1. The Correlation between Height, Weight, and
Income: Reevaluated
Justice Jiang
Course: B2000
Professor Foster
09.18.09

2. 2
Abstract
Using a dataset from the Centers for Disease Control and Prevention for the year 2008,
the correlation between height, weight, and income of American workers is computed. It was
found that, at a significance level of α = 0.01, both men and women living in the U.S. who are
tall (greater than 71” for men and greater than 65” for women) earn 2.9% and 3.2% more than
those who are considered average height, respectively. Both men and women who are short (less
than 68” for men and less than 62” for women) experience penalties in total income of 6.8% and
8.1%, respectively. Males’ income was positively correlated with their weight. Overweight
males (as defined by the body mass index, BMI) earned, on average, 9.5% more than normal
weight males. Females demonstrated the opposite relationship between weight and income, and
women who deviated from the normal weight range earned less.
I. Introduction
Historically, a person’s height or weight has been correlated with their income. The labor
market until the 20th century consisted mainly of physical labor. Income from jobs such as
farming or hunting, which demanded a lot of strength, would understandably be positively
correlated with ones height as well as weight. The taller and fitter the person was, the more
strenuous work they were able to endure, and therefore, the higher pay they would receive for
their labor. A recent study on coal miners in India found that miners with above average height
earned 9-13% more than other workers (Dinda, 2006). This is no surprise since coal mining is a
physically demanding job. The real question is why height and weight still determines income
when most of the labor today is not related to one’s physical abilities. “Theoretically, the
importance of height has evolutionary origins, because animals use height as an index for power
and strength when making fight-or-flight decisions” (Judge and Cable, 2003).

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Recently, studies have shown that there still exists a relationship between one’s height
and income as well as their weight and income (Brunello and d’Hombres, 2007; Cawley, 2004;
Conley and Glauber, 2006; Dinda, 2006; Garcia and Quintana Domeque, 2006; Heineck, 2004;
Heilaman and Stopeck, 1985; Judge and Cable, 2004; Kennedy and Garcia, 1994; Mirta, 2001;
Sarlio-Lähteenkorva et al., 2004). Thus, the question arises as to why companies penalize their
workers for something that is not related to their education or cognitive abilities? Most jobs, in
the labor market today, do not require strenuous physical labor.
Judge and Cable found evidence that height is positively correlated with earnings (Judge
and Cable, 2003). Controlling for variables such as age, weight, and gender, height was found to
be positively correlated with earnings ranging from a correlation of r = 0.24 to 0.35, and with a
significance level of α = 0.01.
The purpose of this paper is to reevaluate the effect of height on one’s income, as well as
the effect of weight on one’s income. We also analyze the correlation between body mass index
(BMI) and income. Observing how one’s body mass index correlates with income reveals how
both height and weight interact in relation to income. The idea behind this comes from
(Hamermesh and Biddle, 1994) who found that non-attractiveness of men and women has a
negative impact on their wages. Hence, those with BMI’s which are categorized as obese or
overweight, may be interpreted by employers as not being attractive and thus, also have a
negative impact on their wages. It has been shown overweight women are discriminated against
in the labor market according to their wages (Conley and Glauber, 2006), this relationship has
been shown to be true for overweight men as well (Brunello, 2007). This may be due to a
psychological phenomenon that tall and slender people are viewed by many as authoritative and

4. 4
motivated (Heineck, 2004), while short and obese people are prejudged to possess laziness and
unsuccessfulness (Harris et al., 1982; Frieze et al., 1990; Frieze et al.,1991).
All studies, which have been stated above, concluded that height has a positive
correlation with income, but not all have shown similar results according to the relationship
between weight and income: Conley et al (2006), Dinda (2006), and Cawley (2004) stated that a
female’s wage is negatively affected by an increase in weight. They also conclude that males,
either have no relationship (Conley and Glauber, 2006), or their income increases as their weight
increases (Dinda, 2006). Brunello (2007) contradicts these results and claims that the wages of
both males and females are negatively affected by increased weight.
The results found in (Dinda, 2006; Brunello, 2007; Cawley, 2004; Conley and Glauber,
2006; Heineck, 2004) are examined and tested by first finding the differences in means of height
and BMI. Next, three regressions are performed. The first regression uses continuous variables of
weight, height, and age. The second regression uses bands of BMI, height, and age in the form of
dummy variables. The third regression analyzes the same variables in the second regression, only
this time using interaction terms, to observe this relationship once again.
This paper is structured as follows: Section II contains a brief overview on the
background and previous research done on this topic. Section III presents the data and methods
used to obtain the results found in Section IV. Section V contains a brief discussion on the results
found and Section VI has concluding remarks.
II. Background and Previous Research
There have been many studies in the past which have addressed discrimination in the
labor market with regards to age, gender, race, and education. Of these demographics which
were studied, height and weight were also included in the pool of articles. One’s appearance

