Is Working for a Start-up Worth It? Evidence from the Semiconductor Industry
Bibliographic record
Abstract
This paper examines the long-term earnings implications of workers’ decisions to work for early-stage firms. Using quarterly data, 1990-2002, from the California Unemployment Insurance System covering workers in California’s semiconductor industry, I compare the career trajectories of charter employees (i.e. employees who leave established firms to join a start-up firm in the start-up’s first quarter of record) with a matched sample of comparable workers at each charter employee’s pre-start-up employer. Estimating a fixed-effects model using the matched sample, I find that joining an early-stage firm has higher expected value and higher variance than staying at an established firm or than changing jobs to a different established firm. Additionally, I demonstrate that firm death and initial public offerings both have very little effect on the earnings levels and trajectories of charter employees. Finally, I look at the coefficient of relative risk aversion at which workers are indifferent between working at a start-up and staying at their previous employer. I conclude that joining a start-up in California’s semiconductor industry is utility maximizing for all workers with a low to moderate level of risk aversion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".