Immigrants and the distribution of income and wealth in the euro area : first facts and implications for monetary policy.
Date: 2024-01-11
Institutions: European Central Bank
Subjects: distribution of wealth, euro area, income, migration, monetary policy, social inequality

Maarten Dossche, Aleksandra Kolndrekaj, 
Maximilian Propst, Javier Ramos Perez, 
Jiri Slacalek 

Discussion Paper Series 

Immigrants and the distribution of 
income and wealth in the euro area: 
first facts and implications for 
monetary policy 

No 21 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

Discussion papers 

Discussion papers are research-based papers on policy relevant topics, offering a broader and more balanced perspective. While being 
partly based on original research, they place the analysis in the wider context of the literature on the topic. They also consider explicitly 
the policy perspective, with a view to develop a number of key policy messages. Their format offers the advantage that alternative 
analyses and perspectives can be combined, including theoretical and empirical work. The selection and distribution of discussion 
papers are subject to the approval of the Director General of the Directorate General Research. 

This paper was originally published as Working Paper no. 2719.

ECB Discussion Paper Series No 211Abstract 

We use household surveys to describe differences in wages, income, wealth and liquid assets 
of households born in their country of residence (“natives”) vs. those born in other EU and non-
EU  countries  (“immigrants”).  The  differences  in  wealth  are  more  substantial  than  the 
differences in wages and incomes: immigrants earn on average about 30% lower wages than 
natives  and  hold  roughly  60%  less  net  wealth.  For  all  variables,  only  a  small  fraction  of 
differences between natives and immigrants—around 30%—can be explained by differences 
in demographics (age, gender, marital status, education, occupation, sector of employment). 
Immigrants  are  more  likely  to  be  liquidity  constrained:  while  about  17%  of  natives  can  be 
labelled as “hand-to-mouth” (holding liquid assets worth less than two weeks of income), the 
corresponding  share  is 20% for  households  born  in  another  EU  country  and  29%  for  those 
born  outside  the  EU.  Employment  rates  of  immigrants  are  substantially  more  sensitive  to 
fluctuations  in  aggregate  employment.  Monetary  policy  easing  stimulates  more  strongly 
employment of individuals born outside the EU. 

Keywords: migration, inequality, monetary policy, distribution of income and wealth 

JEL Codes: J15 (Economics of minorities), D31 (Personal income, wealth, and their 
distributions), E52 (Monetary policy)

ECB Discussion Paper Series No 212Non-technical summary 

Recent empirical and modelling literature has quite extensively analysed various dimensions 

of inequality and household heterogeneity, including issues related to age, education (skills) 

and  gender.  In  contrast,  the  migration  dimension  of  inequality  is  still  under-researched, 

especially  across  European  countries:  little  evidence  is  available  on  economic  differences 

between  euro  area  households  born  in  their  current  country  of  residence  and  those  born 

elsewhere.  

The focus on immigrants is important because they make up about 15% of the population and 

migration  into  the  largest  euro  area  countries  has  increased  significantly  over  the  past  15 

years. In those countries, between 14 and 20% of the people were born in a different country, 

with between 3 and 8% born in another EU country and between 10 and 13% born outside the 

EU. In addition, the share of immigrants in the euro area has risen by around 5 p.p. since 2007. 

This paper provides comprehensive evidence on differences between euro area residents born 

in  the  current  country  of  residence  (“natives”)  vs.  elsewhere  (“immigrants”).  Our  results  are 

representative  for  the  euro  area  and  cover  key  facts  for  several  variables  important  for 

economic  welfare  of  households  and  for  monetary  policy:  hourly  wages,  total  household 

income, net wealth and liquid assets. These variables enter the households’ budget constraints 

and are key determinants of their consumption and welfare. 

We report two sets of results: structural, which reflect facts that persist over many years, and 

cyclical,  which  are  relevant  for  the  response  of  the  economy  to  shocks  and  policies  at  the 

business cycle frequency. As for the structural results, we find that differences in wealth are 

much more substantial than differences in wages and incomes: immigrants earn on average 

about 30% lower wages than natives and hold roughly 60% less net wealth. This difference 

arises  as  wealth,  in  contrast  to  income,  is  a  stock  variable  that  is  accumulated  over  many 

years. In addition, we document that natives are more likely to invest in higher return assets, 

such  as  housing  and  stocks.  Looking  into the  factors  behind the  differences  in  income  and 

wealth between natives and immigrants, we find that only a small fraction (around 30%) can 

be  explained  by  differences  in  demographics  (age,  gender,  marital  status,  education, 

occupation, sector of employment). The rest of the gap is due to unobservable factors arising 

from  differences  in  preferences,  cultural  factors  and  beliefs,  and  differences  in  economic 

opportunities  due  to  discrimination.  These  differences  are  substantial.  The  migrant  gaps 

considerably exceed the gender gaps, which have been extensively studied in the literature, 

but are smaller than racial gaps estimated in US data. 

ECB Discussion Paper Series No 213From  the  cyclical  perspective,  we  report  that  immigrants  are  more  likely  to  be  liquidity 

constrained:  while  about  17%  of  natives  can  be  labelled  as  “hand-to-mouth”  (holding  liquid 

assets worth less than two weeks of income), the corresponding share is 20% for households 

born  in  another  EU  country  and  29%  for  those  born  outside  the  EU.  Employment  rates  of 

immigrants are substantially more sensitive to fluctuations in aggregate employment. Finally, 

monetary policy easing stimulates more strongly employment of individuals born outside the 

EU.  These  results  suggest  that  consumption  of  immigrants  is  more  sensitive  to  aggregate 

economic shocks and monetary policy. 

Our  findings  are  relevant  both  for  researchers  and  policy  makers.  From  a  modelling 

perspective,  our  estimates  can  be  used  to  calibrate  and  test  models  with  household 

heterogeneity,  including  heterogeneous  agent  New  Keynesian  models  (HANK).  Traditional 

models  with  a  single  household  representing  the  whole  household  sector  (representative 

agent) ignore heterogeneity and do not allow studying how shocks and policies affect various 

households.  In  contrast,  HANK  models  provide  a  modelling  framework  in  which  the 

transmission of monetary policy is different (it includes many direct and indirect channels) and 

depends  on  households’  marginal  propensities  to  consume  and  their  interactions  with  the 

structure of households’ balance sheets and income. In addition, the aggregate implications in 

HANK  models  may  differ  from  those  in  representative  agent  models  because  household 

heterogeneity may amplify or dampen the effects of shocks and policies. 

More  specifically,  our  results  document  substantial  differences  between  natives  and 

immigrants in Europe. Modelling analysis of this aspect of household heterogeneity would be 

useful  as  it  would  allow  to  investigate  how  various  policies  affect  welfare  of  native  and 

immigrant households and how such heterogeneity affects aggregate dynamics. In addition, 

our results are informative for policy makers, who need to keep in mind distributional effects of 

alternative  policies  on  various  groups  of  households  and  implications  of  their  actions  for 

inequality. 

ECB Discussion Paper Series No 214 
 
1. 

Introduction 

Recent empirical and modelling literature has quite extensively analysed various dimensions 

of inequality and household heterogeneity, including issues related to age, education (skills) 

and  gender.  In  contrast,  the  migration  dimension  of  inequality  is  still  under-researched, 

especially  across  European  countries:  little  evidence  is  available  on  economic  differences 

between  euro  area  households  born  in  their  current  country  of  residence  and  those  born 

elsewhere.  

This paper provides comprehensive evidence on differences between euro area residents born 

in  the  current  country  of  residence  (“natives”)  vs.  elsewhere  (“immigrants”).  Our  results  are 

representative for the euro area population and cover key facts for several variables important 

for economic welfare of households and for monetary policy: hourly wages, total household 

income, net wealth and liquid assets. These variables enter the households’ budget constraints 

and are key determinants of their consumption and welfare. 