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plays a major role in one’s total income. This is due to the fact that one’s appearance is subject to
the scrutiny of an employer.
As previously stated, the height of a person can give the impression that they are
authoritative, as well as capable (Heineck, 2004). With these preconceived traits, taller people
also gain higher positions in the labor force as well as a better pay (Heilman and Stopeck, 1985;
Hamermesh and Biddle, 1994). Their shorter counterparts tend to be penalized in their wages
(Heineck, 2004). There also is evidence that individuals from low socio-economic groups are
shorter than individuals from higher socio-economic groups (Boström and Diderichsen, 1997).
Tall women in managerial or professional occupations receive a wage premium of about
2.5% with a one-inch increment in height (Mirta, 2001). Another study, which further proved
that height and income are positively correlated, was Judge and Cable’s (2004), which concluded
that each one-inch increase in height results in an increase in annual earnings of, on average,
$789 more a year.
Although much attention has been directed at height and weight as determinant of
income, the underlying cause of income discrepancies has been attributed to appearance or
attractiveness (Hamermesh and Biddle, 1994). We use body mass index to quantify
attractiveness in the regressions presented in Section IV. In a previous study (Brunello, 2007), a
10% increase in the average body mass index reduces the real earnings of males and females by
3.27% and 1.86% respectively.
Other studies negate Brunello’s (2007) results and conclude that overweight males
actually gain a wage premium of 9% in comparison to males in the normal weight range (Dinda,
2006), or are not affected by it (Conley and Glauber, 2006). Rather, only obese females were

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found to have a negative income of about 18% comparative to females in the normal weight
range (Conley and Glauber, 2006).
In this study, all of the previous findings will be reexamined as it explores the effects of
both height and weight on total income for U.S. workers.
III. Data and Method
The data, which was used in this particular study, was from The Behavioral Risk Factor
Surveillance System (BRFSS)1. The (BRFSS) is a collaborative project of the Centers for
Disease Control and Prevention (CDC). This is an ongoing data collection program designed to
measure behavioral risk factors for the adult population (18 years of age or older) living in
households. The basic dataset consisted of 414,509 participants living within the United States or
territories. The data was collected throughout 2008 by means of telephone interviews, and
personal interviews.
The dependent variable for all regressions was either total income or the logarithm of
total income. The individual’s height and weight were the prime variables studied in the
regression and both were treated as exogenous with respect to the total income. Three OLS
models were observed:
Tis = β₀ + β₁His + β₂Wis + β₃Ais + β₄Yis + β₅Dis + εi
(1)
where Ti is the total income of individual (i), of sex (s); β₀ is the constant; His is the continuous
height variable; Wis is the continuous weight variable; Ai is the continuous age variable; Yis is the
polynomial variable being observed (HeightSq, WeightSq, and AgeSq); Dis is the vector of all
1 The web link to the dataset <http://www.cdc.gov/BRFSS/technical_infodata/surveydata/2008.htm#survey>

7. 7
other exogenous dummy variables (race, education, geographical location, and health); and εi is
an error term. Controlling for sex was done by filtering males when observing females and vice-
versa. Three different models were utilized in this study. For Model I, Yis equals “HeightSq”.
For Model II, Yis equals “WeightSq”. For Model III, Yis equals “AgeSq”. Yis accounts for the
nonlinear relationship between total income and height/weight/age. Each squared term was
observed in a different regression in order not to have them interfere with one another. The
second OLS equation is
ln Tis = β₀ + β₁His’ + β₂Bis’ + β₃Ais’ + βDis + εi,
(2)
where Hi’ is the dummy height variable of the individual; Bi’ is the dummy body mass index
variable of the individual; β₃Ais’ is the dummy age variable of the individual; Dis is the vector of
all other exogenous dummy variables (age, education, and race dummies); and εi is an error
term. The third OLS equation is
ln Tis = β₀ + β₁(His’)(His) + β₂(Bis’)(Bis) + β₃(Ais’)(Ais) + βDis + εi,
(3)
where (His’)(His) is the interaction term between the height dummy variable and the continuous
height variable; (Bis’)(Bis) is the interaction term between the body mass index dummy variable
and the continuous body mass index variable; (Ais’)(Ais) is the interaction term between the age
dummy variable and the continuous age variable; Di is the vector of all other exogenous dummy
variables (age, education, and race); and εi is an error term.

