Effects of physical attractiveness, personality, and grooming on academic performance in high school

Labour Economics 16 (2009) 373–382

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Labour Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l a b e c o

Effects of physical attractiveness, personality, and grooming on academic performance in high school Michael T. French a,⁎, Philip K. Robins b, Jenny F. Homer c, Lauren M. Tapsell d a

University of Miami, Department of Sociology, 5202 University Drive, Merrick Building, Room 121F, P.O. Box 248162, Coral Gables, FL, 33124-2030, USA Department of Economics, University of Miami, Jenkins Building, 5250 University Drive, Coral Gables, FL 33146-6550, USA Health Economics Research Group, Sociology Research Center, University of Miami, 5665 Ponce de Leon Blvd., Flipse Building, Room 104, Coral Gables, FL 33124-0719, USA d Health Economics Research Group, Sociology Research Center, University of Miami, 5665 Ponce de Leon Blvd., Flipse Building, Room 112, Coral Gables, FL 33124-0719, USA b c

a r t i c l e

i n f o

Article history: Received 13 February 2008 Received in revised form 6 January 2009 Accepted 9 January 2009 Available online 20 January 2009 JEL classification: I12 I21 J71 Keywords: Physical attractiveness Personality Grooming Adolescents High school grades

a b s t r a c t Using data from the National Longitudinal Study of Adolescent Health (Add Health), we investigate whether certain aspects of personal appearance (i.e., physical attractiveness, personality, and grooming) affect a student's cumulative grade point average (GPA) in high school. When physical attractiveness is entered into the model as the only measure of personal appearance (as has been done in previous studies), it has a positive and statistically significant impact on GPA for female students and a positive yet not statistically significant effect for male students. Including personality and grooming, the effect of physical attractiveness turns negative for both groups, but is only statistically significant for males. For male and female students, being very well groomed is associated with a statistically significant GPA premium. While grooming has the largest effect on GPA for male students, having a very attractive personality is most important for female students. Numerous sensitivity analyses support the core results for grooming and personality. Possible explanations for these findings include teacher discrimination, differences in student objectives, and rational resource allocation decisions. © 2009 Elsevier B.V. All rights reserved.

1. Introduction and background In recent years, economists have expanded the study of labor market discrimination to include the effects of physical attractiveness or beauty. The seminal paper by Hamermesh and Biddle (1994) examines the effects of physical attractiveness on earnings and finds a “plainness penalty” of 5–10% and a slightly lower “beauty premium” for both males and females in the workplace. How much (if any) of the estimated earnings effects reflects discrimination, occupational crowding, or productivity differences is uncertain, although Hamermesh and Biddle's analysis suggests that some degree of employer discrimination is present. Since the original study, Hamermesh and his colleagues have investigated similar topics ranging from the impact of lawyers' appearance on their salaries to the likelihood of attractive politicians being elected (Biddle and Hamermesh, 1998; Hamermesh, 2006; Hamermesh et al., 2002; Hamermesh and Parker, 2005; Pfann et al., 2000). Other recent studies include French (2002), who analyzes self-reported appearance data and finds a beauty premium for female, but not male workers, and Mobius and Rosenblat (2006), who investigate the possible causes of a ⁎ Corresponding author. Tel.: +1 305 284 6039; fax: +1 305 284 5310. E-mail addresses: [email protected] (M.T. French), [email protected] (P.K. Robins), [email protected] (J.F. Homer), [email protected] (L.M. Tapsell). 0927-5371/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.labeco.2009.01.001

beauty premium within an experimental labor market. In addition to the literature focusing on wages, several authors have examined the effect of physical attractiveness on type of employment. Schwer and Daneshvary (2000), for example, study the tendency of “good-looking” people to sort into jobs where appearance is important to performance and how this ultimately influences the upkeep of one's looks. Mocan and Tekin (2006) use the National Longitudinal Study of Adolescent Health (Add Health) to examine the relationship between physical attractiveness and the propensity to engage in criminal behavior. They conclude that “being unattractive increases [the propensity] for a number of crimes, ranging from burglary to selling drugs.” They attribute these effects to rational economic behavior among below average looking individuals who face more obstacles in the labor market. Only a few studies have attempted to capture the effects of other personal appearance characteristics on labor market outcomes, possibly because such measures are typically unavailable (Ritts et al., 1992). Hamermesh et al. (2002) include women's spending on clothing and cosmetics as a proxy for grooming in addition to interviewer appearance ratings. Although beauty increases earnings, spending on beauty enhancements produces only a small additional effect. To better distinguish between physical attractiveness and grooming, Hamermesh and Parker (2005) and Süssmuth (2006) control for whether faculty members wore business attire (ties for men

