Why did unemployment respond so differently to the global financial crisis across countries? Insights from Okun’s Law
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Bibliographic record
Abstract
Abstract The global financial crisis deeply impacted labour markets around the globe. In the case of the United States, some commentators have argued that the subsequent rise in unemployment exceeded previous estimates of the elasticity of the unemployment rate with respect to output growth, a statistical relationship known as Okun’s law. In contrast, others find a stable, long-term estimate of Okun’s coefficient implying that the deviation in unemployment during the crisis resulted from a larger output gap (not a structural shift in the trend). Ultimately, estimates of this relationship will depend on the methodology and data period utilized. Focusing more on short-term fluctuations, changes in unemployment are decomposed to identify the association with other channels of labour market adjustment (hours, productivity and labour force). Results presented in this paper confirm the cross-country variation in the responsiveness of unemployment in the wake of the Great Recession. In the United States, Canada, Spain and other severely affected economies, estimates of Okun’s coefficient increased sharply, departing from pre-crisis levels. In other countries, where unemployment has remained subdued, such as Germany and the Netherlands, the coefficient has fallen dramatically over the short-term. While other factors can explain the heterogeneous impact of the global financial crisis on labour markets in OECD countries, this paper focuses on the contribution of labour market institutions (employment protection legislation) in explaining cross-country differences and shifts in the estimated Okun’s coefficient. In this regard, empirical evidence confirms that the responsiveness in the unemployment rate during the global downturn was lower in countries where workers are afforded greater employment protection such as Germany. JEL codes E24, J64, G01
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it