Migrant networks, regional development and internal migration flows in Brazil

Ribeiro, Ana Carolina Borges Marques; Tai, Silvio H. T. Article Migrant networks, regional development and internal migration flows in Brazil EconomiA Provided in Cooperation with: The Brazilian Association of Postgraduate Programs in Economics (ANPEC), Rio de Janeiro Suggested Citation: Ribeiro, Ana Carolina Borges Marques; Tai, Silvio H. T. (2023) : Migrant networks, regional development and internal migration flows in Brazil, EconomiA, ISSN 2358-2820, Emerald, Bingley, Vol. 24, Iss. 2, pp. 189-204, https://doi.org/10.1108/ECON-06-2022-0052 This Version is available at: https://hdl.handle.net/10419/329549 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. https://creativecommons.org/licenses/by/4.0/ Migrant networks, regional development and internal migration flows in Brazil Ana Carolina Borges Marques Ribeiro and Silvio Hong Tiing Tai Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil Abstract Purpose –This study analyzes the role of migrant networks in the migration flows in relation to the educational level of the migrants and economic growth of the states of origin and destination in Brazil. Design/methodology/approach –Fixed effects estimator applied to microdata. Findings –The results show migrant networks have a significant and positive impact on migration flows of the different educational levels. The economic growth in the destination state accentuates this effect, while the economic growth in the origin state has distinct impacts according to the educational level of the new migrant. Originality/value –The authors investigate the importance of migrant networks in the internal immigration within a developing country with large internal movement of people. In Brazil, the socio-economic condition of the population varies considerably in relation to its geography, which explains the country’s large internal migration flows. Keywords Economic growth, Brazil, Education level, Migrant flows, Migrant networks Paper type Research paper The Brazilian economy, in this respect, provides an interesting example of a country that offers the best of both worlds for a study of migration. Thus, on the one hand, there are no visa problems or racial barriers among the states. On the other hand, the country is so vast and the economic disparities are so wide that, compared with the internal migration in this subcontinent, the international migration among, for example, the countries of Europe might look like a local phenomenon (Sahota, 1968, p. 219). 1. Introduction Externalities originating from migrant networks may be associated with patterns of later migration flows, because they are a form of social capital that individuals can make use of even before migration. Massey et al. (1993) shows that migrant networks can reduce migration costs and facilitate access to jobs and information at the place of reception for new waves of newcomers. This subject has been intensely studied in the international literature on migration (Munshi, 2003;Beine et al., 2011,2015;Beine & Salomone, 2013;Comola & Mendola, 2015;Beine, 2016;Goel & Lang, 2019;Galbis, Wolff, & Herault, 2020;Dagnelie, Mayda, & Maystadt, 2019), establishing a relationship between the existing network of migrants and migratory flows. This article proposes an extension of this analysis to investigate the importance of the socioeconomic context in which this relationship takes place. Given the established literature in which many authors use existing migratory networks to predict migratory flows, this article Regional development and internal migration 189 © Ana Carolina Borges Marques Ribeiro and Silvio Hong Tiing Tai. Published in EconomiA. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/ legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1517-7580.htm Received 7 June 2022 Revised 9 March 2023 Accepted 4 June 2023 EconomiA Vol. 24 No. 2, 2023 pp. 189-204 Emerald Publishing Limited e-ISSN: 2358-2820 p-ISSN: 1517-7580 DOI 10.1108/ECON-06-2022-0052 proposes as a contribution to investigate more about how the effectiveness of this predictor can vary depending on the context. Heterogeneous aspects of this relationship may arise from factors such as the migrant’s educational level, the economic growth in their place of origin, and the economic growth in their destination. To the best of our knowledge, the two latter heterogeneous relationships have not previously been empirically studied in the literature. Three examples can illustrate the importance of these factors and show how the relationship between existing migrant networks and migratory flows can change according to the socioeconomic context. Firstly, unskilled migrants have fewer resources to migrate and may be more penalized, for instance, by poor transport structure, linked to less dynamic economies. In this case, these unskilled migrants may find transport solutions from the existing migrant network to overcome transport difficulties. Secondly, more skilled migrants may be financially better able to seek levels of well-being that are not necessarily related to higher incomes found in more dynamic economies. These skilled migrants can benefit from comparative