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Recommending teams promotes prosocial lending in online microfinance

Principal Investigator(s):  View help for Principal Investigator(s) Yan Chen, University of Michigan

Version:  View help for Version V1

Name File Type Size Last Modified
README.txt text/plain 2.5 KB 11/28/2016 07:37:PM
analysis.do text/x-stata-syntax 2.5 KB 11/29/2016 06:34:AM
choice.csv text/csv 8.2 MB 08/09/2016 09:55:PM
prior_loans.csv text/csv 679.1 KB 08/09/2016 08:52:AM
prior_others.csv text/csv 1.5 MB 08/09/2016 08:52:AM
treatment_effect.csv text/csv 1.7 MB 08/10/2016 08:14:AM

Project Citation: 

Chen, Yan. Recommending teams promotes prosocial lending in online microfinance. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2016-11-29. https://doi.org/10.3886/E100358V1

Project Description

Summary:  View help for Summary This paper reports the results of a large-scale field experiment designed to test the hypothesis that group membership can increase participation and pro-social lending for an online crowdlending community, Kiva. The experiment uses variations on a simple email manipulation to encourage Kiva members to join a lending team, testing which types of team recommendation emails are most likely to get members to join teams as well as the subsequent impact on lending. We find that emails do increase the likelihood that a lender joins a team, and that joining a team increases lending in a short window (one week) following our intervention. The impact on lending is large relative to median lender lifetime loans. We also find that lenders are more likely to join teams recommended based on location similarity rather than team status. Our results suggest team recommendation can be an effective behavioral mechanism to increase pro-social lending.
Funding Sources:  View help for Funding Sources National Science Foundation (BCS-1111019); National Science Foundation (IIS-1054199)

Scope of Project

Subject Terms:  View help for Subject Terms social identity; charitable giving; microfinance; field experiment; recommender systems


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Published Versions

V2 [2016-12-11]

V1 [2016-11-29]

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