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Economics and Human Biology xxx (2005) xxx–xxx
http://www.elsevier.com/locate/ehb
Health and wealth
The late-20th century obesity epidemic in the U.S.
Jay L. Zagorsky *
Center for Human Resource Research, Ohio State University, 921 Chatham Lane,
Suite 100, Columbus, OH 43221, USA
Received 11 May 2005; accepted 11 May 2005
Abstract
Obesity is a rapidly growing public health issue. This paper investigates obesity’s relationship
to individuals’ wealth by analyzing data from a large U.S. longitudinal socio-economic survey.
The results show a large negative association between BMI and White female’s net worth, a
smaller negative association for Black women and White males and no relationship for Black
males. Weight changes and dieting also appear associated with wealth changes. Individuals who
lose small amounts of weight experience little change in net worth, but those who lose large
amounts of weight have a dramatically improved financial position, with Whites showing larger
changes than Blacks.
# 2005 Elsevier B.V. All rights reserved.
JEL classification: I1; D31; J7
Keywords: Obesity; U.S. body mass index; BMI; Weight; Personal wealth; Net worth; Social status; Race;
Ethnicity; Gender
1. Introduction
Obesity is a rapidly growing public health problem throughout the world including the
United States (Fig. 1). Since the early 1960s, Black females experienced the most startling
* Tel.: +1 614 4427332; fax: +1 614 4427329.
E-mail address: [email protected].
1570-677X/$ – see front matter # 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.ehb.2005.05.003
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J.L. Zagorsky / Economics and Human Biology xxx (2005) xxx–xxx
Fig. 1. Share of U.S. Adult Population Obese, by Sex and Ethnicity. Notes: Data for individuals age 20–74 with a
body mass index of 30 or more. The numbers are taken from table 70 of National Center for Health Statistics
(2002).
weight gains, with obesity rates jumping from one-quarter to one-half of all Black
women. Black males, White males and White females all started with much lower
obesity rates but by the year 2000 they, too, were catching up, with obesity rates for all three
groups more than doubling during the time-span (National Center for Health Statistics,
2002).
This development sparked many investigations into both why individuals are
gaining weight and what effects these weight increases are having on individuals.
Zagorsky (2004) explored the relationship between obesity and wealth in the United
States, and this paper expands upon that research by exploring how obesity and
wealth are related based on sex and ethnicity. The paper finds a large negative
relationship between BMI and White female net worth, a smaller negative relationship
for Black women and White males and no relationship for Black males. While these
relationships cannot be proven as causal, they suggest that White women pay the
heaviest financial penalty for being overweight, while Black males experience no
penalty at all.
In addition this research also investigates the relationship between dieting and wealth.
Again while the relationships cannot indicate which direction the causality runs,
individuals who lose a small amount of weight have little change in their net worth, but
individuals who lose large amounts of weight show dramatically improved financial
positions.
One potential reason for the obesity epidemic is economic. Philipson and Posner (2003)
showed that obesity is increasing over time because the inflation-adjusted cost of food has
been falling at the same time that typical jobs have become more sedentary. As it becomes
cheaper to eat and fewer calories are burned during the workday, average weight increases.
Komlos et al. (2004) suggested that changing time preferences are also an important
explanation. Their model posited that people are increasingly focused on short-term
consumption and gratification while increasingly discounting potential future health
consequences, leading individuals to increasingly overeat. Ruhm (2000) found that obesity
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3
was related to economic conditions and that it increased during economic upturns in the
United States.1
Some authors feel obesity also imposes considerable costs to society. Allison et al.
(1999) stated that being overweight shortens lives by greatly increasing the chances of
having serious health problems such as heart disease, stroke and diabetes (National
Institute of Health, 1998). Finkelstein et al. (2003) estimate the cost of obesity to U.S.
society at US$ 75.8 billion in 1998, rivaling the cost of smoking-related diseases. This
figure lead Altman (2003) in a New York Times editorial to propose tax rebates for all U.S.
citizens who can maintain a normal body weight over the course of a year. More recent
research though has suggested that the impact of obesity on health may be greatly
overstated. Flegal et al. (2005) concluded that being overweight was not associated with
excess mortality and that the ‘‘impact of obesity on mortality may have decreased over
time.’’
Beside health, there may be links between obesity and wages. Pagan and Davila (1997)
and Mitra (2001) found that U.S. women who are obese have lower earnings but that obese
men do not experience significant wage penalties. Averett and Korenman (1996) find,
however, that the earnings penalty affected overweight White women but not overweight
African–American women. Cawley (2004) not only corrected women’s height and weight
in the NLSY79 for potential under and over reporting, but also used a clever instrument to
generate consistent estimates.2 He found that extra weight lowered wages for White
women but extra height increases their wages. Extra weight was related to a small negative
wage difference for Hispanic women but showed no relationship to Black women’s wages.
Outside the U.S., in the Netherlands, Mackenbach (1992) finds that obesity was more
prevalent in lower socio-economic groups than higher. Lähteenkorva and Lahelma (1999)
found that overweight and obese Finnish women have lower household income, but not
overweight Finnish men.
While research has documented the relationship between obesity and income, research
has just begun to explore the relationship to wealth. Zagorsky (2004) reports that the net
worth of the obese was roughly half that of those with normal body mass. Fonda et al.