information from the migrant network when arbitrating between economic dynamism and amenities associated with the places of origin and destination of migration. Thirdly, the migrant network can provide information and help with the search for work and job placement of new migrants (whether they are unskilled or skilled), especially in more economically dynamic locations where these relationships may be more complex. This paper empirically verifies the correlation of the interstate migrant network with the interstate migrant flows in Brazil between 2000 and 2010, according to the migrant’s educational level and the economic growth of the places of origin and destination. We investigate the importance of migrant networks in the internal immigration within a developing country with large internal movement of people. In Brazil, the socio-economic condition of the population varies considerably in relation to its geography, which explains the country’s large internal migration flows [1]. Some states in the Northeast Region, such as Maranh~ ao and Piau ıhave per capita GDPs comparable to that of India, while Bras ılia has a per capita GDP comparable to developed countries such as Germany. Indeed, there is a historical and intense immigration from the northeast to the southeast region and to Bras ılia (Aguayo-Tellez, Muendler, & Poole, 2010, p. 840). Another advantage of studying internal migration in a single country is the possibility of eliminating some theoretical mechanisms specific to international immigration, such as those regarding migration policies and visa requirements, remaining only the mechanisms related to migration and installation costs. We find significant and positive association between the migrant network and the flow of migrants. More importantly, we analyze the heterogeneous association of migrant networks with migration flows according to the educational level of the new migrant and the economic growth of the states of origin and destination. On the one hand, the association of the existing migrant network and migration flows is significantly (from a statistical point of view) higher in destination places with higher growth, regardless of the level of education of immigrants. On the other hand, this association is ambiguous in the places of origin with the highest growth, it decreases for the entire sample and for low-skilled immigrants and it increases for highly-skilled immigrants. The paper is organized into five sections. Section 2 reviews the related literature, Section 3 presents the theoretical model that supports this study. Section 4 describes the empirical analysis for the estimation. Section 5 shows the results of the empirical estimates, and finally Section 6 includes the study conclusions. 2. Literature review The size of the migration flow between two locations may be less strongly correlated with wage differences or employment rates, as the effects of these variables on promoting or inhibiting migration may be progressively overshadowed by the reduced costs and risks resulting from the expansion of migrant networks. ECON 24,2 190 Moser (1998) shows social capital can be considered an asset that diminishes individual vulnerability, making it particularly relevant for the poorest individuals. Indeed, the literature shows the benefits networks of compatriots provide, especially the financial benefits, may be more important to less privileged individuals. For such individuals, migration costs represent a higher proportion of their income (Chiquiar & Hanson, 2005;Tai & Ribeiro, 2016). This fact is particularly relevant in developing countries, such as Brazil, in which large proportions of the population have low-incomes, with difficulties related to the poverty of their place of origin. Some individuals while unable to pursue a better life abroad, are able to migrate to the relatively rich centers within the country, such as Bras ılia, S~ ao Paulo and Rio de Janeiro. From a conceptual point of view, migrant networks are thought to have a key role in determining domestic immigration in developing countries, primarily in the immigration of the poorest. A study by Beine et al. (2011) looked at the effects of diasporas [2] and found that they increase international migration flows and decrease the educational level of these flows. The effect of migrant networks seems to be more relevant for the less skilled, because as the networks expand, the costs and risks of migration decrease. Beine and Salomone (2013) analyzed the effects of networks on international migration in relation to education and gender. The results show the impacts vary due to differences in education, that is, such networks have heterogeneous effects regarding high-skilled and low-skilled migrants, as the literature suggests, networks were found to favor the migration of the less qualified. However, regarding gender the impact of networks was statistically identical between men and women of the same level of qualification. Thus, the role of migrant networks would seem to differ according to the qualification level and income of the migrant. This paper extends the analysis and studies the role of the level economic growth of the places of origin and destination of immigration