USDA’s Healthy Eating Index and Nutrition Information. By Jayachandran N.

Variyam, James Blaylock, David Smallwood, Food and Rural Economics Division,

Economic Research Service, USDA, and P. Peter Basiotis, Center for Nutrition Policy

and Promotion, USDA. Technical Bulletin No. 1866.

Abstract

A comprehensive model is developed to measure the extent that nutrition knowledge

and diet-health awareness, among other factors, influence an individual’s Healthy

Eating Index (HEI), USDA’s measure of overall diet quality. This is the first study that

rigorously attempts to examine variation in the index across population groups by controlling for personal and household characteristics and nutrition information levels, as

well as test for the endogeneity of nutrition information. Results indicate that one’s

level of nutrition information has an important influence on one’s HEI and that nutrition information and the HEI are simultaneously determined. Other factors explaining

variations in HEI’s across individuals are income and education levels, race, ethnicity,

and age. Evidence supports the hypothesis that higher education promotes more

healthful food choices through better acquisition and use of health information. 

Keywords: Diet quality, Healthy Eating Index, nutrient demand, nutrition knowledge,

health inputs, health production.

Acknowledgments

The authors thank Brian Gould and Jug Capps for reviewing the manuscript and Nedra

Williams for providing excellent editorial assistance.

Washington, DC 20036-5831 April 1998 
ii ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA

Preface

Background on Nutrition and Health

Nutrition is the bridge between agriculture and health. The American diet—high in fat,

saturated fat, and sodium, and low in calcium and fiber-containing foods such as fruits,

vegetables, and whole grains—is associated with increased risk for several chronic diseases. Diet is a significant factor in the risk of coronary heart disease (CHD), certain

types of cancer, and stroke—the three leading causes of death. In fact, poor diets

and/or sedentary lifestyles are responsible for 14 percent of all deaths in the United

States and at least 20 percent of deaths from CHD and stroke, and 30 percent of cancers may be preventable through diet. 

Diet also plays a major role in the development of diabetes and hypertension. At least

30 percent of diabetes could be prevented through diet and/or control of obesity. New

research also suggests that increased consumption of fruits and vegetables can lower

high blood pressure as effectively as some medications. 

Overweight is another major risk factor for coronary heart disease, stroke, some cancers, diabetes, and hypertension and is associated with diet. The prevalence of overweight has increased 10 percentage points in the last 15 years—more than one in three

adults are now overweight and there is increased prevalence of overweight among children and teenagers. The increasing overweight problem will increase the prevalence of

chronic health problems and at an earlier age. 

Diet is also a risk factor for osteoporosis, which accounts for approximately 1.5 million new fractures annually. Improved diet—in particular, calcium intake—might prevent 40-60 percent of osteoporosis-related hip fractures.

Economic Impacts of Poor Diets

The economic impact analysis for the 1993 nutrition labeling regulations estimated

that a 1-percent reduction in intake of fat and saturated fat and a 0.1-percent reduction

in intake of cholesterol would prevent over 56,000 cases of CHD and cancer, avoid

over 18,000 deaths, and save over 117,000 life-years over 20 years. If all Americans

restricted their intake of dietary fat by reducing consumption of saturated fat and cholesterol, coronary heart disease and cancer mortality rates would fall by 5-20 percent,

depending on age. Overall, 2-percent of adult deaths would be deferred, equivalent to

an increase in average life expectancy of 3-4 months, which would accrue mainly

among those older than 65 years.

USDA’s Economic Research Service (ERS) estimates that improved dietary patterns

could save $43 billion in medical care costs and lost productivity resulting from disability associated with CHD, cancer, stroke, and diabetes in the United States each

year and prevent over 119,900 premature deaths among individuals 55-74 years of age,

valued at $28 billion per year. 

As the average age of the U.S. population continues to rise, the adverse effects of current dietary patterns on chronic health conditions are likely to become an increasing

problem, with important consequences for health expenditures and quality of life during

one’s older years. For example, the direct medical costs of osteoporosis, currently $7

billion yearly, will increase six-fold by the year 2000 and twenty-fold by the year 2040.
Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ iii

Why People Eat Different Diets

No two people eat exactly alike, but what accounts for the vast differences in diet

quality? Diet quality is really the end result of the foods eaten, preparation techniques

used, and other factors influencing the nutrient content of meals. For example, some

people eat diets that are rich in fruits and vegetables while others choose a diet high in

grains, meats or dairy products. Still others dine out frequently and some people prefer

fried foods. In general, four broad categories of factors influence food consumption:

consumer incomes; prices of food and other goods; consumers’ knowledge of health

and nutrition; and tastes and preferences. To change consumption, one of these influences must change. For example, government nutrition education efforts attempt to

change knowledge; commercial advertising may try to influence relative prices or

change tastes; and preferences. Virtually all prior economic studies of diet quality have

focused on income, prices, and consumer personal and household characteristics to

explain variation in food demand. Personal and household characteristics are used as

proxies for tastes and preferences, which are unobservable, as well as for nutrition

knowledge. Of course, knowledge can be measured but few surveys until recently

simultaneously collected nutrition knowledge and consumption data. 

Consumers’ health and nutrition knowledge may differ because of their sociodemographic background. For instance, more educated individuals may acquire more information about the effects of diets on health, and this may induce them to improve the

quality of their diets by, for example, consuming more fruits and vegetables. Similarly

women may be more aware of diet-health relationships than men, and this increased

awareness may be translated to better diets. Conversely, the link between sociodemographics and food and nutrient intake may also reflect consumers’ taste differences.

For example, Hispanics may choose a different type of diet than non-Hispanics purely

due to ethnicity and tradition. Or a person’s food tastes may change with age.

Therefore, the influence of sociodemographic variables on food consumption may

reflect a combination of an informational effect and a taste effect. Some attributes (such

as education) may have a predominantly informational effect, some (such as age) may

have a predominantly taste effect, and some (such as race and ethnicity) may have both

effects. Moreover, the two effects may reinforce each other, or work in opposing ways.

Healthy Eating Index

USDA is the lead Federal agency for human nutrition and fulfills its health responsibility through support for a healthful and abundant food supply, getting food to people

who need it, and promoting healthy dietary choices. USDA introduced the Healthy

Eating Index (HEI) in late 1995 to provide an important new tool for meeting our

nutrition goals. It made available for the first time a single summary measure to monitor changes in food consumption patterns. This Index measures how well the diets of

all Americans conform to the recommendations of the Dietary Guidelines and the

Food Guide Pyramid. It has served as a report on the American diet, allowing

researchers to analyze how Americans eat, and aids USDA in more effectively promoting proper nutrition. Preliminary analysis indicated that the diets of most Americans

need improvement, and some individuals are more likely than others to consume a

poor diet.
iv ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA

The HEI and Nutrition Information

This is the first study to examine the influence of socioeconomic characteristics, nutrition knowledge, and awareness of diet-disease relationships on dietary patterns. The

report makes a strong case that information and knowledge are the keys that will

unlock the door to better diets and in turn better health, longer lives, and children with

improved cognitive and learning abilities. 

