Abstract
This paper examines the effects of a private-sector prison work program called the Prison Industry Enhancement Certification Program (PIECP) on formal unemployment duration, duration of formal employment, and earnings of men and women released from various state prisons between 1996 and 2001. It also investigates the labor market dynamics of formerly incarcerated men and women. The program is found to increase reported earnings and formal employment on the extensive margin, with a stronger impact on the formal employment of women. There is little evidence that it increases formal employment along the intensive margin (i.e., duration of formal employment). Contrary to segmented labor market theories, superior employment (i.e., higher-paying jobs) does not lead to increased job stability. Roughly 92 % of individuals who obtained formal employment in the sample experienced job loss; however, reincarceration rates are too low to explain this fact. An evaluation of labor market dynamics reveals that traditional human capital variables, criminogenic factors, and a few demographic characteristics determine job loss. In addition, black women, single women, and women with more extensive criminal histories face greater barriers in the labor market than their male counterparts.
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Notes
Machin and Meghir (2004) also find the same result in England and Wales.
Although the use of prison labor has been opposed by labor and human rights activists, this research focuses solely on the rehabilitative aspect of prison work programs and seeks to understand whether receiving vocational skills training and work experience while incarcerated improves postrelease labor market outcomes. For a detailed historical discussion of prison labor and the Prison Industry Enhancement Certification Program, please see Cox (2009).
Retrieved from the Federal Justice Statistics Program (http://www.bjs.gov/fjsrc/), and West and Sabol (2008).
Normalized differences for each variable are calculated by dividing the difference in the mean values of each group by the average standard deviation of the groups.
Likewise, due to the availability of additional control variables, a broader range of demographic characteristics of the PIECP sample can be compared to national estimates to explore the extent to which external validity is traded for greater interval validity. A detailed comparison of the PIECP sample to a nationally representative sample of prisoners can be found in Cox (2015).
Please see Appendix A for a more detailed description of OTW, TI, and PIECP jobs.
Please see Cox (2009) for the legislative history and an in-depth analysis of PIECP.
Please see Cox (2009) for a complete listing of certificate holders during that time.
According to the BJA (1999), “Certificate Holder refers to a department of corrections, or an alternate umbrella authority, which is approved by BJA for PIECP Project Certification. Certificate Holders assume monitoring and designation responsibilities with respect to their designated Cost Accounting Centers (CAC). All PIECP prisoner-made goods are produced within (a CAC) that a certificate holder designates within itself, private prisons located in the same state or jurisdiction or, in the case of an umbrella authority within its membership agencies. … Umbrella Authority refers to a type of Certificate Holder which is authorized by law to administer a PIECP Project and which consists of state and/or local departments of correction located within the same state. A certified umbrella authority may designate CACs within its membership agencies, as well as within members’ private prisons, and assumes responsibility for monitoring CAC compliance” (pp. 17007–17009)
Delaware, Missouri, and the Texas Red River County Department of Corrections no longer hold certificates. On May 13, 2004, the Washington State Supreme Court found employing inmates in Class 1 free venture industries to be unconstitutional. However, the legislature proposed an amendment to the constitution that would allow the state to employ such labor. This amendment passed in November 2007. Using a logistic regression in a public choice model, Gallagher and Edwards (1997) attempted to explain the likelihood that a state would participate in PIECP using data from 1985 to 1992. They find that “states with stronger union membership, democratic governors, and high unemployment rates will be less likely to allow PIE projects” (p. 97). However, states with a rehabilitative view of prisons are more likely to participate in PIECP.
According to the BJA (1999), “Cost Accounting Center (CAC) refers to a distinct PIECP goods production unit of the industries system that is managed as a separate accounting entity under the authority of a Certificate Holder. All PIECP production activities are conducted within the context of a designated CAC which, generally is structured either as a customer or employer model for purposes of determining PIECP inmate benefits” (p. 17007).
