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Daytime Locations in Spatial Mismatch: Job Accessibility and Employment at Reentry From Prison

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Demography

Abstract

Individuals recently released from prison confront many barriers to employment. One potential obstacle is spatial mismatch—the concentration of low-skilled, nonwhite job-seekers within central cities and the prevalence of relevant job opportunities in outlying areas. Prior research has found mixed results about the importance of residential place for reentry outcomes. In this article, we propose that residential location matters for finding work, but this largely static measure does not capture the range of geographic contexts that individuals inhabit throughout the day. We combine novel, real-time GPS information on daytime locations and self-reported employment collected from smartphones with sophisticated measures of job accessibility to test the relative importance of spatial mismatch based on residence and daytime locations. Our findings suggest that the ability of low-skilled, poor, and urban individuals to compensate for their residential deficits by traveling to job-rich areas is an overlooked and salient consideration in spatial mismatch perspectives.

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Notes

  1. For a full review of the literature on spatial mismatch, see Ihlanfeldt and Sjoquist (1998) and Kain (1992, 2004).

  2. We use two-tailed tests, as opposed to one-tailed tests, because it is plausible that the associations between job accessibility and employment could run in a negative direction, where spending time in job-rich places is associated with unemployment. For example, individuals could spend time at bars or other undesirable locations in close proximity to jobs that they are not actively trying to obtain.

  3. We conducted two tests to check for heteroskedasticity of residuals: a plot of residuals versus predicted values and Cameron and Trivedi’s decomposition of IM-test. Both indicate that heteroskedasticity is not a concern.

  4. These models are single decrement approaches, meaning that they estimate only one way of leaving the “at-risk” state for employment, by finding work. However, individuals may also leave the project due to recidivism to jail or prison. An analysis of criminal justice records suggests that four of the 131 participants may have left the project due to re-incarceration. This rate is lower than rates of recidivism to prison estimated by the U.S. Bureau of Justice Statistics in 2005 (3 % versus 8 %, see Durose et al. 2014). Given the relatively low risk of recidivating during the study period, we use the single-decrement approach.

  5. We include six of the seven censored individuals who reported work on the first day of the study. For the seventh individual, the NSRP data did not include GPS estimates for the first observed day.

  6. Parks (2004) empirically estimated this parameter using household-level data on employment and residential locations for low-skilled females and arrived at an estimate of −0.058. With that, her estimate weighs jobs at k distance from block group i by 0 minutes = 1; 5 minutes = .75; 10 minutes = .56; and 20 minutes = .31. Using national surveys, we estimate that the distance-to-time ratio for commuting to be approximately 3 to 1. That is, roughly the same proportion of people work 15 minutes away who work 5 miles away, 30 minutes corresponds to 10 miles, and so forth. Thus, we arrived at a decay parameter of −0.058 × 3 = −0.174, where 0 miles = 1; three miles = .59; five miles = .42; 15 miles = .07; 30 miles = .005; and 50 miles = .0002. Only jobs within 50 miles are included.

  7. Information for number of children is missing for one participant and is replaced using the sample mean.

  8. Although these measures describe prior criminal justice contact, tests suggest that multicollinearity is not an issue. Thus, we include these measures as separate variables in the regression models.

  9. The hazard ratio can be converted to a percentage difference in the hazard using the following formula: 100 × (exp(coef.) – 1).

  10. We define low-skilled jobs as those in the following North American Industry Classification System sectors: 11 (agriculture), 23 (construction), 31–33 (manufacturing), 44–45 (retail), 56 (administrative and support and waste management), 72 (accommodation and food services), and 81 (other services). Low-income jobs are restricted to the lowest income category reported in Census LEHD files: $1,250 per month or less. Jobs without a college degree are those in which the LEHD files reports that the incumbent employee does not have a college degree.

  11. We examined whether residential job accessibility moderated the association between daytime accessibility and employment. We found a negative (but nonsignificant) relationship with the interaction, providing circumscribed evidence that daytime locations may be less important among job-seekers who live in job-rich areas.

  12. Kirk (2009) found that changes in residential location pre- and post-incarceration (as the result of Hurricane Katrina) are related to lower recidivism rates, which he attributed to changes in criminogenic peer influences and routine activities.

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Acknowledgments

We thank the New Jersey Parole Board and the NSRP study participants, the journal editors and anonymous reviewers, John Hipp, and the Association for Public Policy Analysis and Management (APPAM) 2015 Big Data Workshop organizers and participants for their helpful comments. We also thank C. J. Gabbe for research assistance. Collection of the NSRP data was funded by the National Institute on Aging of the National Institutes of Health (P30AG024361), the National Science Foundation Law and Social Sciences (SES-1228333), the Fahs-Beck Fund for Research and Experimentation, and the Horowitz Foundation for Social Policy. The Princeton Department of Sociology, Office of Population Research, Center for Information Technology Policy, and the Center for African American Studies provided seed money for the NSRP. The Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24HD047879) provided support for fellowship, research, and travel.

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  1. Department of Criminology, Law and Society, University of California, Irvine, CA, USA

    Naomi F. Sugie

  2. Department of Urban Planning, Luskin School of Public Affairs, University of California, Los Angeles, CA, USA

    Michael C. Lens

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  1. Naomi F. Sugie
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Correspondence to Naomi F. Sugie.

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Sugie, N.F., Lens, M.C. Daytime Locations in Spatial Mismatch: Job Accessibility and Employment at Reentry From Prison. Demography 54, 775–800 (2017). https://doi.org/10.1007/s13524-017-0549-3

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