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Effects of physical attractiveness, personality, and grooming on academicperformance 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, USAb Department of Economics, University of Miami, Jenkins Building, 5250 University Drive, Coral Gables, FL 33146-6550, USAc Health Economics Research Group, Sociology Research Center, University of Miami, 5665 Ponce de Leon Blvd., Flipse Building, Room 104, Coral Gables, FL 33124-0719, USAd 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

a b s t r a c ta r t i c l e i n f o

Article history:Received 13 February 2008Received in revised form 6 January 2009Accepted 9 January 2009Available online 20 January 2009

JEL classification:I12I21J71

Keywords:Physical attractivenessPersonalityGroomingAdolescentsHigh school grades

Using data from the National Longitudinal Study of Adolescent Health (Add Health), we investigate whethercertain aspects of personal appearance (i.e., physical attractiveness, personality, and grooming) affect astudent's cumulative grade point average (GPA) in high school. When physical attractiveness is entered intothe model as the only measure of personal appearance (as has been done in previous studies), it has apositive and statistically significant impact on GPA for female students and a positive yet not statisticallysignificant effect for male students. Including personality and grooming, the effect of physical attractivenessturns 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 thelargest effect on GPA for male students, having a very attractive personality is most important for femalestudents. Numerous sensitivity analyses support the core results for grooming and personality. Possibleexplanations for these findings include teacher discrimination, differences in student objectives, and rationalresource allocation decisions.

© 2009 Elsevier B.V. All rights reserved.

1. Introduction and background

In recent years, economists have expanded the study of labor marketdiscrimination to include the effects of physical attractiveness or beauty.The seminal paper by Hamermesh and Biddle (1994) examines theeffects of physical attractiveness on earnings and finds a “plainnesspenalty” of 5–10% and a slightly lower “beauty premium” for both malesand females in the workplace. How much (if any) of the estimatedearnings effects reflects discrimination, occupational crowding, orproductivity differences is uncertain, although Hamermesh and Biddle'sanalysis suggests that somedegree of employerdiscrimination is present.

Since the original study, Hamermesh and his colleagues have in-vestigated similar topics ranging from the impact of lawyers' appearanceon 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 recentstudies include French (2002), who analyzes self-reported appearancedata and finds a beauty premium for female, but not male workers, andMobius and Rosenblat (2006), who investigate the possible causes of a

beauty premiumwithin an experimental labormarket. In addition to theliterature focusing onwages, several authors have examined the effect ofphysical attractiveness on type of employment. Schwer and Daneshvary(2000), for example, study the tendency of “good-looking” people to sortinto jobs where appearance is important to performance and how thisultimately influences the upkeep of one's looks. Mocan and Tekin (2006)use theNational Longitudinal Studyof AdolescentHealth (AddHealth) toexamine the relationship between physical attractiveness and thepropensity to engage in criminal behavior. They conclude that “beingunattractive increases [the propensity] for a number of crimes, rangingfrom burglary to selling drugs.” They attribute these effects to rationaleconomic behavior among below average looking individuals who facemore obstacles in the labor market.

Only a few studies have attempted to capture the effects of otherpersonal 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 onclothing and cosmetics as a proxy for grooming in addition tointerviewer appearance ratings. Although beauty increases earnings,spending on beauty enhancements produces only a small additionaleffect. To better distinguish between physical attractiveness andgrooming, Hamermesh and Parker (2005) and Süssmuth (2006)control forwhether facultymemberswore business attire (ties formen

Labour Economics 16 (2009) 373–382

⁎ Corresponding author. Tel.: +1 305 284 6039; fax: +1 305 284 5310.E-mail addresses: mfrench@miami.edu (M.T. French), probins@miami.edu

(P.K. Robins), jhomer@miami.edu (J.F. Homer), ltapsell@miami.edu (L.M. Tapsell).

0927-5371/$ – see front matter © 2009 Elsevier B.V. All rights reserved.doi:10.1016/j.labeco.2009.01.001

Contents lists available at ScienceDirect

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j ourna l homepage: www.e lsev ie r.com/ locate / l abeco

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and jackets and blouses for women) in the photographs evaluated byraters. In Hamermesh and Parker (2005), the impact of beauty oninstructor ratings is only slightly smaller after including an indicatorfor business attire. As a sensitivity test, Hamermesh and Parker (2005)re-estimated their mainmodel with only those departments where allfacultymembers had pictures posted online to reduce the possibility ofselection bias if only faculty members with “go-getter” personalitiesvolunteered to post their pictures. Again, including this measureslightly diminished the effects of beauty on teaching evaluations, butdid not change the main results. Other studies use weight and heightinstead of a direct measure of physical attractiveness (Crosnoe andMuller, 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 andpenalties in the labor market stem from differences in attention andassessment by teachers to physically attractive students and theresultant effect on human capital accumulation. A sizeable non-economics literature has examined the influence of physical attrac-tiveness on teacher perceptions of student abilities (Jackson et al.,1995; Kenealy et al., 1988; Ritts et al., 1992). Ritts et al. (1992) reportthat attractive students typically receive higher grades and betterscores on standardized tests than unattractive students. Kenealy et al.(1988) identify favoritism toward girls and determine that “significantsex differences are observed in the teachers' ratings of attractiveness,academic brightness, sociability, and confidence.”

In this paper, we utilize data fromWaves 1 and 3 of the Add Healthsurvey to examinewhether three personal appearance characteristics–physical attractiveness, personality, and grooming–observed just priorto entering high school are significantly related to grades studentsreceived in high school.1 Our expanded definition of personalappearance, derived from extended interviewer observations ofstudents, enables us to identify more precisely which dimensions ofpersonal appearance are the strongest predictors of academicachievement. This approach can offer new insight on the complexrelationship between physical attractiveness per se and variousoutcomes. Using data abstracted from high school records allow usto test for the impact of personal appearance on actual recorded gradepoint average (GPA) rather than subjective measures of studentcompetence or self-reported grades. Finally, we follow the literatureand estimate separate models for males and females.

