The Role of Short-Time Working Schemes During the Global Financial Crisis and Early Recovery
Bibliographic record
Abstract
There has been a strong interest in short-time work (STW) schemes during the global financial crisis. Using data for 23 OECD countries for the period 2004 Q1 to 2010 Q4, this paper analyses the quantitative effects of STW programmes on labour market outcomes by exploiting the country and time variation in STW take-up rates. The analysis takes account of differences in institutional settings across countries that might affect the relationship between labour market outcomes and output and also addresses the endogeneity of STW take-up with respect to labour market conditions. Moreover, special attention is given to the dynamic aspects of the relationship between output and labour market outcomes. The results indicate the STW raises hours flexibility by increasing the output elasticity of working time and helps to preserve jobs in the context of a recession by making employment and unemployment less elastic with respect to output. A key finding is that the timing of STW is crucial. While STW helped preserving a significant number of jobs during the crisis, its continued use during the recovery may have slowed the job-content of the recovery. By the end of 2010, the net effect of STW on employment was negligible or may even have become negative. However, the gross impact of STW on the number of jobs saved per quarter remains large and positive in the majority of countries.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".