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The roots of inequality: Estimating inequality of opportunity from regression trees

Author

Listed:
  • Paolo Brunori

    (University of Florence, Italy)

  • Paul Hufe

    (ifo Munich and LMU Munich, Germany)

  • Daniel Gerszon Mahler

    (University of Copenhagen, Denmark and World Bank)

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 inequality of opportunity. First, they minimize the risk of arbitrary and ad-hoc model selection. Second, they provide a standardized way of trading of 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 & Daniel Gerszon Mahler, 2018. "The roots of inequality: Estimating inequality of opportunity from regression trees," Working Papers 455, ECINEQ, Society for the Study of Economic Inequality.
  • Handle: RePEc:inq:inqwps:ecineq2018-455
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    File URL: http://www.ecineq.org/milano/WP/ECINEQ2018-455.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Equality of opportunity; machine learning; random forests.;
    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
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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