1. The Relationship between First Imprisonment and Criminal Career Development: A Matched Samples Comparison Presentation at the 2 nd Annual Workshop on Criminology and the Economics of Crime June 5-6, Wye Maryland Paul Nieuwbeerta & Arjan Blokland NSCR Daniel Nagin Carnegie-Mellon University

2. Main Question To what extent is there an effect of imprisonment on subsequent criminal career development (here: in the three years after imprisonment)?

3. Criminal propensity Criminal behavior Imprisonment T1T2 = Incapacitation effect = Deterrence effect

4. Hypotheses on effect of imprisonment DLC and Deterrence literature: No effect: –Life circumstances (incl. imprisonment) have no effect Decrease: –Imprisonment causes the punished individual to revise upward his/her estimate of severity and/of likelihood of punishment for future lawbreaking –Rehabilitation, for example by education and vocational training Increase: –‘Imprisonment was not as adverse as anticipated’ –Imprisonment reduces estimate of punishment certainty –Prison is ‘school for crime’ –Labeling: stigmatization socially and economically Different effects for different (groups of) persons: –E.g. for ‘life course persisters’ no effect of imprisonment, for adolescent limited negative effect of imprisonment (imprisonment = ‘snare’)

5. How to test for effects of imprisonment? In a perfect world for science: randomized treatment assignment in an experimental setting –Then by design all differences between people in treatment group and in the non-treatment group are cancelled out However, randomly imposing prison sentences is somewhat difficult and debatable So, we (have to) use: –Data from observational longitudinal studies –A ‘quasi-experimental design’ and –Statistical approaches to control for differences between the treatment and non-treatment group

6. 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.

7. 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)

8. Challenges when examining effects of imprisonment I Challenges: –Crime is age-graded –Men and women differ in criminal behavior –People die –Earlier imprisonment experiences may also influence criminal behavior Solutions used in this paper: –We only examine effects of imprisonment at a certain age: i.e. at age 26, 27 or 28 and examine the number of convictions in next 3 years. –We only examine a selection of persons (N = 3,008): Menexcluding 424 women Persons that did not die before age 31excluding 20 men Persons who pre age 26 had not been imprisonedexcluding 1163 men earlier imprisoned

9. Outcome variable Number of convictions in three year period after imprisonment Imprisonment at ageDep. Var.: convictions at 26 (N = 66)age: 27, 28, 29 27 (N=55)age: 28, 29, 30 28 (N=63)age: 29, 30, 31 Non-imprisoned age 26-28age: 28, 29, 30 Correction for exposure-time / incarceration

10. First time imprisonment between age 26-28 184 (6%) of the 3,008 persons who pre age 26 had not been imprisoned, are imprisoned for the first time at age 26, 27 or 28 Length of imprisonment:

11. Naïve / Baseline comparison

12. Challenges when examining effects of imprisonment II Selection effect: prison sentences are consequence of: –Offender’s prior criminal record –Other characteristics

13. Differences between imprisoned and non-imprisoned

15. Methods Four statistical approaches to account for systematic differences between imprisoned and non-imprisoned: –Regression –Propensity scores matching –Trajectory group matching –Combination of Trajectory group and Propensity score matching

16. Trajectory group matching For more information: See Haviland & Nagin 2005 Semi-Parametric group-based trajectories of lagged outcome variable estimated for non-treated up to age t (here: age 12-25) Outcome variable measured between age t and age t+x (here: age 26-28) Within-groups: compare outcomes from age t forward (here: age 26-28) to assess treatment effect

17. Age–crime curve

18. Four Trajectories

19. Group 0: Effect of imprisonment

20. Group 1: Effect of imprisonment

21. Group 2: Effect of imprisonment

22. Group 3: Effect of imprisonment

23. Conclusion: –Imprisonment increases the number of convictions significantly, i.e. with about 0.6 convictions per year. However: –Although substantial improvement compared to ‘uncontrolled situation’ –Within Trajectory groups no perfect balance between imprisoned and non-imprisoned on criminal history characteristics and personal characteristics was achieved

24. Propensity Score Matching Logistic regression: Dependent variable = imprisonment (0=no, 1=yes), Independent variables = all available (here: –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 Calculate propensity scores: i.e. predicted probabilities to be imprisoned. Match imprisoned persons to non-imprisoned persons with same/similar propensity scores –This creates ‘balance’ on all available characteristics between imprisoned and non-imprisoned (See: Rosenbaum & Rubin1983, 1984, 1985)

25. Combination Trajectory Group Matching & Propensity Score Matching Within each trajectory group the imprisoned are matched to a non-imprisoned person with the same/similar propensity score

26. Group 0: Effect of imprisonment

27. Group 1: Effect of imprisonment

28. Group 2: Effect of imprisonment

29. Group 3: Effect of imprisonment

30. Summary of Estimated Treatment Effects of Imprisonment (in number of convictions per year) Trajectory Group UncontrolledTrajectory Group Matching Combination Traj. Group & Prop. Matching Gr. 00.600.47 Gr. 10.570.53 Gr. 20.330.25 Gr. 30.830.90 All (PATE)0.62 Note: All effects are statistically significant p<0.05

31. Q: What if you look at …..? Participation (i.e. 0 = no conviction, 1 = one or more conviction(s) in a year) [instead of ‘number of crimes’]: –Same conclusions Convictions of specific types of crimes, e.g. property crimes, violent crimes and other crimes [instead of ‘all convictions’] -Same conclusions -Imprisonment at other ages, e.g. 20-22 [instead of at age 26-28]: –Same conclusions

32. Conclusions Conclusion: –In the three years after imprisonment those who have been imprisoned have on average.6 extra convictions per year, compared to the non-imprisoned –Effects of imprisonment are similar across trajectory groups –Conclusions are very similar regardless of method used Theoretical implications: –Results in line with dynamic DLC theories Life circumstance “imprisonment” has effect - even for ‘persistent’ group Policy implications: –Incapacitation effect of imprisonment may partly be nullified by imprisoned offenders subsequently offending at higher rates