2. Conceptual framework

While most of the existing literature has studied the effects ofphysical appearance on adult earnings, the present study focuses on asample of middle and high school students and examines the effects ofan expanded set of personal appearance characteristics on studentgrades. Adolescence is a time of rapid intellectual, mental, and emo-tional developmental, with corresponding physical and social changes(Maggs et al., 1997; Kroger, 2006). It is important to consider theseunique factors when investigating a sample of adolescents andmakingcomparisons to studies of adults. Although physical appearance canchange 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 largemovements in grooming between the beginning ofhigh school and Wave 3, a period of about 6 years. As expected, thesemovements are more pronounced than for the sample of adults inHamermesh and Biddle's (1994) paper, where 93% of respondentsreceived identical ratings in at least 2 of 3 years and one rating leveldifference in the third year.

Identity formation and decisions about whether to conform orchallenge adult conventions also occur during adolescence (Erikson,1968; Maggs et al., 1997; Kroger, 2006). Physical appearance and aca-demic achievement are both avenues through which adolescentsidentify with their peers and rebel against or abide by adult con-ventions. Based partly on studies in the educational psychology andeconomics literature (Anderson and Keith, 1997; Heckman, 2008;Lounsbury et al., 2003; Neisser et al., 1996; Rivkin et al., 2005), weassume that a student is endowedwith a certain level of intelligence orability (proxied by the Peabody Picture Vocabulary Test (PVT) score inour data) as well as an innate degree of physical attractiveness.Students then make decisions about combining these initial endow-ments with a host of variable resources (e.g., time spent studying,selection of friends, participation in extracurricular activities) toachieve certain goals related to their identities. For example, studentswho desire to attend college are likely to invest a greater amount oftheir time in studying and developing their human capital. If physicalattractiveness is correlated with achievement, as has been found instudies of adult labor market behavior, this might suggest possibleteacher discrimination becausewe are controlling (albeit partially) forability and many other observable characteristics that could influencegrades.2 Alternatively, a negative association between physical attrac-tiveness and GPA could reflect decisions bymore attractive students toallocate time to other objectives (such as socializing) rather thanschoolwork. The analysis that follows is not able to definitively resolvethese possible mechanisms, but the results offer new insight into atopic that heretofore has been focused almost exclusively on adultsamples and labor market outcomes.

Another distinct advantage of this paper vis-à-vis most of thepublished literature is our ability to examine personality and groomingin addition to physical attractiveness. Specifically, our main modelposits that GPA is a function of physical attractiveness, personality,grooming, intelligence, and a long list of other variables that are likelyto be related to academic performance. In this model, personality andgrooming are viewed as additional variable resources that studentsmodify to achieve certain goals. A pleasant personality and propergrooming, as perceived by an adult interviewer, may reflect efforts bythe adolescent to conform to adult expectations. Such individualsmight also be likely to devote considerable time and effort to theirschoolwork, as adults look upon these activities approvingly. Con-versely, students who are rebellious against social norms mightpurposely manipulate their grooming habits or personality to conveysuch an image. These appearance traits might be distasteful to adultinterviewers or teachers, but appealing to a particular peer group thatthe student identifies with. In this case, the student may be devotingfewer resources to academic achievement in lieu of group conformityand popularity. Alternative specifications are estimated to furtherprobe the main model and determine whether students manipulatetheir personal appearance to succeed academically.

Following an approach that is similar to earlier investigations(Biddle and Hamermesh, 1998; Hamermesh, 2006; Hamermesh andBiddle, 1994; Hamermesh et al., 2002; Hamermesh and Parker, 2005;Pfann et al., 2000), we treat Wave 1 measures of physical attractive-ness, personality, and grooming as initial endowments that areexogenous because they are measured just prior to or at the point ofentering high school. We select students in 7th, 8th, and 9th grades atWave 1 for the core sample to ensure that the personal appearancevariables are not affected in any meaningful way by high school GPA(which is assigned during Grades 9–12).

1 Hereafter, we use the general term “personal appearance” to refer collectively tophysical attractiveness, personality, and grooming.

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

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The final conceptual point regarding sample formation concernspossible gender differences. Most of the studies of physical attractive-ness and labormarket outcomes among adults have estimated separatemodels 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 psychologyliterature) have also identified gender differences in academic achieve-ment, with females earning higher grades than their male counterparts(Dwyer and Johnson, 1997; Jacob, 2002; Kleinfeld, 1998). In the AddHealth data, overall GPA is significantly higher among females, andmales tend to score lower on personal appearance measures. For thesereasons, we follow the conventional approach in the literature andestimate separate models for male and female students.

3. Methods

Our basic analysis first examines the independent effects ofphysical attractiveness, personality, and grooming on GPA, controllingfor a set of individual, family, and school characteristics. We thenestimate a series of academic achievement equations of the followingform:

GPAi = β0 + βAPhysicali + βPPersonalityi + βGGroomingi +� XβX +� μ i ð1Þ

where GPA is the overall grade point average in high school, Physicalis a set of physical attractiveness measures, Personality is a set ofpersonality measures, Grooming is a set of grooming measures, andX is a set of control variables.3 This preferred specification, whichincludes measures for above- and below-average physical attractive-ness, personality, and grooming, permits a comparison of the relativeimportance of each appearance measure. Standard errors are adjustedfor clustering at the school (primary sampling unit) level. The AddHealth survey provides sampling weights, but we do not use them andchoose instead to directly control for a number of variables related tothe sampling distribution.4

