Abstract
Objectives
Few studies have examined the consequences of neighborhoods for job prospects for people on parole. Specifically, networks between neighborhoods in where people commute to work and their spatial distributions may provide insight into patterns of joblessness because they represent the economic structure between neighborhoods. We argue that the network of neighborhoods provides insight into the competition people on parole face in the labor market, their spatial mismatch from jobs, as well as their structural support.
Methods
We use data from people on parole released in Texas from 2006 to 2010 and create a network between all census tracts in Texas based on commuting ties from home to work. We estimate a series of multilevel models examining how network structures are related to joblessness.
Results
The findings indicate that the structural position of neighborhoods has consequences for people on parole’s joblessness. Higher outdegree, reflecting neighborhoods with more outgoing ties to other neighborhoods, was consistently associated with less joblessness, while higher indegree, reflecting neighborhoods with more incoming ties into the neighborhood, was associated with more joblessness, particularly for Black and Latino people on parole. There was also some evidence of differences depending on geographic scale.
Conclusions
Structural neighborhood-to-neighborhood networks are another component to understanding joblessness while people are on parole. The most consistent support was shown for the competition and structural support mechanisms, rather than spatial mismatch.
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Change history
28 April 2021
A Correction to this paper has been published: https://doi.org/10.1007/s10940-021-09515-8
Notes
Although we use Latino in this study, we recognize that Hispanic ethnicity or gender neutral terms (Latinx) may also be appropriate, and there does not appear to be a strong consensus on which term is preferable: https://www.pewresearch.org/fact-tank/2013/10/28/in-texas-its-hispanic-por-favor/
A contrasting view is Massoglia et al. (2013), who found that white parolees have ‘more to lose’ following incarceration when compared to other groups, and thus white parolees may actually have worse job prospects.
At smaller spatial scales, this isolation might be advantageous for different race/ethnicities to the extent that an ethnic enclave is present for structural support (e.g., see Wilson and Portes 1980).
Our data are of all people released from Texas Department of Criminal Justice custody during the fiscal year. We only focus on people released from prison, and we do not include information on those who were discharged directly from prison or from a substance abuse facility. When we say ‘released’, this indicates that someone was released during these years, and this is not the total count of everyone on parole during this time period.
When we compared our sample sizes by year to the TDCJ reports, they were relatively similar (e.g. see page 34: https://www.tdcj.texas.gov/documents/Statistical_Report_FY2010.pdf). Note also given that we have multiple years of data, a person can be released multiple times (i.e., multiple spells).
Prior to September 2004, the data also do not allow for capturing people who were released multiple times.
The TDCJ staff provided only non-institutional addresses. Prisons, jails, treatment facilities and other institutions are not listed as addresses. We geocoded home addresses for people on parole using a combination of Google and ArcGIS’s 2010 StreetMap data to the address level and joined them to a 2010 census tract. 87% of all releases with address information were able to be geocoded. The demographics between those geocoded and not geocoded were similar, giving us confidence in the data.
We compared the demographics between those with and without residential information, and they were similar.
We also tested models that included disability with unemployment. Indegree was still significant in these models, but not outdegree. Outdegree at the meso level was still significantly associated with reductions in unemployment and disability for Latino people on parole, but not for white or Black people on parole. As such, combining disability and unemployment suggests mixed patterns, implying that disability and unemployment may not always operate in the same way, and arguably should not be combined together. Further, we only focus on those with employment or unemployment information because 1.) it is arguably cleaner to only focus on those who reported being unemployed or employed in the administrative data (we did code those with full employment as zero days unemployed and tested models within and without this distinction and the results were similar), 2.) just because someone was not employed, it’s not clear they are unemployed because they could be a student, retired, or disabled (and it is not necessarily clear they are looking for work or out of work for that matter), and 3.) it also seems plausible that someone reported being ‘employed’ to their parole officer, but they haven’t started work yet (i.e., they got a job but did not start yet). We compared the demographics amongst employed vs. unemployed spells, and they are relatively similar, except white releases were more likely to have an employment spell, while black releases were more likely to have an unemployment spell, which is not surprising given the literature on parole. Accordingly, we examine differences by race/ethnicity in our models.
89.86% of those with address information had employment or unemployment data.
The difference of 54 releases between the analytical sample and the samples in the tables are due to missing data on the covariates (e.g., neighborhood data).
There are a total of 5265 census tracts in the 2010 census in Texas, and thus there are 131 tracts that did not have any people released on parole for our data (2.4% of the tracts in all of Texas).
More information on these data is located here: https://ctpp.transportation.org/
We do not include ties where the sending and receiving nodes are the same in our degree computations (i.e., living and working in the same neighborhood). When constructing measures including same neighborhood ties, they were essentially identical to the measures without these reflexive ties.
