1
Labour market outcomes of immigrants in a South European country:
do race and religion matter?
Giovanna Fullin
Department of Sociology and Social Research
University of Milano Bicocca
Via Bicocca degli Arcimboldi 8 - 20126 Milano (Italy)
Email: [email protected]
ABSTRACT
In the late 1980s and early 1990s South European countries rapidly became magnets for a growing
number of migrants from dozens of developing and East European countries. The performance of
immigrants in the host labour markets strongly differ by country of origin in terms of
unemployment risk and access to highly qualified jobs. This article focuses on these differences and
highlights whether and to what extent they are linked to diversities in country of origin religion and
race. The analysis concerns Italy, a country where the population was highly homogeneous in terms
of religion and ‘racial’ characteristics until twenty years ago. The estimates show that religion plays
a role in explaining differences in terms of unemployment rate only for women, while the
white/non-white divide matters for both sexes. Neither race nor religion have a significant impact in
terms of occupational attainment of migrants in the Italian labour market.
Keywords: Italy, Labour Market, Migrants, Occupation, Race, Religion, Southern Europe,
Unemployment
The final, definitive version of this paper has been published in Work, Employment and Society,
30(3), 2015, pp.391-409, by SAGE Publications Ltd, All rights reserved. ©
Introduction
In the late 1980s and early 1990s South European countries, previously often the origins of
emigration flows, rapidly became magnets for a growing number of immigrants from African,
Asian and, after 1989, East European countries. In contrast to immigration into Northern and
Central Europe, the labour market performance of immigrants shows two opposite facets in South
European countries (Reyneri and Fullin, 2008, 2011; Fullin, 2014). On the one hand,
unemployment rates among immigrants are not much higher than among natives, especially as far
as men are concerned. On the other, the overwhelming majority of immigrants are employed in jobs
at the bottom of the occupational ladder, mainly manual jobs in the secondary labour market, i.e.
agriculture, small-sized construction and manufacturing firms and care and domestic services for
households, despite the fact that their levels of education are medium/high (Arango et al., 2009).
Another key feature of the recent migrations into Southern Europe is the multiplicity and
heterogeneity of migrant nationalities. In Italy and Spain especially, new immigrants come from
dozens of developing and East European countries (Arango et al., 2009), and the performance of
national groups in the host labour markets – within the general trend already depicted - differ in
various respects (activity, employment and unemployment rates and occupational attainments) even
after controlling for individual socio-demographic characteristics (Reyneri and Fullin, 2011; Fullin
and Reyneri, 2011). In this article these differences between national groups, usually explained on a
case by case basis with reference to specific features of their integration into host societies, are
viewed in relation to two dimensions: skin colour and religion.
The impact of somatic differences on performance in the labour market has been widely studied in
the US and in North and Central European contexts (see Bertrand and Mullainathan, 2004; OECD,
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2013), but few studies have concerned themselves with religion differences; and, in general,
empirical evidence on new receiving Southern European countries is lacking (Bail, 2008). As
stressed by Bail (2008: 54) ‘phenotype and religious dress provide visual cues about group
membership that are particularly conspicuous in new immigration countries, precisely because of
their homogeneity’. This issue is especially challenging as regards Italy, a country where the
population, until twenty years ago, was highly homogeneous in terms of religion (Catholic) and in
terms of characteristics that are usually defined as ‘racial’. Italy, after Spain, is the European
country that has received the highest number of immigrants in the last twenty years. The change
was dramatically rapid. In 1990 foreign born residents totalled only 1.5 per cent of the total
population, rising to 7 per cent twenty years later (foreign born residents now number more than
four million). Therefore, ‘race’ and religion are expected to matter in employers’ decisions,
favouring those immigrants who are perceived to be more similar to natives while penalising others.
Furthermore, as stressed by Sniderman et al. (2002), some specific features of the Italian context
make it a high relevant case study in this regard. In fact, while the level of heterogeneity of
migratory inflows is very high in terms of skin colour and religion, immigrants are highly
homogeneous with regards to other aspects that are important for their incorporation in the labour
market. First, Italy has seen the entry of very few refugees, meaning that the majority of immigrants
arriving in recent decades are economic migrants. Second, very few immigrants come from
countries linked to Italy by cultural or political ties which might have otherwise given them
privileged positions in the labour market. Finally, as second generation immigrant workers are still
very few in number, complex issues connected to people born in Italy to foreign parents can be
excluded from the analysis.
Despite these indicators of homogeneity, some large differences between national groups do
concern the risk of unemployment and the position of immigrants on the employment ladder. The
article analyses whether religion and ‘racial’ characteristics play a role in explaining these
differences. Following the suggestion of Przeworski and Teune (1970) and, more recently, of
Goldthorpe (2000), in the analysis the names of the countries of origin have been replaced by the
underlying socio-economic variables that characterise immigrant national groups. This approach
has been proposed by scholars who compare the labour market performances of groups from
multiple origins in multiple destination countries (Fleischmann and Dronkers, 2010; Van Tubergen
et al., 2004; Van Tubergen, 2006). The perspective adopted in this article is similar but focused on a
single destination country. This choice allows a deep analysis of the specificities of the national
context and a focus on comparison between men and women, frequently segregated in two separate
parts of the labour market where differences concerning race and religion are likely to play
dissimilar roles.
