Problem definition : Participants in matching markets face search and screening costs when seeking a match. We study how platform design can reduce the effort required to find a suitable partner. Practical/academic relevance : The success of matching platforms requires designs that minimize search effort and facilitate efficient market clearing. Methodology : We study a game-theoretic model in which “applicants” and “employers” pay costs to search and screen. An important feature of our model is that both sides may waste effort: Some applications are never screened, and employers screen applicants who may have already matched. We prove existence and uniqueness of equilibrium and characterize welfare for participants on both sides of the market. Results : We identify that the market operates in one of two regimes: It is either screening-limited or application-limited. In screening-limited markets, employer welfare is low, and some employers choose not to participate. This occurs when application costs are low and there are enough employers that most applicants match, implying that many screened applicants are unavailable. In application-limited markets, applicants face a “tragedy of the commons” and send many applications that are never read. The resulting inefficiency is worst when there is a shortage of employers. We show that simple interventions—such as limiting the number of applications that an individual can send, making it more costly to apply, or setting an appropriate market-wide wage—can significantly improve the welfare of agents on one or both sides of the market. Managerial implications : Our results suggest that platforms cannot focus exclusively on attracting participants and making it easy to contact potential match partners. A good user experience requires that participants not waste effort considering possibilities that are unlikely to be available. The operational interventions we study alleviate congestion by ensuring that potential match partners are likely to be available.
[1]
A. Roth,et al.
The Job Market for New Economists: A Market Design Perspective
,
2010
.
[2]
Gabriel Y. Weintraub,et al.
Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design
,
2014,
Manag. Sci..
[3]
Itai Ashlagi,et al.
Efficient Dynamic Barter Exchange
,
2017,
Oper. Res..
[4]
Hanna Halaburda,et al.
Competing by Restricting Choice: The Case of Matching Platforms
,
2017,
Management Sciences.
[5]
A. Roth,et al.
Turnaround Time and Bottlenecks in Market Clearing: Decentralized Matching in the Market for Clinical Psychologists
,
1997,
Journal of Political Economy.
[6]
Randall Wright,et al.
Pricing and Matching with Frictions
,
2001,
Journal of Political Economy.
[7]
P. Lions,et al.
Mean field games
,
2007
.
[8]
Philipp Kircher,et al.
Efficiency of Simultaneous Search
,
2008,
Journal of Political Economy.
[9]
John Kennes,et al.
Bidding for Labor
,
2000
.
[10]
Li Zhang,et al.
Customized Regression Model for Airbnb Dynamic Pricing
,
2018,
KDD.
[11]
Gad Allon,et al.
Large-Scale Service Marketplaces: The Role of the Moderating Firm
,
2012,
Manag. Sci..
[12]
Mukund Sundararajan,et al.
Mean Field Equilibria of Dynamic Auctions with Learning
,
2014,
Manag. Sci..
[13]
Alexey Kushnir,et al.
Harmful Signaling in Matching Markets
,
2010,
Games Econ. Behav..