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Table 1
Summary of adult American workers from sample
Population Mean Standard deviation
Total Income (Dollars) 44,549 $48,121.00 28713.607
ReportedHeight(Inches) 44,549 66.50 3.919
ReportedWeight(Pounds) 44,549 175.65 42.887
Age (Year) 44,549 48.82 12.796
BMI 44,549 27.84 6.075
Note: Age is defined as men and women between the ages of 18 and 99.
Source: Behavioral Risk Factor Surveillance System Code Book Report, 2008
Ten main exogenous variables were observed and controlled in this study. The way in
which these variables were modified and controlled is as follows:
Total Income (The study’s dependent variable) was grouped by a range of incomes
within the CDC dataset; beginning with (1 = Less than $10,000) and then increased in
increments of $5,000 until: (6 = $35,000 – Less than $50,000), (7 = $50,000 – Less than
$75,000) and (8 = $75,000 or more). These measures were not acceptable since the increments in
which the income was measure varied. Instead, the median of each group replaces the values (1-
8), with 8 being transformed into $100,000 since it initially stood for “$75,000 or more”. In the
survey “Total Income” was defined as “[the participants] annual household income from all
sources”. Since this study calls for the total income of each specific person, a filter was applied
to only include participants who stated there was only one adult in the household. This implied
that the participant was the only person living in the household.
Age was grouped in bands of ten years, besides the bands “18-24” and “65 and above”. It
also was studied as a continuous variable. The nonlinear relationship between age and total
income was taken into consideration, thus “Agesq” was introduced into the regression as well.

9. 9
Weight was observed as a continuous variable. The nonlinear relationship between
weight and total income was taken into consideration, thus “Weightsq” was introduced into the
regression as well.
Height was grouped for the males as: “Male Height Below 68 in.”, “Male Height 68 – 71
in.”, and “Male Height Above 71 in.” The females’ heights were grouped as: “Female Height
Below 62 in.”, Female Height 62 – 65 in.”, and “Female Height Above 65 in.” The height bands
were representative of the results found in the “National Health Statistics Reports,” by Margaret
A. McDowell, et al. (2008).Using the percentiles in McDowell’s study, bands were made. The
first band consisted of height observations below the 25th percentile. The second band included
height observations in the 25th percentile to 75th percentile. The third band included observations
above the 75th percentile. The regression did not include those with a height above seven feet
tall. There were seventeen participants in total who were above seven feet tall. The decision to
exclude outliers was made in order to exclude observations of people whose height may not be
considered in the “socially acceptable” range, for those above this height level may be penalized
in the labor market (Heineck, 2004). Height was also observed as a continuous variable. The
nonlinear relationship between height and total income was also observed, thus “Heightsq” was
introduced into the regression. Despite the exclusion of the outliers, it is postulated that the
nonlinear relationship may reinforce Heineck’s results. According to Table 2, there appears to be
a positive relationship between height and income for both males and females. As height
increases so does their average total income. Their differences in means will further validate this
finding.

