Individuals who use online lending sites to help others provide significantly more money to borrowers when they are part of a team than when they are not, two studies led by economists and computer scientists at the find.
Based on two randomized field experiments with more than sixty thousand members of , the online lending community, the studies found that team membership appears not merely to correlate with but to drive an increase in the number of loans made by users of the site. In one study, to be published in a special issue of Games and Economic Behavior, Kiva members who joined teams — and have their stats posted on the Kiva leaderboard — contributed about 1.2 loans, or at least $30, per month, more than non-team members. The study also found that when team leaders posted messages on Kiva forums that included a link to a chosen borrower, set a goal, and urged the team to work together to meet it, their teams made an average of eleven more loans per month. The volume of contributions also was more likely to increase when a new member posted a message to the forum that announced a loan to a chosen borrower, included a link to that borrower, and called on other members to help improve the team's ranking than if a post included only one of those elements.
A second , published in Proceedings of the National Academy of Sciences in December, reported that Kiva members could be most effectively nudged to join a team by an email message that included three teams of interest and an explanation of why those teams were included. The study also found that when lenders joined a team, their contributions spiked in the first week, to an average of $392. The study concluded that team membership facilitates information sharing about specific borrowers, which in turn reduces team members' search costs and boosts lending while also increasing pressure on other members to improve the team's ranking on the Kiva leaderboard.
The research could have implications for the field of behavioral economics — and for charitable giving — in that teams seem to discourage free-riding and prod people toward "pro-social" behavior that benefits the public good.
"The indications are that team membership is effective in increasing member contributions among lenders and that making recommendations to lenders to join teams is an effective and inexpensive mechanism to engage community members and increase their contributions," said Yan Chen, the Daniel Kahneman Collegiate Professor of Information in the U-M School of Information and a professor at the U-M Institute for Social Research. "It boils down to the economic principle known as the free-rider problem in public goods provision. You want to encourage the over-contributors and somehow penalize or call out the under-contributors. We think this team structure can help accomplish that."