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Does it Payoff to be Blond in a Non-Blond Neighborhood? Eye Color, Hair Color, Ethnic Composition and Starting Wages

Eastern Economic Journal volume 48, pages 122–146 (2022)Cite this article

Abstract

In this paper, I examine the impact of eye and hair color on wages at one’s first-job after completing schooling. Evidence suggests that having blond/red hair has a positive impact on wages, particularly for white people and females. Using detailed ethnic origin information collected by the Census and using tipping point analysis, I find that individuals with blond/red hair who reside in a county where ethnicities with brown/black hair/eyes constitute the majority, earn around nine percent more compared to individuals with brown/black hair residing in the same county.

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Fig. 1
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Notes

  1. Psychology explains the relationship between attractiveness and treatment by perceivers with general socialization/social expectancy theories and fitness-related evolutionary theories. See Langlois, Kalalanis, Rubenstein, Larson, Hallam and Smoot (2000) for a detailed discussion of these theories.

  2. Based on the hair and eye color maps provided in Coon (1939), Hulse (1963) and Geipel (1969), the Italian population has darker features such as brown/black eyes and brown/black hair color. In addition, Scandinavian people have light-colored eye and hair.

  3. Johnston (2010) assumes that individuals cannot change their natural hair color.

  4. Card et al. (2008) note that fixed-point strategy performs better for smaller places. Since the size of counties varies, I use the “fixed-point” identification procedure.

  5. The results remained mostly the same when alternative restrictions at 50 percent and 70 percent were assumed.

  6. Although I cannot measure individuals’ skin color in the data, in general, light-eye and light-hair colors are associated with lighter skin color (Coon 1939).

  7. Out of 12,686 individuals, 10,876 of them replied to both of these questions by providing their natural hair and eye color, 2 of them refused to answer and 14 of them have invalid skip and there are 1,792 individuals who were not interviewed in 1985. The information on eye and hair color was collected only in the 1985 survey.

  8. I added individuals who chose “other” to this group.

  9. See Online Appendix Table 1 for an example of ethnic groups that are defined as light-featured or dark-featured.

  10. See Online Appendix Table 2 for definitions of variables.

  11. In this figure, for some counties the information is obtained from the 1990 Census since the information is not available in the 1980 Census.

  12. The non-white group consists of people with African American, Asian and Hispanic origins.

  13. See Altonji and Blank (1999)

  14. It is identical to Card et al. (2008)’s figure 5. Only one-third of counties not used for estimating the location of the tipping points are included.

  15. See Barreca et al. (2016) for more details on donut-style regression discontinuity.

  16. The earliest year the ASVAB is administered is 1981.

  17. These questions are: I am a person of worth; I have a number of good qualities; I am inclined to feel that I am a failure; I am able to do things as well as most other people; I felt I do not have much to be proud of; I take a positive attitude toward myself; I am satisfied with myself; I wish I could have more respect for myself; I certainly feel useless at times; At times I think I am no good at all.

  18. The results are robust for BeyondTP and (1- BeyondTP) × Light-Hair as well. They are not presented in the paper for the sake of brevity. However, they are available upon request.

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  1. School of Social Sciences and Global Studies, University of Southern Mississippi, 407 Liberal Arts Building, Hattiesburg, MS, 39406, USA

    Elif S. Filiz

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Filiz, E.S. Does it Payoff to be Blond in a Non-Blond Neighborhood? Eye Color, Hair Color, Ethnic Composition and Starting Wages. Eastern Econ J 48, 122–146 (2022). https://doi.org/10.1057/s41302-021-00194-8

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Keywords

  • Eye and hair color
  • Ethnic composition
  • Tipping point
  • Starting wages

JEL Classification

  • J10
  • J31
  • J71