BMI one’s height and bodyweight to income and employment


BMI and Wages

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Rafaël Schut (11755229)
WG03 | mw. M.L.S. Rijvordt
Introduction to Econometrics

January, 2018

Inhoud1 Introduction. 32 Theoretical framework. 42.1 Human capital theory. 42.1.1 Theory. 42.1.2 Model 42.2 Productivity theory. 52.2.1 Theory. 5* VERGEET GEEN CONCEPTUAL MODEL.. 52.2.2 Model 52.3 Social interaction theory. 72.3.1 Theory. 72.3.2 Model 72.4 Health and medication theory. 82.4.1 Theory. 83 Methods. 93.1 Information about the
dataset 93.1.1 Adjusting the dataset 93.1.2 Operationalization of
the variables. 93.2 Adjusting the models. 113.3 Evaluating the models. 126 References. 13 

 1 Introduction
Aside from the typical qualifications and work experience, various other
physical factors influence one’s ability to acquire better earning jobs and/or
higher salaries. Research has been conducted, observing 3000 employed US
citizens. Of every person, many variables such as their race, height,
bodyweight, religion and income is given. In this paper, the relation between
one’s height and bodyweight to income and employment is investigated. The aim
of the research is to confirm, through data obtained from an empirical experiment,
that job-applicants are not solely judged on their abilities relevant to work,
but on their physical appearance as well. The focus of this paper will be the relation between one’s physical
appearance and their job and income. This paper makes a clear distinction between
men and women; the effect of physical appearance on the job and income will be
calculated separately for each gender. As height and bodyweight are major
aspects of the physical appearance, the research question investigates to what
extent the height and bodyweight influence one’s income and employment. The
topic itself is of great importance, as it confirms the presence of
(unconscious) bias by employers. If this bias of employers happens to be true, having knowledge about this will allow
the job-applicants to prepare themselves better. Through adaptation,
job-applicants can correspond to the image of the ideal person for the job.

The research question will be approached through an analysis of 3000
observations. First of all, the hypothesis of the research will be stated. Secondly,
the results of all observations will be processed in a computer program, known
as EViews. With the use of the
program EViews a regression model
will be constructed. The regression model describes the relation between two
variables, in this case the height and bodyweight in relation to the income and
employment. The regression model will provide insight as to what the relation
between the variables may potentially be. Based on the acquired numbers, a conclusion
will be drawn accordingly.The report is structured into four different parts. First of all, a theoretical
framework will be presented serving as a lens to evaluate the research
question. Secondly, the methods of research will be discussed carefully. In
particular, the results of the observations, which may potentially contain
improper data and outliers, will be discussed thoroughly in order to prevent
false data analysis. Thirdly, the results of the research will be analysed with
the help of EViews building a
foundation for the discussion of the topic. The report will be concluded with a
discussion about the topic and the presence of any unexpected results.    

 2 Theoretical framework
2.1 Conceptual model

2.2 Human capital theory
2.2.1 Theory
Human capital is the accumulation of abilities,
knowledge and experiences of individuals that can be utilized to produce
economic value. Human capital can be gained through cumulative job experience,
and lost through unemployment or simply the passing of time (Berndt, 1991).         Individuals are able to invest in human
capital through formal education; however, firms share this ability through the
possibility of on-the-job training (Berndt, 1991). The broadening of one’s
human capital leads to a higher productivity (Becker 1994). According to the
human capital theory, employees are paid on basis of their productivity. As the
human capital becomes greater, the productivity rises, leading to higher income
for the employee and turnover (per person) for the firm. Therefore, it is
attractive for firms to stimulate and guide the employee in developing the
employees’ human capital, which in turn increases the firm’s capital.
2.2.2 Model

In this paper the
human capital earnings function (HCEF)
constructed in 1974 by Mincer, is
used as the model of the human capital theory:


The variables are defined as follows:  represents earnings, S represents years of completed education, X represents the number of years an
individual has worked since completing schooling, and e is a statistical

logarithm of earnings is the composition of a linear and quadratic equation:
the linear education term and a quadratic function of “years of potential
experience”. Mincer proposed, in the absence of explicit data on experience,
the use of “potential experience”: the years and individual with age A could
have worked, under the assumption that he started school at age 6, took exactly
S years of schooling to finish S years of schooling and started his career
immediately after finishing his schooling: (Card, 1999).

