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The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees

Author

Listed:
  • Paolo Brunori
  • Paul Hufe

    ()

  • Gerszon Daniel Mahler

Abstract

We propose a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. In particular, we illustrate how these methods represent a substantial improvement over existing empirical approaches to measure in equality of opportunity. First, they minimize the risk of arbitrary and ad-hoc model selection. Second, they provide a standardized way of trading off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions.

Suggested Citation

  • Paolo Brunori & Paul Hufe & Gerszon Daniel Mahler, 2018. "The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees," ifo Working Paper Series 252, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  • Handle: RePEc:ces:ifowps:_252
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    References listed on IDEAS

    as
    1. Marc Fleurbaey & Vito Peragine, 2013. "Ex Ante Versus Ex Post Equality of Opportunity," Economica, London School of Economics and Political Science, vol. 80(317), pages 118-130, January.
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    4. Paolo Brunori & Vito Peragine & Laura Serlenga, 2016. "Upward and downward bias when measuring inequality of opportunity," SERIES 05-2016, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Sep 2016.
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    More about this item

    Keywords

    Equality of opportunity; machine learning; random forests.;

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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