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Facilitating the Search for Partners on Matching Platforms: Restricting Agents' Actions

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Listed:
  • Kanoria, Yash

    (Stanford University)

  • Saban, Daniela

    (Stanford University)

Abstract

Two-sided matching platforms, such as those for labor, accommodation, dating, and taxi hailing, can control and optimize over many aspects of the search for partners. To understand how the search for partners should be designed, we consider a dynamic two-sided search model with strategic agents who must spend a cost to discover their value for each potential partner. We find that in many settings, the platform can mitigate wasteful search effort by restricting what agents can see/do. Surprisingly, simple restrictions can improve social welfare even when screening costs are small, and agents on each side are ex-ante homogeneous. In asymmetric markets where agents on one side have a tendency to be more selective (due to smaller screening costs or greater market power), the platform should force the more selective side of the market to reach out first, by explicitly disallowing the less selective side from doing so. This allows the agents on the less selective side to exercise more choice in equilibrium. When agents are vertically differentiated, the platform can significantly improve welfare even in the limit of vanishing screening costs, by forcing one side of the market to propose and by hiding quality information. Furthermore, a Pareto improvement in welfare is possible in this limit.

Suggested Citation

  • Kanoria, Yash & Saban, Daniela, 2017. "Facilitating the Search for Partners on Matching Platforms: Restricting Agents' Actions," Research Papers 3572, Stanford University, Graduate School of Business.
  • Handle: RePEc:ecl:stabus:3572
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    Cited by:

    1. YingHua He & Thierry Magnac, 2022. "Application Costs and Congestion in Matching Markets," The Economic Journal, Royal Economic Society, vol. 132(648), pages 2918-2950.
    2. Kostas Bimpikis & Wedad J. Elmaghraby & Ken Moon & Wenchang Zhang, 2020. "Managing Market Thickness in Online Business-to-Business Markets," Management Science, INFORMS, vol. 66(12), pages 5783-5822, December.
    3. Irene Lo & Vahideh Manshadi & Scott Rodilitz & Ali Shameli, 2020. "Commitment on Volunteer Crowdsourcing Platforms: Implications for Growth and Engagement," Papers 2005.10731, arXiv.org, revised Jul 2021.
    4. Jerry Anunrojwong & Krishnamurthy Iyer & Vahideh Manshadi, 2023. "Information Design for Congested Social Services: Optimal Need-Based Persuasion," Management Science, INFORMS, vol. 69(7), pages 3778-3796, July.
    5. Light, Bar & Weintraub, Gabriel, 2018. "Mean Field Equilibrium: Uniqueness, Existence, and Comparative Statics," Research Papers 3731, Stanford University, Graduate School of Business.
    6. Chengsi Wang & Makoto Watanabe, 2021. "Directed Search on a Platform: Meet Fewer to Match More," Monash Economics Working Papers 2021-02, Monash University, Department of Economics.
    7. Ying-Ju Chen & Tinglong Dai & C. Gizem Korpeoglu & Ersin Körpeoğlu & Ozge Sahin & Christopher S. Tang & Shihong Xiao, 2020. "OM Forum—Innovative Online Platforms: Research Opportunities," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 430-445, May.
    8. He, Yinghua & Magnac, Thierry, 2018. "A Pigouvian Approach to Congestion in Matching Markets," IZA Discussion Papers 11967, Institute of Labor Economics (IZA).
    9. Kostas Bimpikis & Ozan Candogan & Daniela Saban, 2019. "Spatial Pricing in Ride-Sharing Networks," Operations Research, INFORMS, vol. 67(3), pages 744-769, May.
    10. Jun Li & Serguei Netessine, 2020. "Higher Market Thickness Reduces Matching Rate in Online Platforms: Evidence from a Quasiexperiment," Management Science, INFORMS, vol. 66(1), pages 271-289, January.
    11. Itai Ashlagi & Mark Braverman & Yash Kanoria & Peng Shi, 2020. "Clearing Matching Markets Efficiently: Informative Signals and Match Recommendations," Management Science, INFORMS, vol. 66(5), pages 2163-2193, May.

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