Artigos

Comparative analysis of occupational mobility between foreign men and women in Brazil’s formal labour market

Análise comparativa da mobilidade ocupacional entre homens e mulheres estrangeiros no mercado de trabalho formal brasileiro

Claudia Lima Ayer de Noronha
Universidade Federal de Minas Gerais, Brasil
Elaine Meire Vilela
Universidade Federal de Minas Gerais, Brasil

Comparative analysis of occupational mobility between foreign men and women in Brazil’s formal labour market

Revista Brasileira de Sociologia, vol. 8, núm. 19, pp. 148-177, 2020

Sociedade Brasileira de Sociologia

Recepción: 30 Enero 2020

Aprobación: 08 Junio 2020

Abstract: In this article we analyse whether: i) foreign women and men exhibit different occupational mobility patterns in Brazil’s formal labour market; ii) foreign women are less likely to experience up ward mobility thantheir male peers; and iii) the chances of upward mobility among foreign women and men in Brazil’s formal labour market differ by nationality. To examine these questions, we use the data from the Annual Social Information Reports (RAIS) from 1995 to 2015, which refer exclusively to workers employed in the formal labourmarket. The results demonstrate that the chances of mobility of men and women do not differ among all groups of foreigners, since this difference is dependent on the nationality concerned. For those groups that do show differences, the chances of upward mobility are higher amongmen than women.

Keywords: Foreign women and men, Sex, Occupational mobility.

Resumo: Esse artigo objetiva analisar: i) se mulheres e homens estrangeira(o)s têm padrões de mobilidade ocupacional diferentes no mercado de trabalho formal brasileiro; ii) se as mulheres têm menores probabilidades de mobilidade ascendente, quando comparadas aos seus conterrâneos; iii) e se as chances de mobilidade ascendente entre estrangeiros e estrangeiras no mercado de trabalho formal brasileiro se diferenciam por grupo de nacionalidade. Para tanto, utilizamos os dados das Relações Anuais de Informações Sociais (RAIS) de 1995 a 2015 que se referem somente aos trabalhadore(a) s empregado(a)s no mercado formal. Os resultados evidenciam que não são todos os grupos de estrangeiros que apresentam chances de mobilidade diferentes entre homens e mulheres, pois depende da nacionalidade do indivíduo. Para aqueles que apresentam divergências, as chances de mobilidade ascendente são maiores para os estrangeiros comparados às estrangeiras.

Palavras-chave: estrangeiros e estrangeiras, Sexo, Mobilidade ocupacional.

Introduction

Pioneering studies of the occupational mobility of foreigners in a host society, analysing just men, identified a U-curve: an initial decline from the socio-occupational status of the last job in the country of origin to the first job in the destination country, followed by a period of upward career mobility following assimilation and the accumulation of human capital after numerous years living in the new country (CHISWICK 1977, 1978, CHISWICK & PAUL 2005).

However, Portes and Manning (2008) argue that the accumulation of human capital through the immigrant’s[1]assimilation is insufficient to explain the mode of integration of foreigners in the destination country, but there are diverse factors that influence this process, classifiable into three types: 1) individual (sex, language proficiency. age, period since arrival, place of birth, and educational level, for example); 2) family (social and economic capital); 3) contextual (government policy, prejudices existing in the host society, among others).

Furthermore, some studies call attention to a polarization in the occupational structure of immigrants in the host labour market, emphasizingthat a consideration of both the origin and the destination countries of immigrants is fundamental to our understanding of this polarization (SUZIKI 2019, VIRGÍLIO 2019, ADSERÀ& FERRER 2016, ADSERÀ, FERRER & HERRANZ 2020, MAGUID & BUNO 2010).

Respondingto the observed increase in the migratory flows of women and their growing participation in the labour market, there has been anupsurge in studies analysing the processes through which foreign women are integrated into the economically active population, along with their socio-occupational mobility in the host society (POWERS, SELTZER & SHI 1998, AYSA-LASTRA & CACHÓN-RODRIGUEZ 2013A, 2013B, SÁNCHEZ-SOTO & SINGELMANN 2017, ADSERÀ & FERRER 2016, ADSERÀ, FERRER & HERRANZ 2020).

In this context, studies have emerged that question the applicability of the U-curve to women’s experiences, identifying other patterns of insertion (AYSA-LASTRA & CACHÓN-RODRIGUEZ 2013B, VIDAL-COSO & MIRET-GAMUNDI 2014, SÁNCHEZ-SOTO & SINGELMANN, 2017). A study undertaken in Spain by Fernández-Mácias et al. (2015) identifies upward mobility among foreign men and downward mobility among foreign women, or the withdrawal of the latter from the labour market. Also in Spain, Aysa Lastra and Chachón-Rodriguez (2013b) identify an L-pattern for immigrant women with an initial drop in occupational status during the first phase and stagnation (immobility) in the second phase of transition in the labour market.

However, there are studies in countries of the Global North and South[2]showing that foreign women experience an upward mobility (ADSERÀ& CHISWICK 2007, ADSERÀ & FERRER 2016, ADSERÀ, FERRER & HERRANZ 2020, MAGUID & BRUNO 2010). According to these studies, the country of origin, as well as the country of destination, are fundamental to comprehending the different forms of insertion, occupational mobility and salary progressionof immigrants. However, research shows that, compared to their male counterparts, women tend to have fewer chances of upward mobility (MAGUID & BRUNO 2010, BRUNO 2007).

Specifically in the case of Brazil, Suziki (2019) argues that there are national groups of immigrants who accumulate more disadvantages than others in terms of the probability of acquiring better jobs over time. Foreigners originating from the Global South encounter larger barriers to occupational mobility in the Brazilian labour market than those coming from the Global North. However, the author does not compare the occupational trajectory of men and women. Another point to emphasize is that studies of the occupational mobility of foreigners in Brazil arequite scarce in general.

To add to this debate, in this article we intend to answer the following questions: do foreign women have different patterns of mobility compared to foreign men in Brazil’s formal labour market? Are the chances of upward mobility fewer for women compared to male immigrants? Are these differences homogeneousfor all groups of immigrants, irrespective of their country of birth?

We have utilized data from the Annual Social Information Report (RAIS) published by the Ministry of Economy from 1995 to 2015[3], which include information on the registration number in the Social Integration Program (Programa de Integração Social: PIS) of each worker, thus enabling a longitudinal panel analysis.It should be stressed that this database refers to workers employed in the formal labour market only.

The article is divided into three sections, in addition to this introduction and the concluding remarks.The first section comprises a review of the literature, seeking to understand the factors that explain the occupational trajectory of immigrants in their host society, emphasizing the impact of the sex variable. Following this debate, we then present the hypotheses to be tested. Next, we explain the methodology used with a description of the dependent and independent variables, as well as the statistical models. In the third section we introduce our results, aiming to demonstrate whether or not the hypotheses were validated.

