Investigating the Negative Relationship Between Wages and Obesity
Matt Trombley
Department of Economics
UNC – Greensboro
Abstract
A substantial literature has established that obesity is negatively associated with wages,
particularly among females. However, prior research has found limited evidence in support of
the mechanisms hypothesized to drive this relationship. Utilizing data from a single U.S.
telecommunications firm I add to the literature by testing for differences in productivity between
obese and non-obese workers, as well as the possibility of coworker and supervisor
discrimination, using control variables unavailable in prior studies. Consistent with previous
research, I find that obesity is negatively associated with wages among females. Results suggest
that differences in productivity account for over half of this wage penalty, but I find no evidence
that coworker or manager discrimination against obese employees is occurring among males or
females. However, I do find evidence that the wage-obesity penalty among females occurs only
among obese mothers, a result that may suggest differences in unobserved productivity between
obese and non-obese mothers.
October 2013
1. Introduction
Obesity is a substantial health issue in the United States, affecting 35% of adults in 2010
– a proportion projected to increase to 51% by 2030 (Ogden, et al., 2012, Finkelstein, et al.
2012).1 Finkelstein et al. (2009) estimate that in 2008 obesity accounted for 10% of medical
spending in the US at a cost of $147 billion. In addition to direct medical costs, obesity may
incur significant economic costs through lost productivity. Numerous studies link obesity to
greater rates of both absenteeism and presenteeism (i.e. reduced work effort while present on the
job), and it is estimated that in 2008 obesity-related absenteeism and presenteeism cost
employers $40.8 billion in lost productivity (Finkelstein, et al. 2010).2 Perhaps unsurprisingly,
obesity has been linked to lower wages across the demographic spectrum in the United States,
Europe, and even China (see e.g. Cawley, 2004; Greve, 2008; Shimokawa, 2008, Garcia and
Quintana-Domeque, 2007; Lundborg et al., 2007). Subsequent studies using instrumental
variables provide evidence of a causal relationship between body mass and wages that is not
attributable to reverse causality.3
Recently, studies have attempted to uncover the mechanism(s) that may be driving the
wage-obesity relationship. Because results from these efforts have either failed to fully account
for this relationship, or have been inconsistent across studies, establishing the mechanism(s)
behind the wage-obesity relationship remains an important research endeavor. While diminished
productivity provides a likely explanation it is useful to understand why obese workers might be
less productive than their non-obese counterparts. Though some inputs to employees’
1
Body Mass Index (BMI) is the standard scale for establishing a healthy weight to height ratio. BMI is defined as
the ratio of kilograms of weight to squared-meters of height. Healthy BMI is classified within the range [18.5,25),
while [25,30) is classified as overweight, and people with BMI > 30 are classified as obese.
2
For examples of studies regarding obesity and absenteeism/presenteeism, see Howard and Potter, 2012; Goetzel, et
al., 2010; Ricci and Chee, 2005; Tsai et al., 2008; Burton, et al., 1999; Tucker and Friedman, 1998; Schmier, Jones,
and Halpern, 2006; Finkelstein et al, 2010; Gates et al., 2008; Pronk et al., 2004
3
For a thorough review of the wage-BMI instrumental variables literature, see Kortt and Leigh, 2010.
1
production function are essentially immutable (e.g. innate ability), other inputs, such as health
stock, may be subject to change. To the extent that lost productivity is recoverable through
intervention there may be motivation for public or private (i.e. firm-level) health policy to
encourage healthier behaviors among employees. Furthermore, productivity may not tell the full
story.
One hypothesis advanced by Baum and Ford (2004) is that the wage penalty for obesity
that remains unexplained after controlling for typical human capital and demographic variables
may be attributable to discrimination. Carr and Friedman (2005) report that BMI is positively
correlated with perceived workplace discrimination (e.g. rudeness, being treated as less
intelligent, etc.) and that such workplace discrimination is more prevalent among white collar
workers. Obese workers are also more likely to be perceived as lazy, lacking in self-control, of
lower ability, and less likely to get along with and be accepted by coworkers and subordinates
(Rudolph, et al. 2009).
While the potential presence of systematic discrimination may seem more of a legal or
ethical dilemma than an economic one, the socio-economic implications are significant.
Nonwhites, less-educated individuals, and low-income females are more likely to be obese than
others (Ogden, et al. 2010; Flegal, et al., 2012). To the extent that vulnerable populations are
more likely to be obese, the wage effects of discrimination may have repercussions on income
equality or equity of opportunity. Additionally, discrimination may exacerbate productivity
losses, for instance if it limits beneficial cooperation in the workplace. Discrimination against
obese individuals has also been shown to increase the negative health effects of obesity (Schafer
and Ferraro, 2011) which may increase absenteeism or presenteeism costs to employers.
2
My study adds to the literature by attempting to identify the underlying mechanism(s)
that may be confounding the wage-obesity relationship, using detailed measures unavailable in
previous studies. Specifically, I test for differences in productivity between obese and normalweight employees that may be attributable to differences in human capital accumulation or to
differences in employee health (e.g. physical function, sleep quality, or cardiovascular fitness). I
also test for the possibility of peer or coworker discrimination.
This study utilizes a cross-section of employee-level data from a US telecommunications
firm, which offers a number of advantages over standard national-level datasets. First, the data
contain productivity measures not found in national-level data sets. These include detailed
measures of human capital accumulation, health, and a rating of worker presenteeism that may
capture previously unobserved differences in productivity between obese and non-obese
workers. Second, information on the firm’s organizational structure allows me to test if obese
workers are being discriminated against by their peers or supervisors. Third, all employees in
the dataset are high-income earners relative to the US population at large. The lowest paid
employee earns nearly $40,000 per year, and the median worker earns nearly $85,000 annually.
The high income of the sample greatly alleviates concerns about possible reverse causality
between low-income and obesity. Additionally the data contain precise measures of the
variables of interest. Measures of annual salary used to construct hourly wages are retrieved
from administrative records, and BMI measures are directly gathered by trained data collectors.
By eliminating self-reports of BMI and salary, the dependent variable and independent variable
of interest should be less subject to measurement error than in previous studies.4
4
As acknowledged by previous studies (e.g., Burkhauser and Cawley, 2008), BMI is an imperfect measure of
health, as ratios of weight to height do not perfectly account for body frame or muscle mass. To circumvent this
issue, several recent studies have substituted percent body fat or waist size for BMI in the regression equation
(Bozoyan and Wolbring, 2011; Wada and Tekin, 2010; Johansson et al., 2009). Unfortunately the available data do
3
Consistent with previous research I find that obese females earn significantly less than
normal-weight women: a penalty of nearly 7.0%. Controlling for measures of productivity
(human capital investment and health) reduces the estimated wage penalty to 3.5% and renders it
statistically insignificant. I find no evidence of peer or supervisor discrimination among females,
but do find evidence that the wage penalty only occurs among obese mothers, and that obese
females with no children may actually earn slightly more than normal-weight females.
I also find evidence that obese males earn more than normal-weight males. Controlling
for health actually increases the wage premium among males, suggesting that obese males are
more productive than normal-weight males, but that the negative health effects associated with
obesity diminish this productivity advantage. I reject the hypothesis of peer discrimination
against obese males but cannot rule out the possibility of supervisor discrimination. Unlike
females, obese males do not suffer any additional wage penalty after having children.
The remainder of the study is as follows. Section 2 discusses previous attempts to explain the
well-documented negative relationship between wages and obesity. Section 3 introduces the
econometric model, while Section 4 describes the data in detail. Section 5 reports the results of
tests for both differences in productivity and discrimination. Lastly, section 6 explores the
possibility that the wage-obesity relationship is correlated with household fertility decisions.
2. Background and Previous Literature
Numerous studies have examined the relationship between BMI/obesity and wages, with
initial research utilizing standard exogenous OLS methods to establish a negative correlation.
Register and Williams (1990) find that obese females face a significant penalty in hourly wages
compared to their normal-weight counterparts but do not find a wage penalty for males. Pagan
not contain measures of body fat or waist circumference. Since the majority of the literature uses the BMI measures
previously described rather than waist size, using BMI is consistent with providing the best comparison possible.
4
and Davila (1997) corroborate this result, reporting that obese males and females both earn
significantly lower than normal-weight workers, but that lower wages for males can be attributed
to obese males’ choice of occupation. Loh (1993) does not find a relationship between obesity
and wages for males or females but does find that obese males pay a penalty in the growth rate of
hourly wages. Looking specifically at females, Averett and Korenmann (1999) find that
overweight and obese females have lower wages than their normal-weight counterparts, although
the wage penalty is smaller among black females compared to whites. Recently, a subset of the
literature has shifted focus from estimating the relationship between BMI and wages to
identifying the possible mechanism(s) that may be driving the observed negative relationship
between BMI and wages (hereafter referred to as the “wage penalty”).