We report two sets of results: structural, which reflect facts that persist over many years, and 

cyclical,  which  are  relevant  for  the  response  of  the  economy  to  shocks  and  policies  at  the 

business cycle frequency. As for the structural results, we find that the differences in wealth 

are much more substantial than the differences in wages and incomes: immigrants earn on 

average  about  30%  lower  wages  than  natives  and  hold  roughly  60%  less  net  wealth.  This 

difference arises as wealth, in contrast to income, is a stock variable that is accumulated over 

many years. In addition, we document that natives are more likely to invest in higher return 

assets,  such  as  housing  and  stocks.  We  also  find  that  only  a  small  fraction  of  differences 

between  natives  and  immigrants  (around  30%)  can  be  explained  by  differences  in 

demographics (age, gender, marital status, education, occupation, sector of employment). The 

rest of the gap is due to unexplained factors arising from differences in preferences, cultural 

factors, beliefs and differences in economic opportunities due to discrimination. 

From  the  cyclical  perspective,  we  report  that  immigrants  are  more  likely  to  be  liquidity 

constrained:  while  about  17%  of  natives  can  be  labelled  as  “hand-to-mouth”  (holding  liquid 

assets worth less than two weeks of income), the corresponding share is 20% for households 

born  in  another  EU  country  and  29%  for  those  born  outside  the  EU.  Employment  rates  of 

immigrants are substantially more sensitive to fluctuations in aggregate employment. Finally, 

monetary policy easing stimulates more strongly employment of individuals born outside the 

EU.  These  results  suggest  that  consumption  of  immigrants  is  more  sensitive  to  aggregate 

economic shocks and monetary policy. 

ECB Discussion Paper Series No 215Our 

focus 

on 

(first-generation) 

Chart 1: Share of population by country of birth 

immigrants  is  important  because  they 

Percent 

make  up  about  15%  of  the  population 

Immigrants, EU born
Immigrants, non-EU born

and migration into the largest euro area 

countries has increased significantly over 

the past 15 years. In the four largest euro 

area  countries  between  14  and  20%  of 

the  population  has  been  born  in  a 

different country, with between 3 and 8% 

of people born in another EU country and 

between  10  and  13%  of  people  born 

outside the EU (Chart 1). In addition, the 

share of immigrants in the euro area has 

25

20

15

10

5

0

12.5

7.7

DE

11.7

5.0

ES

10.8

3.1

FR

10.1

4.6

IT

Sources: Labour Force Survey 2018-2019. 
Notes: The chart shows the share of people born outside the country of 
residence on total population (in percent). 

risen by around 5 p.p. since 2007 (Chart 1.a in Annex 2).2 In terms of aggregates, immigrants 

account for about 12% of total consumption.  

Our analysis collects facts for the four largest euro area countries from several datasets, which 

provide ex ante harmonized, cross-country comparable micro data. We use the EU Statistics 

on  Income  and  Living  Conditions  (EU-SILC)  for  results  on  hourly  wages.  The  Household 

Finance and Consumption Survey (HFCS) provides a detailed description of household wealth 

and  its  components,  liquid  assets  and  income. The  Labour  Force  Survey  (LFS)  documents 

facts about the labour force participation at quarterly frequency. 

Our  findings  are  relevant  both  for  researchers  and  policy  makers.  From  a  modelling 

perspective,  our  estimates  can  be  used  to  calibrate  and  test  models  with  household 

heterogeneity,  including  heterogeneous  agent  New  Keynesian  models  (HANK).  Traditional 

models  with  a  single  household  representing  the  whole  household  sector  (representative 

agent) ignore heterogeneity and do not allow studying how shocks and policies affect various 

households.  In  contrast,  HANK  models  provide  a  modelling  framework  in  which  the 

transmission of monetary policy is different (it includes many direct and indirect channels) and 

depends  on  households’  marginal  propensities  to  consume  and  their  interactions  with  the 

structure of households’ balance sheets and income. In addition, the aggregate implications in 

2 Our results focus on first-generation immigrants and classify as natives also people born in the current country of 
residence whose ancestors immigrated into the country. The results thus under-estimate the extent of the issue 
given that differences from natives persist even for many second-generation immigrants. 

The focus on the country of birth is informative also because it includes immigrants who may have become citizens 

in their current country of residence but still may have lower incomes or wealth than natives. 

ECB Discussion Paper Series No 216 
 
HANK  models  may  differ  from  those  in  representative  agent  models  because  household 

heterogeneity may amplify or dampen the effects of shocks and policies. 

More  specifically,  our  results  document  substantial  differences  between  natives  and 

immigrants in Europe. Modelling analysis of this aspect of household heterogeneity would be 

useful  as  it  would  allow  to  investigate  how  various  policies  affect  welfare  of  native  and 

immigrant households and how such heterogeneity affects aggregate dynamics. In addition, 

our results are informative for policy makers, who need to keep in mind distributional effects of 

alternative  policies  on  various  groups  of  households  and  implications  of  their  actions  for 

inequality. 

The plan of the paper is as follows. After a brief review of the literature on migrant, racial and 

gender gaps, section 2 focuses on structural differences in wages, income, wealth and liquid 

assets  and  estimates to  what  extent  these  differences  can  be  accounted  for  by observable 

demographics.  Section  3  covers  cyclical  differences—the  share  of  constrained  households 

and the sensitivity of individual employment to aggregate employment (“worker betas”)—which 

are  relevant  for  the  response  of  various  households  and  the  macro-economy  to  short-run 

shocks and policies. Section 4 concludes and highlights some important data gaps. 

Existing literature on migrant, racial and gender gaps and monetary policy 

Existing work analyses migrant, racial and gender gaps mostly in US data, less so in data from 

individual  European  countries.  The  work  on  wealth  gaps  is  much  smaller  than  analyses  of 

wage gaps. The contribution of our paper is that it provides comprehensive evidence on wages, 

income, wealth and liquid assets for the euro area. 

Research  on  migrant  gaps  in  European  countries  documents,  similar  to  our  findings,  that 

wealth gaps are much larger than income and wage gaps. For example, Mathä et al. (2011) 

reports in data from Germany, Italy and Luxembourg that wealth gaps between natives and 

immigrants are sizeable, somewhat narrowing in the upper tail of the wealth distribution. Cobb-

Clark and Hildebrandt (2006) documents corresponding results on migrant wealth gaps for the 

U.S. As for wage gaps, Coppola et al. (2014) estimates substantial differences in Italy, which 

are particularly large for female immigrants and can only to a small extent be explained with 

demographics. For Germany Ingwersen and Thomsen (2019) finds smaller wage gaps which 

can  largely  be  explained  by  observables.  Cupák  et  al.  (2021)  estimates  sizable  pay  gaps 

across European countries, for which the majority (around 70%) tends to remain unexplained, 

with substantial heterogeneity across countries. 

ECB Discussion Paper Series No 217Recently,  the  literature  on  racial  wealth  gaps  in  the  U.S.  has  grown  substantially. The  work 

estimates  very  sizable  and  persistent  wealth  gaps  between  black  and  Hispanic  households 

relative to white households (Bhutta et al., 2020, Derenoncourt et al., 2022 and Boerma and 

Karabarbounis, 2022). These differences matter for transmission of monetary policy (Bartscher 

et al., 2021 and Nakajima, 2021). 

Gender wage gaps have been extensively investigated in the literature (e.g., Altonji and Blank, 

1999, Blau and Kahn, 2017 and Weichselbaumer and Winter-Ebmer, 2005). Kukk et al. (2021) 

provides up-to-date results on gender wealth gaps across European countries. 

