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A multilevel study of perceived risk of
victimization and avoidance behaviour
Prof. dr. L. Pauwels
Drs. W. Hardyns
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 1
Introduction
Fear of crime as a hot issue in theory, empirical
research (Pleysier, 2009; Vanderveen,2006) and
policy (recognizing the cost of fear-policing FOC-
COP; Skogan, Trojanowics- since 1980s, Ponsaers,
2001)
Urban villages often overemphasized as ideal
communities with low levels of crime and fear
(Jacobs, 1961)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 2
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 3
Introduction and goals
In this contribution we are interested in further
unravelling the nature of the geographical
differences in feelings of insecurity.
Assessing the nature of observed area differences
in avoidance behaviour, perceived risk of
victimization, perceived disorder and within-
community victimization, controlling for
compositional effects.
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 4
Introduction and goals
FOC in this study: NOT fear, but two dimensions of
feelings of insecurity:
Avoidance behaviour and perceived risk of victimization
Descriptive results and hypothesis testing
Theory driven:
Vertical theoretical integration using the end-to-end
strategy
Integrating complementary theoretical models of fear at
two levels:
Community level: CE/SD/BW
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 5
Theoretical backdrop
Community structure may either foster or disturb
mechanisms of informal control (social trust and
shared common goals-Bursik and Grasmik, 1993;
Taylor and Harrell, 1996)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 6
Theoretical backdrop
Community characteristics in the present study:
Community stability: structural condition for
the development of local social ties (Sampson,
Raudenbush and Earls, 1997)
Single households: probably the best structural
proxy for lack of control (social eyes) (See
Sampson and Wooldredge, 1987)
Community crime and disorder (Robinson,
Lawton, Taylor and Perkins, 2003) as community
outcome in macro-level theory
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 7
Theoretical backdrop
Disorder, crime, victimization and fear are macrolevel
consequences of community structural instability
From a social ecological perspective individuals are
studied within their context: what are the advantages
of stable communities in a Belgian context?
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 8
Goals and hypotheses
“Do community stability, low informal control and
crime and disorder leave their marks on
(1) attitudinal (perceived risk of victimization) and (2)
behavioural outcomes of individuals (avoidance
behaviour),
-> independent of indicators of population
composition (vulnerability indicators)?”
And can criminal victimization itself be explained by
the same community mechanisms?
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 9
Goals and hypotheses
An analytical approach: what is the “causal link”?
A mechanism-based approach
We introduce multiple mediating links at the
macro level
(1) community instability -> low social ontrol
(2) low social control -> increase in crime and
disorder
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 10
Goals and hypotheses
We introduce two mediating links at the micro
level
(1) Criminal victimization: lifestyle and routine
activities (Maxfield, 1987, Hindelang, Gottfredson,
Garofalo, 1978)
(2) Perceived disorder (Ross & Mirowsky,
1991;Sampson &Raudenbush, 2004)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 11
Socio-economic
Vulnerability
Community Instability
And Disorder
Macro level
Micro level
Perceived disorder
Perceived Risk of
Victimization
Avoidance Behaviour
Within Area
Victimization
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 12
Data and Methods
Federal Survey “Veiligheidsmonitor”: merging of
editions 2002, 2004, 2006
Approx 101,303 respondents clustered in 346
municipalities
Exclusion of municipalities < 40 respondents
(ecological reliability and validity!)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 13
Data and Methods
Hierarchical multilevel models for continuous
variables (ML-estimation)
Block-wise regression models: at each step a
mediating mechanism is introduced
[Quality control: re-analysis with more robust
methods (ordinal hierarchical models, Poisson-based
models and Logistic models, cf violations of
assumptions)
Stable findings of effect parameters]
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 14
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 15
Construct Items Factor
loading
Reliability
(Alpha)
Ecological
reliability
(Lambda)
Unsafety Does it happen that you feel unsafe? -- -- 0.841
Avoidance
behaviour
(3 items)
Does it happen that …
You avoid certain areas in your municipality
because you think they are not safe?
You avoid to open the door for strangers
because you think it is not safe
You avoid leaving home after dark because you
think it is not safe?
