1. Equality and Human Rights Commission - work on equalities data Mark Wright Regional Manager

2. What I’m going to talk about The need, motivation, proportionate response to data collection The national Equality Measurement Framework (EMF) – helping prioritise areas for investigation The Lancaster ‘Review of Equality Statistics’

3. Why do local and regional public bodies use equality data? Get brownie points from inspectorates; fulfill public sector duties Enhance understanding of equality and diversity issues Bench mark, set realistic targets / aspirations Identify priorities that might help them meet LSP or CAA outcome objectives

4. Main ways they use data To do Equality Impact Assessments Checking services delivering for full range of clients/customers Monitoring profile of own workforce Seeking to ensure suppliers and providers of goods and services have appropriate policies in place.

5. Motivations that give extra push Commitment to social inclusion – sometimes organisational, sometimes individual staff Wanting to be an exemplar employer – both for peer admiration and for attracting talent from local labour market Making the business case for activities or for funding bids

6. Some current focus (limited by data availability) Age – Health (older), Health and education (younger) Disability – Analysis of needs of staff and service users (but major definitional issues and aggregation issues) Gender – Good data but main focus is on own workforce: pay, grade, recruitment fairness. Apart from some work by LAs, little on service development/delivery

7. Some current focus (limited by data availability) Race, religion and belief – often tended to be linked. Very little on latter. Focus on own workforce. Some profiling of customers/clients, e.g. PCT patient profiling, but small samples and poor response problems Sexual Orientation – most have relevant anti- discrimination policies in place. But beyond this little progress. Assumption of no data existing; some work with voluntary organisations in order to address data weakness

8. Challenges “It’s too difficult”: Define proportionate solutions –Short term effort compared to long-term and uncertain gain: Critical path of outcomes –More local population data to allow prioritisation of effort –Definitional and conceptual clarity – especially around sexual orientation and disability and appropriate aggregation of small groups

9. Challenges More explicit link to outcomes that organisation will be held to account for –Clearer cause and effect –More case studies/‘promising approaches’ –Data in areas other than education, employment and income

10. Challenges Clearer link between equalities data and issues and social inclusion –Relationship of identity strands to social class and other socio-economic inequalities –What are the characteristics of households that enable or constrain inter-generational mobility Granularity issue –In what areas do equalities issues particularly impact on life chances or quality of life – does this help with prioritising what data should be collected – eg equalities aspects of early years interventions

11. Challenges Influencing future data collection –Development of Integrated Household Survey –Standard survey instruments for each of the strands –2011 census –Development of local administrative data –Engaging local players and pooling information, eg private sector employment data

12. EMF – a guide to focusing limited local resources? Recommended in Equalities Review to prime minister in Feb 2007 and endorsed by ONS review Needed to help –“all public bodies to agree priorities, set targets and evaluate progress towards equality” –Statutory duty of EHRC under 2008 Equality Act to produce triennial report and monitor social outcomes –Prioritise limited data collection and policy development resources at national and local level

13. Equality Measurement Framework: core building blocks Life Physical security Health Education Standard of living Productive and valued activities Participation, influence and voice Individual, family and social life Identity, expression and self- respect Legal security 10 domains Inequality by equality characteristics (gender, transgender, ethnicity, disability, sexual orientation, age, religion/belief, social class.....) 3 aspects (i) outcomes (ii) processes (discrimination, dignity and respect) (iii) autonomy (empowerment, choice and control) Inequality of substantive freedom (inequality in the central and valuable things in life that people can do and be)

14. Equality Measurement Framework: Selection of indicators project  PRINCIPAL AIM: to agree 3 to 5 ‘spotlight’ indicators for each domain  Balancing necessity to be fine-grained and comprehensive with need for sufficient simplicity to motivate  Secondary aim: to identify a longer list of good indicators which can be used for supplementary monitoring for particular issues (including at different geographic levels.

