the relationship between first imprisonment and criminal career development: a matched samples...

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)?

Criminal propensity

Criminal behavior Criminal behavior

Imprisonment Imprisonment

T1 T2

= Incapacitation effect= Deterrence effect

Hypotheses on effect of imprisonmentDLC 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. Exact timing.

– Cause of death (CBS)

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

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, 31Non-imprisoned age 26-28 age: 28, 29, 30

• Correction for exposure-time / incarceration

First time imprisonment between age 26-28

0

5

10

15

20

25

30

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)

Perc

enta

ge

• 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:

Naïve / Baseline comparison

0

0.2

0.4

0.6

0.8

1

1.2

1.4

12 13 14 15 16 17 18 19 20 21 22 23 24 25 t+1 t+2 t+3

Age

All (N=3,008) Imprison. (N=184)

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

0.00

1.00

2.00

3.00

4.00

Num. of conv. age 12-25 Num. of conv. age: 20-25 Num. of conv. age 25

Num

of C

onv.

non-imprisoned (n=2,824) Imprisoned age 26-28 (N = 184)

Differences between imprisoned and non-imprisoned

0.00

0.10

0.20

0.30

0.40

0.50

Non-Dutch Married Children Unemployed Alcohol dep. Drugs dep.

Pro

port

ion

non-imprisoned (n=2,824) Imprisoned age 26-28 (N = 184)

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

0

0.2

0.4

0.6

0.8

1

1.2

1.4

12 13 14 15 16 17 18 19 20 21 22 23 24 25 t+ t+ t+

Age

All (N=3,008)

Four Trajectories

0

0.2

0.4

0.6

0.8

1

1.2

1.4

12 13 14 15 16 17 18 19 20 21 22 23 24 25 t+ t+ t+

AgeGr 0 (40%; N=1200; 4%Imp.) Gr 1 (38%; N=1135; 9% Imp.) Gr 2 (17%; N=519; 3% Imp.)

Gr 3 ( 5%; N=154; 13% Imp.) All (100%; N=3,008; 6% Imp.)

Group 0: Effect of imprisonment

0

0.2

0.4

0.6

0.8

1

1.2

1.4

12 13 14 15 16 17 18 19 20 21 22 23 24 25 t+1 t+2 t+3

Age

Gr 0 (N=1200) Gr 1 (N=1135) Gr 2 (N=519) Gr 3 (N=154) Imprison. (N=44)


0

0.2

0.4

0.6

0.8

1

1.2

1.4

12 13 14 15 16 17 18 19 20 21 22 23 24 25 t+ t+ t+

Age



0

0.2

0.4

0.6

0.8

1

1.2

1.4

12 13 14 15 16 17 18 19 20 21 22 23 24 25 t+ t+ t+

Age

Gr 0 (N=1200) Gr 1 (N=1135) Gr 2 (N=519) Gr 3 (N=154) Imprison (N=20)

• 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


0

0.2

0.4

0.6

0.8

1

1.2

1.4

12 13 14 15 16 17 18 19 20 21 22 23 24 25 t+1 t+2 t+3

Age



0

0.2

0.4

0.6

0.8

1

1.2

1.4

12 13 14 15 16 17 18 19 20 21 22 23 24 25 t+ t+ t+

Age


Summary of Estimated Treatment Effects of Imprisonment (in number of convictions per year)

Trajectory

Group

Uncontrolled Trajectory Group Matching

Combination

Traj. Group & Prop. Matching

Gr. 0 0.60 0.47

Gr. 1 0.57 0.53

Gr. 2 0.33 0.25

Gr. 3 0.83 0.90

All (PATE) 0.62 0.62 0.62Note: 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

the relationship between first imprisonment and criminal career development: a matched samples...

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