OBESITY AND OCCUPATIONAL
ATTAINMENT AMONG THE 50+
OF EUROPE
Petter Lundborg, Kristian Bolin, Sören Höjgård, and
Björn Lindgren
ABSTRACT
This paper brings a European perspective to the mainly U.S.-based literature on the relationship between obesity and labour-market outcomes.
Using micro-data on workers aged 50 and over from the newly developed
SHARE database, the effects of obesity on employment, hours worked, and
wages across 10 European countries were analysed. Pooling all countries,
the results showed that being obese was associated with a significantly
lower probability of being employed for both women and men. Moreover,
the results showed that obese European women earned 10% less than their
non-obese counterparts. For men, however, the effect was smaller in size
and insignificant. Taking health status into account, obese women still
earned 9% less. No significant effect of obesity on hours worked was
obtained, however. Regressions by country-group revealed that the effects
of obesity differed across Europe. For instance, the effect of obesity on
employment was greatest for men in southern and central Europe, while
women in central Europe faced the greatest wage penalty. The results in
this study suggest that the ongoing rise in the prevalence of obesity in
Europe may have a non-negligible effect on the European labour market.
The Economics of Obesity
Advances in Health Economics and Health Services Research, Volume 17, 219–251
Copyright r 2007 by Elsevier Ltd.
All rights of reproduction in any form reserved
ISSN: 0731-2199/doi:10.1016/S0731-2199(06)17009-0
219
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PETTER LUNDBORG ET AL.
1. INTRODUCTION
Obesity, defined as a body mass index (BMI) Z 30,1 is associated with a
number of adverse health outcomes.2 Obesity also appears to be associated
with adverse labour-market outcomes. Controlling for a large number of
individual characteristics, previous studies indicate that for women, and
for white women in particular, obesity is associated with lower wage rate
(Register & Williams, 1990; Sargent & Blanchflower, 1994; Averett &
Korenman, 1996, 1999; Pagan & Davila, 1997; Cawley, 2004; Harper, 2000;
Baum & Ford, 2004; Cawley, Grabka, & Lillard, 2005; Cawley & Danziger,
2005; d’Hombres & Brunello, 2005; Conley & Glauber, 2006) and lower
probability of being employed (Pagan & Davila, 1997; Haskins & Ransford,
1999; Àsgeirsdóttir, 2004; Cawley & Danziger, 2005).3 For men, Register and
Williams (1990), Averett and Korenman (1996), Harper (2000) and Baum
and Ford (2004) found a negative association between obesity and wage rate
for white and Hispanic, but a positive association for black men.4
Àsgeirsdóttir (2004) and Garcia and Quintana-Domeque (2006) did not
find any statistically significant results for the association between obesity
and employment probability for men.
Despite the fact that only obesity (and not overweight or weight-increases
of normal weight people) has been shown to have adverse health effects
(SBU, 2002), there are some studies indicating a negative association between
the value of BMI and wage rate and/or employment probability (Sargent &
Blanchflower, 1994; Cawley, 2000, 2004; Àsgeirsdóttir, 2004; Baum & Ford,
2004; d’Hombres & Brunello, 2005). These results indicate that there may be
other factors than the health effects of obesity that account for the observed
correlation between obesity and labour-market performance.
In the literature, a number of mechanisms have been suggested to be
responsible for the observed correlation between labour-market outcomes
and obesity. These can be broadly divided into those that work through the
productivity of the individual – and, hence, support the view that obesity
causes adverse labour-market outcomes – and those that account for other
channels of influence: first, obesity may affect the individual’s productivity –
the adverse effect on health is assumed to impair productivity5 – and/or
trigger discrimination – employers, co-workers or customers may have
preferences for non-obese employees.6 Second, the correlation may be due to
reversed causality and/or that obesity and labour-market outcomes are
caused by some third unobserved variable (for instance, time preferences).7
Most previous studies on the effects of obesity on labour-market outcomes originate from the United States. There are some recent European
Obesity and Occupational Attainment Among the 50+ of Europe
221
studies, however, using the European Household Panel (d’Hombres &
Brunello, 2005; Garcia & Quintana-Domeque, 2006). Moreover, there
are studies from the United Kingdom (Sargent & Blanchflower, 1994;
Harper, 2000; Morris, 2006), Germany (Cawley et al., 2005) and work in
progress from Iceland (Àsgeirsdóttir, 2004). Most previous U.S. studies
have concerned a relatively young population, since the studies have
been mainly based on data from the National Longitudinal Survey of
Youth. Only Hamermesh and Biddle (1994), Cawley et al. (2005), and
Conley and Glauber (2006) contained both men and women above the
age of 40.
In contrast to the United States, the European labour market comprises
several language areas and several different labour-market legislations. In
addition, there may be differences in the general living conditions between
the U.S. and Europe that are relevant for the way in which employers regard
obesity. Thus, in a European context, the question of to what extent the
effects of obesity on labour-market outcomes differ between different
institutional settings is germane, and, moreover, in principle possible to
analyse. Accordingly, this paper explores a new European cross-national
micro-database in order to study this issue.
In Europe, the prevalence of obesity has risen dramatically during the past
decades (IOTF & EASO, 2002; OECD, 2005, p. 87). It has been estimated
that at least 135 million EU citizens are affected, and about 70 million in
the countries applying to join the EU. Moreover, in the southern parts of
Europe, a third of all children are classified as overweight. The direct health
care costs of obesity have been estimated to 2–5% of total health care costs in
western countries, such as Australia, France, Netherlands, Sweden, and
U.S.A. (Levy, Levy, Le Pen, & Basdevant, 1995).
Our study used data from the Survey of Health, Ageing and Retirement in
Europe (SHARE), which includes information from 11 European countries
on men and women of age 50 and older and their spouses. Thus, our contributions will be (1) to analyse whether or not the effect of obesity on
employment, wages and hours worked varies between the different institutional settings of Europe, (2) to analyse whether or not obesity affects
employment, wages and hours worked for those older than 50 years of age,
(3) to analyse whether or not the effects found for the U.S. labour market
also apply for the European labour market and (4) to analyse to what extent
the effects of obesity on employment, wages and hours worked runs through
reduced health status.
The paper proceeds as follows. First, the data will be presented. Second,
the empirical methods used in the paper are described. Third, the results are
222
PETTER LUNDBORG ET AL.
presented. The paper is concluded with a discussion of the results and some
suggestions for further research.
2. DATA
The Survey of Health, Ageing and Retirement in Europe is a multidisciplinary and cross-national micro-database containing approximately 22,000
Europeans over the age of 50 and from 11 countries. The first wave of data
was collected in 2004. Our analyses included only people who were employed, leaving us with a sample of between 4,189 and 4,330 individuals,
depending on the outcome studied, distributed across 10 countries.
The database contains representative samples from the non-institutionalised population in respective participating country. The countries represent
Northern Europe (Denmark and Sweden), Central Europe (Austria, France,
Germany, Switzerland, Belgium and the Netherlands) and the Southern
Europe (Spain, Italy and Greece). In this paper, data from Belgium was not
included, since it was not yet collected. The database comprises information
on self-reported height and weight, which was used to construct BMI, which,
in turn, was used to construct our indicator of obesity. Moreover, the
database contains health-related variables, for instance, self-reported health,
physical functioning, cognitive functioning, psychological health, well-being,
life satisfaction and health-behaviour, for instance, the utilisation of healthcare facilities; labour-market variables, for instance, wages, hours worked,
current work activity, job characteristics, opportunities to work past retirement age; economic variables, for instance, sources and composition of current income, wealth and consumption. Other variables include education,
housing, and social support variables, for instance, assistance within families,
transfers of income and assets, and social networks.
SHARE follows the design of the U.S. Health and Retirement Study
(HRS) and the English Longitudinal Study of Ageing (ELSA). A description of methodological issues can be found in Börsch-Supan and Jürges
(2005).
2.1. Dependent Variables
Three different labour-market outcomes were addressed in this paper: employment, hourly wage rate and hours worked (past month). These variables
were constructed, using information given by the respondents themselves. In
order to obtain hours worked, the respondent was first asked to state the
Obesity and Occupational Attainment Among the 50+ of Europe
223
number of contracted hours each month in his or her job, excluding meal
breaks and any paid or unpaid overtime. Next, the respondent was asked
about the actual number of hours worked, regardless of the number of
contracted hours, but including any paid or unpaid overtime. In the sample,
the mean number of actual hours worked was somewhat larger than the
mean number of contracted hours; 35 versus 33 h.
