Bibliographic record
Abstract
One of the strongest trends in recent macroeconomic modeling of labor market fluctuations is to treat unemployment inflows as acyclical. This trend stems in large part from an influential paper by Shimer on "Reassessing the Ins and Outs of Unemployment," i.e., the extent to which increased unemployment during a recession arises from an increase in the number of unemployment spells versus an increase in their duration. After broadly reviewing the previous literature, we replicate and extend Shimer's main analysis. Like Shimer, we find an important role for increased duration. But contrary to Shimer's conclusions, we find that even his own methods and data, when viewed in an appropriate metric, reveal an important role for increased inflows to unemployment as well. This finding is further strengthened by our refinements of Shimer's methods of correcting for data problems and by our detailed examination of particular components of the inflow to unemployment. We conclude that a complete understanding of cyclical unemployment requires an explanation of countercyclical inflow rates as well as procyclical outflow rates.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".