The unemployment effects of closing the non-essential activities during the COVID-19 lockdown: The case of 8,108 Spanish municipalities
Bibliographic record
Abstract
We study the labour market impact of the confinement measures implemented in Spain to halt the spread of the COVID-19 pandemic in the first quarter of 2020. We use data from 8,108 municipalities to quantify the short-term impact of the temporary shutdown of non-essential activity on local unemployment rates. Ordinary least squares regressions show that an increment of 10 percentage points in the share of firms with non-essential activities increases the unemployment rate between 0.08 and 0.22 percentage points, depending on the population size of the municipalities. We only find this positive effect in municipalities above 2,395 inhabitants. The lockdown explains around 50% of the observed increase in the unemployment rates of these municipalities. We also look at the impact by gender and age and find that the lockdown of these activities affects males and workers between 25 and 45 years old by relatively more.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 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".