Migrants and Inequality
Immigrazione e disuguaglianza
Federico Giorgi and Alfonso Rosolia
Abstract We compare native-migrant wage and employment differentials across the
major European countries and explore the impact of migration on the country’s
earnings distribution and on that of the EU as a whole. We discuss the major data
limitations encountered when studying migration-related issues at a European level,
rather than at the single member state one.
Abstract Il lavoro confronta differenziali occupazionali e di reddito tra nativi e
immigrati nei principali paesi europei e valuta gli effetti dell’immigrazione sulla
distribuzione delle retribuzioni del singolo paese e dell’UE nel suo complesso. Si
discutono anche i principali ostacoli a un’analisi delle questioni relative
all’immigrazione a livello Europeo insiti nei dati disponibili.
Key words: inequality, migration
1 Introduction
On January 1st 2014, 34 million regular residents in a EU28 country, out of
about half a billion, had a citizenship different from the one of the country of
residence; of these, about 20 million were citizens of a non-EU28 country. Since
2006, that is since when suitable data for most EU28 countries are available, the
population of foreign citizens resident in a EU28 country has increased by 26
percent against an overall population growth of less than 2 percent. These patterns
are bound to continue in the near future. Migration towards more developed
countries has been constantly on the rise; according to the United Nations, since the
1
Federico Giorgi, Bank of Italy. DG Economics, Statistics and Research, Structural Economic
Analysis Directorate; [email protected]:
Alfonso Rosolia, Bank of Italy. DG Economics, Statistics and Research, Structural Economic
Analysis Directorate; [email protected]:
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Federico Giorgi, Alfonso Rosolia
early 90s net migration has been the major source of population growth in advanced
countries; underlying this pattern is the increased propensity to migrate rather than a
larger population of potential migrants. While Eurostat does not directly provide
long run forecasts of migration flows and of foreign resident population, a sense of
the magnitude of such trends can be grasped by comparing the EU28 population
forecast’s main scenario with the no-migration variant: in 2050 the EU28 population
would be lower by 60 million if migration flows were to come to a halt. In the case
of Italy, where Istat releases forecasts also for the resident population of foreign
citizens, the share of migrants will double in the next 30 years, increasing to more
than 18 percent1.
Understanding how these patterns will shape the single European economies is of
utmost importance for the design of appropriate policies. Perhaps more important, in
a framework of increasing European policy coordination and integration, is the
understanding of how these patterns will shape the European Union as a whole. Yet,
differently from what is common for studies focusing on the US and similarly to the
approach taken in other fields of social and economic investigation, studies of
migration in Europe are always conducted from the standpoint of the single member
state, at most keeping a comparative approach. A “European” view of migration is
missing. Two questions come to mind: (1) what would a “European” perspective add
to our understanding of the effects of migration on European economies? (2) are the
available data suitable to such investigation?
In this paper we try to provide some elements to help answer both questions. We
start from the second one, briefly reviewing the available data sources and the major
limitations they pose to a “European” approach to migration. We then move to the
first question: by means of examples and focusing on earnings distributions, we show
that keeping a “European” perspective can make a difference.
2 The data and their limitations
Ideally, studies of issues related to migration, especially as concerns economic
outcomes such as, e.g. migrant-native differentials and complementarities, migrants’
integration, etc., require individual, possibly longitudinal, data on the outcomes of
interests and their potential determinants; in principle, one would like also to
observe, for migrants, their migration history; moreover, to keep a European
perspective, one would like to have harmonized data (that is, collected according to
shared protocols, comparable in terms of informative content). Yet, ideal data are
hardly available in any field. The two main sources of data that allow cross-country
1
All these data obviously refer to regular residents. While harmonized statistics on irregular migration
are not available, in Italy ISMU has regularly produced unofficial estimates of the stock of irregular
residents for most of the past 20 years. According to the latest report (ISMU (2014)), the number of
irregular migrants has remained broadly stable over the past 2 decades, only occasionally being larger
than 500.000.