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and jackets and blouses for women) in the photographs evaluated by raters. In Hamermesh and Parker (2005), the impact of beauty on instructor ratings is only slightly smaller after including an indicator for business attire. As a sensitivity test, Hamermesh and Parker (2005) re-estimated their main model with only those departments where all faculty members had pictures posted online to reduce the possibility of selection bias if only faculty members with “go-getter” personalities volunteered to post their pictures. Again, including this measure slightly diminished the effects of beauty on teaching evaluations, but did not change the main results. Other studies use weight and height instead of a direct measure of physical attractiveness (Crosnoe and Muller, 2004; Loh, 1993), while Frieze et al. (1991) and Harper (2000) consider weight and height together with physical attractiveness. Some researchers (e.g., Loh, 1993; Mocan and Tekin, 2006; Umberson and Hughes, 1987) speculate that beauty premiums and penalties in the labor market stem from differences in attention and assessment by teachers to physically attractive students and the resultant effect on human capital accumulation. A sizeable noneconomics literature has examined the influence of physical attractiveness on teacher perceptions of student abilities (Jackson et al., 1995; Kenealy et al., 1988; Ritts et al., 1992). Ritts et al. (1992) report that attractive students typically receive higher grades and better scores on standardized tests than unattractive students. Kenealy et al. (1988) identify favoritism toward girls and determine that “significant sex differences are observed in the teachers' ratings of attractiveness, academic brightness, sociability, and confidence.” In this paper, we utilize data from Waves 1 and 3 of the Add Health survey to examine whether three personal appearance characteristics– physical attractiveness, personality, and grooming–observed just prior to entering high school are significantly related to grades students received in high school.1 Our expanded definition of personal appearance, derived from extended interviewer observations of students, enables us to identify more precisely which dimensions of personal appearance are the strongest predictors of academic achievement. This approach can offer new insight on the complex relationship between physical attractiveness per se and various outcomes. Using data abstracted from high school records allow us to test for the impact of personal appearance on actual recorded grade point average (GPA) rather than subjective measures of student competence or self-reported grades. Finally, we follow the literature and estimate separate models for males and females. 2. Conceptual framework While most of the existing literature has studied the effects of physical appearance on adult earnings, the present study focuses on a sample of middle and high school students and examines the effects of an expanded set of personal appearance characteristics on student grades. Adolescence is a time of rapid intellectual, mental, and emotional developmental, with corresponding physical and social changes (Maggs et al., 1997; Kroger, 2006). It is important to consider these unique factors when investigating a sample of adolescents and making comparisons to studies of adults. Although physical appearance can change dramatically during puberty, in the Add Health data only 12.4% of adolescents in Grades 7, 8, and 9 at Wave 1 had large movements (i.e., greater than one-category movement in a five-category ranking) in physical attractiveness, 15.2% had large movements in personality, and 11.1% had large movements in grooming between the beginning of high school and Wave 3, a period of about 6 years. As expected, these movements are more pronounced than for the sample of adults in Hamermesh and Biddle's (1994) paper, where 93% of respondents received identical ratings in at least 2 of 3 years and one rating level difference in the third year. 1 Hereafter, we use the general term “personal appearance” to refer collectively to physical attractiveness, personality, and grooming.

Identity formation and decisions about whether to conform or challenge adult conventions also occur during adolescence (Erikson, 1968; Maggs et al., 1997; Kroger, 2006). Physical appearance and academic achievement are both avenues through which adolescents identify with their peers and rebel against or abide by adult conventions. Based partly on studies in the educational psychology and economics literature (Anderson and Keith, 1997; Heckman, 2008; Lounsbury et al., 2003; Neisser et al., 1996; Rivkin et al., 2005), we assume that a student is endowed with a certain level of intelligence or ability (proxied by the Peabody Picture Vocabulary Test (PVT) score in our data) as well as an innate degree of physical attractiveness. Students then make decisions about combining these initial endowments with a host of variable resources (e.g., time spent studying, selection of friends, participation in extracurricular activities) to achieve certain goals related to their identities. For example, students who desire to attend college are likely to invest a greater amount of their time in studying and developing their human capital. If physical attractiveness is correlated with achievement, as has been found in studies of adult labor market behavior, this might suggest possible teacher discrimination because we are controlling (albeit partially) for ability and many other observable characteristics that could influence grades.2 Alternatively, a negative association between physical attractiveness and GPA could reflect decisions by more attractive students to allocate time to other objectives (such as socializing) rather than schoolwork. The analysis that follows is not able to definitively resolve these possible mechanisms, but the results offer new insight into a topic that heretofore has been focused almost exclusively on adult samples and labor market outcomes. Another distinct advantage of this paper vis-à-vis most of the published literature is our ability to examine personality and grooming in addition to physical attractiveness. Specifically, our main model posits that GPA is a function of physical attractiveness, personality, grooming, intelligence, and a long list of other variables that are likely to be related to academic performance. In this model, personality and grooming are viewed as additional variable resources that students modify to achieve certain goals. A pleasant personality and proper grooming, as perceived by an adult interviewer, may reflect efforts by the adolescent to conform to adult expectations. Such individuals might also be likely to devote considerable time and effort to their schoolwork, as adults look upon these activities approvingly. Conversely, students who are rebellious against social norms might purposely manipulate their grooming habits or personality to convey such an image. These appearance traits might be distasteful to adult interviewers or teachers, but appealing to a particular peer group that the student identifies with. In this case, the student may be devoting fewer resources to academic achievement in lieu of group conformity and popularity. Alternative specifications are estimated to further probe the main model and determine whether students manipulate their personal appearance to succeed academically. Following an approach that is similar to earlier investigations (Biddle and Hamermesh, 1998; Hamermesh, 2006; Hamermesh and Biddle, 1994; Hamermesh et al., 2002; Hamermesh and Parker, 2005; Pfann et al., 2000), we treat Wave 1 measures of physical attractiveness, personality, and grooming as initial endowments that are exogenous because they are measured just prior to or at the point of entering high school. We select students in 7th, 8th, and 9th grades at Wave 1 for the core sample to ensure that the personal appearance variables are not affected in any meaningful way by high school GPA (which is assigned during Grades 9–12).

2 Physical attractiveness could also be correlated with unobserved/unmeasured omitted variables (e.g., discipline, organizational skills) that are significantly related to academic achievement, which would tend to counter the teacher discrimination hypothesis.

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The final conceptual point regarding sample formation concerns possible gender differences. Most of the studies of physical attractiveness and labor market outcomes among adults have estimated separate models for men and women (French, 2002; Frieze et al., 1991; Hamermesh and Biddle, 1994; Hamermesh and Parker, 2005; Harper, 2000). Other studies (mainly in the educational and psychology literature) have also identified gender differences in academic achievement, with females earning higher grades than their male counterparts (Dwyer and Johnson, 1997; Jacob, 2002; Kleinfeld, 1998). In the Add Health data, overall GPA is significantly higher among females, and males tend to score lower on personal appearance measures. For these reasons, we follow the conventional approach in the literature and estimate separate models for male and female students. 3. Methods Our basic analysis first examines the independent effects of physical attractiveness, personality, and grooming on GPA, controlling for a set of individual, family, and school characteristics. We then estimate a series of academic achievement equations of the following form: GPAi = β0 + βA Physicali + βP Personalityi + β G Groomingi +
Xβ X +
μ i ð1Þ

where GPA is the overall grade point average in high school, Physical is a set of physical attractiveness measures, Personality is a set of personality measures, Grooming is a set of grooming measures, and X is a set of control variables.3 This preferred specification, which includes measures for above- and below-average physical attractiveness, personality, and grooming, permits a comparison of the relative importance of each appearance measure. Standard errors are adjusted for clustering at the school (primary sampling unit) level. The Add Health survey provides sampling weights, but we do not use them and choose instead to directly control for a number of variables related to the sampling distribution.4 After presenting results from our basic models, we conduct numerous sensitivity analyses to examine the stability of the core findings. Eq. (1) is re-estimated using alternative personal appearance measures, different exclusion criteria, and various control variables. We also estimate several additional models to better understand the potential motivation of high school students to alter their appearance. These specifications are discussed in greater detail in the Results section. 4. Data The analysis uses two waves of data from Add Health, a school-based, longitudinal study of adolescent health-related behaviors and their consequences in young adulthood. Wave 1 was administered during 1994–1995 and included in-home interviews with 20,745 adolescents sampled from 80 high schools and 52 middle schools. The study design ensures the sample is representative of U.S. schools based on region,

