critical essay - parole
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ParoleesPhysicalClosenesstoSocialServices.pdf
Article
Crime & Delinquency 57(1) 102 –129
© 2011 SAGE Publications DOI: 10.1177/0011128708322856
http://cad.sagepub.com
Parolees’ Physical Closeness to Social Services: A Study of California Parolees
John R. Hipp1, Jesse Jannetta1, Rita Shah1, and Susan Turner1
Abstract
This study examines the proximity of service providers to recently released parolees in California over a 2-year period (2005-2006). The addresses of parolee residences and service providers are geocoded, and the number of various types of service providers within 2 miles (3.2 km) of a parolee are measured. “Potential demand” is measured as the number of parolees within 2 miles of a provider. Although racial and ethnic minority parolees have more service providers nearby, these providers appear to be particularly impacted based on potential demand. It is also found that the parolees arguably most in need of social services—those who have spent more time in correctional institutions, have been convicted of more serious or violent crimes in their careers, or are sex offenders—live near fewer social services, or the provid- ers near them appear impacted.
Keywords
parolees, social services, neighborhoods, propinquity
Over the past 25 years, imprisonment rates in the United States have increased dramatically, from 330,000 in prison in 1980 to more than 1.5 million in 2005, a 450% increase (Harrison & Beck, 2006; Lynch & Sabol, 2001). One important
1University of California, Irvine
Corresponding Author: Dr. John R. Hipp, Department of Criminology, Law and Society, University of California, Irvine, 2367 Social Ecology II, Irvine, CA 92697 Email: [email protected]
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implication of this massive increase in incarceration has been the corresponding increase in the number of offenders returning to communities from prison. The number of offenders returning annually from prison to U.S. neighborhoods increased from 170,000 in 1980 to about 700,000 in 2005 (Lynch & Sabol, 2001; Sabol & Harrison, 2007). As a consequence, the number of ex-offenders residing in communities has risen from 1.8 million in 1980 to 4.3 million in 2000 (Raphael & Stoll, 2004). These large numbers of ex-offenders make it all the more important that they have access to the types of social services that may be crucial in preventing recidivism. If the geographic location of offenders returning to communities and the social service providers in those communities are not coterminous, the ability of these offenders to reintegrate into the com- munity may be seriously hampered. This is particularly relevant to reentry models that incorporate the idea of a continuum of care from institutions into the community (Byrne, Taxman, & Young, 2002; Lin & Turner, 2007).
Prisoners returning to their communities often have serious problems with substance abuse, financial problems, family conflict, low educational attain- ment, and lack strong social networks of support (Petersilia, 2003), resulting in difficulties obtaining employment and stable housing and desisting from criminal behavior. If offenders are returning to neighborhoods that do not provide access to the range of services that are important for reintegrating them into the broader community, it stands to reason that they will be less likely to succeed in their postrelease transition and more likely to recidivate. Given the evidence of the importance of services for reintegrating offenders (Zhang, Roberts, & Callanan, 2006) and evidence from the public health and workforce literature that proximity to social services is an important facilitator of accessing them (Allard, Tolman, & Rosen, 2003; Brameld & Holman, 2006; Gregory et al., 2000; Piette & Moos, 1996; Weiss & Greenlick, 2007), inves- tigating the proximity of released offenders to services in the community represents a key first step in understanding successful reentry of offenders.
Nonetheless, evidence regarding the proximity of returned offenders and ser- vices is sparse and somewhat contradictory. Watson et al. (2004) found that few of the organizations providing services to ex-prisoners in Houston were located in the neighborhoods with the greatest concentration of returning offenders. By contrast, Fleming et al. (2005) found that the locations of substance abuse and mental health services in Allegheny County, Pennsylvania, mapped quite closely with the residences of ex-prisoners. However, both of these studies addressed these issues using bivariate analyses only, leaving open the possibility that spurious effects could be driving the results.
We have even less systematic evidence regarding whether there are differ- ences in access to service providers by the characteristics and service needs
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of offenders. That is, do all offenders have equal access to various types of services? Or does this access differ based on demographic characteristics of the offender (e.g., race or age) or the criminal history of the offender (e.g., long-term criminals or violent criminals)? Given their particular need for such social services, do racial/ethnic minorities indeed have reasonable access to such services? And do those with a long history of criminal offending or those who engage in more serious or violent crime—and thus are in need of such services—have reasonable access? Despite the importance of these questions, given the large increase in incarceration of the past 20 years, answers based on empirical evidence are lacking.
We addressed this void by constructing and analyzing a unique data set to test the relative availability of social services to parolees in the state of California in 2 recent years: 2005 and 2006. Because of California’s determinant sentenc- ing laws, parolees account for virtually all releases from prison. In 2006, only 1,994 of 129,811 felons (1.5%) released from state prison were not released to parole supervision (California Department of Corrections and Rehabilitation, 2007). We tested whether the number of social service providers near parolees differs systematically based on the demographic characteristics of parolees or their criminal history. We also tested whether the potential demand of these service providers differs based on these parolee characteristics.
Literature Review Why Might Closeness of Social Services Matter for Offenders?
