WORKING 

PAPERS 

Decomposing the American Obesity Epidemic

Thomas G. Koch 

Nathan E. Wilson 

WORKING PAPER NO. 318 

May 2013 

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BUREAU OF ECONOMICS 

FEDERAL TRADE COMMISSION 

WASHINGTON, DC 20580
Decomposing the American Obesity Epidemic∗

Thomas G. Koch Nathan E. Wilson†

May 14, 2013

Abstract

In recent decades, the prevalence of obesity in America has increased dramatically.

Though it has attracted less attention, the demographic composition of the American population also changed during this period. We decompose the increase in the

average body mass index of the American population over 30 years and show that demographic changes explain a statistically significant but economically marginal amount

of the change. Instead, the rise in average obesity is best explained by increases in BMI

within demographic groups. Furthermore, our results indicate that groups’ experiences

have been heterogeneous with younger women experiencing especially large gains in

weight. We uncover some evidence consistent with the hypothesis that this can be at

least partially attributed to increased labor force participation.

JEL Codes: I12, I18, H51

Keywords: obesity, BMI, demographic change

∗Bureau of Economics, Federal Trade Commission. Contact: 600 Pennsylvania Avenue NW, M-8059,

Washington DC, 20580. Phone: 512-809-8014 and 202-326-3485. E-mail: tkoch@ftc.gov and nwilson@ftc.gov.

Fax: 202-326-2625. The views expressed in this article are those of the authors. They do not necessarily

represent those of the Federal Trade Commission or any of its Commissioners. Many of the findings reported in this paper were originally discussed in a larger manuscript entitled “Decomposing the Origins and

Implications of the American Obesity Epidemic.” Details on changes are available upon request.

†Corresponding author.

1
1 Introduction

Observing the dramatic increase in the average American’s weight over the last few decades

(see, e.g., Cutler et al. (2003)), many commentators and public policy officials have reacted

with alarm, labeling the phenomenon the “obesity epidemic.” Scholarly research on obesity

also has become widespread, but comparatively little has focused carefully on the question

of how and why the average obesity rate has changed over the long term.1 This is worrisome

insofar as the increase in obesity has coincided with several demographic trends that might,

at least partially, also explain it.2 Alternatively, one might wonder whether the changes to the

population average reflect disproportionately large changes for certain demographic groups,

while other groups’ body composition has remained unchanged.

To address these questions, we exploit 40 years of data that include information on

demographics and body composition. These data, collected in the National Health Interview

Survey (NHIS), provide such measures for a representative sample of the population from the

1970s through the current day. Like much of the clinical community, as well as many other

researchers, our proxy for body composition is individuals’ body mass index (BMI), which

is defined as an individual’s weight in kilograms divided by their height in meters squared.

Individuals with BMI’s less than 18.5 are considered unhealthily underweight, while those in

excess of 25 are considered clinically overweight. Having a BMI greater than 30 is considered

clinically obese.3

1The obesity literature has expanded rapidly. For recent surveys, see Rosin (2008) and Philipson and

Posner (2008).

2See, e.g., a recent presentation by the Patient Centered Outcomes Research Institute on obesity

(http://www.pcori.org/assets/PCORI-Obesity-Treatment-Options-Workgroup-Presentations-041613.pdf

(accessed April 24, 2013). In particular, slide 30 indicates that obesity is higher for older Americans,

African-Americans, and Mexican-Americans. All three of these groups grew in prominence during the same

period that obesity is perceived to have increased.

3Despite its increasing commonality as a metric for evaluating weight issues, BMI scores do not map

perfectly to what one might reasonably believe to be an unhealthy body composition. In particular, athletes

and other extremely fit individuals often have BMI scores that qualify as “overweight” due to their high levels

2
Using the NHIS data, we uncover several salient facts: first, the change in average BMI

since the late 1970s can be best explained by changes in BMI within demographic groups.

Thus, the increase in obesity is not being driven by the aforementioned alterations to America’s demographic structure. Indeed, our results show that the variation in the nation’s

demographic composition accounts for less than seven percent of the level change in America’s average BMI. By contrast, within demographic group changes in average BMI account

for 91 percent of the change.