(2004) investigated U.S. retirees and those near retirement and found overweight or obese
women had lower net worth than normal women while overweight and obese men had
higher net worth.
This research expands on the above work by analyzing how obesity and wealth are
related by sex and ethnicity. This connection is important to understand because many
individuals live on more than just their income. During low-income periods many families
spend down their savings or borrow. Conversely, during periods of prosperity many
families build up savings or pay off past debts. Hence, focusing on just current income as
1
These economic issues extend beyond individuals to corporations, some of which find it profitable to
encourage overeating. For example, the Wall Street Journal reports (Parker-Pope, 2003) that many brands of
single serving muffins, brownies and cookies exceed their listed weight so that ‘‘one-third of the products were at
least a full ounce bigger-adding an estimated 100–175 calories more than promised on the package.’’ While in the
past scandals occurred because companies attempted to increase profits by providing short weights and measures,
today some food processing companies are finding it more profitable to overfill.
2
Cawley (2004) linked the NLSY79 child sample with the NLSY79 main file and uses the BMI of children born
to female respondents as his instrument.
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the sole financial measure ignores important ways families cope with life’s financial
changes. This paper focuses on estimating the statistical relationship between body mass,
body type and financial wealth. A theoretical model that explains why the relationship
exists is left for future research since the primary goal is determine if and for whom body
mass and wealth are associated.
2. Data description
There are relatively few national data sets that include both health and wealth
information. One exception is the National Longitudinal Survey of Youth (NLSY79), a
nationally representative panel survey of U.S. youth. The NLSY79 questioned the same
group of people, born between 1957 and 1964, 19 times from its inception to present in
order to provide an in-depth view of socio-economic development. While the NLSY79
began in 1979, this study uses only 12 waves of data, beginning in 1985, the first survey that
included wealth questions, until 2000, the latest publicly available year of wealth data
(Zagorsky, 1997). All data used are publicly available at http://www.bls.gov/nls/
nlsorder.htm.
Beyond the survey’s longitudinal aspects the NLSY79 also over-sampled poor
individuals, Blacks and Hispanics to ensure more accurate results for these groups.3 Due to
space considerations, Hispanic, Asian and Native American respondents were all dropped
from this research. In almost every instance when Hispanics were added back in, their
results fall midway between the White and Black values. Including Asians and Native
Americans in the White category resulted in no significant changes because there are so
few of these respondents in the NLSY79 respondent pool.
All individuals used in this research participated in more than half (>6) of the NLSY79
surveys run since a wealth module was first fielded in 1985. Those who did not meet this
criterion were excluded to ensure the noise caused by respondents with irregular
participation is attenuated. Tests using all respondents who were eligible to be interviewed
show very similar results because the NLSY79 has extremely high rates of participation.4
3. Body mass index
The body-mass-index (BMI), is a measure of an individual weight-to-height ratio [weight
(kg)/height2 (m)]. Following the Centers for Disease Control and Prevention (CDC)
classifications, individuals with a BMI of less than 18.5 are underweight, those with a BMI of
18.5–24.9 are normal, 25.0–29.9 are overweight and 30.0 or more are considered obese.5
3
Given the over sampling, all descriptive and graphical results are reported after being adjusted by the survey
weights (survey variable R02161.00), which removes the over sampling effects and allows the answers to be
considered national totals. Regression results were not weighted. Ethnic separation is done using variable R02147.
Sex separation is done using variable R02148.
4
Among NLSY79 respondents eligible to be interviewed from 1985 to 2000 the average respondent
participated in 11.64 survey, while those who participated more than six times completed 12.4 surveys.
5
CDC categories are found at http://www.cdc.gov/nccdphp/dnpa/bmi.
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NLSY79 youth self-reported their weight 13 times from 1981 until 2000. Response rates
to these weight questions were extremely high with the median respondent answering them
all 13 times (mean response 12.7). Like the overall population, these questions show
individuals in the sample steadily gaining weight. Weight gain from 1981 to 1985 is
expected, since in 1981 the youngest respondent was just 16 years old. Beyond 1985, when
all respondents were 20 or older, weight gain is not biologically expected since respondents
have reached their terminal height. Nevertheless from 1985 to 2000 these individuals
continued to add weight with the average male increasing from 172 to 197 pounds and
females increasing from 132 to 158 pounds.
Some female weight gain is attributable to pregnancy, which temporarily boosts a
women’s BMI and potentially biases results. To check this bias all tables and regressions
were rerun by dropping women who were pregnant during an interview. Since this had no
qualitative and limited quantitative impact, pregnant women are included in this research’s
results.
Research by Cawley (2004) and Ossiander et al. (2004) show that heavier women tend
to underreport their weight. While both authors provide roughly similar adjustment
equations, Cawley’s were used in this research as they were created specifically for the
NLSY79.6 All results were produced using both unadjusted and adjusted data, but only
unadjusted results are reported because correcting made little difference. This matches
Lakdawalla and Philipson (2002) who also found no appreciable change after adjusting.
In 1981, 1982 and 1985, survey respondents were asked to self-report their height.
