Do non-compete agreements help or hurt workers? Evidence from the NLSY97
Bibliographic record
Abstract
While non-compete agreements (NCs) are prevalent, the incentives driving their use and their causal effects on workers remain poorly understood. We develop a model with asymmetric information to show that NCs shift the nature of allocative inefficiency by reducing inefficient quits and increasing inefficient retention, while mitigating the canonical hold-up problem. The model predicts that NCs are more likely to be used in industries with high returns on industry-specific investments, and that signers have longer job tenures, higher wages, and receive more firm-provided investment than similar workers without such agreements. To test these predictions, we use panel data from the NLSY97 and a difference-in-differences research design to estimate the causal impact of signing an NC. We find that NCs raise job tenures by 6% and lead to an immediate wage increase of 10%. Six years after signing, the wage premium falls to 5%. There is also substantial heterogeneity across worker demographics, with non-White, non-college and lower-wage workers experiencing lower wage-growth after signing an NC. While the theory links NCs to firm investment, we find no evidence of increased investment in formal training, suggesting investments prompted by the agreement are likely informal. Our findings caution against blanket bans on NC usage, favoring a more targeted approach focusing on lower-wage workers.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".