Second, our results show that while average waistlines grew for almost all demographic

groups, the magnitude of the increases varied both substantially and systematically. In particular, we found that working-age women have experienced disproportionately large gains

in BMI. Indeed, between the late 1970s and more recent years, the BMIs of women between

the age of 18 and 65 grew by 20-33% more than those of men of similar ages. Similarly

pronounced differences are not observed along racial or ethnic lines.

Overall, our paper contributes to the growing literature on obesity. While the increase in

average BMI over time has been well-documented, as has the existence of substantial variation in obesity across demographic groups, we believe that comparatively little has been done

to link these two phenomena.4 Finding that within demographic group changes dominate

the impact of compositional variation should cause us to refocus efforts at understanding

what behavioral or environmental factors may be involved.

In addition, our focus on identifying both within- and across-group changes can help

clarify the likelihood of different explanations for the overall rise in obesity. As it stands,

the literature has yet to settle on a primary cause, or quantitative division of causes, for

of muscle mass (Burkhauser and Cawley, 2008). Despite this problem, the preference for BMI stems from

its comparative ease of construction from data often collected in surveys and its apparent broad correlation

with other measures of obesity (Bhattacharya and Sood, 2011).

4Baum (2007) and Baum and Ruhm (2009) represent two noteworthy counter-examples. However, as we

discuss further below, those papers data permit them to explore a subset of the issues considered here.

3
the growth in average BMI and obesity. Several leading explanations for the rise of obesity

and BMI in the U.S. include: the relative price of food (Cutler et al., 2003, Lu and Goldman, 2010); proximity to restaurants (Currie et al., 2010, Anderson and Matsa, 2011); and

the changing workplace environment (Lakdawalla and Philipson, 2009). Related work has

considered the role of smoking (Gruber and Frakes, 2006, Courtemanche, 2009).5

The variation in BMI across demographic characteristics, such as gender and age, that

our data show may shed light on the relative merits of these explanations. Given that we

found that almost every group’s BMI increased, our results suggest the relevance of factors

affecting all groups, which is consistent with some past work (Chou et al., 2004). However,

the disproportionate increases of women’s BMIs suggests that other explanations are also

at play. Specifically, since there seems little reason to think that women and men have

systematically different exposure to “supply factors” like foods whose relative prices have

changed, we cautiously interpret our results as consistent with the idea that “demand” factors

like changing female labor force participation – which increased by 16% - 66% depending on

how it’s measured (OECD, 2013, Finkelstein et al., 2005) during our sample period – may

also be at least a partial driver of the obesity epidemic.

2 Data

The NHIS has conducted annual surveys on the health of the U.S. population since the 1960s.

These surveys involve fairly detailed questionnaires, which respondents complete themselves.

We use the IHIS, a harmonized version of the NHIS data, generated by the University of

Minnesota Population Center. Questions on height and weight, asked only of those age 18

5Lakdawalla and Philipson (2009) attempt to decompose the increase in weight attributable to technological change into supply factors (i.e., lower relative food prices) and demand factors (i.e., more sedentary

work conditions). They find that 40 percent can be attributed to the former, while the latter account for 60

percent.

4
and older, have been converted into BMIs, which are consistently available since 1976. The

IHIS also provides harmonized responses to standard demographic information: gender, age

in years, race, and Hispanic ethnicity. As described in the IHIS documentation (2012), the

NHIS has a complex survey design, with sampling weights, PSU and variance strata. All

estimates reported below reflect this survey design.

Our approach to understanding what may underpin the changes in population-level descriptive statistics is to consider and contrast the incidence of obesity during two separate

periods. Our early sample runs from 1976-9, while the late sample runs from 2007-10. Table 2 reports the sample means for the key demographic variables, and overweight/obesity

incidence in the two periods.

Consistent with Cutler et al. (2003) and other scholars who have used different data,

Table 2 shows that the average BMI grew substantially between the early and late periods.