We also tested models that included different thresholds for low income (e.g., $35,000). The results were substantively similar giving us further confidence in our results.
There is no clear agreed upon distance for how to best capture these differing spatial scales, and we choose the 1 mile distance due to the walkability of neighborhoods (1 mile is approximately a 20 min walk, see Talen and Koschinsky, 2013) and 1 mile is also the basis for neighborhood perceptions from the General Social Survey. The 10 mile range is often used to represent the broader neighborhood area (e.g., see Boessen and Hipp, 2015), and we also note that the median distance between home and work zipcodes for people on parole released in Texas is approximately 8 miles, suggesting the 10-mile cut off as being not entirely arbitrary. We also computed a 5 mile cutoff rather than 10 miles, as well as 5 mile spatial lags of neighborhood controls. The two outdegree meso scale measures at 5 and 10 miles are correlated at .85 and the two macro scale measures at .84. There were no substantive differences in the model results. One slight change is that for Latino people on parole, the macro scale rather than meso scale outdegree was associated with reductions in unemployment. Neighborhoods with higher indegree for white people on parole also had significantly more unemployment, suggesting a competition effect similar to Black and Latino people on parole.
Although we only focus on outdegree at various spatial scales, we note that the correlation between meso and macro outdegree is .08, while the correlation between meso and macro indegree is .78, suggesting differences of outdegree over broader spatial scales.
The correlation between the spatial lag of percent unemployed and the logged number of jobs within 10 miles is .2, suggesting that they are related but capturing different features of the local employment context.
Given the size of some tracts in Texas, a 10 mile lag based on distance and centroids does not always include nearby tracts. When this occurred (N = 487), we computed our distance decays based on the five nearest tracts.
Our data only has information on whether someone is currently married at the most recent release.
We cannot determine from the data whether someone is mandatory release or discretionary mandatory released.
Only the most recent sentence length is available. One study of short-term confinement in the Netherlands found that longer sentences were associated with less employment (Ramakers et al. 2014). For those in prison, a longer sentence length could result in more stigma and loss of job skills, resulting in longer jobless spells. An alternative possibility is that a longer sentence length could provide people on parole more motivation to find employment, which might reduce joblessness (Kling 2006).
Given that people on parole have differing numbers of spells, we estimated models that included the number of spells as a series of dummy variables (minus 1), and these results were similar to those shown in the tables. As another approach, we also estimated three-level multilevel models, and they encountered estimation difficulties, although the estimates produced were relatively similar to those shown in the tables. Finally, we also tested models that compared only the first spell of parolees, and the results were substantively similar (for a similar approach for this issue see Hipp, Petersilia, and Turner, 2010).
A question arises as to whether those who spend fewer days on parole are distinct from those who spend more days on parole. While we control for days at an address with employment information as our exposure term, this does not adjust for length of parole overall. To assess this possibility, we estimated ancillary models that included a measure of days on parole, and the inclusion of this measure did not alter our substantive findings. We also tested models that only included people who spent less than 100 days at an address with employment information (about 34% of the data), and the results were similar to those shown in the text.
Collinearity was tested with Philip Ender’s Stata ado file: ‘collin’. All variance inflation factors were under 10. We tested for outliers using studentized residuals from models estimated as linear regressions (the outcome was converted to a rate). We then estimated models without observations with studentized residuals greater than or less than 2, and the results were the same.
When looking at the indegree and outdegree for all kinds of ties (i.e., based on jobs of all incomes) for a typical majority Black, Latino, or white tract in Texas, the values are for majority Black tracts (indegree = 48.1, outdegree = 46.2), for majority Latino tracts (indegree = 54.0, outdegree = 49.2) and White tracts (indegree = 45.0, outdegree 55.9). Black and Latino neighborhoods have more competition, but when looking at outdegree for all jobs, majority white tracts have more structural support. We also emphasize that over half of the tracts in Texas are not majority white, Black, or Latino.
Some people on parole were released during the Great Recession. With a plethora of skilled workers laid off during the recession, we expect parolees to fair even worse in a labor market with many unemployed workers. To test this idea, we used a dummy variable to indicate whether someone was released prior to 2008 and those released from 2008 on. The dummy variable was significant, suggesting those released during the recession had more days unemployed, but the other results were substantively similar. When we interacted this dummy variable with our key measures, it was significant when interacted with indegree. This effect showed that indegree was significantly stronger during the recession as might be expected given the recession context. We also tested models that included year of release as a series of N-1 dummy variables, and their inclusion did not substantively alter the main results.