After presenting results from our basic models, we conductnumerous sensitivity analyses to examine the stability of the corefindings. Eq. (1) is re-estimated using alternative personal appearancemeasures, different exclusion criteria, and various control variables. Wealso estimate several additional models to better understand thepotential 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 twowaves of data fromAddHealth, a school-based,longitudinal study of adolescent health-related behaviors and theirconsequences in young adulthood. Wave 1 was administered during1994–1995 and included in-home interviews with 20,745 adolescentssampled from 80 high schools and 52 middle schools. The study designensures the sample is representative of U.S. schools based on region,

school type, size, and ethnicity. In-home interviews took 1–2 h tocomplete and were administered as a Computer-Assisted PersonalInterview (CAPI)/Audio Computer-Assisted Self Interview (CASI). Forsensitive topics, the respondents listened to pre-recorded questionsusing earphones and entered their responses into a laptop computer. In2001–2002, 15,170 respondents were re-interviewed in Wave 3 whenthey were 18 to 27 years old. Wave 2 (administered approximately oneyear afterWave1)only included adolescents fromWave1whowere stillattending high school while Wave 3 conducted follow-up interviewswithallWave1 respondentswhocouldbe contacted. For this reason, theanalysis focuses specifically on respondents fromWaves 1 and 3.

High school transcripts were requested and abstracted for 88% ofWave 3 respondents. The most common reason for missing GPA datawas difficulty in obtaining records fromvarious high schools. A carefulinvestigation of the missing cases reveals that they have character-istics (e.g., lower income and PVT scores, less parental education) thatare typically associated with lower GPAs. We control for thesecharacteristics in our empirical models explaining GPA, and adjustfor sample selection bias to account for the possibility of non-randomattrition. These results are discussed later in the paper.

The Add Health data have many desirable features pertinent to ourstudy, with interviewer assessments of personal appearance andofficial records of high school grades the most notable. The majority ofresearch studies on physical attractiveness use ratings based onphotographs of the subjects' faces (Hamermesh and Parker, 2005;Ritts et al., 1992; Hamermesh, 2006; Pfann et al., 2000). The potentialproblems associated with the photographmethod are discussed in thereview by Ritts et al. (1992), who hypothesize that such methods failto replicate encounters that occur in real life.

The Add Health interviewers responded to questions on threekey domains of personal appearance—physical attractiveness, person-ality, and grooming.5 Although the subjective nature of “beauty” and“homeliness” may present challenges when using physical attractive-ness ratings in empirical research, multiple studies have confirmedconsistency across evaluators (Jackson et al.,1995; Ritts et al.,1992). Toreduce potential bias, Add Health respondents could not see thequestions or answers in the interviewer section. This material couldonly be accessed by the interviewer with a password and wascompleted after the interviewer left the respondent. Interviewerassessments overcome some of the limitations of the photographmethod, but this approach is not ideal either. The halo effect is one typeof potential bias whereby the interviewer develops a positiveimpression of the respondent over the course of their encounter andthen rates the respondent higher thanhe/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 pre-sented in Fig. 1A and B. Since the interviewers answer a series ofquestions about the relative personal appearance of each respondent,one would expect to see a nearly symmetrical distribution resemblinga normal curvewith “average” appearance as themodal category and afairly equal number of responses on either side of the “average.” Asseen in Fig. 1A, however, there appears to be a preference amonginterviewers for top ranking the adolescents, with about twice thenumber of respondents being designated above average than belowaverage. Indeed, many more individuals were placed in the topcategory (very physically attractive, very attractive personality, or verywell groomed) than in the bottom two categories combined. Toaddress the generosity of the interviewers, we coded the uppercategory by itself, the second and third categories as a combined

3 Including all three personal appearance measures in a single equation raisespotential concern about multicollinearity if the measures are highly correlated.Tabulation of the correlations of the personal appearance measures at Wave 1 revealsthat, while all are significantly different from zero at the 1% level, none exceeds 0.51. Asone would expect, similar rankings for each measure are positively correlated. Forexample, the correlation between being rated as very physically attractive and having avery attractive personality is 0.503 while the correlation between being very physicallyattractive and being very well groomed is 0.501. Although these particular correlationsare sizeable, most are much smaller. To examine this issue further, we calculatedvariance inflation factors (VIFs) for all regressors in the core models. All of the VIFs areless than 5, which is typically considered the threshold for potential multicollinearityproblems.

4 The results are qualitatively similar when the sampling weights are used, althoughsignificance levels change somewhat for a few of the variables (results available onrequest from the authors).

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

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“average” group, and the bottom two categories as a combined “belowaverage” group. This recoding yielded a three-category distributionthat more closely resembles a normal curve (Fig. 1B).6

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

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

6 Other coding strategies yielded similar estimation results (see the sensitivityanalyses below).

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school sample in a manner that would imply causation from personalappearance to high school grades rather than the reverse. To explorethe possibility that personal appearance scores for the same individualchange over time due to interviewer differences and/or physicalchanges that occur during high school, we cross-tabulatedWave 1 andWave 3 personal appearance scores for the full sample (Table 1).Approximately two-thirds of the sample had the identical ranking inthe 3-category distribution from Wave 1 to Wave 3, a span coveringabout 6 years. Moreover, about 85% of the sample had the same or nomore than one category change in ranking when considering fivecategories instead of three (86% for physical attractiveness, 84% forpersonality, and 88% for grooming).

Descriptive statistics for all variables used in the analysis arepresented in Table 2. The overall high school GPA for this sample wasslightly above 2.5, and the average GPA for females (2.699) was sig-nificantly higher than for males (2.390). Female students were signi-ficantly more likely than males to be included in the top appearancecategories, while males were more likely than females to be rated asaverage. All respondentswere administered an abridged version of thePeabody Picture Vocabulary Test (PVT) at the start of their in-homeinterview. On average, PVT scores for male students were significantly

higher than for female students. In addition, several socioecono-mic, demographic, and school-specific variables displayed significantgender differences.