Migrants and Inequality
313
comparisons, at least as concerns Europe, are the European labor force survey
(ELFS) and the European Survey on Income and living conditions (EUSILC).
Although these surveys collect, in particular, individual data on income, employment
and socio-demographic traits, other relevant information is missing. For example,
individual level information on citizenship or country of birth is usually very coarse;
the longitudinal dimension is limited, thus little or nothing is known on migration
histories and, more in general, on an individual history; income and employment
data are not always aligned.
Specifically, ELFS provides a wealth of detailed information on labor market
activity, individual and job characteristics for very large samples; however, it only
has scant information on earnings and incomes. For example, in the past few years
national statistical institutes begun collecting information on wages paid by the job
held at the time of the survey; however, the information is released only in terms of
income deciles computed with respect to the country specific distribution. This
completely prevents the possibility of studying earnings differentials or distributions
in a European perspective, unless brave assumptions are made to anchor the
information on income deciles to country-specific means or other moments of the
distribution obtained from other sources1. EUSILC provides instead rich information
on income and labor earnings collected in the survey’s reference year but is very
limited as concerns labour market activity and job characteristics (e.g. sector of
activity, occupation, etc.); the latter are actually unrelated to the earnings collected
by the survey as they refer to the job held at the time of the interview rather than to
the reference period for which earnings are collected, which further complicates the
adjustment of wage differentials for observable job characteristics as it does not
allow to account for the possibility that, holding other characteristics constant, the
job held by, say, a migrant is different from the one help by a native2 .
Finally, both surveys share a major limitation when it comes to studies of
immigration, namely the unavailability of detailed information at the individual level
on country of birth for foreign born and of residence for foreign citizens: the
information is provided only at a very coarse level, whereby it is known whether the
sampled individual is born in (or a citizen of) the country running the survey, another
EU country, or a non-EU country. In addition to that, over time the very definition of
EU changes in line with the evolution of Union membership and apparently no
backward harmonization is done. This particular feature of both datasets strongly
limits the possibilities of investigating migration issues both in a comparative crosscountry perspective and from a European standpoint. Since as at best one can hope
to compare natives (or citizens) with non-natives (or non-citizens) in a given country,
1
Moreover, a worrisome warning on the reliability of this information appears in the LFS
methodologies: “INCDECIL is included from 2009 subject to availability (a transmission delay of 21
months is allowed in case of use of administrative data). Eurostat further postponed the inclusion of back
– data (INCMON) as analyses of available INCDECIL data showed comparability issues already. For the
time being, analyses intending to use income information from the LFS are hence possible to a very
limited extent only”.
2
Brandolini, Rosolia and Torrini (2012) discuss at length the the main data issues to be dealt with when
using EUSILC for general labor market analyses.
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Federico Giorgi, Alfonso Rosolia
all issues and differentials related to country of origin or citizenship must be left
unexplored although origin country is perhaps one of the most important factor
explaining migrant performance in a given destination. As such, it shapes the
differentials with natives: a US citizen in the UK is presumably very different from
an otherwise comparable (i.e. same age, education, sex, etc.) Moroccan citizen.
Therefore, the country-specific composition of the pool of migrants shapes the
average differentials between migrants and citizens and, as a consequence, crosscountry comparisons as migrant populations in European countries differ – beyond
basic observable individual characteristics – also in the composition by country of
origin. For example, figure 1 reports the shares of EU28 and non-EU28 foreign
citizens resident in each member state as of January 1st, 2014 according to Eurostat;
member states are ranked by size, from smallest to largest. The differences across
countries are pretty striking and most likely hide further major differences when
moving to a higher (though unavailable) level of detail. Perhaps another major
limitation implicit in the dissemination of such coarse data is the impossibility of
studying mobility patterns within the Union of the very European citizens: we can at
best hope of telling them apart from non EU citizens but there is no hope of
exploring who is moving where.
Figure 1: Composition by origin of foreign citizens in EU28 countries, 2014
Migrants and Inequality
513
3 Empirical analysis
We focus our analysis on the labor market experiences of natives and migrants
aged 20-69 in the 4 largest Euro area countries: Germany, France, Italy, Spain. The 4
countries have very different experiences in terms of migration, with Italy and Spain
only recently joining Germany and France and becoming destination of large inflows
(although from very different origins).
