Do Pimples Pay? Acne, Human Capital, and the Labor Market

53 Pages·2017·1.08 MB·English

by  Emory College

Do Pimples Pay?
Acne, Human Capital, and the Labor Market
June 13, 2018
Hugo M. Mialon
Department of Economics
Emory University
1602 Fishburne Drive
Atlanta, GA 30322
[email protected]
Erik T. Nesson
Department of Economics
Ball State University
2000 W University Ave., WB 201
Muncie, IN 47306,
[email protected]
JEL classification: I12, I26, J24
Keywords: Acne, grades, educational attainment, wages
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Do Pimples Pay?
Acne, Human Capital, and the Labor Market

Abstract

We use data from the National Longitudinal Study of Adolescent to Adult Health
to investigate the association between having acne in middle to high school and
subsequent educational and labor market outcomes. We find that having acne is
strongly positively associated with overall grade point average in high school,
grades in high-school English, history, math, and science, and the completion of a
college degree. We also find evidence that acne is associated with higher personal
labor market earnings for women. We further explore a possible channel through
which acne may affect education and earnings.

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1. Introduction
In this paper, we investigate the association between acne and human capital investment.
Acne vulgaris is the eighth most common disease among humans, affecting approximately 645
million people worldwide and 60 to 68 percent of young persons aged 15–19 years (Vos et al.
2015, Hay et al. 2014, Lynn et al. 2016, Institute for Health Metrics and Evaluation 2018).
Having acne during the formative years of adolescence may significantly alter a person’s self-
image and behavior.
We use data from the National Longitudinal Study of Adolescent to Adult Health (Add
Health) to analyze the effects of acne on educational outcomes and labor market earnings. In the
sample, nearly 50 percent of high-schoolers report having pimples occasionally, often, or every
day. We find that having acne in high school is positively associated with overall GPA,
mathematics GPA, and science GPA in high school; positively associated with earning an A in
high-school math, science, history/social studies, and English; and positively associated with
completing a Bachelor’ Degree. The associations are generally stronger for women than for men,
consistent with prior research showing that women are more likely than men to develop anxiety
from having acne (Skroza et al. 2016). We also find some weak evidence that acne is associated
with higher future personal labor market earnings for women.
We further empirically explore a possible mechanism through which acne may affect
education and earnings. In theory, having acne may reduce feelings of being socially accepted,
thereby reducing time spent socializing and increasing time spent studying, which may be
conducive to educational attainment. We find strong evidence that having acne is associated with
feeling less socially accepted and less attractive. Interestingly, we also find that acne is
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associated with reduced participation in sports clubs and increased participation in non-sports
clubs, suggesting a possible shift from physical to intellectual pursuits.
Interpretation of the finding that acne is positively correlated with educational attainment
depends on the degree to which acne is exogenous. An important component of our work is
determining the extent to which acne, and the reporting of acne, are plausibly exogenous. In a
comprehensive review of the medical literature on the causes of acne vulgaris, Bhate and
Williams (2013) find evidence that pubertal maturity, race, and genetics affect acne, but they
find no clear evidence that nutrition, hygiene, natural sunlight, or smoking affect acne. However,
if acne is correlated with parental education and socioeconomic status, then it may be that
parental genes are driving both the appearance of acne and higher education outcomes in
children. We provide evidence that while acne in adolescence is related to certain fixed and
observable characteristics, including age and race, it is not related to measures of socioeconomic
status, including parental education levels or most measures of family structure.
Our paper is related to the growing economics literature on the returns to physical
attractiveness (Hamermesh and Biddle 1994, Hamermesh 2011, French 2002, Borland and Leigh
2014), including papers on the returns to physical attractiveness in particular occupations
(Hamermesh and Parker 2005, Biddle and Hamermesh 1998) and on the returns to particular
aspects of physical attractiveness, including facial attractiveness (Bóo, Rossi, and Urzúa 2013,
Scholz and Sicinski 2015), height and weight (Harper 2000, Deaton and Arora 2009, Cawley
2004), hair color (Johnston 2010), and dressing up (Hamermesh, Meng, and Zhang 2002).
The papers most closely related to ours focus on the association between physical
attractiveness and educational outcomes in high school and college. Using Add Health data,
French et al. (2009) find that measures of physical attractiveness are negatively correlated with
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cumulative GPA in high school. Analyzing data on students at a women’s college, Deryugina
and Shurchkov (2015) find that physical attractiveness is negatively correlated with scores on
standardized college admissions tests. Fletcher (2009) finds that the labor market returns to
physical attractiveness in high school are small compared to the economic returns to academic
ability. These findings are generally consistent with ours. We find that while acne is associated
with reduced measures of physical appearance, it is also associated with increased educational
attainment, which may positively affect labor market earnings.
Examining data from student records and ID cards at a public university, Hernández-
Julián and Peters (2017) find that both female students with below-average and above-average
ratings of attractiveness earn lower grades, suggesting a possible non-monotonicity in the
relationship between physical appearance and grades in college. However, in specifications with
student fixed effects, they only find lower grades for female students with above-average
attractiveness, with the effect being driven by courses taught by male professors. Their finding
suggests that labor market returns to physical attractiveness may be driven by discrimination.
Our results also suggest a possible non-monotonicity, but in the relationship between
severity of acne and earnings. We find that severe acne is related to higher grades and lower
measures of self-esteem and socialization in the short run, but we also find that longer-term
labor-market outcomes are most strongly related to “occasional” (i.e., less severe) acne. Our
findings are consistent with a mechanism where severe acne limits socialization so much as to
affect long-term outcomes that may depend both on studying and on sociability.
Linking data from a dating service and plastic surgery company, Lee and Ryu (2012) find
modest labor market returns to facial plastic surgery. In contrast to acne, plastic surgery
represents both the existence of a health condition (e.g., a crooked nose) and an intervention.
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Thus, plastic surgery may be less exogenous than acne. Similarly, wearing braces represents both
a health condition (crooked teeth) and an intervention. Add Health data also contain information
on wearing braces, allowing us to investigate the relationship between braces and grades as a
robustness check to our findings on the relationship between acne and grades. As with acne, we
find a positive association between wearing braces and grades. However, we do not find as
strong a correlation between braces and socialization as we do between acne and socialization.
To the best of our knowledge, our paper is the first to examine the associations between
acne and educational as well as labor market outcomes. Focusing on acne has the potential to
advance the literature on the human capital and labor market returns to physical attractiveness
since acne is a well-defined medical condition and is arguably a less subjective condition than
general physical attractiveness. The remainder of the paper is organized as follows. Section 2
details our data and methods. Section 3 presents results of OLS regressions and robustness tests.
Section 4 summarizes and discusses implications.