The study found that nutrition information plays a large role in determining the quality

of an individual’s diet. For two individuals with identical sociodemographic characteristics, the one scoring one unit higher on a nutrition knowledge scale also scored four to

five points higher on the HEI scale.

The study shows that the positive effects of income and education on diet quality

found in previous studies are really due to the positive effects of these factors on nutrition information. That is, individuals with greater income or education tend to acquire

more nutrition information and this, in turn, improves the quality of their diets. If this

informational advantage were to disappear, for example through nutrition education

targeted to low-income individuals or starting early in childhood, then individuals with

greater incomes or education may, in fact, have diets that are no better, or possibly

poorer, than individuals with lower incomes or education. This is because individuals

with higher incomes or education may have greater preference for less nutritious convenience foods and may eat out more often. Restaurants meals are usually higher in

fats, sodium, and cholesterol, and lower in fiber than meals prepared at home.

Informational differences also explain the effects of gender, race, ethnicity, and employment status on diet quality. If everything else is held equal, men and women tend to

have the same diet quality. However, on average, women tend to have a higher stock of

nutrition information than men, and this shows up in an HEI 5 points higher for women

than for men. The study shows that blacks and Hispanics are handicapped by relatively

low levels of nutrition information. If their nutrition information levels were brought up

to that of whites or non-Hispanics, other factors being equal, then blacks and Hispanics

would have significantly higher HEIs than whites and non-Hispanics. 

Diet quality tends to improve with age. However, this effect is entirely due to changing

tastes since age has no effect on nutrition information once other sociodemographic

effects are taken into account. Similarly, smokers are as informed about health and

nutrition as non-smokers. Nevertheless, smokers tend to prefer a less healthful diet

than non-smokers and, as a result, tend to have a lower HEI than non-smokers. Diet

quality deteriorates for the overweight although they are equally informed about health

and nutrition as those who are not overweight.
Contents

Page

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

Economic Theory, Nutrition Information, and the Healthy

Eating Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

The Healthy Eating Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Food Group Components of the Healthy Eating Index . . . . . . . . . . . . 4

Other Components of the Healthy Eating Index . . . . . . . . . . . . . . . . . 4

Information Measures and Explanatory Variables . . . . . . . . . . . . . . . . 6

Sample Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Nutrition Information Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Information and the HEI: Differences Across Demographic 

and Socioeconomic Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Explanatory Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Estimated Models and Empirical Results . . . . . . . . . . . . . . . . . . . . . . 11

OLS Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2SLS Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

LV Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

v ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA
Deaths from disease associated with dietary excesses—coronary heart disease, some types of cancer,

stroke, and noninsulin-dependent diabetes mellitus—

account for nearly two-thirds of the deaths each year

in the United States (Singh, Kochanek, and

MacDorman, 1996). Not only do poor diets extract a

heavy toll on individuals, the costs to society are high

and continue to rise. The American Heart Association

estimates that about 1.5 million heart attacks occur

each year, and that coronary heart disease costs

Americans an estimated $52 billion in direct health

care spending and lost productivity (Frazão, 1995).

Associated costs for cancer are even higher, $104 billion. Experts agree that our diets are an important factor in deaths from these diseases but not the only factor (others being genetics, the environment, and

aging). A recent study suggests that dietary factors and

sedentary activity patterns together account for at least

300,000 U.S. deaths per year (McGinnis and Foege,

1993). The Economic Research Service estimates that

illnesses and premature death due to diet-related diseases and conditions costs society about $250 billion

per year (Frazão, 1995).

Among the numerous factors affecting dietary choices,

nutrition knowledge and beliefs about foods and

health—or nutrition information in short—is the most

amenable to modification (Thomas, 1991).

Presumably, increased knowledge of the nutrient content of foods and heightened awareness of diet-health

relationships lead to more healthful food choices. The

close association between diet quality and health, the

high cost of diet-related illnesses, and the possibility

of improving diet quality through better information

have been major motivating factors behind the many

public and private campaigns that encourage people to

eat more healthful diets. Such campaigns include the

USDA Food Guide Pyramid, Dietary Guidelines for

Americans, and the 5-A-Day campaign.

While these campaigns have contributed to better diets

over the years, a considerable gap still remains

between actual and healthful diets (Kennedy, Ohls,

Carlson, and Fleming, 1995). A major problem faced

by nutrition educators and public-health professionals

in their efforts to achieve further dietary improvements

is a lack of specifics on the use of diet-health information by individuals. For example, to what degree does

nutrition information access and use vary across different segments of the population? Likewise, does more

nutrition information help individuals to improve their

diet quality? Any understanding of factors slowing the

adoption of healthful diets requires empirical knowledge of how diet-health information and its effect on

dietary choices vary across the population. Such

empirical knowledge can be useful for targeting nutrition education programs, for promoting and marketing

foods, and for forecasting food consumption trends

(Connor, 1994).

Information plays a key role in the economic theories

of health behavior (Grossman, 1975; Ippolito and

Mathios, 1990; Kenkel, 1991), and several recent

empirical studies have verified that certain forms of

nutrition information influence the intake of selected

nutrients and foods, such as fat, cholesterol, fiber,

eggs, pork, poultry, and fish (Brown and Schrader,

1990; Capps and Schmitz, 1991; Carlson and Gould,

1994; Chern, Loehman, and Yen, 1995; Gould and Lin,

1994; Guthrie and Fulton, 1995; Putler and Frazão,

1994; Variyam, Blaylock, and Smallwood, 1996, 1997;

Yen, Jensen, and Wang, 1996). However, one drawback of these studies is that they attempted to examine

the link between nutrition information and the intake

Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ 1

USDA’s Healthy Eating Index 

and Nutrition Information

Jayachandran N. Variyam

James Blaylock

David Smallwood

P. Peter Basiotis

Introduction
of selected foods or nutrients rather than overall diet

quality. Focusing on the intake and its determinants for

a particular nutrient, such as saturated fat, is useful if

there is concern about its over- or underconsumption.

However, most dietary experts, as well as the Dietary

Guidelines for Americans, advocate a diet that contains

a variety of nutrients and foods but at recommended

levels (U.S. Department of Agriculture and U.S.

Department of Health and Human Services, 1995).

Consequently, a measure of overall diet quality is

desirable for monitoring progress in meeting dietary

guidelines as well as for assessing the influence of

nutrition information on diet quality.

A second drawback of some previous studies is that

they treated nutrition information as an exogenous

determinant of intake. Key intake determinants such as

income and education, as well as unobserved individual heterogeneity, may influence both intake and nutrition information levels simultaneously. Therefore,

treating information as an exogenous determinant will

lead to simultaneous equations bias.

USDA’s Center for Nutrition Policy and Promotion

recently developed an instrument to assess overall diet

quality—the Healthy Eating Index (HEI). USDA

developed this index to provide a single summary

measure of dietary quality in America. Most earlier

dietary assessment instruments focused on specific

components, such as fat and cholesterol, but few

assessed overall diet quality. The HEI combines information on the amount and variety of food in the diet

and compliance with specific dietary recommendations

for food components that should be consumed in limited quantities (U.S. Department of Agriculture, 1995).

