Income Differentials in The Formal Work of Pendular
Migrants in the Northeast States: A Quantile
Regression Approach
Cicera Darla Lopes da Silva, Wellington Ribeiro Justo and Luís
Abel da Silva Filho
Lecturas de Economía - No. 101. Medellín, enero-junio 2024
Migrants in the Northeast States: A Quantile
Regression Approach
Cicera Darla Lopes da Silva, Wellington Ribeiro Justo and Luís
Abel da Silva Filho
Lecturas de Economía - No. 101. Medellín, enero-junio 2024
Lecturas de Economía, 101 (enero-junio 2024), pp. 71-104Página 1 de 1CROSSMARK_logo_3_Test
25/06/2020file:///D:/Paty/LecturasEconomia/LEvol93_2020/LEVol93Abr26_2020/ART_340990/CR...
Cicera Darla Lopes da Silva, Wellington Ribeiro Justo and Luís Abel da Silva Filho
Income Differentials in The Formal Work of Pendular Migrants in the Northeast
States: A Quantile Regression Approach
Abstract: In the Brazilian domestic sphere, more research is needed to address the perspective of migra-
tory commuting associated with differentials in earnings from work, especially in the Northeast region.
Therefore, this article aims to analyze the income differentials from formal work among commuting
migrants from the Brazilian Northeast Region in 2009 and 2019, based on RAIS data and the use
of the method of Quantile Regressions. The results showed that the characteristics of gender, race/color,
length of employment, and education corroborate income differentials in the Northeastern labor market
among commuters. It was verified that the positive effect on income was more remarkable, in both years,
for white men with more than ten years of experience in the job and a higher level of education (master’s
and doctorate), mainly in the higher quantiles of the conditional distribution salary.
Keywords: labor income differentials, commuting migration, formal labor market, quantile regression.
JEL Classification: J0, J15, J61.
Diferenciales de Ingresos en el Trabajo Formal de los Migrantes Pendulares en
los Estados del Noreste: Un enfoque de regresión cuantil
Resumen: En el ámbito doméstico brasilero, se observan pocas investigaciones que aborden la per-
spectiva del desplazamiento migratorio asociado a los diferenciales en la remuneración del trabajo, es-
pecialmente cuando se trata de la región Nordeste. En vista de eso, este artículo tiene como objetivo
analizar los diferenciales de ingresos del trabajo formal entre los migrantes de la Región Nordeste de
Brasil en los años 2009 y 2019, con base en datos del RAIS y el uso del método de regresión por
cuantiles. Los resultados mostraron que las características relacionadas con el género, la raza/color,
la duración del empleo y la educación corroboran los diferenciales de ingresos en el mercado laboral del
noreste entre los viajeros. Se verificó que el efecto positivo sobre el ingreso fue mayor, en ambos años,
para los hombres blancos, con más de diez años de experiencia en el trabajo y mayor nivel de educación
(maestría y doctorado), principalmente en los cuantiles más altos de la distribución salario condicional
Palabras clave: diferenciales de ingresos laborales, migración de desplazamiento, mercado de trabajo
formal, regresión cuantil.
https://doi.org/10.17533/udea.le.n101a353064
Este artículo y sus anexos se distribuyen por la revista Lecturas de Economía bajo los términos de la Licencia Creative
Commons Atribución-NoComercial-CompartirIgual 4.0. https://creativecommons.org/licenses/by-nc-sa/4.0/
25/06/2020file:///D:/Paty/LecturasEconomia/LEvol93_2020/LEVol93Abr26_2020/ART_340990/CR...
Cicera Darla Lopes da Silva, Wellington Ribeiro Justo and Luís Abel da Silva Filho
Income Differentials in The Formal Work of Pendular Migrants in the Northeast
States: A Quantile Regression Approach
Abstract: In the Brazilian domestic sphere, more research is needed to address the perspective of migra-
tory commuting associated with differentials in earnings from work, especially in the Northeast region.
Therefore, this article aims to analyze the income differentials from formal work among commuting
migrants from the Brazilian Northeast Region in 2009 and 2019, based on RAIS data and the use
of the method of Quantile Regressions. The results showed that the characteristics of gender, race/color,
length of employment, and education corroborate income differentials in the Northeastern labor market
among commuters. It was verified that the positive effect on income was more remarkable, in both years,
for white men with more than ten years of experience in the job and a higher level of education (master’s
and doctorate), mainly in the higher quantiles of the conditional distribution salary.
Keywords: labor income differentials, commuting migration, formal labor market, quantile regression.
JEL Classification: J0, J15, J61.
Diferenciales de Ingresos en el Trabajo Formal de los Migrantes Pendulares en
los Estados del Noreste: Un enfoque de regresión cuantil
Resumen: En el ámbito doméstico brasilero, se observan pocas investigaciones que aborden la per-
spectiva del desplazamiento migratorio asociado a los diferenciales en la remuneración del trabajo, es-
pecialmente cuando se trata de la región Nordeste. En vista de eso, este artículo tiene como objetivo
analizar los diferenciales de ingresos del trabajo formal entre los migrantes de la Región Nordeste de
Brasil en los años 2009 y 2019, con base en datos del RAIS y el uso del método de regresión por
cuantiles. Los resultados mostraron que las características relacionadas con el género, la raza/color,
la duración del empleo y la educación corroboran los diferenciales de ingresos en el mercado laboral del
noreste entre los viajeros. Se verificó que el efecto positivo sobre el ingreso fue mayor, en ambos años,
para los hombres blancos, con más de diez años de experiencia en el trabajo y mayor nivel de educación
(maestría y doctorado), principalmente en los cuantiles más altos de la distribución salario condicional
Palabras clave: diferenciales de ingresos laborales, migración de desplazamiento, mercado de trabajo
formal, regresión cuantil.
https://doi.org/10.17533/udea.le.n101a353064
Este artículo y sus anexos se distribuyen por la revista Lecturas de Economía bajo los términos de la Licencia Creative
Commons Atribución-NoComercial-CompartirIgual 4.0. https://creativecommons.org/licenses/by-nc-sa/4.0/
Différences de revenus dans le travail formel des migrants pendulaires dans les
États du Nord-Est: Une approche de régression par quantile
Résumé: Dans la sphère domestique brésilienne, peu de recherches abordent la perspective du dé-
placement migratoire associé aux différentiels de rémunération du travail, en particulier dans la région
du Nord-Est. C’est pourquoi cet article vise à analyser les écarts de revenus du travail formel parmi
les migrants de la région du Nord-Est du Brésil pour les années 2009 et 2019, en se basant sur les
données du RAIS et en utilisant la méthode de régression par quantile. Les résultats ont montré que
les caractéristiques liées au sexe, à la race/couleur, à la durée de l’emploi et à l’éducation corroborent
les écarts de revenus sur le marché du travail du Nordeste parmi les voyageurs. L’effet positif sur les
revenus s’est avéré plus important, pour les deux années, pour les hommes blancs ayant plus de dix ans
d’expérience professionnelle et des niveaux d’éducation plus élevés (maîtrise et doctorat), principalement
dans les quantiles supérieurs de la distribution conditionnelle des salaires.
Mots clés: différentiels de revenus du travail, migration pendulaire, marché du travail formel, régres-
sion quantile.
Cómo citar / How to cite this item:
da Silva, C. D. L., N., Justo, W. R., & Silva Filho, L. A. D. (2023). Income Differentials in
The Formal Work of Pendular Migrants in the Northeast States: A Quantile Regression
Approach. Lecturas de Economía, 101, 71-104.
https://doi.org/10.17533/udea.le.n101a353064
États du Nord-Est: Une approche de régression par quantile
Résumé: Dans la sphère domestique brésilienne, peu de recherches abordent la perspective du dé-
placement migratoire associé aux différentiels de rémunération du travail, en particulier dans la région
du Nord-Est. C’est pourquoi cet article vise à analyser les écarts de revenus du travail formel parmi
les migrants de la région du Nord-Est du Brésil pour les années 2009 et 2019, en se basant sur les
données du RAIS et en utilisant la méthode de régression par quantile. Les résultats ont montré que
les caractéristiques liées au sexe, à la race/couleur, à la durée de l’emploi et à l’éducation corroborent
les écarts de revenus sur le marché du travail du Nordeste parmi les voyageurs. L’effet positif sur les
revenus s’est avéré plus important, pour les deux années, pour les hommes blancs ayant plus de dix ans
d’expérience professionnelle et des niveaux d’éducation plus élevés (maîtrise et doctorat), principalement
dans les quantiles supérieurs de la distribution conditionnelle des salaires.
Mots clés: différentiels de revenus du travail, migration pendulaire, marché du travail formel, régres-
sion quantile.
Cómo citar / How to cite this item:
da Silva, C. D. L., N., Justo, W. R., & Silva Filho, L. A. D. (2023). Income Differentials in
The Formal Work of Pendular Migrants in the Northeast States: A Quantile Regression
Approach. Lecturas de Economía, 101, 71-104.
https://doi.org/10.17533/udea.le.n101a353064
Income Differentials in The Formal Work of Pendular Migrants
in the Northeast States: A Quantile Regression Approach
Cicera Darla Lopes da Silva a, Wellington Ribeiro Justo b and Luís
Abel da Silva Filho c
Introduction. –I. Methodological Procedures. –II. Commuting Migration and
Insertion of Formal Work in The Northeast. –III. Socioeconomic and Demographic
Characterization of Commuting Migrants and Non–Commuting Migrants in the
Northeast – 2009/2019. –IV. Results and Discussions. –Conclusions. –Ethics
Statement. –References.
Original manuscript received on 14 March 2023; final version accepted on 11 November 2023
Introduction
From the 1980s onwards, the dynamics of urban spaces in Brazilian cities
underwent significant transformations, resulting from trends in migratory
flows and urban-regional dynamics throughout the country. In this process,
migratory flows had a relevant influence. The reconfiguration of population
dynamics was partly due to the deconcentration of economic activity over
time, mainly concerning the decade mentioned above, marked by productive
restructuring and regional development policies, which resulted in a rupture
in the trends of migratory flows interregional (Correia & Ojima, 2017) and
the emergence of new displacement axes (Lima, 2018).
a Cicera Darla Lopes da Silva: Professor at Regional University of Cariri – URCA, Departament
of Economics, Crato, Ceará, Brazil. E-mail: ciceradarla.lopes@urca.br
https://orcid.org/0000-0003-4762-4323
b Wellington Ribeiro Justo: Professor at Regional University of Cariri – URCA, Departament of
Economics, Crato, Ceará, Brazil. E-mail: wellington.justo@urca.br
https://orcid.org/0000-0002-4182-4466
c Luís Abel da Silva Filho: Professor at Regional University of Cariri - URCA, Departament of
Economics, Crato, Ceará, Brazil. E-mail: luis.abel@urca.br
https://orcid.org/0000-0002-7453-1678
in the Northeast States: A Quantile Regression Approach
Cicera Darla Lopes da Silva a, Wellington Ribeiro Justo b and Luís
Abel da Silva Filho c
Introduction. –I. Methodological Procedures. –II. Commuting Migration and
Insertion of Formal Work in The Northeast. –III. Socioeconomic and Demographic
Characterization of Commuting Migrants and Non–Commuting Migrants in the
Northeast – 2009/2019. –IV. Results and Discussions. –Conclusions. –Ethics
Statement. –References.
Original manuscript received on 14 March 2023; final version accepted on 11 November 2023
Introduction
From the 1980s onwards, the dynamics of urban spaces in Brazilian cities
underwent significant transformations, resulting from trends in migratory
flows and urban-regional dynamics throughout the country. In this process,
migratory flows had a relevant influence. The reconfiguration of population
dynamics was partly due to the deconcentration of economic activity over
time, mainly concerning the decade mentioned above, marked by productive
restructuring and regional development policies, which resulted in a rupture
in the trends of migratory flows interregional (Correia & Ojima, 2017) and
the emergence of new displacement axes (Lima, 2018).
a Cicera Darla Lopes da Silva: Professor at Regional University of Cariri – URCA, Departament
of Economics, Crato, Ceará, Brazil. E-mail: ciceradarla.lopes@urca.br
https://orcid.org/0000-0003-4762-4323
b Wellington Ribeiro Justo: Professor at Regional University of Cariri – URCA, Departament of
Economics, Crato, Ceará, Brazil. E-mail: wellington.justo@urca.br
https://orcid.org/0000-0002-4182-4466
c Luís Abel da Silva Filho: Professor at Regional University of Cariri - URCA, Departament of
Economics, Crato, Ceará, Brazil. E-mail: luis.abel@urca.br
https://orcid.org/0000-0002-7453-1678
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
In this context, commuting in Brazil gained momentum and importance,
especially in the 1980s/1990s, through the transformations that occurred in
the country during this decade (Delgado et al., 2016).
