Giovanna Fullin and Emilio Reyneri

Low Unemployment and Bad Jobs for Highly Educated New Immigrants: The Case of Italy

ABSTRACT

The article analyses the incorporation of immigrants into the Italian labour market and the difficulties they encounter in accessing both employment and qualified occupations. The analysis is based on the Italian Labour Force Survey and highlights the fact that the great majority of immigrants entering Italy are hardly disadvantaged in comparison to Italians as regards the risk of unemployment, but, in contrast, they are highly disfavoured as regards the socio- professional status of their jobs. Unlike what would happen with the old European immigration, nowadays the segregation of immigrant workers in the lowest ranks of the occupational ladder is not due to their poor education. On the contrary, their disadvantage increases if educational attainment is taken into account. The leading role of low-skilled labour demand and underground economy in shaping immigrants’ integration in the Italian labour market is confirmed by the fact that they have fairly easy access to unskilled and semi- skilled manual jobs, whereas they experience serious difficulties in entering self- employment and in obtaining non-manual jobs.

Keywords: migrations, unemployment, employment, education, Italy 2

IMMIGRANTS IN THE ITALIAN LABOUR MARKET AT A GLANCE

Second only to Spain, Italy is the European country which has received most immigrants in the past fifteen years. The migratory inflow became sizeable in the mid-1990s, and it has skyrocketed since 2001. Because only very few people entered Italy from developed countries, and since Italy does not have an important colonial heritage, immigration flows originated from almost all the developing countries and from Eastern Europe. Up to the late 1990s the largest proportion of immigrants was from North Africa, whereas thereafter the majority of migration inflows originated from Eastern Europe.

Over the years, not just a few non-EU15 immigrants did enter the country without a permit (either by crossing frontiers or by landing clandestinely on the southern coasts); but by far the largest number of unauthorised immigrants entered Italy on short-term visas (for tourism or study purposes, for example), and then overstayed in spite of expired documents. As the number of claims for asylum has been very low, the quota system (implemented in 1995) has been quite scanty and family reunions have become noteworthy only since 1999, the overwhelming majority of immigrants entered Italy (mainly by overstaying) for working reasons, but without a proper permit of stay, which they managed to obtain only subsequently thanks to frequent regularisation drives (Levinson, 2005). The underground economy (which in Italy is a large and deeply rooted phenomenon) has been a major factor in promoting unauthorised immigration (Reyneri, 1998). This is because unauthorised labour migrants tend to enter countries where it is easy for them to live and work for a long period of time, even without a stay permit for working reasons. However, once they were regularized, nearly all immigrant workers moved to the regular economy.

The 2005 Labour Force Survey performed in estimated that the proportion of non-nationals against working age population approaches 5 percent. Over 93 percent of them were from developing and East-European countries, whereas the others originated from EU-15 countries, Switzerland, Japan or the United States. 3

However, migrations in Italy are a quite recent phenomenon. In fact, among the residents coming from high emigration countries who are 15-64 year old, not even 27 percent of them had been living in Italy for 10 years or more,1 and less than 24 percent had spent from 6 to 9 years in the country. Most immigrants are young adults, and the proportion of those aged 25-44 against working age population is 67 percent, which is 22 percentage points higher than the native ratio. The proportion of women is close to 50 percent, but it varies greatly according to their nationality.

Finally, besides those from EU-15 and other Western countries, many people who entered Italy coming from high emigration countries are highly educated, at least by Italian standards (Table 1). The reason for this may be that higher education has been expanding in the developing countries since the 1970s, and it has been widespread in East European countries for a long time. However, it should be borne in mind that emigration is a positively selected process. Such a selection may be driven by both workforce demand and supply, because receiving countries may give preference to highly-educated applicants, and highly-educated youths possess more of the informational, economic and social resources needed to emigrate (Feliciano, 2005; Dumont and Lemaître, 2005; Cheung and Heath, 2007). The main exception is to be found in the old European immigration, when emigrants were selected by agencies to work as unskilled blue-collars (Heath, 2007). By contrast, nowadays, as labour emigration is not only spontaneous, but it is also hampered by restrictive policies implemented by receiving countries, self- selection is strongly positive and migrants are better endowed with human capital (Heath and Yu, 2005). Overcoming hardships and costs of unauthorised entry requires cultural, economic and social resources that may be related to higher levels of education.

Table 1 about here

In Italy the unemployment rate decreased from above 11 percent in the mid-1990s to less than 7 percent in 2006, in parallel with the growth of immigration. However, owing to its segmentation by age, gender and region, the Italian labour market is tighter than it appears, because unemployment rate is very high among young people, especially among females, and very low among prime-age individuals, above all males. Most of the unemployed are young, first-job seekers 4 still living at home with their parents, on whose support they can rely while waiting for a “good job”, whereas almost all singles and breadwinners are employed. Moreover, the regional divide in the Italian unemployment rate is the highest one among developed countries, also because internal mobility is rather poor (Oecd, 2005). Hence, central and northern regions are close to full employment, especially as far as prime-age men are concerned, whereas mass unemployment affects women and youth in the south. Finally, employment is biased towards the poorest jobs: the proportion of managers and professionals is low (over 6 percentage points below the EU-15 average), whereas that of manual workers is still high (5 percentage points above). This employment mix appears to match badly with the increasing educational attainments of youths, so that labour shortages concentrate in manual jobs, above all unskilled, but also skilled ones.

Italy is still considered to have neither a coherent immigration policy nor an inclusive insertion policy (Schierup et al., 2006). Policy making, driven more by political rather than by economic criteria, has been more concerned with fighting (unsuccessfully) against unauthorised entries than with immigrant integration (Zincone, 2006). The 1998 Immigration Act was the first measure that treated immigration as a permanent phenomenon. But its reform, approved by a centre- right government in 2002, revived the guest-worker model by restricting the rules on granting long-term permits and shortening the duration of temporary stay permits to two years. Although immigrant workers have the same social rights as Italians, nevertheless there is no national policy to promote their economic and social integration.

It is within such a context – which greatly differs from past immigration trends, as well as from current ones in central and northern European countries (Castles and Miller, 2003; Schierup et al., 2006) – that we will focus on the integration of immigrants in the labour market, our aim being to highlight, in particular, unemployment risks and their access to qualified jobs.

