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Roma Integration: Skills, Incentives, Policy Options
Martin Kahanec (CEU, IZA, CELSI)Vera Messing (CEU)Klára Brožovičová (CELSI)Brian Fabo (CELSI)
1
The story1. Education as a driver or Roma/non-Roma
exclusion/employment gaps
2. Measuring the role of low education in the labor market
3. Welfare provisions and incentives to work
4. Policy options/conclusions
2
The context
3
The Great Employment Barrier
UNDP/WB/EC, 2011
Minorities at greatest risk of exclusion in the EU
0
5
10
15
20
25
30
35
40
45
Bangla-deshi
African Turks Muslim,MiddleEastern
Former-Yugoslavor Balkan
EasternEuropean
Roma orSinti
Other
Perc
ent
Roma/Sinti top the list
Zimmermann et al. 2008
Risk of exclusion (Policy Matrix)
Zimmermann et al. 2008
Slovakia
Hungarians
Roma
Ruthenians and
Ukrainians
Asians
1
2
3
1 3 5
Risk
Tre
ndRoma: highest possible risk and increasing
What barriers?
Zimmermann et al. 2008
0
10
20
30
40
50
60
Perc
ent
Minorities in general Minorities at greatest risk
Education next to discrimination
The role of education1. Segregation (O’Higgins, 2010), discrimination
(Kahanec and Zimmermann, 2011) etc. lead to gaps in educational attainment
2. Educational gaps are perpetuated through intergenerational transmission (Kahanec and Yuksel, 2010)
3. Bottom line – Roma heavily overrepresented among the low educated
7
Roma/non-Roma human capital gaps
8
Male Female
Education (years)
Bad health assessment (self-
reported, %) Education (years)
Bad health assessment (self-
reported, %)
Country Roma non-Roma Romanon-
Roma Romanon-
Roma Roma non-Roma
Albania 4.6 9.7 11 8 4.2 9.5 14 10
Bosnia and Herzegovina 5.5 11.1 19 16 3.9 9.9 21 16
Bulgaria 7.1 11.1 11 13 6.2 11.3 13 18
Croatia 5.4 11.0 12 11 3.5 10.1 12 10
Czech Rep. 7.1 11.1 11 7 6.2 11.3 10 8
Hungary 8.4 11.0 15 18 7.5 10.5 18 21
Moldova 4.9 10.2 30 27 3.8 10.7 37 34
Montenegro 5.0 11.0 8 8 2.6 10.2 9 8
Romania 6.1 10.8 19 17 4.9 10.2 22 28
Serbia 6.7 11.0 18 12 4.9 10.6 22 19
Slovakia 9.1 12.0 6 7 8.8 11.9 7 8
Macedonia 6.9 10.8 13 8 5.1 10.2 16 9UNDP/WB/EC, 2011
Low education and employment
D19.1
Low education across countries
Education Low Medium High
Bulgaria 18.28 58.83 22.89
Spain 44.14 22.75 33.12
Hungary 18.22 64.46 17.32
Romania 22.72 62.07 15.21
Slovakia 6.86 75.98 17.16
Variation across the studied countries
Messing et al. 2012; EU LFS 2010
The role of low education: Spain
11
Activity Empl. Perm. Full Time Activity Empl. Perm. Full Time
Low Edu. -0.0415* -0.110*** -0.0345 0.00736 -0.187*** -0.0836*** -0.0115 -0.115***
High Edu. 0.0468*** 0.0592*** 0.0549*** 0.00607 0.152*** 0.0644*** 0.0234** 0.0685***
Age -0.00765***0.00333*** 0.00072 0.000649** -0.0101*** 0.00533*** 0.00585*** 0.00221***
Age (15-22) -0.123*** -0.0245* 0.0360** -0.0141* -0.147*** -0.0119 -0.0184 0.0382**
Age (52-62) -0.400*** -0.0718*** -0.244*** -0.0590*** -0.570*** -0.0782*** -0.190*** -0.172***
Not Capital -0.0464*** -0.0341** -0.0908*** -0.0111 -0.0810*** -0.0102 -0.0922*** -0.0329*
Sparse 0.00582 0.0340*** -0.0606*** 0.0132*** -0.00505 -0.0112 -0.0517*** -0.00954
Children -0.113*** 0.0511 0.0719 0.0515*** -0.158*** -0.0575 0.113* -0.0835*
Children (#) 0.00368 -0.0213 -0.0383 -0.0170** -0.0277 0.011 -0.0596** -0.00636
Children 3+ -0.0394** -0.00286 -0.0253 0.0136 -0.0479* -0.0425 0.0595 0.0174
Married 0.160*** 0.0928*** 0.0455*** 0.0264*** -0.0365*** 0.0241*** 0.00405 -0.0298***
52-62_Low -0.0172 -0.0211 -0.0616*** -0.0062 -0.0648*** -0.0292* -0.0822*** -0.0901***
