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Income Inequality in Latin America: Data Challenges and Availability

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Abstract

This article discusses the main challenges faced by nowadays researchers when measuring income inequality in Latin America. The discussion is articulated around the two main approaches to summarize the distributional problem: factor and personal inequality. The article underlines the difficulties, mainly imposed by data availability for inequality measurement under both of them. The article proposes specific lines to advance in the improvement of data quality and availability for inequality measurement (and understanding) in the region.

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Notes

  1. For discussions about recent evolution of inequality in Latin America, see Cornia (2010), López Calva and Lustig (2011), Gasparini et al. (2011), Birdsall et al. (2011), ECLAC (2012), among others.

  2. A constant value of labor share of two thirds is considered by many researchers as plausible.

  3. National accounts are calculated by central banks in Chile, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Nicaragua, Paraguay, Uruguay and Venezuela. They are compiled by statistical agencies in Argentina, Bolivia, Brasil, Colombia, México, Panamá and Perú.

  4. These countries are: Bolivia, Brasil, Chile, Colombia, Costa Rica, Ecuador, Honduras, México, Nicaragua, Panamá, Paraguay, Perú, Uruguay y Venezuela.

  5. This information can be found in Cepalstat, www.eclac.org.

  6. UNIDO compiles the Industrial Statistics Database (INDSAT) and the OECD compiles the Structural and Demographic Business Statistics Database (SDBS).

  7. According to Atkinson (1986), this method was first proposed by Feinstein.

  8. They study the impact of factors that may affect the labor share, such as the price of imported materials or capital augmenting technical progress, union bargaining and labor adjustment costs (hiring and firing costs).

  9. In some countries, like Brazil and Uruguay, the first household surveys date from the late 60 s, but they were interrupted during some years. The World Bank initiative, Living Standards Measurement Study (LSMS)-developed in the 80 s—helped to increase the capacity of statistical institutes to perform household surveys. LSMS surveys collect data on consumption, income, savings, employment, health, education, fertility, nutrition, housing and migration. In the region, they were carried in Brazil, Ecuador, Guatemala, Nicaragua, Panamá and Perú. Details of this experience can be found in Grosh and Glewwe (1995) and Deaton (1997). Later on, in 1996, MECOVI program, a joint initiative by ECLAC; IADB and the World Bank, also helped countries to generate good quality information about living conditions in the region. Nowadays, most Latin American countries have annual data on income distribution. Chile and Mexico have surveys every two years, whereas Guatemala and Nicaragua have more sporadic surveys. Details about household surveys in Latin America can be found at Cepal Statistics’ web page, http://interwp.cepal.org/badehog/default2.asp.

  10. In particular, this article refers to comparability in terms of harmonization of measurement and definitions, which is only one component of the broader problem of comparability of household surveys (see Kish 1994).

  11. In Brazil and Panamá income is measured in gross terms, as well as in Colombia in the case of salaried workers.

  12. For estimations of equivalence scales for the region see Alonzo and Mancero (2011). Inequality measures from CEDLAS, discussed later on this text, are calculated on equivalized terms.

  13. A detailed comparison of incomes from household surveys and national accounts for Uruguay shows that household surveys capture only 40 % of housing rents going to households, and 23 % of interests from household’s bank deposits (Amarante et al. 2007). In the case of France, a comparison of household surveys and national account suggests that the Gini coefficient of household income could be underestimated by 2 % points due to underreporting of capital income (Atkinson and Bourguignon 2000). Barros et al. (2007) analyze underestimation of income for the PNAD in Brazil, and find that eventough household income underestimation in the household survey is high, this does not substantially affect the measurement of income inequality or of its evolution.

  14. An exception was the case of Chile until 2012, where the official figures on poverty and inequality were calculated using ECLAC’s adjusted household data.

  15. The regional data sets included in LIS are: Brazil (2006), Colombia (2004, 2007, 2010), Mexico (1984, 1989, 1992, 1994, 1996, 1998, 2000, 2002, 2004), Peru (2004) and Uruguay (2004). See http://www.lisproject.org.

  16. See Cepalstat, http://estadisticas.cepal.org/cepalstat/WEB_CEPALSTAT/Portada.asp.

  17. http://sedlac.econo.unlp.edu.ar/esp/.

  18. http://www.wider.unu.edu/research/Database/en_GB/database/.

  19. This is not an exhaustive record. At the national levels, there exist other panels covering specific areas or populations, usually designed for specific purposes, and whose results can not necessary be generalized.

  20. See http://epp.eurostat.ec.europa.eu/portal/page/portal/microdata/echp.

  21. The first wave included data for Belgium, Denmark, France, Germany, Greece, Ireland, Italy, Luxembourg, The Netherlands, Portugal, Spain, United Kingdom. Later on, Austria and Sweden were added.

  22. See http://epp.eurostat.ec.europa.eu/portal/page/portal/microdata/eu_silc.

  23. See https://www.iser.essex.ac.uk/euromod.

  24. http://www.commitmentoequity.org/.

  25. The concept of augmented wealth proposed by Wolff (1996) includes human capital or other comparable measure of future earnings possibilities.

  26. LWS includes datasets from 12 countries: Austria, Canada, Cyprus, Finland, Germany, Italy, Japan, Luxembourg, Norway, Sweden, the United Kingdom, and the United States. See http://www.lisdatacenter.org/our-data/lws-database/.

  27. This first wave includes data from Belgium, Germany, Greece, Spain, France, Italy, Cyprus, Luxembourg, Malta, Netherlands, Austria, Portugal, Slovenia and Slovakia. See http://www.ecb.int/home/html/researcher_hfcn.en.html.

  28. See www.efh.cl.

  29. See Lindert (2000) and Morrison (2000) for reviews of previous estimations of top income shares combining tax shares and national accounts.

  30. The major part of the data constructed has been complied as part of The World Top Incomes Database, a joint initiative of Alvaredo, Atkinson, Piketty and Saez. This database includes data for Argentina and Colombia (whereas there is work in progress for Brazil, Chile and Uruguay). For details see http://topincomes.g-mond.parisschoolofeconomics.eu/.

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Acknowledgments

The author is greatful to Simone Cecchini, Xavier Mancero, Cecilia Rossel and Andrea Vigorito for useful comments on earlier versions of this article.

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  1. Social Development Division, ECLAC, Av. Dag Hammarskjöld 3477, Santiago, Chile

    Verónica Amarante

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Correspondence to Verónica Amarante.

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Amarante, V. Income Inequality in Latin America: Data Challenges and Availability. Soc Indic Res 119, 1467–1483 (2014). https://doi.org/10.1007/s11205-013-0564-8

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