1. Katarina Mathernova, World Bank 16 May 2011

2.  The right thing to do!  Political opportunity – greater awareness; political momentum at the EU level – April 5 th Communication  Makes economic sense – World Bank study on Benefits of Roma Inclusion

3. Economic argument for Roma inclusion

4.  4 country study: Bulgaria, Czech Republic, Romania and Serbia  Majority populations in these countries are aging. Roma share of new labor market entrants is high and growing  Large employment gap. Biggest driver is the large educational gap, especially at the secondary level  Closing labor market gap can increase national incomes by up to Euro 5.5 billion and tax revenues by Euro 1.5 billion in these 4 countries

5. Study – Roma Inclusion: An Economic Opportunity Focus: Inclusion in Employment Countries: Bulgaria, Czech Republic, Romania, Serbia Quantitative analysis: 7 household surveys Qualitative analysis: interviews with 222 stakeholders

6. Study: Four key messages *Roma inclusion is smart economics *Roma want to contribute and have the potential to do so *There is knowledge about what needs to be addressed *Resources are available

7. Roma are much less likely to be working than non-Roma % Employed

8. Roma with jobs earn much less than non- Roma Relative average wages: majority is 100%

9. Young Roma are entering labor markets at much higher rates than aging majority populations % Population 0-15 years old

10. Equal labor market opportunities would generate billions of euros annually in extra output Euros

11. Equal labor market opportunities would generate fiscal benefits of hundreds of millions of euros annually Euros

12. Fiscal benefits are many times larger than the public spending on education Assume it would cost 50% more per Roma child Assume Roma currently complete primary and 10% completes secondary Assume no Roma attends pre-primary or tertiary Fiscal benefits would be >3 times the needed resources to bridge education gap

13. Facts do not accord with common perceptions: Roma want to work but cannot find jobs Male LFP Female LFP % Working age population participating in labor force

14. Facts do not accord with common perceptions: vast majority of Roma do not depend on social assistance % Households receiving social assistance

15. Education facts accord with perceptions: the vast majority of Roma do not have a secondary education or higher % Working age population with secondary and/or vocational

16. Roma Inclusion Requires a Multi- Dimensional Approach Priority areas include: Employment activation policies Ensuring equal education opportunities Addressing housing inequities Closing health disparities

17. Katarina Mathernova 16 May 2011

18. LAU 1 level (‘nuts 4’) – 262 municipalities (2005)

19.  East Asia: Cambodia, China, Indonesia, Laos, Papua New Guinea, Philippines, Thailand, Vietnam  South Asia: Bangladesh, India, Nepal, Sri Lanka  Latin America: Bolivia, Brazil, Chile, Colombia, Dominica, Ecuador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru  Africa: Burkina Faso, Cape Verde, Central African Republic, Cote d’Ivoire, Gabon, Gambia, Guinea, Ghana, Kenya, Madagascar, Malawi, Mali, Mauritania, Mozambique, Namibia, Niger, Rwanda, Senegal, Sierra Leone, South Africa, Tanzania, Uganda, Zambia,  North Africa: Morocco, Tunesia, Egypt, Yemen, Jordan  Eastern Europe and FSU: Albania, Azerbaijan, Bulgaria, Kazakhstan, Tajikistan

20. Source: “EU legislation on the 2011 Population and Housing Censuses” (Eurostat 2011, ISSN 1977-0375) In summary:  Household survey like EU-SILC have breadth of indicators, but sample sizes too small to be representative for local area units  Population censuses do allow small areas calculations but frequently lack breadth of indicators necessary to calculate main poverty indicators

21. Common Household Background Characteristics EU-SILC or other detailed survey Common Household Background Characteristics National Population Census Background characteristics unique to EU- SILC Household Welfare Indicator(s) such as at-risk-of-poverty in EU-SILC Step 0 Step 1 Household Welfare Indicator(s) such as at-risk-of-poverty not in census Step 2 POVERTY MAP(S)

22. LAU 1 level (‘nuts 4’) – 262 municipalities (2005)

23. Main Findings  Considerable variation in poverty levels across municipalities: 3%-40% of individuals  Considerable variation in poverty levels across municipalities within the same district  Poorest areas characterized by relatively higher shares of ethnic minorities (Roma and Turk households)  Poorest areas characterized by lacking in human capital endowment and in infrastructure

24.  Poverty maps can be very useful tool to target poorest areas with inclusion programs  Poverty maps have been implemented around the world. If data are available, production of poverty maps takes several months  Policy relevance and adoption of poverty maps enhanced through considerable outreach and capacity building  Population censuses being implemented throughout the EU in 2011 and availability of annual EU-SILC survey data are promising