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
Main Question
• To what extent is there an effect of imprisonment
on subsequent criminal career development
(here: in the three years after imprisonment)?
T1
T2
Criminal behavior
Criminal behavior
Imprisonment
Imprisonment
Criminal propensity
= Incapacitation effect
= Deterrence effect
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’)
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
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.
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. Exac
timing.
– Cause of death (CBS)
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):
• Men
• Persons that did not die before age 31
• Persons who pre age 26 had not been imprisoned
excluding 424 women
excluding 20 men
excluding 1163 men earlier
imprisoned
Outcome variable
• Number of convictions in three year period after
imprisonment
• Imprisonment at age
26 (N = 66)
27 (N=55)
28 (N=63)
Non-imprisoned age 26-28
Dep. Var.: convictions at
age: 27, 28, 29
age: 28, 29, 30
age: 29, 30, 31
age: 28, 29, 30
• Correction for exposure-time / incarceration
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:
30
25
Percentage
20
15
10
5
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Imposed sentence (in weeks)
Naïve / Baseline comparison
Challenges when examining
effects of imprisonment II
• Selection effect: prison sentences are
consequence of:
– Offender’s prior criminal record
– Other characteristics
Differences between
imprisoned and non-imprisoned
4.00
Num of Conv.
3.00
2.00
1.00
0.00
Num. of conv. age 12-25
Num. of conv. age: 20-25
Num. of conv. age 25
non-imprisoned (n=2,824) Imprisoned age 26-28 (N = 184)
Differences between
imprisoned and non-imprisoned
0.50
Proportion
0.40
0.30
0.20
0.10
0.00
Non-Dutch
Married
Children
non-imprisoned (n=2,824)
Unemployed
Alcohol dep.
Imprisoned age 26-28 (N = 184)
Drugs dep.
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
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
Age–crime curve
Four Trajectories
Group 0: Effect of imprisonment
Group 1: Effect of imprisonment
Group 2: Effect of imprisonment
Group 3: Effect of imprisonment
• 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
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)
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
Group 0: Effect of imprisonment
Group 1: Effect of imprisonment
Group 2: Effect of imprisonment
Group 3: Effect of imprisonment
Summary of Estimated
Treatment Effects of Imprisonment
(in number of convictions per year)
Trajectory
Group
Gr. 0
Uncontrolled Trajectory Group
Matching
Combination
Traj. Group &
Prop. Matching
0.60
0.47
Gr. 1
0.57
0.53
Gr. 2
0.33
0.25
Gr. 3
0.83
0.90
0.62
0.62
All (PATE)
0.62
Note: All effects are statistically significant p<0.05
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
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