MIGRATION
Edited by Elzbieta Gozdziak, Georgetown University
doi:10.1111/j.1468-2435.2009.00594.x
Low Unemployment and Bad Jobs
for New Immigrants in Italy
Giovanna Fullin and Emilio Reyneri
ABSTRACT
The article analyses the incorporation of immigrants into the Italian labour
market and the difficulties they encounter in accessing both employment
and qualified occupations. The analysis is based on the Italian Labour
Force Survey and highlights the fact that the great majority of immigrants
entering Italy are hardly disadvantaged in comparison to Italians as
regards the risk of unemployment, but, in contrast, they are highly disfavoured as regards the socio-professional status of their jobs. Unlike what
would happen with the old European immigration, nowadays the segregation of immigrant workers in the lowest ranks of the occupational ladder
is not due to their poor education. On the contrary, their disadvantage
increases if educational attainment is taken into account. The leading role
of low-skilled labour demand and underground economy in shaping immigrants’ integration in the Italian labour market is confirmed by the fact
that they have fairly easy access to unskilled and semi-skilled manual jobs,
whereas they experience serious difficulties in entering self-employment and
in obtaining non-manual jobs.
IMMIGRANTS IN THE ITALIAN LABOUR MARKET AT A GLANCE
Second only to Spain, Italy is the European country that has received
the most immigrants in the past fifteen years. The migratory inflow
became sizeable in the mid-1990s, and has skyrocketed since 2001.
Because only very few people entered Italy from developed countries,
and since Italy does not have an important colonial heritage, immigration flows originated from almost all the developing countries and from
Eastern Europe. Up to the late 1990s, the largest proportion of immigrants was from North Africa, whereas thereafter the majority of migration inflows originated from Eastern Europe.
Dipartimento di Sociologia e Ricerca Sociale, Università di Milano Bicocca, Milan.
2010 The Authors
International Migration 2010 IOM
International Migration Vol. 49 (1) 2011
ISSN 0020-7985
Published by Blackwell Publishing Ltd.,
9600 Garsington Road, Oxford OX4 2DQ, UK,
and 350 Main Street, Malden, MA 02148, USA.
Unemployment in Italy
119
Over the years, not just a few non-EU15 immigrants entered the country
without a permit (either by crossing frontiers or by landing clandestinely
on the southern coasts); but by far the largest number of unauthorised
immigrants entered Italy on short-term visas (for tourism or study purposes, for example), and then overstayed in spite of expired documents.
As the number of claims for asylum has been very low, the quota system
(implemented in 1995) has been quite scanty, and family reunions have
become noteworthy only since 1999, the overwhelming majority of immigrants entered Italy (mainly by overstaying) for working reasons, but
without a proper permit of stay, which they managed to obtain only subsequently thanks to frequent regularisation drives (Levinson, 2005). The
underground economy (which in Italy is a large and deeply rooted
phenomenon) has been a major factor in promoting unauthorised immigration (Reyneri, 1998). This is because unauthorised labour migrants
tend to enter countries where it is easy for them to live and work for a
long period of time, even without a work permit. However, once they were
regularized, nearly all immigrant workers moved to the regular economy.
The Labour Force Survey performed in 2005 estimated that the proportion of non-nationals against working age population approaches 5 per
cent. Over 93 per cent of them were from developing and Eastern European countries, whereas the others originated from EU-15 countries,
Switzerland, Japan or the United States. However, migration into Italy is
a quite recent phenomenon. In fact, among the residents coming from
high emigration countries who are between 15 and 64 years old, not even
27 per cent of them had been living in Italy for 10 years or more,1 and less
than 24 per cent had spent between six to nine years in the country. Most
immigrants are young adults, and the proportion of those aged 25-44
against working age population is 67 per cent, which is 22 percentage
points higher than the native ratio. The proportion of women is close to
50 per cent, but it varies greatly according to their nationality.
Finally, besides those from EU-15 and other Western countries, many
people who entered Italy coming from high emigration countries are
highly educated, at least by Italian standards (Table 1). The reason for
this may be that higher education has been expanding in the developing
countries since the 1970s, and it has been widespread in Eastern European countries for a long time. However, it should be borne in mind
that emigration is a positively selected process. Such a selection may be
driven by both workforce demand and supply, because receiving countries may give preference to highly-educated applicants, and highlyeducated youths possess more of the informational, economic and social
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49.9
59.2
56.5
75.2
42.9
47.9
53.6
37.3
51.2
32.6
39.6
38.2
64.7
48.5
51.1
44.2
17.2
37.2
60.0
58.3
30.7
75.2
69.2
52.0
76.3
64.6
44.8
55.8
No school
and lower
secondary
Source: Our elaboration from Istat, Labour force survey, 2005.
Italians born in Italy, EU15 & Oecd
Italians born in developing countries
Eu15 & Oecd
Other Eastern Europe
Albania
Ex Yugoslavia
Romania & Bulgaria
Center-south Asia
Eastern Asia
Other North Africa
Morocco
Central Africa
South America
Total-High Emigration countries
% of
women
Gender
38.6
39.9
41.8
44.4
34.8
37.3
64.0
20.5
27.3
34.9
20.3
30.4
46.8
37.4
Upper
secondary
Education
10.3
15.9
41.0
18.3
5.2
4.4
5.3
4.3
3.5
13.1
3.4
5.0
8.4
6.8
Tertiary
5.0
13.5
29.2
17.9
13.6
26.0
19.1
12.4
15.5
12.1
14.7
18.1
18.3
Till 2
years
13.5
19.5
45.9
29.3
23.4
47.0
26.0
21.5
21.5
22.8
23.7
39.8
31.2
3–5
years
9.4
16.7
15.0
32.8
21.9
21.0
30.3
16.0
22.0
25.6
23.1
22.2
23.7
6–9
years
Year since migration
GENDER, EDUCATIONAL LEVEL AND YEAR SINCE MIGRATION BY NATIONALITY
TABLE 1
72.2
50.4
10.0
19.9
41.1
6.1
24.6
50.0
40.9
39.5
38.6
20.0
26.8
>9
years
120
Fullin and Reyneri
Unemployment in Italy
121
resources needed to emigrate (Feliciano, 2005; Dumont and Lemaı̂tre,
2005; Cheung and Heath, 2007). The main exception is to be found in
the old European immigration, when emigrants were selected by agencies to work as unskilled blue-collars (Heath, 2007). By contrast, nowadays, as labour emigration is not only spontaneous, but is also
hampered by restrictive policies implemented by receiving countries, selfselection is strongly positive and migrants are better endowed with
human capital (Heath and Yu, 2005). Overcoming hardships and costs
of unauthorised entry requires cultural, economic and social resources
that may be related to higher levels of education.