To assess the respondent’s hourly wage rate, the respondent was first
asked about the frequency of payment. Second, he or she was asked about
how much the last payment was, before deductions for tax, national insurance or pension and health contributions, union dues and so on. Together
with the information on contracted hours, the hourly wage rate was then
calculated.8
2.2. Obesity
A measure of BMI was constructed using self-reported information on height
and weight. Obesity was defined as having a BMI of 30 or above. The average
person in the sample had a BMI of 26, and 14% were classified as obese.
2.3. Background Variables
Essential background information such as age, gender, years of education,
marital status, number of children and country of birth were included as
explanatory variables. The typical person was a married, 55-year-old person
with two children. He or she had 12 years of education and was born in the
country where he or she resided. Smokers constituted 28% of the sample
and the typical person consumed alcohol once or twice a week. Two
additional variables, potentially affecting the wage rate, were included.
These were an indicator of part-time work and a variable indicating whether
the person worked in the public or in the private sector. In the sample, 18%
worked part-time and 28% in the public sector.
Four health indicators were used. The first variable was self-reported
health, measured on a 5-point scale where 1 indicated perfect health and
5 bad health. The second and third variables indicated the number of chronic
conditions and symptoms, respectively, that the respondent reported. Finally,
a variable indicating limitations to mobility was created, taking on the value
one, if the respondent reported any such limitations.
Separate dummies for each country were included in order to account for
unobserved factors at the country-level affecting the wage rate and hours
worked. A description of all variables used is given in Table 1.
224
Table 1.
PETTER LUNDBORG ET AL.
Descriptives. All Variables. Mean and Standard Deviation.
Mean
Dependent variables
Employed
Log of contracted hoursa
Log of hourly wage ratea
Independent variables
Obese
Age
Age squared
Female
Years of education
Public sectora
Part-timea
Country of birth
Married
Smoker
Former smoker
Frequent alcohol
consumption
Self-reported bad health
Number of chronic
conditions
Symptoms
Mobility
Austria
Spain
Netherlands
Germany
Italy
France
Denmark
Greece
Switzerland
Sweden
Instruments
Household obese
Oldest child
Only sisters
a
1 if employed
Log(1+contracted hours worked)
Log(1+hourly wage rate)
SD
0.46
2.27
3.48
0.50
0.61
0.44
1 if obese (BMI Z 30)
Age in years
Age2
1 if female
Total number of years of education
1 if working in the public sector
1 if working part-time (20 h or less per
week)
1 if born in country where the interview
took place
1 if married
1 if smoker
1 if former smoker
1 if drinking once a week or more
0.18
56.26
3191.19
0.57
11.01
0.28
0.16
0.39
5.12
562.91
0.50
4.16
0.45
0.37
0.91
0.29
0.77
0.27
0.26
0.56
0.42
0.44
0.44
0.50
1 if self-reported bad or very bad health
Number of chronic conditions out of 14
listed
Number of symptoms out of 11 listed
Number of limitations with mobility
out of 10 described
0.16
1.14
0.36
1.23
1.21
0.93
1.40
1.66
0.10
0.07
0.15
0.14
0.13
0.08
0.09
0.09
0.04
0.10
0.30
0.26
0.36
0.35
0.33
0.27
0.29
0.29
0.21
0.30
0.11
0.31
0.29
0.19
0.45
0.40
1 if any other member of the household
is obese
1 if the respondent was the oldest child
1 if the respondent have only sisters
Conditional on being employed.
Obesity and Occupational Attainment Among the 50+ of Europe
225
3. METHOD
The first outcome studied was whether or not the respondent was employed.
Since the indicator of employment was a binary variable, we used the
standard probit model. The employment equation was formulated as:
E i ¼ f ðOi ; HS i ; X i ; C i ; i Þ,
(1)
where Ei takes on the value one if the person i is employed. Oi is an
indicator of obesity, HSi indicates health status, Xi socio-economic and
demographic characteristics, Ci country and ei an unobserved error term.
Following the convention in the literature on obesity/labour-market outcomes, the wage equation was formulated as:
ln W i ¼ f ðOi ; HS i ; X i ; J i ; C i ; Zi Þ,
(2)
where lnWi denotes the log of the hourly wage rate of person I, Ji job
characteristics and Zi an unobserved error term. The corresponding equation
for hours worked is as follows:
ln HW i ¼ f ðOi ; HS i ; ln W i ; X i ; J i ; C i ; ui Þ,
(3)
where lnHWi denotes the log of hours worked. The independent variables
are the same as in Eq. (2), with the exceptions that the log of the hourly
wage rate now was included, and the indicator of part-time work excluded.
Eqs. (2) and (3) were estimated using OLS regression.
Since obesity is a potentially endogenous variable in the employment,
wage- and hours-worked regressions, due to reverse causality and/or because both obesity and wages/hours worked are related to some third factor,
instrumental-variables (IVs) methods were considered. In the case of the
wage- and hours-worked equations, the conventional 2SLS-estimator used
in most previous studies (e.g. Behrman & Rosenzweig, 2001; Cawley, 2000,
2004; Àsgeirsdóttir, 2004) may, however, produce inconsistent standard errors, since the use of individual-level cross-section data means that heteroscedasticity is likely to be a problem. If so, tests of overidentifying
restrictions and endogeneity may be invalid. In order to better deal with
heteroscedasticity of unknown form, we used the IV-GMM estimator,
described by Baum, Schaffer, and Stillman (2003), which allows for efficient
estimation under unknown heteroscedasticity. Since GMM comes at the
cost of possibly poor finite sample performance, we tested for heteroscedasticity using the Pagan and Hall (1983) test, designed for testing
for the presence of heteroscedasticity in the context of IV estimation.9
226
PETTER LUNDBORG ET AL.
In the employment equation, where the dependent variable was dichotomous, we used the Amemiya generalized least squares (AGLS) estimator,
which estimates a probit model with an endogenous explanatory variable.
The computations were performed using the IVPROB programme in
STATA, which provides asymptotically efficient standard errors. In the
first-stage regression, the endogenous explanatory variable was treated as
linear functions of the instruments and the exogenous variables. In the second stage, the prediction from the first stage was included as an explanatory
variable in the main equation. For a summary of how AGLS works, see
Maddala (1983, pp. 247–252).
Good instruments should be (1) correlated with the potentially endogenous right-hand-side variable and (2) orthogonal to the error process. We
tested the former requirement by conducting an F-test of the joint significance of the instruments, as suggested by Bound, Jaeger, and Baker (1995).
It has been suggested, as a rule of thumb, that an F-statistic below 10 is a
cause of concern, since it signals a problem of weak instruments (Staiger &
Stock, 1997). The second requirement, the validity of the overidentifying
restrictions, was tested in the wage- and hours-worked equations via the
J statistic by Hansen (1982). This is a commonly used test for testing the
suitability of the instruments within the context of GMM. In the employment equation, the validity of the exclusion restrictions was tested by including all but one of the instruments in the structural equation that control
for endogeneity and testing their joint significance with a Wald test (Bollen
et al., 1995). The test result does not depend on which instrument is left out.
With valid exclusion restrictions, these should not be significant predictors
of employment after controlling for obesity.
Once instruments that fulfilled our requirements were found, we tested the
hypothesis that obesity could be treated as an exogenous variable in the
regressions. In the wage- and hours-worked equations, we used a version of
the test allowing for robust estimation, the C-statistic (Baum et al., 2003). In
the probit regression, we computed a test of exogeneity for a probit model
proposed by Smith and Blundell (1986).
Finding decent instruments, in order to deal with the potential endogeneity of obesity, is a challenging task.10 We struggled a great deal with
various candidates before settling for the following. The two first instruments related to the birth order and the sibling sex composition of the
respondent. The birth order of the respondent, and especially being the
oldest child, has been found to be associated with a greater risk of developing obesity as an adult (Stettler et al., 2000; Celi et al., 2003). The effect
has been attributed to various factors connected to the gestational period
Obesity and Occupational Attainment Among the 50+ of Europe
227
and postnatal period. Birth order has also been found to predict educational
outcome as an adult (Conley & Glauber, 2005). However, since we control
for education, birth order should at least not be correlated with the unobserved factors also determining occupational attainment for that reason.