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Table 2
Total yearly earnings of male employed Americans by height
Male Height Band Population Average Income Standard deviations
Heightabove 71 inches 5253 $57,872.17 29629.4
68-71 inches (referenceheight) 7113 $55,146.91 29161.9
Heightbelow68 inches 2563 $50,272.14 29081.2
Total 14929
Female HeightBand
Heightabove 65 inches 10947 $47,225.04 28120.5
62-65 inches (referenceheight) 15127 $43,987.41 27422.1
Heightbelow62 inches 3546 $38,427.10 26073.5
Total 29620
Table 2.1
Height Percentiles (Inches)
Percentiles Male Female
10 66 61
25 68 63
50 70 65
75
90
72
74
66
68
Employment status was categorized as: “Employed For Wages”, “Self-Employed”,
“Out of work for more than 1 year”, Out of work for less than 1 year”, “Home maker”,
“Student”, “Retired”, and “Unable to work”. Of the sub-groups not included in the regression
were: “Self-employed”, “A home maker”, “Retired”, “Unable to work”, and “Refused (to
answer)”. Height was not as crucial in determining income for those who are self-employed
(Cinnirella, 2009). The regression was restricted to only those who were employed for wages and
students. The students which are also employed may have found the occupation of “student” was
a better demographic description, but these participants can also be categorized as “employed for
wages”. Since observations with zero income were filtered, these students working for wages are
able to be captured and utilized in this regression as well.
Education was broken up into four groups: “No High School”, “Grade 12 or GED”,
“Some College”, and “College graduate”.

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Race had classifications as “White”, “African American”, “Asian”, “American Indian”,
“Native Hawaiian”, and “Hispanic”. In the BRFSS survey, there was a specific question which
asked if the participant were Hispanic. All the different races defined in this study reported that
they were not Hispanic in this separate question, otherwise they were considered “Hispanic”.
Overall Health was scored by a rating of 1 to 5; 1 being “excellent” and 5 being “poor”.
This was at the participant’s discretion. The study did not include in the population those who
reported their health as “poor”. By not including participants with poor health in the regression,
the chance of error is reduced. For instance, a person who is tall and has a normal body mass
index may have a low total income due to physical restrictions which are not severe enough to
consider that person completely handicapped.
Geographical Location was grouped by each region of the United States as:
“Northeast”, “Midwest”, “South”, and “West”.
Body Mass Index consisted of groups such as: “Obese”, “Overweight”, “Normal
Weight”, and “Underweight”. According to the Center for Disease Control and Prevention the
formula for calculation one’s body mass index is: weight (lb) / [height (in)]2 x 703. BMI’s below
18.5 are considered underweight. BMI’s ranging between 18.5 and 24.9 are considered normal
weight. BMI’s ranging between 25.0 and 29.9 are considered overweight. BMI’s 30.0 and above
are considered obese. According to Table 3, the majority of this dataset contained observations
which were overweight. It also appears that males whom are overweight or obese earn more than
those who are normal weight, and that females who are normal weight earn the most. Their
differences in means will further validate this find.

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Table 3
Total yearly earnings of employed Americans by BMI
Male BMI Population Average Income Standard deviations
Underweight 96 $45,026.04 28553.95
Normal Weight(referenceBMI) 4020 $52,866.29 29573.8
Overweight 6595 $57,350.64 29410.1
Obese 4218 $54,537.10 29076.3
Total 14929
Female BMI
Underweight 497 $43,868.21 29304.2
Normal Weight(referenceBMI) 10818 $48,133.67 29181.9
Overweight 9223 $44,191.97 27307.5
Obese 9081 $40,576.48 25399.0
Total 29620
Table 3.1
BMI Percentiles
Percentiles Male Female
10 22.38 20.80
25 24.40 23.17
50 27.12 26.60
75
90
30.51
34.43
31.09
36.31
After grouping the variables, a filter was applied to refine the regression. Total income
was filtered so that it only included values greater than 99. Filtering removed irrelevant values
such as 77 and 99, which were labeled as “Don’t know” and “Refused”. Including these values
skews the results. This was true for other variables as well such as weight and height.
After setting all the controls for the variables, and refining the data to avoid any biased
results, the sample was left with 49,181 valid observations (11.86% of the total number of
observations from the dataset). This included 14,929 male observations, and 29,620 female
observations. Precautions for heteroskedasticity were applied by using a macro written by
Andrew F. Hayes, at Ohio State University, called “hreg.sps”, before any regression was
performed.

