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National Agricultural Library Cataloging Record

Mancino, Lisa The role of economics in eating choices and weight 

outcomes.

(Agriculture information bulletin ; no. 791

1. Diet--Economic aspects--United States. 2. Diet--Social 

aspects--United States.

3. Americans--Food. 4. Obesity--Economic aspects

-United States.

5. Weight loss--Economic aspects--United States.

I. Lin, Biing-Hwan II. Ballenger, Nicole. III. United States. 

Dept of Agriculture Economic Research Service IV. 

Title

RA645.O23
Abstract

We use data from the USDA’s 1994-96 Continuing Survey of Food Intakes

by Individuals and the 1994-96 Diet and Health Knowledge Survey to ascertain whether economic factors help explain weight differences among

adults. Weight differs among demographic subgroups, and differences in

specific behaviors, health awareness, and eating patterns can be linked to

weight outcomes. An economic framework helps explain how socioeconomic factors affect an individual’s ability to achieve good health. Our

results suggest that income, household composition, and formal education

help explain variation in behaviors and attitudes that are significantly associated with weight outcomes.

Keywords: obesity, CSFII, DHKS, weight, age, income, education,

race/ethnicity.

Acknowledgments

The authors thank Elise Golan, Joanne Guthrie, Betsey Kuhn, Michael

LeBlanc, Benjamin Senauer, and Sid Clemens for their review comments on

earlier drafts of this report. Thanks to Kathy Kassel for helping with the

initial presentation of this research. Thanks also to Dale Simms and Susan

DeGeorge for editing, design, and production. Any remaining errors and

omissions are solely the responsibility of the authors. 

The Role of Economics 

in Eating Choices and

Weight Outcomes

Lisa Mancino, Biing-Hwan Lin, and 

Nicole Ballenger

United States

Department

of Agriculture

www.ers.usda.go

A Report from the Economic Research Service

September 2004

Agriculture

Information

Bulletin

Number 791

This report is based on a presentation to the USDA’s Research Education and 

Economics Advisory Board in October 2003.
Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Which Behaviors and Attitudes Affect Risk? . . . . . . . . . . . . . . . . . . . . . . . .2

Economic Factors May Help Explain Behaviors and Attitudes:

Our Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Economic Factors May Help Explain Behaviors and Attitudes:

Our Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Appendix: Who Is More at Risk? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

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Summary

The incidence of obesity has risen all across America, among all population

groups. Not everyone, however, is equally at risk of becoming overweight or

obese, or at risk for the same reasons. Understanding weight differences is

one approach to finding solutions to obesity and its associated health costs.

Much of the variation in body weight is related to differences in what we eat

and how active we are. To encourage specific behavioral changes, however,

we need to understand the motivation behind these behaviors. 

The purpose of this report is to:

• Identify if and how the risk of being either overweight or obese differs

among various demographic subgroups.

• Determine if differences in specific eating and physical activity behaviors—as well as knowledge, attitudes, and perceptions that affect such

behaviors—can be linked with weight outcomes.

• Examine whether economic factors—such as income and time constraints—appear to help explain differences in behaviors and attitudes

that affect weight outcomes.

We use multivariate analysis to identify significant correlates of overweight

and obesity. The data come from USDA’s 1994-96 Continuing Survey of

Food Intakes by Individuals and the 1994-96 Diet and Health Knowledge

Survey.

Certain behaviors and attitudes are significantly associated with alternative

weight outcomes. Individuals who exercise more frequently, watch less television, drink fewer sugary beverages, and eat a higher quality diet are more

likely to have a healthy body weight. Differences in attitudes about diet and

health also correlate with weight differences. Compared with women of

healthy weight, overweight and obese women are less likely to believe they

have control over their weight. Likewise, overweight and obese men are less

likely to accurately assess their weight status; nearly 60 percent of overweight and obese men consider themselves to have a healthy weight. 

Differences in obesity rates across population subgroups indicate that

socioeconomic factors significantly and systematically affect an individual’s

ability to achieve good health. Variables of particular interest to economists

seem to contribute to variation in these behaviors and attitudes. Individuals

with higher incomes tend to make greater investments in their own health.

They watch less TV and eat a higher quality diet. Men with higher incomes

are more accurate about their weight status, while women with higher

incomes drink fewer sugary beverages, exercise more frequently, and are

more confident that they can control their weight. 

Time constraints may limit personal investments in healthier behaviors.

These time constraints seem to correlate with household composition.

