Bibliographic record
Abstract
In this paper we ask whether policies targeting a reduction in crime rates through changes in education outcomes can be considered an effective and cost-viable alternative to interventions based on harsher punishment alone. In particular we study the effect of subsidizing high school completion. Most econometric studies of the impact of crime policies ignore equilibrium effects and are often reduced-form. This paper provides a framework within which to study the equilibrium impact of alternative policies. We develop an overlapping generation, life-cycle model with endogenous education and crime choices. Education and crime depend on different dimensions of heterogeneity, which takes the form of differences in innate ability and wealth at birth as well as employment shocks. PSID, NIPA and CPS data are used to estimate the parameters of a production function with different types of human capital and to approximate a distribution of permanent heterogeneity. These estimates are used to pin down some of the model's parameters. The model is calibrated to match education enrolments, aggregate (property) crime rate and some features of the wealth distribution. In our numerical experiments we find that policies targeting crime reduction through increases in high school graduation rates are more cost-effective than simple incapacitation policies. Furthermore, the cost-effectiveness of high school subsidies increases significantly if they are targeted at the wealth poor. We also find that financial incentives to high school graduation have radically different implications in general and partial equilibrium (i.e. the scale of the programmes can substantially change its outcomes).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".