Finally, it has to be stressed that, although the labour market penalisation of immigrants on grounds
of skin colour and religion is likely to be caused by discrimination, this article does not focus
directly on discrimination as such. Studies based on labour market survey data can only estimate
whether differences in employment performances between various groups persist after taking
differences in observable factors into consideration (e.g. age, education, citizenship). Other research
strategies (Bertrand and Mullainathan 2004, OECD 2008 and 2013) have better tools to detect
when some individuals receive unequal treatment because of their appearance or religion. However,
this article, in contrast to mainstream studies on discrimination, does not compare immigrants with
natives, but analyses differences between national groups. By keeping the foreign-born status
constant, a status which generally penalises all ethnic groups in the Italian labour market (Fullin,
2011), the penalties linked to ‘racial’ characteristics and religion can be better highlighted as rough
indicators of discrimination. Moreover, in contrast to field experiments, this article has the merit of
not focusing on specific ethnic groups and a few occupations, but on all ethnic groups, looking at
their probabilities of being employed and chances of attaining high-ranking occupational status.
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How do religion and ‘race’ matter in hiring decisions?
A well-established sociological literature, mainly based on fieldwork carried out in the English-
speaking countries, suggests that hiring decisions by employers are often affected by characteristics
of workers, including skin colour and religion (Portes and Rumbaut, 2006). As highlighted by Alba
(2005) social boundaries separating immigrant groups from natives may be permeable to a greater
or lesser extent, and they do not operate in precisely the same way in every society. In particular, in
contrast to the US, religion is a key institutional factor in the demarcation of native/immigrant
boundaries in many European societies, and it is likely to be of great significance in the South
European countries, like Italy and Spain, which are characterised by a strong (Catholic) religious
culture. Following Alba (2005: 27), it should be stressed that this boundary is ‘constructed from
cultural, legal, and institutional materials that are already at hand and thus [it] depends in a path-
dependent way on the prior histories of the societies and groups involved’. In particular, given the
crucial role played by the Catholic church as a connecting agency between destination countries,
(such as Italy and Spain), and less developed Catholic countries (such as Sri Lanka, the Philippines,
Cape Verde and Poland) for inflows of female migrants willing to work as elderly care-givers and
housekeepers (Arango et al., 2009), the impact of religious differences is expected to be stronger for
women than for men. The characteristics of the Mediterranean welfare regime – where the use by
households of female immigrant labour is the most viable alternative to the very scant direct
provision of care services by the State (Sciortino, 2004) – are likely to make this gender divide
much stronger than in other national contexts. Another factor that may make religious differences
more important for immigrant women than for men is the work environment for care activities. In
the Mediterranean countries, care tends to be provided by female immigrants within the houses of
the families that have hired them. The need to share the private spaces of the home is likely to
influence hiring decisions, influencing employers’ preferences for selecting immigrants from
Catholic countries for work of this kind (Catanzaro and Colombo, 2009). For the same reason also
similarities of somatic characteristics are likely to matter in this respect.
As regards differences linked to ‘racial’ characteristics, two preliminary remarks are necessary. The
first concerns terminology. In this article the term ‘race’ will be used as the simplest way to denote
somatic differences linked to skin colour. The second remark concerns justification of this choice.
The emergence of a body of literature that calls attention to the social constructedness of racial
distinctions (Morning, 2009) discourages a-problematic references to somatic differences between
immigrant groups. In Italy, however, the concept of race has not been codified from either the
theoretical or the political/institutional point of view (Queirolo Palmas and Rahola (eds), 2011),
even if a latent ethnic-racial view of the Italian nation still persists (Andall, 2002: 400; Allasino et
al., 2004; Bail, 2008). However, studies on prejudices and stereotypes are only partially useful for
the analysis conducted by this article, because it is unclear how and to what extent hiring decisions
are influenced by them. In particular, as far as the cases of Italy and Spain are concerned, the
literature has stressed a quite marked incongruity between negative public opinion and positive
employers’ attitudes to immigrants as workers useful for filling low-skilled job vacancies
(Ambrosini, 2000; Solé and Parella, 2003). In this regard, the impact of racial and religious
differences on hiring decisions is likely to differ by industry. While, as already stressed, households
are likely to take such differences into account when recruiting people to work in their private
spaces and/or take care of their children and elderly relatives, studies on discrimination show that
race is taken into account especially by employers selecting workers, such as waiters or
salespersons, whose jobs involve relations with customers, whereas it matters less in hiring
decisions made by manufacturing or construction firms (Allasino et al., 2004; Andall, 2002; Solé
and Parella, 2003). In fact, employers may account for prejudices of customers.