3 Including all three personal appearance measures in a single equation raises potential concern about multicollinearity if the measures are highly correlated. Tabulation of the correlations of the personal appearance measures at Wave 1 reveals that, while all are significantly different from zero at the 1% level, none exceeds 0.51. As one would expect, similar rankings for each measure are positively correlated. For example, the correlation between being rated as very physically attractive and having a very attractive personality is 0.503 while the correlation between being very physically attractive and being very well groomed is 0.501. Although these particular correlations are sizeable, most are much smaller. To examine this issue further, we calculated variance inflation factors (VIFs) for all regressors in the core models. All of the VIFs are less than 5, which is typically considered the threshold for potential multicollinearity problems. 4 The results are qualitatively similar when the sampling weights are used, although significance levels change somewhat for a few of the variables (results available on request from the authors).

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school type, size, and ethnicity. In-home interviews took 1–2 h to complete and were administered as a Computer-Assisted Personal Interview (CAPI)/Audio Computer-Assisted Self Interview (CASI). For sensitive topics, the respondents listened to pre-recorded questions using earphones and entered their responses into a laptop computer. In 2001–2002, 15,170 respondents were re-interviewed in Wave 3 when they were 18 to 27 years old. Wave 2 (administered approximately one year after Wave 1) only included adolescents from Wave 1 who were still attending high school while Wave 3 conducted follow-up interviews with all Wave 1 respondents who could be contacted. For this reason, the analysis focuses specifically on respondents from Waves 1 and 3. High school transcripts were requested and abstracted for 88% of Wave 3 respondents. The most common reason for missing GPA data was difficulty in obtaining records from various high schools. A careful investigation of the missing cases reveals that they have characteristics (e.g., lower income and PVT scores, less parental education) that are typically associated with lower GPAs. We control for these characteristics in our empirical models explaining GPA, and adjust for sample selection bias to account for the possibility of non-random attrition. These results are discussed later in the paper. The Add Health data have many desirable features pertinent to our study, with interviewer assessments of personal appearance and official records of high school grades the most notable. The majority of research studies on physical attractiveness use ratings based on photographs of the subjects' faces (Hamermesh and Parker, 2005; Ritts et al., 1992; Hamermesh, 2006; Pfann et al., 2000). The potential problems associated with the photograph method are discussed in the review by Ritts et al. (1992), who hypothesize that such methods fail to replicate encounters that occur in real life. The Add Health interviewers responded to questions on three key domains of personal appearance—physical attractiveness, personality, and grooming.5 Although the subjective nature of “beauty” and “homeliness” may present challenges when using physical attractiveness ratings in empirical research, multiple studies have confirmed consistency across evaluators (Jackson et al., 1995; Ritts et al., 1992). To reduce potential bias, Add Health respondents could not see the questions or answers in the interviewer section. This material could only be accessed by the interviewer with a password and was completed after the interviewer left the respondent. Interviewer assessments overcome some of the limitations of the photograph method, but this approach is not ideal either. The halo effect is one type of potential bias whereby the interviewer develops a positive impression of the respondent over the course of their encounter and then rates the respondent higher than he/she “deserves” on all criteria. Moreover, ratings are based on an assessment by one interviewer, whereas multiple raters are often used in studies using photographs (Süssmuth, 2006). Hundreds of interviewers participated in Waves 1, 2, and 3 of Add Health. The distributions of our personal appearance variables are presented in Fig. 1A and B. Since the interviewers answer a series of questions about the relative personal appearance of each respondent, one would expect to see a nearly symmetrical distribution resembling a normal curve with “average” appearance as the modal category and a fairly equal number of responses on either side of the “average.” As seen in Fig. 1A, however, there appears to be a preference among interviewers for top ranking the adolescents, with about twice the number of respondents being designated above average than below average. Indeed, many more individuals were placed in the top category (very physically attractive, very attractive personality, or very well groomed) than in the bottom two categories combined. To address the generosity of the interviewers, we coded the upper category by itself, the second and third categories as a combined

5 A list of the entire questions and additional information about the interviewer section of Add Health can be found in Appendix A.

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Fig. 1. A. Distribution of Wave 1 personal appearance ratings (5 categories). B. Distribution of Wave 1 personal appearance ratings (3 categories).

“average” group, and the bottom two categories as a combined “below average” group. This recoding yielded a three-category distribution that more closely resembles a normal curve (Fig. 1B).6 6 Other coding strategies yielded similar estimation results (see the sensitivity analyses below).

As noted earlier, the primary analysis sample includes adolescents in grades 7, 8, and 9 during the Wave 1 interview. We used the Wave 2 personal appearance measures (collected one year after Wave 1) whenever available for respondents in 7th and 8th grade to measure physical attractiveness, personality, and grooming as close to the start of high school as possible. The intent here was to construct a pre-high

M.T. French et al. / Labour Economics 16 (2009) 373–382

school sample in a manner that would imply causation from personal appearance to high school grades rather than the reverse. To explore the possibility that personal appearance scores for the same individual change over time due to interviewer differences and/or physical changes that occur during high school, we cross-tabulated Wave 1 and Wave 3 personal appearance scores for the full sample (Table 1). Approximately two-thirds of the sample had the identical ranking in the 3-category distribution from Wave 1 to Wave 3, a span covering about 6 years. Moreover, about 85% of the sample had the same or no more than one category change in ranking when considering five categories instead of three (86% for physical attractiveness, 84% for personality, and 88% for grooming). Descriptive statistics for all variables used in the analysis are presented in Table 2. The overall high school GPA for this sample was slightly above 2.5, and the average GPA for females (2.699) was significantly higher than for males (2.390). Female students were significantly more likely than males to be included in the top appearance categories, while males were more likely than females to be rated as average. All respondents were administered an abridged version of the Peabody Picture Vocabulary Test (PVT) at the start of their in-home interview. On average, PVT scores for male students were significantly