Regarding its concept, commuting migration can be understood as the
displacement that occurs daily between the municipality of residence of the
individual and another different municipality, with the purpose of work and
study (Mouraet al., 2005; Sobreira, 2007), being predominant in the main
metropolitan areas, and may also extend; for smaller agglomerations (Del-
gado et al., 2016).
As with other types of mobility, commuting incurs financial and time
costs. Thus, the decision to commute is made in an environment involv-
ing individual motivations and circumstances that change throughout life and
space (de Brito et al. 2018). However, such a decision to work in a munici-
pality other than that of residence is rational, where individuals who opt for
commuting would do so for a greater wage return or a gain in well-being re-
lated to better housing conditions in the place of residence, considering the
costs of commuting to work (Stutzer & Frey, 2008).
To Sobreira (2007), those who commute in general obtain a higher income
when compared to those who do not travel to work in another municipality
far from their residence; this is because, from the author’s point of view, such
mobility is associated with socioeconomic differences in society, where popu-
lations of different classes carry them out. Each location has a characteristic
that conditions selectivity in such displacements.
Within the empirical perspective of migration, several works discuss the
existence of income differentials between migrants and non-migrants, both in
the national and international economic literature (Batista & Cacciamali, 2009;
Chiswick, 1978; de Aguiar et al., 2018; de Beaumont & Yang, 2008; Gama &
Machado, 2014; Santos, 2018; Santos & Lelis, 2018; Silva-Filho, 2017; Silva-
Filho & Resende, 2018). These studies, however, are consensual, and in all of
them, it is observed that migrants earn higher incomes than non-migrants.
As for commuting, according to Santos and Lelis (2018), only some stud-
ies in Brazil seek to explore the income earned by workers associated with
commuting. This gap is even more remarkable when considering research
75
In this context, commuting in Brazil gained momentum and importance,
especially in the 1980s/1990s, through the transformations that occurred in
the country during this decade (Delgado et al., 2016).
Regarding its concept, commuting migration can be understood as the
displacement that occurs daily between the municipality of residence of the
individual and another different municipality, with the purpose of work and
study (Mouraet al., 2005; Sobreira, 2007), being predominant in the main
metropolitan areas, and may also extend; for smaller agglomerations (Del-
gado et al., 2016).
As with other types of mobility, commuting incurs financial and time
costs. Thus, the decision to commute is made in an environment involv-
ing individual motivations and circumstances that change throughout life and
space (de Brito et al. 2018). However, such a decision to work in a munici-
pality other than that of residence is rational, where individuals who opt for
commuting would do so for a greater wage return or a gain in well-being re-
lated to better housing conditions in the place of residence, considering the
costs of commuting to work (Stutzer & Frey, 2008).
To Sobreira (2007), those who commute in general obtain a higher income
when compared to those who do not travel to work in another municipality
far from their residence; this is because, from the author’s point of view, such
mobility is associated with socioeconomic differences in society, where popu-
lations of different classes carry them out. Each location has a characteristic
that conditions selectivity in such displacements.
Within the empirical perspective of migration, several works discuss the
existence of income differentials between migrants and non-migrants, both in
the national and international economic literature (Batista & Cacciamali, 2009;
Chiswick, 1978; de Aguiar et al., 2018; de Beaumont & Yang, 2008; Gama &
Machado, 2014; Santos, 2018; Santos & Lelis, 2018; Silva-Filho, 2017; Silva-
Filho & Resende, 2018). These studies, however, are consensual, and in all of
them, it is observed that migrants earn higher incomes than non-migrants.
As for commuting, according to Santos and Lelis (2018), only some stud-
ies in Brazil seek to explore the income earned by workers associated with
commuting. This gap is even more remarkable when considering research
75
76
on the country’s Northeast region. However, some studies in the literature
attest to the existence of income differentials in favor of those who choose to
commute compared to those who do not commute (Lameira, 2016; Santos,
2018; Santos & Lelis, 2018; Sidrim & Fusco, 2019).
It is worth noting, however, that to date, no study analyzes wage differ-
entials due to migratory commuting in formal work. In Brazil, commuting
in formal work can be verified through a micro database from the Ministry
of Labor. Thus, it is possible to know which employed people work in a
municipality and live in another municipality different from the one where
they work. Furthermore, the uniqueness of this article occurs when it is ob-
served that all the literature on migratory commuting in Brazil considers other
databases and does not analyze the formal sector of the country’s economy.
Knowing that this sector is what ensures social guarantees for workers in the
country, investigating commuting in formal work is relevant as an object of
this investigation.
It should also be noted that this article aims to observe wage differentials
throughout the conditional distribution of income, since there is, in the migra-
tory commuting of formal work in the Northeast, a migration to large urban
centers of people with low levels of employment opportunities. work in its
surroundings, as there still is, however, there is a commuting mobility from
large urban centers to the periphery of people with high levels of formal quali-
fications who live in the centers and work in the surrounding cities. Therefore,
analyzing the points of the conditional distribution of work through quantile
regressions is appropriate for this investigation. In this sense, its originality
ensures academic relevance to a study of this nature to understand the dynam-
ics of migratory commuting in formal work in the Northeast.
Given the above, this study aims to analyze the impacts of socioeconomic
and demographic characteristics on the differentials in income from formal
work among commuting migrants in Northeast Brazil. The study is based on
the RAIS database and uses the Quantile Regression method. Furthermore,
the current search justifies contributing empirically to the base of knowledge
about commuting migrations; given the lack of studies whose themes are
focused on this perspective of migratory commuting and the formal labor
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
on the country’s Northeast region. However, some studies in the literature
attest to the existence of income differentials in favor of those who choose to
commute compared to those who do not commute (Lameira, 2016; Santos,
2018; Santos & Lelis, 2018; Sidrim & Fusco, 2019).
It is worth noting, however, that to date, no study analyzes wage differ-
entials due to migratory commuting in formal work. In Brazil, commuting
in formal work can be verified through a micro database from the Ministry
of Labor. Thus, it is possible to know which employed people work in a
municipality and live in another municipality different from the one where
they work. Furthermore, the uniqueness of this article occurs when it is ob-
served that all the literature on migratory commuting in Brazil considers other
databases and does not analyze the formal sector of the country’s economy.
Knowing that this sector is what ensures social guarantees for workers in the
country, investigating commuting in formal work is relevant as an object of
this investigation.
It should also be noted that this article aims to observe wage differentials
throughout the conditional distribution of income, since there is, in the migra-
tory commuting of formal work in the Northeast, a migration to large urban
centers of people with low levels of employment opportunities. work in its
surroundings, as there still is, however, there is a commuting mobility from
large urban centers to the periphery of people with high levels of formal quali-
fications who live in the centers and work in the surrounding cities. Therefore,
analyzing the points of the conditional distribution of work through quantile
regressions is appropriate for this investigation. In this sense, its originality
ensures academic relevance to a study of this nature to understand the dynam-
ics of migratory commuting in formal work in the Northeast.
Given the above, this study aims to analyze the impacts of socioeconomic
and demographic characteristics on the differentials in income from formal
work among commuting migrants in Northeast Brazil. The study is based on
the RAIS database and uses the Quantile Regression method. Furthermore,
the current search justifies contributing empirically to the base of knowledge
about commuting migrations; given the lack of studies whose themes are
focused on this perspective of migratory commuting and the formal labor
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
market in the Brazilian Northeast, this work thus becomes a pioneer in the
subject and advances on such literature.
In addition to this introduction, the article is structured in five sections.
The second section presents the methodological procedures, such as the de-
scription of the data and the econometric method used. The third section
presents descriptive statistics and discussions in light of current literature; the
results and discussions are presented in the following section. Furthermore,
finally, in the fifth section, there are the final considerations.
I. Methodological Procedures
This section aims to demonstrate the methodological procedures adopted
in the study, where the data source and the econometric method will be de-
scribed. Thus, the database used was taken from the Annual List of Social
Information (RAIS) of the Secretariat for Social Security and Employment
of the Ministry of Labor and Social Security. This data source was chosen
because it has information on workers inserted in the formal labor market
throughout the national territory and because it constitutes an essential source
for analyzing the migratory flows of workers for formal work in Brazil.
A. Coverage area and time frame
The chosen study area corresponds to the Northeast Region of Brazil,
considering all its federal states with a time frame that comprises the years
2009 and 2019, according to the availability of RAIS data.
B. Description of variables
The variables used in the present study are described in Table 1. These
variables have both socioeconomic and demographic characteristics that can
influence the differentials in earnings from work and are widely accepted by
the national literature on migrations and differentials in earnings, being used
as control variables both on the migration decision and on labor income dif-
ferentials (Freguglia, 2007; Gama & Hermeto, 2017; Maciel & Oliveira, 2011;
Silva Filho, 2017; Silva-Filho; 2019).
77
market in the Brazilian Northeast, this work thus becomes a pioneer in the
subject and advances on such literature.
In addition to this introduction, the article is structured in five sections.
The second section presents the methodological procedures, such as the de-
scription of the data and the econometric method used. The third section
presents descriptive statistics and discussions in light of current literature; the
results and discussions are presented in the following section. Furthermore,
finally, in the fifth section, there are the final considerations.
I. Methodological Procedures
This section aims to demonstrate the methodological procedures adopted
in the study, where the data source and the econometric method will be de-
scribed. Thus, the database used was taken from the Annual List of Social
Information (RAIS) of the Secretariat for Social Security and Employment
of the Ministry of Labor and Social Security. This data source was chosen
because it has information on workers inserted in the formal labor market
throughout the national territory and because it constitutes an essential source
for analyzing the migratory flows of workers for formal work in Brazil.
A. Coverage area and time frame
The chosen study area corresponds to the Northeast Region of Brazil,
considering all its federal states with a time frame that comprises the years
2009 and 2019, according to the availability of RAIS data.
B. Description of variables
The variables used in the present study are described in Table 1. These
variables have both socioeconomic and demographic characteristics that can
influence the differentials in earnings from work and are widely accepted by
the national literature on migrations and differentials in earnings, being used
as control variables both on the migration decision and on labor income dif-
ferentials (Freguglia, 2007; Gama & Hermeto, 2017; Maciel & Oliveira, 2011;
Silva Filho, 2017; Silva-Filho; 2019).
77
78
Table 1. Description of the variables used based on the 2009/2019 RAIS
Migrates Binary (1) for people who lived in a different municipality than
where they worked in 2009 and 2019, respectively; (0) for those
residing in the same municipality of work according to the RAIS
in 2009 and 2019.
Sex Binary (1) for individuals declared male; (0) for female.
Age Age of the reference person in the research.
White man For white race/color men.
White woman For women of white race/color.
Yellowman For men of race/color yellow.
Woman yellow For women of race/yellow color.
Black man For black race/color men.
Black woman For women of black race/color.
Man brown For men of mixed race/color.
Woman brown For women of mixed race/color.
Farming For formal workers allocated in the agricultural sector.
Industry For formal workers allocated in the industry sector.
Construction For formal workers allocated in the civil construction sector.
Trade sector For formal workers allocated in the trade sector.
Services For formal workers allocated in the service sector.
Public administration For formal workers allocated to public administration.
Education, culture and health
services, and other services.
For formal workers allocated in the education, culture, and
health services sector and other services.
Domestic services For formal workers allocated in the domestic services sector.
Disabled person For formal workers with disabilities.
Industry opting for the simple
national
For formal workers allocated in companies that opted for taxa-
tion of the simple national.
Micro For formal workers in micro-enterprises.
Small For formal workers of small companies.
Average For formal workers in medium-sized companies.
Big For formal workers of large companies.
Up to 1 year For workers who have been in employment for up to one year.
Continued
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Table 1. Description of the variables used based on the 2009/2019 RAIS
Migrates Binary (1) for people who lived in a different municipality than
where they worked in 2009 and 2019, respectively; (0) for those
residing in the same municipality of work according to the RAIS
in 2009 and 2019.
Sex Binary (1) for individuals declared male; (0) for female.
Age Age of the reference person in the research.
White man For white race/color men.
White woman For women of white race/color.
Yellowman For men of race/color yellow.
Woman yellow For women of race/yellow color.
Black man For black race/color men.
Black woman For women of black race/color.
Man brown For men of mixed race/color.
Woman brown For women of mixed race/color.
Farming For formal workers allocated in the agricultural sector.
Industry For formal workers allocated in the industry sector.
Construction For formal workers allocated in the civil construction sector.
Trade sector For formal workers allocated in the trade sector.
Services For formal workers allocated in the service sector.