2.  A structural perspective 

This section documents differences in hourly wages, income, wealth and liquid assets across 

the three groups of households. We first present the unconditional evidence, which does not 

control  for  differences  in  various  demographic  factors  (such  as  gender,  marital  status, 

education and occupation). Then we estimate how much of the gaps can be accounted for by 

differences in demographics. 

The  four  variables  we  analyse—wages,  income,  wealth  and  liquid  assets—enter  the 

household’s  budget  constraint  and  are  relevant  for  economic  decisions  of  households.  For 

hourly  wages  we  restrict  our  sample  to  the  population  of  employed  individuals  aged  18-64 

years  (and  exclude  people  who  are  unemployed  or  inactive).  In  contrast,  we  report  the 

evidence on income, net wealth and liquid assets for all households (in line with large existing 

literature documenting inequality for these variables). Total income includes various sources 

of  household  income  in  addition  to  employment  (and  self-employment)  income,  such  as 

pensions, financial and rental income, unemployment benefits and transfers, and is informative 

about the inflow of resources into a household’s budget constraint. In contrast to income, net 

wealth,  which  consists  of financial  and  real  assets  net  of total  liabilities,  is  a  stock  variable, 

which accumulates over (many) years. Liquid assets are resources immediately available to 

smooth consumption in case households face an adverse shock to their income or wealth and 

have  been  found  to  be  a  key  determinant  of  the  marginal  propensity  to  consume  out  of 

transitory income shocks (MPC). 

2.1  Unconditional evidence 

Hourly  wages  of  natives  are  substantially  higher  than  wages  of  immigrants,  across  all  age 

groups (Chart 2.a). The profiles are increasing with age for all three migration groups. Workers 

born in another EU country earn by about 20% less than natives, and those born outside the 

ECB Discussion Paper Series No 218 
EU  earn  by  about  25%  less.  The  gap  between  wages  of  natives  and  immigrants  remains 

substantial over the life cycle. 

Chart 2: Key economic variables by age and country of birth 

a: Gross hourly wages 

b: Gross annual household income 

EUR 

Natives
Immigrants, EU Born
Immigrants, non-EU Born

EUR thousands 

Natives
Immigrants, EU born
Immigrants, non-EU born

16

14

12

10

8

6

4

2

0

50

45

40

35

30

25

20

15

10

5

0

18-34

35-44

45-54

55-64

<35

35-44

45-54

55-64

65-74

>74

c: Net wealth 

EUR thousands 

Natives
Immigrants, EU born
Immigrants, non-EU born

d: Liquid assets 

EUR thousands 

Natives
Immigrants, EU born
Immigrants, non-EU born

200

180

160

140

120

100

80

60

40

20

0

12

10

8

6

4

2

0

<35

35-44

45-54

55-64

65-74

>74

<35

35-44

45-54

55-64

65-74

>74

Sources:  Household  Finance  and  Consumption  Survey  2010,  2014,  2017;  EU  Statistics  on  Income  and  Living  Conditions  2009-2018,  Italy: 
2009-2017. 
Notes: Hourly wages are calculated for employed individuals aged 18-64. Gross household income includes employee income, income from 
self-employment,  pensions,  financial  income,  rental  income,  unemployment  benefits  and  transfers.  Net  wealth  consists  of  financial and  real 
assets (including housing), net of total liabilities (mortgage and non-mortgage debt). Liquid assets include deposits, directly held mutual funds, 
stocks and bonds, net of liquid liabilities (overdraft debt and credit card debt). Due to data limitations the chart on hourly wages shows data for 
France, Italy and Spain; the remaining charts show data for Germany, France and Italy. All reported numbers are medians. 

Total gross incomes of natives tend to exceed somewhat incomes of people born in another 

EU country and substantially incomes of people born outside the EU. Chart 2.b shows median 

total gross household income for all households (workers and non-workers). Households born 

outside the EU tend to earn lower income than households born in another EU country and 

native households. Total income includes in addition to wages also other sources of income, 

such  as  social  benefits,  pensions  and  financial  income.  The  fact  that  the  income  gaps  are 

ECB Discussion Paper Series No 219 
 
 
 
somewhat lower than wage gaps reflects the progressivity of the system of taxes and social 

benefits. 

Differences in holdings of net wealth between natives and immigrants are larger than for wages 

and  income  (Chart  2.c).  Households  headed  by  a  native  accumulate  substantially  higher 

median wealth than EU born and in particular non-EU born households.3 The differences are 

large  and  persist  over  the  life  cycle,  suggesting  little  convergence  before  retirement: 

immigrants accumulate much less wealth even at higher age.4 The gap is particularly large for 

households born outside the EU, who at the age of 55, for example, own a median net wealth 

of only roughly EUR 40,000, compared to EUR 180,000 for natives. There could be various 

reasons for why the differences in wealth highly exceed those in income. First, in contrast to 

income, wealth is a stock variable that is accumulated over many years. The initial immigrant 

wealth  at  arrival  is  likely  lower  than  wealth  of  comparable  natives.  In  addition,  wealth 

accumulation in the current country of residence tends to be faster than in the country of birth 

(thanks to higher incomes). In addition, natives are more likely to invest in assets with higher 

return (such as housing and stocks), to receive inheritance, and to have access to financial 

assistance from relatives or friends.5 

Immigrants also hold a much lower stock of liquid assets (Chart 2.d). The difference is again 

particularly striking for non-EU born households who only hold median liquid assets of around 

EUR 1,500 at the age of 55, compared to roughly EUR 11,000 for natives (and around EUR 

8,000 for EU born immigrants). Given the negative relationship between liquid assets and the 

marginal propensities to consume often estimated in data and implied by models (see, e.g., 

Ganong  et  al.,  2020),  these  very  low  holdings  of  liquid  assets  make  spending  of  non-EU 

immigrants exposed to adverse shocks (and result in a high share of constrained households, 

see Chart 8 in section 3 below). 

3 Net wealth includes assets held both in the country of residence and abroad. 
4 Given that older immigrants likely spent a longer time in the current country of residence, one would expect the 

gaps to decrease with age. 

For EU born immigrants, median wealth for households older than 65 is quite close to the wealth of natives. This 

may be affected by selection, with some immigrants moving back to their country of birth for retirement. 

5 Natives tend to save more than EU born households, which in turn save more than non-EU born households. This 
can  be  due to  the  fact that native households tend to  have higher incomes, or due to  differences  in  cultural 
factors or beliefs (such as desire for wealth accumulation and attitudes toward thrift (see, e.g., Bisin and Verdier, 
2011, Haliassos et al., 2017, Fuchs-Schündeln et al., 2020, and Fleck and Monninger, 2020). Zillessen (2022) 
finds  that  while  immigrants  without  a right  to  citizenship  save  30%  less  than natives,  once  immigrants  have 
access  to  citizenship,  they  save  as  much  as  natives  when  individual  characteristics  such  as  labour  market 
outcomes are accounted for. 

ECB Discussion Paper Series No 2110 
Compared  to  gender  gaps,  which  have  extensively  been  studied  in  the  literature,  these 

differences between natives and immigrants are substantial, especially for net wealth and liquid 

assets. For example, the gender gap for mean wages is around 10-15% and for mean wealth 

around 35%.6 In comparison, across the three groups by country of birth, for wages the gaps 

are roughly 25% and 35% respectively for people born in other EU countries and outside the 

EU.  For  income  the  corresponding 

Chart 3: Ownership of assets and liabilities 

migrant  gaps  are  by  about  10  p.p. 

Percent 

Natives

Immigrants, EU born

Immigrants, non-EU born

lower,  reflecting  the  progressivity  of 

the systems of social benefits. For net 

wealth  and  liquid  assets  the  gaps 

across  migration  groups  are  larger 

than  across  genders,  amounting  for 

non-EU  immigrants  to  roughly  60% 

and for EU immigrants to roughly 40% 

(for both net wealth and liquid assets).7  

In  contrast,  compared  to  racial  gaps 

reported  in  US  data,  the  differences 

60

50

40

30

20

10

across migrant groups in the euro area 

Main Residence

Self-empl Business

Mortgage

Shares

are smaller. For example, Bartscher et 

al. 

