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In Search of the Intermittent Offender: A Theoretical and Statistical Journey. Megan C. Kurlychek , Ph.D. Assistant Professor Shawn Bushway , Ph.D. Associate Professor School of Criminal Justice University at Albany. Goals.
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In Search of the Intermittent Offender: A Theoretical and Statistical Journey Megan C. Kurlychek, Ph.D. Assistant Professor Shawn Bushway, Ph.D. Associate Professor School of Criminal Justice University at Albany
Goals • Describe population of individual trajectories underlying age crime curve • Identify process of desistance • Is intermittency real? • How do these different models reflect/impact practice?
Starting Point • Lifecourse criminologists care about individual lifecourse trajectory/criminal career • Descriptive: Age Crime Curve Debate • What is the underlying distribution that determines the Age- Crime Curve • Explanatory: Thornberry 1987: • “The manner in which reciprocal effects and developmental changes are interwoven in the interactional model can be explicated by the concept of behavioral trajectories.(p. 882)
What Has Happened Since? • Panel models • Growth Curve Models (GCM) HLM • Group-based Trajectories Model (GTM) Proc Traj • Generalized Mixture Models (GMM) Mplus • Much annoying banter about which model is “Right”
Bushway, S., G. Sweeten, P. Nieuwbeerta (2009) Measuring Long Term Individual Trajectories of Offending Using Multiple Methods. Journal of Quantitative Criminology 25:259–286
What Did We Do? • Compared individual trajectories from three models: • 1) Individual time series for every person • 2) Growth Curve model (HLM) • 3) Group Trajectory model (Traj)
Criminal Career and Life Course Study (CCLS) Sample: • 4.615 persons convicted in 1977 • 4% random sample of all persons convicted in 1977 • Oversample of persons convicted for serious offenses, undersample of persons convicted for traffic incidents • 500 women (10%) • 20% non-native (Surinam, Indonesia) • Mean age in 1977: 27 years; youngest: 12; oldest 79 • Data from year of birth until 2003: for most over 50 years.
CCLS Data For all persons we have information on: • Full criminal conviction histories (Rap sheets) • Timing, type of offense, type of sentence, incarceration. • Life course events: • Various types: marriage, divorce, children, moving, death (GBA & Central Bureau Heraldry) – incl. Exact timing. • Cause of death (CBS) • Data = conviction for periods not dead or incarcerated
Desistors • An individual who has a period where offending probability is statistically greater than zero, followed by at least 5 years when probability of offending is statistically indistinguishable from zero.
Comparison of Desistors MODEL Desistors (% of sample) ITM 60.8% GCM 27.5% GTM 36.4% • ITM more flexible, better captures change (but with error).
Conclusion • Lots of “up and down” • Could be noise • Could be intermittency • Can’t tell with conviction data – even with 50 years! • Need another approach - recidivism/survival models?
In Search of the Intermittent Offender: A Theoretical and Statistical Journey Megan C. Kurlychek, Ph.D. Assistant Professor Shawn Bushway, Ph.D. Associate Professor School of Criminal Justice University at Albany
Criminal Career Research • Traditional Question: • “When does a criminal career start and when does it end.” • Traditional Answer (Blumstein 1986)
Instantaneous Desistance • Go immediately to zero • Very consistent with parole/probation models • Pragmatic • Fits qualitative work: Going (and staying) straight (Maruna)
Hazards • Probability that you are going to offend in this period given that you have not offended yet • Used in latest round of reentry models • When does ex-offender “look like” non offender in terms of offending
Barnett Modification • Starting point • Active career • Ending point (instantaneous desistance) • A few people restart career (Intermittency)
Theoretical Intermittency • Matza (1964) • Drift: Offenders “flirt” with criminal activity. • Horney, Osgood and Rowe (1995): • “local-life circumstance” • “Relapse” • ZIP Parameter in Trajectory Models
Alternative: Glide Path Desistance as a process: “glide” path towards zero ( Bushway et al. 2001, Laub and Sampson 2001)
Theoretical Glide Path • Differential Association Theory/Social Learning Theory • Social Control Theory “Social bonds do not arise intact and full-grown but develop over time like a pension plan funded by regular contributions” Laub, Nagin and Sampson (1998)
In Hazard Model • Both can explain FAT Tail • People still at high(er) risk after many years • BUT – Glide Path should be smooth declining hazard rate • Intermittency – bumpy declining hazard
Our Data • Crime Control Effects of Sentencing in Essex County New Jersey, 1978-1997. • Judge questionnaires completed by 18 judges in Essex County NJ on cases sentenced in 1976-77. • Follow up information was collected through 1997 • New Jersey Offender Based Transaction System Computerized Criminal History • New Jersey Department of Corrections Offender based Correctional Information System • US Department of Justice Interstate Identification Index
Sample and Methods • All offenders with probation or short jail sentences (n=661) • Follow for 20 years • Apply parametric survival time distributions and employ graphical comparisons and goodness of fit statistics
Measures • Dependent Variable: New arrest • Independent Variables: • Age of offender • Prior Probations and Violations • Race, Gender, Type/Seriousness of Offense, Judge’s perception of risk
Three Distributions • Exponential • Assumes constant rate of offending • Hazard drops fast • High rate offenders – everyone who hasn’t desisted offends quickly • Weibull • Smoothly declining hazard rate • Lognormal • Allows hazard rate to go up and down
Three Distributions • Exponential = Original Criminal Career • Weibull = Glide path • Lognormal = Intermittency
Why the Lognormal • “Upswing” in the beginning • OR • Fat Tail (intermittency)
Conclusions • Glide path looks more realistic than strict intermittency • People experience reduced risk as they last longer on parole • But, don’t go to zero very quickly • Desistance takes time
Next Steps • Multi-Event Hazard • What happens after arrest? • For people who have not offended for 5 years? • Intermittency: should start offending again at a regular rate • Glide path: should continue to decrease in offending rate
Policy Implications/Questions • Most people don’t desist “instantaneously” • Declining risk • Recidivate or not mentality may miss declining risk • Is it feasible to tolerate “less” offending? • Do current practices implicitly acknowledge reality? • Do changes in other behavior (work/housing/family) serve as proxy for “declining hazard”