These data and the corresponding weight information were used to calculate BMI.7 Using
the BMI cut-offs, described above, individuals were classified as underweight, normal,
overweight and obese. The percentage in each category demonstrates the growing obesity
trend. From 1985 to 2000 the overall number of obese individuals increased from 7% to
26%. Breaking the numbers down by ethnicity and sex shows Black females were the most
likely to be obese in both 1985 (14%) and in 2000 (44%). Black males in 1985 had the
lowest rate of all four groups (5%) but by 2000 ranked second highest (32%). White males
(7%) and females (6%) started relatively low in 1985 but both more than tripled their rates
(25% males and 21% females) by 2000.8
4. Wealth
The NLSY79 asked each respondent a variety of detailed asset and debt questions in 11
out of the 13 surveys fielded from 1985 to 2000. These 11 wealth modules all follow the
same pattern. Respondents were first asked if they or their spouse currently owe a debt or
have an asset. If they answered yes, the interviewer asked them to state the current market
value as of the interview date. By summing all the asset answers and subtracting from that
6
An example of how to use Cawley’s corrections is that a White male’s actual height should be
73.02747 (0.0450414 reported height) + 0.0066118 (reported height2 70.55754822).
7
For years beyond 1985 the respondent’s terminal height was used in the formula. For the small number of
cases (1.4–106% respondents) where the person reported shrinking over time, the middle height was used.
8
These percentages differ from those reported in Fig. 1 because they are based on a different data set.
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Fig. 2. Net worth of Whites in 2000 by BMI and gender.
total all debt answers a respondent’s total net worth was created for each survey year.
Repeating this procedure for all 11 surveys created a net worth series for each respondent
(Zagorsky, 1999) as shown in Eq. (1). All net worth value are adjusted into year 2000 US$
to account for inflation.9
Net worth ¼ home value mortgage property debt þ cash saving þ stock
þ trusts þ business=farm=re equity business=farm=re debt
þ car value car debt þ possessions other debt þ IRA
þ 401 K þ CD:
(1)
Overall median individual net worth increased from US$ 5050 in 1985 to US$ 65,500 in
2000. Calculating median net worth broken down by subgroups showed the typical White
male (US$ 7424 in 1985; US$ 82,500 in 2000) and White female (US$ 6792 in 1985; US$
87,500 in 2000) have dramatically more wealth than Black male (US$ 1321 in 1985; US$
8200 in 2000) or Black female (US$ 158 in 1985; US$ 6650 in 2000).
Net worth in 2000 by BMI reveals an inverted V shaped pattern for all four groups
(Figs. 2 and 3).10 Low net worth is associated with low BMI. The peak net worth figures are
found in the BMI mid-range. Finally, high BMI levels are associated with lower net worth.
White females’ peak net worth is at the low end of the normal range (BMI 20), White males
and Black females peak net worth at the upper end of the normal range (BMI 24) but Black
males peak net worth occurs in the obese range (BMI 32).
9
Missing wealth values were imputed using the procedure described by Camphuis (1993) for the Netherlands
Socio-Economic Panel. Camphuis’ technique causes some data smoothing, but the high response rates to wealth
questions result in little imputation being needed.
10
Each respondent’s BMI was rounded into categories two BMI units wide. The graphs show the median net
worth of each category. Categories with less than 25 respondents are not graphed.
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Fig. 3. Net worth of Blacks in 2000 by BMI and gender.
In absolute dollar terms the largest drop from peak net worth occurs for White females.
However, other groups experience relatively large decreases. For example, comparing
White females with the highest net worth with those whose BMI is 10 points higher shows a
net worth that is 44% smaller. Black females with the highest net worth have 55% greater
wealth than those who have a BMI 10 points greater.
One drawback using these net worth figures is that for married respondents net worth is
a family-level measure, while body mass pertains to individuals. Since the NLSY79 does
not ask respondents about their spouse’s height or weight there is the potential that the
pattern found in these figures are influenced by the spouse’s BMI instead of the
respondents. Rerunning the data first by separately tracking married and unmarried
respondents and second by adjusting the net worth for the number of adults in the
household shows the same general patterns as discussed above. Accounting or not
accounting for marriage did not change the inverted V shapes.
5. Demographic profile
Table 1 provides a demographic overview of individuals who participated in the year
2000 survey and completed more than six surveys since 1985. The mean BMI of 27.2 falls
into the middle of the overweight range, with Black females (BMI = 29.7) the heaviest. The
second line tracks the number of adult years the respondent was overweight or obese.11
Since age 18 the subjects have been overweight on an average 8.3 years, with the average
White males, Black males and Black females all being overweight about a decade. Given
that respondents were just 38.7 years old in 2000 White male, Black male and Black female
subjects have been overweight more than half their adult life.
11
Since the NLSY79 measures BMI only 13 times, interpolation was used to create a yearly BMI classification
for each respondent starting from age 18 years.