In Italy, the unemployment rate decreased from above 11 per cent in the
mid-1990s to less than 7 per cent in 2006, in parallel with the growth of
immigration. However, owing to its segmentation by age, gender and
region, the Italian labour market is tighter than it appears, because
unemployment rate is very high among young people, especially among
females, and very low among prime-age individuals, above all males.
Most of the unemployed are young, first-job seekers still living at home
with their parents, on whose support they can rely while waiting for a
‘‘good job’’, whereas almost all singles and breadwinners are employed.
Moreover, the regional divide in the Italian unemployment rate is the
highest one among developed countries, also because internal mobility is
rather poor (OECD, 2005). Hence, central and northern regions are
close to full employment, especially as far as prime-age men are concerned, whereas mass unemployment affects women and youth in the
south. Finally, employment is biased towards the poorest jobs: the proportion of managers and professionals is low (over 6 percentage points
below the EU-15 average), whereas that of manual workers is still high
(5 percentage points above). This employment mix appears to match
badly with the increasing educational attainments of youths, so that
labour shortages concentrate in manual jobs, above all unskilled, but
also skilled ones.
Italy is still considered to have neither a coherent immigration policy
nor an inclusive insertion policy (Schierup, et al., 2006). Policymaking,
driven more by political rather than by economic criteria, has been more
concerned with fighting (unsuccessfully) against unauthorised entries
than with immigrant integration (Zincone, 2006). The 1998 Immigration
Act was the first measure that treated immigration as a permanent phenomenon. But its reform, approved by a centre-right government in
2002, revived the guest-worker model by restricting the rules on granting
long-term permits and shortening the duration of temporary stay per 2010 The Authors
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122
Fullin and Reyneri
mits to two years. Although immigrant workers have the same social
rights as Italians, nevertheless there is no national policy to promote
their economic and social integration.
It is within such a context – which greatly differs from past immigration
trends, as well as from current ones in central and northern European countries (Castles and Miller, 2003; Schierup, et al., 2006) – that we will focus on
the integration of immigrants in the labour market, our aim being to highlight, in particular, unemployment risks and their access to qualified jobs.
DATA AND RESEARCH METHODOLOGY
Our analysis, which draws on the 2005 Labour Force Survey covering
around 170,000 individuals, is focused on respondents aged between 15
and 64 years. The sample is based on the population register and slightly
underestimates the weight of non-nationals,2 but the Labour Force Survey
undoubtedly provides the richest and most reliable dataset concerning
migrants in the Italian labour market. We use nationality to identify
immigrants but, as far as Italians are concerned, we also use information
about country of birth in order to make a distinction between those born
either in Italy or in EU-15 and OECD countries (who can be considered
second-generation emigrants returned to Italy) and those born in high
emigration countries. Only slightly more than 60 per cent of Italians who
were born in emigration countries are real migrants who have acquired
citizenship through naturalisation (almost all by means of marriage with
Italian citizens). The others were born in South American countries, where
even distant descendents of Italian emigrants retain dual citizenship, or
they have repatriated from Libya, a former Italian colony.
The small number of cases obliged us to collapse data related to immigrants from many countries into a few groups: former Yugoslavia
(mainly Serbia-Montenegro and Macedonia, but also Bosnia, Slovenia
and Croatia), other Eastern European countries (Ukraine, Poland,
Moldova), Central Southern Asia (India, Bangladesh, Pakistan, Sri
Lanka), Eastern Asia (Philippines and China), North Africa (mainly
Tunisia, Egypt, but also Near and Middle East), Central Africa (Senegal,
Nigeria and Ghana), and South America (mainly Ecuador and Peru).
Owing to gender contrasts found in labour market patterns in relation to
both Italians and immigrants, statistical models were run separately by gender. Independent variables include age, education, family status and region
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Unemployment in Italy
123
(North, Centre and South). Models concerning only immigrants also
included a variable regarding the number of years since migration. The Italian labour force survey classifies educational qualifications obtained abroad
according to the Italian grid, which does not specify vocational courses.
Therefore, we used the ISCED classification and collapsed data into four
categories in order to reduce the risk of misunderstandings in the recoding.
Family status was recoded by collapsing information on marital status and
the presence of children. Finally, in order to analyse the occupational status
of immigrants, we used the class scheme proposed by Erikson, Goldthorpe
and Portocarero (1979), which we collapsed into a four-item variable.3
LOW ETHNIC PENALTY FOR LABOUR PARTICIPATION
AND UNEMPLOYMENT
In 2005, the unemployment rate among people from developing and
East-European countries residing in Italy was only 30 per cent higher
than among natives (10.3% vs. 7.8%). That gap is much narrower than
in Central and Northern European countries, in spite of wide differences
between immigrant groups (Table 2). Such a result is usually explained
by the fact that those who entered Central and Northern Europe were
mainly refugees, whereas in Italy they were labour migrants who filled
job shortages, although their entry was unauthorised in most cases (Salt,
et al., 2004; Diez Guardia and Pichelman, 2006). This conclusion, however, needs to be qualified in light of two peculiarities of the Italian economic and social fabric: a poor welfare system, and the sharp NorthSouth divide.
Because Italy is the developed country where unemployment benefits are
the least generous, the unemployed must rely mainly on their families’
support. This explains why over 52 per cent of native job-seekers are
youths living at home with their parents, while singles and breadwinners
amount to less than 24 per cent. By contrast, not even 10 per cent of
immigrant job-seekers can rely on the modest support from their parents, whereas 40 per cent are either singles or breadwinners and can rely
only on rather meagre benefits. Thus, the much lower proportion of
long-term job-seekers found among immigrants (23% vs. 41% for
natives) may be due to the fact that immigrants from non-EU countries
cannot afford to remain unemployed for longer than 6 months because,
before that time-frame elapses, they will be forced to get a job, or to
switch back to an illegal resident status or, as a last resort, to leave the
country.
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6.3
6.2
5.5
5.9
5.9
4.1
4.6
1.8
5.5
11.3
10.2
11.3
9.3
7.0
Source: Our elaboration from Istat, Labour force surveys, 2005.