The most straightforward explanation for the wage penalty is that obese workers are less
productive than normal-weight ones. Baum and Ford (2004) suggest that one reason for
differences in productivity may be due to differences in human capital accumulation. For
example, individuals with high future discount rates are both more likely to forego investment in
health (and become heavier) and to invest less in training or studying that may lead to future
reward. To test for this effect, the authors include an interaction between obesity and tenure.
This allows average returns to experience to vary between obese and non-obese workers, which
may signal the presence of myopic behavior on the part of obese workers. Using data from the
1979 National Longitudinal Survey of Youth (NLSY79) the authors find that obese workers have
significantly lower returns to tenure, consistent with lower human capital investment (although
the authors concede it may be due to discrimination in training opportunities). 5 Atella et al.
(2008) attempt to recreate this result using data from the European Household Community Panel
5
Although the tenure-interaction model renders the obesity term insignificant in Baum and Ford (2004), no
subsequent study utilizing the NLSY79 has attempted to recreate these results.
5
(EHCP). Rather than tenure, obesity is interacted with an indicator variable for participation in a
training program. The authors find no evidence that returns to training vary by BMI category
among European workers.
A second productivity-related hypothesis is that obese workers are less productive due to
health limitations caused by obesity. Brunello and D’Hombres (2009) and Baum and Ford
(2004) control for health using a binary indicator for “poor health” or “health limitations,”
respectively, while Atella et al. (2008) use a measure of absenteeism (days of work missed in the
last four work weeks due to illness). All three studies fail to find evidence that obese workers
are less productive due to lower average health. Johansson et al. (2009) do find that a binary
indicator for “good health” is significantly correlated with wages among Finnish workers, but
inclusion of the measure only slightly attenuates the observed wage penalty for obesity.
Ultimately, no supplemental control for productivity has been able to fully account for the
observed wage penalty, leading researchers to consider alterative explanations.
Baum and Ford (2004) and Atella et al. (2008) both hypothesize that the wage penalty
remaining after attempts to control for employee productivity may be due to some form of
discrimination.6 Baum and Ford (2004) test for the possibility that the penalty is due to customer
discrimination (i.e. customers prefer to deal with non-obese rather than obese workers). To do
so, the authors create a binary indicator signaling that an employee works in a “customeroriented” job. This indicator is interacted with obesity to determine if obese workers in
6
A third hypothesis posits that total compensation for obese employees is consistent with normal-weight employees,
but that wages decrease as employers shift compensation to cover higher insurance premiums incurred by obese
workers but borne by the firm. Bhattacharya and Bundorf (2009) find strong evidence for this effect in the US, but
Baum and Ford (2004) find that obese American workers with employer-provided insurance actually earn more than
obese workers without it. Atella et al. (2008) also fail to find evidence for the insurance hypothesis among
European workers.
6
customer-oriented jobs earn lower wages than non-obese workers in the same jobs. However, the
authors find no evidence for discrimination by customers.
Two recent studies take this strategy a step further, finding evidence for the possibility of
customer or coworker discrimination. Han, Norton, and Stearns (2009) include a set of six
“interpersonal skills” (e.g. speak-signal, supervise, instruct), which are defined by the U.S.
Employment Service, and which correspond to the skill deemed most critical for a given
occupation. (For instance, economics professors require “instructing” skill, while non-academic
economists would require “speaking-signaling” skills.) The authors interact indicators for
overweight and obese with five of the six skills to test if heavier workers face different returns
for a given interpersonal skill. (“Taking instructions,” presumed to be the least “social” of the job
skills, serves as the reference category). The authors find that obese white and black women face
significantly higher wage penalties for two of the five social skills (“serve” and “speak-signal”),
but that obese Hispanic women actually earn higher wages than normal-weight women in
occupations using those two skill sets.
A similar approach is undertaken by Johar and Katayama (2012). The authors classify all
respondents as belonging to “social” or “non-social” occupations. Social occupations are those
deemed to require “authority” or “nurturance,” (e.g. management/supervision, sales, teaching,
customer service). Separate models are estimated for the social and non-social occupation
subsamples. The authors find that obese females face a wage penalty regardless of occupation
type, but that those in social occupations face nearly double the wage penalty as those in
nonsocial occupations. Obese males only face a wage penalty in social occupations, while those
in nonsocial occupations do not earn less than normal-weight males.
7
Although both studies find some evidence supporting a discrimination hypothesis neither
is able to discern the particular kind of discrimination occurring (customer, coworker, or
supervisor). The current data include information regarding each employee’s “work group,” a
group of employees that reports to the same manager. I can use this organizational information
to account for peer attributes that may affect coworker discrimination, as well as the possibility
of discrimination by an employee’s direct supervisor. Moreover, the data contain detailed
measures of physical function, cardiovascular health, and presenteeism that allow me to test for
differences in productivity between obese and non-obese workers to an unprecedented degree.
3. Econometric Model
The standard approach to estimating the effect of BMI on wages is an adaptation of the
classic Mincer (1974) wage model, which expresses the log of hourly wages as a linear function
of a vector of control variables (X) and the independent variables of interest (overweight and
obese).7,8 The relationship is described by the equation
ln(Wij) =
β + δ1OVERWEIGHTi + δ2OBESEi + εij
(1)
where OVERWEIGHTi and OBESEi refer to indicators for each of these two BMI categories,
with BMI < 25 (normal weight) serving as the reference category.9 Wij refers to the hourly wage
rate of individual i in work group j, and Xi is a set of standard individual-level controls outlined
in Section 4.2.
7
Several studies utilize semi- or non-parametric methods to account for a nonlinear relationship between BMI and
wages (Kline and Tobias 2008; Shimokawa 2008; Ruhm and Gregory, 2009; Hildebrande and Van Kerhm, 2010;
Kan and Jae-Lee, 2012 ) The current paper is concerned primarily with the mechanism driving the wage penalty,
rather than precise estimation of the distribution of the penalty. This is at odds with the semi- and non-parametric
literature in the field, and therefore such approaches are not considered in this study.
8
A separate branch of the literature utilizes a quantile regression approach to determine if the observed relationship
varies by level of income (Atella, Pace, and Vuri, 2008; Johar and Katayama, 2012). Given the homogeneity of
wages and occupations within the current sample, as well as the small sample size, this method is considered
inappropriate for the present analysis.
9
I omit the “underweight” category as only 5 respondents meet this definition. Therefore the normal-weight
category refers to anyone with a BMI of less than 25, rather than the strict definition of individuals with a BMI
between 18.5 and 25.
8
Estimates of δ = [δ1 δ2] are expected to be zero since clinical BMI classification should
not be correlated with wages per se. However, the set of basic controls may omit (or fail to fully
account for) factors that affect wages and are correlated with obesity, such as human capital
accumulation, health, or discrimination. Therefore, estimates of δ that are significantly different
from zero may indicate that ̂ is biased by one or more omitted variables. I assume that
εij =
γ + uij
(2)
where Zij is a vector of individual and group-level controls for productivity and/or discrimination
that are not contained in Xi. For one or more elements of Zij, corr(OBESEi,Zij|Xi) ≠ 0 so that bias
in the estimates of ̂ is attributable to some subset of Zij.10
Accounting for this possibility, the full equation of interest can be written as
ln(Wij) =
β + λ1OVERWEIGHTi + λ2OBESEi +
γ + uij
(3)
The residual error uij is a stochastic shock that satisfies the assumption E[uij|Xi,Zij] = 0. It is
assumed that uij may be correlated between individuals in the same group j and consequently, all
models cluster standard errors at the workgroup level.11
If Zij and BMI category are correlated then including Zij in the model should correct for
omitted variable bias and ̂ should be different in magnitude compared to ̂ I can determine
whether a given subset of Zij accounts for a significant portion of the estimated wage-obesity
relationship (̂) by testing the null hypotheses that ̂ 1 - ̂ 1 =
(and the same for ̂ 2 and ̂ 2). To
10
Some omitted variables cannot be accounted for by including a specific variable (i.e. a binary proxy for
discrimination) but can be tested for by allowing the relationship between ln(Wij) and Xij or Zij to vary between
obese and normal-weight employees through the use of interaction terms. In the interest of notational efficiency
these interactions were left out of Equation 3.
11
Due to the multilevel nature of the data, a hierarchical linear model (HLM) was considered. The HLM assumes
that the variance of wages is homoscedastic within workgroups, and also that the distribution of the variance is
known. The standard random effects approach loosens the assumption that the variance distribution is known but
retains the assumption of within-group homoscedasticity. Clustered standard errors do not require the within-group
homoscedasticity assumption. Choosing the least efficient but most general method for estimating standard errors
makes hypothesis testing as conservative as possible and the reported results more robust to faulty assumptions.