(2021)  estimate 

that  black 

Sources: Household Finance and Consumption Survey 2017. 
Notes:  The  chart  shows  the  fraction  of  households  holding  a  given  asset  or 
liability. Due to data limitations the chart shows data for Germany,  France  and 
Italy. 

households in the U.S. own on average only 11% of the wealth of white households and earn 

about 50% of the income compared to white households. 

Looking into the composition of assets and liabilities, native households are more likely to own 

a house, stock or business and hold a mortgage (Chart 3). Immigrants are substantially less 

likely to own their main residence and business wealth or participate in the stock market. On 

the other hand, homeowners among immigrants are much more likely to hold a mortgage than 

homeowners  among  natives—in  line  with  about  the  evidence  on  the  lower  availability  of 

inheritances and family resources for immigrants: while around a half of homeowners among 

immigrants  hold  a  mortgage,  for  natives  the  corresponding  share  is  around  one  third.  In 

particular the difference in owning the main residence is substantial because housing is a large 

assets  and  a  key  driver  of  wealth,  and  households  who  do  not  own  their  residence  do  not 

6 See OECD (2021) for data on gender wage gaps and Kukk et al. (2021) for estimates of gender wealth gaps. 
7 These gaps refer to means, to be consistent with the numbers given for gender gaps (and with the Oaxaca–Blinder 

decompositions discussed below). 

ECB Discussion Paper Series No 2111 
 
benefit from increases in house prices and tend to accumulate much less wealth than home-

owners.8 In sum, immigrants benefit much less from increases in asset prices and are more 

financially vulnerable, resulting in their lower welfare due to lower wealth accumulation over 

the life cycle and due to worse consumption smoothing in the short run. 

The migrant gaps depend on the time spent in the current country of residence (Chart 4). For 

the case of wage gaps, the differences between natives and immigrants is only about 15-20% 

for immigrants who have been in the country for more than 10 years. In contrast, for immigrants 

who arrived less than 10 years ago the wage gaps are at least twice as high. In addition, for 

those immigrants wages do not increase with age, so that the wage gaps relative to natives 

widen at higher age. 

Chart 4: Hourly wages by length of time in the current country of residence 

a: Immigrants, EU born 

b: Immigrants, non-EU born 

EUR 

Natives
Less than 10 years in the country of residence
More than 10 years in the country of residence

Natives
Less than 10 years in the country of residence
More than 10 years in the country of residence

16

14

12

10

8

6

4

2

0

16

14

12

10

8

6

4

2

0

18-34

35-44

45-54

55-64

18-34

35-44

45-54

55-64

Age (years)

Age (years)

Sources: EU Statistics on Income and Living Conditions 2003-2018, Italy: 2009-2017. 
Note: Hourly wages are calculated for employed individuals aged 18-64 (the self-employed are excluded). Due to data limitations the chart on 
hourly wages shows data for France, Italy and Spain; the remaining charts show data for Germany, France and Italy. All reported numbers are 
medians. 

2.2  Conditional evidence: accounting for the role of demographics 

While these univariate results are informative about the differences across the three migrant 

groups, multivariate analysis is needed to better understand what drives the differences, i.e., 

whether the differences can be explained by different demographics across the groups (e.g., 

age, gender, marital status or education). To see to what extent this is the case, we estimate 

8 For evidence on long-run differences in returns between real estate and other (financial) assets, see Jordà et al. 

(2019). 

ECB Discussion Paper Series No 2112 
 
the  Oaxaca–Blinder  decomposition,  which  is  a  standard  tool  used  to  separate  the  role  of 

observable differences (due to demographics) and the remaining differences which cannot be 

explained by observable factors and are instead ascribed to other factors, such as preferences, 

culture and omitted variables not included in the regressors (see Annex 1 for a description of 

the  decomposition).  The  explanatory  variables  that  control  for  the  key  observable 

demographics  are  age,  gender,  marital  status,  education  (included  as  a  proxy  for  skills), 

presence  of  a  child  in  the  household,  occupation,  the  sector  of  employment,  employment 

dummy and self-employment dummy.9 

Chart 5.a: Oaxaca–Blinder decomposition for 

Chart 5.b: Oaxaca–Blinder decomposition for 

hourly wages and household income  

liquid assets and net wealth 

Percent 

Explained
Unexplained

Percent 

Explained
Unexplained

35

30

25

20

15

10

5

0

180

160

140

120

100

80

60

40

20

0

Immigrants, EU
born

Immigrants, non-EU
born

Immigrants, EU
born

Immigrants, non-EU
born

Immigrants, EU
born

Immigrants, non-
EU born

Immigrants, EU
born

Immigrants, non-
EU born

Hourly wages

Huosehold income

Net wealth

Liquid assets

Sources: Household Finance and Consumption Survey 2010, 2014, 2017; EU Statistics on Income and Living Conditions 2009-2018, Italy: 2009-
2017. 
Note: The charts decompose the average gaps between native and immigrant households into a part explained by observable variables and an 
unobserved part. The observable variables are: age, gender, marital status, education, presence of a child in the household, occupation, the 
sector of employment, employment dummy, self-employment dummy and time fixed effects. Net wealth and liquid assets were transformed using 
the inverse hyperbolic transformation (to account for the presence of zero and negative values). The top and bottom 5 percent of values were 
winsorised. 

The differences between natives and immigrants are large for hourly wages and household 

income and very substantial for net wealth and liquid assets. Charts 5.a and 5.b confirm the 

earlier results that hourly wages of natives on average are about 25% higher than wages of 

people born in another EU country and by 35% higher than wages of people born outside the 

EU. The  corresponding  gaps  for  household  income  are  somewhat  lower,  roughly  17%  and 

9 Our explanatory variables are the same for all four dependent variables. They consist of variables typically included 
in Oaxaca-Blinder (and other) decompositions for wage and wealth gaps. Marital status, gender and presence 
of  children  in  the  household  are  0-1  dummy  variables.  Education  is  split  into  the  following  three  categories: 
below secondary, secondary and tertiary. Age is measured using the following brackets: 18-34, 35-44, 45-54, 
55-64 and 75 years and above. Occupation is based on the 1-character ISCO-08 classification and consists of 
10 groups. The sector of employment is based on the 2008 NACE classification of economic activities, grouped 
into  12  areas.  For  income,  net  wealth  and  liquid  assets  we  include  0-1  indicators  of  employment  and  self-
employment. 

ECB Discussion Paper Series No 2113 
 
 
26% for EU born and non-EU born households, respectively. The gaps for net wealth and liquid 

assets are substantially higher than the gaps for hourly wages and income (panel 5.b). 