Accessing services after release from prison is necessary for the successful integration of most, if not all, offenders released from prison. Parolees face numerous challenges during the reentry process, and social services can assist them in meeting those challenges (Petersilia, 2003). For instance, employment services can provide information on job openings, job training, and assistance with job search techniques such as interviewing and résumé writing. Housing services can help parolees secure a stable residence, a necessary first step in community reintegration. Parolees may also need substance abuse treatment, legal assistance, family services, transportation help, and other services.
There is considerable evidence that the utilization of various social services has positive consequences for ex-offenders. For instance, postrelease attendance at community-based substance abuse programs is associated with less substance use and reduced recidivism (Anglin, Prendergast, Farabee, & Cartier, 2002; Visher & Courtney, 2007; Wexler, DeLeon, Thomas, Kressel, & Peters, 1999). Program evaluation evidence suggests that community employment programs reduce
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recidivism (Bouffard, Mackenzie, & Hickman, 2000). Zhang et al. (2006) found that meeting the service goal of one of the constituent programs of California’s Preventing Parolee Crime Program (PPCP) was associated with about 15% lower recidivism rates, and that parolees who participated in multiple programs had even better outcomes. The fact that only about 40% of the parolees in this same study met at least one of these program goals highlights the importance of actual utiliza- tion of available services.
We suggest that one important characteristic that might increase the extent to which ex-offenders access services is physical proximity to the provider. We build on the behavioral model of health care access from the public health literature, which posits that services located near populations in need is an important enabling resource and that the interaction of this closeness with predisposing service-seeking characteristics and need (both perceived and assessed) on the part of individuals will increase the likelihood of accessing these services (Anderson, 1995). Although this model was based on nonof- fender populations, there seems little reason to suppose that it would not operate similarly for the ex-offender population. For our population of parol- ees, accessing services might increase simply because the presence of proxi- mate services makes ex-offenders more aware of them. Beyond simple awareness of services, nearby services might encourage utilization because they require the expenditure of less time and fewer resources on the part of ex-offenders. That is, traveling longer distances takes more time and thus can be perceived as burdensome for some parolees who experience other time demands in their lives. Furthermore, simply obtaining transportation for traveling the longer distances can pose an additional burden for parolees: Those relying on public transportation may find that longer distances result in a nonlinear increase in travel time due to the peculiarities of negotiating public transportation routes.
Nonetheless, there is little evidence regarding the extent to which proximity to social services contributes to service utilization by ex-offenders. Qualitative studies of the dynamics of prisoner reentry have found that lack of access to transportation (La Vigne, Wolf, & Jannetta, 2004; Visher, Palmer, & Gouvis Roman, 2007) and lack of information regarding the existence of service providers (Visher & Farrell, 2005; Visher et al., 2007) deter ex-offenders from accessing services. Issues of difficulty with transportation as a barrier to employment for residents of low-income urban communities, to which a large proportion of offenders return, are well-established (Blumenberg & Manville, 2004). This is particularly salient given evidence of racial disparities in access to transportation (Hess, 2005).
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On the other hand, there is a much larger literature in the public health field showing that physical closeness increases access to various types of services. For instance, multiple studies have provided support for the proposition that proximity to health care services results in increased service utilization (Brameld & Holman, 2006; Gregory et al., 2000; Piette & Moos, 1996; Weiss & Greenlick, 2007). Proximity to social services has been shown to increase the likelihood that welfare recipients will access those services (Allard et al., 2003). Likewise, proximity to employment opportunities increases the likeli- hood that welfare recipients will be employed and transition off welfare rolls (Allard & Danziger, 2003; Blumenberg & Ong, 1998). Welfare recipients are a useful reference group because they frequently lack job skills, have low levels of educational attainment, and suffer from mental health problems and substance abuse (Allard et al., 2003), difficulties that are also common among offenders. Given this evidence, it seems plausible to presume that physical closeness to providers enables access to these services for offenders as well.
Is the Presence of Services More Important for Some Types of Ex-Offenders? Although the question has been explored less, it is important to ask whether there are systematic differences in the types of offenders who have greater access to social services. We consider three characteristics that often indicate parolees in particular need of such services: (a) their race or ethnicity, (b) their criminal history, and (c) whether they are a sex offender.
First, it is likely that accessing various social services is particularly impor- tant for racial/ethnic minorities. For instance, given the considerable prior evidence that African Americans experience discrimination in the labor force regardless of whether they are ex-offenders (Pager, 2003; Pager & Quillian, 2005), it may be particularly important that minorities have adequate access to employment services. A study finding that employers felt “soft” skills—such as motivation and good customer relations—are the most important and that African American men are frequently lacking such skills suggests that employ- ment services that address such skills may pay significant dividends for minorities (Moss & Tilly, 1996). Access to housing services may also be particularly important for racial/ethnic minorities. Several studies have shown that Whites are more likely to own a home than are Blacks or Latinos, and there is evidence that the gap in homeownership is growing (for a review, see Painter, Gabriel, & Myers, 2001). Zhang et al. (2006) found that African Americans were more likely than other racial and ethnic groups to access
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services in the Preventing Parolee Crime Program relative to their representa- tion in the parolee population, again suggestive of particular needs for such services. In general, research indicates that racial, ethnic, and cultural factors affect whether people access services in general (Allard et al., 2003; Williams, Pierce, Young, & Van Dorn, 2001).
