Size-Dependent Gender Gaps in Entrepreneurship: The Case of Chile*
Bibliographic record
Abstract
This paper documents differences in firm size depending on whether their manager is a man or a woman and studies the aggregate implications of these gender gaps in Chile. We document that in 2007 less than a quarter of firms are managed by women and that this gap takes its largest value for managers with tertiary education or more. In terms of their number of workers, female-run firms are on average about three times smaller than those run by men. Moreover, the ratio of men to women managers is always above one, but it is much higher for large and medium firms than for small or micro ones. These differences remain significant after controlling for several manager and firm characteristics. We then use an extended version of the theoretical framework developed in Cuberes and Teignier (2016) to incorporate these facts and obtain quantitative predictions about their effects on aggregate productivity and income in Chile. We find that the observed gender gaps in entrepreneurship in Chile generate a fall in aggregate productivity and aggregate income of 7.5%.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".