Public administration For formal workers allocated to public administration.
Education, culture and health
services, and other services.
For formal workers allocated in the education, culture, and
health services sector and other services.
Domestic services For formal workers allocated in the domestic services sector.
Disabled person For formal workers with disabilities.
Industry opting for the simple
national
For formal workers allocated in companies that opted for taxa-
tion of the simple national.
Micro For formal workers in micro-enterprises.
Small For formal workers of small companies.
Average For formal workers in medium-sized companies.
Big For formal workers of large companies.
Up to 1 year For workers who have been in employment for up to one year.
Continued
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
Table 1. Continuation
More than 1 to 2 For workers who have been in employment for one to two years.
More than 2 to 3 For workers who have been in employment for two to three
years.
More than 3 to 5 For workers who have been in employment for three to five
years.
More than 5 to 10 For workers who have been in employment for five to ten years.
More than 10 For workers who have been in employment for more than ten
years.
Uneducated or with incom-
plete primary education
For people who had no education or had at least incomplete
primary education.
Complete primary education
and incomplete secondary ed-
ucation
For people who had completed elementary school and incom-
plete high school.
Complete high school and in-
complete higher education
For people who had completed high school and incomplete
higher education.
Complete higher education For people who had completed higher education.
Master’s degree For people who have a master’s degree.
Doctorate For people who had doctorates.
Income from work Income per hour worked.
Source: Own elaboration based on data from the RAIS 2009/2019.
C. Descriptions of the Quantile Regression Method
The Quantile Regression method was proposed by Bassett and Koenker
(1978). Since then, it has been widely used in studies of an empirical nature
that seek to analyze how the quantiles of a dependent variable change with
variations in the independent variables. Thus, they make it possible to verify
the impact of the explanatory variables (independent) on the different points
of the conditional distribution of the explained variable (dependent), making
it possible to explore more significant amounts of information from the data.
Furthermore, they differ from the Ordinary Least Squares (OLS) method,
which only estimates the average effect of a variable on the conditional distri-
bution of a dependent variable.
79
Table 1. Continuation
More than 1 to 2 For workers who have been in employment for one to two years.
More than 2 to 3 For workers who have been in employment for two to three
years.
More than 3 to 5 For workers who have been in employment for three to five
years.
More than 5 to 10 For workers who have been in employment for five to ten years.
More than 10 For workers who have been in employment for more than ten
years.
Uneducated or with incom-
plete primary education
For people who had no education or had at least incomplete
primary education.
Complete primary education
and incomplete secondary ed-
ucation
For people who had completed elementary school and incom-
plete high school.
Complete high school and in-
complete higher education
For people who had completed high school and incomplete
higher education.
Complete higher education For people who had completed higher education.
Master’s degree For people who have a master’s degree.
Doctorate For people who had doctorates.
Income from work Income per hour worked.
Source: Own elaboration based on data from the RAIS 2009/2019.
C. Descriptions of the Quantile Regression Method
The Quantile Regression method was proposed by Bassett and Koenker
(1978). Since then, it has been widely used in studies of an empirical nature
that seek to analyze how the quantiles of a dependent variable change with
variations in the independent variables. Thus, they make it possible to verify
the impact of the explanatory variables (independent) on the different points
of the conditional distribution of the explained variable (dependent), making
it possible to explore more significant amounts of information from the data.
Furthermore, they differ from the Ordinary Least Squares (OLS) method,
which only estimates the average effect of a variable on the conditional distri-
bution of a dependent variable.
79
80
The use of Quantile Regressions, according to Buchinsky (1998), allows
for reducing the presence of Outliers by percentiles and presents more fair
estimates, that is, robust when compared to Ordinary Least Squares (OLS),
which present only average estimates.
Therefore, this study resorts to Quantile Regression to estimate the ef-
fects of socioeconomic and demographic characteristics on the earnings dif-
ferentials commuters, in quantiles of yi (10, 50, 90), Quantile ten, fifty (me-
dian), and ninety. Estimations based on these points are intended to explore
the lower and upper tails, the middle, and the left and right tails of the con-
ditional distribution of earnings, verifying disparities in labor income existing
in such quantiles.
In this study, the explained or dependent variable assumes the function
of the natural logarithm of labor income (ln_rendatrab), which is explained
through a set of socioeconomic and demographic characteristics of individ-
uals and their work occupations (sex and race/color, age, age², sector of oc-
cupation, size of establishment, length of employment and education). The
aim is to analyze the effects of each of these variables on the quantiles of the
distribution of labor income for commuters for formal work in the Northeast.
Thus, if (xiyi), i = 1, . . . , n, represents a random sample of commuting
formal workers in the Northeast, in which it xi assumes the function of a
vector of (K × 1) explanatory variables and yiis the dependent variable to be
explained at the various points of the conditional distribution of income, the
θ-ésimo quantile of the explained variable yi is described as follows:
F 1 = inf {y : F (y) ≥ 0} (1)
where F is described as an unconditioned distribution function of (y). If there
is a linear relationship between the explained variable y and its explanatory
variables (x), the mathematical representation of the equation is presented
and expressed by:
yi = x′
iβ + μi (2)
Thus, in (2), β it refers to a vector of estimated parameters and the quan-
tiles yi(10, 50, 90) conditional distribution of labor income defined from the
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
The use of Quantile Regressions, according to Buchinsky (1998), allows
for reducing the presence of Outliers by percentiles and presents more fair
estimates, that is, robust when compared to Ordinary Least Squares (OLS),
which present only average estimates.
Therefore, this study resorts to Quantile Regression to estimate the ef-
fects of socioeconomic and demographic characteristics on the earnings dif-
ferentials commuters, in quantiles of yi (10, 50, 90), Quantile ten, fifty (me-
dian), and ninety. Estimations based on these points are intended to explore
the lower and upper tails, the middle, and the left and right tails of the con-
ditional distribution of earnings, verifying disparities in labor income existing
in such quantiles.
In this study, the explained or dependent variable assumes the function
of the natural logarithm of labor income (ln_rendatrab), which is explained
through a set of socioeconomic and demographic characteristics of individ-
uals and their work occupations (sex and race/color, age, age², sector of oc-
cupation, size of establishment, length of employment and education). The
aim is to analyze the effects of each of these variables on the quantiles of the
distribution of labor income for commuters for formal work in the Northeast.
Thus, if (xiyi), i = 1, . . . , n, represents a random sample of commuting
formal workers in the Northeast, in which it xi assumes the function of a
vector of (K × 1) explanatory variables and yiis the dependent variable to be
explained at the various points of the conditional distribution of income, the
θ-ésimo quantile of the explained variable yi is described as follows:
F 1 = inf {y : F (y) ≥ 0} (1)
where F is described as an unconditioned distribution function of (y). If there
is a linear relationship between the explained variable y and its explanatory
variables (x), the mathematical representation of the equation is presented
and expressed by:
yi = x′
iβ + μi (2)
Thus, in (2), β it refers to a vector of estimated parameters and the quan-
tiles yi(10, 50, 90) conditional distribution of labor income defined from the
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
quantiles of the conditional distribution of errors, as follows the equation
below:
P r
(
yi ≤ y
xi
)
= Fμθ
(
y − x′βθ
xi
)
, 1 = 1, . . . , n (3)
From the mathematical representation of equation (3), the Quantile Re-
gression model can be defined as follows:
Qθ
( yi
xi
)
= x′
iβθ + F 1 (θ) (4)
Quantile Regression, the quantiles yi (10, 50, 90)must be read as uncon-
ditional, being the solution of a maximization problem. Therefore, the βθ
quantile regression estimator (equation 4) needs to be defined from an objec-
tive function:
min
β
1
n
∑
i:yi≥x′
iβ
θ ∣
∣yi − x′
iβ∣
∣ + ∑
i:yi<x′
iβ
(1 − θ) ∣
∣yi − x′
iβ∣
∣
= min
β
1
n
n∑
i=1
ρθ(uθi ) (5)
Differently from what we have in estimates by Ordinary Least Squares,
with estimation by Quantile Regression, there is the minimization of absolute
values of the variables since the solution is obtained through linear program-
ming. Therefore, the model presented in equation (6) represents a conditional
function of each quantile of the explained variable y given a matrix x of ex-
planatory variables, defined below:
Qyi
( θ
x
)
= Xβ (θ) , onde θ = [0, 1] (6)
In this way, at each of the quantiles the effect of the explanatory variables
contained in yi(10, 50, 90), on the explained variable y(lnrendatrab), at each
81
quantiles of the conditional distribution of errors, as follows the equation
below:
P r
(
yi ≤ y
xi
)
= Fμθ
(
y − x′βθ
xi
)
, 1 = 1, . . . , n (3)
From the mathematical representation of equation (3), the Quantile Re-
gression model can be defined as follows:
Qθ
( yi
xi
)
= x′
iβθ + F 1 (θ) (4)
Quantile Regression, the quantiles yi (10, 50, 90)must be read as uncon-
ditional, being the solution of a maximization problem. Therefore, the βθ
quantile regression estimator (equation 4) needs to be defined from an objec-
tive function:
min
β
1
n
∑
i:yi≥x′
iβ
θ ∣
∣yi − x′
iβ∣
∣ + ∑
i:yi<x′
iβ
(1 − θ) ∣
∣yi − x′
iβ∣
∣
= min
β
1
n
n∑
i=1
ρθ(uθi ) (5)
Differently from what we have in estimates by Ordinary Least Squares,
with estimation by Quantile Regression, there is the minimization of absolute
values of the variables since the solution is obtained through linear program-
ming. Therefore, the model presented in equation (6) represents a conditional
function of each quantile of the explained variable y given a matrix x of ex-
planatory variables, defined below:
Qyi
( θ
x
)
= Xβ (θ) , onde θ = [0, 1] (6)
In this way, at each of the quantiles the effect of the explanatory variables
contained in yi(10, 50, 90), on the explained variable y(lnrendatrab), at each
81
82
point of the conditional distribution of income earned in formal work by
commuting migrants is captured x.
Where, for everyone i, w as the neperian logarithm of labor income, while
the covariates are defined in frame 11.
II. Commuting Migration and Insertion of Formal Work in The Northeast
This section presents, through a quantitative approach, the occurrence of
commuting due to formal work, as well as the average income from this work
in the municipalities of the Northeast in the years 2009 and 2019.
Figure 1 shows the concentration of commuting migrants in the formal
labor market in the municipalities of the Northeast region in 2009 and 2019.
Those who carry out migratory commuting are concentrated in a more sig-
nificant proportion, mainly in the municipalities that are part of the capitals
of each state and, nevertheless, to a lesser extent in the municipalities sur-
rounding these metropolises. Among them, Fortaleza, the capital of the state
of Ceará, is highlighted, followed by the municipality of Recife, the capital
of Pernambuco, and Salvador in Bahia, as the main ones in terms of higher
concentration.
Moreover, it is also observed that in the rest of the other states that form
the Northeast region, in what belongs to their capitals, the same dynamics of
these flows can be verified; the capitals of these states are where commuting
migrants for formal work go the most, this goes against what the national
literature points out, that commuting predominates in urban agglomerations,
mainly in metropolitan regions (Cintra et al., 2009; Delgado et al., 2016). Thus,
it is associated with the integration and functional interdependence that char-
acterize conurbation regions, namely, metropolitan regions or clusters special-
ized in a specific economic activity (Oliveira & Givisiez, 2018).
1 In estimating the Quantile Regression, the variables “person with a disability” and “industries
opting for Simples Nacional” were disregarded, as they are not relevant, according to the
economic literature, in determining wages.
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
point of the conditional distribution of income earned in formal work by
commuting migrants is captured x.
Where, for everyone i, w as the neperian logarithm of labor income, while
the covariates are defined in frame 11.
II. Commuting Migration and Insertion of Formal Work in The Northeast
This section presents, through a quantitative approach, the occurrence of
commuting due to formal work, as well as the average income from this work
in the municipalities of the Northeast in the years 2009 and 2019.
Figure 1 shows the concentration of commuting migrants in the formal
labor market in the municipalities of the Northeast region in 2009 and 2019.
Those who carry out migratory commuting are concentrated in a more sig-
nificant proportion, mainly in the municipalities that are part of the capitals
of each state and, nevertheless, to a lesser extent in the municipalities sur-
rounding these metropolises. Among them, Fortaleza, the capital of the state
of Ceará, is highlighted, followed by the municipality of Recife, the capital
of Pernambuco, and Salvador in Bahia, as the main ones in terms of higher
concentration.
