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 1 st 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