We experimentally study the impact of relatively large 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.
Publisher
American Economic Review
Published On
Oct 26, 2023
Authors
Gharad Bryan, Dean Karlan, Adam Osman
Tags
enterprise loans
Egypt
entrepreneurs
profits
credit allocation
psychometric data
risk management
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