While racial- or ethnic-minority parolees may have a particularly acute need for various social services, there is also reason to expect that they may live near many such services. For instance, research has shown that urban census tracts with high poverty rates are located in closer proximity to social service provid- ers than are census tracts with lower poverty rates (Allard, 2004), and there have been similar findings on a national scale in New Zealand (Pearce, Witten, Hiscock, & Blakely, 2007). Thus, offenders living in low-income, urban areas are likely to be proximate to more social services than are offenders living elsewhere. These neighborhood differences, combined with patterns of resi- dential segregation in which minorities often reside in such low-income neigh- borhoods, can result in differential proximity to service providers along racial and ethnic lines (Allard, 2004).
Second, it is crucial to understand whether the criminal history of an ex-offender is related to the ex-offender’s proximity to services. This is an important factor for two reasons. First, criminal history, in terms of both severity of the most recent offense and the extent of prior criminal activity, is a key determinant of recidivism risk for offenders (Gottfredson & Gottfredson, 1986). Therefore, access to services that might address the needs of serious, violent, or habitual offenders and make them less likely to reoffend is very important from a public safety standpoint. Second, offenders with more-serious convictions or with multiple convictions will have served longer prison sentences on average. This greater time of incar- ceration may affect both their service needs (e.g., employment, housing) and their willingness to access necessary services. Nonetheless, despite this presumably greater need for services, one study found negligible differences between ex- offenders who did and did not access services in terms of number of prior incar- cerations and commitment offense (Zhang et al., 2006).
Third, a special category of offender type to consider is sex offenders, given that they have been the subject of particular public concern and interest in recent years, as demonstrated by the passage in 2006 of Proposition 83 in California (popularly known as “Jessica’s Law”). Sex offenders have treatment needs specific to their offenses and are subject to residency restrictions that are likely to affect their proximity to services. This is particularly the case in California after the passage of Jessica’s Law, the residency restriction component of which makes large portions of many California urban areas off limits to sex offenders
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for residence purposes (California Coalition Against Sexual Assault, 2006). To the extent that such residency restrictions limit the access to services of this particular population, such laws may produce a rather undesirable unintended consequence.
Are All Service Providers Alike? An analysis of the proximity of ex-offenders to social services that considers only the attributes of the ex-offenders illustrates only half the picture. Equally important is the capacity of the service providers to furnish those services. Spatial proximity to social services is greatest for poor populations in urban areas, rela- tive to suburban areas, but social service providers in urban areas are also proxi- mate to many more low-income households (Allard, 2004). As a result, service provision in low-income urban neighborhoods may fall short of demand despite greater proximity to low-income populations that are likely to need those services. Returning ex-offenders tend to cluster in a few urban areas, and even within a few neighborhoods within those urban areas (La Vigne, Kachnowski, Travis, Naser, & Visher, 2003; Solomon, Thomson, & Keegan, 2004; Watson et al., 2004). Service providers may be concentrated in those areas as well, but service providers in those areas may also be proximate to more ex-offenders who need their services than are service providers in other areas.
This likely occurs because of a dynamic process. On one hand, many service providers may choose to locate in neighborhoods with large numbers of ex- offenders (potential clients). On the other hand, ex-offenders returning from prison may choose to reside in neighborhoods with large numbers of available services. In addition, when ex-offenders choose to change residences, they may select a neighborhood on the basis of the number of services there. This dynamic process suggests an equilibrium solution of neighborhoods with both many service providers and many ex-offenders. Of course, other social processes can also drive the system toward such a result, including the income level and housing costs of neighborhoods, as well as the racial/ethnic composition. Our interest here is simply in observing the equilibrium solution of these processes in this study, not in attempting to tease out causal relationships.
Unfortunately, we do not have information on the capacity or utilization levels of the service providers in our study. Allard (2004) adopted a strategy of estimating what he termed “potential demand”: the number of persons living near each service provider. We employed this strategy in this study. Although it provides only a rough estimate of the impact of parolee clustering on service access, as service providers may differ in the number of persons to whom they
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can provide services at any given time, it does allow for a rough analysis of the differential burden on the service provision environment of ex-offenders returning to California communities.
Data We addressed our research questions of the number of social service providers near different types of parolees, and their potential demand, by using a unique data set with information on parolees in the state of California in 2005 and 2006, their addresses during this time, and information on social services geared toward these returning parolees. The data on parolees were obtained from the California Department of Corrections and Rehabilitation (CDCR, 2007). We defined our sample as those who began parole at some point between January 1, 2005, and December 31, 2006. These data provide information on all parolees during the period, the dates of entry to and exit from a CDCR institution, and certain characteristics of the parolees. We geocoded all addresses of a parolee during this 2-year period and placed them at a specific latitude–longitude point. Addresses were geocoded with a success rate of 81% for the parolees and 89% for the service providers, and analyses were performed on these parolees.