Compared with single parents, married parents have a higher quality diet,

eat breakfast more often, and drink fewer sugary beverages. 

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Formal education also seems to motivate individuals to make greater investments in their own health. Individuals with a college education watch less

TV, eat a higher quality diet, drink fewer soft drinks, and eat breakfast more

often. Women with a college education have a greater feeling of control over

their own weight and exercise more frequently.

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Introduction

Obesity rates among adult Americans have doubled within the past 25 years.

In 1999-2000, nearly 65 percent of U.S. adults were either overweight or

obese (Centers for Disease Control and Prevention, 2003). Obesity accounts

for $117 billion a year in direct and indirect economic costs, it is associated

with 300,000 deaths each year, and it will soon overtake tobacco as the

leading cause of preventable deaths (U.S. Department of Health and Human

Services, 2000). Magnifying the public cost, Medicaid and Medicare were

estimated to have paid for over half of all U.S. medical expenses related to

overweight and obesity in 1998 (Finkelstein et al., 2003). 

The risk of being overweight1 has been increasing among children as well

(CDC; Joliffe, 2004). Over 15 percent of children age 6 to 19 are either

overweight or at risk of becoming overweight, according to recent estimates.

For children age 6-12, this represents a 135-percent increase from 1976. For

children age 12-19, the increase is 210 percent. For an adolescent, the probability of childhood obesity persisting into adulthood is as high as 80

percent (American Academy of Pediatrics, 2003). So the trends in overweight and obesity are likely to continue if left unchecked. 

The incidence of obesity has risen across America and among all population

groups. Not everyone, however, is equally at risk of becoming overweight or

obese, or at risk for the same reasons (see appendix). Understanding weight

differences and predispositions to obesity is one approach to finding solutions. Understanding differences in risk can be used to tailor education and

intervention campaigns and more efficiently allocate funds. 

One determinant of body weight is a person’s genetic makeup. Medical

studies have shown that genetic differences explain a significant amount of

weight variation both among individuals and over time for a given individual (CDC; Schousboe et al., 2004; Allison et al., 1996). However, much

of the variation in body weight is also related to behavior: what we eat and

how active we are. This is cause for hope because behaviors are amenable to

change. Our first objective is to see if we can link specific eating and physical activity behaviors—as well as knowledge, attitudes, and perceptions

that affect such behaviors—with weight outcomes. 

To promote specific behavioral changes, we need to understand the motivation behind these behaviors. Economic analysis is suited to this task because

it helps explain how individuals choose to allocate their limited resources—

including their time and money—to eating a healthful diet, engaging in

physical activity, and being informed about diet, health, and weight relationships. More important, are economic factors (costs, income, etc.) behind

differences in behaviors and attitudes that affect weight outcomes?

Data

The data used in this study come from the USDA’s 1994-1996 Continuing

Survey of Food Intakes by Individuals (CSFII) and the companion Diet and

Health Knowledge Survey (DHKS).2 The CSFII collects information on

food intake in the United States, and also records demographic information

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1Children are considered to be at risk

of being overweight if their BMIs are

between the 85th and 95th sex- and

age-specific percentiles from the 2000

CDC growth charts. Those with BMIs

at or above the 95th percentile are considered to be overweight.

2Although there are more recent data

on individual food consumption and

body weight, these data lack health

knowledge information and some

sociodemographic variables that allow

us to look more closely at how economic factors help explain the relationship among behaviors, attitudes,

and weight outcomes.
on each respondent’s household. CSFII respondents are also asked to report

their weight and height. The DHKS provides information on people’s attitudes and knowledge about dietary guidelines and their ability to practice

this knowledge. In each CSFII household, the DHKS was administered to

one adult over 19 years old who reported at least 1 day of food intake. To

maintain a clear link between dietary knowledge/attitudes and an individual’s nutrient intake, our data set included only the adults who answered

both the CSFII and DHKS, amounting to 5,364 individuals.

Which Behaviors and Attitudes 

Affect Risk?

Are particular behavioral patterns of eating and physical activity associated

with an individual’s weight category? To create weight categories, we

grouped respondents based on their self-reported body mass index (BMI)

(table 1). BMI is the ratio of weight (in kilograms) to height squared (in

meters). Following weight classifications used by the CDC, National Institutes of Health, and World Health Organization, respondents who reported

BMIs between 18.5 and 25 were classified as having a healthy weight,

BMIs of 25-30 were overweight, and BMIs of 30 or above were classified

as obese. 