The use of the log transformation knows various benefits. Firstly, the
distribution of log hourly earnings closely resembles the normal distribution,
making the transformation useful for data analysis. In addition, the
transformation allows the success of the HCEF, as the distribution is closely
approximated by the standard HCEF equation (Card, 1999). Another benefit is
that it can be mathematically proven that the coefficients in the equation of a
log function indicate percentage increases. Therefore the interpretation of the
meaning behind the coefficients is easier. Lastly, the log transformation is
known for its resistance against the impact of extreme outliers.

2.3 Productivity theory

2.3.1 Theory
Perhaps one of
the major objectives of a firm is to yield high productivity. Research displays
a positive correlation between obesity and presenteeism, that is, a reduced
productivity on the job. In particular obesity
has been shown to obstruct the employee’s productivity in physically-demanding jobs
(Baum and Ford, 2004). Health
effects on productivity are concentrated among the most obese workers with BMIs
of 35.0 and greater, suggesting that employers should consider workplace
interventions targeting obesity. Even modest weight loss could result in
hundreds of dollars of improved productivity costs per worker each year (Gates,
Succop, Brehm, Gillespie & Sommers, 2008).                                                                    Furthermore,
as firms do not gain any turnover from absent employees, the absenteeism
related to obesity causes foregone earnings to the firm.

2.3.2 Model
In a report (Han, Norton & Stearns, 2009), the estimation model considers
the log hourly wages as dependent variables, which are modeled as a function of
weight status, age, other covariates and individual fixed effects, resulting in
the main equations:

                         ln() = h ,   )                                                                                                           (2)

Where suffixes i and t stand for individual and time, respectively. BMI in two dummy
variables for overweight and obese and with normal weight or underweight as the
reference groups. Categorical variables are chosen to measure incremental
effects of moving up one clinical weight classification on the probability of
employment and log wages. This specification does not impose a constant
marginal effect of a one-unit increase in BMI on the probability of employment
or log wages across the entire BMI range.                                         Recent research displays that being
overweight adversely affects the employment across all race-gender subgroups
except black women and men1.
Therefore, SkillType is a vector of dummy variables representing interpersonal
skills required in each occupation.                     and denote other control variables at the
individual and state levels, respectively. M stands for time-invariant
individual fixed effects and e stands for independently identically distributed
error terms. Individual-level covariates include years of work experience.                                                                                                                   Furthermore,
the wage equation is modified by including interactions of BMI groups and age
and a three-way interaction of BMI groups, age, and characteristics of
interpersonal skills required in each occupation. The modifications allow
estimating any changes in the effect of BMI groups on hourly wages with age and
characteristics of interpersonal skills in each occupation.
2.4 Health and medication theory

2.4.1 Theory
It has been shown that “obesity is associated with a 36 percent increase in
inpatient and outpatient spending and a 77 percent increase in medications.” As
a result, employers, who provide health insurance, are less inclined to pay
high wages as the labor costs of overweight and obese workers are high (Sturm,
2002).                                                                                    Moreover,
research stated that body weight increases health expenditures more steadily
for women than men (Wee et al., 2005). If employers are to (partly) cover health
costs for the employees, the greater expenses could possibly dissuade employers
from hiring overweight women in comparison to
overweight men.  
2.4.2 Model
The model consists of individuals’ wages that are related to BMI and other
variables (X is a vector of variables that affect wages), at time t, according
to equation:
+ ²                                                                                         
            (1)where  is the
residual. Exogeneity of BMI assures that OLS estimates of ? can be interpreted as a consistent
estimate of the true effect of BMI on wages. However, it can be reasoned that
BMI is endogenous, as BMI could be influenced by non-genetic factors such as
individual choices and environment and the possibility that obesity may be
influenced by wages. Endogeneity of BMI instead leads to inconsistent estimates
of the effect of weight on wages.                                                                                                                                     In an attempt to
identify the possible sources of endogeneity, the residual in (1) is decomposed
into a genetic component (G), a non-genetic component (NG) and a residual (?), independent and identically
distributed random variables over individuals and time (De Sousa, 2012):                                  

 3 Methods
3.1 Information about the dataset
The National Longitudinal Survey of Youth 1979 (NLSY79) was a panel survey
conducted by the Bureau of Labor Statistics in the United States. In this
paper, the research will be done with the help of the dataset from NLSY79. The
dataset that is used to investigate the research question, dates from the
period of 1979 to 1994. All data has been conducted using US citizens. In the
beginning 3,003 males and 3,108 females participated in the sample; however,
for this research question, only 3000 observations will be used. In the
sixteen-year period, every two years the participants were interviewed. For
every participant, the education, training, employment, marital status,
fertility, health, child care and assets and income were held up-to-date.

