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Inheritances and wealth inequality: a machine learning approach

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  • Salas-Rojo, Pedro
  • Rodríguez, Juan Gabriel

Abstract

This paper explores the relationship between received inheritances and the distribution of wealth (financial, non-financial and total) in four developed countries: the United States, Canada, Italy and Spain. We follow the inequality of opportunity (IOp) literature and − considering inheritances as the only circumstance− we show that traditional IOp approaches can lead to non-robust and arbitrary measures of IOp depending on discretionary cut-off choices of a continuous circumstance such as inheritances. To overcome this limitation, we apply Machine Learning methods (‘random forest’ algorithm) to optimize the choice of cut-offs and we find that IOp explains over 60% of wealth inequality in the US and Spain (using the Gini coefficient), and more than 40% in Italy and Canada. Including parental education as an additional circumstance −available for the US and Italy− we find that inheritances are still the main contributor. Finally, using the S-Gini index with different parameters to weight different parts of the distribution, we find that the effect of inheritances is more prominent at the middle of the wealth distribution, while parental education is more important for the asset-poor.

Suggested Citation

  • Salas-Rojo, Pedro & Rodríguez, Juan Gabriel, 2022. "Inheritances and wealth inequality: a machine learning approach," LSE Research Online Documents on Economics 120916, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:120916
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    Cited by:

    1. Pedro Salas-Rojo & Juan Gabriel Rodríguez, 2021. "The distribution of wealth in Spain and the USA: the role of socioeconomic factors," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 389-421, September.
    2. Daniel Graeber & Viola Hilbert & Johannes König, 2023. "Inequality of Opportunity in Wealth: Levels, Trends, and Drivers," CEPA Discussion Papers 69, Center for Economic Policy Analysis.
    3. Roth, Steve, 2022. "Distributional Haig-Simons Income Accounts for U.S. Households, 2000-2019," MPRA Paper 114524, University Library of Munich, Germany.
    4. Graeber, Daniel & Hilbert, Viola & König, Johannes, 2023. "Inequality of Opportunity in Wealth: Levels, Trends, and Drivers," IZA Discussion Papers 16488, Institute of Labor Economics (IZA).
    5. Thibaut Plassot & Isidro Soloaga & Pedro J. Torres, 2022. "A Random Forest approach of the Evolution of Inequality of Opportunity in Mexico," Working Papers 614, ECINEQ, Society for the Study of Economic Inequality.
    6. Balwant Singh Mehta & Siddharth Dhote & Ravi Srivastava, 2023. "Decomposition of Inequality of Opportunity in India: An Application of Data-Driven Machine Learning Approach," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 66(2), pages 439-469, June.
    7. Daniel Graeber & Viola Hilbert & Johannes König, 2023. "Inequality of Opportunity in Wealth: Levels, Trends, and Drivers," SOEPpapers on Multidisciplinary Panel Data Research 1193, DIW Berlin, The German Socio-Economic Panel (SOEP).
    8. Domenico Moramarco, 2023. "Fairness and Gini decomposition," Working Papers 650, ECINEQ, Society for the Study of Economic Inequality.
    9. Moramarco, Domenico, 2023. "Fairness and Gini decomposition," Economics Letters, Elsevier, vol. 233(C).

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    More about this item

    Keywords

    inequality of opportunity; inheritances; machine learning; parental education; wealth inequality;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • G00 - Financial Economics - - General - - - General

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