In Search of the Intermittent In Search of the Intermittent Offender: Offender:

A Theoretical and Statistical A Theoretical and Statistical JourneyJourney

Megan C. Kurlychek, Ph.D.Megan C. Kurlychek, Ph.D.Assistant ProfessorAssistant Professor

Shawn Bushway, Ph.D.Shawn Bushway, Ph.D.Associate ProfessorAssociate Professor

School of Criminal JusticeSchool of Criminal JusticeUniversity at AlbanyUniversity at Albany

GoalsGoals Describe population of individual trajectories Describe population of individual trajectories

underlying age crime curveunderlying age crime curve

Identify process of desistanceIdentify process of desistance

Is intermittency real?Is intermittency real?

How do these different models reflect/impact How do these different models reflect/impact practice?practice?

Starting PointStarting Point

Lifecourse criminologists care about Lifecourse criminologists care about individual lifecourse trajectory/criminal individual lifecourse trajectory/criminal careercareer

Descriptive: Age Crime Curve DebateDescriptive: Age Crime Curve Debate What is the underlying distribution that determines What is the underlying distribution that determines

the Age- Crime Curvethe Age- Crime Curve

Explanatory: Thornberry 1987: Explanatory: Thornberry 1987: ““The manner in which reciprocal effects and The manner in which reciprocal effects and

developmental changes are interwoven in the developmental changes are interwoven in the interactional model can be explicated by the concept interactional model can be explicated by the concept of behavioral trajectories.(p. 882)of behavioral trajectories.(p. 882)

What Has Happened Since?What Has Happened Since?

Panel modelsPanel models Growth Curve Models (GCM) HLMGrowth Curve Models (GCM) HLM Group-based Trajectories Model (GTM) Proc TrajGroup-based Trajectories Model (GTM) Proc Traj Generalized Mixture Models (GMM) MplusGeneralized Mixture Models (GMM) Mplus

Much annoying banter about which model Much annoying banter about which model is “Right”is “Right”

,

2 3, 0, 1, , 2, , 3, , i ti t i i i t i i t i i ty Age Age Age e

Bushway, S., G. Sweeten, P. Bushway, S., G. Sweeten, P. Nieuwbeerta (2009) Nieuwbeerta (2009)

Measuring Long Term Individual Measuring Long Term Individual Trajectories of Offending Using Trajectories of Offending Using Multiple Methods. Multiple Methods. Journal of Journal of Quantitative CriminologyQuantitative Criminology 25:259–286 25:259–286

What Did We Do?What Did We Do?

Compared individual trajectories Compared individual trajectories from three models:from three models:

1) Individual time series for every 1) Individual time series for every personperson

2) Growth Curve model (HLM)2) Growth Curve model (HLM) 3) Group Trajectory model (Traj)3) Group Trajectory model (Traj)

Criminal Career and Life Course Criminal Career and Life Course Study (CCLS)Study (CCLS)

Sample:Sample: 4.615 persons convicted in 19774.615 persons convicted in 1977

4% random sample of all persons convicted in 19774% random sample of all persons convicted in 1977 Oversample of persons convicted for serious Oversample of persons convicted for serious

offenses, undersample of persons convicted for offenses, undersample of persons convicted for traffic incidentstraffic 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 79Mean age in 1977: 27 years; youngest: 12; oldest 79 Data from year of birth until 2003: for most over 50 Data from year of birth until 2003: for most over 50

years.years.

CCLS DataCCLS DataFor all persons we have information on:For all persons we have information on:

Full criminal conviction historiesFull criminal conviction histories (Rap sheets) (Rap sheets) Timing, type of offense, type of sentence, Timing, type of offense, type of sentence,

incarceration.incarceration.

Life course eventsLife course events:: Various types: marriage, divorce, children, moving, Various types: marriage, divorce, children, moving,

death (GBA & Central Bureau Heraldry) – incl. Exact death (GBA & Central Bureau Heraldry) – incl. Exact timing.timing.

Cause of death (CBS)Cause of death (CBS)

Data = conviction for periods not dead or Data = conviction for periods not dead or incarceratedincarcerated

Average Curves: Raw Data & ITMAverage Curves: Raw Data & ITM

Job 2: Compare Best estimates of Individual paths

DesistorsDesistors

An individual who has a period where An individual who has a period where offending probability is statistically offending probability is statistically greater than zero, followed by at greater than zero, followed by at least 5 years when probability of least 5 years when probability of offending is statistically offending is statistically indistinguishable from zero.indistinguishable from zero.

