Ethnic variation in criminological experiences: an analysis of BCS data, 2001-2006

1. Ethnic variation in criminological experiences: an analysis of BCS data, 2001-2006 Dr. Paula M. Kautt, University of Cambridge UPTAP Workshop 2008 University of Leeds, 18-19 March 2008 Kautt UPTAP Workshop, 19 March 2008

2. The British Crime Survey (BCS) is an in-depth, face-to-face victimisation survey that utilises a nationally representative sample of adult, private household residents Although many studies use its data to examine a broad range of criminological questions Few multivariate analyses use it to examine variation in criminological perceptions within and between Black and Minority Ethnic (BME) groups victimisation fear of crime performance of the criminal justice system (CJS) Existing multivariate studies largely rely on BCS data from years prior to 2001 To date few studies use multilevel modelling techniques to analyse BCS data The current study remedies this by Focusing on differences by BME status Using single and multilevel modelling techniques Using data from 2001 through 2006 This span enables exploration of how recent events (e.g. 9/11 and 7/7) may have changed how BME status relates to respondents’ criminological perceptions. Examining three key dependent variable families experience of victimisation fear of crime perceptions of the CJS British Crime Survey Data Kautt UPTAP Workshop, 19 March 2008

3. Additional Contribution • This strategy incorporates a more specific measure of area than the previous research • Rather than geographical regions, it uses Police Force Area (PFA)/Criminal Justice Area (CJA) in the multilevel analyses • Results can be more readily translated into specific policy recommendations than those of previous research • These areas constitute geographically-based management divisions for the CJS since 2001 • Provides a more specific view of the area characteristics that victimisation, fear of crime and perceptions of the CJS. Kautt UPTAP Workshop, 19 March 2008

4. BCS Reported Victimisation • Research consistently indicates that BME group members experience higher crime victimisation than comparable whites • However, the level of victimisation varies between specific BME groups • Studies focusing on the household or individual level • Identify several factors highly correlated with respondent ethnicity, which significantly affect victimisation risk, and vary by offence type • Strongly suggest interactive effects between respondent ethnicity, crime type and other factors predictive of victimisation. • Studies focussing on neighbourhood and region • Identify additional area-level factors, which significantly influence victimisation rates • Indicate that the effect of several characteristics (both household and area-level) varies significantly by geographic region Kautt UPTAP Workshop, 19 March 2008

5. BCS Fear of Crime • Although studies show that fear of crime often overestimates likelihood of victimisation, existing research suggests a strong connection between the two • Multivariate evidence examining BCS fear of crime shows that several factors predicting victimisation also predict fear of crime • Moreover, like victimisation, the constellation of factors affecting fear of crime also varies by crime type. • Research further suggests that area characteristics also significantly affect fear of crime • For example, greater area-level BME populations (measured independently for blacks and Asians) are associated with increased reported fear of crime Kautt UPTAP Workshop, 19 March 2008

6. BCS Perceptions of the CJS • Research indicates that perceived control over crime directly influences fear of crime • This suggests a link between fear of crime and perceptions of criminal justice system • Yet, multivariate analyses of BCS respondent perceptions of the CJS are uncommon • There are also conflicting results • Clancy et al (2001) find that being a member specific BME group significantly affects reported assessment of CJS agencies while Jansson (2006) does not uncover such effects • These disparate results suggest that further exploration is necessary to discern the connection between these factors Kautt UPTAP Workshop, 19 March 2008

7. Research Questions • What theoretically relevant factors significantly predict BCS respondent/household reported : • Victimisation? • Fear of crime? • Perceptions of the CJS? • Do the significant factors vary by the BME status of the respondent? (Overall, in terms of strength (magnitude) or direction of effect?) • Do the above patterns change over time (e.g. pre/post prominent events)? • Do the assessed outcomes vary significantly by the PFA/CJA within which the respondent/household is situated? • Do PFA/CJA level factors significantly predict the measured outcomes? • Do PFA/CJA level factors (notably ethnic heterogeneity and composition) significantly influence the respondent/household level predictors? • Do the effects of PFAs/CJAs and their characteristics vary by respondent BME status? Kautt UPTAP Workshop, 19 March 2008

8. Victimisation, fear of crime and perceptions of the CJS are inextricably tied to one another as well as to culture, locale and ethnicity In relation to locale and views of crime, Jackson (2004: 946) notes: ‘…public attitudes toward crime express and gather meaning within a context of judgements, beliefs and values regarding law and order and the social make-up of one’s community and society’ He later ties this to culture and ethnicity, observing: ‘incivility may also symbolize the presence of a variety of sub-cultural groups whose behaviour is seen as different or foreign, with differing values, norms and behaviour (Jackson 2004: 948)’ Ethnic experience is more individualised and context-dependent than empirical research generally takes into account This research aims to embrace and incorporate different minority perspectives in order to better discern ‘the role of ethnicity (‘whiteness’, ‘blackness’, ‘Asianess’, or some ‘otherness’) in explaining offendings, victimization and criminal justice practices (Phillips and Bowling 2003: 271)’ Theoretical Framework: Two Assumptions Kautt UPTAP Workshop, 19 March 2008

9. The Data • BCS • Data from 2001 through 2006 provide the three families of dependent variables and the respondent/household level control variables • Because ethnic minorities comprise only a small proportion of the BCS sample (approximately 10% for all BME groups combined), pooling data across years will be necessary to meaningfully analyse responses by specific ethnic group for some analytical models. • Data from PFA/CJA level • This information, whilst pre-existing, is not available in a single data set • Data are manually compiled from available sources • 2001 Census and published reports • the National Audit Office, Crown Prosecution Service, Her Majesty’s Court Service (HMCS), Department for Constitutional Affairs (DCA), the Police Service, the Home Office • These two data sets will be linked via the PFA identifier present in BCS data from 2001 onward Kautt UPTAP Workshop, 19 March 2008