Last but not least, the size of employer must be considered. The heads of small companies – who
deal with relatively low personnel turnover and who are often directly involved in production and
service activities – are hypothesised to be more likely to consider race and religion differences in
their hiring decisions than are the HR managers of large-sized companies (Allasino et al., 2004;
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Carlsson and Rooth, 2007). As immigrants are more likely than natives to be employed in small
firms, this issue is likely to be of major importance in countries, like Italy and Spain, which have a
particularly large presence of firms with fewer than 10 employees.1
Given the above hypotheses, the analysis will focus on the Italian case in order to determine
whether and to what extent being non-white or non-Christian has an effect among immigrants in
terms of higher unemployment risks and lower quality of jobs, and whether these effects differ by
gender.
The Italian case
As already stated, the presence of immigrants in the Italian labour market is characterised by a high
homogeneity in terms of reasons of entry and a high heterogeneity in terms of country of origin. In
2010 the Labour Force Survey identified more than 120 countries of origin, and immigrants
belonging to the biggest 10 national groups represented only 57 per cent of the total immigrant
population (Table 1). Moreover, the country mix has changed substantially over time. Since the late
1990s inflows from South America, Asia and above all from Eastern Europe have largely replaced
those from northern Africa and the Middle East. The largest immigrant groups include both the
oldest (such as Moroccans and Albanians) as well as the more recent (such as Romanians and
Ukrainians). Only 5 per cent of immigrants come from the EU15 and other highly developed
countries. Unauthorised immigration used to be a fairly widespread phenomenon, but frequent
regularisation drives and EU enlargement have drastically reduced the number of immigrants
without regular right to remain (Reyneri, 1998).
The differences between national groups in terms of labour market performances are marked, and
they concern both the risk of unemployment and segregation in manual jobs (Table 1). These
inequalities, which form the focus of this article, remain after controlling for age, education, family
status, and years since migration (Fullin and Reyneri, 2011).
‘Table 1 here’
Male and female immigrant workers are concentrated in two segments of the Italian labour market,
with men from developing countries working mainly in manufacturing, construction, retail and
catering, and women segregated in housekeeping and elderly care activities2. This sector alone
accounts for more than 40 per cent of total immigrant female employment, with the percentage
reaching 80 percent for some national groups. In fact, as in other South European countries, in
recent decades recourse to immigrant women as domestic workers and elderly care-providers has
greatly increased in Italy (Sciortino, 2004). Finally Italy has a particularly large presence of small-
sized firms, and immigrant employees concentrate in them more than Italian-born: according to LFS
data, in 2010 61 per cent of male immigrants worked for firms with fewer than 15 employees,
versus 25 per cent of Italian-born.
There are relatively few studies on the role of racial and religious differences in explaining the
labour market performances of immigrant groups in Italy, and those studies that exist place greater
emphasis on the importance of prejudices and stereotypes regarding specific national groups
(Allasino et al., 2004; Andall, 2000; Sniderman et al., 2002). Nevertheless, to use the typology
developed by Alba (2005), in the Italian context both racial and religious divides may be considered
as ‘bright boundaries’ (i.e. which involve no ambiguity about membership), given that the Italian
population was very homogeneous in terms of racial characteristics and religion until recently.
Moreover, according to World Values Survey data, Italy, after Ireland and the US, records the
highest values for the main indicators of religiosity among post-industrial societies,3 and the strong
predominance of the Catholic faith is evident with 80 to 90 percent of the Italian population
declaring themselves Catholic (Cartocci 2011). As regards the impact of religious differences on the
labour market performance of immigrant groups, the importance of parish churches, voluntary
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associations linked to the Catholic Church (Andall, 1998 and 2000) and networks of missionaries in
the origin countries of immigrants (Palidda, 2000) need to be pointed out. Already in the 1970s and
1980s, these were the main local institutions on which immigrants relied when seeking employment
(Andall, 1998 and 2000; Fullin et al., 2009), and which households used as the recruitment channels
for care-givers and housekeepers. Although in most cases, access to the services provided by such
associations is open to all immigrants, regardless of their religion and country of origin, immigrants
from Catholic countries are more likely to know and be in contact with them.
The Italian context has been little studied as far as labour market discrimination based on race is
concerned (Zanfrini 2010). However, comparative analyses (Allasino et al., 2004; Zegers de Beijl,
2000) show that the discrimination rate is slightly higher in Italy than in other European countries.
A recent study carried out in Italy by Sniderman et al. (2002) begins from the assumption that race
is especially stigmatising because it is visible in a way that differences of nationality are not.
Nevertheless, fieldwork showed the peculiar character of prejudice in Italy. Negative stereotypes
and prejudices seem to apply to all immigrants wherever they come from and whatever they look
like. This does not mean that race does not matter. For instance, Africans are more likely to be
judged by Italians ‘inferior by nature’ compared with Eastern European immigrants. Nevertheless in
some cases, Sniderman et al. (2002) show that hostility to blacks is less strong than (or, at least,
equal to) hostility to immigrants from Eastern European countries. Other public polls regarding
immigrants (ISPO and Ministry of Interior, 2007; SWG and IARD, 2010) show similar results:
immigrants from Romania and Albania seem to suffer more stigmatisation than those from central
Africa, although the former are more similar to Italian-born in terms of physical appearance and (for
Romanians) in terms of religion. Prejudices seem to be more influenced by the damning pictures of
these ethnic groups painted in the mass media (Maneri, 2011) than by simple somatic differences
(SWG and IARD, 2010).