9
determine whether ̂ and ̂ are significantly different I jointly estimate equations (1) and (3). I
then use a Wald statistic to test the null hypothesis that ̂ and ̂ are identical.12
Rejection of this hypothesis provides evidence that a given subset of Zij is (one of) the
explanatory mechanism(s) underlying the wage-obesity penalty. This approach is an adaptation
of Baron and Kenny’s (1986) test for mediating effects. The key distinction is that I do not
hypothesize any direction of causality between Zij and BMI category. I am not testing whether a
variable mediates the wage-obesity relationship, but rather whether a given variable is correlated
strongly enough with both wages and obesity to cause significant bias in the estimated obesity
coefficient. Although statistical testing cannot “prove” the presence of omitted variables bias,
statistical evidence, coupled with valid theoretical consideration, can make a strong case for Zij
as an explanatory mechanism for the wage-obesity penalty. Elements of Zij include controls for
employee productivity (human capital accumulation and health) as well as controls for peer or
supervisor discrimination. These elements will be detailed in Sections 4.3 and 5.3.
4. Data
4.1 Work Family and Health Network
The data were obtained from the Work, Family, and Health Network (WFHN). The
WFHN was created by the National Institutes of Health (NIH) and Centers for Disease Control
and Prevention (CDC) in order to study the relationship between work, family life, and health
outcomes. The WFHN is comprised of four research centers, a translational coordinating center
(TCC), and a data and methods coordinating center (DCC). The four research centers are the
University of Minnesota, Penn State University, Harvard University in conjunction with Portland
State, and Michigan State in conjunction with Purdue University. The Kaiser Permanente Center
12
Wald tests are 1 tailed with p < 0.1 as the cutoff for “significant” difference. The residuals (ej) used to estimate ̂ 2
are clustered at the work group level. For K parameters in equation (1) and L parameters in equation (3), the
resulting covariance matrix for the estimates is KL x KL, accounting for correlation between X ij and Zij.
10
for Health Research serves as the TCC, and RTI International serves as the DCC. Members of
the WFHN were tasked with determining the health effects of an intervention intended to:
a) increase employees’ control over their work time and b) improve supervisor and coworker
support for employees’ family and personal lives. Two firms were selected for intervention, one
of which is an American telecommunications firm. A single cross-section of data from this firm,
collected prior to the intervention, is utilized in the present study.
The sample is limited to full-time, permanent (non-contractor) employees, each of whom
belongs to one of 115 “work groups.”13 The J work groups are collections of employees who
report to the same manager and may collaborate with each other frequently. Employees operate
at one of thirteen sites, and all sites are located in one of two urban locations in two separate
states.14 The average work group size in the sample is approximately 12. The average work site
hosts about 58 employees.
Employees are classified by the firm’s human resources (HR) department as either
support personnel (e.g. tech support, project coordinators) or core personnel (those directly
involved in the firm’s core business). Core personnel are further distinguished between staff and
senior employees. Within the firm there are also four broad job functions. Each workgroup
emphasizes or is entirely devoted to one of the four functions. Each employee in the sample is
designated by HR as belonging to one of four job categories based on the function emphasized
by her or his workgroup.15
13
The data do not contain any “blue collar” support personnel (e.g. custodians, security).
In addition to work site indicators all models include an indicator for those who did not report a work site.
15
The distinction between core/support and job category assignment are defined for the benefit of researchers and
are not official administrative divisions. However, these divisions represent real and significant differences between
tasks performed and potentially the compensation that accompanies each task. Disclosure of details regarding the
four job functions is prohibited.
14
11
4.2 Standard Variables
Consistent with the literature, the dependent variable of interest is the natural log of
hourly wages. Annual income is obtained from administrative records, and annual hours are
constructed by multiplying self-reported average weekly hours by 52. Hourly wages (Wij) are
the ratio of annual income to annual hours. Controls for hourly wage (Xij) include age, tenure
with the firm, education, race, nativity, married/cohabitating status, number of children, job
category, an indicator to differentiate between support and core employees, and indicators for
state and worksite.16,17,18 Following the literature, separate models are run for males and females.
Observations with missing data for BMI or salary are dropped from the sample.19 All
missing observations are assumed to be missing at random (Rubin, 1976) and the observed
subsample is thus representative of the full population of non-executive, white-collar employees
within the firm. Due to the small sample size, extra attention is paid to outliers that may have an
undue influence on the parameter estimates. As a final step I drop any observation that fulfills
both of two conditions: belonging to either the top or bottom 1-percentile of BMI, and belonging
to either the top or bottom 1-percentile of log hourly wages. This resulted in one additional male
and two additional females being dropped, leaving a final sample of 452 men and 295 women.
Summary statistics are provided in Tables 1a (females) and 1b (males).
Respondents’ annual salaries average about $82,000 for females and $88,000 for males:
equivalent to hourly wages of roughly $35 for females and $38 for males. The majority of the
16
The binary division between support and core workers is a qualitative distinction in job type not captured by the
four job categories. The difference between staff and senior workers may reflect differences in ability or human
capital accumulation and therefore this distinction is not considered until later models in Section 5.3.
17
A common strategy in wage models is to include a squared age term, but including a squared term to the
specification rendered both the linear and squared term insignificant, and the linear and quadratic terms were jointly
insignificant. Therefore only a linear age term was adopted in the final model.
18
Race enters the equation as a binary white/nonwhite variable. Roughly half of non-whites are Indian-Asians,
while about 20% are “Other Asian”, 10% are African American, and the remainder are Pacific Islander, Native
American, or “Other.”
19
See Appendix I for information regarding missing data.
12
sample has at least a four-year degree with virtually the entire remainder having some college
education and only a few holding just a high school diploma.20 Employees are generally midcareer with a mean age of about 46 (minimum 26, maximum 64) and 12-15 years of experience
with the current company. Roughly 25% of the sample is nonwhite, while about 32% of males
and 19% of females were born abroad. Approximately 85% of males and 71% of the females are
married or cohabitating, and both subsamples have slightly less than two children, on average.
Both men and women have an average BMI of about 28, about 1.5 units higher than the
national average, with females having a slightly higher variance. About 32% of females and
42% of males are clinically overweight (< 25 BMI < 30), while about 30% of males and 32% of
females are clinically obese (BMI > 30). The proportions overweight and obese are roughly
consistent with unadjusted national averages (except for the slightly high proportion of
overweight males).21
Comparisons between obese and normal-weight employees indicate significant
differences between the two subsamples. Compared to their normal-weight coworkers, obese
females are significantly less likely to be married, nonwhite, or born abroad. The average obese
female is also about three years older than her normal-weight peers, with an extra two years of
tenure with the present firm. Despite the additional experience, obese females earn nearly $3 less
per hour than normal-weight women, a significant difference equivalent to nearly 1/2 of a
20
Testing found no significant difference in wages between those with just a high school diploma and those with
some college but no bachelor’s degree. Individuals without a bachelor’s degree do not appear to have diminished
standing with the firm. Employees without a degree are more likely to be classified as senior than staff, and are
represented across all four job classes. The primary difference is that employees without a degree have been with
the company longer, suggesting a longer path to promotion, or the possibility that older employees were
“grandfathered” in before more rigorous selection was enacted.
21
Parametric studies typically model the relationship between BMI and wages by utilizing a linear measure of BMI,
possibly paired with a quadratic term, or by creating a set of categorical BMI indicator variables defined as
underweight (BMI<18.5), overweight (25<BMI<30), or obese (BMI>30), with the normal range (18.5 < BMI < 25)
as the reference group. Testing suggests a nonlinear relationship between wages and BMI that is best captured by
the categorical measures.
13
standard deviation. However, obese females are nearly 20% less likely to have completed a 4year degree, and 10 percentage points less likely to be “senior” core personnel. Among males,
obese workers actually earn significantly more than their normal-weight peers, a difference of
$1.50, or about 1/5 of a standard deviation. Obese males are significantly less likely to be
nonwhite or born abroad, and also significantly less likely to have finished college. Like females,
obese males in the sample are roughly three years older with an additional two years of
experience with the firm.
