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The Racialized Consequences of Jail Incarceration on Local Labor Markets

Race and Justice (2022)

Published onMay 24, 2022
The Racialized Consequences of Jail Incarceration on Local Labor Markets


As a racialized labor market institution, the criminal justice system shapes racial patterns in local labor markets through processes of exclusion and marginalization. How do local county jails contribute to these dynamics? To examine that question, the relationship between county-level jail and employment rates is examined across the U.S. between 2007 and 2017. The study uses a System Generalized Method of Moments (GMM) dynamic panel model approach that structurally controls for simultaneous determination and other sources of endogeneity. A racial stratification analysis identifies a negative relationship between jail and employment in the urban counties with the highest percentage of Black residents aged 15 to 64, whereas areas with the lowest percentage of Black residents have a positive relationship between jail and employment. These racially differential spillover effects suggest that the impact of jail incarceration on employment is significantly racialized at this level of analysis.

CITATION: Thomas, C. (2022). The Racialized Consequences of Jail Incarceration on Local Labor Markets. Race and Justice,


As a racialized labor market institution, the criminal justice system shapes racial patterns in unemployment, labor market participation, job stability, earnings, and other labor outcomes, through processes of exclusion and marginalization from the labor market (Smith & Broege, 2020; Smith & Simon, 2020; Warner, Kaiser, & Houle, 2020; Zatz & Stoll, 2020). Scholars have argued that the criminal justice system often functions to exclude Black communities, in particular, from employment, especially employment in more desirable, stable jobs (Augustine, 2019; Zatz, 2021). These patterns contribute to what Dawson (2016: 157) terms the “continued systemic economic subordination of Black communities” (Zatz, 2020).

Multiple forms of criminal justice contact contribute to this race-specific labor market disruption in distinct ways. Arrests, for example, have been shown to lead to a smaller proportion of weeks worked per year for Black individuals compared to their White counterparts (Fernandes, 2020), while prison incarceration has been shown to have many racialized harms on labor market outcomes both individually (Apel & Sweeten, 2010; DeWitt & Denver, 2020) and at more macro levels of analysis (Christian et al., 2006; Lopez-Aguado, 2016).

Some studies have included local jail incarceration in their analyses of racialized labor market impacts, but they have often not disentangled jail from prison incarceration (Kirk & Wakefield, 2018). This persists despite jail incarceration affecting a much wider swath of people per year than does prison incarceration, so the impact on communities, especially communities of color, could be even broader than that of prison incarceration (Turney & Conner, 2019). Jail incarceration is also distributed in starkly racially unequal ways: in counties with the highest percent Black residents, both jail capacity and jail admission rates for Black individuals are substantially higher than in other counties. In fact, urban Black men are eight times more likely than urban White men to experience jail incarceration by the age of 38 (Western et al., 2021). Thus, local jails are a potentially impactful but under-examined racialized labor market institution.

This study fills in this gap in the literature by examining the racialized consequences of jail incarceration on local labor markets. Using national, county-level administrative data from 2007 to 2017, the study applies a racial moderation analysis, stratifying U.S. counties by the percentage of Black residents aged 15-64 each year, then deploying a System Generalized Method of Moments (GMM) dynamic panel model to examine the relationship between jail and employment in urban and non-urban counties with the highest percentage of Black residents. The study concludes with a discussion of the implications and new research questions opened up by this novel extension of the criminal justice, race, and labor markets literature to an overlooked type of incarceration, an unexplored level of analysis, and a powerful method of controlling for the endogeneity that plagues so much observational research.

Literature Review and Hypotheses

Three Theories Explaining the Racialized Effect of Jail on Employment

At the macro level, racial threat theories suggest that harsher jail incarceration would be more likely in areas with larger proportions of people of color, because such demographic changes would be viewed as a threat to the political and economic influence of the White population, prompting more severe social control of those perceived threats (Durante, 2020; Duxbury, 2021). Some research testing racial threat theory has found that high Black populations are associated with racial disparities in prison incarceration rates (Myers & Talarico, 1987; Weidner, Frase & Schultz, 2005) and police force size (Kent & Jacobs, 2005), but other studies have not found empirical support for the association between racial threat and arrests (Ousey & Lee, 2008; Parker, Stults & Rice, 2005), incarceration decisions (Ulmer & Johnson, 2004), or sentence lengths (Feldmeyer & Ulmer, 2011; Omori, 2017). Other research has found that the effect of racial threat on prison admissions is race-/ethnic-specific (Durante, 2020). Together, this inconsistent body of research suggests that the impact of racial threat varies depending on the criminal justice process being studied (Stewart, Warren, Hughes, & Brunson, 2017).

