WELFARE, WEALTH AND WORK – A NEW GROWTH PATH FOR EUROPE
A European research consortium is working on empirical
foundations for a new socio-ecological growth model
Ethnic diversity and the labour market
integration of immigrants
Thomas Horvath, Peter Huber,
FIW Workshop, WIFO
Diversity of foreign born
(increase 2004 -2011)
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Employment rate differential native –
foreign born
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Literature on economic effects of ethnic diversity
Theory:
 benefits in production may arise if workers from different cultures present different
complementary skills and abilities => increase of productivity.
 costs may be increased co-ordination costs among different groups and loss of trust
potentially resulting from increased ethnic diversity => loss of productivity.
Empiricis
 Productivity - Ottaviano and Peri 2005, 2006, Ratna et al. 2009, Sparber 2010, Dohse and
Gold 2014
 Growth - Easterly and Levine 1997
 Employment and wages of natives - Suedekum et al. 2014, Bellini et al. 2008, Nathan 2011,
Ottaviano and Peri 2005, 2006
 Innovation, entrepreneurship, workplace satisfaction - Niebuhr 2010, Audretsch et al. 2010,
Longhi 2011
 Recent results on regional level indicate a positive impact of diversity on regional
development due to increase in labour productivity. Literature pertaining to the
national level often finds a negative impact of ethnic diversity on productivity.
 Our contribution – impact on labour market integration of foreign born.
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Literature on regional demographics and labour market outcomes of
immigrants
Theory
 Networks and ethnic segregation may foster contacts among immigrants => facilitates job
matching and labour market integration
 May also reduce incentives to invest in host country specific human capital (language) =>
may reduce (long term) integration prospects
Empirics
 Segregation : empirical literature so far (mostly concerned with the US) has remained
rather inconclusive (Cutler et al. 2008)
•
•
•

Early studies => negative impact of segregation (e.g. Cutler and Glaeser 1997)
Later studies => no relationship or a reversed relationship (e.g. Collins and Margo, 2000)
Quasi experimental evidence => positive effects (Piil Damm, 2009, Edin et al 2003)
Networks: Quite few studies showing positive effects of share of own group in a region
(e.g.Patel and Vella 2007, Toussaint-Comeau 2008) or frequency of actual contacts on
labour market integration.
•
•
Some results suggesting that networks lead to integration into social benefits and lower language
learning (Chiswick and Miller 2002, Pohjola, 1991, Bertrand et al. 1998).
Also many extensions: contacts to natives may be more valuable than contacts to immigrants
(Kanas et al. 2012), quality of network may matter…
=> Our contribution: ethnic diversity of a region may be a further variable impacting
on employment and unemployment probabilities among immigrants
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Contribution
Central Question:
 What is the impact of ethnic diversity on labour market integration of
immigrants?
Differentiate:
 By education groups
 For recent and established
Results
 Robust positive average impact of ethnic diversity on employment
probabilities of both recent as well as established immigrants,
 Employment prospects of highly educated recent immigrants improve
more than those of less educated recent immigrants as ethnic diversity
increases :
Outline:
 Theory:
 Data:
 Results
 Conclusions
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Theory: Why are immigrants different?



Consider a region which hosts a total of M equally sized groups of immigrants from
different countries or ethnicities (m). The population size (𝑛𝑚 ) of each group is 𝑛𝑚 =
𝑁/𝑀 (N the total population of the region).
Labour is the only input to production and wages are rigid such that 𝑤 = 𝑤 + 𝜉𝑖 with
𝜉𝑖 normally distributed with mean zero and variance 𝜎𝜉 .
Employers cannot directly observe the productivity of immigrant workers.
•
•
•
Observe group membership and a signal (𝜃𝑖𝑚 ), such as educational attainment.
Signal measures productivity with some imprecision such that 𝜃𝑖𝑚 = 𝑞𝑚 + 𝜀𝑖𝑚 , with 𝜀𝑖𝑚
distributed normally with mean zero and variance 𝜎𝜀𝑚 .
Employers also know that the average productivity of workers of immigrant group 𝑚 is
identically and independently normally distributed with mean 𝜇𝑚 and variance 𝜎𝑚 .
 Classical statistical discrimination setup: expected productivity of a member of
an ethnic group (𝒎) given signal 𝜽𝒊𝒎 is distributed normally with mean
𝑬 𝒒𝒎 𝜽𝒊𝒎 = 𝝁𝒎 + 𝝈𝒎 (𝜽𝒊𝒎 − 𝝁𝒎 ) (𝝈𝒎 +𝝈𝜺𝒎 )
and variance
𝑽 𝒒𝒎 𝜽𝒊𝒎 = 𝝈𝜺𝒎 𝝈𝒎 (𝝈𝒎 +𝝈𝜺𝒎 ).
(Phelps 1972 and Aigner and Cain 1977)
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Theory: Effects of ethnic Diversity
Expected
Productivity
𝑬 𝒒𝒎 𝜽𝒊𝒎 , 
Signal
𝑬 𝒒𝒎 𝜽𝒊𝒎 ,  = 𝝁𝒎 + 𝝈𝒎 (𝜽𝒊𝒎 − 𝝁𝒎 ) (𝝈𝒎 +𝝈𝜺𝒎 )
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Empirical questions
 What is aggregate effect of diversity on labour
market integration?
 Dominance of signal noise or heterogeneity of
immigrants (i.e. more or less positiive effects on
more highly educated)?
 More relevant for imigrant groups where
information level is lower (i.e. recent versus
established immigrants)?
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Method