For these 4 countries we specifically study the distribution of labor earnings of
employees, adjusted for hours and months worked during the year, the monthly fulltime equivalent wage (MFTE), and on their labor market attachment which we
measure with the number of full-time equivalent months worked during the reference
year.
Figure 2 displays the unconditional native-foreigner difference in the cumulative
distribution of MFTE wages in the 4 countries as of 2007; wage levels are adjusted
to account for cross-country differences in price levels using Eurostat Purchasing
power parity indexes so that they are comparable across countries.
Figure 2: Distribution function of earnings, differences native-foreigners
The figure shows that wage distributions of natives and foreigners are more
unequal in Italy and Spain, where 25 percent more of the foreign population has
earnings below 2000 euros per full-time month worked; in Germany the excess share
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Federico Giorgi, Alfonso Rosolia
is only about 15 percent and in France only 5 percent; the share of native and foreign
high earners, that is workers on the upper end of the wage distribution, are instead
roughly equal in all countries suggesting that differences in the distributions of
earnings happen in the lower tails. Along with differences in the distribution of
monthly full-time equivalent earnings there are also differences in terms of labor
market participation. Figure 3 displays the distribution of full-time months worked
during the reference year for foreigners and natives in the 4 countries in 2007. The
figure highlights three important features: first, migrants in Italy and Spain are more
likely than natives to participate in the labor market while in Germany and France
the opposite is true1; second, foreigners in Italy and Spain participate on average
more that foreigners in Germany and France, while for natives only Italy ranks
slightly below the other countries; third, conditional on participating, the average
number of full-time months worked does not differ substantially across countries and
citizenships.
Figure 3: Distribution of FTE months worked
1
Importantly, note that we do not distinguish between unemployed and inactive individuals.
Migrants and Inequality
3.1
713
Native-migrant wage differentials
This evidence partly reflects the different composition of migrant and native
populations. Table 1 reports for the 4 countries the composition of the foreign
population by sex, age and education. In Germany, Italy and France more than half
of the migrant population aged 20-69 is female; in Germany the share of low
educated migrants is at around 30 percent against more than 40 percent in the other 3
countries; in Germany and France more than 40 percent of the migrants are older
than 50, while in Italy and Spain more than half of the migrants are younger than 40.
Table 1: Composition of foreign population by sex, education, age
Germany
France
Italy
Spain
M
F
M
F
M
F
M
F
42,6
57,4
48,3
51,7
46,9
53,1
50,1
49,9
Less than high school
20-29
30-39
40-49
50-59
60-69
12,0
1,8
3,2
2,8
1,9
2,4
17,3
2,4
4,2
3,8
2,2
4,7
21,7
1,3
3,4
3,8
5,8
7,4
25,8
1,8
4,0
6,1
7,9
6,0
22,4
4,4
8,6
6,1
2,3
1,0
22,3
4,8
8,0
4,3
3,7
1,5
23,4
5,9
7,2
6,4
2,8
1,0
19,7
4,8
6,4
4,9
2,1
1,5
High school
20-29
30-39
40-49
50-59
60-69
15,6
1,7
2,9
2,8
1,9
6,2
20,2
2,7
3,9
3,6
3,1
6,9
15,2
1,6
2,4
5,3
3,5
2,4
15,2
2,4
3,3
3,8
3,1
2,6
19,9
4,5
7,3
5,2
2,0
0,9
22,9
4,9
8,2
6,4
2,5
0,9
15,5
4,9
5,6
3,0
1,5
0,6
18,6
5,3
6,1
4,9
1,8
0,7
College or more
20-29
30-39
40-49
50-59
60-69
15,0
1,4
3,4
3,9
2,8
3,6
19,9
2,6
7,5
3,9
2,9
3,1
11,4
0,9
3,3
3,0
2,6
1,6
10,7
1,8
3,3
2,7
1,9
1,0
4,6
0,6
1,8
1,2
0,5
0,5
7,9
0,7
2,8
2,3
1,5
0,6
11,2
1,1
4,4
3,1
1,7
0,9
11,6
1,9
5,1
2,9
1,1
0,7
These differences reflect both the history of migration of the country and its
attractiveness for certain profiles. While an investigation of these determinants is
beyond the scope of the paper1 we try to assess the differentials in terms of earnings
and labor participation abstracting from observable differences. To do so we
estimate a country-specific conditional quantile regressions of log MFTE wages on a
dummy for being a foreign citizen and a set of dummies capturing in a flexible way
1
Cingano, Giorgi and Rosolia (2013) offer evidence in these respects.