Some studies have applied the theory to the jail context, examining the impact of racial threat on higher jail incarceration rates (Carmichael, 2005) and the severity of bail practices (Hood & Schneider, 2019). One recent qualitative study also features an example of how some jail decision-makers might treat particular groups more harshly due to local demographics: a county sheriff justified high jail rates in one newly Latinx part of his county by explaining that “the population down there is from out of the area, and they bring a different culture” (Norton, 2018: 5). This is an emerging body of research, so there is an open question about the extent to which racial threat applies to the economic impacts of jail incarceration.

Second, what Irwin, Davidson, & Hall-Sanchez (2013) call racialized crime theories find a historic line between systems of racial control and contemporary criminal justice policies. Researchers in this tradition attribute such patterns to factors like racial domination from historic racist processes and institutions that serve as a “symbolic template” for how communities of color are punished and controlled (Perkinson, 2010; Petersen & Ward, 2015). A mechanism explaining the persistence of behaviors and institutional relations over time is the theory of behavioral path dependence, according to which once the social and political behaviors “of a community [have] proceeded down a certain path, the behaviors and culture of that community can become entrenched and difficult to reverse” due to inter-generational socialization and institutional reinforcement (Acharya, Blackwell, & Sen, 2018: 28). Research in this tradition focuses on historical continuities in processes of racial domination like colonialization (Hawkins, 2011), racially restrictive residential process (Bass, 2001), and the general demoralization of people of color (Keahiolalo-Karasuda, 2010; Tatum, 2017). Closely related are theories of structural or systemic racism (Sewell, 2020; Unnever et al., 2021). These historically contiguous processes of structural racial control could help explain why the effect of jail on employment would be moderated by local racial composition.

The third theory explaining how the employment effect of jail could be moderated by race is what I am calling racialized vicarious stigma. It has been widely found that incarceration has harsher impacts on communities of color because of the disproportionate concentration of formerly incarcerated people in those neighborhoods (Roberts, 2004; Wilson, 2012). This concentration affects not only them but also the labor-market prospects of others in their community due to processes like social isolation and neighborhood stigma (Clear, 2007). Such vicarious stigma can become particularly detrimental to people of color when it is racialized (that is, when people of color in places with concentrated incarceration are inherently perceived to be justice-involved or otherwise stigmatized by proximity to incarceration). Support for the micro-foundations of this theory includes individual-level research about how ban the box reforms had the unintended consequence of increased racial discrimination because employers used race as a proxy for history of incarceration (Agan & Starr, 2018). Similarly, perceptions of community danger and risk have been found to be driven not by crime rates, but by the perceived Blackness of those communities (Mears et al., 2013; Quillian & Pager, 2010; Rodriguez, 2013). Black individuals, in particular, are perceived as justice-involved when they live in areas where incarceration is concentrated, and in turn their whole communities are stigmatized as dangerous and undesirable (Anderson, 2015).1 Such racialized vicarious stigma could explain how the effect of jail on employment is racially moderated.

Empirical Evidence on the Racialized Effect of Jail on Employment

Prior research has persuasively demonstrated that prison incarceration disrupts individual employment for people across all racial groups (Apel & Sweeten, 2010; Bushway, Stoll, & Weiman, 2007; Holzer, Raphael, & Stoll, 2006; Kling, 2006; Loeffler, 2013; Pager, Western, & Sugie, 2009; Western, Braga, Davis, & Sirois, 2015; Western, Kling, and Weiman, 2001). Other research has demonstrated a similar pattern at more macro levels of analysis like neighborhoods, cities, and counties (Clear, 2007; Duck, 2015; Tilly, Moss, Kirschenman, and Kennelly, 2001). An emerging body of research on the general consequences of jail incarceration on employment has also found a negative association (Apel & Powell, 2019; Dobbie, Goldin, & Yang, 2018; Pogrebin, Dodge & Katsampes, 2001; Freudenberg et al., 2008; Grau et al., 2021).

