Repeated Moral Hazard with Worker Mobility via Directed On-the-Job Search
Bibliographic record
Abstract
I develop a model of dynamic employment contracts by integrating an optimal contracting problem into an equilibrium search framework. The proposed framework enables us to analyze the interaction between the contracting problem and the endogenously evolving outside environment via worker mobility, and I characterize the optimal long-term wage contract as well as the optimal incentive compatible effort-tenure profile. The optimal contract exhibits an increasing wage-tenure profile for two reasons: 1) it induces the workers to be more likely to stay in their current contracts, and 2) it can induce the workers to make efforts when the current up-front wages cannot. The optimal incentive-compatible effort also has an increasing profile due to an interaction between 1) the workers' fear of losing their jobs, and 2) their incentive to obtain better outside offers. I then show the existence of an equilibrium. The equilibrium inherits the ``block recursivity'' developed by Shi (2008) and Menzio and Shi (2008); that is, individuals' optimal decisions and optimal contracts are independent of the distribution of workers.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".