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Federico Giorgi, Alfonso Rosolia
observable differences in socio-demographic characteristics1. Figure 4 reports the
estimated coefficient for the dummy Foreigner at each decile along with its 95
percent confidence interval. Again, the cross-country comparison highlights
important differences.
Figure 4: Conditional quantile regressions, Foreigner dummy, 2007
In Germany and France there is virtually no wage difference between foreigners
and natives in the top 30 percent of the distribution. However, Germany is the
country with the largest difference in the lower end: the tenth decile of the wage
distribution of migrants is about a half of that of similar natives; although the
differences shrink as we move along the distribution, the migrants median is still
about 20 percent lower than the natives’. In France, instead, differences in the lower
end of the distributions are not as sharp and, actually, lower than those detected in
Italy and Spain. In these two latter countries, the wage difference is in favour of
natives all along the distribution: in Italy, however, the wage differential declines as
we move towards higher wages while in Spain it increases. These results imply that
the (conditional) distribution of migrants earnings is much more unequal than that of
natives in Germany, and, to a much lesser extent, in Italy and France while it tends to
be less dispersed than that of comparable natives in Spain.
1
Specifically, dummies for the interaction of 5-year age class, sex, education. Unfortunately, the data do
not allow to add to the set of explanatory variables the sector of activity and the job title.
Migrants and Inequality
3.2
913
Native-migrant labor market participation differentials
These differences in the wage distributions of migrants and natives highlighted
above may partly reflect the different patterns of participation (hence, selection) into
the labor market described before (fig. 3): countries with a higher presence of
migrants in the labor market that have also only recently become destination of
major migration flows may attract (and offer employment opportunities to) different
profiles, especially along unobserved dimensions. Indeed, estimating an ordered
probit for the (discretised) number of months worked in the year shows that migrant
status affects differently, even accounting for observable characteristics, the
probability of being employed. The effect of foreign citizenship quantified by this
simple exercise is, ceteris paribus, different across countries: in Germany, France
and Spain it decreases, ceteris paribus, labor market attachment while in Italy it
apparently makes no substantial difference1. Given the nature of the empirical model,
however, the coefficient in itself is not very informative as to the size of the
probability differential. We thus report, in figure 5, the difference between the
probability of a certain amount employment in the reference year estimated for
migrants and for natives with the same observable characteristics2. We focus on the
probability of being employed less than 3 full-time equivalent months (including
non employment) and on the probability of being employed at least 10 full-time
equivalent months. The difference is plotted, for each country, against the
probability of the specific employment level of natives. Several facts stand out. First,
Italy is unique in that migrants otherwise similar to natives are more likely to have
longer employment spells and less likely to stay out of the labor market; while this
was already clear from the unconditional evidence reported in figure 3, the case of
Spain shows how relevant observable differences can be. In fact, while unconditional
differences in figure 3 pointed to an employment advantage of migrants with respect
to natives similar to that detected in Italy, the probability differentials displayed in
figure 5 show that this is no longer the case once observable differences are
accounted for: migrants in Spain, just like those in Germany and in France, are less
likely to record long employment spells and more likely to stay out of employment.