Using these weight categories, we questioned whether various measures of

caloric consumption, caloric expenditures, and attitudes about diet and

health correlate well with weight outcomes.

Eating and Physical Activity

We analyzed how the differences among individuals in terms of calories

expended and calories consumed correlate with weight differences among

individuals.3

Calories Expended: Exercise and Television

Watching

As proxies for calories expended, we considered the number of hours a

respondent spent watching television, averaged over 2 days, and whether he

or she exercised more than once a week. (We cannot ascertain how well

these days represent their usual patterns of calories consumed and calories

expended.) 

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3This was done by comparing the

mean values of food intake, physical

activity, and dietary knowledge for

individuals with a healthy body weight

against the mean values for individuals

classified as either overweight or

obese.

Table 1—BMI definition and classifications

Healthy weight Overweight Obese

BMI1 18.5-25 25-30 > 30

6’ 137-185 lbs 185-220 lbs > 220 lbs

5’6’’ 115-155 lbs 155-185 lbs > 185 lbs

1Body Mass Index (BMI) = Weight (Kilograms)

Height2 (Meters)
For both men and women, a smaller proportion of overweight and obese

individuals exercised more than once a week. Also, the number of hours

spent watching TV is significantly4 higher for overweight and obese individuals than for healthy-weight individuals.

Calories Consumed: Quantity, Quality,

and Eating Patterns

To assess the relationship between calories consumed and body weight, we

analyzed the total energy an individual consumed, averaged over 2 days.

The quality of an individual’s diet can also influence overall caloric intake;

for example, foods that are lower in energy density,5 such as broccoli, can

promote a feeling of fullness and reduce caloric demand compared to foods

that are higher in energy density, such as French fries. As a measure of diet

quality, we used the 2-day average of an individual’s healthy eating index

(HEI) score (Basiotis et al.; table 2). We also included the number of

Pyramid servings consumed from each food group, and the percentage of

calories derived from sugary beverages. 

Nutrition studies have pointed to a relationship between an individual’s

body weight and various eating patterns, such as snacking between meals,

eating breakfast, the amount of time between eating occasions, and eating

foods prepared outside the home (Ma et al., 2003; Nicklas et al., 2001). As

indicators of such dietary patterns, we included the percent of calories

derived from foods consumed away from home, whether or not the individual consumed breakfast on the recall days, how long the individual went

between eating occasions, and how often he or she ate a snack. 

We found that:

• Individuals with a healthy body weight consume fewer calories than

individuals who are overweight. However, obese individuals report 

consuming fewer calories than either healthy-weight or overweight 

individuals.6

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4Statistical significance refers to the

likelihood that the difference observed

between two groups is due to chance

alone and there is no true systematic

difference between these groups. In

this study, a finding is referred to as

significant if there is a less than 5 percent probability that the difference

observed would occur by chance alone

if there were no differences between

the groups being compared (i.e., a pvalue of less than 0.05).

5Energy density is the total energy

(calories) from a specific food divided

by its weight.

0 10 20 30 40 50 60

Percent of the population that exercise less than once a week

Weight differences by exercise frequency

Women

Men

Figure 1

Healthy weight Overweight and obese

6This counterintuitive finding could be

because individuals who are obese

may under-report more often and to a

higher degree than individuals with a

healthy weight. (McCrory et al., 2002;

Variyam, 2003b).
• For both men and women, individuals with a healthier body weight also

score higher on the HEI. This difference is statistically significant for

women. 

• Healthy-weight women tend to eat significantly more fruit and less meat,

snack more often, and eat breakfast more regularly than women who are

overweight or obese. 

• Overweight and obese women go significantly longer intervals between

meals than healthy-weight women, and receive more of their daily calories from fast-food restaurants.

Knowledge, Perceptions, and Attitudes

An individual’s eating and exercise behaviors may be influenced by his or

her awareness of relationships among diet, physical activity, health, and

weight. We looked at three aspects of diet and health awareness: knowledge,

perceptions, and attitudes. 

As an indicator of knowledge, we used information on whether an individual was correctly able to identify how many servings of dairy, fruit,

vegetables, grains, and meat are recommended in the Food Guide Pyramid.

We found no significant correlation between knowledge about food servings

and body weight category. For example, both healthy-weight and overweight men are about equally able to identify the correct number of food

servings. Questions about serving recommendations may be poor measures

of an individual’s actual knowledge about health and nutrition.

