Estimating the impact of incarceration on subsequent offending trajectories: Deterrent, criminogenic, or null effect?

Journal of Criminal Law and Criminology

Volumn 98, Issue 1, 2007, Pages 207-254

  Bhati, Avinash Singh ^a   Piquero, Alex R ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 57749156512     PISSN: 00914169     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (109)

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4. Jenifer Warren, Packed Prisons Brace for New Crush, LA. TIMES, Apr. 22, 2006, at Al.
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8. See Thomas J. Miles & Jens Ludwig, The Silence of the Lambdas: Deterring Incapacitation Research, 23 J. QUANTITATIVE CRIMINOLOGY (2007);
9. Alex R. Piquero & Alfred Blumstein, Does Incapacitation Reduce Crime?, 23 J. QUANTITATIVE CRIMINOLOGY (2007);
10. William Spelman, What Recent Studies Do (and Don't) Tell Us About Imprisonment and Crime, 27 CRIME & JUST. 419 (2000).
11. See, e.g., FRANKLIN E. ZIMRING & GORDON HAWKINS, DETERRENCE (1973).
12. See Daniel S. Nagin, Criminal Deterrence Research at the Outset of the 21st Century, 23 CRIME & JUST. 1 (1998);
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15. Greg Pogarsky & Alex R. Piquero, Can Punishment Encourage Offending? Investigating the "Resetting" Effect, 40 J. RES. CRIME & DELINQ. 95 (2003);
16. Douglas A. Smith & Patrick R. Gartin, Specifying Specific Deterrence: The Influence of Arrest on Future Criminal Activity, 54 AM. SOC. REV. 94 (1989).
17. EDWIN M. SCHUR, RADICAL N ONINTERVENTION (1973);
18. FRANK TANNENBAUM, CRIME AND THE COMMUNITY (1938).
19. Ross Matsueda, Reflected Appraisals, Parental Labeling, and Delinquency: Specifying a Symbolic Interactionist Theory, 97 AM. J. SOC. 1577 (1992);
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22. David A. Ward & Charles R. Tittle, Deterrence or Labeling: The Effects of Informal Sanctions, 14 DEVIANT BEHAV. 43 (1993).
23. Jon Bernburg et al., Official Labeling, Criminal Embeddedness, and Subsequent Delinquency: A Longitudinal Test of Labeling Theory, 43 J. RES. CRIME & DELINQ. 67 (2006);
24. Jon Bernburg & Marvin D. Krohn, Labeling, Life Chances, and Adult Crime: The Direct and Indirect Effects of Official Intervention in Adolescence on Crime in Early Adulthood, 41 CRIMINOLOGY 1287 (2003).
25. Lawrence W. Sherman, Defiance, Deterrence, and Irrelevance: A Theory of the Criminal Sanction, 30 J. RES. CRIME & DELINQ. 445 (1993).
26. See Raymond Paternoster & Alex Piquero, Reconceptualizing Deterrence: An Empirical Test of Personal and Vicarious Experiences, 32 J. RES. CRIME & DELINQ. 251 (1995);
27. Alex R. Piquero et al., Discerning Unfairness Where Others May Not: Low Self- Control and Unfair Sanction Perceptions, 42 CRIMINOLOGY 699 (2004);
28. Alex R. Piquero & Raymond Paternoster, An Application of Stafford and Warr's Reconceptualization of Deterrence to Drinking and Driving, 35 J. RES. CRIME & DELINQ. 5 (1998);
29. Nicole L. Piquero & Leana Allen Bouffard, A Preliminary and Partial Test of Specific Defiance, 26 J. CRIME & JUST. 1 (2003).
30. See, e.g., LAWRENCE W. SHERMAN, POLICING DOMESTIC VIOLENCE (1992).
31. See Alex R. Piquero et al., The Criminal Career Paradigm, 30 CRIME & JUST. 359 (2003).
32. See Daniel S, Nagin & Raymond Paternoster, On the Relationship of Past to Future Participation in Delinquency, 29 CRIMINOLOGY 163 (1991).
33. See, e.g., MICHAEL R. GOTTFREDSON & TRAVIS HIRSCHI, A GENERAL THEORY OF CRIME (1990).
34. Daniel S. Nagin & Raymond Paternoster, Population Heterogeneity and State Dependence: State of the Evidence and Directions for Future Research, 16 J. QUANTITATIVE CRIMINOLOGY 117 (2000).
35. Id.
36. JOHN H. LAUB & ROBERT J. SAMPSON, SHARED BEGINNINGS, DIVERGENT LIVES: DELINQUENT BOYS AT AGE 70 (2003);
37. Raymond Paternoster et al., Generality, Continuity, and Change in Offending, 13 J. QUANTITATIVE CRIMINOLOGY 231 (1997).
