Social structures (e.g., racial/ethnic status, state of residence) appear to have stronger effects on arrest outcomes among this sample than theoretically relevant predictors do, such as age and level of education. When racial/ethnic status and SVORI site were excluded from the nested duration models, higher levels of educational attainment increased the time that men remained in the community before rearrest. Further education was no longer significantly associated with rearrest when racial/ethnic status and state of residence were included in the model. These findings may reflect state-level differences in arrest rates; it is not clear to what extent higher rates of arrests among African Americans are the result of racial profiling or similar justice practices (see Figures 4.1-4.6 for graphs of pre-SVORI arrest rates by racial/ethnic status; Tables 4.8 and 4.9 for duration model results).
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Racial/Ethnic Differences in Rates of Offending and Arrest
Disproportionate minority involvement in the criminal justice system likely reflects real differences in levels of policing, prosecutorial discretion, and criminal justice sanctioning, especially for less serious crimes that may go unreported or unobserved by police (the
“differential criminal justice system selection hypothesis”) (Piquero & Brame, 2008). However, racial/ethnic differences in arrest rates may also reflect differential rates of involvement, if minorities are more likely to remain involved in criminal activity over the life course (the “differential involvement hypothesis”) (Anderson, 1999; D’Alessio & Stolzenberg, 2003; McNulty & Bellair, 2003; Piquero & Brame, 2008).
Between these two divergent perspectives, a middle position exists, which hypothesizes that police and criminal justice processes discriminate against minorities, but that individual, social, and structural factors contribute to higher rates of serious crime among minorities (Piquero & Brame, 2008; Piquero, MacDonald, & Parker, 2002). For instance, racial differences between African American and White male former prisoners in timing to first violent felony disappeared when controlling for local unemployment rate and access to manufacturing jobs (Bellair & Kowalski, 2011). The association between unemployment and violent offending has also been observed among African American prisoners in Florida; for these men, rising African American unemployment increased the likelihood of a new felony offense within 2 years of release (Mears, Wang, & Bales, 2014).
The results of studies using self-reported information often differ from studies that use official records, which consistently show higher arrest rates among African Americans and other minorities than among Whites (Piquero & Brame, 2008). Ideally, self-report measures would provide insight into the source of the racial disparities in criminal justice involvement.
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Unfortunately, older studies that have collected official records and self-reported information on official records revealed significant differences by racial/ethnic status in the accuracy of self- reported information (Farrington, Stouthamer-Loeber, Van-Kammen, & Schmidt, 1996;
Hindelang, Hirschi, & Weis, 1981; Huizinga & Elliott, 1986). These original studies often used adolescent samples that engaged in less serious forms of delinquent behavior, so the results may not generalize to adult prisoners with extensive criminal records (Piquero & Brame, 2008). Recent studies have provided mixed evidence to support the validity of self-report measures (Jolliffe et al., 2003; Maxfield, Luntz Weiler, & Spatz Widom, 2000; Piquero & Brame, 2008; Piquero, Schubert, & Brame, 2014; Sampson, Morenoff, & Raudenbush, 2005).
State-Level Differences in Arrest Rates
The duration and path models revealed significant differences in the odds of arrest by state location. These differences likely reflect differential rates of criminal activity by individuals within each state, although multiple potential sources of variation also exist at the local and state levels. First, policing practices vary across localities and states, which in turn influence the likelihood and timing of arrest. Second, prosecutorial discretion at the local level influences the odds that an arrest leads to prosecution, conviction, and eventually imprisonment. The length of time imprisoned for a given offense varies across states, as do prison conditions and access to programming within prisons.
State recidivism rates reflect the cumulative impact of these local and statewide variations in criminal justice practices, rendering it difficult to compare outcomes across states. In the case of the repeated-events duration model, residents from two states (Maryland and Washington) showed large increases in the baseline hazard of rearrest. Recidivism rates for these two states were the second- and third-highest rates, respectively, of the 11 states included in the duration
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models (Pew Center on the States, 2011; Rosenwald, 2011). The reduced time to first arrest for Maryland and Washington prisoners therefore reflects, in part, the policing or supervision practices in these states.
Finally, states appeared to recruit participants into the SVORI evaluation using different aspects of the SVORI enrollment criteria. Several states enrolled high proportions of men with recent drug convictions, most notably Iowa (58%) and Maryland (66%). Property offenders frequently exhibit the highest rates of rearrest and return to prison, relative to drug and property offenders, but none of the states appeared to use property convictions as criteria for enrollment into the SVORI evaluation (Lattimore & Steffey, 2009).
States that enrolled the highest proportions of violent offenders included Kansas (61%), Nevada (88%), Ohio (58%), and Washington (65%). Violent offenders often exhibit the lowest
recidivism rates, in comparison to drug and property offenders. This is born out for Nevada, which had the lowest statewide recidivism rate in 2004, of the states in the sample (Sentencing Project, 2010).
However, in the case of Washington State, the enrollment criteria were designed to recruit a high-risk, high-needs sample (e.g., under 35 years old and fitting one or more categories
reflecting heightened risk or needs). Men from Washington State who fit these criteria and were selected for the SVORI intervention were then mandated to receive services (Lattimore & Steffey, 2009). The LSEM results showed that men from Washington State were significantly more likely to have reoffended within each 6-month reference period, so policing practices do not fully account for state-level differences in rearrest.
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