The Index was applied to the 1989-90 USDA

Continuing Survey of Food Intake by Individuals

(CSFII) and examined for variability across income

and demographic groups. These simple cross-tabulations revealed that an individual’s HEI improved with

higher income and more education, that women tended

to score higher than men, and that scores were higher

than average for children and older people. However,

these findings may change in a statistical analysis of

the data whereby the influence of one variable on the

HEI can be separated from the effects of other variables. In simple cross-tabulations, one cannot be certain if the observed variability in the HEI across

income groups, for example, is due solely to income or

whether education is also playing a role since income

and education are closely related. The purpose of this

report is to estimate the effect of nutrition information

on overall diet quality as measured by the HEI. We

control for an extensive set of personal and household

characteristics that simultaneously influence both

nutrition information and the HEI, and we use several

model specifications and estimation methods to assess

the stability of our results.

2 ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA
Many of the economic analyses of the demand for

foods and nutrients are based on the conceptual framework provided by the theory of household production

developed by Becker and the characteristics model of

consumer demand developed by Lancaster (Behrman

and Deolalikar, 1988; Pitt and Rosenzweig, 1985;

Senauer and Garcia, 1991). In this framework, households combine various inputs to produce “commodities,” such as the health of family members, so as to

maximize a joint utility function. The inputs (for

example, food and medical care) derive their value by

supplying characteristics (nutrients and medical services) necessary for the production of the commodities

(health). Subject to the constraints of household technology and resources, utility maximization generates

individual and household demand functions for the

inputs and characteristics.

Assume that a representative household with M members has a joint utility function, U:

(1)

where F is a matrix of foods consumed, and z and h

are vectors of nonfoods and health status for each family member. Health and food intakes enter directly into

the utility function because good health is valued in

itself and because foods are consumed for reasons

other than their nutritional value, such as taste. We

assume the utility function satisfies certain conditions.

First, if the level of food, nonfoods, or health status

increases, so does a person’s utility level, U'>0.

Second, these increases in utility occur at a deceasing

rate as any component of the utility function reaches

ever higher levels, U"<0.

Given household income and market prices, the preference function 1 is maximized, subject to three sets of

constraints. First, the health of each family member is

constrained by the health production technology:

(2)

where HEIm is the Healthy Eating Index value (as a

measure of overall diet quality) of the mth household

member, and gm is a vector of nonfood health inputs

such as exercise and medical services. The efficiency

of producing health from HEIm and gm is conditional

on xm, a vector of personal and household characteristics, and um , an exogenous health endowment beyond

the individual’s or household’s control.

Second, expenditures are constrained to equal household income:

(3)

where p denotes prices, I is household income, and i is

a unit vector.

Third, the HEI input into the health production function is constrained by the production technology:

(4)

where Q is a matrix of fixed weights representing

nutrient levels in each food, number of servings,

and a unit vector to count the kinds of foods eaten; 

and f is the vector of food consumed by the mth

household member.

Under the assumption that the relevant functions have

desirable properties to ensure unique interior solutions,

the first-order conditions to maximize equation 1 subject to the three constraints give, among other relations, a member-specific HEI demand equation as a

function of prices, income, personal and household

characteristics, and um.

Introducing diet-health information explicitly into the

model reflects its role as a factor mediating part of the

influence of xm on hm. For example, consider a key

component of xm: education. More educated people are

more efficient producers of health because they are

more informed about the true effects of inputs on

health. That is, they have higher allocative efficiency—

the ability to select a better input mix (Grossman and

Kaestner, 1995). Education, therefore, affects health

through information. Other personal characteristics

that influence an individual’s acquisition and use of

information, such as income, play a similar role in the

production of health.1

Making the role of information explicit, the reducedform HEI demand function for the mth household

member may be written as:

(5)

where p is a vector of prices, I is the household

income, and km is a vector of nutrition 

information variables.

Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ 3

Economic Theory, Nutrition Information, and the Healthy Eating Index

max , , , , , UU U U

F

z ,z= aF hf ′ > 0 0 ′ <

h h HEI u m M m m mm m = c , , , ,,, g x h = 1 K

i Fp zp ′b F z + =g I,

HEI , m m = Qf

HEI f ,I , ,u , m mmm = cpxk h

1

Personal characteristics affect health production through productive efficiency (that is, amount of health output from given

amounts of inputs) and through tastes related to ethnic and cultural

factors (Grossman and Kaestner, 1995).
The Healthy Eating Index measures how well people’s

diets conform to recommended healthful eating patterns. The index provides a picture of foods people are

eating, the amount of variety in the diet, and compliance with specific Dietary Guidelines recommendations. A score on the index represents the sum of 10

different dietary components. Each component has a

possible range of 0 to 10. The maximum overall score

is 100 points. The following 10 dietary components

are included in the index based on different aspects of

a healthful diet:

• Components 1-5 measure the extent to which a person’s diet conforms to the Food Guide Pyramid

serving recommendations for the grain, vegetable,

fruit, milk, and meat groups.

• Component 6 measures total fat consumption as a

percentage of total food energy intake.

• Component 7 measures saturated fat consumption as

a percentage of total food energy intake.

• Component 8 measures total cholesterol intake.

• Component 9 measures total sodium intake.

• Component 10 reflects the amount of variety in a

person’s diet over a 3-day period.

Food Group Components of the 

Healthy Eating Index

The HEI reflects dietary intake in relation to the five

major food groups (grain, meat, milk, vegetable, and

fruit) in the Food Guide Pyramid (U.S. Department of

Agriculture, 1992). The number of recommended servings depends on an individual’s caloric requirements

(table 1). For example, recommended servings of vegetables range between three for a 1,600-calorie diet to

five for a 2,800-calorie diet.

For each of the five food group components of the

index, individuals who consumed the recommended

number of servings received a maximum score of 10.

A score of zero was assigned for any food group

where no items from that food group were eaten.

Scores between zero and 10 were calculated in proportion to the number of servings consumed. For example, if the recommended number of servings was 8 and

an individual consumed 4 servings, the component

score for the individual is 5 points (one-half of 10).

USDA, in developing the index, interpolated serving

recommendations from the Food Guide Pyramid for

individuals with food energy requirements different

from the 1,600-, 2,200-, and 2,800-calorie levels in the

Guide. For example, food energy requirements for

children between the ages of 1 and 3 are less than

1,600 calories. The recommended number of servings

was retained at the minimum (for example, two for

fruits), but the serving size was scaled down to be proportionate with their energy requirements. The exception was for the milk group, where adult serving sizes

were retained. This approach is consistent with the

guidance provided in the Food Guide Pyramid.

Conversely, the Food Guide Pyramid provides no 

guidance for adjusting serving sizes for adult males

between the ages of 15 and 50 whose energy requirements are greater than 2,800 calories. USDA decided

that, in lieu of increasing serving sizes for this age

group, food portions would be capped at the maximum

number of recommended servings for a food group,

such as 11 for grains.

