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)?

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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

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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

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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

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Martin Kahanec Tel/Fax: +36 1 235 3097Email: kahanecm@ceu.hu

Department of Public PolicyCentral European UniversityNador utca 9Budapest 1051Hungarypublicpolicy.ceu.hu

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Read more: NEUJOBS report D19.1