Post on 11-Jan-2016
Inclusion of Immigrants into Welfare: The Myths and the Veracity in the EU
Martin KahanecCentral European University
Institute for the Study of Labor (IZA)
Central European Labour Studies Institute (CELSI)
Credits to: Alan Barrett, Corrado Giulietti,
Martin Guzi, Bertrand Maitre, Klaus Zimmermann, et al.
June 2012, Bratislava
What are we interested in, and why?
• Immigrant welfare receipt is a controversial issue
– Immigrants more likely to have worse socio-economic outcomes (…)
– Concerns that immigrants disproportionally participate in (abuse) welfare (Cohen, Razin and Sadka, 2009 and Nannestad, 2006)
– Concerns that immigrants constitute a fiscal burden for host countries (De Giorgi and Pellizzari, 2009)
Immigrants across the EU
Highest shares CY, IE, BE, AT, SE, UK; lowest RO, BG, PL, SK, HU, CZ.
0
5
10
15
20
25
RO
BG PL
SK
HU
CZ FI
LT
PT
DK
ES SI
NL
GR IT
EE
LV
FR
UK
SE
AT
BE IE CY
Other
EUN
EU12
EU15+EFTA
Source: Kahanec, 2012. EU LFS 2010
Poverty among immigrants
Figure 4.9: Estimated marginal impact of migrant status on support receipt: At risk of poverty
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
LU BE CY FI GR SE FR AT UK NO CZ ES IT IE DK NL PT DE* IS PL
Non-EU EU
Source: EU-SILC (2008). Notes: *All migrants for Germany.
Unskilled immigrants?
Non-EU immigrants well-educated, especially in NMSs. Less skilled than natives are EUNs in the EU15, other immigrants in eg ES and FI.
c) Percent high-educated EUN immigrants and natives
d) Percent high-educated other immigrants and natives
DEIT
SK
PT
NL
UKLV
PL LT
SIFR
CY
BE
DK
HU
CZ
EE
AT
FI
IE
GR
ES
RO
SE
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Percent high skilled, natives
Per
cen
t h
igh
ski
lled
, EU
N
DE
IT
SK
PT
NL
UK
LV
PL
LTSI
FR
CY
BE
DKHU
BG
CZ
EE
AT
FI
IE
GRES
RO
SE
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Percent high skilled, natives
Per
cen
t h
igh
ski
lled
, oth
er o
rig
in
Tertiary education. Source: Kahanec, 2012. EU LFS 2010
Brain waste?
Non-EU immigrants more often work in less-skilled occupations (especially ES, IT, AT, DE< SE, NL), except for some NMSs.
c) Percent high-skilled EUN immigrants and natives
d) Percent high-skilled other immigrants and natives
SE
RO
ES
GR
IE
FI
AT
EECZ
BGHU
DK
BE
CYFR
SI
LT
PLLV
UKNL
PT
SK
ITDE
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
10 15 20 25 30 35 40
High-skilled share, natives
Hig
h-s
kile
d s
har
e, E
UN
SE
RO
ES
GR
IE
FI
AT
EE
CZ
BG
HU
DKBE
CY
FR
SI
LTPL
LV
UKNL
PT
SK
IT
DE
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
10 15 20 25 30 35 40
High-skilled share, natives
Hig
h-s
kile
d s
har
e, o
ther
ori
gin
ISCO 1-3. Source: Kahanec, 2012. EU LFS 2010
Ratio of proportions of immigrants and natives: Unemployment support
0
1
2
3
4
5
NO FI IS PL AT UK IT GR LU FR DK SE DE* BE PT NL ES CY IE CZ
Non-EU EU
Estimated marginal impact of immigrant status on support receipt: unemployment, sickness and disability
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
FI DK AT FR NO LU DE* IT GR NL BE IS UK SE PT ES PL IE CY CZ
Non-EU EU
Ratio of proportions of immigrants and natives: Unemployment support for those who are unemployed
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
LU DK GR UK IT CZ IS DE* PL AT FI FR PT ES BE NO CY SE IE NL
Non-EU EU
So…
• Immigrants more likely to be poor
• Not necessarily less educated than natives
• But downskilling
• Take up welfare more frequently
• But have inadequate access to welfare
• Do they shop for welfare?
What do we do? • We take unemployment benefits spending (UBS) in GDP
a measure of welfare (for now)– Aggregate measure, “generosity index”
• We explicitly account for the possible endogeneity of welfare spending
• We concentrate on Europe as a cluster of welfare-heterogeneous countries among which migration is relatively easy
• We have panel data with a good number of observations
Data
• Gross inflows of foreigners/population, 16-64: OECD-SOPEMI• UBS and other welfare measures/GDP: OECD Social Expenditure
Database (SOCX)• Contextual variables: (unemployment rate, per-capita GDP, etc):
World Development Indicators (WDI) online database. • Unbalanced panel with 248 observations, 19 EU countries 1993-
2008
Econometric model
where:
mit - immigrant inflows as percentage in total population in country i at time t
Xit-1 - UBS as a percentage of GDP
Zit-1 is the matrix that includes the immigration rate (networks), per-capita GDP, unemployment rate.
All explanatory variables are lagged, as we assume lagged response of potential immigrants. This may also alleviate the endogeneity problem but only partially if at all (see below).