5. Results

5.1. Basic findings

Table 3 presents selected coefficient estimates for the linearregressions of overall high school GPA on personal appearance and along list of covariates. Models A–C (males) and E–G (females) includeonly one set of personal appearancemeasures, whileModels D (males)and H (females) include all three personal appearance measures. Inaddition to the personal appearance measures, all specificationscontrol for the PVT score, grade, race, children in household, birthorder, mother's education, whether a parent received public assis-tance, whether the household is two-parent, school size, school type,school location, average class size, whether a school dress code isenforced, racial composition of the school, region, and oversampling ofcertain groups. The complete regression results for Models D and H(our preferred models) are presented in Appendix Table B.

Table 1Cross 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.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.

The shaded areas represent the combined categories for defining the variables used in the empirical models.1This 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 1.2This rating is based on the Wave 3 assessment for all respondents.

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Turning first to the male students, when physical attractivenessis the only measure of personal appearance (Model A), being veryphysically attractive is positively related to GPA, while being belowaverage physical attractiveness is negatively related. The positiveeffect for being very physically attractive, however, is not statisticallysignificant. The plainness penalty for male students is −0.146 pointsin overall GPA, which in percentage terms is about −6.11% (−0.146/2.390). Models B (personality) and C (grooming) strongly indicatethat being rated in the highest category is associated with a gradepremium and being rated below average is associated with a gradepenalty. 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 correspond-ing beauty premium/penalty. In the model with all three personalappearance measures (Model D), the coefficient estimate for beingvery 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 malestudents, grooming continues to deliver the biggest quantitativeeffect 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 attractive-ness, personality, and grooming are positively and significantlyrelated to GPA when these measures are entered individually in theregressions. In Model E, female students who are very physicallyattractive gain a modest boost of 0.080 (0.080/2.699=2.96%) pointsin overall GPA. The estimated premiums for personality andgrooming are about two times larger in magnitude. A marginallysignificant GPA penalty is found for below average personality(−0.128, Model F) and grooming (−0.197, Model G). When all threedimensions of personal appearance are included (Model H), theresults are similar to those for male students (Model D) in terms ofdirection, but not necessarily statistical significance. In particular,the coefficient estimate for being very physically attractive becomesnegative, but it is not statistically significant. A significant GPApremium is present for female students with very attractivepersonalities (0.145) and exceptional grooming (0.114). Unlike thelarge and significant result for males, poorly groomed female studentsdo not incur a statistically significant GPA penalty.

Some notable findings also pertain to the other independentvariables in these regressions (see Appendix Table B). Most of theseresults are similar for males and females. As expected, PVT score,intended to control somewhat for ability and intelligence, is positive andsignificantly related toGPA.Hispanics andAfricanAmericanshave lowerGPAs thanWhites, and other non-White races have higher GPAs amongmales. Having a resident mother who attended college, residing in atwo-parent household, and attending a small school are all positivelyassociated with GPA, while receiving public assistance is negativelyrelated to GPA. These effects may reflect the types and amount ofresources being devoted to the student's academic activities.

5.2. Robustness checks

To gauge the precision and stability of our core findings, weconducted a series of robustness checks using our preferred models Dand H in Table 3. The results are presented in Tables 4A (males) and B(females).

First,Wave3personal appearancemeasures are used insteadofWave1 measures to account for the possibility that appearance may havechanged for some of the respondents during high school (Column a).Second, all models are re-estimated with the full sample at Wave 1instead of just the7th, 8th, and9th graders (Columnb). Third, anaveragescore of personal appearance rankings is constructed from Waves 1, 2,and 3, (Column c) and of personal appearance rankings from Waves 1and 3 (Column d) using the original analysis sample. Finally, the coremodel is re-estimated using adolescents in 7th and 8th grades, but not9th grade, at Wave 1 (Column e).

Numerous other robustness checks of Models D and H were alsoconducted, the results of which are not reported in the tables, but areavailable on request from the authors. First, the three personalappearance categories are redefined strictly from the originalquestions as above average (top two categories), average (middlecategory), and below average (bottom two categories). Second,covariates are included for graduation status (92% of our samplereported receiving a high school degree), age, and body mass index(BMI). Third, the models are re-estimated using only Whiteinterviewers, using only female interviewers, and then controlling

Table 2Variable 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 2.699 2.556(0.906) (0.844) (0.886)

Personal appearance measuresVery physically attractive (W1)⁎⁎⁎ 0.085 0.178 0.135Average physical attractiveness (W1)⁎⁎⁎ 0.856 0.777 0.814Less than average physical attractiveness (W1)⁎⁎ 0.058 0.045 0.051Very attractive personality (W1)⁎⁎⁎ 0.108 0.173 0.143Average personality attractiveness (W1)⁎⁎⁎ 0.836 0.787 0.810Less than average personality attractiveness(W1)⁎⁎⁎

0.056 0.041 0.048

Very well groomed (W1)⁎⁎⁎ 0.087 0.139 0.115Average grooming (W1)⁎⁎⁎ 0.875 0.832 0.852Less than average grooming (W1)⁎ 0.038 0.029 0.033

Control variablesPeabody picture vocabulary test(PVT) score⁎⁎⁎

101.558 99.233 100.311(14.536) (14.519) (14.572)

Grade 7 0.301 0.310 0.306Grade 8 0.287 0.304 0.296Grade 9⁎ 0.411 0.386 0.398White 0.660 0.657 0.658Hispanic 0.124 0.121 0.122Black⁎⁎ 0.202 0.230 0.217Other race⁎⁎⁎ 0.138 0.113 0.125Number of children under age 18 in thehousehold

1.358 1.418 1.390(1.195) (1.255) (1.228)

Oldest child⁎⁎ 0.317 0.289 0.302Resident mother attended college ⁎⁎⁎ 0.451 0.419 0.434Resident mother or father receivespublic assistance⁎⁎