Recommended servings from the five food groups by

gender and age of an individual are presented in table

2. For example, the recommended daily servings of

fruits range from two for children ages 1-3 to four for

males age 15-50. 

Other Components of the 

Healthy Eating Index

The five other components of the Healthy Eating Index

are related to the consumption of fat, saturated fat,

cholesterol, and sodium; and variety of diet.

Fat and Saturated Fat: The index scores for fat and

saturated fat are related to their consumption in 

proportion to total food energy. Fat intakes less than or

equal to 30 percent of total calories are given a score

of 10. The score declines to zero when the proportion

of fat to total calories is 45 percent or more. Intakes

between 30 and 45 percent were scored proportionately. Saturated fat intakes of less than 10 percent of total

calories received a score of 10, while zero points were

given for saturated fat intakes of 15 percent or more 

of calories. 

Cholesterol and Sodium: Scores for cholesterol and

sodium were each given based on milligrams consumed in the diet. A score of 10 was given for cholesterol intakes less than or equal to 300 milligrams daily.

4 ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA

The Healthy Eating Index

Table 1—Recommended numbers of servings per day

at food energy levels discussed in the Food

Guide Pyramid Bulletin

Calories Grains Vegetables Fruits Milk Meat

Number of servings

1,600 6 3 2 2 2.0

2,200 9 4 3 2 2.4

2,800 11 5 4 2 2.8

Source: U.S. Department of Agriculture, 1992.
Zero points were given for intakes at or over 450 milligrams. Intermediate scores were given for intakes

between the two limits. For sodium, a score of 10 was

earned for intakes less than or equal to 2,400 milligrams. A zero score was given for sodium intakes at

4,800 milligrams or higher.

Variety: Dietary variety was assessed by totaling the

number of “different” foods eaten by an individual in

amounts sufficient to contribute at least one-half of a

serving in a particular food group. Food mixtures were

broken into their component ingredients and assigned

to relevant food groups. Similar types of foods were

grouped together and counted only once in measuring

the score for variety. A maximum score of 10 was

awarded if 16 or more different food items were 

consumed over a 3-day period. A score of zero was

given if six or fewer food items were consumed.

Intermediate scores were awarded proportionately for

consumption between the cutoffs.

Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ 5

Table 2—Recommended number of servings per day for age/gender categories

Age/gender category Kilocalories Grains Vegetables Fruits Milk Meat

Children 1-3 1,300 6.0a 3.0a 2.0a 2.0a 2.0a

* 1,600 6.0 3.0 2.0 2.0 2.0

Children 4-6 1,800 7.0 3.3 2.3 2.0 2.1

Females 51+ 1,900 7.4 3.5 2.5 2.0 2.2

Children 7-10 2,000 7.8 3.7 2.7 2.0 2.3

Females 11-50 2,200 9.0 4.0 3.0 2.0 2.4

Males 51+ 2,300 9.1 4.2 3.2 2.0 2.5

Males 11-14 2,500 9.9 4.5 3.5 3.0 2.6

* 2,800 11.0 5.0 4.0 2.0 2.8

Males 19-50 2,900 11.0 5.0 4.0 2.0b 2.8

Males 15-18 3,000 11.0 5.0 4.0 2.0 2.8

aPortion sizes are reduced for children age 1-3. bIs 3 servings for persons age 11 to 24.

* RDA levels included in the Food Guide Pyramid.

Source: U.S. Department of Agriculture, 1992.
Sample Description

Data from USDA’s 1989-90 Continuing Survey of

Food Intakes by Individuals (CSFII) and the companion Diet and Health Knowledge Survey (DHKS) provided the basis for this report. The 1989-90 CSFII/

DHKS surveys were conducted by USDA’s Human

Nutrition Information Service (HNIS). Two independent samples of households—the “basic” or all-income

sample and low-income sample—were selected using

a multistage, stratified selection procedure targeted at

private households in the 48 contiguous States. In the

1989-90 surveys, 15,801 housing units were selected,

which, after screening, resulted in 5,554 eligible

households, of which 4,406 (79.3 percent) participated.

The CSFII survey collects information on what, when,

and where Americans eat and how much they eat.

Each CSFII participant was asked to provide 3 consecutive days of dietary data. The first day’s data were

collected in an in-home interview using a 1-day

dietary recall. The second and third days’ data were

collected using a self-administered 2-day dietary

record. Social, economic, and demographic characteristics of survey participants are also included in the

CSFII. There were 11,552 individuals living in the

4,406 participating households; 7,816 (67.7 percent)

completed the 3-day record. 

In the CSFII survey, each food item eaten was recorded using a coding system that contains about 6,700

food codes. USDA’s Agricultural Research Service

(ARS) maintains a database with the nutrient composition for each food code. The amount of nutrients in

each food was calculated by multiplying the amount of

food eaten by its nutritive value.

A DHKS respondent, usually the household’s main

meal planner, was contacted by telephone about 6

weeks after collection of the dietary data and asked

questions about knowledge of and attitudes toward

diet, health, and food safety issues. Among the 4,406

participating households, 3,805 (86.4 percent) completed the DHKS.

The surveys have been used to describe food consumption behavior and to assess the nutritional content of

diets. Results from the surveys have major implications for policies relating to food production and marketing, food safety, food assistance, and nutrition education. The surveys are a major component of the

National Nutrition Monitoring and Related Research

Program, a set of related Federal activities intended to

provide regular information on the nutritional status of

the U.S. population.

Our analysis is restricted to the main meal planner/preparer of the sample households since diet-health

knowledge of other household members was not collected. After eliminating cases with missing values,

our final sample consisted of 2,442 observations out of

3,805 with complete 3-day intake data.

Nutrition Information Measures

We used responses to two sets of questions in the

DHKS to develop measures of meal planners’ nutrition

information. The first measure represents the “nutrient

content knowledge” (NCK) of meal planners.

Respondents were asked to choose the correct answer

from each of a series of binary-choice questions about

sources and occurrence of various nutrients in common

food items. We used 21 questions to construct a measure of a respondent’s nutrient content knowledge (table

3). The NCK measure represents the number of correct

answers given by a respondent. Therefore, the minimum

score is zero and the maximum is 21. On average,

respondents answered about 15 questions correctly.

The NCK questions probed a respondent’s knowledge

of the fiber, cholesterol, and fat content of foods. For

example, respondents were asked to identify which of

two foods has the higher fiber content: fruit or meat,

cornflakes or oatmeal, popcorn or pretzels. They were

also asked to identify which foods contain more cholesterol: liver or T-bone steak, butter or margarine,

skim or whole milk. Other questions probed knowledge about different kinds of fat, the types of foods

that contain cholesterol, and the relationship between

fat and cholesterol.