10. Analyses • The following analyses will first be conducted on ethnically pooled data before being run on subsets representing different BME groups • The coefficients from subset analyses will be compared via the z-test for equality of coefficients to indicate whether the same factors influencing DVs in the ethnically pooled analyses hold true for specific BME groups or if their significance and magnitude changes with the ethnicity of the respondent • Single level analyses estimating • The significant predictors of the DVs, interaction effects • Multilevel analyses to determine • How PFA/CJA level factors affect the respondent level dependent variables • How the effect of respondent level factors varies by PFA/CJA (random effects) • Estimations of how much of the variance in the dependent variables is explained by both the PFA/CJA and PFA/CJA-level factors • Exploratory time-series analyses Kautt UPTAP Workshop, 19 March 2008

11. Dependent Variables-Victimisation • This is captured in two ways • Prevalence : the likelihood of being victimised • Modelled as a dichotomy at the respondent/household level • Responses will initially be categorised as victimised (1) versus non-victimised (0). • From this, cases where victimisation is reported can be further analysed by victimisation type where incidents are categorised as acquisitive (0) or expressive (1). This can be further refined such that acquisitive victimisations are compared against one another • Logistic regression will be used to identify which case-level factors significantly predict the type of victimisation experienced • Incidence : the number of times one has been victimised • Incidence is captured as a right and left-censored count variable • The same comparative approach outlined for prevalence will also be applied to incidence. In other words, the prevalence of general victimisation incidence will be modelled first, followed by independent models of acquisitive and expressive victimisation incidence and so forth. • Negative binomial and Tobit analysis will be used to model this outcome Kautt UPTAP Workshop, 19 March 2008

12. Fear of Crime • Represented in two ways • A scale from 1 to 10 • 1 represents fear of crime having no effect over the respondent’s quality of life and 10 represents a total effect • Ordered Logistic Regression will be used to model this dependent variable • A ‘fear of crime’ index, based upon a compilation of respondent responses on a series of ordinal questions dealing with fear of crime, will be constructed • These include questions as to whether the respondent ‘feel(s) safe walking alone at night’ as well as ‘during the day’ and if s/he ‘worr(ies) about being the victim of crime’ in a reverse ordinal scale as well as dichotomous indicators for fear • This yields a right and left bounded, interval-level variable which will be analysed with both negative binomial and Tobit analysis Kautt UPTAP Workshop, 19 March 2008

13. Perception of CJS • This is captured in two ways • A series of dichotomies • These BCS questions directly ask respondents if they view ‘too lenient sentencing’ and ‘too few police’ as major causes of crime in Britain • Analysed via Logistic regression • An index score based on a series of four-point reverse ordinal scales assessing respondent confidence in various aspects of the CJS • These several include several four (e.g. ‘effectiveness’ and ‘confidence’ measures) and five (e.g. ‘how good a job’ or ‘satisfaction with contact’) point scales for assessments of both the CJS generally as well as specific CJS elements (e.g. the police, the juvenile courts, the Crown Prosecution Service, etc.) • modelled using Negative Binomial and Tobit analysis Kautt UPTAP Workshop, 19 March 2008

14. Timetable-Progress to date • January-March 2008 • Literature review conducted—complete* • Review of BCS data collection procedures and codebooks for years 2001-2006—complete • Obtain, recode and clean BCS data sets for years 2001-2006—complete • Link BCS data sets for years 2001-2006—complete • Preliminary analysis of BCS data (univariate and bivariate statistics)—ongoing • Obtain PFA level data from electronic and non-electronic sources—ongoing • Identification of Census District distribution within PFAs—ongoing Kautt UPTAP Workshop, 19 March 2008

15. What follows are some preliminary analyses of fear of crime by race, ethnicity, year and month This is for all years combined The N of the total data set = 30,641 867 for Asian 583 for Black 27,886 for White Recall that these questions are the product of a sub-sample so are not available for all 250,000+ cases since April 2001 FCI (Fear of Crime Index) A note on race and ethnicity In the BCS data, these are combined into a single variable capturing both Here disaggregated by race to look at ethnicity (only Asian) independently Differences in sample size may account for the results shown here If so, it addresses the utility of BCS data in examining BME issues If not, it shows striking results A Sampler of Early Results Kautt UPTAP Workshop, 19 March 2008

16. Mean Score FoC affects QoL by Month and Race Kautt UPTAP Workshop, 19 March 2008

17. Mean Score FoC affects QoL by Month and Ethnicity (Asian) Kautt UPTAP Workshop, 19 March 2008

18. Mean Score FoC affects QoL by Year and Race Kautt UPTAP Workshop, 19 March 2008

19. Mean Score FoC affects QoL by Year and Ethnicity (Asian) Kautt UPTAP Workshop, 19 March 2008

20. Mean FCI by Month and Race Kautt UPTAP Workshop, 19 March 2008

21. Mean FCI by Month and Ethnicity (Asian) Kautt UPTAP Workshop, 19 March 2008

22. Mean FCI by Year and Race Kautt UPTAP Workshop, 19 March 2008

23. Mean FCI by Year and Ethnicity (Asian) Kautt UPTAP Workshop, 19 March 2008

24. More to Follow • These are merely preliminary analyses on a small subset of the available data • COMING SOON • Multivariate analyses • Partitioned by Race • Partitioned by Ethnicity • Multilevel analyses • Exploratory time series • STAY TUNED… Kautt UPTAP Workshop, 19 March 2008