Bibliographic record
Abstract
Using data from the Survey of Income and Program Participation (SIPP) covering 1990-2011, we document that a surprisingly large number of workers return to their previous employer after a jobless spell and experience more favorable labor market outcomes than job switchers.Over 40% of all workers separating into unemployment regain employment at their previous employer; over a fifth of them are permanently separated workers who did not have any expectation of recall, unlike those on temporary layoff.Recalls are associated with much shorter unemployment duration and better wage changes.Negative duration dependence of unemployment nearly disappears once recalls are excluded.We also find that the probability of finding a new job is more procyclical and volatile than the probability of a recall.Incorporating this fact into an empirical matching function significantly alters its estimated elasticity and the time-series behavior of matching efficiency, especially during the Great Recession.We develop a canonical search-and-matching model with a recall option where new matches are mediated by a matching function, while recalls are free and triggered both by aggregate and job-specific shocks.The recall option is lost when the unemployed worker accepts a new job.A quantitative version of the model captures well our cross-sectional and cyclical facts through selection of recalled matches.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".