Measuring Heterogeneity in Job Finding Rates among the Non-Employed Using Labor Force Status Histories
Bibliographic record
Abstract
We construct a novel measure of the duration of joblessness using the labor force status histories in the four-month CPS panels. For those out of the labor force (OLF) and the unemployed, the job finding rate declines with the duration of joblessness. This duration measure dominates other existing measures in the CPS for predicting transitions from non-employment to employment. For those OLF, the variation in job finding rates explained by the duration of joblessness is five times larger than the variation explained by the self-reported desire to work or reasons for not searching. For the unemployed, the job finding rate declines with the self-reported duration of unemployment only to the extent that this variable correlates with the duration of joblessness. The two duration measures are not equivalent, and the discrepancy between them is not a classification error. Instead, the self-reports of unemployment durations refer to how long the respondent looked for work, often disregarding short-term jobs or including periods of employment while searching. Using our novel measure, we provide new estimates of the duration distribution of the unemployed and reexamine current approaches to misclassification error in the CPS.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".