When Does Beauty Pay? A Large-Scale Image-Based Appearance Analysis on Career Transitions

References

• Altonji JG (2005) Employer learning, statistical discrimination and occupational attainment Amer. Econom. Rev. 95:(2)112–117.Crossref, Google Scholar
• Altonji JG, Smith AA Jr, Vidangos I (2013) Modeling earnings dynamics. Econometrica 81(4):1395–1454.Crossref, Google Scholar
• Amos B, Bartosz L, Satyanarayanan M (2016) OpenFace: A general-purpose face recognition library with mobile applications. Technical Report No. CMU-CS-16-118, Carnegie Mellon University, Pittsburgh.Google Scholar
• Arunachalam, Shah R, Manisha (2010) The prostitute's allure: Examining returns to beauty, productivity and discrimination. IZA Discussion Papers No. 5064, Institute for the Study of Labor (IZA), Bonn.Google Scholar
• Baker G, Gibbs M, Holmstrom B (1994) The internal economics of the firm: Evidence from personnel data. Quart. J. Econom. 109(4):881–919.Crossref, Google Scholar
• Becker HS, Geer B (1957) Participant observation and interviewing: A comparison. Human Organ. 16(3):28–32.Crossref, Google Scholar
• Biddle JE, Hamermesh DS (1998) Beauty, productivity, and discrimination: Lawyers’ looks and lucre. J. Labor Econom. 16(1):172–201.Crossref, Google Scholar
• Chen BC, Chen CS, Hsu WH (2014) Cross-age reference coding for age-invariant face recognition and retrieval. European Conf. Comput. Vision (Springer, Cham, Switzerland), 768–783).Google Scholar
• Dion K, Berscheid E, Walster E (1972) What is beautiful is good. J. Personality Soc. Psych. 24(3):285–290.Crossref, Google Scholar
• Doorley K, Sierminska E (2015) Myth or fact? The beauty premium across the wage distribution in Germany. Econom. Lett. 129:29–34.Google Scholar
• Fang H, Moro A (2011) Theories of statistical discrimination and affirmative action: A survey. Benhabib J, Bisin A, Jackson MO, eds. Handbook of Social Economics, vol. 1 (North-Holland, San Diego), 133–200.Google Scholar
• Fruhen LS, Watkins CD, Jones BC (2015) Perceptions of facial dominance, trustworthiness and attractiveness predict managerial pay awards in experimental tasks. Leadership Quart. 26(6):1005–1016.Crossref, Google Scholar
• Gan J, Li L, Zhai Y, Liu Y (2014) Deep self-taught learning for facial beauty prediction. Neurocomputing 144:295–303.Crossref, Google Scholar
• Gao L, Li W, Huang Z, Huang D, Wang Y (2018) Automatic facial attractiveness prediction by deep multi-task learning. 2018 24th International Conference on Pattern Recognition (ICPR) (IEEE, Piscataway, NJ), 3592–3597.Google Scholar
• Gayle G, Golan L, Miller R (2012) Gender differences in executive compensation and job mobility. J. Labor Econom. 30(4):829–872.Crossref, Google Scholar
• Gunarathne P, Rui H, Seidmann A (2022) Racial bias in customer service: Evidence from Twitter. Inform. Systems Res. 33(1):43–54.Link, Google Scholar
• Hamermesh D, Biddle JE (1994) Beauty and the labor market. Amer. Econom. Rev. 84(5):1174–1194.Google Scholar
• Hirsch BT, Macpherson DA (2004) Wages, sorting on skill, and the racial composition of jobs. J. Labor Econom. 22(1):189–210.Crossref, Google Scholar
• Kokkodis M (2021) Dynamic, multidimensional, and skillset-specific reputation systems for online work. Inform. Systems Res. 32(3):688–712.Link, Google Scholar
• Kokkodis M, Lappas T (2020) Your hometown matters: Popularity-difference bias in online reputation platforms. Inform. Systems Res. 31(2):412–430.Link, Google Scholar
• Kulka RA, Kessler JB (1978) Is justice really blind?–The influence of litigant physical attractiveness on juridical judgment. J. Appl. Soc. Psych. 8(4):366–381.Crossref, Google Scholar
• Langer N, Gopal RD, Bapna R (2020) Onward and upward? An empirical investigation of gender and promotions in information technology services. Inform. Systems Res. 31(2):383–398.Link, Google Scholar
• Lanitis A, Taylor CJ, Cootes TF (2002) Toward automatic simulation of aging effects on face images. IEEE Trans. Pattern Anal. Machine Intelligence 24(4):442–455.Crossref, Google Scholar
• Lee HCB, Ba S, Li X, Stallaert J (2018) Salience bias in crowdsourcing contests. Inform. Systems Res. 29(2):401–418.Link, Google Scholar