With respect to the racialized effects of incarceration, many studies of the impact of prison incarceration on employment acknowledge how individual and structural racism amplify damage from incarceration (Murakawa & Beckett, 2010; Warner et al., 2020; Western & Pettit, 2005). At the individual level, some studies have found a larger effect of imprisonment on employment among Black and Latinx people than White people (Holzer, 2009; Western, 2006). For example, Pager (2003) found the consequences of a prison record for job call-backs are more severe for Black than White people. Time in prison (or, by extension, jail) “acts as an absorbing status that feeds into a process of cumulative disadvantage … hallmarked by discrimination” (Sykes & Maroto, 2016: 130).

However, some other recent individual-level studies using instrumental variable approaches have found that the negative effects of prison on employment are concentrated among White men who had had jobs before incarceration (Harding et al., 2018; Mueller-Smith, 2015). These authors’ interpretations are that people of color’s labor market trajectories tend to be less affected because they are more likely to already be more socially excluded from labor markets, concluding that “imprisonment’s role in exacerbating racial inequalities in the labor market is primarily in its incapacitation effects” (Harding et al., 2018: 41). In the jail context, a study on the individual employment consequences of jail and probation has similarly found the disruptions to employment concentrated among White individuals with prior employment (Menefee et al., 2021).

At the county or community level, qualitative studies in social control traditions have documented the disproportionate, concentrated effects of prison incarceration on joblessness in communities of color (Lopez-Aguado, 2016; Rios, 2011), with some studies giving attention to local short-term jail incarceration rates. Duck (2015), for example, performed a ten-year ethnography of a primarily Black, urban Northeastern neighborhood where marginalized young men commonly earn money via drug dealing. He found that in such communities, jail and its ensuing fines and fees are, along with over-policing and other processes of harsh social control, part of “a matrix of factors that stymie [residents’] ambitions to get ahead” and lead to what he calls “precarious living” (Duck, 2015: 113). In another county-level study, Sabol & Lynch (2003) use an instrumental variable approach to find that changes in the annual number of prisoners returning to a county are negatively related to employment levels for Black individuals in those counties. Those prison effects on jobs are not significant for White people in those counties, which is inconsistent with individual-level studies like those of Menefee and colleagues, Harding and colleagues, and Mueller-Smith, but consistent with the other studies like Western’s and Sykes and Maroto’s that find that race moderates the relationship between imprisonment and employment at multiple levels of analysis.

Although the racialized effects of county-level jail incarceration on employment have not received the scholarly attention warranted for such a policy-relevant question, it is plausible that the same mechanisms that moderate the effect of aggregate prison rates would operate similarly for jail rates. Like prison incarceration, jail is particularly concentrated in communities and counties with higher concentrations of people of color (Eaglin & Solomon, 2015; Turney, 2021). Further, at the individual level, bail decisions have been found to be significantly more lenient for White defendants compared to defendants of color (Kutateladze et al., 2014; Sacks et al., 2015). These initial inequalities in pretrial detention and jail incarceration have been found to operate as a racial-ethnic stratification process over time, where racially differential bond amounts increase time detained, and that time detained then reinforces racial inequalities in justice processing (Martinez et al., 2020). This system of individually racially stratified pretrial processes and their multiplying downstream consequences could provide a distinct micro-foundation explaining how racially differentiated impacts of jail incarceration might arise at the macro level.

Overall, there is disagreement in the literature on race, incarceration, and labor markets as to whether counties with differing concentrations of Black residents would be more affected by jail incarceration. The closest quantitative study would be Sabol and Lynch’s, which found a significant impact of prison on Black employment but not on White employment. Further, even if individual stints in jail did not have much racially differential impact on that individual’s immediate joblessness, in counties with higher levels of people of color, widespread, repeated jail cycling due to factors such as racially disproportionate misdemeanor punishment (Kohler-Hausmann, 2018) could accumulate to create an overall weakening of local labor markets. Thus, prior literature suggests that the impact of jail incarceration could differ across communities of different racial makeups.

Current Study and Hypothesis

This study fills an important gap in the literature about the racialized consequences of jail incarceration on local labor markets. It also extends the literature by focusing on the macro level of analysis to compare how the effects of jail on employment might vary by county racial composition. When examining this relationship, the county is the most relevant aggregate unit of analysis, since U.S. jail and bail practices are usually determined by county-level officials like prosecutors, judges, and county jail administrators (Hood & Schneider, 2019; Pfaff, 2012). Urban and non-urban counties are compared because urban jails are qualitatively very different from suburban or rural jails, as far as experiences within different types of jails, policies of jail administrations, and physical and social distance from other communities (Turney & Conner, 2019). Further, urban versus rural labor market processes are very different due to the larger, more heterogeneous labor markets in urban counties (Crampton, 1999).

