Earnings differentials between immigrants and natives: The role of occupational attainment

Aringa, Carlo Dell; Lucifora, Claudio; Pagani, Laura Article Earnings differentials between immigrants and natives: The role of occupational attainment IZA Journal of Migration Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Aringa, Carlo Dell; Lucifora, Claudio; Pagani, Laura (2015) : Earnings differentials between immigrants and natives: The role of occupational attainment, IZA Journal of Migration, ISSN 2193-9039, Springer, Heidelberg, Vol. 4, pp. 1-18, https://doi.org/10.1186/s40176-015-0031-1 This Version is available at: https://hdl.handle.net/10419/149432 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ ORIGINAL ARTICLE Open Access Earnings differentials between immigrants and natives: the role of occupational attainment Carlo Dell’Aringa 1 , Claudio Lucifora 2 and Laura Pagani 3* * Correspondence: laura.pagani@ unimib.it 3 Università di Milano Bicocca, Milan, Italy Full list of author information is available at the end of the article Abstract This paper brings new evidence to the existing literature on earnings differentials and returns to human capital for immigrants and natives. It is the first paper analysing this topic using data drawn from the Italian Labour Force Survey, a large nationally representative dataset. We show that returns to human capital are considerably lower for immigrants as compared to natives and that there is no return to pre-immigration work experience, suggesting imperfect transferability of human capital. In the second part of the paper we explore models of occupational attainment among immigrants and the native born. Our findings suggest that, contrary to what is observed for natives, immigrants’human capital does not contribute to getting access to high-paying occupations. JEL classification: J31, J24, J61, F22 Keywords: Immigration; Earnings; Returns to human capital; Occupation 1 Introduction In recent years, Italy experienced a marked increase in immigration. The population share of migrants rose very rapidly, from 1.1 per cent (738,000) in 1995 to 7 per cent (4,235,000) in 2010. EU enlargement, since 2007, further contributed to the increasing of migration flows from eastern European countries. Migrants are generally younger and more active in the labour market; hence, when computed on the labour force, their share is close to 9 per cent (in 2010). This significant and rapid growth of immigrants constitutes a substantial (supply) shock, which is expected to affect both employment and earnings differentials of immigrants relative to natives. This paper investigates the process of wage determination for migrants and natives. Empirical research has shown, for different countries, that wages and returns to human capital are generally lower for immigrants as compared to the native-born (Chiswick 1978, Dustmann 1993, Baker and Benjamin 1994, Shields and Wheatly Price 1998, Friedberg 2000, Chiswick and Miller 2008). This is often explained with reference to the low portability of immigrants’human capital (i.e., pre-immigration education and work experience). Due to the poor quality of data with information on migrants, in Italy we lack sound empirical evidence –based on nationally representative data – on immigrants’earnings differentials. 1 The existing studies that have investigated the migrants pay gap in Italy either used administrative archives or surveys limited to specific regions. Accetturo and Infante (2010) analyse earnings differentials in a © 2015 Dell’Aringa et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 DOI 10.1186/s40176-015-0031-1 large Italian northern region (Lombardy). Given the cross sectional nature of the data,theyarenotabletoidentifyassimilation. They find that returns to education for immigrants located in this region are, on average, much lower as compared to natives (0.7 to 0.9 per cent versus 4.7 to 6.1 per cent). They also show that immigrants’returns to education, when compared to natives, remain low even over time, which they interpret as lack of assimilation. It should be noted, however, that Lombardy is one of the most economically advanced region in Italy, and it can not be considered as representative of overall Italian migration. Venturini and Villosio (2008) use administrative panel data drawn from the social security archives (INPS) to investigate the labour market assimilation of foreign workers in Italy. Their analysis focuses on earnings and employment status of male workers. However, a severe shortcoming of these data is that there is no information on educational attainment of both migrants and natives such that it is not possible to estimate the contribution of education