Italians born in Italy, EU15 & Oecd
Italians born in developing countries
Eu15 & Oecd
Other Eastern Europe
Albania
Ex Yugoslavia
Romania & Bulgaria
Center-south Asia
Eastern Asia
Other North Africa
Morocco
Central Africa
South America
Total high emigration countries
Unemployment
rate
Men
73.7
75.4
83.7
85.8
90.2
85.4
88.4
90.3
86.1
86.7
87.8
84.8
79.7
87.1
Activity
rate
10.0
14.3
6.0
15.2
27.1
17.5
7.7
30.8
3.2
37.3
26.6
17.3
11.5
15.5
Unemployment
rate
Women
50.3
50.7
58.7
72.0
47.9
53.2
72.0
29.3
71.6
34.3
34.9
68.3
70.7
58.7
Activity
rate
UNEMPLOYMENT AND ACTIVITY RATES BY GENDER AND NATIONALITY
TABLE 2
7.8
10.2
5.7
12.6
12.0
9.0
6.1
6.5
4.4
15.5
13.6
13.3
10.6
10.3
Unemployment
rate
Total
62.0
60.8
69.6
75.4
72.1
69.9
79.6
67.6
78.7
69.6
66.8
78.5
73.9
73.4
Activity
rate
124
Fullin and Reyneri
Unemployment in Italy
125
Furthermore, immigrants concentrate in central-northern regions, where
the labour market is tighter. Hence, in the area where most of them live,
their unemployment gap with respect to natives is much wider than at
national level. In fact, the ratio between the unemployment rates is two
and a half in the North and one and a half in the Centre. Such gaps,
however, are only due to immigrants’ higher risk of entering unemployment, because their average length of job-seeking is much shorter than
that of natives even in central-northern regions. On the other hand,
because immigrants are much more prone to geographical mobility than
natives, in the South their unemployment rate is even lower.
Besides regional settlement, the unemployment gap between immigrants
and natives may also depend on their different personal characteristics.
To focus on this aspect we shall use the concept of ‘‘ethnic penalty’’,
which refers to the net disadvantages that immigrants continue to experience in the labour market even after their observed personal characteristics have been taken into account (Heath and Cheung, 2007).
Referring to the results of a regression, we can show the extent to which
gaps in the probability of avoiding unemployment persist between immigrants and natives who, besides living in the same regional area, are
alike in terms of age, education and family status. Such analyses should
always be carried out separately for men and women, because, generally
speaking, in Italy, male and female immigrants enter different labour
markets, that is, mainly private companies for the former, households
for the latter. Furthermore, by comparing models for the whole sample,
whose results are ‘‘driven’’ by natives who constitute the sample’s great
bulk, with those including immigrants only, we should be able to determine whether education affects the risk of unemployment for immigrants and natives in the same way.
Especially in the first generation, several immigrants may be ‘‘discouraged unemployed’’, that is, neither in education nor looking for a job;
and women from some ethnic groups may have very low labour-market
participation owing to their traditional values, so that those who are
active (i.e., employed or job-seekers) may be ‘‘positively selected’’. In
both cases, sizeable differences in activity rates (equal to the ratio
between the number of people employed and looking for a job and the
total population aged 15-64) between immigrant groups (Table 2) may
distort the results on the risk of unemployment, with the real disadvantages of less active groups being underestimated (Heath and Cheung,
2007). This might be our case, as, although the average activity rate of
all immigrants does not differ from that of natives, differences between
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Fullin and Reyneri
groups are quite significant: 15 percentage points for men and even 40
points for women. To deal with this problem, we used a Heckman probit selection model involving a two-stage binary regression that enabled
us to take account of both the probability of being active and that of
avoiding unemployment at the same time. However, the likelihood ratio
test of the equations’ independence of the fitted Heckman selection models was not found to be significant for either men or women, thus indicating that there is no correlation between errors in the two equations;
that is to say, that the unobservables affecting labour market participation have not affected the risk of unemployment (results available on
request).
Therefore, simple logistic regression models were preferred in order to
stress the ethnic penalty as regards both labour market participation
(taking into consideration all the population aged 15-64) and risk of
unemployment (focusing only on active population, i.e., only employed
and job-seekers). For both men and women, two models were estimated:
the first one included immigrants and natives, whereas the second one
included immigrants only. The parameters associated with the groups of
immigrants in the first models could be estimated as the sizes of ‘‘ethnic
penalties’’ (if they were negative) or ‘‘ethnic bonuses’’ (if they were positive) experienced by those groups compared to Italians with the same
personal characteristics (age, education, family status) and living in the
same region.
As regards labour market participation,4 most of the male immigrant
groups are advantaged in comparison to Italians, whereas most of the
female groups are more or less largely disadvantaged (Table 3). Because
those results are controlled for age, the high labour-market participation
by male immigrants is not only due to the low proportion of elderly
individuals among them, but also to their higher activity rates, in particular among youths. Eastern Asian immigrants are a notable exception
to low labour market participation by female immigrants, because
women from China and the Philippines are largely advantaged in comparison to their Italian counterparts, whose activity rate is the lowest in
Europe, however. By contrast, women from Morocco, other North-African countries and Central Southern Asia (Pakistan, India, Sri Lanka
and Bangladesh) appear to be almost excluded from the labour market,
presumably for cultural reasons.
As regards the risk of unemployment, the ethnic penalty is evident for
both men and women because for most immigrants groups the probability
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Nationality
Italians born in Italy, EU15 & Oecd
Italians born in developing countries
Eu15 & Oecd
Ex Yugoslavia
Romania & Bulgaria
Albania
Other Eastern Europe
Centre-South Asia
Eastern Asia
Morocco
Other North Africa
Central Africa
South America
Education
No school & primary
Lower secondary
Upper secondary
Tertiary
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International Migration 2010 IOM
ref.
0.41***
0.92***
1.67***
ref.
)0.22**
)0.26
0.00
0.49
0.84***
0.41
0.74**
0.57
0.74***
0.40
0.43
0.71
All
B
Men
ref.
0.72***
1.13***
1.91***
Reference =
Italians born
in developing
countries
Controlled
for nationality
Migrants only
b
ref.
0.73***
1.61***
2.40***
ref.
)0.47**
)0.77**
)0.50***
)0.07
)0.41***
0.05
)0.99**
0.72***
)0.76***
)1.50***
0.51***
)0.15
All
b
Women
ref.