DATA AND RESEARCH METHODOLOGY

Our analysis, which draws on the 2005 Labour Force Survey covering around 170,000 individuals, is focused on respondents aged between 15 and 64 years. The sample is based on the population register and slightly underestimates the 5 weight of non-nationals,2 but the Labour Force Survey undoubtedly provides the richest and most reliable dataset concerning migrants in the Italian labour market. We use nationality to identify immigrants but, as far as Italians are concerned, we also use information about country of birth in order to make a distinction between those born either in Italy or in EU-15 and OECD countries (who can be considered second-generation emigrants returned to Italy) and those born in high emigration countries. Only slightly more than 60 percent of Italians who were born in emigration countries are real migrants who have acquired citizenship through naturalisation (almost all by means of marriage with Italian citizens). The others were born in South American countries, where even distant descendents of Italian emigrants retain dual citizenship, or they have repatriated from Libya, a former Italian colony.

The small number of cases obliged us to collapse data related to immigrants from many countries into a few groups: former Yugoslavia (mainly Serbia-Montenegro and Macedonia, but also Bosnia, Slovenia and Croatia), other Eeastern European countries (Ukraine, Poland, Moldova), Central Southern Asia (India, Bangladesh, Pakistan, Sri Lanka), Eastern Asia (Philippines and China), North Africa (mainly Tunisia, Egypt, but also Near and Middle East), Central Africa (Senegal, Nigeria and Ghana), South America (mainly Ecuador and Peru).

Owing to gender contrasts found in labour-market patterns in relation to both Italians and immigrants, statistical models were run separately by gender. Independent variables include age, education, family status and region (North, Centre and South). Models concerning only immigrants also included a variable regarding the number of years since migration. The Italian labour force survey classifies educational qualifications obtained abroad according to the Italian grid, which does not specify vocational courses. Therefore, we used the ISCED classification and collapsed data into four categories in order to reduce the risk of misunderstandings in the recoding. Family status was recoded by collapsing information on marital status and the presence of children. Finally, in order to analyse the occupational status of immigrants, we used the class scheme proposed by Erikson, Goldthorpe and Portocarero (1979), which we collapsed into a four- item variable.3 6

LOW ETHNIC PENALTY FOR LABOUR PARTICIPATION AND UNEMPLOYMENT

In 2005, the unemployment rate among people from developing and East- European countries residing in Italy was only 30 percent higher than among natives (10.3 vs. 7.8 percent). That gap is much narrower than in Central and Northern European countries, in spite of wide differences between immigrant groups (Table 2). Such a result is usually explained by the fact that those who entered Central and Northern Europe were mainly refugees, whereas in Italy they were labour migrants who filled job shortages, although their entry was unauthorised in most cases (Salt et al., 2004; Diez Guardia and Pichelman, 2006). This conclusion, however, needs to be qualified in the light of two peculiarities of the Italian economic and social fabric: a poor welfare system, and the sharp North-South divide.

Table 2 about here

Because Italy is the developed country where unemployment benefits are the least generous, the unemployed must rely mainly on their families’ support. This explains why over 52 percent of native job-seekers are youths living at home with their parents, while singles and breadwinners amount to less than 24 percent. By contrast, not even 10 percent of immigrant job-seekers can rely on the modest support from their parents, whereas 40 percent are either singles or breadwinners and can rely only on rather meagre benefits. Thus, the much lower proportion of long-term job-seekers found among immigrants (23 vs. 41 percent for natives) may be due to the fact that immigrants from non-EU countries cannot afford to remain unemployed for longer than 6 months because, before that time-frame elapses, they will be forced to get a job, or to switch back to an illegal resident status or, as a last resort, to leave the country.

Furthermore, immigrants concentrate in central-northern regions, where the labour market is tighter. Hence, in the area where most of them live, their unemployment gap with respect to natives is much wider than at national level. In fact, the ratio between the unemployment rates is two and a half in the North and one and a half 7 in the Centre. Such gaps, however, are only due to immigrants’ higher risk of entering unemployment, because their average length of job-seeking is much shorter than that of natives even in central-northern regions. On the other hand, because immigrants are much more prone to geographical mobility than natives, in the South their unemployment rate is even lower.

Besides regional settlement, the unemployment gap between immigrants and natives may also depend on their different personal characteristics. To focus on this aspect we shall use the concept of "ethnic penalty", which refers to the net disadvantages that immigrants continue to experience in the labour market even after their observed personal characteristics have been taken into account (Heath and Cheung, 2007). Referring to the results of a regression, we can show the extent to which gaps in the probability of avoiding unemployment persist between immigrants and natives who, besides living in the same regional area, are alike by age, education and family status. Such analyses should always be carried out separately for men and women, because, generally speaking, in Italy male and female immigrants entered different labour markets, i.e. mainly private companies for the former, households for the latter. Furthermore, by comparing models for the whole sample, whose results are “driven” by natives who constitute the sample's great bulk, with those including immigrants only, we should be able to determine whether education affects the risk of unemployment for immigrants and natives in the same way.

Especially in the first generation, several immigrants may be “discouraged unemployed”, i.e. neither in education nor looking for a job; and women from some ethnic groups may have very low labour-market participation owing to their traditional values, so that those active (i.e. employed or job-seekers) may be “positively selected”. In both cases, sizeable differences in activity rates (equal to the ratio between the number of people employed and looking for a job and the total population aged 15-64) between immigrant groups (Table 2) may distort the results on the risk of unemployment, with the real disadvantages of less active groups being underestimated (Heath and Cheung, 2007). This might be our case, as, although the average activity rate of all immigrants does not differ from that of natives, differences between groups are quite significant: 15 percentage points for men and even 40 points for women. To deal with this problem, we used a 8

Heckman probit selection model involving a two-stage binary regression that enabled us to take account of both the probability of being active and that of avoiding unemployment at the same time. However, neither for men nor for women the likelihood ratio test of the equations’ independence of the fitted Heckman selection models was found to be significant, thus indicating that there is no correlation between errors in the two equations, that is to say, that the unobservables affecting the labour market participation have not affected the risk of unemployment (results available on request).