1. Comprehending the occupational trajectory of foreigners in the host society

The first studiesof the occupational mobility of foreigners in their host society, which emerged from classic assimilation theory, showed a U-curve in the insertion of immigrants into the labour market. An initial fall in the immigrant’s occupational status was seen to occur due to issuesfaced in the destination society, such as language difficulties, or problem in recognizing university qualifications and previous work experience. However, with more time spent living in the host country, combined with theconsolidation of social networks, learning the official language of the host society, and acquisition of social[4]and symbolic capitals,[5] as well as the possibleobtainmentof residence and work permits, along with recognition of academic qualifications, immigrants succeed in improving their occupational status and complete the U-curve (CHISWICK & PAUL 2005, RUEF 2017).

In debate with classic assimilation theory,segmented assimilation theory emerged, premised on the idea that modern societies are diverse and segmented, meaning that the form in which immigrants become inserted into the host society depends on three kinds of conditioning factors: 1) individual variables, including factors relating to the person’s exposure tothe host society, such as the ability to speak the language, age on arrival, place of birth, educational level, and racial and gender characteristics; 2) family variables, which relate to social and economic capitals; 3) contextual variables, which concern the diversity found in the country of destination, including the different government policies, values and prejudices existing in the host society (PORTES & MANNING, 2008, EGREJA & PEIXOTO, 2011).

In general, the first studies from both these theoretical approaches analysed samples of foreign men, excluding women, due to evidence that the labour market shows distinct dynamics for men and women and due to the greater participation of men in the labour market compared to women (CHISWICK, 1977 AND 1978, CHISWICK & PAUL, 2005).

With the increase in the flows of female migrants and their increasing participation in the labour market, there has been a new wave of analyses of the processes of integrating foreign women into the economically active population, along with their socio-occupational mobility in the destination country (POWERSET AL. 1998, AYSA-LASTRA & CACHÓN-RODRIGUEZ 2013A AND 2013B, SÁNCHEZ-SOTO & SINGELMANN 2017, ADSERÀ & FERRER 2016, ADSERÀ, FERRER & HERRANZ 2020).

Inspired bysegmented assimilation theory, studies have questioned the application of the U-curve model to women’s mobility, identifying other patterns that better fit the trajectory of female immigrants in the labour market.These author suggest that immigrant women have different mobility patterns to men, concentrated in precarious jobs with little possibility of upward mobility (POWERS ET AL. 1998, AYSA-LASTRA & CACHÓN-RODRIGUEZ 2013B, VIDAL-COSO & MIRET-GAMUNDI 2014, SÁNCHEZ-SOTO & SINGELMANN, 2017). Aysa-Lastra and Cachón-Rodriguez (2013b) analyse foreign women in Spain and identify an L-shaped mobility pattern, describing an initial drop in occupational status compared to the last job in the country of origin and the first job in the destination country, followed by occupational immobility after insertion in the host labour market. The authors classify the mobility pattern of female immigrants in Spain as ‘solid patterns’ due to the high rates of stagnation in occupational status. Similarly, Fernández-Mácias et al. (2015),also in Spain, identify that foreign women have more chance of downward mobility and many of them end up leaving the labour market.

However, the studies by Adserà, Ferrer and Herranz (2020), Adseràand Ferrer (2016) and Adseràand Chiswick (2007) demonstrate that this pattern is not the same in other countries such as Canada, the United Kingdom and Sweden, where these authors show that foreign women do not remain stuck in low-skilled jobs. They experienced upward mobility, more quickly among more highly educated women.Over time,foreign women end up occupying jobs that require analytic skills and less physical effort. Furthermore, such studies demonstrate that the countries of origin and destination are fundamental to comprehending the forms of insertion, occupational mobility and salary progression of immigrants.

However, specifically in relation to the group of highly qualified women, some authors indicate that the upward mobility of these foreign women in the host labour market is even more difficult than for men (PURKAYASTHA, 2005; KOFMAN & RAGHURAM, 2006). The main obstacles to the upward trajectory of women are the many fields in the labour market still dominated by men and, consequently, the devalorisation of roles occupied by women and the domestic responsibilities traditionally attributed to them.

It is worth emphasizing that this international literature refers to research on immigrants moving from developing or developed countries to developed countries (from the South or North to the North), unlike Brazil, which, over recent decades, had taken insome foreigners migrating North-South but mainly South-South.Moreover, many of these studies also analyse the informal labour market, which is not the focus of our analysis. Few studies exist on socio-occupational mobility that compare female and male foreigners originating from South-South migration.

The studies of mobility produced by Bruno (2007), Maguid and Bruno (2010) and Virgílio (2019) comparing South American men and women migrating to Argentina or Chile demonstrate that foreign women move through more unskilled occupations than foreign men, and also that these women tend to present higher chances of downward occupational mobility than their male compatriots. However, there are differences in terms of the pattern of mobility by sex when the country of origin is considered. One example are Bolivian and Paraguayan immigrants in Buenos Aires, where Bolivian women tend to have a higher level of upward mobility than their male compatriots. This is in contrast to Paraguayan women who have much lower levels of upward mobility compared to men from their country. In the case of the latter foreign women, the level of immobility is high and the pattern of concentration in the occupational niche of domestic services is very high, even in the formal labour market, which does not happen with Bolivian women (Maguid & Bruno 2010).

Studies of occupational mobility among foreign workers in Brazil are still scarce, likewise research examining the effects of sex on the labour trajectories of immigrants (PERES, 2012; PERES & BAENINGER, 2017; VILELA, COLLARES & NORONHA, 2015; VILELA, NORONHA & CAMPOS 2019, SUZIKI, 2019). In general, these Brazilian studies analyse the insertionof foreigners in the occupational structure, but do not trackmobility over time (except Suziki 2019). On the occupational mobility of immigrant workers in Brazil, Suziki (2019) shows evidence of unequal paths. Analysing the formal labour market, the author verifies that some nationalities accumulate higher chances of mobility than others. The barriers to insertion and upward occupational mobility in the labour market are higher in the case of immigrants from the Global South than those encountered by foreigners from the Global North. The immigrant’s origin is fundamental to comprehending her or his socio-occupational position in the host society. Nonetheless, the work of Suziki (2019) cannot be extrapolated to the country as a whole given the qualitative methodology used by the author and the absence of any comparative analysis of female and male foreigners, the focus of our article here.

Seeking to complement this debate, we propose to test: i)whether immigrant women have different mobility patterns to men, caused by structural features of Brazil’s formal labour market, characterized as sexually segregated; ii) whether foreign women generally have fewer chances of upward mobility than men with similar socioeconomic characteristics; iii) whether,however, the impact of the sex variable is heterogeneous among the differentgroups of nationalities migrating to the country.