Outcome Measures The data on social services available to parolees comes from CDCR provider database. Although this data set does not include all service providers avail- able in California, it was constructed for parole agents to guide parolees toward service providers, which suggests that it captures the most important service providers. That is, it is these providers that parolees will be made aware of; in addition, should parolees discover additional service providers, this information can be added to the database by the parole agent. We geo- coded these organizations on the basis of the address provided and placed them at a specific latitude–longitude point. We initially created a taxonomy of 13 types of services of importance to parolee reintegration and classified each organization on the basis of the type of services it provides. Because we are theoretically interested in the availability of services to parolees, and not the existence of providers, we allowed a service provider to be counted for each type of service it provided. Given that the initial analyses using these 13 categories showed considerable similarity over the different types of services offered, we collapsed the 13 categories into 4 broad categories: (a) social services, (b) self-sufficiency (financial, transportation, employment, education, identification, and legal services), (c) family and housing, and (d) linking with the community (community and networking services).1 These
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broader categories eased the interpretation of the results (the full results for the 13 categories are provided in the Appendix).
For each parolee in our sample, we calculated the number of social service organizations offering each type of service within 2 miles (3.2 km) of the parolee’s current address. Although 2 miles is a somewhat arbitrary figure, it does comport with the distance used in prior work and has been suggested as an important distance by county social service administrators (Allard, 2004; Allard et al., 2003).2 We measured distance from parolee address to service provider “as the crow flies,” based on the latitude and longitude of the parolees and the services. This approach provides a more precise assessment of the presence of services near parolees than do approaches that simply count the number of service providers in a parolee’s census tract. Our outcome measures are the number of social service organizations providing a particular type of service within 2 miles of the parolee. Table 1 provides the summary statistics for our analysis variables. The average number of service providers within
Table 1. Summary Statistics for Measures Used in Analyses, California Parolees in 2005-2006
Independent Variables Number %
African American 63,185 28.3 Latino 63,109 28.3 Asian 1,409 0.6 Other race/ethnicity 7,578 3.4 Female 30,290 13.6 Registered sex offender 22,066 9.9
M SD
Age 35.6 9.8 Property offenses 0.350 0.714 Violent offenses 0.329 0.789 Total violations on record 3.468 2.892 Days spent in CDCR institutions 1,184.9 1,252.2 Outcome measures, types of service providers Self-sufficiency 9.982 12.853 Family and housing 7.568 11.410 Community networking 5.867 7.568 Social services 4.592 7.226 Average number of parolees within 403.62 387.42 2 miles (3.2 km) of service
N = 223,129 person observations.
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2 miles of parolees ranged from 4.59 (for social services) to 9.98 (for self- sufficiency services).
As another outcome measure, we computed a proxy for server capacity by calculating the “potential demand” for services. We accomplished this by first calculating the number of parolees within 2 miles of each service provider on the initial date of our study period (January 1, 2005)—this is the potential demand—and then calculating for each parolee the average potential demand for all service providers within 2 miles of the specific parolee. This gives a sense of how affected the service providers near the parolee are. In our sample, service providers near the average parolee had approximately 400 other parolees residing within 2 miles. We log transformed this outcome to reduce the possibility of extreme cases as well as to ease interpretation of the results.3
Given that parolees are able to change residences, the unit of analysis for our study is parolee address spells. Whereas about half the parolees had a single address during the study period, others moved about. We included information for all the addresses of particular parolees (and the number of service providers each address placed them near) and accounted for this nonindependence in the analyses by computing the standard errors using a Huber/White correction. In this sample, 50% did not change residences, 26% moved just once, 12% moved twice, and 12% moved more than twice.
Characteristics of Parolees We took into account several parolee characteristics to determine their impact on the number of services near a parolee. For all parolees, we had information from their criminal record on their total number of prior offenses, number of prior property offenses, number of prior violent offenses, and total number of days spent in a CDCR institution. By California statute, violent offenses include all murders, about 80% of rapes, 50% of assaults, and 40% of robberies. Serious offenses include all of the above four violent offenses as a subset, as well as property crimes as defined in California Penal Code Sections 667.5(c), 1192.7(c), and 1192.8. Consequently, 60% of burglaries and about 95% of arsons are included as serious crimes (for a complete description of these categories, see Greenwood et al., 1994, pp. 44-47). For each parolee, we also computed the total number of days spent in CDCR institutions during the parolee’s lifetime. This measure revealed parolees with a long record of institutionalization and hence perhaps a particular need for services. We also created an indicator of whether the parolee was a sex offender.
We also accounted for demographic characteristics of the parolees. To account for racial/ethnic differences, we created measures indicating whether
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a parolee was African American, Latino, Asian, White, or Other. Given that ex-offenders likely have different service needs as they age (for example, Uggen, 2000, suggested that employment services may be particularly effec- tive for older ex-offenders as opposed to younger ones), we created a measure of the age of a parolee at the first date at an address. To take into account pos- sible nonlinear effects of age, we also included measures of age squared and age cubed to test their relationship to relative closeness of services.4 Given the evidence that female ex-offenders have service needs that differ signifi- cantly from those of male offenders (Bloom, Owen, & Covington, 2002), we created an indicator of women parolees to account for possible gender differ- ences in access to services.