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Table 1
Demographics of the NLSY79 Sample, 2000
Body mass index
Number of years overweight
Average age in years
Mean family income (US$)
Mean net worth (US$)
Highest grade completed
Percent self employed
Married in 2000 (%)
Ever divorced (%)
Number of children
Have health insurance (%)
Yearly change in home value (%)
Mean spending on food (US$)
Weighted % of all respondents
Number of respondents
Overall
White male
White female
Black male
Black female
27.2
8.3
38.7
65042
184942
13.5
7.4
56.8
29.0
1.7
75.6
7.4
4827
100
7335
27.7
10.1
38.6
70952
213414
13.6
10.6
58.6
26.4
1.5
74.9
8.1
5000
38.7
2268
26.1
5.9
38.7
68338
208163
13.7
5.9
63.9
33.4
1.8
79.1
8.1
4935
39.1
2279
28.4
10.1
38.5
42562
55802
12.4
5.5
33.8
21.9
1.8
61.8
3.0
4056
7.2
1402
29.7
10.0
38.8
36803
40310
13.0
2.9
32.3
26.6
2.1
73.7
3.9
3944
7.5
1386
Notes: Hispanic, Asian and Native American respondents are eliminated from the overall category. Selfemployment rates are based on answers in the ‘‘class of worker’’ variables.
The fourth line shows that Whites have the highest income (males US$ 70,952; females
US$ 68,338) followed by Black males (US$ 42,562) and Black females (US$ 36,803).12
The mean net worth line tracks wealth in 2000 and shows males have more wealth than
females and Whites more wealth than Blacks. The average respondent completed high
school (12 years) and received 1.5 years of additional schooling.13 The next line shows
White males (10.6%) had the largest self-employment rate, while Black females (2.9%)
had the lowest.
While the NLSY79 provides detailed information on income and wealth, it does so in
only a limited way on capital appreciation and expenditures. For capital appreciation
the only variable is the rate of change in home value, which is largest for White females
(8.1%) but smallest for Black males (3.0%).14 Expenditure data were collected only
during the early 1990s, when the survey asked questions about food spending.15 Mean
overall spending on food during the early 1990s was US$ 4800 per year. However, when
the figure is broken down by ethnicity and gender, Black females (US$ 3944) report
spending least. Unfortunately, outside of food the survey does not include other
12
Total yearly income is calculated by combining for the respondent and their spouse or partner; all wage, salary,
military pay, self-employment income, and government transfers such as unemployment insurance and welfare.
13
Highest grade completed is taken from NLSY79 variable R65404.
14
There were 29,566 observations where an individual owned a home and provided its value in two consecutive
surveys. The calculations have an unknown bias because the NLSY79 public data set does not record if the
consecutive surveys are referring to the same home. Additionally, if a respondent improves the home’s value by
renovating, the value increases even if un-renovated houses in the neighborhood experience no capital appreciation.
15
From 1990 to 1994 the NLSY79 survey asked respondents how much money they spent on; food purchased at
home using money other than food stamps, food delivered to the home, food purchased for eating outside the
home, and the value of all food stamps they were given. Total food spending per year is the summation of these
four categories from each survey.
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spending information, such as medical outlays.16 Finally, the last rows of the table show in
2000 more than 7300 individuals responded to the survey, with Whites representing 62%
and Blacks 38% of the respondents.
6. Regression results
The pictures suggest a visual association between net worth and BMI. One problem with
the visual approach is that other factors, such as the different educational and income levels
of the four groups are not held constant. Hence, the pictures might not be showing the
relationship between BMI and net worth, but instead the relationship both have with a third
factor. To eliminate this concern net worth regressions were run holding constant the
factors described in the previous section. Before the results are presented a number of
issues, direction, multiple dependent variables, outliers and data clustering need to be
discussed, because each problem affects the regressions.
The first problem is determining whether body mass or net worth should be the
explanatory or right-hand variable. This research uses net worth as the dependent or lefthand variable and BMI as an explanatory or right-hand variable for a number of reasons.
First, the author’s primary research interest is identifying the correlates of poverty and
wealth. Using net worth on the left side and BMI on the right provides some additional
information to understand this topic. Second, previous research, cited in the introduction,
looks at the relationship between body mass and income. By using net worth on the lefthand side this paper follows and expands upon the earlier results.
Third, inheritance data in the NLSY79 allow a direct test to check if wealth impacts
BMI. In most NLSY79 surveys since 1989 respondents are asked if they received a large
(>US$ 500) inheritance, and if so what year they inherited and the gift’s value. Since
inheritances are exogenous changes, these data provide a method of testing if positive
exogenous wealth shocks reduce weight. Regressions were run using the data from the
1058 respondents who had both a positive inheritance amount (independent variable) and a
valid change in BMI (dependent variable). The regression’s inheritance coefficient was
statistically and quantitatively zero in all cases, indicating positive wealth shocks have no
impact on body mass.17 While these reasons suggest why net worth is on the left-hand-side
of each equation, all regression results found in the rest of this research are only tests of the
strength of association, not causation.
The next issue is that in time series regressions there is usually just one dependent
variable and a set of explanatory independent variables. Yet, in the NLSY79 case there is a
series of dependent wealth variables for each respondent at multiple points in time as well
as a series of independent explanatory variables, some of which are time depending. The
strategy below follows Allison (1995, p. 223) and creates multiple observations for each
16
Having medical outlays data are important because they would show if the obese are limited in their ability to
accumulate wealth because of higher medical care expenditures.
17
The change in BMI was from the year the inheritance was received to the following year. Respondents who
inherited multiple times were not used in the regressions. The baseline regression used just inheritances as the
explanatory variable. Additional regressions, which added variables for inheritance squared, ethnicity and sex did
not improve results.