4.3 – Additional Controls for Productivity
The control variables (Xij) in Section 4.2 are those that are included as standard in
virtually every wage model in the wage-obesity literature. However, these controls may be
insufficient to fully account for differences in productivity between obese and normal-weight
workers (as evidenced by the significant wage-obesity relationship typically estimated in the
literature). The WFHN data include additional controls (Zij) for employee productivity that
allow me to more precisely account for differences in productivity that remain after accounting
for the standard controls.22
The additional productivity measures allow me to consider the two productivity-based
hypotheses introduced earlier (human capital investment and health). The first set of variables
controls for possible differences in accumulation of occupation-specific human capital, i.e. skills
and abilities that directly translate to the job (and which should entail a mixture of workplace
experience and natural ability). Core personnel in the data are differentiated between “staff” and
“senior” level workers. Senior level workers do not possess administrative authority over staff
workers. Rather, senior status indicates a meaningful accumulation of human capital that has
22
Tests for discrimination rely on organizational data rather than specific variables. The approach for these methods
will be outlined in Section 5.3.
14
resulted in one or more promotions. 23 Indicator variables accounting for this division should
capture potential differences in human capital accumulation between obese and non-obese
employees. Among both males and females roughly 60% of employees have reached senior
status, and approximately 35% are staff level (with the remainder belonging to support
positions). Obese females are significantly less likely to be senior-level employees, a difference
of nearly 10 percentage points, although this discrepancy does not hold among males.
The second set of variables contains proxies for facets of health that may affect
productivity, including physical function, sleep, and cardiovascular health. The first health
proxy is a comprehensive measure of physical function, rated on a scale from 0-100. This
variable is constructed from nine separate questions, which assess self-reported limitations on
everyday activities such as walking up stairs, carrying groceries, and bending/stooping, among
others. Responses may be either “Yes, limited a lot”; “Yes, limited some” or “No, not limited at
all.” The responses are transformed by the DCC (RTI, International) into a 0-100 scale based on
scoring devised by the RAND Corporation. 24 A score of 100 reflects full functionality (able to
run and play sports) and 0 is barely functional (health severely limits all everyday activities).
Although software development is not a physically strenuous job, it is still reasonable to assume
that diminished physical function may affect on-the-job performance. In general, the sample is
highly functional, with average ratings over 90%. However, obese employees are significantly
limited compared to their normal-weight coworkers, a difference of 10 percentage points
23
Staff may be either “Staff I” or “Staff II” and seniors as “Senior I” or “Senior II”. Therefore someone joining the
firm as a Staff I would need two promotions to reach Senior I, while someone hired as a Senior I could be promoted
and still appear in the data as a “Senior.” The distinction between sub-levels I and II is unavailable in the data.
24
The questions comprising the physical limitations measure are derived from the Medical Outcomes Study 36-Item
Short-Form Survey (Ware and Sherbourne, 1992). Scoring from 0-100 is based on the RAND 36-Item Health
Survey 1.0 (www.rand.org/health/surveys_tools/mos/mos_core_36item_scoring.html) .
15
(roughly 1 standard deviation) among females and 5 percentage points (roughly ½ a standard
deviation) among males.
Another way in which obesity may result in reduced productivity is if obesity
substantially disrupts sleep. Obesity is significantly correlated with obstructive sleep disorder
(aka “sleep apnea”), which in turn has been associated with decreased cognitive function
(Engleman and Douglas, 2004; Ulfberg et al., 1996; Vgontzas et al., 1994). Although the data
lack an exact measure of sleep apnea, they do contain a self-reported indicator variable for “loud
snoring.” The indicator denotes positive response to the question “During the past month, have
you ever snored loudly, or been told you were snoring loudly?”25 Severity of snoring has been
identified as one of the primary predictors of sleep apnea among obese patients (Vgontzas et al.,
1994). Therefore, controlling for loud snoring may serve as a proxy for the effect of obesity on
sleep. Approximately 1/4 of females and 1/3 of males report loud snoring. Females are more
likely to snore heavily by 30 percentage points, while for males the differences is 15 percentage
points.
The final set of health variables contains four measures of cardiovascular health including
blood serum levels of C-reactive protein (CRP), a biomarker for inflammation; blood serum
levels of cholesterol; blood pressure; and heart rate. 26 All four of these measures capture
elements of risk for cardiovascular disease, as well as overall cardiovascular fitness. Measures
of cholesterol are transformed into a ratio of total to HDL (“good”) cholesterol, a measure that is
25
This question distinguishes people who have reported snoring (but not loudly) from people who have reported
“loud” snoring. Nested within the “loud snoring” indicator are respondents who have “Snorted/gasped” or “stopped
breathing/struggled for breath.” These sub-measures are not considered separately due to the small number of
respondents (particularly females) who suffer from these conditions. Moreover, analysis by Maislin et al. (1995)
suggests that loud snoring is nearly as correlated with apnea as snorting/gasping, and more correlated with apnea
than stopped breathing/struggled for breath.
26
Data for C-Reactive protein and cholesterol are missing for 7% and 13% of the sample, respectively. Several
observations were also missing data on heart rate. Since these are not the variables of interest and are assumed to be
missing at random, missing values were imputed using a modified regression-based EM algorithm. See Appendix 1
for details.
16
more strongly correlated with heart disease than total cholesterol or LDL (“bad”) cholesterol and
that may better capture the negative lifestyle behaviors associated with BMI (Kinosian et al,
1994). Measures of both CRP and the cholesterol ratio appear to follow a lognormal distribution
(and to be heteroskedastic in relation to log wages), and so these variables are transformed by the
natural log before entering the model. Measures of blood pressure and heart rate refer to the
mean value of three measures obtained on three separate days. The average blood pressure
measure is used to construct an indicator for high blood pressure (hypertension), which refers to
systolic pressure greater than 140 mmHg or diastolic pressure greater than 90 mmHg.
Among both males and females, obese employees have significantly worse measures of
cardiovascular health compared to their normal-weight coworkers. Obese females measure
nearly one standard deviation higher for cholesterol ratio and CRP levels, and are approximately
25 percentage points more likely to have clinically high blood pressure. Females also have
significantly higher heart rates, although the difference is not substantial (about 2.5 beats per
minute: less than 1/5 a standard deviation). Obese males also have higher measures of
inflammation and cholesterol ratio compared to normal-weight males, equal to approximately1/2
of a standard deviation. Obese males are also 25 percentage points more likely to have high
blood pressure by, and have a heart rate approximately 5 bmp (1/2 standard deviation) higher
than normal-weight employees. Taken together, the set of health variables suggests that obese
workers are less healthy than normal-weight employees in physical function, cardiovascular
health, and potentially in quality of sleep. Incorporating these measures should capture
differences in productivity that are attributable to health.
To account for unexplained differences in productivity that may remain, I include one
additional measure of productivity. Relative presenteeism indicates workers’ self-assessment of
17
their performance relative to workers with similar jobs. This performance measure does not
control for any specific hypothesized cause of differences in productivity but may serve as a
general indicator for whether differences in wages are attributable to differences in productivity.
The variable is reported as a ratio of an individual’s perception of their own job performance and
the perceived “average” performance for somebody else with their job (each rated on a scale of
1-10, with 1 being poor performance and 10 being exemplary). The relative presenteeism
measure was initially developed by the World Health Organization in order to quantify the
workplace costs of health problems in terms of reduced job performance. Tests of validity show
a significant and monotonic relationship between the presenteeism self-assessment and
employer-based ratings (e.g. peer assessments, supervisor assessments, or work audits) among a
variety of occupations (airline reservation agents, customer service representatives, automobile
company executives, and railroad engineers). (Kessler, et al., 2003). Summary statistics indicate
that obese females report significantly lower levels of productivity, although the difference is not
substantial (equivalent to approximately 1/5 of a standard deviation). Among males obese
employees do not appear to be less productive than their normal-weight coworkers, although an
effect may net out conditional on other control variables. Tests for differences in productivity
between obese and normal-weight workers are provided in Section 5.2.
5. Results
5.1 –Baseline Results
Results from the initial model containing only standard control variables (equation 1) are
reported in Table 2. Among females, overweight employees earn approximately 5.7% less than
those in the normal-weight category, while obese employees earn 7.0% less, on average. Neither
overweight nor obese males earn significantly different wages to their normal-weight coworkers.
18
The lack of a significant wage-obesity relationship for males is consistent with the literature,
which has found mixed results regarding the BMI-wage relationship among men. The most
plausible explanation is that BMI is a more imperfect measure of physical health for males, since
BMI does not distinguish muscle from other body mass. It may also be the case that the wageBMI pathway operates differently for males versus females, a possibility that will be explored
further in the remainder of Section 5. Tables 3a and 3b report the results of tests for the
productivity hypotheses (human capital accumulation, health) among females and males,
respectively. Results examining the peer and supervisor discrimination hypotheses for both
males and females are reported in Table 4. For the sake of brevity, only results for obese
workers relative to normal-weight workers will be reported in results tables moving forward. All
subsequent models include indicators for overweight, and all interaction models include an
overweight interaction term in addition to the reported obese interaction term. Results for
overweight employees are qualitatively similar to those for obese ones.