Chart 6: Oaxaca–Blinder decompositions depending on the age of arrival in the country 

a: Gross hourly wages 

b: Gross household income 

Percent 

Explained
Unexplained

40

35

30

25

20

15

10

5

0

Immigrants, EU
born

Immigrants, non-EU
born

Immigrants, EU
born

Immigrants, non-EU
born

Moved older than 18

Moved at 18 or younger

c: Net wealth 

Percent 

Explained
Unexplained

180

160

140

120

100

80

60

40

20

0

Immigrants, EU
born

Immigrants, non-
EU born

Immigrants, EU
born

Immigrants, non-
EU born

Moved older than 18

Moved at 18 or younger

Percent 

Explained
Unexplained

30

25

20

15

10

5

0

-5

Immigrants, EU
born
Moved older than 18

Immigrants, non-
EU born

Immigrants, EU
born
Moved at 18 or younger

Immigrants, non-
EU born

d: Liquid assets 

Percent 

Explained
Unexplained

180

160

140

120

100

80

60

40

20

0

Immigrants, EU
born

Immigrants, non-
EU born

Immigrants, EU
born

Immigrants, non-
EU born

Moved older than 18

Moved at 18 or younger

Sources:  Household  Finance  and  Consumption  Survey  2010,  2014,  2017;  EU  Statistics  on  Income  and  Living  Conditions  2009-2018,  Italy: 
2009-2017. 
Note: The charts compare estimates for people who moved to the country of residence before the age of 18 and after. The charts decompose 
the  average  gaps  between  native  and  immigrant  households  into  a  part  explained  by  observable  variables  and  an  unobserved  part.  The 
observable variables are: age, gender, marital status, education, presence of a child in the household, occupation, the sector of employment, 
employment dummy, self-employment dummy and time fixed effects. Net wealth and liquid assets were transformed using the inverse hyperbolic 
transformation (to account for the presence of zero and negative values). The top and bottom 5 percent of values were winsorised. 

Only a small fraction of the gaps—around 30%—can be explained by the demographics, while 

the rest remains unexplained. The explained share of gaps is roughly stable across the two 

groups  of  immigrants  and  across  the  four  variables.  It  is  typically  positive,  suggesting  that 

natives  tend  to  be  older,  and  more  educated,  variables  that  from  the  life  cycle  perspective 

ECB Discussion Paper Series No 2114 
 
 
 
correlate with higher wages and wealth.10 However, even once accounting for these factors, 

the bulk of the gap remains unexplained. 

Although the Oaxaca–Blinder decompositions suggest a large contribution from unexplained 

factors, the data do not allow us to disentangle the contribution to the gaps from differences in 

preferences,  cultural  factors,  beliefs  and  differences  in  economic  opportunities  due  to 

discrimination.11 

The gaps vary across the distribution (Chart 5 in Annex 2). For wealth and liquid assets the 

gaps persist all over the distribution: they are about twice as large at the 25th percentile of the 

distribution compared to the 75th percentile. For wages and income, the gaps are broadly stable 

across the distribution and the share of the explained part also tends to be stable. 

The gaps are substantially smaller for people who moved into their current country of residence 

at a young age and who spent there a longer time (Chart 6). Comparison of the two cohorts of 

people shows that the gaps persist even for people who moved into the country before the age 

of  18,  but  are  by  roughly  60%  lower than  for people  who  arrived  as  adults,  both for people 

arriving from other EU countries and those arriving from outside the EU. This finding suggests 

how much the length of time spent in the current country of residence reduces the gaps. (The 

comparison  of  gaps  includes  the  fact  that  younger  people  may  integrate  more  easily.) 

Interestingly, the share of the explained part (in blue) in the total gaps is higher for younger 

immigrants, so that observable characteristics explain relatively more of the gaps.12 Looking 

further into whether the length of stay matters more than the age at arrival, the evidence on 

wage  gaps  points  in  that  direction  although  we  are  facing  limitations  due  to  limited  sample 

sizes (see Chart 8 in Annex 2). 

10  The  results  for  wealth  are  qualitatively  robust  to  including  employment  among  explanatory  variables  and 

restricting the sample to employed households only (see Charts 6 and 7 in Annex 2). 

11 The explanatory variables in Oaxaca–Blinder include basic demographics, determining variables (to some extent 
exogenous),  which  were  fixed  at  the  time  when  the  regressor  of  interest  was  determined.  The  explanatory 
variables should not include “bad controls” or outcomes (such as home-ownership status in the case of wealth 
regressions). The goal of the decompositions is thus not to maximize the explanatory power of the regression. 
For more detailed discussion see Angrist and Pischke (2008), section 3.2.3. 

12 Considering the gaps for people who arrived as children is also used in the literature to alleviate concerns related 
to  the  selection  bias  in  our decompositions  due  to  the  fact  that  some  of  the  adult  immigrants  endogenously 
chose their country of residence, and which may affect the share of the explained part of the gaps. This selection 
bias for some people who arrive as adults suggests that the above estimates of gaps are conservative. 

Our data do not make possible for us to identify second-generation immigrants (people born in the current country 
of  residence  whose  parents  were  born  in  a  different  country).  Algan  et  al.  (2010)  compare  labour  market 
outcomes (earnings, labour force participation) of the first- and second-generation immigrants and find that the 
progress in closing the differences with respect to natives varies across countries. The UK has particularly large 
differences for the first generation but also much improved outcomes for the second generation. Evidence of 
progress  in  France  and  Germany  is  not  so  clear-cut.  Individuals  who  moved  into  their  current  country  of 
residence before or during their early teens are sometimes referred to as 1.5 generation immigrants, so Chart 
6 is informative about how quickly the gaps reduce across generations. 

ECB Discussion Paper Series No 2115 
2.3 

Intergenerational dimension 

There  is  some  evidence  that  the  gaps 

Chart 7: Share of children at risk of poverty 

faced  by  first  generation  immigrants 

Percent 

translate  across  generations.  Chart  7 

documents that children born to families 

of  immigrants,  especially  those  from 

outside  the  EU,  face  roughly  twice  as 

high a risk of poverty (as defined by the 

share of people in the group earning less 

than 60% of median disposable income) 

as children of natives. The gap between 

natives  and  immigrants  has  persisted 

over time.13 As the economic fortunes of 

children  born  in  immigrant  families  are 

significantly lower than those of children 

born in native families, a full convergence 

Main respondent native
Main respondent immigrant EU born
Main respondent immigrant non-EU born

45

40

35

30

25

20

15

10

5

0

2011

2012

2013

2014

2015

2016

2017

2018

2019

Sources:  EU  Statistics  on  Income  and  Living  Conditions,  2011-2018,  Italy: 
2011-2017. 
Notes:  The  chart  depicts  the  share  of  households  with  children  below  14 
years at risk of poverty. The at-risk-of-poverty rate is the share of people with 
an equivalised disposable income (after social transfer) below the at-risk-of-
poverty  threshold,  which  is  set  at  60%  of  the  national median equivalised 
disposable income after social transfers.  

in  income  and  wealth  between  natives  whose  ancestors  were  also  natives  and  children  of 

immigrants may take several generations to complete. 

3 

A cyclical perspective  

This  section  focuses  on  the  implications  of  differences  across  the  three  migrant  groups  of 

households  for  monetary  policy  at  the  business  cycle  frequency  (at  a  horizon  of  several 

quarters).  The  literature  on  the  heterogeneous  agent  New  Keynesian  models  (HANK)  has 

identified  that two  objects  are  important to  pin  down  the response  of  the  macroeconomy to 

shocks and policies in the short run: the share of constrained households and the sensitivity 

of incomes of individual households to changes in aggregate employment (sometimes called 

“worker betas”). The share of constrained households (households with low holdings of liquid 

assets) affects monetary transmission because their spending is more sensitive to income and 

wealth  shocks,  i.e.,  they  have  higher  marginal  propensities  to  consume  than  the  remaining 

households  who  hold  adequate  liquid  assets.  The  sensitivity  of  incomes  of  individual 

13 Chart 7 documents that the gap in children at risk of poverty has not been closing, i.e., incomes of disadvantaged 
families  have  not  been  converging  toward  aggregate  median  income.  Focusing  on  the  absolute  levels  both 
incomes of disadvantaged families and the median income has been rising over time. Also indicators of material 
deprivation  (such  as  inability  to  pay  rent,  keep  home  adequately  warm,  face  unexpected  expenses,  go  on 
holiday) declined between 2013 and 2019, including for immigrants. 

ECB Discussion Paper Series No 2116 
 
 
households matters because following a monetary easing which stimulates aggregate demand 

and aggregate income, employment and incomes of some households respond more strongly 

than employment and incomes of others. 

