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Decade of Roma Inclusion Implementation. Purpose of collecting data and its possible application Andrey Ivanov Human Development Adviser, Bratislava RSC. Presentation outline. Methodology and sampling Levels of comparability; difference between DATA and INDICATORS
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Decade of Roma Inclusion Implementation Purpose of collecting data and its possible application Andrey Ivanov Human Development Adviser, Bratislava RSC
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
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
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
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)
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)
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
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?
Education - 22% of Roma children attending “Schools for disabled”
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)