Custodial versus non-custodial sentences: Long-run evidence from an anticipated reform
Bibliographic record
Abstract
We study the relative impact of custodial and non-custodial sentences on later crime and labor-market outcomes in Denmark, a country where detention conditions are particularly good. To do so, we take advantage of a large-scale reform of the Danish legislation implemented in 2000, whereby incarcerationwas replaced by a non-custodial sentence for most drunk-driving crimes, which represented a quarter of all custodial sentences passed in 1999. Our first key finding is that stakeholders anticipated the consequences of the reform and that wealthier offenders managed to postpone their trial until after the reform came into force to avoid prison. To measure the relative impact of incarceration, we therefore use a novel instrumental variable approach exploiting quasi-exogenous variation in the probability of being tried after the reform, and therefore incarcerated, based on offenders’ crime date. We follow sampled individuals over a 15-year period and find that incarcerated offenders commit more crimes and have weaker ties to the labor market after release. Additionally, first-time offenders are more negatively affected than repeat offenders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".