Peer-to-peer lending and bias in crowd decision-making

PLoS One. 2018 Mar 28;13(3):e0193007. doi: 10.1371/journal.pone.0193007. eCollection 2018.

Abstract

Peer-to-peer lending is hypothesized to help equalize economic opportunities for the world's poor. We empirically investigate the "flat-world" hypothesis, the idea that globalization eventually leads to economic equality, using crowdfinancing data for over 660,000 loans in 220 nations and territories made between 2005 and 2013. Contrary to the flat-world hypothesis, we find that peer-to-peer lending networks are moving away from flatness. Furthermore, decreasing flatness is strongly associated with multiple variables: relatively stable patterns in the difference in the per capita GDP between borrowing and lending nations, ongoing migration flows from borrowing to lending nations worldwide, and the existence of a tie as a historic colonial. Our regression analysis also indicates a spatial preference in lending for geographically proximal borrowers. To estimate the robustness for these patterns for future changes, we construct a network of borrower and lending nations based on the observed data. Then, to perturb the network, we stochastically simulate policy and event shocks (e.g., erecting walls) or regulatory shocks (e.g., Brexit). The simulations project a drift towards rather than away from flatness. However, levels of flatness persist only for randomly distributed shocks. By contrast, loss of the top borrowing nations produces more flatness, not less, indicating how the welfare of the overall system is tied to a few distinctive and critical country-pair relationships.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Financing, Organized / economics*
  • Financing, Personal / economics*
  • Humans
  • Investments / economics*
  • Models, Economic
  • Peer Group*
  • Poverty / economics
  • Poverty / prevention & control

Grants and funding

This research was sponsored by the Northwestern University Institute on Complex Systems (NICO), the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 (the ARL Network Science CTA), the Army Research Office (ARO) grant W911NF-16-1-0524, and NU SP0033419. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory or the U.S. government.