We estimate linear probability model
and
𝑦𝑖 (𝑚 )𝑟𝑡 = 𝛼0 + 𝛼1 𝜌𝑟𝑡 + 𝛽𝑋𝑖(𝑚 )𝑟𝑡 + 𝛾𝑍𝑟𝑡 + 𝜋 + 𝜇 + 𝜏 + 𝜁𝑖 (𝑚 )𝑟𝑡
Q1
𝑦𝑖 (𝑚 )𝑟𝑡 = 𝛼0 + 𝛼𝑙 𝜌𝑟𝑡 𝐿𝑖 + 𝛼𝑚 𝜌𝑟𝑡 𝑀𝑖 + 𝛼ℎ 𝜌𝑟𝑡 𝐻𝑖 + 𝛽𝑋𝑖 (𝑚 )𝑟𝑡 + 𝛾𝑍𝑟𝑡 + 𝜋 + 𝜇 + 𝜏 + 𝜁𝑖 (𝑚 )𝑟𝑡

Q2
Separately for recent established immigrants
Q3
Where
y = measure of immigrant labour market integration (individual level)
 = diversity
L,M,H = indicator education level
X = individual level controls
Z = region specific controls (regional e.g. unemployment rate)
 = sending country fixed effect
 = sending country fixed effect
 = time fixed effect
Note: Standard errors all clustered on region year
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Method issues

Missing variables:
•

As pointed out by Bertrand et al (1998) using data from more than one time
period that allows for controlling for region of residence, sending country and
time fixed effects should do away with many of the missing variable problems
that plague standard cross-sectional analyses on this topic.
Endogeneity (Dujardin and Goffette-Nagot 2010)
•
quasi experimental techniques or using of sibling data => we do not have the
necessary data
• sample restriction = focusing on a group of persons that is unlikely to have had a
choice in their original location decision. E.g. persons who moved to a particular
country as children and whose location decision was therefore taken for them by
their parents = leads to too few observations
=> We use Instrumental variables => two suggested and used in literature
(Cutler, 2000, Betrand et al. 2008)
• Prediction of values based on country and sending groups specific occupation
structure
• Prediction of values based on country and sending groups specific sector
structure
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Data

ELFS from 2004 to 2011
•
•
•
•
representative survey conducted in all EU27-countries and Norway
asks respondents on their country of birth and (if born abroad) on their duration of stay in the
respective country.
Provides information on NUTS2 level in most countries (NUTS1 in Austria).
Contains
•
•
•
•

demographic characteristics (age, gender, marital status number of children and many others),
labour market status (employed, unemployed and inactive) according to ILO definitions,
Can differentiate only between 8 country groups of sending region
Provides annual duration of residence for p to 10 years only
Caveats
•
German LFS does not ask the question of country of birth and in some countries (in
particular EU10) samples of foreign born to small to allow for meaningful analysis.
• Also some countries only one Nuts 2 region (creates problem with instrument)
 We end up with 15 EU countries (Austria, Belgium, Czech Republic, Finland, France,
Greece, Hungary, Italy, Ireland, Slovakia, Portugal, Slovenia, Spain, Sweden, UK) and
Norway
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Dependent variables & definitions
 Dependent variable
• Employment = 1 if foreign born is employed acording to ILO
definition, 0 else
• Unemployment = 1 if foreign born is unemployed acording to ILO
definition 0 if employed else
 Definition
• Foreigner= foreign born
 Sample Split (Dictated by data)
• Recent immigrant = in country of residence for 10 or less years
• Established immigrant = in country of residence for more than10
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Measuring diversity