in this relationship. Some conceptual arguments support the existence of heterogeneous effects. Firstly, underdeveloped places may have poor transportation infrastructure with no direct access to formal train, bus or air transport services. The World Health Organization (WHO, 2018) shows that Brazil reported 19 road traffic deaths per 100,000 inhabitants in 2016, makingit the 14th worst result on a list of 175 countries. The history of the President Luiz In acio Lula da Silva’s migration from Pernambuco to S~ ao Paulo on an open-back truck [3] for lack of any other economically accessible means of transport illustrates this situation. Rioja (2003) shows the effectiveness of public infrastructure in Latin America corresponds to 74% of that of public infrastructure in industrialized countries. As in the case of the president Lula, this precariousness particularly affects the least skilled individuals who cannot afford to surmount the inadequate transport infrastructure. In this case, the experience accumulated by the migrant network can provide information and help regarding alternative forms of transport. By contrast, better qualified individuals, although also penalized, are better able to overcome the problem by using their own resources for transportation, such as their own car. According to this argument, the association of the existent migrant network with migratory flows of unskilled individuals would be expected to diminish with the economic dynamism, particularly of the place of origin. This is because migrants in general may avoid destinations that lack a minimally adequate transport infrastructure, and network performance is not observed in these cases. Secondly, the most qualified individuals can derive specific benefits from the migrant network when departing from more developed locations. The literature shows that young skilled individuals seek employment opportunities in places with a business environment, while retired qualified individuals are attracted by quality of life rather than business environment (Gabriel & Rosenthal, 2004;Chen & Rosenthal, 2008). While the quality of the business environment may be indicated by the economic dynamism of the destination, the quality of life afforded by the amenities available in smaller or coastal cities lacks any such objective indicators. Comparative information on the quality of life in the destination, which may compensate for a better economic condition in the origin may be provided by the tacit Regional development and internal migration 191 knowledge held by the migrant network. A fellow countryman that is aware of the amenities available in the destination can provide a comparative assessment that supports the decision to migrate. According to this argument, the association of the migrant network with migratory flows of skilled individuals would be expected to increase with the economic dynamism of the places of origin. Thirdly, more economically dynamic target destinations may also have higher installation and adaptation costs. These may be incurred in a passive manner, for example due to formal landlord and tenant relationships that require the existence of a guarantor as a prerequisite for renting accommodation. In this case, a network of fellow countrymen may provide information about the real estate market and/or a guarantor. Such destinations may also have more complex, formal working relationships involving larger companies, which also provides scope for installed migrant networks to provide help. Barriers to migration in more developed locations may be more proactive in nature. Feler and Henderson (2011) show that “Localities in developed countries often enact regulations to deter low-income households from moving in”, the authors study how the Brazilian dictatorship prevented the internal migration of unskilled individuals by withholding water supplies to small houses where low-income migrants are likely to live. According to these arguments, the association of the network with the migrant flow generally increases with the economic condition of the place of destination and is accentuated for new, less qualified migrants. 3. Theoretical foundation The main theoretical models regarding the migration decision were developed by Borjas (1987) and Chiquiar and Hanson (2005). The work of Beine et al. (2011) added to earlier migration decision models by including the effects of networks on the likelihood of migrating. Beine and Salomone (2013) added the gender dimension to the model. As the aim of this paper is to analyze the role of networks on the size and composition of migration flows in Brazil between 2001 and 2010, the theoretical model of this study is based on the model proposed by Beine et al. (2011). A worker with units of human capital h, receives a wage wihat his place of origin i, while wi is the price of skills at that place i. To facilitate the description of the model, the time index t has been omitted from the equations. Individual utility is linear in income, but also depends on the characteristics of the place of origin, such as public expenditure, climate, and amenities, which are denoted by Ai. The utility of the type h individual, born at location iremaining at place