To return to the hypotheses presented in the previous section, the impact of race and religious
differences are expected to exhibit a strong gender divide, given the segmentation of the Italian
labour market and the gendered distribution of immigrants among industries. On the one hand,
Italian households – the main employers of female immigrants – are hypothesised to be likely to
discriminate on grounds of religion and race when recruiting housekeepers and elderly caregivers.
On the other hand, since the employment of immigrant men in Italy is concentrated in construction
and manufacturing, a smaller or null impact of racial and religious differences is expected in terms
of unemployment risk and occupational attainment for male immigrant groups compared with
female ones. Nevertheless, the small size of firms and the presence of an immigrant male workforce
also in the catering and hospitality industries may make racial characteristics important in the
labour market performance of men.
Data, variables and methods
The analysis reported in what follows was based on the Italian Labour Force Survey (LFS). To
increase the number of foreign born cases, the data from 2005 to 2010 were pooled. The analysis
focused on first generation immigrants aged 15-64 years. After all respondents born in a foreign
country had been selected, the specificities of migration inflows to Italy made it necessary to narrow
the sample selection further. Two groups of respondents were excluded: those born within the EU15
and other highly developed countries – because they are strongly different from all other immigrant
groups, are mostly high skilled, and have jobs very similar to those of Italian-born (Recchi and
Favell, 2009) – and those holding Italian citizenship and born in Libya, Ethiopia and Eritrea, since
they were likely to be return emigrants or second-generation Italian emigrants in those countries.
After exclusion of these two groups of immigrants, which can be considered ‘outliers’, the analysis
focused on the differences among all the other national groups in terms of labour market
performance.
The Labour Force Survey is undoubtedly the richest and most solid source of data on immigrants in
the Italian labour market. The sample is based on the population register and as a result its main
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shortcoming is a bias in favour of the most settled immigrant population (ISTAT, 2007). The
estimates were based on around 48,200 cases, of which 33,300 concerned immigrants active in the
labour market from 126 countries of origin.
Dependent variables
First, the active population was selected, and that subsample was used to examine the odds
of being employed versus being unemployed (the probabilities of being employed versus non-
employed (unemployed or inactive) were also estimated with similar results). Second, in order to
analyse occupational attainment, the Erikson, Goldthorpe and Portocarero (1979) scheme,
identifying 7 main classes, was collapsed into two categories, in order to focus on the manual/non-
manual divide: manual (skilled and unskilled) workers, on one hand, and routine non-manual
employees, self-employed and white collar workers as a single residual category on the other. The
reasoning behind this categorisation was that in Italy the distribution of immigrant workers is
markedly concentrated in manual occupations4: almost 70 per cent of immigrant workers are in the
lowest categories, although the proportion of self-employment is not particularly low among men
(14 per cent) (Fullin, 2011; Fullin and Reyneri, 2011). In contrast to most of the international
literature, poorly-skilled non-manual jobs were separated from their manual counterparts. As has
already been explained in other studies (Fullin and Reyneri, 2011) in Italy, access to non-manual
occupations is, at present, the crucial issue regarding the employment of immigrants since the
manual/non-manual divide appears much stronger than in other European countries (Maurice et al.,
1986) while the differences between skilled and unskilled blue-collar workers are less marked in
terms of working conditions and social status. In fact, as already highlighted, in Italy most skilled
blue-collar labourers work in very small-sized factories, where career opportunities are very limited,
employment stability is weak and working conditions are poor.
The results of the models are presented in terms of average marginal effects, which express the
average of the variation induced in the probability of interest (of being employed and of obtaining a
specific occupational position) by a marginal change in an independent variable for each individual.
Independent variables
The two independent variables on which the analysis focused – race and religion – could not be
derived from Labour Force Survey data. Consequently, as in other studies, birthplace was used as a
proxy5 (Model and Lin, 2002; OECD, 2008). Therefore, the religion most widespread in the country
of origin was used as a proxy for the religion of all immigrants born in that particular country or, at
least, as an indicator of the cultural distance between the country of origin and the country of
destination (Fleischmann and Dronkers, 2010). Given the predominance of Catholics among the
native population, the variable was dichotomised (Christian/non Christian) (Table 2). Following a
similar logic, all the interviewed immigrants from a country where the majority of the population
has a non-white racial appearance were considered ‘non-white’, while people from countries where
the majority of the population is white were considered white (Table 2). As already stressed, this
dichotomous variable – labelled ‘race’– is obviously only a proxy for visible somatic differences
between the majority of immigrants from a country and the native population that may cause
discrimination (Alba, 2005). Given that the native population is almost entirely white, it can be
assumed that in Italy the divide that matters is between white and non-white (Sniderman et al.,
2002). Consequently, all non-white immigrants were collapsed into a single category.