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Table 2—Healthy Eating Index definition

The HEI score is the sum of 10 components that represent different

aspects of a healthful diet. The maximum score for each component

of the Index is 10. The minimum score is 0. The maximum total HEI

score is 100.

• Components 1-5: Reflect how well an individual’s diet conforms to

the Food Guide Pyramid in terms of daily consumption of grains

(bread, cereal, rice, and pasta), vegetables, fruits, milk (milk, yogurt,

and cheese), and meat (meat, poultry, fish, dry beans, eggs, and nuts).

• Components 6-7: Measure an individual’s fat and saturated fat consumption as a percent of total calories. To obtain perfect scores for

these components, individuals must consume no more than 30 percent of calories from fat and no more than 10 percent of daily calories

from saturated fats.

• Components 8-9: Reflect the degree to which an individual limits her

cholesterol and sodium intake.

• Component 10: Reflects the level of variety in an individual’s diet.

Based on the total HEI score, an individual’s diet will fall into one of

three categories: Good (above 80), Needs Improvement (51-80), and

Poor (50 and lower).

Source: The Healthy Eating Index : 1999-2000, 

www.usda.gov/cnpp/Pubs/HEI/HEI99-00report.pdf
People’s perceptions about the healthfulness of their current body weight

may affect their willingness to invest in more healthful behaviors. For

example, someone who does not know he or she is overweight may make

different food choices than someone who has been told by his or her doctor

to lose weight and limit caloric intake. To verify this, we first created a variable that compared one’s BMI to whether one considered oneself to be too

heavy, too thin, or just right (Kuchler and Varyiam, 2003; Mancino and

Kinsey, 2004). We next estimated the correlation between an individual’s

perceived body weight and BMI.

Nearly 60 percent of overweight and obese men believed they have a

healthy body weight. This is significantly more than the share of men actually categorized as healthy according to their BMI. This “disconnect” likely

influences the extent to which many men invest in healthful behaviors. In

some ways, this inspires hope and might indicate an easy fix. If men are

more aware of what their body weight or BMI should be, they might make

different eating and exercise decisions. 

Attitudes about diet and health may also have an impact on health behaviors (Kuchler and Lin, 2002; Lin et al., 2004). For example, people who do

not believe their food choices can affect their overall health would be more

likely to make less healthful food choices than those who strongly believe in

such a link. To ascertain attitudes, we used answers to how important

respondents felt it was to maintain a healthy weight, how much control they

felt they had over their own body weight, and how nutrition (versus prices

or convenience) influenced their food choices. 

Women in the healthy-weight category were significantly more likely to

disagree with the statement “some people are born fat, some are born thin.”

In other words, those women who believe their weight is under their own

control are more likely to have a healthy weight than other women. This

makes sense. If you have a low sense of self efficacy in terms of controlling

your body weight, why would you invest in behaviors not always enjoyable

to you?

Significantly more healthy-weight women than overweight women indicated

the importance of nutrition when buying food. Significantly more overweight women indicated that taste and storability are the most important

attributes when buying food. As to storability, we found that significantly

more overweight women shop once a month or less. This may suggest that

overweight women are buying fewer nonstorable fresh items in favor of

goods that are processed, which may ultimately affect the healthfulness of

their diets. 

Economic Factors May Help Explain Behaviors 

and Attitudes: Our Framework

We developed an economic framework to determine if and how economic

factors might explain differences in the behaviors and attitudes that we

found to be correlated with weight outcomes. This framework is based on

the biological relationship between calories consumed and calories

expended, where excess body weight results when someone routinely

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consumes more calories than he or she expends. How many calories we

choose to consume and how many calories we expend daily are influenced

by a constellation of factors including the price of goods and services, a

person’s income, time constraints, cooking skills, level of education, gender,

age, cultural background, and genetic endowment (Frazao, 1999). Each

factor may play a multifaceted role, affecting an individual’s knowledge

about health and nutrition, his or her choice of what to eat, and how many

calories he or she expends. 

Prices 

Economic theory assumes that relative differences in prices can partially

explain differences among individuals in terms of their food choices, leisure

activities, and knowledge about diet and health. For example, avocados may

cost less in California than in Minnesota. If buyers of avocados were alike

in every other way, we would expect the Californian to purchase more

avocados than the Minnesotan. We may also expect relative differences in

the price or accessibility of information to shape an individual’s knowledge

and attitudes about diet and health. Public health campaigns, which reduce

the price of information, tend to focus on areas or populations that are most

at risk of coming in contact with a specific health threat. For example,

Texans may have different attitudes and knowledge about how to avoid West

Nile virus than people in Montana. Economic theory also assumes that the

full price of a good or service includes its monetary price as well as other

costs, such as travel costs and time. Thus, an individual living near a public

park may choose to be more active than someone else living in an area with

few walking trails, sidewalks, or bike trails.

