1. 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

2. Goals Describe population of individual trajectories underlying age crime curve Describe population of individual trajectories underlying age crime curve Identify process of desistance Identify process of desistance Is intermittency real? Is intermittency real? How do these different models reflect/impact practice? How do these different models reflect/impact practice?

3. Starting Point Lifecourse criminologists care about individual lifecourse trajectory/criminal career Lifecourse criminologists care about individual lifecourse trajectory/criminal career Descriptive: Age Crime Curve Debate Descriptive: Age Crime Curve Debate What is the underlying distribution that determines the Age- Crime Curve What is the underlying distribution that determines the Age- Crime Curve Explanatory: Thornberry 1987: 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) “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)

4. What Has Happened Since? Panel models Panel models Growth Curve Models (GCM) HLM Growth Curve Models (GCM) HLM Group-based Trajectories Model (GTM) Proc Traj Group-based Trajectories Model (GTM) Proc Traj Generalized Mixture Models (GMM) Mplus Generalized Mixture Models (GMM) Mplus Much annoying banter about which model is “Right” Much annoying banter about which model is “Right”

5. Bushway, S., G. Sweeten, P. Nieuwbeerta (2009) Measuring Long Term Individual Trajectories of Offending Using Multiple Methods. Journal of Quantitative Criminology 25:259–286

6. What Did We Do? Compared individual trajectories from three models: Compared individual trajectories from three models: 1) Individual time series for every person 1) Individual time series for every person 2) Growth Curve model (HLM) 2) Growth Curve model (HLM) 3) Group Trajectory model (Traj) 3) Group Trajectory model (Traj)

7. Criminal Career and Life Course Study (CCLS) Sample: 4.615 persons convicted in 1977 4.615 persons convicted in 1977 4% random sample of all 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 Oversample of persons convicted for serious offenses, undersample of persons convicted for traffic incidents 500 women (10%) 500 women (10%) 20% non-native (Surinam, Indonesia) 20% non-native (Surinam, Indonesia) Mean age in 1977: 27 years; youngest: 12; oldest 79 Mean age in 1977: 27 years; youngest: 12; oldest 79 Data from year of birth until 2003: for most over 50 years. Data from year of birth until 2003: for most over 50 years.

8. CCLS Data For all persons we have information on: Full criminal conviction histories (Rap sheets) Full criminal conviction histories (Rap sheets) Timing, type of offense, type of sentence, incarceration. Timing, type of offense, type of sentence, incarceration. Life course events: Life course events: Various types: marriage, divorce, children, moving, death (GBA & Central Bureau Heraldry) – incl. Exact timing. Various types: marriage, divorce, children, moving, death (GBA & Central Bureau Heraldry) – incl. Exact timing. Cause of death (CBS) Cause of death (CBS) Data = conviction for periods not dead or incarcerated Data = conviction for periods not dead or incarcerated

9. Average Curves: Raw Data & ITM

13. Job 2: Compare Best estimates of Individual paths

14. 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. 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.

15. Comparison of Desistors MODELDesistors (% of sample) ITM 60.8% GCM 27.5% GTM 36.4% ITM more flexible, better captures change (but with error). ITM more flexible, better captures change (but with error).

16. Conclusion Lots of “up and down” Lots of “up and down” Could be noise Could be noise Could be intermittency Could be intermittency Can’t tell with conviction data – even with 50 years! Can’t tell with conviction data – even with 50 years! Need another approach - recidivism/survival models? Need another approach - recidivism/survival models?

17. 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

18. Criminal Career Research Traditional Question: Traditional Question: “When does a criminal career start and when does it end.” “When does a criminal career start and when does it end.” Traditional Answer (Blumstein 1986) Traditional Answer (Blumstein 1986)

19. Instantaneous Desistance Go immediately to zero Go immediately to zero Very consistent with parole/probation models Very consistent with parole/probation models Pragmatic Pragmatic Fits qualitative work: Going (and staying) straight (Maruna) Fits qualitative work: Going (and staying) straight (Maruna)

20. Hazards Probability that you are going to offend in this period given that you have not offended yet Probability that you are going to offend in this period given that you have not offended yet Used in latest round of reentry models Used in latest round of reentry models When does ex-offender “look like” non offender in terms of offending When does ex-offender “look like” non offender in terms of offending