These considerations lead to my main research question: what is the impact of jail on local employment rates in urban counties with the highest levels of Black residents? Urban jail incarceration rates are hypothesized to have strongly negative impacts on employment rates in urban counties with the highest levels of Black residents aged 15 to 64, since jail incarceration disrupts not just the daily lives of those jailed, but also the sensitive interconnected local economies where high levels of jail incarceration are concentrated. When a significant proportion of residents are cycling in and out of jail, such as in heavily policed Black communities, entire counties are expected to suffer significant indirect economic costs and stigma. Based on prior findings about how prison differentially affects communities with relatively high concentrations of Black residents, these jail effects on employment are expected to be strongest in urban counties with the highest levels of Black residents aged 15 to 64, due to racial threat, structural racism, and racialized vicarious stigma.


Data and Measures

I test this hypothesis by analyzing a dataset of 30,310 county-years of harmonized administrative data on jail, employment, and other relevant variables across the U.S. from 2007 to 2017. I operationalize the focal explanatory variable of jail incarceration as the sum of people admitted to jails in a county each year divided by the sum of people living in that county that year. I focus on total annual jail admissions because counties vary on relevant jail detention policies, such as housing federal detainees in local jails, but jail admission rates are less affected than jail population rates by such policy differences. The jail data, from the Vera Institute of Justice Incarceration Trends dataset, combine two national, county-level sources from the Bureau of Justice Statistics: the Census of Jails (COJ), which is conducted every five years, and the Annual Survey of Jails for the years between the COJ (Kang-Brown, 2015). Six states are excluded (Alaska, Connecticut, Delaware, Hawaii, Rhode Island, and Vermont) because of their combined jail-prison systems.

As my focal outcome, I operationalize the dependent variable of employment as each county’s average annual employment-to-population ratio, also called the EPOP or the employment rate (OECD, 2019). This is simply the sum of people employed in a county divided by that county’s residents aged 15 to 64. For the numerators, I gather local area counts of employed people from the U.S. Bureau of Labor Statistics, where it is measured monthly and averaged over 12 months. Employed people are defined as persons who, during the week including the 12th day of each month, worked in paid employment for at least one hour or had a job but were absent from work during the reference week. The working-age population denominators come from the U.S. Census Bureau’s annual, county-level Population Estimates Program.

I calculate the EPOP rather than using the more commonly reported U-1 unemployment rate because the denominator of the latter includes only the active civilian labor force: people who have actively looked for a job in the last four weeks. The U-1 explicitly excludes discouraged workers, those marginally attached to the labor force, and the long-term unemployed, so it could strongly underestimate the effects of incarceration on people marginally attached to the labor force. Scholars often use the EPOP when analyzing communities with high levels of discouraged workers to capture “invisible inequality” in unemployment (Pettit, 2012). This is especially apt because even after relatively short stints of incarceration, many people experience stable rather than transient states of formal labor force nonparticipation (Apel & Powell, 2019).

For the racial stratification analysis, I measure racial composition as the percentage of Black residents aged 15 to 64 in a county, as reported by Census Bureau’s Population Estimates Program. People in this age range are at highest risk of jail incarceration. I divide the distribution into quartiles and use a dummy variable indicating whether the county’s Black population is in the highest 25% percentile. This variable is used as an indirect measure of racial threat (Thomas, Moak, & Walker, 2013).

To compare urban and non-urban counties, I use the National Center for Health Statistics (2019) urban-rural classification scheme, and what they classify as large central metro counties, in particular. These are counties in metropolitan statistical areas (MSA) with 1 million or more residents that “contain the entire population of the largest principal city of the MSA, or are completely contained in the largest principal city of the MSA, or contain at least 250,000 residents of any principal city of the MSA” (ibid.).

For control variables, I include the proportion of each county’s population who are non-Latinx White and aged 15 to 64, to account for this dimension of the time-variant racial context (Black et al., 2014). I also include population density per million, to account for inter-county migration and changes in county labor supply (Elhorst, 2003). These two controls are obtained from the U.S. Census. I additionally include the index crime rate to explore whether changes in incidence of major crime categories could account for any estimated associations between jail and employment (Greenberg, 2001). I obtain this index crime variable from the Uniform Crime Reporting database. Finally, I include a control variable for each year to control for external events that all counties experienced at the same time.

