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Polarization measurement and inference in many dimensions when subgroups can not be identified

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  • Anderson, Gordon

Abstract

The most popular general univariate polarization indexes for discrete and continuous variables are extended and combined to describe the extent of polarization between agents in a distribution defined over a collection of many discrete and continuous agent characteristics. A formula for the asymptotic variance of the index is also provided. The implementation of the index is illustrated with an application to Chinese urban household data drawn from six provinces in the years 1987 and 2001 (years spanning the growth and urbanization period subsequent to the economic reforms). The data relates to household adult equivalent log income, adult equivalent living space, which are both continuous variables and the education of the head of household which is a discrete variable. For this data set combining the characteristics changes the view of polarization that would be inferred from considering the indices individually.

Suggested Citation

  • Anderson, Gordon, 2011. "Polarization measurement and inference in many dimensions when subgroups can not be identified," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 5, pages 1-19.
    • Handle: RePEc:zbw:ifweej:201111
    DOI: 10.5018/economics-ejournal.ja.2011-11
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    References listed on IDEAS

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    1. Alberto Alesina & Enrico Spolaore, 1997. "On the Number and Size of Nations," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(4), pages 1027-1056.
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    3. Joan Esteban & Debraj Ray, 2005. "A Comparison of Polarization Measures," Working Papers 310, Barcelona School of Economics.
    4. Jean-Yves Duclos & Joan Esteban & Debraj Ray, 2004. "Polarization: Concepts, Measurement, Estimation," Econometrica, Econometric Society, vol. 72(6), pages 1737-1772, November.
    5. Anderson, Gordon & Crawford, Ian & Leicester, Andrew, 2011. "Welfare rankings from multivariate data, a nonparametric approach," Journal of Public Economics, Elsevier, vol. 95(3), pages 247-252.
    6. Chiara Gigliarano & Karl Mosler, 2009. "Constructing indices of multivariate polarization," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 7(4), pages 435-460, December.
    7. Gordon Anderson, 2008. "The empirical assessment of multidimensional welfare, inequality and poverty: Sample weighted multivariate generalizations of the Kolmogorov–Smirnov two sample tests for stochastic dominance," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(1), pages 73-87, March.
    8. Gordon Anderson & Maria Grazia Pittau & Roberto Zelli, 2011. "Partially Identified Poverty Status: A New Approach to Measuring Poverty and the Progress of the Poor," Working Papers tecipa-421, University of Toronto, Department of Economics.
    9. Koshevoy, G. A. & Mosler, K., 1997. "Multivariate Gini Indices," Journal of Multivariate Analysis, Elsevier, vol. 60(2), pages 252-276, February.
    10. Anderson, Gordon, 2004. "Toward an empirical analysis of polarization," Journal of Econometrics, Elsevier, vol. 122(1), pages 1-26, September.
    11. Jean-Yves Duclos & David E. Sahn & Stephen D. Younger, 2006. "Robust Multidimensional Poverty Comparisons," Economic Journal, Royal Economic Society, vol. 116(514), pages 943-968, October.
    12. Gordon Anderson, 2010. "Polarization Of The Poor: Multivariate Relative Poverty Measurement Sans Frontiers," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(1), pages 84-101, March.
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    Cited by:

    1. Stelios Rozakis & Athanasios Kampas, 2022. "An interactive multi-criteria approach to admit new members in international environmental agreements," Operational Research, Springer, vol. 22(4), pages 3461-3487, September.
    2. Gordon Anderson & Oliver Linton & Jasmin Thomas, 2017. "Similarity, dissimilarity and exceptionality: generalizing Gini’s transvariation to measure “differentness” in many distributions," METRON, Springer;Sapienza Università di Roma, vol. 75(2), pages 161-180, August.
    3. Tanzhe Tang & Amineh Ghorbani & Flaminio Squazzoni & Caspar G. Chorus, 2022. "Together alone: a group-based polarization measurement," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3587-3619, October.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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