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Multidimensional Poverty in Brazil in the Early 21st Century: Evidence from the Demographic Census

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Abstract

This paper examines multidimensional poverty in Brazil in 2000 and 2010, based on the microdata of the Demographic Censuses. Our analysis is disaggregated into five classes of municipalities according to their degree of urbanisation and remoteness, highlighting wide rural–urban inequalities in the levels and dynamics of poverty. We compare estimates of traditional monetary poverty with multidimensional poverty measures based on two methods: (i) the Alkire-Foster counting identification approach; and (ii) the Permanyer two-stage poverty identification approach. The two-stage approach introduces the concepts of complementarity/substitutability within and across poverty dimensions, which enables a more precise identification of the population targeted by anti-poverty policies. All methods highlight substantial progress in poverty alleviation. In absolute terms, the reduction in the incidence of multidimensional poverty was significantly larger in the initially poorest areas—rural and intermediate municipalities, as well as those in the North and North–East regions. Important advances were made in standard of living, especially in the access to electricity, durable consumer goods and private bathroom in the households in rural and intermediate municipalities. However, remote municipalities remain relatively poorer from any perspective, facing more difficulties in reducing monetary poverty.

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Fig. 1

Data source: IBGE (2017b)

Fig. 2

Data source: Demographic Census (IBGE 2017a)

Fig. 3

Data source: Demographic Census (IBGE 2017a)

Fig. 4

Data source: Demographic Census (IBGE 2017a)

Fig. 5

Data source: Demographic Census (IBGE 2017a)

Fig. 6

Data source: Demographic Census (IBGE 2017a)

Fig. 7

Data source: Demographic Census (IBGE 2017a)

Fig. 8

Data source: Demographic Census (IBGE 2017a)

Fig. 9

Data source: Demographic Census (IBGE 2017a)

Fig. 10

Data source: Demographic Census (IBGE 2017a)

Fig. 11

Data source: Demographic Census (IBGE 2017a)

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Availability of Data and Materials

Available upon request to the authors.

Notes

  1. Poor sanitation means that households lack access to any of the following services: general water supply, sanitary sewage or septic tank, and garbage collection (IBGE 2011a).

  2. Brazil does not have an official poverty line. The Brazilian government uses different income criteria to select beneficiaries of social programs. The Benefício de Prestação Continuada (BPC), for instance, which consists of a monthly allowance of a minimum wage, is aimed at the elderly and the disabled people with a monthly household income per capita below a quarter of the minimum wage. For more information on this and other social security benefits, see: https://www.inss.gov.br/beneficios/ (in Portuguese).

  3. The multidimensional poverty measure released for 119 countries contained only the first three dimensions. Six countries contained data for the five dimensions: Ecuador, Indonesia, Iraq, Mexico, Tanzania, and Uganda (World Bank 2018). It is worth noting that the Commission on Global Poverty did not recommend the inclusion of monetary poverty among the dimensions of the multidimensional poverty index (World Bank 2017).

  4. A scalar index is required for quantifying the incidence of multiple deprivations but not sufficient. For instance, the Human Development Index (HDI) is a scalar index that does not provide any information about the joint distribution of its dimensional indices—health, education, and income (UNDP 2018).

  5. For simplicity of presentation, following Alkire et al. (2015), the term dimension here refers to each variable. In the empirical application, the term indicator refers to each variable, while dimension refers to groups of indicators.

  6. The term individual may refer to a person or a household, depending on the available data and the choice of the unit of identification.

  7. The dimensional monotonicity principle requires that if a poor person, who is not deprived in all dimensions, becomes deprived in an additional dimension then poverty should increase (Alkire et al. 2015). In other words, if the deprivation score of any individual identified as multidimensionally poor increases (decreases), then the overall poverty should also increase (decrease).

  8. Distribution sensitivity concerns the inequality among the poor. According to this principle, an increase (decrease) in overall poverty due to an increase (decrease) in the deprivation score of a multidimensionally poor person should be greater the higher his or her score is.

  9. This section is based on Permanyer (2019) and also in a previous and extended version of it available at: https://www.ucm.es/data/cont/media/www/pag-37515/Permanyer_Mar16.pdf.

  10. In the two-stage identification approach, it is worth noting that the weighting structure of the AF method is not used, as the identification of the poor is based on the deprivations matrix \({\mathbf{g}}^{0}\), and not the weighted matrix \({\stackrel{-}{\mathbf{g}}}^{0}\).

  11. Some notations here differ from those presented by Permanyer (2019), in order to keep the same notation of the AF method in Sect. 3.1.1, and prevent any ambiguous interpretation.

  12. The superscripts \(w\) and \(b\) refers to within-dimension and between-dimensions, respectively.

  13. The Census tract is the minimum territorial unit—subdivisions of a municipality or municipality equivalent—for data collection.

  14. Two or more municipalities with strong population integration due to commuting to work or study, or contiguity between urban areas (in Portuguese, arranjos populacionais).