The ELFS provides the possibility to differentiate between 8 immigrant
groups. (EU15 countries, EU12 countries, other European Countries, North
Africa and the Near East, other African countries, Asia, South America and
ROW).
This information can be used to construct two measures of ethnic diversity.
The first of these is the fractionalization index.
𝑀
2
𝑚=1 𝑠𝑚𝑟𝑡

𝜚𝑟𝑡 𝐹 = 1 −

used in much of the literature (e.g. Brunow and Brenzel 2012, Desmet,
Ortuno-Ortin and Wacziarg 2012, Easterly and Levine 1997)
Criticized by some authors as it puts a very strong emphasis on large
(majority) groups. Audretsch, et al. (2010), Trax et al. (2012) and Dohse and
Gold (2013) use the Theil index,

𝐻𝑟𝑡 =
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− 𝑀
𝑚=1 𝑠𝑚𝑟𝑡 ln(𝑠𝑚𝑟𝑡 )
ln(𝑀)
Controls

Control variables
•
•
•
•
•
•
•
•
Share of same ethnicity immigrants in same region
10 Age dummies
Gender
Marital status
Education
National employment/ unemployment rate
Years of resdinence
Citizenship
•
•
•
Region fixed effects
Sending Country fixed effects
Year fixed effects
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Descriptives
Unemployment probability
Employment probability
Fractionalisationa)
Entropy a)
Share own group a)
Unemployment rate natives a)
Employment rate natives a)
Female
Compulsory Education
Medium education
High Education
Citizenship
Age*
Years of residence**
>10 years of residence
Married
2004
0.114
0.647
0.630
0.633
0.024
0.079
0.652
0.511
0.374
0.378
0.248
0.428
40.039
4.520
0.620
0.636
2011
0.160
0.635
0.691
0.710
0.026
0.099
0.644
0.524
0.357
0.381
0.252
0.366
39.906
5.854
0.553
0.602
Overall
0.126
0.656
0.672
0.687
0.025
0.080
0.653
0.518
0.368
0.382
0.250
0.381
39.671
5.392
0.543
0.616
S: European labour force survey, own calculations. Table reports means, *) Age calculated at median of 5 year
age groups. **Years of residence – average for migrants with less than 10 years residence a) Measured at regional
level (standard deviation across regions)
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Results: Overall Recent
(1)
Dependent Variable:
Divesity
Share own group
Employment rate
Female
Medium Education
High Education
Citizenship
Years of residence
Married
Observations
R-squared
F-value
17| Event, Date
0.176**
(0.0720)
0.287***
(0.0957)
1.495***
(0.1520)
-0.221***
(0.0058)
0.0457***
(0.0049)
0.108***
(0.0073)
-0.00331
(0.0049)
0.0126***
(0.0009)
-0.0703***
(0.0047)
431754
0.153
(2)
(3)
(1)
(2)
(3)
Employment Probability
Unemployment Probability
Diversity = Fractionalisation
0.645***
0.763***
-0.120
-0.335***
-0.444***
(0.1530)
(0.1690)
(0.0734)
(0.1060)
(0.1320)
0.977***
0.983***
-0.0779
-0.356***
-0.400***
(0.1200)
(0.1150)
(0.0862)
(0.1050)
(0.1030)
1.530***
1.538***
1.649***
1.655***
1.656***
(0.1580)
(0.1600)
(0.0977)
(0.1000)
(0.1010)
-0.221***
-0.221***
0.0398***
0.0399***
0.0399***
(0.0058)
(0.0058)
(0.0036)
(0.0036)
(0.0036)
0.0454***
0.0454***
-0.0207***
-0.0205***
-0.0205***
(0.0048)
(0.0048)
(0.0035)
(0.0035)
(0.0035)
0.109***
0.110***
-0.0484***
-0.0487***
-0.0488***
(0.0071)
(0.0071)
(0.0048)
(0.0048)
(0.0047)
-0.00442
-0.00433
0.0000612
0.000382
0.000346
(0.0049)
(0.0049)
(0.0042)
(0.0042)
(0.0042)
0.0125***
0.0125***
-0.00539***
-0.00538***
-0.00539***
(0.0009)
(0.0009)
(0.0007)
(0.0007)
(0.0007)
-0.0705***
-0.0705***
0.0108***
0.0110***
0.0111***
(0.0047)
(0.0047)
(0.0035)
(0.0035)
(0.0035)
431754
431754
316231
316231
316231
0.152
0.151
0.0734
0.073
0.0727
Cragg-Donald F statistic for instrument relevance
121763.9
146984.9
113254.9
129773.2
Results: Education groups recent immigrants
(1)
Compulsory education*diversity
Medium education*diversity
High education*diversity
N
Rsq
F-value
Comp. Ed.*div.=Med. Ed.*div
Comp. Ed.*div.=Tert. Ed.*div
Med. Ed.*div.=Tert. Ed.*div
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(2)
(3)
(1)
(2)
(3)
Employment Probability
Unemployment Probability
Diversity = Fractionalisation
0.0452
0.372**
0.497***
-0.0981
-0.317***
-0.445***
(0.0874)
(0.1640)
(0.1770)
(0.0856)
(0.1210)
(0.1430)
