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ight, weight, and entry earnings of female graduates Taiwan

ng-Lin Tao *

artment of Economics, Soochow University, Taipei, Taiwan

ntroduction

Over the last few decades, there has been muchdence pointing to a significantly positive link betweenght and earnings (Loh, 1993; Sargent and Blanch-

er, 1994; Harper, 2000; Schultz, 2002; Heineck,5; Hubler, 2009; Kortt and Leigh, 2010). Not until

ently, however, has the reasoning behind the link beeniberately explored. Economists have proposed at leastr types of answers to explain this link. They arguedt height raises earnings through four possible paths:-cognitive ability, cognitive ability, physical capacity,

occupation.The non-cognitive ability path is proposed by Persico et2004). They found that, even though tall adults’ average

ges are higher, tall adults who experience a late growth

spurt do not have a significantly higher wage. They hencepostulated that tall teenagers are more likely to participatein sports and clubs, and that these activities facilitate thedevelopment of participants’ social skills (non-cognitiveabilities), which in turn enhances their earnings inadulthood. Their argument was challenged by Case andPaxson (2008), who posited that height is simplycorrelated with cognitive ability. Medical studies showedthat both cognitive ability and height growth are driven bythe same endowment. Of course, it could be that bothcognitive and non-cognitive abilities enhance tall people’sperformances in the labor market, as indicated by Schickand Steckel (2010) and Lindqvist (2012).

Lundborg et al. (2009) employed data for males frommilitary enlistments in Sweden and found that physicalcapacity explains a much higher proportion of the linkbetween height and earnings than do cognitive and non-cognitive ability. The argument of Lundborg et al. (2009) issimilar to that of Schultz (2002) and Dinda et al. (2006),who argued that tall people earn more because they arehealthier. Finally, Heineck (2008) challenged the common

T I C L E I N F O

le history:

ived 19 January 2013

ived in revised form 19 December 2013

pted 19 December 2013

lable online 2 January 2014

lassification:

ords:

ht

ght

istical discrimination

eptual bias

ings

A B S T R A C T

Using a data set of Taiwanese female graduates in 2006, this study finds that height and

earnings are positively correlated for full-time workers. However, it is not because tall

individuals went to better colleges or received better grades (cognitive ability), not

because they are gifted with superior physical strength or because they have participated

in more extracurricular activities (non-cognitive ability), and not because they work in a

highly paid occupation. We find that statistical discrimination (or perceptual bias) is most

likely to play a role in determining the entry earnings of female graduates. In addition, we

find that an estimator of the height premium for females is downward-biased if weight is

omitted from the model.

� 2014 Published by Elsevier B.V.

Correspondence to: 56, Kuei-Yang Street, Section 1, Taipei 10048,

an. Tel.: +886 2 23111531x3669; fax: +886 2 23822001.

E-mail address: hltao@scu.edu.tw

Contents lists available at ScienceDirect

Economics and Human Biology

jo u rn al ho m epag e: h t tp : / /ww w.els evier .c o m/lo cat e/ehb

0-677X/$ – see front matter � 2014 Published by Elsevier B.V.

://dx.doi.org/10.1016/j.ehb.2013.12.006

H.-L. Tao / Economics and Human Biology 13 (2014) 85–9886

consensus by arguing that the height premium vanishesonce occupational effects of height are considered.

Without arguing that tall individuals are smarter, aremore inclined to participate in social activities, or areendowed with more physical strength, psychologists havedemonstrated that it is simply the result of a basic humanperceptual bias that height per se is a socially desirableasset. Jackson and Ervin (1992) called the perceptual biasthe height stereotype. In economics, the height stereotypeis referred to as ‘‘statistical discrimination,’’ as Case andPaxson (2008) stated in their conclusion. Literally, thestatistical discrimination is not exactly equivalent to theperceptual bias. Both the statistical discrimination andperceptual bias mean that some observable characteristicsare used to infer some unobservable characteristics. In thecase of the former, although generally useful, the inferenceis usually associated with some degree of bias. On thecontrary, in the case of the latter, the inference is usuallyuseless. Classical psychological studies, such as those ofBruner and Goodman (1947) and Dukes and Bevan (1952),have shown that people perceive more valuable objects asbeing larger or heavier than those that are less valuable.Height is an example of such a biased perception. The‘‘bias’’ might originate from the animal instinct withinhuman beings. Animals use height or size to gauge theircompetitors’ capability before engaging in a fight ordeciding whether to flee (Judge and Cable, 2004).Civilization has not completely eliminated the animalinstinct inside our body. The argument is underpinned byMurray and Schmitz (2011). They specifically argued thatthe instinct acquired through evolution in our ancestors’violent times has manifested itself in our modern times bypeople preferring leaders of greater physical stature.

Consequently, this ‘‘animal instinct’’ can explain whywe naively judge a person’s value and status based on hisor her height (Dannenmaier and Thumin, 1964; Wilson,1967; Lerner and Moore, 1974; Lechelt, 1975; Higham andCarment, 1992),2 which makes height itself a type ofvaluable asset. That is, given cognitive, non-cognitive,physical capability, and occupations, tall individuals stillreceive higher rewards from the labor market. Sincecognitive, non-cognitive, and physical capability are likelyto tilt the age-earnings profile, it is not an easy task toconfirm the prevalence of the perceptual bias of height.This is what Case and Paxson (2008) stated in their finalsentence: ‘‘to follow a cohort regularly’’ to examinestatistical discrimination of height. Given cognitive, non-cognitive, and physical capability, an alternative way isusing new graduates who have just entered the labormarket to investigate this issue. The earnings of these newentrants in the labor market have not yet grown. That is,cognitive, non-cognitive, and physical capability have notinfluenced the growth of earnings. As Case and Paxson

(2008) put it: ‘‘to see whether the height premium islargest early on in workers’ tenure on the job.’’ Therefore,one of the purposes of this study is to use the data for thesenew entrants to examine whether the statistical discri-mination of height prevails.

In a sense, Persico et al. (2004) argued that theadvantage of height has to do with ‘‘nurture’’, while Caseand Paxson (2008) argued that it arises from ‘‘nature’’. Ifthe advantage of height comes from ‘‘nature’’, then it mustuniversally prevail. Such prevalence must be found even insome Asian countries in which parents do not encouragetheir children to join sports clubs, as described by Chua(2011). When the advantage of height results from‘‘nurture’’, the advantage of height will be found incountries in which adolescents are encouraged to parti-cipate in sports activities, e.g., the United States, but is notto be found in Taiwan. Consequently, the conclusion of thisstudy has some implications for the debate between‘‘nature’’ and ‘‘nurture.’’

Previous work on the height premium has largelyfocused on males. It will be intriguing to investigate all-female data. This study uses Taiwan’s data for female

graduates who have just entered the labor market toinvestigate whether height is related to the entry earningsbecause of the aforementioned factors, or because ofheight per se.

