The Labor Market, the Decision to Become an Entrepreneur, and the Firm Size Distribution
Bibliographic record
Abstract
Why do some people become entrepreneurs, and how do labor markets affect this choice? This paper addresses this question using a matching model with occupational choice and heterogeneity in both ability as a worker and ex ante unknown productivity of firm startups. Key effects are the following: labor market conditions affect incentives to start firms differently for workers and the unemployed, with repercussions on aggregate productivity; and they affect the expected value of firm creation due to the possibility of failure. These effects go beyond the standard impact of labor market conditions on firms ’ employment policy and value. The correlation of observed productive ability and potential productivity significantly shapes the firm size distribution, suggesting that the empirical correlation is positive but far from perfect. Finally, the model allows for a comparatively flexible lower tail of the firm size distribution and can explain the existence and persistence of small, low-productivity firms with low profits: their owners have low outside options in the labor market.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".