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Table 2
Regression results
Body mass
Year
Total income in US$
Age in years
Highest grade completed
Self employed (T/F)
Married (T/F)
Ever divorced (T/F)
Number of children
Health insurance (T/F)
Home value change
Annual food spending
Constant
R2
F Statistic
N
Overall
(US$)
White male
(US$)
White female
(US$)
Black male
(US$)
Black female
(US$)
1306***
4800***
0.80***
2205***
3986***
34713***
13136***
5496***
2768***
8043***
57618***
5.94***
118480***
0.28
222.9***
69732
524
6021***
0.95***
3468***
4712***
40089***
6579***
10783***
480
14189***
61050***
5.03***
160517***
0.30
148.3***
21449
1829***
7108***
0.76***
3112***
4313***
30602***
10657***
6098*
1415
15720***
$55210***
7.29***
165286***
0.27
165.4***
21260
20.2
1428***
0.75***
437
2480***
14630***
2471
1516
1206*
624
$42194***
1.74***
35445***
0.22
61.2***
13217
585***
2014***
0.22**
499
2174***
11947*
11973***
695
3684***
1540
$33620***
3.70***
35930***
0.15
40.6***
13255
Notes: (***) is significant at 99% level (**) at 95% and (*) at 90%. BMI is the absolute value of the respondent’s
BMI 22.5. Coefficients represent the change in wealth associated with a one unit change in each explanatory
variable. Mean values of the explanatory variables are found in Table 1 and described in Section 5. Adjusted
standard errors available upon request. Dependent variable: net worth.
respondent, one for each year of wealth data. Allison showed that this technique does not
result in biased estimates or inflated test statistics.
The third problem with NLSY79 wealth regressions is to ensure the small number of
extremely wealthy individuals do not exert undue influence on the results. Thus, two sets of
regressions are run. The first analysis, which uses net worth as the dependent variable,
eliminates the top 1% of wealth holders in each year (Table 2). The results in Table 3, which
use the natural log transformation, are based on all available data since the natural log
transformation automatically reduces the impact of extreme values.
Moreover, the NLSY79 is a multi-stage clustered sample. This means that the initial
panel was created by first randomly choosing areas and then within those areas randomly
selecting households. This two-step process clustered respondents together, reducing the
amount of time interviewers spent travelling for each face-to-face interview, but resulting
in many respondents sharing similar social and demographic characteristics. Because of
the clustering survey results appear more homogeneous than actually found in the U.S. and
the unadjusted regression coefficients appear extraordinarily precise. To adjust for
geographic clustering a linear Generalized Estimating Equation, or GEE framework (Liang
and Zeger, 1986) was used to estimate the coefficients and their adjusted standard errors
instead of ordinary least squares regression.18
The regressions were run after adjusting for these technical issues. Because of space
limitations Tables 2 and 3 contain only the most important regressions. Regressions are
18
All GEE regressions were computed using the Sudaan 9.0 software package.
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11
Table 3
Regression results
Overall
Body mass
Year
Ln(income)
Age in years
Highest grade completed
Self employed (T/F)
Married (T/F)
Ever divorced (T/F)
Number of children
Health insurance (T/F)
Home value change
Annual food spending
Constant
R2
F Statistic
N
***
0.08
0.19***
0.55***
0.12***
0.14***
1.08***
2.29***
0.22**
0.55***
1.50***
2.57***
0.0003***
6.01***
0.19
2549.5***
69659
White male
0.02
0.18***
0.56***
0.18***
0.10***
1.06***
1.12***
0.53***
0.34***
1.93***
2.16***
0.0002***
6.24***
0.15
1760.6***
21666
White female
***
0.12
0.22***
0.49***
0.17***
0.10***
0.53**
2.16***
0.71***
0.46***
1.61***
2.45***
0.0003***
6.06***
0.17
2161.7***
21479
Black male
Black female
0.03
0.06***
0.48***
0.01
0.21***
0.84**
1.53***
0.10
0.07
1.61***
3.15***
0.0003***
2.76***
0.16
656.8***
13235
0.07***
0.22***
0.44***
0.10**
0.11**
1.40***
2.91***
0.34*
0.69***
0.93***
2.79***
0.0004***
6.18***
0.16
313.1***
13276
Notes: The coefficients show the percentage change in wealth. (***) is significant at 99% level (**) at 95% and (*)
at 90%. BMI is the absolute value of the respondent’s BMI minus 22.5. For additional information see the notes to
Table 2 and the text. Dependent variable: Ln (net worth).
omitted which produced similar qualitative results such as using BMI raised to higher
powers,19 including fixed respondent effects20 modifying the list of explanatory
demographic variables21 and adding lagged BMI.22 Last, to simplify the exposition
instead of using BMI directly, an adjusted value was used. Regressions in Tables 2 and 3
use the absolute value of the difference between an individual’s BMI value and the target
value of 22.5. Using this difference enables BMI coefficients to be interpreted as the impact
of being above or below a target near the middle of the normal range.23
The coefficients of the ‘‘BMI’’ variable in the ‘‘Overall’’ column indicates that a one
unit change in BMI away from the normal target of 22.5 is associated with a reduction in
wealth by US$ 1306 or 8%, holding all other factors constant. The regressions for Black
males show no relationship between net worth and BMI (coefficients +US$ 20, +3%). The
19
Adding BMI raised to higher powers resulted in statistically insignificant higher order terms.