Moreover, to the extent that birth order affects later health outcomes, we
also control for the latter in the regressions.11
Experimenting with various variables reflecting the number of siblings
and their sex composition, one variable was in particular found to predict
obesity among women. This was an indicator variable of whether or not
having only sisters. The variable was used as a proxy for growing up with
sisters only.12 We believe that the effect of the variable may reflect the
choices of food and activities in the household when growing up, and
thereby affect the risk of developing obesity later in life.13
Finally, we used a variable indicating the presence of any other obese
household member.14 Unfortunately, we could not construct a variable indicating only the presence of an obese child or parent. Such an indicator
would have been preferred, since parents share about half of their genes with
each child and about half of the variation in weight has been found to be
genetic (Commuzie & Allison, 1998). In SHARE, however, since eligible
participants were all household members 50+ and their partners, children
below 50 were not included. Since very few had children 50+ living in the
household, an indicator variable of child obesity was not meaningful. Moreover, the fraction having an obese parent living in the household was too
small to make the creation of an indicator variable of parent obesity meaningful. Our instrument variable will, therefore, indicate the presence of an
obese spouse. Spousal correlation in eating behaviours and BMI has
been found in a number of studies (Tambs et al., 1991; Provencher et al.,
2005). Moreover, there is evidence of assortative mating by bodyweight
(Silventoinen, Kaprio, Lahelma, Viken, & Rose, 2003). Our identifying assumption is thus that the presence of an obese spouse is correlated with the
obesity status of the respondent, but not with the unobserved factors affecting occupational attainment. We admit that this assumption is stronger than
would have been the case, if we were able to use the presence of an obese
child/parent as instrument. For example, bias may result if the weight of the
spouse is still correlated with some unobserved trait or background characteristic of the respondent that may also affect his/her occupational attainment. For instance, living in a low socio-economic neighbourhood may lead
to both worse employment prospects and an increased risk of developing
obesity. Spousal obesity may, thus, be correlated with unobserved neighbourhood characteristics of the respondent. More directly, as suggested by an
228
PETTER LUNDBORG ET AL.
anonymous referee, the instrument may reflect reverse causality if poor people are more likely to be obese, and obese people tend to marry each other.
The results should, therefore, be interpreted with some caution.
4. RESULTS
4.1. Descriptive Statistics
In Table 2, the prevalence of obesity, the mean employment rate, median and
mean wages and mean hours worked by country are shown. As revealed in the
table, the prevalence of obesity varies to a substantial extent across Europe. A
north–south gradient can be detected, where the northern countries Denmark
and Sweden have a lower prevalence of obesity than most countries in central
and southern Europe, Switzerland being the exception. In turn, the countries
in southern Europe have higher obesity rates than several of the countries in
central Europe. The employment rate is greatest in the northern countries
Sweden and Denmark, and lowest in Austria, Spain and Italy. Median wages
are lower in southern than in central and northern Europe. In the Table, a
pattern can be detected where the countries with the lowest median wages, e.g.
Spain, Italy, Greece and Austria, also have the highest prevalence of obesity.
For hours worked, no similar pattern can be immediately detected. Hours
worked are, for instance, roughly similar in northern and southern Europe,
whereas the prevalence of obesity differs quite dramatically.
Table 2.
Southern group
Spain
Italy
Greece
Central group
France
Netherlands
Germany
Austria
Switzerland
Nordic group
Denmark
Sweden
n
Descriptives. Main Variables. Mean and Median by Country
(SD).
Obesity (Mean)
Employed
(Mean)
Wage Rate
(Median)
Wage Rate
(Mean)
0.24 (0.43)
0.19 (0.39)
0.21 (0.40)
0.34 (0.47)
0.28 (0.45)
0.47 (0.50)
7.01
10.18
9.11
11.92 (26.66)
13.52 (10.29)
13.01 (16.30)
40
36
40
36.16 (10.41)
33.35 (10.36)
35.62 (10.73)
0.19
0.16
0.16
0.23
0.14
0.46
0.45
0.44
0.29
0.61
(0.50)
(0.50)
(0.50)
(0.45)
(0.49)
12.44
16.75
15.19
10.97
22.40
16.93
23.09
19.48
19.67
32.28
(23.95)
(32.64)
(28.76)
(66.34)
(54.60)
35
33.6
37.5
38.5
40
33.41
29.64
32.37
35.28
35.78
0.62 (0.49)
0.72 (0.45)
10,149
21.22
14.12
3,960
23.19 (14.59)
15.82 (66.35)
3,960
37
40
4,040
34.72 (9.71)
36.70 (8.24)
4,040
(0.39)
(0.37)
(0.37)
(0.42)
(0.35)
0.15 (0.36)
0.15 (0.36)
10,149
Hours Worked
(Median)
Hours Worked
(Mean)
(10.22)
(10.70)
(11.78)
(13.27)
(15.28)
Obesity and Occupational Attainment Among the 50+ of Europe
229
4.2. Employment
Table 3 shows the results from the regressions on the employment probability. Excluding the health status variables, the marginal effect of being obese
was negative (0.07) and significant for the full sample. The effect differed by
gender, though. For women, the effect was 0.05, whereas the corresponding
effect for men was twice as high, 0.10. Including the health-status variables,
however, substantially reduced the magnitude of the effect of obesity on
employment probabilities, as shown in columns 5–7. For women the effect
was reduced from 0.05 to 0.01, while for the corresponding reduction was
from 0.10 to 0.04. Moreover, the effects were no longer statistically significant. Consequently, it seems that the effect of obesity on employment
mainly went through its effect on the health of the individual.
Several of the health variables showed significant effects on the probability of employment. Self-reported bad health, for instance, was associated
with a large reduction in the employment probability for both men and
women. The effect was to lower the employment probability by 0.21 for
men and 0.13 for women. For men, other results were that age, the
number of children, education, being married, being born in the resident
country and being a frequent alcohol consumer showed significant and
positive effects on the probability of employment, while the opposite effect
was obtained for age squared, being a smoker, number of chronic conditions, reduced mobility, bad self-reported health and the number of symptoms. For women, age, education and being a frequent alcohol consumer
showed significant and positive effects on the employment probability,
whereas age squared, the number of children, being married, the number of
chronic conditions, reduced mobility and self-reported bad health showed
the opposite effects.
Next, we treated obesity as an endogenous variable. A summary of the
results are shown in Table 4. For men, two instruments indicating whether or
not the respondent was the oldest child and whether there was any other obese
person in the household was used. These were found to predict obesity well in
the first-stage regression. An F-test of joint insignificance of the instruments
yielded an F-statistics of 21.88, and the hypothesis was rejected at the 1%
level. The F-statistic was well above the suggested rule of thumb (Staiger &
Stock, 1997). Moreover, the instruments passed the test of overidentifying
restrictions, suggesting that they could be validly excluded from the main
equation. The effect of treating obesity as endogenous in the regression for
men was to increase the magnitude of the marginal effect to 0.296. The
effect was, however, far from being significant. Moreover, Smith–Blundell
Effect of Obesity on Probability of Employment for Full Sample and for Women and Men
Separately. Probit Regression with Robust Standard Errors.