0.58***
0.84***
1.23***
Reference =
Italians born
in developing
countries
Controlled for
nationality
Migrants only
b
PROBABILITY OF BEING ACTIVE FOR 15–64 YEARS OLD RESIDENTS (FULL TIME STUDENTS EXCLUDED).
LOGISTIC BINARY REGRESSION
TABLE 3
Unemployment in Italy
127
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0.34***
ref.
0.53**
0.53*
0.40
2.28***
3,209
0.118
0.267
2.36***
105,871
0.239
0.382
107,724
0.270
0.361
ref.
)0.90***
)0.63***
)0.09**
0.01
All
b
ref.
0.69***
0.12
)0.06
0.11
Migrants only
b
ref.
0.74***
0.14***
)0.81***
0.40***
*= 10% significance, ** = 5% significance, *** = 1% significance.
Family status
Living alone
Living with partner & children
Living with partner without children
Youth living with parents
Single parent
Living in Italy
Till 2 years
From 3 to 5 years
From 6 to 9 years
10 years & over
Constant
Controlled for age and region
Number of cases
Cox and Snell R2
Nagelkerke R2
All
B
Men
TABLE 3 (CONTINUED)
Women
3,875
0.198
0.267
ref.
0.69***
0.54***
0.86***
0.74***
ref.
)1.92***
)1.13***
0.28
)0.04
Migrants only
b
128
Fullin and Reyneri
Nationality
Italians born in Italy, EU15 & Oecd
Italians born in developing countries
Eu15 & Oecd
Ex Yugoslavia
Romania & Bulgaria
Albania
Other Eastern Europe
Centre-South Asia
Eastern Asia
Morocco
Other North Africa
Central Africa
South America
Education
No school & primary
Lower secondary
Upper secondary
Tertiary
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ref.
0.63***
1.00***
0.86***
ref.
)0.27
)0.85**
)0.55
)0.56*
)0.35
0.00
1.13**
0.39
)0.94***
)1.05***
)1.11***
)1.00***
All
b
Men
ref.
)0.20
0.05
0.41
Reference =
Italians born in
developing
countries
Controlled for
nationality
Migrants only
b
ref.
0.50***
1.11***
1.15***
ref.
)0.70***
)0.57**
)0.95***
)0.34
)1.67***
)0.94**
)1.49**
1.69**
)1.38***
)2.29***
)1.00***
)0.88***
All
b
Migrants only
b
ref.
0.15
0.37*
0.16
Reference =
Italians born in
developing
countries
Controlled for
nationality
Women
PROBABILITY OF AVOIDING UNEMPLOYMENT FOR 15–64 YEARS OLD ACTIVE RESIDENTS. LOGISTIC BINARY REGRESSION
TABLE 4
Unemployment in Italy
129
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58,964
0.088
0.184
2.48***
2,936
0.038
0.097
1.55***
ref.
0.45*
1.05***
0.92***
2.68***
85,398
0.074
0.199
ref.
)0.14***
0.02
)0.71***
)0.46***
All
b
ref.
0.01
)0.07
)1.24***
0.69
Migrants only
b
ref.
0.76***
0.53***
)0.63***
0.07
*= 10% significance, ** = 5% significance, *** = 1% significance.
Family status
Living alone
Living with partner & children
Living with partner without children
Youth living with parents
Single parent
Living in Italy
Till 2 years
From 3 to 5 years
From 6 to 9 years
10 years & over
Constant
Controlled for age and region.
Number of cases
Cox and Snell R2
Nagelkerke R2
All
b
Men
TABLE 4 (CONTINUED)
Women
2,321
0.079
0.137
ref.
0.71***
0.88***
1.13***
1.50***
ref.
)1.08***
)0.89***
)1.01***
)0.91***
Migrants only
b
130
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Unemployment in Italy
131
of avoiding unemployment is lower than for their Italian counterparts,
and for several groups the gap is quite large. Men from Central Southern Asia (Indians and Pakistanis) and women from Eastern Asia (Chinese and Filipinos) are an important exception as their probability of
avoiding unemployment is largely above that of natives having the same
personal characteristics, probably because they filled wide shortages in
the Italian labour market.
To sum up, only people from Eastern Asia are more active and less
unemployed than their native counterparts, and for women the advantage is substantial. By contrast, immigrants from former Yugoslavia,
EU-15 and OECD countries, as well as Italians born in a developing
country, are always disadvantaged. Such a finding is not unexpected for
people from former Yugoslavia because many of them are among the
few refugees to have entered Italy. On the other hand, the finding for
those from EU-15 and OECD countries and Italians born in a developing country would be surprising if we did not consider that most of
them have the same characteristics (prime age, higher education) as
natives with the best labour market situations. Finally, naturalisation by
itself appears not to have a positive impact on immigrants’ integration
into the labour market, perhaps because the overwhelming majority of
them obtained Italian citizenships through marriage.
When comparing the model including the whole sample, whose
outcomes are ‘‘driven’’ by natives, with that including immigrants only,
differentials between parameters as regards the probabilities of being
active by family status show that immigrant women living with a partner are much more disadvantaged than their native counterparts.5 The
reason for that may be cultural, but the penalisation is especially high
for immigrant women with children, who face huge difficulties in conciliating childcare with paid employment since public care services in Italy
are rather poor, private ones are too expensive, and, unlike natives,
immigrant women cannot rely on the support of any relatives.
In Italy, the level of educational attainment is positively related to the
probability of avoiding unemployment (as shown by the parameters of
models including the whole sample that are all significantly positive). By
contrast, such a relation does not seem to exist for immigrants because,
for both genders, the parameters for all education levels are not significantly different from zero. Hence, higher education does not protect
immigrants against the risk of unemployment. The poor performance of
highly-educated immigrants in comparison to poorly educated ones is
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Fullin and Reyneri
usually explained by the fact that, for first-generation immigrants, a
higher education cannot involve a greater endowment of human capital
(Heath and Cheung, 2007b). Firstly, skills acquired in a different educational system may be useless because human capital is often country-specific (Heath and Yu, 2005); secondly, foreign qualifications may not be
recognised by the receiving country; and thirdly, educated immigrants
may not have a good command of the receiving country’s language,
which is necessary to gain access to qualified occupations. Those
hypotheses seem to be well-suited to explain either the professional
downgrading of highly educated immigrants or the behaviour of wellsettled highly educated immigrants, who can afford to wait for a long
time for a good job. However, they do not fit when the focus is on
employment position and on recent immigrants who do not have sufficient economic and social resources that would enable them to wait for
a long time for work. Therefore, other hypotheses are needed, and
should all be based on the fact that in Italy employment opportunities
for immigrants are mainly for unskilled positions.
