Labour market institutions and unemployment volatility: evidence from OECD countries
Bibliographic record
Abstract
Using publicly available data for a group of 20 OECD countries, we find that the cyclical volatility of the unemployment rate exhibits substantial cross-country and time variation. We then investigate empirically whether labour market institutions can account for this observed heterogeneity and find that the impact of various institutions on cyclical unemployment dynamics is quantitatively strong and statistically significant. The hypothesis that labour market institutions could increase the volatility of unemployment by reducing match surplus is not supported by the data. In fact, unemployment benefits, taxation and employment protection appear to reduce the volatility of unemployment rates. In addition, we find that the precise nature of union bargaining has important implications for cyclical unemployment dynamics, with union coverage and density having large and offsetting effects. Finally, we provide evidence suggesting that interactions between shocks and institutions matter for cyclical unemployment fluctuations. However, institutions only account for about one quarter of the explained variation, which implies that they are important but they are not the entire story.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".