Health Status Excluded
Full Sample
Health Status Included
Men
Full Sample
Women
Men
(0.019)
(0.020)
(0.000)
(0.006)
0.101 (0.023)
0.287 (0.042)
0.003 (0.000)
0.012 (0.007)
0.021 (0.016)
0.257 (0.022)
0.003 (0.000)
0.003 (0.005)
0.206 (0.012)
0.026 (0.002)
0.044 (0.014)
0.017 (0.015)
0.070 (0.012)
0.063 (0.022)
0.004 (0.015)
0.027 (0.006)
0.038 (0.005)
0.170 (0.018)
0.003 (0.006)
0.320 (0.018)
0.376 (0.015)
0.298 (0.020)
0.210 (0.027)
0.409 (0.012)
0.281 (0.020)
0.350 (0.016)
0.352 (0.017)
0.229 (0.022)
9,973
0.007 (0.020)
0.193 (0.021)
0.002 (0.000)
0.019 (0.006)
0.039 (0.025)
0.309 (0.045)
0.003 (0.000)
0.018 (0.007)
0.029 (0.002)
0.024 (0.018)
0.010 (0.019)
0.068 (0.015)
0.019 (0.026)
0.055 (0.018)
0.033 (0.008)
0.030 (0.006)
0.130 (0.022)
0.008 (0.007)
0.341 (0.013)
0.349 (0.014)
0.299 (0.017)
0.238 (0.023)
0.354 (0.012)
0.210 (0.023)
0.328 (0.015)
0.325 (0.016)
0.196 (0.023)
5,660
0.018 (0.003)
0.081 (0.023)
0.032 (0.022)
0.051 (0.021)
0.133 (0.036)
0.077 (0.024)
0.018 (0.009)
0.054 (0.008)
0.214 (0.029)
0.022 (0.010)
0.126 (0.046)
0.359 (0.035)
0.201 (0.047)
0.091 (0.057)
0.441 (0.029)
0.374 (0.034)
0.313 (0.035)
0.310 (0.037)
0.234 (0.042)
4,313
0.075 (0.015)
0.240 (0.021)
0.003 (0.000)
0.007 (0.004)
0.214 (0.012)
0.029 (0.002)
0.067 (0.014)
0.003 (0.014)
0.094 (0.012)
0.072 (0.021)
0.011 (0.014)
0.053
0.183
0.002
0.022
0.032 (0.002)
0.042 (0.017)
0.021 (0.019)
0.086 (0.015)
0.037 (0.025)
0.042 (0.018)
0.024 (0.002)
0.113 (0.022)
0.004 (0.021)
0.090 (0.020)
0.121 (0.036)
0.091 (0.023)
0.309 (0.019)
0.378 (0.015)
0.307 (0.020)
0.192 (0.028)
0.410 (0.013)
0.277 (0.020)
0.349 (0.016)
0.365 (0.016)
0.245 (0.021)
9,983
0.341 (0.014)
0.355 (0.014)
0.304 (0.017)
0.226 (0.025)
0.354 (0.013)
0.205 (0.024)
0.330 (0.016)
0.331 (0.016)
0.208 (0.023)
5,662
0.112 (0.044)
0.352 (0.034)
0.231 (0.044)
0.082 (0.057)
0.454 (0.027)
0.372 (0.033)
0.315 (0.034)
0.356 (0.034)
0.261 (0.040)
4,321
Note: Robust standard errors in parentheses.
Significant at 5%.
Significant at 1%.
PETTER LUNDBORG ET AL.
Obese
Age
Age squared
Number of children
Female
Years of education
Smoker
Former smoker
Frequency of alcohol consumption
Country of birth
Married
Number of chronic conditions
Mobility
Self-reported health
Symptoms
Greece
Italy
Spain
Switzerland
Austria
France
Netherlands
Germany
Denmark
Observations
Women
230
Table 3.
Obesity and Occupational Attainment Among the 50+ of Europe
231
Table 4. Effects of Obesity on Employment. Obesity Treated as an
Endogenous Variable. Instrumental Variables Probit Regression.
Marginal Effects.
Women
Obese
F-test of first-stage instruments (p-value
of null of jointly ¼ 0)
p-value of null hypothesis of valid
exclusion restrictions
Smith–Blundell exogeneity test
0.43 (0.14)*
42.68 (po0.01)
–
5.31 (p ¼ 0.02)
Men
0.296 (0.274)
21.88 (po0.01)
P ¼ 0.276
2.355 (p ¼ 0.125)
exogeneity test could not reject the null hypothesis that obesity was exogenous, hence supporting the specification presented in Table 3.
For women, the variable indicating whether or not the respondent was the
oldest child did not predict obesity in the first-stage regression. It was, thus,
dropped, leaving the variable indicating the presence of any other obese
person in the household as the sole instrument in the first-stage regression.
The predictive power of the latter variable was good, however, and the
F-test yielded an F-statistic of 42.68, which was significant at the 1% level.
Since only one instrument was used, our exclusion restriction could not be
tested. Treating obesity as endogenous for women dramatically increased
the magnitude of the effect to 0.43. Moreover, the effect was significant at
the 6% level (p ¼ 0.056). The Smith–Blundell test rejected the hypothesis of
obesity being exogenous for women (p ¼ 0.02). The result shows that treating obesity as exogenous may understate the true effect for women, if anything. The result should be interpreted with caution, though, since we were
not able to formally test the exclusion restriction and since the strict
exogeneity of the instrument could be discussed.
In Table 5, the impact of obesity on the employment probability is presented by country-group. In order to preserve space, only the coefficients of
the obesity variable are shown. For the Nordic countries (Denmark and
Sweden), the association between obesity and the employment probability
for the full sample was significant at only the 10% level. The effect of being
obese was to lower the employment probability by 0.054. The effect was
negative for both men and women but was not significant when men and
women were analysed separately. Including the health-status variables, the
effect of obesity became positive. The effect was insignificant for both the
full sample, and in the regression by gender, though.
232
PETTER LUNDBORG ET AL.
Table 5. Effect of Obesity on the Probability of Employment.
Regressions by Country-Group. OLS with Robust Standard Errors.
Health Status Excluded
Full Sample
Women
Health Status Included
Men
Full Sample
Nordic countries
0.049 (0.046)
0.060 (0.048)
0.022 (0.033)
Obese
0.054 (0.033)
Observations
1,915
1,061
854
1,915
Central European countries
Obese
0.084 (0.020)0.068 (0.025)0.106 (0.033) 0.025 (0.022)
Observations
5,141
2,887
2,254
5,135
South European countries
0.018 (0.025)0.108 (0.041) 0.035 (0.024)
Obese
0.055 (0.024)
Observations
2,927
1,714
1,213
2,923
Women
Men
0.027 (0.047)
1,061
0.012 (0.048)
854
0.021 (0.028)
2,885
0.027 (0.036)
2,250
0.008 (0.025)
1,714
0.076 (0.042)
1,209
Note: Robust standard errors in parentheses.
Significant at 10%.
Significant at 5%.
Significant at 1%.
For the central European countries (Germany, France, Netherlands, Austria and Switzerland), being obese showed a statistically significant effect,
lowering the employment probability by 0.084. The effect was somewhat
stronger among men (0.106) than among women (0.068). In both cases,
the effect was significant at the 1% level. Adding the health-status variables
substantially reduced the magnitude of the effect. The effect was no longer
significant, neither for the full sample, nor for the analyses by gender.
For the southern European countries (Spain, Italy and Greece), the effect
of being obese was statistically significant at the 5% level, when excluding
the health-status variables. The effect was to lower the employment probability by 0.055. The effect, however, was almost ten times greater among
men than among women. In the former case, the effect was significant at the
1% level and lowered the employment probability by 0.108. Among women,
the effect (0.018) was not significant. Adding health status to the regressions lowered the effect of being obese and rendered it insignificant for the
full sample. Among men, the effect was significant at the 10% level and
somewhat reduced in magnitude (0.076).
4.3. Hours Worked
The results regarding the impact of obesity on hours worked are shown in
Table 6. When excluding the health status variables, the effect of obesity on
contracted hours for the full sample was positive but not significant. The
Effect of Obesity on Hours Worked for the Full Sample and for Women and Men Separately. OLS
Regression with Robust Standard Errors.
Health Status Excluded
Full Sample
Women
Men
Full Sample
Women
Men
0.010 (0.019)
0.081 (0.020)
0.084 (0.023)
0.001 (0.000)
0.011 (0.005)
0.028 (0.030)
0.036 (0.027)
0.055 (0.025)
0.001 (0.000)
0.015 (0.008)
0.036 (0.024)
0.135 (0.028)
0.218 (0.051)
0.002 (0.000)
0.006 (0.007)
0.009 (0.020)
0.084 (0.020)
0.089 (0.024)
0.001 (0.000)
0.009 (0.005)
0.048 (0.030)
0.039 (0.027)
0.059 (0.025)
0.001 (0.000)
0.012 (0.008)
0.017 (0.024)
0.141 (0.028)
0.222 (0.051)
0.002 (0.000)
0.005 (0.006)
0.273 (0.014)
0.014 (0.002)
0.000 (0.000)
0.018 (0.004)
0.000 (0.000)
0.007 (0.003)
0.262 (0.014)
0.012 (0.002)
0.000 (0.000)
0.016 (0.004)
0.035 (0.015)
0.001 (0.016)
0.004 (0.016)
0.025 (0.015)
0.034
0.002
0.009
0.040
0.027 (0.024)
0.045 (0.015)
0.154 (0.030)
0.183 (0.030)
0.115 (0.039)
0.112 (0.037)
0.147 (0.029)
0.141 (0.027)
0.322 (0.023)
0.262 (0.026)
0.124 (0.023)
0.044 (0.033)
0.107 (0.022)
0.148 (0.052)
0.176 (0.046)
0.115 (0.062)
0.252 (0.054)
0.244 (0.046)
0.147 (0.043)
0.493 (0.035)
0.349 (0.039)
0.154 (0.030)
(0.021)
(0.024)
(0.024)
(0.022)
0.014
0.015
0.015
0.011
(0.020)
(0.020)
(0.020)
(0.018)
0.016 (0.035)
0.030 (0.021)
0.148 (0.035)
0.181 (0.040)
0.110 (0.045)
0.067 (0.047)
0.039 (0.034)
0.126 (0.029)
0.138 (0.025)
0.132 (0.033)
0.076 (0.034)
0.035 (0.015)
0.006 (0.016)
0.009 (0.016)
0.020 (0.015)
0.029 (0.024)
0.046 (0.015)
0.145 (0.030)
0.171 (0.030)
0.108 (0.039)
0.111 (0.037)
0.136 (0.030)
0.133 (0.027)
0.314 (0.022)
0.253 (0.026)
0.117 (0.023)
0.033
0.008
0.011
0.034
(0.021)
(0.024)
(0.024)
(0.022)
0.049 (0.033)
0.109 (0.021)
0.135 (0.052)
0.166 (0.046)
0.100 (0.063)
0.252 (0.054)
0.235 (0.047)
0.141 (0.043)
0.481 (0.035)
0.343 (0.039)
0.147 (0.029)
0.000 (0.000)
0.007 (0.003)
0.014
0.009
0.007
0.008
(0.020)
(0.020)
(0.020)
(0.018)
0.018 (0.036)
0.028 (0.021)
0.144 (0.035)
0.168 (0.039)
0.111 (0.046)
0.066 (0.046)
0.027 (0.033)
0.116 (0.029)
0.132 (0.025)
0.120 (0.032)
0.068 (0.034)
233
Obese
Hourly wage rate
Age
Age squared
Number of
children
Female
Years of
education
Public sector
Smoker
Current smoker
Frequent alcohol
consumption
Country of birth
Married
Greece
Italy
Spain
Switzerland
Austria
France
Netherlands
Germany
Denmark
Health Status Included
Obesity and Occupational Attainment Among the 50+ of Europe
Table 6.
