INTERNATIONAL TRADE, WAGES, AND SKILL PREMIUMS IN BRAZILIAN MUNICIPALITIES FROM 2000 TO 2010

Mendes, Krisley; Hidalgo, Álvaro Barrantes; Luchine, André Araújo

doi:10.1590/198055272608

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Revista de Economia Contemporânea

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Artigos • Rev. econ. contemp. 26 • 2022 • https://doi.org/10.1590/198055272608 copy

INTERNATIONAL TRADE, WAGES, AND SKILL PREMIUMS IN BRAZILIAN MUNICIPALITIES FROM 2000 TO 2010

COMÉRCIO INTERNACIONAL, SALÁRIOS E PRÊMIOS POR QUALIFICAÇÃO NOS MUNICÍPIOS BRASILEIROS NO DECÊNIO 2000-2010

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

This study aims to assess wage and skill premium responses to international trade in Brazil, determining different local specialization patterns with municipal data. We followed the theoretical model developed by Venables and Limão, who state that distance is a non-neutral variable in determining trade patterns. We used Chiquiar’s empirical model, which includes pooled regression with microdata and feasible generalized least squares in three steps. Selection bias and omitted variable biases were evaluated based on Redding and Schott and Oster. Results show that wage and skill premium responses to international trade are heterogeneous and depend on the geographic location. International trade seems to help reducing interregional inequalities in Brazil. However, it seems to intensify inter-skill wage inequalities in more distant zones, like Midwest and North Brazil. Thus, the reduction in income inequality due to a greater trade liberalization is not homogeneous in all regions of this developing country. Therefore, policies that geographically spread the benefits of international trade within Brazil would have to consider the occurrence of these regionally heterogeneous responses.

KEYWORDS:
Specialization zones; input prices; distance; municipalities; wages

Movements in the regression coefficients			Mesoregion classification
Inclusion of variables	Variations in $(β_{0} + β_{D z_{i}})$	Variations in $(β_{3} + β_{D z A E_{i}})$
Second regression compared to the first - Factor endowment	Undefined	Undefined	Zone I
Third regression compared to the second - Trade exposure	Decreased	Unchanged	Zone I
Second regression compared to the first - Factor endowment	Unchanged	Unchanged	Zone II
Third regression compared to the second - Trade exposure	Increased	Decreased	Zone II
Second regression compared to the first - Factor endowment	Increased	Increased	Zone III
Third regression compared to the second - Trade exposure	Unchanged	Unchanged	Zone III
Second regression compared to the first - Factor endowment	Unchanged	Unchanged	Zone IV
Third regression compared to the second - Trade exposure	Decreased	Increased	Zone IV
Second regression compared to the first - Factor endowment	Undefined	Undefined	Zone V
Third regression compared to the second - Trade exposure	Unchanged	Unchanged	Zone V

Variables	Description	Source
Dependent variable (y_it )
Wage (Y _iz )	Annual average value of the absolute wage received by individual i in municipality z at 2000 prices, with the 2010 values deflated by the IGP-di.	RAIS and FGV
Variables of interest (x_z )
Openness coefficient	Ratio between the sum of municipal exports and imports and the GDP in dollars of municipality z.	SECEX, IBGE, and IPEADATA
Fraction of personnel employed in agriculture and livestock	Logarithm of the participation of the personnel employed in agriculture and livestock per municipality.	RAIS
Fraction of personnel employed in industry	Logarithm of the participation of the personnel employed in industry per municipality.	RAIS
Distance from the economic center	Calculation according to Smarzynska (2001SMARZYNSKA, B. K. Does relative location matter for bilateral trade flows? Journal of Economic Integration, v. 16, n. 3, p. 379-398, 2001. DOI: https://doi.org/10.11130/jei.2001.16.3.379. https://doi.org/10.11130/jei.2001.16.3.3... )
Individual controls
Experience	Calculated as age - 6 - years of schooling.	RAIS
Experience²/100	The variable “experience” squared.	RAIS
Years of schooling⁹ 9 Years of schooling may be inaccurate, as they are estimated based on the schooling level reported. When the schooling level was incomplete, we attributed the number of years that corresponds to half of the way to complete it. In this sense, the variable “experience” is also innacurate.	The worker’s degree of education, obtained by converting the achieved education level into years.	RAIS
Age group dummy	Age group of individual i in municipality z. In this study, we considered individuals aged 18 to 64 years old. The base group is from 30 to 39 years old.	RAIS
Sector dummy	Sector in which individual i works, with trade as the base group.	RAIS
Municipal controls
Spillover	Average schooling level of workers in municipality z, representing the human capital spillover.	RAIS
Municipality size dummy	Municipality size range. Two categories: less and more than 500,000 inhabitants. The base group was a population of less than 500,000 inhabitants, representing the urban agglomeration effect.	IBGE
Altitude	Altitude of the municipality, according to the political-administrative division into force in 2000.	IPEADATA
Rainfall	Average of the estimates of quarterly rainfall averages.	IPEADATA
Labor factor endowment	Percentage of the economically active population within the total population.	IBGE
Earth factor endowment	Amount of planting area, in natural logarithm¹⁰ 10 Regarding municipalities that did not present planting areas in the years studied, we attributed the maximum value of the planting area of municipalities within the same population range in the group. .	IPEADATA/ IBGE
Capital factor endowment	Vehicle fleet as proxy, in natural logarithm.	DENATRAN