Moreover, it is also observed that in the rest of the other states that form
the Northeast region, in what belongs to their capitals, the same dynamics of
these flows can be verified; the capitals of these states are where commuting
migrants for formal work go the most, this goes against what the national
literature points out, that commuting predominates in urban agglomerations,
mainly in metropolitan regions (Cintra et al., 2009; Delgado et al., 2016). Thus,
it is associated with the integration and functional interdependence that char-
acterize conurbation regions, namely, metropolitan regions or clusters special-
ized in a specific economic activity (Oliveira & Givisiez, 2018).
1 In estimating the Quantile Regression, the variables “person with a disability” and “industries
opting for Simples Nacional” were disregarded, as they are not relevant, according to the
economic literature, in determining wages.
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Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
Figure 1. Commuting migrants inserted in the formal labor market in municipalities in
the Northeast, 2009/2019
Source: Own elaboration based on RAIS data – 2009/2019.
Concerning 2019, there is a certain homogeneity in the concentration of
formal commuting workers in all states of the Northeast. However, in this
period, there was a more significant intensification of these flows both in the
metropolitan areas of each state and in some municipalities in their interior,
for the latter, probably due to the development of economic activity. In this
regard, as highlighted on the map, the case of the municipalities that form
the Mesoregion of the São Francisco Valley in Bahia, particularly Juazeiro.
Such dynamics in this municipality are associated with the development of
irrigated agriculture in the Juazeiro/Petrolina Pole, which contributed to the
expansion of production and productivity of crops intended for the foreign
market instead of food production (da Silva & de Souza, 2018).
Furthermore, there is a significant concentration of commuting migrants
in some cities in southern Bahia and the extreme west of Bahia, the latter
being an integral part of the dynamics of MATOPIBA’s agribusiness.
83
Figure 1. Commuting migrants inserted in the formal labor market in municipalities in
the Northeast, 2009/2019
Source: Own elaboration based on RAIS data – 2009/2019.
Concerning 2019, there is a certain homogeneity in the concentration of
formal commuting workers in all states of the Northeast. However, in this
period, there was a more significant intensification of these flows both in the
metropolitan areas of each state and in some municipalities in their interior,
for the latter, probably due to the development of economic activity. In this
regard, as highlighted on the map, the case of the municipalities that form
the Mesoregion of the São Francisco Valley in Bahia, particularly Juazeiro.
Such dynamics in this municipality are associated with the development of
irrigated agriculture in the Juazeiro/Petrolina Pole, which contributed to the
expansion of production and productivity of crops intended for the foreign
market instead of food production (da Silva & de Souza, 2018).
Furthermore, there is a significant concentration of commuting migrants
in some cities in southern Bahia and the extreme west of Bahia, the latter
being an integral part of the dynamics of MATOPIBA’s agribusiness.
83
84
It is also observed that for both years, Mossoró, located in the Mesore-
gion of the West Potiguar, stands out among the other municipalities in the
state, which are far from the capital and present a considerable concentra-
tion of commuting migrants in the formal labor market. Along the same
lines, the city of Imperatriz, located in Maranhão, also stands out regarding
commuting migrants. Indeed, this concentration is associated with the city’s
economy, which is based on the tertiary sector, represented mainly by trade
and services. Recently, a new industrialization process has begun centered on
furniture production and the presence of an industry focused on pulp and
paper production (Borges et al, 2014).
In addition to these, it should be noted that among the other central
metropolitan regions of each state that make up the Northeast region con-
cerning the concentration of formal commuting workers, the capital, São Luís
obtained a relative share of the flows of commuting migrants in formal work
for both periods mentioned in the study, as well as in neighboring municipal-
ities. The result is linked to the great concentration of formal employment
in the expanding sectors of civil Construction and Commerce (Holanda &
Júnior, 2015).
In both years, the metropolitan areas of Fortaleza, Recife, and Salvador
showed the same trend in the concentration of commuting migrants in the
formal labor market among the other regions of the Northeast. The dynamics
of these municipalities in becoming the largest concentrations of commuting
migrants in the entire Northeast region are mainly related to their population
aspects, such as density and economic aspects, GDP, sectors of activities, and
a higher income from work, which constitute attractive factors for the occur-
rence of pendulum movements.
Thus, from what was ascertained, there is a greater concentration of com-
muting migrants, mainly in the largest municipalities, in terms of economic
dynamics, that is, the capitals of each state in the Northeast, especially in its
metropolitan areas. However, it should be noted that as we move away from
these large metropolises, there is a reduction in the concentration of commut-
ing migrants, except for some municipalities that maintain solid economic
activity on the rise.
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
It is also observed that for both years, Mossoró, located in the Mesore-
gion of the West Potiguar, stands out among the other municipalities in the
state, which are far from the capital and present a considerable concentra-
tion of commuting migrants in the formal labor market. Along the same
lines, the city of Imperatriz, located in Maranhão, also stands out regarding
commuting migrants. Indeed, this concentration is associated with the city’s
economy, which is based on the tertiary sector, represented mainly by trade
and services. Recently, a new industrialization process has begun centered on
furniture production and the presence of an industry focused on pulp and
paper production (Borges et al, 2014).
In addition to these, it should be noted that among the other central
metropolitan regions of each state that make up the Northeast region con-
cerning the concentration of formal commuting workers, the capital, São Luís
obtained a relative share of the flows of commuting migrants in formal work
for both periods mentioned in the study, as well as in neighboring municipal-
ities. The result is linked to the great concentration of formal employment
in the expanding sectors of civil Construction and Commerce (Holanda &
Júnior, 2015).
In both years, the metropolitan areas of Fortaleza, Recife, and Salvador
showed the same trend in the concentration of commuting migrants in the
formal labor market among the other regions of the Northeast. The dynamics
of these municipalities in becoming the largest concentrations of commuting
migrants in the entire Northeast region are mainly related to their population
aspects, such as density and economic aspects, GDP, sectors of activities, and
a higher income from work, which constitute attractive factors for the occur-
rence of pendulum movements.
Thus, from what was ascertained, there is a greater concentration of com-
muting migrants, mainly in the largest municipalities, in terms of economic
dynamics, that is, the capitals of each state in the Northeast, especially in its
metropolitan areas. However, it should be noted that as we move away from
these large metropolises, there is a reduction in the concentration of commut-
ing migrants, except for some municipalities that maintain solid economic
activity on the rise.
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Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
Figure 2 shows the average income from formal work according to the
municipalities in the Northeast region in 2009 and 2019. higher income from
formal work compared to those on a smaller scale.
Figure 2. Average income from formal work in the municipalities of the Northeast,
2009/2019
Source: Own elaboration based on data from RAIS, 2009/2019.
In 2009, the number of municipalities that were part of the smaller scale
was 1238, reducing to 861 municipalities in 2019, meaning an improvement in
average income. Concerning the municipalities that were part of the scale with
the highest average income from formal work 2009, the numbers were 84, in-
creasing to 49 municipalities in 2019, showing a deconcentration of the average
income from formal work.
It is possible to observe that the states of Ceará and Piauí, notably, com-
paring the two years of analysis of the study, were the ones that had the high-
est number of municipalities allocated in other scales of average income from
formal work. For the year 2009, the municipalities in Ceará with the highest
85
Figure 2 shows the average income from formal work according to the
municipalities in the Northeast region in 2009 and 2019. higher income from
formal work compared to those on a smaller scale.
Figure 2. Average income from formal work in the municipalities of the Northeast,
2009/2019
Source: Own elaboration based on data from RAIS, 2009/2019.
In 2009, the number of municipalities that were part of the smaller scale
was 1238, reducing to 861 municipalities in 2019, meaning an improvement in
average income. Concerning the municipalities that were part of the scale with
the highest average income from formal work 2009, the numbers were 84, in-
creasing to 49 municipalities in 2019, showing a deconcentration of the average
income from formal work.
It is possible to observe that the states of Ceará and Piauí, notably, com-
paring the two years of analysis of the study, were the ones that had the high-
est number of municipalities allocated in other scales of average income from
formal work. For the year 2009, the municipalities in Ceará with the highest
85
86
average income from work were the municipalities of General Sampaio, Catunda,
Banabuiú, Catarina, Saboeiro, Antonina do Norte, Granjeiro and Missão Velha,
whereas, in 2019, these municipalities in Ceará reduced for more minor scales
of medium income, while others emerged and stood out on a larger scale, as in
the case of São Gonçalo do Amarante and Jati, the latter being associated with
the transposition works of the São Francisco River to the Cinturão das Águas in
Ceará. Jati is an eligible municipality, which in turn contributes to improvements
in the economy, and, therefore, in the average labor income of the municipality.
In this same line of analysis, the states of Bahia, Sergipe, Pernambuco,
Alagoas, and Paraíba were the ones that suffered minor changes in the high-
est scales of average labor income over the two years of analysis. In 2009 and
2019, the Bahian municipalities of Sobradinho, Jaguarari, Andorinha, Barro-
cas, and those around Salvador remained on the average income scale of for-
mal work about the highest income scale. In 2019, there was a significant
improvement in the average income from formal work for municipalities in
the Far West of Bahia integrated into the soy export complex.
Furthermore, in the states of Maranhão and Rio Grande do Norte, there
was a deconcentration of average income from formal work for the municipal-
ities included in the scale with the highest average income from work when
comparing the years of analysis. In 2009, the Maranhão municipalities of
Godofredo Viana, Capinzal do Norte, Governador Archer, Senador Alexan-
dre Costa, and Estreito were some of the other municipalities that stood out
in the scale of higher average income from formal work. Of these, only the
municipalities of Godofredo Viana and Capinzal do Norte remained in 2019
on the same income scale mentioned above, and the others reduced to lower
average income scales. Furthermore, there is also an emergence of several
municipalities with the improvement in the scale of the average income from
formal work, compared to 2009, in general terms for the entire state.
Comparing the two maps, it is possible to verify the predominance of
the smaller scale of average income from formal work represented by the
light-yellow color, indicating an average income of R $500.00 for most mu-
nicipalities in the Northeast region. It is also verified that the municipalities
within this scale 2009 emerged in 2019 to a second smaller scale, represented
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
average income from work were the municipalities of General Sampaio, Catunda,
Banabuiú, Catarina, Saboeiro, Antonina do Norte, Granjeiro and Missão Velha,
whereas, in 2019, these municipalities in Ceará reduced for more minor scales
of medium income, while others emerged and stood out on a larger scale, as in
the case of São Gonçalo do Amarante and Jati, the latter being associated with
the transposition works of the São Francisco River to the Cinturão das Águas in
Ceará. Jati is an eligible municipality, which in turn contributes to improvements
in the economy, and, therefore, in the average labor income of the municipality.
In this same line of analysis, the states of Bahia, Sergipe, Pernambuco,
Alagoas, and Paraíba were the ones that suffered minor changes in the high-
est scales of average labor income over the two years of analysis. In 2009 and
2019, the Bahian municipalities of Sobradinho, Jaguarari, Andorinha, Barro-
cas, and those around Salvador remained on the average income scale of for-
mal work about the highest income scale. In 2019, there was a significant
improvement in the average income from formal work for municipalities in
the Far West of Bahia integrated into the soy export complex.
Furthermore, in the states of Maranhão and Rio Grande do Norte, there
was a deconcentration of average income from formal work for the municipal-
ities included in the scale with the highest average income from work when
comparing the years of analysis. In 2009, the Maranhão municipalities of
Godofredo Viana, Capinzal do Norte, Governador Archer, Senador Alexan-
dre Costa, and Estreito were some of the other municipalities that stood out
in the scale of higher average income from formal work. Of these, only the
municipalities of Godofredo Viana and Capinzal do Norte remained in 2019
on the same income scale mentioned above, and the others reduced to lower
average income scales. Furthermore, there is also an emergence of several
municipalities with the improvement in the scale of the average income from
formal work, compared to 2009, in general terms for the entire state.
Comparing the two maps, it is possible to verify the predominance of
the smaller scale of average income from formal work represented by the
light-yellow color, indicating an average income of R $500.00 for most mu-
nicipalities in the Northeast region. It is also verified that the municipalities
within this scale 2009 emerged in 2019 to a second smaller scale, represented
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
by the darker yellow color, indicating an average income range of R $1 567.50.
It was also found that the municipalities in 2009 that were part of the scale
with the highest average income from formal work, represented by red, indi-
cating an average income of R $2 612.50, started to be allocated in 2019 to a
second larger scale middle-income, represented by the color yellow, with an
average income between R$ 2 090.002.
However, these results highlighted a supposed deconcentration of North-
east municipalities’ average income from formal work when comparing 2009
with 2019. It is also observed that, in some municipalities, mainly in areas
with more incredible economic performance, the average income from work
is either in the highest income range or in the second highest due to the perfor-
mance of its economy. Thus, they become the main destinations of formally
employed people who commute.
