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Difficulties and solutions arising from an ESRC-UPTAP funded research project Dr. Paula M. Kautt PowerPoint Presentation
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Difficulties and solutions arising from an ESRC-UPTAP funded research project Dr. Paula M. Kautt

Difficulties and solutions arising from an ESRC-UPTAP funded research project Dr. Paula M. Kautt

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Difficulties and solutions arising from an ESRC-UPTAP funded research project Dr. Paula M. Kautt

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  1. Methodological and analytical issues in using the British Crime Survey to model the impact of respondent Ethnicity: Difficulties and solutions arising from an ESRC-UPTAP funded research project Dr. Paula M. Kautt

  2. Ethnicity and the BCS Background

  3. The British Crime Survey • The British Crime Survey (BCS)is a face-to-face victimisation survey utilising a nationally representative sample of adult, private household residents • Since 1982, it has been used to examine a broad range of criminological questions • Yet, few analyses use it to examine variation in criminological experiences between black and minority ethnic groups (BME) or the factors influencing those experiences • Several methodological issues are associated with doing so • Design changes in 2001 means that data from previous years are not comparable • Today’s discussion focuses on post-2001 BCS issues

  4. BCS Sampling Strategy • BCS data • Are collected year-round • Use a 12 month reference point • Purposefully include roughly 1,000 casesper PFA (Police Force Area) • Are sampled via Postcode Address File (PAF) • Stratified by various factors • Include a non-White boost sample

  5. BCS Structure: Follow up Modules • BCS Questionnaire • Core battery of questions that all participants answer • Four distinct follow-up modules • Attitudes to the Police • Attitudes to the CJS • Crime Prevention • Ad hoc • These are also sometimes divided into sub-modules • Each respondent answers questions from only onefollow-up module (and sub-module) • Which follow-up module is pre-assigned, preserving the sampling frame

  6. Ethnicity and the BCS Defining Ethnicity

  7. Ethnic/racial categories Skin Colour ‘Other’ Religion Subgroups? Active? National/Geographic origin or Birth country Can move from country to country Language Native/first language Main language spoken at home Fluency in dominant language Citizenship Naturalised or Native? Immigration Status Country EU, former colony Regions Legal or illegal? Length of time in country Appearance Immutable characteristics Mutable characteristics Cultural affiliation Self-declaration Name Ancestry/Family origin All of the above for ancestors Ethnicity and Ways of Capturing It

  8. Components of Self-Identified Ethnic Identity (as modified from Hirschman & Perez, 2007) Collective Memory Group History Family Socialization Community Attachment & Participation ANCESTRY ETHNIC IDENTITY Culture & Language Citizenship & ImmigrationStatus Physical Appearance Religion & its Importance Reflected Appearance

  9. Components of Ascribed Ethnic Identity Skin colour and Physical features Social Customs, Practices Dress Name Ascribed Ethnicity Accent Proficiency in English Religious Practice

  10. The Sixteen BCS Categories of Ethnicity • White - British • White - Irish • White - Other White Background • Mixed - White and Black Caribbean • Mixed - White and Black African • Mixed - White and Asian • Mixed - Any Other Mixed Background • Asian or Asian British - Indian • Asian or Asian British - Pakistani • Asian or Asian British - Bangladeshi • Asian or Asian British - Other Asian Background • Black or Black British - Caribbean • Black or Black British - African • Black or Black British - Other Black Background • Chinese • ‘Other’ Ethnic Group

  11. BCS-Captured Ethnicity Benefits Liabilities Triple-barrelled question Traditional ‘race’ Asian, Black, White National origin Pakistani, Indian, etc Regional origin African, Caribbean Inconsistent and oversimplified Non-representative ‘Nationality’ and ‘Country of birth’ only covers the British Isles Unwieldy 15 Dummy variables for analysis • Same 16 categories as the UK Census • Permits consistency between these data • Respondent classifies self by categories • Improvement over capturing only national origin • Latest version captures year of arrival in country

  12. Changing Ethnic Definitions • Recommended improvements to the ethnicity measure are slated for the 2011 UK Census • Two ethnic categories to be added • Arab (under ‘Other’) • ‘Travelers’ and ‘gypsies’ (under White) • Plus a ‘write in’ option for ‘Other’ • In addition, it also captures • Language • Respondent’s main language • How well respondent speaks English • What does this mean for the BCS?