Note that this effect is controlling for the unemployment rate in the neighborhood. We estimated ancillary models without the unemployment rate in the neighborhood or nearby area and the results were nearly identical, further indicating that our structural network measures are distinct from neighborhood unemployment. We also tested interactions between unemployment and our degree measures, and none were significant. Finally, we also estimated models with neighborhood unemployment as the outcome and our indegree and outdegree measures as predictors along with our other neighborhood measures. While indegree was not a significant predictor, we did find that outdegree was associated with less unemployment, suggesting some of the effect of unemployment is through structural outdegree (i.e., lack of external support). This also indicates that the effect of indegree is distinct from unemployment.
One possibility is that high values of outdegree may indicate more joblessness due to the broader diversity of economic links, and low values of outdegree indicate an isolation effect and thus also more joblessness (a convex ‘U-shaped’ association). We tested these ideas with models that included a nonlinear effect for both indegree and outdegree (a squared term). We found no evidence of this pattern for indegree or outdegree.
Although not of primary interest for this project, a concern is residential mobility of people on parole. For our study, if someone moved (even within the same neighborhood), it is a new spell. To assess this issue, we estimated models that only focused on the first address reported by someone on parole. The results were largely similar to those in the tables, which gives us further confidence in the results.
This result was unexpected, and future research with other datasets might examine different approaches to assessing this negative association. One possibility is that this effect has to do with the fact that our data is only of the most recent sentence length. Another possibility is that this negative effect is due to people with longer sentences being more motivated and supported while on parole (see also Kling 2006 for a similar finding). We also tested whether this effect was nonlinear, and while it was significant, when plotted, it appears relatively linear.
To further assess these patterns, we also estimated some ancillary models that tested interactions between race and our degree measures. The results indicated that the effect of indegree did not significantly differ between white and Black people on parole and white and Latino people on parole. The results were otherwise substantively similar.
An alternative strategy would be to adjust our spatial degree measures by the opportunity for forming ties. We assessed this by dividing the meso outdegree measure by the number of neighborhoods within 1 to 10 miles. When multiplying this by 100, it is the percent of neighborhoods that a neighborhood is tied to within this buffer. On average, a tract is tied to 16% of possible tracts within the 1 to 10 mile buffer. This adjusted outdegree measure was never significant when included in the models, but it was significant for black people when we logged this measure given the excessive skew (the coefficient was negative, and a one standard deviation increase in logged adjusted outdegree was associated with 3.9% fewer jobless days). Thus, this approach yielded results somewhat consistent with those in the paper. We also emphasize that a degree standardization strategy is not of substantive interest in this study given that we are comparing the role of a neighborhood within the broader constellation of the entire state and not necessarily those bounded to one particular metro or region. We also point readers to our supplemental models shown in "Appendix B" that test differences by size of population in the broader area.
We also estimated ancillary models including interactions of our various degree measures at the meso and macro spatial scales with the poverty and unemployment rates. None of the interactions were significant. We also tested these models by race/ethnicity of the person on parole and found no differences. Thus, these results are not consistent with a spatial mismatch explanation.
When looking at the descriptive statistics, in urban areas, the average low income indegree is 10.5 and outdegree is 11.7, but in rural areas these values are just 2.6 and 3.0. Urban people on parole also spend 2 weeks longer jobless on average.
We also tested models separately by race/ethnicity. While some of the interactions were significant, when we plotted them, we do not see any substantive difference, suggesting this is likely due to our large sample size. As such, these results are largely consistent with those in "Appendix B".
To further examine the robustness of our results, we tested whether the patterns differ for rural or urban areas. We defined areas with more than 50,000 people within 20 miles as urban (everyone else was rural). We used a population cutoff of 50,000 within 20 miles because the United States Census defines this as the minimum city population size for the central city of a metropolitan area, and as the total maximum population for a micropolitan area. The inclusion of this dummy variable did not substantively change the findings (whether or not we included the logged population measure within 20 miles). We also tested a series of interactions between the urban/rural dummy variable and our network of neighborhoods measures, and none of these interactions were significant.
To compute this measure, we again used the CTPP commuting edgelist data. We computed the outdegree of a neighborhood for all jobs, then multiplied this by the number of jobs in the neighborhoods in which all residents worked. We then logged this measure given the skew. This measure is correlated with our low income outdegree measure at .33.
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Boessen, A., Hipp, J.R. The Network of Neighborhoods and Geographic Space: Implications for Joblessness While on Parole. J Quant Criminol 38, 597–636 (2022). https://doi.org/10.1007/s10940-021-09510-z
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DOI: https://doi.org/10.1007/s10940-021-09510-z