Data and Code for: Big Loans to Small Businesses: Predicting Winners and Losers in an Entrepreneurial Lending Experiment
Domaine:
socioeconomicType de record:
datasetpaper
Créateur:
BryKarOsm
Éditeur:
ICP
Hôte:
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. Response Rates: Overall response rate was 96% Applicants to a microfinance organization Approved loan applicants to "Tamouh" program at Alexandria Business Association. face-to-face interview; other; Data collection received CAPMAS approval from government of Egypt.
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