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higher than for female students. In addition, several socioeconomic, demographic, and school-specific variables displayed significant gender differences. 5. Results 5.1. Basic findings Table 3 presents selected coefficient estimates for the linear regressions of overall high school GPA on personal appearance and a long list of covariates. Models A–C (males) and E–G (females) include only one set of personal appearance measures, while Models D (males) and H (females) include all three personal appearance measures. In addition to the personal appearance measures, all specifications control for the PVT score, grade, race, children in household, birth order, mother's education, whether a parent received public assistance, whether the household is two-parent, school size, school type, school location, average class size, whether a school dress code is enforced, racial composition of the school, region, and oversampling of certain groups. The complete regression results for Models D and H (our preferred models) are presented in Appendix Table B.

Table 1 Cross tabulations of Wave 1 and Wave 3 personal appearance measures.

The shaded areas represent the combined categories for defining the variables used in the empirical models. The shaded areas represent the combined categories for defining the variables used in the empirical models. 1 This rating is based on the Wave 1 assessment for respondents in grades 9 through 12 at Wave 1 and the Wave 2 assessment for respondents in grades 7 and 8 at Wave 1. 1 2 This rating is based on the Wave 1 assessment for respondents in grades 9 through 12 at Wave 1 and the Wave 2 assessment for respondents in grades 7 and 8 at Wave 1. This rating is based on the Wave 3 assessment for all respondents.

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Table 2 Variable means (SD) for respondents in Grades 7, 8, and 9 at Wave 1 (N = 5365). Males (N = 2487)

Females (N = 2878)

Full sample

Outcome measure

Mean (SD)

Mean (SD)

Mean (SD)

Overall GPA⁎⁎⁎

2.390 (0.906)

2.699 (0.844)

2.556 (0.886)

0.085 0.856 0.058 0.108 0.836 0.056

0.178 0.777 0.045 0.173 0.787 0.041

0.135 0.814 0.051 0.143 0.810 0.048

0.087 0.875 0.038

0.139 0.832 0.029

0.115 0.852 0.033

101.558 (14.536) 0.301 0.287 0.411 0.660 0.124 0.202 0.138 1.358 (1.195) 0.317 0.451 0.094

99.233 (14.519) 0.310 0.304 0.386 0.657 0.121 0.230 0.113 1.418 (1.255) 0.289 0.419 0.114

100.311 (14.572) 0.306 0.296 0.398 0.658 0.122 0.217 0.125 1.390 (1.228) 0.302 0.434 0.105

0.674 0.223 0.505 0.272 0.933 0.296 0.182 0.522 25.817 (4.767) 0.865 0.563 0.814 0.260 0.188 0.137 0.414 0.680

0.686 0.213 0.496 0.291 0.931 0.294 0.186 0.520 25.850 (4.765) 0.856 0.565 0.819 0.261 0.196 0.137 0.407 0.670

Personal appearance measures Very physically attractive (W1)⁎⁎⁎ Average physical attractiveness (W1)⁎⁎⁎ Less than average physical attractiveness (W1)⁎⁎ Very attractive personality (W1)⁎⁎⁎ Average personality attractiveness (W1)⁎⁎⁎ Less than average personality attractiveness (W1)⁎⁎⁎ Very well groomed (W1)⁎⁎⁎ Average grooming (W1)⁎⁎⁎ Less than average grooming (W1)⁎ Control variables Peabody picture vocabulary test (PVT) score⁎⁎⁎ Grade 7 Grade 8 Grade 9⁎ White Hispanic Black⁎⁎ Other race⁎⁎⁎ Number of children under age 18 in the household Oldest child⁎⁎ Resident mother attended college ⁎⁎⁎ Resident mother or father receives public assistance⁎⁎ Two parent household⁎⁎ Attending small school⁎ Attending medium school Attending large school⁎⁎⁎ Public school Urban school Rural school Suburban school Average class size

0.699 0.201 0.485 0.314 0.927 0.293 0.190 0.518 25.887 (4.763) Dress code enforced⁎ 0.846 More than 66% of school is White 0.567 Percentage of full-time teachers that are White 0.824 Midwest 0.261 West 0.204 Northeast 0.138 South 0.398 Not a member of an oversampled group⁎ 0.657

Standard deviations reported in parentheses for continuous variables. Overall GPA has a minimum of 0 and a maximum of 4. Peabody PVT Score has a minimum of 10 and a maximum of 137. Number of children under age 18 in the household has a minimum of 0 and a maximum of 11. Average class size has a minimum of 10 and a maximum of 39. ⁎Statistically significant differences between males and females, p b 0.10. ⁎⁎Statistically significant differences between males and females, p b 0.05. ⁎⁎⁎Statistically significant differences between males and females, p b 0.01. (Kruskal Wallis tests for equality of populations.)

Turning first to the male students, when physical attractiveness is the only measure of personal appearance (Model A), being very physically attractive is positively related to GPA, while being below average physical attractiveness is negatively related. The positive effect for being very physically attractive, however, is not statistically significant. The plainness penalty for male students is − 0.146 points in overall GPA, which in percentage terms is about −6.11% (− 0.146/ 2.390). Models B (personality) and C (grooming) strongly indicate that being rated in the highest category is associated with a grade premium and being rated below average is associated with a grade penalty. The grooming premium (0.263)/penalty (− 0.492) is larger