Variables	I			II			III
	2000	2010	Change	2000	2010	Change	2000	2010	Change
Years of schooling	0.0903^a	0.0842ª	-0.00619	0.0875ª	0.0822ª	-0.00530	0.0871ª	0.0819ª	-0.00523
	(0.00606)	(0.00239)	(0.0055)	(0.00575)	(0.00188)	(0.0054)	(0.00567)	(0.00178)	(0.00533)
Experience	0.0495ª	0.0303ª	-0.0192ª	0.0481ª	0.0296ª	-0.0185ª	0.0480ª	0.0296ª	-0.0184ª
	(0.0008)	(0.0005)	(0.0010)	(0.00080)	(0.00053)	(0.00101)	(0.00080)	(0.00054)	(0.00101)
Experience²	-0.0627ª	-0.0338ª	0.0290ª	-0.0606ª	-0.0327ª	0.0279ª	-0.0604ª	-0.0325ª	0.0279ª
	(0.00181)	(0.00061)	(0.00203)	(0.00166)	(0.00063)	(0.00188)	(0.00165)	(0.00063)	(0.00189)
Women	-0.269ª	-0.223ª	0.0457ª	-0.265ª	-0.222ª	0.0434ª	-0.265ª	-0.220ª	0.0451ª
	(0.00454)	(0.00405)	(0.00413)	(0.00419)	(0.00392)	(0.00375)	(0.00416)	(0.00393)	(0.00376)
Age	Yes	Yes		Yes	Yes		Yes	Yes

Sectors	Yes	Yes		Yes	Yes		Yes	Yes

Mesoregional dummies	Yes	Yes		Yes	Yes		Yes	Yes

Mesoregional dummies iterated with education	Yes	Yes		Yes	Yes		Yes	Yes

Municipal characteristics	No	No		Yes	Yes		Yes	Yes

Trade exposure	No	No		No	No		Yes	Yes

Constant	5.058ª	5.716ª	0.658ª	4.324ª	5.237ª	0.913ª	3.533ª	4.879ª	1.346ª
	(0.0451)	(0.0355)	(0.0355)	(0.0734)	(0.0528)	(0.0583)	(0.211)	(0.0988)	(0.184)

Observations	3,700,633	3,118,047	6,818,680	3,700,633	3,118,047	6,818,680	3,700,633	3,118,047	6,818,680
R²	0.444	0.346	0.504	0.455	0.353	0.514	0.458	0.356	0.517

Instituto de Economia da Universidade Federal do Rio de Janeiro Avenida Pasteur, 250 sala 114, Palácio Universitário, Instituto de Economia, 22290-240 Rio de Janeiro - RJ Brasil, Tel.: 55 21 3938-5242 - Rio de Janeiro - RJ - Brazil
E-mail: rec@ie.ufrj.br

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[1] Correspondência para: Krisley Mendes Contato: krisley@unb.br