0.094 0.114 0.105

Two parent household⁎⁎ 0.699 0.674 0.686Attending small school⁎ 0.201 0.223 0.213Attending medium school 0.485 0.505 0.496Attending large school⁎⁎⁎ 0.314 0.272 0.291Public school 0.927 0.933 0.931Urban school 0.293 0.296 0.294Rural school 0.190 0.182 0.186Suburban school 0.518 0.522 0.520Average class size 25.887 25.817 25.850

(4.763) (4.767) (4.765)Dress code enforced⁎ 0.846 0.865 0.856More than 66% of school is White 0.567 0.563 0.565Percentage of full-time teachers that areWhite 0.824 0.814 0.819Midwest 0.261 0.260 0.261West 0.204 0.188 0.196Northeast 0.138 0.137 0.137South 0.398 0.414 0.407Not a member of an oversampled group⁎ 0.657 0.680 0.670

Standard deviations reported in parentheses for continuous variables. Overall GPA has aminimum of 0 and a maximum of 4. Peabody PVT Score has a minimum of 10 and amaximum of 137. Number of children under age 18 in the household has a minimum of0 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, pb0.10. ⁎⁎Statisticallysignificant differences between males and females, pb0.05. ⁎⁎⁎Statistically significantdifferences between males and females, pb0.01. (Kruskal Wallis tests for equality ofpopulations.)

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for interviewer characteristics. Fourth, to better account for economicwell being and living conditions, Models D and H are re-estimatedwith the addition of three variables based on interviewer responses:whether the respondent lived in a single family home, whether theinterviewer felt concerned for his/her safety when going to therespondent's home, and whether the building where the respondentlives was poorly or very poorly kept. Fifth, 159 respondents withphysical disabilities (based on self-reports) are excluded from thesample. Finally, to account for unobservable differences acrossschools, Models D and H are re-estimated with school fixed-effectsinstead of the control variables for school characteristics.

For male students, the most consistent results across all of thesealternative specifications are for very well groomed students (positiveand significant) and less than average groomed students (negative andsignificant). The negative and marginally significant effect of beingvery physically attractive (Model D) is not statistically significant inmost of the othermodels. There is some evidence in Table 4A and otherspecifications of a statistically significant grade premium associatedwith having a very attractive personality, but the core model in Table 3does not support this result.

Overall, results from the sensitivity tests for female students aremore consistent and stable than those for male students. The grade

Table 4BSelected linear regression results for sensitivity analyses of personal appearance onoverall GPA (females).

Personal appearancemeasures

Models

a b c d e

Very physically attractive 0.033 −0.058⁎⁎ −0.050 0.003 −0.037(0.049) (0.027) (0.062) (0.037) (0.053)

Less than average physicalattractiveness

0.070 −0.033 −0.084 −0.010 −0.025

(0.064) (0.049) (0.098) (0.110) (0.108)Very attractive personality 0.093⁎⁎ 0.137⁎⁎⁎ 0.210⁎⁎⁎ 0.137⁎⁎⁎ 0.163⁎⁎⁎

(0.038) (0.024) (0.049) (0.038) (0.046)Less than average personality 0.032 −0.108⁎ −0.250⁎⁎ 0.064 −0.097

(0.071) (0.055) (0.122) (0.110) (0.099)Very well groomed 0.160⁎⁎⁎ 0.130⁎⁎⁎ 0.207⁎⁎⁎ 0.192⁎⁎⁎ 0.051

(0.044) (0.024) (0.055) (0.041) (0.058)Less than average grooming −0.252⁎⁎⁎ −0.115 −0.139 −0.089 −0.198

(0.089) (0.071) (0.156) (0.176) (0.132)N 2878 6365 2541 2878 1767R2 0.301 0.280 0.300 0.301 0.281

Notes: Model “a” uses personal appearance measures from Wave 3 and includesrespondents in grades 7, 8, and 9 at Wave 1. Model “b” uses personal appearancemeasures fromWave 1 and includes all respondents atWave 1. Model “c” uses the meanof the personal appearance ratings from Waves 1, 2, and 3 and includes respondents ingrades 7, 8, and 9 at Wave 1. Model “d” uses the mean of the pre- and post-personalappearance ratings used in the coremodels and includes respondents in grades 7, 8, and9 at Wave 1. Model “e” uses personal appearance measures from Wave 1 and includesrespondents in grades 7 and 8 at Wave 1. Coefficient estimates and robust standarderrors (in parentheses) are reported. Standard errors were adjusted for clustering at theschool level (primary sampling unit). All specifications also controlled for PVT score,grade, race, children in household, birth order, mother's education, whether a parentreceived public assistance, two-parent household, school size, school type, schoollocation, average class size, school dress code, racial composition of school, region, andoversampling of certain groups.⁎Statistically significant at pb0.10; ⁎⁎statistically significant at pb0.05; ⁎⁎⁎statisticallysignificant at pb0.01.

Table 3Selected 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) Females (N=2878)

A B C D E F G H

Very physically attractive (W1) 0.055 −0.122⁎ 0.080⁎⁎ −0.047(0.052) (0.064) (0.035) (0.044)

Below average physical attractiveness (W1) −0.146⁎⁎ −0.005 −0.114 −0.044(0.073) (0.077) (0.072) (0.079)

Very attractive personality (W1) 0.145⁎⁎⁎ 0.081 0.173⁎⁎⁎ 0.145⁎⁎⁎(0.050) (0.066) (0.030) (0.036)

Below average personality attractiveness (W1) −0.182⁎⁎ −0.114 −0.128⁎ −0.091(0.081) (0.083) (0.067) (0.069)

Very well groomed (W1) 0.263⁎⁎⁎ 0.274⁎⁎⁎ 0.163⁎⁎⁎ 0.114⁎⁎(0.055) (0.065) (0.036) (0.044)

Below average grooming (W1) −0.492⁎⁎⁎ −0.468⁎⁎⁎ −0.197⁎ −0.155(0.102) (0.109) (0.106) (0.112)

R squared 0.219 0.222 0.235 0.237 0.290 0.294 0.293 0.297

Notes: Coefficient estimates and robust standard errors (in parentheses) are reported. Standard errors were adjusted for clustering at the school level (primary sampling unit). Allspecifications 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 pb0.10; ⁎⁎statistically significant at pb0.05; ⁎⁎⁎statistically significant at pb0.01.

