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Problems with SWIID: the case of South Africa

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  • Martin Wittenberg

    (DataFirst, University of Cape Town)

Abstract

The information contained in databases of summary statistics should look plausible when viewed in context. Judged by that criterion the Standardized World Income Inequality Database (SWIID) comes up short with its South African data. Figure 1 contains the series as extracted from the SWIID web site (Solt 2014b). The 95% confidence bands suggest that inequality in 1965, at the height of apartheid, was significantly lower than in the 2000s. This, however, flies in the face of much other evidence. For instance it is well-known that Black mine workers' wages were static in real terms from the early twentieth century right up to the 1970s (e.g. van der Berg 1989). The wages of white miners, by contrast, increased, so that the ratio of White to Black mine wages reached its maximum of twenty to one in 1969 (Devereux 1983, p.18). Simkins (1979) estimated the Gini coefficient in 1970 at 0.71, which seems more in line with the political and social realities. Trying to understand how the SWIID may have arrived at such a misleading estimate is instructive about the types of problems that may be lurking elsewhere in the database.There are four potential sources of error in SWIID: measurement error, model error, imputation error and sampling error.

Suggested Citation

  • Martin Wittenberg, 2015. "Problems with SWIID: the case of South Africa," SALDRU Working Papers 148, Southern Africa Labour and Development Research Unit, University of Cape Town.
  • Handle: RePEc:ldr:wpaper:148
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    References listed on IDEAS

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    1. Stephen Jenkins, 2015. "World income inequality databases: an assessment of WIID and SWIID," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(4), pages 629-671, December.
    2. Mr. Desmond Lachman & Mr. Kenneth Bercuson, 1992. "Economic Policies for a New South Africa," IMF Occasional Papers 1992/003, International Monetary Fund.
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    Cited by:

    1. Francisco G. Ferreira & Nora Lustig & Daniel Teles, 2015. "Appraising cross-national income inequality databases: An introduction," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(4), pages 497-526, December.
    2. Krieger, Tim & Renner, Laura, 2020. "Polygyny, inequality, and social unrest," Discussion Paper Series 2020-02, University of Freiburg, Wilfried Guth Endowed Chair for Constitutional Political Economy and Competition Policy.
    3. Le, Thai-Ha & Nguyen, Canh Phuc & Su, Thanh Dinh & Tran-Nam, Binh, 2020. "The Kuznets curve for export diversification and income inequality: Evidence from a global sample," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 21-39.
    4. Florian Dorn, 2016. "On Data and Trends in Income Inequality around the World," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(4), pages 54-64, December.
    5. repec:ces:ifodic:v:14:y:2016:i:4:p:19267790 is not listed on IDEAS
    6. Grunewald, Nicole & Klasen, Stephan & Martínez-Zarzoso, Inmaculada & Muris, Chris, 2017. "The Trade-off Between Income Inequality and Carbon Dioxide Emissions," Ecological Economics, Elsevier, vol. 142(C), pages 249-256.
    7. Christian Houle, 2017. "Inequality, ethnic diversity, and redistribution," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(1), pages 1-23, March.
    8. Florian Dorn, 2016. "On Data and Trends in Income Inequality around the World," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(04), pages 54-64, December.
    9. Christian Houle, 2017. "Inequality, ethnic diversity, and redistribution," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(1), pages 1-23, March.

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