IDEAS home Printed from https://ideas.repec.org/p/bai/series/series_wp_02-2024.html
   My bibliography  Save this paper

Biases in inequality of opportunity estimates: measures and solutions

Author

Listed:
  • Domenico Moramarco

    (University of Bari)

  • Paolo Brunori

    (University of Firenze and London School of Economics)

  • Pedro Salas-Rojo

    (London School of Economics)

Abstract

In this paper we discuss some limitations of using survey data to measure inequality of opportunity. First, we highlight a link between the two fundamental principles of the theory of equal opportunities - compensation and reward - and the concepts of power and confidence levels in hypothesis testing. This connection can be used to address, for example, whether a sample has sufficient observations to appropriately measure inequality of opportunity. Second, we propose a set of tools to normatively assess inequality of opportunity estimates in any type partition. We apply our proposal to Conditional Inference Trees, a machine learning technique that has received growing attention in the literature. Finally, guided by such tools, we suggest that standard tree-based partitions can be manipulated to reduce the risk of compensation and reward principles. Our methodological contribution is complemented with an application using a quasi-administrative sample of Italian PhD graduates. We find a substantial level of labor income inequality among two cohorts of PhD graduates (2012 and 2014), with a significant portion explained by circumstances beyond their control.

Suggested Citation

  • Domenico Moramarco & Paolo Brunori & Pedro Salas-Rojo, 2024. "Biases in inequality of opportunity estimates: measures and solutions," SERIES 02-2024, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Aug 2024.
  • Handle: RePEc:bai:series:series_wp_02-2024
    as

    Download full text from publisher

    File URL: http://www.seriesworkingpapers.it/RePEc/bai/series/SERIES_WP_02-2024.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Francisco H. G. Ferreira & Jérémie Gignoux, 2011. "The Measurement Of Inequality Of Opportunity: Theory And An Application To Latin America," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 57(4), pages 622-657, December.
    3. John E. Roemer & Alain Trannoy, 2016. "Equality of Opportunity: Theory and Measurement," Journal of Economic Literature, American Economic Association, vol. 54(4), pages 1288-1332, December.
    4. Daniele Checchi & Vito Peragine, 2010. "Inequality of opportunity in Italy," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 8(4), pages 429-450, December.
    5. Alexander W. Cappelen & James Konow & Erik ?. S?rensen & Bertil Tungodden, 2013. "Just Luck: An Experimental Study of Risk-Taking and Fairness," American Economic Review, American Economic Association, vol. 103(4), pages 1398-1413, June.
    6. 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.
    7. Alexander W. Cappelen & Astri Drange Hole & Erik Ø Sørensen & Bertil Tungodden, 2007. "The Pluralism of Fairness Ideals: An Experimental Approach," American Economic Review, American Economic Association, vol. 97(3), pages 818-827, June.
    8. Juan Carlos Escanciano & Joel Robert Terschuur, 2022. "Machine Learning Inference on Inequality of Opportunity," Papers 2206.05235, arXiv.org, revised Oct 2023.
    9. Francisco H.G. Ferreira & Jérémie Gignoux, 2011. "The Measurement of Inequality of Inequality of Opportunity: Theory and an Application to Latin America," Post-Print halshs-00754503, HAL.
    10. Xavier Ramos & Dirk gaer, 2016. "Approaches To Inequality Of Opportunity: Principles, Measures And Evidence," Journal of Economic Surveys, Wiley Blackwell, vol. 30(5), pages 855-883, December.
    11. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    12. Brunori, Paolo & Hufe, Paul & Mahler, Daniel, 2023. "The roots of inequality: estimating inequality of opportunity from regression trees and forests," LSE Research Online Documents on Economics 118220, London School of Economics and Political Science, LSE Library.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Domenico Moramarco & Paolo Brunori & Pedro Salas-Rojo, 2024. "Biases in inequality of opportunity estimates: measures and solutions," Working Papers 675, ECINEQ, Society for the Study of Economic Inequality.
    2. Paul Hufe & Andreas Peichl & John Roemer & Martin Ungerer, 2017. "Inequality of income acquisition: the role of childhood circumstances," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 49(3), pages 499-544, December.
    3. Paolo Brunori & Paul Hufe & Daniel Gerszon Mahler, 2017. "The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees," Working Papers - Economics wp2017_18.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    4. Brunori, Paolo & Hufe, Paul & Mahler, Daniel Gerszon, 2021. "The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees and Forests," IZA Discussion Papers 14689, Institute of Labor Economics (IZA).
    5. 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.
    6. Xavier Ramos & Dirk Van de gaer, 2021. "Is Inequality of Opportunity Robust to the Measurement Approach?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(1), pages 18-36, March.
    7. John E. Roemer & Alain Trannoy, 2013. "Equality of Opportunity," Cowles Foundation Discussion Papers 1921, Cowles Foundation for Research in Economics, Yale University.
    8. Karin Hederos & Markus Jäntti & Lena Lindahl, 2017. "Gender and inequality of opportunity in Sweden," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 49(3), pages 605-635, December.
    9. Stefano Filauro & Flaviana Palmisano & Vito Peragine, 2023. "The Evolution of Inequality of Opportunity in Europe," Working Papers 644, ECINEQ, Society for the Study of Economic Inequality.
    10. Andreas Peichl & Martin Ungerer, 2016. "Accounting for the spouse when measuring inequality of opportunity," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 47(3), pages 607-631, October.
    11. Vito Peragine & Federico Biagi, 2019. "Equality of opportunity: theory, measurement and policy implications," JRC Research Reports JRC118542, Joint Research Centre.
    12. Brunori, Paolo & Hufe, Paul & Mahler, Daniel, 2023. "The roots of inequality: estimating inequality of opportunity from regression trees and forests," LSE Research Online Documents on Economics 118220, London School of Economics and Political Science, LSE Library.
    13. Pedro Salas-Rojo & Juan Gabriel Rodríguez, 2022. "Inheritances and wealth inequality: a machine learning approach," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 27-51, March.
    14. Ana Suárez Álvarez & Ana Jesús López Menéndez, 2018. "Assessing Changes Over Time in Inequality of Opportunity: The Case of Spain," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(3), pages 989-1014, October.
    15. Camarero Garcia, Sebastian, 2022. "Inequality of Educational Opportunities and the Role of Learning Intensity," Labour Economics, Elsevier, vol. 74(C).
    16. Song, Yang & Zhou, Guangsu, 2019. "Inequality of opportunity and household education expenditures: Evidence from panel data in China," China Economic Review, Elsevier, vol. 55(C), pages 85-98.
    17. Rafael Carranza, 2023. "Upper and Lower Bound Estimates of Inequality of Opportunity: A Cross‐National Comparison for Europe," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(4), pages 838-860, December.
    18. Carlos Gradín & Gabriela Zapata-Román, 2024. "Unpacking inequality of opportunity in Chile: the role of birth circumstances using a Shapley decomposition," Working Papers 676, ECINEQ, Society for the Study of Economic Inequality.
    19. Dirk Van de gaer & Xavier Ramos, 2020. "Measurement of inequality of opportunity based on counterfactuals," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 55(3), pages 595-627, October.
    20. Davillas, Apostolos & Jones, Andrew M, 2020. "Ex ante inequality of opportunity in health, decomposition and distributional analysis of biomarkers," Journal of Health Economics, Elsevier, vol. 69(C).

    More about this item

    Keywords

    Equality of opportunity; Machine learning; PhD graduates; Compensation; Reward.;
    All these keywords.

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bai:series:series_wp_02-2024. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Annalisa Vinella (email available below). General contact details of provider: https://edirc.repec.org/data/debarit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.