38. See Julie D. Horney et al., Criminal Careers in the Short-Term: Intra-Individual Variability in Crime and Its Relation to Local Life Circumstances, 60 AM. SOC. REV. 655 (1995);
39. John H. Laub et al., Trajectories of Change in Criminal Offending: Good Marriages and the Desistance Process, 63 AM. SOC. REV. 225 (1998);
40. Alex R. Piquero et al., Crime in Emerging Adulthood, 40 CRIMINOLOGY 137 (2002).
41. ALFRED BLUMSTEIN ET AL., CRIMINAL CAREERS AND "CAREER CRIMINALS" (1986).
42. See Miles & Ludwig, supra note 6;
43. Piquero & Blumstein, supra note 6;
44. Spelman, supra note 6.
45. See HAAPANEN, supra note 4.
46. It is also worth pointing out that arrest rates increased shortly before the current sentence, which is consistent with research showing that offenders' criminal activity is highest just prior to their current sentence.
47. Of course, such simple before/after comparisons are not without their limitations, see Michael D. Maltz et al., An Artifact in Pretest-Posttest Designs: How It Can Mistakenly Make Delinquency Programs Look Effective, 4 EVALUATION REV. 4 (1980), and a more rigorous examination would require a determination of what would have happened to these arrest rates if the sentences had not been imposed.
48. Figure reproduced from HAAPANEN, supra note 4, at 93 fig. 7.1.
49. John H. Laub & Robert J. Sampson, Turning Points in the Life Course: Why Change Matters to the Study of Crime, 31 CRIMINOLOGY 301 (1993).
50. See LAUB & SAMPSON, supra note 21.
51. id. 151-72.
52. Id. at 169, 188.
53. Id. at 128.
54. To be sure, the Lyman School experience was not the same for all the males, nor did it have the same sort of outcome for all of the males. Unlike the generally positive, deterrent experiences for Angelo, Leon, and Henry, David described the Lyman School experience as horrible, and Ralph encountered a labeling effect that caused his high school principal to target him for "things he didn't do." Id. at 232-35. Further, Laub and Sampson noted that it was difficult to understand why among those men who had adverse experiences, like Victor, some did not react negatively by committing further crime or failing in adult roles. Id. at 131.
55. Id. at 291. Using a longer time series of the same data, Wimer et al. found that imprisonment was associated with higher rates of arrest, but that the criminogenic effect of arrest was fragile when they applied specific methods for causal inference with nonexperimental data. Christopher Wimer et al., A New Approach to Estimating Time-Varying Causes and Outcomes, With Applications to Incarceration and Crime, in APPLIED DATA ANALYTIC TECHNIQUES FOR TURNING POINTS RESEARCH (Pat Cohen et al. eds., 2008).
56. Richard Rosenfeld et al., The Contribution of Ex-Prisoners to Crime Rates, in PRISONER REENTRY IN AMERICA (Jeremy Travis & Christy Visher eds., 2005).
57. Paul Nieuwbeerta et al., The Relationship Between First Imprisonment and Criminal Career Development: A Matched Samples Comparison (2007) (unpublished manuscript, on file with Netherlands Institute for the Study of Crime and Law Enforcement).
58. Shawn Bushway et al., Connecting Desistance and Recidivism: Measuring Changes in Criminality over the Lifespan, in AFTER CRIME AND PUNISHMENT: PATHWAYS TO OFFENDER REINTEGRATION 97 (Shadd Maruna & Russ Immarigeon eds., 2004).
59. JEREMY TRAVIS & CHRISTY VISHNER, PRISONER REENTRY AND CRIME IN AMERICA (2005).
60. PATRICK A. LANGAN & DAVID J. LEVIN, RECIDIVISM OF PRISONERS RELEASED IN 1994 (2002).
61. ALAN J. BECK & B.E. SHIPLEY, RECIDIVISM OF PRISONERS RELEASED IN 1983 (1989).
62. To be sure, our measure of prior arrests is reflective of only one component of an offender's prior criminal history.
63. Alex R. Piquero et al., Assessing the Impact of Exposure Time and Incapacitation on Longitudinal Trajectories of Criminal Offending, 16 J. ADOLESCENT RES. 54 (2001).
64. See LANGAN & LEVIN, supra note 40, at 14.
65. It should be noted, however, that the proportion of cases in Virginia that seem to be recorded as having some confinement as a result of the last arrest is too low (3% in the prerelease sample and 2% in the post-release sample). In all likelihood, this is an error in the data system. Despite that, in this Article, we have used this variable as it is.
