From the Ground Up: Gender, Self-Employment, and Space in a Colombian Housing Project
Bibliographic record
Abstract
Researchers have documented a persistent wage gap between self-employed men and women. In this paper, we identify a novel intervention that boosts women’s earnings and reduces this gap in an informal, residential marketplace. We argue that micro-spatial resources offer specific, gendered advantages to female business owners. We show how gendered constraints on women’s labor market activity intersect with spatial resources to powerfully influence their likelihood of running a home-based 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, close the gender earnings gap in self-employment by 58.5%, and earn an income that lifts them above the poverty line. Overall, this study offers a new perspective on how gender and micro-geography intersect to shape self-employment, and shows how self-employment can function as a lever for women’s economic mobility in developing countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".