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)