2. Data and methods

We used data from the Annual Social Information Report (RAIS) from 1995 to 2015, which characterize the individuals and companies in whichBrazil’s formal workforce are inserted. However, this database possesses some limitations that deserve highlighting: 1) it presents only information on workers employedin the formal market and by companies[6], meaning that the article’s conclusions apply exclusively to such workers;[7] 2) there is no information onemployers and self-employed workers; 3) the year of entry into Brazil is shown only from 2011 onwards. As a solution we selected for analysis only individuals who appear in the databases from 2011 to 2015; 4) the existence of errors inthe data arising from the fact that the reportsare administrative records that are not checked for consistency (STERNBERG, 2001); 5) there is no information on the foreigner’s earlier occupation in the country of origin. In this article, therefore, we do not analyse the potential U-curve trajectory experienced by immigrants. Instead the immigrant’s work trajectory is examined solely on the basis of the second phase of the curve, comparing the first occupation in the host country (more precisely, the first that appears in the RAIS records) with the current occupation (again more precisely, the occupation in the last year that the person appears in the database).

The first stage of the methodology relates to the transformation of the RAIS’s unit of analysis from the employment relation, as originally construed, to the individual. In so doing, we follow the steps mapped by Noronha (2018), based on the use of the registration number in the Social Integration Program (Programa de Integração Social: PIS). We then selected those workers whose trajectory in Brazil’s formal labour market appearsover at least three complete years, since this is the minimum period indicated as necessary for the immigrant to show some mobility in the local market (SÁNCHEZ-SOTO & SINGELMANN, 2017).

In addition, the analysis considers just the fourteen groups of foreigners with the highest number of observations (all those over 15,000 observations, representing a minimum of3% of the total). We adopted this strategy for two reasons: 1) to maintain a high enough number of countries for a diversified analysis, including the flows of immigrants from both the Global North and South, but not so broad that the fluidity of the text would be compromised; 2) to achieve a more parsimonious model insofar asthe origin variable will be included in the equation as a control variable.

Following these procedures, the panel of data we constructed contains 75,343 individuals over a twenty-one-year period, totalling 499,794 observations. By nationality, this total breaks down as: Portuguese (22%), Argentinean (11%), Chilean (10%), Bolivian (8%), Uruguayan (7%), Haitian (7%),Paraguayan (6%), Italian (5%), Spanish (5%), Japanese (4%), Chinese (4%), American (US) (4%), German (4%), and French (3%).

To test our hypotheses, we used the multi-levelmixed-effects linear regressionmodel. We constructed the database in longitudinal form, classified as unbalanced: in other words, information does not exist for each individual in every year, since there are numerous situations that may remove a worker from the RAIS records, such as entering the informal market, being laid off, leaving to become an entrepreneur, retirement and death.

We based our construction of the dependent variable referring to the measure of socio-occupational mobility on theInternational Socio-Economic Index of Occupational Status (ISEI), an index developed by Ganzeboom, De Graaf and Treiman (1992), which attributes occupation scores in order to maximize occupation as an intervening variableof income and educational level.The index ranges from 16 to 90.

To apply the ISEI to the Brazilian context, we converted the occupational codes recorded in the RAIS (in accordance with the Brazilian Classification of Occupations: CBO 2002) to the International Standard Classification of Occupations (ISCO 88). After classifying all the occupations in accordance with ISCO 88, we then built the International Socio-Economic Index of Occupational Status, as described by Noronha (2018).The measure of occupational mobility is constructed by subtracting the ISEI of the migrant worker’s first occupation recorded in the database from the ISEI ofthe last occupation (the final year in which the person appears in the database).

In terms of independent variables, the models include information relating to: a) the worker’s human capital and demographic characteristics; b)the process of assimilation in Brazil and the social and cultural differences; c) the operational sector of the companies employing the foreign workers; d) the status of the first occupation, based on the EGP classification (Carvalhaes 2015). Detailed information on these variables appears in the appendices.

Here it is worth explaining our inclusion of the predict_Formal variable as a control in the model, created in order to correct selection bias in the sample. This bias derives from the fact that the RAIS includes only workers from the formal job market. However, a large number of immigrants are known to be employed in informal jobs in Brazil. To correct this bias, the predict_Formal variable was estimated, which refers to the probability of a worker being employed in the formal market rather than the informal. To create this variable, data from the 2010 Demographic Census relating to individuals in the informal sector was added to each year of the RAIS, and a logistic regression was applied to the data. This logistic regression included the following independent variables: age, sex, schooling, foreign or Brazilian, disabled, and region of the country in which the person is resident. This regression generated a score for the likelihood of the worker being employed in the formal sector, based on the observed variables.

In relation to the statistical models used in the analyses, we initially estimate a model including all the foreign workers.[8]Next, we estimate specific models for each group of foreigners. This process generatedfifteen equations.

3. Results

3.1 Descriptive analysis

The sample analysed comprises 53,692 (71.2%) men and 21,651 (28.8%) women, with the percentages between men (averaging 70%) and women (30%)unvarying over time.The Haitians are the group with the smallest percentage of women(14%) and the Paraguayans with the largest (40%). This result differs from findings in other South American countries like Argentina and Chile, which have higher percentages of foreign women employed in the labour market (Maguid & Bruno 2010, Bruno 2007). One possible explanation may be that we are analysing data exclusively taken from the formal labour market. However, we believe this is not the case, given that these percentages are very close to the percentage of foreigners entering the country, as can be verified in the 2015 OBMIGRA report (Oliveira, Pereira & Quintino 2015).

In relation to occupations with the highest percentages over the years, we identify the position of administrative manager among men (3% of the total) and retail seller among women (6%). Four occupational positions stand out with higher percentages for both sexes: administrative manager, retail seller, administrative assistant and office assistant. However, we observe a higher segmentation and polarization of occupations among men: in other words, among those occupations with the highest proportion of foreigners there exist both positions with higher occupational status, like managers, and positions at the base of the occupational rankings, like labourersand production line workers. These findings corroborate the evidence found in studies by Adserà and Ferrer (2016) and Virgílio (2019) in Canada and Santiago (Chile), respectively, which identify an occupational polarization, as well as an occupational dispersal of immigrants.

We also analysed the existence of occupational niches on entering and leaving the labour market. In terms of entry occupation, neither sex presents high percentages of concentration in lower-skilled occupations – that is, in unskilled manual labour like general services, cleaning or construction.Furthermore, for men and women, there is an increase of foreigners employed in occupational groups in their final job that demand more analytic and management skills compared to their entry job. In the final employment, there is a higher percentage of workers occupying posts such as “directors and managers,”“science professionals and intellectuals,”“technicians and secondary education professionals.” On the other hand, there is a notable decline in all those occupational groups demanding fewer qualifications when we compare the start and end of the labour trajectory.

This data suggests that foreign men and women demonstrate a tendency to upward mobility in the Brazilian labour market. These findings closely match the studies of Adserà, Ferrer and Herranz (2020), Adserà and Ferrer (2016),and Adserà and Chiswick (2007),which show that, with the passage of time, foreign women end up occupying jobs that demand more analytic skills and less physical effort.

