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On Measuring Income Polarization: An Approach Based on Regression Trees

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  • Mussini Mauro

    (Department of Economics, University of Verona, Via dell’Artigliere 8, Verona, ( Italy ))

Abstract

This article proposes the application of regression trees for analysing income polarization. Using an approach to polarization based on the analysis of variance, we show that regression trees can uncover groups of homogeneous income receivers in a data-driven way. The regression tree can deal with nonlinear relationships between income and the characteristics of income receivers, and it can detect which characteristics and their interactions actually play a role in explaining income polarization. For these features, the regression tree is a flexible statistical tool to explore whether income receivers concentrate around local poles. An application to Italian individual income data shows an interesting partition of income receivers.

Suggested Citation

  • Mussini Mauro, 2016. "On Measuring Income Polarization: An Approach Based on Regression Trees," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 221-236, June.
    • Handle: RePEc:vrs:stintr:v:17:y:2016:i:2:p:221-236:n:1
    DOI: 10.21307/stattrans-2016-015
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    References listed on IDEAS

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    1. Ambra Poggi & Jacques Silber, 2010. "On Polarization And Mobility: A Look At Polarization In The Wage–Career Profile In Italy," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(1), pages 123-140, March.
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    More about this item

    Keywords

    polarization; regression trees; recursive partitioning; ANOVA; JEL D31; D63; C14;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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