Chart  8:  Share  of  constrained  (hand-to-

Chart 9: Sensitivity of individual employment 

mouth) households by country of birth 

to aggregate employment by country of birth 

Percent 

Poor hand-to-mouth
Wealthy hand-to-mouth

30

25

20

15

10

5

0

Natives

Immigrants, EU born

Immigrants, non-EU
born

Sources: Household Finance and Consumption Survey 2017. 
Notes: The chart shows the share of the two types of hand-to-mouth 
households  for  native  households,  households  born  in  another  EU 
country and those born in a country outside the EU. The estimates are 
based on an aggregate of France, Germany, Italy and Spain. 

1.9

1.7

1.5

1.3

1.1

0.9

0.7

0.5

Natives

Immigrants, EU born

Immigrants, non-EU
born

Sources: Labour Force Survey 2005-2019, quarterly data. 
Notes:  The  chart  shows  the  sensitivity  of  individual  employment  to 
aggregate  employment  for  native  households,  households  born  in 
another EU country and those born in a country outside the EU. The 
estimates  average  to  1  and  are  based  on  an  aggregate  of  France, 
Germany,  Italy  and  Spain.  The  lines  indicate  the  95%  confidence 
interval. 

The  share  of  constrained  households  is  substantially  larger  for  EU  and  especially  non-EU 

immigrants than for natives (Chart 8). Following Kaplan et al. (2014), constrained households 

hold liquid assets worth less than two weeks of income. Depending on whether they own illiquid 

assets (most importantly housing), they are denoted either as poor hand-to-mouth or wealthy 

hand-to-mouth.14 While among natives 15% of households are constrained, for immigrants the 

share of constrained households is 18% and 29% for EU born and non-EU born, respectively. 

The difference is mainly driven by the share of poor hand-to-mouth households, which is for 

non-EU  born  households  more  than  twice  higher  than  for  natives.  These  differences 

correspond to our previous finding that immigrants hold substantially lower amounts of liquid 

assets (Chart 2.d) and are less likely to be home-owners (Chart 3). EU born and in particular 

14 We define constrained households following Kaplan, Violante and Weidner (2014). They are either poor hand-to-
mouth or wealthy hand-to-mouth. Poor hand-to-mouth households are those with zero or negative illiquid wealth 
and net liquid assets close to zero, if positive, and close to the credit limit, if negative. Wealthy hand-to-mouth 
households have liquid assets defined in the same way, but also have positive holdings of net illiquid assets. By 
“close” we mean no more than half of their monthly disposable labour income away from zero or the credit limit. 

ECB Discussion Paper Series No 2117 
 
 
 
 
non-EU born immigrants thus tend to have less liquid assets available to smooth their spending 

than native households.15 

Employment of immigrants is particularly sensitive to the business cycle: they disproportionally 

lose during recessions, and strongly benefit from recoveries. Chart 9 shows the estimates of 

the  sensitivity  of  employment  of  individuals  to  changes  in  aggregate  employment  (“worker 

betas”).  For  each  of  the  three  groups,  the  sensitivity  is  estimated  by  regressing  individual 

employment status on the aggregate employment rate. By construction, across all households 

the sensitivity averages to 1. A sensitivity higher than 1 indicates that incomes of that group of 

households  react  particularly  strongly  to  aggregate  shocks.  Chart  9  indicates  that  the 

sensitivity among immigrants is substantially higher, amounting to 1.15 and 1.65 for EU born 

and non-EU born respectively, compared to 0.9 for natives.16 

Monetary  easing  strongly  reduces  unemployment  of  people  born  outside  the  EU.  Chart  10 

estimates 

the 

responses 

of 

Chart 10: Effects of monetary easing on 

unemployment  rates  to  the  ECB’s Asset 

unemployment rate by country of birth 

Purchase Programs undertaken in 2014-

Percentage points 

2018. The  simulation  follows  Lenza  and 

Slacalek 

(2018)  and  accounts 

for 

heterogeneity in job finding rates across 

standard 

demographic 

variables. 

Households  born  outside 

the  EU 

benefitted  particularly  strongly  as  their 

unemployment  rate  dropped  by  roughly 

1.5 p.p., while the unemployment rate for 

the  remaining  households  declined  by 

0.3-0.5  p.p.  The  key  factor  is  that  the 

overall pool of non-employed individuals 

Natives

Immigrants, EU born

Immigrants, non-EU born

0.0

-0.2

-0.4

-0.6

-0.8

-1.0

-1.2

-1.4

-1.6

-1.8

Sources:  Household  Finance  and  Consumption  Survey  2014,  based  on 
Lenza and Slacalek (2018). 
Notes: Due to data limitations the chart shows data for Germany, France and 
Italy. 

15 Ganong et al. (2020) document that black and Hispanic households in the U.S. hold less liquid assets and have 

substantially higher marginal propensities to consume than white households. 

16 These estimates correspond to Aaronson et al. (2019), who estimate that in the U.S. the labor market experiences 
of less advantaged groups (as measured with unemployment rates) are more cyclically sensitive than the labor 
market experiences of more advantaged groups, and to Friedrich et al. (2021) with evidence for Sweden.  
Chart  4  in  Annex  2  presents  a  more  detailed  breakdown.  Immigrants  from  new  EU  Member  States,  advanced 
economies (including North America, Australia, New Zealand and non-EU European countries), South America 
and in particular Africa have a higher employment sensitivity than natives, while those from EU-15, Near and 
Middle East and Asia have a lower sensitivity. 

The  results  are  not  driven  by  seasonal  workers  because  the  Labour  Force  Survey  does  not  include  a 
disproportionate share of people who have lived in the current country of residence for less than one year. 

ECB Discussion Paper Series No 2118 
 
is  relatively  larger  for  individuals  born  outside the  EU. These  results correspond  well  to the 

estimates in Chart 9, as they focus specifically on the effects of nonstandard monetary policy 

(while  results  in  Chart  9  are  unconditional  regarding  the  cause  of  the  decline  in  aggregate 

employment). 

These differences imply that monetary policy easing stimulates more strongly employment of 

immigrants. At the same time, given that immigrants tend to hold fewer assets (including the 

household main residence, business wealth, and shares), they benefit to a lesser extent from 

the stimulating effects of monetary easing on asset prices. 

4 

Conclusions  

We document substantial differences in income and wealth between individuals born in their 

current country of residence (natives) and elsewhere (immigrants). Only about 30% of these 

differences can be explained by different demographics, the rest is due to unobservable factors 

(such as preferences, norms, beliefs, culture or discrimination). The gaps diminish slowly with 

the length of stay in the country of residence: gaps for people who arrived to their country of 

residence before the age 18 are still large, but about 60% lower than for people who arrived 

as adults.  

In addition to having a direct impact on welfare of households, lower income and wealth also 

affect  the  transmission  of  monetary  policy  to  those  households  and  the  response  of  the 

economy to cyclical shocks. We document that people born abroad are much more likely to be 

constrained  (i.e.,  accumulate  much  less  liquid  assets),  their  employment  is  particularly 

sensitive to the business cycle, and their unemployment declines strongly following a monetary 

easing. Even if only descriptive, these facts suggest that consumption of immigrants is more 

volatile over the business cycle and can be particularly stimulated by lower interest rates. 

While  the  analysis  documents  that  socio-economic  differences  between  natives  and 

immigrants  are  very  substantial,  better  data  are  needed  to  uncover  the  underlying  drivers. 

Although the Oaxaca–Blinder decompositions suggest a large contribution from unexplained 

factors, the available data do not allow us to disentangle the contribution from differences in 

preferences, cultural factors, beliefs, and differences in economic opportunities. As it is well-

known  that  discrimination  is  widespread  in  several–education,  labour,  housing–markets  (cf. 

