IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1906.06747.html
   My bibliography  Save this paper

Shape Matters: Evidence from Machine Learning on Body Shape-Income Relationship

Author

Listed:
  • Suyong Song
  • Stephen S. Baek

Abstract

We study the association between physical appearance and family income using a novel data which has 3-dimensional body scans to mitigate the issue of reporting errors and measurement errors observed in most previous studies. We apply machine learning to obtain intrinsic features consisting of human body and take into account a possible issue of endogenous body shapes. The estimation results show that there is a significant relationship between physical appearance and family income and the associations are different across the gender. This supports the hypothesis on the physical attractiveness premium and its heterogeneity across the gender.

Suggested Citation

  • Suyong Song & Stephen S. Baek, 2019. "Shape Matters: Evidence from Machine Learning on Body Shape-Income Relationship," Papers 1906.06747, arXiv.org.
  • Handle: RePEc:arx:papers:1906.06747
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1906.06747
    File Function: Latest version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-594, July.
    2. Anne Case & Christina Paxson, 2008. "Stature and Status: Height, Ability, and Labor Market Outcomes," Journal of Political Economy, University of Chicago Press, vol. 116(3), pages 499-532, June.
    3. Song, Suyong, 2015. "Semiparametric estimation of models with conditional moment restrictions in the presence of nonclassical measurement errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 95-109.
    4. Böckerman, Petri & Vainiomäki, Jari, 2013. "Stature and life-time labor market outcomes: Accounting for unobserved differences," Labour Economics, Elsevier, vol. 24(C), pages 86-96.
    5. Pierre-André Chiappori & Sonia Oreffice & Climent Quintana-Domeque, 2012. "Fatter Attraction: Anthropometric and Socioeconomic Matching on the Marriage Market," Journal of Political Economy, University of Chicago Press, vol. 120(4), pages 659-695.
    6. Nicola Persico & Andrew Postlewaite & Dan Silverman, 2004. "The Effect of Adolescent Experience on Labor Market Outcomes: The Case of Height," Journal of Political Economy, University of Chicago Press, vol. 112(5), pages 1019-1053, October.
    7. Karim Chalak & Halbert White, 2011. "Viewpoint: An extended class of instrumental variables for the estimation of causal effects," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(1), pages 1-51, February.
    8. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
    9. Hausman, Jerry A & Taylor, William E, 1983. "Identification in Linear Simultaneous Equations Models with Covariance Restrictions: An Instrumental Variables Interpretation," Econometrica, Econometric Society, vol. 51(5), pages 1527-1549, September.
    10. Wada, Roy & Tekin, Erdal, 2010. "Body composition and wages," Economics & Human Biology, Elsevier, vol. 8(2), pages 242-254, July.
    11. Deaton, Angus & Arora, Raksha, 2009. "Life at the top: The benefits of height," Economics & Human Biology, Elsevier, vol. 7(2), pages 133-136, July.
    12. Courtemanche, Charles & Pinkston, Joshua C. & Stewart, Jay, 2015. "Adjusting body mass for measurement error with invalid validation data," Economics & Human Biology, Elsevier, vol. 19(C), pages 275-293.
    13. John Cawley, 2004. "The Impact of Obesity on Wages," Journal of Human Resources, University of Wisconsin Press, vol. 39(2).
    14. Nicola Persico & Andrew Postlewaite & Dan Silverman, 2001. "The Effect of Adolescent Experience on Labor Market Outcomes: The Case of Height, Third Version," PIER Working Paper Archive 04-013, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 15 Mar 2004.
    15. John Karl Scholz & Kamil Sicinski, 2015. "Facial Attractiveness and Lifetime Earnings: Evidence from a Cohort Study," The Review of Economics and Statistics, MIT Press, vol. 97(1), pages 14-28, March.
    16. Petter Lundborg & Paul Nystedt & Dan-Olof Rooth, 2014. "Height and Earnings: The Role of Cognitive and Noncognitive Skills," Journal of Human Resources, University of Wisconsin Press, vol. 49(1), pages 141-166.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1906.06747. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: http://arxiv.org/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.