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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: giovanna.fullin@unimib.it

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.

14

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.