Respondents identified the correct answer to some of

the comparisons more easily than others. Over 90 percent correctly identified whole-wheat bread as containing more fiber than white bread, but only 56 percent

knew that kidney beans contained more fiber than lettuce. Likewise, virtually everyone, 95 percent, knew

that skim milk has less cholesterol than whole milk,

but only 52 percent correctly identified liver as containing more cholesterol than a T-bone steak. The same

held true for the questions concerning fat content.

Most knew that ice cream contained more fat than

sherbet and that fried chicken was higher in fat than

roasted chicken, but far fewer knew that a porterhouse

steak contained more fat than a round steak. When

respondents were asked what kind of fat (saturated or

polyunsaturated) is more likely to be a liquid rather

than a solid, only 30 percent could identify polyunsaturated fat as the correct answer. Less than 40 percent

of the respondents knew that cholesterol is found only

in animal products.

6 ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA

Information Measures and Explanatory Variables
Table 4 lists the questions used to construct a variable

measuring the meal planners’ awareness of diet-health

problems. These questions take the general form: Have

you heard about any health problems that might be

related to how much of a particular nutrient a person

eats? There are eight such questions. About 85 percent

of the respondents indicated that they had heard of

health problems associated with salt, but less than 50

percent said the same for fiber and iron. We constructed a “diet-health awareness” variable (DHA) by

adding together the positive responses for each of the

eight questions. Thus, the variable has a lower limit of

zero (respondent had heard of no problems associated

with any of the nutrients) and an upper limit of eight

(respondent had heard of health problems associated

with each of the eight nutrients). This is similar to

Kenkel’s (1991) measure of health knowledge in his

study of smoking, alcohol use, and exercise.

Information and the HEI: Differences Across

Demographic and Socioeconomic Groups

Before we systematically isolate and analyze the

impacts of individual socioeconomic and demographic

characteristics on the Healthy Eating Index and the

knowledge and awareness variables, it is instructive to

examine the average index, knowledge, and awareness

scores within different population groups. Examining

the average scores within groups gives an indication of

which influences are likely to be important in building

statistical models. However, the following descriptive

analysis should be interpreted cautiously since some

characteristics are highly correlated. For example,

higher HEI scores that are associated with higher 

education levels may be partially caused by higher

income, since education and income levels are positively correlated.

USDA has developed a grading scale to rate overall

diet quality as measured by the HEI. The scale rates

Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ 7

Table 3—Nutrient content knowledge questions and percent responses

Question Correct Incorrect

Percent

Which has more fiber?

Fruit or meat 77.7 22.3

Cornflakes or oatmeal 79.5 20.5

Whole-wheat bread or white bread 91.8 8.2

Orange juice or an apple 74.0 26.0

Kidney beans or lettuce 56.3 43.7

Popcorn or pretzels 73.6 26.4

Which has more cholesterol?

Liver or T-bone steak 52.3 47.7

Butter or margarine 87.2 12.8

Egg whites or yolks 84.6 15.4

Skim milk or whole milk 95.0 5.0

Which has more fat?

Regular hamburger or ground round 87.8 12.2

Loin pork chops or spare ribs 72.0 28.0

Hot dogs or ham 61.3 38.7

Peanuts or popcorn 90.5 9.5

Yogurt or sour cream 85.9 14.1

Porterhouse steak or round steak 58.8 41.2

Ice cream or sherbet 95.0 5.0

Roast chicken leg or fried chicken leg 94.6 5.4

Which kind of fat (saturated, polyunsaturated) is more likely to be a

liquid rather than a solid? Or are they equally likely to be liquids? 29.6 70.4

Is cholesterol found in vegetables and vegetable oils, animal products,

or all foods containing fat or oil? 38.7 61.3

If a food is labeled cholesterol-free, is it also low in saturated fat,

high in saturated fat, or either? 55.6 44.4

Source: 1989-90 Diet Health Knowledge Survey.

Table 4—Diet-health awareness questions and percent

responses

Question Yes No

Percent

Have you heard about any health

problems that might be related

to how much:

Fat a person eats? 71.3 28.7

Saturated fat a person eats? 58.6 41.4

Fiber a person eats? 48.8 51.2

Salt a person eats? 84.7 15.3

Calcium a person eats? 59.3 40.7

Cholesterol a person eats? 81.7 18.3

Sugar a person eats? 79.6 20.4

Iron a person eats? 47.5 52.5

Source: 1989-90 Diet Health Knowledge Survey.
index scores of greater than 80 as “good,” scores of

51-80 as “needs improvement,” and scores less than 51

as “poor.” Table 5 reports results of tabulating the HEI

grades and the nutrition information variables against

key socioeconomic groups. We found that higher

scores are clearly associated with increased knowledge

about the nutrient content of foods as well as about

diet-health awareness. For example, individuals with

scores rated good answered, on average, two more

questions correctly about nutrient content than people

with a poor HEI score. 

Age appears to be strongly associated with higher HEI

scores. On average, people over age 69 scored 10

points higher than individuals under 30 years old.

However, there was not a clear association between

age and nutrient knowledge or diet-health awareness.

On the other hand, women had higher HEI scores than

men and higher nutrient knowledge and diet-health

awareness levels.

Race and ethnicity appear to have some influence on

HEI scores as well as on nutrient knowledge and

awareness. Whites had higher HEI scores on average

than Blacks, but Hispanics and non-Hispanic scores

were virtually identical. Non-Hispanics’ nutrient content knowledge and diet-health awareness scores were

higher than Hispanics’. 

We found that higher education and incomes were systemically related to higher knowledge of the nutrient

content of foods, more awareness of diet-health problems, and higher HEI scores. Vegetarians have higher

index scores, but their knowledge and awareness levels

appear to be about the same as nonvegetarians.

Smokers had lower index scores than nonsmokers and

slightly lower knowledge and awareness scores.

Explanatory Variables

Table 6 lists the explanatory variables hypothesized to

affect nutrition information and/or HEI. The variables

fall into three broad categories: household characteris8 ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA

Table 5—Nutrition information and the healthy eating index across selected sociodemographic groups

Nutrient content Diet-health Healthy eating 

knowledge (NCK) awareness (DHA) index (HEI)

HEI:

Less than 51 14.41 4.71 44.99

51-80 15.45 5.33 64.79

Greater than 80 16.55 6.04 88.09

Age:

Less than 30 15.09 4.84 59.28

31-49 15.67 5.64 61.51

50-69 15.68 5.44 67.17

Over 69 14.74 4.84 69.33

Gender:

Male 14.75 4.95 60.59

Female 15.56 5.39 64.79

Race:

White 15.74 5.49 64.78

Black 13.76 4.41 59.66

Other 14.12 4.47 63.56

Ethnic origin:

Non-Hispanic 15.55 5.37 64.04

Hispanic 13.56 4.60 64.11

Income per capita:

Less than $3,801 14.28 4.72 59.52

$3,801-5,400 14.69 4.74 63.47

$5,401-10,200 15.30 5.18 64.52

$10,201 or above 16.57 6.06 66.83

Education:

Less than high school 14.10 4.53 62.57

High school 15.56 5.20 62.97

More than high school 16.56 6.21 66.67

Vegetarian:

Vegetarian 15.61 5.18 67.21

Nonvegetarian 15.41 5.32 63.95

Smoking:

Smoker 15.04 4.93 58.63

Nonsmoker 15.55 5.45 65.98

Source: 1989-90 CSFII, DHKS.
Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ 9

Table 6—Description of variables

Variable description Name Mean

Dependent variables:

Healthy eating index HEI 64.1

Nutrient content knowledge NCK 15.4

Diet-health awareness DHA 5.3

Independent variables:

Household characteristics

Annual income before taxes Income 22.8

Household size Household size 2.6

Children present (less than 20 years old) Children 37.9

Participate in Women, Infants, and Children Program WIC 4.2

Participate in Food Stamp Program FSP 13.0

Head of household status:

Only female head Female head 34.6

Only male head Male head 11.6

Both male and female heads (omitted) — 53.8

Region:

Midwest Midwest 24.2

South South 37.7

West West 19.3

Northeast (omitted) — 18.8

Urbanization:

Suburb Suburban 42.8

Nonmetro Nonmetro 26.8

City (omitted) — 30.4

Personal characteristics 

Education:

Completed high school High school 35.6

Attended but did not complete college Some college 18.4

Completed college College 7.4

Completed post-graduate degree Post-graduate 6.8

Less than high school (omitted) — 31.8

Age (years) Age 48.9

Sex-female Female 82.3

Race:

Black Black 13.6

Other1 Other 1.4

White (omitted) — 85.0

Ethnic origin-Hispanic Hispanic 6.8

Employment status:

Not employed Not employed 54.4

Employed part-time Part-employed 14.0

Employed full-time (omitted) — 31.6

Smoke cigarettes now Smoker 26.3

Former, not current, smoker Quit smoking 17.9

Vegetarian Vegetarian 3.2

Body Mass Index (BMI) BMI 25.8

Watch more than 5 hours TV per day TV5 20.8

Received diet advice from physician or dietitian Diet advice 10.2

Compare nutrients when shopping:

Always Nutri-comp1 14.4

Sometimes Nutri-comp2 42.3

Rarely/never (omitted) — 43.3

Survey-related:

Year of CSFII-DHKS

1990 1990 49.1

1989 (omitted) — 50.9

Amount of food eaten (day 1):

Less than usual LTU1 17.1

More than usual MTU1 6.8

Usual (omitted) — 76.1

Amount of food eaten (day 2):

Less than usual LTU2 13.5

More than usual MTU2 3.6

Usual (omitted) — 82.9

Amount of food eaten (day 3):

Less than usual LTU3 13.0

More than usual MTU3 4.0

Usual (omitted) — 83.0

Note: Household income, household size, age, and BMI are continuous variables. The standard deviations are 21.4, 1.6, 18.3, and 5.5, respectively.

All other independent variables are dummy variables. 1Asian/Pacific Islander, Aleut, Eskimo, or American Indian.
tics, personal characteristics, and survey-related controls. Most of the household and sociodemographic

variables such as income, household size, age, sex,

race, and schooling have been used in previous nutrient intake studies (Behrman and Deolalikar, 1988;

Gould and Lin, 1994; Morgan, 1986). The regional,

urbanization, and survey-year dummy variables are

expected to capture any cross-sectional price variation

across households.

Income is represented by gross household income

before taxes for the year before the survey. Higher

income may provide increased access to dietary information and thus indirectly increase diet quality

(Ippolito and Mathios, 1990). On the other hand,

intake of meat products and less nutritious convenience foods may rise as income increases, causing a

negative direct effect on diet quality. Which of these

effects will dominate is uncertain and needs to be

determined empirically. Household size, presence of

children in the household, household head status, and

the employment status of the meal planner all likely

influence the household’s allocation of resources as

well as the time spent in shopping for and preparing

food (Gawn et al., 1993; Horton and Campbell, 1991).

Hence, these variables are likely to influence both

nutrition information and diet quality. The Women,

Infants, and Children (WIC) and Food Stamp Program

(FSP) variables are included to capture the nutrition

effects of program participation (Basiotis, Hirschman,

and Kennedy, 1996; Butler and Raymond, 1996).

Education is predicted to have a positive, indirect

effect on diet quality by increasing the allocative efficiency of health production (Grossman, 1975;

Grossman and Kaestner, 1995; Ippolito and Mathios,

1990; Kenkel, 1991). However, as in the case of

income, the direct effect of schooling on intake due to

variations in tastes is difficult to predict and remains to

be empirically determined.

The role that women have often played in food preparation and shopping leads us to expect they have a

higher stock of nutrition information than men. The

race, ethnicity, and age variables are expected to capture variations in information, food preferences, and

consumption induced by cultural backgrounds, cohort

effects, and dietary habits.

Smoking is related to health risk perception and smokers may value health less than nonsmokers

(McPhillips, Eaton, and Gans, 1994; Viscusi, 1990).

This leads us to expect a negative direct effect of

smoking on diet quality. All else equal, vegetarians are

expected to have higher HEI’s since HEI components

emphasize fruit and vegetable consumption. Body

mass index (BMI) is a ratio of body weight (in kilograms) divided by the square of height (in meters).

Individuals with higher BMI’s may receive more of

their calories from foods high in fats and fewer calories from foods rich in complex carbohydrates (Dattilo,

1992), and hence we expect BMI to have a negative

direct effect on HEI.

Meal planners’ use of nutrition information sources is

captured by whether the person watches 5 or more

hours of television each day (TV5), whether he or she

receives dietary advice from a physician or a dietitian

(diet advice), and whether the person compares nutrients while shopping (nutri-comp1, nutri-comp2).

While some amount of television watching may help a

person gain information, an excessive amount (defined

as 5 or more hours per day) is likely to hinder information gathering by curtailing alternative activities

such as reading (Carlson and Gould, 1994). Both

receiving dietary advice and the practice of comparing

nutrients while shopping are expected to be positively

correlated with a respondent’s nutrition information

level (Gould and Lin, 1994; Kenkel, 1991; Moorman

and Matulich, 1993). Finally, some variation in the

intake data is likely to depend on whether the person

reported each day’s food intake to be less than usual or

more than usual. A set of six binary variables is used

to control for these survey-related effects. The omitted

categories are those reporting “usual” intake.

10 ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA
OLS Models

Table 7 reports the main empirical results. Column 1

presents results from a linear model for HEI:

(6)

where X

p represents the explanatory variables. The

model does not include information variables and was

estimated by ordinary least squares (OLS). The standard errors of the coefficient estimates were corrected

for heteroskedasticity using White’s procedure

(Greene, 1995, p. 261). This model is similar to that of

Basiotis, Hirschman, and Kennedy (1996). It gives the

net effects of explanatory variables on the HEI, without reference to the theoretical framework underlying

the inclusion of nutrition information variables, km, in

the reduced-form equation 5. These estimates provide

a useful benchmark against which we can compare our

other estimated models. 