The influence of being overweight is more substantial inthe case of females than males (Tiggemann, 1994;McKinley, 1998; Clay et al., 2005; Furnham et al., 2002;Franklin et al., 2006). The evidence also shows that beingoverweight reduces the earnings of females more thanthose of males (Register and Williams, 1990; Loh, 1993;Averett and Korenman, 1996; Cawley, 2004). Accordingly,in addition to height, this study will investigate therelation between weight and earnings. It has beenobserved that both height and weight are significantlylinked to earnings. Eliminating weight in a wage regressionmight lead to this omitted variable being embodied in theerror term, and this error term might be correlated withthe crucial explanatory variable, height. The present studywill show that the correlation between the error term andheight gives rise to a downwardly biased estimator.

In addition to Section 1, Section 2 introduces the data.Section 3 illustrates the empirical model to determinewhether stature influences a person’s career status oneyear after college graduation, and Section 4 presents thewage regression equation that is used in the sample-selection model. Moreover, Section 5 will demonstrate thatomitting weight in the earnings regression might under-estimate the influence of height on earnings for femalesamples. Section 6 concludes.

2. Data sources and statistics

The data are obtained from the Taiwan IntegratedPostsecondary Education Database (TIPED) compiled bythe Center for Educational Research and Evaluation,National Taiwan Normal University. The data consist oftwo waves of surveys. In 2004, Taiwan’s Ministry ofEducation collected the information for all junior collegestudents (164,725 students) in 140 colleges. Stratified

2 A blatant example is to compare a successful individual with a failed

individual on the basis of perceptual height. The former U.S. President

Richard Nixon was actually taller than former President Jimmy Carter.

One year after the inauguration of the former President Jimmy Carter, that

is, four years after Nixon stepped down because of the Watergate scandal,

college students were asked to compare the heights of these two former

Presidents. Most students perceived that Carter was taller (Keyes, 1980).

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H.-L. Tao / Economics and Human Biology 13 (2014) 85–98 87

pling was employed to randomly select a sampleich accounted for roughly 25% of the population, that is,899 junior students. 30,272 students responded to thestionnaire. These 48,899 students were again surveyed006 when they graduated from college one year later,

16,387 graduates responded. Observations for theond wave are fewer because they were difficult to trace

most males are obliged to serve in the military afterduation. Because the present study is focused on four-r college students, two-year and three-year collegedents are excluded from the sample. Male observations also ruled out since most of them are in the militarying the second wave. After deleting missing data andosing individuals who appeared in both wave surveys,9 observations remain in the final sample.

The attrition rate, from the first wave to the secondve, is about 46%. To examine whether the attritionnges the characteristics of the female sample in theond wave, Figs. A1 and A2 in Appendix respectivelysent the frequencies of height and weight in the firstve. Regardless of whether the height figures or theight figures are considered, the frequencies of bothves look very much alike (figures of the second wave are

shown). The Chi-square goodness-of-fit tests areducted, and neither test rejects the null hypotheses.3

The questions in both surveys included height, weight,jor, and college type. The data on physical strength wereained from the survey on junior students. One of thestions respondents were asked was: ‘‘How is yoursical strength?’’ Respondents answered by choosing

‘‘very weak,’’ ‘‘weak,’’ ‘‘average,’’ ‘‘strong,’’ and ‘‘veryng.’’ ‘‘Strong’’ and ‘‘very strong’’ have been reclassified

‘‘strong.’’ The degree of extracurricular activities wasresented by the number of clubs in which a respondentticipated while in college.4 In the survey conducted oner after graduation, the information collected coveredployment status, earnings, job information, averageege grade, student loans, and work experience duringege. Work experience referred to the experience gainedile enrolled in college. In Taiwan, high school studentse intense classes in school. They spend the whole day inool, and after school they usually go to cram school.thermore, Taiwanese parents do not think that highool students are sufficiently independent to work in theor market.Colleges are divided into four types, namely, publiceges, private colleges, public technological colleges,

private technological colleges. In general, high schoolduates go to general colleges, while vocational high

school graduates go to technological colleges. Roughlyspeaking, public colleges are more competitive thanprivate colleges, and general colleges are more competitivethan technological colleges. Table 1 presents the samplestructure based on status and the average earnings of full-time workers one year after graduation.

Table 1 shows that the heights and weights of mostfemale graduates are between 155 cm and 170 cm, andbetween 45 kg and 60 kg, respectively. Table 1 also showsthat BMI of most female graduates are between 18.5 and24.5 Individuals whose heights are less than 150 cm aremore likely to be unemployed and less likely to enroll ingraduate school. Female graduates who are less than 40 kgor more than 60 kg are less likely to enroll in graduateschool. Individuals less than 40 kg are more likely to beunemployed. The height and weight are not measured butself-reported. Some epidemiological studies have shownthat people usually underreport their weight and over-report their height, for example, Spencer et al. (2002),Palta et al. (1982), and Brener et al. (2003). Despite thesystematic difference between measured and self-reportedstatures, they concluded that the differences are small andthe correlation between these two statures is very high (forexample, Spencer et al. (2002) reported that r > 0.9). Moreimportantly, they concluded that self-reported height andweight are valid for identifying relationships in epidemio-logical studies and for calculating BMI, and are highlyreliable.6 Possible bias due to overstatement of height andunderstatement of weight will be discussed later. Descrip-tive statistics of selected variables are displayed in Table 2.Fig. 1 presents scatter diagram of earnings vs. height offull-time workers.

3. Height, weight, and initial career status

To most graduates, the immediate post-school choicesare graduate study (ICS = 0), full-time employment(ICS = 1), part-time employment (ICS = 2), unemployment(ICS = 3), and others (ICS = 4). The status one year aftergraduation is what they did at the time of the survey.7 Amultinomial logit model is used to fit the post-schoolchoices. In addition to the investigation of possible

Height is divided into four groups, under 150, 150–160, 161–170, and

170 cm. The Chi-square value is 5.55, which is less than 7.81, the

cal value when there are 3 degrees of freedom and the significance

l is 5%. Weight is divided into five groups, under 40, 40–50, 51–60,

70, and over 70 kg. The Chi-square value is 9.32, which is less than

, the critical value when there are 4 degrees of freedom and the

ificance level is 5%.

This study follows Persico et al. (2004) and uses extracurricular

ities to represent non-cognitive ability. The evidence shows that

acurricular activities do benefit noncognitive ability. For example,

5 BMI = weight(kg)/(height(m))2. The Department of Health (DOH) of

the Taiwan suggests that a BMI of 18.5–24 is recommended, 24–27 is

overweight, greater than 27 is obesity, and less than 18.5 is being

underweight. The recommended BMI by DOH is slightly different from

the recommended BMI by the World Health Organization (WHO).6 Official data on the measured height and weight of people after they

left junior high school are not available in Taiwan. The Bureau of Health

Promotion conducted a ‘‘National Health Interview Survey’’ in 2005. This

survey asked respondents to report their approximate or measured

height or weight. No matter which one they reported, the data were self-

reported, and not measured. However, this data set is closest to the

measured data that are available. For females aged between 19 and 24,

the reported height and weight are 159.89 cm and 52.62 kg, respectively.