III. Socioeconomic and Demographic Characterization of Commuting
Migrants and Non-Commuting Migrants in the Northeast
– 2009/2019
Table 1 presents the descriptive statistics of the variables used, present-
ing the socioeconomic and demographic characteristics of commuting mi-
grants and non-commuting migrants of formal work in the Northeast region
of Brazil for the years 2009 and 2019.
In 2009, it was observed that 68% of the formally employed were both com-
muting migrants, predominantly male and non-commuting migrants, with an av-
erage age of 33.56 years (commuting migrants) and 33.22 years (non-migrants).
In 2019, this participation went to 64% for commuting migrants and 56% for
non-migrants, indicating a reduction in the participation of men in commuting
and the formal labor market and, in effect, an improvement in the participation
of women in the pendular migratory dynamics. These results are in line with
the findings by Gama and Machado (2014), and Silva-Filho (2019); the latter
showed that for the state of Bahia, greater participation of men concerning mi-
grant women and non-migrants in the condition of formal occupation of work
in the municipalities of Bahia, for the years 2000 and 2010.
2 Monetary values are in 2019 BRL and were deflated by the INPC.
87
by the darker yellow color, indicating an average income range of R $1 567.50.
It was also found that the municipalities in 2009 that were part of the scale
with the highest average income from formal work, represented by red, indi-
cating an average income of R $2 612.50, started to be allocated in 2019 to a
second larger scale middle-income, represented by the color yellow, with an
average income between R$ 2 090.002.
However, these results highlighted a supposed deconcentration of North-
east municipalities’ average income from formal work when comparing 2009
with 2019. It is also observed that, in some municipalities, mainly in areas
with more incredible economic performance, the average income from work
is either in the highest income range or in the second highest due to the perfor-
mance of its economy. Thus, they become the main destinations of formally
employed people who commute.
III. Socioeconomic and Demographic Characterization of Commuting
Migrants and Non-Commuting Migrants in the Northeast
– 2009/2019
Table 1 presents the descriptive statistics of the variables used, present-
ing the socioeconomic and demographic characteristics of commuting mi-
grants and non-commuting migrants of formal work in the Northeast region
of Brazil for the years 2009 and 2019.
In 2009, it was observed that 68% of the formally employed were both com-
muting migrants, predominantly male and non-commuting migrants, with an av-
erage age of 33.56 years (commuting migrants) and 33.22 years (non-migrants).
In 2019, this participation went to 64% for commuting migrants and 56% for
non-migrants, indicating a reduction in the participation of men in commuting
and the formal labor market and, in effect, an improvement in the participation
of women in the pendular migratory dynamics. These results are in line with
the findings by Gama and Machado (2014), and Silva-Filho (2019); the latter
showed that for the state of Bahia, greater participation of men concerning mi-
grant women and non-migrants in the condition of formal occupation of work
in the municipalities of Bahia, for the years 2000 and 2010.
2 Monetary values are in 2019 BRL and were deflated by the INPC.
87
88
Table 2. Socioeconomic and demographic characterization of commuting migrants and
non-commuting migrants in the Northeast, 2009/2019
Variables 2009 2019
Commuting
Non-Migrant
(%)
Commuting
Migrant (%)
Commuting
Non-Migrant
(%)
Commuting
Migrant (%)
Sex 0.68 0.68 0.56 0.64
Age 33.22 33.56 35.9 35.67
White man) 20.7 17.9 15.3 12.3
White woman) 12.4 10.5 13.2 8.6
Yellow man) 0.8 0.7 0.3 0.5
Woman (yellow) 0.4 0.3 0.3 0.4
Black man) 4.6 4.3 3.4 4.2
Black woman) 1.4 1.3 two 1.8
Man (brown) 41.7 44.9 36.8 46.7
Woman (brown) 17.9 20.1 28.7 25.6
Farming 5.5 6.3 6.8 4.2
Industry 21.9 17.5 8.5 18.1
Construction 11.8 11 4.4 8.5
Trade sector 20.4 27.4 23.6 25.3
Services 25.2 23.9 25.4 29
Public administration 5 3 4.8 3.6
Education, culture, health
and other services
10.2 10.9 26.5 11.2
Domestic services 0 0 0 0
Disabled person 1 0.6 0.5 1.4
Industry opting for the simple
national
18 31.4 58.3 20.8
Micro 25.5 39.8 53.3 29.7
Small 21.1 25.5 24.6 23.9
Average 21.8 17.9 8.4 21.5
Big 31.6 16.8 13.7 24.9
Up to 1 year 49.7 49.6 38.5 33.4
Continued
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Table 2. Socioeconomic and demographic characterization of commuting migrants and
non-commuting migrants in the Northeast, 2009/2019
Variables 2009 2019
Commuting
Non-Migrant
(%)
Commuting
Migrant (%)
Commuting
Non-Migrant
(%)
Commuting
Migrant (%)
Sex 0.68 0.68 0.56 0.64
Age 33.22 33.56 35.9 35.67
White man) 20.7 17.9 15.3 12.3
White woman) 12.4 10.5 13.2 8.6
Yellow man) 0.8 0.7 0.3 0.5
Woman (yellow) 0.4 0.3 0.3 0.4
Black man) 4.6 4.3 3.4 4.2
Black woman) 1.4 1.3 two 1.8
Man (brown) 41.7 44.9 36.8 46.7
Woman (brown) 17.9 20.1 28.7 25.6
Farming 5.5 6.3 6.8 4.2
Industry 21.9 17.5 8.5 18.1
Construction 11.8 11 4.4 8.5
Trade sector 20.4 27.4 23.6 25.3
Services 25.2 23.9 25.4 29
Public administration 5 3 4.8 3.6
Education, culture, health
and other services
10.2 10.9 26.5 11.2
Domestic services 0 0 0 0
Disabled person 1 0.6 0.5 1.4
Industry opting for the simple
national
18 31.4 58.3 20.8
Micro 25.5 39.8 53.3 29.7
Small 21.1 25.5 24.6 23.9
Average 21.8 17.9 8.4 21.5
Big 31.6 16.8 13.7 24.9
Up to 1 year 49.7 49.6 38.5 33.4
Continued
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
Table 2. Continuation
Variables 2009 2019
Commuting
Non-Migrant
(%)
Commuting
Migrant (%)
Commuting
Non-Migrant
(%)
Commuting
Migrant (%)
More than 1 to 2 16.8 16.7 17.9 16.4
More than 2 to 3 9 9 10.4 10.2
More than 3 to 5 9.8 9.7 12.2 13
More than 5 to 10 8.8 8.7 14 16.8
More than 10 6 6.2 7 10.2
Uneducated or with incom-
plete primary education
24.2 23.4 9.7 12.1
Complete primary education
and incomplete secondary ed-
ucation
19.8 21.6 11.7 13.6
Complete high school and in-
complete higher education
47.9 47.6 61.6 61.6
Complete higher education 7.9 7.2 16.2 12.2
Master’s degree 0.2 0.2 0.6 0.4
Doctorate 0.1 0 0.2 0.1
Income from work 1897.64 1754.39 1835.96 2081.78
Source: Own elaboration based on data from the RAIS 2009/2019.
Regarding education, 47.6% of the formally employed who carry out
migratory commuting mostly have completed high school and incomplete
higher education, while non-commuting migrants have a slightly higher per-
centage, with 47.9%. In 2019, a significant increase in this percentage was
noted for both groups, with each in the order of 61.6%. It is essential to
highlight that, comparing the years of study, greater participation of both
groups in the other higher levels of education is observed, indicating an im-
provement in education and a reduction in the participation of these groups
in lower levels of education, converging with the results pointed out by other
works (Correia, 2020; Ramalho & Brito, 2016; Sidrim, 2018; Sidrim & Fusco,
2019).
89
Table 2. Continuation
Variables 2009 2019
Commuting
Non-Migrant
(%)
Commuting
Migrant (%)
Commuting
Non-Migrant
(%)
Commuting
Migrant (%)
More than 1 to 2 16.8 16.7 17.9 16.4
More than 2 to 3 9 9 10.4 10.2
More than 3 to 5 9.8 9.7 12.2 13
More than 5 to 10 8.8 8.7 14 16.8
More than 10 6 6.2 7 10.2
Uneducated or with incom-
plete primary education
24.2 23.4 9.7 12.1
Complete primary education
and incomplete secondary ed-
ucation
19.8 21.6 11.7 13.6
Complete high school and in-
complete higher education
47.9 47.6 61.6 61.6
Complete higher education 7.9 7.2 16.2 12.2
Master’s degree 0.2 0.2 0.6 0.4
Doctorate 0.1 0 0.2 0.1
Income from work 1897.64 1754.39 1835.96 2081.78
Source: Own elaboration based on data from the RAIS 2009/2019.
Regarding education, 47.6% of the formally employed who carry out
migratory commuting mostly have completed high school and incomplete
higher education, while non-commuting migrants have a slightly higher per-
centage, with 47.9%. In 2019, a significant increase in this percentage was
noted for both groups, with each in the order of 61.6%. It is essential to
highlight that, comparing the years of study, greater participation of both
groups in the other higher levels of education is observed, indicating an im-
provement in education and a reduction in the participation of these groups
in lower levels of education, converging with the results pointed out by other
works (Correia, 2020; Ramalho & Brito, 2016; Sidrim, 2018; Sidrim & Fusco,
2019).
89
90
In addition, it notes that commuting migrants with higher education in-
creased from 7.2% in 2009 to 12.2% in 2019, while non-commuting migrants
increased from 7.9% to 16.2% respectively. However, the results indicate that
for the years referred to in this study, non-commuting migrants have, on av-
erage, a higher level of education (more schooling) compared to commuting
migrants, converging with results presented by de Brito et al. (2018).
Concerning average income from work3, it is possible to identify that
non-commuting migrants in 2009 earned an average income of R $1 897.64,
which is relatively higher than that of the commuting migrants, which was
only R $1 754.39. However, this relationship changes when compared to the
year 2019, where the average income earned by the group of non-commuting
migrants reduces to R$ 1 835.96, while there is an improvement in the average
income for commuting migrants, increasing to R $2 081.78.
Over the period covered in this analysis, it appears that the group of non-
commuting migrants in the condition of formal occupation in the labor mar-
ket in 2009 earned an average income from work higher than the group of
commuting migrants of R $143.30, while in 2019, commuting migrants had a
higher average income from work compared to commuting non-migrants in
the order of BRL 245.82, which may indicate an inversion of positions. This
result is consistent with the findings by da Silva-Filho et al. (2021) for the
Midwest region of Brazil.
IV. Results and Discussions
This section aims, using the Quantile Regression Method (QR), to analyze
and capture the effect of socioeconomic and demographic characteristics on
the income differentials of commuting workers in the formal economy in
what comprises the Northeast region of Brazil in the period 2009 and 2019
in different income quantiles (10, 50 and 90). Indeed, using that method has
advantages as it makes it possible to compare income differentials and returns
to commuting in different quantiles of the conditional distribution of income
3 The income considered in this study is real income. Thus, the values presented in terms of
average earnings from work were monetarily corrected and deflated, and therefore a compar-
ison between years can be made.
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
In addition, it notes that commuting migrants with higher education in-
creased from 7.2% in 2009 to 12.2% in 2019, while non-commuting migrants
increased from 7.9% to 16.2% respectively. However, the results indicate that
for the years referred to in this study, non-commuting migrants have, on av-
erage, a higher level of education (more schooling) compared to commuting
migrants, converging with results presented by de Brito et al. (2018).
Concerning average income from work3, it is possible to identify that
non-commuting migrants in 2009 earned an average income of R $1 897.64,
which is relatively higher than that of the commuting migrants, which was
only R $1 754.39. However, this relationship changes when compared to the
year 2019, where the average income earned by the group of non-commuting
migrants reduces to R$ 1 835.96, while there is an improvement in the average
income for commuting migrants, increasing to R $2 081.78.
Over the period covered in this analysis, it appears that the group of non-
commuting migrants in the condition of formal occupation in the labor mar-
ket in 2009 earned an average income from work higher than the group of
commuting migrants of R $143.30, while in 2019, commuting migrants had a
higher average income from work compared to commuting non-migrants in
the order of BRL 245.82, which may indicate an inversion of positions. This
result is consistent with the findings by da Silva-Filho et al. (2021) for the
Midwest region of Brazil.
IV. Results and Discussions
This section aims, using the Quantile Regression Method (QR), to analyze
and capture the effect of socioeconomic and demographic characteristics on
the income differentials of commuting workers in the formal economy in
what comprises the Northeast region of Brazil in the period 2009 and 2019
in different income quantiles (10, 50 and 90). Indeed, using that method has
advantages as it makes it possible to compare income differentials and returns
to commuting in different quantiles of the conditional distribution of income
3 The income considered in this study is real income. Thus, the values presented in terms of
average earnings from work were monetarily corrected and deflated, and therefore a compar-
ison between years can be made.