As far as immigrants’ behaviour is concerned, higher education can be
regarded as a ‘‘proxy’’ for greater personal resources, which enable
immigrants to obtain more information, to control their behaviour better and direct it to their migratory goals, to learn the language beforehand, and to manage themselves more efficiently in the labour market.
Temporary immigrants have been considered the best example of homo
oeconomicus because they are usually committed to making as much
money as possible, with no concern for the social status of their jobs
because they keep their home society as a reference (Piore, 1979). Thus,
highly educated immigrants should ‘‘displace’’ poorly-educated ones,
with the consequence that their probability of avoiding unemployment
should be higher. Since that has not occurred, we may surmise that
some highly educated immigrants either do not have a merely instrumental approach to migration (as ethnographic studies have reported),
or possess fairly good economic resources, so that they are scarcely
prone to fill just any poor job vacancy. On the other end, as far as
labour demand is concerned, we can guess that perspective employers
may either not be able to evaluate the human capital of immigrants with
different educational levels or even be afraid of hiring over-educated
workers for very poor jobs.
The model including only people born abroad shows that integration
into the labour market is positively related to the length of stay,
although the trend is not straightforward. As regards labour-market
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Unemployment in Italy
133
participation, the probability among more settled immigrants is higher
only for women, but after a ‘‘leap’’ from less than two years to three to
five years, the parameters do not increase significantly; whereas for men,
after a small increase from less than two years to three to five years the
probability appears to be steady. Such a difference may be due to the
fact that many women joined their partners and entered the labour market only at a later stage, whereas all men immediately started to look
for a job. The relation with the length of stay is positive, with an asymptotic trend for the probability of avoiding unemployment as well, given
that after nine years the parameters for both men and women tend to
increase only slightly or not at all. We may suppose that after that time
the process of integration of immigrants into the Italian labour market
has stabilised, although no information on those who left the country is
available and we can only ground on studies carried out on other cases6
the assumption that return migration is not a selective process. However, we can guess that such a positive trend as regards the probability
of avoiding unemployment might be overestimated because long-term
unemployed non-EU immigrants ‘‘disappear’’ from municipal registers,
which labour force surveys are based on, as they are not entitled to
renew their permit of stay.
A BRAIN WASTE: THE ETHNIC PENALTY AS REGARDS
OCCUPATIONAL STATUS
As far as occupational status is concerned, the original EGP class scheme
identified 7 main classes, which we recoded into four categories. In the
Italian case, as shown in Table 5, the distribution of immigrant workers
markedly concentrates in manual occupations (76% of workers from
emigration countries are in the lowest three EGP classes). Consequently,
we decided to keep poorly-skilled non-manual jobs separate from their
manual counterparts. In fact, we assumed that access to non-manual
occupations (service class and routine non-manual employees) is at present the crucial issue as regards the employment of immigrants. In Italy,
the manual ⁄ non manual divide is much stronger than in other European
countries, whereas differences between skilled and unskilled blue-collars
are less marked in terms of working conditions and social status. This is
because in Italy most skilled blue-collars work in small factories and in
the national cultural tradition manual work is often despised.7
A quite large proportion of immigrants (nearly one-third versus slightly
more than 17% for Italians) have managed to obtain skilled manual
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IVa Small proprietors. artisans with employees
IVb Small proprietors. artisans without employees
IVc Farmers
V Lower grade technicians
VI Skilled manual workers
VIIa Semi and unskilled manual workers
VIIb Agricultural workers
Petty
bourgeoisie
Skilled manual
workers
Semi and unskilled
manual workers
Total
I Higher-grade professionals
II Lower-grade professionals
IIIa Routine non manual employees - higher grade
IIIb Routine non manual employees - lower grade
White collars
100.0
14.8
2.0
2.2
15.2
3.2
8.0
2.0
6.4
21.1
21.0
3.9
Italians born
in Italy, EU15
and Oecd
100.0
25.4
1.0
1.9
20.0
1.5
7.2
2.3
6.8
17.7
12.9
3.4
Italians
born in
developing
countries
EMPLOYMENT BY NATIONALITY AND CLASS POSITION
TABLE 5
100.0
7.3
2.0
2.7
6.6
1.2
4.8
1.4
15.5
39.3
17.9
1.2
Eu15 &
Oecd
100.0
45.1
0.2
1.4
31.2
1.2
5.0
4.7
0.8
4.2
4.4
1.7
Emigration
countries
134
Fullin and Reyneri
Unemployment in Italy
135
jobs. But this does not mean that such immigrants have substantially
improved their social status, because they are mainly employed in small
manufacturing firms (over 50% of them work in firms employing 10
workers or fewer), which are characterized by low wages, poor social
protection and low social recognition. On the other hand, the fact that
in Italy on-the-job training is the main way to enter skilled manual jobs
makes career paths hardly institutionalised and the insertion of immigrants into those occupations much easier than in other countries, like
Germany or Denmark, where skill certificates are needed. The proportion of the petty bourgeoisie among immigrants is not much lower than
among Italians8 (10.9% versus 13.2%). By contrast, the gap between
immigrants and Italians is extremely wide as regards access to non-manual work – 11.1 per cent versus 52.5 per cent. Moreover, the immigrant
groups attaining the highest proportions (those from South America,
other Eastern European countries, and former Yugoslavia) score barely
16-17 per cent, whereas only 4-6 per cent of people from Central Southern Asia and Morocco have managed to obtain white-collar jobs. The
fact that over 73 per cent of people from EU15 and OECD countries
work as white collars confirms that they are a professional elite. Finally,
the distribution of Italians born in a developing or Eastern European
country appears to be much closer to that of natives than the one of
immigrants, contrary to what was seen for unemployment.
In the case of the old European immigration, the confinement of
migrants to the lowest ranks of the occupational ladder was due to their
poor educational attainment. We presume that this is not the case here,
however, because many immigrants entering Italy are highly educated
young adults. In order to check the extent to which individual characteristics explain such differences in social position between immigrants and
natives, we used a multinomial logistic regression model, assuming that
classes are independent and unranked categories. We took the lowest
level – unskilled and semi-skilled manual workers – as the reference category for the dependent variable and, as usual, we carried out separate
analyses for women and men, controlling for age and level of education.