Therefore, simple logistic regression models were preferred in order to stress the ethnic penalty as regards both labour market participation (taking into consideration all the population aged 15-64) and risk of unemployment (focusing only on active population, i.e. only employed and job-seekers). For both men and women, two models were estimated: the first one included immigrants and natives, whereas the second one included immigrants only. The parameters associated with the groups of immigrants in the first models could be estimated as the sizes of “ethnic penalties” (if they were negative) or “ethnic bonuses” (if they were positive) experienced by those groups compared to Italians with the same personal characteristics (age, education, family status) and living in the same region.

As regards labour-market participation,4 most of male immigrant groups are advantaged in comparison to Italians, whereas most of the female groups are more or less largely disadvantaged (Table 3). Because those results are controlled for age, the high labour-market participation by male immigrants is not only due to the low proportion of elderly individuals among them, but also to their higher activity rates, in particular among youths. Eastern Asia immigrants are a notable exception to low labour-market participation by female immigrants, because women from China and the Philippines are largely advantaged in comparison to their Italian counterparts, whose activity rate is the lowest in Europe, however. By contrast, women from Morocco, other North-African countries and Central Southern Asia (Pakistan, India, Sri Lanka and Bangladesh) appear to be almost excluded from the labour market, presumably for cultural reasons.

Table 3 about here 9

As regards the risk of unemployment, the ethnic penalty is evident for both men and women because for most immigrants groups the probability of avoiding unemployment is lower than for their Italian counterparts, and for several groups the gap is quite large. Men from Central Southern Asia (Indians and Pakistanis) and women from Eastern Asia (Chinese and Filipinos) are an important exception as their probability of avoiding unemployment is largely above that of natives having the same personal characteristics, probably because they filled wide shortages in the Italian labour market.

Table 4 about here

To sum up, only people from Eastern Asia are more active and less unemployed than their native counterparts, and for women the advantage is substantial. By contrast, immigrants from former Yugoslavia, EU-15 and OECD countries, as well as Italians born in a developing country, are always disadvantaged. Such a finding is not unexpected for people from former Yugoslavia because many of them are among the few refugees to have entered Italy. On the other hand, the finding for those from EU-15 and OECD countries and Italians born in a developing country would be surprising if we did not consider that most of them have the same characteristics (prime age, higher education) as natives with the best labour market situations. Finally, naturalisation by itself appears not to have a positive impact on immigrants’ integration into the labour market, perhaps because the overwhelming majority of them obtained Italian citizenships through marriage.

When comparing the model including the whole sample, whose outcomes are “driven” by natives, with that including immigrants only, differentials between parameters as regards the probabilities of being active by family status show that immigrant women living with a partner are much more disadvantaged than their native counterparts.5 The reason for that may be cultural, but the penalisation is especially high for immigrant women with children, who face huge difficulties in conciliating childcare with paid employment since public care services in Italy are rather poor, private ones are too expensive, and, contrary to natives, immigrant women cannot rely on the support of any relatives. 10

In Italy the level of educational attainment is positively related to the probability of avoiding unemployment (as shown by the parameters of models including the whole sample that are all significantly positive). By contrast, such a relation does not seem to exist for immigrants because, for both genders, the parameters for all education levels are not significantly different from zero. Hence, higher education does not protect immigrants against the risk of unemployment. The poor performance of highly-educated immigrants in comparison to poorly educated ones is usually explained by the fact that, for first-generation immigrants, a higher education cannot involve a greater endowment of human capital (Heath and Cheung, 2007b). Firstly, skills acquired in a different educational system may be useless because human capital is often country-specific (Heath and Yu, 2005); secondly, foreign qualifications may not be recognised by the receiving country; thirdly, educated immigrants may not have a good command of the receiving country’s language, which is necessary to gain access to qualified occupations. Those hypotheses seem to be well suited to explain either the professional downgrading of highly educated immigrants or the behaviour of well-settled highly educated immigrants, who can afford to wait for a long time for a good job. However, they do not fit when the focus is on employment position and on recent immigrants who do not have sufficient economic and social resources that would enable them to wait for a long time for work. Therefore, other hypotheses are needed, and should all be based on the fact that in Italy employment opportunities for immigrants are mainly for unskilled positions.

As far as immigrants’ behaviour is concerned, higher education can be regarded as a “proxy” for greater personal resources which enable immigrants to obtain more information, to control their behaviour better and direct it to their migratory goals, to learn the language beforehand, and to manage themselves more efficiently in the labour market. Temporary immigrants have been considered the best example of “homo oeconomicus” because they are usually committed to making as much money as possible, with no concern for the social status of their jobs because they keep their home society as a reference (Piore, 1979). Thus, highly educated immigrants should “displace” poorly-educated ones, with the consequence that their probability of avoiding unemployment should be higher. Since that has not occurred, we may surmise that some highly educated 11 immigrants either do not have a merely instrumental approach to migration (as ethnographic studies have reported), or possess fairly good economic resources, so that they are scarcely prone to fill just any poor job vacancy. On the other end, as far as labour demand is concerned, we can guess that perspective employers may either not be able to evaluate the human capital of immigrants with different educational levels or even be afraid of hiring over-educated workers for very poor jobs.

The model including only people born abroad shows that integration into the labour market is positively related to the length of stay, although the trend is not straightforward. As regards labour-market participation, the probability among more settled immigrants is higher only for women, but after a “leap” from less than 2 years to 3-5 years, the parameters do not increase significantly; whereas for men, after a small increase from less than 2 years to 3-5 years the probability appears to be steady. Such a difference may be due to the fact that many women joined their partners and entered the labour market only at a later stage, whereas all men immediately started to look for a job. The relation with the length of stay is positive, with an asymptotic trend for the probability of avoiding unemployment as well, given that after 9 years the parameters for both men and women tend to increase only slightly or not at all. We may suppose that after that time the process of integration of immigrants into the Italian labour market has stabilised, although no information on those who left the country is available and we can only ground on studies carried out on other cases6 the assumption that return migration is not a selective process. However, we can guess that such a positive trend as regards the probability of avoiding unemployment might be overestimated because long-term unemployed non-EU immigrants “disappear” from municipal registers, which labour force surveys are based on, as they are not entitled to renew their permit of stay.

