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Institutional Placement and Illegal Earnings: Examining the Crime School Hypothesis

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Prison is like high school with knives.

- Raegan Butcher.

Abstract

Objectives

The objective of the current study is to examine the hypothesis that correctional environments can facilitate the accumulation of “criminal capital” and might actually encourage offending by serving as a school of crime.

Methods

We use panel data from a sample of 615 serious juvenile offenders who reported illegal earnings and information regarding institutional stays over a 7 year period. There are two separate measures that can contribute to criminal capital within institutions: the prevalence of friends in the institution who have committed income generating crimes and the length of institutional stays as a cumulative dosage. We account for unobserved heterogeneity using fixed effects estimation.

Results

Both institutional measures, total number of days incarcerated and exposure to deviant peers, predict a positive increase in an individual’s daily illegal wage rate, even after eliminating fixed unobserved heterogeneity and controlling for important time-varying confounders. For total number of days institutionalized, there were positive though marginally declining returns to length of stay.

Conclusions

For juveniles, placement in institutions can have important negative consequences. Our findings extend and complement findings showing that institutionalization might help increase opportunities in the illegal market. This not only undermines prison as a crime-prevention strategy but could also promote longer and more successful criminal careers.

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Notes

  1. To be clear, juvenile offenders are placed in various types of settings, each with a different focus (e.g., correctional vs. residential). We use the term “institution” to represent all settings in which juveniles are placed.

  2. Quantifying the monetary returns associated with the accumulation of human capital (e.g., the returns to education) is a widely studied problem in empirical economics (e.g., Carneiro et al. 2011).

  3. The idea that formal control institutions can be “schools for crime” has a long history both in criminology and literature. In Victorian England, Dickens wrote extensively about the malignant consequences of bridewells, workhouses, and debtors prisons (Richardson 2012). At about the same time, the journalist and neophyte social researcher Henry Mayhew conducted comprehensive examinations of both the London poor (most of whom had been touched by workhouses or prisons), and London prisons. His books London Labor and the London Poor (2012) and The Criminal Prisons of London (2011) document the fact that Victorian institutions for the poor and criminal were more successful in teaching crime and vice than they were at reforming. A similar theme was picked up by the life histories of Chicago School theorists, as detailed in Shaw’s The Jack Roller (1930).

  4. It is important to note that some scholars, such as Gottfredson and Hirschi (1990) argue that there is no skill required for the commission of most crimes. At the same time, qualitative work in criminology, especially in burglary (Wright and Decker 1994), drug dealing (Jacobs 1996), and violent offending (Topalli 2005) suggests that offenders do possess specialized knowledge with regard to the successful commission of crimes, target selection, and detection avoidance.

  5. Other methods based on exogenous variation have been used to confront the selection problem, such as experiments (Berecochea and Jaman 1981; Gaes and Camp 2009), regression discontinuity approaches (Chen and Shapiro 2007; Hjalmarsson 2009), and natural experiments (Drago et al. 2009).

  6. Information regarding the rationale and overall design of the study can be found in Mulvey et al. (2004a, b), while details regarding recruitment, a description of the full sample, and the study methodology are discussed in Schubert et al. (2004). Additional information about the study can be found at: www.pathwaysstudy.pitt.edu.

  7. The types of facilities include: jail/prison, detention, youth detention, contract residential, and contract residential mental health.

  8. The self-reported illegal earnings variable is highly skewed and the median daily wage rate is considerable lower ($50.88). Nonetheless, we observe average daily illegal wage rates that are slightly higher than previous work and might be because the serious offending nature of our sample. McCarthy and Hagan (2001) found that the average daily wages for homeless youth engaged in drug dealing was $101. Freeman (1996) found that among a sample of Boston youth, weekly offenders earned $448.

  9. There were 214 observations for which at some point in the observation period, days in custody were not observed for one period. In these cases, we treated the missing days as zero for the purpose of generating a cumulative number of days, understanding that this strategy essentially creates a lower bound estimate for total days in custody. As a robustness check, we also estimated all models using only full data and found substantively identical results.

  10. Haynie and Osgood (2005) have noted one limitation of self-reported peer measures is that they may overestimate the true influence of peers due to issues of projection. While we recognize this possibility, we offer two points as to why this potential problem would be less problematic for our findings. First, our analysis considers only within person variability, meaning any fixed reporting bias by an individual would be differenced away. Second, even if the peer effect is upwardly biased, this would not explain the observed independent days effect, and in fact would only make the gap between these two effect sizes larger, therefore calling even more emphasis to a secondary explanation beyond a ‘crime school’ explanation.

