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Inheritances and Wealth Inequality: a Machine Learning Approach

Author

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  • Pedro Salas-Rojo
  • Juan Gabriel Rodr guez
Abstract
This paper explores how the inheritances received influence the distribution of wealth (financial, non-financial and total) in four developed −but substantially different− countries: the United States, Canada, Italy and Spain. Following the inequality of opportunity literature, we first group individuals into types based on the inheritances received. Then, we estimate the between-types wealth inequality to approximate the part of overall wealth inequality explained by inheritances. After showing that traditional approaches lead to non-robust and arbitrary results, we apply Machine Learning methods to overcome this limitation. Among the available computing methods, we observe that the random forests is the most precise algorithm. By using this technique, we find that inheritances explain more than 65% of wealth inequality (Gini coefficient) in the US and Spain, and more than 40% in Italy and Canada. Finally, for the US and Italy, given the availability of parental education, we also include this circumstance in the analysis and study its interaction with inheritances. It is observed 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

  • Pedro Salas-Rojo & Juan Gabriel Rodr guez, 2020. "Inheritances and Wealth Inequality: a Machine Learning Approach," LWS Working papers 32, LIS Cross-National Data Center in Luxembourg.
    • Handle: RePEc:lis:lwswps:32
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    3. 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).
    4. 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.
    5. Roth, Steve & Bezemer, Dirk, 2024. "Introducing the THIAs: Total Haig-Simons U.S. Household Income Accounts, 1960–2021," MPRA Paper 121242, University Library of Munich, Germany.
    6. Moramarco, Domenico, 2023. "Fairness and Gini decomposition," Economics Letters, Elsevier, vol. 233(C).
    7. Domenico Moramarco, 2023. "Fairness and Gini decomposition," Working Papers 650, ECINEQ, Society for the Study of Economic Inequality.
    8. Roth, Steve, 2022. "Distributional Haig-Simons Income Accounts for U.S. Households, 2000-2019," MPRA Paper 114524, University Library of Munich, Germany.

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    More about this item

    Keywords

    Wealth inequality; inheritances; Machine Learning; inequality of opportunity; parental education;
    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
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

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