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
(Maciel & Oliveira, 2011). In this way, the 10th, 50th, and 90th percentiles
of the conditional wage distribution will be investigated, that is, the lowest
wages earned by workers in the first ten percent of wages, in the median, and
the higher wages received by workers in the ninety percent of income.
Thus, observing Table 24, Considering the variables that refer to individ-
ual characteristics, it is noted that men of race/yellow color earned higher
wages than those of the reference category (white man) in all quantiles of the
conditional distribution of wages, as well as it appears that the greater the
wage distribution, the greater the differential earned by them in 2009. On
the other hand, it is observed that regardless of race and color, women earn
lower incomes than men, highlighting black women at the 0.90 quantiles as
those earning the lowest income from work, earning 41% less.
Concerning 2019, at quantile 0.10, only yellow, black, and brown men
earned wages higher than white men (reference category), but this cannot be
said for the other quantiles analyzed and for women. On the median, women
of the yellow race/color earned the lowest salaries than those in the omit-
ted category and concerning the others. Likewise, in the 0.90 quantiles of
income, black women. Based on these results, it is evident that the character-
istics related to sex and race/color harm wage differentials, especially when
observing the highest income quantiles, where income inequalities are more
attenuated, ranging from meeting with the findings in the economic literature
(Carvalho et al., 2006; de Brito et al., 2018; Gama & Hermeto, 2017; Matos
& Machado, 2006; Silva-Filho et al., 2018).
4 The variables “Yellow Man” at quantiles 0.50 and 0.90 for the year 2019; “Public Administra-
tion” at quantile 0.10 in 2019 did not show statistical significance. Only the variable “Yellow
Woman” at quantile 0.10 in the year 2019 showed statistical significance at the 10% level.
And the variable “Black Man” at the 0.50 quantile for 2019 obtained statistical significance
at the 5% level.
91
(Maciel & Oliveira, 2011). In this way, the 10th, 50th, and 90th percentiles
of the conditional wage distribution will be investigated, that is, the lowest
wages earned by workers in the first ten percent of wages, in the median, and
the higher wages received by workers in the ninety percent of income.
Thus, observing Table 24, Considering the variables that refer to individ-
ual characteristics, it is noted that men of race/yellow color earned higher
wages than those of the reference category (white man) in all quantiles of the
conditional distribution of wages, as well as it appears that the greater the
wage distribution, the greater the differential earned by them in 2009. On
the other hand, it is observed that regardless of race and color, women earn
lower incomes than men, highlighting black women at the 0.90 quantiles as
those earning the lowest income from work, earning 41% less.
Concerning 2019, at quantile 0.10, only yellow, black, and brown men
earned wages higher than white men (reference category), but this cannot be
said for the other quantiles analyzed and for women. On the median, women
of the yellow race/color earned the lowest salaries than those in the omit-
ted category and concerning the others. Likewise, in the 0.90 quantiles of
income, black women. Based on these results, it is evident that the character-
istics related to sex and race/color harm wage differentials, especially when
observing the highest income quantiles, where income inequalities are more
attenuated, ranging from meeting with the findings in the economic literature
(Carvalho et al., 2006; de Brito et al., 2018; Gama & Hermeto, 2017; Matos
& Machado, 2006; Silva-Filho et al., 2018).
4 The variables “Yellow Man” at quantiles 0.50 and 0.90 for the year 2019; “Public Administra-
tion” at quantile 0.10 in 2019 did not show statistical significance. Only the variable “Yellow
Woman” at quantile 0.10 in the year 2019 showed statistical significance at the 10% level.
And the variable “Black Man” at the 0.50 quantile for 2019 obtained statistical significance
at the 5% level.
91
92Table 3. Differentials in earnings from work among commuting migrants in formal work in the Northeast according to
socioeconomic and demographic characteristics, 2009/2019
Variables Dependent variable: lnrendatrab
(Qtl. 0.10- 2009) (Qtl. 0.10 - 2019) (Qtl. 0.50 - 2009) (Qtl. 0.50 - 2019) (Qtl. 0.90 - 2009) (Qtl. 0.90 -2019)
White woman -0.025 ∗∗∗ -0.013 ∗∗∗ -0.135 ∗∗∗ -0.075 ∗∗∗ -0.322 ∗∗∗ -0.189 ∗∗∗
(0.0003) (0.0003) (0.001) (0.001) (0.003) (0.003)
Black man -0.004 ∗∗∗ 0.003 ∗∗∗ -0.013 ∗∗∗ -0.004 ∗∗ -0.101 ∗∗∗ -0.066 ∗∗∗
(0.001) (0.0005) (0.001) (0.002) (0.003) (0.005)
Black woman -0.026 ∗∗∗ -0.009 ∗∗∗ -0.144 ∗∗∗ -0.082 ∗∗∗ -0.410 ∗∗∗ -0.270 ∗∗∗
(0.0004) (0.0004) (0.001) (0.002) (0.005) (0.005)
Yellowman 0.016 ∗∗∗ 0.006 ∗∗∗ 0.069 ∗∗∗ -0.006 0.098 ∗∗∗ -0.018
(0.002) (0.002) (0.003) (0.006) (0.011) (0.017)
Yellow woman -0.015 ∗∗∗ -0.002 ∗ 0.021 ∗∗∗ -0.090 ∗∗∗ -0.306 ∗∗∗ -0.213 ∗∗∗
(0.002) (0.001) (0.003) (0.005) (0.011) (0.017)
Brown man -0.003 ∗∗∗ 0.002 ∗∗∗ -0.005 ∗∗∗ 0.006 ∗∗∗ -0.080 ∗∗∗ -0.031 ∗∗∗
(0.0003) (0.0003) (0.001) (0.001) (0.002) (0.003)
Brown woman -0.029 ∗∗∗ -0.010 ∗∗∗ -0.144 ∗∗∗ -0.070 ∗∗∗ -0.395 ∗∗∗ -0.223 ∗∗∗
(0.0003) (0.0003) (0.001) (0.001) (0.002) (0.003)
Age 0.004 ∗∗∗ 0.002 ∗∗∗ 0.013 ∗∗∗ 0.008 ∗∗∗ 0.035 ∗∗∗ 0.021 ∗∗∗
(0.0001) (0.00004) (0.0001) (0.0002) (0.0004) (0.0005)
Age2 -0.00003 ∗∗∗ -0.00002 ∗∗∗ -0.0001 ∗∗∗ -0.0001 ∗∗∗ -0.0002 ∗∗∗ -0.0001 ∗∗∗
(0.00000) (0.00000) (0.00000) (0.00000) (0.00001) (0.00001)
Industry 0.027 ∗∗∗ 0.023 ∗∗∗ 0.053 ∗∗∗ 0.026 ∗∗∗ 0.080 ∗∗∗ -0.099 ∗∗∗
(0.001) (0.0004) (0.001) (0.001) (0.003) (0.004)
Construction 0.066 ∗∗∗ 0.035 ∗∗∗ 0.196 ∗∗∗ 0.184 ∗∗∗ 0.255 ∗∗∗ 0.081 ∗∗∗
(0.001) (0.001) (0.001) (0.002) (0.003) (0.005)
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
socioeconomic and demographic characteristics, 2009/2019
Variables Dependent variable: lnrendatrab
(Qtl. 0.10- 2009) (Qtl. 0.10 - 2019) (Qtl. 0.50 - 2009) (Qtl. 0.50 - 2019) (Qtl. 0.90 - 2009) (Qtl. 0.90 -2019)
White woman -0.025 ∗∗∗ -0.013 ∗∗∗ -0.135 ∗∗∗ -0.075 ∗∗∗ -0.322 ∗∗∗ -0.189 ∗∗∗
(0.0003) (0.0003) (0.001) (0.001) (0.003) (0.003)
Black man -0.004 ∗∗∗ 0.003 ∗∗∗ -0.013 ∗∗∗ -0.004 ∗∗ -0.101 ∗∗∗ -0.066 ∗∗∗
(0.001) (0.0005) (0.001) (0.002) (0.003) (0.005)
Black woman -0.026 ∗∗∗ -0.009 ∗∗∗ -0.144 ∗∗∗ -0.082 ∗∗∗ -0.410 ∗∗∗ -0.270 ∗∗∗
(0.0004) (0.0004) (0.001) (0.002) (0.005) (0.005)
Yellowman 0.016 ∗∗∗ 0.006 ∗∗∗ 0.069 ∗∗∗ -0.006 0.098 ∗∗∗ -0.018
(0.002) (0.002) (0.003) (0.006) (0.011) (0.017)
Yellow woman -0.015 ∗∗∗ -0.002 ∗ 0.021 ∗∗∗ -0.090 ∗∗∗ -0.306 ∗∗∗ -0.213 ∗∗∗
(0.002) (0.001) (0.003) (0.005) (0.011) (0.017)
Brown man -0.003 ∗∗∗ 0.002 ∗∗∗ -0.005 ∗∗∗ 0.006 ∗∗∗ -0.080 ∗∗∗ -0.031 ∗∗∗
(0.0003) (0.0003) (0.001) (0.001) (0.002) (0.003)
Brown woman -0.029 ∗∗∗ -0.010 ∗∗∗ -0.144 ∗∗∗ -0.070 ∗∗∗ -0.395 ∗∗∗ -0.223 ∗∗∗
(0.0003) (0.0003) (0.001) (0.001) (0.002) (0.003)
Age 0.004 ∗∗∗ 0.002 ∗∗∗ 0.013 ∗∗∗ 0.008 ∗∗∗ 0.035 ∗∗∗ 0.021 ∗∗∗
(0.0001) (0.00004) (0.0001) (0.0002) (0.0004) (0.0005)
Age2 -0.00003 ∗∗∗ -0.00002 ∗∗∗ -0.0001 ∗∗∗ -0.0001 ∗∗∗ -0.0002 ∗∗∗ -0.0001 ∗∗∗
(0.00000) (0.00000) (0.00000) (0.00000) (0.00001) (0.00001)
Industry 0.027 ∗∗∗ 0.023 ∗∗∗ 0.053 ∗∗∗ 0.026 ∗∗∗ 0.080 ∗∗∗ -0.099 ∗∗∗
(0.001) (0.0004) (0.001) (0.001) (0.003) (0.004)
Construction 0.066 ∗∗∗ 0.035 ∗∗∗ 0.196 ∗∗∗ 0.184 ∗∗∗ 0.255 ∗∗∗ 0.081 ∗∗∗
(0.001) (0.001) (0.001) (0.002) (0.003) (0.005)
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
Variables Dependent variable: lnrendatrab
(Qtl. 0.10- 2009) (Qtl. 0.10 - 2019) (Qtl. 0.50 - 2009) (Qtl. 0.50 - 2019) (Qtl. 0.90 - 2009) (Qtl. 0.90 -2019)
Trade sector 0.049 ∗∗∗ 0.034 ∗∗∗ 0.049 ∗∗∗ 0.042 ∗∗∗ -0.075 ∗∗∗ -0.117 ∗∗∗
(0.001) (0.0004) (0.001) (0.001) (0.003) (0.003)
Services 0.043 ∗∗∗ 0.038 ∗∗∗ 0.078 ∗∗∗ 0.077 ∗∗∗ 0.031 ∗∗∗ -0.040 ∗∗∗
(0.001) (0.0004) (0.001) (0.001) (0.003) (0.003)
Public Administration 0.099 ∗∗∗ 0.009 0.223 ∗∗∗ 0.070 ∗∗∗ 0.303 ∗∗∗ 0.150 ∗∗∗
(0.001) (0.007) (0.002) (0.002) (0.005) (0.008)
Education, culture,
health, and other
services.
