Social and Economic Convergence Across Brazilian States Between 1990 and 2010

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Notes

  1. 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. 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. 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. 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. 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. 6.

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

  7. 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. 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. 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. 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. 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. 12.

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

  13. 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.

References

  1. Alkire, S., & Foster, J. (2011). Understandings and misunderstandings of multidimensional poverty measurement. Journal of Economic Inequality, 9(2), 289–314.

    Article  Google Scholar 

  2. Alkire, S., & Santos, M. E. (2010). Acute multidimensional poverty: A new index for developing countries. United Nations development programme human development report office background paper, 2010/11.

  3. Anselin, L. (1988). Model validation in spatial econometrics: A review and evaluation of alternative approaches. International Regional Science Review, 11(3), 279–316.

    Article  Google Scholar 

  4. Anselin, L., & Rey, S. (1991). Properties of tests for spatial dependence in linear regression models. Geographical Analysis, 23(2), 112–131.

    Article  Google Scholar 

  5. Azzoni, C. R., Menezes Filho, N., de Menezes, T., & Silveira-Neto, R. (2000). Geography and income convergence among Brazilian states. Inter-American Development Bank Discussion Paper, 3096.

  6. Barro, R. J. (1991). Economic growth in a cross section of countries. The Quarterly Journal of Economics, 106(2), 407–443.

    Article  Google Scholar 

  7. Barro, R., & Sala-i Martin, X. (2003). Economic growth (2nd ed.). Cambridge, MA: MIT Press.

    Google Scholar 

  8. Barro, R. J., & Sala-i Martin, X. (1992). Convergence. Journal of Political Economy, 100(2), 223–251.

    Article  Google Scholar 

  9. Barro, R. J., Sala-i Martin, X., Blanchard, O. J., & Hall, R. E. (1991). Convergence across states and regions. Brookings Papers on Economic Activity, 1991(1), 107–182.

    Article  Google Scholar 

  10. Baumol, W. J. (1986). Productivity growth, convergence, and welfare: What the long-run data show. American Economic Review, 76(5), 1072–1085.

    Google Scholar 

  11. Becker, G. S., Philipson, T. J., & Soares, R. R. (2005). The quantity and quality of life and the evolution of world inequality. American Economic Review, 95(1), 277–291.

    Article  Google Scholar 

  12. Cerqueira, D., de Lima, R. S., Bueno, S., Valencia, L. I., Hanashiro, O., Machado, P. H. G., et al. (2017). Atlas da violência 2017. Rio de Janeiro: Instituto de Pesquisa Econômica Aplicada.

    Google Scholar 

  13. Cerqueira, D., & Lobão, W. (2003). Determinantes da criminalidade: uma resenha dos modelos teóricos e resultados empíricos. IPEA Discussion Paper, 956.

  14. Corbucci, P. R., Kubota, L. C., & Meira, A. P. B. (2016). Evolução da educação superior privada no Brasil: da reforma universitária de 1968 à década de 2010. Radar, 46, 7–12.

    Google Scholar 

  15. Costa, G. O. T., Machado, A. F., & Amaral, P. V. (2018). Vulnerability to poverty in Brazilian municipalities in 2000 and 2010: A multidimensional approach. EconomiA, 19(1), 132–148.

    Article  Google Scholar 

  16. Cravo, T. A., Becker, B., & Gourlay, A. (2015). Regional growth and SMEs in Brazil: A spatial panel approach. Regional Studies, 49(12), 1995–2016.

    Article  Google Scholar 

  17. da Silva, J. J., Bruno, M. A. P., & Silva, D. B. N. (2020). Pobreza multidimensional no Brasil: uma análise do período 2004–2015. Brazilian Journal of Political Economy, 40(1), 138–160.

    Article  Google Scholar 

  18. de Almeida, R. D. C. (2018). Ensaios sobre convergência, crescimento econômico e desigualdade entre os estados brasileiros. Ph.D. thesis, Catholic University of Brasília.

