Reducing Crime by Reducing Risk of Crime
Carlos Vilalta, Marcelo Bergman and Gustavo Fondevila1
2nd International Conference on Governance, Crime and Justice Statistics
Mexico City, June 2014
1. Center for Economic Research and Education (CIDE) and University Tres de Febrero (UNTREF)
More data at: www.carlosvilalta.net and www.geocrimen.cide.edu
Overview
•
•
•
•
•
Crime is a political problem
Go beyond counting → analytical measures
Ask victims and offenders
Provide solutions = Risk indicators
Not all solutions apply everywhere
– Byrne: “Age-appropriate, culture-appropriate, and problemappropriate” solutions
– Latessa: “Poor correctional programs increase recidivism among
low risk and high risk offenders”
• Q: What to measure then?
• A: Understand criminal motivation
• Cut the chase 1: Goal is not only to count well but to reduce
victimization rates across the region
We asked Offenders
• Data sources:
• Regional and national representative samples of prison inmate
populations in 2013.4 N = 5,700
• Units of analysis:
•
– Regions: Mexico City, Sao Paulo, and Buenos Aires
– Countries: Chile, El Salvador, and Peru
Data Analysis:
– Hierarchical binary logistic regression (HBLR) to assess
independent risk factors of reoffending
– Odd ratios were generated
– Statistical significance was set at p < 0.10 for all analyses
Source: Bergman M. Fondevila G. and Vilalta C. 2013. Prison inmate population surveys of Latin
America. Countries: Argentina, Brazil, Chile, El Salvador, Mexico, and Peru. n = 5,700
Offenders and repeat offenders
• How many crimes could have been prevented?
Prison population
95% C.I.
Committed a crime 6
months before arrested
Chile
36,350
33.2%, 39.8%
13,268
Buenos Aires
11,259
26.4%, 34.4%
3,423
Sao Paulo
110,086
12.4%, 17.6%
16,513
Peru
47,726
11.6%, 15.4%
6,443
Mexico City
34,253
8.3%, 11.5%
3,391
El Salvador
19,988
4.5%, 7.1%
1,159
259,662
-
44,197
Total
(17.0%)
Source: Bergman M. Fondevila G. and Vilalta C. 2013. Prison inmate population surveys of Latin
America. Countries: Argentina, Brazil, Chile, El Salvador, Mexico, and Peru. n = 5,700
Theories
• Social Disorganization 1942 + new formulations
– Community-level patterns of inequality give rise to the social
isolation and geographical concentration of the deprived, which
then leads to structural barriers and cultural adaptations
undermining social organization and ultimately the control of
antisocial behaviors and crime
• Strain theory 1992
– Actual or expected failure to achieve socially valued goals, lack of
positive stimuli, and presence of negative stimuli result in strain
and anger leading to antisocial behaviors and crime
Theories
• Social Disorganization:2
• Strain theory:3
Correlates as solutions
• Integrated theoretical model
Family economic
dissatisfaction
Length of
residence
Importance of
richness
Trust in neighbors
Social
Disorganization
theory
Gangs in
neighborhood
Criminal
friendships during
childhood
Criminal in the
family of childhood
In Italics a likely mutual correlate
Committed
a crime
6 months
before
arrest
Not having a job
Strain theory
Parental alcohol
abuse
Parental physical
abuse
Have been
convicted before
Regression results
Mexico
Sao Paulo
Buenos Aires
Chile
El Salvador
Peru
1.007
1.033
1.293
0.961
1.057
0.980
Importance of richness
1.389***
0.829
0.903
1.356**
1.215
1.146
Did not have a job
2.765***
1.747
4.699***
3.066***
1.421
3.832***
Parental phys. abuse
1.046
1.554
1.138
0.883
1.198
1.381
Parental alcohol abuse
1.172
1.214
0.816
1.218
1.152
1.022
1.791***
1.147
0.672
1.293
1.294
3.907***
Distrust in neighbors
0.931
0.797
0.814
1.021
1.574
1.322
Gangs in neighborhood
1.274
3.684***
0.967
1.280
2.724**
2.031**
Criminal in family of childhood
1.602
1.774
1.990**
0.786
2.207**
1.169
1.824**
2.439**
3.718***
1.546
3.130***
2.249***
1.005
0.937***
0.970*
0.983
0.986
0.996
0.892
0.576
2.390
1.004
0.537
0.767
0.651***
0.923
0.694***
0.781**
0.866
0.856*
Intercept
0.005***
0.020
0.009*
0.076
0.001***
0.001***
Nagelkerke´s pseudo R2
0.183
0.304
0.359
0.183
0.214
0.271
Correctly classified
81.7%
74.8%
71.5%
68.0%
90.7%
86.7%
Model Chi-Square (sig.)