Redoing the regressions in a fixed effect framework to account for respondent specific changes not tracked by
the demographic items did not qualitatively or quantitatively change the results. For example the fixed effect
regression based on the overall regression found in Table 2’s first column is; net worth = US$ 134,285 US$
1650 body mass + US$ 4891 year + US$ 0.495 income + US$ 2172 age + US$ 691 high grade + US$ 15,132
self employed + US$ 18,752 married + US$ 11,628 ever divorced + US$ 4784 number of children + US$ 751
health insurance + US$ 40,902 home value chng. + US$ 5.8 food spending + respondent specific terms. For space
reasons the additional >7000 coefficients that track respondent specific effects are not included.
21
Adding variables that tracked health status and number of ailments did not improve the equations overall R2.
Including a total of all previously earned income reduced the current income coefficient but did not improve R2.
22
Lagged BMI values were both qualitatively and quantitatively close to zero, indicating the relationship is just
contemporaneous.
23
Additional simplifications are; the age variable is true age minus 16 years, the year variable is the true year
minus 1979 and the highest grade completed is the true grade 12.
20
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regressions for White males (US$ 524, 2%) show a small relationship. The regressions
for Black females (US$ 585, 7%) show a medium relationship. The largest relationship
between the two variables is for White females (US$ 1829, 12%). These regressions
show a stronger wealth-BMI association for females than males, and for Whites than for
Blacks.
The coefficients on the year and age variables indicate that as the cohort matures wealth
increases. Each additional survey year adds to overall wealth by US$ 4800 or 19% and
being a year older within the cohort increases wealth by US$ 2205 or 12%. The overall
income coefficient shows those earning 1% more are associated with a net worth that is
0.55% higher. Each additional year of education boosts net worth by 14%, with Black
males (21%) see the biggest percentage effect from schooling and White males (US$ 4712)
the biggest absolute effect.
The US$ 34,713 and 108% overall coefficient on self-employed confirms Bates’ (1997)
research, which shows entrepreneurs and business owners have much higher wealth
holdings than average. Marriage also boosts net worth with married respondents having a
net worth 229% that of the non-married. Like the BMI coefficients, the marriage effect is
significantly larger for females than for males. Ever experiencing a divorce destroys wealth
for Whites but the coefficient is not statistically distinguishable from zero for Blacks. The
negative coefficients on the number of children suggest each additional son or daughter has
a wealth dampening effect. The last three lines show that respondents who have health
insurance and whose homes increase in value have significantly higher net worth but
spending on food does not impact wealth.
One possible explanation why the relationship between BMI and net worth is larger for
women than for men is a marriage effect. If women with a higher BMI have a lower chance
of being married, their net worth could be lower since there are fewer adults in the
household saving money. To check this, all regressions were rerun with net worth divided
in half in every wave that the respondent was married, to account for twice as many adults
in the household. The adjusted regression’s key coefficients on body mass are very similar
quantitatively to those in Table 2 (overall US$ 731)24 and almost exactly the same as
those in Table 3 (overall US$ 0.08).25 Hence, accounting or not accounting for marriage
did not influence the results.
To check the robustness of these results, dummy variables, which classified the
respondent as underweight, normal, overweight or obese were created and used to replace
BMI in the regressions. Since this replacement did not change most of the demographic and
socioeconomic values Table 4 lists just the new variables’ coefficients. Because a
respondent must be in one category, the omitted case is the normal category. The top
section of the table shows a negative relationship between those overweight and their total
net worth (overall US$ 7519) and an even larger negative relationship for those who are
obese (overall US$ 16,777). Similar results are seen in the table’s bottom, which uses the
log of net worth. There is a negative relationship between those overweight and their total
24
The other key coefficients with net worth as the dependent variable are White male US$ 170; White female
US$ 969; Black male +US$ 122; and Black female US$ 409.
25
The other key coefficients with the natural log of net worth as the dependent variable are White male US$
0.02; White female US$ 0.12; Black male +US$ 0.03; and Black female US$ 0.07.
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13
Table 4
Key coefficients using broad classifications instead of BMI as explanatory variables
Overall
White Male
White Female
Black Male
Black Female
Left hand side net worth
Underweight
599
Overweight
7519***
Obese
16777***
10964
1898
4880
4900
10876***
26072***
530
559
541
240
2816***
7979***
Left hand side Ln (net Worth)
Underweight
0.08
Overweight
0.14**
Obese
0.88***
0.50
0.04
0.12
0.01
0.35**
1.65***
0.11
0.41***
0.44*
0.63
0.18
0.84***
Note: The omitted category are people with normal body mass. (***) is significant at 99% level (**) at 95% and (*)
at 90%.
net worth (overall 14%) and an even larger negative relationship for those who are obese
(overall 88%).26
Overall, the results show that changes in White female’s body mass and type are
associated with the largest wealth impact. Comparing BMI coefficients from Table 3,
shows the typical Black female experiences a wealth change about half as big as
experienced by White females when their body mass changes by one point away from a
BMI of 22.5. White males experience about half the change experienced by Black females,
but body mass changes have no negative relationship with the wealth of Black males, after
holding other demographic factors constant.