Second, the differentials display a U-shape: they are larger, and sizeably so in
Germany, France and Spain, for profiles whose employment experience in the case
of natives is more polarized, that is that imply probabilities of both extreme
outcomes (less than 3 months vs more than 10) around one half. In other words,
native-migrant differences are not so large for profiles for which specific
employment outcomes are highly likely among natives, while differences grow large
as employment outcomes of natives become more uncertain. In this latter case,
migrants turn out to have, compared with similar natives, a very large disadvantage
in Germany, France and Spain and a small advantage in Italy.
1
Importantly, these results account for a limited set of observables (full interactions of age class, sex and
education) and only include employees and non-employed.
2
That is the estimated probability difference for each cell given by the combination of sex, education
and age.
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Federico Giorgi, Alfonso Rosolia
Figure 5: Migrant-Native employment probability differentials
3.3
How does selection of migrants and natives into employment
affect their wage distributions?
The fact that self-selection into employment along dimensions unobserved to the
econometrician is likely to shape the measured role of wage determinants has been
long known and investigated (Heckman (1979)). However, much less has been done
to assess how self-selection shapes results of quantile estimates of such determinants
(among the few exceptions, Buchinsky (1998) and Albrecht, van Vuuren and
Vroman (2009)) and, as a consequence, no consolidated methodology has emerged
yet. Reviewing the few existing methodological contributions is far from the scope of
this paper; however, all share the basic principle of the Heckman correction for
empirical models of the conditional expectation of a given outcome in that the
quantile regression is augmented with an index function for the likelihood of the
observation being self-selected in the estimation sample. A crucial requirement for
each of the methods proposed is obviously the existence of an exclusion restriction,
that is of some variables that can be credibly taken to affect the selection in the
sample but not the outcome itself. The EUSILC survey is not of much help in this
respect, as the information collected hardly provide such information. In a very
coarse attempt to address the selection issue, we exploited the (self-reported)
information on own general health status, on the existence of chronic conditions, on
Migrants and Inequality
1113
limitations of activity because of health conditions as well as the time taken to
achieve the reported educational title as exclusion restrictions. We thus estimated a
probit model for having reported positive earnings in the survey year and, mimicking
what proposed by Buchinsky (1998), augmented the previous conditional quantile
regressions with a third order polynomial of the estimated probability of being
employed. Figure 6 replicates figure 4 with the addition of the estimates for the
migrant dummy from the quantile regression adjusted for selection. Under the strong
assumption that the chosen exclusion restrictions are valid, accounting for selfselection reduces the native-migrant differentials all along the distribution in
Germany and France by a generally statistically significant amount; in Italy, instead,
differentials are only mildly affected and in the opposite direction while in Spain
they are generally unaffected.
Figure 6: Selection into employment
The change recorded in Germany and France is also quantitatively relevant;
moreover, while in Germany it is larger at the lower hand of the distribution in
France it appears rather constant throughout it. These patterns are consistent with the
fact that, even only in some segments of the distribution, part of the wage
differentials are traceable to the fact that a different subsets of the population selects
in the labor market. In Germany and France, it is profiles with lower earnings
potential all else equal; moreover, in Germany this effect seems stronger at the lower
end while in France it is rather constant; in Italy, instead, the comparison of the
quantile effects seem to suggest that, all else equal, migrants selected in the labor
market have a (slightly) higher earnings potential than comparable natives.
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3.4
The effects of migrant population on the overall wage
distribution
So far we have explored the differences in the distribution of earnings of
migrants and of natives. The results are however silent as to how the presence of
migrants in a country shapes the overall distribution of earnings, the general
equilibrium effect of migration. Studies of immigration typically focus on
quantifying the differences in certain outcomes between natives and migrants (e.g.
wage differentials, inequality differentials, etc.) or on assessing the impact of their
presence on natives’ outcomes (e.g. employment and earnings opportunities of
natives when more foreigners are in the market). In the latter case, the focus is
typically on employment probabilities and on average wages. In keeping with the
spirit of this paper, however, we are interested in investigating the effect of a given
share of migrants in the country on the overall (marginal) earnings distribution and,
as a consequence, on measures of overall inequality. This is no easy task as it
requires the ability of relating the incidence of migrant population on the marginal
earnings distribution while accounting for a host of individual characteristics that
may in themselves affect the distribution. We rely on a method recently proposed by
Firpo, Fortin and Lemieux (2009; FFL henceforth). FFL “[…] propose a new
computationally simple regression method to estimate the impact of changing the
distribution of explanatory variables, X, on the marginal quantiles of the outcome
variable, Y […]”.