In addition, we observe that foreign women in the formal market are generally not employed in jobs at the base of the occupational structure, but either in intermediary service activities or as science professionals and intellectuals. This data contradicts some of the literature’s predictions, which classify foreign women as secondary workers, employed mainly in care professions.Onepossible explanation for this discrepancycould be that the literature refers to foreigners migrating from poor countries to rich countries, unlike foreigners migrating to Brazil who tend to come from developed or developing countries to a developing country. A second possible explanation is that we are dealing solely with the formal market, while previous studies do not specify whether the type of market is formal or informal, recognizing thatundocumented and heavily exploited foreign women potentially work in the informal sector. Another possible explanation is the fact that Brazil has a labour pool in precarious services (principally the general service, cleaning, and care sectors) generated by internal migration, making this space uncompetitive for foreign workers.

In terms of the average evolution of the ISEI with time recorded in the RAIS (Graph 1), we identified that the ISEI of women remains higher than those of men over time. The data suggest that the greater the experience on the formal work market, the higher the occupational status, principally for women. This result corroborates previous studies in both developing and developed countries, demonstrating that more experience in work, and more assimilation, increased the occupational status of immigrants (CHISWICKET al., 2005, POWERS et al., 1998). However, the result presents a higher average value of occupational status for women than for men.

Average evolution of the ISEI with time recordedin the RAIS
Graph1
Average evolution of the ISEI with time recordedin the RAIS
Source: data elaborated by the authors based on RAIS 1995 to 2015.

On the average evolution of the ISEI with time spentliving in Brazil (Graph 2), there is a tendency for an average increase of the ISEI, with occasional small dips, but without reducing the foreigner’s status below the ISEI of the first job. These results run counter to the findings on immigrants in Spain contained in the studies of Vidal-Coso and Miret-Gamundi (2014) and Aysa-Lastro and Cachón (2013) who suggest a pattern of stagnation of immigrants in low-skilled occupations. However, our results corroborate other studies that identify upward mobility in occupational status for men and women (CHISWICK 1977 AND 1978, ADSERÀ& CHISWICK 2005, ADSERÀ& FERRER 2016; VÍRGÍLIO 2019, to cite some examples).

Average evolution of the ISEI with time spent living in Brazil
Graph 2
Average evolution of the ISEI with time spent living in Brazil
Source: data elaborated by the authors based on RAIS 1995 to 2015.

In terms of the mobility rates obtained through the ISEI, the results indicate that men and women are fairly close across all dimensions (immobility or upward and downward mobilities: see Table 1),with foreigners in Brazil generally displaying higher rates of immobility. Even so, it is worth highlighting a small percentage (0.06%) higher upward mobility among men compared to women. Based on these descriptive analyses, we can identify that women have average ISEIs higher than those of men and that women’s trajectories of mobility are not very different to men’s, but they do show a small disadvantage in terms of percentage of upward mobility. We can call attention to the fact that women have a higher percentage of immobility. Perhaps this is not a significant disadvantage for foreign women, given that they present a higher average ISEI than their male peers over time.

Table1
(Im)mobilityfor foreign men and women, calculated by subtractingthe ISEI of the first job from the ISEI of the last job
ResultVariable (ISEI final – ISEI initial)
WomenMen
Immobility Rate49.6%49.1%
Upward Mobility Rate28.8%29.6%
Downward Mobility Rate21.6%21.3%
Total100%100%
Source: data elaborated by the authors based on RAIS 1995 to 2015.

Nonetheless, these results do not allow us to infer whether foreign women have more or fewer chances of upward mobility compared to foreign men in Brazil’s formal labour market due to the fact of being women. To verify this possibility, a more advanced analysis is needed, controlling other factors.

3.2 Advanced analyses: model estimators

In relation to the results of the econometric model, we found that being male has a positive and significant impact on the individual’s upward mobility. The estimated coefficient demonstrates that, on average, men have a 0.9-point increase in the upward mobility indicator compared to women (see the result of the coefficient in the appendix for Model 1) Based on this result, we validated the following hypotheses: i) immigrant women have different mobility patterns to men; and ii) foreign women have fewer chances of upward mobility compared to foreign men. These results demonstrate that sex is a factor influencing the trajectory of mobility in the Brazilian labour market.

We also found that immigrant women are not generally concentrated in general service/cleaning or care occupations. On average, they also have a higher occupational status than men and higher percentages of immobility, and have fewer chances of upward mobility. Generally speaking, their lower chances of upward mobility corroborate previous studies. It should be stressed that this result concerns immigrants employed in the formal labour market, recognizing that the foreign workforce is characterized by workers with better qualifications and a higher educational level than the informal market. This fact perhaps explains the low concentration of foreign women and men in precarious occupations, diverging from other studies(AYSA-LASTRA & CACHÓN, 2013; FERNANDEZ-MACIÁS et al., 2015; VIDAL-COSO & MIRETA-GAMUNDI, 2014; AMONG OTHERS).

In the study by Maguid and Bruno (2010) on the employment and occupational mobility of Bolivians and Paraguayans in the metropolitan area of Buenos Aires, within the formal labour market, Paraguayan women were indeed found to be concentrated in domestic services and did not leave this sector over time, remaining defined by high levels of immobility. However, the same did not occur among Bolivian women who achieved higher levels of upward mobility than their male counterparts. Consequently, the question remains: does the inequality between immigrant men and women vary according to the worker’s country of birth?

We analysed the distribution of occupational groups at the start of the labour trajectory of foreigners and the occupational groups of the last occupation for each country of the immigrants. In all groups of foreigners, men and women, there is a percentage increase of professionals in the posts of directors and managers at the end of the labour trajectory, except among Haitian women. For immigrants from countries of the Global North, this occupational group presented higher growth compared to other categories in the socio-occupational hierarchy. In other words, for all foreigners from countries of the Global North at the end of the professional career, the category with the highest percentage growth was directors and managers, a finding applicable to both men and women, which corroborates the studies of Suziki (2019).

Among foreign workers from countries of the Global South, three distinct patterns can be perceived. Argentineans, Chileans, Uruguayans and Chinese follow the trend of immigrants from the countries of the Global North where the highest levels of growth are observed among the occupations of directors and managers, over 4%at the end of their career. Among Paraguayans and Bolivians, on the other hand, there is a small growth of approximately 1% only, but other occupational groups stand out. For Bolivians, there is a clear increase at the end of the labour trajectory of “technicians and secondary education professionals,” with the same registered among Paraguayan men. Among Paraguayan women, there is a notable growth in administrative support workers at the end of the career. For Haitians, women can be seen to enter as “service workers, store and market sellers,” especially cleaners (15%), while men enter as “skilled workers, factory workers and building craftsmen, mechanics and other trades” and a growth of both sexes in their final employment as “service workers, store and market sellers.”We highlight the fact that Haitian women show a marked expansion in the group of “service workers” with a 9% increase at the end of the career and a reductionof “plant, machine and assembly workers.” One possible explanation for the situation of Haitians is the short period of migratory flow to Brazil. Some studies (ADSERÀ & FERRER, 2016; ADSERÀ, FERRER & HERRAZ; 2020) argue that a period of 14 to 20 years is needed residing in the country of destination for more skilled occupations to be attained.

