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The University of Texas Inequality Project Global Inequality Data Sets, 1963–2008: Updates, Revisions and Quality Checks

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Abstract

The UTIP-UNIDO data set of industrial pay inequality is a panel comprised of the between-groups component of Theil’s T statistic measured in different countries and years across a stable and consistent set of industrial sectors. The Theil method is described in full elsewhere (Conceição, Ferreira and Galbraith 1999). Initially computed by Galbraith, Lu and Darity (1999) and updated by Galbraith and Kum (2004), the UTIP-UNIDO data set has the virtue of providing dense, consistent, accurate measures, and it has the limitation of being restricted to the inequality of inter-industrial pay. Its principal direct interest for economists is the study of common trends and of common factors affecting inequality, such as interest rates, debt crises, changing financial regimes, technology and trade. It has also proved to be a sensitive measure of major political events. Perhaps most important, the UTIP-UNIDO measures prove to be an effective instrument for estimating Gini coefficients for gross household income inequality, and this permits the construction of a dense and consistent panel of such estimates, complementing and extending the direct measurements available from other sources.

Keywords

  • Income Inequality
  • Income Distribution
  • Contemporary Issue
  • Income Mobility
  • Luxembourg Income Study

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2016 James K. Galbraith, Béatrice Halbach, Aleksandra Malinowska, Amin Shams and Wenjie Zhang

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Galbraith, J.K., Halbach, B., Malinowska, A., Shams, A., Zhang, W. (2016). The University of Texas Inequality Project Global Inequality Data Sets, 1963–2008: Updates, Revisions and Quality Checks. In: Stiglitz, J.E., Guzman, M. (eds) Contemporary Issues in Microeconomics. International Economic Association Series. Palgrave Macmillan, London. https://doi.org/10.1057/9781137529718_2

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