For missing data, I use listwise deletion when there are missing observations in the conjunction of the full set of model variables. I use listwise deletion because it has been shown to be less biased and inefficient than other approaches like multiple imputation when data are not missing at random (Pepinsky, 2018). One common approach is to use listwise deletion when the proportions of missing data are below approximately 5% and it is implausible that there are confounding patterns in the missingness (i.e., if the potential impact of the missing data is likely negligible) (Jakobsen et al., 2017). This approach results in a total of 523 county-years deleted (i.e., approximately 1.7% of the full set of 30,833 county-years deleted), yielding 30,310 county-years in the final analytic sample. Examining the deleted county-years did not suggest any plausibly confounding patterns.

Analytic Strategy

To reduce distortions from simultaneous determination and endogenous regressors that would bias OLS estimates in this panel dataset, I use a dynamic panel model: the two-step system GMM estimator (Blundell & Bond, 2000). Arellano and Bover (1995) developed the system GMM to combine the earlier Arellano-Bond difference GMM estimator with the level GMM estimator to analyze dynamic panel models (that is, models that include lagged values of the dependent variable as regressors) (Roodman, 2009). System GMM estimators take advantage of the complex lag structure of such panel data to create a large array of instruments that are individually weak but jointly relevant, thereby achieving greater efficiency than two-stage least squares or other instrumental variable estimators in this context (Greenberg, 2014). They are often used in macro-level contexts when there is not a theoretically relevant instrumental variable available, but the dataset has a higher number of panels than time periods, as in this study (Agnew et al., 2011; Beck & Goldstein, 2018). The system GMM has been used effectively in a number of important recent studies of crime and criminal justice (see, for example, Manning et al. (2018), Rosenfeld & Fornango (2014), or Wu, Koper, & Lum (2021) for recent uses of this GMM dynamic panel model approach in similar macro-level longitudinal criminal justice analyses).

Here, the two-step system GMM model uses multiple lags of the endogenous jail variable, the employment dependent variable, and the exogenous regressors to construct sets of internal instrumental variables that are jointly highly correlated with the regressors but less correlated with the structural error term. More formally:

(ΔEmplitEmplit)=(0β0)+β1(ΔEmplit1Emplit1)+β2(ΔEmplit2Emplit2)+θ(ΔXitXit)+(Δεitµi+εit)  \binom{{\mathrm{\Delta}Empl}_{it}}{{Empl}_{it}} = \binom{0}{\beta_{0}} + \beta_{1}\binom{{\mathrm{\Delta}Empl}_{it - 1}}{{Empl}_{it - 1}} + \beta_{2}\binom{{\mathrm{\Delta}Empl}_{it - 2}}{{Empl}_{it - 2}} + \mathbf{\theta}\binom{\mathrm{\Delta}\mathbf{X}_{\mathbf{it}}}{\mathbf{X}_{\mathbf{it}}} + \binom{\mathrm{\Delta}\varepsilon_{it}}{µ_{i} + \varepsilon_{it}}\ \

The two-step system GMM model estimates two equations simultaneously, both of which estimate the same coefficients. In both dynamic equations, two lags of the employment outcome are included on the right-hand side to account for serial auto-correlation. Then, in the first equation, on top, all variables are transformed into first differences to remove time-constant unobserved heterogeneity. The second equation below uses the same variables untransformed – that is, their levels. The model then uses two sets of internal instruments derived from two distinct set of lags to instrument future values so that they are not correlated with the structural error term: levels of lags of the differences as instruments in the difference GMM on the top, and differences of lags of the levels as instruments in the level GMM on the bottom. Finally, to minimize post-treatment bias (Montgomery, Nyhan, and Torres 2018), I followed the step-by-step system-GMM model selection procedure of Kripfganz (2019), optimizing only on the standardized information criteria.


Table 1 presents descriptive statistics for the variables in the analytic sample. A preliminary assessment of bivariate correlations suggests that urban jail admission rates are moderately negatively correlated with employment rates at the county level.2 As discussed above, even if there is an association between jail and employment, there are reasons to expect that jail and employment could affect each other in a feedback loop (also known as simultaneous determination). Thus, I ran Granger tests of causal order and found statistically significant evidence for bidirectional causality between jail and employment, which suggests that traditional OLS regression estimates would be strongly biased by simultaneous determination.