to the assimilation process, which is instead one of the main contributions of our paper. They find no differences in earnings between immigrants and natives at the beginning of the working life, but earnings profiles diverge over time with work experience, pointing to a lack of assimilation that is persistent. Battisti (2013) uses the INPS Veneto Worker History dataset, an administrative longitudinal linked employer-employee dataset that covers the population of private-sector workers of the Italian administrative region of Veneto for the years 1982–2001, and documents a large and growing wage gap between foreign-born and native-born workers. However, the focus on a single Italian region (Veneto) and the lack of information on educational attainment are both limiting factors in terms of national representativeness and in providing evidence on migrants’returns to education. This paper brings new evidence to the existing literature on earnings differentials and returns to human capital for immigrants and native Italians. To our knowledge, this is the first paper which uses a large representative dataset with information on both earnings and foreign status (i.e., Italian Labour Force Survey, LFS) to investigate earnings differentials and the role of human capital (both education and work experience) at the national level. The Italian case is particularly interesting since the share of highly educated migrants is one of the lowest among OECD countries. In 2007 migrants with tertiary attainment were just 12.2 per cent (a lower value is found only for Austria, 11.3 per cent, and Poland, 11.9 per cent). This sharply contrasts with the migration pattern of countries such as Ireland or Canada, where the same share is around 40 per cent. 2 One limitation, however, that our papers shares with other studies using crosssectional data is that there are serious threats to identification of the labour market assimilation of foreign workers. In this case, as shown in the literature, the parameters of interest may be confounded with immigration cohort quality (Borjas, 1985), selective out-migration (Lubotsky, 2007) or age-at-arrival effects. Nonetheless, most studies which focus on the effects of immigration on earnings are usually forced to use large crosssectional data (Census, Labour Force Surveys) because large datasets are needed to guarantee representativeness of the immigrant population (see, for example, Chiswick and Miller, 2007 and Friedberg, 2000). The limits related to cross section data analysis will be discussed when presenting the econometric results and in the interpretation of coefficient estimates. We distinguish between the effects of human capital acquired domestically and abroad on earnings and investigate the patterns of immigrants’skill transferability. Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 Page 2 of 18 We allow for differences in the returns to human capital (both education and work experience) between immigrants and natives and for differences in returns to home and destination country work experience (Friedberg 2000). 3 In line with previous findings, we show that returns to immigrants’education are lower as compared to that of natives. We also find that pre-immigration work experience grants no returns in the Italian labour market and that years of post-migration labour market experience are rewarded at a considerably lower rate for immigrants when compared to natives. Our main results are confirmed for specific migrant groups defined according to their country of origin and to their age at immigration. Our paper also contributes to the analysis of occupational attainment among immigrants and the native born. In particular, we analyse the role of human capital in governing the allocation of immigrants, as compared to native workers, in the occupational hierarchy (Chiswick and Miller 2007). Our findings suggest that wage differentials for immigrants take place mainly within, rather than between, occupations. In other words, contrary to what is observed for natives, immigrants’human capital does not seem to contribute to getting access to high-paying occupations. This contrasts with the empirical evidence provided by Chiswick and Miller (2007) for the US, where they show that education is the key factor for immigrants, determining access to high-paying occupations as compared to natives. The latter may show the existence of occupational segregation in the Italian labour market, which we interpret as a “glass-ceiling” effect for immigrant workers located in the upper part of the wage distribution. The above results prove robust to a number of alternative specifications. The rest of the paper is organised as follows. The next section describes the data used and presents some descriptive evidence. Section 3 presents different specifications for wage equations and compares returns to human capital of immigrants and natives. In section 4, we estimate both inter-occupational and intra-occupational wage differentials as well as their patterns over the earnings distribution using quantile regressions. Section 5 presents some sensitivity checks, while section 6 concludes. 