Body shape matters: Evidence from machine learning on body shape-income relationship.

The association between physical appearance and income has been of central interest in social science. However, most previous studies often measured physical appearance using classical proxies from subjective opinions based on surveys. In this study, we use novel data, called CAESAR, which contains...

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Autores principales:	Suyong Song, Stephen Baek
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a99f3606b5a14e6aa188bb5119a52697
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id	oai:doaj.org-article:a99f3606b5a14e6aa188bb5119a52697
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spelling	oai:doaj.org-article:a99f3606b5a14e6aa188bb5119a526972021-12-02T20:08:51ZBody shape matters: Evidence from machine learning on body shape-income relationship.1932-620310.1371/journal.pone.0254785https://doaj.org/article/a99f3606b5a14e6aa188bb5119a526972021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254785https://doaj.org/toc/1932-6203The association between physical appearance and income has been of central interest in social science. However, most previous studies often measured physical appearance using classical proxies from subjective opinions based on surveys. In this study, we use novel data, called CAESAR, which contains three-dimensional (3D) whole-body scans to mitigate possible reporting and measurement errors. We demonstrate the existence of significant nonclassical reporting errors in the reported heights and weights by comparing them with measured counterparts, and show that these discrete measurements are too sparse to provide a complete description of the body shape. Instead, we use a graphical autoencoder to obtain intrinsic features, consisting of human body shapes directly from 3D scans and estimate the relationship between body shapes and family income. We also take into account a possible issue of endogenous body shapes using proxy variables and control functions. The estimation results reveal a statistically significant relationship between physical appearance and family income and that these associations differ across genders. This supports the hypothesis on the physical attractiveness premium in labor market outcomes and its heterogeneity across genders.Suyong SongStephen BaekPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254785 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Suyong Song Stephen Baek Body shape matters: Evidence from machine learning on body shape-income relationship.
description	The association between physical appearance and income has been of central interest in social science. However, most previous studies often measured physical appearance using classical proxies from subjective opinions based on surveys. In this study, we use novel data, called CAESAR, which contains three-dimensional (3D) whole-body scans to mitigate possible reporting and measurement errors. We demonstrate the existence of significant nonclassical reporting errors in the reported heights and weights by comparing them with measured counterparts, and show that these discrete measurements are too sparse to provide a complete description of the body shape. Instead, we use a graphical autoencoder to obtain intrinsic features, consisting of human body shapes directly from 3D scans and estimate the relationship between body shapes and family income. We also take into account a possible issue of endogenous body shapes using proxy variables and control functions. The estimation results reveal a statistically significant relationship between physical appearance and family income and that these associations differ across genders. This supports the hypothesis on the physical attractiveness premium in labor market outcomes and its heterogeneity across genders.
format	article
author	Suyong Song Stephen Baek
author_facet	Suyong Song Stephen Baek
author_sort	Suyong Song
title	Body shape matters: Evidence from machine learning on body shape-income relationship.
title_short	Body shape matters: Evidence from machine learning on body shape-income relationship.
title_full	Body shape matters: Evidence from machine learning on body shape-income relationship.
title_fullStr	Body shape matters: Evidence from machine learning on body shape-income relationship.
title_full_unstemmed	Body shape matters: Evidence from machine learning on body shape-income relationship.
title_sort	body shape matters: evidence from machine learning on body shape-income relationship.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a99f3606b5a14e6aa188bb5119a52697
work_keys_str_mv	AT suyongsong bodyshapemattersevidencefrommachinelearningonbodyshapeincomerelationship AT stephenbaek bodyshapemattersevidencefrommachinelearningonbodyshapeincomerelationship
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Body shape matters: Evidence from machine learning on body shape-income relationship.

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