5.2 – Productivity
The unexplained gap in wages between obese and normal-weight employees suggests
that there may be a significant difference in the average productivity of employees in each BMI
category. There are two broad hypotheses that may account for this difference in productivity.
The first is that normal-weight and obese workers have different levels of human capital
accumulation that cannot be discerned by standard control variables. The second is that obese
workers have poor health compared to normal-weight employees, which leads to missed work, or
reduced productivity while at work. Tests for these two hypotheses are conducted one at a time
in order to better distinguish the exact mechanism that may be biasing the obesity coefficient.
Mechanisms that are found to be significantly correlated with wages are then combined to see if
19
a full model of productivity is able to completely account for the wage-obesity penalty estimated
in Model (1).
Controls for the human capital hypothesis enter the model in one of two ways. Consistent
with Baum and Ford (2004) and Atella et al. (2008) I include an interaction between job tenure
and the overweight and obesity indicators (Model 2). This allows returns to experience to vary
by BMI category. The interaction coefficient allows me to test whether returns to tenure differ
between obese and normal-weight workers. For the second approach, I replace the indicator for
“core” jobs with two indicators differentiating between staff and senior positions (Model 3). The
division between the staff and senior level provides a distinct indication of overall human capital
accumulation. If obese employees accumulate human capital at a different rate than normalweight employees (or if they are discriminated against in promotion), they may be
disproportionately represented at the staff level. If this is the case, controlling for the staff/senior
distinction should attenuate the coefficients for the overweight and obese indicators.
Model 2, which includes interactions between tenure and overweight/obese, does not
provide any evidence that obese males or females receive different returns to tenure within the
firm. For both males and females the tenure-interaction coefficient is insignificant, and the
change in the obesity coefficient is also statistically insignificant.27 Accounting for the
distinction between staff and senior employees in Model 3 fails to explain any substantial
amount of wage differences between obese and normal-weight males, although both staff and
senior employees earn significantly more than support personnel. Among females staff
27
To consider the total horizon of professional time, I construct a variable “potential experience” (PE) where
PE = age-22 for college graduates, PE = age-20 for those with some college, and PE = age-18 for those with just a
high school diploma. Replacing tenure and tenure-obesity interactions with PE and PE-obesity interactions yielded
qualitatively similar results to the tenure-interaction model, albeit with coefficients slightly larger in magnitude.
However, in the PE model, neither the interactions nor the overweight/obesity coefficients are significant among
males or females.
20
employees do not earn significantly more than support personnel, but seniors earn roughly 23%
more per hour. Inclusion of these variables decreases the magnitude of the estimated wageobesity relationship from -7.0% to -5.5%, a statistically significant reduction. This suggests that
differences in human capital accumulation account for nearly 1/4 of the observed wage penalty
among females.
To account for the remaining wage differences I also test for three facets of health that
may be biasing the estimated relationship between wages and obesity. These include physical
function, sleep quality, and cardiovascular fitness. Model 4 includes a measure of physical
function to determine if limitations in daily activity result in reduced productivity on the job,
while Model 5 introduces an indicator for loud snoring to test whether poor sleep quality is
associated with productivity. Model 6 adds measures for cholesterol ratio and C-reactive
protein, as well as heart rate, and an indicator for hypertension, in order to test whether
cardiovascular health may be correlated with productivity on the job. Results for the
productivity models are reported in Tables 3a (females) and 3b (males).
Model 4 shows that differences in productivity are not attributable to physical limitations.
The measure for physical limitations is insignificantly correlated with wages, and does not
change the coefficient for overweight or obese males or females. Introducing an indicator for
loud snoring in Model 5 also fails to significantly alter the overweight or obese coefficients
among males or females, and the coefficient on loud snoring is insignificant. Adding controls
for cardiovascular health (Model 6) significantly attenuates the wage penalty for obese females
but leaves a significant penalty of -5.5%. Among males controls for cardiovascular health
increase the wage premium for obesity from 2.2% to 6.6%, a significant change. This suggests
that obese males with average cardiovascular health are more productive than their normal-
21
weight coworkers, but that the diminished cardiovascular health that often accompanies obesity
means that the average obese male is not significantly more productive than a normal-weight
male. Overall, cardiovascular health seems to be a significant omitted variable among both
males and females.
Model 7 combines controls for cardiovascular health, loud snoring, and the indicators for
staff and senior to determine if a joint model of productivity is able to explain a significant
portion of the wage-obesity relationship. (Physical function and tenure-interactions are omitted
due to their lack of correlation with wages). Among males the health and human capital variables
essentially cancel each other out. Controlling for human capital accumulation reduces the wage
premium significantly, whereas including controls for cardiovascular health significantly
increases the wage premium. The net effect is a small positive increase that leaves a large but
insignificant wage premium of 3.7%. For females the combined model reduces the obesity
penalty from -7.0% to -3.3%, a significant reduction.
Model 8, the final productivity model, controls for relative presenteeism to see if ratings
of productivity on the job can account for the large remaining differences in wages between
obese and normal-weight employees. Among both males and females the presenteeism measure
is uncorrelated with wages, and does not alter estimates of the overweight or obesity coefficients,
indicating that presenteeism is unable to account for potential remaining differences in
productivity. Although the female wage penalty and male wage premium are statistically
insignificant, the sign and magnitude of the estimates are not inconsistent with the previous
literature, suggesting that meaningful unexplained wage differences may still exist between
obese and normal-weight employees. Consistent with prior research I consider the possibility
that the remaining differences in wages may be attributable to discrimination.
22
5.3 Discrimination
This section utilizes three approaches to test for possible discrimination. The first two
use work group-level measures to test for coworker discrimination. Work group-level measures
include the proportion of obese coworkers in an employee’s workgroup, and the proportion of
the work group of opposite gender in each employee’s workgroup. 28,29 Research has shown that
individuals are more likely to discriminate publicly when it is considered socially acceptable to
do so (Crandall, Eshelman, and O’Brien, 2002). Presumably as the proportion of obese workers
in a work group increases, the acceptability of discrimination towards obese members of that
work group will diminish. The proportion of work group members of the opposite gender is also
hypothesized to affect the probability of discrimination. The most likely explanation is that
discrimination towards obese individuals may be at least partially discrimination based on
attractiveness, the social norms of which may be more likely to be applied to the opposite
gender. However, the possibility that workers discriminate more against obese coworkers of the
same gender is left open. The peer discrimination models include an interaction between the
indicators for overweight/obese and the measures of proportion opposite gender and proportion
obese. This provides a test of whether the wage penalty varies based on either workgroup
characteristic. The third discrimination model utilizes workgroup fixed effects to test whether
unobserved work group attributes are driving the wage penalty among obese workers. 30 Recall
that each work group reports to a single manager. Therefore key unobserved work group
28
The sample mean of the workgroup characteristics is consistent with the sample mean of the characteristics
themselves (i.e. the mean proportion of obese coworkers across workgroups equals the proportion of the overall
sample that is obese).
29
An attempt was made to generate gender-specific obesity proportions, but there were several workgroups that had
only one male or female, making this approach infeasible.
30
Hausman testing rejects the pooled model in favor of workgroup fixed effects. However, the pooled model is
maintained for two reasons: first, as seen in the results, fixed effects reveal a positive rather than negative bias,
suggesting that the biased pooled results are a conservative estimate. Second, the fixed effects model substantially
reduces the available power making it virtually impossible to test for any of the proposed underlying mechanisms.
23
characteristics include the attributes of the manager and her or his potential attitude towards
obese subordinates. The first two discrimination models should account for the primary channels
through which coworker discrimination would manifest. Therefore the work group fixed effects
model can be thought of as a manager fixed effects model that is able to account for direct
supervisor discrimination. Results of the discrimination models are reported in Table 4.
Models 9 and 10 in Table 4 show that among females there is no evidence for coworker
discrimination. The proportion of male coworkers in a work group is insignificantly correlated
with wages, and the coefficients on the interaction terms do not indicate that the relationship
between work group gender composition and wages varies by BMI category. Controlling for the
proportion of obese coworkers in one’s work group reduces the observed wage penalty to -1.5%,
although this is not a significant change from the estimate in Model (8). The proportion of obese
coworkers is insignificantly correlated with wages, and the coefficient for the obesity interaction
is also insignificant. Moreover, the sign of the interaction coefficient for both overweight and
obese employees is the opposite of the hypothesized direction. The wages for both obese and
overweight females diminishes as the proportion of obese coworkers increase.
Among males the proportion of females in the workgroup (Model 9) has no effect on the
wage of overweight or obese males. However, as the proportion of obese workers in a group
increases, the wages of overweight and obese males actually fall significantly (Model 10). In
this model an obese male with no other obese group members earns a significant wage premium
of approximately 10%, while obese males in a group comprised entirely of obese coworkers earn
roughly the same as normal-weight males.