Table 1. Summary Statistics for jail and employment model, U.S. counties 2007 to 2017.

Table Description automatically generated

Table 2. System GMM dynamic panel estimation of predictors of employment rate, U.S. counties 2007 to 2017.

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To reduce bias from endogeneity and the simultaneous determination identified with the Granger tests, I use a dynamic panel model to test my hypothesis that the effect of jail on employment will be strongly negative in urban counties with the highest percent Black residents. Specifically, I use the system GMM when comparing the relationship between jail and employment in the counties with the highest percent Black residents and the lower percent Black residents. As discussed above, two lags of the employment variable are included and reported as predictors on the right-hand side of the equation, since they are associated with both the current value of employment and the focal jail variables.

The results in Table 2 support my hypothesis: there is statistically significant evidence for a substantial negative relationship between jail and employment in urban counties with the highest percent Black residents (specifically, the EPOP decreases by 0.07 for every one-unit increase in the jail admission rate in those counties, holding constant other variables in the model and structurally accounting for other sources of bias). Intriguingly, once taking that effect into account, the other urban counties demonstrate the opposite effect. In urban counties with lower percent Black residents, this dynamic panel model actually provides compelling evidence for a statistically significant and positive relationship between jail and employment (specifically, the EPOP increases by 0.02 for every one-unit increase in the jail admission rate in those counties).

The model’s post-estimation diagnostics are reported in the table. Most crucially, the dynamic panel models pass the Arellano-Bond test of auto-correlation at order 2 and higher, suggesting that the model successfully accounts for the serial auto-correlation that can befuddle panel data analysis. It also passes two Sargan-Hansen tests of the overidentifying restrictions (in which the Null hypothesis is that the overidentifying restrictions are valid). These strong post-estimation test diagnostics suggest that distortions from auto-correlation or overidentification can be ruled out for the results. Additionally, the estimates for the parsimonious set of controls in the model were consistent in direction with prior research. As sensitivity analyses, the pattern between urban jail and employment in highest percent Black counties remains similar in alternate model specifications using t-statistics, long-run estimates, and models excluding outliers.3

Figure 1 shows the predictive margins of the three-way interaction effect of counties’ jail incarceration rates, highest percent Black residents, and urbanicity on counties’ employment rates. The urban and non-urban graphs show how slopes change across standard deviations of the jail incarceration rate. On the left are the estimates for non-urban counties, where the slopes for highest percent Black counties and lower percent Black counties are statistically indistinguishable (though the estimated EPOP in counties with the highest percent of Black residents, in blue, is downshifted, reflecting a consistently lower EPOP across standard deviations of jail incarceration). There is not strong evidence in non-urban counties for a distinctly racialized effect of jail on employment.

Figure 1. Predictive margins of interaction of jail rate, percent Black residents, and urbanicity on employment rate, U.S. counties 2007 to 2017.

predictive margins of interaction of jail rate, percent Black residents, and urbanicity on employment rate, U.S. counties 2007 to 2017

On the right are the results of main interest in this study: the estimates for urban counties. Here, the blue line again indicates the estimated relationship between jail and employment in the highest percent Black counties, and it is sharply downward sloping across standard deviations of jail incarceration. Unexpectedly, the black line indicating the estimated relationship in the lower percent Black counties is noticeably upward sloping across standard deviations of jail incarceration. The strength of this relationship is demonstrated by the distance between the lines fanning out in the counties with higher rates of jail incarceration. Together, these two slopes indicate that, structurally controlling for confounders, there is evidence that the relationship between jail and employment is strongly negative in the highest percent Black urban counties, and positive in the lower percent Black urban counties.

Discussion and Conclusion

This study began with the question of how we can extend research on prisons as a racialized labor market institution to this other far-reaching element of the criminal justice system, county jails. Do those jails similarly function as mechanisms of racialized social control in particular local labor markets? This study’s dynamic panel analysis of the racially differentiated relationship between jail incarceration and local labor markets provides statistically significant evidence in support of the hypothesis that local jails do contribute to processes of labor market exclusion and marginalization for residents of urban counties in the highest quartile of percentage of Black residents. Specifically, in those counties there is an estimated 0.07 unit decrease in the employment rate for every one-unit increase in the jail admission rate. This study has identified a threshold of urban racial composition at which jail incarceration significantly reduces local levels of employment.