iis given by (for the sake of clarity, we have omitted the index t for the time dimension): uiiðhÞ¼wihþAiþ ε i(1) The utility obtained when the same individual migrates to location jis given by: uijðhÞ¼wjhþAjCij þ ε j(2) Where Cij captures the migration costs to be absorbed by the individual migrant, these costs depend on factors such as the geographical distance between locations i(origin) and j (destination), denoted by dij, the social and cultural characteristics of the destination and origin locations ðxi;xjÞ, as well as the level of human capital of the individual hand the network of migrants (stock) from the location iresiding in j, denoted by Mij, of the economic growth of the places of origin and destination ðyi;yjÞand the interaction between Mij and these levels of development ðMij 3yi;Mij 3yjÞ:Thus migration costs can be described as: CijðhÞ¼cdij;xi;xj;yi;yj;Mij;Mij 3yi;Mij 3yj;h(3) ECON 24,2 192 This paper analyzes the association of the migrant network (Mij) with migration flows with different educational levels, as well as the association of migration flows with the interactions of the migrant network with the economic growth of places of origin (Mij 3yi) and destination (Mij 3yj). Networks are assumed to reduce transport, information and assimilation costs. Thus, we expect the migrant network has a positive association with migration flows. The association of migration flows with the interaction of the migrant network with the economic growth of the place of origin (Mij 3yi) may vary according to the educational level of the migrants. On the one hand, a negative coefficient is expected for the interaction between migration network and economic growth in the place of origin for low-skilled migrants. Such migrants are the most penalized by the lack of transport infrastructure typical of poorlydeveloped regions and can thus gain greater benefits from the knowledge of transport alternatives accumulated by the migrant network. On the other hand, a positive coefficient is expected for the interaction between the migrant network and economic growth in the place of origin for highly-skilled migrants. Such migrants can use the comparative knowledge obtained from the migrant network to arbitrate a migration decision by comparing the economic condition in the place of origin and amenities at the proposed destination. The coefficient of interaction of the migrant network with the economic growth of the destination (Mij 3yj) is expected to be positive for all educational levels in the migration flows. In this case, the network would be expected to provide assistance with job-related activities, which are more complex in more dynamic destinations. The size of the native population of migration age in place iis denoted by Nii. After analyzing the set of variables that influence migration flows, we can write equation (4): lnNijðhÞ NiiðhÞ¼ðwjwiÞhþðAjAiÞcdij;xi;xj;yi;yj;Mij;Mij 3yi;Mij 3yj;h(4) The model presented in equation (4) allows us to analyze the main features of migration flows, especially how migrant networks influence the size of such flows. 4. Materials and methods This section describes the variables associated with the interstate migration flows in Brazil from 2001 to 2010, their size and their educational composition by educational levels in different Federal Units (FUs) of origin and destination. In line with the theoretical model, the impact of migrant networks among other factors associated with recent migration flows were evaluated. 4.1 Data The estimates are based on microdata from the 2010 Census microdata, produced by the Brazilian Institute of Geography and Statistics (IBGE) that have been aggregated by federative unit of origin and destination. To calculate the migrant networks, we considered the stock of individuals born in FU i, residing in FU j, at time t-1. To calculate the migration flows, we considered the stock of individuals previously living in FU iand who migrated to FU j, in year t. The year of migration twas calculated considering the reported length of time in the FU of residence in 2010. The sample consists of 7020 observations, which describe flows and networks of 702 pairs ij from 2001 to 2010. Four levels of education were considered: incomplete primary school, complete primary school, complete secondary school and tertiary education. Low-skilled individuals were defined as those who had completed primary school and high-skilled individuals those who had completed secondary school or tertiary education. Table 1 shows the stock of migrants from 2000 to 2009 by FU of birth. Regional development and internal migration 193 Of the ten FUs with the highest stock/resident population ratio, eight are from the Northeast. The FU with the largest stock of emigrants in relation to its population is Para ıba, followed by Piau ıand Alagoas. Table 2 shows the ranking of the 15 largest paired ij flows. The cumulative flows from 2001 to 2010 are generally composed of low-skilled migrants. In addition, most of the ij pairs with the largest flows are, in general, also those with the largest migration networks. For example, Bahians living in S~ ao Paulo have the second largest network of individuals residing in a different state from where they were born, and the BA-SP pair has the largest flow of ij pairs from 2001 to 2010, moreover 73% of this volume is composed of low-skilled migrants. The ij pair with the lowest ratio of flow of low-skilled is Acre-Rio Grande do Sul, followed by Roraima-Para ıba, with less than 2% of the flow being low-skilled. These pairs are also placed 640th and 575th, respectively, in the volume of migrant networks [4]. 