2. Data and Methods
We use data from Add Health, run by the Eunice Kennedy Shriver National Institute of
Child Health and Human Development (Harris et al. 2017). Add Health began in 1994-1995
with a sample of 90,000 students in grades 7-12 and has followed up with three additional waves,
each including about 15,000 of the original Wave I students. Wave II includes in-home
interviews one year after Wave I in 1996, Wave III includes in-home interviews in 2001-2002,
and Wave IV includes both in-home interviews and biological samples from 2007-2008.
Importantly for our research question, Add Health contains information regarding acne.
In particular, in Wave I, when respondents are in grades 7-12, they are asked “how often have
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you had skin problems, such as itching or pimples?” with responses ranging from 0 (never) to 4
(every day). We code respondents as having skin problems if they respond with a 2,
(occasionally), 3 (often) or a 4 (every day). Add Health also asks about acne medication use in
Wave III, which we use to test the robustness of our original measure.
Additionally, Add Health contains measures of self-esteem and socialization in Wave I,
including questions regarding whether the respondent thinks he or she has good qualities, “Do
you agree or disagree with the following statement? You have a lot of good qualities,” likes
themselves the way they are, “Do you agree or disagree with the following statement? You like
yourself just the way you are,” and feels socially accepted, “Do you agree or disagree with the
following statement? You feel socially accepted.” For each of these, Add Health asks
respondents whether they strongly agree (1), agree (2), neither agree nor disagree (3), disagree
(4), or strongly disagree (5). We create indicator variables which are 1 if the respondent strongly
agrees or agrees with the statement and 0 otherwise. The survey further contains reports from
the interviewers on respondents’ physical attractiveness, personality attractiveness, and
grooming. We use these responses to create three indicator variables which are equal to 1 if the
interviewer strongly agrees (response of 5) or agrees (response of 4) that the respondent has the
quality in question. Finally, we also examine indicators for whether respondents participate in
athletics in Wave I and in non-athletic clubs in Wave I.
Add Health further contains many measures of academic performance, both concurrently
with the question regarding skin problems in Wave I, and over time. In Wave I, respondents are
asked to report their grades in their most recent English, mathematics, history or social studies,
and science classes. We use these responses to create indicators for whether respondents received
an “A” grade in these classes. In Wave III of the Add Health data, when respondents were
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between 18 and 24 years of age, Add Health requested access to respondents’ high school
transcripts. About 80 percent of respondents in Wave III were successfully matched to
transcripts. We use the transcripts to record the cumulative high school GPAs for all classes
overall, mathematics classes, and science classes measured on the traditional four-point scale.1 In
Wave IV, when respondents were between 24 and 32 years of age, Add Health asked questions
regarding educational attainment, and we record whether respondents graduated from high
school, completed some college (including receiving an Associate’s degree), received a
Bachelor’s degree, and completed some form of graduate education. Finally, we examine
measures of income and wealth in Wave IV, including personal earnings and household
earnings.2
The survey also records many demographic characteristics, and we include indicators for
age at the time of the Wave I survey, gender (male, female, missing), Hispanic status, race
(white, black, Asian, Native American, and other), being born in the United States, living with a
mother and/or father in Wave I, being adopted and the number of individuals in the household in
Wave I. We also include indicators for parents' education levels and for the school in which the
student attended in Wave I.
Our main identification strategy uses a standard OLS model as follows:
(1)
where is the outcome of in𝑦𝑦te𝑖𝑖r=est𝛽𝛽, 0 +𝛽𝛽1 𝑠𝑠i𝑠𝑠s 𝑠𝑠a𝑛𝑛n𝑖𝑖 +ind𝑥𝑥i𝑖𝑖c𝛽𝛽a2to+r f𝜎𝜎o𝑠𝑠r +w𝑒𝑒h𝑖𝑖ether the student reported that
they ha𝑦𝑦d𝑖𝑖 skin problems, is a vecto𝑠𝑠r 𝑠𝑠o𝑠𝑠f𝑛𝑛 o𝑖𝑖ther controls as described above, are school fixed
effects, and is an error𝑥𝑥 𝑖𝑖term, clustered at the school level. In our main res𝜎𝜎ul𝑠𝑠ts, we weight our
𝑒𝑒𝑖𝑖
1 Add Health did not calculate overall GPAs for other course categories because larger variation in course titles and
contents would have made it difficult to consistently assign courses into these categories across students and across
schools.
2 Household earnings are reported in ranges. We convert these ranges to a cardinal number by using the midpoint of
each range. The top range is “150,000 or more,” and we use 150,000*1.5 as the value for this category.
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regressions by Add Health sample weights. In robustness checks, we verify that our results are
robust to either estimating unweighted OLS models, probit models for dichotomous dependent
variable models, and propensity score matching models, as well as other variations on our
preferred specifications. We discuss these robustness tests in Section 4b.