0.587
0.615
0.647
0.642 0.881
Perceived risk of
victimization
(4 items)
Within the next 12 months, …
How do you perceive the risk of your household
to become victim of burglary
How do you perceive your personal risk
to become victim of physical violence
or being threatened with physical
violence
How do you perceive your risk of becoming
victim of theft without violence or threats with
violence
How do you perceive your personal risk of
becoming victim of a traffic related offence
0.641
0.697
0.819
0.505
0.756 0.805
Perceived
Disorder and
Crime
(13 items)
Do you consider …as a problem in your
neighbourhood:
Theft of bicycles
Theft from car
Threats
Soiled house fronts/buildings
Incivilities from groups of adolescents
Harassing men and women on the streets
Car accidents
Litter in public
Destruction of phone cabins and bus stops
Burglary in homes or other buildings
Violence
Incivilities related to drug users
Car theft
0.504
0.665
0.704
0.655
0.621
0.754
0.576
0.560
0.660
0.588
0.825
0.729
0.720
0.908 0.917
Measurement issues
Stable
reliability
coefficienst
over time
(2002,
2004,2006)
Measuring within area victimization
Total within area victimization is a count variable
that summarizes the different times that a person
(or household) was set out for eight different
phenomena within the municipality of residence.
Eight phenomena: (1) burglary with theft, (2) attempted
burglary, (3) car-theft, (4) theft from car, (5) vandalism on
cars, (6) violent theft, (7) physical violence and (8) being
threatened with violence.
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 16
Results of hierarchical ML models
Intra class correlations (empty random intercept
versus controlling for compositional effects)
Avoidance behaviour (6.62%-6.44%)
Perceived risk of victimization (3.72%- 3.83%)
Within area victimization (1.96% - 2.15%)
Perceived disorder (10.04%-10.06%)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 17
Results of hierarchical ML models
Perceived risk of
victimization
ICC
(1) Background variables
-M*
+ age*
-single HH*
- lower education*
Avoidance behaviour
ICC
(1) Background variables
-M *
+ age*
+ single HH*
+ lower education*
- home owners*
+Length of residence
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 18
Results of hierarchical ML models
Perceived risk of victimization and avoidance
behavior as dependent variables yield similar
results
(2) Moderating effect of within area victimization
and perceived disorder
(3) Independent contextual effect of community
stability
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 19
Results of hierarchical ML models
(4) Community stability also explains individual
differences in within area victimization and perceived
disorder
Suggests that context does matter in different ways.
Some future directions:
Level of analysis? Measurement issues?
Stability across age groups- suggested interaction
affects
Context matters- but: strongly dependent of
explanandum: (“putting space at its place- Titta, 2008”)
Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 20
Slide 21
"cities are not made from their roofs,
stone walls, bridges and canals but from
men able to grasp opportunities and make
the most of them"
Alcaeus, BC 7th
century

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Multilevel stable communities esc1

  • 1. A multilevel study of perceived risk of victimization and avoidance behaviour Prof. dr. L. Pauwels Drs. W. Hardyns Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 1
  • 2. Introduction Fear of crime as a hot issue in theory, empirical research (Pleysier, 2009; Vanderveen,2006) and policy (recognizing the cost of fear-policing FOC- COP; Skogan, Trojanowics- since 1980s, Ponsaers, 2001) Urban villages often overemphasized as ideal communities with low levels of crime and fear (Jacobs, 1961) Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 2
  • 3. Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 3
  • 4. Introduction and goals In this contribution we are interested in further unravelling the nature of the geographical differences in feelings of insecurity. Assessing the nature of observed area differences in avoidance behaviour, perceived risk of victimization, perceived disorder and within- community victimization, controlling for compositional effects. Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 4
  • 5. Introduction and goals FOC in this study: NOT fear, but two dimensions of feelings of insecurity: Avoidance behaviour and perceived risk of victimization Descriptive results and hypothesis testing Theory driven: Vertical theoretical integration using the end-to-end strategy Integrating complementary theoretical models of fear at two levels: Community level: CE/SD/BW Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 5
  • 6. Theoretical backdrop Community structure may either foster or disturb mechanisms of informal control (social trust and shared common goals-Bursik and Grasmik, 1993; Taylor and Harrell, 1996) Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 6
  • 7. Theoretical backdrop Community characteristics in the present study: Community stability: structural condition for the development of local social ties (Sampson, Raudenbush and Earls, 1997) Single households: probably the best structural proxy for lack of control (social eyes) (See Sampson and Wooldredge, 1987) Community crime and disorder (Robinson, Lawton, Taylor and Perkins, 2003) as community outcome in macro-level theory Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 7
  • 8. Theoretical backdrop Disorder, crime, victimization and fear are macrolevel consequences of community structural instability From a social ecological perspective individuals are studied within their context: what are the advantages of stable communities in a Belgian context? Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 8