The figures are very close to those in our data (see Table 2).7 The survey question does not specify what items are in ‘‘others.’’ In

general, graduates who are not in the labor market or in school are

classified as ‘‘others.’’ For example, ‘‘others’’ may include female

graduates who married and withdrew from the labor market, or who

ricks and Eccles (2005, 2008) found that extracurricular activities

ance self-esteem, resilience, and psychological adjustment.

were preparing for exams to enter graduate programs in Taiwan or

overseas.

H.-L. Tao / Economics and Human Biology 13 (2014) 85–9888

influences of height (H) and weight (W) on a person’sinitial career status, the results of the multinomial logitmodel hold for the sample selection model in the nextsection. The explanatory variables in the multinomiallogit model consist of stature, physical strength (PS),extracurricular activities (EC), academic characteristics(AC), student loans, and work experience. There is no clearexpectation as to how stature influences the initial careerstatus. If tall females have higher cognitive ability andnon-cognitive ability, as existing studies have argued,they might have a higher probability of pursuing amaster’s degree, and a higher probability of finding abetter job as well. It is not clear which status is more likelyto occur to tall females.

Because a general college provides an academicorientation, its graduates are more inclined to pursuemaster’s degrees. A technological college is employ-ment-oriented, and thus its graduates are more inclinedto enter the labor market. Graduates who performwell in college are more inclined to pursue a master’sdegree. Graduates who have student loans are morelikely to encounter financial problems. It follows thatthey are more likely to enter the labor market, and lesslikely to go to graduate school. Graduates who havework experience are more inclined to enter the labormarket.

Let the utility of initial career status (ICS) j to individuali be Ui(ICS = j). Individual i will choose status j if status j isassociated with the maximal utility among all choices thathe/she can choose.

ICS = j, if and only if Ui(ICS = j) � Max Ui(ICS 6¼ j), for allj = 0,1, 2,. . ., k

Ui j ¼ b0; j þ b1; jHi þ b2; jWi þ b3; jPSi þ b j;4EC

þ b5; jACi þ b6; jPi þ ui j (1)

The probability of individual i choosing the initial careerstatus j is as follows:

ProbðICSi ¼ jÞ ¼ eZib j

1 þP4

j¼1 eZib j

h i j ¼ 1; 2; 3; 4

ProbðICSi ¼ jÞ ¼ 1

1 þP4

j¼1 eZib j

h i j ¼ 0

(2)

In (1), b is the coefficient and u is the error term. P is apersonal trait matrix. In (2), all coefficients and indepen-dent variables are compressed into vector b and matrix Z,

Table 1

Stature, status, and full-time earnings of Taiwanese females.

Total

obs.

(%) Grad.

study

(%) Full-time

job

(%) Part-time job (%) Unemployment (%) Others (%) Full-time

earningsa

Height (cm)

<150 49 1 6 12 26 53 3 6 11 22 3 6 26,846

150 � H < 155 580 12 126 22 302 52 51 9 72 12 29 5 27,212

155 � H < 160 1475 30 342 23 724 49 107 7 217 15 85 6 28,349

160 � H < 165 1799 37 375 21 973 54 130 7 227 13 94 5 28,374

165 � H < 170 768 16 177 23 378 49 49 6 104 14 60 8 28,294

�170 218 4 47 22 107 49 26 12 27 12 11 5 28,665

Weight (kg)

<40 48 1 8 17 24 50 5 10 10 21 1 2 26,135

40 � W<45 475 10 111 23 235 49 25 5 70 15 34 7 28,023

45 � W<50 1390 28 324 23 707 51 113 8 177 13 69 5 28,622

50 � W<55 1373 28 312 23 713 52 92 7 177 13 79 6 28,281

55 � W<60 828 17 180 22 422 51 58 7 110 13 58 7 28,182

60 � W<65 401 8 75 19 209 52 34 8 59 15 24 6 27,962

W � 65 374 8 63 17 200 53 39 10 55 15 17 5 27,305

BMI

<18.5 1192 24 265 22 605 51 90 8 167 14 65 5 28,626

18.5 � BMI < 21 2098 43 486 23 1075 51 142 7 274 13 121 6 28,334

21 � BMI < 24 1149 24 250 22 587 51 88 8 148 13 76 7 27,966

24 � BMI < 27 305 6 51 17 168 55 27 9 45 15 14 5 27,263

�27 145 3 21 14 75 52 19 13 24 17 6 4 27,140

Sum 4889 100 1073 2510 366 658 282

a The values are New Taiwan dollars per month. One US dollar is about 30 New Taiwan dollars.

Fig. 1. Scatter diagram of earnings vs. height for full-time workers of

female college graduates.

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H.-L. Tao / Economics and Human Biology 13 (2014) 85–98 89

pectively. The results of the multinomial logit model aresented in Table 3.8 Table 3 shows that height does note a significant influence on the initial career status. Asussed above, how height influences the initial careerus is not clear. Weight reduces the likelihood ofsuing a master’s degree, while it increases thebability of the occurrence of the status of engaging inart-time job.9 Graduates from public colleges are morely to enroll in graduate school and to be unemployed,ile they are less likely to take a full-time job. It takesger for graduates from public colleges to find a better. Graduates with higher college grades are more likelyenroll in graduate school, and less likely to be

unemployed. As expected, good academic characteristicsraise the probability of enrolling in graduate school.Literature graduates are more likely to be unemployed.10

Student loans and work experience decrease the likelihoodof pursuing a master’s degree, but increase the probabilityof engaging in a full-time job. Student loans constitute theexclusion restriction of the first stage of the sampleselection model.

4. Height, weight and entry earnings

This section explores the relationships between heightand earnings. Because only those graduates who chose toengage in a full-time job were observed to have earnings,the generalized sample selection model proposed by Lee(1983) will be employed to analyze the full-time workers.If the sample of full-time workers is not random or is notrepresentative, then the estimator of the height coeffi-cient will be biased. Using the sample selection modelensures consistent estimators, and the estimators are alsoefficient. When the sample is not representative, theestimators of the sample selection model are stillconsistent but not efficient. In this case, the estimatorsof OLS are efficient as well as consistent. In addition, thepart-time job probably is a temporary job. The decisions ofworkers and employers in relation to a temporary job arelikely to be distinct from those for a long-term job. Thus,the sample selection model merely considers full-timejobs ( j = 1).

le 2

criptive statistics of selected variables.