0.042 ∗∗∗ 0.037 ∗∗∗ 0.123 ∗∗∗ 0.128 ∗∗∗ 0.061 ∗∗∗ 0.174 ∗∗∗
(0.001) (0.001) (0.001) (0.001) (0.004) (0.005)
Domestic services 0.019 ∗∗∗ 0.006 ∗∗∗ -0.021 ∗∗∗ -0.032 ∗∗∗ -0.180 ∗∗∗ -0.332 ∗∗∗
(0.001) (0.001) (0.005) (0.003) (0.0022) (0.005)
Small 0.029 ∗∗∗ 0.015 ∗∗∗ 0.093 ∗∗∗ 0.066 ∗∗∗ 0.173 ∗∗∗ 0.121 ∗∗∗
(0.0002) (0.0003) (0.001) (0.001) (0.002) (0.002)
Average 0.052 ∗∗∗ 0.037 ∗∗∗ 0.179 ∗∗∗ 0.183 ∗∗∗ 0.297 ∗∗∗ 0.354 ∗∗∗
(0.0003) (0.001) (0.001) (0.002) (0.002) (0.005)
Big 0.051 ∗∗∗ 0.176 ∗∗∗ 0.187 ∗∗∗ 0.336 ∗∗∗ 0.234 ∗∗∗ 0.256 ∗∗∗
(0.0003) (0.002) (0.001) (0.001) (0.002) (0.005)
More than 1 to 2 0.019 ∗∗∗ 0.006 ∗∗∗ 0.017 ∗∗∗ 0.012 ∗∗∗ -0.006 ∗∗∗ -0.018 ∗∗∗
(0.0002) (0.0002) (0.001) (0.001) (0.002) (0.002)
More than 2 to 3 0.034 ∗∗∗ 0.018 ∗∗∗ 0.040 ∗∗∗ 0.030 ∗∗∗ 0.047 ∗∗∗ 0.017 ∗∗∗
(0.0003) (0.0004) (0.001) (0.001) (0.002) (0.003)
More than 3 to 5 0.040 ∗∗∗ 0.030 ∗∗∗ 0.080 ∗∗∗ 0.053 ∗∗∗ 0.084 ∗∗∗ 0.042 ∗∗∗
(0.0004) (0.0003) (0.001) (0.001) (0.002) (0.003)
More than 5 to 10 0.052 ∗∗∗ 0.040 ∗∗∗ 0.099 ∗∗∗ 0.090 ∗∗∗ 0.146 ∗∗∗ 0.107 ∗∗∗
(0.0004) (0.0004) (0.001) (0.001) (0.003) (0.003)
93
Variables Dependent variable: lnrendatrab
(Qtl. 0.10- 2009) (Qtl. 0.10 - 2019) (Qtl. 0.50 - 2009) (Qtl. 0.50 - 2019) (Qtl. 0.90 - 2009) (Qtl. 0.90 -2019)
Trade sector 0.049 ∗∗∗ 0.034 ∗∗∗ 0.049 ∗∗∗ 0.042 ∗∗∗ -0.075 ∗∗∗ -0.117 ∗∗∗
(0.001) (0.0004) (0.001) (0.001) (0.003) (0.003)
Services 0.043 ∗∗∗ 0.038 ∗∗∗ 0.078 ∗∗∗ 0.077 ∗∗∗ 0.031 ∗∗∗ -0.040 ∗∗∗
(0.001) (0.0004) (0.001) (0.001) (0.003) (0.003)
Public Administration 0.099 ∗∗∗ 0.009 0.223 ∗∗∗ 0.070 ∗∗∗ 0.303 ∗∗∗ 0.150 ∗∗∗
(0.001) (0.007) (0.002) (0.002) (0.005) (0.008)
Education, culture,
health, and other
services.
0.042 ∗∗∗ 0.037 ∗∗∗ 0.123 ∗∗∗ 0.128 ∗∗∗ 0.061 ∗∗∗ 0.174 ∗∗∗
(0.001) (0.001) (0.001) (0.001) (0.004) (0.005)
Domestic services 0.019 ∗∗∗ 0.006 ∗∗∗ -0.021 ∗∗∗ -0.032 ∗∗∗ -0.180 ∗∗∗ -0.332 ∗∗∗
(0.001) (0.001) (0.005) (0.003) (0.0022) (0.005)
Small 0.029 ∗∗∗ 0.015 ∗∗∗ 0.093 ∗∗∗ 0.066 ∗∗∗ 0.173 ∗∗∗ 0.121 ∗∗∗
(0.0002) (0.0003) (0.001) (0.001) (0.002) (0.002)
Average 0.052 ∗∗∗ 0.037 ∗∗∗ 0.179 ∗∗∗ 0.183 ∗∗∗ 0.297 ∗∗∗ 0.354 ∗∗∗
(0.0003) (0.001) (0.001) (0.002) (0.002) (0.005)
Big 0.051 ∗∗∗ 0.176 ∗∗∗ 0.187 ∗∗∗ 0.336 ∗∗∗ 0.234 ∗∗∗ 0.256 ∗∗∗
(0.0003) (0.002) (0.001) (0.001) (0.002) (0.005)
More than 1 to 2 0.019 ∗∗∗ 0.006 ∗∗∗ 0.017 ∗∗∗ 0.012 ∗∗∗ -0.006 ∗∗∗ -0.018 ∗∗∗
(0.0002) (0.0002) (0.001) (0.001) (0.002) (0.002)
More than 2 to 3 0.034 ∗∗∗ 0.018 ∗∗∗ 0.040 ∗∗∗ 0.030 ∗∗∗ 0.047 ∗∗∗ 0.017 ∗∗∗
(0.0003) (0.0004) (0.001) (0.001) (0.002) (0.003)
More than 3 to 5 0.040 ∗∗∗ 0.030 ∗∗∗ 0.080 ∗∗∗ 0.053 ∗∗∗ 0.084 ∗∗∗ 0.042 ∗∗∗
(0.0004) (0.0003) (0.001) (0.001) (0.002) (0.003)
More than 5 to 10 0.052 ∗∗∗ 0.040 ∗∗∗ 0.099 ∗∗∗ 0.090 ∗∗∗ 0.146 ∗∗∗ 0.107 ∗∗∗
(0.0004) (0.0004) (0.001) (0.001) (0.003) (0.003)
93
94Variables Dependent variable: lnrendatrab
(Qtl. 0.10- 2009) (Qtl. 0.10 - 2019) (Qtl. 0.50 - 2009) (Qtl. 0.50 - 2019) (Qtl. 0.90 - 2009) (Qtl. 0.90 -2019)
More than 10 0.092 ∗∗∗ 0.073 ∗∗∗ 0.312 ∗∗∗ 0.220 ∗∗∗ 0.403 ∗∗∗ 0.299 ∗∗∗
(0.001) (0.001) (0.002) (0.002) (0.003) (0.004)
Complete primary edu-
cation and incomplete
secondary education
0.032 ∗∗∗ 0.011 ∗∗∗ 0.076 ∗∗∗ 0.034 ∗∗∗ 0.197 ∗∗∗ 0.081 ∗∗∗
(0.0003) (0.0003) (0.001) (0.001) (0.002) (0.003)
Complete high school
and incomplete higher
education
0.063 ∗∗∗ 0.024 ∗∗∗ 0.189 ∗∗∗ 0.095 ∗∗∗ 0.552 ∗∗∗ 0.237 ∗∗∗
(0.0003) (0.0003) (0.001) (0.001) (0.002) (0.002)
Complete higher edu-
cation
0.373 ∗∗∗ 0.141 ∗∗∗ 1,239 ∗∗∗ 0.805 ∗∗∗ 1,833 ∗∗∗ 1,306 ∗∗∗
(0.003) (0.002) (0.002) (0.002) (0.003) (0.005)
Master’s degree 0.955 ∗∗∗ 1,005 ∗∗∗ 2021 ∗∗∗ 1,638 ∗∗∗ 2,620 ∗∗∗ 2,083 ∗∗∗
(0.044) (0.018) (0.011) (0.010) (0.028) (0.0022)
Doctorate 0.610 ∗∗∗ 1,544 ∗∗∗ 2,175 ∗∗∗ 2,029 ∗∗∗ 3,127 ∗∗∗ 2,194 ∗∗∗
(0.042) (0.025) (0.030) (0.014) (0.0141) (0.020)
Constant 2,841 ∗∗∗ 3,082 ∗∗∗ 2,709 ∗∗∗ 3,000 ∗∗∗ 2,697 ∗∗∗ 3,173 ∗∗∗
(0.001) (0.001) (0.002) (0.003) (0.008) (0.010)
Comments 3,374,922 1,367,917 3,374,922 1,367,917 3,374,922 1,367,917
Note: Significances: *10%; **5%; and ***1%. In parentheses, the p-value.
Source: Own elaboration based on data from RAIS 2009/2019.
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
(Qtl. 0.10- 2009) (Qtl. 0.10 - 2019) (Qtl. 0.50 - 2009) (Qtl. 0.50 - 2019) (Qtl. 0.90 - 2009) (Qtl. 0.90 -2019)
More than 10 0.092 ∗∗∗ 0.073 ∗∗∗ 0.312 ∗∗∗ 0.220 ∗∗∗ 0.403 ∗∗∗ 0.299 ∗∗∗
(0.001) (0.001) (0.002) (0.002) (0.003) (0.004)
Complete primary edu-
cation and incomplete
secondary education
0.032 ∗∗∗ 0.011 ∗∗∗ 0.076 ∗∗∗ 0.034 ∗∗∗ 0.197 ∗∗∗ 0.081 ∗∗∗
(0.0003) (0.0003) (0.001) (0.001) (0.002) (0.003)
Complete high school
and incomplete higher
education
0.063 ∗∗∗ 0.024 ∗∗∗ 0.189 ∗∗∗ 0.095 ∗∗∗ 0.552 ∗∗∗ 0.237 ∗∗∗
(0.0003) (0.0003) (0.001) (0.001) (0.002) (0.002)
Complete higher edu-
cation
0.373 ∗∗∗ 0.141 ∗∗∗ 1,239 ∗∗∗ 0.805 ∗∗∗ 1,833 ∗∗∗ 1,306 ∗∗∗
(0.003) (0.002) (0.002) (0.002) (0.003) (0.005)
Master’s degree 0.955 ∗∗∗ 1,005 ∗∗∗ 2021 ∗∗∗ 1,638 ∗∗∗ 2,620 ∗∗∗ 2,083 ∗∗∗
(0.044) (0.018) (0.011) (0.010) (0.028) (0.0022)
Doctorate 0.610 ∗∗∗ 1,544 ∗∗∗ 2,175 ∗∗∗ 2,029 ∗∗∗ 3,127 ∗∗∗ 2,194 ∗∗∗
(0.042) (0.025) (0.030) (0.014) (0.0141) (0.020)
Constant 2,841 ∗∗∗ 3,082 ∗∗∗ 2,709 ∗∗∗ 3,000 ∗∗∗ 2,697 ∗∗∗ 3,173 ∗∗∗
(0.001) (0.001) (0.002) (0.003) (0.008) (0.010)
Comments 3,374,922 1,367,917 3,374,922 1,367,917 3,374,922 1,367,917
Note: Significances: *10%; **5%; and ***1%. In parentheses, the p-value.
Source: Own elaboration based on data from RAIS 2009/2019.
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
Despite the coefficient of the age variable, it is verified that, both in 2009
and in 2019, earnings grew with increasing age in the quantiles analyzed, as
expected. However, the effect is more significant for the first year than the
second since there is a slight reduction in the coefficients in 2019. However,
the growth of earnings takes place up to a certain level since, when age be-
comes relatively high, earnings start to decrease, as shown by the results of its
quadratic form “age2”, which in turn was also expected, and verified in other
studies (Gama & Hermeto, 2017; Silva-Filho, 2017).
About the sectors of activity, it appears in 2009 that formal workers em-
ployed in public administration earned, on average, the highest wages com-
pared to those employed in agriculture (reference category) in all quantiles of
the conditional distribution of wages. While at the 0.50 quantile (median) and
the 0.90 quantiles, those employed in the domestic services sector achieved
the lowest salary levels, respectively, 2.1% and 18% less. These findings par-
tially corroborate the results found by Santos Júnior et al. (2005), de Brito et
al. (2018), and Santos (2018).
Considering 2019, compared to workers in the agricultural sector, work-
ers allocated in other categories obtain incomes higher than these. Since, at
a quantile of 0.10, the highest salaries on average were achieved by those em-
ployed in the service sector (3.8% more), similar to what was found by Julião
and Rocha (2020). On the median, as verified by Santos and Lelis (2018),
those allocated to civil construction earned, on average, the highest earnings
from work (18.4% more) than the other categories and the reference cate-
gory. In the highest quantile of income, the 0.90 quantile, the highest earnings
from work were achieved by those allocated to education, culture, health, and
other services. As in 2009, the lowest wages on average were earned by those
employed in domestic service activities both at the median and at the 0.90
quantiles. This result can be justified by the fact that the activity of domestic
services is considered a menial occupation and one of the subsectors with the
worst remuneration, even if the salary in kind is considered, as pointed out by
Melo (1998).