in magnitude than the personality premium (0.145)/penalty (−0.182), and both sets of estimates are larger than the corresponding beauty premium/penalty. In the model with all three personal appearance measures (Model D), the coefficient estimate for being very physically attractive turns negative and statistically significant (−0.122), which is counter to the effect found in previous studies. Later, we offer a possible explanation for this result. For male students, grooming continues to deliver the biggest quantitative effect on overall GPA (premium = 0.274 for very well groomed; penalty = − 0.468 for below average grooming). For female students, the upper categories for physical attractiveness, personality, and grooming are positively and significantly related to GPA when these measures are entered individually in the regressions. In Model E, female students who are very physically attractive gain a modest boost of 0.080 (0.080/2.699 = 2.96%) points in overall GPA. The estimated premiums for personality and grooming are about two times larger in magnitude. A marginally significant GPA penalty is found for below average personality (− 0.128, Model F) and grooming (− 0.197, Model G). When all three dimensions of personal appearance are included (Model H), the results are similar to those for male students (Model D) in terms of direction, but not necessarily statistical significance. In particular, the coefficient estimate for being very physically attractive becomes negative, but it is not statistically significant. A significant GPA premium is present for female students with very attractive personalities (0.145) and exceptional grooming (0.114). Unlike the large and significant result for males, poorly groomed female students do not incur a statistically significant GPA penalty. Some notable findings also pertain to the other independent variables in these regressions (see Appendix Table B). Most of these results are similar for males and females. As expected, PVT score, intended to control somewhat for ability and intelligence, is positive and significantly related to GPA. Hispanics and African Americans have lower GPAs than Whites, and other non-White races have higher GPAs among males. Having a resident mother who attended college, residing in a two-parent household, and attending a small school are all positively associated with GPA, while receiving public assistance is negatively related to GPA. These effects may reflect the types and amount of resources being devoted to the student's academic activities. 5.2. Robustness checks To gauge the precision and stability of our core findings, we conducted a series of robustness checks using our preferred models D and H in Table 3. The results are presented in Tables 4A (males) and B (females). First, Wave 3 personal appearance measures are used instead of Wave 1 measures to account for the possibility that appearance may have changed for some of the respondents during high school (Column a). Second, all models are re-estimated with the full sample at Wave 1 instead of just the 7th, 8th, and 9th graders (Column b). Third, an average score of personal appearance rankings is constructed from Waves 1, 2, and 3, (Column c) and of personal appearance rankings from Waves 1 and 3 (Column d) using the original analysis sample. Finally, the core model is re-estimated using adolescents in 7th and 8th grades, but not 9th grade, at Wave 1 (Column e). Numerous other robustness checks of Models D and H were also conducted, the results of which are not reported in the tables, but are available on request from the authors. First, the three personal appearance categories are redefined strictly from the original questions as above average (top two categories), average (middle category), and below average (bottom two categories). Second, covariates are included for graduation status (92% of our sample reported receiving a high school degree), age, and body mass index (BMI). Third, the models are re-estimated using only White interviewers, using only female interviewers, and then controlling

M.T. French et al. / Labour Economics 16 (2009) 373–382

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Table 3 Selected linear regression results for the effects of personal appearance at Wave 1 on overall GPA (respondents in grades 7, 8, and 9 at Wave 1). Personal appearance measures

Models Males (N = 2487) A

Very physically attractive (W1)

Females (N = 2878)

B

C

D

E 0.080⁎⁎ (0.035) − 0.114 (0.072)

0.263⁎⁎⁎ (0.055) − 0.492⁎⁎⁎ (0.102) 0.235

− 0.122⁎ (0.064) −0.005 (0.077) 0.081 (0.066) −0.114 (0.083) 0.274⁎⁎⁎ (0.065) − 0.468⁎⁎⁎ (0.109) 0.237

0.055 (0.052) − 0.146⁎⁎ (0.073)

Below average physical attractiveness (W1) Very attractive personality (W1)

0.145⁎⁎⁎ (0.050) −0.182⁎⁎ (0.081)

Below average personality attractiveness (W1) Very well groomed (W1) Below average grooming (W1) R squared

0.219

0.222

F

G

H

0.163⁎⁎⁎ (0.036) − 0.197⁎ (0.106) 0.293

− 0.047 (0.044) − 0.044 (0.079) 0.145⁎⁎⁎ (0.036) − 0.091 (0.069) 0.114⁎⁎ (0.044) − 0.155 (0.112) 0.297

0.173⁎⁎⁎ (0.030) − 0.128⁎ (0.067)

0.290

0.294

Notes: Coefficient estimates and robust standard errors (in parentheses) are reported. Standard errors were adjusted for clustering at the school level (primary sampling unit). All specifications also controlled for PVT score, grade, race, children in household, birth order, mother's education, whether a parent received public assistance, two-parent household, school size, school type, school location, average class size, school dress code, racial composition of school, region, and oversampling of certain groups. ⁎Statistically significant at p b 0.10; ⁎⁎statistically significant at p b 0.05; ⁎⁎⁎statistically significant at p b 0.01.

for interviewer characteristics. Fourth, to better account for economic well being and living conditions, Models D and H are re-estimated with the addition of three variables based on interviewer responses: whether the respondent lived in a single family home, whether the interviewer felt concerned for his/her safety when going to the respondent's home, and whether the building where the respondent lives was poorly or very poorly kept. Fifth, 159 respondents with physical disabilities (based on self-reports) are excluded from the sample. Finally, to account for unobservable differences across schools, Models D and H are re-estimated with school fixed-effects instead of the control variables for school characteristics.

For male students, the most consistent results across all of these alternative specifications are for very well groomed students (positive and significant) and less than average groomed students (negative and significant). The negative and marginally significant effect of being very physically attractive (Model D) is not statistically significant in most of the other models. There is some evidence in Table 4A and other specifications of a statistically significant grade premium associated with having a very attractive personality, but the core model in Table 3 does not support this result. Overall, results from the sensitivity tests for female students are more consistent and stable than those for male students. The grade

Table 4A Selected linear regression results for sensitivity analyses of personal appearance on overall GPA (males).

Table 4B Selected linear regression results for sensitivity analyses of personal appearance on overall GPA (females).