• Lee S-Y, Rui H, Whinston AB (2019) Is best answer really the best answer? The politeness bias. MIS Quart. 43(2):579–600.Crossref, Google Scholar
• Liang L, Xie D, Jin L, Xu J, Li M, Lin L (2017) Region-aware scattering convolution networks for facial beauty prediction. 2017 IEEE Internat. Conf. Image Processing (ICIP) (IEEE, Piscataway, NJ), 2861–2865.Google Scholar
• Liu Y, Pant G, Sheng ORL (2020) Predicting labor market competition: Leveraging interfirm network and employee skills. Inform. Systems Res. 31(4):1443–1466.Link, Google Scholar
• Mobius M, Rosenblat T (2006) Why beauty matters. Amer. Econom. Rev. 96(1):222–235.Crossref, Google Scholar
• Niessen-Ruenzi A, Ruenzi S (2019) Sex matters: Gender bias in the mutual fund industry. Management Sci. 65(7):3001–3025.Link, Google Scholar
• Olivola C, Todorov A (2010) Elected in 100 milliseconds: Appearance-based trait inferences and voting. J. Nonverbal Behav. 34(2):83–110.Crossref, Google Scholar
• Page L, Brin S, Motwani R, Winograd T (1999) The PageRank Citation Ranking: Bringing Order to the Web (Stanford InfoLab, Stanford, CA).Google Scholar
• Park J, Konana P, Gu B, Kumar A, Raghunathan R (2013) Information valuation and confirmation bias in virtual communities: Evidence from stock message boards. Inform. Systems Res. 24(4):1050–1067. Link, Google Scholar
• Phelps ES (1972) The statistical theory of racism and sexism. Amer. Econom. Rev. 62(4):659–661.Google Scholar
• Ruffle BJ, Shtudiner ZE (2014) Are good-looking people more employable? Management Sci. 61(8):1760–1776.Link, Google Scholar
• Sabzehzar A, Burtch G, Hong Y, Raghu TS (2022) Putting religious bias in context: How offline and online context shape religious bias in online pro-social lending. MIS Quart. 47(1):33–62.Crossref, Google Scholar
• Santos J, Reynaldo A (1999) Cronbachs alpha: A tool for assessing the reliability of scales. J. Extension 37(2):1–5.Google Scholar
• Scholz JK, Sicinski K (2015) Facial attractiveness and lifetime earnings: Evidence from a cohort study. Rev. Econom. Statist. 97(1):14–28.Google Scholar
• Sin I, Stillman S, Fabling R (2022) What drives the gender wage gap? Examining the roles of sorting, productivity differences, bargaining, and discrimination. Rev. Econom. Statist. 104(4):636–651.Crossref, Google Scholar
• Treble J, van Gameren E, Bridges S, Barmby D (2001) The internal economics of the firm: Further evidence from personnel data. Labour Econom. 8(5):531–552.Crossref, Google Scholar
• Usui E (2015) Occupational gender segregation in an equilibrium search model. IZA J. Labor Econom. 4:1–14.Google Scholar
• Walker TB, Feloni R (2020) Google’s Unconscious Bias Training Presentation (Business Insider). Accessed June 8, 2020.Google Scholar
• Wei Y, Malik N (2023) Unstructured data, econometric models, and estimation bias. USC Marshall School of Business Research Paper Sponsored by iORB. Accessed 19 October 2023, https://ssrn.com/abstract=4113608.Google Scholar
• Xie D, Liang L, Jin L, Xu J, Li M (2015) SCUT-FPB: A benchmark dataset for facial beauty perception. 2015 IEEE Internat. Conf. Systems, Man, and Cybernetics (IEEE, Piscataway, NJ), 1821–1826.Google Scholar
• Xu J, Jin L, Liang L, Feng Z, Xie D, Mao H (2017) Facial attractiveness prediction using psychologically inspired convolutional neural network (PI-CNN). 2017 IEEE Internat. Conf. Acoustics, Speech and Signal Processing (ICASSP) (IEEE, Piscataway, NJ).Google Scholar
• Yang K, Lau RYK, Abbasi A(2022) Getting personal: A deep learning artifact for text-based measurement of personality. Inform. Systems Res. 34(1):194–222.Link, Google Scholar
• Yang M, Adomavicius G, Burtch G, Ren Y (2018) Mind the gap: Accounting for measurement error and misclassification in variables generated via data mining. Inform. Systems Res. 29(1):4–24.Google Scholar
• Zhang Y, Zhou Q (2017) Estimation for time-invariant effects in dynamic panel data models with application to income dynamics. Econom. Statist. 9:62–77.Google Scholar
• Zhang Z, Song Y, Qi H (2017) Age progression/regression by conditional adversarial autoencoder. Proc. IEEE Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 5810–5818).Google Scholar