234
Table 6. (Continued )
Health Status Excluded
Full Sample
Number of
chronic
conditions
Mobility
Self-reported
health
Symptoms
Constant
Observations
R-squared
2.036 (0.609)
3,948
0.15
Women
2.531 (0.626)
2,035
0.15
Health Status Included
Men
1.652 (1.356)
1,913
0.09
Full Sample
Women
Men
0.004 (0.007)
0.002 (0.012)
0.004 (0.009)
0.024 (0.009)
0.073 (0.033)
0.029 (0.012)
0.050 (0.047)
0.019 (0.013)
0.090 (0.045)
0.001 (0.008)
1.900 (0.617)
3,944
0.16
0.000 (0.011)
2.423 (0.634)
2,034
0.15
0.006 (0.010)
1.734 (1.354)
1,910
0.10
Note: Robust standard errors in parentheses.
Significant at 5%.
Significant at 1%.
PETTER LUNDBORG ET AL.
Obesity and Occupational Attainment Among the 50+ of Europe
235
effect was qualitatively different for men and women, however. For men, the
effect was negative, whereas the opposite was true for women. In neither
case was the effect significant. When self-reported health, chronic conditions, symptoms and mobility were added, the effect of obesity was reduced
for men, from 0.36 to 0.017, and amplified for women from 0.028 to
0.048. The effects were, again, statistically insignificant. In sum, the inclusion of the health-status variables did not affect the effect of obesity on
hours worked to any important extent.
Several of the health-status variables showed significant effects on hours
worked. Self-reported bad health showed a negative and statistically significant effect on hours worked for men, but not for women. For men, the
effect was to decrease the number of hours worked by 9%. Reduced mobility was associated with 3% reduction in the hours worked for women,
but showed no effect on the hours worked by men. For men, other results
were that age and education showed a positive and significant association
with hours worked, while the opposite was true for the hourly wage rate
and age squared. For women, age and education showed a positive effect
on hours worked, whereas age squared and being married showed a negative effect.
In Table 7, we show the results when treating obesity as endogenous. For
men, we used the same instruments as in the employment regression, i.e.
whether the respondent is the oldest child and whether there is any other
obese member of the household. These instruments predict reasonably well
in the first-stage regression; the test of joint significance yielded an F-statistic
of 9.21, which was significant at the 1% level. Moreover, the hypothesis that
the exclusion restrictions were valid could not be rejected, as suggested by
the Hansen J statistic of 0.014 (p ¼ 0.91). Treating obesity as endogenous
Table 7.
Effects of Obesity on Hours Worked. Obesity Treated as an
Endogenous Variable. IV-GMM Regression.
Women
Obese
F-test of first-stage instruments (p-value of
null of jointly ¼ 0)
Overidentification test, Hansen J statistic
(p-value of null of valid exclusion
restrictions)
Exogeneity test, C-statistic, (p-value of null
of exogeneity)
Men
0.050 (0.029)
6.65 (po0.01)
0.186 (0.238)
9.21 (p>0.01)
1.42 (p ¼ 0.23)
0.014 (p ¼ 0.91)
0.63 (p ¼ 0.43)
0.77 (p ¼ 0.38)
236
PETTER LUNDBORG ET AL.
among men, the effect became positive, but was still insignificant. The hypothesis of obesity being exogenous could not be rejected (p ¼ 0.38).
For women, the indicator of whether or not being the oldest child was,
again, insignificant in predicting obesity in the first-stage regression. The
variable indicating the presence of any other obese member of the household, however, was highly significant. After experimenting with a number of
different instruments, we also added the variable indicating whether or not
the respondent only had sisters alive. The two instruments were significant,
both individually and jointly, yielding an F-statistic of 6.65. Moreover, the
hypothesis that the instruments could be validly excluded from the main
equation could not be rejected (p ¼ 0.23). The effect of obesity, once treating it as endogenous, became negative (0.236). It was, however, insignificant. Furthermore, the hypothesis that obesity was exogenous could not be
rejected (p ¼ 0.43).
Table 8 presents the results from the regressions on hours worked by
country-group. For the Nordic countries (Denmark and Sweden), the effect
of being obese was negative and twice as high among men (0.010) than
among women (0.005). The effect, however, was not significant in any of
the regressions. It could be noted that the effect of being obese was negative,
when excluding the health-status variables, but became positive for the full
sample and for women, when including them.
For the central European countries (Germany, France, Netherlands,
Austria and Switzerland), the effect of being obese was positive but insignificant for the full sample. This result disguised some interesting differences
Table 8.
Effects of Obesity on the Hours Worked. Regressions by
Country-Group. OLS.
Health Status Excluded
Full Sample
Nordic countries
Obese
0.014 (0.033)
Observations
1,240
Central European countries
Obese
0.020 (0.029)
Observations
2,023
South European countries
Obese
0.027 (0.038)
Observations
685
Health Status Included
Women
Men
0.005 (0.044)
708
0.010 (0.041)
532
0.022 (0.034)
1,240
0.051 (0.048)
708
0.011 (0.042)
532
0.101 (0.044)
1,031
0.062 (0.037)
992
0.038 (0.029)
2,020
0.107 (0.044)
1,030
0.025 (0.037)
990
0.047 (0.073)
296
0.008 (0.038)
389
0.021 (0.038)
684
0.037 (0.071)
296
0.006 (0.039)
388
Note: Robust standard errors in parentheses.
Significant at 10%.
Significant at 5%.
Full Sample
Women
Men
Obesity and Occupational Attainment Among the 50+ of Europe
237
by gender, though. For women, the effect was positive (0.101) and significant at the 5% level. This result suggests that being obese was associated
with 10% more hours worked. Among men, however, the effect was negative (0.062) at the 10% level. Controlling for health status rendered this
negative effect insignificant. Among women, including health status did not
change the effect of obesity to any great extent; it was still positive, similar in
magnitude, and significant at the 5% level.
In the southern European countries (Spain, Italy and Greece), the effect
of being obese was negative, and substantially greater in magnitude among
women (0.047) than among men (0.008). The effect was not statistically
significant for neither women, nor men. This result did not change when
including the health variables.
4.4. Hourly Wage Rate
Table 9 shows the results from the wage equations. When excluding the
health-status variables, the effect of obesity for the full sample was negative
and statistically significant. Obese people had a 7.4% lower wage rate that
non-obese people. The effect differed by gender, though. While the effect for
women was significant and large, 0.10, the corresponding effect among
men was roughly half in size (0.05) and insignificant.