Table 4ASelected linear regression results for sensitivity analyses of personal appearance onoverall GPA (males).

Personal appearancemeasures

Models

a b c d e

Very physically attractive 0.093 −0.055 0.002 0.091⁎ −0.064(0.063) (0.036) (0.139) (0.054) (0.088)

Less than average physicalattractiveness

−0.180⁎ 0.030 −0.110 −0.172 −0.162⁎

(0.103) (0.039) (0.096) (0.144) (0.095)Very attractive personality 0.153⁎⁎⁎ 0.085⁎⁎ 0.102 0.112⁎⁎ 0.106

(0.058) (0.040) (0.104) (0.047) (0.088)Less than average personality −0.068 −0.111⁎⁎ 0.026 −0.097 −0.105

(0.089) (0.048) (0.140) (0.135) (0.125)Very well groomed 0.045 0.214⁎⁎⁎ 0.206 0.153⁎⁎ 0.273⁎⁎⁎

(0.076) (0.036) (0.138) (0.063) (0.084)Less than average grooming −0.048 −0.353⁎⁎⁎ −0.453⁎⁎⁎ −0.417⁎⁎ −0.311⁎⁎

(0.076) (0.055) (0.123) (0.164) (0.146)N 2487 5677 2158 2487 1464R2 0.228 0.229 0.239 0.230 0.257

Notes: Model “a” uses personal appearance measures from Wave 3 and includesrespondents in grades 7, 8, and 9 at Wave 1. Model “b” uses personal appearancemeasures fromWave 1 and includes all respondents at Wave 1. Model “c” uses the meanof the personal appearance ratings from Waves 1, 2, and 3 and includes respondents ingrades 7, 8, and 9 at Wave 1. Model “d” uses the mean of the pre- and post-personalappearance ratings used in the coremodels and includes respondents in grades 7, 8, and9 at Wave 1. Model “e” uses personal appearance measures from Wave 1 and includesrespondents in grades 7 and 8 at Wave 1. Coefficient estimates and robust standarderrors (in parentheses) are reported. Standard errors were adjusted for clustering at theschool level (primary sampling unit). All specifications also controlled for PVT score,grade, race, children in household, birth order, mother's education, whether a parentreceived public assistance, two-parent household, school size, school type, schoollocation, average class size, school dress code, racial composition of school, region, andoversampling of certain groups.⁎Statistically significant at pb0.10; ⁎⁎statistically significant at pb0.05; ⁎⁎⁎statisticallysignificant at pb0.01.

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premium found in Model H for a very attractive personality isvirtually identical in terms of magnitude and statistical significanceacross all of the alternative specifications. There is slightly morevariability in the significance level of the grade premium forvery well groomed female students, but this is also highly stable.Finally, a few of the alternative specifications produced asignificant grade penalty among very physically attractive andpoorly groomed female students, but these effects were more oftennot significant.

5.3. Further analyses

To better understand our core results and to explore themotivation of high school students to use personal appearance toattain their goals (see Section 2), we conducted several furtheranalyses.7 Because the returns from personality and grooming maydepend upon an adolescent's initial level of physical attractiveness,we evaluate the effects of personality and grooming on GPA afterexcluding adolescents with below average physical attractiveness.With this conditional sample, a significant grade penalty emergesfor males with unpleasant personalities while the coefficients forbeing well groomed and poorly groomed are similar in magnitudeand significance to those in Model D. All four measures ofpersonality and grooming are significant in the expected directionfor females. Thus, among students with at least average physicalattractiveness, both personality and grooming are likely to have asignificant impact on GPA.

In our basic models, relatively large effects were found forgrooming, which is perhaps more easily manipulated by studentsthan physical attractiveness or personality. As explained in Section2, being better groomed might be at least partially driven by anattempt to conform to adult expectations, while adopting moreradical grooming habits might be part of an effort to disassociatewith adult conventions and/or fit in with certain peer groups. To testthese possible mechanisms, Models D and H are re-estimated withtwo additional indicator variables representing “conformists” and“rebels.” Conformists (11.22% of the sample) are defined as studentswith average or higher physical attractiveness and the highestcategory of grooming, and rebels (2.02% of the sample) are definedas those with average or higher physical attractiveness and thelowest category of grooming. The results indicate that being aconformist or rebel does not have a significant effect on GPA foreither gender, suggesting that the mediating effects of grooming donot counteract the direct effect of being physically attractive. Wenote, however, that the analysis may lack statistical power for rebelsbecause they represent only 2.3% of male students and 1.7% offemale students. Small sample sizes also impeded our attempts toinvestigate other combinations of grooming and physical attractive-ness, as 75% of the sample in grades 7, 8, and 9 at Wave 1 were ratedas average in grooming and physical attractiveness (see AppendixTable C).

Because academic achievement is one of several outcomes thathigh school students may pursue, we evaluated the effects ofpersonal appearance on three measures of risky behavior atWave 3: number of drinking days in the past year, number ofbinge drinking days in the past year, and number of timesmarijuana was used in the past 30 days. Overall, few personalappearance measures are significantly related to these otheroutcomes. Relative to the average category, the results for femalestudents indicate that those who are very physically attractivedrink alcohol more often, while those who are rated as belowaverage in physical attractiveness report significantly fewer drink-

ing days, but more marijuana use. Males with highly ratedpersonalities report fewer drinking days. Very physically attractivemales as well as those with less than average physical attractive-ness report using marijuana fewer times than males ranked in themiddle category of physical attractiveness.