  19. Dorius, S. F. (2008). Global demographic convergence? A reconsideration of changing intercountry inequality in fertility. Population and Development Review, 34(3), 519–537.

    Article  Google Scholar 

  20. dos Santos, L. B., Miranda, R. B., & Moreira, T. B. S. (2015). A pobreza no Brasil e as estratégias de superação. Revista de Economia e Agronegócio, 10(3), 359–396.

    Google Scholar 

  21. Easterlin, R. A. (1996). Does satisfying material needs increase human happiness? In R. A. Easterlin (Ed.), Growth triumphant: The twenty-first century in historical perspective (Vol. 10, pp. 131–136). Ann Arbor: University of Michigan Press.

    Google Scholar 

  22. Easterly, W. (1999). Life during growth. Journal of Economic Growth, 4(3), 239–276.

    Article  Google Scholar 

  23. Ehrl, P. (2017). Minimum comparable areas for the period 1872–2010: An aggregation of Brazilian municipalities. Estudos Econômicos, 47(1), 215–229.

    Google Scholar 

  24. Ehrl, P., & Monasterio, L. (2019). Skill concentration and persistence in Brazil. Regional Studies, 53(11), 1544–1554.

    Article  Google Scholar 

  25. Ehrl, P., & Monasterio, L. (2021). Spatial skill concentration agglomeration economies. Journal of Regional Science, 61(1), 140–161.

    Article  Google Scholar 

  26. Ferreira, A. (2000). Convergence in Brazil: Recent trends and long-run prospects. Applied Economics, 32(4), 479–489.

    Article  Google Scholar 

  27. Gonçalves, R., & Ehrl, P. (2021). Efeitos econômicos da Zona Franca de Manaus. Pesquisa e Planejamento Econômico.

  28. Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.

    Article  Google Scholar 

  29. Hobijn, B., & Franses, P. H. (2001). Are living standards converging? Structural Change and Economic Dynamics, 12(2), 171–200.

    Article  Google Scholar 

  30. Islam, N. (1995). Growth empirics: A panel data approach. The Quarterly Journal of Economics, 110(4), 1127–1170.

    Article  Google Scholar 

  31. Kenny, C. (2005). Why are we worried about income? Nearly everything that matters is converging. World Development, 33(1), 1–19.

    Article  Google Scholar 

  32. Kuznets, S. (1946). National income and its composition, 1919–1938. New York: NBER.

    Google Scholar 

  33. LeSage, J. P., & Pace, R. K. (2009). Introduction to spatial econometrics (statistics, textbooks and monographs). Boca Raton: CRC Press.

    Book  Google Scholar 

  34. Loureiro, P. R., Moreira, T., Nascimento, A., & Ellery, R. (2018). Does the political party in the government increase intentional homicide in Brazil? Review of Development Economics, 22(2), 706–726.

    Article  Google Scholar 

  35. Loureiro, P. R., Moreira, T. B. S., & Ellery, R. (2017). The relationship between political parties and tolerance to criminality: A theoretical model and empirical evidences for Brazil. International Journal of Social Economics, 44(12), 1871–1891.

    Article  Google Scholar 

  36. Magalhães, A., Hewings, G. J. D., & Azzoni, C. R. (2005). Spatial dependence and regional convergence in Brazil. Investigaciones Regionales, 6, 5–20.

    Google Scholar 

  37. Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407–437.

    Article  Google Scholar 

  38. Marchante, A. J., & Ortega, B. (2006). Quality of life and economic convergence across Spanish regions, 1980–2001. Regional Studies, 40(5), 471–483.

    Article  Google Scholar 

  39. Mendes, C. C., Chebenova, D., & Lorena, A. C. (2019). 30 years of the Brazilian Federal Constitution: Perspectives for Brazilian federalism. Rio de Janeiro: Instituto de Pesquisa Econômica Aplicada (IPEA).