66. The archived data contain numerous errors in this variable. We have incorporated corrections in our analysis that were suggested by the BJS and other researchers. This includes release-type-specific changes made for the states of California and Michigan as well as case-by-case changes made for the North Carolina release cohort. In addition, data from Delaware were missing all conditional-release types. Therefore, when modeling the effect of the release mechanism, we analyzed data by groups of states. Another known problem with these data is that the Maryland cohort is missing detailed offense-level information for each arrest event. For our analysis, since we used all arrest events (irrespective of offense type) in modeling the criminal history accumulation process, we only needed offense information pertaining to the current release (which is available for all observations). Therefore, in our analysis, all known problems with the archived data were accounted for.
67. Richard W. Harding & Ross A. Mailer, An Improved Methodology for Analyzing Age- Arrest Profiles: Application to a Western Australian Offender Population, 13 J. QUANTITATIVE CRIMINOLOGY 349 (1997).
68. Multiple-clock models allow researchers to capture several other dimensions of time when studying event histories. See KAZUO YAMAGUCHI, EVENT HISTORY ANALYSIS (1991);
69. Lee A. Lillard, Simultaneous Equations for Hazards: Marriage Duration and Fertility Timing, 56 J. ECONOMETRICS 189 (1993).
70. CHARLIES F. MANSKI, ANALOG ESTIMATION METHODS IN ECONOMETRICS (1988).
71. Edwin T. Jaynes, Information Theory and Statistical Mechanics, 106 PHYSICAL REV. 620 (1957);
72. Edwin T. Jaynes, Information Theory and Statistical Mechanics II, 108 PHYSICAL REV. 171 (1957).
73. C.E. Shannon, A Mathematical Theory of Communication, 27 BELL SYS. TECHNICAL J. 379 (1948).
74. See, e.g., Arnold Zellner, Bayesian Methods and Entropy in Economics and Econometrics, in MAXIMUM ENTROPY AND BAYESIAN METHODS (W.T. Grandy, Jr. & L.H. Schick eds., 1991);
75. Hang K. Ryu, Maximum Entropy Estimation of Density and Regression Functions, 56 J. ECONOMETRICS 397 (1993);
76. Arnold Zellner & R.A. Highfield, Calculation of Maximum Entropy Distributions and Approximation of Marginal Posterior Distributions, 37 J. ECONOMETRICS 195 (1988). For recent theoretic and applied work in this field, see 12 ADVANCES IN ECONOMETRICS: APPLYING MAXIMUM ENTROPY TO ECONOMIC PROBLEMS (T.B. Fomby & R. Carter Hill eds., 1997);
77. AMOS GOLAN ET AL., MAXIMUM ENTROPY ECONOMETRICS: ROBUST ESTIMATION WITH LIMITED DATA (1997);
78. RON C. MITTELHAMMER ET AL., ECONOMETRIC FOUNDATIONS (2000);
79. Amos Golan, Information and Entropy Economics-Editor's View, 107 J. ECONOMETRICS (2002);
80. Esfandiar Maasoumi, A Compendium of Information Theory in Economics and Econometrics, 12 ECONOMETRIC REV. 137 (1993);
81. Ehsan S. Soofi, Capturing the Intangible Concept of Information, 89 J. AM. STAT. ASS'N 1243 (1994).
82. See Ryu, supra note 52.
83. Id.
84. Peter J. Huber, The Behavior of Maximum Likelihood Estimators Under Nonstandard Conditions, in 1 PROCEEDINGS OF THE FIFTH SYMPOSIUM ON MATHEMATICAL STATISTICS AND PROBABILITY 221 (1967);
85. Michael E. Ezell et al., Modeling Multiple Failure Time Data: A Survey of Variance-Corrected Proportional Hazard Models with Empirical Applications to Arrest Data, 33 SOC. METHODOLOGY 111 (2003);
86. Halbert White, A Heteroskedasticity- Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity, 48 ECONOMETRICA 817 (1980).
87. See, e.g., PAUL D. ALLISON, EVENT HISTORY ANALYSIS (1984);
88. HANS-PETER BLOSSFELD ET al., EVENT HISTORY ANALYSIS: STATISTICAL THEORY AND APPLICATION IN THE SOCIAL SCIENCES (1989);
89. EVENT HISTORY ANALYSIS IN LIFE COURSE RESEARCH (Karl U. Mayer & Nancy B. Tuma eds., 1990).
90. Ebrahimi and Soofi present another way to approach this problem by redefining the hazards into probabilities and noting that the measure reduces to the traditional Kullback- Leibler divergence measure with an appropriate normalization and a ratio of survival functions. Nader Ebrahimi & Ehsan S. Soofi, Presentation at a Conference in Honor of Arnold Zellner: Recent Developments in the Theory, Method, and Application of Information and Entropy Econometrics: Static and Dynamic Information for Duration Analysis (Sept. 19-21, 2003), available at http://www.american.edu/cas/econ/faculty/golan/ golan/Papers/8-20soofi.pdf;
91. see also Ehsan S. Soofi et al., Information Distinguishability with Application to Analysis of Failure Data, 90 J. AM. STAT. ASS'N 657 (1995).