When health-status variables were added, the effect of obesity for the full
sample was still negative and statistically significant, but slightly reduced in
magnitude. Being obese was now associated with having a 5.8% lower wage
rate. Interestingly, the inclusion of the health variables did not change the
effect of being obese among women to any great extent. The effect was still
significant and only slightly reduced to 0.09. For men, the effect was more
than halved, from 0.05 to 0.02, but was still insignificant. The result
obtained for women suggests that only a slight part of the effect of obesity on
wages worked via lower productivity due to worse health status, and that the
major part of the effect must be explained by other factors, such as employer
discrimination.
Few of the health indicators showed any significant effect of the wage
rate. Reduced mobility was associated with a significantly lower wage rate
among men, but the effect among women was insignificant. Other findings
were that among men, age, education, working part-time, being married and
being a frequent drinker were associated with a significantly higher wage
rate, whereas the opposite effect was obtained for age squared, and working
in the public sector. For women, age and education were significantly and
238
Table 9.
Effect of Obesity on Hourly Wage Rate for the Full Sample and for Women and Men Separately.
OLS Regression with Robust Standard Errors.
Health Status Excluded
Full Sample
Women
Men
0.090 (0.036)
0.051 (0.025)
0.000 (0.000)
0.015 (0.010)
0.000 (0.000)
0.052 (0.004)
0.001 (0.033)
0.007 (0.027)
0.007 (0.026)
0.020 (0.029)
0.043 (0.024)
0.020 (0.032)
0.144 (0.063)
0.001 (0.001)
0.014 (0.009)
0.000 (0.000)
0.049 (0.004)
0.230 (0.073)
0.057 (0.028)
0.056 (0.032)
0.009 (0.029)
0.105 (0.029)
0.055 (0.033)
0.035 (0.038)
0.092 (0.061)
0.053 (0.033)
0.031 (0.038)
0.041 (0.019)
0.023 (0.025)
0.068 (0.030)
0.039 (0.019)
0.022 (0.025)
0.442 (0.041) 0.360 (0.063) 0.509 (0.053) 0.435 (0.041) 0.347 (0.064)
0.260 (0.039) 0.229 (0.055) 0.300 (0.055) 0.251 (0.039) 0.222 (0.055)
0.423 (0.050) 0.416 (0.084) 0.434 (0.061) 0.416 (0.050) 0.406 (0.083)
0.101 (0.061)
0.064 (0.030)
0.508 (0.053)
0.286 (0.055)
0.441 (0.060)
0.074 (0.024)
0.056 (0.023)
0.001 (0.000)
0.002 (0.007)
0.200 (0.018)
0.053 (0.002)
0.045 (0.031)
0.023 (0.019)
0.032 (0.021)
0.015 (0.020)
0.074 (0.018)
Men
0.099 (0.035) 0.051 (0.032)
0.049 (0.025)
0.138 (0.063)
0.000 (0.000)
0.001 (0.001)
0.016 (0.010)
0.013 (0.009)
Full Sample
0.058 (0.025)
0.059 (0.023)
0.001 (0.000)
0.001 (0.007)
0.195 (0.018)
0.053 (0.003)
0.051 (0.004)
0.052 (0.003)
0.052 (0.031)
0.004 (0.033)
0.212 (0.073)
0.005 (0.027)
0.056 (0.028) 0.022 (0.020)
0.009 (0.026)
0.065 (0.032) 0.027 (0.021)
0.020 (0.029)
0.004 (0.029)
0.018 (0.021)
0.046 (0.023)
0.110 (0.029)
0.070 (0.018)
PETTER LUNDBORG ET AL.
Obese
Age
Age squared
Number of children
Female
Years of education
Part-time
Public
Smoker
Former smoker
Frequency of alcohol
consumption
Country of birth
Married
Greece
Italy
Spain
Women
Health Status Included
0.386 (0.043)
0.349 (0.059)
0.445 (0.062)
0.385 (0.043)
0.355 (0.060)
0.235 (0.043) 0.247 (0.061) 0.221 (0.060) 0.227 (0.043) 0.235 (0.062)
0.004 (0.033)
0.050 (0.048)
0.048 (0.046)
0.010 (0.033)
0.055 (0.048)
0.086 (0.026)
0.110 (0.038)
0.078 (0.036)
0.088 (0.026)
0.113 (0.039)
0.177 (0.031) 0.193 (0.038) 0.141 (0.049) 0.171 (0.031) 0.186 (0.038)
0.184 (0.025)
0.199 (0.035)
0.166 (0.037)
0.190 (0.025)
0.204 (0.035)
0.007 (0.009)
0.004 (0.013)
0.682 (0.613)
3,948
0.30
0.739 (0.646)
2,035
0.27
Note: Robust standard errors in parentheses.
Significant at 5%.
Significant at 1%.
1.677 (1.716)
1,913
0.30
0.018 (0.009)
0.051 (0.034)
0.003 (0.009)
0.592 (0.619)
3,944
0.30
0.016 (0.011)
0.079 (0.047)
0.015 (0.011)
0.699 (0.649)
2,034
0.27
0.437 (0.062)
0.211 (0.061)
0.043 (0.045)
0.075 (0.037)
0.130 (0.049)
0.170 (0.037)
0.016 (0.012)
0.032 (0.016)
0.012 (0.048)
0.023 (0.013)
1.821 (1.737)
1,910
0.31
Obesity and Occupational Attainment Among the 50+ of Europe
Switzerland
Austria
France
Netherlands
Germany
Denmark
Number of chronic
conditions
Mobility
Self-reported health
Symptoms
Constant
Observations
R-squared
239
240
PETTER LUNDBORG ET AL.
positively associated with the wage rate, while age squared showed a negative association.
Table 10 shows the results from the regressions treating obesity as endogenous. We struggled hard to find suitable instruments to be used in the
wage equations. For women, we settled for the variable indicating whether
or not the respondent had only sisters. While significant at the 5% level in
the first-stage regression, an F-statistic of 4.21 was obtained, suggesting a
potential problem of weak instruments. The effect of the obesity variable
(0.12) was quite similar to the effect obtained when treating obesity as
exogenous, but the precision of the estimate was very low (p ¼ 0.87). Moreover, we were unable to reject the hypothesis that obesity was exogenous.15
For men, few variables were found to be suitable as instruments. Again, a
single instrument was used; the variable indicating whether or not the respondent was the oldest child. Its predictive power was weak, however, and
the variable was only significant at the 7% level in the first-stage regression.
Consequently, the precision of the estimated obesity effect (0.86,
SE ¼ 0.97) was very low and should be interpreted with great caution.
The hypothesis of obesity being exogenous could not be rejected by the
Durbin–Wu–Hausman test (p ¼ 0.32).
In Table 11, we present regressions by country-group. For the Nordic
countries (Denmark and Sweden), the effect of being obese was to lower the
wage by 4.7%. The effect was, however, only significant at the 12% level.
For women, the effect was larger (0.063) than the corresponding effect
among men (0.024). In neither case was the effect significant, though.
Adding the health-status measures reduced the magnitude of the effect for
both men and women, but the effect was still insignificant in both cases.
In the group of central European countries (Germany, France, Netherlands, Austria and Switzerland), being obese showed a negative and statistically significant effect on the wage rate. The estimated coefficient suggested
Table 10.
Effects of Obesity on Wages. Obesity Treated as an
Endogenous Variable. 2SLS Regression.
Obese
F-test
F-test of first-stage instruments (p-value of null of
jointly ¼ 0)
Exogeneity test, Durbin–Wu–Hausman test, (p-value of
null of exogeneity)
Women
Men
0.12 (0.73)
4.21 (po0.05)
–
0.86 (0.97)
3.15 (p ¼ 0.07)
–
0.003 (p ¼ 0.96)
0.99 (p ¼ 0.32)
Obesity and Occupational Attainment Among the 50+ of Europe
241
Table 11. Effects of Obesity on the Wage Rate. Regressions by
Country-Group. OLS with Robust Standard Errors.
Health Status Excluded
Full Sample
Women
Health Status Included
Men
Nordic
countries
Obese
0.047 (0.030)
0.063 (0.044) 0.024 (0.040)
Observations
1,240
708
532
Central
European
countries
Obese
0.113 (0.038)0.153 (0.055) 0.062 (0.049)
Observations
2,023
1,031
992
South
European
countries
Obese
0.049 (0.054)
0.016 (0.081) 0.036 (0.067)
Observations
685
296
389
Full Sample
0.029 (0.032)
1,240
Women
Men
0.046 (0.046) 0.001 (0.043)
708
532
0.096 (0.039) 0.146 (0.056) 0.025 (0.051)
2,020
1,030
990
0.038 (0.056)
684
0.008 (0.082) 0.015 (0.070)
296
388
Note: Robust standard errors in parentheses.