The analysis of the mobility rates through the calculation of the ISEIsfor each country of birth of the immigrants indicates that all these groups show higher levels of immobility, except Chilean men and Haitian women who have higher percentages of upward mobility, 38% and 37% respectively. The groups with the highest percentages of immobility are Chinese women and men, with 69% and 64% respectively, followed by Bolivian men and women with 62% and 59%. Meanwhile, downward mobility attainshigher percentages among Haitian men (29%), Chilean men (28%) and Haitian women (27%).

The central finding is that there are different occupational trajectories for men and women among distinct national groups, whether from the Global North or South. We did not identifya downward mobility pattern for foreign women. In general, men have a higher or similar rate of downward occupational mobility to women. But the question remains:is sex a factor that influences these differences?

In the econometric models separated by each nationality, the results indicate that among Argentineans, Paraguayans, Uruguayans, Italians, Japanese and Chinese there is no statistically significant effect on the probability of upward mobility between men and women. Among Bolivians, Chileans, Germans, Spanish, (US) Americans, French and Portuguese, on the other hand, being a man represents a positive effect on the upward mobility of workers, indicating a lower chance for foreign women. Finally, in the case of Haitians, women have the highest probabilities of achieving upward mobility compared to men (see the result of the coefficients in the appendix for Model2).

These findings corroborate our third hypothesis concerning the heterogeneous impact of the sex variable depending on country of birth of the immigrant. Furthermore, the results show thatwomen are generally either in a similar situation to their male peers or relatively worse in terms of upward mobility.

In this sense, the findings underline the studies that argue that the country of birth is fundamental to our comprehension of the immigrant’semployment situation in the labour market of the host country, whether male or female

(ADSERÀ, FERRER & HERRANZ, 2020; VIRGÍLIO, 2019; PORTES & MANNING, 2008). However, it is worth emphasizing that the individual’s nationality is more important than whether the immigrant comes from the Global North or South.

4. Final considerations

The results found in this study arefairly significant. They dialogue with the international literature, which affirms that: i) immigrant women, in general, are employed in worse jobs than men; ii)they either tend to present fewer chances of upward social mobility in the labour market of the host society compared to men; iii) or their chances of upward social mobility are null – that is, they do not present chances of mobility in a U-curve pattern but in an L-shape.

We stress that, in the case of our study, it was not possible to measureU-curve upward mobility, as diverse studies have done. This is because our source of data contains no information on the occupation of immigrants in the country of origin. For this reason, we examine the possibility of a J-shaped mobility among international male and female immigrants. In other words, comparing the person’s occupation on entry into the Brazilian labour market and the most recentoccupation, we have sought to encounter indicators of upward mobility with an emphasis on gender. The aim is to ascertain whether: i) sex is a variable that influences the trajectory of (im)mobility of the person under study; ii) women are at a disadvantage with regard to the chances of upward occupational mobility; and iii) the chances of social mobility among men and women differ when we consider the nationality of immigrants.

Another point to highlight is that our data has limitations, the most significant being the fact that we analysed only those immigrants employed in Brazil’s formal labour market by companies, thus excluding those working for individuals. Our results, therefore, may be quite different from those encountered in previous studies that coverthe labour market as a whole (formal and informal, thus excluding female domestic workers for individuals, an occupation held by many foreign women). Furthermore, the database does not contain any information on employers and self-employed workers, positions occupied by many international immigrants. This fact can generate an underestimation of the upward mobility (in the case of employers) and the upward and downward mobility (in the case of the self-employed) of those individuals being studied.

In the analysed data, in general, we did not observe, as indicated by some earlier studies, a concentration of foreign women in low status occupations in the care sector, except among Haitians, or a concentration of men in unskilled industrial jobs. We did find that the majority of foreign women are employed in Brazil’s formal market in non-manual routine occupations, considering the period of assimilation into the host society. One probable explanation for this finding resides in the differences between migrations from poor to rich countries and from rich to poor countries. It was not expected, of course, that foreign women arriving from rich countries, like Germans, French and Americans, would become employed in more low-qualified occupations in Brazil (like the care sector). However, the same was not expected in the case of female immigrants from poor countries, like Bolivian, Paraguayan and Peruvian women, among others, which is what we found. These results differ, therefore, from those encountered by Bruno (2007) and Maguid and Bruno (2010) with regard to Paraguayan and Peruvian women in the metropolitan area of Buenos Aires. Hence, the issue of migration from the Global North or South may not be a good explanation.

Two other possible explanations for this result seem more pertinent, therefore: i) we analysed documented women employed in the formal market, who may perhaps be positively selected in relation to human capital and other characteristics valorised on the labour market, unlike foreign women working in the informal market; ii) Brazil has a labour pool in the service sector generated by internal migration, where foreign women, due to the competition, are unable to become inserted and end up escaping the more precarious services. These are questions that need to be analysed in future studies.

In terms of the insertion of foreign men in the formal market, we can notea concentration of workers in high-level professional posts and a tendency for an increase in male immigrants occupying skilled manual jobs.The explanations are the same as the case for women.

Men and women display a similar pattern in terms of the average evolution of the ISEI according to time recordedin the RAIS, a variable utilized as a proxy for experience in the labour market. It was also foundthat the ISEI of women remains higher than men’s over time.For both sexes, there is a tendency for the ISEI to increase on average.

For foreigners as a whole, the sex variable has an effect on comprehending the occupational trajectories of foreigners in Brazil, with men generally showing higher probabilities of upward mobility than women. In other words, the model utilized for all foreigners confirms our overall supposition that female immigrants have different mobility patterns to men with lower probability of upward mobility, as shownby the international literature. However, when we analysed the fourteen national groups separately, the difference is positive for men among a higher portion (seven groups). For the six remaining groups no statistical difference is observed.Meanwhile, for Haitians the difference is negative for men. This case should be analysed in more detail in future studies.

This evidence demonstrates that detailed analyses are needed of the situation experienced by foreigners in the Brazilian labour market. In other words, if we only undertake a descriptive analysis of the occupational situation and mobility among men and women, we do not encounter a very different situation between them.In some cases, we might even conclude that women find themselves in a better situation, since, for instance, they presenthigher values of socio-occupational status than men over time.

However, as we proceed deeperinto the analyses, controlling other important variables in order to examine the real effect of sex on occupational mobility, we identify something very interesting: the pattern found among male and female foreigners is that female immigrants do not have more chances of upward mobility than their male counterparts (except for Haitian women).

Moreover, this analysis enables us to counterthe assertion of Aysa-Lastro and Cachón (2013) of the persistence of an L-shaped model for women, reflecting consistently high levels of stagnation in jobs low on the occupational hierarchy. Our results show that although women do have a pattern of fewer chances of upward mobility than men, they do not remain stagnant in the initial occupations over time. Women present rates of upward mobility and, if we consider some groups, have similar chances as their male counterparts. And as we have seen, this is not only the case for foreign women from countries of the Global North: the same applies to women from the Global South too.