7. Losing or gaining weight
Dieting and exercising to control weight is an important aspect of many people’s lives. In
one national U.S. health survey overseen by the Centers for Disease Control almost threequarters (70.5%) stated that they were eating either fewer calories or less fat to lose or keep
from gaining weight during 2000. In the same survey over 60% (60.7%) stated they were
using physical activity or exercise to lose or keep from gaining weight.27 While modifying
exercise and eating habits affect weight, the economic benefits or consequences are unknown.
While many individuals diet and exercise to lose weight, almost one-half of the
individuals followed by this research kept their same broad BMI classification since 1985.
Table 5 indicates the percentage keeping their BMI classification constant over time. The
top two lines show that 28.9% of the persons were always in either the underweight or
normal categories, while 21.3% were always in either the overweight or obese categories.
The next lines break the broad categories down into finer groupings and shows slightly
26
Because so few NLSY79 respondents are in the underweight category, none of the underweight coefficients
are statistically significant.
27
Data are from the Behavioral Risk Factor Surveillance System, a national survey run by the National Center
for Chronic Disease Prevention and Health Promotion. The survey is done annually by telephone by each state’s
health department. Weight control questions are asked in all even years. The figures quoted are from the weight
control section of the 2000 survey, found online at http://apps.nccd.cdc.gov/brfss.
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Table 5
Percentage of persons maintaining BMI classification from 1985 to 2000
Overall
White male
White female
Black male
Black female
Always underweight or normal (%)
Always overweight or obese (%)
28.9
21.3
21.2
27.9
40.9
13.3
16.4
23.4
17.4
26.6
Always
Always
Always
Always
0.2
21.7
3.7
3.9
0.1
19.2
7.3
3.6
0.4
27.4
0.8
3.3
0.2
14.1
3.0
3.2
0.3
11.8
7.6
9.5
underweight (%)
normal (%)
overweight (%)
obese (%)
Note: The top two rows contain both individuals who stay in one category and individuals who move between the
two classifications.
more than one-fifth (21.7%) of all persons were persistently in the normal category and
about 4% were persistently always overweight or always obese.
The next four columns repeat the analysis but break the categories down by ethnicity
and gender. Looking at the broad categories in the top two lines shows White females
(40.9%) and White males (21.2%) are most likely to always be underweight or normal.
White males (27.9%) and Black females (26.6%) are most likely to always be either
overweight or obese. Combining the bottom four lines shows White females (total 31.9%)
are the most likely to stay consistently in one of the four finer weight categories, while
Black males (total 20.5%) are the least likely.
While half of all individuals did not change their broad classification, half did. Does
losing or gaining weight have any relationship with losing or gaining wealth? Since the
NLSY79 tracks the same individuals over time, one can calculate the amount BMI and net
worth changes from survey-to-survey.28 This first difference of each series shows that the
average person’s BMI increases about one-quarter of a point (median 0.22, mean 0.25) per
year. At the same time the difference from survey-to-survey in net worth is about US$ 3000
(median US$ 2435, mean US$ 4339). Together these figures denote that roughly every four
years the typical person gains both one BMI and US$ 10,000–17,000 in wealth.
Regressions in Table 6 explore the year-to-year change in net worth using the change in
each respondent’s BMI from year-to-year and the change in BMI squared as key
explanatory variables.29 Since the BMI change coefficient takes both positive and negative
values and is not statistically distinguishable from zero, this term has little explanatory
power.30 The BMI squared term, however, is both quantitatively large (maximum US$
114) and is statistically distinct from zero for all but Black males.
This means that small weight changes have little relationship to net worth changes but
larger weight changes are related to large net worth changes. For example, using the overall
regression coefficients shows that when the typical young person decreased their BMI by
28
For surveys that cover a 2-year time period the changes were divided in half for consistency so that all data
represent yearly changes.
29
Squaring BMI allows weight changes to affect wealth nonlinearly and allows individuals losing or gaining a
large amount of weight to potentially have a bigger impact on their wealth than those changing their weight
slightly.
30
To prevent extreme outliers from influencing the results, data from surveys where the respondent’s change in
BMI was more than 15 points or net worth changed by more than US$ 250,000 were eliminated. All surveys in
which female respondents stated they were pregnant were also excluded.
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15
Table 6
Regression results
BMI change
BMI2 change
BMI changet-1
Income change
Net wortht1 change
Survey year
Became married
Became divorced
Constant
R2
F Statistic
N
Overall
(US$)
White male
(US$)
White female
(US$)
Black male
(US$)
Black female
(US$)
136
98***
126
0.05***
0.21***
755***
8503***
5194***
2002***
0.05
309***
48893
172
110**
70
0.05**
0.21***
937***
5769***
5202**
1589**
0.05
106***
16876
48
114***
154
0.05***
0.21***
722***
14966***
6416***
3129***
0.05
86***
13873
326
4.5
193
0.07
0.33***
391***
5040***
163
627
0.11
154***
9719
198
25*
81.6
0.06**
0.26***
182***
3640**
3187
1053**
0.07
75***
8422
Notes: (***) is significant at 99% level (**) at 95% and (*) at 90%. Adjusted standard errors available upon
request. Dependent variable: year-to-year net worth change.
one point their wealth increased by US$ 234 (US$ 136 + US$ 98). However, when a
boomer decreased from the middle of the overweight category (BMI 27.5) to the middle of
the normal category (BMI 21.7), a fall of 5.8 points, the regression indicates their wealth
changed by more than four thousand dollars (US$ 4085).
