Who Becomes an Entrepreneur? Labor Market Prospects and Occupational Choice
Bibliographic record
Abstract
Why do some people become entrepreneurs (and others don’t)? Why are firms so het-erogeneous, and many firms so small? To start, the paper briefly documents evidence from the empirical literature that the relationship between entrepreneurship and education is U-shaped; that many entrepreneurs start a firm “out of necessity”; that most firms are small, remain so, yet persist in the market; and that returns to entrepreneurship have a much larger cross-sectional variance than returns to wage work. Popular models of firm heterogeneity cannot easily account for the U-shape or for the persistence of low-productivity firms. The paper shows that these facts can be explained in a dynamic model of occupational choice between wage work and entrepreneurship where agents are heterogeneous in their ability as workers, and starting entrepreneurs face uncertainty about their project’s productivity. Then, under weak conditions, the most and the least able individuals choose to become ∗I would like to thank the associate editor (Toshihiko Mukoyama) and two anonymous referees for detailed comments that helped improve the paper. I would also like to thank Andrea Caggese, Antonio Ciccone, Rus-
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".