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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".