Borrowing Requirements, Credit Access, and Adverse Selection: Evidence from Kenya
Bibliographic record
Abstract
Do the stringent formal sector borrowing requirements common in many developing countries restrict credit access, technology adoption, and welfare? When a Kenyan dairy's savings and credit cooperative randomly offered some farmers the opportunity to replace loans with high down payments and stringent guarantor requirements with loans collateralized by the asset itself — a large water tank — loan take-up increased from 2.4% to 41.9%. (In contrast, substituting joint liability requirements for deposit requirements did not affect loan take up.) There were no repossessions among farmers allowed to collateralize 75% of their loans, and there was only a 0.7% repossession rate among those offered 96% asset collateralization. A Karlan-Zinman test based on waiving borrowing requirements ex post finds evidence of adverse selection with lowered deposit requirements, but not of moral hazard. A simple model and rough calibration suggests that adverse selection may deter lenders from making welfare-improving loans with lower deposit requirements, even after introducing asset collateralization. We estimate that 2/3 of marginal loans led to increased water storage investment. Real effects of loosening borrowing requirements include increased household water access, reductions in child time spent on water-related tasks, and greater school enrollment for girls.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".