Note that the type of model the private sector uses will determine the benefit structure to the inmate. According to the BJA’s (1999) PIECP federal guidelines, “PIECP projects must provide inmate workers appropriate benefits comparable to those made available by the Federal or State Government to private sector employees, including workers’ compensation and, under certain circumstances, Social Security” (p. 17011). Nonetheless, some states prohibit inmates from receiving workers’ compensation. However, “provision of comparable workers’ compensation benefits is acceptable as long as the CAC can demonstrate comparability of such benefits with those secured by the Federal or State Government for private sector employees” (BJA 1999, p. 17011). Moreover, if the employer model is used, then Social Security benefits must be provided to the prisoner. However, if the customer model is used, then “the BJA recognizes the applicability of other provisions of Federal law which may operate to preclude the provision of PIECP inmates with certain benefits, including Social Security” (BJA 1999, p. 17011).
I am grateful to Cindy Smith for her guidance and willingness to share the data.
Segmented labor market theory (SLM) is often referred to as a theory of dual labor markets because it argues that there are two distinct labor markets: the primary labor market and the secondary labor market. The primary labor market consists of “jobs in large firms and/or unionized jobs, which tend to be better jobs—higher paying, more promotion possibilities, better working conditions, and more stable work. The secondary labor market, which roughly overlaps large sections of the external labor market, contains the low-paid jobs that are held by workers who are discriminated against and who have unstable working patterns” (Cain 1976, p. 1222). Traditionally, economists view preferences for work as exogenous variables that help to explain an individual’s labor market achievements. However, SLM theorists argue that preferences are actually endogenous and can be determined by success in the labor market. In particular, discrimination and other systematic or random influences that cause individuals to enter the secondary labor market can trigger antiwork sentiments among low-income workers, thereby keeping them in a position of hardship (Cain 1976). Theoretically, this implies that employment can help to rehabilitate an offender; however, it would take a decent (i.e., in the primary labor market) job to draw individuals out of a life of crime.
There are originally 1,309 observations. However, one observation is dropped due to having a negative value for time served and 91 observations with a recorded race of minority other are excluded. Moreover, there are two observations in which time from release to formal employment and time from formal employment to job loss indicated the ex-offender was employed during the follow-up period. However, the censored employment variable was coded to the contrary. Because all of the other employment variables indicated employment was obtained, the censored variable was corrected to match the rest of the data. A descriptive analysis comparing this sample to the Smith (2009) data determined that individuals in these data are about the same age at release and incur roughly the same number of disciplinary reports during incarceration as the superset to the data (i.e., the Smith 2009 data). However, the individuals in the smaller sample seem to be worse off than those in the superset to the data as measured by criminal history, marital status, preincarceration employment, and preincarceration earnings. In particular, the individuals in the data utilized in this study have on average greater prior arrests, more prior convictions, more previous incarcerations, have a larger proportion that have no formal employment prior to incarceration, have a greater proportion of individuals that earned wages less than $20,234 prior to confinement, and have a larger percentage of individuals that are single.
The other than work (OTW) and traditional industries (TI) groups comprise the control group and (as previously mentioned) could also perform the same tasks as PIECP participants depending on how the state classifies the work performed by TI and OTW workers. Moreover, OTW participants could earn wages comparable to TI participants. TI workers could be classified as those who perform work similar to PIECP workers (however, unlike PIECP, they may earn a nominal wage or nothing at all) and those who perform institutional maintenance. Finally, PIECP tasks can vary from habitual and labor intensive to very skilled.
Even though there is no official cutoff to determine when normalized differences are too large, Imbens (2013) suggests that variables with a normalized difference greater than .5 should be considered too large to be adequately controlled for using regression analysis.
Assuming the unconfoundedness assumption holds.
Tied data occurs when multiple failures happen at the same point in time (Cameron and Trivedi 2005).
This assumes φ(x,β) > 0.
