Prior Criminal Risk Heterogeneity in Youth Employment Outcomes

In Simple Words

Imagine throwing life jackets into a swimming pool where most people are standing in knee-deep water and only a few are in the deep end. The life jackets do nothing for those in shallow water because they were never at risk of drowning, but they save the lives of the few swimming in deep waters.

Research question

Universal social interventions often display muted aggregate impacts because low-risk individuals have minimal baseline rates of negative outcomes. Assessing overall averages obscures massive behavioral shifts occurring within high-risk subgroups.

Participants

N = 163,447 youth (5,092 at-risk youth with prior arrests, 158,355 low-risk youth without prior arrests), New York City.

Study Design

Subgroup-stratified 2SLS instrumental variables analysis of randomized lottery winners versus losers across four program years (2005–2008).

Results

Low-risk youth (97% of the cohort) show precisely estimated zero effects for contemporaneous crime reduction, ruling out arrest decreases greater than 0.9 per 1,000 youth. Virtually all statistically significant reductions in summer arrests and convictions are concentrated in the 3% at-risk population.

Takeaway

1. Allocate program slots and recruitment outreach preferentially to individuals with prior criminal justice contact rather than relying on broad universal lotteries.

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Plain-English glossary

Original explanations, informed by the references below. Examples are illustrations, not data from the studies above. The research text stays unchanged.

Baseline

The starting measurements in a study. Researchers use these to compare what participants were like at the start with what happens later.

ExampleBefore a training programme starts, researchers measure workers’ earnings. That first measurement is the baseline.

GET-IT glossary · Baseline characteristics ↗Read more about baseline →

Statistically significant

A label used when a result passes a chosen statistical threshold, often p < 0.05. It does not show how large or useful an effect is. A result that is not statistically significant does not prove there is no effect.

ExampleA study reports p = 0.03 against a 0.05 threshold. It passes that test, but the effect could still be too small to matter.

American Statistical Association · Statement on p-values ↗Read more about statistically significant →

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