These assumptions are undoubtedly strong, but they made it possible to exploit the Labour Force
Survey dataset for the analysis, that is the largest and most interesting reliable source of information
concerning the integration of immigrants into the Italian labour market. Moreover, as far as religion
is concerned, the use of country of birth as a proxy is not so different from the stereotypes that are
hypothesised to be used by employers and households when recruiting and selecting immigrant
workers for a job. Given the high heterogeneity of migration inflows to Italy, the aforementioned
categorizations allowed comparison among immigrants born in countries where the majority of the
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population is white and Christian (i.e. Poland, Romania), white and non-Christian (i.e. Albania,
Bosnia), non-white and Christian (i.e. Philippines, Brazil, Peru) and non-white and non-Christian
(i.e. China, India, Morocco).
Control variables
Following other studies (Fleischmann and Dronkers, 2010; Van Tubergen et al., 2004; Van
Tubergen, 2006), besides the usual controls for individual characteristics (such age, education,
family status), some other features affecting the economic incorporation of immigrant groups in
destination countries were included in the analysis. First of all, as highlighted by a large body of
literature (Portes and Rumbaut, 2006; Portes and Sensenbrenner, 1993), ethnic networks in the
receiving country can be both an important resource for immigrants (in particular by helping them
find jobs) as well as a potential penalty (by segregating them in low-skilled jobs). Therefore the size
and ‘quality’ (in terms of human capital (Borjas, 1995)) of networks was taken into consideration.
Additionally, in the literature the economic development of the country of origin is used as a proxy
for the quality of human capital and skills transferability (above all diploma recognition) among
immigrants in the destination country (Fernandez and Fogli, 2009; Van Tubergen, 2004). As for
race and religion, information on ethnic networks is also unavailable in the LFS dataset. The size of
the networks was taken into consideration by introducing a variable equal to the mean size of the
immigrant group (people from the same country of origin) present in Italy between 2005 and 2010
(LFS data). Unfortunately, no data were available on the size of immigrant groups at local level
(Kalter and Kogan 2006). With regard to the human capital (HC) embedded in ethnic networks, two
variables were combined by a factorial score analysis: the average level of education of the ethnic
group in Italy (percentage of people with tertiary and upper secondary education within the ethnic
group) (source: LFS) and Gross Domestic Product (GDP) per capita of the country of origin
(calculated as purchasing power parity as a share of USA GDP) (source: Economist 2007)
(Fernandez and Fogli, 2009). As the distribution by education level varies greatly by gender, two
different variables have been computed as regards the quality of ethnic network6.
A second aspect taken into consideration concerns the self-selection processes in the country
of origin which determine who emigrates and who does not, and which is likely to have an
influence on the performance of migrants in the destination labour market. In particular, the
literature stresses that greater distance increases migration costs and difficulties (Borjas, 1987), so
that those who travel long distances should be positively selected (Van Tubergen, 2006). Therefore,
the number of kilometres between the capital city of the home country and the Italian capital was
included in the models. Nevertheless the self-selection of migrants within their country of origin
can also be affected by the propensity to participate in the labour market. In particular, cultural
factors can help explain the huge cross-national differences in the participation rates of women in
the Italian labour market (from 36 per cent for Morocco to 90 per cent for the Philippines),
according to differences in national cultures and traditions concerning gender roles, while male
activity rates are generally very high for all the ethnic groups. Thus, the effects of home country
culture concerning working behaviours of women also need to be highlighted, and disentangled
from penalisation occurring in the destination labour market, as a strong selection effect is likely to
occur in the case of immigrant women participating in the labour market, because important non-
random factors can influence their activity patterns (Van Tubergen, 2004; Heath and Cheung,
2007). For this reason, a Heckman probit regression model was used to estimate the probability of
being employed (outcome equation), taking into consideration the probability of being active versus
inactive (selection equation) in a previous step (conditional probabilities). This two stage regression
assumed that there is at least one independent variable that affects the probability of being active,
but does not affect the probability of being employed. In this case the variable is the female activity
rate in the country of origin (equal to the ratio between the number of women employed or
unemployed and the female population of working age) (source: World Bank) which is likely to be
strongly linked with the activity rate of female immigrants in Italy, while its impact on the
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probability of being employed (given participation in the labour market) can be considered weak7
(as in Antecol, 2000; Van Tubergen et al., 2004).
Last but not least, knowledge of the Italian language was taken into account because it has a crucial
impact on both the job seeking activities of unemployed immigrants and the quality of the jobs of
employed ones. As no information on languages spoken was available in the dataset, a dummy
variable classified all immigrants speaking neo-Latin languages (South Americans and Romanians)
and Albanians (who have wide access to Italian television channels) as potentially good Italian
speakers in comparison with all others.