Significant at 5%.
Significant at 1%.
that being obese was associated with a 11.3% lower wage rate. The effect
was more than twice as large among women (0.153) than among men
(0.062). Moreover, the effect was only significant among women, where it
was significant at the 1% level. Adding the health-status variables only
slightly affected the effect of being obese. For the full sample, the effect was
reduced to 0.096 , while it among women was reduced to 0.146. In the
former case, the effect was significant at the 5% level, whereas it was significant at the 1% level in the latter case. For men, the effect of being obese
on the wage rate was still insignificant.
For the southern European countries (Spain, Italy and Greece), the effect
of being obese was to reduce wages by 4.9%. The effect, which was more
than twice as high among men as among women, was not significant,
though, for either men or women. Adding health measures substantially
reduced the magnitude of the effects, but it remained insignificant.
5. DISCUSSION
This paper brings a European perspective to the mainly U.S.-based literature on the relationship between obesity and labour-market outcomes.
242
PETTER LUNDBORG ET AL.
Using data from the newly developed SHARE database, we were able to
address whether the effects of obesity on labour-market outcomes differed
across Europe. In contrast to the U.S., the European labour market comprises several language areas and several different labour-market legislations. In addition, there are differences in the general living conditions
between the U.S. and Europe that are relevant for the way in which employers (and employees) regard obesity. Our study is, to the best of our
knowledge, the first to use the SHARE-database to study the relationship
between obesity and labour-market performance. In addition, we were able
to contribute to the literature by investigating if there were similar effects of
obesity on labour-market outcomes for older individuals, as for younger
individuals, who have been the primary focus for prior studies. Finally,
using an extensive set of health measures we were able to address to what
extent the effects of obesity on employment, wages and hours worked ran
through reduced health status.
First, pooling all 10 countries, the results showed that being obese was
associated with a significantly lower probability of being employed for both
men and women. When controlling for health status, however, the effects
became insignificant. Second, the results suggested that being obese was
associated with a wage penalty of approximately 7%. The effect differed by
gender, though. For women, the wage penalty was 10% and significant,
whereas among men it was 5% and insignificant. Taking the health status of
the individual into account, obese women still earned 9% less, whereas the
effect among men, although still insignificant, was reduced to 2%. In other
words, after the potential negative productivity aspects of being obese were
taken into account, a significant wage penalty persisted for women. One
explanation for this may be employer discrimination. The data, however,
did not allow us to test this hypothesis. Third, we analysed the effect of
obesity on hours worked. The results showed no significant effects of obesity
on hours worked, neither for men nor for women.
Regressions were also performed on three different country groups, according to a north–south gradient. The results showed that the effect of
obesity on labour-market outcomes varied to a substantial extent across
Europe. Starting with the probability of employment; in the Nordic group
the effect was negative and significant when analysing men and women
together. When conducting separate analyses by gender, however, the effect
was still negative, but not significant. Including health status rendered the
effect of obesity on the employment probability smaller in magnitude and
insignificant for the full sample. In the central European group, the effect of
obesity on employment was negative and significant for both men and
Obesity and Occupational Attainment Among the 50+ of Europe
243
women. The effect was greater than the corresponding effect obtained in the
Nordic group. Including health status greatly reduced the magnitude of the
effect for both men and women and rendered it statistically insignificant. In
the southern group, the effect of obesity on employed was, interestingly,
only significant for men. The effect on employment on obesity among men
was large, 0.11, and was still significant when including health status,
although it was somewhat reduced in magnitude. In sum, the greatest effect
on employment was obtained for men in southern and central Europe,
where being obese reduced the employment probability by almost 11 percentage points.
Regarding wages, the effect of being obese was negative in all country
groups, but only significant for women in the central European group. Here,
being obese reduced wages by 15%. The effect was still significant, and only
slightly reduced in magnitude, when including health status. Consequently, it
appears as if the effects of obesity on wages among central European women
to a large extent had to do with other factors than health. Results supporting
the discrimination hypothesis have been obtained in a couple of prior studies
(Hamermesh & Biddle, 1994; Harper, 2000; Baum & Ford, 2004).16 Regarding hours worked, the effect of obesity was again only significant in the central
European group. Here, the effect had different signs for men and women.
Among women, being obese increased the number of hours worked, whereas
the opposite effect was obtained for men. The effect was significant for both
men and women. The effect for women was still significant, and almost unchanged in magnitude, when including health status, whereas among men, the
effect became substantially reduced in magnitude and insignificant.
The hypothesis that obesity was exogenous in the regressions was tested,
since there are good reasons to suspect that obesity may be endogenous. We
were unable, though, to reject the hypothesis of obesity being exogenous in
most cases. Similar results were obtained by Cawley (2004), Pagan and
Davila (1997) and Àsgeirsdóttir (2004). In the case of employment among
women, however, we rejected the hypothesis of exogeneity. The effect of
treating obesity as endogenous in this case was to increase the effect on
employment. Our instruments, the presence of other obese persons in
the household, being an oldest child, and having sisters only, were subject
to standard econometric tests, which they passed. It should be noted,
though, as discussed in the methods section, that ideal instruments, such as
the obesity of a parent and child, could not be obtained. Using spousal
obesity as an instrument may lead to bias if, for instance, neighbourhood
status influences both spousal obesity and occupational attainment of the
respondent.
244
PETTER LUNDBORG ET AL.
Moreover, in some cases, the predictive power of the instruments was
weak, suggesting that the results from the instrumental variables regressions
should be interpreted with caution.
While this study used instrumental variables techniques to deal and test for
endogeneity of obesity in our regressions, other techniques have been used in
other studies. Consequently, results may to some extent depend on the techniques used. In those studies that have applied more than one of the strategies, it is, however, difficult to conclude if differences in results depend on
the choice of strategy. For instance, Averett and Korenman (1996), Behrman
and Rosenzweig (2001) and Baum and Ford (2004) all get insignificant
effects of BMI when using sibling differences. However, this may be an effect
of the limited number of observations in these two approaches. Behrman and
Rosenzweig (2001), also failed to find significant effects of BMI when using
instrumented BMI, but again this may be caused by their limited sample. In
contrast, Cawley (2000, 2004), using a larger sample, found consistent effects
across all models. Using sibling differences was not possible in our case, since
no information on siblings was present in the dataset.
It should be noted that the indicator of obesity used in the present study
was based on self-reports of height and weight, which both may be subject
to measurement errors. If the latter are randomly distributed, the estimates
of the obesity effects may be biased downwards, i.e. our estimates may be
conservative. If measurement errors are not random, but, for instance, vary
systematically between countries, the expected bias would be harder to predict. We are aware of no studies, however, that have examined variations in
the accuracy of self-reported weight and height in different cultural settings.
It should be noted, though, that Cawley (2000) found that his results did not
change, when correcting for measurement errors in self-reported weight and
height.
Even though prior studies almost exclusively focused on younger populations, our estimates do not differ radically. In Baum and Ford (2004), for
instance, a wage penalty of approximately 5% of being an obese woman was
found in the model including individual- and family-fixed effects. Among
men, the corresponding effect was 0.7% in the individual fixed-effects
model. Larger effects were obtained in Averett and Korenman (1996, 1999),
where being an obese white woman was associated with a wage penalty of
17% (Averett & Korenman, 1999). Being man and obese lowered the wage
rate by 8% (Averett & Korenman, 1996).
Only one prior study analysed the relation between body mass and hours
worked (Cawley & Danziger, 2004). The results are not easily comparable,
though, since BMI, instead of obesity, was used as an explanatory variable.
Obesity and Occupational Attainment Among the 50+ of Europe
245
Here, it was found that among women aged 18–54 the effect of a one-unit
increase in BMI was to lower the number of hours worked by 1.6%, suggesting a BMI elasticity of 0.514. The sample was small (n ¼ 651), however, and covered only single mothers receiving cash-welfare benefits in one
urban county in Michigan.
According to WHO, the prevalence of obesity in European countries is
currently about 10–20% in men and about 10–25% in women (WHO, 2000).
Moreover, even though there are difficulties in obtaining internationally
comparable time-series data, there seems to be a consensus that obesity rates
are rapidly increasing all over the world. In Europe, the increase has been
about 10–40% during the 1990’s (WHO, 2000). The results of this study
suggest that the ongoing rise in the prevalence of obesity in Europe may
have a non-negligible effect on the European labour market.