Standard conditional quantile regressions explored so far estimate the effect of a
given explanatory variable on the distribution of the outcome variable conditional
the value of other explanatory variables; in other words, they measure how a given
quantile of the earnings distribution of a specific individual profile changes when
this individual is a native as opposed to him being a migrant. Obviously, this result
does not say much about the consequences of the marginal quantile of raising the
foreign population in a country. The method proposed by FFL returns instead this
latter effect, that is the impact on the quantile of the marginal distribution, i.e. the
unconditional quantile, of a change in the share of migrants in the country.
Moreover, the method can be easily generalized to any other functional of the
marginal distribution1.
The method allows to perform several exercises. For example, it allows us to
study and compare the effect, all else equal, of raising the share of migrants in a
given member state on the overall earnings distribution of the member state and on
the overall EU earnings distribution as well as measuring the effect of raising the
share of migrants in the EU while keeping, along with other determinants, their
distribution across member states constant. The results of these three specific
exercises are reported in figure 7; specifically, the figure displays the effect of a
1
Other methods to recover the marginal distribution of a given outcome from the estimation of
conditional quantiles have been proposed (Melly (2005); Machado and Mata (2005)) but all involve a
higher order of computational complexity and, importantly, do not allot to recover the effect of a single
explanatory variable on the marginal distribution.
Migrants and Inequality
1313
(small) increase in the share of migrants on the decile of the marginal earnings
distribution1 (both specified in the legend).
Figure 7: The unconditional effect of migration: member-states vs Europe
The figure clarifies the gains of keeping a European perspective: raising the share
of foreigners in the EU while keeping their distribution across countries constant has
an inequality enhancing effects with stronger depressing effects on lower deciles
(dashed line); if the additional foreigners were located in Germany, however, the
decline in the lower decile of the EU distribution would be much larger against a
more homogeneous effect in the rest of the distribution; locating them in Italy or
Spain would have non monotonic U-shaped effect on the distribution, with larger
negative effects between the 2nd and 5th deciles. Importantly, in all cases, except
perhaps France, the effect on the member state earnings distribution and on the EU
one are rather different; the case of Spain is perhaps the most striking: more migrants
in the country imply a decline in country-specific inequality measures, as top deciles
decrease more, but an increase in the EU inequality as the opposite is true.
1
For the sake of exposition, we have considered only the EU4 (Germany, France, Italy and Spain);
completing the analysis with an explicit consideration of all countries however poses no substantial
problems.
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Federico Giorgi, Alfonso Rosolia
4 Conclusions
This paper offers a reflection on the need of changing the standpoint of studies of
migration to one where the destination country is the European Union, rather than
single member states. This change of perspective would allow to jointly grasp the
determinants and the consequences of within country (i.e. Europe) migration as well
as of migration from abroad (i.e. outside Europe). This change of perspective is all
the more needed as (1) the integration process stalls as it would help make clear its
enormous benefits and favour its development, and (2) Europe, rather than single
member states, increasingly becomes the target of large (regular as well as irregular)
inflows which therefore require a common and shared policy to be dealt with. In this
sense, the question “What is the impact of immigration on the European Union
society and economy” should be the question at the forefront of policy oriented
quantitative analysis.
The exercises presented in this paper have shown, focusing on earnings
dispersion, that migration has different effects in different European Union member
states and that migration to different member states has different effects on the
European Union. Part of these differences are presumably traceable to the different
characteristics of the foreign populations in each member state. Unfortunately the
available data, that is the only individual surveys harmonized and jointly run at the
European level (EUSILC and ELFS), prevent from an adequate characterization of
such populations in terms of origin country, migration history and job characteristics.
To be able to address relevant questions in the presumably most relevant European
perspective much has to change in terms of data collection.
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