Bibliographic record
Abstract
In this paper, we develop a tractable model of the labor market where workers search for jobs both while unemployed and while on the job. Search is directed in the sense that each worker chooses to search for the offer that provides the optimal tradeoff between the probability of obtaining the offer and the increase in the value relative to the worker's current employment. There are both aggregate and match-specific shocks, on which the wage path in an offer can be contingent. We characterize the equilibrium analytically and show that the equilibrium is unique and socially efficient. On the quantitative side, we calibrate the model to the US data to measure the effect of aggregate productivity fluctuations on the labor market. We find that productivity fluctuations account for approximately 64% of the cyclical volatility in US unemployment. Moreover, productivity fluctuations generate the same matrix of correlations between unemployment and other labor market variables as in the US. In particular, the Beveridge curve is negatively sloped over business cycles, and the magnitude of the slope is the same as in the data. In light of these findings, we conclude that productivity shocks are one of the main forces driving labor market fluctuations over business cycles. Furthermore, we find that recessions have a cleansing effect on the economy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".