Big Loans to Small Businesses: Predicting Winners and Losers in an Entrepreneurial Lending Experiment
在埃及实验中发现，大额贷款对小微企业平均影响不大，但用心理数据训练的机器学习能识别出利润大增的“优等生”和利润下降的“差生”，说明贷款分配决策对总收入很重要，而现有做法导致严重错配。
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 “ 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 existing practice leads to substantial misallocation. We argue that some entrepreneurs are overoptimistic 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.