Cargando…
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...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323889/ https://www.ncbi.nlm.nih.gov/pubmed/34329322 http://dx.doi.org/10.1371/journal.pone.0254785 |
_version_ | 1783731325512974336 |
---|---|
author | Song, Suyong Baek, Stephen |
author_facet | Song, Suyong Baek, Stephen |
author_sort | Song, Suyong |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8323889 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83238892021-07-31 Body shape matters: Evidence from machine learning on body shape-income relationship Song, Suyong Baek, Stephen PLoS One Research Article 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. Public Library of Science 2021-07-30 /pmc/articles/PMC8323889/ /pubmed/34329322 http://dx.doi.org/10.1371/journal.pone.0254785 Text en © 2021 Song, Baek https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Song, Suyong Baek, Stephen Body shape matters: Evidence from machine learning on body shape-income relationship |
title | 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_short | 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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323889/ https://www.ncbi.nlm.nih.gov/pubmed/34329322 http://dx.doi.org/10.1371/journal.pone.0254785 |
work_keys_str_mv | AT songsuyong bodyshapemattersevidencefrommachinelearningonbodyshapeincomerelationship AT baekstephen bodyshapemattersevidencefrommachinelearningonbodyshapeincomerelationship |