A BRAIN WASTE: THE ETHNIC PENALTY AS REGARDS OCCUPATIONAL STATUS

As far as occupational status is concerned, the original EGP class scheme identified 7 main classes, which we recoded into four categories. In the Italian 12 case, as shown in Table 5, the distribution of immigrant workers markedly concentrates in manual occupations (76 percent of workers from emigration countries are in the lowest three EGP classes). Consequently, we decided to keep poorly-skilled non-manual jobs separate from their manual counterparts. In fact, we assumed that access to non-manual occupations (service class and routine non- manual employees) is at present the crucial issue as regards the employment of immigrants. In Italy, the manual/non manual divide is much stronger than in other European countries, whereas differences between skilled and unskilled blue- collars are less marked in terms of working conditions and social status, because in Italy most skilled blue-collars work in small factories and in the national cultural tradition manual work is often despised.7

Table 5 about here

A quite large proportion of immigrants (nearly one third versus slightly more than 17 percent for Italians) have managed to obtain skilled manual jobs. But this does not mean that such immigrants have substantially improved their social status, because they are mainly employed in small manufacturing firms (over 50 percent of them works in firms employing 10 workers or fewer), which are characterised by low wages, poor social protection and low social recognition. On the other hand, the fact that in Italy on-the-job training is the main way to enter skilled manual jobs makes career paths hardly institutionalised and the insertion of immigrants into those occupations much easier than in other countries, like Germany or Denmark, where skill certificates are needed. The proportion of the petty bourgeoisie among immigrants is not much lower than among Italians,8 i.e. 10.9 percent versus 13.2 percent. By contrast, the gap between immigrants and Italians is extremely wide as regards access to non-manual work - 11.1 percent versus 52.5 percent. Moreover, the immigrant groups attaining the highest proportions (those from South America, other Eastern European countries, and former Yugoslavia) score barely 16-17 percent, whereas only 4-6 percent of people from Central Southern Asia and Morocco have managed to obtain white- collar jobs. The fact that over 73 percent of people from EU15 and OECD countries work as white collars confirms that they are a professional elite. Finally, 13 the distribution of Italians born in a developing or Eastern European country appears to be much closer to that of natives than the one of immigrants, contrary to what was seen for unemployment.

In the case of the old European immigration, the confinement of migrants to the lowest ranks of the occupational ladder was due to their poor educational attainment. We presume that this is not the case here, however, because many immigrants entering Italy are highly educated young adults. In order to check the extent to which individual characteristics explain such differences in social position between immigrants and natives, we used a multinomial logistic regression model, assuming that classes are independent and unranked categories. We took the lowest level – unskilled and semi-skilled manual workers – as the reference category for the dependent variable and, as usual, we carried out separate analyses for women and men, controlling for age and level of education. Table 6 highlights that workers from emigration countries have negative odds of being in all the classes, therefore, they have a lower probability than their Italian counterparts of attaining any social status higher than semi and unskilled manual work, which is the reference category. The probability is rather lower for access to the petty bourgeoisie, and dramatically lower for access to the white-collar class. ethnic penalties always seem to be much heavier for female immigrants than for males, the reason being the extraordinary concentration of female immigrants in housekeeping and elderly care.

Table 6 about here

In order to analyse differences among immigrants groups and the impact of immigrants' individual characteristics on access to higher social positions, we estimated two other models excluding Italians born in Italy and in EU-15 and OECD countries, and including the length of stay.9

As regards men, Table 7 highlights that the odds of entering the white-collar class are more or less negative for all immigrants except for workers from EU-15 and OECD countries, whereas for some immigrant groups the odds of entering the skilled manual-worker class and the petty bourgeoisie are not statistically different from the reference group, and even positive in some cases. We can, therefore, 14 identify two broader groups. The first comprises immigrant workers from Central Africa, Morocco, Central Southern Asia and Eastern Asia, who experience an ethnic penalty for all higher occupational statuses juxtaposed with the unskilled and semi-skilled manual class. By contrast, immigrant workers in the second group, which includes those from Albania, other Eastern Europe, other North Africa, former Yugoslavia, Romania and Bulgaria, experience an ethnic penalty only for access to the white–collar class, and, moreover, the penalty is generally lower than for the first group. Immigrant workers from South America lie in between, because they have a positive coefficient for the skilled manual class and a negative one for both the petty bourgeoisie and white–collar classes. The case of men from Central Southern Asia (India, Pakistan, Sri Lanka and Bangladesh) and from Eastern Asia (China and the Philippines) is interesting because they combine a very bad performance concerning the occupational status with a high ethnic bonus as regards the probability of avoiding unemployment (Table 4). We may presume that men from those countries are the only ones who have managed to attain a probability of avoiding unemployment even higher than that of natives because they are the most willing to fill even the lowest job vacancies.

Table 7 about here

The results for women are very different. As shown in Table 8,10 coefficients are always negative for all immigrant women except for those from EU-15 and OECD countries. As for men, the least penalised are Albanians, although women show a relative disadvantage in comparison to Italians who were born in developing countries (negative coefficients), whereas that is not the case for men, who have positive odds of entering both skilled manual jobs and the petty bourgeoisie. On the other hand, the most heavily penalised are women from North Africa and Asia (mainly women from Philippines, India, China and East Asia, with a few cases from Sri Lanka and Bangladesh). As stressed for men, also the case of women from Asia is interesting because they have a quite high probability of avoiding unemployment (Table 4), but a high risk of attaining bad social positions. 15

For both male and female immigrant workers, the probability of entering the skilled manual class is not affected either by the level of education or by the length of stay in Italy, given that coefficients are not significant. By contrast, the probability of entering the white-collar class increases greatly for more educated immigrants and for those who entered Italy before the mid-1990s. Only a long stay in the country also fosters access to the petty bourgeoisie, which, however, is not affected by the level of education. Of course, also those results are based on the assumption that no selection affected returns, as it was emphasised by the few studies dealing with that issue.11

Table 8 about here

In order to determine the impact of education, we compared the outcomes of two logistic regression models (for men and women) predicting the odds of entering the white-collar class. The first one included only age as control variable, while the second included education level as well. As Graphs 1a and 1b report, in both models coefficients are negative for almost all immigrant groups, and disadvantages further increase once education is controlled for. Such a result confirms that the high education level of some immigrant groups does not help them to achieve better social positions; on the contrary, it makes their penalisation more evident. Workers (men and women) from Central Africa and men from Eastern Asia seem to represent an exception. The different outcome by gender for workers from Eastern Asian countries is largely due to the fact that most of the men are Chinese and most of the women are Filipinos. Contrary to Chinese men, many Filipino women are highly educated and they are strongly penalised because most of them work as housekeepers. The worsening in the ethnic penalty when account is taken of the education is especially marked for workers from EU-15 and OECD countries, other Eastern European countries, Romania and Bulgaria, and, for men, also from the other North African countries. A very large proportion of immigrants from those countries are highly educated, so that their concentration in unskilled and semi-skilled manual occupations must be interpreted as an effect of very strong ethnic penalties. 16