2 Data and descriptive statistics Weusedatadrawnfromthe2009waveofthe Italian Labour Force Survey (LFS), a nationally representative dataset with information on workers’earnings as well as a foreign identifier (i.e., individuals with non-Italian citizenship). 4 Country of birth is often used, instead of nationality, to define migrant status; note however that in our dataset, the two definitions are equivalent since all but eight non-Italian citizens are also foreign-born. The LFS only covers foreigners registered at municipal registry offices; hence, the study does not consider illegal immigration. We restrict our sample to migrants from Eastern Europe, Asia, Central and South America and Africa, while we exclude foreigners from EU15, North America, Oceania and Japan. 5 As commonly done in the literature (among others, Baker and Benjamin 1994 and Chiswick and Miller 2007), we focus the analysis on males only. Indeed, female migration patterns have been shown to be quite different from that of males, both in terms of purposes (i.e., family reunions) and with respect to the specific labour market segment where it is concentrated (mainly the household service sector). Our final sample contains 94,269 individuals, with 7,252 (7.69 per cent) immigrants and 87,017 (92.31 per cent) Italian Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 Page 3 of 18 citizens. 6 Our variable of interest, as recorded in the LFS, is net monthly earnings (which excludes occasional elements of pay such as annual productivity bonuses, allowances, pay for non-customary overtime, etc.). Table 1 shows some basic characteristics of the sample separately for immigrants and natives. 7 Average monthly earnings are much lower for immigrants (−20 per cent) as compared to Italians, while working hours are higher for the latter group. Immigrants are younger (5 years), have resided in Italy on average for 10 years, and their work experience, while being, on average, lower, is almost equally split between Italy and their country of origin. 8 Moreover, immigrants tend to be less educated (approximately 1.5 years) 9 and more frequently hired on “non-standard”contracts (15 versus 10 per cent). Finally, immigrants are mainly located in Northern regions, as compared to Italians (68 versus 48 per cent), while they are under-represented in the South (11 versus 36 per cent). Table2reportsaverageearningsacrossquartiles of the distribution separately by education and work experience for natives and immigrants –i.e., for the latter both pre-immigration and post-immigration measures are reported. 10 Earnings levels are positively associated with both education and work experience for both natives and immigrants, but the relationship is stronger for natives: comparing the first quartile with the fourth quartile, average education is 3 years higher for natives and only 1.1 years higher for immigrants. The same holds for overall work experience: from the first to the fourth quartile, average work experience ranges from 21 to over 27 years for Italians and from 21 to 23 years for immigrants. 11 At a descriptive level, the evidence presented shows that earnings levels are higher and exhibit a steeper progression along the distribution for Italians as compared with immigrants. 3 Earnings equations and the immigrants’wage differential We specify a standard human capital earnings equation, which represents our workhorse model, lnðwiÞ¼αþδ0WTiþδ1Mþδ2EDiþδ3EDiMðÞþδ4EXPHiþδ5EXPDi þδ6EXPDiM  þδ7Xiþμi ð1Þ Table 1 Summary statistics Natives Immigrants Mean Std. Dev. Mean Std. Dev. Net monthly wage 1372.50 563.79 1097.71 343.74 Weekly working time 39.13 6.92 40.19 6.93 Age 41.85 10.94 36.99 9.32 Education (years) 10.94 3.46 9.36 3.95 Work experience (natives) 24.91 11.75 - - Pre-immigration work experience - - 11.80 8.73 Post-immigration work experience - - 9.82 5.60 Years since migration - - 10.05 5.61 Full time 0.96 0.20 0.94 0.24 Married 0.61 0.49 0.59 0.49 Permanent worker 0.89 0.31 0.85 0.36 Nr obs 87017 7252 Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 Page 4 of 18 where ln(w i )is the log of net monthly earnings, WT is weekly hours worked, Mis a dummy variable for immigrant status, ED iseducationinyears,andEXP is potential work experience, which, for migrants, is split between the part acquired in the home country (EXP H ,H=home) and the part acquired in the destination country (EXP D , D=Destination). 