REPORT

An Analysis of Incarceration, Crime, Unemployment and Rural Spaces

By Rashawn Ray, PhD

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The Issue

Thousands of ex-offenders are released every month across the United States. They are mothers, fathers, sons and daughters who have made mistakes, paid their debts to society and are ready to begin anew. Yet most find themselves locked out of opportunity to find employment, get housing or gain an education due to background checks.

The current system for federal offenders turns a prior offense into a life sentence and prohibits them from gaining a foothold in society. Surveys have shown that only 12.5% of employers are willing to hire an ex-offender and the results bear that out—51% of ex-offenders are unable to find work within a year of their release.  Unable to move on and build a new life, many lose hope and turn to substance abuse and eventually a return to crime and jail.

1

in 3

Americans have a criminal record

Any criminal record—even an arrest that never led to conviction —can be a lifelong barrier to opportunity.

9

in 10

Employers use background checks

Applicants with a record are nearly half as likely to get a callback or a job offer.

$87

Billion

In lost GDP every year

Shutting people with records out of the labor market takes a huge toll on the U.S. economy.

The Solution

The Clean Slate Act is an opportunity to give Americans a second chance at fully participating in society.
  • Automatically seals an individual’s federal criminal record for simple drug possession one year after successfully completing his or her sentence.
  • Automatically seals arrest records and other related records for individuals who have been acquitted, exonerated or never had charges filed against them.
  • Establishes new procedures to allow individuals to petition to seal records for nonviolent offenses that are not automatically sealed.
  • Permits law enforcement to access sealed records for public safety reasons, such as investigatory purposes and background checks for firearms.
  • Protects employers from liability for any claim arising out of the misconduct of an employee if the misconduct relates to a sealed criminal record.

Overview

Historically, attention to issues of incarceration and unemployment have focused on low-income urban communities. Comparatively little is understood about low-income rural communities. Does long-term incarceration, including for federal crimes, for Federal non-violent crimes impact rural communities in the same manner as urban communities, and do these relationships hold equally for Black and White formerly incarcerated?

Unemployment remains high among released prisoners after servicing their sentences for non-violent crimes. This unemployment contributes to recidivism, community unemployment, and fatherlessness, perpetuating a cycle of disadvantage which contributes to future crime. Links between incarceration of non-violent offenses and future unemployment have been demonstrated in samples of former offenders, both at the State and Federal level. The current analysis examines this issue at the State and Congressional District unemployment levels. Proportion of individuals experiencing prison sentences, including for non-violent crimes, is correlated with unemployment at both the Congressional District and State level. This relationship persists regardless of a person’s race/ethnicity or rural/urban location, suggesting prison sentences for non-violent crimes predict unemployment for both urban Black and rural White communities. In conclusion, White-majority rural communities suffer long-term economic and social costs in the same manner as urban Black communities. The long-term impact of incarceration for non-violent crimes increases the chances of unemployment and child poverty.

Introduction and Literature Review

The United States has an unusually high prison and incarcerated population in comparison to other industrialized nations. Indeed, the proportion of United States (U.S.) citizens incarcerated or under community supervision is the highest in the world; however, per-capita numbers have fallen in recent years (Pew Research Center, 2021). Ostensibly the focus was on drug and other crimes related to high-crime, low-income, urban neighborhoods with high proportions of non-White minorities. Harsh sentencing was implemented in the 1990’s to deal with both soaring homicide rates and drug epidemics. Though, homicides aside, the U.S. does not experience relatively-high violent crime rates, with assault rates lower than countries such as France, Belgium, the United Kingdom, New Zealand, or Australia (Ferguson & Smith, 2021). One question worth asking is:what impact does imprisonment for non-violent crime have on offenders’ potential for rehabilitation and reintegration into U.S. society upon release, and do experiences for non-White urban communities hold also for White-majority rural environments? That is, given this process is understandably dependent upon employment. In this paper, past research on offender samples will be reviewed before analyzing several unique data sets to examine the impact of long-term imprisonment on employment at the societal level, particularly with an eye on whether patterns that are established for urban non-white environments hold for White majority rural environments.
Research on Offender Samples
Upon release from prison, reintegration into society is dependent upon seeking employment; however, many employers require background checks for potential employees. Upon discovering a prior history of incarceration - even if for non-violent crimes- such employers generally decline offering employment to former inmates. As a consequence, unemployment rates among former non-violent offenders are far higher than the general populace - hitting rates around 38% or even higher among former inmates without a college degree (Lockwood et al., 2012).

