Better Loans or Better Borrowers? Lifting Credit Constraints for Women-Owned Firms in Ethiopia
Bibliographic record
Abstract
Purpose Evidence on the relationship between credit and firm growth among micro, small, and medium enterprises relies heavily on the microfinance literature, which finds that credit has only modest impacts on business performance. Our study investigates the impact of meso-credit : larger capital infusions with longer maturities to women entrepreneurs in Ethiopia. We study the impact of individual-liability loans with an average size of USD12,000, or eight times larger than the typical microfinance group-lending offering. Study design We leverage panel survey data collected from nearly 2000 firms in three post-loan intervention time periods over five years. We employ difference-in-differences and propensity score matching methods to estimate the impact of the loans to women-owned, formal enterprises, relative to a comparison group not offered loans. Findings Our results suggest that large, individual-liability loans have a significant positive impact on firm growth, with gains of 30% in business income and 50% in employment for firms that borrow, relative to those that do not. Loans also increase the likelihood of survival of existing firms. These effects are not merely driven by positive selection during the loan appraisal process; we find that loans matter even after accounting for borrower characteristics. Contributions Our paper is one of the first to evaluate the effect of loans to women in the “missing middle” and serves as a proof of concept that larger capital infusions for firms can promote growth. Implications Policymakers that seek to support the growth of firms should consider redesigning loan products to target underserved market segments, with larger and better-fit credit.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".