Full Income

An individual’s full income is the fusion of the amount of money, time, and

ability he or she has to either purchase or produce goods and services

(Becker, 1965). For example, someone may allocate his or her time, money,

and ability to painting the exterior of his or her house, or may hire a

contractor to do the job. The choice depends on the price the contractor

charges versus the price of materials, on the time needed to paint the exterior (which is related to painting skills), and on what else this person could

be doing (opportunity costs).

Economic theory uses the concept of full income to explain differences in

choices regarding food and physical activity. For example, as individuals’

wages increase, their incomes increase and they have more money to spend

on everything: food, other goods, and leisure activities. Wage increases also

inflate the value of their time because time spent working has increased in

value relative to time spent in leisure or working around the house. This

change may compel individuals to purchase goods and services that are

more convenient, more prepared, or higher quality. An increase in wage

rates also raises the full cost of ill health, which includes the cost of medical

treatment needed to regain health and time lost working. As such, individuals with higher incomes should have more incentives to invest in their own

health (Grossman, 1972). They may choose to make these investments by

eating more healthfully and engaging in more active pursuits. 

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However, there are opposing forces that may compel individuals to gain

weight as income increases. As time spent working in the market becomes

more valuable, an individual may devote more time to such work by

spending less time at leisure. With sedentary work, this reallocation of time

will lead to fewer calories expended. If this is not offset by a reduction in

calories consumed, then an individual will likely gain weight. As time spent

working in the market becomes more valuable, an individual may also

choose to purchase foods that are more prepared. If these foods are higher

in calories, or if that individual is less aware of the caloric and nutrient

content of these purchased foods, weight gain is again likely. 

Other factors related to time allocation may affect how individuals choose

which foods to buy and how much to exercise. For one, the extra costs

incurred from poor nutrition should be at least as high for individuals with

children as for individuals with no children.7 However, the extra costs of

preparing food may actually be lower for individuals with children. The

time costs to prepare a meal for two people are not much different than to

prepare a meal for four. Also, many food manufacturers offer volume

discounts. Thus, as individuals prepare food for more people, the relative

time and monetary costs decline, while the extra benefits of preparing a

healthful meal remain at least as high. Therefore, economic theory predicts

that, all else equal, an individual with children is more likely to make

healthful food choices than an individual without children. 

Opposing forces are at work here too. Some factors may compel individuals

to gain weight as family size increases. This is more likely for single parents

who are responsible for both providing the family income and tending to

housework. As these individuals devote more time to working and tending

to household chores, they will have less time for active pursuits. They may

also choose to purchase foods that are more prepared. If these time substitutions lead to fewer calories expended or less awareness of the caloric and

nutrient content of foods eaten, weight gain is again likely. 

Education 

Formal education may also promote greater self-investment in health.

People with more education may be better able to obtain, process, interpret,

and apply information that shapes their knowledge and attitudes about nutrition. Investing in education may reflect the degree to which people are

forward looking and thus willing to make greater investments in their own

health. Moreover, education is highly correlated with income; individuals

with higher levels of education tend to earn higher incomes. Thus, they may

choose to make greater investments in their own health by eating healthfully, being physically active, and enhancing their knowledge about health

and nutrition. 

Age 

Age may have opposite effects on an individual’s willingness to invest in his

or her own health. On one hand, as people grow old, their health declines.

They may become more aware of the links between diet and health, possibly

from visits to the doctor or illnesses among friends. Thus, the perceived

benefit of investing in health may increase. Since metabolism slows with

age, we would expect older individuals to expend less energy and eat

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7Traditional economic models typically assume individuals consider only

their own well-being. However, an

individual’s well-being is likely influenced by that of others, especially

their children’s or other family members. 
smaller portions than younger individuals. This, along with reduced flexibility and bone density, may limit an older person’s ability to be physically

active.

Gender

Assuming equal levels of physical activity, ages, and height, men typically

have higher metabolisms than women. This, in turn, should induce women

to consume fewer calories than men. Also, differences in marketing techniques and recommendations from physicians may also lead to variation, by

gender, in diet and health awareness. Physicians may tell men less often to

monitor their calcium intake because men are less likely to suffer from

osteoporosis. Men, therefore, may be less aware of a link between loss of

bone density and inadequate calcium consumption. 