Comparison of DesistorsComparison of Desistors

MODELMODEL Desistors (% of sample)Desistors (% of sample)

ITM ITM 60.8%60.8%

GCM GCM 27.5%27.5%

GTM GTM 36.4%36.4%

ITM more flexible, better captures ITM more flexible, better captures change (but with error). change (but with error).

ConclusionConclusion

Lots of “up and down”Lots of “up and down” Could be noise Could be noise Could be intermittencyCould be intermittency

Can’t tell with conviction data – even Can’t tell with conviction data – even with 50 years! with 50 years!

Need another approach - Need another approach - recidivism/survival models?recidivism/survival models?

In Search of the Intermittent In Search of the Intermittent Offender: Offender:

A Theoretical and Statistical A Theoretical and Statistical JourneyJourney

Megan C. Kurlychek, Ph.D.Megan C. Kurlychek, Ph.D.Assistant ProfessorAssistant Professor

Shawn Bushway, Ph.D.Shawn Bushway, Ph.D.Associate ProfessorAssociate Professor

School of Criminal JusticeSchool of Criminal JusticeUniversity at AlbanyUniversity at Albany

Criminal Career ResearchCriminal Career Research

Traditional Question:Traditional Question: ““When does a criminal career When does a criminal career

start and when does it end.”start and when does it end.” Traditional AnswerTraditional Answer

(Blumstein 1986)(Blumstein 1986)

Instantaneous DesistanceInstantaneous Desistance

Go immediately to zeroGo immediately to zero

Very consistent with parole/probation Very consistent with parole/probation modelsmodels Pragmatic Pragmatic

Fits qualitative work: Going (and Fits qualitative work: Going (and staying) straight (Maruna)staying) straight (Maruna)

HazardsHazards

Probability that you are going to Probability that you are going to offend in this period given that you offend in this period given that you have not offended yethave not offended yet

Used in latest round of reentry Used in latest round of reentry modelsmodels When does ex-offender “look like” non When does ex-offender “look like” non

offender in terms of offendingoffender in terms of offending

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

0.000

0.050

0.100

0.150

0.200

0.250

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Haz

ard

Rat

e

Years of Follow-Up

Full Sample Predicted and Actual Hazards: Desistance = .33 P = .3

Predicted Actual

Barnett ModificationBarnett Modification

Starting pointStarting point Active careerActive career Ending point (instantaneous desistance)Ending point (instantaneous desistance) A few people restart career (Intermittency)A few people restart career (Intermittency)

Theoretical IntermittencyTheoretical Intermittency

Matza (1964)Matza (1964) Drift: Offenders “flirt” with criminal Drift: Offenders “flirt” with criminal

activity.activity.

Horney, Osgood and Rowe (1995):Horney, Osgood and Rowe (1995): “ “local-life circumstance”local-life circumstance”

““Relapse”Relapse” ZIP Parameter in Trajectory ModelsZIP Parameter in Trajectory Models

Alternative: Glide PathAlternative: Glide Path

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

Theoretical Glide PathTheoretical Glide Path

Differential Association Theory/Social Differential Association Theory/Social Learning TheoryLearning Theory

Social Control TheorySocial Control Theory

““Social bonds do not arise intact and full-Social bonds do not arise intact and full-grown but develop over time like a grown but develop over time like a

pension plan funded by regular pension plan funded by regular contributions” Laub, Nagin and Sampson contributions” Laub, Nagin and Sampson

(1998) (1998)

In Hazard ModelIn Hazard Model

Both can explain FAT Tail Both can explain FAT Tail People still at high(er) risk after many People still at high(er) risk after many

yearsyears

BUT – Glide Path should be smooth BUT – Glide Path should be smooth declining hazard ratedeclining hazard rate

Intermittency – bumpy declining Intermittency – bumpy declining hazardhazard

Our DataOur Data Crime Control Effects of Sentencing in Essex Crime Control Effects of Sentencing in Essex

County New Jersey, 1978-1997.County New Jersey, 1978-1997.

