The heterogeneous effects of COVID-19 on labor markets: People’s movement and non-pharmaceutical interventions
Bibliographic record
Abstract
The paper investigates the heterogeneous effect of a policy-induced decline in people's mobility on the Japanese labor market outcome during the early COVID-19 period. Regressing individual-level labor market outcomes on prefecture-level mobility changes using policy stringency index as an instrument, our two-stage least squares estimator presents the following findings. First, the number of people absent from work increased for all groups of individuals, but the magnitude was greater for workers with non-regular employment status, low-educated people, females especially with children, and those aged 31 to 45 years. Second, while work hours decreased for most groups, the magnitude was especially greater for business owners without employees and those aged 31 to 45. Third, the negative effect on unemployment was statistically significant for older males who worked as regular workers in the previous year. The impact was particularly considerable for those aged 60 and 65, thus suggesting that they lost their re-employment opportunity due to COVID-19. Fourth, all these adverse effects were greater for people working in service and sales occupations. Fifth, a counterfactual experiment of more stringent policies indicates that while an average worker would lose JPY 3857 in weekly earnings by shortening their work hours, the weekly loss for those aged 31 to 45 years and working in service and sales occupations would be about JPY 13,842.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".