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

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  • Brunori,Paolo
  • Hufe,Paul
  • Mahler,Daniel Gerszon

Abstract

This paper proposes a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. It illustrates how these methods represent a substantial improvement over existing empirical approaches to measure inequality of opportunity. First, the new methods minimize the risk of arbitrary and ad hoc model selection. Second, they provide a standardized way to trade 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

  • Brunori,Paolo & Hufe,Paul & Mahler,Daniel Gerszon, 2018. "The roots of inequality : estimating inequality of opportunity from regression trees," Policy Research Working Paper Series 8349, The World Bank.
  • Handle: RePEc:wbk:wbrwps:8349
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    Cited by:

    1. Paolo Brunori & Guido Neidhöfer, 2021. "The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(4), pages 900-927, December.
    2. Ana Suárez Álvarez & Ana Jesús López Menéndez, 2020. "Trends in Inequality of Opportunity for Developing Countries: Does the Economic Indicator Matter?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(2), pages 503-539, June.
    3. Hufe, Paul & Peichl, Andreas & Weishaar, Daniel, 2021. "Lower and Upper Bound Estimates of Inequality of Opportunity for Emerging Economies," IZA Discussion Papers 14680, Institute of Labor Economics (IZA).
    4. Paolo Brunori & Vito Peragine & Laura Serlenga, 2019. "Upward and downward bias when measuring inequality of opportunity," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 52(4), pages 635-661, April.
    5. Brunori, Paolo & Ferreira, Francisco H. G. & Peragine, Vito, 2021. "Prioritarianism and equality of opportunity," LSE Research Online Documents on Economics 108868, London School of Economics and Political Science, LSE Library.
    6. Chen, Xi & Yan, Binjian & Gill, Thomas M., 2020. "Childhood Circumstances and Health Inequality in Old Age: Comparative Evidence from China and the United States," IZA Discussion Papers 13460, Institute of Labor Economics (IZA).
    7. Vincenzo Carrieri & Apostolos Davillas & Andrew M. Jones, 2020. "A latent class approach to inequity in health using biomarker data," Health Economics, John Wiley & Sons, Ltd., vol. 29(7), pages 808-826, July.
    8. Paul Hufe & Andreas Peichl & Daniel Weishaar, 2019. "Lower and Upper Bounds of Inequality of Opportunity in Emerging Economies," ifo Working Paper Series 301, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    9. Hufe, Paul & Peichl, Andreas & Stöckli, Marc, 2018. "Ökonomische Ungleichheit in Deutschland – ein Überblick," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 185-199.
    10. Andreas Peichl & Marc Stöckli, 2018. "Ungleichheit und Umverteilung in Deutschland: Trends und Handlungsoptionen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(15), pages 18-22, August.
    11. Till van Treeck & Judith Niehues & Galina Kolev & Piotr Pysz & Peter Hampe & Andreas Peichl & Marc Stöckli & Georg Cremer, 2018. "Wie gerecht ist die Welt? – Soziale Ungleichheit und Wirtschaftswachstum," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(15), pages 03-25, August.
    12. Daniel Gerszon Mahler & Xavier Ramos, 2019. "Equality of Opportunity in Four Measures of Well‐Being," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(S1), pages 228-255, November.
    13. Tista Kundu & Arnaud Lefranc, 2020. "Inequality of Opportunity in Indian Society," CSH-IFP Working Papers 0014, Centre de Sciences Humaines, New Delhi, revised May 2020.
    14. Paolo Brunori & Paul Hufe & Daniel Mahler, 2018. "Wurzeln der Ungleichheit – Ist Ungleichheit gleich ungerecht?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(05), pages 18-22, March.
    15. Paul Hufe & Andreas Peichl & Daniel Weishaar, 2018. "Intergenerationelle Einkommensmobilität: Schlusslicht Deutschland?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(20), pages 20-28, October.
    16. European Commission, 2018. "Tax Policies in the European Union: 2018 Survey," Taxation Survey 2018, Directorate General Taxation and Customs Union, European Commission.
    17. Arnaud Lefranc & Tista Kundu, 2020. "Inequality of Opportunity in Indian Society," Working Papers hal-02539364, HAL.
    18. Pedro Salas-Rojo & Juan Gabriel Rodríguez, 2019. "The distribution of wealth in the U.S. and Spain: the role of socio-economic factors," Working Papers 506, ECINEQ, Society for the Study of Economic Inequality.
    19. Leonardo Gasparini & Irene Brambilla & Andrés César & Guillermo Falcone & Carlo Lombardo, 2020. "The Risk of Automation in Argentina," CEDLAS, Working Papers 0260, CEDLAS, Universidad Nacional de La Plata.

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    More about this item

    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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