15_22_Low 0.0567*** 0.0264* 0.112*** 0.0199*** 0.0860*** 0.0216 0.123*** 0.109***
NotCap_Low -0.000792 0.0124 0.00404 0.00663 0.0732** -0.0204 -0.00374 0.0433
Sparse_Low 0.0163 0.00488 -0.0835*** -0.00978 -0.0204 0.0146 -0.0890*** 0.0199
Constant 0.634*** 0.0996*** 0.180*** 0.0856*** 0.930*** 0.0455* 0.0265 0.213***
Observations 26,666 21,126 17,197 17,197 26,972 17,196 13,860 13,860
Pseudo R 2̂ 0.1857 0.0854 0.0347 0.0765 0.1835 0.0715 0.0328 0.0474
Male Female
Messing et al. 2012; EU LFS 2010Low education detrimental for activity, employment, hours
The role of low education: Slovakia
12Messing et al. 2012; EU LFS 2010
Activity Empl. Perm. Full Time Activity Empl. Perm. Full Time
Low Edu. -0.309*** -0.195*** 0.0404 -0.00952** -0.372*** -0.195*** -0.0246 -0.0103
High Edu. 0.0336***0.103*** -0.0045 0.00581*** 0.0738***0.145*** -0.0038 0.0207***
Age -0.0229***-0.0005 -0.00121*-0.000129 -0.0195***0.00239***0.0007 -5.9E-05
Age (15-22) -0.0019 -0.0194* 0.0445***0.00286 -0.272*** -0.0301**-0.0285**-0.0105*
Age (52-62) -0.681*** -0.0874***-0.0053 -0.0012 -0.762*** -0.0906***-0.0735***-0.0145**
Not Capital -0.0084 -0.0449***0.0116 0.00590*** -0.0091 -0.105*** 0.00152 0.00842**
Sparse -0.0136***-0.0459***-0.0066 -0.00375***0.0207***-0.0165**-0.0129**-0.00108
Children -0.125*** 0.0858***-0.0355 -0.00422 -0.251*** 0.0896***-0.0554 -0.0163
Children (#) 0.0175* -0.0337***0.00711 0.00464 -0.0284**-0.0446***0.0177 0.00887
Children 3+ -0.0066 -0.011 -0.0429* -0.0133*** -0.0503**-0.0112 0.0259 -0.0270**
Married 0.132*** 0.128*** -0.0202**0.00221 0.0497***0.0219***-0.0025 0.00291
52-62_Low 0.253*** 0.160*** 0.0555 0.00321 0.108*** 0.112*** 0.0981***-0.00785
15_22_Low -0.170*** 0.106*** -0.164** -0.0102** -0.280*** -0.0459 N/ A N/ A
NotCap_Low -0.0298 -0.144*** -0.0321 -0.00101 0.05 -0.0867**0.0484 -0.018
Sparse_Low 0.0838***-0.0047 -0.0389 -0.000535 0.0574***-0.0507***-0.0624 0.00428
Constant 1.302*** 0.245*** 0.264*** 0.0331*** 1.325*** 0.224*** 0.204*** 0.0930***
Observations 26,884 19,628 16,145 16,145 27,267 15,515 12,663 12,663
Pseudo R 2̂ 0.351 0.1512 0.0051 0.0561 0.2534 0.1385 0.0099 0.0295
Male Female
Low education very detrimental for activity and employment
The role of low education: Slovakia
13Messing et al. 2012; EU LFS 2010
Activity Empl. Perm. Full Time Activity Empl. Perm. Full Time
Low Edu. -0.309*** -0.195*** 0.0404 -0.00952** -0.372*** -0.195*** -0.0246 -0.0103
High Edu. 0.0336***0.103*** -0.0045 0.00581*** 0.0738***0.145*** -0.0038 0.0207***
Age -0.0229***-0.0005 -0.00121*-0.000129 -0.0195***0.00239***0.0007 -5.9E-05
Age (15-22) -0.0019 -0.0194* 0.0445***0.00286 -0.272*** -0.0301**-0.0285**-0.0105*
Age (52-62) -0.681*** -0.0874***-0.0053 -0.0012 -0.762*** -0.0906***-0.0735***-0.0145**
Not Capital -0.0084 -0.0449***0.0116 0.00590*** -0.0091 -0.105*** 0.00152 0.00842**
Sparse -0.0136***-0.0459***-0.0066 -0.00375***0.0207***-0.0165**-0.0129**-0.00108
Children -0.125*** 0.0858***-0.0355 -0.00422 -0.251*** 0.0896***-0.0554 -0.0163
Children (#) 0.0175* -0.0337***0.00711 0.00464 -0.0284**-0.0446***0.0177 0.00887
Children 3+ -0.0066 -0.011 -0.0429* -0.0133*** -0.0503**-0.0112 0.0259 -0.0270**
Married 0.132*** 0.128*** -0.0202**0.00221 0.0497***0.0219***-0.0025 0.00291
52-62_Low 0.253*** 0.160*** 0.0555 0.00321 0.108*** 0.112*** 0.0981***-0.00785
15_22_Low -0.170*** 0.106*** -0.164** -0.0102** -0.280*** -0.0459 N/ A N/ A
NotCap_Low -0.0298 -0.144*** -0.0321 -0.00101 0.05 -0.0867**0.0484 -0.018