Reducing or increasing BMI by 10 points, a large weight change, shows a different
relationship for each ethnicity and gender group.31 A 10 point BMI decline is associated with
a wealth increase of US$ 12,720 for White males, US$ 11,880 for White females, US$ 4480
for Black females and a decrease (US$ 2810) for Black males.
The third row of the table contains the lagged BMI coefficient, which is labeled BMIt1.
This coefficient checks if previous weight changes are related to current wealth. Given that
the coefficient is never statistically different from zero in all cases there appears to be no
relationship.
The other factors included in the regressions are the change in income, if the respondent
changed their marital status, the survey year to capture the general upward growth in
boomer wealth, and a lagged valued of net worth change to reduce the impact of serial
correlation on the results. The coefficients on these terms show that every dollar increase in
income is associated with a 5–7¢ increase in net worth.32 Each year net worth increases on
an average by US$ 755, while becoming married boosts net worth by roughly US$ 8500
and becoming divorced reduces it by roughly US$ 5200. The 21¢ coefficient on lagged
net worth shows that individuals whose net worth increases in one survey tend to give back
roughly one-fifth of that wealth increase by the next survey, while individuals whose net
worth falls tend to increase their wealth by the next.
Overall, the regressions show a relationship between wealth changes and weight loss
squared. Since the data underlying these regressions are not from a controlled experiment,
it is impossible to definitively state how dieting affects wealth or the direction of causality.
31
32
An example of a 10 point BMI reduction is a 6-foot tall man who drops from 250 to 175 pounds.
This change is formally called the marginal propensity to save.
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Nevertheless, the regressions suggest for the typical individual who loses or gains a few
pounds there is almost no change in wealth, while for those losing or gaining large amounts
of weight there is a large change.
8. Conclusion
Obesity has been declared a new public health epidemic. This research investigated if
obesity has any relationship to the wealth of Whites and Blacks, males and females.
Graphing net worth against BMI reveals an inverted V shaped pattern for all four groups
with White females experiencing peak net worth at the low end of the normal range (BMI
20), White males and Black females experiencing peak net worth at the upper end of the
normal range (BMI 24) and Black males peaking in the obese range (BMI 32).
Regression results, which adjust for a variety of demographic differences not accounted
for in the graphs, reveal that each one-unit increase in a typical young person’s BMI is
associated with roughly a US$ 1300 or 8% reduction in wealth. This reduction, however,
differs dramatically by ethnicity and gender. BMI changes are not associated with changes
in Black male’s wealth, are associated with small negative changes in White male’s wealth,
medium negative changes on Black female’s wealth and large negative changes on White
female’s wealth. Future research needs to develop theoretical models that explain why this
relationship exists for these U.S. data.
Since, dieting and exercising to control weight are commonplace, regressions were run
to investigate the relationship of body mass changes and wealth changes. In general the
data show overall that individuals losing or gaining large amounts of weight also have
corresponding large changes in wealth. Slight weight changes, however, are not associated
with any significant wealth changes.
Obesity has been a rapidly growing public health issue in the United States. From the
late 1970s–2000 the number of adults classified as obese roughly doubled. Philipson
(2001) points out how obesity is connected to smoking, city planning and marriage. He
observes that antismoking policies increase obesity since smoking suppresses appetite;
sprawling cities result in people driving more and walking less, which increases obesity;
and reductions in marriageable men result in unmarried women being less concerned about
their weight, increasing obesity. Combining these ideas with the increased focus on more
immediate gratification described in Komlos et al. (2004) suggests obesity issues will
continue growing as a major health problem.
This research’s investigation into obesity and net worth suggests another connection:
between obesity and limited savings. During the period when the U.S. experienced surging
obesity the national savings rate plummeted, from 10.2% in 1980 to 2.3% in 2001.33 While
this research did not investigate the linkage between obesity and savings, wealth changes
are often driven by savings changes.34
33
National savings figures are taken from the BEA’s ‘‘Personal Saving as a Percentage of Disposable Personal
Income’’ spreadsheet located online at http://www.bea.gov/bea/dn1.htm.
34
BMI might have no direct impact on savings, but rather there might be other underlying factors such as
different time preferences that affect both individuals’ weight and their capability to accumulate money.
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J.L. Zagorsky / Economics and Human Biology xxx (2005) xxx–xxx
17
The inverse relationship between BMI and wealth highlighted by this paper suggests
that BMI changes are potentially negatively related to savings rates. If BMI changes are
negatively associated with savings, then continued BMI increases could presage further
declines in the U.S. savings rate. Future research needs to understand how dangerous
obesity is to a nation’s wealth and savings. While medical research shows that being
overweight is dangerous to one’s health this research shows obesity is potentially
dangerous to one’s wealth.
Acknowledgement
I wish to thank Dr. Robert Sege for his helpful discussion of obesity.
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