Abstract
This paper reconsiders the Brazilian wage curve using individual data from the National Household Survey at 27 Federative Units over the period 2002–2009. We find evidence in favor of the Brazilian wage curve with an unemployment elasticity of −0.08. We also find that males in Brazil are significantly more responsive to local unemployment rates (−0.13) than their female counterparts. In fact, we find that the unemployment elasticity for women is statistically insignificant. Applying gender-specific unemployment rates, the elasticity for men decreases to −0.09, while the elasticity for women remains statistically insignificant. This paper also finds that the estimates for Brazilian wage curve are completely different for the case of formal and informal workers.
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Notes
In Portuguese “Pesquisa Nacional por Amostra de Domicílios” (PNAD).
The National Sample Survey (PNAD) is obtained annually except for Census years. The last two censuses were conducted in 2000 and 2010. So, the period 2001–2009 is the largest and most recent annual information sequence.
It is worth noting that the informality was even higher in 2002, when 37.25 % employees lacked a formal labor contract.
See Baltagi and Blien (1998) for more details.
As much as we like to have more regions for our study of Brazil, we believe that the Federative Unit is the lowest level of territorial aggregation for which the National Household Survey remains statistically representative.
Note that dynamics are not feasible here, since these survey data do not necessarily follow the same individual over the entire period.
We tried to include spatial spillovers (using different weights matrices) at the level of Federative Units. However, these spatial spillovers were not statistically significant at this level of territorial aggregation and we do not report these results here.
The results on the other control variables are available upon request.
The control variables are exactly the same as in previous regressions.
Contrary to our results, Ramos et al. (2009) also find that the unemployment elasticity for women working in the informal sector is higher than that for men in Colombia.
Fair reasons can be bad discipline, insubordination, employment abandonment, criminal acts, etc.
In Portuguese “Pesquisa Nacional por Amostra de Domicílios” (PNAD).
Or −0.246 with Heckman correction.
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We dedicate this paper in honor of Kajal Lahiri’s many contributions to empirical economics and econometrics.
Appendices
Data Appendix
The data set used in the present study is based on the National Household Survey (from Portuguese PNAD—Pesquisa Nacional por Amostra de Domicílios) for the period 2002–2009. After excluding the self-employed or paid family workers, the unemployed, inactive and missing observations, we are left with over 739,490 observations. The data provide representative information on the state level where the employee is located.
In this study, we use individual hourly wage deflated by an index especially developed by Corseuil and Foguel (2002) for this survey and updated each year by the Brazilian Institute of Geography and Statistics (from Portuguese IBGE—Instituto Brasiliero de Geografia e Estatística). We also calculated the unemployment rate at the state level for each year.
The control variables used in the regressions are the following:
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Age of the individual
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Gender Female = 1 and male = 0.
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Race Black = 1 and white = 0
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Household type This variable includes four categories: couple without children = 1; couple with children = 2; single mother with children = 3; other household types = 4.
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Education This variable includes four different categories: up to 7 years of education = 1; more than 7 and up to 10 years of education = 2; more than 10 and up to 14 years of education = 3; 15 or more years of education = 4.
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The individual’s years of tenure at the firm.
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Regions We distinguish among the 27 states of Brazil, also called Federative Units (UF).
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Occupation This variable includes nine categories: managers; professionals in the sciences and arts; technicians; administrative workers; service workers; vendors and trade service providers; agricultural workers; workers producing goods and services, and repair and maintenance; members of the armed forces and auxiliary.
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Industry classification This variable includes 12 categories: agricultural; other industrial activities; manufacturing industry; construction; trade and repair; accommodation and food; transport, storage and communication; public administration; education, health and social services; domestic services; other community, social and personal services; other activities.
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Formality of employment Formal employee = 1 and informal employee = 0.
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Sector Public = 1 and private = 0.
Summary statistics
Variable | Observations | Mean | SD | Min | Max |
|---|---|---|---|---|---|
Year | 739,490 | 2005.688 | 2.270173 | 2002 | 2009 |
Urban/rural areas | 739,490 | 0.908660 | 0.288092 | 0 | 1 |
Hourly wage | 739,490 | 4.455756 | 2.792494 | 1.5 | 15 |
Unemployment rate | 739,490 | 8.468754 | 2.258912 | 3.75 | 20.36 |
Working hours weekly | 739,490 | 41.23837 | 9.250804 | 10 | 60 |
Age | 739,490 | 34.12132 | 11.55742 | 15 | 75 |
Gender | 739,490 | 0.438409 | 0.496192 | 0 | 1 |
Race | 739,490 | 0.518010 | 0.499676 | 0 | 1 |
Tenure | 739,490 | 5.115286 | 6.790269 | 0 | 60 |
Sector | 739,490 | 0.103849 | 0.305064 | 0 | 1 |
Formality of employment | 739,490 | 0.680597 | 0.466246 | 0 | 1 |
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Baltagi, B.H., Rokicki, B. & de Souza, K.B. The Brazilian wage curve: new evidence from the National Household Survey. Empir Econ 53, 267–286 (2017). https://doi.org/10.1007/s00181-016-1105-5
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DOI: https://doi.org/10.1007/s00181-016-1105-5