale increases best responses under both mechanisms, which is likely due to the increase in the precision of subjects’ beliefs about others’ strategies.

Keywords: matching; school choice; experiment; scale (search for similar items in EconPapers)
JEL-codes: C78 C92 D47 D82 (search for similar items in EconPapers)
Date: 2017
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ftp://ftp.gate.cnrs.fr/RePEc/2017/1702.pdf (application/pdf)

Related works:
Journal Article: Matching in the large: An experimental study (2018)
Working Paper: Matching in the large: An experimental study (2018)
Working Paper: Matching in the Large: An Experimental Study (2017)
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