12 The interaction terms with the immigrant dummy allow for the returns to education (ED i *M) and experience (EXP D *M) to differ between natives and migrants, while Xis a vector of personal and job characteristics (marital status, full-time, permanent job). 13 Note that the term EXP D *M is usually measured as ‘years since migration’and interpreted as capturing the yearly returns to migration (i.e., since arrival in Italy). The coefficient on the immigrant dummy Mvirtually measures the (expected) earnings gap between immigrants and natives upon arrival. In our empirical analysis, we first estimate a restricted version of equation (1) where we set the returns to schooling for both immigrants and Italians to be the same (i.e. δ 3 =0)andwherewedonotdifferentiatebetweenpre-andpostimmigration work experience for immigrants (i.e. δ 4 =δ 5 ). We then release the above restrictions and estimate the more flexible specification shown in equation (1), which allows for differences in the returns to human capital between immigrants and natives and for differences in the returns to home and destination country work experience. For immigrants, the overall returns to education are given by δ 2 +δ 3 , while the returns to post-immigration work experience are δ 5 +δ 6 . Discrimination, occupational segregation or imperfect transferability of human capital in the Italian labour market will show-up as a negative sign on the coefficients of the interaction terms δ 3 and δ 6 –for schooling and experience, respectively–which represents the earnings penalty that immigrants face with respect to native workers. The various specifications of equation (1) that we estimate –i.e., restricted and unrestricted as well as with and without additional controls –are reported in Table 3. When returns to education and experience are restricted to be the same between immigrants and natives (columns 1 and 2), we find a 10 per cent earnings penalty for immigrants upon arrival (7.7 per cent when controlling for industry and firm size). Interestingly, the coefficient on work experience in Italy for immigrants is negative and statistically significant in the first column, suggesting that immigrants’relative earnings decrease by 0.2 per cent per year after migration. However, when controlling for industry and firm size, the coefficient is no longer statistically significant. A direct comparison of estimated coefficients suggests that the earnings penalty following migration is partly due to immigrants’concentration in small firms or low-wage industries. Estimated returns Table 2 Distribution of human capital by wage quartiles Natives Immigrants Education Work experience Monthly net wage Education Work experience Pre-immigration work experience Post-immigration work experience Monthly net wage Wage quartile 1 9.87 21.23 830.73 8.75 20.76 12.09 8.66 712.87 2 10.06 25.06 1175.75 9.26 21.07 11.8 9.29 1033.72 3 10.92 26.06 1403.22 9.67 21.89 11.64 10.22 1189.18 4 12.92 27.3 2082.1 9.84 23 11.65 11.36 1496.51 Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 Page 5 of 18 to education and work experience are, respectively, 4.5 and 0.77 per cent (column 1) and 3.6 and 0.64 per cent (column 2) when additional controls are included. The restricted version, however, is easily rejected by the data. When we fit the unrestricted specification, as reported in equation (1), the estimated returns to education are, respectively, 4.9 and 4 per cent for natives and 0.79 and 0.66 per cent for immigrants (see columns 3 and 4). Incidentally, given that almost all immigrants in our sample completed their education in their country of origin, an important point to raise is whether the quality of schooling is effectively comparable between origin and destination country. Should the latter not to be true, differences in returns could reflect the imperfect transferability of degree due to differences in the quality of schooling between countries. In order to check the robustness of our result, we replicated the analysis controlling for a measure of country’s school quality. In particular, we used information drawn from the OECD’s “Programme for International Student Assessment”(PISA) regarding the average test score in mathematics and science from primary through end of secondary school (as in Hanushek and Woessmann 2009). Our results proved to be largely unaffected (See the Additional file 1: Table S1 for results). 