Data and Code for: Matching in the Large: An Experimental Study

Principal Investigator(s): View help for Principal Investigator(s) Yan Chen, School of Information, University of Michigan; Ming Jiang, Antai College of Economics and Management, Shanghai Jiao Tong University; Onur Kesten, Tepper School of Business, Carnegie Mellon University; Stéphane Robin, Université de Lyon, CNRS; Min Zhu, School of Business, Beijing Normal University

Version: View help for Version V2

Name	File Type	Size	Last Modified
graphs			05/20/2018 10:13:AM
Fct_Test_Vuong.ado	text/plain	3.6 KB	05/20/2018 06:13:AM
analysis.do	text/x-stata-syntax	18.2 KB	05/20/2018 06:13:AM
codebook.txt	text/plain	8.6 KB	05/20/2018 06:13:AM
file_description.txt	text/plain	737 bytes	05/20/2018 06:13:AM
human.dta	application/x-stata	844.9 KB	05/20/2018 06:13:AM
human_eff.dta	application/x-stata	23.5 KB	05/20/2018 06:13:AM
human_stab.dta	application/x-stata	19.9 KB	05/20/2018 06:13:AM
robot_emp.dta	application/x-stata	390.2 KB	05/20/2018 06:13:AM
robot_truth_ia.dta	application/x-stata	138 KB	05/20/2018 06:13:AM

Project Citation:

Chen, Yan, Jiang, Ming, Kesten, Onur, Robin, Stéphane, and Zhu, Min. Data and Code for: Matching in the Large: An Experimental Study. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-09-28. https://doi.org/10.3886/E103521V2

Project Description

Summary: We compare the performance of the Boston Immediate Acceptance (IA) and Gale--Shapley Deferred Acceptance (DA) mechanisms in a laboratory setting where we increase the number of participants per match. In our experiment, we first increase the number of students per match from 4 to 40; when we do so, participant truth-telling increases under DA but decreases under IA, leading to a decrease in efficiency under both mechanisms. Furthermore, we find that DA remains more stable than IA, regardless of scale. We then further increase the number of participants per match to 4,000 through the introduction of robots. When robots report their preferences truthfully, we find that scale has no effect on human best response behavior. By contrast, when we program the robots to draw their strategies from the distribution of empirical human strategies, we find that our increase in scale increases human ex-post best responses under both mechanisms.

Funding Sources: National Science Foundation (SES-0962492); Agence Nationale de la Recherche (ANR-13-BSHS1-0010); National Natural Science Foundation of China (71704013); ANR (ANR-11-IDEX-007); Shanghai Pujiang Program (17PJC059)

Scope of Project

Subject Terms: matching; experiment; school choice; scale

Geographic Coverage: Beijing, China

Time Period(s): 5/30/2012 – 7/3/2012; 5/6/2013 – 5/12/2013

Collection Date(s): 5/30/2012 – 7/3/2012; 5/6/2013 – 5/12/2013

Universe: College students in Beijing, China

Data Type(s): experimental data

Collection Notes: All sessions are conducted in Chinese at the Experiment Economics Laboratory and the Finance Simulation Laboratory at Beijing Normal University between June 2012 and May 2013. The subjects are students from Beijing Normal University and the Beijing University of Posts and Telecommunications. No subject participates more than once. We conduct 12 independent sessions for the all-human treatments and 320 independent observations for the human–robot treatments, with a total of 736 subjects. The exchange rate is 5 experiment points for 1 RMB for all sessions. Each subject also receives a participation fee of 5 RMB. The average earning (including participation fee) is 63.8 RMB.

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V2 [2020-09-28]

V1 [2020-09-07]

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