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IV. Results
Total income increased as the participants heights increased, for both males and females
(Table 2). The t-statistics for both of the sexes prove that their differences in mean are
statistically significant between all the height bands (Table 4). This was the first step in
concluding that one’s income and height is positively correlated.
Table 4
Results of t-statistic, in absolute value, for equality of mean total income for different height-bands
Male Height Band Heightabove 71
inches
68-71 inches Heightbelow68
inches
Heightabove 71 inches 5.09 10.78
68-71 inches 7.27
Heightbelow68 inches
Female HeightBand Heightabove 65
inches
62-65 inches Heightbelow62
inches
Heightabove 65 inches 9.27 17.12
62-65 inches 11.32
Heightbelow62 inches
Overweight men earned the highest average income, while normal weight women earned
the highest income (Table 3). The t-statistics, from their differences in mean, prove that the
highest average total income for both genders have differences which are statistically significant
relative to each sex’s reference body mass index, normal weight, (Table 5). For females, this was
the first step in concluding that one’s weight, relative to their height, is correlated with income.
For males, this is the first step in proving that the relationship between weight and total income is
different from that of females. The regressions on the variables of height and income, and weight
and income, will provide further evidence on the results found from Table 4 and Table 5.

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Table 5
Results of t-statistic, in absolute value, for equality of mean total income for different body mass indexes
Male BMI Obese Overweight Normal Weight Underweight
Obese 4.89 2.58 3.23
Overweight 7.59 4.20
Normal Weight 2.66
Underweight
Female BMI Obese Overweight Normal Weight Underweight
Obese 9.28 19.52 2.45
Overweight 9.87 0.24
Normal Weight 3.17
Underweight
Observing the correlation between height and income; weight and income; age and
income, using polynomial variables, allowed for this study to estimate the slopes for each
relationship. Upon observation of Table 6 (see page 20), Eq. (1), the following statistics were
calculated: for males, at α = 0.01, each marginal increase in height equates to a marginal increase
in total income by $290.47 (Model III). This supports Judge and Cable’s (2004) finding that each
one-inch increase in height results in a $789 increase in income a year. The inclusion of heights
squared term reveals that, at α = 0.05 this positive correlation changes after 74in (90th percentile
Table 2.1), where the marginal increase in height translates into a negative marginal return in
total income. This supports Heineck’s (2004) findings, which suggests that there is a wage
penalty for males above a socially accepted height. At α = 0.01, each marginal increase in weight
equates to a marginal increase in total income by $42.15 (Model III). Above 267 lbs, marginal
increase in weight translates into a negative marginal return in total income. At α=0.01, each
marginal increase in weight equates to a marginal increase in total income by $175.30 (Model I).
The marginal increase in total income, relative to each marginal increase in a male’s age, became
negative after 52 years old.

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For females, α = 0.01, each marginal increase in height equates to a marginal increase in
total income by $530.83 (Model III). The inclusion of heights squared term reveals that this
positive correlation changes after 71in, where the marginal increase in height translates into a
negative marginal return in total income. At α = 0.01, each marginal increase in weight equates
to a marginal decrease in total income by -$21.39 (Model III). Weight was not statistically
significant for females; however, if “Height (Inches)” was removed from the regression the
values became significant at α = 0.01. With the new values for the weight coefficients, the point
at which marginal return on total income become negative with relation to a female’s weight is
after 192lbs. Weight was not as strongly correlated with total income as height. By removing
height, the correlation between weight and income was statistically significant, which enabled
the calculation of the peak of the slope. The marginal increase in total income, relative to each
marginal increase in a female’s age, became negative after 52 years old. Visual representations
of these polynomial equations are viewable in Table 6.1 (see page 21).
Table 7 (see page 22), Eq (2), supports the hypothesis that taller workers earn more. Male
workers who are tall earn 2.9% more and those who are short earn 6.8% less than workers who
are in the reference range. Female workers who are tall earn 3.2% more and those who are short
earn 8.1% less than the reference height. This result supports Heineck’s (2004) study, which
finds that taller male German workers gain 1 to 1.3% and taller females gain about 1% more than
the reference height; while the shorter male workers end up earning 3 to 6% and the shorter
females earn around 2% less than the reference height. Male workers who are overweight or
obese actually tend to earn more than those who are normal weight by 9.5% to 4.7%,
respectively. Females who are overweight or obese are penalized with a total income that is 2.7%
to 8.3% lower than a female who is normal weight, respectively. This provides further support

