Long-Term Unemployment and the Great Recession: The Role of Composition, Duration Dependence, and Non-Participation
Bibliographic record
Abstract
We explore the extent to which composition, duration dependence, and labor force non-participation can account for the sharp increase in the incidence of long-term unemployment (LTU) during the Great Recession.We first show that compositional shifts in demographics, occupation, industry, region, and the reason for unemployment jointly account for very little of the observed increase in LTU.Next, using panel data from the Current Population Survey for 2002-2007, we calibrate a matching model that allows for duration dependence in the exit rate from unemployment and for transitions between employment (E), unemployment (U), and non-participation (N).We model the job-finding rates for the unemployed and non-participants, and we use observed vacancy rates and the transition rates from E-to-U, E-to-N, N-to-U, and U-to-N as the exogenous "forcing variables'' of the model.The calibrated model can account for almost all of the increase in the incidence of LTU and much of the observed outward shift in the Beveridge curve between 2008 and 2013.Both negative duration dependence in the job-finding rate for the unemployed and transitions to and from non-participation contribute significantly to the ability of the model to match the data after 2008.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".