![]() ![]() Furthermore, such classical measurements of body shape are too sparse to describe the whole-body structure. The estimation results for the association between height (or BMI) and family income confirm that the reporting errors have substantial impacts on the estimated coefficients. In contrast, the nonparametric conditional mean and nonlinear quantile regressions show that there are substantial nonclassical errors in the reported weight of both genders. Thus, it would be more plausible to impose a restriction on the conditional quantile of the reporting error of height than the conditional mean (see Bollinger Hu and Schennach and Song ). The quantile regression provides that the conditional median of the reporting error is independent of the true height. The nonparametric estimation of the conditional expectations of the reporting errors in height, given the true height, shows that the reporting error for female height is nonclassical in that the reporting error and the true height are dependent. We investigate the properties of reporting errors using a nonparametric conditional mean and nonlinear quantile functions. Consequently, measurement errors in the body shapes would make it difficult to correctly estimate the true relation. In addition, measurements such as height, weight, and BMI are too sparse to characterize detailed body shapes (see Kan and Lee ). This presents a possibility of attenuation bias from reporting errors on physical appearance, in estimating the relationship between physical appearance and labor market outcomes. However, these measurements of physical appearance are acquired through subjective survey responses. Hamermesh and Biddle studied the impact of facial attractiveness on wages and demonstrated significant beauty premium. Cawley estimated the effects of BMI on wages and reported that weight lowers the wages of white females. They found apparent height premium in the labor market outcomes. and Case and Paxson analyzed the association between height and wages. In studies on the association between physical attractiveness and labor market outcomes, height, weight, and body mass index (BMI) have been popular choices, as measurements of physical appearance. The data after applying our proposed methods is held in a public repository at We also uploaded the dataset as a Supporting information file.įunding: The author(s) received no specific funding for this work.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The original data underlying the results presented in the study are available from the website for the Civilian American and European Surface Anthropometry Resource Project at. Received: OctoAccepted: JPublished: July 30, 2021Ĭopyright: © 2021 Song, Baek. ![]() University of Luxembourg and Luxembourg Institute of Socio-Economic Research (LISER), LUXEMBOURG This supports the hypothesis on the physical attractiveness premium in labor market outcomes and its heterogeneity across genders.Ĭitation: Song S, Baek S (2021) Body shape matters: Evidence from machine learning on body shape-income relationship. The estimation results reveal a statistically significant relationship between physical appearance and family income and that these associations differ across genders. We also take into account a possible issue of endogenous body shapes using proxy variables and control functions. 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 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. In this study, we use novel data, called CAESAR, which contains three-dimensional (3D) whole-body scans to mitigate possible reporting and measurement errors. However, most previous studies often measured physical appearance using classical proxies from subjective opinions based on surveys. The association between physical appearance and income has been of central interest in social science.
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