Transcript DEC Course on Poverty and Inequality Analysis Module 4

Joost de Laat (Phd)
Senior Economist
Human Development Europe
and Central Asia
The World Bank
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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
http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/principles_characteristics
Example: Nuts 3 in Bulgaria represent 28 regions; Nuts 2
is 6 regions; Nuts 1 is 2 regions
http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/local_administrative_units
LAU 1 level (‘nuts 4’) – 262 municipalities (2005)
In summary:
 Household survey like EU-SILC have breadth
of indicators, but sample sizes too small to
be representative for local area units
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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)
Background
characteristics
unique to EUSILC
Step 1
Household Welfare
Common Household Background
Indicator(s) such as
Characteristics
at-risk-of-poverty in
EU-SILC or other detailed survey
EU-SILC
Step 0
Step 2
Common Household Background
Characteristics
National Population Census
Household Welfare
Indicator(s) such as
at-risk-of-poverty
not in census
POVERTY MAP(S)
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Highly disaggregated databases of:
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Poverty
Inequality
Average income/consumption
Calorie intake
Under-nutrition
Other indicators (health, employment etc)
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Goals
◦ Identify poor municipalities
◦ Serve a basis for targeting for poverty reduction
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Implementation: Joint team (Data Users’ Group)
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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
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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
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Municipal level indicators estimated
◦ Poverty rate, poverty depth, severity of poverty, and
Gini coefficients
Main Findings
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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
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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)
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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