Sparse_Low 0.0838***-0.0047 -0.0389 -0.000535 0.0574***-0.0507***-0.0624 0.00428
Constant 1.302*** 0.245*** 0.264*** 0.0331*** 1.325*** 0.224*** 0.204*** 0.0930***
Observations 26,884 19,628 16,145 16,145 27,267 15,515 12,663 12,663
Pseudo R 2̂ 0.351 0.1512 0.0051 0.0561 0.2534 0.1385 0.0099 0.0295
Male Female
Low education worse for the young or rural; the elderly can cope better
The activity penalty: All countries
14Messing et al. 2012; EU LFS 2010Romania most severe, then Slovakia, Hungary and Bulgaria, Spain
The employment penalty: All countries
15Messing et al. 2012; EU LFS 2010Slovakia most severe, then Hungary, Bulgaria, Spain and Romania
Incentives
D19.1
Does it pay to work (>50% return)?
17
Scenario Bulgaria Spain Hungary Romania Slovakia
single Employed 227.49 688.16 335.61 164.57 393.58
Unemployed 47.71 414.07 140.63 45.52 84.03
family - 2 children Employed 525.45 1423.46 745.66 359.7 849.78
emp/unemp 297.92 1149.37 550.67 195.13 725.22
Unemployed 203.92 875.29 355.69 142.03 310.86
family - 5 children Employed 641.57 1494.17 920.27 405.55 1238.56
emp/unemp 403.51 1220.05 725.28 240.98 918.38
Unemployed 381.22 946.00 530.3 180.63 524.79
single parent Employed 293.96 735.27 418.44 231.5 617.06
Unemployed 183.16 461.21 223.45 151.08 251.83
Monetary incentives to work smaller with children Assume min wage, control for benefits, taxes (Messing 2012)
Does it pay to work (>33% return)?
18
Scenario Bulgaria Spain Hungary Romania Slovakia
single Employed 227.49 688.16 335.61 164.57 393.58
Unemployed 47.71 414.07 140.63 45.52 84.03
family - 2 children Employed 525.45 1423.46 745.66 359.7 849.78
emp/unemp 297.92 1149.37 550.67 195.13 725.22
Unemployed 203.92 875.29 355.69 142.03 310.86
family - 5 children Employed 641.57 1494.17 920.27 405.55 1238.56
emp/unemp 403.51 1220.05 725.28 240.98 918.38
Unemployed 381.22 946.00 530.3 180.63 524.79
single parent Employed 293.96 735.27 418.44 231.5 617.06
Unemployed 183.16 461.21 223.45 151.08 251.83
Monetary incentives rather small especially for large families
Assume min wage, control for benefits, taxes (Messing 2012)
Policy options
Summary• Roma attain substandard labor market outcomes
• Education is a key factor
• Low education indeed leads to lower participation, employment, job quality
• If you are low educated (min wage), returns to working may be small or negative, especially for families with children
20
Imagine a world… A policy reflection• There are a number of policies that could help to get
at equal education
• The key appears to be breaking three vicious circles– substandard socioeconomic outcomes reinforce each
other for people, families (within and over generations), and communities;
– they fuel negative attitudes and perceptions, leading to ill-chosen policies;
– segmentation is perpetuated through (statistical) discrimination
• But imagine a world where Roma educational attainment matches that of non-Roma
21
Would we be done?• Research: gaps only partly due to differences in
education and other variables – much of the gaps is due to differences in returns to characteristics, i.e. unequal treatment (O’Higgins 2010, Drydakis 2012, Milcher and Fischer 2011)
• Bottom line: Educational equality is necessary, but not sufficient
22
Martin Kahanec Tel/Fax: +36 1 235 3097Email: [email protected]
Department of Public PolicyCentral European UniversityNador utca 9Budapest 1051Hungarypublicpolicy.ceu.hu
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Read more: NEUJOBS report D19.1