Culture and Genetics

A person’s cultural background and genetic endowment may also affect his

or her body weight via health awareness, food choices, and level of physical

activity. Cultural background may cause a person to make different food

choices, have different notions about the links between diet and health,

prefer different types of physical activities, and hold different views on what

his or her ideal body weight should be. Also, an individual with Type I

diabetes may be both more aware of the nutrient content of food and more

mindful of food choices (to monitor glucose levels) than someone without

diabetes. 

In summary, our economic approach is based on the assumption that the

foods we consume, the energy we expend, and what we know about diet and

health affect our body weight. In turn, these behaviors and attitudes are

influenced by prices, full income, education, age, gender, genetics, and

culture.

This economic framework enables us to look at if, and how, economic

factors correlate with—and therefore might explain—behaviors and attitudes that affect weight outcomes. 

Economic Factors May Help Explain Behaviors 

and Attitudes: Our Findings

To apply our economic framework, we used multivariate statistical analysis

to assess the relationship between specific behavioral patterns and attitudes

and an individual’s income, level of education, household structure (which

may be a proxy for time constraints), geographic location (which may be a

proxy for variations in price and availability of food), age, and race/

ethnicity. Multivariate analysis allows us to gauge the influence of one

specific socioeconomic or demographic variable on behavior. For example,

we can estimate how increasing an individual’s age by 1 year will affect the

amount of television that that individual watched each day, holding all other

influences constant. 

The correlation between socioeconomic factors and weight outcomes varied

significantly by gender when estimating how the risk of being either over8

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weight or obese differed among demographic subgroups (see appendix). For

that reason, we ran separate estimations for men and women (table 3). To

interpret, keep in mind that each of the variables listed in the row along the

top of the table represents a specific behavior or attitude. Each behavior or

attitude was hypothesized to be jointly determined by the socioeconomic

and demographic variables listed in the first column. Thus, the “+ (Women)”

in the second column, first row indicates that among women, income is

positively correlated with exercise. In other words, a woman with a higher

income is likely to exercise more often than a woman with a lower income.

A negative sign in the next column of the first row shows that both men and

women with higher incomes watch less television than those with lower

incomes. 

Region/Location 

One important limitation of the CSFII is that it contains no information on

price of foods bought and eaten. However, when purchases are made within

a short timeframe, it is reasonable to assume that slight variation in prices

across households can be captured by information on the household’s

regional location (Variyam, 2003b). Thus, geographic location and whether

or not an individual lives in an urban, suburban, or rural area are often

included in models as proxies of systematic differences in food prices and

expenditures. Regional variables may also correlate with cultural differences

(southern cuisine is famous for its fried food and barbeque), and neighborhood or lifestyle differences (some suburban areas have fewer sidewalks for

exercise while some urban areas have fewer supermarkets). Our analysis

suggests that these location variables do correlate with some weight-related

behaviors:

• Men and women who live in the West have a higher quality diet and

drink fewer sugary beverages. 

• Men and women in rural areas have a lower quality diet.

• Men who live in urban areas are more likely to believe that their body

weights are healthy when in fact they are either overweight or obese.

• Women in the South eat breakfast more often than women in other

regions.

Full Income 

Economic theory suggests that demand for goods and services used to maintain or improve one’s health increases with income (Grossman, 1972). We

used the household’s Poverty Income Ratio (PIR) to group individuals into

three different income categories: low, middle, and high (Lin et al., 2004).

The PIR is the ratio of a household’s income to the Federal poverty guideline for that household’s number of family members.8 In this study, a household was considered to be low income if its PIR fell below 185 percent of

the Federal poverty level. Households above 185 percent typically do not

qualify for most social programs, such as Head Start, the Food Stamp

Program, the National School Lunch Program, and the Children’s Health

Insurance Program. An individual whose household income fell between

185 and 300 percent of the poverty level was classified as middle income.

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8For example, the 2004 Poverty

Guideline for a family of 2 individuals

is $12,490. If this household reported

an income of $24,980, its PIR would

be 2 and its income would be 200 percent of the Federal poverty level.
Individuals with household incomes that exceeded 300 percent of the poverty

level were classified as high income. 

Our empirical results show that income and healthy weight behaviors are

correlated in the following ways:

• Compared with all other explanatory variables, income had the strongest

marginal impact on diet quality and the amount of time spent watching

TV. Both men and women with higher incomes watch less TV and eat a

higher quality diet.