The overweight fraction of the U.S. population (i.e., those whose BMI is greater than or

equal to 25) grew by almost 50 percent between the early and late periods. The fraction

whose BMI qualifies them as clinically obese (i.e., those whose BMI is greater than or equal

to 30) increased by more than 150 percent. However, the Table also indicates that America’s

population has changed dramatically during the last 40 years. Hispanic ethnicity more than

doubled. Similarly, though less commented upon in the popular media, the black population

also expanded by a substantial amount.6 Simultaneously, the male share of the population

grew modestly. Meanwhile, the age distribution shows evidence of major alterations: the

youngest group (18-30) shrinks by six percentage points between periods, while the older

groups, except for those in their early middle-age, grow in their proportion. This is consistent

with the aging of the “baby boom” generation.

6The relative increase of respondents identifying as African-American can also be seen in Census data:

http://www.infoplease.com/ipa/A0922246.html (accessed April 25, 2013).

5
Variable Entire Sample Early (1976-9) Late (2007-10)

Demographic

Male 0.48 0.46 0.49

Hispanic 0.11 0.05 0.13

White 0.84 0.88 0.82

Black 0.11 0.10 0.12

Age Group

18-30 0.26 0.30 0.24

31-40 0.18 0.18 0.18

41-50 0.18 0.16 0.19

51-64 0.22 0.21 0.23

65+ 0.16 0.16 0.17

Weight Metrics

BMI 26.26 24.36 27.21

Overweight (BMI≥25) 0.55 0.39 0.63

Obese (BMI≥30) 0.21 0.10 0.26

True Obs 336252 246239 90013

Table 1: Sample Means for the NHIS, 1976-9 and 2007-2010, using sample weights. The final

row indicates the actual number of surveyed individuals in each of the different

periods (i.e., unweighted).

6
Taken together, one might suspect that the dramatic changes in population structure

could explain a significant portion of the increase in obesity. This is because all of relatively

more prominent groups are positively correlated with higher BMI levels (Chou et al., 2004).

The empirical analysis below explicitly evaluates this possibility.

3 Understanding the Increase in Average Obesity

3.1 Within-Group or Across-Group Changes?

Cross-sectional analyses have demonstrated sizable cleavages in obesity and body-mass composition across demographic groups (Chou et al., 2004, Wang and Beydoun, 2007). In order

to infer to what extent these cleavages matter in terms of explaining the change in the population average between time periods, we begin by constructing 100 demographic categories

defined by the interaction of gender, Hispanic ethnicity, five race categories7, and five age

groups.8

Figure 1 plots the percentage changes in average BMI for each of the groups between the

two time periods (1976 to 1979; and 2007 to 2010) sorted by magnitude.9It demonstrates

that an increase in BMI was strikingly common across groups: 90 of the different groups

experienced an increase in their average BMI. Due to these increases, we found that nearly

all groups’ average BMIs qualified as at least overweight in the later period. These results offer

support to the hypothesis that the change in the overall incidence of unhealthy weight levels

reflects changes in common behaviors rather than alteration in the demographic composition

of the U.S. population.

Though the magnitude of many of the increases in BMI shown in Figure 1 are striking, it

7White, black, Aboriginal Indian (e.g., Cherokee or Inuit), Asian, and other.

8These age groups vary by age in years: 18 to 30, 31 to 40, 41 to 50, 51 to 64, and those 65 to 85.

9

It was not possible to estimate changes for three groups due to the thinness of the sample.

7
-10

0 10 20 30 40

Percent Growth in BMI by Group, 1976-9 vs. 2007-10

Figure 1: Percent change in average BMI, by gender-age-race-Hispanic ethnicity groups.

should be noted that the demographic groups are parsimoniously constructed and unevenly

sized. For example, the demographic group experiencing the largest increase in average BMI,

a clear outlier, is composed of late middle-aged women claiming both to be Native-American

and have Hispanic ethnicity. Thus, its magnitude may, at least in part, reflect survey sampling

issues. Therefore, to gain a fuller understanding of the documented within-group changes, the

across-group changes (e.g., shifts in the demographic distribution), and how they respectively

impact the average overall BMI for the U.S. population in the sample, we perform a BlinderOaxaca decomposition.