Graphs 1a and 1b about here

We can conclude that, unlike what occurred in European immigration from past centuries (Cheung and Heath, 2007), for new immigrants in Italy taking their human capital endowment into account does not reduce their ethnic penalty at all as regards access to higher occupational statuses, but it actually increases it. Contrary to the probability of avoiding unemployment, in this case the scant “portability” of skills acquired abroad, a poor command of the receiving country’s language, and difficulties in obtaining recognition of foreign educational qualifications appear to matter a great deal. However, because competition for the few “good jobs” in the Italian labour market is fierce, we cannot exclude that some discriminatory processes may also be at work. Specific data are still lacking, but a survey on employers’ behaviour regarding recruitment for semi-skilled manual jobs showed that the discrimination is even higher in Italy than in Spain, Belgium, Germany and the Netherlands (Allasino et al., 2004).

Finally, in order to analyse whether and to what extent the pay-offs of education differ between immigrants and Italians, we inserted interaction terms between education and migratory status into the regression model of the odds of attaining the white-collar class (Table 9). To simplify the outcomes, we collapsed nationality into a dummy variable: Italians and workers from European and OECD developed countries versus all other workers. The negative coefficients of the interaction terms appear to confirm that immigrants receive lower returns on their educational investments than Italians do. Furthermore, a comparison of the main effects by education level, which refer to Italians only, with the net effects (resulting from the sum of main effects and the coefficients of the interaction terms) shows that for both Italians and immigrants the odds of entering non- manual classes increase the higher the level of education is, but coefficients for immigrants grow much more slowly (from 0.54 for lower secondary to 1.13 for tertiary education) than those for Italians (from 1.05 to 4.08).

Table 9 about here

CONCLUSION 17

Generally speaking, we can conclude that a great many immigrants who have entered Italy are hardly penalised in comparison with Italians as regards the risk of unemployment, but are severely penalised as regards the socio-professional status of their jobs. Because most immigrant workers have entered the country without working permits and have been forced to work off-the-books for rather long periods of time, when they gain entitlement to hold a registered job through a regularisation drive, the quality of their jobs only seldom upgrades, even in the case of highly-educated workers.

The segregation of immigrants in manual jobs, as well as their relatively low probability of being unemployed, do not depend on their personal characteristics but rather on the mismatch between labour demand and native labour supply, as well as on a sharp labour-market segmentation by age, gender, region and educational attainment. The trade-off between the risk of unemployment and a poor job is accentuated by a serious lack of qualified labour demand, 12 a not very generous welfare state, and scant (de facto) regulation of the labour market. The leading role of labour demand in shaping immigrants’ integration into the Italian labour market is confirmed by the fact that they have fairly easy access to skilled blue-collar jobs, which have a low social status in Italy, whereas they are almost entirely excluded from the least qualified non-manual jobs, which enjoy quite a good social standing.

Unlike the case of the old European immigration, the segregation of immigrant workers in the lowest ranks of the occupational ladder is not at all due to their poor education. On the contrary, their penalisation in comparison to Italians increases if educational attainment is taken into account. There are many reasons why the return to education for recent immigrants is very low. However, the phenomenon is so striking that we can imagine that a social closure mechanism might be at work, one that is grounded less on discrimination than on family and personal networks that are by far the main means to obtain the few good jobs available in the Italian labour market. In other countries, immigrants have managed to avoid the barriers to their occupational upgrading by rapidly entering self-employment, but this does not appear to be the case in Italy, because only a long stay in the country fosters access to the petty bourgeoisie. The reason for that 18 may be that self-employment is still very widespread in Italy and has a good social status, so that formal and informal barriers slow down the entry of immigrants, who can fill vacancies only in the most burdensome independent activities (from catering to construction).

By contrast, taking education into account reduces the penalisation of immigrants in regard to the risk of unemployment. However, the best educated immigrants do not have a greater probability of avoiding unemployment than the lowest-educated ones, perhaps because not just a few of them are not willing to take whatever poor job is offered to them. The probability of avoiding unemployment grows with the length of stay for two reasons. On the one hand, the process of assimilation enables immigrants to acquire language skills, improve their qualifications, and gain better understanding of labour-market institutions. On the other hand, economic needs force immigrants to downgrade their professional expectations.

As regards differences by gender, immigrant women are generally more penalised than men in relation to the risk of unemployment, but their ethnic penalisation is even greater for occupational status, because the overwhelming majority of them work in housekeeping and elderly care. A breakdown by country of origin has enabled us to show that only for women and men from Asia there is a trade-off between a fairly good performance in the probability of avoiding unemployment and a very high risk of obtaining bad jobs. On the other hand, people from EU-15 and OECD countries are affected by a reverse trade-off, because they are severely penalised in their probability of avoiding unemployment, but their probability of obtaining qualified positions is higher than that of their Italian counterparts. Albanian men are close to that situation, whereas for the other immigrant groups the two ethnic penalisations go together, with immigrants from Northern and Central Africa being in the worst position.

The Italian labour market exerts an important pull effect on unauthorised immigration because of its huge underground economy and confines authorised immigrants to poorly qualified jobs. For those jobs there are large labour shortages because native job-seekers have higher social expectations and are able to wait before accepting a job. But in the medium term such a social equilibrium may be precarious, because immigrants are greatly over-educated for the jobs that 19 they are forced to accept, and their expectations of occupational mobility – now very limited – are likely to become higher in the near future.