Meana SD Mean SD Mean SD

ature Sectorb Firm scalec

Height 160.2 cm 5.31 Public sector 11% 32% Less than 3 1% 10%

Weight 52.34 kg 7.91 Private sector 77% 42% 3–10 17% 37%

BMI 20.38 2.81 Other sectors 11% 32% 11–30 17% 38%

ajor 31–50 8% 27%

Business 27% 44% Occupation 51–100 11% 32%

Social 8% 27% Manager 1% 12% 101–500 17% 38%

Literature 24% 42% Salesman 12% 32% 501–1000 7% 25%

Science 11% 31% Teacher 11% 32% More than 1000 21% 41%

Medicine 7% 25% Professional 7% 26%

Engineering 7% 26% Technician 9% 29% Earningsd

Media 5% 22% Clerk 35% 48% Earnings 28,201 7174

Others 10% 30% Non-skilled 22% 42%

llege life

Extracurricular 1.4 1.37

Work experience 86% 35%

The statistics of the first and the second columns are based on the full sample, i.e., 4889 observations. The statistics of the other columns are based on the

time worker sample, i.e., 2510 observations.

‘‘Sector’’ indicates that graduates work in the public or the private sector.

‘‘Firm scale’’ is measured by the number of employees in a firm.

Earnings are expressed in New Taiwan dollars.

The multinomial logit model assumes the independence of irrelevant

rnatives (IIA). That is, the odds ratios are independent of the other

rnatives. The IIA assumption can be examined by the Hausman test

wing Greene (2008, p. 847). By dropping each alternative in each test,

e are four Hausman chi-squared values. The chi-squared values

ciated with dropping Y = 1, Y = 2, Y = 3, and Y = 4 are 28.89, 2.71,

5, �0.36, respectively. The degrees of freedom for each of the tests

ber 60, and the critical value for a = 5% is 79.082. Theoretically, the

squared value must be positive. The Limdep 8.0 manual (p. E19-37)

ains that the right conclusion for the negative values should be 0 or a

ll positive value (also see Footnote 4 in Hausman and McFadden,

4). Therefore, the IIA assumption cannot be rejected. However, the IIA

mption is rejected by the Small–Hsiao test. Fry and Harris (1996,

8) and Cheng and Long (2007) have shown that both the Hausman test

the Small–Hsiao test are weak. The empirical results in the later

ions will show that the sample-selection effects in most models are

nificant or that the results of the sample-selection models are very

e to those of the OLS models in Appendix.

Married females choose to engage in a part-time job usually because

amily responsibilities. Since Taiwanese marry late (the females’

age age at first marriage is about 29 years old in 2010), instead of

ily responsibilities, young female graduates choosing a part-time job

a very different reason. Taking a part-time job is probably a

promise as a result of waiting for a full-time job, or is seen as an

10 Height might influence what type of college to enter and what major

to choose. If this is the case, multicollinearity might occur. The average

heights in four types of college and nine majors are compared. The

average heights are very close across majors and college types. The

ANOVA F tests for height in nine majors and in four college types are 1.831

rim measure as the student prepares to try again to enroll in graduate

ol.

and 0.657, respectively. The 5% critical value for the former is 1.940, and

for the latter is 2.607. Neither of the null hypotheses is rejected.

H.-L. Tao / Economics and Human Biology 13 (2014) 85–9890

ICS = j, if and only if U(ICS = j) � Max U(ICS 6¼ j)

½ln EijICSi ¼ 1� ¼ g0 þ g1ln Hi þ g2PSi þ g3ECi þ Xig

þrsef½V f ðZib jÞZib j�

F½V f ðZib jÞ�

( )þ ei;

¼ g0 þ g1 ln Hi þ g2PSi þ g3ECi

þXig þ ali þ ei; (3)

where V j ¼ F�1 eZib j

1þP4

j¼1e

Zib j

!, li ¼

fðV jÞFðV jÞ

, a = rse.

In (3), ln E is the logarithm of earnings, X is the matrix ofindependent variables, g is the coefficient vector, r is thecorrelation coefficient between e and the error term u ofEq. (1), se is the standard deviation of the error term e, f isthe probability density function of a standard normaldistribution, F is the probability accumulation function of astandard normal distribution, and j represents the choice ofa full-time job. Height is expressed in logarithmic form, so itscoefficient represents the elasticity of earnings on height.

X includes college type, college average grade, workexperience, major, and job characteristics. The jobcharacteristics consist of private or public sector, occupa-tion, the scale of the firm (number of employees), and thefirm’s location. The public sector is the reference group.Occupation is divided into seven groups: Managers andProfessionals (O1), Salesmen (O2), Teachers (O3), AssociateProfessionals (O4), Technicians (O5), Clerks and other staff(O6), and Non-technical workers (O7). Managers andProfessionals are the reference group. The scale of the firmis divided into eight groups, and a scale which is less thanthree employees is the reference group. According to theofficial districts of Taiwan, for the location of the firmTaiwan is divided into 23 districts, and Taipei city is the

To investigate how height influences earnings, thisstudy employs the strategy applied by Persico et al. (2004),Case and Paxson (2008), and Lundborg et al. (2009). Theydropped some explanatory variables in their regressionmodels to identify whether height influences earningsthrough these explanatory variables. Table 4 presentsseveral sample selection models to examine throughwhich channels height raises earnings. Column 1 considersheight only, column 2 is the full model, and columns 3, 4, 5,and 6 drop physical strength, extracurricular activities,academic variables, and job characteristics, respectively.Contrary to the expectation and findings in the literature,none of the coefficients of height is significant. It seemsthat the way in which height is correlated with earningsdiffers from existing findings which usually come frommale data in Western countries. Since height mightinfluence earnings conditional on body size, columns 7and 8 further include the BMI. The insignificant results ofheight remain, and even the coefficients of the BMI areinsignificant.11 Table 4 shows that either height isirrelevant to earnings or some important factor is omittedfrom the models.

The model then incorporates, instead of the BMI, theweight logarithm to represent body size. As Persico et al.(2004) pointed out, while short individuals are more likelyto be obese, after introducing weight into the model, adecrease in the coefficient for height implies that eitherheight serves as a proxy for weight or the effect of heighton earnings is channeled through weight. Since obesepersons usually have lower earnings, if short persons aremore likely to be obese, excluding weight from the modelwidens the earnings gap between the tall and the short. Inthis case, the height premium will be overestimated.

Table 3

Marginal effects of stature and academic characteristics on initial career status (multinomial logit model).

