Job Displacement, Unemployment, and Crime: Evidence from Danish Microdata and Reforms
Bibliographic record
Abstract
This paper estimates the individual impact of a worker’s job loss on his/her criminal activity. Using a matched employer-employee longitudinal data set on unemployment, crime, and taxes for all residents in Denmark, the paper builds each worker’s timeline of job separation, unemployment, and crime. The paper focuses on displaced workers: high-tenure workers who lose employment during a mass-layoff event at any point between 1990 and 1994 (inclusive). Initial local industrial specialization suggests that the growth of manufacturing imports and the Nordic financial crisis in the early 1990s explain a significant share of mass layoffs. Placebo tests display no evidence of trends in crime prior to worker separation. Using Denmark’s introduction of the Act on an Active Labor Market at the end of 1993, we estimate the impacts of activation and of a reduction in benefit duration on crime: crime is lower during active benefits than during passive benefits and spikes at the end of benefit eligibility. We use policy-induced shifts in the kink formula relating prior earnings to unemployment benefits to estimate the separate impacts of labor income and unemployment benefits on crime: the results suggest that unemployment benefits do not significantly offset the impact of labor income losses on crime.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".