Personal appearance measures

Models

Personal appearance measures

Models a

b

d

e

Very physically attractive

0.093 −0.055 (0.063) (0.036) − 0.180⁎ 0.030

Very physically attractive

0.033 (0.049) 0.070

− 0.058⁎⁎ −0.050 (0.027) (0.062) − 0.033 − 0.084

0.003 (0.037) −0.010

− 0.037 (0.053) − 0.025

(0.049) 0.137⁎⁎⁎ (0.024) − 0.108⁎ (0.055) 0.130⁎⁎⁎ (0.024) − 0.115 (0.071) 6365 0.280

(0.110) 0.137⁎⁎⁎ (0.038) 0.064 (0.110) 0.192⁎⁎⁎ (0.041) − 0.089 (0.176) 2878 0.301

(0.108) 0.163⁎⁎⁎ (0.046) − 0.097 (0.099) 0.051 (0.058) − 0.198 (0.132) 1767 0.281

Less than average physical attractiveness

a

(0.103) 0.153⁎⁎⁎ (0.058) Less than average personality − 0.068 (0.089) Very well groomed 0.045 (0.076) Less than average grooming −0.048 (0.076) N 2487 0.228 R2 Very attractive personality

b

(0.039) 0.085⁎⁎ (0.040) − 0.111⁎⁎ (0.048) 0.214⁎⁎⁎ (0.036) −0.353⁎⁎⁎ (0.055) 5677 0.229

c

d

e

0.002 (0.139) − 0.110

0.091⁎ (0.054) − 0.172

− 0.064 (0.088) − 0.162⁎

(0.096) 0.102 (0.104) 0.026 (0.140) 0.206 (0.138) −0.453⁎⁎⁎ (0.123) 2158 0.239

(0.144) 0.112⁎⁎ (0.047) −0.097 (0.135) 0.153⁎⁎ (0.063) − 0.417⁎⁎ (0.164) 2487 0.230

(0.095) 0.106 (0.088) − 0.105 (0.125) 0.273⁎⁎⁎ (0.084) − 0.311⁎⁎ (0.146) 1464 0.257

Notes: Model “a” uses personal appearance measures from Wave 3 and includes respondents in grades 7, 8, and 9 at Wave 1. Model “b” uses personal appearance measures from Wave 1 and includes all respondents at Wave 1. Model “c” uses the mean of the personal appearance ratings from Waves 1, 2, and 3 and includes respondents in grades 7, 8, and 9 at Wave 1. Model “d” uses the mean of the pre- and post-personal appearance ratings used in the core models and includes respondents in grades 7, 8, and 9 at Wave 1. Model “e” uses personal appearance measures from Wave 1 and includes respondents in grades 7 and 8 at Wave 1. Coefficient estimates and robust standard errors (in parentheses) are reported. Standard errors were adjusted for clustering at the school level (primary sampling unit). All specifications also controlled for PVT score, grade, race, children in household, birth order, mother's education, whether a parent received public assistance, two-parent household, school size, school type, school location, average class size, school dress code, racial composition of school, region, and oversampling of certain groups. ⁎Statistically significant at p b 0.10; ⁎⁎statistically significant at p b 0.05; ⁎⁎⁎statistically significant at p b 0.01.

Less than average physical attractiveness

(0.064) 0.093⁎⁎ (0.038) Less than average personality 0.032 (0.071) Very well groomed 0.160⁎⁎⁎ (0.044) Less than average grooming − 0.252⁎⁎⁎ (0.089) N 2878 2 0.301 R Very attractive personality

c

(0.098) 0.210⁎⁎⁎ (0.049) − 0.250⁎⁎ (0.122) 0.207⁎⁎⁎ (0.055) − 0.139 (0.156) 2541 0.300

Notes: Model “a” uses personal appearance measures from Wave 3 and includes respondents in grades 7, 8, and 9 at Wave 1. Model “b” uses personal appearance measures from Wave 1 and includes all respondents at Wave 1. Model “c” uses the mean of the personal appearance ratings from Waves 1, 2, and 3 and includes respondents in grades 7, 8, and 9 at Wave 1. Model “d” uses the mean of the pre- and post-personal appearance ratings used in the core models and includes respondents in grades 7, 8, and 9 at Wave 1. Model “e” uses personal appearance measures from Wave 1 and includes respondents in grades 7 and 8 at Wave 1. Coefficient estimates and robust standard errors (in parentheses) are reported. Standard errors were adjusted for clustering at the school level (primary sampling unit). All specifications also controlled for PVT score, grade, race, children in household, birth order, mother's education, whether a parent received public assistance, two-parent household, school size, school type, school location, average class size, school dress code, racial composition of school, region, and oversampling of certain groups. ⁎Statistically significant at p b 0.10; ⁎⁎statistically significant at p b 0.05; ⁎⁎⁎statistically significant at p b 0.01.

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premium found in Model H for a very attractive personality is virtually identical in terms of magnitude and statistical significance across all of the alternative specifications. There is slightly more variability in the significance level of the grade premium for very well groomed female students, but this is also highly stable. Finally, a few of the alternative specifications produced a significant grade penalty among very physically attractive and poorly groomed female students, but these effects were more often not significant. 5.3. Further analyses To better understand our core results and to explore the motivation of high school students to use personal appearance to attain their goals (see Section 2), we conducted several further analyses.7 Because the returns from personality and grooming may depend upon an adolescent's initial level of physical attractiveness, we evaluate the effects of personality and grooming on GPA after excluding adolescents with below average physical attractiveness. With this conditional sample, a significant grade penalty emerges for males with unpleasant personalities while the coefficients for being well groomed and poorly groomed are similar in magnitude and significance to those in Model D. All four measures of personality and grooming are significant in the expected direction for females. Thus, among students with at least average physical attractiveness, both personality and grooming are likely to have a significant impact on GPA. In our basic models, relatively large effects were found for grooming, which is perhaps more easily manipulated by students than physical attractiveness or personality. As explained in Section 2, being better groomed might be at least partially driven by an attempt to conform to adult expectations, while adopting more radical grooming habits might be part of an effort to disassociate with adult conventions and/or fit in with certain peer groups. To test these possible mechanisms, Models D and H are re-estimated with two additional indicator variables representing “conformists” and “rebels.” Conformists (11.22% of the sample) are defined as students with average or higher physical attractiveness and the highest category of grooming, and rebels (2.02% of the sample) are defined as those with average or higher physical attractiveness and the lowest category of grooming. The results indicate that being a conformist or rebel does not have a significant effect on GPA for either gender, suggesting that the mediating effects of grooming do not counteract the direct effect of being physically attractive. We note, however, that the analysis may lack statistical power for rebels because they represent only 2.3% of male students and 1.7% of female students. Small sample sizes also impeded our attempts to investigate other combinations of grooming and physical attractiveness, as 75% of the sample in grades 7, 8, and 9 at Wave 1 were rated as average in grooming and physical attractiveness (see Appendix Table C). Because academic achievement is one of several outcomes that high school students may pursue, we evaluated the effects of personal appearance on three measures of risky behavior at Wave 3: number of drinking days in the past year, number of binge drinking days in the past year, and number of times marijuana was used in the past 30 days. Overall, few personal appearance measures are significantly related to these other outcomes. Relative to the average category, the results for female students indicate that those who are very physically attractive drink alcohol more often, while those who are rated as below average in physical attractiveness report significantly fewer drink-