1. The 2005 Labour Force Survey does not provide more details, but the trend of stay permits leads us to presume that few people entered Italy 15 years before. 2. As not only unauthorised immigrants, but also seasonal immigrants and about 10 percent of those authorised are not recorded in the population registers (Istat, 2007), analyses based on the labour force survey in Italy are a bit biased in favour of the most settled immigrant population. To increase the number of non-nationals, we pooled the data from all the four 2005 waves, which for the first time provided information distinguished by nationality and country of birth, excluding data from second and third interviews to the same household, in order to avoid duplications. 3. That classification, which lets us focus also on immigrant workers entering self-employment, is illustrated by Reyneri and Fullin in this issue. 4. We excluded full-time students, since there are few of them among first-generation immigrants, because their absence from the labour market does not depend either on traditional values or on a discouragement effect. 5. We obtained a similar result by introducing an interaction between the family status and a variable opposing immigrants to Italians into two binary logistic regression models for the whole sample (results available on request). 6. See references quoted by Reyneri and Fullin in this issue. 7. Italy is like France in the classic comparison with Germany carried out by Maurice et al. (1986). 8. The petty bourgeoisie includes only medium-low level self-employment, because high level self-employed are classified in the service class. For this reason the proportion of the petty bourgeoisie among Italians is much lower than the proportion of all the self-employed workers and is close to the proportion calculated among immigrants. 9. The reference group for nationality becomes that of Italians born in developing countries, who are the immigrants most similar to natives. 10. In this model, we collapsed all immigrants from North Africa and Asia to avoid problems concerning the small number of cases. 11. See references quoted by Reyneri and Fullin in this issue. 12. So that more and more young Italian workers are over-educated for their jobs (Barbieri and Scherer, 2007).

REFERENCES

Allasino, E., E. Reyneri, A. Venturini and G. Zincone, 2004 “Labour market discrimination against migrant workers in Italy”, International Migration Papers, No. 67, ILO, Geneva. Barbieri, P. and S. Scherer 2007 Flexibilizing the Italian labour market, FlexCareer, Working Paper, University of Bamberg. Castles, S. and Miller, M. J. 2003 The Age of Migration, New York: Palgrave. Cheung, S-Y. and A. Heath 20

2007 “The comparative study of ethnic minority disadvantage”, in Heath and Cheung (2007a): 1-44. Diez Guardia, N. and K. Pichelman 2006 “Labour migration patterns in Europe. Recent trends, future challenges”, European Economy, European Commission Directorate-General for Economic and Financial Affairs n. 256, September. Dumont, J.-C. and G. Lemaître 2005 “Counting immigrants and expatriates in OECD countries: a new perspective”, OECD Economic Studies, n. 40/1. Erikson R., J.H. Goldthorpe and L. Portocarero 1979 “Intergenerational class mobility in three western European societies: England, France and Sweden”, British Journal of Sociology, 30: 303-343. Feliciano, C. 2005 “Does selective migration matter? Explaining ethnic disparities in educational attainment among immigrants’ children”, International Migration Review, 39(4): 841-871. Gazenboom H. B. G. and D. J. Treiman 1996 “Internationally comparable Measures of occupational status for the 1988 International Standard Classification of Occupations”, Social Science Research, 25: 201-239. Kesler, C. 2006 “Social policy and immigrant joblessness in Britain, Germany and Sweden”, Social Forces, vol. 85 (2): 743-770. Heath, A. 2007 “Cross-national patterns and processes of ethnic disadvantage”, in Heath and Cheung (2007a): 639-695. Heath, A. and S. Yu 2005 “Explaining ethnic minority disadvantage”, in A. Heath, J. Ermisch and D. Gallie (Eds.), Understanding social change, Oxford University Press, Oxford: 187-224 Heath A. and S.-Y. Cheung, 2007a (Eds.) Unequal Chances. Ethnic Minorities in Western Labour Markets, Oxford University Press Oxford. 2007b “Nice work if you can get it: Ethnic penalties in Great Britain”, in Heath and Cheung (2007a): 507-550. Istat 2007 “La popolazione straniera regolarmente presente in Italia”, Nota informativa, 11 aprile. Levinson, A. 2005 The regularisation of unauthorised migrants: Literature survey and country case studies, Centre on Migration, Policy and Society, University of Oxford. Maurice, M., F. Sellier and J.J. Silvestre, 1986 The social foundations of industrial power. A comparison of France and Germany, MIT Press, Cambridge, MA. Oecd 2005 Economic Surveys: Italy, Paris. Piore, M. 1979 Birds of passage. Migrant labor and industrial societies, Cambridge University Press, Cambridge, MA. Reyneri, E. 1998 “The role of the underground economy in irregular migration to Italy: cause of effect?”, Journal of Ethnic and Migration Studies, 24 (2): 313-331. Salt, J., J. Clark, and P. Wanner 2004 “International labour migration”, Population Studies, n. 44, Council of Europe Publishing, Strasbourg. Schierup, C.-U., P. Hansen and S.Castles 2006 Migration, Citizenship and the European welfare state, Oxford University Press, Oxford. Zincone, G. 2006 “The making of policies: Immigration and immigrants in Italy”, Journal of Ethnic and Migration Studies, 32 (3): 347-375.

TABLE 1. GENDER, EDUCATIONAL LEVEL AND YEAR SINCE MIGRATION BY NATIONALITY 21

Gender Education Year since migration No school % of and lower Upper Till 2 3 - 5 6 - 9 > 9 women secondary secondary Tertiary years years years years

Italians born in 49.9 51.1 38.6 10.3 Italy, EU15 & Oecd Italians born in 59.2 44.2 39.9 15.9 5.0 13.5 9.4 72.2 developing countries Eu15 & Oecd 56.5 17.2 41.8 41.0 13.5 19.5 16.7 50.4 Other Eastern 75.2 37.2 44.4 18.3 29.2 45.9 15.0 10.0 Europe Albania 42.9 60.0 34.8 5.2 17.9 29.3 32.8 19.9 Ex Yugoslavia 47.9 58.3 37.3 4.4 13.6 23.4 21.9 41.1 Romania & 53.6 30.7 64.0 5.3 26.0 47.0 21.0 6.1 Bulgaria Center-south Asia 37.3 75.2 20.5 4.3 19.1 26.0 30.3 24.6 Eastern Asia 51.2 69.2 27.3 3.5 12.4 21.5 16.0 50.0 Other North Africa 32.6 52.0 34.9 13.1 15.5 21.5 22.0 40.9 Morocco 39.6 76.3 20.3 3.4 12.1 22.8 25.6 39.5 Central Africa 38.2 64.6 30.4 5.0 14.7 23.7 23.1 38.6 South America 64.7 44.8 46.8 8.4 18.1 39.8 22.2 20.0 Total - High Emigration 48.5 55.8 37.4 6.8 18.3 31.2 23.7 26.8 countries Source: Our elaboration from Istat, Labour force survey, 2005.