The control variables concerning size and quality of network, female activity rate and distance have
been recoded into three-item categorical variables (high/medium/low) in order to make marginal
effects more understandable. Finally, the following observed personal characteristics of immigrants
were included in the models as control variables: age (four categories), education (four categories),
family status (five categories), citizenship (three categories: Italian and EU15 citizenship, new EU-
member-states and non-EU-countries citizenship), region (three categories), and years since
migration (five categories). A control for the year of the survey was also included, since the
business economic cycle changed in the middle of the period analysed. Correlations among control
variables were checked, and collinearity tests ruled out the possibility that the estimations were
biased.
‘Table 2 here’
Another and more important source of bias could have been the use of the country of origin to
impute information at individual level that was not available in the dataset (as in Fleischmann and
Dronkers (2010), Van Tubergen et al. (2004) and Van Tubergen (2006)), such as religion, race, size
and quality of networks, distance from origin country, language knowledge and female activity rate
(Table 2). All these variables were clustered by country of birth – i.e. for all the individuals coming
from the same country the same values were imputed - therefore there could be a violation of a
regression assumption concerning the independence of residuals. More precisely the standard error
estimates could be biased because residuals are not indepedent within individuals belonging to the
same country. As a consequence, the estimated standard errors could be smaller than the correct
ones, leading to consider statistically significant also regression parameteres that are not so
(Moulton, 1990). A way to deal with this problem in econometric literature is using robust
regression models, taking into account the data clusterization. These method makes adjustments in
the standard error estimates and correct them increasing their values. In the following analyses the
cluster option8 (Nichols and Schaffer, 2007) – available for STATA – was applied.
The risk of unemployment
In Italy the male unemployment rate among immigrants shows small variations while the female
equivalent varies strongly across national groups (Table 1). Descriptive statistics (available on
request) show an unemployment rate slightly higher for non-white immigrants (men and women)
and for men from non-Christian countries, while a strong disadvantage affects women from non-
Christian countries, who suffer an unemployment rate about 9 percentage points higher than those
from Christian countries. In order to analyse whether real penalisations occur, a regression model
controlling for other characteristics (age, education, family status, years since migration,
citizenship, distance from the country of origin, size and HC quality of networks, language, region
and year of the survey) is needed. Thus, excluding inactive people, two binary logistic regression
models of the likelihood of being employed versus unemployed (Table 3) were carried out. Both the
models included a cluster option.
‘Table 3 here’
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The most interesting result here is the sharp gender divide concerning the role of religion, which has
a significant and strong impact on the probability of avoiding unemployment for women, but no
effect on unemployment risks for men. In particular, the probabilities of being employed for
immigrant women from non-Christian societies are 11 percentage points lower than those for
women from Christian countries, while no differences linked to religion affect men. As regards the
role of race, a negative effect is estimated not only for women but also for men, since non-white
immigrants have a lower probability of obtaining a job than white immigrants, all other
characteristics being equal.
As explained in the data and variables section, further analyses must be devoted to women
in order to take into account the selection process concerning the participation to the labour market
(female activity rates vary strongly across ethnic groups, in contrast to male rates). For this reason,
a Heckman probit regression model has been estimated. Since the concern here is with marginal
effects estimated for the independent variables (third part of Table 3), detailed results are not
presented (but available on request). It is important to stress, however, that the rho coefficient
differs significantly from zero, confirming that the Heckman two-stage estimation model is able to
capture some selection bias. As far as race and religion are concerned, the marginal effects
estimated by the Heckman regression are still significant and show the same signs as those
estimated by the model not taking selection into consideration. Therefore the previous results seem
confirmed.
Occupational attainment
As already stressed (Table 1), the differences between immigrant national groups are strong in
terms of social position. To check the extent to which race and religion affect the quality of the jobs
that immigrants can find in the Italian labour market, a binomial logistic regression model was used
to estimate the probabilities of avoiding manual jobs (Table 4). As before, a cluster option was
included, and models controlled for age, level of education, years since migration, region,
citizenship, size and HC quality of ethnic networks, language, distance from the country of origin
and year of the survey.
‘Table 4 here’
The estimated marginal effects show that in the Italian context, with all the control variables listed
above being equal, race seems unimportant in explaining differences among immigrants in terms of
occupational attainment for both genders. On the other hand, the religion of the country of origin
has a significant marginal effect only for men, with immigrants from non-Christian societies having
higher probabilities of avoiding manual jobs. The last result is explained by the probability of
entering self-employment, which is quite high for migrants coming from some non-Christian
countries like China and Egypt. As stressed in the data and variable section, the dichotomisation of
the variable concerning occupational attainment (manual/non manual) implies the inclusion of self-
employment on the broad category of non manual occupations. Once the self-employed are
excluded from the sample the marginal effect of religion for men becomes not significant (second
part of Table 4). Nevertheless, as independent occupations would require ad hoc investigations that
cannot be carried out here, the analysis does not delve any deeper into this point.
