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Referrals: peer screening and enforcement in a consumer credit field experiment

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  • Bryan, Gharad
  • Karlan, Dean
  • Zinman, Jonathan

Abstract

Empirical evidence on peer intermediation lags behind many years of lending practice and a large body of theory in which lenders use peers to mitigate adverse selection and moral hazard. Using a simple referral incentive mechanism under individual liability, we develop and implement a two-stage field experiment that permits separate identification of peer screening and enforcement effects. We allow for borrower heterogeneity in both ex-ante repayment type and ex-post susceptibility to social pressure. Our key contribution is how we deal with the interaction between these two sources of asymmetric information. Our method allows us to identify selection on the likelihood of repayment, selection on the susceptibility to social pressure, and loan enforcement. We estimate peer effects on loan repayment in our setting, and find no evidence of screening (albeit with an imprecisely estimated zero) and large effects on enforcement. We then discuss the potential utility and portability of the methodological innovation, for both science and for practice.

Suggested Citation

  • Bryan, Gharad & Karlan, Dean & Zinman, Jonathan, 2015. "Referrals: peer screening and enforcement in a consumer credit field experiment," LSE Research Online Documents on Economics 59009, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:59009
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    Cited by:

    1. Thorsten Beck & Patrick Behr, 2017. "Individual versus Village Lending: Evidence from Montenegro," Review of Development Economics, Wiley Blackwell, vol. 21(4), pages 67-87, November.
    2. Marup Hossain & Conner Mullally, 2022. "Using evaluation data to predict loan performance among poor borrowers: The case of BRAC’s asset transfer and microcredit programmes," Development Policy Review, Overseas Development Institute, vol. 40(3), May.
    3. Afridi, Farzana & Dhillon, Amrita & Sharma, Swati, 2024. "The ties that bind us: Social networks and productivity in the factory," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 470-485.
    4. Guido Friebel & Matthias Heinz & Mitchell Hoffman & Nick Zubanov, 2023. "What Do Employee Referral Programs Do? Measuring the Direct and Overall Effects of a Management Practice," Journal of Political Economy, University of Chicago Press, vol. 131(3), pages 633-686.
    5. Biener, Christian & Eling, Martin & Landmann, Andreas & Pradhan, Shailee, 2018. "Can group incentives alleviate moral hazard? The role of pro-social preferences," European Economic Review, Elsevier, vol. 101(C), pages 230-249.
    6. Gunnsteinsson, Snaebjorn, 2020. "Experimental identification of asymmetric information: Evidence on crop insurance in the Philippines," Journal of Development Economics, Elsevier, vol. 144(C).
    7. Cai, Yong & Rafi, Ahnaf, 2024. "On the performance of the Neyman Allocation with small pilots," Journal of Econometrics, Elsevier, vol. 242(1).
    8. Moina Rauf & Zahid Pervaiz, 2016. "Replacing Contracts with Handshakes: A Study of Social Networks of Entrepreneurs in the Weaving Sector," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 55(4), pages 331-348.
    9. Yong Cai & Ahnaf Rafi, 2022. "On the Performance of the Neyman Allocation with Small Pilots," Papers 2206.04643, arXiv.org, revised Jun 2024.
    10. M. Ali Choudhary & Anil K. Jain, 2022. "Credit access and relational contracts: An experiment testing informational and contractual frictions for Pakistani farmers," International Finance Discussion Papers 2022, Board of Governors of the Federal Reserve System (U.S.).
    11. Emily Breza & Arun G. Chandrasekhar, 2015. "Social Networks, Reputation and Commitment: Evidence from a Savings Monitors Experiment," NBER Working Papers 21169, National Bureau of Economic Research, Inc.

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    More about this item

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

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