The Blinder-Oaxaca methodology allows researchers to decompose the magnitude of the

difference in average population outcomes into portions relating to observable differences in

the composition of the population and portions relating to genuinely different reactions. This

decomposition can be understood by considering the following standard linear regression of

individual i’s BMI:

BMIi = Xiβ + i. (1)

8
In this regression, X is a vector of the demographic group indicator variables. The parameters, β, reflect the average BMI within each group. When Equation (1) is estimated

separately for each period t, Xiβˆ equals the expected value of BMI in that period for individual i. Straightforwardly, this implies that the population average in a given period is just

E[BMI|period = t] = E[Xt]βt, or the expected population composition weighted by each

group’s innate BMI-level.

As documented above, there is a close to 3 point BMI point difference in average BMI

levels (i.e., E[BMI|early]−E[BMI|late] ≈ −3) across the early (1976-1979) and late (2007-

10) time periods. In order to understand the explanation for this change, the Blinder-Oaxaca

(BO) decomposition rewrites the difference between the average values of BMI in each period

as:

E[BMI|early] − E[BMI|late] =E[Xe]βe − E[Xl]βl

=E[Xe − Xl]βl + E[Xl](βe − βl) + [E[Xe] − E[Xl]](βe − βl),

(2)

where the subscripts e and l indicate the early and late periods, respectively.

In order to better understand what exactly the BO provides, it is useful to explain

each of the elements on the righthand side of Equation (2). The first term will capture the

amount of the change in population averages due to changes in the relative sizes of groups. In

other words, if a particular group with a high innate tendency towards obesity becomes more

prevalent, then we can ascribe some increase in the average to the demographic changes. The

second term reflects the amount of the change in population averages that is attributable

to within demographic group changes in innate BMI levels. Thus, this element will provide

insight into the possibility that very large changes for one group mask relative stasis for

9
others. Finally, the third term corresponds to the interaction in changes in frequency of the

group and the average BMI within that group.

Our estimation of Equation (2) show that while both compositional and within-group

changes increased the population’s average BMI between periods, the second term dominates.

Indeed, we find that 91 percent of the almost three BMI point difference between the two

periods can be tied to the changes in average BMI within groups. In contrast, less than seven

percent corresponds to changes in the make-up of the population; one and a half percent is

left for the interaction term. In other words, consistent with the impression given by Figure

1, we find that within-group changes in BMI levels dominate any impact of changes to the

demographic composition of the U.S. population over time.10

It is worth spending a moment to contextualize these findings relative to previous work

examining how the national obesity rate may have been influenced by changing demographic

composition. In particular, Baum (2007) takes an approach that is not dissimilar in spirit

to our analysis, looking at the demographic correlates of obesity in the National Health

and Nutrition Examination Survey (NHANES) data, and seeing how those demographic

factors vary between 1988-1994 and 1999-2002. However, unlike our analysis, that paper

holds the relationship between demographics and obesity constant over time. This ignores

the possibility of large within-group changes, which we show is key to understanding the

overall growth in BMI over the long-run.11 Alternatively, Baum and Ruhm (2009) use the

National Longitudinal Survey of Youth (NLSY), following a cohort over time. Here, the

restriction is the opposite: the relationship between obesity and demographics is allowed

to vary over time, but only because the cohort is growing older. This, however, makes it

impossible to compare old to young cohorts over time.

10Details on the results for individual groups are available upon request.

11Insofar as both of the Baum (2007) samples are drawn from a roughly similar era, the “structural”

changes that occur within groups may be of sufficiently small magnitude as to be irrelevant.

10
Moreover, while the prior work signally advanced our understanding of the role that

demographics may play in magnifying the increase in average obesity, their research designs

and data necessarily limit their ability to investigate different hypotheses about the long-run

drivers of the obesity epidemic.12 Our much longer sample and its comprehensive coverage

of the popuation allow us to begin to address these questions. Therefore, we now build

non-parametrically on our decomposition approach in order to determine whether different

demographic segments consistently grew more obese than others, and if there are patterns

in theses results that support some existing hypotheses more than others.

3.2 Has the Change in the Prevalence of Obesity Varied Across

Groups?

In addition to indicating that many groups’ incidence of obesity increased, Figure 1 suggested

that groups’ experiences were quite heterogeneous. To understand the key dimensions of

this variability, we examine the impacts on different collections of the demographic groups.

Below, we focus on gender and age categories. In unreported analyses, we examined whether

differences also existed across racial groups. Although some variation was observed, on the

whole, the results were less striking than those for gender and age. Details are available upon

request.