Concerning the length of stay on the job of the workers, it appears that,
in both years, except for those who were on the job between 1 and 2 years,
95
Despite the coefficient of the age variable, it is verified that, both in 2009
and in 2019, earnings grew with increasing age in the quantiles analyzed, as
expected. However, the effect is more significant for the first year than the
second since there is a slight reduction in the coefficients in 2019. However,
the growth of earnings takes place up to a certain level since, when age be-
comes relatively high, earnings start to decrease, as shown by the results of its
quadratic form “age2”, which in turn was also expected, and verified in other
studies (Gama & Hermeto, 2017; Silva-Filho, 2017).
About the sectors of activity, it appears in 2009 that formal workers em-
ployed in public administration earned, on average, the highest wages com-
pared to those employed in agriculture (reference category) in all quantiles of
the conditional distribution of wages. While at the 0.50 quantile (median) and
the 0.90 quantiles, those employed in the domestic services sector achieved
the lowest salary levels, respectively, 2.1% and 18% less. These findings par-
tially corroborate the results found by Santos Júnior et al. (2005), de Brito et
al. (2018), and Santos (2018).
Considering 2019, compared to workers in the agricultural sector, work-
ers allocated in other categories obtain incomes higher than these. Since, at
a quantile of 0.10, the highest salaries on average were achieved by those em-
ployed in the service sector (3.8% more), similar to what was found by Julião
and Rocha (2020). On the median, as verified by Santos and Lelis (2018),
those allocated to civil construction earned, on average, the highest earnings
from work (18.4% more) than the other categories and the reference cate-
gory. In the highest quantile of income, the 0.90 quantile, the highest earnings
from work were achieved by those allocated to education, culture, health, and
other services. As in 2009, the lowest wages on average were earned by those
employed in domestic service activities both at the median and at the 0.90
quantiles. This result can be justified by the fact that the activity of domestic
services is considered a menial occupation and one of the subsectors with the
worst remuneration, even if the salary in kind is considered, as pointed out by
Melo (1998).
Concerning the length of stay on the job of the workers, it appears that,
in both years, except for those who were on the job between 1 and 2 years,
95
96
in the quantile 0.90, those who were on the job for more than one year earn,
on average, higher earnings than those who stay less than a year in their jobs
(reference category). It is also noted that, as the length of stay in the job in-
creases, the wages earned by workers rise compared to the reference category,
being more expressive for workers who were in the job whose period is over
ten years in all the analyzed quantiles. These results are like the findings of
other studies, such as those by Silva-Filho et al. (2017a) which showed in-
creasing wage returns in the ranges of time in employment and the highest
wages being earned by workers with employment time over ten years.
Regarding the variables that reflect education, the positive signs of their
coefficients indicate a positive relationship between education and earnings
from work in both years. In 2009, it stood out in the 0.10 quantile that for-
mally employed with a master’s degree, on average, earned the highest wages
compared to the reference category (workers without education or with in-
complete primary education), making about 95.5% more income. In the me-
dian (quantile 0.50) and the quantile 0.90, it is noted that the higher the level
of education, the greater the income from work earned by commuting mi-
grant workers in the Northeast, with those who had a doctoral degree, on
average, achieving the highest wages. In 2019, the results were like those of
2009, where salary returns grew as the level of education increased. Therefore,
the highest salaries, on average, were earned by those who had completed a
doctorate in all field’s quantiles analyzed.
This result, in addition to corroborating the theory of human capital pro-
posed by Schultz (1961) and Becker (1962), where individuals seek to invest
in higher levels of formal education to increase their productivity and obtain
higher income, also demonstrates that schooling, among the observable char-
acteristics of individuals, is one of the ones that have the most significant
impact on income differentials, converging with other studies both in the in-
ternational and national literature (de Aguiar et al., 2018; Chiswick, 1999; de
Beaumont & Yang, 2008; Loureiro, 2018; Mincer, 1974; Silva-Filho et al.,
2017a; Silva-Filho et al. 2021).
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
in the quantile 0.90, those who were on the job for more than one year earn,
on average, higher earnings than those who stay less than a year in their jobs
(reference category). It is also noted that, as the length of stay in the job in-
creases, the wages earned by workers rise compared to the reference category,
being more expressive for workers who were in the job whose period is over
ten years in all the analyzed quantiles. These results are like the findings of
other studies, such as those by Silva-Filho et al. (2017a) which showed in-
creasing wage returns in the ranges of time in employment and the highest
wages being earned by workers with employment time over ten years.
Regarding the variables that reflect education, the positive signs of their
coefficients indicate a positive relationship between education and earnings
from work in both years. In 2009, it stood out in the 0.10 quantile that for-
mally employed with a master’s degree, on average, earned the highest wages
compared to the reference category (workers without education or with in-
complete primary education), making about 95.5% more income. In the me-
dian (quantile 0.50) and the quantile 0.90, it is noted that the higher the level
of education, the greater the income from work earned by commuting mi-
grant workers in the Northeast, with those who had a doctoral degree, on
average, achieving the highest wages. In 2019, the results were like those of
2009, where salary returns grew as the level of education increased. Therefore,
the highest salaries, on average, were earned by those who had completed a
doctorate in all field’s quantiles analyzed.
This result, in addition to corroborating the theory of human capital pro-
posed by Schultz (1961) and Becker (1962), where individuals seek to invest
in higher levels of formal education to increase their productivity and obtain
higher income, also demonstrates that schooling, among the observable char-
acteristics of individuals, is one of the ones that have the most significant
impact on income differentials, converging with other studies both in the in-
ternational and national literature (de Aguiar et al., 2018; Chiswick, 1999; de
Beaumont & Yang, 2008; Loureiro, 2018; Mincer, 1974; Silva-Filho et al.,
2017a; Silva-Filho et al. 2021).
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
Conclusions
The present work aimed to investigate the effects of socioeconomic and
demographic characteristics on the income differentials of formal commuting
workers in the Northeast region of Brazil in the years 2009 and 2019. The
regression method used quantiles based on RAIS data to achieve this pur-
pose. This article presents an unprecedented contribution to the literature
on income differentials from the formal work of commuting migrants in the
northeast, as there is no investigation to date that uses this database and this
empirical approach, justified by the dynamics of the commuting migration
periphery-center and center-periphery.
The initial evidence demonstrated by the descriptive statistics revealed
that the commuting migrants of formal work in the Northeast, as well as the
group of non-commuting migrants, were, in both years, male, predominantly
of brown color, employed in the commerce and services sector, allocated to
microenterprises, and were in employment for up to one year. They also had
similar levels of education, with complete high school and incomplete higher
education significantly improving their education level. Regarding income
from work, it was found that commuting workers earned lower payments
than those who did not opt for migratory commuting in the first year, but in
the last year analyzed, the income earned became higher.
Quantile regressions found that the characteristics related to gender,
race/color, and time in the job and education corroborated positive effects on
the income differentials among northeastern commuters, especially at higher
income quantiles.
In general, in 2009, it was observed that the highest earnings from work
were earned by yellow men, regardless of race and color, with men earning
higher wages than women, with these disparities being more expressive in the
upper (or higher) quantiles of the conditional wage distribution. In addition,
higher-income differentials were also found for those allocated in the public
administration sector, with a more extended period of employment (over ten
years), who had the highest levels of education (master’s and doctorate).
97
Conclusions
The present work aimed to investigate the effects of socioeconomic and
demographic characteristics on the income differentials of formal commuting
workers in the Northeast region of Brazil in the years 2009 and 2019. The
regression method used quantiles based on RAIS data to achieve this pur-
pose. This article presents an unprecedented contribution to the literature
on income differentials from the formal work of commuting migrants in the
northeast, as there is no investigation to date that uses this database and this
empirical approach, justified by the dynamics of the commuting migration
periphery-center and center-periphery.
The initial evidence demonstrated by the descriptive statistics revealed
that the commuting migrants of formal work in the Northeast, as well as the
group of non-commuting migrants, were, in both years, male, predominantly
of brown color, employed in the commerce and services sector, allocated to
microenterprises, and were in employment for up to one year. They also had
similar levels of education, with complete high school and incomplete higher
education significantly improving their education level. Regarding income
from work, it was found that commuting workers earned lower payments
than those who did not opt for migratory commuting in the first year, but in
the last year analyzed, the income earned became higher.
Quantile regressions found that the characteristics related to gender,
race/color, and time in the job and education corroborated positive effects on
the income differentials among northeastern commuters, especially at higher
income quantiles.
In general, in 2009, it was observed that the highest earnings from work
were earned by yellow men, regardless of race and color, with men earning
higher wages than women, with these disparities being more expressive in the
upper (or higher) quantiles of the conditional wage distribution. In addition,
higher-income differentials were also found for those allocated in the public
administration sector, with a more extended period of employment (over ten
years), who had the highest levels of education (master’s and doctorate).
97
98
As for 2019, the results are like those contacted in the first year of anal-
ysis. Again, the variables corresponding to sex and race/color could express
greater returns in terms of income, especially for white men, in the highest
quantiles of the conditional wage distribution. Likewise, it is worth noting
that higher levels of education, such as a doctorate and more experience at
work, result in higher earnings for commuting workers at different points in
the conditional distribution of profits from work.
Finally, this article provides actual results for the empirical literature that
deals with commuting migrations. The contributions are made to show the
impacts exerted by the socioeconomic and demographic characteristics on
the income differentials of formal work in the Northeast region as a whole
and information about the profile of commuting migrants of legal profession
in the area. However, the research presents some limitations imposed by the
data source itself.
For future studies, alternative databases are suggested, allowing the incor-
poration of other variables considered necessary for the analysis of commut-
ing flows, as in the case of the average travel time and residence sector, among
others. It is also recommended that non-commuting workers be considered in
the econometric model to verify the existence of income differentials between
the groups of commuters and non-commuters to complement this study.
Ethics Statement
This research article did not work with a person or groups of persons for
the generation of data used in the methodology, therefore it did not require
the endorsement of an Ethics Committee for its realization.
References
Bassett Jr, G., & Koenker, R. (1978). Asymptotic Theory of Least Absolute
Error Regression. Journal of the American Statistical Association, 73(363),
618-622. https://doi.org/10.2307/2286611
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
As for 2019, the results are like those contacted in the first year of anal-
ysis. Again, the variables corresponding to sex and race/color could express
greater returns in terms of income, especially for white men, in the highest
quantiles of the conditional wage distribution. Likewise, it is worth noting
that higher levels of education, such as a doctorate and more experience at
work, result in higher earnings for commuting workers at different points in
the conditional distribution of profits from work.
Finally, this article provides actual results for the empirical literature that
deals with commuting migrations. The contributions are made to show the
impacts exerted by the socioeconomic and demographic characteristics on
the income differentials of formal work in the Northeast region as a whole
and information about the profile of commuting migrants of legal profession
in the area. However, the research presents some limitations imposed by the
data source itself.
For future studies, alternative databases are suggested, allowing the incor-
poration of other variables considered necessary for the analysis of commut-
ing flows, as in the case of the average travel time and residence sector, among
others. It is also recommended that non-commuting workers be considered in
the econometric model to verify the existence of income differentials between
the groups of commuters and non-commuters to complement this study.
Ethics Statement
This research article did not work with a person or groups of persons for
the generation of data used in the methodology, therefore it did not require
the endorsement of an Ethics Committee for its realization.
References
Bassett Jr, G., & Koenker, R. (1978). Asymptotic Theory of Least Absolute
Error Regression. Journal of the American Statistical Association, 73(363),
618-622. https://doi.org/10.2307/2286611
Lecturas de Economía -Lect. Econ. - No. 101. Medellín, enero-junio 2024
Lopes da Silva, C. D., Justo, W. R. and da Silva Filho L. A.: Income Differentials in...
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0982009000100008
Becker, G. S. (1962). Investment in Human Capital: A Theoretical Analy-
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org/stable/1829103
Borges, J. P. S., Carniello, M. F., & Tadeucci, M. S. R. (2014) Caracterização
do desenvolvimento industrial do município de Imperatriz – MA: uma
análise dos últimos 13 anos. III Congresso Internacional de Ciência,
Tecnologia e Desenvolvimento. CICTED, 2014. https://unitau.br/file
s/arquivos/category_154/MPH1500_1427394717.pdf
Buchinsky, M. (1998). Recent Advances in Quantile Regression Models: A
Practical Guideline for Empirical Research. Journal of Human Resources,
33(1), 88-126. https://doi.org/10.2307/146316
Carvalho, A. D., Néri, M., & Silva, D. D. N. (2006). Diferenciais de salários
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de Geografia e Estatística]. https://hdl.handle.net/10438/934
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los, T. D. (2009). Movimento pendular da população na Região Sul. Ob-
servatório das Metrópoles. Universidade Federal do Rio de Janeiro.
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1058/4403?mode=full
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dias brasileiras (Master’s tesis, Universidade Federal de Viçosa). https:
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