4. Results
Table 1 shows summary statistics from our sample. Almost half of the students in our
sample report skin problems in Wave I. Students reporting skin problems have higher grades in
classes, are slightly older, slightly more likely to be female, much more likely to be white or
Asian, report lower levels of self-esteem and social acceptance, are less likely to be in sports
clubs but more likely to be in non-sports clubs, and are slightly less likely to live with their father
and mother and to have been born in the United States.

4a. Regression Results
We begin our results by showing evidence that the skin problems question in Wave I is
related to acne. In Wave III, when respondents are between the ages of 18 and 24, they are asked
if they have taken prescription medication for acne in the previous 12 months. Table 2 shows
results from models estimating the probability of reporting medication for acne in Wave III as a
function of reporting skin problems in Wave I. We show results for our entire sample and broken
down by men, women, whites, blacks, and white women. For most demographics, especially
women and whites, reporting skin problems in Wave I is strongly associated with prescription
acne medication use in Wave III. For women, reporting skin problems in Wave I increases the
probability of acne medication use in Wave III by 4.7 percentage points.
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We next consider the physiological determinants of having acne. It is important to know
whether any associations we find between acne and education are plausibly causal, which would
be the case if having acne is to some degree exogenous, conditional on observable
characteristics. Like physical traits related to attractiveness, acne is heritable. Genetics (having
a first-degree relative who had acne) has been identified as a risk factor for acne, while there is
little evidence of an association with diet, hygiene, or smoking (Bhate and Williams 2013). We
additionally provide evidence that while certain individual characteristics are predictive of
reporting skin problems in Wave I, these characteristics are fixed and observable, and acne in
adolescence is not related to socioeconomic status, suggesting that acne may have some
exogenous component. To this end, we regress having self-reported skin conditions in Wave I
on the demographic variables included in our regressions. Table 3 shows results from these
models. As with Table 2, we show results for our entire sample and five demographic
subsamples. While reporting skin problems in Wave I is related to gender, race, and age (for
whites), it is not generally statistically significantly related to living with one’s mother or father,
with one’s mother’s education, or with one’s father’s education.
Next, we show the effects of skin problems on short-run and medium-run education
outcomes, measured by self-reported academic achievement in Wave I and cumulative high-
school GPA reported in Wave III. Table 4 shows results from these regressions. All models
include the control variables discussed above. Skin problems are related to statistically
significant increases in grades in English, mathematics, history/social studies, and science.3 Skin
problems are also associated with increases in cumulative overall high-school GPA and
cumulative science high-school GPA, and we find some evidence that skin problems are related
3 We also create a “calculated GPA” variable instead of examining whether students received an “A” in the class.
To this end, we assigned numbers to the reported grades (A=4, B=3, C=2, D or F=0.5). We discuss these results in
Section 4b.
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