  11. The 12 items include: destroy property, hit someone, sold drugs, consumed alcohol, carry a knife, carry a gun, owned a gun, fight, hurt someone in a fight, stole something worth more than $100, stole a car, and enter a building to steal.

  12. There were 117 missing values for peer ratings among individuals who reported being in a facility. Among the 117 we imputed the mean rating that the individual reported across all their stays. To ensure that imputation did not change the results, we also ran the model without the imputed values. Results were almost identical.

  13. The 11 items include: shoplifted, bought/received/sold stolen property, used checks/credit cards illegally, stolen car/motorcycle, sold marijuana, sold other drugs, stole something worth more than $100, enter a building to steal, took something by force using weapon, took something by force no weapon, and prostitution.

  14. Due to the irregularity of periods in which individuals report illegal earnings, since incarceration leaves large gaps in the panel, we are able to observe meaningful variation in the change in age.

  15. Similar to our facility peers measure, we recognize the possibility of projection bias. However, due to our estimation strategy and our outcome of interest, we believe that projection bias is not overly problematic for the current study.

  16. Tables 4 and 6 display cumulative days in custody so that the coefficients reflect per 100 days incarcerated.

  17. We also considered a set of specifications for Table 5 in which we lagged the peer exposure measure. The results show a similarly positive effect of peer exposure in the cross-sectional regression, though slightly smaller in magnitude from the results reported in Table 5. Conversely, we observed a null result in the fixed effects model. However, lagging this variable created several issues. Notably we lost ~11 % of our cases for whom we did not observe a lagged peer measure, and this problem was exacerbated in the fixed effects estimation where the total number of cases for whom we observed multiple periods—for which we were able to calculate a change—was reduced even further. Thus, we include the summary of results in this footnote.

  18. As mentioned, we also considered a set of parallel supplementary analyses in which we examined binary involvement in illegal income-generating activities (i.e., reporting any illegal income in a period) using logit with similar specification as reported here. This allowed us to retain all individuals in the sample in the model (as opposed to excluding zero wage earners). The results, which are available upon request from the lead author, are substantively similar in that they indicate a positive relationship between both total days and peer exposure with the outcome.

  19. We evaluated this marginal effect in our log-linear model using the model coefficients to generate the following formula: [.001 − 2(1.49E−7)(40)] × 30, where 40 is the first quintile of cumulative days incarcerated and 30 is the size of the dosage. In this formula, we converted the coefficients to reflect days rather than the reported days/100.

  20. Ideally, we would have specified the model as a person × days dosage and fit the model nonparametrically. However, our measure of facility peer exposure was categorical and prevented such a specification.

  21. With respect to employers, Holzer (1996: 90, emphasis added) observed that “[e]mployers much choose among applicants on the basis of the skills and personal characteristics they perceive them [job seekers] to possess.

  22. An alternative behavioral explanation for this finding is the sunk cost fallacy. In other words, some individuals may view their time in incarceration as a sunk cost, and the need to justify it by continuing to produce returns to this ‘investment’ may lead some individuals to irrationally conclude that continuing participation in illegal activities is merited. As the present study includes no behavioral intentions to assess this possibility, it remains a speculation in need of empirical research.

  23. McCarthy and Hagan (2001) used engaging in drug crimes only as their measure of specialization. Drug crimes are unique and are qualitatively different from other types of crime (Thompson and Uggen 2012).

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Authors and Affiliations

  1. Department of Sociology and Criminology, Pennsylvania State University, University Park, PA, USA

    Holly Nguyen

  2. Department of Criminology and Criminal Justice, University of Maryland, College Park, MD, USA

    Thomas A. Loughran & Ray Paternoster

  3. Columbia Law School, Columbia University, New York, NY, USA

    Jeffrey Fagan

  4. Program in Criminology, University of Texas at Dallas, Dallas, TX, USA

    Alex R. Piquero

Authors
  1. Holly Nguyen
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  2. Thomas A. Loughran
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  3. Ray Paternoster
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  4. Jeffrey Fagan
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  5. Alex R. Piquero
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Correspondence to Holly Nguyen.

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Nguyen, H., Loughran, T.A., Paternoster, R. et al. Institutional Placement and Illegal Earnings: Examining the Crime School Hypothesis. J Quant Criminol 33, 207–235 (2017). https://doi.org/10.1007/s10940-016-9291-z

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  • DOI: https://doi.org/10.1007/s10940-016-9291-z

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