Table 6 highlights that workers from emigration countries have negative
odds of being in all the classes, therefore, they have a lower probability
than their Italian counterparts of attaining any social status higher than
semi and unskilled manual work, which is the reference category. The
probability is significantly lower for access to the petty bourgeoisie, and
dramatically lower for access to the white-collar class. Ethnic penalties
always seem to be much heavier for female immigrants than for males,
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0.32
0.34
ref.
)0.05
)0.23***
85,612
ref.
0.26
)0.74***
Petty
bourgeoisie
b
Models control for age and education.
*= 10% significance, ** = 5% significance, *** = 1% significance.
Italians
Eu15 & Oecd
Emigration countries
Number of cases
Pseudo R square
Cox and Snell
Nagelkerke
Skilled manual
class
b
Men
ref.
0.04
)2.67***
b
White collars
0.33
0.37
ref.
)0.17
)0.86***
56,897
Skilled manual
class
b
ref.
)0.03
)1.94***
Petty
bourgeoisie
b
Women
ref.
)0.49*
3.49***
White
collars
b
MULTINOMIAL LOGISTIC REGRESSION MODEL OF THE LIKELIHOOD OF ENTERING HIGHER SOCIAL CLASSES FOR 15–64 YEARS
OLD WORKERS. REFERENCE CATEGORY: SEMI AND UNSKILLED MANUAL CLASS (REGRESSION COEFFICIENTS)
TABLE 6
136
Fullin and Reyneri
Unemployment in Italy
137
the reason being the extraordinary concentration of female immigrants
in housekeeping and elderly care.
In order to analyse differences among immigrants groups and the impact
of immigrants’ individual characteristics on access to higher social positions, we estimated two other models excluding Italians born in Italy
and in EU-15 and OECD countries, and including the length of stay.9
As regards men, Table 7 highlights that the odds of entering the
white-collar class are more or less negative for all immigrants except
for workers from EU-15 and OECD countries, whereas for some
immigrant groups the odds of entering the skilled manual-worker class
and the petty bourgeoisie are not statistically different from the reference group, and even positive in some cases. We can, therefore, identify two broader groups. The first comprises immigrant workers from
Central Africa, Morocco, Central Southern Asia and Eastern Asia,
who experience an ethnic penalty for all higher occupational statuses
juxtaposed with the unskilled and semi-skilled manual class. By contrast, immigrant workers in the second group, which includes those
from Albania, other Eastern Europe, other North Africa, former
Yugoslavia, Romania and Bulgaria, experience an ethnic penalty only
for access to the white–collar class, and, moreover, the penalty is generally lower than for the first group. Immigrant workers from South
America lie in between, because they have a positive coefficient for the
skilled manual class and a negative one for both the petty bourgeoisie
and white–collar classes. The case of men from Central Southern Asia
(India, Pakistan, Sri Lanka and Bangladesh) and from Eastern Asia
(China and the Philippines) is interesting because they combine a very
bad performance concerning the occupational status with a high ethnic
bonus as regards the probability of avoiding unemployment (Table 4).
We may presume that men from those countries are the only ones
who have managed to attain a probability of avoiding unemployment
even higher than that of natives because they are the most willing to
fill even the lowest job vacancies.
The results for women are very different. As shown in Table 8,10 coefficients are always negative for all immigrant women except for those
from EU-15 and OECD countries. As for men, the least penalised are
Albanians, although women show a relative disadvantage in comparison
to Italians who were born in developing countries (negative coefficients),
whereas that is not the case for men, who have positive odds of entering
both skilled manual jobs and the petty bourgeoisie. On the other hand,
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TABLE 7
MULTINOMIAL LOGISTIC REGRESSION MODEL OF THE LIKELIHOOD OF ENTERING
HIGHER SOCIAL CLASSES FOR 15–64 YEARS OLD MALE IMMIGRANT WORKERS.
REFERENCE CATEGORY: SEMI AND UNSKILLED MANUAL CLASS (REGRESSION
COEFFICIENTS)
Skilled
manual
class
b
Constant
Education
Tertiary
Upper secondary
Lower secondary
No school & primary
Living in Italy
10 years and over
From 6 to 9 years
From 3 to 5 years
Till 2 years
Nationality
South America
Central Africa
Morocco
Other North Africa
Eastern Asia
Centre South Asia
Romania e Bulgaria
Ex Yugoslavia
Albania
Other Eastern Europe
EU15 & Oecd
Italians born in developing
countries
pseudo R square
Cox and Snell
Nagelkerke
Number of cases
Petty
bourgeoisie
b
White
collars
b
)1.02***
)2.18***
)0.05
0.08
0.07
ref.
)0.19
)0.28
)0.27
ref.
3.12***
1.59***
0.66**
ref.
0.07
0.19
0.23
ref.
0.66***
0.26
0.22
ref.
1.03***
0.40
)0.07
ref.
0.11
)0.90***
)0.47***
0.12
)0.64**
)0.87***
0.01
0.02
0.45***
0.33
0.02
ref.
)0.78*
)1.85***
)1.02***
0.25
)0.21
)0.71***
0.18
0.15
0.38*
0.39
0.82**
ref.
)0.39
)1.31***
)2.30***
)0.98***
)0.87***
)2.08***
)1.43***
)1.19***
)1.42***
)0.54
1.31***
ref.
0.24
0.29
0.32
3,126
Model controls for age.
*= 10% significance, ** = 5% significance, *** = 1% significance.
the most heavily penalised are women from North Africa and Asia
(mainly women from Philippines, India, China and East Asia, with a
few cases from Sri Lanka and Bangladesh). As stressed for men, the case
of women from Asia is also interesting because they have a quite high
probability of avoiding unemployment (Table 4), but a high risk of
attaining bad social positions.
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Unemployment in Italy
TABLE 8
MULTINOMIAL LOGISTIC REGRESSION MODEL OF THE LIKELIHOOD OF ENTERING
HIGHER SOCIAL CLASSES FOR 15–64 YEARS OLD FEMALE IMMIGRANT WORKERS.
