Labor Market Mobility and Expectation Management: Evidence from Enforceability of <scp>Noncompete</scp> Provisions*
Bibliographic record
Abstract
ABSTRACT This study examines how managers' use of expectation management is affected by their labor market mobility, which we measure by the enforceability of noncompete provisions in their employment contracts. Exploiting quasinatural experiments, our difference‐in‐differences analyses provide new causal insights to the growing literature on how managers' career concerns affect their disclosure choices. Consistent with a less mobile labor market imposing more pressure on managers to achieve earnings expectations, we predict and find that managers in US states that tightened enforcement of noncompete provisions are more likely to manage analyst expectations downward. We also find that downward expectation management is used to a greater extent than other tools such as real and accrual‐based earnings management. Additional analysis shows that the increase in expectation management is more pronounced for CEOs with lower general skills or shorter tenures, for firms with more independent boards, and for industries that are more homogeneous. Our path analysis suggests a significant link between increased use of expectation management after tightened noncompete enforcement and meeting and beating earnings expectations, which in turn is linked to lower executive turnover. Overall, our findings suggest that expectation management is an important channel through which noncompete enforcement reduces executive labor market mobility. Our study sheds light on the underlying mechanism through which labor market mobility affects disclosure choices and has important implications for both firms and regulators on the use and enforcement of noncompete provisions.
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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 | 0.005 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".