Method Given that our outcome measures are counts of the number of social service providers within a 2-mile radius of a parolee, we estimated fixed effects negative binomial regression models. The negative binomial model treats the outcome measure as a Poisson distribution with an additional parameter with an assumed gamma distribution to account for the overdispersion created by the noninde- pendence of events. Whereas a simplistic approach would simply compare all parolees, it is arguably not appropriate to compare the number of service provid- ers near parolees living in urban areas with the number near parolees living in more suburban or rural areas. One strategy is to account for these differences by including county-level variables capturing important differences over coun- ties and to estimate a multilevel model. A risk with such an approach is that failing to include all relevant county-level covariates will result in biased coef- ficients at the parolee level. Given this, and the fact that we were not interested in explaining differences between counties in the current research project, a safer approach was to simply condition out all differences between counties through a fixed effects approach. We adopted the fixed effects approach advocated by Allison and Waterman (2002) because it appropriately conditions out differences across counties.5 In this approach we are estimating the following model:
y = α + Pβ + COUNTYδ (1)
where y is the number of social services within 2 miles of the parolee, α is an intercept, P is the particular characteristic of interest of the parolee that has β effect on the outcome, COUNTY is a matrix of K − 1 indicators for the K counties in California, and δ is a vector of the effects of each of these counties. Note that whereas this strategy of accounting for differences across tracts by
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including indicator variables results in the ‘incidental parameters’ problem for logistic regression models, Allison and Waterman (2002) highlighted that such is not the case in the negative binomial regression model. In this model, we were effectively comparing parolees only with other parolees living in the same county. For the model using the potential demand for the providers near a particular parolee as the outcome measure, we estimated Equation 1 with a normally distributed error term (an ordinary least squares model), given that this is a continuous measure. All analyses were estimated in Stata 9.2. We tested for and found no evidence of multicollinearity problems or outliers in any of these models.
Results Relationship Between Returning Parolees and Proximity to Services
We begin by focusing on the relative closeness of various social service pro- viders to our sample of parolees. An advantage of the negative binomial regres- sion model is that the exponentiated coefficients are easily interpreted as percentage effects on the outcome measure. For instance, the model with the number of self-sufficiency service providers near parolees as the outcome in Table 2 (column 1) shows that an African American parolee has 26.0% more such providers within 2 miles, on average, than a White parolee has. A Latino parolee has 7.5% more self-sufficiency service providers within 2 miles than a White parolee has.
It is also clear that the general pattern for race/ethnicity is similar across these different types of service providers. African Americans have more providers of all types nearby, ranging from 20.3% more social service providers on average than Whites have to 29.4% more family and housing providers. Latinos also have more providers nearby than Whites have, though considerably fewer than African Americans have. Service providers near Latinos range from, on average, 5.6% more social service providers than Whites have to 7.5% more self-sufficiency service providers. The rates are similar for Other race/ethnicity parolees. Although these results may seem somewhat surprising, recall that prior studies have sug- gested that poverty areas—the types of neighborhoods minorities in general, and minority parolees in particular, tend to live in—have more services nearby (Allard, 2004). Although minorities have more social service providers nearby, these pro- viders may be particularly impacted. We explore this possibility next.
Whereas the number of nearby service providers implicitly assumes that all service providers are equally impacted—and therefore their services are equally
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available to the nearby parolees—it may be that the providers near some parolees are overburdened in terms of capacity relative to need. If this is the case, obtain- ing the needed services from such providers may be difficult. We addressed this question in the final column of Table 2, in which the outcome is our measure
Table 2. Number of Service Providers Within 2 Miles (3.2 km) of Parolee, by Characteristics of Parolee
Number of Service Providers Within 2 Miles (3.2 km)
Number of Family Parolees Near Independent Self- and Community Social Services Variables Sufficiency Housing Networking Services (Logged)
Age (× 1,000) 7.630** 8.516** 7.092** 6.834** 8.562** (18.24) (18.93) (16.63) (14.63) (11.02) Age squared (× 1,000) 0.126** 0.139** 0.118** 0.125** 0.180** (4.70) (4.94) (4.36) (4.19) (3.69) Age cubed (× 1,000) −0.007** −0.008** −0.007** −0.006** −0.010** −(5.75) −(6.30) −(5.47) −(4.28) −(4.12) African American 0.231** 0.258** 0.235** 0.185** 0.652** (34.07) (36.29) (34.64) (25.10) (53.60) Latino 0.072** 0.057** 0.062** 0.055** 0.277** (10.41) (7.76) (8.84) (7.10) (21.44) Asian 0.037 0.040 0.049 0.018 0.157 (1.13) (1.11) (1.47) (0.47) (2.43) Other race/ethnicity 0.077** 0.070** 0.080** 0.068** 0.197** (5.18) (4.43) (5.39) (4.03) (6.95) Female 0.014 0.011 0.009 0.025** 0.092** (1.82) (1.33) (1.20) (2.86) (6.65) Years in prison 0.004** 0.005** 0.004** 0.003** 0.014** (4.13) (4.42) (4.17) (2.78) (7.62) Violent offenses −0.022** −0.027** −0.020** −0.021** −0.016** −(5.34) −(6.43) −(4.85) −(4.67) −(2.25) Property offenses −0.024** −0.023** −0.023** −0.022** −0.027** −(5.90) −(5.54) −(5.61) −(4.94) −(3.69) Sex offender 0.033** 0.047** 0.036** 0.036** 0.149** (3.59) (4.81) (3.89) (3.54) (8.69) Just released 0.056** 0.062** 0.058** 0.054** −0.028** from prison (8.43) (8.77) (8.66) (7.30) −(2.70) Intercept 1.968** 1.350** 1.479** 0.700** 5.266** (177.35) (127.37) (140.50) (50.08) (267.63) R2 0.023 0.035 0.025 0.045 0.209
Note: Fixed effects (by county) negative binomial regression models. Standard errors corrected for clustering by parolee. N = 223,129; t values are in parentheses. **p < .01 (two-tailed test).