92. To be sure, the method described here is not the only way one can study trajectories of offending patterns over time. There exists a large literature in criminology that aims to model the trajectories of offending patterns over the life course of individuals using groupbased modeling techniques. See, e.g., DANIEL S. NAGIN, GROUP BASED MODELS OF DEVELOPMENT (2005);
93. Alex R. Piquero, Taking Stock of Developmental Trajectories of Criminal Activity over the Life Course, in THE LONG VIEW OF CRIME: A SYNTHESIS OF LONGITUDINAL RESEARCH (Akiva Liberman ed., 2008). Responding to concerns raised by Hagan and Palloni, see John Hagan & Alberto Palloni, Crimes as Social Events in the Life Course: Reconceiving a Criminological Controversy, 26 CRIMINOLOGY 87 (1988), Nagin and Land demonstrated that group-based trajectory models are well suited to take into account the order of arrest events. Daniel S. Nagin & Kenneth C. Land, Age, Criminal Careers, and Population Heterogeneity: Specification and Estimation on a Nonparametric, Mixed Poisson Model, 31 CRIMINOLOGY 327 (1993). Similarly, the approach developed here is not incompatible with approximating unobserved heterogeneity via finite mixture modeling strategies, at least in theory. Therefore, it would be a profitable extension of the current work to include distinct group-based heterogeneity in the models as well. For example, it is reasonable to expect that the Lagrange Multiplier should vary randomly across individuals. As such, using a finite mixture model to obtain a finite set of Lagrange Multipliers as well as group membership probabilities could add further clarity to the classification of individuals' incarceration as having had a deterrent, a criminogenic, or a null effect on their future offending patterns. For the approach to have practical utility, however, the emphasis should remain on attempting to construct counterfactual trajectories for each and every individual in the sample (not just for groups). In this Article, we have relied solely on available attributes to model the heterogeneity in the evolution of the hazards.
94. AVINASH S. BHATI, URBAN INSTITUTE, STUDYING THE EFFECTS OF INCARCERATION ON OFFENDING TRAJECTORIES: AN INFORMATION THEORETIC APPROACH (2006), available at http://www.urban.org/publications/411427.html.
95. Detailed state-specific estimates of the hazard models are available upon request.
96. PAUL D. ALLISON, SURVIVAL ANALYSIS USING THE SAS SYSTEM: A PRACTICAL GUIDE (1996).
97. Id. at 16.
98. See Piquero et al., supra note 16.
99. See Rosenfeld et al., supra note 36.
100. These findings are not intended to provide any specific policy recommendations. Rather, they are presented as a means of showcasing the utility of the proposed analytical strategy in assisting practitioners in decision-making. For instance, state and local authorities that have sufficiently detailed information about the programs in which releasees participated while in prison or the kinds of assistance being offered to them after release, whether they have employment available upon release, whether they are returning to a family with strong ties, etc., could all be used in the type of model described above in an attempt to study how these variables (many of which are choices available to practitioners and policy-makers) can increase or decrease the likelihood that a releasee will be deterred from future crime.
101. Further, some caution with respect to the counter-labeling theory result should be noted. Our assessment of the labeling perspective was based on whether offenders released from prison have arrest trajectories that were greater than would be expected from their past arrest history and trajectory. One reviewer noted that it was unusual in the U.S. system of justice and punishment that the first time a damaging label is applied occurs when an offender is sentenced to prison; rather, the vast majority of offenders released from prison have had prior juvenile incarceration and adult prison experiences, been sentenced to local jails, been placed on community supervision one or more times, or been adjudicated as a juvenile delinquent or adult criminal. Thus, although other researchers have recently considered the effect of first-time imprisonment on subsequent offending trajectories, see Nieuwbeerta et al., supra note 37, our view that labeling theory holds little weight with respect to the study findings awaits replication, extension, and better measures of the labeling process.
102. See MICHAEL J. HINDELANG ET AL., MEASURING DELINQUENCY (1981).
103. See Laub et al., supra note 22.
104. Daniel S. Nagin et al., Life Course Turning Points: The Effect of Grade Retention on Physical Aggression, 15 DEV. & PSYCHOPATHOLOGY 343 (2003).
105. Robert Brame et al., On the Development of Different Kinds of Criminal Activity, 29 SOC. METHODS & RES. 319 (2001).
106. See Nieuwbeerta et al., supra note 37.
107. See LAUB & SAMPSON, supra note 21, at 291.
108. TODD R. CLEAR & ERIC CADORA, COMMUNITY JUSTICE (2003);
109. JEREMY TRAVIS, BUT THEY ALL COME BACK: FACING THE CHALLENGES OF PRISONER REENTRY (2002).