0.235***
0.730***
0.852***
-0.140*
-0.347***
-0.461***
(0.0708)
(0.1560)
(0.1750)
(0.0723)
(0.1050)
(0.1360)
0.266***
0.787***
0.914***
-0.119*
-0.337***
-0.423***
(0.0761)
(0.1740)
(0.1920)
(0.0719)
(0.1050)
(0.1280)
431754
431754
431754
316231
316231
316231
0.153
0.152
0.152
0.0734
0.073
0.0727
Cragg-Donald F statistic for instrument relevance
59991.4
72676.1
43753.5
54208.8
Tests for equal coeffcients (P-Value)
0.000
0.000
0.000
0.271
0.526
0.737
0.003
0.000
0.000
0.665
0.742
0.730
0.515
0.367
0.361
0.544
0.813
0.421
Results: Established Immigrants
Divesity
Observations
R-squared
F-value
Compulsory education*diversity
Medium education*diversity
High education*diversity
N
Rsq
F-value
Employment Probability
Unemployment Probability
Overall
-0.0034
0.229***
0.220***
0.009
-0.00527
-0.0239
(0.0435)
(0.0805)
(0.0763)
(0.0366)
(0.0612)
(0.0569)
676454
676454
676454
676454
676454
676454
0.158
0.157
0.157
0.158
0.157
0.157
Cragg-Donald F statistic for instrument relevance
140834.8
173051.7
125571.4
155101
Education Groups
0.019
0.187**
0.175**
-0.0424
-0.0681
-0.0895
(0.0491)
(0.0875)
(0.0830)
(0.0395)
(0.0657)
(0.0608)
-0.0158
0.240***
0.235***
0.0368
0.0122
-0.00842
(0.0453)
(0.0799)
(0.0759)
(0.0383)
(0.0616)
(0.0578)
-0.0228
0.274***
0.267***
0.0383
0.0372
0.0271
(0.0521)
(0.0876)
(0.0852)
-(0.0378)
-(0.0612)
-(0.0579)
676454
676454
676454
502160
502160
502160
0.158
0.157
0.157
0.057
0.0567
0.0567
Cragg-Donald F statistic for instrument relevance
70351.8
86457.5
62762.3
77529.1
Tests for equal coeffcients (P-Value)
0.254
0.118
0.088
0.154
0.243
0.239
0.413
0.146
0.140
0.134
0.307
0.118
0.823
0.345
0.388
0.925
0.166
0.622
Comp. Ed.*div.=Med. Ed.*div
Comp. Ed.*div.=Tert. Ed.*div
Med. Ed.*div.=Tert. Ed.*div
Source: European Labour Force Survey, own calculations. All estimates include age-group, region (NUTS2), sending country group, year dummies and other control variables (as in table 2 and 3)
which are not reported. Values in parentheses are cluster corrected standard errors at the region and year level. ***, ** and * indicate significance at the 1%, 5% and 10% level. (1) OLS
(uninstrumented) results (2) Instruments are predicted shares of nationality based on national occupation structure (3) Instruments are predicted shares of nationality based on national sector structure.
19| Event, Date
Summary


Robust positive average impact of ethnic diversity on employment probabilities
of both recent as well as established immigrants,
After instrumenting, ethnic diversity has a positive impact on the employment
probability of the least skilled recent and established immigrants.
=> positive effects of ethnic diversity on average productivity also found in
earlier studies on natives are strong enough to compensate for any negative
effects of increased heterogeneity on low educated immigrants.

Employment prospects of highly educated recent immigrants improve more
than those of less educated recent immigrants as ethnic diversity increases.
 dominance of immigrants heterogeneity effect over signal noise effect.

No evidence of such a differential impact of ethnic diversity on employment
probabilities for established migrants
=> information problems with respect to the productivity of immigrant workers
due to increased diversity are less severe.
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Conclusions



Differences in effects on different skill groups remain statistically insignificant
for the unemployment risks of both established and recent immigrants
For established immigrants ethnic diversity is an altogether insignificant
determinant of their unemployment risk.
=> Suggests some labour supply side reaction of immigrants in more diverse
regions that may be focus of future research.
Results are robust across a number of specifications and to measuring
diversity by both the fractionalisation index and the Theil index,
.
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Thank you for your attention
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