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of average potential demand for the providers near a parolee. These analyses show considerable evidence that the service providers near minorities are indeed overburdened. Given that the outcome measure in this linear regression model is natural log transformed, we once again can interpret the coefficients in terms of percentages. Thus, the service providers near African American parolees have 65% more parolees within 2 miles than do the providers near White parolees. Given that, on average, an African American parolee has about 23% more service providers nearby than does a White parolee, these combined results suggest that minority parolees in general, and African American parolees in particular, live clustered in neighborhoods with many service providers but also many more parolees. The net result may well be overburdened service providers in these neighborhoods. The pattern is similar for other minority parolees: Latinos, for instance, have about 6% more service providers nearby, on average, than Whites have, but these service providers have 28% more potential demand in their catchment areas. For Other race/ethnicity parolees, these percentages are 7% and 20%.
Turning to the other demographic measures, we see that the effect for women is much more modest than the race/ethnicity effects: Whereas females have 2.5% more social service providers nearby than males have, the differences for the other types of services are not significantly different when comparing males and females. On the other hand, there is evidence here that the providers near women are more impacted. The service providers near female parolees have, on average, about 9% more parolees nearby than do service providers near male parolees.
The effects of age are somewhat stronger. To get a sense of the magnitude of these effects for age, we plotted in Figure 1 the marginal effect on the number of nearby self-sufficiency service providers as age increases, holding all other variables in the model constant. This figure highlights the nonlinear effect of age, in which older parolees tend to be located nearer to more self-sufficiency providers than are younger parolees. If younger parolees have more need for such services (given their greater propensity to recidivate), this suggests that those most in need are not necessarily the ones with the most service providers nearby. The effect of age on the other types of service providers is generally similar to that depicted in Figure 1. Whereas older parolees have more service providers nearby, these providers appear to be more impacted, based on our measure of potential demand in the catchment areas. Figure 2 illustrates this nonlinear effect by plotting the effect of age using the coefficients from the model in the last column of Table 2. Note that Figure 2 largely follows the pattern in Figure 1 for the number of nearby services by age of parolee. This suggests that older parolees are clustered in neighborhoods that have more providers, but also more parolees.
116 Crime & Delinquency 57(1)
We next turn to the results for our more direct measures of parolees most in need of such services. The general pattern for parolees most in need of services is that they either live near fewer providers or else live near more potentially impacted providers. First, we see consistent evidence that parolees who have spent long periods behind bars have more service providers nearby. For instance, an additional 3.5 years in prison (a 1-standard-deviation increase in this sample) increases the number of social service providers nearby 1.1%, the number of self-sufficiency and community networking services 1.5%, and the number of family and housing service providers 1.7%. Although significant,
–0.1
–0.05
0
0.05
0.1
0.15
0.2
18 22 26 30 34 38 42 46 50 54 58
Age
P ro
po rt
io n
ch an
ge in
pr ov
id er
s
Figure 1. Marginal Effect of Age on Number of Self-Sufficiency Service Providers Nearby
Figure 2. Marginal Effect of Age on Potential Demand for Service Providers
–0.1
–0.05
0
0.05
0.1
0.15
0.2
18 22 26 30 34 38 42 46 50 54 58
Age
P ro
po rt
io n
ch an
ge in
pr ov
id er
d em
an d
Hipp et al. 117
these are rather modest effects. Furthermore, it appears that the providers near these long-term offenders may be more overburdened than other providers. Each additional 3.5 years behind bars results in 5% more parolees near these providers.
On the other hand, the most troubled parolees—those with more property and violent crimes on their permanent record—actually have fewer service providers nearby. Each additional property crime and each additional violent crime on a parolee’s record reduce the number of various nearby service providers by between 2% and 2.7%. This effect is observed for all these types of service providers.
The story for the special case of sex offenders is a similar one: living near providers who may well be overburdened. Although there is variability in the closeness of sex offenders to different types of service providers, these ex- offenders have, on average, nearly 4% more service providers nearby than ex-offenders who are not sex offenders have. Nonetheless, it should be high- lighted that these service providers have about 15% more parolees nearby than do the service providers near ex-offenders who are not sex offenders. Again, to the extent that such providers are limited in their ability to serve nearby parolees, sex offenders may have difficulty in accessing these services.6
Finally, an unexpected finding was the evidence here that those just released from prison tend to have more service providers nearby. In general, a parolee just released from prison has between 5% and 6% more providers nearby than does a parolee who has moved to a new address since reentering the community. However, there is no evidence that these providers near parolees who have just reentered the community are more overburdened. This may suggest that later residential mobility decisions by parolees are taking them to worse neighbor- hoods vis-à-vis service providers.