Bertrand and Mullainathan, 2004), better data are key to assess the role that different structural 

policies can play in reducing disparities across natives and immigrants. This is relevant from a 

monetary  policy  perspective,  as  it  determines  the  interaction  between  monetary  policy  and 

economic inequality. 

ECB Discussion Paper Series No 2119 
One  particular  aspect  of  inequality  that  in  Europe  is  still  under-researched  and  particularly 

relevant for future work are racial and ethnic disparities. This is to a large extent due to a lack 

of  data.  Contrary  to  the  US  (e.g.,  the  US  Census),  most  euro  area  countries  do  not 

systematically record any ethnic information on their citizens.17 In some countries (e.g., France) 

even the voluntary reporting of such information is strongly regulated. While this tradition rests 

to a large extent on historical grounds, it hampers a debate that is based on facts and scientific 

research. It is in this context that the EU has recently taken several initiatives to improve the 

collection  of  equality  data.18 Availability  of  new  data  on  these  aspects  would  be  valuable  to 

support future analysis in this area, also with a focus on the implications for monetary policy. 

17 Several existing surveys do collect information on people’s ethnicity (e.g. the European Social Survey). However, 
this  information  is  usually  not  available  together  with  information  on  income  or  wealth  of  the  same  person, 
making it impossible to document the differences in income or wealth across ethnic groups. While this may not 
apply to all euro area countries, it excludes a comparison across countries and a comprehensive assessment 
of how these inequalities interact with the common monetary policy. 

18  Racial  differences  have  received  increasing  attention  as  in  important  dimension  of  economic  inequality, 
particularly in the United States. While racial differences in the US have been analysed for many years, recent 
events have sparked a renewed interest in the differences in opportunities across ethnic groups (e.g., Edwards 
et al., 2019). Recent work has documented how racial inequality in income and wealth continued to grow over 
the past decades (Bartscher et al., 2021) and how it leads to increasing macroeconomic costs (Buckman et al., 
2021).  Moreover,  several  studies  in  the  context  of  the  Federal  Reserve’s  monetary  policy  strategy  review 
(Aaronson et al., 2019, Feiveson et al., 2020) suggest that racial inequality interacts with monetary policy. 

ECB Discussion Paper Series No 2120 
 
 
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ECB Discussion Paper Series No 2122 
Annex 1: Oaxaca–Blinder decomposition 

Seminal work of Oaxaca (1973) and Blinder (1973) decomposes differences between groups 
of households into an observed and an unobserved part. The method divides the group mean 
difference (𝜇(cid:2869) − 𝜇(cid:2870)) into two terms. The first one, commonly known as quantity effect, accounts 
for  differences  between  the  groups  in  observable  characteristics  (such  as  demographic 
variables). This term reflects that different compositions lead to unequal average outcomes. 
The  second  term  captures  the  differences  in  coefficients,  i.e.,  returns  to  observable 
characteristics. Given the same characteristics in individuals belonging to two distinct groups, 
the effects on the variable of interest are not the same. It is also known as coefficient effect, 
because it shows differences in returns for the two groups. 

We apply the method considering two groups, natives and immigrants denoted by the index i 
=  {N,  I},  an  outcome  variable  Y,  logarithm  of  income,  and  a  set  of  explanatory  variables  X 
containing demographic information like age, educational attainment and marital status. Let 𝜇(cid:3036) 
denote  the  unconditional  sample  mean  of  group  i.  We  want  to  understand  what  drives  the 
difference between the means 𝜇(cid:3015) − 𝜇(cid:3010). A positive difference indicates that natives have higher 
income  than  immigrants.  Denoting  the  unconditional  mean  for  each  group  as:  𝜇(cid:3036) = 𝐸(𝑌(cid:3036)) =
𝑋(cid:3364)(cid:3036)𝛽(cid:3013), their difference can be written as: 

(cid:4593)
𝐸(𝑌(cid:3015)) − 𝐸(𝑌(cid:3010)) = (cid:3435)𝐸(𝑋(cid:3364)(cid:3015)) − 𝐸(𝑋(cid:3364)(cid:3010))(cid:3439)

𝛽(cid:3015)   + 𝐸(𝑋(cid:3364)(cid:3010))(cid:4593)( 𝛽(cid:3015) − 𝛽(cid:3010)). 

The  first  term  on  the  right  hand  side,  (𝐸(𝑋(cid:3364)(cid:3015)) − 𝐸(𝑋(cid:3364)(cid:3010)))′ 𝛽(cid:3015),  captures  disparities  in  the 
composition of the underlying population evaluated with the coefficients of the reference group, 
natives in our analysis. For example, if natives are older than immigrants, according to the life-
cycle theory, their earnings should be higher. The second term, 𝐸(𝑋(cid:3364)(cid:3010))′( 𝛽(cid:3015) − 𝛽(cid:3010)) captures the 
differences in returns arising from the same set of characteristics. For example, if an additional 
year of experience has a higher impact on earnings of natives than immigrants, then 𝛽(cid:3015) > 𝛽(cid:3010).  

The  size  of  the  explained  component  is  given  by  the  first  term;  the  rest  of  the  gap  is 
unexplained.  The  unexplained  part  reflects  the  effect  of  missing  explanatory  variables  and 
other  factors.  In  practice  it  is  very  hard  to  account  for  differences  across  households  with 
observed  characteristics  only.  This  implies  that  the  second,  unexplained  term  is  driven  by 
factors,  such  as  differences  in  preferences,  beliefs,  norms  and  cultural  factors  and 
discrimination or barriers. 

ECB Discussion Paper Series No 2123 
 
14

12

10

8

6

4

2

0

Annex 2: Additional charts 
Chart 1.a: Share of immigrant households on 
total population  
Percent 

Chart  1.b:  Composition  of 
households by region of birth 
Share among immigrants 

immigrant 

DE immigrants, non-EU Born
ES immigrants, non-EU Born
FR immigrants, non-EU Born
IT immigrants, non-EU Born

DE immigrants, EU-Born
ES immigrants, EU Born
FR immigrants, EU Born
IT immigrants, EU-Born

EU-15
EU-NMS 13
Advanced Economies
Africa

Asia
South America
Middle East

100

90

80

70

60

50

40

30

20

10

0

DE

ES

FR

IT

Sources: Labour Force Survey, 2018-2019. 
Notes: The chart shows the composition of immigrant households by 
region  of  provenance.  EU-NMS13  comprises  the  thirteen  countries 
that  joined  the  EU  from  2004.  Advanced  Economies  contain  North 
America, Australia, New Zealand, and non-EU European countries. 

2005

2007

2009

2011

2013

2015

2017

2019

Sources: Labour Force Survey, 2005-2019.  
Notes:  The  chart  shows  the  evolution  of  the  fraction  of  households 
born in other EU state, and outside the EU, in time. Computation based 
on the quarterly Labour Force Survey 2005-2019 for Italy, Spain and 
France, and 2018-2019 for Germany.  

Chart  2.a:  Share  of  immigrant  households 
across income quintiles 
Percent 

Chart  2.b:  Share  of  immigrant  households 
across quintiles of net wealth 
Percent 

Immigrants, EU born
Immigrants, non-EU born

Immigrants, EU born
Immigrants, non-EU born

18

16

14

12

10

8

6

4

2

0

1

2

3

4

5

25

20

15

10

5

0

Sources Household Finance and Consumption Survey, 2017.  
Notes: The chart shows the fraction of immigrant households across 
quintiles of gross household income.  

1

2

3

4

5

Sources: Household Finance and Consumption Survey, 2017. 
Notes: The chart shows the fraction of immigrant households across 
quintiles of net wealth. 