14 Table 3 Baseline earnings equation VARIABLES (1) (2) (3) (4) (5) (6) Immigrant −0.1039*** −0.0772*** 0.4222*** 0.3428*** 0.2082*** 0.1754*** (0.008) (0.007) (0.016) (0.016) (0.016) (0.016) Education 0.0453*** 0.0360*** 0.0493*** 0.0402*** 0.0246*** 0.0215*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Experience abroad −0.0005 0.0001 0.0002 0.0005 (0.000) (0.000) (0.000) (0.000) Experience in Italy 0.0082*** 0.0069*** 0.0060*** 0.0054*** (0.000) (0.000) (0.000) (0.000) Education x immigrant −0.0414*** −0.0336*** −0.0193*** −0.0165*** (0.001) (0.001) (0.001) (0.001) Experience in Italy x immigrant −0.0048*** −0.0032*** −0.0025*** −0.0017*** (0.001) (0.001) (0.001) (0.001) Experience 0.0077*** 0.0064*** (0.000) (0.000) YSM −0.0019*** −0.0009 (0.001) (0.001) Constant 5.4179*** 5.4541*** 5.3605*** 5.3995*** 6.2061*** 6.2503*** (0.013) (0.015) (0.013) (0.015) (0.021) (0.024) Observations 93,982 93,982 93,982 93,982 93,982 93,982 R-squared 0.407 0.445 0.417 0.451 0.482 0.502 Personal and job characteristics YES YES YES YES YES YES Regional fixed-effects YES YES YES YES YES YES Industry fixed-effects and firm size NO YES NO YES NO YES Occupations NO NO NO NO YES YES Robust standard errors in parentheses. Control for working time is included in all specifications. ***p<0.01. Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 Page 6 of 18 In the last two columns of Table 3, we also add a large set of occupational dummies and estimate the model both including (column 6) and not including (column 5) industry and firm-size dummies. In both cases the returns to education for both natives and immigrants are further reduced. We will further delve into this issue in the following section. The returns to work experience also offer some interesting insights. First, pre-immigration work experience seems not to be valued in the Italian labour market. Second, there is a penalty for immigrants (as shown by the negative and statistically significant coefficient of the interaction term, δ 6 in equation 1) on the returns to work experience. Particular care should be used in interpreting these results due to the cross-sectional nature of the data and the potential selection bias induced by return migration. For instance, if the most successful migrants are more likely to return to their country of origin, least squares estimates of work experience in the destination country are likely to be biased downward. Moreover, since the contribution of Borjas (1985), it is well known that working with a cross-section can lead to bias in the estimation of the relationship between years since migration and work experience in the destination country and wages (cohort effects). In this case, for instance, if the more recent immigration cohorts have a lower (higher) unobserved ability, least squares estimates will lead to an upward (downward) bias in the estimated coefficient of work experience in Italy. In order to adequately address these issues, longitudinal data are needed. In this respect, Venturini and Villosio (2008) is the only paper that analyses wage differentials for migrants using a nationally representative panel dataset. Although data limitations do not allow them to study the role of education in the assimilation process, their findings are close to ours. Namely, they find that immigrants’and natives’wage profiles diverge with on-the-job experience. More importantly, for the purpose of our paper, they show that even when selective return migration and cohort effects are taken into account, the main results still hold. This may suggest that cohort effects do notplayamajorroleinItalianmigrationpatterns. One explanation may be related to the fact that immigration is a relatively recent phenomenon in Italy: our data show that 85% of migrants arrived in Italy less than 15 years before 2009, the date of the survey we are using. Hence, it can be argued that cohort quality may not have changed much across the various waves of migration in such a relatively short period. As a partial attempt to control for potential changes in cohort quality, we re-estimated equation (1) adding cohort dummies interacted with the immigration dummy. These cohort dummies intend to capture some cohort-specific unobserved characteristics affecting migrants’wage. Results are largely unchanged. Overall, we find that returns to human capital in the destination country (both education and work experience) are considerably lower for immigrants as compared to natives. 