Recognizing this, the federal government, along with many States, Counties, and municipalities, has developed programs for individuals with criminal records and prior experiences with incarceration. These programs generally include work training, work release, vocational trainings, and Ban the Box initiatives. Evidence suggests these programs can be effective in reducing post-release criminal recidivism (Graham et al.,2014). This suggests societal investment in programs that aid offenders in obtaining employment are beneficial for offenders themselves and also society via reduced crime. Furthermore, evidence suggests society experiences down-stream impacts from reduced employment, such as fatherlessness or absentee fathers (Sum et al., 2011). Given present and non-abusive fathers reduce violent behavior among adolescent males in particular (Mackey & Mackey, 2003), investing in programs which increase the prevalence of involved fathers appears to be a clear societal good.
Training for employment is, however, is just one part of the picture. If employers are reluctant to hire returning citizens due to concerns about their honesty, integrity, or reliability, work training can only be so effective. Lack of employment is a critical barrier which former inmates face upon reintegration to society, and employer perceptions of former inmates can be unfavorable, resulting in reluctance to hire (Graffam et al., 2008). Reluctance to hire violent criminals is - not surprisingly - higher for violent criminals than for non-violent criminals. Though, reluctance is observed for both crime-types, particularly for inmates with fewer work qualifications (Cerda et al., 2015). Interestingly, training efforts aimed at employers to reduce stigma and increasing hiring of returning citizens appear only modestly effective (Batastini et al, 2017). As such, evidence suggests so-long as criminal history remains accessible by employers, employers will negatively evaluate potential employees based on this information. This process is understandable at the individual level, yet appears to contribute to negative outcomes at the societal level.
Societal Level Data
Relationships between formerly incarcerated populations and unemployment at the societal level is complex and more data would certainly be welcome in this realm. Some data available at present suggests the experience of former inmates on unemployment can take several forms. Simply, ex-offenders never being hired is one. So-too is the fragility of employment for many returning citizens. That is, ex-offenders are often hired at lower rates than non-offenders, as well as are let-go first during times of economic recession (D’Alessio et al., 2014). Unemployment can then result in increases in crime, particularly for property crimes (Phillips & Land, 2012).
This can lead to tension for the criminal justice system - primarily concerned with reducing crime rates - and increased incarceration, which can be effective in reducing crime. Though, this comes at considerable cost in terms of employment and long-term recidivism (Cappell & Sykes, 1991). This can create tension among policy-makers who, on one hand may wish to be tough on crime - believing leniency may result in increased crime rates - but who also recognize reintegration and rehabilitation are important in preventing recidivism of offenders.One other aspect that has not been examined closely in prior literature is the influence of race and space on incarceration and unemployment. For instance, it is possible that the experiences of Black individuals living in low-income urban environments simply differ from the experiences of White individuals living in low-income rural environments. As such, this research will focus on this perceived race-urban-rural distinction.
The Current Study
As indicated, more data on the societal impacts of space, long-term incarceration for non-violent crimes, and unemployment is desirable. As such, the current study sought to examine these issues using several databases to determine whether incarceration rates predict unemployment at the Congressional District and State level for both urban and rural spaces.
Specifically, the current study sought to investigate several research questions:
  1. Does this effect also include relationships with child poverty rates?
  2. Do these effects persist when ethnicity is controlled?
  3. Do White-majority rural communities experience similar relationships as do better-researched Black-majority urban environments?
  4. Does prison incarceration predict unemployment at the level of Congressional Districts and States?

Methods

Databases
Several databases were compiled for examination within the current study. First, a database was developed for a sample of 437 U.S. Counties representing multiple Congressional Districts and States. For this dataset, incarceration trends were obtained from the Vera Institute of Justice (2022). Data on demographics, child poverty rates, and rurality were obtained from the County Health Rankings Dataset (Remington, EtAl, 2015). Finally, data on poverty was obtained from U.S. Census data and data on unemployment from the U.S. Department of Labor.

The second dataset used sentencing and crime data from the United States Sentencing Commission (USSC) (United States Sentencing Commission, 2022). This dataset provides state level data on Federal crimes, broken down by violent and non-violent crime types. Demographic information is provided by U.S. Census data and unemployment data by the Bureau of Labor Statistics (2022).
Data Analysis
Data analysis will take-on several types. First, bivariate correlations between main predictor and outcome variables will be provided. Second, multiple regression analyses will be conducted, considering the impact of short-term jail and long-term prison incarceration ,as well as violent crime rate, percentage Black and percentage rural on unemployment and child poverty at the County level. Correlations between poverty and long-term prison incarceration will also be considered for individual Congressional Districts with more than five-counties represented in the dataset. Finally, for the first USSC dataset, correlations between non-violent offending prison sentences and unemployment will be considered, controlling for race.