7 To conserve space, these results are discussed in the text and the full regression results are available from the authors on request.

ing days, but more marijuana use. Males with highly rated personalities report fewer drinking days. Very physically attractive males as well as those with less than average physical attractiveness report using marijuana fewer times than males ranked in the middle category of physical attractiveness. Finally, we constructed a personal appearance “index” equal to the cumulative score for the three personal appearance measures at Wave 1. Using 1 for the lowest category and 5 for the highest category, the personal appearance index has a range from 3 to 15. As expected, the personal appearance index has a positive and statistically significant effect on GPA for both males and females. A quantitative interpretation should be avoided, however, because the numerical rankings are on an ordinal scale and do not have any meaningful cardinal properties. Nonetheless, the results suggest that students may be able to trade-off different characteristics of their personal appearance to enhance academic achievement. 6. Qualifications and limitations Before summarizing our results and offering some explanations for them, it is important to acknowledge some data and statistical limitations of our study. First, we treat Wave 1 measures of personal appearance as exogenous because students in 7th, 8th, and 9th grades at Wave I were used in the analyses. This timing convention ensures that the personal appearance variables were not affected by the students' high school grades. While reverse causality is highly unlikely, omitted variables bias is not. It is possible that our personal appearance measures may be capturing the effects of other important and omitted predictors of academic performance such as discipline, organizational skills, parental involvement in schooling, and unobserved teacher heterogeneity (i.e., teacher skills and effectiveness). We control for a long list of covariates related to school-level, family, and personal characteristics, but the analysis may still suffer from omitted variable bias. Second, if personal appearance changes dramatically during high school, then the current two-period models with a 6-year lag may introduce considerable measurement error when estimating the effects of personal appearance on GPA. To address this issue, we presented data in Table 1 showing that most respondents had little or no movement in physical attractiveness, personality, or grooming between Waves 1 and 3. We also presented results in our robustness analysis that use personal appearance measures from Wave 3 in addition to a combination of measures from Waves 1, 2, and 3. Third, interviewers tended to inflate the personal appearance rankings, as a greater percentage of respondents were rated above average than below average. We addressed this problem by recoding the five categories into three categories that displayed a more symmetrical distribution, with the highest category being the only above average ranking. Inflated scoring patterns were found in other studies as well (French, 2002; Hamermesh and Biddle, 1994; Harper, 2000). Like others, we do not view inflated rankings as a major concern because it is unlikely to be systematic across individuals, particularly as it relates to GPA. Finally, GPA records data are missing for 21% of our core analysis sample. To account for the possibility of non-random attrition, we reestimated our main model using the Heckman selection correction procedure (Heckman, 1976; Heckman, 1979). The selection equation included county-level variables that are associated with the missing GPA data (i.e., median household income, unemployment rate, proportion of population aged 25 and older without a high school diploma or a GED, per capita expenditures on education, and total crime rate per 100,000 population). After taking selection into account, the magnitude of the personal appearance coefficients was slightly smaller, but those that were statistically significant remained so. Based on these results, we conclude that the missing GPA cases have no appreciable effect on our main findings.

M.T. French et al. / Labour Economics 16 (2009) 373–382

7. Summary and conclusions The main objective of our study was to determine how personal appearance of high school students (based on interviewer ratings of physical attractiveness, personality, and grooming) affects overall high school GPA. To our knowledge, this is the first economic study of the relationship between personal appearance and academic performance, and one of the few studies in the literature to utilize longitudinal data with more than one dimension of personal appearance (Hamermesh et al., 2002). The study serves as a natural complement to the much more voluminous existing literature on physical attractiveness and labor market success. The results from our basic model suggest that when considered alone, physically attractive female students receive a grade premium, while physically unattractive male students receive a grade penalty (Table 3). However, when personality and grooming–two resources students combine with physical attractiveness and innate ability to achieve academic, social, and other goals–are included in the model, the positive effect of physical attractiveness on high school GPA turns negative for both genders and statistically significant for males. A statistically significant grade premium for well-groomed male and female students is also present, and an even larger penalty is found for poorly groomed male students. Female students with pleasant personalities also receive a grade premium. The grade premiums and penalties generated for grooming for male students are larger than those for female students. Personality is the only characteristic with a greater impact for female students. Results for female students are a bit more robust to sensitivity checks than those for males. Though somewhat speculative, we offer two possible explanations for our results. The first relates to how students apply fixed and variable resources to meet their objectives. Part of this decisionmaking process may involve attempts to establish an identity that either conforms to or challenges adult norms. Female students who appear personable to the Add Health interviewers and who are well groomed may be choosing to conform to adult expectations. As part of this effort, they may also be investing more time in schoolwork instead of socializing, since academic achievement is highly valued by teachers and other adults. The same type of mechanism can help explain the results for male students. Minimal investment in grooming by some males students may be a conscience effort to challenge social norms, fit in with certain peer groups, and disparage academic success. Similarly, the negative and significant coefficient for very physically attractive male students could reflect substitution of time and goods. Males who invest time and effort in improving their physical attractiveness or who are naturally more physically attractive may choose to socialize more than studying. They also may be complacent about their studies because they believe their attractiveness will provide a sufficient boost to academic and labor market success. A second, and perhaps more extreme interpretation of our findings, would point to teacher bias in favor of or against certain types of students. Teachers may assume that students who are wellgroomed and/or have pleasant personalities are more intelligent or capable than other students, leading to more generous grading practices. Female students who present a pleasant demeanor and all students who invest in grooming may be rewarded for their efforts in the form of a grade premium. Conversely, teachers may impose a grade penalty on male students who invest little in grooming or use unconventional attire as a form of dissension. Along the same lines, the grade penalty associated with being very physically attractive (males) may occur because teachers view these students as privileged or in some way less deserving than less attractive students. One way to reduce potential teacher bias, if present, would be to implement a policy in which students identify themselves on exams and other assignments with codes rather than names, social security

