This document summarizes research on measuring and analyzing social exclusion across 6 countries in the region. It presents the development of a Social Exclusion Index using a multidimensional poverty approach. Survey data from 2700 households was collected and analyzed alongside data characterizing local community conditions. The index was applied to Serbia as a case study, contextualizing results based on statistical data from municipalities. While not all analyzed indicators showed clear relationships, additional qualitative research and more localized data is recommended to better understand links between social exclusion and local characteristics.

1. Secondary source contextualization of survey dataHDCA Annual conference, The Hague, 7th September 2011 Andrey Ivanov, Human Development Advisor, UNDP BRC

2. Acknowledgements This presentation is summarizing the results of the regional project on ‘Social Inclusion in CEE’ and thus benefits from the inputs and ideas of the entire team involved The additional computation of indicators was done by Mihail Peleah, UNDP BRC The follow-up research in Serbia was conducted by Pavle Golicin and BrankaAndjelkovicfrom Public Policy Research Centre, Belgrade

3. Summary The Social Exclusion Index The Social Exclusion Chain The local context – how to grasp it? Piloting the approach at regional and country levels Broader opportunities for further application

4. The Social Exclusion Index Application of the Multidimensional Poverty Approach and Alkire-Foster method ‘Dual cutoff’ method: within dimension: based on deprivation with respect to given dimension across dimensions: overall threshold (number of deprivations) beyond which a person is considered socially excluded

5. Construction of the index Three dimensions of social exclusion (with 8 indicators each): Economic: Deprivation in incomes, basic needs, access to employment, financial services; material needs and lack of amenities; housing and ICT-related exclusion. Social services: Access to and affordability of education and health services; other public services, such as public utilities. Participation: Deprivation in political, cultural and social participation; political, cultural and social support networks. Threshold: 9

6. Data sources Social Exclusion Survey in 6 countries of the region conducted in November 2009 Kazakhstan, Macedonia, Moldova, Serbia, Tajikistan, Ukraine Sample of 2700 households; In the case of Serbia 3,001 interviews in total (2,401 with members of the general population, plus two boosters with 300 Roma, and 300 internally displaced persons) 10 interviews per PSU (7 in Kazakhstan)

8. Social exclusion profiles

9. The exclusion chain Individual characteristics gender, ethnicity, health status Feedback to traits Positive: empowered, educated, Negative – accident as consequence of informal labor Inclusion Drivers of Exclusion Local context: rural, mono-town Positive reinforcing feedback i.e. vote, voice or action Institutions, policies and values Exclusion Negative feedback i.e. informality, unemp5loyment

10. Adding The local context Assumptions Individuals in each PSU share the same conditions Conditions do not change overnight Survey data can be enriched by a number of variables characterizing the local specifics related to exclusion Caveat: correlation doesn’t automatically mean causality Data source: expert-level assessments of the socioeconomic status of the individual PSUs in each country (done by national teams)

11. Local infrastructure 6 countries average

12. Mono-company towns 6 countries average

13. Exclusion and corruption 6 countries average

14. Environmental disasters 6 countries average

15. Serbia: The impact of the crisis Values for Serbia using the experts’ estimates as in the 6 countries average)

16. Serbia: Mono-company towns Values for Serbia using the experts’ estimates as in the 6 countries average)

17. Serbia: Infrastructure and migration Values for Serbia using the experts’ estimates as in the 6 countries average)

18. QUantifying local conditions Experts’ estimates are not sufficient. The ‘gold mine’ is in the existing statistical data on local level In-depth assessment and data base of local level indicators for Serbia from National statistics, local administrations from Google Maps (for distances) Method: The sample of the social exclusion survey broken down into three equal groups of PSUs – with low, medium and high value of respective parameter (using Stata command -xtile-) The SEI calculated for each group groups This approach combine both possibility of analysis and adequate sample size

19. Variables used Basic demographic characteristics (population by age, sex, ethnicity) Education (number of pupils by educational level, type of establishments, teachers) Health: number of doctors, natlity, mortality Employment by sectors and unemployment (registered) Local budgets (revenue and expenditure) Voters turnout Distances to social infrastructure (medical, school, restaurant)

20. Social exclusion and health SEI for Serbia contextualized for different local characteristics using the regular statistical data for municipalities where individual PSUs are located

21. Social exclusion and education SEI for Serbia contextualized for different local characteristics using the regular statistical data for municipalities where individual PSUs are located

22. Employment and activity rate SEI for Serbia contextualized for different local characteristics using the regular statistical data for municipalities where individual PSUs are located

23. Exclusion and political activity SEI for Serbia contextualized for different local characteristics using the regular statistical data for municipalities where individual PSUs are located

24. Social exclusion and remoteness SEI for Serbia contextualized for different local characteristics using the regular statistical data for municipalities where individual PSUs are located

25. Exclusion and infrastructure SEI for Serbia contextualized for different local characteristics using the regular statistical data for municipalities where individual PSUs are located

26. Not all criteria work… Values for Serbia using the experts’ estimates as in the 6 countries average)

27. Preliminary conclusions Not all indicators behave ‘adequately’ Due to small number of observations? Due to imprecise data? Because they contradict initial expectations? Additional qualitative research necessary to establish causality links A lot of data still not directly available but worth the effort acquiring, like Pollution and other environmental characteristics Vehicles park – newly registered, type, average age Electricity consumption, mobile phones traffic Traffic and other accidents, deaths by cause Registered crimes profiles …much more This can be particularly useful for instruments like HBS and LFS implemented on regular basis Because local conditions do not change in the short run, the method makes possible contextualizing data from earlier surveys For longer time periods, the change of the locally observed characteristics can be tracked as well (data exists) and then correlated with the change of household status recorded by HBS providing entirely new dimension to existing data

28. Next steps Mainstream the SEI into existing national statistical instruments, namely HBS adding ‘social exclusion’ component Mapping the potentially available information in existing data (from line ministries, companies, other sources) sets that could be used for social exclusion mapping and contextualization Investigate the feasibility tracking the dynamics of local conditions Test methods for addressing intra-settlements disparities (like GPS coordinates of surveyed households) Investigate the opportunities for local-level monitoring involving members of the respective communities – both collection of data and reporting through on-line application

29. Thank you! For more information Check out our web-site: www.undp.sk/socialinclusion …and our blog: http://europeandcis.undp.org/blog/