Big Loans to Small Businesses: Predicting Winners and Losers in an Entrepreneurial Lending Experiment

We experimentally study the impact of substantially larger enterprise loans in Egypt. Larger loans generate small average impacts, but machine learning using psychometric data reveals that ”top-performers” (those with the highest predicted treatment effects) substantially increase profits, while profits drop for poor-performers. The large differences imply that lender credit allocation decisions matter for aggregate income, yet we find that existing practice leads to substantial misallocation. We argue that some entrepreneurs are over-optimistic and squander the opportunities presented by larger loans by taking on too much risk, and show the promise of allocations based on entrepreneurial type relative to firm characteristics.

Written with Dean Karlan (Northwestern University, IPA, J-PAL, and NBER and Adam Osman (University of Illinois at Urbana-Champaign, and J-PAL).