Discussion and conclusions
This article has sought to determine whether and to what extent race and religion play a role in
explaining differences among ethnic groups in terms of risk of unemployment, on the one hand, and
occupational attainment on the other. Although the definition of some variables was based on rough
10
indicators (because of the lack of data), the analysis highlighted some interesting results on the
Italian case that could concern also other national cases, in particular the South European countries.
As far as the unemployment risks are concerned, a gender divide in the impact of religion is very
evident: while immigrant women from Christian countries have significantly less probabilities of
being unemployed than other immigrant women, this is not the case of men. This result can be
explained by the gendered distribution among industries characteristic of immigrants in the Italian
labour market, where female employment is concentrated in care activities for households and male
employment in the manufacturing and construction industries. Discrimination on the basis of
religion were expected to be more frequent in the hiring decisions of households than in the
recruitment strategies of firms. The mainstream literature on employment discrimination does not
consider the difference between the recruitment/selection strategies of companies and households
(focusing only on the former) but this issue is likely to be very important in all the South European
countries where, in recent decades, family assistance has been partially replaced by private welfare
based on immigrant labour (Sciortino, 2004).
The second result – differences in terms of race have a significant impact on the probability of
being unemployed for both sexes – can again be interpreted by referring to the specificities of the
industries in which the employment of immigrants concentrates. In particular, in the case of female
immigrants, reference can again be made to the prejudices of households against non-white
immigrants when they select housekeepers and caregivers. The explanation for the male counterpart
is more complex and stresses the relevance of two non-trivial factors impacting on the incidence of
discrimination against non-white immigrants: a) size of firms – employers of small firms, working
shoulder to shoulder with their employees and facing low turnover rates, are more likely to
discriminate than are HR managers of large sized companies – and b) the frequency of customer
contacts, which induces employers to consider the prejudice of customers. While the latter
phenomenon has been investigated in several national contexts (Zegers de Beijl, 2000; Andall,
2000; Solé and Parella, 2003), further research is needed to explore whether similar results
concerning firms size apply to other countries.
As far as the quality of jobs is concerned, regressions showed that closer similarity to the native
population – i.e. being white and from a predominantly Christian country – seems unconnected
with the probabilities of avoiding manual jobs compared with other immigrants for both women and
men (once self-employment is excluded from the analysis). Does this result mean that no
discrimination occurs on the basis of race and religion? First, it should be borne in mind that the
analysis did not consider race and religion impacts per se, but only insofar as they explained the
better/worse performances of some national groups compared with others, keeping the status of
being born abroad constant. Moreover, even if the specific features of the new immigration
countries – which only recently and very rapidly have become destinations of large migration
inflows – might suggest that religion and race differences matter (Bail, 2008), the strong
penalisation of almost all immigrant groups explains this initially unexpected result for the Italian
case. In a strongly segmented labour market, where immigrants are largely segregated in the
secondary sector, racial and religious differences have relatively low impacts on occupational
attainments, their variance being compressed within low-skilled jobs. As stressed by Sniderman et
al. (2002:128), unlike the US, in Italian society, which is characterised by the absence of any
tradition of immigration (and of any recent history of slavery) and by fixed social and cultural
boundaries, ‘hostility to blacks by virtue of their being black is overshadowed by hostility to
immigrants whatever their race’.
In this respect further analyses are needed to determine whether similar results can be found for
other South European countries characterised – like Italy – by the strong segregation of immigrants
in low skilled occupations.
1 According to OECD data, in Italy 30.3 per cent of employees in the manufacturing sector work in
small firms (fewer than 20 employees), while the percentage is 8.3 in the UK and 17 in France.
11
2 Housekeeping and elderly care activities were classified as manual jobs (see Table 1).
3 For instance, in 2001 40 per cent of respondents in Italy declared that they attended religious
activities once or more than once per week, while the percentages recorded in other European
countries were much lower: for example, 14 per cent in the UK and 8 per cent in France (Norris and
Inglehart, 2004). 4 See footnote 2.
5 The source of information on prevalent religion and race is the World Atlas of the International
Herald Tribune. The complete classifications are available on request. 6 On the other hand no significant gender differences affect the distribution of the variable regarding
the size of ethnic networks. 7 The selection equation also controls for age, education, family status, years since migration,
citizenship and distance from the home country. Size and quality of ethnic networks, language, race
and religion were included only in the outcome equation, as they are likely to have an impact on
job searches but not on decisions concerning participation in the labour market. 8 Unfortunately, this option is not provided by STATA for the Heckman model, but the comparison
between logistic models concerning women with and without the cluster option suggests that this
does not cause significant estimation problems.
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ACKNOWLEDGEMENTS
An earlier version of this article was presented by me and Emilio Reyneri at the IMISCOE International
Workshop “Matching Context and Capacity: The Economic Integration of Immigrants”, European
University Institute, Florence (June 2009). I presented a second version at the ECSR Conference at
University College Dublin (December 2011). I am extremely grateful to the participants at these conferences
for their comments and, most of all, to Emilio for his help and direct involvement in the first phases of this
work. Last but not least, I would like to thank the three anonymous referees for their extremely useful
suggestions and Marcello Maneri and Fabio Quassoli for their comments.
