Analyzing Impact of First Imprisonment on Criminal Career Development

1. The Relationship between First Imprisonment and Criminal Career Development: A Matched Samples Comparison Paul Nieuwbeerta & Arjan Blokland NSCR Daniel Nagin Carnegie-Mellon University

2. Main Question • What is the effect of imprisonment on the subsequent criminal career development of those actually imprisoned? • Methodology builds upon work with Amelia Haviland (Rand) and Paul Rosenbaum (Penn) that combines propensity score matching and group-based trajectory modeling

3. Possible Effect of Imprisonment on Crime • On the wider society—general deterrence • On the criminality of the imprisoned individual • Incapacitation (-) • Specific Deterrence (-) • Rehabilitation (-) • Labeling/stigma (+) • School of crime (+)

4. Criminal Career and Life Course Study CCLS Data Sample: • 5.164 persons convicted in 1977 in the Netherlands • 4% random sample of all persons convicted in 1977 • 500 women (10%) • 20% non-Dutch (Surinam, Indonesia) • Mean age in 1977: 27 years; youngest: 12; oldest 79 • Data from year of birth until 2003: for most over 50 years.

5. CCLS Data • Full criminal conviction histories (Rap sheets) • Timing, type of offense, type of sentence, imprisonment. • Life course events (N=4,615): • Various types: marriage, divorce, children, moving, death (GBA & Central Bureau Heraldry) – incl. Exact timing. • Cause of death (CBS)

6. Outcome variable • Number of convictions in three year period after year of first-time imprisonment

7. Outcome variable • Number of convictions in three year period after year of first-time imprisonment • First-time imprisonment effects measured by age from 18 to 39

9. Outcome variable • Number of convictions in three year period after year of first-time imprisonment • First-time imprisonment effects measured for ages 18 to 39 • Limit analysis to persons with sentences of less than 1 year • 80% less than 6 months • 99% less than 1 year

10. Outcome variable • Number of convictions in three year period after year of first-time imprisonment • First-time imprisonment effects measured for ages 18 to 39 • Limit analysis to persons with sentences of less than 1 year • Correction for exposure-time / incarceration

11. Estimating the effect of imprisonment on the imprisoned: Some important contingencies and challenges • Prior experience with imprisonment • Limit analysis to first-time imprisonment effects

12. Estimating the effect of imprisonment on the imprisoned: Some important contingencies and challenges • Prior experience with imprisonment • Age

14. Estimating the effect of imprisonment on the imprisoned: Some important contingencies and challenges • Prior experience with imprisonment • Age—exact matching on age

15. Estimating the effect of imprisonment on the imprisoned: Some important contingencies and challenges • Prior experience with imprisonment • Age • Sex—Males only

16. Estimating the effect of imprisonment on the imprisoned: Some important contingencies and challenges • Prior experience with imprisonment • Age • Sex • Prior trajectory of offending • Estimate effects contingent on prior trajectory of offending

17. Estimating the effect of imprisonment on the imprisoned: Some important contingencies and challenges • Prior experience with imprisonment • Age • Sex • Prior trajectory of offending • Selection—Imprisonment more likely for higher propensity offenders

18. Differences in prior records of those imprisoned at age 26-28 and those convicted but not imprisoned

19. Other differences between imprisoned and non-imprisoned

20. Overview of Approach • Focus on the effect of first-time imprisonment • Match individuals who are the same age • Estimate effects of first-time imprisonment by age from 18-38 • Males only • Estimate effects contingent on trajectory of prior offending • Use risk set matching to balance measured differences between the imprisoned and the non-imprisoned

21. Use Group-based Trajectory Modeling to Test for Prior Offending Contingencies • Based on finite mixture modeling • Poisson distribution this application • Cubic link function for rate • Designed to identify clusters of individuals with similar trajectories of prior offending • Trajectory groups can be thought of as latent strata of the pre-treatment time path of the outcome variable

22. Trajectories of Number of Convictions: age 12 - 20, age 12 - 25 and age 12-30

23. Trajectories of Number of Convictions (cont.)

24. What is a propensity score? • Propensity score is the probability of imprisonment as a function of variables such as prior record and conviction offense characteristics • Propensity score matching balances imprisoned and non-imprisoned on these variables • Rules them out as potential confounders • Important caution: Still may be unmeasured confounders

25. Risk Set Matching to Balance Measured Covariate Differences • Imprisoned at age t matched with up to 3 non-imprisoned but convicted at t with same probability of imprisonment at t • Time dependent propensity for imprisonment at t based on covariates measured up to t • Propensity for imprisonment at t measured by logit model of imprisonment at t

26. Constructing the Propensity Score • Logistic regression • Independent variables • Characteristics of Conviction Offense • Violence, property.. • Severity • Criminal history characteristics: • Num. of convictions age 12-25, 20-25 and at 25, • Age of first registration, age of first conviction, • Trajectory group membership probabilities. • Personal Characteristics: • Age in 1977, non-Dutch, Unemployed around age 25, • Number of years married at age 25, Married at age 25, • Number of years children at age 25, children at age 25, • Alcohol and/or drugs dependent around age 25

27. Box plots of propensity scores: Full sample

28. Significant differences before and after matching • Before Matching (partial listing) • Convictions 12-25 (also by type) • Convictions 20-25 (also by type) • Convictions 25 (also by type) • Numerous Conviction offence characteristics • Age in ’77 • Non-Dutch • # of children at 25

29. Box plots of propensity scores: Matched sample

30. Significant differences before and after matching • Before Matching (partial listing) • Convictions 12-25 (also by type) • Convictions 20-25 (also by type) • Convictions 25 (also by type) • Numerous Conviction offence characteristics • Age in ’77 • Non-Dutch • # of children at 25 • After matching • Cohort (marginal) • # violent convictions past 5 years (marginal)

34. Further Analyses • Analysis of more recent data—1997 conviction cohort • Analysis of groups on the “margin” of imprisonment • Analysis of mediating processes—What is the source of the criminogenic effect • Bounding ala Manski and Nagin (1998) to account for the possible effects of “hidden bias”

35. Conclusions • Conclusion: • First-time imprisonment appears to increase conviction rate by .4 convictions per year in first 3 years after imprisonment • No 1st imprisonment effects apparent after age 25 • Theoretical implications—Criminogenic effects of first-time imprisonment outweigh any preventive effects for the individual who is sanctioned • Policy implications: • Incapacitation and general deterrent effect of imprisonment may partly be nullified by imprisoned offenders subsequently offending at higher rates