1. Joost de Laat (Phd) Senior Economist Human Development Europe and Central Asia The World Bank

2.  2012 Slovakia Poverty Mapping project – Statistical Office/WB  What are poverty maps? Going from high level NUTS to small LAU areas  Combining 2011 census information with EU-SILC survey information as a (potential) way to poverty mapping  Bulgaria poverty mapping case study

4. http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/principles_characteristics

5. Example: Nuts 3 in Slovakia represent 8 regions

6. http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/local_administrative_units

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

8. 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

9. 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)

10.  Highly disaggregated databases of: ◦ Poverty ◦ Inequality ◦ Average income/consumption ◦ Calorie intake ◦ Under-nutrition ◦ Other indicators (health, employment etc)

11.  Goals ◦ Identify poor municipalities targeting for poverty reduction ◦ Serve a basis for targeting for poverty reduction  Implementation: Joint team  Implementation: Joint team (Data Users’ Group) ◦ Leadership of the Ministry of Labor and Social Policy (MLSP) ◦ Technical expertise of the National Statistical Institute (NSI) ◦ Active involvement of leading Bulgarian academics ◦ World Bank financing and technical assistance trough a Capacity Building Institutional Development Fund (IDF) grant  Outcomes ◦ 2003 and 2005 poverty incidence maps

12.  Methodology ◦ Data sources: 2001 Census and 2001 and 2003 Bulgaria Integrated Household Surveys (BIHS), and district level indicators ◦ BIHS: 2,500-3,023 households, representative at NUTS 1 (Sofia, urban, rural level) ◦ 30 common indicators between Census and BIHS ◦ Standard “small-area estimation” procedure  Municipal level indicators estimated ◦ Poverty rate, poverty depth, severity of poverty, and Gini coefficients

13. 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 Turkish households)  Poorest areas characterized by lacking in: o human capital endowment (prevalence of people with low education attainment, or elderly pensioners), and o infrastructure

14.  Policy use ◦ Strategic poverty documents, e.g.  The National Plan for Poverty Reduction 2005-2006  Strategy for Reduction of Poverty and Social Exclusion 2006-08  District Development Strategies 2005-2015 ◦ Targeting of antipoverty interventions  Program for Poverty Reduction in the (13) Poorest Municipalities  Targeting of Social Investment Fund (SIF) projects included in a multi-dimensional continuous scoring formula applied for ranking of municipal proposals, along with other indicators Social Investment and Employment Promotion Project (WB)

15.  Appropriate for targeting Poverty maps can be very useful tool to target poorest areas  Implemented around the world.  Window of opportunity: 2011 Censuses and annual EU-SILC survey data  Involve community of Roma stakeholders to identify Roma communities on poverty map and build ownership – Slovak Roma Atlas