Finally, we constructed a personal appearance “index” equal tothe cumulative score for the three personal appearance measures atWave 1. Using 1 for the lowest category and 5 for the highestcategory, the personal appearance index has a range from 3 to 15. Asexpected, the personal appearance index has a positive andstatistically significant effect on GPA for both males and females. Aquantitative interpretation should be avoided, however, because thenumerical rankings are on an ordinal scale and do not have anymeaningful cardinal properties. Nonetheless, the results suggestthat students may be able to trade-off different characteristics oftheir personal appearance to enhance academic achievement.

6. Qualifications and limitations

Before summarizing our results and offering some explanationsfor them, it is important to acknowledge some data and statisticallimitations of our study. First, we treat Wave 1 measures of personalappearance as exogenous because students in 7th, 8th, and 9th gradesat Wave I were used in the analyses. This timing convention ensuresthat the personal appearance variables were not affected by thestudents' high school grades. While reverse causality is highly un-likely, omitted variables bias is not. It is possible that our personalappearancemeasuresmay be capturing the effects of other importantand omitted predictors of academic performance such as discipline,organizational skills, parental involvement in schooling, and unob-served 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 fromomitted variable bias.

Second, if personal appearance changes dramatically during highschool, then the current two-period models with a 6-year lag mayintroduce considerable measurement error when estimating the effectsof personal appearance on GPA. To address this issue, we presented datain Table 1 showing that most respondents had little or no movement inphysical attractiveness, personality, or grooming betweenWaves 1 and3.We also presented results in our robustness analysis that use personalappearance measures from Wave 3 in addition to a combination ofmeasures from Waves 1, 2, and 3.

Third, interviewers tended to inflate the personal appearancerankings, as a greater percentage of respondents were rated aboveaverage than below average. We addressed this problem by recodingthe five categories into three categories that displayed a more sym-metrical distribution, with the highest category being the only aboveaverage ranking. Inflated scoring patterns were found in other studiesas well (French, 2002; Hamermesh and Biddle, 1994; Harper, 2000).Like others, we do not view inflated rankings as a major concernbecause it is unlikely to be systematic across individuals, particularlyas it relates to GPA.

Finally, GPA records data are missing for 21% of our core analysissample. To account for the possibility of non-random attrition, we re-estimated our main model using the Heckman selection correctionprocedure (Heckman, 1976; Heckman, 1979). The selection equationincluded county-level variables that are associated with the missingGPA data (i.e., median household income, unemployment rate, pro-portion of population aged 25 and older without a high schooldiploma or a GED, per capita expenditures on education, and totalcrime rate per 100,000 population). After taking selection intoaccount, the magnitude of the personal appearance coefficients wasslightly smaller, but those that were statistically significant remainedso. Based on these results, we conclude that the missing GPA caseshave no appreciable effect on our main findings.

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

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7. Summary and conclusions

The main objective of our study was to determine how personalappearance of high school students (based on interviewer ratings ofphysical attractiveness, personality, and grooming) affects overall highschool GPA. To our knowledge, this is the first economic study of therelationship between personal appearance and academic performance,and one of the few studies in the literature to utilize longitudinal datawith more than one dimension of personal appearance (Hamermeshet al., 2002). The study serves as a natural complement to the muchmore voluminous existing literature onphysical attractiveness and labormarket success.

The results from our basic model suggest that when consideredalone, physically attractive female students receive a grade pre-mium, while physically unattractive male students receive a gradepenalty (Table 3). However, when personality and grooming–tworesources students combine with physical attractiveness and innateability to achieve academic, social, and other goals–are included inthe model, the positive effect of physical attractiveness on highschool GPA turns negative for both genders and statisticallysignificant for males. A statistically significant grade premium forwell-groomed male and female students is also present, and an evenlarger penalty is found for poorly groomed male students. Femalestudents with pleasant personalities also receive a grade premium.The grade premiums and penalties generated for grooming for malestudents are larger than those for female students. Personality is theonly characteristic with a greater impact for female students. Resultsfor female students are a bit more robust to sensitivity checks thanthose for males.

Though somewhat speculative, we offer two possible explana-tions for our results. The first relates to how students apply fixed andvariable resources to meet their objectives. Part of this decision-making process may involve attempts to establish an identity thateither conforms to or challenges adult norms. Female students whoappear personable to the Add Health interviewers and who are wellgroomed may be choosing to conform to adult expectations. As partof this effort, they may also be investing more time in schoolworkinstead of socializing, since academic achievement is highly valuedby teachers and other adults. The same type of mechanism canhelp explain the results for male students. Minimal investment ingrooming by some males students may be a conscience effort tochallenge social norms, fit in with certain peer groups, and disparageacademic success. Similarly, the negative and significant coefficientfor very physically attractive male students could reflect substitutionof time and goods. Males who invest time and effort in improvingtheir physical attractiveness or who are naturally more physicallyattractive may choose to socialize more than studying. They also maybe complacent about their studies because they believe theirattractiveness will provide a sufficient boost to academic and labormarket success.

A second, and perhaps more extreme interpretation of our find-ings, would point to teacher bias in favor of or against certaintypes of students. Teachers may assume that students who are well-groomed and/or have pleasant personalities are more intelligent orcapable than other students, leading to more generous gradingpractices. Female students who present a pleasant demeanor and allstudents who invest in grooming may be rewarded for their effortsin the form of a grade premium. Conversely, teachers may impose agrade penalty on male students who invest little in grooming or useunconventional 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 pri-vileged or in some way less deserving than less attractive students.One way to reduce potential teacher bias, if present, would be toimplement a policy in which students identify themselves on examsand other assignments with codes rather than names, social security

numbers, or other identifying information, effectively making thegrading process anonymous.

Unfortunately, we are not able to conclusively determine whetherthe relationships between personal appearance and high schoolgrades arise because of investments by students to establish theiridentities and attain academic objectives, teacher discrimination inassigning grades, or real differences in academic performance. If thefirst mechanism is dominant, then fully informed students areprobably acting as rational economic agents. Because we control atleast partially for student ability and many other characteristics,teacher discrimination is still a real possibility. Regardless of whichof these causal mechanisms is most plausible, we find that personalappearance matters at a relatively young age in influencing out-comes that may shape future success in college, the labor market,and family formation. To the extent that human capital accumulationand ultimate labor market success depend on high school grades,this is a noteworthy finding.

