Unemployment in the Great Recession: A Comparison of Germany, Canada and the United States
Bibliographic record
Abstract
This paper investigates the potential reasons for the surprisingly different labor market performance of the United States, Canada, Germany, and several other OECD countries during and after the Great Recession of 2008-09. Unemployment rates did not change substantially in Germany, increased and remained at relatively high levels in the United States, and increased moderately in Canada. More recent data also show that, unlike Germany and Canada, the U.S. unemployment rate remains largely above its pre-recession level. We find two main explanations for these differences. First, the large employment swings in the construction sector linked to the boom and bust in U.S. housing markets can account for a large fraction of the cross-country differences in aggregate labor market outcomes for the three countries. Second, cross-country differences are consistent with a conventional Okun relationship linking GDP growth to employment performance. In particular, relative to pre-recession trends there has been a much larger drop in GDP in the United States than Germany between 2008 and 2012. In light of these facts, the strong performance of the German labor market is consistent with other aggregate outcomes of the economy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".