16. 16
for both Dinda (2006) and Conley et al (2006) which concludes that male’s BMI’s have a
positive relationship with their total income, but for females, obesity is associated with a
reduction in their wages compared to those in the range of normal weight.
Next, height, weight, and BMI are considered as segmented continuous variables. The
results from Table 8 (see page 23), Eq. (3), indicate that for each additional inch increment in
height, the mean total income of male and female workers whom are tall increases by 0.04% or
0.05% more than their reference height, respectively. Again, this supports Heineck’s (2004)
results. According to Table 8, every incremental increase in BMI results in a 0.3% increase in
total earnings for males who are overweight; once a male is obese, this then turns into a 0.1%
increase. For women there is a negative effect. For every marginal increase in BMI, the mean
total income of overweight women decreases by 0.1%, and for obese women the negative effect
increases and total income decreases by 0.3%. Again, this supports both Dinda (2006) and
Conley et al (2006), as well as negates Brunello’s (2007) results. Table 8 also reinforces the
findings in both Table 6 and Table 7.
A scatter plot of these correlations does not present the results found in this study. The
dependent variable “total income” is not continuous, thus the scatter plot only shows a dense
congregation of points at each level of total income. These discrete points in the graph are not
distinguishable visually.
V. Discussion
Excluding the relationship between weight and income for males, it is now clear that
height and weight are relative to one’s total income. The results of this study are parallel to other
similar studies done on this topic, and they negate others. This is probably due to differing study

17. 17
designs as well as heterogeneous populations being studied, in terms of different countries being
studied and other factors.
Measuring the BMI of an individual is only the first step in measuring someone’s
physical appearance. One’s BMI ignores the specification of fat to fat-free weight due to muscle
mass and bones (e.g. Barry Bonds is considered overweight). Muscle mass can be seen as
attractive since it shows that they are healthy and enabled. By this view point, muscle mass can
actually promote one’s physical appearance, which is a factor in explaining why males who are
overweight gain more than males who are have a normal weight. Females tend to not have as
much muscle as males, which is why their BMI measurement is strongly related to their physical
appearance. The fat mass of an observation should have also been taken into consideration, since
it is the percentage of fat which would be deemed unattractive to employers. This measurement
was not allowed to be studied due to the variables available in the dataset.
An alternate hypothesis as to why overweight males earn more is because they may be
working so much, that they are not able to exercise. The stress also can induce them to eat more
and gain more weight. This in turn would cause the males relationship between weight and
income to be the opposite of females weight and income relationship. Controlling for the amount
of time one works as well as the activities one does outside of work would have lead to less
biased results. These variables were also not available in the dataset used.
The dataset limited number of variables which could be controlled as well as the
variables used. The BRFSS’s original grouping of income did not ensure accurate results. Once
the data reached up to $75,000 is was classified as “$75,000 or more” and the variables before
that increased from a five-thousand dollar gap, to a fifteen –thousand dollar gap, and then a
twenty-five-thousand dollar gap. Grouping the participants’ income levels in this manner limits

18. 18
the results. Observing total income as a continuous variable would have allowed for a more
accurate analysis. Taking the median of each grouping allowed for a slightly more accurate
regression. Transforming the value “8” ($75,000 or more) to $100,000 also permits the
observation of high earners, but this was only an estimate of the average income in this grouping.
In the grouping of “$75,000 or more”, there are approximately 96,400 people, which is almost
one-fourth of the total sample. This is a huge disadvantage when calculating the correlation.
Another issue that occurred was the fact that this survey did not include general
occupations of the working class. One’s occupation may change the degree of the correlation
between height and income due to the key roles that one is entitled to under different
occupations. A telemarketer does not utilize their height in order to persuade their consumers
into purchasing their product, which may be the opposite case for a car salesman. The data also
did not take into account part-time work and full-time work. The employment was specified as
anyone who is working for wages. This also affects the data since part-time workers often make
less than full-time workers.
The results were restricted to only those who lived in the house by themselves. This
forced a lot of the observations to be omitted. In turn, bias may have been created. The mean,
median, and mode of age in the sample were approximately around fifty years old. Older adults
living alone are more prone to being depressed (Cheng, 2008). This indicates that the sample had
a higher chance of including observations with depression. Since depression negatively affects
one’s wage, this allows for biased results (Cseh, 2008).
Although these restrictions on the dataset are valid, this study still was able to produce
the same results as prior studies. In the future, the time at work, fat mass, as well as activities