Acknowledgements

This research uses data from Add Health, a program projectdesigned by J. Richard Udry, Peter S. Bearman, and Kathleen MullanHarris, and funded by a grant P01-HD31921 from the NationalInstitute of Child Health and Human Development, with cooperativefunding from 17 other agencies. Special acknowledgment is dueRonald R. Rindfuss and Barbara Entwisle for assistance in theoriginal design. Persons interested in obtaining data files from AddHealth should contact Add Health, Carolina Population Center, 123W. Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@unc.edu). We gratefully acknowledge Hai Fang, two anonymousreviewers, and participants in seminars at the Departments ofEconomics and Sociology at the University of Miami and at the ListerHill Center for Health Policy at the University of Alabama atBirmingham School of Public Health for their comments andsuggestions on earlier versions of the paper, and William Russelland Carmen Martinez for editorial and administrative support. Theauthors are entirely responsible for the research and resultsreported in this paper and their position or opinions do notnecessarily represent those of the Carolina Population Center orthe University of Miami.

Appendix A

Table AAdd health questions for personal appearance.

Q1. How physically attractive is the respondent?1. Very unattractive2. Unattractive3. About average4. Attractive5. Very attractive

Q2. How attractive is the respondent's personality?1. Very unattractive2. Unattractive3. About average4. Attractive5. Very attractive

Q3. How well groomed was the respondent?1. Very poorly groomed2. Poorly groomed3. About average4. Well groomed5. Very well groomed

Notes: These questions were part of the interviewer remarks. The interviewer wasasked to describe the respondent, the neighborhood, and the circumstances andsurroundings of the interview as part of a separate section that could only be accessedby the interviewer using a password. Respondents were unable to review theinterviewers' questions or responses, and these questions were to be completed assoon as possible after leaving the respondent.

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Appendix B

Table BFull set of linear regression results for the effects of personal appearance at Wave 1 onoverall GPA (respondents in grades 7, 8, and 9 at Wave 1).

Explanatory variables Males (Model D) Females (Model H)

Coef. SE Coef. SE

Very physically attractive (w1) −0.122⁎ (0.064) −0.047 (0.044)Belowaveragephysicalattractiveness(w1) −0.005 (0.077) −0.044 (0.079)Very attractive personality (w1) 0.081 (0.066) 0.145⁎⁎⁎ (0.036)Below average personality (w1) −0.114 (0.083) −0.091 (0.069)Very well groomed (w1) 0.274⁎⁎⁎ (0.065) 0.114⁎⁎ (0.044)Below average grooming (w1) −0.468⁎⁎⁎ (0.109) −0.155 (0.112)PVT score 0.014⁎⁎⁎ (0.001) 0.018⁎⁎⁎ (0.001)Missing PVT score −0.064 (0.084) 0.074 (0.075)7th grade 0.103⁎ (0.052) 0.089⁎⁎ (0.042)8th grade 0.025 (0.050) 0.090⁎⁎ (0.044)Hispanic −0.165⁎⁎⁎ (0.061) −0.285⁎⁎⁎ (0.063)African American −0.361⁎⁎⁎ (0.063) −0.277⁎⁎⁎ (0.048)Other race 0.143⁎⁎ (0.060) 0.057 (0.053)Numberofchildrenb18 inthehousehold −0.011 (0.016) −0.003 (0.014)Oldest child −0.012 (0.029) 0.043 (0.031)Resident mother attended college 0.311⁎⁎⁎ (0.035) 0.264⁎⁎⁎ (0.034)Resident mother/father public assistance −0.138⁎⁎ (0.060) −0.244⁎⁎⁎ (0.048)Two parent household 0.233⁎⁎⁎ (0.040) 0.185⁎⁎⁎ (0.034)Small school 0.220⁎⁎⁎ (0.083) 0.188⁎⁎⁎ (0.069)Medium school 0.013 (0.065) 0.079 (0.052)Public school 0.007 (0.115) −0.069 (0.084)Urban school −0.064 (0.065) −0.036 (0.061)Rural school −0.092 (0.083) −0.024 (0.067)Average class size −0.004 (0.006) 0.002 (0.004)Dress code 0.046 (0.058) 0.048 (0.057)More than 66% of school is White −0.031 (0.074) 0.004 (0.061)% of full-time teachers that are White −0.111 (0.175) 0.014 (0.154)Missing residential mother's education −0.153⁎⁎⁎ (0.053) −0.060 (0.064)Missing school level variables 0.013 (0.049) 0.049 (0.098)Midwest −0.146⁎⁎ (0.072) −0.202⁎⁎⁎ (0.066)West 0.074 (0.066) −0.041 (0.052)Northeast −0.170⁎⁎ (0.078) −0.249⁎⁎⁎ (0.059)Oversampling of certain groups −0.027 (0.037) 0.002 (0.037)N 2487 2878R2 0.237 0.297

Notes: standard errors were adjusted for clustering at the school level (primarysampling unit).⁎Statistically significant at pb0.10; ⁎⁎statistically significant at pb0.05; ⁎⁎⁎statisticallysignificant at pb0.01.

Appendix C

Table CCross tabulations of Wave 1 physical attractiveness and grooming (N=5365).

Wave 1 rating of physicalattractiveness1

Wave 1 rating of grooming1

Less than average Average Very well groomed Total

Less than average 1.32 3.56 0.24 5.13Average 1.98 75.17 4.21 81.36Very attractive 0.04 6.47 7.01 13.51Total (N=5365) 3.34 85.20 11.46 100.00Pearson chi2(4)=1.8e+03 pb0.000

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

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