Conclusion Although recent scholarship has noted the large increase in prison incarceration during the past 20 years and the potentially important role that service providers play in reintegrating these parolees into society, little systematic evidence exists regarding whether parolees live near these service providers. Given the literature on other populations, suggesting that physical distance plays a large role in whether those in need actually utilize services, there is a crucial need to know whether parolees live near service providers. This is an important consideration for reentry models that stress the continuity of services from institutions into the community (Byrne et al., 2002; Lin & Turner, 2007). Our study has utilized a unique data set to address these questions, as well as the question of whether certain types of parolees live near more service providers.
118 Crime & Delinquency 57(1)
By also asking whether physical closeness to service providers differs sys- tematically based on the characteristics of parolees, our findings have illumi- nated important differences. One key finding is that the parolees arguably most in need of services—specifically, those who have spent more time in CDCR institutions, have committed more property or violent offenses in their careers, or are sex offenders—tend either to live near fewer service providers or to be near providers that may well be overburdened. Although we could not directly measure how overburdened providers were but instead measured the number of parolees living near such providers, a key question is the accuracy of this proxy. Note that our approach assumes that the capacity of these providers is the same. For this proxy to be flawed requires that the service providers located closest to these more long-term and serious offenders systematically have greater capacity levels. We know of no evidence supporting this conjecture.
The question of overburdened service providers also played a large role in the story regarding minority parolees. Although we found that Latino and African American parolees actually have more service providers near them than do White parolees, these providers have far more potential demand in their catchment areas. In general, it appeared that the level to which these providers were overburdened was about 2 to 4 times greater than the advan- tage minorities obtained from living near more providers. Paralleling the discussion regarding long-term and serious-crime parolees above, it would be necessary for service providers near minority parolees to have systemati- cally larger capacity levels than the providers near White parolees for these findings to be overturned. Although we are aware of no such evidence, this does suggest an avenue for future research.
A somewhat unexpected finding was that the first address after release from prison appears to place parolees near more service providers. Given that we know of no systematic programs by the CDCR to place parolees in advanta- geous locations—instead, parolees are generally allowed to locate at their own discretion, and often into the same neighborhoods they left prior to imprison- ment (Visher & Farrell, 2005)—this may suggest that in their subsequent resi- dential moves, parolees are moving into neighborhoods that are subpar vis-à-vis the nearby presence of service providers—an additional direction for future research.
The fact that residential moves appear to be taking parolees to areas where there are fewer service providers indicates a potential problem of which policy makers must be made aware. The factors driving this process need to be understood and suggest another avenue for future research. Understanding these factors would provide insight for policy makers: If parolees are moving out of service-rich neighborhoods, then steps would need to be taken to assist
Hipp et al. 119
parolees in staying in residences proximate to services. On the other hand, if parolees are simply unaware of the lack of services in their new neighborhoods, then a policy geared toward providing information on the existence of such services would be called for. This suggests a need for collaboration between providers of housing service to parolees and agencies with an awareness of the physical location of such services.
Despite the uniqueness of our data and the importance of our findings, certain limitations should be acknowledged. First, our data contained information only on parolees and service availability in one state. Although these data allowed us to carefully explore the predictors of parolee proximity to services, the gen- eralizability of our findings hinges on the extent to which this state is representa- tive of other states. Confidence in the findings will therefore be increased by replications on other states. Second, our data were limited to 2 recent years. The generalizability of our findings to other times should thus be viewed with caution. Nonetheless, the recency of the data at least provides important evidence on the current status of parolees’ access to social services designed to serve them. Third, we did not have information on all service providers available in California, suggesting that we may have missed some potential providers. However, our data set was constructed for parole agents to guide parolees toward such providers, which suggests that it captures most service providers of which parolees will be aware.
Fourth, we had limited information on the characteristics of these parolees. Although we took into account a few key demographic characteristics, as well as some information on their criminal history, we lacked information regarding their marital status, children, income or education level, whether they own their home, and their social support resources. Although this is a limitation common to nearly all data sources on parolees, the importance of these constructs for the reintegration of parolees suggests an important avenue for future research. These characteristics have important implications for the type of services these parolees need, and future work should match the closeness of service providers to the specific needs of parolees. Indeed, the CDCR has recently implemented a survey instrument (Correctional Offender Management Profiling for Alternative Sanc- tions) that is geared toward assessing such needs among California parolees and that may be useful for future assessments. In addition, the combination of the informal social resources a parolee has, on one hand, and access to the formal resources provided by these service providers, on the other, is likely important for understanding the successful reintegration of parolees into the community.
Finally, we did not have information on the actual capacity level or current demand of these service providers. Following other studies, we employed a proxy of the number of parolees living nearby the providers. This is, of course, an approximation given that these providers serve other populations as well,
120 Crime & Delinquency 57(1)
and measuring the actual capacity levels of these providers, as well as their current demand, is a crucial next step for future analyses. It is possible that if parolees reside in neighborhoods with many nonparolee residents who also utilize such services, these providers are even more overburdened than we have estimated here—another avenue for future research. Furthermore, although we focused simply on the closeness to service providers under the assumption that this will affect the degree to which parolees access such services, future researchers will want to explicitly test the effect this physical closeness has on the actual use of these providers. Although the public health literature suggests that there are likely important effects, there is still a crucial need for research on this issue for the specific population of parolees.