Results

Vera Institute/County Health Dataset
First, we present bivariate correlations between all study variables. In this dataset, urban/rurality is represented by population density. In addition to significance, we use the r = .10 threshold as minimal for interpretation as hypothesis supportive in order to reduce the potential for false positives due to statistical noise (see Ferguson & Heene, 2021 for discussion). Additionally, raw data used to create the following graphs are located within the Appendices.
Figure 1. Bivariate Correlations Between Study Variables
A few of these relationships wherein * denotes significant findings bear noting. First, long-term prison (Prison Pop) is a better predictor of negative outcomes (unemployment and child poverty) than is jail (Jail Pop). Prison populations are associated with higher unemployment, poverty, and child poverty as well as higher rates of violent crime; however, population density was unrelated as a variable, suggesting that these relationships held for both urban and rural environments.

We used U.S. Department of Agriculture (2022) guidelines to delineate whichCongressional Districts were considered rural or urban. Looking at Districts which are rural, and majority (60+%) White (n = 8), we compared these to urban, majority Black (25%) Districts (n = 3). Congressional Districts in the database were specifically selected if they met these criteria, resulting in 8 White-majority rural Congressional Districts and 3 Black-over represented urban Congressional Districts. Most districts are more blended in terms of race and rurality and, as such, these districts represent the more extreme examples. Although their numbers are few, there placement at opposite extremes regarding race and rurality suggest a pattern, given their similarities, likely to hold for Congressional Districts that mix communities that are minority-majority and White-majority and are blended in terms of rural/urban divides. As we can see below, prison population and poverty rates were similar across these Congressional Districts, though unemployment was higher in Black majority districts.
Figure 2. Mean Rates of Prison Population, Unemployment and Poverty for White and Black
Majority Congressional Districts
We further conducted Ordinary Least Squares (OLS) multiple regressions with both unemployment and child poverty as outcome variables. This type of analysis allows us to consider the impact of multiple variables at the same time. By doing so, we can examine whether a relationship between two variables (e.g., prison incarceration and unemployment) holds, once other factors (race, rurality) are controlled. Variable Inflation Factor (VIF) results for these analyses indicated an absence of multicollinearity, as all VIFs were below 2.0. Predictor variables included jail and prison population, percentBlack and percent rural. Both the model for unemployment (R = .488, R2 adj = .231, F(4,419) = 32.72, p < .001) and child poverty (R = .748, R2 adj = .555, F(4, 419) = 133.09, p< .001) Individual predictor results are as follows:
Figure 3. Regression Results for Unemployment and Child Poverty Outcomes
These numbers are standardized regression coefficients and can be treated similar to correlation coefficients. In other words, they represent the correlation between two variables, once the other variables in the model are controlled. Thus, for example, prison population and unemployment are correlated .113, even with race and rurality (and jail population) controlled. These results clarify that, even when controlling for rurality and race, prison populations are associated with increased unemployment as well as increased child poverty. As such, phenomena related to imprisonment are consistent across communities, including both Black urban communities as well as White rural communities as well as communities which are blended.

We also examined the correlation between prison population and employment in specific Congressional Districts with more than five-counties in the dataset. This number allowed for more robust correlations. Focusing on districts with multiple counties in the dataset allows for more robust correlation coefficients that are less likely due simply to chance. Results for these Congressional Districts with correlations above .10 are as follows.
Figure 4. Correlation between Prison Population and Employment in Congressional Districts with more than five-Counties
As can be seen from these results, although there are a few exceptions, the relationship between prison population per-capita and unemployment is consistent across most Congressional Districts. When considering only Counties with White majorities, the results were similar for correlations .10 or above:
Figure 5. Correlation between Prison Population Per-Capita and Unemployment
Note that as the requirement of at least five-counties for analysis was maintained, some Congressional Districts available for the first set of analyses no longer met the threshold for five-Counties when considering only White majority counties. Or, put more directly, when focusing on White majority counties, this removed more blended counties from the analysis. As a result, we had fewer districts for this analysis. As can be seen, relationship between incarceration and unemployment remained consistent across most districts when only White-majority counties were considered.
The USSC Dataset
We examined the consistency of the current results from the first dataset by examining the USSC dataset. This dataset allows us to examine the degree to which violent and non-violent federal convictions exist within each state and US territory. Within Figure 6, average Non-Violent Federal Offenders per-100,000 Residents by States and Territories are demonstrated for the fiscal years of 2015 through 2020. Per-100,000 residents, New Mexico had the highest proportion of Non-Violent Federal Offenders (M = 181.51), or over nine times the U.S. average of 19.5 non-violent federal offenders per 100,000 residents.
Figure 6. Non-Violent Federal Offenders per-100,000 Residents by States and Territories, 2015-2020
Figure 2 demonstrates the Violent Federal Offenders per-100,000 Residents by State and Territories within the fiscal years of 2015 through 2020. Herein, Alaska averaged the most Violent Federal Offenders per-100,000 Residents (M = 24.06), over-five-times the US-average. Moreover, Alaska’s Percent of BlackResidents was 2.78%, or approximately five-times less than the US-average.
Figure 7. Violent Federal Offenders per-100,000 Residents by States and Territories, 2015-2020
We were also able to determine the relationship between Federal Non-Violent prison populations with unemployment. As such, correlational analyses were conducted in two ways; first) by examining all prisoners across US States and District of Columbia; and secondly) by rerunning the correlation, controlling for percent of Black population in the State to control for race. For this dataset the uncontrolled correlation between Federal imprisonment for non-violent crimes and unemployment was .174. When controlling for race, the correlation actually increased to .241. Both of these results surpassed our threshold for meaningful results (Ferguson & Heene, 2021). As such, federal incarceration for non-violent offenses is associated with unemployment at the state level for both White and Black offenders. Further, these results held when race was controlled, suggesting they are of equally important to Black majority and White majority communities.

