Bibliographic record
Abstract
Self-employment is an important component of many development strategies aiming to enhance earnings and employment among low-income populations. However, women tend to earn less than men through self-employment, calling into question the effectiveness of self-employment as a tool for bolstering women's earnings. In this paper, we identify a novel intervention that boosts women's returns from self-employment and narrows the gender earnings gap in an informal, residential market. We argue that micro-spatial resources offer gender-specific advantages to female business owners. We show how gendered constraints on women's labor market activity intersect with spatial resources to influence their likelihood of running a business and their self-employment earnings. Using data from a Colombian public housing complex, we find that the randomly assigned location of a resident's apartment significantly influences women's business activity, but not men's. Women who run informal, home-based businesses from favorable locations earn more than twice as much as comparable women, narrowing the gender earnings gap by 58.5% and earning an income that lifts them above the poverty line. This study offers a new perspective on how gender and micro-geography intersect to shape self-employment. More broadly, it reveals how an important but often-overlooked factor, micro-spatial variation, influences economic development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.104 | 0.031 |
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".