That the service provider database does not include information about the capacity of these service providers is itself telling and suggests policy implica- tions. Correctional and parole agencies need to know not only what service providers exist that might be of use to parolees, but the capacity of both indi- vidual providers and the service provision environment as a whole to serve ex-offenders. To that end, it would be valuable to conduct an analysis of exist- ing service capacity in the communities to which large numbers of parolees return in order to determine which provider services are most appropriate for which types of parolees, where service capacity is insufficient, and which capacity gaps are most significant (in terms of the most lacking or overburdened services and the services with the greatest impact on offender recidivism and reintegration). With the information from that analysis, corrections and parole agencies can work to link parolees with services that can meet their needs and strategically allocate resources to fill service capacity gaps.
Despite these limitations, it should be highlighted that the uniqueness of our data allowed us to explore important questions that have heretofore not been addressed. With more parolees returning to neighborhoods after a long period of mass incarceration, understanding the relative access to social services of these returning parolees is absolutely crucial, especially as California corrections incorporates a logic model that considers linkages with the community as part of the reentry phase (National Research Council, 2007). Our finding that minority residents live in neighborhoods in which the service providers may well be more overburdened based on our measure of potential demand suggests the possibility of inequality in access to services and also suggests a policy need to address this inequality. And our finding that the parolees who have spent more time in prison or been convicted of more serious offenses live near fewer service providers or near providers that may be overburdened because of larger potential demand suggests an important policy implication for officials in guiding service providers to areas most in need of such services.
121
T a b
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Hipp et al. 125
Authors’ Note
We thank the Center for Evidence-Based Corrections at the University of California– Irvine for providing access to the data used in the analyses.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interests with respect to the author- ship and/or publication of this article.
Funding
The author(s) received no financial support for the research and/or authorship of this article.
Notes
1. We also empirically tested the degree of clustering among the initial 13 types of ser- vices. A principal components analysis of the number of such types of services near the parolees in our sample found that they loaded on a single factor. This suggests that there is considerable clustering of all types of services in particular geographic areas. We therefore focused on models with the four theoretically derived outcome measures described here.
2. We also estimated our models using a 5-mile circle around parolees and found very similar results.
3. With a log-transformation, the coefficients of this model can be interpreted as percentage changes in the outcome measure. We also estimated models with the unlogged outcome, and the results were substantively the same.
4. We tested higher level polynomials and found no significant effects. We also cre- ated a series of categorical measures of the age of parolees and found a similar nonlinear effect. The age categories were (a) less than or equal to 18 years of age; (b) 19-21 years of age; (c) 22-25 years; (d) 26-29 years; (e) 30-34 years; (f) 35-39 years; (g) 40-44 years; (h) 45-49 years; (i) 50-54 years; (j) 55-59 years; and (k) 60 years and older.
5. As Allison and Waterman (2002) discussed, the conditional fixed effects negative binomial regression of Hausman, Hall, and Griliches (1984) does not appropriately account for differences across units as it accounts only for the difference in the distribution of the overdispersion across units rather than accounting for the dif- ferences in the parameters.
6. Our data come from the time before the passage and implementation of “Jessica’s Law” in California. The additional mobility restraints of this law will likely affect sex offenders’ access to services even further than what we observed in this study.
126 Crime & Delinquency 57(1)
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Bios
John R. Hipp is an assistant professor in the department of Criminology, Law and Society, and Sociology at the University of California–Irvine. His research interests focus on how neighborhoods change over time, how that change both affects and is affected by neighborhood crime, and the role networks and institutions play in that change. He has published substantive work in such journals as American Sociological Review, Criminology, Social Forces, Social Problems, Mobilization, City & Community, Urban Studies, and Journal of Urban Affairs. He has published methodological work in such journals as Sociological Methodology, Psychological Methods, and Structural Equation Modeling.
Hipp et al. 129
Jesse Jannetta is a research specialist in the Center for Evidence Based Corrections at the University of California–Irvine. He received his master’s degree in public policy from the John F. Kennedy School of Government at Harvard University in 2005. His work for the Center has included projects on GPS monitoring of sex offender parolees, adapting the COMPSTAT management system to a correctional agency, the role of the Division of Juvenile Justice in the California juvenile justice system, the scope of correctional control in California, and assessment of CDCR programs in terms of evidence-based program design principles. His primary interest is in applied research for policy application. He has also served on the California Comprehensive Approaches to Sex Offender Management Task Force and as a member of the support team to the CDCR Expert Panel on Adult Offender and Recidivism Reduction Programming.
Rita Shah is a criminology, law, and society doctoral student at the University of California–Irvine. Her research interests include the effect of neighborhood character- istics on parolee outcomes, the lived experience of parole, and women in the criminal justice system.
Susan Turner, PhD, is a professor of criminology, law, and society and codirector of the Center for Evidence-Based Corrections at the University of California–Irvine. Before joining UCI in 2005, she was a senior behavioral scientist at the RAND Corporation in Santa Monica, California, for more than 20 years. Her areas of expertise include the design and implementation of randomized field experiments and research collaborations with state and local justice agencies. She is a member of the American Society of Criminology and the Association for Criminal Justice Research (California) and is a fellow of the Academy of Experimental Criminology.