  • 9. Goals and hypotheses “Do community stability, low informal control and crime and disorder leave their marks on (1) attitudinal (perceived risk of victimization) and (2) behavioural outcomes of individuals (avoidance behaviour), -> independent of indicators of population composition (vulnerability indicators)?” And can criminal victimization itself be explained by the same community mechanisms? Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 9
  • 10. Goals and hypotheses An analytical approach: what is the “causal link”? A mechanism-based approach We introduce multiple mediating links at the macro level (1) community instability -> low social ontrol (2) low social control -> increase in crime and disorder Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 10
  • 11. Goals and hypotheses We introduce two mediating links at the micro level (1) Criminal victimization: lifestyle and routine activities (Maxfield, 1987, Hindelang, Gottfredson, Garofalo, 1978) (2) Perceived disorder (Ross & Mirowsky, 1991;Sampson &Raudenbush, 2004) Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 11
  • 12. Socio-economic Vulnerability Community Instability And Disorder Macro level Micro level Perceived disorder Perceived Risk of Victimization Avoidance Behaviour Within Area Victimization Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 12
  • 13. Data and Methods Federal Survey “Veiligheidsmonitor”: merging of editions 2002, 2004, 2006 Approx 101,303 respondents clustered in 346 municipalities Exclusion of municipalities < 40 respondents (ecological reliability and validity!) Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 13
  • 14. Data and Methods Hierarchical multilevel models for continuous variables (ML-estimation) Block-wise regression models: at each step a mediating mechanism is introduced [Quality control: re-analysis with more robust methods (ordinal hierarchical models, Poisson-based models and Logistic models, cf violations of assumptions) Stable findings of effect parameters] Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 14
  • 15. Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 15 Construct Items Factor loading Reliability (Alpha) Ecological reliability (Lambda) Unsafety Does it happen that you feel unsafe? -- -- 0.841 Avoidance behaviour (3 items) Does it happen that … You avoid certain areas in your municipality because you think they are not safe? You avoid to open the door for strangers because you think it is not safe You avoid leaving home after dark because you think it is not safe? 0.587 0.615 0.647 0.642 0.881 Perceived risk of victimization (4 items) Within the next 12 months, … How do you perceive the risk of your household to become victim of burglary How do you perceive your personal risk to become victim of physical violence or being threatened with physical violence How do you perceive your risk of becoming victim of theft without violence or threats with violence How do you perceive your personal risk of becoming victim of a traffic related offence 0.641 0.697 0.819 0.505 0.756 0.805 Perceived Disorder and Crime (13 items) Do you consider …as a problem in your neighbourhood: Theft of bicycles Theft from car Threats Soiled house fronts/buildings Incivilities from groups of adolescents Harassing men and women on the streets Car accidents Litter in public Destruction of phone cabins and bus stops Burglary in homes or other buildings Violence Incivilities related to drug users Car theft 0.504 0.665 0.704 0.655 0.621 0.754 0.576 0.560 0.660 0.588 0.825 0.729 0.720 0.908 0.917 Measurement issues Stable reliability coefficienst over time (2002, 2004,2006)
  • 16. Measuring within area victimization Total within area victimization is a count variable that summarizes the different times that a person (or household) was set out for eight different phenomena within the municipality of residence. Eight phenomena: (1) burglary with theft, (2) attempted burglary, (3) car-theft, (4) theft from car, (5) vandalism on cars, (6) violent theft, (7) physical violence and (8) being threatened with violence. Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 16
  • 17. Results of hierarchical ML models Intra class correlations (empty random intercept versus controlling for compositional effects) Avoidance behaviour (6.62%-6.44%) Perceived risk of victimization (3.72%- 3.83%) Within area victimization (1.96% - 2.15%) Perceived disorder (10.04%-10.06%) Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 17
  • 18. Results of hierarchical ML models Perceived risk of victimization ICC (1) Background variables -M* + age* -single HH* - lower education* Avoidance behaviour ICC (1) Background variables -M * + age* + single HH* + lower education* - home owners* +Length of residence Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 18
  • 19. Results of hierarchical ML models Perceived risk of victimization and avoidance behavior as dependent variables yield similar results (2) Moderating effect of within area victimization and perceived disorder (3) Independent contextual effect of community stability Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 19
  • 20. Results of hierarchical ML models (4) Community stability also explains individual differences in within area victimization and perceived disorder Suggests that context does matter in different ways. Some future directions: Level of analysis? Measurement issues? Stability across age groups- suggested interaction affects Context matters- but: strongly dependent of explanandum: (“putting space at its place- Titta, 2008”) Prof. Dr. Lieven Pauwels- ESC Ljubljana 2009 20
  • 21. Slide 21 "cities are not made from their roofs, stone walls, bridges and canals but from men able to grasp opportunities and make the most of them" Alcaeus, BC 7th century