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Social and Economic Convergence Across Brazilian States Between 1990 and 2010

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Abstract

The present paper analyzes the convergence in economic and social terms across Brazilian states from 1990 to 2010. We argue that a more ample perspective is enlightening because income convergence does not necessarily go hand in hand with social convergence and income is not the only relevant aspect of well-being. Social convergence is captured by selected indicators, such as years of study, life expectancy at birth and the absence of crime. Using OLS, fixed effects and spatial dependence models, we find that GDP per capita has the highest dispersion across states and its absolute convergence is relatively slow. Social conditions, in contrast, have become considerably more equal and seem to converge towards a unique steady state at half-lives between 8 and 12 years. Absence of crime shows a peculiar behavior and a non-linear inconclusive convergence path.

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  1. According to Easterly (1999), conflicting views about the relation between living conditions and economic growth are far from new in the literature. While early development economists were optimistic about the positive impact of growth on a range of health, education and political indicators, the second generation of economists and political scientists contested these conclusions.

  2. Using other related variables such as the literacy rate or infant mortality qualitatively yields the same results, see the preliminary version of this research in de Almeida (2018).

  3. Brazil is the largest economy in South America and one of the most unequal. For example, the Southeast region is responsible for 55.2% of the country’s GDP, while the North region is much larger in terms of area but participates with only 5.2%. According to recent data from the Atlas of Human Development (available at: http://atlasbrasil.org.br), the state of São Paulo has one of the highest Municipal Human Development Indexes (IDHM) in the country, reaching 0.829, while the state of Alagoas has one of the lowest indexes, of around 0.667. This scenario was even worse in 2000, when the IDHM of Alagoas was 0.471 and that of São Paulo was 0.702.

  4. The North is formed by 7 states, the Northeast by 9 states, the Central-West by 3 states plus the Federal District, the Southeast by 4 and the South by 3 states. Therefore, Brazil is made up of 26 states plus the Federal District, where the country’s capital Brasilia is located.

  5. Still, one needs to be aware that within each region, state and municipality, pronounced disparities between those indicators exist, too. Note, however, that a more in depth discussion of the within regional inequalities is beyond the scope of this research.

  6. From the mid-2000s onwards, there was a commodity boom that strongly favored the agribusiness in Brazil.

  7. The MHDI is an indicator that measures the population’s quality of life. It consists of three indices: education, longevity and income. The higher the indicator, the better the quality of life.

  8. For results regarding the MPI in Brazilian municipalities in 2000 and 2010, see Costa et al. (2018). Overall, Brazil obtained a MPI of 0.039 in the year 2010 and was ranked at 39\(^{th}\) position among 109 developing countries.

  9. We did not opt for a panel data model with random effects because if the fixed effects do indeed capture variables that influence the economies’ states and thus the convergence rate, they will also be correlated with the economies’ initial size. The Hausman (1978) test corroborates the appropriateness of the fixed effects model in the present case.

  10. The queen matrix considers, in addition to the common boundaries, the common vertices (nodes), which is equivalent to the movement of the queen in chess. In this case, the matrix element assumes value 1 if the regions i and j are neighbors, and value 0, if i and j are not neighbors. The main diagonal has all elements equal to zero, by definition.

  11. Some microeconomic reforms were also of great relevance, such as the new bankruptcy law and the introduction of payroll-deductible loans. The adjustments made in addition to the external scenario of strong global growth and higher commodity prices between 2003 and 2010 strongly marked the performance of the Brazilian economy.

  12. Since the variation of interest in the FE model is within states, we report the within-\(R^2\) in these cases.

  13. Moran’s I can vary between − 1 and + 1 and indicate the degree spatial autocorrelation similar to the usual Spearman correlation coefficient. Moran’s I is obtained through regression using the growth rate of the variable and a value of the initial variable, both taken in mean values.

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Acknowledgements

We are grateful to Benjamin Tabak and two anonymous referees for their comments and suggestions. The authors acknowledge financial support by the CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior), the FAP-DF (Fundação de apoio à pesquisa do Distrito Federal) Grant Number 10509.56.40698.08042016, and the CNPq (National Council for Scientific and Technological Development).

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  1. University Center FAG, Toledo, Brazil

    Rubiane Daniele Cardoso de Almeida

  2. Economia, Catholic University of Brasília, SGAN 916, Brasília, 70790-160, Brazil

    Philipp Ehrl & Tito Belchior Silva Moreira

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  1. Rubiane Daniele Cardoso de Almeida
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Correspondence to Philipp Ehrl.

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de Almeida, R.D.C., Ehrl, P. & Moreira, T.B.S. Social and Economic Convergence Across Brazilian States Between 1990 and 2010. Soc Indic Res 157, 225–246 (2021). https://doi.org/10.1007/s11205-021-02659-x

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