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A Quasi-Experimental Evaluation of the Impact of Public Assistance on Prisoner Recidivism

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Abstract

Introduction

The Welfare Act of 1996 banned welfare and food stamp eligibility for felony drug offenders and gave states the ability to modify their use of the law. Today, many states are revisiting their use of this ban, searching for ways to decrease the size of their prison populations; however, there are no empirical assessments of how this ban has affected prison populations and recidivism among drug offenders. Moreover, there are no causal investigations whatsoever to demonstrate whether welfare or food stamp benefits impact recidivism at all.

Objective

This paper provides the first empirical examination of the causal relationship between recidivism and welfare and food stamp benefits

Methods

Using a survival-based estimation, we estimated the impact of benefits on the recidivism of drug-offending populations using data from the National Corrections Reporting Program. We modeled this impact using a difference-in-difference estimator within a regression discontinuity framework.

Results

Results of this analysis are conclusive; we find no evidence that drug offending populations as a group were adversely or positively impacted by the ban overall. Results apply to both male and female populations and are robust to several sensitivity tests. Results also suggest the possibility that impacts significantly vary over time-at-risk, despite a zero net effect.

Conclusion

Overall, we show that the initial passage of the drug felony ban had no measurable large-scale impacts on recidivism among male or female drug offenders. We conclude that the state initiatives to remove or modify the ban, regardless of whether they improve lives of individual offenders, will likely have no appreciable impact on prison systems.

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Notes

  1. In fact, the ban itself was a relatively obscure provision in a much larger piece of legislation. Congressional records show that the ban provision saw <2 min of total debate (Mauer 2002; Petersilia 2003).

  2. Since the NCRP does not capture alternative measures of recidivism (e.g., rearrest, reconviction, incarceration in jail, etc), we could not explore alternative definitions in our analysis. However, return to prison is a useful and important measure (e.g., Hunt and Dumville 2016; Langen and Levin 2002; Durose et al. 2014). It is often used as a metric for evaluating programs, assessing trends and gauging impacts for other correctional issues of interest, often in concert with other metrics such as rearrest or reconviction (e.g., Bales et al. 2005; Spivak and Damphousse 2006; Steurer and Smith 2003).

  3. The closest source to a nationally representative picture we could locate comes from the Bureau of Justice Statistics Inmate Survey, which provides limited information on welfare receipt before an arrest and during an offender’s childhood. This survey does not track offenders over time.

  4. In fact, there is explicit mention of trading benefits for drugs and the associated penalties in the SNAP benefit application form in Louisiana. (http://www.dcfs.louisiana.gov/assets/docs/searchable/EconomicStability/Applications/OFS4_4I.pdf).

  5. The analysis of Butcher and LaLonde raises an interesting question of whether state agencies are in fact complying with the federal law. Though we cannot say with absolute certainty that every state complies, evidence gathered for this research (e.g., SNAP application forms asking about drug conviction status, and a conversation with a Massachusetts congressional representative) suggests that policies have resulted in operational changes at the agency level. (http://www.dcfs.louisiana.gov/assets/docs/searchable/EconomicStability/Applications/OFS4_4I.pdf).

  6. Gabor and Botsko (1998) report that 10 states opted out of the ban on food stamps in the year following the PRWORA ban. Those results were based on a survey of states and only report responses for the food stamp portion of the ban. Our independent research has led us to conclude that only 4 states had fully opted out of both aspects of the ban (i.e. completely removed restrictions to both SNAP and TANF).

  7. Broadly, states adopt three types of partial reforms: (1) requirements for offenders to participate in or complete treatment before receiving benefits; (2) allowance for drug offenders who committed less serious crimes to access benefits; and (3) allowance for offenders to receive benefits after a probationary period following release.

  8. New York has data back going back to 1994, but opted out immediately after the ban was passed. We separately tested our pooled estimation with and without New York and found no difference in findings between models.

  9. Apparent confusion by states as to what is meant by “ban modification” has led to reporting error in the State Options Report, and subsequently, confusion in the literature as to what states have adopted what policies and when. For example, although Iowa imposes some drug rehabilitation services (or other requirements) for former drug felons, FNS reports show it has opted out since 2006.

  10. A large number of studies have used date/time as an assignment variable modeled within an RD framework. Table 5 in Lee and Lemieux (2010) provides a nice summary of many such studies. Because time is the forcing variable, our approach can also be described as an “event study”—language more common to various social science disciplines.

  11. We argue that prison admission is a good proxy for date of conviction. Prior to conviction, most offenders are housed in jails rather than prisons. After conviction, most offenders are moved to prison quickly.

  12. <15% of offenders in our analytic sample are convicted of more than two offenses and, of these, <2% have nondrug offenses for their first two offenses and a drug-related offense for their third offense. Since we cannot know whether this third offense is a felony or misdemeanor, we treat these cases as nondrug offenders.

  13. For reference, computations of detectable effects performed in Stata (using the stpower command) show that sample sizes of 200, 500, 3,000, and 10,000 can detect minimum differences in recidivism rates of roughly 0.40, 0.25, 0.10, and 0.05 respectively. These computations assume a two-sided test of a Cox model where the standard deviation of the Ban/Mod covariate is 0.5, power is 0.8, and alpha is 0.05.

  14. We also conducted empirical tests of impacts excluding Florida and Minnesota. Results from these tests did not change the core findings of this paper.

  15. The difference in the sum of the squared residuals between these competing trends is <0.0001.

  16. Results are available upon request.

  17. This paper proposes one alternative construction for a “new crime” using a time-based rule that classifies all prison admissions as new crimes when no release from prison has been observed within the prior year. We test the sensitivity of our results to this classification rule (as well as a 3-year variant of this rule) and find no discernable difference in our findings.

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Acknowledgements

This work was supported by Grant Nos. 2010-BJ-CX-K067 and 2015-R2-CX-K135 awarded by the Bureau of Justice Statistics, Office of Justice Programs, US Department of Justice. For this work, Thomas Rich served as Project Director along with Principal Investigators William Rhodes and Gerry Gaes. Points of view in this document are those of the authors and do not represent the official position of the US Department of Justice. The authors are responsible for any errors in the paper.

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Authors and Affiliations

  1. Abt Associates, 55 Wheeler St., Cambridge, MA, 02451, USA

    Jeremy Luallen, Jared Edgerton & Deirdre Rabideau

Authors
  1. Jeremy Luallen
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  2. Jared Edgerton
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  3. Deirdre Rabideau
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Corresponding author

Correspondence to Jeremy Luallen.

Appendix

Appendix

See Fig. 3.

Fig. 3
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State-by-state illustrations of the proportion drug and nondrug offenders returning to prison within 3 years of release, based on prison admission date centered at Aug 22, 1996

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Luallen, J., Edgerton, J. & Rabideau, D. A Quasi-Experimental Evaluation of the Impact of Public Assistance on Prisoner Recidivism. J Quant Criminol 34, 741–773 (2018). https://doi.org/10.1007/s10940-017-9353-x

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