Study Full-time job Part-time job Unemployment Others

Constant 1.49% 8.62% �10.04% 21.50% �21.57%**

Height 0.12% �0.06% �0.03% �0.13% 0.10%

Weight �0.22%*** 0.08% 0.10%* 0.06% �0.02%

Physical strength �0.54% 4.21%** �0.73% �2.23% �0.71%

Extracurricular activities �0.22% �0.81% 0.63%** �0.16% 0.56%**

Pub. tech. college �17.02%*** 30.87%*** �4.52%** �6.39%*** �2.93%*

Private college �4.96%*** 15.15%*** �3.41%*** �6.27%*** �0.52%

Private tech. college �31.47%*** 40.50%*** �0.24% �6.33%*** �2.46%**

70–79 (grade) �4.72%** 4.73%* �0.35% �1.49% 1.82%

80–89 9.14%*** �1.96% �1.51% �6.17%*** 0.50%

�90 18.98%*** �3.44% �1.14% �14.04%*** �0.35%

Business �1.70% 5.75%** �1.23% �0.65% �2.17%*

Literature �5.25%*** �3.92% 2.93%** 5.70%*** 0.53%

Science 10.79%*** �6.48%* 1.37% �4.04%* �1.63%

Medicine 6.73%** �6.31%* �2.75% 3.08% �0.76%

Engineering 19.80%*** �7.45%* �1.92% �6.70%** �3.73%*

Media �5.37%* 6.54% �1.72% 2.42% �1.87%

Others 6.23%** �7.89%** 1.40% 0.38% �0.13%

Student loan �6.06%*** 8.39%*** 0.50% �1.24% �1.59%**

Work experience �9.79%*** 15.23%*** 1.52% �5.28%*** �1.68%*

Number of obs.: 4889; x2 = 1006.39

The choices for the multinomial logit model are graduate study, full-time job, part-time job, unemployment, and others.

* 10% level of significance.

** 5% level of significance.

*** 1% level of significance.

11 The results are similar if the model incorporates the BMI logarithm.

reference group.

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H.-L. Tao / Economics and Human Biology 13 (2014) 85–98 91

It is worth noting that performance in the labor marketld influence a person’s stature, particularly, weight.t is, stature might be endogenous. Past studies whichestigated the relationship between stature and earningsided the endogeneity problem by using stature invious years. For example, Averett and Korenman (1996)

Cawley (2004) used data on stature for seven yearsviously, Harper (2000) used data on stature for 10 yearsviously, and Conley and Glauber (2005) used data onure for 13–15 years previously. It is not persuasive that

stature of more than ten years before is similar to ason’s current stature. The height and weight which thesent study uses were reported in the respondents’

graduation. Therefore, the weight the present study uses isthat of about two or three years previously. It ensures thatthe weight variable does not vary substantially, and it alsoeschews the endogeneity problem.12

Six models of the wage regression results of thesample selection model including weight are presentedin Table 5. Model I considers stature only, Model II is the

le 4

lts of sample-selection models of logarithm earnings for full-time workers only (without weight).

Column 1

(height only)

Column 2 (full model) Column 3 (without

physical strength)

Column 4 (without

extracurricular

activities)

Coeff. t-Value Coeff. t-Value Coeff. t-Value Coeff. t-Value

nstant 9.31 14.58*** 10.07 18.60*** 10.06 18.57*** 10.06 18.58***

g(Height) 0.14 1.15 0.06 0.55 0.07 0.66 0.06 0.55

ysical strength 0.02 1.96* 0.02 2.27**

tracurricular activities 0.00 1.25 0.00 1.70*

b. tech. college �0.06 �2.12** �0.08 �2.78*** �0.05 �1.90*

ivate college �0.04 �2.59*** �0.05 �3.17*** �0.04 �2.47**

ivate tech. college �0.09 �2.99*** �0.11 �3.75*** �0.08 �2.80***

–79 (grade) 0.01 0.70 0.01 0.49 0.01 0.86

–89 0.02 1.56 0.02 1.54 0.02 1.62

0 0.02 0.72 0.02 0.81 0.02 0.71

ork experience 0.02 1.00 0.01 0.52 0.02 1.34

ajor No Yes Yes Yes

ivate/public sector No Yes Yes Yes

cupation No Yes Yes Yes

m’s scale No Yes Yes Yes

cation No Yes Yes Yes

0.26 16.10*** 0.04 0.78 0.01 0.15 0.05 1.15

Value 127.03 26.08 26.46 26.52

servations 2510 2510 2510 2510

Column 5 (without

academics)

Column 6 (without

job characteristics)

Column 7 (BMI) Column 8 (BMI)

Coeff. t-Value Coeff. t-Value Coeff. t-Value Coeff. t-Value

nstant 9.94 18.35*** 9.95 17.00*** 9.29 14.43*** 10.07 18.43***

g(Height) 0.06 0.53 0.06 0.55 0.15 1.16 0.06 0.56

I 0.00 0.21 0.00 0.06

ysical strength 0.03 2.89*** 0.02 1.93* 0.02 1.96*

tracurricular activities 0.00 0.95 0.01 2.26** 0.00 1.25

b. tech. college �0.10 �3.10*** �0.06 �2.12**

ivate college �0.09 �5.15*** �0.04 �2.59***

ivate tech. college �0.15 �4.66*** �0.09 �2.99***

–79 (grade) 0.01 0.98 0.01 0.70

– 89 0.03 2.64*** 0.02 1.56

90 0.04 1.59 0.02 0.72

ork experience 0.05 3.94*** 0.04 2.04** 0.15 4.01***

ajor Yes Yes Yes Yes

ivate/public sector Yes No Yes Yes

cupation Yes No Yes Yes

m’s scale Yes No Yes Yes

cation Yes Yes Yes Yes

0.162 9.489*** 0.053 1.026 0.256 16.068*** 0.037 0.784

Value 28.89 20.73 84.67 25.61

servations 2510 2510 2510 2510

10% level of significance.

5% level of significance.

* 1% level of significance.

12 In addition to in the junior year, height and weight are surveyed one

year after graduation. The mean height and weight for one year after

graduation are 160.29 cm and 52.24 kg, respectively. These figures are

close to the measures of their junior year (see Table 2). Their statures

very stable in 2–3 years.

ior year, while earnings used were those one year aftervery

are

H.-L. Tao / Economics and Human Biology 13 (2014) 85–9892

full model, and Models III, IV, V, and VI drop the physicalstrength, extracurricular activities, academic variables,and job characteristics, respectively. If the concern ofPersico et al. (2004) prevails, adding weight into themodel reduces the coefficient of height. However, all thecoefficients of the height logarithm in these models aremuch greater than those in Table 4, and are at leastsignificant at the 10% level. The concern of Persico et al.(2004) is not what we observe in Tables 4 and 5. In thenext section we will propose a possible reason to explainwhy the magnitudes and the significances of the height

coefficients substantially increase after weight is intro-duced in the model.

The coefficients of l at the bottom of Table 5 aresignificant at the 1% level in Models I and V. Thecoefficients of l are insignificant in the other models.The insignificance indicates that the inclusion of theacademic variables causes the multicollinearity in therespective models. It is true that l is a nonlinear function ofthe academic variables based on step 1 (the multinomiallogit model), but Nawata (1993) and Puhani (2000) haveargued that, even with the exclusion restriction, l might be

Table 5

Results of sample-selection models of logarithm earnings for full-time workers only (with weight).