    Google Scholar 

  40. Moran, P. A. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society. Series B (Methodological), 10(2), 243–251.

    Article  Google Scholar 

  41. Neumayer, E. (2003). Beyond income: Convergence in living standards, big time. Structural Change and Economic Dynamics, 14(3), 275–296.

    Article  Google Scholar 

  42. Quah, D. T. (1996). Empirics for economic growth and convergence. European Economic Review, 40(6), 1353–1375.

    Article  Google Scholar 

  43. Resende, G. M., de Carvalho, A. X. Y., Sakowski, P. A. M., & Cravo, T. A. (2016). Evaluating multiple spatial dimensions of economic growth in Brazil using spatial panel data models. Annals of Regional Science, 56(1), 1–31.

    Article  Google Scholar 

  44. Rey, S. J., & Montouri, B. D. (1999). US regional income convergence: A spatial econometric perspective. Regional Studies, 33(2), 143–156.

    Article  Google Scholar 

  45. Rocha, W. M., Monasterio, L. M., & Ehrl, P. (2016). Qual foi o impacto do FIES nos salários? Radar, 46, 33–38.

    Google Scholar 

  46. Rodríguez-Posé, A., & Tselios, V. (2015). Toward inclusive growth: Is there regional convergence in social welfare? International Regional Science Review, 38(1), 30–60.

    Article  Google Scholar 

  47. Royuela, V., & García, G. A. (2015). Economic and social convergence in Colombia. Regional Studies, 49(2), 219–239.

    Article  Google Scholar 

  48. Sachsida, A., Mendonça, M. J. C., & Moreira, T. B. S. (2015). O impacto de diferentes tipos de repressão legal sobre as taxas de homicídio entre os estados brasileiros. Revista Brasileira de Políticas Públicas, 5(3), 99–122.

    Google Scholar 

  49. Schneider, S., & Cassol, A. (2014). Diversidade e heterogeneidade da agricultura familiar no brasil e algumas implicações para políticas públicas. Cadernos de Ciência & Tecnologia, 31(2), 227–263.

    Google Scholar 

  50. Sen, A. (1976). Real national income. Review of Economic Studies, 43(1), 19–39.

    Article  Google Scholar 

  51. Silva, M. N. F. (2018). Capital público e investimentos privados no Nordeste brasileiro: Bahia, Pernambuco, Rio Grande do Norte e Ceará. Turismo e Sociedade, 11(1), 86–112.

    Article  Google Scholar 

  52. Soares, F. V., Ribas, R. P., & Osório, R. G. (2010). Evaluating the impact of Brazil’s Bolsa Familia: Cash transfer programs in comparative perspective. Latin American Research Review, 45, 173–190.

    Article  Google Scholar 

  53. Solow, R. M. (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70(1), 65–94.

    Article  Google Scholar 

  54. Todaro, M. P. (1997). Economic development (6th ed.). New York: Longman.

    Google Scholar 

  55. Williamson, J. G. (1965). Regional inequality and the process of national development: A description of the patterns. Economic Development and Cultural Change, 13(4, Part 2), 1–84.

    Article  Google Scholar 

  56. Young, A. T., Higgins, M. J., & Levy, D. (2008). Sigma convergence versus beta convergence: Evidence from US county-level data. Journal of Money, Credit and Banking, 40(5), 1083–1093.

    Article  Google Scholar 

  57. Zanchi, V., Maciel, D., & Ehrl, P. (2021). Direct and indirect effects between Individualism, Institutions and the Homicide Rate. Social Indicators Research, 153(3), 1167–1195.

    Article  Google Scholar 

Download references

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).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Philipp Ehrl.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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 (2021). https://doi.org/10.1007/s11205-021-02659-x

Download citation

Keywords

  • Convergence
  • Inequality
  • Economic growth
  • Social indicators
  • HDI
  • Brazil

JEL Classification

  • C23
  • O47
  • R11