Further research should aim at identifying reasons for the north–south
difference in the effects of obesity on employment, wages and hours worked
obtained in this study. Candidates are differences in the institutional framework between countries, for instance in labour-market regulations, labourmarket structure, anti-discrimination laws and tax schedules. If, for instance,
anti-discrimination laws are stricter in Denmark and Sweden, compared with
countries in central Europe, this could be part of the explanation as to why
obesity showed no significant effect on wages in the former countries.
Further work should also try to investigate in more detail the mechanisms
by which obesity affects labour-market outcomes in Europe. The results in
this study suggests that part of the explanation may lie in the health effects of
obesity but that a large part remains to be explained. One candidate for the
remaining part is employer discrimination. Investigating discrimination
effects using data on occupations may be a fruitful way to proceed. Such
data may become available in the SHARE database in due time, when individual occupations are coded into comparable categories across countries.
NOTES
1. BMI is measured by dividing weight by length (in metres) in square. Overweight
is usually defined as 25 r BMIo30 and obesity as BMI Z 30.
2. It has been shown that obesity is a risk factor for several health problems such
as, hypertension, dyslipidemia, insulin resistance, hyperinsulinemia, abnormal pulmonary function and several types of cancers (Hubert, Feinleib, McNamara, &
Castelli, 1983; National Institutes of Health, 1985; Council of Scientific Affairs of the
American Medical Association, 1988; Pi-Sunyer, 1991, 1993; Bray, 1992; Abbott
et al., 1994; SBU, 2002).
246
PETTER LUNDBORG ET AL.
3. Exceptions are Loh (1993; no effect on the current wage but a negative effect
on wage-growth), Hamermesh and Biddle (1994; no wage effects), Averett and
Korenman (1999; positive employment effect for white women) and Garcia and
Quintana-Domeque (2006; few significant correlations between obesity and employment/wages).
4. Again, Loh (1993) finds no effect on the current wage, but a negative effect on
wage-growth, for the men.
5. Only a few studies, however, include variables that account for differences in
health (Gortmaker, Must, Perrin, Sobol, & Dietz, 1993; Averett & Korenman, 1996;
Harper, 2000; Àsgeirsdóttir, 2004).
6. Obesity may cause discrimination, if employers have preferences for non-obese
employees, or if fellow workers have preferences for working with non-obese colleagues, or if customers prefer to be served by non-obese attendants. See, for instance, Hamermesh and Biddle (1994) or Baum and Ford (2004).
7. Reversed causality may be present if, for instance, inferior labour-market outcomes cause depression and depression, in turn, causes obesity (Autrey, Strover,
Reatig, & Casper, 1986; Cawley, 2000). Alternatively, inferior labour-market outcomes may cause obesity if fattening foods are relatively cheaper (Cawley, 2004).
Further, people who discount the future more heavily are less likely to invest in their
human capital (affecting labour-market outcomes negatively) and more inclined to
present consumption (increasing the likelihood of weight gain) (Cawley, 2000, 2004;
Baum & Ford, 2004). Thus, the observed correlation may also be caused by some
third factor.
8. For the non-euro countries, i.e. Sweden, Denmark and Switzerland, we used the
average annual exchange rate of 2004 to convert the amounts into euros. The exchange rates used were 9.1243 for Sweden, 7.4399 for Denmark and 1.5438 for
Switzerland (ECB, 2005).
9. Other tests are available as well, such as the Breusch and Pagan (1979) and
Godfrey (1978) test. These tests, however, will only be valid tests for heteroscedasticity in the IV-regression, if heteroscedasticity is only present in that equation and
nowhere else in the system (Pagan & Hall, 1983). The Pagan and Hall test relaxes this
requirement and is, therefore, employed in our case.
10. Three strategies have been employed in the empirical literature in order to take
into account that the body mass may be an endogenous variable. The first strategy is
to use lagged indicators of body mass (Gortmaker et al., 1993; Sargent & Blanchflower, 1994; Averett & Korenman, 1996, 1999; Haskins & Ransford, 1999; Cawley,
2004; Conley & Glauber, 2005, 2006). This assumes that lagged values are uncorrelated with the current wage (or employment probability) residual and that there is
no serial correlation in the wage residuals. This strategy removes potential contemporaneous effects of wages on body mass, but does not address problems of lagged
third factors (for instance time-preferences) affecting both lagged body mass and
current wages.
The second strategy (Averett & Korenman, 1996; Behrman & Rosenzweig, 2001;
Baum & Ford, 2004; Cawley, 2004; Conley & Glauber, 2005, 2006) is to estimate the
wage- and employment-probability equations after taking differences in both outcomes and explanatory variables with another individual with highly correlated
genes and environmental background (e.g. a same-sex sibling or twin). This strategy
assumes that all unobserved heterogeneity is constant within the pairs of individuals
Obesity and Occupational Attainment Among the 50+ of Europe
247
compared and, therefore, is eliminated by the differentiation. It also assumes that
wages do not influence body mass, implying that the differenced variable is uncorrelated with the differenced wage residual. However, if there is unobserved heterogeneity that is not shared between siblings (or twins), the latter assumption may not
hold.
The third strategy is to use variables that are uncorrelated with wages, but affect
body mass, to instrument the latter variable (Pagan & Davila, 1997; Behrman &
Rosenzweig, 2001; Àsgeirsdóttir, 2004; Cawley, 2000, 2004). The main difficulty of
using this strategy is to find credible instruments.
11. We also tried an indicator variable of being the only child, but this variable
was never significant. This result was similar to the one obtained by Celi et al.
(2003).
12. Admittedly, there may be some measurement errors in the variable,
since some respondents may have deceased sisters or brothers. It might be
expected, though, that since the respondents are at most 64 years old, the overwhelming majority of respondents will have sisters and/or brothers that are still
alive.
13. We experimented with other birth-order and family instruments as well, such
as being the youngest child, having no siblings and the number of sisters and brothers. None of these, however, predicted obesity among women in the first-stage regression.
14. The variable was constructed by assigning the value 1 to those having at
least one obese member of the household, other than the respondent him/herself,
and the value 0 to those having no other obese person in the household or having
no other household members at all. Obviously, the latter category is not affected by
the instrument. However, it is not unusual in the economics literature to have instrumental variables estimates where not every respondent is affected by the instrument. Such instrumental variables provide an estimate of the causal effect for the
group whose behaviour is actually affected by the instrument (Imbens & Angrist,
1994). In our case, since we also have additional instruments besides the indicator
of the presence of an obese household member, we do not have to rely solely on
the former instrument, which only identifies the effect for those having household
members.
15. Since only one instrument was used, the C-statistic could not be calculated.
Therefore, we used the Durbin–Wu–Hausman test. Consequently, we did not employ
GMM-estimation in this case but rather used the standard 2SLS. The test yielded a
test statistic of 0.003, and the hypothesis of obesity being exogenous could not be
rejected (p ¼ 0.96).
16. Harper (2000) found indications of employer discrimination based on overall appearance, but not because of obesity, for men. However, for women, the
results were the opposite, i.e. indicating employer discrimination due to obesity,
but not because of general appearance. Baum and Ford (2004) found that indications of employer discrimination for men strongly depended on model specification. For women, they found employer discrimination according to obesity. While
Hamermesh and Biddle (1994), found no significant effect of obesity per se, they
could not refute the hypothesis of overall appearance/beauty being both positively
correlated with individual productivity and negatively correlated with employer
discrimination.
248
PETTER LUNDBORG ET AL.
ACKNOWLEDGEMENT
This paper uses data from the early release 1 of SHARE 2004. This release is
preliminary and may contain errors that will be corrected in later releases.
The SHARE data collection has been primarily funded by the European
Commission through the 5th framework programme (project QLK6-CT2001-00360 in the thematic programme Quality of Life programme area).
Additional funding came from the U.S. National Institute on Aging (U01
AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, Y1-AG4553-01 and OGHA 04-064). Data collection in Austria (through the Austrian Science Foundation, FWF), Belgium (through the Belgian Science
Policy Administration) and Switzerland (through BBW/OFES/UFES) was
nationally funded. The SHARE data set is introduced in Börsch-Supan et al.
(2005); methodological details are contained in Börsch-Supan and Jürges
(2005). Additional funding for the Swedish participation in the SHARE
data collection project came from the Bank of Sweden Tercentenary Foundation, the Swedish Council for Working Life and Social Research and the
Swedish Social Insurance Agency. The research reported in this chapter was
supported by a grant from the Swedish Council for Working Life and Social
Research, which is gratefully acknowledged.
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