Figure 2 plots the average BMI (with 95% confidence intervals), by age, gender, and time.

Unlike in our earlier analyses, we now exclude all individuals over 70 due to the small number

of people in the sample over 70, which made it difficult to compellingly identify age-related

trends for these individuals. Our results indicate that, on average, men and women of all age

12For example, the relatively short, and recent, time-frame of Baum (2007) make it difficult to identify

what factors have changed. Similarly, the ability to construct a control or quasi-control group, with more

limited exposure to the potential cause, is limited in Baum and Ruhm (2009) by the fact that it is a cohort

study. Thus, any cause with national reach would affect each member of the cohort for equal time.

11
20 21 22 23 24 25 26 27 28 29 30 Average BMI (95% CI)

20 30 40 50 60 70

Age

1976-1979 2007-2010

(a) Men 20 21 22 23 24 25 26 27 28 29 30 Average BMI (95% CI)

20 30 40 50 60 70

Age

1976-1979 2007-2010

(b) Women

Figure 2: Average BMI, by age, gender, and time period.

12
8 10 12 14 % Change Average BMI (95% CI)

20 30 40 50 60 70

Age (Grouped)

Women Men

(a) Both genders, differences between time periods -6 -4 -2

0

2 % Double Difference of Average BMI (95% CI)

20 30 40 50 60 70

Age (Grouped)

Men - Women

(b) Difference between genders

Figure 3: Percent changes in BMI by age and gender.

13
groups were significantly heavier in the latter period. However, the figures make it equally

clear that not all groups’ average BMIs grew at the same rate. In particular, it looks like the

percentage increase may have been larger for women across many ages.

Unfortunately, the age-gender-time period cells are not large enough to reliably estimate whether such differences-in-differences are statistically significant. Therefore, we create

aggregated groups using the same age categories described above; however, to ensure consistency with Figure 2, we continue to only use individuals aged 70 and younger. Figure 3(a)

plots the percent change in average BMI across the two time periods, by gender across the

different age groups. Figure 3(b) shows the plot of the difference between the two lines.13

The figures show that women of working age, i.e., those aged 18 to 65, saw their average

BMI grow 2-5 percent points more than those of equal-aged men between the late 1970s and

more recent years. Relative to the average change within age-gender groups, these differences

are economically and statistically significant. Older men (i.e., those 65-70) experienced larger

proportional gains on average than women in their peer group; however, the difference is not

statistically significant.

We believe these estimates have significant value as a means of evaluating some of the

various theories about the drivers of the American obesity epidemic. Since Figure 1 showed

that most groups experienced significant increases, it suggests the appropriateness of looking

for factors that will affect all population groups like falling relative costs of unhealthy food

and increasingly sedentary work roles. Unfortunately, the data do not permit us to say which

such mechanisms are most influential. However, our findings regarding the cleavage between

men and women do suggest that theories that accomodate different effects across gender and

age also are important, at least on the margin. In particular, we interpret our results – albeit

13In generating the results shown in Figures 3(a) and 3(b), we exploit the fact that the first difference of

logged values closely approximates percentage changes.

14
extremely cautiously – as supporting the idea that weight gain may be related to changes

in workplace environment, such as increased female labor force participation. After all, time

series data suggest that women’s role in the formal workplace increased dramatically during

our period of study (OECD, 2013, Finkelstein et al., 2005).

Although Lakdawalla and Philipson (2009) do find a substantial role for demand factors

(i.e., more sedentary work conditions), and Finkelstein et al. (2005) highlight the possible

role of women’s returning to the labor force, our conclusion is inconsistent with some of the

prior literature. For example, Gomis-Porqueras et al. (2011) interpret mixed results of an

identification scheme based on the earned income tax credit as inconsistent with female labor

force participation’s correlation with obesity. Somewhat similarly, cross-border analyses by

Cutler et al. (2003) do not suggest a role for female labor force participation. However,

these results rely on cross-sectional variation within only a small sample, and the difficulty

of identifying effects in cross-country regression frameworks is well-known (Commander and

Svejnar, 2011).