It is unclear what is causing this result. If work groups comprised almost entirely of obese
workers are less productive as a group, then higher proportions of obesity should be negatively
24
correlated with wages among normal-weight workers as well. An alternative explanation is that
normal-weight workers are able to outperform more of their immediate peers if they are in a
work group with a high proportion of obese coworkers. If this were the case, then overweight
workers should also perform better in comparison in comparison to obese employees. However,
the overweight interaction term is actually more negative than the obesity interaction for both
males and females, suggesting that whatever factor is diminishing wages for heavier employees
the effect is greater for overweight than obese employees. While the underlying explanation of
these results is uncertain, the results do not suggest that peer-level discrimination is an issue
within the sample. I turn next to the possibility of supervisor discrimination.
Controlling for unobserved workgroup (manager) attributes (Model 4) increases the wage
premium among obese males from 3.7% to 4.9%. The overweight coefficient has an equally
substantial shift in magnitude but does not become statistically significant. Although the
direction of bias from unobserved workgroup attributes is consistent with manager
discrimination, it would seem contradictory for obese males to be discriminated against and still
earn more than their normal-weight peers. It is possible that obese males are more productive
than normal-weight males, but that the full premium earned from this productivity difference are
only realized under non-discriminating managers. Controlling for work group fixed effects
among female employees increases the magnitude of the wage penalty by approximately one
percentage point for both overweight and obese employees. This suggests that the coefficients in
the pooled work group model are positively biased - the opposite of what would be expected if
manager discrimination was a dominant factor, and a result at odds with the results from the
male fixed effects model.
25
The combined results of the discrimination models suggest that the wage penalty among
female employees is not attributable to mistreatment at work, while obese males appear to earn a
wage premium and may be subject to supervisor discrimination. The final section considers the
possibility that the female wage penalty and male wage premium that remains unaccounted for
may be attributable to household fertility decisions. Although this is expected to affect females
more than males, males are included in the analysis for comparison of the results.
6 - Children
One hypothesis that has not been previously considered in the literature is that having
children is a mechanism that may both increase BMI and decrease wages. Such a mechanism
would help to explain the discrepancy in wage penalty between obese males and females
sometimes encountered in the literature (e.g., Cawley, 2004; Greve, 2008; Johannson, 2009;
Hildebrande and Van Kern, 2010). Women have larger biological roles in pregnancy and
childbirth that may affect body mass or disrupt human capital accumulation, and in the US
women still handle the majority of child care responsibilities (Craig, 2006). Children are a
standard control variable in the literature and therefore not the source of any omitted variables
bias. What has not been accounted for is the possibility that the relationship between children
and wages differs between obese and normal-weight women, or that the relationship between
wages and obesity differs between mothers and non-mothers.
Research has suggested that women who are obese in early adulthood are less likely to
ever have children and that those who do will have fewer children than mothers who were not
obese in early adulthood (Frisco and Weden, 2013; Frisco et al., 2012). Therefore measures of
fertility may provide information on the timing of obesity. The timing of obesity may influence
the wage-obesity relationship in ways that can be captured by this variation. To control for this
26
possibility I estimate equation (3) treating interactions between overweight/obese and the total
number of children the respondent has ever had as Zij. Results are presented in Table 5.
Results from Models 12 and 13 show that omitting children from the model does not
have any effect on the estimated obesity coefficients for males or females. As expected,
including an interaction between children and obesity (Model 14) does not produce any
significant results among males. However, results from Model 14 demonstrate a significant
difference in the wage-obesity relationship between obese mothers and non-mothers. Obese
females appear to pay a significant penalty of over 5% for each additional child. Obese females
without children no longer face any wage penalty, and may in fact earn a wage premium. This
appears to be a binary distinction: including an interaction between obesity and an indicator for
“any children” results in an estimated wage penalty of 9.5% for the average obese mother. These
results do not hold for overweight females, who face no additional wage penalty for fertility.
Moreover, overweight females without children continue to incur a wage penalty of
approximately 4%, although the estimate remains insignificant.
These results suggest that the timing of obesity has important implications for future
wages. Females who are obese in early adulthood, at the beginning of their careers, do not seem
to encounter any wage penalty relative to their normal-weight coworkers (although the current
model cannot control for the ways in which early obesity may affect decisions to attend college
or choice of career). However, females who do not begin their careers obese but become obese
in the intervening years after having one or more children seem to bear the entire observed wage
penalty in the current sample.
The results also indicate that there are important underlying differences between mothers
who become obese and those who do not since there is no wage penalty associated with children
27
among normal-weight mothers. The most likely explanation is that the process of becoming
obese is a manifestation of unobserved differences in productivity between normal-weight and
obese mothers. Females who are more efficient at work may also receive greater returns to
investment in health.
It may be possible that these differences between obese and normal-weight mothers
manifest as differences in human capital accumulation, although accounting for the distinction
between staff and senior employees cannot account for the wage penalty attributed to obese
mothers. It may be the case that obese mothers are more likely to be single mothers, which may
influence both body mass and productivity. However, controlling for single-motherhood (Model
15) does not significantly change the coefficient on the child-obesity interaction, suggesting that
having a spouse present is not the primary factor that differentiates obese mothers from normalweight mothers. The exact mechanism underlying this result remains an issue for future
research.
7. Conclusion
Consistent with previous literature I find that a negative relationship exists between
wages and obesity among females in the current sample, and find some evidence for a wage
premium among males. To explain this result, I test for differences in productivity between
obese and normal-weight employees. I find evidence that human capital investment and
cardiovascular health are both key omitted variables. Controlling for these factors explains over
half of the wage penalty among obese females, and actually reveals a significant wage premium
among obese males. In particular, controls for cardiovascular health reduce the wage penalty
among females by approximately 1.5 percentage points, and increase the wage premium among
28
males by nearly 4 percentage points. This suggests a substantial portion of productivity losses
attributable to obese workers may be recoverable through appropriate health intervention.
There is some evidence for supervisor discrimination among males. Accounting for
unobserved work group attributes using a work group fixed-effects approach suggests that the
obesity coefficient estimated in the pooled work group model is negatively biased. Among
females unobserved work group characteristics positively bias the obesity coefficient from the
pooled model, which calls into question whether any supervisor discrimination is occurring, or
whether the negative bias in the male obesity coefficient is attributable to some other source.
However, I find no evidence for peer discrimination among males or females. Subsequent
analyses indicate that obese females with children are the primary recipients of the wage-penalty
for obesity in the sample: obese females without children may actually earn more than normalweight women, while obese mothers incur a roughly 5% wage penalty per additional child, on
average. The difference between obese mothers and obese non-mothers is likely attributable to
differences in the timing of obesity: obese females with no children or fewer children are more
likely to have been obese at early adulthood, whereas females with more children are likely to
have become obese after having one or more child. It is uncertain why this distinction in the
timing of obesity results in a wage penalty, as controls for human capital are unable to account
for any of the observed difference.
The wage difference between obese mothers and normal-weight mothers is likely
attributable to unobserved differences in productivity. However, this discrepancy cannot be
ascribed to human capital accumulation or single-parenthood.31 Uncovering the exact
mechanism driving the productivity difference between obese and normal-weight females is a
31
Although without a full panel of adulthood it is impossible to account for past spells of single-motherhood that
may have affected investments in health or interruptions in work experience.
29
promising avenue for future research: a better understanding of this mechanism could yield
valuable information for employers or policymakers wishing to improve maternal health and
well-being.
These results may also inform research on women’s wages in other branches of the
literature. For instance, the motherhood wage penalty established in the labor literature (see e.g.
Budig and England, 2001) may fall more heavily on obese than normal-weight women. Lastly,
these results may provide guidance for future wage-obesity analyses at the national level. If the
frequently observed wage penalty for obese women is primarily attributable to obese mothers
across region and industry, this may provide justification for public policy aimed at promoting
maternal health. Such policy could help to recover some of the lost productivity and medical
spending currently attributable to obesity.