Model I (stature only) Model II (full model) Model III (without

physical strength)

Model IV (without

extracurricular

activities)

Coeff. t-Value Coeff. t-Value Coeff. t-Value Coeff. t-Value

Constant 8.89 13.45*** 9.61 17.25*** 9.60 17.21*** 9.61 17.23***

log(Height) 0.29 2.07** 0.23 1.96* 0.24 2.06** 0.22 1.92*

log(Weight) �0.08 �2.42** �0.09 �3.39*** �0.09 �3.40*** �0.09 �3.32***

Physical strength 0.02 1.92* 0.02 2.28**

Extracurricular activities 0.00 1.43 0.00 1.87*

Pub. tech. college �0.07 �2.49** �0.09 �3.15*** �0.06 �2.24**

Private college �0.05 �2.87*** �0.06 �3.46*** �0.05 �2.72***

Private tech. college �0.10 �3.39*** �0.12 �4.15*** �0.10 �3.17***

70–79 (grade) 0.01 0.52 0.00 0.31 0.01 0.70

80 – 89 0.02 1.59 0.02 1.57 0.02 1.66*

� 90 0.02 0.92 0.02 1.00 0.02 0.90

Work experience 0.01 0.54 0.00 0.06 0.01 0.92

Major No Yes Yes Yes

Private/public sector No Yes Yes Yes

Occupation No Yes Yes Yes

Firm’s scale No Yes Yes Yes

Location No Yes Yes Yes

l 0.26 16.10*** 0.02 0.33 �0.01 �0.32 0.03 0.73

F-Value 86.83 25.93 26.3 26.34

Observations 2510 2510 2510 2510

Model V (without

academics)

Model VI (without

job characteristics)

Model VII (without height)

Coeff. t-Value Coeff. t-Value Coeff. t-Value

Constant 9.51 17.00*** 9.46 15.70*** 10.67 81.82***

log (Height) 0.21 1.76* 0.24 1.92*

log (Weight) �0.08 �3.02*** �0.10 �3.32*** �0.07 �2.82***

Physical strength 0.03 2.99*** 0.02 1.90* 0.02 2.03**

Extracurricular activities 0.00 1.01 0.01 2.43** 0.00 1.37

Pub. tech. college �0.11 �3.47*** �0.07 �2.38**

Private college �0.10 �5.43*** �0.05 �2.79***

Private tech. college �0.17 �5.05*** �0.10 �3.26***

70–79 (grade) 0.01 0.80 0.01

80–89 0.03 2.66*** 0.02

�90 0.05 1.77* 0.02

Work experience 0.04 3.75*** 0.03 1.59 0.01 0.68

Major Yes Yes Yes

Private/public sector Yes No Yes

Occupation Yes No Yes

Firm’s scale Yes No Yes

Location Yes Yes Yes

l 0.16 9.40*** 0.03 0.57 0.02 0.49

F-Value 28.59 20.57 26.3

Observations 2510 2510 2510

* 10% level of significance.

** 5% level of significance.

*** 1% level of significance.

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H.-L. Tao / Economics and Human Biology 13 (2014) 85–98 93

hly linearly correlated with the independent variablesich repeatedly appear in steps 1 and 2. The multi-inearity reduces the efficiency of the estimation. Withlticollinearity, Leung and Yu (1996) postulated thatsed OLS estimators might be better than unbiased butfficient estimators of the sample selection model.endix shows some of the estimations of the OLS

dels. Their signs and significance are consistent withse of the sample selection models.The coefficients of the weight logarithms are negative

significant at least at the 5% level. The results are asected for all models. Model I shows that both the height

arithm and weight logarithm are significant at the 5%el. An increment of 1% in height increases earnings by9%, while an increment of 1% in weight decreasesnings by 0.08%. Model II includes a full set oflanatory variables. Although the coefficient of theght logarithm is still significant at the 10% level, it iswn to decline from 0.29 to 0.23.13 Evaluated at therage height (160.2 cm), the height premium amounts to42,14 that is, the earnings increase by 0.142% with anrement of 1 centimeter of height. The estimate is closehe height coefficient of the German female (0.2%) and

Australian female (0.15%, but insignificant) estimatedHeineck (2005) and Kortt and Leigh (2010), respec-ly. Using British data, Case and Paxson (2008) foundt the earnings increase by between 0.1% and 0.7% withadditional centimeter of height.15 The estimates in thiser are at the lower boundary of these estimated values.ould be that the study uses a data set in which thenings are those of new entrants in the labor market.ght has not yet influenced the growth of earnings. Theer possible reason is that height is self-reported andht cause an underestimation of the height premium.

s will be discussed later. Recall that all our observations college graduates. It is then important to note that thisult is obtained although there is not as much variationheight as there would be if we were examining allcational levels (Cinnirella et al., 2011).

To further explore whether the deletion of physicalngth, extracurricular activities, and academic and job

characteristics cause the results of the estimations tosubstantially change, Models III, IV, V and VI respectivelydrop physical strength, extracurricular activities, academiccharacteristics, and job characteristics. Compared to thefull model, the absolute magnitudes of the coefficients ofthe height logarithm and of the coefficients of the weightlogarithm do not substantially decline.16 In these fourmodels, the model that drops academic characteristicspossesses the smallest coefficient of the height logarithm,while the one dropping job characteristics possesses thegreatest coefficient of the height logarithm. Comparedwith the full model, ruling out physical strength orextracurricular activities in Model III or Model IV doesnot reduce the explanatory power of stature. It suggeststhat height does not influence earnings through physicalstrength or extracurricular activities. Consequently, theresults do not support the view that height raises earningsbecause it enhances physical strength or enhancesinclination to participate in club or sport activities, asargued by Lundborg et al. (2009) and Persico et al. (2004).

Similarly, the magnitude of the coefficient of the heightlogarithm in Model V (without academic characteristics)does not increase, compared to that in the full model. Itindicates that tall graduates who have higher earnings donot earn more because they went to a better college or hadbetter grades. Psychological and intelligence studies havedemonstrated that academic performance and cognitiveability are positively correlated (Marjoribanks, 1976;Rohde and Thompson, 2007; Dickens, 2008) Whenacademic performance and cognitive ability are positivelycorrelated, it seems that the present results do not supportthe view that height enhances earnings because of highercognitive ability. Model VI shows that dropping jobcharacteristics increases the magnitude of the coefficientof the height logarithm. As a result, height raises earningsand it is not because tall individuals have good jobcharacteristics, consistent with (Case et al., 2009).