19. 19
outside of work should be controlled in addition to the variables already incorporated in this
study.
VI. Conclusion
Using polynomial expressions, dummy variables, and interaction terms, the 2008 BRFSS
dataset was calculated and analyzed in depth. The results show that males and females heights
are associated with total income. Females weight is negatively correlated with total income,
while for males, weight is positively correlated. In the U.S., males who are considered tall gain
an income premium of 2.9% over males who are considered normal height. Females who are tall
earn 3.2% more than average height females. Males who are overweight gain 9.5% more than
those who are within the normal BMI weight rating, and females who either have a lower BMI or
a higher BMI than the normal rating are penalized. These results are statistically significant α =
0.01. The correlation between one’s height and income, as well as one’s weight and income, has
been proven to be correlated with a correlation coefficient of r = 0.443 for males and r = 0.533
for females2. Although the dataset restricted the ability to further explore the correlation between
ones height and income, as well as their weight and income, the results found in this study still
support the prior results found in other studies.
“Physical height deserves equal footing with other types of physical attributes that garner
serious scholarly attention, such as attractiveness and weight.” (Judge and Cable 2004)
2 This was calculated from the √R² in Table 6, and then compared with the formula | 𝑟| ≥
2
√𝑛
to see the
relationship.

20. 20
Table 6
Results of height and weight effects on total income of American workers
Variables
Model I
Males
Model II Model III
Height (Inches) 3016.458** 148.454* 290.466***
Height² -20.474* ― ―
Weight (Pounds)
Weight²
Age (Years)
Age²
African American
Asian
Native Hawaiian
American Indian
Other (Non-Hispanic)
Multiracial(Non-Hispanic)
Hispanic
53.797**
―
175.299***
―
-6342.207***
5125.7***
-660.982
-8834.523***
-3808.901*
-3794.514**
-4551.906***
308.556***
-0.577 ***
167.766***
―
-6416.696***
5754.257***
-231.562
-8926.132***
-3763.020
-3551.038**
-4695.241***
42.152***
―
1949.319***
-18.590***
-6448.424***
5469.657***
352.138
-8937.185***
-2803.289
-3407.806**
-4098.507***
Midwest Region
South Region
West Region
Grades 12 or GED
Some College
College Grad
VeryGood Health
Good Health
Fair Health
-7446.059***
-1529.923***
-2501.45***
7969.114***
15219.839***
30668.465***
-4750.665***
-9985.847***
15638.658***
-7430.174***
-1566.521***
-2509.214***
7990.277***
15200.266***
30654.823***
-4898.783***
10068.592***
-15420.9***
-7538.886***
-1469.718**
-2680.356***
6683.3***
13823.528***
29181.372***
-4937.022***
10103.332***
15660.593***
R² .214 .216 .229
Model I
Females
Model II Model III
Height (Inches) 6797.966*** 552.324*** 530.829***
Height² -48.109*** ― ―
Weight (Pounds)
Weight²
Age (Years)
Age²
African American
Asian
Native Hawaiian
American Indian
Other (Non-Hispanic)
Multiracial(Non-Hispanic)
Hispanic
-11.326***
―
122.494***
―
-3693.720***
4205.276***
470.767
-6913.162***
-366.996
-2160.052**
-5745.677***
25.441 (64.571***)
-.097*(-.168***)
121.325***
―
-3785.885***
4148.402***
326.243
-6941.211***
-426.387
-2187.195**
-5940.148***
-21.389***
―
2086.786***
-20.049***
-3518.381***
4823.456***
769.634
-6454.084***
223.719
-1517.247
-5811.193***
Midwest Region
South Region
West Region
Grades 12 or GED
Some College
College Grad
VeryGood Health
Good Health
Fair Health
7511.169***
2056.857***
-3580.653***
7058.181***
14250.582***
33339.656***
-3507.319***
-7937.230***
-11799.783***
-7514.132***
-2044.274***
-3591.289***
7157.323***
14367.246***
33476.906***
3557.391***
7993.350***
11835.369***
-7437.960***
-2126.792***
-3994.198***
6553.431***
13400.304***
31713.049***
-3569.661***
-7956.451***
-11984.496***
R² .272 .272 .294
Note: (i) Values were measure in U.S. Dollars. (ii) (***), (**),and (*) denote statistically significant at a 1%, 5%, and 10% level,respectively.(iii) Dummy variables were used for Region,
Education, and Health. (iiii) Variables which were included in the constant were: “White”, “Northeast Region”, “No High School”, and “Excellent Health”. (iiiii) Males and females were
regressed separately. (iiiiii) The values in parenthesis in the Females Model II for “Weight (Pounds)” and “WeightSq” were computed not including “Height (Inches)”.