30
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35
Standard
Variables
(X)
BMI
Overweight
Obese
Hourly Wage
Log Hourly Wage
Married or Cohabitating
Total Number of Children
Nonwhite
Born Abroad
College Graduate (4-year)
Age
Tenure with Firm (years)
Support Personnel
Productivity Staff
Controls
Senior
(Z)
Physical Function
Loud Snoring
Presenteeism
C-Reactive Proteina
Cholesterol Ratiob
Hypertension
Heart Rate
Table 1a – Summary Statistics Female
Full Sample (n = 295)
Obese (n = 92 )
Mean
Standard
Mean
Standard
Deviation
Deviation
28.28
6.61
36.05***
5.68
31.18%
0.46
31.18%
0.46
35.23
6.96
33.63***
7.04
3.54
0.20
3.49***
0.21
70.85%
0.46
54.34%***
0.50
1.63
1.24
1.73*
1.34
24.07%
0.43
17.39%*
0.38
19.32%
0.40
8.70%***
0.28
66.44%
0.47
55.43%***
0.50
46.88
8.38
48.49**
7.68
16.04
9.94
17.66**
10.19
5.76%
0.23
8.70%
0.28
36.27%
0.48
38.04%
0.49
57.97%
0.49
53.26%**
0.50
92.63%
13.38
87.14***
17.69
25.76%
0.44
40.22%***
0.49
1.13
0.22
1.10*
0.19
3.06
4.34
5.74***
6.24
3.87
1.09
4.17***
1.18
31.18%
0.46
43.48%***
0.50
72.35
11.10
73.66*
12.39
Normal Weight (n=111)
Mean
Standard
Deviation
22.44
1.75
36.69
3.59
72.97%
1.48
27.03%
27.03%
75.68%
45.11
15.01
5.41%
32.43%
62.16%
96.00
10.82%
1.14
1.53
3.61
18.92%
71.37
6.74
0.19
0.45
1.19
0.45
0.45
0.43
9.14
9.96
0.23
0.47
0.49
7.61
0.31
0.21
2.25
0.94
0.39
10.58
Notes: a: C-Reactive Protein is measured in mg/L. For reference, CRP levels below 1.0 are considered low-risk for heart disease, 1.0-2.99 is considered average
risk, and greater than 3.0 is high risk. b: A cholesterol ratio below 3.5 is considered optimal, 3.5-5 is normal, and >5 is considered high.
Standard
Variables
(X)
BMI
Overweight
Obese
Hourly Wage
Log Hourly Wage
Married or Cohabitating
Total Number of Children
Nonwhite
Born Abroad
College Graduate (4-year)
Age
Tenure with Firm (years)
Support Personnel
Productivity Staff
Controls
Senior
(Z)
Physical Function
Loud Snoring
Presenteeism
C-Reactive Proteina
Cholesterol Ratiob
Hypertension
Heart Rate
Table 1b – Summary Statistics Male
Full Sample (n = 452)
Obese (n = 135 )
Mean
Standard
Mean
Standard
Deviation
Deviation
28.10
4.90
33.74***
4.38
42.92%
0.50
29.87%
0.46
37.70
7.81
38.65*
8.36
3.61
0.22
3.63*
0.23
84.74%
0.36
85.93%
0.35
1.64
1.43
1.83**
1.49
29.20%
0.46
17.04%***
0.38
31.89%
0.44
16.30%***
0.37
83.63%
0.37
73.33%***
0.44
45.12
8.70
46.96***
9.04
12.07
8.16
12.60**
8.11
6.86%
0.25
4.44%*
0.21
33.41%
0.47
34.07%
0.48
59.73%
0.49
61.48%
0.49
95.28
10.21
92.96***
12.30
32.74%
0.47
40.00%**
0.49
1.87
2.32
2.53***
2.86
4.47
1.35
4.60**
1.11
45.35%
0.50
57.04%***
49.69
69.75
11.33
73.40***
11.96
1.16
0.25
1.13
0.24
Normal Weight (n=123)
Mean
Standard
Deviation
23.04
1.55
37.16
3.60
81.30%
1.32
45.53%
47.97%
91.87%
43.24
10.82
10.47%
33.33%
56.10%
97.02
23.58%
1.04
4.31
33.33%
68.26
1.16
7.28
0.20
0.39
1.25
0.50
0.50
0.27
9.38
7.35
0.31
0.47
0.50
6.57
0.43
1.08
1.68
0.047
10.73
0.21
Notes: a: C-Reactive Protein is measured in mg/L. For reference, CRP levels below 1.0 are considered low-risk for heart disease, 1.0-2.99 is considered average
risk, and greater than 3.0 is high risk. b: A cholesterol ratio below 3.5 is considered optimal, 3.5-5 is normal, and >5 is considered high.
Table 2 – The Relationship Between Wages and Obesity with Standard Control Variables (Model 1)
Overweight
Obese
Married or Cohabitating
Total Number of Children
Nonwhite
Born Abroad
College Graduate (4-year)
Age
Tenure with Firm (10 years)
Core (Staff and Senior)
Female (n = 295)
-0.057*
(0.030)
-0.070**
(0.031)
0.023
(0.035)
-0.005
(0.011)
-0.058*
(.0230)
0.053
(0.040)
0.045
(0.027)
0.004**
(0.002)
-0.002
(0.019)
0.183**
(0.072)
Male (n = 452)
-.005
(0.022)
0.031
(0.025)
0.017
(0.026)
0.005
(0.009)
-0.012
(0.024)
0.046*
(0.026)
0.050
(0.031)
0.004***
(0.001)
0.008
(.015)
0.248***
(0.036)
Note: Dependent variable is log hourly wages. The overweight and obese coefficients report wages relative to normal-weight employees (BMI < 25). The model
includes indicators for worksite, state, and job category. Standard errors in parentheses.
* Significant at the 0.1 level
** Significant at the 0.05 level
*** Significant at the 0.01 level
Table 3a – Potential Productivity-Related Explanations for the Wage-Obesity Relationship (Females)
Obese
Tenure (10 years)
Core (Staff and Senior)
Obese x Tenure (10 yrs)
Staff
Senior
Physical Function (10 pts)
Heavy Snore
Log Cholesterol Ratio
Log C-Reactive Protein
Hypertension
Heart Rate (10 bpm)
Presenteeism
(2)
-0.098*
(0.057)
-0.014
(0.027)
0.180**
(0.071)
0.017
(0.032)
(3)
-0.055* #
(0.030)
-0.006
(0.017)
(4)
-0.071**
(0.032)
-0.002
(0.019)
0.182**
(.073)
(5)
-0.064*
(0.033)
-0.004
(0.019)
0.181**
(0.072)
(6)
-0.055* #
(0.032)
-0.002
(0.019)
0.182**
(0.070)
0.093
(0.074)
0.243***
(0.075)
-0.001
(0.012)
-0.022
(0.027)
-0.058
(0.039)
-0.017*
(0.010)
0.038
(0.024)
0.016**
(0.008)
(7)
-0.035 #
(0.032)
-0.008
(0.016)
(8)
-0.035 #
(0.030)
-0.006
(0.016)
0.090
(0.071)
0.240***
(0.072)
-0.004
(0.011)
-0.030
(0.024)
-0.053
(0.034)
-0.019**
(0.009)
0.046**
(0.022)
0.016*
(0.009)
0.090
(0.070)
0.241***
(0.071)
-0.004
(0.011)
-0.030
(0.024)
-0.054
(0.035)
-0.019**
(0.009)
0.042*
(0.022)
0.016*
(0.009)
-0.015
(0.057)
Note: Dependent variable is log hourly wage Models control for employee’s state, site, age, race, nativity, marital status, number of children, tenure with the
firm (in years), and job category, unless reported otherwise. All models also contain an indicator for overweight, and overweight is interacted with tenure in
model (2). The normal-weight category therefore serves as the point of reference for all obesity and obesity-interaction coefficients. Standard errors in
parentheses. # Signifies that the obesity coefficient is significantly different from the coefficient estimate in model (1).
* Significant at the 0.1 level
** Significant at the 0.05 level
*** Significant at the 0.01 level
Table 3b – Potential Productivity-Related Explanations for the Wage-Obesity Relationship (Males)
Obese
Tenure (10 years)
Core (Staff and Senior)
Obese x Tenure (10 yrs)
Staff
Senior
Physical Function (10 pts)
Heavy Snore
Log Cholesterol Ratio
Log C-Reactive Protein
Hypertension
Heart Rate (10 bpm)
Presenteeism
(2)
0.031
(0.034)
-0.002
(0.022)
0.250***
(0.035)
0.001
(0.026)
(3)
0.012 #
(0.023)
-0.003
(0.013)
(4)
0.032
(0.026)
0.008
(0.015)
0.247***
(0.036)
(5)
0.035
(0.025)
0.008
(0.015)
0.025***
(0.036)
(6)
0.066** #
(0.026)
0.008
(0.015)
-0.235***
(0.036)
0.147***
(0.034)
0.330***
(0.036)
0.002
(0.009)
-0.027
(0.017)
0.002
(0.038)
-0.027***
(0.009)
-0.029
(0.018)
-0.003
(0.008)
(7)
0.037
(0.024)
-0.002
(0.013)
(8)
0.037
(0.024)
-0.002
(0.013)
0.142***
(0.035)
0.322***
(0.037)
-0.002
(0.009)
-0.012
(0.016)
0.029
(0.033)
-0.019**
(0.009)
-0.030*
(0.017)
-0.002
(0.007)
0.144***
(0.035)
0.323***
(0.036)
-0.002
(0.009)
-0.012
(0.015)
0.029
(0.033)
-0.019**
(0.009)
-0.031*
(0.017)
-0.002
(0.007)
-0.017
(0.032)
Note: Dependent variable is log hourly wage. Models control for employee’s state, worksite, age, race, nativity, marital status, number of children, tenure with
the firm (10 years), job category, and an indicator for core jobs, unless reported otherwise. All models also contain an indicator for overweight, and overweight
is interacted with tenure in model (2). The normal-weight category therefore serves as the point of reference for all obesity and obesity-interaction coefficients.