22. Test of desistance using hazards Barnett et. al. (1989)

23. Barnett Modification Starting point Starting point Active career Active career Ending point (instantaneous desistance) Ending point (instantaneous desistance) A few people restart career (Intermittency) A few people restart career (Intermittency)

24. Theoretical Intermittency Matza (1964) Matza (1964) Drift: Offenders “flirt” with criminal activity. Drift: Offenders “flirt” with criminal activity. Horney, Osgood and Rowe (1995): Horney, Osgood and Rowe (1995): “local-life circumstance” “local-life circumstance” “Relapse” “Relapse” ZIP Parameter in Trajectory Models ZIP Parameter in Trajectory Models

25. Alternative: Glide Path Desistance as a process: “glide” path towards zero ( Bushway et al. 2001, Laub and Sampson 2001)

26. Theoretical Glide Path Differential Association Theory/Social Learning Theory Differential Association Theory/Social Learning Theory Social Control 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)

27. In Hazard Model Both can explain FAT Tail Both can explain FAT Tail People still at high(er) risk after many years People still at high(er) risk after many years BUT – Glide Path should be smooth declining hazard rate BUT – Glide Path should be smooth declining hazard rate Intermittency – bumpy declining hazard Intermittency – bumpy declining hazard

28. Our Data Crime Control Effects of Sentencing in Essex County New Jersey, 1978-1997. 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. Judge questionnaires completed by 18 judges in Essex County NJ on cases sentenced in 1976-77. Follow up information was collected through 1997 Follow up information was collected through 1997 1. New Jersey Offender Based Transaction System Computerized Criminal History 2. New Jersey Department of Corrections Offender based Correctional Information System 3. US Department of Justice Interstate Identification Index

29. Sample and Methods All offenders with probation or short jail sentences (n=661) All offenders with probation or short jail sentences (n=661) Follow for 20 years Follow for 20 years Apply parametric survival time distributions and employ graphical comparisons and goodness of fit statistics Apply parametric survival time distributions and employ graphical comparisons and goodness of fit statistics

30. Measures Dependent Variable: New arrest Dependent Variable: New arrest Independent Variables: Independent Variables: Age of offender Age of offender Prior Probations and Violations Prior Probations and Violations Race, Gender, Type/Seriousness of Offense, Judge’s perception of risk Race, Gender, Type/Seriousness of Offense, Judge’s perception of risk

31. Three Distributions Exponential Exponential Assumes constant rate of offending Assumes constant rate of offending Hazard drops fast Hazard drops fast High rate offenders – everyone who hasn’t desisted offends quickly High rate offenders – everyone who hasn’t desisted offends quickly Weibull Weibull Smoothly declining hazard rate Smoothly declining hazard rate Lognormal Lognormal Allows hazard rate to go up and down Allows hazard rate to go up and down

32. Three Distributions Exponential = Original Criminal Career Exponential = Original Criminal Career Weibull = Glide path Weibull = Glide path Lognormal = Intermittency Lognormal = Intermittency

33. Goodness of Fit Tests

36. Why the Lognormal “Upswing” in the beginning “Upswing” in the beginning OR OR Fat Tail (intermittency) Fat Tail (intermittency)

37. Models t0 to t5

38. Weibull Frailty Model

39. High and Low Risk Offenders

40. Conclusions Glide path looks more realistic than strict intermittency Glide path looks more realistic than strict intermittency People experience reduced risk as they last longer on parole People experience reduced risk as they last longer on parole But, don’t go to zero very quickly But, don’t go to zero very quickly Desistance takes time Desistance takes time

41. Next Steps Multi-Event Hazard Multi-Event Hazard What happens after arrest? What happens after arrest? For people who have not offended for 5 years? For people who have not offended for 5 years? Intermittency: should start offending again at a regular rate Intermittency: should start offending again at a regular rate Glide path: should continue to decrease in offending rate Glide path: should continue to decrease in offending rate

42. Policy Implications/Questions Most people don’t desist “instantaneously” Most people don’t desist “instantaneously” Declining risk Declining risk Recidivate or not mentality may miss declining risk Recidivate or not mentality may miss declining risk Is it feasible to tolerate “less” offending? Is it feasible to tolerate “less” offending? Do current practices implicitly acknowledge reality? Do current practices implicitly acknowledge reality? Do changes in other behavior (work/housing/family) serve as proxy for “declining hazard” Do changes in other behavior (work/housing/family) serve as proxy for “declining hazard”