Working Paper: CEPR ID: DP16573
Authors: Gharad Bryan; Dean Karlan; Adam Osman
Abstract: 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 treatmenteffects) 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.
Keywords: entrepreneurship; enterprise credit; heterogeneous treatment effects; psychometric data; small and medium enterprises
JEL Codes: D22; D24; L26; M21; O12; O16
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Larger loans (G51) | Increase in profits for top performers (L25) |
| Larger loans (G51) | Decrease in profits for poor performers (L25) |
| Larger loans (G51) | Increase in wage bill for top performers (J33) |
| Larger loans (G51) | Increase in productivity for top performers (D29) |
| Larger loans (G51) | Increase in household expenditures for top performers (D12) |
| Larger loans (G51) | Decrease in wage bill for poor performers (J33) |
| Allocation of larger loans based on entrepreneurial type (L26) | Better outcomes (I14) |
| Loan officer perceptions (G21) | Misallocation of loans (H81) |