21. 21
Table 6.1
Polynomial Charts
x-axis is weight in lbs
x-axis is height in inches and age in years
0
5000
10000
15000
20000
25000
30000
35000
40000
45000 1
15
29
43
57
71
85
99
113
127
141
155
169
183
197
211
225
239
253
267
281
295
ChangeInTotalIncome
Weight V.S. Total Income
Males Weight
Females Weight
0
50000
100000
150000
200000
250000
300000
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77
ChangeInTotalIncome
Height & Age V.S Total Income
Males Height
Females Heigth
Males Age
Females Age

22. 22
Table 7
Results of height-dummy, age-dummy, and BMI-dummy with respect to the log of total income of
American workers
Variable Males Females
Tall (Height Above 71in)
Normal (Height 68in-71in)
Short (Height Below 68in)
Tall (Height Above 65in)
Normal (Height 62in-65in)
Short (Height Below 62in)
Under Weight
Normal Weight
Over Weight
Obese Weight
Age 18-24
Age 25-34
.029***
Reference Height
-.068***
―
―
―
-11.8**
Reference BMI
.095***
.047***
Reference Age
.308***
―
―
―
.032***
Reference Height
-.081***
-.089***
Reference BMI
-.027***
-.083***
Reference Age
.271***
Age 35-44 .475*** .478***
Age 45-54 .472*** .545***
Age 55-64 .468*** .516***
Age 65-Above .308*** .350***
African American
Asian
Native Hawaiian
American Indian
Other Race (Non Hispanic)
Multiracial (Non Hispanic)
Hispanic
Grades 12 or GED
Some College
College Grad
Midwest Region
South Region
West Region
R²
-.157***
.094**
-.091
-.252***
-.101**
-.093**
-.144***
.264***
.425***
.728***
-.131***
-.024*
-.040***
.197
-.107***
.047
-.054
-.183***
-.008
-.079***
-.215***
.324***
.538***
.975***
-.163***
-.060***
-.074***
.284
Note: (i) Values represent the change in percentage of an individual’stotal incomerelativeto the reference
variable.(ii) (***), (**), and (*) denote statistically significantata 1%, 5%, and 10% level, respectively.(iii)
Variables which were included in the constant were: “Height 68in-71in”,“Height 62in-75in”,“Normal Weight”,
“Age18-24”,“White”, “No High School”, and “Northeast Region”. (iiii) Males and females were regressed
separately.

23. 23
Table 8
Results of interaction (height x dummy), (age x dummy), and (BMI x dummy) with respect to the log of
total income of American workers
Variable Males Females
Tall (Height Above 71in)
Normal (Height 68in-71in)
Short (Height Below 68in)
Tall (Height Above 65in)
Normal (Height 62in-65in)
Short (Height Below 62in)
Under Weight
Normal Weight
Over Weight
Obese Weight
Age 18-24
Age 25-34
.0004***
Reference Height
-.001***
―
―
―
-.007**
Reference BMI
.003***
.001***
Reference Age
.009***
―
―
―
.0005***
Reference Height
-.001***
-.005***
Reference BMI
-.001***
-.003***
Reference Age
.008***
Age 35-44 .011*** .011***
Age 45-54 .009*** .010***
Age 55-64 .007*** .008***
Age 65-Above .004*** .004***
African American
Asian
Native Hawaiian
American Indian
Other Race (Non Hispanic)
Multiracial (Non Hispanic)
Hispanic
Grades 12 or GED
Some College
College Grad
Midwest Region
South Region
West Region
R²
-.158***
.094**
-.088
-.252***
-.102**
-.096**
-.146***
.265***
.428***
.730***
-.131***
-.024*
-.039***
.196
-.104***
.042
-.051
-.183***
-.006
-.076***
-.214***
.323***
.539***
.975***
-.162***
-.059***
-.073***
.284
Note: (i) Values represent the change in percentage of an individual’stotal incomerelativeto the reference
variable.(ii) (***), (**), and (*) denote statistically significantata 1%, 5%, and 10% level, respectively.(iii)
Variables which were included in the constant were: “Height 68in-71in”,“Height 62in-75in”,“Normal Weight”,
“Age18-24”,“White”, “No High School”, and “Northeast Region”. (iiii) Males and females were regressed
separately.

24. 24
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