Based on these findings, new questions emerge, which we intend to analyse in future studies. For instance: what is the effect of education on the occupational trajectory of foreign workers? Do educational qualifications in Brazil have a better return on the foreigner’s upward mobility compared to those acquired in another country? And what is the effect of colour/race on the immigrant’s mobility?

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Appendix1 – Description of the dependent and independent variables used in the models

Dependent variable

Box1
Variable dependent used for estimation
VariableTypeDescription
MobilityContinuousSubtraction of theISEI (International Socio-Economic Index ofOccupational) of the first occupation from the ISEI of the last occupation. That is, Mobility =ISEI last occupation - ISEI first occupation
Source: authors’ elaboration

Box2
Independent variables used in the models
VariableTypeLevelDescription
SexBinary21= Men
0= Women
WhiteBinary21= White
0= Non-white
AgeContinuous1Age of individual, in years
Age2Continuous1Age squared
Education level
Educ2Binary11= Complete primary and incomplete secondary education/
0= Illiterate or incomplete primary education/
Educ3Binary11=Complete secondary or incomplete higher education/
0= Illiterate or incomplete primary education/
Educ4Binary11= Complete higher education/
0= Illiterate or incomplete primary education/
Educ_BrasilBinary11= Education acquired in Brazil/
0= Education acquired abroad
Age on migrating
17 to 24Binary21= Group of people who migrated between 17 and 24/
0= Group of people who migrated aged 16 or under
25 to 40Binary21= Group of people who migrated between 25 and 40/
0= Group of people who migrated aged 16 or under
Over 41Binary21= Group of people who migrated aged 41 and over/
0= Group of people who migrated aged 16 or under
Time of residenceTime range of residence in Brazil
fx_resid2Binary11= Time of residence in Brazil between 4 and 5 years/
0= Time of residence in Brazil between 0 and 3 years
fx_resid3Binary11= Time of residence in Brazil between 6 and 10 years/
0= Time of residence in Brazil between 0 and 3 years
fx_resid4Binary11= Time of residence in Brazil between 11 and 20 years/
0= Time of residence in Brazil between 0 and 3 years
fx_resid5Binary11= Time of residence in Brazil over 21 years/
0= Time of residence in Brazil between 0 and 3 years
TimeEmpContinuous1Time of employment in company
TContinuous1Continuous variable between 0 and 20, corresponding to the years from 1995 to 2015
TimeContinuous2Number of times that the foreigner appears in the RAIS database
EmploymentBinary11= Active employment/
0 = Inactive employment
Nationality1
ArgentinaBinary21 = Argentinean / 0 = Portuguese
ChileBinary21 = Chilean / 0 = Portuguese
BoliviaBinary21 = Bolivian / 0 = Portuguese
UruguayBinary21 = Uruguayan / 0 = Portuguese
HaitiBinary21 = Haitian / 0 = Portuguese
ParaguayBinary21 = Paraguayan / 0 = Portuguese
ItalyBinary21 = Italian / 0 = Portuguese
SpainBinary21 = Spanish / 0 = Portuguese
JapanBinary21 = Japanese / 0 = Portuguese
ChinaBinary21 = Chinese / 0 = Portuguese
USABinary21 = American / 0 = Portuguese
GermanyBinary21 = German / 0 = Portuguese
Class typology of the first occupation (EGP classification)Continuous
Egp1Binary21= High level professionals
0= Agricultural workers and others in primary prod.
Egp2Binary21= Low level professionals
0= Agricultural workers and others in primary prod.
Egp3Binary21= Non-manual routine, higher education
0= Agricultural workers and others in primary prod.
Egp4Binary21= Services and sales
0= Agricultural workers and others in primary prod.
Egp5Binary21= Manual work supervisors
0= Agricultural workers and others in primary prod.
Egp6Binary21= Skilled manual work
0= Agricultural workers and others in primary prod.
Egp7Binary21= Semi-skilled and unskilled manual work
0= Agricultural workers and others in primary prod.
predict_FormalContinuous2Probability of worker being in the formal sector
Company sector
Sector2Binary11 = Rigid sector / 0 = Traditional sector
Sector3Binary11 = Flexible sector / 0 = Traditional sector
Sector4Binary11 = Public sector / 0 = Traditional sector
Company size2
MediumBinary11 = Companies between 40 and 99 employees/
0 = Companies between 4 and 49 employees
LargeBinary11 = Companies over 100 employees/
0 = Companies between 4 and 49 employees
Region of the country
NorthBinary11 = North / 0 = Southeast
NortheastBinary11 = Northeast / 0 = Southeast
Centre-WestBinary11 = Centre-West / 0 = Southeast
SouthBinary11 = South / 0 = Southeast
Source: authors’ elaboration1 The Portuguese are the immigrants used as a reference in the majority of the statistical equations since they form part of the group with the highest representativeness in the labour market due to their size, and because this is the group with least social distance from Brazilians.2 The parameters of the Inter-Union Department of Statistics and Socioeconomic Studies (DIEESE) were used as a reference for this classification.

Appendix 2 –Table of mobility calculated by subtracting ISEI of first job from ISEI of last job in the RAIS, according to nationality and the foreigner’s sex

Countries of the Global South
Result – ARGENTINAMenWomen
Immobility44%46%
Upward mobility33%31%
Downward mobility23%23%
Total100%100%
Result – BOLIVIAMenWomen
Immobility62%59%
Upward mobility21%25%
Downward mobility17%16%
Total100%100%
Result – CHILEMenWomen
Immobility34%38%
Upward mobility38%37%
Downward mobility28%25%
Total100%100%
Result – PARAGUAYMenWomen
Immobility53%53%
Upward mobility26%27%
Downward mobility22%21%
Total100%100%
Result – URUGUAYMenWomen
Immobility45%46%
Upward mobility31%30%
Downward mobility24%24%
Total100%100%
Result – HAITIMenWomen
Immobility40%36%
Upward mobility31%37%
Downward mobility29%27%
Total100%100%
Result – CHINAMenWomen
Immobility64%69%
Upward mobility22%18%
Downward mobility13%13%
Total100%100%
Source: data elaborated by the authors on the basis of RAIS 1995 to 2015.

Countries of Global North
Result – SPAINMenWomen
Immobility51%52%
Upward mobility27%27%
Downward mobility22%21%
Total100%100%
Result – USAMenWomen
Immobility59%59%
Upward mobility24%24%
Downward mobility17%17%
Total100%100%
Result – FRANCEMenWomen
Immobility55%59%
Upward mobility23%23%
Downward mobility22%18%
Total100%100%
Result – ITALYMenWomen
Immobility53%52%
Upward mobility30%29%
Downward mobility18%20%
Total100%100%
Result – GERMANYMenWomen
Immobility53%53%
Upward mobility26%29%
Downward mobility21%18%
Total100%100%
Result – JAPANMenWomen
Immobility55%60%
Upward mobility27%23%
Downward mobility18%17%
Total100%100%
Result – PORTUGALMenWomen
Immobility47%49%
Upward mobility31%30%
Downward mobility22%21%
Total100%100%
Source: data elaborated by the authors on the basis of RAIS 1995 to 2015.