Judge questionnaires completed by 18 judges in Judge questionnaires completed by 18 judges in Essex County NJ on cases sentenced in 1976-77. Essex County NJ on cases sentenced in 1976-77. Follow up information was collected through 1997Follow up information was collected through 1997

1.1. New Jersey Offender Based Transaction System New Jersey Offender Based Transaction System Computerized Criminal HistoryComputerized Criminal History

2.2. New Jersey Department of Corrections Offender New Jersey Department of Corrections Offender based Correctional Information Systembased Correctional Information System

3.3. US Department of Justice Interstate Identification US Department of Justice Interstate Identification IndexIndex

Sample and MethodsSample and Methods

All offenders with probation or short All offenders with probation or short jail sentences (n=661)jail sentences (n=661)

Follow for 20 yearsFollow for 20 years

Apply parametric survival time Apply parametric survival time distributions and employ graphical distributions and employ graphical comparisons and goodness of fit comparisons and goodness of fit statisticsstatistics

MeasuresMeasures

Dependent Variable: New arrestDependent Variable: New arrest

Independent Variables:Independent Variables: Age of offenderAge of offender Prior Probations and ViolationsPrior Probations and Violations Race, Gender, Type/Seriousness of Race, Gender, Type/Seriousness of

Offense, Judge’s perception of riskOffense, Judge’s perception of risk

Three DistributionsThree Distributions

ExponentialExponential Assumes constant rate of offendingAssumes constant rate of offending Hazard drops fast Hazard drops fast

High rate offenders – everyone who hasn’t High rate offenders – everyone who hasn’t desisted offends quicklydesisted offends quickly

WeibullWeibull Smoothly declining hazard rateSmoothly declining hazard rate

LognormalLognormal Allows hazard rate to go up and downAllows hazard rate to go up and down

Three DistributionsThree Distributions

Exponential = Original Criminal Exponential = Original Criminal CareerCareer

Weibull = Glide pathWeibull = Glide path

Lognormal = Intermittency Lognormal = Intermittency

Goodness of Fit TestsGoodness of Fit Tests

Dif. p Dif. pExponential 223.9 0.0000 284 0.0000

Weibull 60.3 0.0000

LognormalWeibull

0.2

.4.6

.81

Haza

rd fu

nct

ion

0 5 10 15 20analysis time

Weibull regression

0.1

.2.3

.4H

aza

rd fu

nct

ion

0 5 10 15 20analysis time

Log-normal regression

Why the LognormalWhy the Lognormal

“ “Upswing” in the beginningUpswing” in the beginning

OROR

Fat Tail (intermittency) Fat Tail (intermittency)

Models t0 to t5Models t0 to t5

-log l. BIC -log l BIC Diff -LoglpFulln=661 -1217.37 2525.65 -1187.22 2465.358 60.292 0.0000After 1 yearn=464 -790.13 1666.21 -783.61 1653.16 13.04 0.0000After 2 yearsn=374 -563.86 1210.65 -558.01 1198.95 11.7 0.0000After 3 yearsn=328 -449.29 979.68 -441.25 963.61 16.08 0.0000After 4 yearsN=300 -394.97 869.7 -390.66 859.98 8.62 0.0033After 5 yearsn=278 -351.36 781.51 -349.5 777.85 3.72 0.0538

Weibull Lognormal

Weibull Frailty ModelWeibull Frailty Model0

.2.4

.6H

aza

rd fu

nctio

n

0 5 10 15 20analysis time

Weibull regression

High and Low Risk OffendersHigh and Low Risk Offenders0

.2.4

.6.8

1H

aza

rd fu

nctio

n

0 5 10 15 20analysis time

class=1 class=2class=3 class=4

Log-normal regression

ConclusionsConclusions

Glide path looks more realistic than Glide path looks more realistic than strict intermittency strict intermittency

People experience reduced risk as People experience reduced risk as they last longer on parolethey last longer on parole

But, don’t go to zero very quicklyBut, don’t go to zero very quickly Desistance takes time Desistance takes time

Next StepsNext Steps

Multi-Event HazardMulti-Event Hazard

What happens after arrest?What happens after arrest? For people who have not offended for 5 For people who have not offended for 5

years?years? Intermittency: should start offending again Intermittency: should start offending again

at a regular rateat a regular rate Glide path: should continue to decrease in Glide path: should continue to decrease in

offending rate offending rate

Policy Implications/QuestionsPolicy Implications/Questions Most people don’t desist “instantaneously”Most people don’t desist “instantaneously”

Declining riskDeclining risk

Recidivate or not mentality may miss declining riskRecidivate 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) Do changes in other behavior (work/housing/family) serve as proxy for “declining hazard” serve as proxy for “declining hazard”