In order to shed new light on the possibility that women’s increased participation in the

labor force may partially explain the difference in weight gain across genders, we leverage

the information on employment status that is present in the NHIS. To do this, we generate

a binary variable that indicates whether or not a person is working.14 We then examined

whether or not the data indicate that employment status had differential effects on men and

women of different ages’ expected change in BMI.

Figures 4(a) and 4(b) show plots of the difference between the percent change in average

BMI across the two time periods depending on whether an individual was employed or not,

14The NHIS categorizes people into multiple bins. We reduce this dimensionality by setting our indicator

equal to 1 if the respondent answers that they are working, even if not for pay. The variable is set to 0 if

the respondent says that they are out of the labor force or unemployed (for any reason). The small number

of observations involving responses outside of these categories are dropped.

15
by gender across the different age groups. Table 2 provides much the same information by

estimating regressions of BMI and the log of BMI on interactions between the variables of

interest after netting out the impact of gender-age-time effects. Both the regression results

and the graphed results of our non-parametric analyses offer striking evidence in favor of the

hypothesis that women’s increased labor force participation at least partially explains the

difference in growth rates between men and women.

Table 2 shows that employment is associated with relatively higher BMIs in the later

period, which is consistent with increasingly sedentary work roles. This already suggests

one reason that women’s BMIs may have risen relative to that of men, who were already

well-represented in the workforce. Moreover, Figure 4(b) and Table 2 show that labor force

participation actually is correlated with slower growth in men’s BMI. In contrast, our data

show that the opposite is true for women: employed individuals on average experienced

approximately 2 percent large increases in BMI than those out of the labor force. Figure 4(b)

shows that this is especially true for younger women.15 Thus, women’s increased participation

in the labor force is especially correlated with gains in BMI.

Overall, we do not claim that our data constitute dispositive evidence as to the causal

impact of employment on body composition. For example, the Figures cannot explain the

fact that the overall data suggest that labor force participation rates changed approximately

equally for women of many different age groups, while weight gain appears to have disproportionately affected the relatively young. This may suggest that the nature of work was

more likely to change for younger women. Such caveats notwithstanding, we do interpret our

findings as strong motivation for exploring in more controlled settings what role employment

may have played. We look forward to subsequent research on this topic.

15Moreover, if employment is somewhat endogenous, as intuition and the prior literature (Cawley, 2004,

Morris, 2007) suggest, then these simple averages will understate the true relationship between employment

and obesity.

16
-6 -4 -2

0

2

4 % Double Difference of Average BMI (95% CI)

20 30 40 50 60 70

Age (Grouped)

Working Women - Non-Working Women

(a) Difference in women’s change in BMI by employment status -6 -4 -2

0

2

4 % Double Difference of Average BMI (95% CI)

20 30 40 50 60 70

Age (Grouped)

Working Men - Non-Working Men

(b) Difference in men’s change in BMI by employment status

Figure 4: Changes in BMI by age, gender, and employment status.

17
Table 2: Relationship between age, gender, time, employment status and BMI.

BMI ln(BMI)

b/se b/se

1(Male) 0.14 0.01**

0.15 0.01

1(Employed) -0.68*** -0.03***

0.06 0

1(Late Period) 5.21*** 0.20***

0.14 0

1(Male & Employed) 1.02*** 0.04***

0.09 0

1(Employed & Late) 0.11 0.01*

0.1 0

1(Employed & Late & Male) -0.39*** -0.02***

0.15 0.01

Age-Gender-Period FE Yes Yes

N 231768 231768

* p<0.10, ** p<0.05, *** p<0.01. Standard errors account for survey sampling.

4 Conclusion

Using rich data on Americans’ health statuses over the past 30 years, we assess the relationship between population characteristics and body composition. Consistent with other

research, we show that the distribution of BMI in America has shifted noticeably to the right,

increasing the incidence of obesity. Decomposing this effect, we show that the change was

not principally driven by alterations to the underlying demographic makeup of the American population occuring during this time. Instead, we find evidence that the overwhelming

majority of demographic groups experienced gains in their average BMI. In addition, we find

strong evidence that the gains were not equally distributed. In particular, there is evidence

of particularly large gains in women’s BMIs, and these gains appear associated with employment status. We believe these results help to clarify thinking about the different economic

theories for the rise in obesity.

18
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