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

Journal: American Economic Review

Date: 20240901

Author: Bryan, Gharad; Karlan, Dean; Osman, Adam

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 "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.

Link: Google Scholar

Key Findings

Heterogeneous Impact of Large Loans

Top-performing entrepreneurs increased profits by 55% with larger loans, while poor-performers saw profits decrease by 52%, highlighting the importance of targeting credit allocation.

Effective Credit Allocation Matters

Psychometric data better predicts loan performance than traditional metrics. Current loan officer allocation practices lead to substantial misallocation of credit.

Risk and Optimism Drive Performance

Top performers were more risk-averse and realistic, while poor performers were overly optimistic and took excessive risks with larger loans.

Impact on Business Profits by Performance Group

  • Top performers saw monthly profits increase by 8,611 EGP
  • Poor performers experienced profit decrease of 8,180 EGP
  • Control group mean profits were 15,649 EGP

Business Performance Metrics Across Groups

  • Top performers increased revenues by 50,942 EGP
  • Top performers saw 133% increase in wage bill
  • Bottom group experienced declines across all metrics

Loan Repayment Performance

  • 76% of control group had perfect repayment vs 63% of treatment group
  • Average days late: 12.7 for control vs 26.8 for treatment
  • All loans were eventually repaid in full

Contribution and Implications

  • Demonstrates importance of entrepreneur characteristics over firm characteristics in credit allocation
  • Suggests need to reform loan officer incentives to focus on business growth potential rather than just default risk
  • Highlights value of psychometric data in improving lending decisions and reducing credit misallocation
  • Shows potential for substantial gains in profitability through better targeting of larger loans

Data Sources

  • Profits Chart: Constructed using data from Table 4, Panel A showing GATES estimates for profits across quartiles
  • Performance Metrics Chart: Based on Table 4, Panel B showing Conditional Group Average Treatment Effects
  • Repayment Chart: Created using data from Table 3 on loan repayment behavior metrics