Three essays on economic inequality

This doctoral dissertation is divided in three chapters. All of them deal with aspects related to the measurement of economic inequality, but each one has a distinct topic and puts its focus on a specific standpoint. Inheritances and Wealth Inequality: A Machine Learning Approach. This chapter explo...

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Detalhes bibliográficos
Autor: Salas Rojo, Pedro
Formato: tesis doctoral
Fecha de publicación:2022
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/3954
Acesso em linha:https://hdl.handle.net/20.500.14352/3954
Access Level:acceso abierto
Palavra-chave:330.52(043.2)
Wealth
Riqueza
Economía
53 Ciencias Económicas
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spelling Three essays on economic inequality(Tres ensayos sobre desigualdad económica)Salas Rojo, Pedro330.52(043.2)WealthRiquezaEconomía53 Ciencias EconómicasThis doctoral dissertation is divided in three chapters. All of them deal with aspects related to the measurement of economic inequality, but each one has a distinct topic and puts its focus on a specific standpoint. Inheritances and Wealth Inequality: A Machine Learning Approach. This chapter 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. Overcoming this limitation, we apply Machine Learning methods to optimize the choice of cut-offs (‘random forest’ algorithm) 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...Universidad Complutense de MadridRodríguez, Juan GabrielUniversidad Complutense de Madrid20222022-11-1520222022-11-15doctoral thesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/doctoralThesisapplication/pdfhttps://hdl.handle.net/20.500.14352/3954reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/39542026-06-02T12:44:21Z
dc.title.none.fl_str_mv Three essays on economic inequality
(Tres ensayos sobre desigualdad económica)
title Three essays on economic inequality
spellingShingle Three essays on economic inequality
Salas Rojo, Pedro
330.52(043.2)
Wealth
Riqueza
Economía
53 Ciencias Económicas
title_short Three essays on economic inequality
title_full Three essays on economic inequality
title_fullStr Three essays on economic inequality
title_full_unstemmed Three essays on economic inequality
title_sort Three essays on economic inequality
dc.creator.none.fl_str_mv Salas Rojo, Pedro
author Salas Rojo, Pedro
author_facet Salas Rojo, Pedro
author_role author
dc.contributor.none.fl_str_mv Rodríguez, Juan Gabriel
Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 330.52(043.2)
Wealth
Riqueza
Economía
53 Ciencias Económicas
topic 330.52(043.2)
Wealth
Riqueza
Economía
53 Ciencias Económicas
description This doctoral dissertation is divided in three chapters. All of them deal with aspects related to the measurement of economic inequality, but each one has a distinct topic and puts its focus on a specific standpoint. Inheritances and Wealth Inequality: A Machine Learning Approach. This chapter 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. Overcoming this limitation, we apply Machine Learning methods to optimize the choice of cut-offs (‘random forest’ algorithm) 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...
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-11-15
2022
2022-11-15
dc.type.none.fl_str_mv doctoral thesis
http://purl.org/coar/resource_type/c_db06
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/3954
url https://hdl.handle.net/20.500.14352/3954
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Complutense de Madrid
publisher.none.fl_str_mv Universidad Complutense de Madrid
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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