Discussion

Recent years have seen renewed questions regarding whether long-term imprisonment for non-violent crimes and the consequences post-release regarding a felony record may contribute to issues of unemployment. Past research with samples of offenders demonstrates unemployment rates are very high among offenders including non-violent offenders, contributing to poverty and recidivism. The current analysis added to this data by considering the issue at the societal level. Results were consistent with prior data suggesting long-term imprisonment including for non-violent offenses is a predictor of unemployment rates at the County, Congressional District and State level. This is important, given Figure 6 and Figure 7 demonstrate most Federal crimes are non-violent.

The current results suggest long-term imprisonment - but not short-term jails - are associated with both unemployment and child poverty. The latter finding is important because it suggests the potential consequences of this relationship may extend beyond merely those for the former offenders. Removal of primary caregivers from the family has straightforward economic impacts on youth left behind. Given a high proportion of former inmates are male, economic deprivation contributes to fatherlessness. Fatherlessness being a consistent predictor of negative outcomes, an issue true for both Black and White families (Rambert, 2021). As such, policies which can return employed fathers to families is in the national interest.

Our data suggest relationships between imprisonment for non-violent crimes and unemployment and child poverty hold regardless of the racial composition of the communities under study. To put it directly, this is not a Black issue or a White issue, nor an urban issue or a rural issue. Rather the relationship between imprisonment and unemployment is consistent across most communities. Thus, policies which address this relationship - breaking this cycle - have a good chance of reducing recidivism, increasing employment, and improving issues related to fatherlessness. As such, the long-term benefits from such programs may be intergenerational and cross-cultural in nature.

As criminal histories for non-violent offenders which are required to be reported to employers create an obvious roadblock for employment, reconsideration of these policies, and laws provide a broad societal good. In this sense the Clean Slate Act provides a positive avenue for increasing non-violent offenders’ participation in employment, in their families, and decreasing their involvement with the criminal justice system which would provide resource relief for taxed States, Congressional Districts, and municipalities. Our results consistently pointed to associations between incarceration, including at the federal level, unemployment and poverty, for both rural White, urban Black and all communities in between. Thus, reducing roadblocks to employment for former non-violent offenders, potentially benefits all communities.
Limitations
As with all studies, the current study does have limitations that bear mentioning.First, all data are correlational and, as such, causal conclusions cannot be made. Second our results demonstrate consistent relationships between imprisonment and unemployment at the community level, consistent with prior studies at the level of individual offenders. Though, predicting the impact of specific policies such as the Clean SlateAct is speculative and can only be evaluated through the initiation of the program itself.Nonetheless, we find it unlikely that improving economic prospects for former non-violent offenders is likely to exacerbate negative outcomes.

Conclusion

Prior research with offender populations has demonstrated links between criminal history and unemployment after release. Our analyses at the societal level are consistent with these individual-level studies, suggesting that incarceration history predicts unemployment and child poverty at the rural as well as urban level. Public policy which improves released offenders’ ability to seek employment has the potential to improve these outcomes, including not only for released offenders but for their families as well.