Incarceration wages and wages after release are indirectly controlled for through PIECP. These variables are not included in the analysis to avoid overcontrolling for the effects of PIECP. In other words, if PIECP is beneficial because it increases postincarceration wages, then including this variable in the analysis will eliminate the effect of PIECP.
However, because the data are measured in quarters and don’t indicate how many times an individual changes jobs in a quarter, this is an imperfect measure of job stability because it only shows whether the individual maintained employment, not the number of changes in employment during the observation period.
Please see Cox (2015) for a detailed discussion of the imputation procedure.
To recapture these observations, the Cox model is reestimated by assigning observations that have a time from release to formal employment of zero (i.e., obtaining a job at discharge from prison) with the smallest recognizable number that is close to zero but not equal to zero. This can be done for the Cox estimator because it is the ranking of the numbers that is most important when estimating the coefficients.
It should be noted that type of release (supervised release, work release, etc.) cannot be controlled for in the model. Thus, an alternative explanation is that PIECP workers are more likely to participate in work release, suggesting that work release is a mediator variable through which part of the effect of PIECP occurs. Controlling for work release, therefore, might attenuate the effect of PIECP on the duration of formal unemployment.
Thanks to an anonymous referee for pointing this out.
Anyone with a previous occupation is hypothesized to have a shorter duration of unemployment, a greater duration of formal employment, and greater earnings than those who were unemployed or out of the labor force prior to prison.
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Conflict of Interest
I volunteered to assist Dr. Cindy Smith with data management of the supplementary data to the National Evaluation of the Prison Industry Enhancement Certification Program (Smith 2009), which was used in this study. Dr. Smith also served on my dissertation committee. Additionally, I serve as a program evaluator for Correction Enterprises, the North Carolina Department of Public Safety’s prison industrial work program. I am unaware of additional (potential) conflicts of interest.
Appendices
Appendix A: Definitions of PIECP, TI, and OTW (Smith et al. 2006, pp. 28–31)
Other than work (OTW)
Individuals involved in other prison activities (e.g., education or drug treatment programs) but not industry work. Those classified as OTW are not necessarily inactive while incarcerated. In addition, individuals classified as OTW and TI could perform the same task (i.e., laundry). However, one state may classify the task as OTW and the other as TI. OTW can be classified into two groups: (1) prisoner who decide not to work and (2) those located in compulsory work states (which require the prisoner to provide labor or go to school) that select jobs that demand minimal exertion and time. Some receive a nominal wage comparable to TI (e.g., $0.25 per hour).
Traditional Industries (TI)
There are two kinds of TI workers. One group performs work comparable to PIECP; however, the prisoner does not earn a market wage and the manufactured goods cannot be purchased in the private sector. For instance, the inmate may not earn anything or could make a nominal amount, such as a minimum of $0.25 per hour up to about $1.25 per hour. The second group of workers does institutional maintenance (e.g., semiskilled maintenance, administrative support, etc.). Each state determines what is considered a traditional industry in that state.
Prison Industry Enhancement Certification Program
Tasks range from habitual manual labor (e.g., assembly line) to highly skilled labor (e.g., sheet metal welding). Prisoners who work in facilities featuring the employer and manpower models have normal dealings with and are managed by a free-world employee. This could transform the corrections atmosphere to an employment setting during the workday. This program is explained in detail in the body of the article; thus, the reader is referred to the section on PIECP for a more detailed discussion of the program.
Appendix B
Kaplan-Meier recidivism survival curves
Kaplan-Meier survival curves formal employment versus incarceration
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Cox, R. The Effect of Private Sector Work Opportunities in Prison on Labor Market Outcomes of the Formerly Incarcerated. J Labor Res 37, 412–440 (2016). https://doi.org/10.1007/s12122-016-9229-0
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DOI: https://doi.org/10.1007/s12122-016-9229-0
Keywords
- Prison labor
- Incarceration
- Prisoner reentry
- Unemployment duration
- Employment duration
- Formerly incarcerated
- Earnings
- Prison industry enhancement certification program
- Job training programs