Expanding Microenterprise Credit Access: Using Randomized Supply Decisions to Estimate the Impacts in Manila
Why this work is in the frame
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Bibliographic record
Abstract
Microcredit seeks to promote business growth and improve well-being by expanding access to credit. We use a field experiment and follow-up survey to measure impacts of a credit expansion for microentrepreneurs in Manila. The effects are diffuse, heterogeneous, and surprising. Although there is some evidence that profits increase, the mechanism seems to be that businesses shrink by shedding unproductive workers. Overall, borrowing households substitute away from labor (in both family and outside businesses), and into education. We also find substitution away from formal insurance, along with increases in access to informal risk-sharing mechanisms. Our treatment effects are stronger for groups that are not typically targeted by microlenders: male and higher-income entrepreneurs. In all, our results suggest that microcredit works broadly through risk management and investment at the household level, rather than directly through the targeted businesses.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it