Appendix 3 - Results from the estimated econometric models

MODEL 1
With the sex variable included and Portuguese as group of reference
mobCoef.P>z
sex_rev.94004460.000
employment_-.135340.000
age-.0952530.000
age2.00046280.000
fx_resid2.33250940.000
fx_resid3.53175560.000
fx_resid4.70327650.000
fx_resid5.56455670.000
time.26617340.000
timeEmp.05182830.000
educ25.9907650.000
educ37.6954790.000
educ410.804570.000
educ_brasil-.12119120.000
Argentina-.13464230.393
Bolivia-2.1700550.000
Chile-.81192980.000
Paraguay-3.4817620.000
Uruguay-1.863630.000
Germany1.5156310.000
Spain.47915050.025
USA1.3047670.000
France1.0809050.000
Italy.47376870.024
Haiti-37713460.000
Japan.12835650.564
China.63596180.002
id_17_24-.77202490.000
id_25_40.09555750.365
id_41_over.86304140.000
sector1.1585570.000
sector2-.01945360.005
sector4-.15234180.000
medium.06810970.000
large.23920630.000
predict_Formal-14827760.000
white.38783620.002
north-1.8875830.000
northeast-1.4907050.000
centrewest-.43917040.000
south-.58377410.000
T.06986280.000
egp_11-14.984280.000
egp_12-12.094240.000
egp_13-6.879550.000
egp_14-5.3662560.000
egp_15-6.2983780.000
egp_16-2.3099840.000
egp_174.3719120.000
_cons13.76440.000
Source: data elaborated by the authors on the basis of RAIS 1995 to 2015.

MODEL 2
For each group of foreigners separately. 14 equations estimated for each foreign nationality
VariablesArgentinaBoliviaChileParaguayUruguayGermanySpain
Coef.P>zCoef.P>zCoef.P>zCoef.P>zCoef.P>zCoef.P>zCoef.P>z
sex_rev.58578550.0521.336040.0001.0352680.011.04131950.866.00850340.9801.5927910.0071.7388680.000
employment_-.13056590.000-.16349080.000-.15396490.000-.07883470.000-.13443450.000-.06803620.007-.09530710.000
age-.10901150.000-.13270490.000-.12871050.000-.01633370.143-.12745010.000-.098940.000-.08542420.000
age2.00044750.000.0011520.000.00106320.000-.000490.000.00077270.000.00051370.000.00047920.000
fx_resid2.18578650.000.26111480.000.25132050.000.26364090.000.23577530.000.0668560.051.35215590.000
fx_resid3.31098940.000.46855540.000.27720830.000.33950140.000.45157980.000.07770920.039.62306390.000
fx_resid4.37502460.000.56264740.000.03182080.550.33135610.000.47084340.000.24553970.000.95113980.000
fx_resid5.17527010.000.4031850.000-.49693590.000.16875210.017.29215370.000.18785640.0101.0179230.000
time.38622560.000.28969050.000.18514030.000.33193350.000.39953340.000.30165410.000.16135780.000
timeEmp.05803270.000.05078360.000.05998660.000.09015510.000.06350120.000.03270520.000.04798580.000
educ28.3801690.0005.3867850.0007.8880040.0003.6753770.0005.3322940.00011.638220.0007.5572140.000
educ39.9145810.0006.7380620.0009.3172970.0004.9374960.0006.738180.00013.131790.0009.7893510.000
educ411.726540.00011.223530.00011.919870.00010.815580.0009.8202940.00014.476580.00012.693250.000
educ_brasil-.01663040.364-.19344550.000-.12298290.000-.07758720.001-.13665490.000.23472770.000-.19498910.000
age_17_24-1.8602440.000-1.3950260.000-2.3597550.000-.59628470.039-1.3614570.002-.86510020.382.54545770.476
age_25_40.06526020.852-.60065930.075-2.0426760.000-.1018510.742-.39234570.3351.7709890.005.66632380.224
age_41_over.84683130.090-1.5904840.009-2.2965360.0031.0103860.104.35669070.5712.6705620.0011.0629940.134
sector1.22022890.000-.17228570.093.21979760.023.17990610.007.50844090.000.03045040.814.13278660.291
sector2.03429120.067-.04391660.102.00616550.785.04587090.095.00474380.851-.04386630.193-.05461260.126
sector4-.28400480.000-.04020580.414-.33718670.000-.38928890.000-.14521720.076-.21762560.023-.07100140.494
medium.03740260.037.10803960.000.0630980.006-.01950690.493.07255740.004.08936950.005.05927720.081
large.15487860.000.12003170.000.1625770.000.07730890.004.17473120.000.15811370.000.24510670.000
predict_ Formal-14.73750.000-15.831220.000-18.428790.000-10.404590.000-10.62540.000-9.4609430.000-15.064220.000
white.75683540.093.18806460.4191.6605510.002.0093570.975-.10880320.856.87524060.4931.4589470.056
north-1.5769260.000-.91314320.000-2.0390320.000-1.1232270.000-2.0700880.000-.92063870.000-1.5188840.000
northeast-1.2468710.000-1.3349280.000-2.0342920.000-.99519770.000-1.1104350.000-.70294030.000-1.0704410.000
centrewest-.36027470.000-.46941010.000-.5738670.000-.20733070.039-.25777820.026-.28146190.130-.68297680.000
south-.51163620.000-.41155750.000-.81681580.000-.39807550.000-.5594020.000-.23618820.002-.27620650.012
T.12423860.000.07635950.000.09471120.000.08727960.000.06969320.000.06501220.000.03101110.005
egp_11-13.758120.000-14.504440.000-18.634060.000-14.734920.000-13.273890.000-9.2480450.018-21.755640.000
egp_12-11.193430.000-9.7714190.000-14.784790.000-10.345710.000-10.261410.000-5.5364220.159-18.405740.000
egp_13-5.5422240.002-6.2972170.000-8.3807540.007-5.751420.000-4.4664670.000-.91707170.816-13.720790.001
egp_14-5.2991620.002-3.8201750.002-6.7948980.028-4.4233560.000-3.0432480.004.53688680.893-10.953390.008
egp_15-4.92910.006-7.4414940.000-7.9767460.010-7.8768860.000-3.5263250.0031.5527860.695-12.018160.003
egp_16-1.814270.300-4.9584430.000-4.526730.141-2.265970.000-.22968470.8296.0328280.130-9.766710.017
egp_176.4253120.0003.5106470.0066.583450.0363.2291430.0006.6468970.00019.773990.000-1.7619910.677
_cons10.267460.00014.899360.00018.417820.0006.9948240.0008.2951760.000-2.4079890.56117.75020.000
Source: data elaborated by the authors on the basis of RAIS 1995 to 2015.