381

numbers, or other identifying information, effectively making the grading process anonymous. Unfortunately, we are not able to conclusively determine whether the relationships between personal appearance and high school grades arise because of investments by students to establish their identities and attain academic objectives, teacher discrimination in assigning grades, or real differences in academic performance. If the first mechanism is dominant, then fully informed students are probably acting as rational economic agents. Because we control at least partially for student ability and many other characteristics, teacher discrimination is still a real possibility. Regardless of which of these causal mechanisms is most plausible, we find that personal appearance matters at a relatively young age in influencing outcomes that may shape future success in college, the labor market, and family formation. To the extent that human capital accumulation and ultimate labor market success depend on high school grades, this is a noteworthy finding. Acknowledgements This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@unc. edu). We gratefully acknowledge Hai Fang, two anonymous reviewers, and participants in seminars at the Departments of Economics and Sociology at the University of Miami and at the Lister Hill Center for Health Policy at the University of Alabama at Birmingham School of Public Health for their comments and suggestions on earlier versions of the paper, and William Russell and Carmen Martinez for editorial and administrative support. The authors are entirely responsible for the research and results reported in this paper and their position or opinions do not necessarily represent those of the Carolina Population Center or the University of Miami. Appendix A Table A Add health questions for personal appearance. Q1. How physically attractive is the respondent? 1. Very unattractive 2. Unattractive 3. About average 4. Attractive 5. Very attractive Q2. How attractive is the respondent's personality? 1. Very unattractive 2. Unattractive 3. About average 4. Attractive 5. Very attractive Q3. How well groomed was the respondent? 1. Very poorly groomed 2. Poorly groomed 3. About average 4. Well groomed 5. Very well groomed Notes: These questions were part of the interviewer remarks. The interviewer was asked to describe the respondent, the neighborhood, and the circumstances and surroundings of the interview as part of a separate section that could only be accessed by the interviewer using a password. Respondents were unable to review the interviewers' questions or responses, and these questions were to be completed as soon as possible after leaving the respondent.

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Appendix B Table B Full set of linear regression results for the effects of personal appearance at Wave 1 on overall GPA (respondents in grades 7, 8, and 9 at Wave 1). Explanatory variables Very physically attractive (w1) Belowaveragephysicalattractiveness(w1) Very attractive personality (w1) Below average personality (w1) Very well groomed (w1) Below average grooming (w1) PVT score Missing PVT score 7th grade 8th grade Hispanic African American Other race Number of children b 18 in the household Oldest child Resident mother attended college Resident mother/father public assistance Two parent household Small school Medium school Public school Urban school Rural school Average class size Dress code More than 66% of school is White % of full-time teachers that are White Missing residential mother's education Missing school level variables Midwest West Northeast Oversampling of certain groups N R2

Males (Model D)

Females (Model H)

Coef.

SE

Coef.

SE

− 0.122⁎ − 0.005 0.081 − 0.114 0.274⁎⁎⁎ − 0.468⁎⁎⁎ 0.014⁎⁎⁎ −0.064 0.103⁎ 0.025 − 0.165⁎⁎⁎ −0.361⁎⁎⁎ 0.143⁎⁎ − 0.011 − 0.012 0.311⁎⁎⁎ − 0.138⁎⁎ 0.233⁎⁎⁎ 0.220⁎⁎⁎ 0.013 0.007 − 0.064 − 0.092 − 0.004 0.046 − 0.031 − 0.111 − 0.153⁎⁎⁎ 0.013 − 0.146⁎⁎ 0.074 − 0.170⁎⁎ −0.027 2487 0.237

(0.064) (0.077) (0.066) (0.083) (0.065) (0.109) (0.001) (0.084) (0.052) (0.050) (0.061) (0.063) (0.060) (0.016) (0.029) (0.035) (0.060) (0.040) (0.083) (0.065) (0.115) (0.065) (0.083) (0.006) (0.058) (0.074) (0.175) (0.053) (0.049) (0.072) (0.066) (0.078) (0.037)

− 0.047 − 0.044 0.145⁎⁎⁎ −0.091 0.114⁎⁎ − 0.155 0.018⁎⁎⁎ 0.074 0.089⁎⁎ 0.090⁎⁎ −0.285⁎⁎⁎ − 0.277⁎⁎⁎ 0.057 − 0.003 0.043 0.264⁎⁎⁎ −0.244⁎⁎⁎ 0.185⁎⁎⁎ 0.188⁎⁎⁎ 0.079 −0.069 −0.036 −0.024 0.002 0.048 0.004 0.014 −0.060 0.049 − 0.202⁎⁎⁎ − 0.041 − 0.249⁎⁎⁎ 0.002 2878 0.297

(0.044) (0.079) (0.036) (0.069) (0.044) (0.112) (0.001) (0.075) (0.042) (0.044) (0.063) (0.048) (0.053) (0.014) (0.031) (0.034) (0.048) (0.034) (0.069) (0.052) (0.084) (0.061) (0.067) (0.004) (0.057) (0.061) (0.154) (0.064) (0.098) (0.066) (0.052) (0.059) (0.037)

Notes: standard errors were adjusted for clustering at the school level (primary sampling unit). ⁎Statistically significant at p b 0.10; ⁎⁎statistically significant at p b 0.05; ⁎⁎⁎statistically significant at p b 0.01.

Appendix C Table C Cross tabulations of Wave 1 physical attractiveness and grooming (N = 5365). Wave 1 rating of physical Wave 1 rating of grooming1 attractiveness1 Less than average Average Very well groomed Total Less than average 1.32 Average 1.98 Very attractive 0.04 Total (N = 5365) 3.34 2 Pearson chi (4) = 1.8e + 03 p b 0.000

3.56 75.17 6.47 85.20

0.24 4.21 7.01 11.46

5.13 81.36 13.51 100.00

1 This rating is based on the Wave 1 assessment for respondents in grade 9 at Wave 1 and the Wave 2 assessment for respondents in grades 7 and 8 at Wave 1 (when available).

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