Katarina Mathernova
16 May 2011
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The right thing to do!
Political opportunity – greater awareness; political
momentum at the EU level – April 5th
Communication
Makes economic sense – World Bank study on
Benefits of Roma Inclusion
Economic argument for
Roma inclusion
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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
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
Study: Four key messages
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Roma inclusion is smart economics
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Roma want to contribute and have the
potential to do so
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There is knowledge about what needs to
be addressed
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Resources are available
Roma are much less likely to be working than
non-Roma
% Employed
100
80
70
63
56
60
50
41
40
40
51
36
20
0
Bulgaria
Czech Republic
Non-Roma
Romania
Roma
Serbia
Roma with jobs earn much less than nonRoma
Relative average wages: majority is 100%
100
100
100
100
100
80
69
60
51
43
39
40
20
0
Bulgaria
Czech Republic
Non-Roma
Romania
Roma
Serbia
Young Roma are entering labor markets at
much higher rates than aging majority
populations
% Population 0-15 years old
Equal labor market opportunities would
generate billions of euros annually in extra
output
Euros
2,980
3000
2500
2000
1500
1,070
526
1,048
887
1000
367
500
252
0
Bulgaria (2007)
Czech Republic*
(2008)
Lower population est.
Romania (2008)
Serbia (2007)
Higher population est.
Equal labor market opportunities would
generate fiscal benefits of hundreds of
millions of euros annually
Euros
675
700
600
500
400
260
300
200
260
257
202
128
62
100
0
Bulgaria (2007)
Czech Republic*
(2008)
Lower population est.
Romania (2008)
Serbia (2007)
Higher population est.
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
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Fiscal benefits would be >3 times the needed
resources to bridge education gap
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Facts do not accord with common perceptions:
Roma want to work but cannot find jobs
% Working age population participating in labor force
100
100
80
79
85
84
75
68
80
70 72
61
68
60
60
40
40
20
20
0
0
59
58
55
49
37
40
28
Bulgaria
Czech Romania
Republic
Male
LFP Roma
Majority
group
Serbia
Bulgaria
Czech Romania
Republic
Female
LFP Roma
Majority
group
Serbia
Facts do not accord with common perceptions:
vast majority of Roma do not depend on social
assistance
% Households receiving social assistance
100
80
60
40
25
20
16
12
0
Bulgaria
Romania
Proportion of population (%)
Serbia
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
100
87
80
80
77
75
60
40
20
20
13
12
13
0
Bulgaria
Czech Republic
Majority Group
Romania
Roma
Serbia
Roma Inclusion Requires a MultiDimensional Approach
Priority areas include:
• Employment activation policies
• Ensuring equal education opportunities
• Addressing housing inequities
• Closing health disparities
Katarina Mathernova
16 May 2011
LAU 1 level (‘nuts 4’) – 262 municipalities (2005)
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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
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)
LAU 1 level (‘nuts 4’) – 262 municipalities (2005)
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 Turk
households)
Poorest areas characterized by lacking in human
capital endowment and in infrastructure
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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