0.001
0.001
0.001
0.001
0.001
0.001
Hosmer-Lemershow (sig.)
0.133
0.968
0.314
0.859
0.364
0.156
Strain theory:
Family economic dissatisfaction
Have been convicted before
Social disorganization theory:
Criminal friends in childhood
Length of residence
Controls:
Gender
Age group
Summary of results
• Risk indicator matches across regions and countries
Mexico
Sao Paulo
Buenos Aires
Chile
El Salvador
Peru
Mexico
-
1
2
2
1
3
Sao Paulo
1
-
1
0
2
2
Buenos Aires
2
2
-
1
2
1
Chile
2
0
1
-
0
1
El Salvador
1
2
2
0
-
2
Peru
3
2
2
1
2
-
9
7
8
4
7
9
Total
Summary of results
• Risk indicators for each region and country
Theory
Correlate
Matches
Regions / Countries:
Social disorganization theory
Criminal friends in childhood
5
Mexico, Sao Paulo, Buenos Aires, El Salvador, Peru
Strain theory
Did not have a job
4
Mexico, Buenos Aires, Chile, Peru
Social disorganization theory
Gangs in neighborhood
3
Sao Paulo, El Salvador, Peru
Strain theory
Importance of richness
2
Mexico, Peru
Strain theory
Have been convicted before
2
Mexico, Peru
Social disorganization theory
Criminal in family of childhood
2
Buenos Aires, El Salvador
Social disorganization theory
Length of residence
2
Sao Paulo, Buenos Aires
Strain theory
Economic dissatisfaction
0
Strain theory
Parental physical abuse
0
Strain theory
Parental alcohol abuse
0
Social disorganization theory
Distrust in neighbors
0
-
Summary of results
• Most capable indicators in terms of Odd Ratios (SOR)
Theory
Correlate
Ranking
Strain theory
Did not have a job
1
Social disorganization theory
Criminal friends in childhood
2
Social disorganization theory
Gangs in neighborhood
3
Strain theory
Have been convicted before
4
Social disorganization theory
Criminal in family of childhood
5
Strain theory
Importance of richness
6
Social disorganization theory
Length of residence
7
Discussion
• How to reduce criminal risk across the region?
Rank
Correlate
Policy actions
Where?
1
Did not have a job
Education and employment opp. for youth
Mexico, Buenos Aires, Chile, Peru
2
Criminal friends in childhood
Community informal controls
Mexico, Sao Paulo, Buenos Aires,
El Salvador, Peru
3
Gangs in neighborhood
Community informal controls
Mexico, Sao Paulo, El Salvador,
Peru
4
Have been convicted before
Education and employment opp. for ex-convicts
Mexico, Peru
5
Criminal in family of childhood
Follow up of ex-convicts
Buenos Aires, El Salvador
6
Importance of richness
Anomic pressures < Econ. opportunity
Mexico, Peru
7
Length of residence
Community networking
Sao Paulo, Buenos Aires
Discussion
• Cut the chase 2: Policy action is difficult but necessary
• Cut the chase 3: Move now beyond counting and “indicate”
•
•
•
•
•
•
what to do
At risk: Youngsters that do not have a job, have had criminal
friends, live in neighborhoods with gangs, have been
convicted before, and have had a family member in jail
Not at risk: Economic dissatisfaction, neither parental
physical abuse or alcohol abuse, nor distrust in neighbors
Disrupt key social disorganization and strain patterns
Focus on developmental prevention and correctional
programs
Promote social organization of communities
Put money into pro-social occupations and organizations
Thanks!
[email protected]
www.geocrimen.cide.edu
References
1. Institutional adscription
2. Adapted from Shaw and McKay (1942), Sampson and Groves
(1989) and Sampson (2011)
3. Adapted from Agnew (1992)
4. Bergman M. Fondevila G. and Vilalta C. (2013). Prison inmate
population surveys of Latin America. Countries: Argentina, Brazil,
Chile, El Salvador, Mexico, and Peru.