Slow recoveries, worker heterogeneity, and the zero lower bound
Bibliographic record
Abstract
We show that a new Keynesian model with search and matching labor market frictions, worker heterogeneity, and a zero lower bound on the nominal interest rate is able to account qualitatively for several characteristics of the Great Recession and the sluggish recovery of the U.S. job market. With worker heterogeneity, a slow recovery lowers the average productivity of the pool of unemployed workers as less productive workers experience a higher inflow into unemployment and lower outflow from unemployment. This compositional effect lowers the expected surplus for firms of creating new jobs. Compared to a model with homogeneous workers, worker heterogeneity in a persistent downturn results in: (1) a much larger increase in unemployment; (2) a similar output recovery but a delayed and much slower recovery of unemployment (a jobless recovery); (3) little downward wage pressure despite considerable slack in the labor market; (4) a fall in measured match efficiency and a long-lived shift in the Beveridge curve. These results are obtained despite the assumption that wages are fully flexible. We show that the employment consequences of the zero lower bound on the nominal interest rate are small if workers are homogeneous but are large in the presence of worker heterogeneity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".