It is interesting to note that the absolute magnitudes ofthe coefficients of college grade and college type in ModelVI are substantially greater than those in other models.These results suggest that academic characteristicsenhance earnings through job characteristics. Both collegetype and average grade represent the goodness ofacademic characteristics. The effect of the college type ismuch stronger than that of the average grade. It shows thatthe marginal return from diligently studying to enroll in abetter college is greater than that of diligently studying toenhance the average grade in college. These results areconsistent with Tao (2008).17

In Model V, when academic characteristics are dropped,the coefficient of work experience becomes positivelysignificant. Its significance implies that work experience

TIPED provides information on parents’ educational attainments.

n the father’s educational attainment is included in the models,

ticollinearity occurs in some regressions. The magnitudes of the

ht coefficient do not substantially change as the father’s educational

inment is added in the model, but their standard errors are

tantially enlarged by about three times. For example, the height

ficient becomes 0.217 in Model V after the inclusion of the father’s

cational attainment, a little higher than the 0.206 in Table 4, but it

s out to be insignificant. Adding the father’s educational attainment

ll OLS models (Appendix), corresponding to Table 5, does not

tantially change the significance and magnitude of the height

ficient. It demonstrates that multicollinearity reduces the signifi-

e of the height coefficient in the process of the sample-selection

el. See more discussions about multicollinearity in the sample-

ction in Nawata (1993) and Puhani (2000). For this reason, the father’s

cational attainment is not included in our model.

A 1% increment of height is 1.602Ycm. Hence, it is calculated by 0.228/

2 = 0.142.

See column (5) to column (8) in their Table 6. Height is recorded

16 The correlation between height and college grade is very small, about

0.002. It is not surprising that height does not influence earnings through

the college grade.17 Most past studies of height investigated how height is correlated with

earnings, and some recent studies have begun to investigate why height is

correlated with earnings. Tao (2008) is one of the former, while the

ches. By dividing their height coefficients by 2.54 cm we obtain the

0.7% range.

present study is one of the latter. The basic issues discussed in these two

studies differ.

H.-L. Tao / Economics and Human Biology 13 (2014) 85–9894

and academic characteristics are correlated in some way.Working while enrolled in college might retard learningand delay graduation (Ehrenberg and Sherman, 1987), butit might help students to adapt to a work environmentbefore engaging in a full-time job, foster adequate workhabits and attitudes, and facilitate the development ofpersonal responsibility. Furthermore, early work experi-ence enables graduates to know what kind of job they arebest suited for. The significantly positive coefficient ofwork experience indicates that the advantages of earlywork experience might dominate, but the dominancevanishes when academic characteristics are controlled for.Since the earnings are observed only one year aftergraduation, they do not guarantee that the advantage ofthe early work experience will persist. Light (1999) andHotz et al. (2002) argued that the earnings advantagedisappears in later life.

It should be stressed that height and weight are self-reported, and might contain measurement error. Theestimators might then attenuate to zero. If this is the case,both the absolute magnitudes of the coefficients of heightand of weight are underestimated. Consequently, the trueabsolute magnitudes of these two coefficients areprobably greater and more significant. Since height isusually over-reported, it is also likely that the heightpremium is underestimated. The possible measurementerror and over-reporting problems might cause theestimated height premium to be lower than the truevalue and less significant.18 However, weight is probablyunderstated. The understatement is likely to overestimatethe coefficient of weight. It is then unclear whether thecoefficient of weight is overestimated or underestimated.Additionally, the relationships between earnings andheight and between earnings and weight might not becausal. For example, an insulin-like growth factorinfluences height growth as well as cognitive develop-ment in the brain (Berger, 2001; Schick and Steckel, 2010).Height is correlated with, but does not cause, cognitiveability.

5. Omission of weight

Without controlling for weight, the coefficients ofheight logarithm in Table 4 are only about one fourth ofthat in any model which includes the weight logarithmin Table 5. The coefficient of the height logarithm turnsout to be significant when the weight logarithm isincluded in the models. Model VII in Table 5 furtherdrops height but retains weight to see if the coefficientof the weight logarithm will substantially change. Itturns out that the absolute magnitude of the coefficientof the weight logarithm declines from �0.09 (Model II)

to �0.07 (Model VII). Excluding either height orweight from the model substantially changes themagnitude of the coefficient of stature, which is left inthe model. In spite of the reduction in significance, thecoefficient of the weight logarithm in Model VII is stillsignificant at the 1% level. As a result, the exclusion ofeither height or weight results in an underestimation oroverestimation of the coefficient for the other aspect ofstature.

Tables 4 and 5 together demonstrate that, becauseheight and weight are correlated, the exclusion of eitherone leads to the omitted variable problem, and mightlead to a biased estimator. This underestimation oroverestimation can be illustrated by a simple example.Suppose that the true model is ln Ei = g0 + g1ln Hi + g2ln -Wi + ei, and that the specified model is ln Ei = g0

0 +g10ln Hi + ei

0, where weight is omitted in the specifiedmodel. The expected value of the biased estimator ofheight is presented in (4). Obviously, the covariancebetween height and weight is positive, and the truecoefficient of weight (g2) is negative. Consequently, thesecond term on the right-hand side of (4) is negative, andcauses the expected value of g1

0 to be smaller than thetrue value. A downward bias occurs. It can be inferredthat the coefficient of weight is overestimated as height isomitted. Model VII and Table 4 show exactly the biasedconsequence.

Eðg 01Þ ¼ g1 þ g2ECovðln Hi; ln WiÞ

Varðln HiÞ

� �(4)

Table 1 shows that the relation between weight andearnings might not be linear. Taking the logarithm ofweight in the model allows for the possible nonlinearity.To ensure that the conclusion of the variable omission doesnot result from the misspecification of the earningsequation with respect to stature, the earnings equationis respecified without taking the logarithm of height andweight, and is a function of height, height squared, weight,and weight squared. The results of these sample-selectionmodels are presented in Table 6. Except for stature, allother explanatory variables are the same as for the fullmodel in Table 5. For ease of exposition, the results otherthan the stature are not presented in Table 6. Model XIII inTable 6 includes height, height squared, weight, andweight squared, and all the coefficients of stature areinsignificant. Model IX drops the height squared, and thecoefficient of height becomes significant at the 10% level,but the coefficients of weight are still insignificant. ModelX further drops weight squared, and both the coefficientsof height and weight are significant. The results of staturein Model X are consistent with those in Table 5. Model XIfurther drops weight, and the results are similar to thosemodels in Table 4. That is, the coefficient of height is muchsmaller than that of the height coefficient when weight isin the model. Moreover, after controlling for the otherfactors, it appears that there is no nonlinear relationbetween stature and earnings.

To further confirm that omitting weight might under-estimate the height premium for female observations, thisstudy employs another data set for Taiwan – the Panel

18 Some studies have employed instrumental variables to deal with the

possible measurement error or endogeneity of height, for example,

Schultz (2002, 2003) and Gao and Smyth (2010). Schultz (2002)

concluded that some instrumental variables (regional prices and

ethnic/language/race) are more applicable to developing countries than

the United States. Gao and Smyth (2010) also applied their instrumental

variables (number of health institutions and location) to a developing

country, namely, China.