15. Selection of indicators Legitimacy  consultation with general public and indivs/grps at particular risk of discrimination and disadvantage on list of domains & sub-domains  specialist and stakeholder consultation on selection of indicators 1.Draw up selection criteria 2.Develop long list of possible indicators 3.Conduct face-to-face consultation with subject specialists and stakeholders 4.Formulate provisional short list of indicators for each domain 5.Hold web consultation with subject specialists and stakeholders 6.Revise short lists; detailed assessment of statistical robustness 7.Sign off by national governments and EHRC Procedure:

16. eg: Productive and valued activities domain Provisional spotlight indicators Indicator 1: Employment rate (LFS / IHS) Indicator 2: Earnings (LFS / IHS) –risk of low earnings –pay gaps Indicator 3: Occupational segregation (horizontal) (LFS / IHS) Indicator 4: Discrimination in employment (Fair Treatment at Work Survey) Indicator 5: Unpaid care and free time –free time (ONS Omnibus Time Use Module) –recognition and gains from unpaid care and childcare (ELSA plus?)

17. eg: Legal security domain Provisional spotlight indicators Indicator 1: Equal protection and support for victims of crime Indicator 2: Organisational discrimination and unfair treatment by the CJS (objective) Indicator 3: Organisational discrimination and unfair treatment by the CJS (subjective) Indicator 4: Deprivation of liberty: Numbers and conditions Indicator 5:Equal protection and support for individuals with justiciable civil justice problems

18. Make best use of existing data Make sure known about: ONS review, Lancaster Review, IDeA Review as well as local and regional Pooling across years and/or booster samples Adding equality characteristic questions Modifying question wording to broaden coverage or improve clarity of definition Adding indicator questions to existing surveys

19. Lancaster Statistics Review Will be published mid October 450 page resource Suggests data and indicators to consid Shows what nationally collected data are available in sufficient sample at regional level – and sometimes local. Limited on additional local/regional sources Use as a resource; informs indicator selection for national EMF

20. Lancaster Statistics Review 2.METHODOLOGY AND KEY CONCEPTS 6 2.1Introduction 6 2.2Defining key concepts 6 2.3Criteria of quality of statistics10 2.4Review of existing and suggested indicators14 2.5Data resources: surveys, administrative data and indicator sets19 3.EVALUATION: STRENGTHS AND CHALLENGES21 3.1Introduction21 3.2Strands: availability, definitions, classifications, developments 21 3.3Geographic areas37 3.4Technical aspects: gaps, rates and thresholds40 3.5Domains 4.CONCLUSIONS: RECOMMENDATIONS FOR HEADLINE INDICATORS59 4.1Introduction59 4.2Potential headline indicators61 4.3Data needs to support proposed headline indicators91 4.4Technical appendix: definitions and data sources95

21. Lancaster Data Review 5.KEY STATISTICS100 5.1Introduction100 5.2Index of key statistics103 6.DATA RESOURCES253 6.1Introduction253 6.2List of data sources256 6.3Surveys256 6.4Administrative data sources377 6.5Indicator data sets400

22. Example of key statistic Key statistic: Attainment in Higher Education Technical definition Attainment of first or upper second class degree by first degree qualifiers Class of first degree by first degree qualifiers Strand availability Sex Ethnicity/race Disability Age Geographic Area availability United Kingdom Great Britain England Wales Scotland Northern Ireland Data source Higher Education Statistics Agency Examples of current or suggested use Office of Disability Issues Race Equality in Public Services (DCLG)

23. Example of data resource Taking Part: The National Survey of Culture Leisure and Sport Content Data for the Taking Part Survey are collected on areas of interest to the Department of Culture, Media, and Sport. These areas include arts, sports, museums, broadcasting, gambling and volunteering and information is gathered on individuals’ participation, attendance, and attitudes etc. Sample size 28,117 (2005/06) Sample design Multi-stage stratified unclustered random sample using the Small Users Postal Address File (PAF) Frequency Annual Strands Sex Ethnicity/race Disability Age Religion/belief Social class (NS – SEC)

24. Example of data resource Standard region/country Number in sample Percent Tyne and Wear2,3982.0 Rest of North East3,2732.7 Greater Manchester4,7163.9 Merseyside2,5032.0 Rest of North West5,8624.8

25. Equality and Human Rights Commission - work on equalities data Mark Wright Regional Manager