MODEL 2
For each group of foreigners separately. 14 equations estimated for each foreign nationality (continuation)
VariablesUSAFranceItalyHaitiJapanChinaPortugal
Coef.P>zCoef.P>zCoef.P>zCoef.P>zCoef.P>zCoef.P>zCoef.P>z
sex_rev1.1727220.0082.0096440.000.56047580.282.62324410.008.65985570.179.26619750.4041.145110.000
employment_-.06298790.003-.0572480.015-.04299360.029-.01064390.057-.15811090.000-.08932140.000-.11580130.000
age-.06036580.000-.09311520.000-.03907380.000-.00272660.766-.04284290.000-.09771710.000-.11702150.000
age2.0005380.000.00060320.000.00048060.000-.00016830.177.00018080.026.00045510.000.0007670.000
fx_resid2.10660420.000.14071330.000.22318860.000-.01358230.200.19278970.000.1361420.000.30803770.000
fx_resid3.19858230.000.24600840.000.38592090.000-.20180880.000.21155060.000.20329280.000.58305490.000
fx_resid4.2552850.000.33546440.000.73656180.000-.62808780.000.37427630.000.23308820.000.68722390.000
fx_resid5.23296620.002.25325560.001.93392580.000-1.2995490.000.38753450.000.16627350.000.43152640.000
time.1618450.002.14914890.006.14747750.000.95002440.000.25391450.000.36910020.000.22787620.000
timeEmp.02106560.022.02101560.025.0480550.000.08854050.000.02648990.000.03750530.000.04638680.000
educ22.2736470.000-4.038560.0006.8850340.0001.1657310.0006.5988820.0004.1633080.0005.628220.000
educ3.8707820.0003.8142560.0008.3242610.0001.4601790.0007.9305620.0005.1620420.0007.6321620.000
educ45.9163260.0005.5069770.00010.098060.0004.735030.00010.32570.0006.4885190.00011.007130.000
educ_brasil-.23446820.000.19798250.000-.11027740.000.0195710.002-.1348580.000-.16570430.000-.31590740.000
age_17_24.05820410.940.29545440.753-.6017330.415-1.013540.063.99759540.175-.54143620.244-.07428660.807
age_25_40-.31580230.5671.3900340.051.67940780.171-1.5139290.0042.188330.000-.64951370.099.39883590.138
age_41_over.19091080.7831.1310420.1811.1548920.073-2.0321510.0003.2025260.000-.48520990.4031.3113710.001
sector1-.07135720.324-.08064280.303-.02598470.797-.04280050.100.56712880.000.08783690.126.10737170.072
sector2-.03988140.269-.04895470.135-.12624960.000.01137710.106-.02334130.432-.0325250.093-.1652860.000
sector4-.16633740.060-.25778750.002-.02470350.737-.63253580.000-.20559530.028.00978570.871-.10580380.003
medium-.01304240.660.06906890.021.02360350.325-.01917470.027.06475550.031-.07707870.000.07630650.000
large.04163870.159.0531570.071.17461590.000.01326210.055.19721520.000-.0315560.111.27087420.000
predict_ Formal-4.7895230.000-6.3523540.000-10.948980.000-5.0441940.000-12.11760.000-8.4635350.000-12.353270.000
white1.5645670.0161.433860.065.43122680.563-.6708690.012.58467880.459.76541110.208.74583340.030
north-.07738770.775-.89257220.000-1.0344240.000-.58937070.000-1.1728270.000-.76345180.000-1.4060650.000
northeast-.09059750.408-.64595810.000-1.0409690.000-.5083810.000-1.5960360.000-.9735760.000-1.3874320.000
centrewest-.13399270.302-.75305970.000-.41527390.000-.13412520.000-.50168690.003-.08753730.237-.49771450.000
south.32872930.000-.36771940.000-.37070070.000-.20820320.000-.78374680.000-.39518280.000-.57778480.000
T-.00207390.855.0414450.000-.00960110.258.13464040.000.04223860.000.08742550.000.02656250.000
egp_11-8.2670280.191-13.397070.028-17.242140.000-16.70690.000-14.216820.000-9.5798010.000-17.911390.000
egp_12-5.9647970.346-10.295280.093-14.914290.000-19.807290.000-13.114240.000-7.0945570.001-14.405340.000
egp_13.50581690.936-5.2677390.390-10.063370.006-10.236930.000-6.902660.000-4.0683530.065-9.2982830.000
egp_144.1799550.512-2.8224490.646-8.3739790.022-9.6663050.000-4.4496610.011-3.8921270.077-7.9245750.000
egp_153.2015870.616-2.7751070.652-8.0431290.027-20.728280.000-3.9274110.026-2.7573990.224-7.566950.000
egp_1610.970560.087-.20842750.973-5.6945650.117-2.6209130.001-1.2623480.468-.78183050.725-5.1908090.000
egp_1717.212720.00913.471830.0357.0061020.064.99966940.2216.9819730.00110.747040.0005.8440570.000
_cons2.9616550.6438.4159370.17412.469090.0013.0429950.0058.8060030.0008.2271370.00014.57370.000
Source: data elaborated by the authors on the basis of RAIS 1995 to 2015

Notes

[1] In this article, we use the terms immigrant and foreigner to refer to all those workers whose nationality is other than Brazilian. Though aware that these concepts are not synonymous, to avoid repetitions and maintain the fluidity of the text, we consider the two terms interchangeable here given that we are analysing only international immigrants without Brazilian nationality, that is, foreigners.
[2] In terms of classifying foreign workers from the Global South, we assume that these are immigrants born in countries defined by the methodology of the World Economic Situation and Prospects as those with a transitional or developing economy. In other words, these are countries that are not part of Europe (whether or not members of the European Union), North America (United States and Canada) or other developed countries like Japan, New Zealand and Australia, southern hemisphere countries defined as belonging to the Global North (UN 2014).
[3] This time frame was selected because: a) the RAIS data became more consistent and precise from 1995 on, as Cavalcanti et al. (2015) point out; b) 2015 was the final year made available by the Ministry of Economy for the Stratification and Labour Study Laboratory of the Federal University of Minas Gerais at the time when this research was undertaken.
[4] Social capital can be defined as the investment and use of resources embedded in social relations for expected returns (Lin 2006).
[5] The acquisition of symbolic capital by the foreigner is understood as the assimilation of tastes, values, ‘ways of communicating,’ ‘good manners’ and other notions prevalent in the destination country.
[6] In other words, it does not include domestic workers employed by individuals, thus excluding domestic workers in family homes. However, it does include cleaners and general service workers.
[7] The data from the 2010 Demographic Census indicates that 55.76% of foreigners are employed in the formal labour market.
[8] In the case of this model, the immigrant group of reference are the Portuguese, given their number and the fact of being the group with the least social distance from Brazilians.
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