The interaction effect of gender and ethnicity in loan approval: A Bayesian estimation with data from a laboratory field experiment
Bibliographic record
Abstract
Abstract Microfinance targets women and uses loan provision as a tool for empowerment, which translates into better household nutrition, improved education, and a scale down of domestic violence. However, ethnic discrimination in microfinance may exist in countries with a segregated indigenous population. We assessed this possibility with a field experiment in Bolivia. The controlled laboratory experiment evaluated whether credit officers rejected microloan applications based on the interaction effect of ethnicity and gender of potential borrowers. Point estimates of a Bayesian mixed‐effects logistic regression, estimated with the experimental data, indicate that nonindigenous women have double the chance of loan approval, but indigenous women have only 1.5 times the chance of loan approval when compared with men. While the findings about gender are limited, the evidence for the interaction of gender and ethnicity is more robust and suggests the existence of positive taste‐based discrimination favorable for nonethnic women in Bolivia. We conclude that the affirmative actions towards women promoted by development agencies and microfinance institutions must not overlook ethnicity as an important factor for financial policies of sustainable development. In practice, these policies should be aimed at identifying and reducing both social desirability bias and the structural barriers to financial inclusion that indigenous women may face when trying to obtain access to a loan.
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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 | 0.049 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".