REFERENCE CATEGORY: SEMI AND UNSKILLED MANUAL CLASS (REGRESSION
COEFFICIENTS)
Skilled
manual class
b
Education
Tertiary
Upper secondary
Lower secondary
No school & primary
Living in Italy
10 years and over
From 6 to 9 years
From 3 to 5 years
Till 2 years
Nationality
South America
Central Africa
North Africa
Asia
Romania e Bulgaria
Ex Yugoslavia
Albania
Other Estern Europe
EU15 & Oecd
Italians born in developing
countries
Constant
Number of cases
Pseudo R square
Cox and Snell
Nagelkerke
Petty
bourgeoisie
b
White
collars
b
0.45
0.70***
0.30
ref.
0.49
0.27
0.16
ref.
3.60***
2.25***
1.06***
ref.
)0.02
0.06
)0.37**
ref.
0.77**
0.19
)0.13
ref.
1.27***
0.65**
0.01
ref.
)0.53**
)0.34
)0.86***
)1.05***
)0.42*
)0.14
)0.67***
)0.39*
0.13
ref.
)1.36***
)1.57***
)1.14***
)0.85***
)1.14***
)0.35
)0.97***
)0.73**
0.87**
ref.
)1.42***
)1.67***
)2.04***
)2.20***
)1.46***
)1.06***
)1.64***
)1.47***
1.06***
ref.
)0.89*
)1.92***
0.16
2,228
0.34
0.37
Model controls for age
*= 10% significance, ** = 5% significance, *** = 1% significance.
For both male and female immigrant workers, the probability of entering the skilled manual class is not affected by either the level of education or the length of stay in Italy, given that coefficients are not
significant. By contrast, the probability of entering the white-collar class
increases greatly for more educated immigrants and for those who
entered Italy before the mid-1990s. Only a long stay in the country also
fosters access to the petty bourgeoisie, which, however, is not affected
by the level of education. Of course, those results are also based on the
assumption that no selection affected returns, as was emphasized by the
few studies dealing with that issue.11
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Fullin and Reyneri
In order to determine the impact of education, we compared the outcomes of two logistic regression models (for men and women) predicting
the odds of entering the white-collar class. The first one included only age
as control variable, while the second included education level as well. As
Graphs 1a and 1b report, in both models, coefficients are negative for
almost all immigrant groups, and disadvantages further increase once
education is controlled for. Such a result confirms that the high education
level of some immigrant groups does not help them to achieve better
social positions; on the contrary, it makes their penalisation more evident.
Workers (men and women) from Central Africa and men from Eastern
Asia seem to represent an exception. The different outcome by gender for
workers from Eastern Asian countries is largely due to the fact that most
of the men are Chinese and most of the women are Filipinos. Contrary to
Chinese men, many Filipino women are highly educated and they are
strongly penalised because most of them work as housekeepers. The
GRAPH 1A
LOGIT OF ENTERING HIGH LEVEL CLASSES (SALARIAT AND ROUTINE NON
MANUAL EMPLOYEES) MALE WORKERS 15-64
1.00
Without education
With education
0.50
0.00
–0.50
–1.00
–1.50
–2.00
–2.50
–3.00
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Unemployment in Italy
GRAPH 1B
LOGIT OF ENTERING HIGH LEVEL CLASSES (SALARIAT AND ROUTINE NON
MANUAL EMPLOYEES) FEMALE WORKERS 15–64
0.25
Without educaƟon
With educaƟon
–0.25
–0.75
–1.25
–1.75
–2.25
–2.75
–3.25
worsening in the ethnic penalty when account is taken of the education is
especially marked for workers from EU-15 and OECD countries, other
Eastern European countries, Romania and Bulgaria, and, for men, also
from the other North African countries. A very large proportion of immigrants from those countries are highly educated, so that their concentration in unskilled and semi-skilled manual occupations must be interpreted
as an effect of very strong ethnic penalties.
We can conclude that, unlike what occurred in European immigration
from past centuries (Cheung and Heath, 2007), for new immigrants in
Italy taking their human capital endowment into account does not
reduce their ethnic penalty at all as regards access to higher occupational statuses, but it actually increases it. Contrary to the probability of
avoiding unemployment, in this case the scant ‘‘portability’’ of skills
acquired abroad, a poor command of the receiving country’s language,
and difficulties in obtaining recognition of foreign educational qualifications appear to matter a great deal. However, because competition for
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Fullin and Reyneri
the few ‘‘good jobs’’ in the Italian labour market is fierce, we cannot
exclude that some discriminatory processes may also be at work. Specific
data are still lacking, but a survey on employers’ behaviour regarding
recruitment for semi-skilled manual jobs showed that the discrimination
is even higher in Italy than in Spain, Belgium, Germany and the Netherlands (Allasino, et al., 2004).
Finally, in order to analyse whether and to what extent the pay-offs of
education differ between immigrants and Italians, we inserted interaction
terms between education and migratory status into the regression model
of the odds of attaining the white-collar class (Table 9). To simplify the
outcomes, we collapsed nationality into a dummy variable: Italians and
workers from European and OECD developed countries versus all other
workers. The negative coefficients of the interaction terms appear to
confirm that immigrants receive lower returns on their educational
investments than Italians do. Furthermore, a comparison of the main
effects by education level, which refer to Italians only, with the net
effects (resulting from the sum of main effects and the coefficients of the
interaction terms) shows that for both Italians and immigrants, the odds
of entering non-manual classes increase the higher the level of education
is, but coefficients for immigrants grow much more slowly (from 0.54
TABLE 9
LOGISTIC REGRESSION MODEL OF THE LIKELIHOOD OF ENTERING HIGH LEVEL
CLASSES (SALARIAT AND ROUTINE NON MANUAL EMPLOYEES) FOR 15–64 YEARS
OLD IMMIGRANT WORKERS. REFERENCE CATEGORY. ALL MANUAL CLASSES AND
PETTY BOURGEOISIE (REGRESSION COEFFICIENTS)
b
Education
Nationality
Interaction terms
No school & primary
Lower secondary
Upper secondary
Tertiary
Emigration countries
Emigration countries * No school & primary
Emigration countries * Lower secondary
Emigration countries * Upper secondary
Emigration countries * Tertiary
Constant
Number of cases
)2 log likelihood
R-square of Cox and Snell
R-square of Nagelkerke
Model controls for age and gender.
*= 10% significance, ** = 5% significance, *** = 1% significance.
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1.05
2.23
4.08
)2.08
***
***
***
***
)0.51
)1.88
)2.96
)0.97
142,508
140960.63
0.33
0.44
**
***
***
***
Unemployment in Italy
143
for lower secondary to 1.13 for tertiary education) than those for Italians (from 1.05 to 4.08).
