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.
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Acknowledgements
We acknowledge comments from LIS Datacenter seminar participants, Juan César Palomino, Dmitry Petrov, Paolo Brunori, Amaia Palencia, Alberto Palacios, Pablo de la Vega, Jordi Domenech, Josep Espasa, Piotr Paradowski, Johannes Zahnner and participants at the 14th RGS Conference, the XXVIII Encuentro de Economía Pública, the 9th ECINEQ Meeting and the 2021 EsCOE Conference on Economic Measurement. Two anonymous referees also provided very useful suggestions. This paper has received financial support from the Ministerio de Economía y Competitividad of Spain under project PID2019-107161GB-C33) and Comunidad de Madrid (Spain) under project H2019/HUM-5793-OPINBI-CM. Salas-Rojo also received financial support from the Horizon 2020 Research and Innovation Programme under Grant Agreement 730998, the InGRID-2 project, and the Ministerio de Ciencia, Innovación y Universidades through FPU grant 17/735. The usual disclaimer applies.
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Salas-Rojo, P., Rodríguez, J.G. Inheritances and wealth inequality: a machine learning approach. J Econ Inequal 20, 27–51 (2022). https://doi.org/10.1007/s10888-022-09528-8
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DOI: https://doi.org/10.1007/s10888-022-09528-8