The Relationship between First Imprisonment and Criminal Career Development: A Matched Samples Comparison Presentation

1. The Relationship between First Imprisonment and Criminal Career Development: A Matched Samples Comparison Presentation at the 2nd 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. T1 T2 = Incapacitation effect= Deterrence effect Criminalbehavior Criminalbehavior Criminal propensity Imprisonment Imprisonment

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 examiningeffects 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): • Men excluding 424 women • Persons that did not die before age 31 excluding 20 men • Persons who pre age 26 had not been imprisoned excluding 1163 men earlier imprisoned

9. Outcome variable • Number of convictions in three year period after imprisonment • Imprisonment at age Dep. 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 26-28 age: 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

14. 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) 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