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

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 ahistoric 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, notless, indicating how the welfare of the overall system is tied to a few distinctive and critical country-pair relationships.

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Document Details

Document Type
Technical Report
Publication Date
Mar 28, 2018
Accession Number
AD1071681

Entities

People

  • Boleslaw Szymanski
  • Brian Uzzi
  • Empke-agnes Horvat
  • Gyorgy Korniss
  • Jonathan Bakdash
  • Jyaram Uparna
  • Panagiotis Karampourniotis
  • Pramesh Singh

Organizations

  • Northwestern University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Engineered Resilient Systems
  • Human Systems

DTIC Thesaurus Topics

  • Commerce
  • Complex Systems
  • Economics
  • Geography
  • International Trade
  • Investments
  • Mechanics
  • Military Research
  • Money
  • Network Science
  • New York
  • Probability
  • Regression Analysis
  • Simulations
  • Social Networks
  • Statistical Mechanics
  • United States

Readers

  • Combustion Dynamics and Shock Wave Physics.
  • International Relations and European Studies
  • Regression Analysis.