Unemployment in the Great Recession: A Comparison of Germany, Canada and the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it