The Effect of Education on Crime: Evidence from Prison Inmates, Arrests, and Self-Reports
Bibliographic record
Abstract
We estimate the effect of high school graduation on participation in criminal activity accounting for endogeneity of schooling. We begin by analyzing the effect of high school graduation on incarceration using Census data. Instrumental variable estimates using changes in state compulsory attendance laws as an instrument for high school graduation uncover a significant reduction in incarceration for both blacks and whites. When estimating the impact of high school graduation only, OLS and IV estimators estimate different weighted sums of the impact of each schooling progression on the probability of incarceration. We clarify the relationship between OLS and IV estimates and show that the "weights" placed on the impact of each schooling progression can explain differences in the estimates. Overall, the estimates suggest that completing high school reduces the probability of incarceration by about .76 percentage points for whites and 3.4 percentage points for blacks. We corroborate these findings using FBI data on arrests that distinguish among different types of crimes. The biggest impacts of graduation are associated with murder, assault, and motor vehicle theft. We also examine the effect of drop out on self-reported crime in the NLSY and find that our estimates for imprisonment and arrest are caused by changes in criminal behavior and not educational differences in the probability of arrest or incarceration conditional on crime. We estimate that the externality of education is about 14-26% of the private return to schooling, suggesting that a significant part of the social return to education comes in the form of externalities from crime reduction.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".