Standard errors in parentheses. # Signifies that the obesity coefficient is significantly different from coefficient estimate in model (1).
* Significant at the 0.1 level
** Significant at the 0.05 level
*** Significant at the 0.01 level
Table 4 – Potential Discrimination Explanations for the Wage-Obesity Relationship
Females
Obese
Proportion Opposite Gender
Obese X Proportion OG
(9)
-0.031
(0.060)
-0.060
(0.10)
0.005
(0.101)
Proportion Obese
Obese
Proportion Opposite Gender
Obese X Proportion OG
Proportion Obese
Obese x Proportion Obese
(11)
-0.067**
(0.032)
0.084
(0.096)
-0.066
(0.105)
Obese x Proportion Obese
Males
(10)
-0.016
(0.049)
(9)
(10)
(11)
0.019
(0.041)
-0.047
(0.51)
0.044
(0.90)
0.107** #
(0.034)
0.049*
(0.025)
0.159**
(0.067)
-0.239***
(0.087)
Note: Dependent variable is log hourly wage. Models control for employees’ state, site, age, race, nativity, marital status, number of children, tenure with the firm (in
years), job category, indicators for staff and senior, an indicator for loud snoring, as well as cholesterol ratio, CRP plasma concentration, hypertension, and heart rate. All
models also contain an indicator for overweight, and overweight is interacted with proportion opposite gender in model (9) and proportion obese in model (10). The
normal-weight category therefore serves as the point of reference for all obesity and obesity-interaction coefficients. Standard errors in parentheses. # Signifies that the
obesity coefficient is significantly different from the obesity coefficient in model (7), the full productivity model.
* Significant at the 0.1 level
** Significant at the 0.05 level
*** Significant at the 0.01 level
Table 5 – Household Fertility as a Potential Explanation for the Wage-Obesity Relationship
Females
Obese
(12)
-0.035
(0.033)
Children
Married
0.020
(0.030)
(13)
-0.033
(0.032)
-0.011
(0.011)
0.030
(0.033)
(14)
0.053#
(0.043)
0.010
(0.014)
0.026
(0.045)
-0.053***
(0.017)
(15)
0.046#
(0.045)
0.012
(0.014)
0.016
(0.042)
-0.066***
(0.022)
0.021
(0.023)
(13)
0.037
(0.024)
0.005
(0.007)
-0.001
(0.024)
(14)
0.031
(0.030)
0.011
(0.012)
0.001
(0.023)
0.002
(0.012)
(15)
0.029
(0.031)
0.011
(0.012)
0.006
(0.026)
0.038
(0.029)
-0.037
(0.028)
Obese x Children
Obese x Married x Children
Males
Obese
(12)
0.039
(0.024)
Children
Married
Obese x Children
Obese x Married x Children
0.006
(0.022)
Note: Models control for employee’s state, site, age, race, nativity, marital status, number of children, tenure with the firm (in years), job category, indicators for
staff and senior, an indicator for loud snoring, as well as cholesterol ratio, CRP plasma concentration, hypertension, and heart rate. All models also contain an
indicator for overweight, and overweight is interacted with children in models (14) and (15). The normal-weight category therefore serves as the point of reference
for all obesity and obesity-interaction coefficients. Standard errors in parentheses. # signifies that the obesity coefficient differs significantly from model (13) –
which is identical to model (7). Models 12 and 13 are re-runs of Model (7) – the full productivity control model - with and without a control for children.
* Significant at the 0.1 level
** Significant at the 0.05 level
*** Significant at the 0.01 level
Appendix I – Missing and Imputed Data
Reason Omitted
Salary nonresponse
BMI nonresponse
TOTAL
Omitted Observations
Females
19 (5.9%)
6 (2.0%)
25 (7.9%)
Males
36 (7.2%)
5 (1.1%)
41 (8.4%)
Values in parentheses refer to percentage of original (full) sample dropped for each reason
Observations were dropped from the analysis sample if respondents did not provide their salary or a BMI value. Nineteen
females and thirty-six males failed to provide salary data. This represents approximately 6% of all females and 7% of all males. Six
females and five males failed to provide their BMI. This represents 2% of females and 1% of males. Among the remainder of the
control variables in Model (1) no nonresponses are recorded. The following table reports differences in means between nonrespondents and respondents. On average, non-responding females have less tenure, and are more likely to be nonwhite or foreign.
Male non-respondents also have less tenure, and more likely to be nonwhite or foreign. They are also significantly younger, on
average. These marginal differences do not suggest that weight or salary-level are correlated with item non-response.
Summary Statistics for Observations with Omitted Data
Females
Males
Omitted
Retained
Omitted
Retained
Married or
60.00%
70.85%
85.37%
84.74%
Cohabitating
(0.50)
(0.46)
(0.34)
(0.36)
Total Number of
1.52
1.63
1.61
1.64
Children
(1.45)
(1.24)
(1.28)
(1.43)
Nonwhite
36.00%*
24.07%
48.78%*
29.20%
(0.49)
(0.43)
(0.49)
(0.46)
Born Abroad
32.00%*
19.32%
53.65%*
31.89%
(0.48)
(0.40)
(0.53)
(0.44)
College Graduate
76.00%
66.44%
88.80%
83.63%
(4-year)
(0.44)
(0.47)
(0.32)
(0.37)
Age
46.52
46.88
41.95*
45.12
(12.56)
(8.38)
(9.20)
(8.70)
Tenure with Firm
11.12*
16.04
8.80*
12.07
(years)
(10.87)
(9.94)
(8.60)
(8.16)
Support Personnel
12.00%
5.76%
9.75%
6.86%
(0.33)
(0.23)
(0.30)
(0.25)
Staff
24.00%
36.27%
29.27%
33.41%
(0.44)
(0.48)
(0.46)
(0.47)
Senior
64.00%
57.97%
60.98%
59.73%
(0.50)
(0.49)
(0.49)
(0.49)
* Indicates significant difference in means between omitted and retained observations. Omitted observations were
left out of the sample due to missing salary, missing BMI, or because the respondent was the only responder in
her/his group.
C-Reactive Protein
Cholesterol Ratio
Heart Rate
Comparison of Means Between Imputed and Reported Health Variables
Females
Males
Imputed
Non-imputed
Imputed
1.65*
3.14
1.88
(2.10)
(4.42)
(0.84)
[15]
[280]
[34]
3.76
3.88
4.48
(0.25)
(1.15)
(0.26)
[30]
[265]
[58]
73.40
72.35
68.85
(0.43)
(11.14)
(3.88)
[2]
[293]
[4]
Non-imputed
1.87
(2.41)
[418]
4.47
(1.44)
[394]
69.76
(11.38)
[448]
Standard errors in parenthesis. N in brackets.
* Indicates significant difference between imputed and non-imputed means.
Among control variables the only item non-response occurred among biological measures that proxy for cardiovascular health. Due to
the relatively high number of non-responses values were imputed for missing observations rather than dropping the entire observation
from the sample. Imputation followed the modified regression-based EM algorithm detailed in Cameron and Trivedi (2006) Section
27.5 (pp. 931-932). X1 refers to control variables for observations with no missing responses. X2 indicates control variables for
observations with a missing response for one of the above biological variables. There are N1 complete observations and N2 incomplete
observations. The algorithm is as follows:
Estimate ̂ using the N1 complete observations.
Estimate 2 using only N1 complete observations.
Generate N2 estimates of the missing values: ̂ MIS = X2 ̂
Estimate ̂ [ ̂ MIS] = 2( + X2[X1ʹX1]-1X2ʹ)
5. Generate adjusted values ̂ = ( ̂ -1/2 ̂ MIS) um where um is a Monte Carlo draw from the N(0,
element by element multiplication.
6. Using the augment sample (y1 and ̂ )obtain a revised estimate of ̂
7. Repeat steps 1-6 using the revised ̂ in step 1 at each iteration.
1.
2.
3.
4.
2
) distribution and denotes
Consistent with Aitkin and Aitkin (1996) this algorithm is cycled until the difference between successive log likelihood values is less
than 10-5