ECB Discussion Paper Series No 2124 
 
 
 
 
 
 
 
 
 
Chart 3: Age profiles of median gross hourly wages across countries 

a: France 
EUR 

Natives
Immigrants, EU Born
Immigrants, non-EU Born

b: Italy 
EUR  

Natives
Immigrants, EU Born
Immigrants, non-EU Born

16

14

12

10

8

6

4

2

0

16

14

12

10

8

6

4

2

0

18-34

35-44

45-54

55-64

18-34

35-44

45-54

55-64

c: Spain 
EUR  

Natives
Immigrants, EU Born
Immigrants, non-EU Born

14

12

10

8

6

4

2

0

18-34

35-44

45-54

55-64

Sources: EU Statistics on Income and Living Conditions 2009-2018, Italy: 2009-2017. 
Notes: Hourly wages are calculated for employed individuals aged 18-64 (the self-employed are excluded). Due to data limitations the chart on 
hourly wages shows data for France, Italy and Spain. All reported numbers are medians. 

ECB Discussion Paper Series No 2125 
 
 
 
 
 
 Chart  4:  Sensitivity  of 
aggregate employment by region of birth 

individual  employment 

to 

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Natives

EU-15 EUNMS-13 Advanced
economies

Africa

Asia

South
America

Middle East

Sources: Labour Force Survey 2005-2019, quarterly data. 

Notes:  The  chart  shows  the  sensitivity  of  individual  employment,  to  aggregate  the 
aggregate employment rate for various groups of households. EU-NMS 13 contains the 
thirteen  countries  that  joined  the  EU  from  2004. Advanced  economies  comprise  North 
America,  Australia,  New  Zealand,  and  non-EU  European  countries.  The  estimates 
average to 1 and are based on an aggregate of France, Germany, Italy and Spain. The 
lines indicate the 95% confidence interval. 

ECB Discussion Paper Series No 2126 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chart 5: Decompositions across the distribution, percentiles P25, P50 (median), P75 

a: Gross hourly wages 
Percent 

b: Gross household income 
Percent 

c: Net wealth 
Percent 

d: Liquid assets 
Percent 

Sources: Household Finance and Consumption Survey 2010-2017; EU Statistics on Income and Living Conditions 2009-2018, Italy: 2009-2017. 
Note: The charts use the method of Chernozhukov et al. (2013) to decompose the gaps between native and immigrant households into a part 
explained by observable variables and an unobserved part at various quantiles of the distribution of the gaps. The observable variables are age, 
gender,  marital  status,  education,  presence  of  a  child  in  the  household,  occupation,  the  sector  of  employment,  employment  dummy,  self-
employment dummy and time fixed effects. Net wealth and liquid assets were transformed using the inverse hyperbolic transformation (to account 
for the presence of zero and negative values). The top and bottom 5 percent of values were winsorised. 

ECB Discussion Paper Series No 2127 
 
 
 
 
 
 
 
Chart 6: Oaxaca-Blinder decomposition for net wealth—Robustness to excluding employment 
status and sector of employment from explanatory variables 

A: Net wealth: Baseline decomposition 

B: Net wealth: Decomposition without 
employment status and sector of employment 
variables 

Percent 

Explained
Unexplained

Percent 

Explained
Unexplained

180

160

140

120

100

80

60

40

20

0

Immigrants, EU born

Immigrants, non-EU born

180

160

140

120

100

80

60

40

20

0

Immigrants, EU born

Immigrants, non-EU born

Sources: Household Finance and Consumption Survey 2010, 2014, 2017. Germany, France, and Italy.  
Note: The charts decompose the average gaps between native and immigrant households into a part explained by observable variables and an 
unobserved part. For the baseline specification (panel A) the explanatory variables are: age, gender, marital status, education, presence of a 
child in the household, employment status, occupation, the sector of employment, employment dummy, self-employment dummy and time fixed 
effects.  Panel  B  excludes  employment  status  and  sector  of  employment  from  explanatory  variables.  Net  wealth  and  liquid  assets  were 
transformed using the inverse hyperbolic transformation (to account for the presence of zero and negative values). The top and bottom 5 percent 
of values were winsorised. 

Chart 7: Oaxaca-Blinder decomposition for net wealth—Robustness restricting the sample to 
the employed only 
a: Age profiles 
EUR thousands 
Natives
Immigrants, EU born
Immigrants, non-EU born

b: Oaxaca-Blinder decomposition 
Percent 

Explained
Unexplained

160

250

200

150

100

50

0

140

120

100

80

60

40

20

0

<35

35-44

45-54

55-64

Sources: Household Finance and Consumption Survey 2010, 2014, 2017. Germany, France, and Italy.  
Note: The right chart decomposes the average gap between native and immigrant households into a part explained by observable variables 
and an unobserved part. The sample is restricted to households whose reference person is employed and aged less than 65 years. For the 
baseline specification explanatory variables are: age, gender, marital status, education, presence of a child in the household, occupation, the 
sector  of  employment,  self-employment  dummy  and  time  fixed  effects.  Net  wealth  and  liquid  assets  were  transformed  using  the  inverse 
hyperbolic transformation (to account for the presence of zero and negative values). The top and bottom 5 percent of values were winsorised. 

Immigrants, EU born

Immigrants, non-EU born

ECB Discussion Paper Series No 2128 
 
 
 
 
 
Chart 8: Oaxaca-Blinder decomposition for hourly wages—Breakdown by 
age of arrival and time spent in the current country of residence 

Percent 

Explained
Unexplained

50

40

30

20

10

0

Immigrants, EU
Born

Immigrants,
non-EU Born

Immigrants, EU
Born

Immigrants,
non-EU Born

Immigrants, EU
Born

Immigrants,
non-EU Born

Immigrants, EU
Born

Immigrants,
non-EU Born

Above 18, less than 10 years Above 18, more than 10 years Below 18, less than 10 years Below 18, more than 10 years

Sources: EU Statistics on Income and Living Conditions 2003-2018, Italy: 2003-2017. 
Note: The observable variables are age, gender, marital status, education, presence of a child in the household, 
occupation and the sector of employment and time fixed effects. 

ECB Discussion Paper Series No 2129 
 
 
 
 
Acknowledgements  
We thank participants in the Household Finance and Consumption Network research seminar and Niccolò Battistini, Claus Brand, 
Davide Debortoli, Davide Di Laurea, Virginia Di Nino, Michael Ehrmann, Nicola Fuchs-Schündeln, Johannes Gareis, Dimitris 
Georgarakos, Michael Haliassos, Arthur Kennickell, Geoff Kenny, Omiros Kouvavas, Luc Laeven, Philip Lane, Michele Lenza, Adrian 
Monninger, Beatrice Pierluigi, Isabel Schnabel, João Sousa, Oreste Tristani and Guido Wolswijk for comments, and Omiros Kouvavas 
for sharing his code with us. 
All opinions expressed are personal and do not necessarily represent the views of the European Central Bank or the European System 
of Central Banks. This paper uses data from the Eurosystem Household Finance and Consumption Survey. 

Maarten Dossche 
European Central Bank, Frankfurt am Main, Germany; email: Maarten.Dossche@ecb.europa.eu 

Aleksandra Kolndrekaj 
European Central Bank, Frankfurt am Main, Germany; email: Aleksandra.Kolndrekaj@ecb.europa.eu 

Maximilian Propst 
University of Cyprus, Nicosia, Cyprus; email: propst.maximilian@ucy.ac.cy 

Javier Ramos Perez 
Northwestern University, Evanston, United States; email: javihardcode@gmail.com 

Jiri Slacalek 
European Central Bank, Frankfurt am Main, Germany; email: jiri.slacalek@ecb.europa.eu 

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This paper can be downloaded without charge from ECB’s website or from RePEc: Research Papers in Economics. Information on all of 
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ISBN 978-92-899-6289-6 

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