Schools and neighborhood crime: The effects of dropouts and high-performing schools on juvenile crime

Abstract

Objectives: We argue that assessing the level of crime concentration across cities has four challenges: 1) how much variability should we expect to observe; 2) whether concentration should be measured across different types of macro units of different sizes; 3) a statistical challenge for measuring crime concentration; 4) the temporal assumption employed when measuring high crime locations.Methods: We use data for 42 cities in southern California with at least 40,000 population to assess the level of crime concentration in them for five different Part 1 crimes and total Part 1 crimes over 2005-12. We demonstrate that the traditional measure of crime concentration is confounded by crimes that spatially locate due to random chance. We also use two measures employing different temporal assumptions: a historically adjusted crime concentration measure, and a temporally adjusted crime concentration measure (a novel approximate solution that is simple for researchers to implement).Results: There is much variability in crime concentration over cities in the top 5% of street segments. The standard deviation across cities over years for the temporally adjusted crime concentration measure is between 10% and 20% across crime types (with the average range typically being about 15% to 90%). The historically adjusted concentration has similar variability and typically ranges from about 35% to 100%. Conclusions:The study provides evidence of variability in the level of crime concentration across cities, but also raises important questions about the temporal scale when measuring this concentration. The results open an exciting new area of research exploring why levels of crime concentration may vary over cities? Either micro-or macro-theories may help researchers in exploring this new direction.

Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models

“…The results show that school dropout is associated with an increase in serious violent robberies, but schools with good grades will not have a significant impact on these types of crimes. Finally, by comparing the juvenile crime model with the juvenile and adult crime model, the viewpoint that reasonable measures should be performed on juvenile delinquency is supported [16].…”

The neighborhood effects on the online financial investment of rural households: Evidence from China

“…Moreover, focus is also put on individual gambling participation, entrepreneurial behavior, and donation behavior (Goldberg et al, 2014, Monson et al, 2018, Drouvelis&Marx, 2020) [29][30][31] . In addition, the impact of neighborhood effects on individual wrong behaviors results in suggestions on negative behaviors -reducing individual crime rate and illegal decisions of companies through social multipliers (Kim, 2016,Berg&Nelson, 2016, Gerlinger&Hipp, 2020) [32][33][34] .…”

Action Research in a Juvenile Detention School: New Processes, Paradigms, and Possibilities

“…Students who drop out have higher rates of aggravated assaults and robberies, and students with disabilities have disparate outcomes in juvenile justice involvement, length of sentences, and worse outcomes than students without disabilities (Gerlinger & Hipp, 2020;Kincaid & Sullivan, 2019). There was a gap in the literature concerning the experiences of first-time detained juvenile delinquents.…”