CONCLUSION
Generally speaking, we can conclude that a great many immigrants who
have entered Italy are hardly penalised in comparison with Italians as
regards the risk of unemployment, but are severely penalised as regards
the socio-professional status of their jobs. Because most immigrant
workers have entered the country without working permits and have
been forced to work off-the-books for rather long periods of time, when
they gain entitlement to hold a registered job through a regularisation
drive, the quality of their jobs only seldom upgrades, even in the case of
highly-educated workers.
The segregation of immigrants in manual jobs, as well as their relatively
low probability of being unemployed, do not depend on their personal
characteristics but rather on the mismatch between labour demand and
native labour supply, as well as on a sharp labour-market segmentation
by age, gender, region and educational attainment. The trade-off
between the risk of unemployment and a poor job is accentuated by a
serious lack of qualified labour demand, 12 a not very generous welfare
state, and scant (de facto) regulation of the labour market. The leading
role of labour demand in shaping immigrants’ integration into the Italian labour market is confirmed by the fact that they have fairly easy
access to skilled blue-collar jobs, which have a low social status in Italy,
whereas they are almost entirely excluded from the least qualified nonmanual jobs, which enjoy quite a good social standing.
Unlike the case of the old European immigration, the segregation of
immigrant workers in the lowest ranks of the occupational ladder is not
at all due to their poor education. On the contrary, their penalisation in
comparison to Italians increases if educational attainment is taken into
account. There are many reasons why the return to education for recent
immigrants is very low. However, the phenomenon is so striking that we
can imagine that a social closure mechanism might be at work, one that is
grounded less on discrimination than on family and personal networks
that are by far the main means to obtain the few good jobs available in
the Italian labour market. In other countries, immigrants have managed
to avoid the barriers to their occupational upgrading by rapidly entering
self-employment, but this does not appear to be the case in Italy, because
2010 The Authors
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Fullin and Reyneri
only a long stay in the country fosters access to the petty bourgeoisie.
The reason for that may be that self-employment is still very widespread
in Italy and has a good social status, so that formal and informal barriers
slow down the entry of immigrants, who can fill vacancies only in the
most burdensome independent activities (from catering to construction).
By contrast, taking education into account reduces the penalisation of
immigrants with regard to the risk of unemployment. However, the best
educated immigrants do not have a greater probability of avoiding
unemployment than the lowest-educated ones, perhaps because not just
a few of them are not willing to take whatever poor job is offered to
them. The probability of avoiding unemployment grows with the length
of stay for two reasons. On the one hand, the process of assimilation
enables immigrants to acquire language skills, improve their qualifications, and gain better understanding of labour-market institutions. On
the other hand, economic needs force immigrants to downgrade their
professional expectations.
As regards differences by gender, immigrant women are generally more
penalised than men in relation to the risk of unemployment, but their ethnic penalisation is even greater for occupational status, because the overwhelming majority of them work in housekeeping and elderly care. A
breakdown by country of origin has enabled us to show that only for
women and men from Asia is there a trade-off between a fairly good performance in the probability of avoiding unemployment and a very high
risk of obtaining bad jobs. On the other hand, people from EU-15 and
OECD countries are affected by a reverse trade-off, because they are
severely penalised in their probability of avoiding unemployment, but their
probability of obtaining qualified positions is higher than that of their Italian counterparts. Albanian men are close to that situation, whereas for the
other immigrant groups the two ethnic penalisations go together, with
immigrants from Northern and Central Africa being in the worst position.
The Italian labour market exerts an important pull effect on unauthorised immigration because of its huge underground economy and the confinement of authorised immigrants to poorly qualified jobs. For those
jobs, there are large labour shortages because native job-seekers have
higher social expectations and are able to wait before accepting a job.
But in the medium term, such a social equilibrium may be precarious
because immigrants are greatly over-educated for the jobs that they are
forced to accept, and their expectations of occupational mobility – now
very limited – are likely to become higher in the near future.
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Unemployment in Italy
145
NOTES
1. The 2005 Labour Force Survey does not provide more details, but the
trend of stay permits leads us to presume that few people entered Italy 15
years before.
2. As not only unauthorised immigrants, but also seasonal immigrants and
about 10 per cent of those authorised are not recorded in the population
registers (Istat, 2007), analyses based on the labour force survey in Italy are
a bit biased in favour of the most settled immigrant population. To
increase the number of non-nationals, we pooled the data from all the four
2005 waves, which for the first time provided information distinguished by
nationality and country of birth, excluding data from second and third
interviews to the same household, in order to avoid duplications.
3. That classification, which lets us focus also on immigrant workers entering
self-employment, is illustrated by Reyneri and Fullin in this issue.
4. We excluded full-time students, since there are few of them among first-generation immigrants, because their absence from the labour market does not
depend either on traditional values or on a discouragement effect.
5. We obtained a similar result by introducing an interaction between the family status and a variable opposing immigrants to Italians into two binary
logistic regression models for the whole sample (results available on
request).
6. See references quoted by Reyneri and Fullin in this issue.
7. Italy is like France in the classic comparison with Germany carried out by
Maurice, et al. (1986).
8. The petty bourgeoisie includes only medium-low level self-employment,
because high level, self-employment is classified in the service class. For this
reason the proportion of the petty bourgeoisie among Italians is much
lower than the proportion of all the self-employed workers and is close to
the proportion calculated among immigrants.
9. The reference group for nationality becomes that of Italians born in developing countries, who are the immigrants most similar to natives.
10. In this model, we collapsed all immigrants from North Africa and Asia to
avoid problems concerning the small number of cases.
11. See references quoted by Reyneri and Fullin in this issue.
12. So that more and more young Italian workers are over-educated for their
jobs (Barbieri and Scherer, 2007).
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Allasino, E., E. Reyneri, A. Venturini, and G. Zincone
2004 ‘‘Labour market discrimination against migrant workers in Italy’’,
International Migration Papers, No. 67, ILO, Geneva.
Barbieri, P., and S. Scherer
2007 ‘‘Flexibilizing the Italian labour market’’, FlexCareer Working
Paper, University of Bamberg, Bamberg.
2010 The Authors
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Fullin and Reyneri
Castles, S., and M.J. Miller
2003 The Age of Migration, Palgrave, New York.
Cheung, S-Y., and A. Heath
2007 ‘‘The comparative study of ethnic minority disadvantage’’, in A.
Heath and S-Y. Cheung (Eds), Unequal Chances. Ethnic Minorities
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