The short-term impact of the 2020 pandemic lockdown on employment in Greece
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 analyzes the short-term employment impact of the COVID-19 lockdown in Greece during the first few months following the pandemic onset. During the initial lockdown period, aggregate employment was lower by almost 9 percentage points than it would have been expected based on pre-pandemic employment trends. However, due to a government intervention that prohibited layoffs, this was not due to higher separation rates. The overall short-term employment impact was due to lower hiring rates. To uncover the mechanism behind this, we use a difference-in-differences framework, and show that tourism-related activities, which are exposed to seasonal variation, had significantly lower employment entry rates in the months following the pandemic onset compared to non-tourism activities. Our results highlight the relevance of the timing of unanticipated shocks in economies with strong seasonal patterns, and the relative effectiveness of policy interventions to partly absorb the consequences of such shocks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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