  13. What’s Missing: Self-SelectedEthnic Identity Collective Memory Group History Family Socialization Culture & Language ANCESTRY Community Attachment & Participation ETHNIC IDENTITY Citizenship & Immigration Status Physical Appearance Religion & its Importance Reflected Appearance

  14. What’s missing?: AscribedEthnic Identity Social Customs & Practices Skin colour and Physical features Dress Name Ascribed Ethnicity Accent Proficiency in English Religious Practice

  15. Improving BCS Ethnicity Measures • Census ethnicity measure must be incorporated into the BCS to ensure comparability • This does not mean this need be the onlyethnicity measure included • Options: • Include separate indicators for each pertinent aspect • Language as with the Census • Consistently capture comprehensive measures for Nationality and Country of Birth • Present the ‘write-in’ results for the ‘other’ ethnicities in the data set • Use existing Multi-faceted Ethnicity Classification Instruments • e.g. Multi-Ethnic Identity Measure (MEIM) • Combining methods • Do nothing; continue with current classification

  16. Ethnicity and the BCS Analytical Solutions

  17. Anomalies of BCS data • As captured, only ‘Whites’ can be simply ‘British’ • ‘White-British’ • It is not possible for ‘Black’ or ‘Asian’ respondents to simply be ‘British’ • Rather, specific ethnicity is always attached. For example • ‘Black or Black-British– Caribbean’ • ‘Asian or Asian-British—Indian’ • The Data do not capture different ‘White’ ethnicities beyond ‘British’, ‘Irish’ and ‘Other’ • ‘English,’ ‘Scottish’ and ‘Welsh’ are the ‘Other’ options • Thus, other ‘White’ ethnicities, such as ‘Polish’, are not captured

  18. Practical Analytical Solutions • The BCS ethnicity categorisation scheme permits partitioning at two tiers • General Ethnicity • A larger group differentiated mainly by traditional ‘race’ groupings • White, Black, Asian • ‘Other’ • Specific Ethnicity • A more refined comparison of groups falling within a general ethnicity • Under ‘Asian’, the sub-groups ‘Indian’, ‘Pakistani’, ‘Bangladeshi’ can be compared • Partially addresses consistency issue • This split is precisebut potentially non-representative of reality

  19. What about ‘Other’? • The general ‘Other’ category can be treated as another General Ethnicity • The ‘Black—Other’ and ‘Asian—Other’ can also be treated as other Specific Ethnicity categories • HOWEVER: currently no meaningful way to talk about these groups because they • Comprise an unknown numberof different ethnicities • The proportion of these ethnicities is also unknown

  20. And ‘Mixed’? • Because of relative rarity ‘Mixed’ ethnicity cases are difficult to analyse • Analytically they can be • Merged into the specifically-named BME group for analysis • The supposition being that they will appear to be a member of that group to a third party • Pooled by year (see below) for independent analysis • Limited techniques available due to low counts

  21. Comparison Problems • Sample Size for Ethnicity • Majority of respondents are ‘White British’ • Much smaller numbers of the Asian and Black General Ethnicities • Makes meaningful statistical comparisons between ‘White’ and other General Ethnicities problematic • There are even fewer of the specific ethnicities • These are not evenly distributed • e.g. There are many more Indians than Chinese • Especially problematic for the ‘follow-on’ modules and sub-modules

  22. Solution?: Pooling the Data • Because BMEs comprise only a small proportion of the BCS sample (approximately 10% total across all BME groups), can be combined across years to: • Meaningfully analyse responses by specific ethnic group • Provide sufficient numbers to support ethnically separate analyses • Despite this, for some categories, there are still too few cases even when all data since 2001 is used • In such instances, an ethnically pooled model is the only option • NOTE:when pooling by year, only indicators available for all years can be used • e.g. religion and sexual orientation cannot be controlled in analyses employing data from years prior to their inclusion

  23. Incorporating Location: Criminal Justice Areas • England and Wales are currently divided into 43 Criminal Justice Areas • These specify jurisdictional and administrative boundaries for CJS agencies • The Police Service • The Courts • The Prison Service • The Probation Service • This is the smallest geographic subunit universally* available in the BCS • Distribution of BMEs varies substantially across them • Important for multilevel analyses

  24. Representation: PFA & Ethnicity

  25. To understand the complexity, multiply by ten…

  26. PFA Example One: Respondent Ethnicity Counts London (2001-7)

  27. PFA Example Two: Respondent Ethnicity Counts Cumbria (2001-7)

  28. PFA Sample Size Solution?: Analysis by Ethnic Centres • Low counts in minority ethnicities make the BCS unfit for examining ethnic differences nationwide • London and the West Midlands have the highest concentrations • Focus analyses on these areas, which will : • Maximise the number of ethnic minority respondents • Better represent actual diversity levels and composition • Better capture how crime-related outcomes vary by ethnicity • Use Local Authority rather than PFA as the smallest geographic subunit (special license only)

  29. Conclusions • Using the BCS to analyse the impact of minority ethnicity status over criminological outcomes is difficult but not impossible • Currently, definitional and methodological compromises must be made • Improvements to the BCS could make these compromises unnecessary • Limited utility for comparisons by context and geographic location • Wider release of the ‘special license’ data would ease this