This finding is consistent with and extends to the jail context the similarly macro-level qualitative studies of Duck (2015), Lopez-Aguado (2016), and Rios (2011), as well as the quantitative results of Sabol and Lynch (2003), who found a significant impact of prison on Black employment but not on White employment (though, as discussed below, this study looks at the percentage of Black residents in a county as a contextual factor rather than examining employment rates by race). This finding about macro-level jail incarceration stands in contrast to some individual-level studies of the racially stratified effects of jail and prison incarceration on employment that found that the biggest negative effects are among relatively advantaged White men with prior jobs (especially the Menefee et al. (2021) study of jail incarceration, as well as the studies of prison, employment and race by Harding and colleagues (2018) and Mueller-Smith (2015)). The finding is more consistent with other individual-level prison, employment, and race studies like Western’s (2006) and Sykes and Maroto’s (2016) that found the largest employment effects among Black individuals. This study’s key finding suggests that, as found in some prior research and extending other prison research to jails, the context of Black population concentration moderates the relationship between jail incarceration and employment.

Unexpectedly, the study also identified another strongly racialized relationship in the urban counties with fewer Black residents. There, for every one-unit increase in the jail admission rate there is an estimated 0.02 unit increase in the employment rate. Together, these results suggest there is not one story about the racialized effect of jail on urban labor markets, there are two opposing stories. In the subset of urban counties with the highest percent Black residents, local jail incarceration functions to suppress local employment, while in the majority of urban counties, that effect is not just invisibilized – in those counties, jail actually has a positive effect on local labor markets. It is a tale of two counties.

This study does have limitations. One important limitation is that the model measures overall local employment, rather than race-specific employment levels, so the effects identified in counties with the most Black residents are not necessarily driven by Black residents of those counties. There is a trade-off between the richness of data that could address such questions and the heavy data requirements of the dynamic panel model – when trying to use race-specific employment data at the county level annually across the U.S., there was too much missing data for the model to work. Future research using different analytic approaches could fruitfully examine whether the effects identified in this study are driven by Black employment or a spillover effect across all racial groups in the counties with the most Black residents.

Another potential limitation to the convincingness of the evidence depends on the validity of the assumptions of the relatively complex dynamic panel models. One advantage of dynamic panel models is that their most important assumption is directly testable with the Arellano-Bond test of autocorrelation, and this model passes that test as well as the test of over-identification. Similarly, it could be argued that the model does not ensure proper temporal order for causal claims because the jail rates are not lagged. However, an advantage of dynamic panel models is that their use of lags and differences of lags ensures proper temporal order. Nonetheless, this study uses observational data at the macro level, with all the well-established problems with that type of panel data (Spelman, 2013). Within the body of literature using observational data to answer policy-urgent questions on these and related topics, this study is a methodological step forward.

Despite these data limitations, the study contributes to the conflicting literature on race, incarceration, and labor markets, providing compelling evidence that labor markets in urban counties with higher concentrations of Black residents are negatively affected by jail incarceration, as well as evidence of a smaller but still significant effect in the opposite direction in urban counties with fewer Black residents. Although the administrative dataset does not allow for direct theory testing, the results are consistent with the three theoretical mechanisms discussed above: racial threat, systemic racism, and racialized vicarious stigma.

With respect to the literature on racial threat, the key results align with prior research on prison and racial threat, where higher Black populations lead to more aggressive social control. However, these findings extend the application of the theory to the employment effects of jail incarceration. Since there has not been consistent empirical evidence for racial threat in all criminal justice contexts (see Stewart et al., 2017), these findings make a meaningful contribution about a new dimension of the theory. Future studies could directly test how racial threat amplifies these effects of jail incarceration by using other data sources or approaches. For example, multilevel studies could examine how living in areas with more Black residents could moderate the individual effects of jail.

The key findings are also consistent with historic systemic racism theories, since the negative effect of jail on employment in Black communities aligns with historically contiguous processes of structural racial control. How jail functions in these communities could involve historic racist processes serving as symbolic templates for punishment and control today. This theory is not directly tested, and future research could use other data or methods to identify continuities between today’s racialized processes and the legacy of historic racist systems. For example, Acharya, Blackwell, and Sen (2018) use innovative statistical, survey, and spatial methods to quantify how the legacy of slavery predicts current White political attitudes, and similar approaches could be applied to studies of jail and labor markets to identify such patterns as well as potential interventions to mitigate or attenuate any such historical path dependence.