15 The findings that immigrants receive no return to their pre-immigration work experience and that foreign education is valued less than domestic education are common to other studies in the literature (among others, Friedberg 2000 and Chiswick and Miller 2008). Finally, it is interesting to notice that the earnings gap between natives and immigrants upon arrival is mainly explained by the lower returns to immigrants’human capital: the gap is close to zero (other things being equal) when both natives and immigrants have (roughly) ten years of schooling, and it becomes negative at higher levels of schooling, while work experience matters less. 16 Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 Page 7 of 18 3.1 Estimates by area of origin and by age at immigration In order to investigate heterogeneity across different groups of the immigrant population, we now extend the analysis to assess whether the estimated effects are different according to the area of origin. Considering our sample of male employees, the most represented national groups who are resident in Italy are: Romanians (19.3 per cent), Albanians (16.2 per cent) and Moroccans (11.9 per cent), followed by migrants from the former Yugoslavia (Macedonia, Kosovo, Serbia, Bosnia-Herzegovina and Croatia, 8 per cent), India (5.8 per cent), Philippines (3.2 per cent) and Tunisia (3.1 per cent). We reestimate the human capital penalty for immigrants specifying a dummy for each immigrants’groups. More specifically, we defined the following immigrant groupings: Eastern Europe, Africa, Asia (excluding Japan) and Latin America. Results are reported in Table 4. With respect to returns to education, the highest penalty is found for Asian migrants and the lowest for Latin-Americans. This finding may indicate, as shown in the literature, that language skills play an important part in the returns to human capital: Spanish-speaking migrants from Central and South America –given the greater lexical proximity between the Spanish and the Italian languages –are more likely to become proficient in Italian as compared to Asian. The education penalty, however, is rather large also for some immigrant groups from Eastern Europe and some Balkan countries, including Romanians and Albanians, whose proficiency in Italian is generally rather good. 17 Experience in the home country is not valued for any migrant group, while an interesting result emerges when considering work experience in Italy: we find no penalty for immigrants from Europe and Latin America, while for Asians and Africans work experience in the destination country is less valued as compared to native workers. Table 4 Estimates by area of origin and by age at immigration a Wage penalty Return on pre-immigration work experience Education Post-migration work experience Area of origin Eastern Europe −0.0419*** −0.0015 Ref (0.001) (0.001) Africa −0.0413*** −0.0045*** 0.0002 (0.002) (0.001) (0.001) Asia −0.0454*** −0.0065*** −0.0012 (0.002) (0.002) (0.001) Latin America −0.0377*** −0.0047 −0.0004 (0.004) (0.003) (0.002) Age at immigration Less or equal to 20 −0.0386*** −0.0017 Ref (0.003) (0.001) More than 20 −0.0424*** −0.0058*** −0.0011** (0.001) (0.001) (0.001) Robust standard errors in parentheses. ***p<0.01, **p<0.05. a specification as in columns 3 of Table 3. Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 Page 8 of 18 While providing new and important evidence for the economic performance of migrants in the Italian labour market, some important questions are left for future research. For example, future studies should try to assess what part of the observed wage penalties for immigrant workers depends on imperfect transferability of educational attainment and what part is related to the existence of discrimination or occupational segregation in the Italian labour market. Endnotes 1 A number of studies have investigated the displacement effect of immigration on native workers’employment and wages for Italy. For example, Gavosto, Venturini and Villosio (1999) find no effect of immigration on natives’earnings and mixed results for (un)employment. 2 Moreover, OECD’s evaluations suggest that Italy is the country with the lowest tendency to attract more highly educated immigrants on average, given its country of origin mix (OECD, 2008). 3 Friedberg (2000) showed that the returns to schooling obtained in the country (i.e., Israel) for immigrants was lower as compared to natives (8 and 10 per cent respectively) and that for immigrants, the returns to schooling acquired abroad was even lower (7 per cent). 