TABLE 2. UNEMPLOYMENT AND ACTIVITY RATES BY GENDER AND NATIONALITY Men Women Total Unemploy- Activity Unemploy- Activity Unemploy- Activity ment rate rate ment rate rate ment rate rate Italians born in Italy, EU15 & 6.3 73.7 10.0 50.3 7.8 62.0 Oecd Italians born in developing 6.2 75.4 14.3 50.7 10.2 60.8 countries Eu15 & Oecd 5.5 83.7 6.0 58.7 5.7 69.6 Other Eastern Europe 5.9 85.8 15.2 72.0 12.6 75.4 Albania 5.9 90.2 27.1 47.9 12.0 72.1 Ex Yugoslavia 4.1 85.4 17.5 53.2 9.0 69.9 Romania & Bulgaria 4.6 88.4 7.7 72.0 6.1 79.6 Center-south Asia 1.8 90.3 30.8 29.3 6.5 67.6 Eastern Asia 5.5 86.1 3.2 71.6 4.4 78.7 Other North Africa 11.3 86.7 37.3 34.3 15.5 69.6 Morocco 10.2 87.8 26.6 34.9 13.6 66.8 Central Africa 11.3 84.8 17.3 68.3 13.3 78.5 South America 9.3 79.7 11.5 70.7 10.6 73.9 Total high emigration countries 7.0 87.1 15.5 58.7 10.3 73.4 countries

TABLE 3. PROBABILITY OF BEING ACTIVE FOR 15-64 YEARS OLD RESIDENTS (FULL TIME STUDENTS EXCLUDED). LOGISTIC BINARY REGRESSION 22

Men Women All Migrants All Migrants only only Β β β β Nationality Italians born in Italy, EU15 & Oecd ref. ref. Italians born in developing countries - 0.22** - 0.47** Eu15 & Oecd - 0.26 Controlled - 0.77** Controlled Ex Yugoslavia 0.00 for - 0.50*** for Romania & Bulgaria 0.49 nationality - 0.07 nationality Albania 0.84*** - 0.41*** Other Eastern Europe 0.41 Reference = 0.05 Reference = Centre-South Asia 0.74** Italians born - 0.99** Italians born Eastern Asia 0.57 in developing 0.72*** in developing Morocco 0.74*** countries - 0.76*** countries Other North Africa 0.40 - 1.50*** Central Africa 0.43 0.51*** South America 0.71 - 0.15 Education No school & primary ref. ref. ref. ref. Lower secondary 0.41*** 0.72*** 0.73*** 0.58*** Upper secondary 0.92*** 1.13*** 1.61*** 0.84*** Tertiary 1.67*** 1.91*** 2.40*** 1.23*** Family status Living alone ref. ref. ref. ref. Living with partner & children 0.74*** 0.69*** - 0.90*** -1.92*** Living with partner without children 0.14*** 0.12 - 0.63*** -1.13*** Youth living with parents - 0.81*** - 0.06 - 0.09** 0.28 Single parent 0.40*** 0.11 0.01 -0.04 Living in Italy Till 2 years ref. ref. From 3 to 5 years 0.53** 0.69*** From 6 to 9 years 0.53* 0.54*** 10 years & over 0.40 0.86*** Constant 2.36*** 2.28*** 0.34*** 0.74*** Controlled for age and region Number of cases 105,871 3,209 107,724 3,875 Cox and Snell R2 0.239 0.118 0.270 0.198 Nagelkerke R2 0.382 0.267 0.361 0.267 *= 10% significance, ** = 5% significance, *** = 1% significance

TABLE 4. PROBABILITY OF AVOIDING UNEMPLOYMENT FOR 15-64 YEARS OLD ACTIVE RESIDENTS. LOGISTIC BINARY REGRESSION 23

Men Women All Migrants All Migrants only only β β β β Nationality Italians born in Italy, EU15 & Oecd ref. ref. Italians born in developing countries - 0.27 -0.70*** Eu15 & Oecd - 0.85** Controlled -0.57** Controlled Ex Yugoslavia - 0.55 for -0.95*** for nationality Romania & Bulgaria - 0.56* nationality - 0.34 Albania - 0.35 -1.67*** Other Eastern Europe 0.00 Reference = -0.94** Reference = Centre-South Asia 1.13** Italians born -1.49** Italians born Eastern Asia 0.39 in developing 1.69** in developing Morocco -0.94*** countries -1.38*** countries Other North Africa - 1.05*** -2.29*** Central Africa - 1.11*** -1.00*** South America - 1.00*** -0.88*** Education No school & primary ref. ref. ref. ref. Lower secondary 0.63*** - 0.20 0.50*** 0.15 Upper secondary 1.00*** 0.05 1.11*** 0.37* Tertiary 0.86*** 0.41 1.15*** 0.16 Family status Living alone ref. ref. ref. ref. Living with partner & children 0.76*** 0.01 -0.14*** - 1.08*** Living with partner without children 0.53*** - 0.07 0.02 - 0.89*** Youth living with parents - 0.63*** - 1.24*** -0.71*** - 1.01*** Single parent 0.07 0.69 -0.46*** - 0.91*** Living in Italy Till 2 years ref. ref. From 3 to 5 years 0.45* 0.71*** From 6 to 9 years 1.05*** 0.88*** 10 years & over 0.92*** 1.13*** Constant 2.48*** 2.68*** 1.55*** 1.50*** Controlled for age and region. Number of cases 85,398 2,936 58,964 2,321 Cox and Snell R2 0.074 0.038 0.088 0.079 Nagelkerke R2 0.199 0.097 0.184 0.137 *= 10% significance, ** = 5% significance, *** = 1% significance