BIOGRAPHY
Giovanna Fullin is assistant professor of Economic Sociology at the Department of Sociology and Social
Research of the University of Milano Bicocca (Italy). She is involved in a comparative research project on
drivers of growth and ethnic inequality in the labour market (Horizon 2020). She coordinated, with Emilio
Reyneri, two International research groups on integration of immigrants into the labour market (Network of
Excellence EQUALSOC) and she edited two special issues on this topic (published by International
Migration and International Journal of Comparative Sociology). She teaches Sociology of Labour Market
and she conducts research on migrations and labour market, low wage retail jobs and front line service
workers.
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TABLES
Table 1 - Unemployment rate, occupational attainment and percentage of total active immigrant population in Italy (first largest national groups) - Mean 2010
MEN WOMEN
Unemployment
rate
Occupational attainment
(% of manual jobs)
% of total active
immigrant population
Unemployment rate
Occupational attainment
(% of manual jobs)
% of total active
immigrant population
Romania 9,3 74,7 19,1 15,1 79,3 22,7 Albania 10,7 69,9 10,9 21,2 73,8 6,5 Morocco 17,2 81,8 8,8 22,4 77,6 3,3 Ukraine 8,7 81,2 1,4 5,7 89,5 7,4 Philippines 5,7 93,7 2,7 3,2 96,8 5,3 Poland 8,6 77,3 1,4 7,2 64,5 4,1 India 7,1 73,5 3,1 15,5 46,0 0,7 Moldova 15,5 72,0 1,7 8,4 90,1 3,9 Peru 10,7 77,8 1,8 10,7 76,8 3,2 China 11,1 48,6 2,3 9,4 41,6 2,0 Natives 7,3 39,4 9,3 22,2 Source: Labour Force Survey data
Table 2 - Variables clustered by country of origin (15 largest national groups)
Race Religion
Quality of
network - Men
Quality of
network -
Women
Size of
network
Female activity
rate in the country
of origin (2000) Distance
Knowledge of
Italian
language
Romania White Christian
High Medium Medium High Medium Easy
Albania White Non-christian
Medium Low Medium Medium Low Easy
Morocco Non-white Non-christian
Low Low High Low Medium Not easy
Ukraine White Christian
Medium Medium Low High Medium Not easy
Philippines Non-white Christian
Medium Low High Medium High Not easy
Poland White Christian
High High High Medium Medium Not easy
India Non-white Non-christian
Low Low Medium Low High Not easy
Moldova White Christian
Medium Medium Low High Medium Not easy
Peru Non-white Christian
Medium Medium Medium High High Easy
China Non-white Non-christian
Low Low Medium High High Not easy
Tunisia Non-white Non-christian Low Low Medium Low Low Not easy
Brasile Non-white
Christian Medium
Medium High Medium High Easy
Argentina White
Christian High
High Low Medium High Easy
Ecuador Non-white
Christian Medium
Low Low Medium High Easy
Senegal Non-white
Non-christian Low
Low Small Medium High Not easy
Data on the other 111 national groups are available on request.
Table 3 - Average marginal effects on probabilities of being employed (vs unemployed) for immigrant men and women
MEN WOMEN WOMEN -
HECKMAN
Coef. P>z [95% Interval] Coef. P>z [95% Interval] Coef. P>z [95% Interval]
Race White Ref. Ref. Ref.
Non white -0.05 0.000 -0.07 -0.03 -0.08 0.006 -0.14 -0.02 -0.04 0.005 -0.06 -0.01
Religion Christian Ref. Ref. Ref.
15
Non Christian 0.00 0.832 -0.02 0.02 -0.11 0.003 -0.18 -0.04 -0.07 0.000 -0.10 -0.04
Regression models control also for age, educational level, family status, year since migration, macroregion of residence,
citizenship, size and HC quality of ethnic networks, distance from the country of origin, language, year of the survey.
Heckman regression model includes in the selection equation the female activity rate in the country of origin
In bold significant (at least 5 percent level) marginal effects.
Tab. 4 Average marginal effects on probabilities of avoiding manual jobs for immigrant MEN and WOMEN
MEN WOMEN
Coef. P>z [95% Conf. Interval] Coef. P>z [95% Conf. Interval]
Race White Ref. Ref.
Non white 0.00 0.976 -0.06 0.06 0.01 0.795 -0.07 0.09
Religion Christian Ref. Ref.
Non Christian 0.14 0.015 0.03 0.25 0.13 0.172 -0.05 0.30
Self employed excluded from the sample
Race White Ref. Ref.
Non white 0.01 0.667 -0.03 0.04 0.01 0.823 -0.06 0.08
Religion Christian Ref. Ref.
Non Christian 0.03 0.175 -0.01 0.08 0.05 0.419 -0.06 0.15
Regression models control also for age, educational level, family status, year since migration, macroregion of residence,
citizenship, size and HC quality of ethnic networks, distance from the country of origin, language, year of the survey.
In bold significant (at least 5 percent level) marginal effects.
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