4 In order to improve the quality of data on foreigners, the LFS employs a number of ad hoc strategies to collect data on the immigrant population. For example, interviews Table 7 Baseline earnings equations - common support (1) (2) (3) (4) VARIABLES Immigrant 0.3980*** 0.3293*** 0.1972*** 0.1675*** (0.016) (0.016) (0.016) (0.016) Education 0.0475*** 0.0390*** 0.0238*** 0.0208*** (0.000) (0.000) (0.000) (0.000) Education x immigrant −0.0398*** −0.0326*** −0.0186*** −0.0159*** (0.001) (0.001) (0.001) (0.001) Work experience (natives) 0.0078*** 0.0068*** 0.0058*** 0.0053*** (0.000) (0.000) (0.000) (0.000) Pre-immigration work experience (immigrant) −0.0006 0.0001 0.0001 0.0005 (0.000) (0.000) (0.000) (0.000) Post-immigration work experience (immigrant) −0.0045*** −0.0031*** −0.0024*** −0.0016*** (0.001) (0.001) (0.001) (0.001) Constant 5.3788*** 5.4124*** 6.2101*** 6.2727*** (0.014) (0.015) (0.026) (0.028) Observations 88,546 88,546 88,546 88,546 R-squared 0.416 0.451 0.481 0.500 Personal and job characteristics YES YES YES YES Regional fixed-effects YES YES YES YES Industry fixed-effects and firm size NO YES NO YES Occupations NO NO YES YES Robust standard errors in parentheses. Control for working time is included in all specifications. ***p<0.01. Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 Page 15 of 18 in households with a foreigner head are made using the Capi technique (Computer assisted personal interviewing) instead of the Cati technique (Computer assisted telephoning interviewing). Moreover, since 2004, further constraints referring to foreigners separately by gender and citizenship have been introduced into the procedure of computing individual weights. 5 Immigration from these countries is very limited in Italy (it represents just 3 per cent of the whole sample of migrants), and, most importantly, it is very different in terms of education and skills from immigration from the rest of the world. 6 Despite this sample selection, it is likely that also male migrant employees have quite different characteristics as compared to natives, and this could bias results. To consider this point, in the robustness check, we re-estimate the model enforcing a common support in personal and job characteristics between immigrants and natives. 7 Results of the t-test on the equality of means for migrants and natives show that the means are statistically different from each other for all the variables in Table 1. 8 Note that the small difference between years since migration and experience in destination country (less than 3 months) is due to a small number of foreigners who have acquired part of their education in Italy. 9 The LFS provides information on schooling levels (i.e., highest educational level achieved), which was converted in years of education with reference to the Italian educational system. Obviously, in some cases this conversion might be imprecise. 10 The sample has been split into natives and immigrants, and quartiles for each group have been determined independently. This implies that the quartile cut-offs for the two groups may differ. 11 Interestingly, experience in home country for immigrants is smaller at higher wage levels, while experience in the domestic country is greater at higher wage levels, although the observed increase is lower as compared to Italians. 12 Potential work experience is measured as age minus education minus six years, while pre-immigration work experience is equal to age at immigration minus education, minus six years. Since in our sample 97 per cent of immigrants completed their studies before arriving in Italy, we do not split immigrants’education between the parts acquired in home and in destination country. We replicated estimates excluding the few immigrants who completed their education in Italy, but results are unchanged (results are available upon request). 13 All specifications include regional fixed effects. 14 Since the school quality indicator is computed on OECD countries, many observations for non-OECD countries are lost. 15 We also experimented with a specification with quadratic work experience. Although the coefficient on the quadratic term is statistically significant, its size is close to zero, and results do not change when compared to the linear specification. Hence, we only report the most parsimonious (linear) specification. 16 The high positive immigrants’earnings gap estimated upon arrival, as in columns (3) and (4) in Table 3, can be explained by the fact that there are very few individuals in the sample with less than 10 years of schooling. 17 The neo-latin Romanian language is quite similar to the Italian language, and Italian TV channels are usually broadcasted on Albanian television. We replicated estimates Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 Page 16 of 18 splitting European migrants between those coming from Albania and Romania and those coming from other European countries, but we found no statistically significant differences in the penalties between the two groups. 18 In our sample, this effect is likely to be very small as most immigrants completed their education in their country of origin. 