TABLE 5. EMPLOYMENT BY NATIONALITY AND CLASS POSITION 24

Italians born in Italians Eu15 Italy, born in Emigration & EU15 developing countries Oecd and countries Oecd I Higher-grade professionals 6.4 6.8 15.5 0.8 II Lower-grade professionals 21.1 17.7 39.3 4.2 White collars IIIa Routine non manual employees - higher grade 21.0 12.9 17.9 4.4 IIIb Routine non manual employees - lower grade 3.9 3.4 1.2 1.7 IVa Small proprietors. artisans with employees 3.2 1.5 1.2 1.2 Petty bourgeoisie IVb Small proprietors. artisans without employees 8.0 7.2 4.8 5.0 IVc Farmers 2.0 2.3 1.4 4.7 Skilled manual V Lower grade technicians 2.2 1.9 2.7 1.4 workers VI Skilled manual workers 15.2 20.0 6.6 31.2 Semi and unskilled VIIa Semi and unskilled manual workers 14.8 25.4 7.3 45.1 manual workers VIIb Agricultural workers 2.0 1.0 2.0 0.2 Total 100.0 100.0 100.0 100.0

TABLE 6. MULTINOMIAL LOGISTIC REGRESSION MODEL OF THE LIKELIHOOD OF ENTERING HIGHER SOCIAL CLASSES FOR 15-64 YEARS OLD WORKERS. REFERENCE CATEGORY: SEMI AND UNSKILLED MANUAL CLASS (REGRESSION COEFFICIENTS) Men Women Skilled Petty Skilled White Petty White manual bourgeoisi manua collars bourgeoisie collars class e l class β β β β β β Italians ref. ref. ref. ref. ref. ref. Eu15 & Oecd -0.05 0.26 0.04 -0.17 -0.03 -0.49* Emigration countries -0.23*** -0.74 *** -2.67*** -0.86 *** -1.94 *** 3.49*** Number of cases 85,612 56,897 Pseudo R square Cox and Snell 0.32 0.33 Nagelkerke 0.34 0.37 Models control for age and education *= 10% significance, ** = 5% significance, *** = 1% significance

TABLE 7. MULTINOMIAL LOGISTIC REGRESSION MODEL OF THE LIKELIHOOD OF ENTERING HIGHER SOCIAL CLASSES FOR 15-64 YEARS OLD MALE IMMIGRANT WORKERS. REFERENCE CATEGORY: SEMI AND UNSKILLED MANUAL CLASS (REGRESSION COEFFICIENTS) 25

Skilled manual class Petty bourgeoisie White collars β β β Constant 0.24 -1.02 *** -2.18 *** Education Tertiary -0.05 -0.19 3.12 *** Upper secondary 0.08 -0.28 1.59 *** Lower secondary 0.07 -0.27 0.66 ** No school & primary ref. ref. ref. Living in Italy 10 years and over 0.07 0.66 *** 1.03 *** From 6 to 9 years 0.19 0.26 0.40 From 3 to 5 years 0.23 0.22 -0.07 Till 2 years ref. ref. ref. Nationality South America 0.11 -0.78 * -0.39 Central Africa -0.90 *** -1.85 *** -1.31 *** Morocco -0.47 *** -1.02 *** -2.30 *** Other North Africa 0.12 0.25 -0.98 *** Eastern Asia -0.64 ** -0.21 -0.87 *** Centre South Asia -0.87 *** -0.71 *** -2.08 *** Romania e Bulgaria 0.01 0.18 -1.43 *** Ex Yugoslavia 0.02 0.15 -1.19 *** Albania 0.45 *** 0.38 * -1.42 *** Other Eastern Europe 0.33 0.39 -0.54 EU15 & Oecd 0.02 0.82 ** 1.31 *** Italians born in developing countries ref. ref. ref. pseudo R square Cox and Snell 0.29 Nagelkerke 0.32 Number of cases 3,126 Model controls for age *= 10% significance, ** = 5% significance, *** = 1% significance

TABLE 8. MULTINOMIAL LOGISTIC REGRESSION MODEL OF THE LIKELIHOOD OF ENTERING HIGHER SOCIAL CLASSES FOR 15-64 YEARS OLD FEMALE IMMIGRANT WORKERS. REFERENCE CATEGORY: SEMI AND UNSKILLED MANUAL CLASS (REGRESSION COEFFICIENTS) Skilled manual class Petty bourgeoisie White collars β β β Education Tertiary 0.45 0.49 3.60 *** 26

Upper secondary 0.70 *** 0.27 2.25 *** Lower secondary 0.30 0.16 1.06 *** No school & primary ref. ref. ref. Living in Italy 10 years and over -0.02 0.77 ** 1.27 *** From 6 to 9 years 0.06 0.19 0.65 ** From 3 to 5 years -0.37 ** -0.13 0.01 Till 2 years ref. ref. ref. Nationality South America -0.53 ** -1.36 *** -1.42 *** Central Africa -0.34 -1.57 *** -1.67 *** North Africa -0.86 *** -1.14 *** -2.04 *** Asia -1.05 *** -0.85 *** -2.20 *** Romania e Bulgaria -0.42 * -1.14 *** -1.46 *** Ex Yugoslavia -0.14 -0.35 -1.06 *** Albania -0.67 *** -0.97 *** -1.64 *** Other Estern Europe -0.39 * -0.73 ** -1.47 *** EU15 & Oecd 0.13 0.87 ** 1.06 *** Italians born in developing countries ref. ref. ref. Constant 0.16 -0.89 * -1.92 *** Number of cases 2,228 Pseudo R square Cox and Snell 0.34 Nagelkerke 0.37 Model controls for age *= 10% significance, ** = 5% significance, *** = 1% significance

TABLE 9. LOGISTIC REGRESSION MODEL OF THE LIKELIHOOD OF ENTERING HIGH LEVEL CLASSES (SALARIAT AND ROUTINE NON MANUAL EMPLOYEES) FOR 15-64 YEARS OLD IMMIGRANT WORKERS. REFERENCE CATEGORY. ALL MANUAL CLASSES AND PETTY BOURGEOISIE (REGRESSION COEFFICIENTS) β Education No school & primary *** Lower secondary 1.05 *** Upper secondary 2.23 *** Tertiary 4.08 *** Nationality Emigration countries -2.08 *** Interaction terms Emigration countries * No school & primary *** Emigration countries * Lower secondary -0.51 ** Emigration countries * Upper secondary -1.88 *** Emigration countries * Tertiary -2.96 *** Constant -0.97 *** Number of cases 142,508 -2 log likelihood 140960.63 R-square of Cox and Snell 0.33 R-square of Nagelkerke 0.44 Model controls for age and gender *= 10% significance, ** = 5% significance, *** = 1% significance 27