Accounting for the Racial Property Crime Gap in the US: A Quantitative Equilibrium Analysis
Bibliographic record
Abstract
This paper studies the effects of both labor market conditions and asset poverty on the property crimes involvement of American males. Since the mid 60’s the property crimes arrest rate has been four times higher for black males if compared to white ones. Another set of stylised facts show for the first demographic group lower educational levels and worse labor market outcomes, with the African Americans supplying less hours of labor, gaining lower wages, experiencing both higher unemployment duration and rates. At the same time, more than 30% of black households had a negative net worth. A dynamic general equilibrium model is developed, exploiting these facts to quantitatively assess the race crime gap, that is the difference in crime explained by the difference in observables. The model is calibrated relying on US data and solved numerically. The model captures well relevant dimensions of the crime phenomenon, such as the inmates composition by race, employment status and education. Simulation results show that the observed poverty and labor market outcomes account for as much as 90% of the arrest rates ratio. Finally the model is used to compare two alternative policy experiments aimed at reducing the aggregate crime rate: increasing the expenditure on police seems to be cost effective, when compared to an equally expensive lump-sum subsidy targeted to the high school dropouts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".