1 / 15

Identifying most vulnerable (Roma) communities in Bulgaria

Identifying most vulnerable (Roma) communities in Bulgaria. Joost de Laat (Phd) Senior Economist Human Development Europe and Central Asia The World Bank. Outline. 2012 Bulgaria Poverty Mapping project – Statistical Office/ World Bank / European Commission

chick
Download Presentation

Identifying most vulnerable (Roma) communities in Bulgaria

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Identifying most vulnerable (Roma) communities in Bulgaria Joost de Laat (Phd) Senior Economist Human Development Europe and Central Asia The World Bank

  2. Outline • 2012 Bulgaria Poverty Mapping project – Statistical Office/ World Bank / European Commission • 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

  3. Estimating EU Poverty Indicators @ LAU Levels

  4. How to go from ‘high-level’ NUTS…? http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/principles_characteristics

  5. Example: NUTS 3 Example: Nuts 3 in Bulgaria represent 28 regions; Nuts 2 is 6 regions; Nuts 1 is 2 regions

  6. …down to ‘Local Administrative Units’ LAU levels 1 and 2? http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/local_administrative_units

  7. LAU 1: Bulgaria Poverty Incidence Map LAU 1 level (‘nuts 4’) – 262 municipalities (2005)

  8. Estimating EU Poverty Indicators @ LAU levels: Main Challenge In summary: • Household survey like EU-SILC have breadthof 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 Source: “EU legislation on the 2011 Population and Housing Censuses” (Eurostat 2011, ISSN 1977-0375)

  9. Small Area Estimation: CombineCensus and Survey Information Step 1 Background characteristics unique to EU-SILC Common Household Background Characteristics EU-SILC or other detailed survey Household Welfare Indicator(s) such as at-risk-of-poverty in EU-SILC Step 0 Step 2 Household Welfare Indicator(s) such as at-risk-of-poverty not in census Common Household Background Characteristics National Population Census POVERTY MAP(S)

  10. What are Poverty Maps? • Highly disaggregated databases of: • Poverty • Inequality • Average income/consumption • Calorie intake • Under-nutrition • Other indicators (health, employment etc)

  11. Bulgaria Poverty Map Case Study • Goals • Identify poor municipalities • Serve a basis for targeting for poverty reduction • 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 • Outcomes • 2003 and 2005 poverty incidence maps

  12. Bulgaria Poverty Map Case Study • 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. Bulgaria Poverty Map Case Study 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: • human capital endowment (prevalence of people with low education attainment, or elderly pensioners), and • infrastructure

  14. Bulgaria Poverty Map Case Study • 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. Identify vulnerable communities concluding Remarks • 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 or HBS survey data • Involve community of Roma stakeholders to identify Roma communities on poverty map and build ownership

More Related