The sample and independent variables used by

Basiotis, Hirschman, and Kennedy (1996) are different

from ours. Their sample included all CSFII respondents, while our sample is limited to main meal planners responding to both CSFII and DHKS. This

means, for example, that their sample includes children whereas ours does not. The results in column 1,

therefore, are different from theirs. To capture nonlinear effects of income and age on diet quality, Basiotis,

Hirschman, and Kennedy (1996) include squared and

cubic terms of these variables. We experimented with

different specifications for income, age, and BMI, and

found that a log specification for income and linear

specification for age and BMI were appropriate.2

According to the linear OLS model, and based on significance at the 10-percent level or lower, the profile of

a meal planner with a high HEI value is an older,

White, nonsmoking, highly educated female with high

household income, low BMI, not employed or

employed part-time, and residing in the Northeast.

Household variables, except income and geographic

region, have no significant effect on HEI. Whether the

individual is vegetarian and whether he or she quit

smoking also has no influence on HEI. As noted earlier, previous studies have suggested interpreting the

effects of education and income in equation 1 as

reflecting informational differences (Ippolito and

Mathios, 1990; Kushi and others, 1988). Higher

income may promote greater access to information and

higher education may increase information processing

efficiency. Therefore, both are hypothesized to have a

positive effect on health behavior, such as diet quality. 

In columns 2 and 3 of table 7, we report OLS estimates for models that include nutrition information as

an explanatory variable:

(7)

where INFO is either nutrient content knowledge

(NCK) or diet-health awareness (DHA). Estimated

coefficients for both of these regressors are positive

and highly significant, confirming that a higher information level is related to better diet quality as measured by the HEI.3 Although the coefficients of the

information variables are estimated at high significance levels, the estimated elasticities are rather

small; 0.155 for NCK and 0.049 for DHA. Holding

other explanatory variables constant, a 1-percent rise

in the nutrient content knowledge score results in a

0.155-percent increase in the meal planner’s HEI, and

a 1-percent rise in the diet-health awareness score

results in a 0.049-percent increase in the meal planner’s HEI. Based on these elasticities, NCK seems to

be a better predictor of the HEI than does DHA. This

is likely because NCK is measured from a much larger set of components (21) and has a much smaller

coefficient of variation (0.19) than DHA, which has

only eight components and a coefficient of variation

of 0.43 (tables 1 and 2).

Compared with model 1, the estimated effects of most

other explanatory variables change when information

measures are added in models 2 and 3 of table 7. The

effects of income, education, and gender decline, while

those of race (Black) and Hispanic ethnicity rise. The

effects of age, BMI, and smoking, however, remain

Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ 11

Estimated Models and Empirical Results

HEI X u p p

p 1

P

=+ + =α α 0 ∑ ,

2

The dependent variable HEI takes on values ranging from 0 to

100. Given this fixed range, a question may arise as to whether a

transformation of HEI is necessary to ensure that the predicted values are also bound within this range. This is not strictly applicable

here because HEI is not a proportion, a probability, or a percentage

for which such a transformation is typical, but rather a score constructed to be between 0 and 100. We did, however, estimate models where the dependent variable was expressed as log(HEI/100-

HEI) and obtained quite similar inferences as reported here. See

Putler and Frazão (1994) for use of such a transformation.

HEI X INFO u p p

p 1

P

=+ + + =ααβ 0 ∑ ,

3

We estimated an OLS model that included both NCK and

DHA. The estimated coefficients (t-values) were 0.534 (5.52) and

0.372 (3.22) respectively. The decrease in the DHA coefficient in

this case is due to the relatively high correlation between NCK and

DHA; the correlation coefficient is 0.462. To retain compatibility

with subsequent models, we do not report these results in table 7;

see also footnote 4.
stable. This indicates that the sociodemographic variables were capturing some of the effects of information in model 1. However, equation 7 does not address

the question whether all or only some of the effects of

education, income, and other sociodemographic variables are through information. As noted earlier, the

effect, especially of income, is often unclear; higher

income may promote increased intake of fat-rich foods

such as meats and thus influence HEI negatively— the

direct effect. At the same time, higher income may

provide greater access to information and thus indirectly influence HEI positively. The relative influence

of these different effects can be seen by explicitly

modeling the relationship between sociodemographics

and nutrition information:

(8)

where INFO is either NCK or DHA, γ’s are unknown

parameters, and v is a random error term.

In estimating the parameters of equations 7 and 8, we

take account of the correlation between u and v due to

unobserved heterogeneity—that is, correlation due to

factors that are not included among the regressors,

such as unobserved preferences that affect both HEI

and information. If corr(u,v) ≠ 0, the OLS estimator of

parameters in equation 7 suffers from simultaneous

equations or endogeneity bias and is inconsistent. We

tested whether NCK and DHA in models 2 and 3 are

endogenous by applying a test of simultaneity. The test

involves adding OLS residuals from equation 8 as an

explanatory variable in equation 7 and applying a t-test

to the estimated coefficient of the residual (Pindyck

and Rubinfeld, 1991, pp. 303-304). The t-values for

NCK and DHA residuals in such a test were -5.69 and

12 ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA

Table 7—Determinants of diet quality (absolute t-values in parentheses)

Explanatory variable OLS OLS OLS 2SLS 2SLS LV

(1) (2) (3) (4) (5) (6)

Nutrient content knowledge — 0.643* — 4.131* — —

(NCK) (7.00) (5.21)

Diet-health awareness — — .596* — 3.803* —

(DHA) (5.34) (3.34)

Nutrition information — — — — — 5.053*

(INFO) (5.41)

Log income 1.465* 1.099* 1.264* -.888 .177 -1.392***

(3.68) (2.78) (3.19) (1.38) (.30) (1.91)

High school 1.128*** .672 .951 -1.802*** -.001 -2.151**

(1.83) (1.09) (1.54) (1.87) (.00) (2.12)

Some college 3.891* 3.129* 3.403* -1.005 .771 -2.312

(5.17) (4.13) (4.52) (.76) (.60) (1.52)

College 5.221* 4.14* 4.386* -1.714 -.117 -4.102***

(4.99) (4.02) (4.22) (.95) (.06) (1.85)

Postgraduate 5.529* 4.183* 4.466* -3.121 -1.248 -6.173**

(5.12) (3.86) (4.09) (1.52) (.54) (2.37)

Age .185* .183* .185* .176* .187* .178 *

(10.23) (10.26) (10.32) (7.52) (8.67) (7.31)

Female 4.991* 4.142* 4.73* -.466 3.320* -.862

(4.96) (4.13) (4.74) (.27) (2.69) (.50)

Black -1.613** -.932 -1.320*** 2.765** .260 3.395**

(2.14) (1.22) (1.73) (2.06) (.23) (2.49)

Other -.807 .082 -.180 4.905 3.196 6.643**

(.35) (.04) (.08) (1.58) (1.41) (2.22)

Hispanic 1.890** 2.894* 2.225** 8.339* 4.026* 8.901*

(2.00) (2.99) (2.37) (4.57) (3.13) (4.86)

Part-employed 1.737** 1.626** 1.484** 1.026 .122 .154

(2.35) (2.21) (2.02) (1.10) (.12) (.15)

Not employed 1.185*** 1.207** 1.092*** 1.326*** .586 .959

(1.92) (1.98) (1.78) (1.70) (.79) (1.15)

Children -1.095 -.996 -.988 -.462 -.416 -.131

(1.41) (1.29) (1.27) (.46) (.43) (.12)

Household size -.016 .007 -.029 .134 -.102 .067

(.07) (.03) (.13) (.44) (.37) (.20)

See notes at end of table. —Continued

INFO X v q q

q 1

Q

=+ + =γ γ 0 ∑ ,
-3.37, respectively. Therefore, the exogeneity of nutrition information is strongly rejected.

