Decade of Roma Inclusion Implementation

1. Decade of Roma Inclusion Implementation Purpose of collecting data and its possible application Andrey Ivanov Human Development Adviser, Bratislava RSC

2. Presentation outline • Methodology and sampling • Levels of comparability; difference between DATA and INDICATORS • Brief outlook at the data for Czech Republic • Possible usage of the data for policy purposes and for the Decade implementation

3. Nature of the survey • Integrated household survey containing household and individual modules • “Status” data and not “attitudes” information • Main interviewee – head of the household • Two separate questionnaires (status of the household and of each individual member) • Provides basis for comparisons in all countries in SEE and CEE with sizeable Roma minorities and other vulnerable groups like IDPs and refugees where relevant ( two or three separate samples) • Universe studied – households in areas with compact Roma population (municipalities or neighborhoods with share of Roma population at and above the national average), majorities living in close proximity to Roma and IDPs/refugees where relevant

4. The sampling model assumptions • Census understate absolute numbers but reflect the structure and distribution (“where those people are?”) • Comparability with the “majority in proximity” equally important as comparability with national average (perhaps even more important) • Majority boosters – a “benchmark” sample for comparisons between Roma and majorities living in close proximity to Roma (i.e. in similar socioeconomic environment) • Map vulnerability of groups with common socio-economic patterns

5. Sample design • Universe defined as average and above share of Roma in each AU; • Sampling clusters were determined using estimations of Roma organizations • Individual respondents identified using random route selection • The major challenge - “Who is Roma?” Compromise between self-identification and external identification with three levels of identification: • Self-identification (reflected in the census) to identify the distribution and size of sampling clusters • External identification (local activists, Roma experts, social workers) to identify the specific location of sampling clusters • Potential respondents’ “implicit confirmation of the external identification” (identifying the individual respondents)

6. “Decade” countries samples

7. Data and Indicators • The survey provides data on the status (both of individuals and of the households). Example of data: levels of HH incomes or educational status or age of respondents • Based on the data indicators are computed using individual records (poverty rates based on income or expenditure data or enrollment rates based on educational status and age of respondents) • Data is fixed, indicators may vary (for example depending on the poverty line chosen)

8. Levels of comparability • Between different sampled groups (Roma and majority living in close proximity to Roma) • Between Roma and status of the average population (reflected in HBS, LFS) • Between Roma populations in different countries with similar socioeconomic conditions

9. Data and its application for the National Action Plans • Why do we need data if we already know that the situation is dire? • What kind of data? • How to read and understand it? • How to avoid misinterpretation?

10. What the data shows: Poverty

11. Employment

12. Self-employment and small business

13. Equal employment opportunities

14. Education - 22% of Roma children attending “Schools for disabled”

15. Access to housing

16. Social exclusion and housing

17. Social exclusion and modern communication

18. Access to health services

19. Conclusions and next steps • Quantitative data is necessary to outline the real magnitude of disparities • It helps build persuasive message and receive broad constituencies’ support Decade implementation • It suggests the areas needing more work (the empty boxes in blue, which still need to be filled in)