References

Batastini, A. B., Bolaños, A. D., Morgan, R. D., & Mitchell, S. M. (2017). Bias in hiring applicants with mental illness and criminal justice involvement: A follow-up study with employers. Criminal Justice and Behavior, 44(6), 777–795. https://doi.org/10.1177/0093854817693663

Black Demographics (2022). Black Population by State. Retrieved from: https://blackdemographics.com/population/black-state-population/

Bureau of Labor Statistics. (2022). Local Area Unemployment Statistics. Retrieved from: https://www.bls.gov/web/laus/laumstrk.htm

Cappell, C. L., & Sykes, G. (1991). Prison commitments, crime, and unemployment:A theoretical and empirical specification for the United States, 1933–1985. Journal ofQuantitative Criminology, 7(2), 155–199. https://doi.org/10.1007/BF01268628

Cerda, J. A., Stenstrom, D. M., & Curtis, M. (2015). The role of type of offense and work qualifications on perceived employability of former offenders. American Journal of Criminal Justice, 40(2), 317–335. https://doi.org/10.1007/s12103-014-9244-8

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Lockwood, S., Nally, J. M., Ho, T., & Knutson, K. (2012). The effect of correctional education on postrelease employment and recidivism: A 5-year follow-up study in the state of Indiana. Crime & Delinquency, 58(3), 380–396. https://doi.org/10.1177/0011128712441695

Mackey, W. C., & Mackey, B. (2003). The Presence of Fathers in Attenuating YoungMale Violence: Dad as a Social Palliative. Marriage & Family Review, 35(1–2), 63–75.https://doi.org/10.1300/J002v35n01_05

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Phillips, J., & Land, K. C. (2012). The link between unemployment and crime rate fluctuations: An analysis at the county, state, and national levels. Social Science Research,41(3), 681–694. https://doi.org/10.1016/j.ssresearch.2012.01.001

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Appendices

Appendix A

Bivariate Correlations between Study Variables
Appendix B

Mean Rates of Prison Population, Unemployment and Poverty for White and Black Majority Congressional Districts
Appendix C

Regression Results for Unemployment and ChildPoverty Outcomes
Appendix D

Correlation between Prison Population and Employment in Congressional Districts with more than five-Counties
Appendix E

Correlation between Prison Population Per-Capita and Unemployment

About the Authors

Howard Henderson, PhD
is the founding director of the Center for Justice Research and Professor of Justice Administrationat Texas Southern University. His research focuses on structural and cultural predictors of criminal justice system disparities.

Chris Ferguson, PhD

is an affiliated researcher with the Center for Justice Research Texas Southern University. His researchexamines clinical offender and juvenile justice populations as well as evaluations for child protective services.

Sven Smith, J.D., PhD

is an affiliated researcher with the Center for Justice Research at Texas Southern University. Svenserved as the lead researcher/supervisor on The American Jury Project.

Stephen G. Van Geem, PhD

is an assistant professor in the Department of Sociology and Anthropology at Utah State University. Stephen is the Undergraduate Program Director.

Suggested Citation
Henderson, H., Ferguson, C., Smith, S., Van Geem, S. (2022). Understanding the Link Between Incarceration, Crime, Unemployment, and Rural Spaces. Texas Southern University Center for Justice Research.
Acnowledgement
We would like to express our deepest appreciation to Jack Sevil, research associate at the Center for justice Research, for hisassistance with the analysis and editorial support. His willingness to tough through the long hours ensured that this projectreached its completion.
About The Center For Justice Research
The Center for Justice Research (CJR), housed in Texas Southern University’s Barbara Jordan -Mickey Leland School of Public Affairs, provides culturally-responsive solutions to mass incarceration. The Center’s initiativesare situated on four pillars: Research and Data, Policy Advocacy, Researcher Development and Strategic Engagement. CJR’sefforts move theory to practice, advise key stakeholders, and diversifies the justice research space. Founded in 2018, CJR wasinitially funded as a research initiative born out of a partnership between the Thurgood Marshall College Fund, Charles KochFoundation, and Koch Industries to explore the removal of barriers to opportunities in historically under-resourced communities. From the beginning, CJR has worked on projects supported by the Department of Justice, National Science Foundation,National Hockey League, National Collaborative on Gun Violence Research, Chevron, and the Houston Endowment. Thesupporters of CJR all share a deeply rooted interest in dismantling mass incarceration, maintaining public safety and creating aprocedurally just criminal justice system. Because of our partnerships, CJR has the unique opportunity to investigate problemsand provide solutions for the complexities of the criminal justice system. Through these relationships, the Center’s researchersturn data into culturally-sensitive solutions for professionals, policymakers, and advocates. Sources of the Center’s funding andour projects are outlined in greater detail on the website.