Information asymmetries and identification bias in P2P social microlending

Frederick J. Riggins and David M. Weber

Information Technology for Development, 2017, vol. 23, issue 1, 107-126

Abstract: The Internet has created new opportunities for peer-to-peer (P2P) social lending platforms, which have the potential to transform the way microfinance institutions raise and allocate funds used for poverty reduction. Depending upon where decision-making rights are allocated, there is the potential for identification bias whereby lenders may be motivated to give to specific projects with which they have an affinity without regard to whether it represents a sound financial investment. Using data collected from Kiva, we present empirical evidence that distant upstream lenders do not have adequate information about local business and loan conditions to make sound microfinance funding decisions, but instead make decisions based on identification biases. Furthermore, more information provided on the P2P lending site about the prospective loan does not improve the lender’s information about the loan conditions, but rather exacerbates the identification bias effect.

Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://hdl.handle.net/10.1080/02681102.2016.1247345 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:titdxx:v:23:y:2017:i:1:p:107-126

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/titd20

DOI: 10.1080/02681102.2016.1247345

Access Statistics for this article

Information Technology for Development is currently edited by Sajda Qureshi

More articles in Information Technology for Development from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().