  15. Such as established by IBGE in the REGIC’s project (Regiões de Influência das Cidades), the three higher levels in the hierarchy: metropolis, regional capital, and sub-regional centre.

  16. As 58 new municipalities were created between 2000 and 2010, the same rural–urban typology of 2010 is assumed in the year 2000.

  17. In the study Consultations with the Poor in Brazil (World Bank 1999), which was part of a global research in 23 countries using participatory methods (Narayan et al. 2000), the adequate provision of basic public services (sanitation, education, infrastructure, and health services) was deemed a precondition to overcome poverty.

  18. The literature offers alternative normative criteria for the selection of dimensions and respective indicators. For instance, in contrast to participatory methods (e.g. Narayan et al. 2000) yielding a context-specific response, Nussbaum (2003, 2011) deduces a list of core capabilities meant to have universal validity.

  19. It was roughly equivalent to the international poverty line of US$ 2 per person per day (2011 PPP) (Campello and Neri 2014).

  20. Regarding cross-tabulations and equations for computing the measures of correlation (Cramer’s V) and redundancy (\({R}^{0}\)), see Alkire et al. (2015, Sect. 7.3).

  21. By the end of 2019, the Luz para Todos program had reached 16.8 million people in the Brazilian countryside. Program results, including data by state and region, are available at: https://eletrobras.com/pt/Paginas/Luz-para-Todos.aspx.

  22. Lasso de La Vega (2010) presents this first deprivation curve as an increasing step function that is right-continuous, with the horizontal axis displaying the identification cut-offs ranked in decreasing order. In this paper, because there are five classes of municipalities, the graphs are presented in the usual way to facilitate visualization.

  23. When the curves intersect, it is still possible to establish dominance conditions by restricting the set of \(k\).

  24. Taking into account that there are five classes of municipalities and twelve poverty cut-offs, the table of results from the statistical tests is not presented here. However, it is available upon request to the authors.

  25. Table 9 in the Appendix shows all these results.

  26. Based on the microdata sample of the Demographic Census, 0.1% of the Brazilian population corresponded to 218.491 people deprived in all indicators in 2010, of which 60% lived in predominantly rural municipalities.

  27. These results are not comparable with the global MPI since the components of the indices are not the same. Despite the relevance of the global MPI in cross-country comparisons, it is important to remember that the deprivation cut-offs are not suitable for Brazil, particularly with regard to the legislation on education (Brasil 2016). The latest MPI figures (UNDP & OPHI 2020) point out that the Brazilian population experienced 1.6% of the total possible deprivations they could experience in 2015, considering the missing indicator on nutrition and incomplete indicator on child mortality (the survey—PNAD 2015—did not collect the date of child deaths). The multidimensional poverty headcount (\(H\)) (i.e. population with a deprivation score of at least 33%) was estimated at 3.8% (7.9 million people in 2015), with an intensity of deprivation (\(A\)) of 42.5%. For more information on PNAD 2015, see: https://www.ibge.gov.br/estatisticas/multidominio/condicoes-de-vida-desigualdade-e-pobreza/9127-pesquisa-nacional-por-amostra-de-domicilios.html.

  28. Besides, it is arguably harder to compare other monetary and non-monetary indices meaningfully (think, for instance, of trying to compare the monetary square poverty gap against the non-monetary adjusted headcount ratio).

  29. See footnote 2.

  30. Osorio et al. (2011) show that the main changes in income poverty between 2004 and 2009 were a result of inclusive growth through the labour market, real increases in the minimum wage, and increases in coverage and benefits of targeted cash transfers. Regarding non-contributory benefits like BPC and Bolsa Família, Barros et al. (2010) found that each of them explains about 10% of the overall reduction in income inequality between 2001 and 2007. Over the period 2001–2011, Hoffmann (2013) estimates the contribution of these benefits to the decline in income inequality between 15 and 20%.

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Funding

Funding was provided by the CAPES Foundation—Ministry of Education of Brazil to the first author, as a postdoctoral research Grant (Project No. 88887.351760/2019-00).

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  1. Institute of Economics, University of Campinas (UNICAMP), Rua Pitágoras, 353, Barão Geraldo, Campinas, São Paulo, 13083-857, Brazil

    Adriana Stankiewicz Serra & Alexandre Gori Maia

  2. Leeds University Business School, Maurice Keyworth Building, Leeds, LS6 1AN, UK

    Gaston Isaias Yalonetzky

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Appendix

Appendix

See Appendix Table 9.

Table 9 \(\it H\), \(\it A\) and \(M_{0}\): Alkire-Foster counting method and two-stage identification of the poor—Brazil—2000 and 2010
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Stankiewicz Serra, A., Yalonetzky, G.I. & Maia, A.G. Multidimensional Poverty in Brazil in the Early 21st Century: Evidence from the Demographic Census. Soc Indic Res 154, 79–114 (2021). https://doi.org/10.1007/s11205-020-02568-5

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