This study’s findings could also be consistently explained by a mechanism of vicarious racialized stigma, such that the stigma of jail incarceration can become racialized and extended to all Black residents in the communities where it is concentrated. Surveys of employers or audit studies could fruitfully explore the extent to which that mechanism contributes to the negative effect of jail on employment in high percent Black areas, as well as the extent to which non-Black residents of such counties might also be vicariously stigmatized. In future research, directly testing each of these theories could help elucidate which if any mechanism primarily explains the key patterns quantitatively identified in this study.

With respect to the unexpected finding of a smaller but significant positive association between jail and employment in lower-percent-Black urban labor markets, there are multiple potential explanations. Chirakijja (2018) has documented how prisons are often important local employers of staff across multiple geographies, and the same pattern could hold for county jails. Thus, the positive relationship between jail and employment in those areas could simply be due to the increase in jobs from people working in county jails. That increase in jail jobs might also occur in counties with the highest percentage of Black residents, but it might be obscured by even more powerful negative labor market effects that do not occur in areas with fewer Black residents. For example, the net of criminal justice tends to be cast wider in Black communities, so it might be more likely to sweep in people who would otherwise be formally employed. With the dynamics of racial threat or structural racism less prominent, perhaps jails in lower-percent-Black areas are less likely to contain people whose jobs could be disrupted by even short stints in jail. These are speculative explanations that deserve serious attention and direct testing in future studies to begin to understand the mechanisms explaining this unexpected pattern.

Policy Implications

This study suggests that the impact of jails as a labor market institution is a local story that varies between counties by relative racial composition, and that even in the years after the historical peak of mass incarceration, urban counties with the most Black residents have been experiencing challenging collateral employment consequences from local jail incarceration. In the same period, many cities have started experimenting with bail and jail reform, and those experiments continue to be as deeply contested as previous historical waves of reform (Rabinowitz, 2021). The findings from this study can inform and complicate those policy debates about jail decarceration and bail reform.

First, one of the main arguments against prison and jail decarceration is that doing so would impose large economic costs from the lost jobs in prisons and jails, which can be a major employer in areas hurt by deindustrialization and other economic trends (Eason, 2017; Schept, 2015). However, such calculations fail to account for how jails have large countervailing economic costs, especially in certain communities. This study contributes to that discourse by providing evidence that local jail incarceration rates seem to have a perceptible negative impact on local urban employment rates in Black communities in particular, which could complicate decision-makers’ social ledgers of costs and benefits. When evidence is presented that jail can function as a form of harsh social control of Black communities, rather than primarily as a source of jobs in struggling communities, those communities might be more open to policy experiments.

Second, since other studies have found that recent increases in jail incarceration are largely due to increases in pretrial detention (National Research Council, 2014), this study has strong policy implications about changing our system of cash bail or other pretrial detention reforms. Most people held in pretrial detention are held there due to a failure to make bail. Yet every single person in pretrial detention is legally innocent. Many are there on low-level charges, and others are factually innocent (even if later pressured into guilty pleas from coercive pretrial detention) (McCoy, 2005). Although this study does not distinguish between those held in jails pretrial and not, there is face validity to the idea that those held in pretrial detention could be affected even more strongly due to the moral injury of experiencing incarceration while legally or even factually innocent. Through the use of pretrial detention, local governments might not be punishing crime per se, but rather punishing poverty (or at least the inability to pay bail). This study goes further to suggest that in urban Black communities, local governments are punishing that poverty not once but twice: first by depriving the legally innocent of their liberty (arguably without full due process of law), and second by significantly harming the local economies to which they must eventually return. Such arguments support the urgent need for reforms to a system of cash bail that thwarts rather than preserves justice and fairness.

There have now been decades of research exploring inequality and the collateral consequences of incarceration, but if jails have been included they have mostly been combined into an aggregate measure with prison rates. Beyond the direct effects of jail incarceration on jailed individuals and their families, this study suggests there are important employment spillover effects on urban Black communities – something about increases in jail incarceration in counties with the most Black residents seems to be affecting employment in those counties above and beyond the disruptions to the lives of those directly affected. Jail incarceration in urban areas with the highest levels of Black residents leads to significantly less employment, whereas in urban areas with fewer Black residents jail leads to significantly more employment. Our next challenge is explaining the mechanisms underlying this form of racialized social control.


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