4.2 Variables associated with migration flows In the theoretical model described in equation (4), the main variables associated with migration flows are the wage differential (specific to each skill level), the features of the places of origin and destination or amenities, and the migration costs. In the empirical analysis, the specific factors of the ij pairs that influence migration costs are captured by the geodetic distance between the FUs of origin and destination. The lagged [5] per capita GDPs of the origin and destination FUs were used to capture the economic growth (as origin and destination fixed effects are applied, the coefficients of the per capita Federal unit Number of migrants Stock/population Para ıba 984,881 0.261 Piau ı733,833 0.236 Alagoas 588,959 0.189 Pernambuco 1,603,596 0.182 Paran a 1,841,041 0.176 Bahia 2,387,659 0.170 Maranh~ ao 1,089,190 0.166 Minas Gerais 3,093,606 0.158 Cear a 1,234,696 0.146 Sergipe 294,005 0.142 Tocantins 163,556 0.118 Esp ırito Santo 413,669 0.118 Rio Grande do Norte 372,250 0.118 Mato Grosso do Sul 227,582 0.093 Goi as 526,410 0.088 Rio Grande do Sul 889,918 0.083 Santa Catarina 514,535 0.082 Acre 50,108 0.068 Distrito Federal 157,836 0.061 Par a 425,381 0.056 Mato Grosso 168,538 0.056 Rio de Janeiro 634,663 0.040 S~ ao Paulo 1,583,686 0.038 Amazonas 118,570 0.034 Rond^ onia 50,080 0.032 Amap a 18,655 0.028 Roraima 11,830 0.026 Source(s): 2010 Census, table created by authors Table 1. Stock of migrants by FU of birth ECON 24,2 194 GDPs capture only their within variation, i.e. economic growth) of the federative units, which may also reflect the effect of the wage differential on migration [6]. The fixed effects of the origin FU that capture the combined effect of all unobserved timeconstant features of the origin FU iwere included. The same fixed effects also capture all the migration-related impediments or facilitations specific to the origin FU (xi)inequation (1). Similarly, the fixed effects of the destination FU that capture the combined effect of unobserved time-constant features of the destination FU, as well as the migration-related impediments or facilitations to the destination FU were also included. The inclusion of these fixed effects for origin and destination also captures the effects of the amenities (Aiand Aj). Time fixed effects from 2001 to 2010 were also included, as were cluster effects in the FU ij pairs due to the possibility that the random errors of the ij pairs could be correlated. Finally, the migrant network (i.e. the stock of individuals born in FU iresiding in FU jat time t-1) are denoted by the variable (Mij). The introduction of these variables provides a specification for the migration flow, now explaining the tindex of the time dimension: ln½NijtðhÞ ¼ α 0þ α 1lnMijðt−1Þþ α 2lnðdijÞþ α 3lnPIBpciðt−1Þ þ α 4lnPIBpcjðt−1Þþγiþγjþγtþ ε ijt (5) Where NijtðhÞis the flow of migrants observed in year tborn in FU iresiding in FU j, with the educational level h,Mijðt−1Þis the size of the migrant network in the ij pair in t-1. Fixed effects for FU i,FUjand tare captured by γi,γjand γt, respectively. The main estimable equation including interactions of the existent migrant network with lagged per capita GDP is shown in equation (6): ln½NijtðhÞ ¼ β0þβ1lnMijðt−1Þþβ2lnMijðt−1Þ3yiþβ3lnMijðt−1Þ3yjþβ4lnðdijÞ þβ5lnPIBpciðt−1Þþβ6lnPIBpcjðt−1Þþγiþγjþγtþ ε ijt (6) ij pair Accumulated flow Low-skilled flow Low-skilled % Migrant network Network ranking BA-SP 238,867 174,411 0.73 1,365,687 2 MG-SP 202,070 115,773 0.57 1,407,474 1 SP-MG 170,921 98,430 0.58 263,560 14 PE-SP 113,573 86,393 0.76 831,640 4 SP-BA 108,442 72,939 0.67 97,255 50 SP-PR 129,059 69,342 0.54 424,331 6 PR-SC 116,894 69,080 0.59 270,218 12 PR-SP 115,993 65,425 0.56 879,776 3 MA-PA 78,679 62,087 0.79 339,027 8 CE-SP 69,450 50,091 0.72 401,854 7 DF-GO 84,490 49,613 0.59 71,771 66 RS-SC 100,971 47,080 0.47 331,613 9 PI-SP 54,212 42,088 0.78 206,565 20 AL-SP 50,682 40,784 0.80 294,823 10 SP-PE 52,886 36,530 0.69 43,018 99 Source(s): 2010 Census, table created by authors Table 2. Ranking of the 15 largest accumulated migration flows and networks Regional development and internal migration 195 4.3 Econometric issues The estimation of migration flows, eq. (5) involves econometric issues that may cause inconsistency if estimated using ordinary least squares (OLS) alone. The first problem is related to the occurrence of zero values for the dependent variable, “migration flow”. The second problem is the potential correlation between migrant networks, denoted by ln Mijt with the random error, denoted by ε ijt. The presence of unobservable components that affect both the size of the migrant network and the characteristics of new migrants can lead to endogeneity problems in the estimations. For example, in Santa Catarina discrimination against individuals born in the neighboring state of Rio Grande do Sul might inhibit them from emigrating there. 