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H.-L. Tao / Economics and Human Biology 13 (2014) 85–98 95

dy of Family Dynamics (PSFD).19 In 2004, the PSFDveyed respondents’ height and weight. Therefore, datathe year 2004 are used. After deleting missing data,re are 687 females.20 Table 7 presents the results of theple-selection model of females. The coefficient of the

ght logarithm is not significant when weight is dropped, including weight in the model renders the coefficient

of the height logarithm significant at the 5% level. Theomission of weight underestimates the females’ heightpremium.21

6. Conclusion

This paper first examines whether the initial careerstatus after college graduation depends on stature,physical strength, and academic characteristics. Theresults reveal that weight is negatively correlated withthe probability of enrolling in graduate school. However,height is not correlated with the initial career status. In the

le 6

lts of sample-selection earnings logarithm models with weight squared.

Model XIII (with weight

squared and height

squared)

Model IX (with weight

squared)

Model X (with weight) Model XI (without

weight)

Coeff t Coeff t Coeff t Coeff t

ight 0.010 0.46 0.001 1.860* 0.001 1.94* 0.0003 0.53

ight-sq �0.00003 �0.40

eight �0.0001 �0.03 0.0002 0.06 �0.002 �3.48***

eight-sq �0.00001 �0.46 �0.00002 �0.57

25.06 25.50 25.95 26.08

servations 2510 2510 2510 2510

ther explanatory variables are the same with the full model in Table 5. Note in this table that logarithms of height and weight are not taken so as to

w weight squared and height squared.

10% level of significance.

* 1% level of significance.

le 7

lts of earnings regression for females—PSFD data.

With weight Without weight

Coeff t-Value Coeff t-Value

nstant 1.55 0.41 �0.15 �0.04

g(Height) 1.07 1.46 1.63 2.03**

g(Weight) �0.29 �1.69*

ucation (years) 0.08 4.03*** 0.08 3.90***

e 0.09 4.29*** 0.10 4.43***

e2 �0.001 �2.88*** �0.001 �2.91***

giona Yes Yes

cupationa Yes Yes

�0.08 �0.15 �0.15 �0.27

servations 687 687

38.96 36.64

0.448 0.450

There are 5 regions and 6 types of occupations.

Female data are used in the sample selection model. The result of the first step is not shown.

10% level of significance.

5% level of significance.

* 1% level of significance.

The PSFD consists of three main samples. The first, the second, and the

d main samples include 994 individuals born between 1953 and 1964,

9 individuals born between 1935 and 1954, and 1152 individuals born

een 1964 and 1976, respectively. Each sample is randomly selected

Taiwan’s population. The first, the second and the third main

ples were first surveyed in 1999, 2000 and 2003, respectively. These

e samples have been surveyed annually. As of 2011, the first, second,

third main samples had been conducted in 13 waves, 12 waves, and 9

es of surveys, respectively.

1358 female observations are used in the first step of the sample-

ction model, and 687 female observations are used in the second step.

result of the first step probit model (on whether the female works) is

21 A referee provides an alternative explanation. Slimness, rather than

height or weight, is the factor that matters. In controlling for weight, the

influence of slimness is embodied in height. That is, the positive

significance of height implies that slimness, rather than height per se,

pays. Given weight, the higher earnings of tall people is not because they

are tall, but because they are slim. In the same vein, in controlling for

shown. The explanatory variables of the probit model include age,

cation, marriage, and whether the spouse has a job.

height, the negative coefficient of weight reflects the influence of

slimness.

H.-L. Tao / Economics and Human Biology 13 (2014) 85–9896

determination of the initial career status, academiccharacteristics are more important than height. Betteracademic characteristics enhance the probability ofpursuing a master’s degree, and reduce the probabilityof being unemployed.

In addition, this paper investigates the relation betweenearnings and stature, academic characteristics, physicalstrength, job characteristics, and extracurricular activities.An increment of 1% in height and in weight respectivelyincreases and decreases earnings by 0.2–0.29% and 0.07–0.1% (Table 5). It is found that height, weight, and physicalstrength are all significantly correlated with earnings.Height and physical strength are positively correlated withearnings, while weight is negatively correlated withearnings. For female college graduates in Taiwan, theheight premium prevails for full time workers. This resultis similar to those found in other studies (Hubler, 2009;Schultz, 2003; Bockerman et al., 2010). The results of thisstudy suggest that the height premium does not arisebecause tall individuals went to a better college or receivedbetter grades (cognitive ability), or because they havesuperior physical strength, or because they participated inmore extracurricular activities (non-cognitive ability). Thisleaves statistical discrimination the possible factorexplaining why height per se is positively correlated with

earnings. This conclusion should be interpreted morecarefully since it is not derived from any direct evidence.That is, not only is the measurement of the perceptual biasor the statistical discrimination unavailable, but thecausality is also difficult to demonstrate. Some of thenon-cognitive ability might be accumulated before college,and cannot be captured by college extracurricular activ-ities. When tall individuals accumulate more non-cogni-tive human capital before college, it might be miscountedinto the effect of the perceptual bias on earnings. Furtherstudies can employ a longer and more detailed panel dataset to investigate why height raises earnings.

Finally, this study argues that the omission of weightmight cause a downward bias of the height premium forthe Taiwanese female sample. However, it is not likely tobe a universal phenomenon. At least, this case does notoccur in Persico et al. (2004) who used a data set of malesin Western countries. In their study, incorporating orexcluding weight does not change the coefficient of height.Whether the omission of weight causes an underestimatedheight premium could be a gender and cultural matter.

Appendix

See Figs. A1 and A2 and Table A.1.

Fig. A1. Frequency of height in the first wave.

Fig. A2. Frequency of weight in the first wave.

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H.-L. Tao / Economics and Human Biology 13 (2014) 85–98 97

erences

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le A.1

lts of OLS models of full-time logarithm earnings.

Model AI (full model) Model AII (without

physical strength)

Model AIII (without weight)

Coeff. t-Value Coeff. t-Value Coeff. t-Value

nstant 9.62 17.08*** 9.59 17.02*** 10.11 18.51***

g (Height) 0.23 1.97** 0.24 2.02** 0.06 0.57

g (Weight) �0.10 �3.42*** �0.09 �3.35***

ysical strength 0.02 1.90* 0.02 1.78*

tracurricular activities 0.004 1.55 0.01 1.83* 0.004 1.49

b. tech. college �0.08 �4.95*** �0.08 �4.97*** �0.08 �4.94***

ivate college �0.05 �4.62*** �0.05 �4.64*** �0.05 �4.69***

ivate tech. college �0.11 �9.23*** �0.11 �9.23*** �0.11 �9.21***

–79 (grade) 0.01 0.45 0.01 0.41 0.01 0.50

–89 0.02 1.60 0.02 1.53 0.02 1.61

0 0.02 0.97 0.02 0.95 0.02 0.85

ork experience 0.01 0.44 0.01 0.40 0.01 0.62

ajor Yes Yes Yes

ivate/public sector Yes Yes Yes

cupation Yes Yes Yes

m’s scale Yes Yes Yes

cation Yes Yes Yes

0.38 0.38 0.37

servations 2510 2510 2510

10% level of significance.

5% level of significance.

* 1% level of significance.

results. Journal of Human Resources 22 (1) 1–23.

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