Information of every participant is divided into four different categories. The
first category conducts personal variables of the participant are given: sex,
ethnicity, age (in 2002), educational background, marital status and faith. Secondly,
the respondent is asked about their family background, including years of
schooling of both parents; the possession of library cards in the family when
the respondent was 14; as well as the number of siblings and financial status
of the family (poverty). Lastly work-related information is asked from the
respondent, earnings, work experience, category of employment; place of
residence and pay set by collective bargaining.

It is noteworthy that some samples have been discontinued as they did not
correspond to the aim of having results representative for US citizens.

3.2 Operationalization of the variables
As the aim for this paper is to provide reliable results and confirm potential
relations, certain dummy variables, control variables and adjustments are
the sex of the respondent is of great importance, as in previous research from
Blau and Kahn (1994) a wage gap between males and females was found. The respondent’s
sex is processed as a dummy control variable: females receive the number 0 and
men receive the number 1.  
addition, it is of great importance to note that the BMI is an index
calculated on basis of the bodyweight and height of the respondent. The BMI serves
as an indication to what weight group the person is in, ranging from underweight
to obesity. A BMI under 18.5 is classified as underweight and a normal weight
is a BMI ranging from 18.5 to 24.9. Any value greater than 25 is considered
overweight and any value above 30 is classified as obesity. In this paper, the BMI serves as a dummy variable. If the
BMI of the respondent takes a value classified as either overweight or obese,
the value will be 0. For a BMI value ranging between underweight and normal
weight, the value is 1.                                                                               In order to estimate the
effect of BMI on income, it is of great significance to bear in mind that not
every respondent has the same profession. Consequently, the variable SkillType
will be regarded as a dummy variable. The variable will take the value of 0 for
white-collar work and 1 for blue and pink-collar workers.             Lastly,
due to the positive correlation between work experience and wages (Becker,
1994), the control variable of work experience is introduced.

 3.2 Overview of variables
In order to keep a proper overview of the dataset, the relevant variables are
listed in the table below, with their abbreviations and definitions.








A value based on one’s
height and bodyweight, indicating if a person is underweight, overweight,
“normal” weight or obese
The respondent’s weight measured in pounds (lbs)


The respondent’s height
measured in feet and inches


The respondent’s income
measured in USD ($) per hour


A variable indicating
whether the respondent is male or female



The category of profession
of the respondent


The respondent’s accumulated
working experience in years



The natural logarithm of

1: Variables used in the dataset and their abbreviations as well as definitions

3.3 Evaluating the models
The aim of this research is to evaluate whether there is any correlation
between BMI and level of income. As three theory models will be discussed, a
comparison of the results from the regression analysis will be drawn.

3.3.1 Regression

In order to
compare the different models, the multiple regression analysis will be applied
to every model. The three estimated equations have been created in such a way
that allows OLS to be applied as estimator for the coefficients. Statistical
properties provide information about the estimated equations and their
reliability. In this paper, the , p-values and t-statistics of the coefficients will provide
insight as to which model yields the most reliability.


 6 References
Becker, G. S. (1994). Human capital revisited. In Human Capital: A
Theoretical and Empirical Analysis with Special Reference to Education (3rd
Edition) (pp. 15-28). The University of Chicago Press.

Berndt, E. R.
(1991). The practice of econometrics: classic and contemporary. Reading:

Card, D. (1999). The causal effect of education on earnings. Handbook of
labor economics, 3, 1801-1863.

De Sousa, S. (2012). Does size matter? A propensity score approach to the
effect of BMI on labour market outcomes. EUI Florence.

Gates, D. M., Succop, P., Brehm, B. J., Gillespie, G. L., & Sommers, B. D.
(2008). Obesity and presenteeism: the impact of body mass index on workplace
productivity. Journal of Occupational and Environmental
Medicine, 50(1), 39-45.

Han, E., Norton, E. C., & Stearns, S. C. (2009). Weight and wages: fat
versus lean paychecks. Health economics, 18(5), 535-548.

Manning, A. (2000). Movin’on up: Interpreting the earnings–experience
profile. Bulletin of Economic Research, 52(4), 261-295.

Wee, C. C., Phillips, R. S., Legedza, A. T., Davis, R. B., Soukup, J. R.,
Colditz, G. A., & Hamel, M. B. (2005). Health care expenditures associated
with overweight and obesity among US adults: importance of age and race. American
Journal of Public Health, 95(1), 159-165.


1 In particular, professions that require interpersonal
skills, with presumably more social interactions, the negative relationship
between BMI and wages is amplified (Han, Norton & Stearns, 2009).