19 Simon et al. (2011) analyse the determinants of occupational mobility of immigrants between their origin countries and Spain. In line with our results, they find that the downgrading with respect to occupational status in origin is significantly higher for older-at-immigration immigrants. 20 Obviously, in this way, we account for age-at-arrival effects only to the extent that differences between the two groups are constant below and above 20. We grouped age-at-arrival using different thresholds (i.e., above and below both 25 and 30), and results, available upon request, are qualitatively the same. 21 As discussed in Chiswick and Miller (2007), occupational fixed effects are generally not included in the earnings equation because they can be considered either as a grouped variant of the dependent variable or an alternative measure of the labour market outcome. Their inclusion, however, can shed light on the indirect channels through which earnings gains are achieved, that is, through occupational attainment. More educated and more experienced workers have in general access to occupations that are ranked higher-up in the occupational ladder and pay higher wages. 22 As previously noted, this evidence contrasts with that reported by Chiswick e Miller in their study on the U.S using census data (Chiswick and Miller, 2007). 23 Notice that while estimating equation (1) on the full sample, the returns on preimmigration work experience were not statistically significant, when estimates are performed separately on natives and migrants’samples, we find that the coefficient, albeit very small, is positive and significant. 24 We performed the same exercise also using a less aggregated one-digit classification (9 occupational groups), and results, available upon request, support the same conclusions. 25 In practice, we re-estimated equation (1) with and without occupational controls (i.e. as in Table 3 columns 3 and 5) and reported in Figure 2 the coefficient estimates of the schooling interaction term. We do not perform the same exercise for work experience because the difference between coefficients controlling or not for occupations is not statistically significant and because of the limitations related to cohort effects and selective return migration when analysing the effects of post-migration work experience. 26 The full set of estimates are not reported here for lack of space, but they are available upon request. 27 Note that this can also be consistent with the hypothesis that immigrants at the bottom of the distribution are more favourably selected on the basis of unobserved characteristics as compared to immigrants located at the top; hence, the smaller gap could also be attributed in part to higher ability and motivation of immigrants with respect to natives at lower deciles (see Chiswick, 1978). 28 In particular, we use the geometric mean of earnings in the occupation (i.e., the mean of log earnings) using 37 occupational groups. Dell’Aringa et al. IZA Journal of Migration (2015) 4:8 Page 17 of 18 Additional file Additional file 1: Table S1. Baseline earnings equations with control for school quality a .Table S2. Mean monthly wage (€)byoccupation. Competing interest The IZA Journal of Migration is committed to the IZA Guiding Principles of Research Integrity. The authors declare that they have observed these principles. Acknowledgements We would like to thank the editor and an anonymous referee for the useful comments received. We also thank participants at the 9th IZA Annual Migration Meeting (AM 2 ) (Bonn, 2012) and at the 25 th EALE Conference, (Turin, 2013); we also thank Marco Francesconi and Paolo Pinotti and all participants at the Workshop “Current Issues in Population Economics: Family and Migration”(Milan, 2013) and participants at the Immigration 1 session of the ESPE Conference (Bern, 2012). Responsible editor: Amelie Constant. Author details 1 Università Cattolica del Sacro Cuore, Milan, Italy. 2 Università Cattolica del Sacro Cuore and IZA, Milan, Italy. 3 Università di Milano Bicocca, Milan, Italy. Received: 7 October 2014 Accepted: 16 March 2015 References Accetturo A, Infante L (2010) Immigrant Earnings in the Italian Labour Market. Giornale degli Economisti e Annali di Economia 69(1):1–28 Baker M, Benjamin D (1994) The Performance of Immigrants in the Canadian Labor Market. Journal of Labor Economics 12(3):369–405 Battisti M (2013) High Wage Workers and High Wage Peers. Ifo Working Paper No. 168. Borjas GJ (1985) Assimilation, Changes in Cohort Quality, and the Earnings of Immigrants. Journal of Labor Economics 3(4):463–489 Buchinsky M (1998) Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research. Journal of Human Resources 33(1):88–126 Chiswick BR (1978) The Effect of Americanization on the Earnings of Foreign-born Men. 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