• Women with higher incomes drink fewer sugary beverages, such as fruit

drinks and soft drinks, and have a higher sense of self-efficacy regarding

weight control. Also, a higher proportion of women with higher incomes

indicate that they exercise at least once a week.

• Men with higher incomes are more accurate in reporting their weight status.

Household Type 

Time is a scarce resource for all households, both low and higher income.

Waking hours can be spent working for pay, working in the home (cleaning,

cooking, caring for children, upkeep), investing in our health (exercising,

cooking a healthy meal, reading about health and nutrition), or simply

enjoying our free time (reading a book, watching TV, or socializing). Our

analysis suggests that for some households, time constraints—or preferred

allocations of time—may limit personal investments in healthier behaviors.

For example, married couples with children may be better able attend to their

health than single parents. 

• Compared with all other explanatory variables, household structure has

the largest impact on an individual’s consumption of sugary beverages

and whether he or she consumes breakfast. Men and women who are

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Table 3—How socioeconomic variables correlate with food choices, physical activity, and 

dietary awareness

Excercise TV Healthy Sugary Breakfast Women: Men:

Eating beverages a sense of accurately

Index control over assessing

score own weight weight status

Income + (Women) - + - (Women) + +

College education + (Women) -+ -++

Married with 

children - (Men) - (Men) + - +

- (West) + (Southern

Residence + (Rural) - (West) women)

Race and + (Black + (Black + (White and + (White 

ethnicity men) women) Hispanic) women) + (White) - (Hispanic)

Age - + + - + -

Calories out Calories in Dietary awareness
married with children drink fewer sugary beverages, and eat breakfast

more often than single parents. They also have a higher quality diet.

• Household composition has the strongest impact on whether men exercise more than twice a week. Married men with children exercise less

frequently than single men with no children. However, they also tend to

watch less TV than men without children.

Education 

Formal education may motivate an individual to make greater investments in

his or her own health. Indeed, we find that education is correlated with those

behaviors that are associated with a healthy weight.

• Men and women with a college education watch less TV, eat a higher

quality diet, drink fewer soft drinks, and eat breakfast more often. 

• Compared with all other explanatory variables, whether or not a woman

went to college has the strongest impact on whether or not she exercises

more than twice a week. 

• Women with a college education have a greater sense of control over

their own weight. 

Age 

On one hand, older people may find it more difficult to be physically active,

due to loss of bone density and increased risk of disease. On the other hand,

increasing risk of disease may also provide greater incentives to eat healthfully. Our analysis supports both conjectures about the relationship between

age and weight-related behaviors.

• Older men and women indicated they exercise less frequently and watch

more television.

• Older individuals appear to make investments in their heath by trying to

eat more healthfully. They have a higher quality diet, drink fewer sugary

beverages, and eat breakfast more often. 

Race and Ethnicity 

Ethnic differences may partly account for variations in what people choose

to eat, their attitudes about physical activity or various forms of exercise,

and their awareness of health and nutrition. 

• Men and women who are either Hispanic or non-Hispanic White eat a

higher quality diet than non-Hispanic Black men and women.

• Non-Hispanic White women were more likely to report that they ate

breakfast on both survey days.

• Non-Hispanic Black women watch more hours of television than women

of other races. 

• Non-Hispanic Black men indicated they exercise more frequently than

men of other races.

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• Compared with all other explanatory variables, ethnicity is estimated to

have the strongest impact on an individual’s dietary awareness. Hispanic

men are most likely to believe that their body weights are fine, when in

fact they are overweight or obese. White women disagree most with the

idea that they have little control over their own body weight.

Conclusion

Increasing rates of overweight and obesity present serious challenges to the

future quality of public health, health care, aggregate productivity, and

quality of life among Americans. Health education and outreach programs

can be designed to influence the two levers that individuals use to manage

their weight: the foods they eat and the energy they expend. 

This report illuminates how certain socioeconomic and demographic factors

may relate to an individual’s consumption of a healthful diet, engagement in

physical activity, and understanding of the links among diet, exercise, and

good health. Our analysis finds that certain behaviors and attitudes—such as

diet quality, breakfast eating, exercise, TV watching, and perceptions of

control over one’s own weight—are associated with weight outcomes. Also,

variables of particular interest to economists—such as household structure,

income, and formal education—may help explain variation in these behaviors and attitudes. These insights might be used to tailor nutrition outreach

messages within specific demographic subgroups. For example, it seems

that there may be constraints common among single parents that make it

difficult for them to find time for physical activity, prepare nutritious foods,

and eat meals at regular intervals. The next step would be to explore ways to

relieve such constraints. 