Fixed country and year dummies, so variation only within countries and beyond systemic shocks. Population weights.
'1 1it it it i t itm x z γ
Results (OLS, non-EU)
(a) (b) (c) (d) Non-EU immigrants
UBS 0.058 * 0.061 * 0.066 *** (0.028) (0.031) (0.021) Stock of non-EU immigrants 0.141 *** 0.129 *** 0.123 *** 0.079 * (0.028) (0.026) (0.028) (0.039) Per-capita GDP 0.017 *** 0.019 *** 0.018 *** 0.007 (0.007) (0.007) (0.007) (0.004) Unemployment rate -0.007 -0.015 -0.005 -0.026 (0.018) (0.017) (0.016) (0.015) Constant -0.056 *** -0.063 *** -0.053 *** -0.02 (0.023) (0.024) (0.021) (0.014)
2R 0.64 0.65 0.68 0.52
a - wihout UBS; b - with UBS; c - with other welfare components (health, family, pension); d – no weights
Endogeneity of UBS
• OLS results point at a welfare magnet for non-EU immigrants• But we have an endogeneity problem: UBS may be a function of
immigrationA) Immigrants themselves directly increase UBS take up or decrease
average GDP
B) Policy reaction to immigration may cut/expand UBS
So we need to take care of reverse causality – 2SLS
• We need an instrument that is correlated with UBS, but not with immigration
• We propose “the number of parties in the ruling coalition”
• Argument: with a relatively large number of parties in coalition, it is difficult to impose austerity on spending. Or, there are more parties with interest to spend (and win voters)
• Simultaneously, this instrument is unlikely to be directly correlated with immigration.
• Is this instrument relevant?
First stage: UBS on # of coalition parties
0.0
2.0
4.0
6
0 2 4 6 8
EU immigrants Non-EU immigrants
IV GMM IV GMM
UBS 0.040 -0.013 -0.003 -0.004 (0.065) (0.029) (0.007) (0.022) Stock of immigrants 0.133 *** 0.115 *** 0.075 *** 0.073 *** (0.018) (0.011) (0.009) (0.014) Per-capita GDP 0.019 *** 0.015 *** 0.000 0.000 (0.003) (0.002) (0.001) (0.001) Unemployment rate -0.012 -0.013 *** 0.000 0.002 (0.011) (0.006) (0.001) (0.003) Constant -0.068 *** -0.054 *** 0.001 0.002 (0.012) (0.007) (0.002) (0.005) N 248 248 248 248
Notes: robust standard errors in parentheses. */**/*** indicate significance at the 10/5/1% level. All models are estimated by fixed effects and contain year dummies. All regressions are weighted by the counts of individuals in each country in the year 2000. Instrument is the number of parties in the winning parliamentary coalition. IV estimates are computed using the Stata command xtivreg2 developed by M.E. Schaffer. GMM estimates are obtained using the Stata command xtabond2 developed by D. Roodman.
Results
Interpretations
• Immigrants are more likely to be poor• They are not necessarily less educated, but their skills are not
transferable (LM problem)• They are more likely to be in welfare take up, but not because
there is something special about migrants.• And also not because they would abuse welfare• Rather, they seem to be at higher risk due to their characteristics
and they face barriers to access to welfare (welfare problem)• Integration and selection of immigrants!• What should we do?
What Do Ethnic Minorities Want?
01020304050607080
Per
cent
• Almost all minorities want to change their situation (86% of all respondents, 98% of minority respondents)
• Mainly in paid employment, education, attitudes and housing
01020304050607080
Minorities in general Minorities at greatest risk
0
10
20
30
40
50
60
Perc
ent
Minorities in general Minorities at greatest risk
Integration barriers and desired intervention
0
5
10
15
20
25
30
None General public Specific public Business NGOs Other
Perc
ent
All respondents Minority respondents
0
10
20
30
40
50
60
70
Equal treatment Specific provisions Positive discrim. Other
Percent
All respondents Minority respondents
Preferred policy principles(minorities in general and at greatest risk)
0
10
20
30
40
50
60
70
Equal treatment Specific provisions Positive discrim. Other
Percent
All respondents Minority respondents
• Equal treatment!
• But some room for positive action
Germany
Turks
ex-Soviet Union
ex-Yugoslav
Africans
1
2
3
1 3 5
Risk
Tre
nd
Slovakia
Hungarians
Roma
Ruthenians and
Ukrainians
Asians
1
2
3
1 3 5
Risk
Tre
nd
Policy Matrix •Based on the Expert Opinion Survey
• A tool to compare and scale the situation of minorities
• The four largest minorities in each country
• Measuring the risk of labor market exclusion and its trend
• The NE corner desires most policy attention
Conclusions
• Serious demographic challenges
• Severe ethnic divides in the EU (LM, downskilling, poverty)
• Welfare state helps but the discussion is misguided (lack of access rather than abuse, no welfare magnet etc)
• Ethnic minorities want change (attitudes, labor market access, etc)
• Missed opportunities
• Policy action needed
Martin Kahanec Tel/Fax: +36 1 235 3097Email: kahanecm@ceu.hu
Department of Public PolicyCentral European UniversityNador utca 9Budapest 1051Hungarywww.publicpolicy.ceu.hu