4.3.1 Zero value for the dependent variable. Among the migration flows analyzed, the flow of individuals with complete primary schooling constitutes the largest number of nonexistent observations (equal to zero), representing 30% of the sample in this group, followed by the migration flows of individuals with tertiary education, for which there are 1,929 flow observations equal to zero, representing 27% of the sample in this group. In the other flows, 17% of the observations are equal to zero. There are no observations whose migrant network is zero. These occurrences of zero values in the estimation of equation (5) using OLS could lead to inconsistent estimates. Using the natural logarithm specification reduces the zero observations in the sample, which would likely result in biased estimates of the association of networks and other variables on migration flows. Excluding these observations could underestimate the impact of variables that affect migration costs, such as distance or the migrant network. Two techniques are available to address this problem. The first is to use the Poisson regression, which is based on pseudo-maximum likelihood estimates. This procedure is common in the trade literature [7], Poisson estimates are viable for analyzing the impact of the networks on the flows. The other way involves using techniques that take into account a potential selection bias via two-stage Heckman estimation. In general, when analyzing migration flows it is first necessary to estimate a selection equation - to estimate the probability that a given ij pair will have a positive migration flow. The usual procedure implies the use of an instrument in the probit equation, that is, a bilateral variable that influences the probability of observing a migrant flow between the two FUs, but does not influence the volume of the flow. Finding such an instrument is hard work, Wooldridge (2008) reports that an instrument for this procedure is not absolutely necessary. We chose to perform two-stage Heckman regressions without any instrument, similarly to Beine et al. (2011). 4.3.2 Unobservable variables correlated with networks. Another important econometric issue is the possibility that unobservable bilateral components may affect the size of the migrant networks (Mij) and the dependent variables (migration flows by educational level). For example, socioeconomic and cultural patterns between an FU iand an FU jmay simultaneously affect the migrant network (stock) and recent migration flows as well as the selection of these flows. These effects will be included in the error term, which in turn leads to some kind of omitted variable bias and some correlation between Mijt and its interactions with the error term, ε ijt. To mitigate this problem, it is necessary to use an instrument, a variable correlated with the size of the migrant network, but uncorrelated with the migration flows from 2001 to 2010. For this purpose, the variable “migrant stock par ij from 1974 to 1980”, extracted from the 1980 census, was used. To construct the stock, individuals younger than 45 years old in 1980 were excluded from the sample, thus avoiding their inclusion in the migration flows from 2001 to 2010. As the state of Tocantins had not yet been created in the 1980s, the flows ECON 24,2 196 8. This number is obtained by 0.0906 50.1*0.07*ln(416195) 416195 is the average for networks from Table 2 9. This number is obtained by 0.194 50.1*0.15*ln(416195) 416195 is the average for networks from Table 2 References Aguayo-Tellez, E., Muendler, M. A., & Poole, J. P. (2010). Globalization and formal sector migration in Brazil. World Development,38(6), 840–856. Beine, M. (2016). The role of networks for migration flows: An update. International Journal of Manpower,37(7), 1154–1171. Beine, M., & Salomone, S. (2013). Network effects in international migration: Education versus gender. 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She has experience in economics, with emphasis on mathematical, econometric and statistical methods and models, working mainly on the following topics: Brazil, migration, migrant networks, wage premium, labor market and entrepreneurship. Silvio Hong Tiing Tai holds a degree in civil engineering from the Polytechnic School of the University of S~ ao Paulo and the Institut National des Sciences Appliqu eees in Rennes, France (2000); master’s degree and Ph.D. in International Economics from the University Paris 1 Panth eon-Sorbonne with an Eiffel Excellence Scholarship (2009) and postdoctoral degree in Economics at the University of Geneva funded by Marie Curie Research Training Networks of the European Commission (2010) and postdoctoral degree at Sophia University (Tokyo, Japan) funded by PRINT from CAPES (2019). He was Assistant Professor at the Paris-Sud XI University from 2010 to 2019 and Professor at the Pontifical Catholic University of Rio Grande do Sul from 2011 to 2022. He has been Professor at the State University of Santa Catarina since 2023. Silvio Hong Tiing Tai is the corresponding author and can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] ECON 24,2 204