It would be misleading to suggest that weight differences are entirely the

result of personal choices. It would also be naive to suggest that personal

choices are entirely shaped by the economic variables we considered.

However, economic factors do seem to influence behaviors that may lead to

weight differences among individuals. 

Clearly, an individual’s culture, environment, and genetic makeup also play

a role in determining his or her weight. These influences likely affect

anyone’s desire and ability to have the physique of either Arnold

Schwarzenegger or Audrey Hepburn. However, such differences should not

completely preclude maintaining a healthy body weight—at least not for the

three out of five Americans who are currently overweight or obese.

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Appendix

Who Is More at Risk?

The variables we used to identify the risk confronted by specific socioeconomic/demographic subpopulations are the variables available in our data

set: an individual’s gender, race/ethnicity, age, household composition,

income, and level of education.1 We also included regional indicators, such

as Midwest, South, Northeast, and West, as well as whether the individual

lived in an urban, rural, or suburban area. 

Gender 

We found that men are more likely than women to be overweight, and that

women are more likely than men to be either a healthy weight or obese. The

BMI classification system is designed to be gender neutral, so why men are

more likely to be overweight and women are more likely to be obese is not

easily explained.

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1 We would have also liked to include

information on the amount of time

spent preparing food per day; more

detailed information about the household's time constraints; the availability

of grocery stores, restaurants, and fastfood places in the household's vicinity;

and an individual's measured—as

opposed to reported—BMI.

Women

Men

Percent

0 20 40 60 80 100

Healthy weight Overweight Obese

Weight differences by gender

Figure 1a
Age 

Weight distributions change significantly with age. This in itself is not too

surprising. For most people, metabolism slows with age. However, our data

suggest that an energy imbalance tends to occur earlier for men than for

women. For example, there are significantly more women age 30-49 with a

healthy weight than men of the same age. 

Race/ethnicity

Women who are non-Hispanic White or Hispanic are significantly more

likely to have a healthy weight than non-Hispanic Black women. One’s

racial/ethnic background may influence food choices, as well as one’s attitudes about diet, exercise, weight, and health. 

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Weight differences by gender and age

Figure 2a

Men Women

Percent Percent

Healthy weight Overweight Obese

0 20 40 60 80 100 0 20 40 60 80 100

50 and 

older

30-49

Under 30

Healthy weight Overweight Obese

Percent

Weight differences by race/ethnicity

Figure 3a

Hispanic

Non-Hispanic

Black

Non-Hispanic

White

Women

0 20 40 60 80 100
Income 

Women with higher incomes are significantly more likely to have a healthy

body weight than those with low incomes. Likewise, low-income women

are significantly more likely to be obese. Among men, we found that weight

outcomes did not differ significantly across income categories. 

Education

Both men and women who had not gone to college are significantly more

likely to be overweight or obese. Men and women who had attended at least

some college are more likely to have a healthy body weight. Education level

may be a predictor of diet and health knowledge, or a good proxy for an

individual’s investment in his or her own health. 

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Weight differences by gender and income

Figure 4a

Men Women

Percent Percent

Healthy weight Overweight Obese

0 20 40 60 80 100 0 20 40 60 80 100

High

income

Middle

income

Low

income

Weight differences by gender and education level

Figure 5a

Men Women

Percent Percent

Healthy weight Overweight Obese

0 20 40 60 80 100 0 20 40 60 80 100

College

No college
Household type 

Women who are married with children are more likely to have a healthy

body weight than single women with children. In a household with two

adults, both may contribute to the household’s income and share in childrearing and other household duties, freeing up time and money to pursue

healthier behaviors. 

Region/Location 

For both men and women, individuals who live in the West are significantly

more likely to have a healthy weight. Men who live in rural areas are significantly more likely to be obese than men in urban or suburban areas.

Whether a woman lived in an urban, rural, or suburban area was not significantly related to her weight category.

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Women's weight differences by household composition

Figure 6a

Single 

with 

children

Married

with 

children

Single 

no 

children

Married

no 

children

Percent

Healthy weight Overweight Obese

0 20 40 60 80 100

Weight differences by sex and region/location

Figure 7a

Men Women

Percent Percent

Healthy weight Overweight Obese

0 20 40 60 80 100 0 20 40 60 80 100

Rural

Suburban

Urban