2SLS Models

To account for the endogeneity of the information variables, we estimated equation 7 by two-stage least

squares (2SLS). The results are reported as models 4

and 5 in table 7. The information variables are

regressed on all the determinants of HEI and four indicators of the use of different sources of information:

excess television watching, whether respondent

received advice from physician or dietitian about dieting, and whether respondent compares (always/sometimes) nutrients in foods while shopping (Gould and

Lin, 1994; Kenkel, 1991; Moorman and Matulich,

1993). Estimates from these first-stage regressions are

reported in table 8, columns 1 and 2. In each case, the

standard errors of the coefficient estimates were corrected for heteroskedasticity using White’s procedure

(Greene, 1995).

Estimated coefficients for NCK and DHA in table 7,

models 4 and 5, are considerably higher than corresponding estimates in models 2 and 3.4 Both the NCK

Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ 13

Table 7—Determinants of diet quality (absolute t-values in parentheses)—continued

Explanatory variable OLS OLS OLS 2SLS 2SLS LV

(1) (2) (3) (4) (5) (6)

Female head -.004 .165 .070 1.079 .466 1.239

(.01) (.25) (.11) (1.24) (.59) (1.33)

Male head .787 .615 .971 -.316 1.963 .493

(.64) (.51) (.80) (.19) (1.35) (.30)

BMI -.128* -.139* -.130* -.200* -.139* -.199*

(3.00) (3.26) (3.03) (3.62) (2.69) (3.40)

Smoker -4.592* -4.492* -4.447* -3.952* -3.667* -3.477*

(8.12) (8.01) (7.89) (5.45) (5.05) (4.41)

Quit smoking -.151 -.243 -.185 -.738 -.366 -.801

(.23) (.37) (.28) (.88) (.48) (.92)

Vegetarian 1.711 1.508 1.789 .407 2.211 .841

(1.23) (1.06) (1.28) (.21) (1.32) (.47)

FSP -.328 -.335 -.367 -.380 -.582 -.524

(.39) (.40) (.44) (.34) (.56) (.47)

WIC -.181 -.022 .159 .840 1.988 2.004

(.16) (.02) (.14) (.56) (1.23) (1.16)

Midwest -1.473** -1.762** -1.488** -3.332* -1.568*** -3.188*

(2.05) (2.48) (2.08) (3.58) (1.87) (3.14)

South -2.094* -1.970** -2.036** -1.297 -1.725** -1.166

(3.08) (2.94) (3.01) (1.50) (2.16) (1.28)

West -1.150 -1.313*** -1.26 -2.016** -1.824** -2.476**

(1.46) (1.69) (1.60) (2.25) (2.00) (2.38)

Suburban .172 .179 .230 .212 .542 .425

(.30) (.32) (.40) (.30) (.81) (.57)

Nonmetro .056 .054 .090 .041 .271 .169

(.09) (.08) (.14) (.05) (.36) (.20)

1990 .183 .009 .090 -.932 -.407 -1.158***

(.39) (.02) (.20) (1.53) (.72) (1.74)

Intercept 40.780* 35.810* 40.013* 8.843 35.885* .000

(9.30) (8.22) (9.19) (1.07) (6.86)

R2 .213 .229 .222 .114 .124 .229

Test of schooling — — 1.243 .673 9.098

restrictions (.29) (.61) (.059)

Note: *, **, and *** indicate coefficient estimates significant at 1-, 5-, and 10-percent levels, respectively, under two-sided t-test. All models also included six

dummy variables indicating whether each of the 3-day intakes was less than usual or more than usual; usual is the omitted category; see table 4. The R2 for 2SLS

and LV models are the squared correlations between observed and predicted HEI values

4

We estimated a 2SLS model that included both NCK and

DHA. While the coefficient for NCK came out positive and significant in this model, the estimate of the DHA coefficient was

insignificant. This is due to the very high correlation between predicted values of NCK and DHA; the estimated correlation was

0.904. Therefore, we do not report the results of this model. We

also examined possible nonlinearities in the information effects by

estimating the HEI equations with the square of the predicted values of NCK and DHA added as regressors. The results showed that

a small nonlinearity may exist at the lower ranges of NCK and

DHA, but for the most part the relationships are linear. For example, the average derivative of HEI with respect to NCK is 4.143,

which is almost identical to the linear estimate of 4.131 in model

4, table 7.
and DHA estimates are more than six times their OLS

estimates. The elasticity of HEI with respect to nutrition information is close to one (0.995) for NCK and

one-third (0.316) for DHA. The increase in size of the

estimated information effects under 2SLS is an indication that much of the effect of the sociodemographic

variables on HEI occurs through nutrition information.

This is most clearly illustrated by the estimated effects

of income and education in models 4 and 5. Most of

these estimates are insignificant. At the same time, the

first-stage estimates in table 8, models 1 and 2, show

that income and education have highly significant positive effects on NCK and DHA. 

In model 1, table 7, the education effects show a clear

monotonic pattern, increasing with larger effects for

higher education. The first-stage results in table 8

show that this monotonic pattern is generated by the

steadily increasing informational effects of higher education. In the last row of table 7, we report F-statistics

for excluding the education dummy variables from the

HEI equation, with p-values given in parentheses.

These restrictions cannot be rejected for both NCK

and DHA.

These results suggest that the indirect effects of

income and education occurring through information

almost completely explain their net effects on HEI.

Thus, the role of education and income in determining

diet quality, at least as measured by HEI, appears to be

wholly information-related. Our findings support the

view proposed in the health economics literature that

education influences both the choice of health inputs

and health by increasing information about these

inputs. Increased information leads to greater allocative efficiency, that is, the ability to select and use a

better input mix (Grossman and Kaestner, 1995). Our

findings also suggest that the favorable effects of

schooling on health inputs (in our case, overall diet

quality as measured by HEI) persists even at higher

levels of schooling, thus supporting Grossman’s finding about positive education effects on health.

The estimated effects of gender, race, and ethnicity in

the 2SLS models provide additional evidence regarding the informational effects of sociodemographic variables. When NCK is the information variable, the

coefficient for the female dummy variable becomes

insignificant in the 2SLS model. This contrasts with

the significant positive effects in the OLS models 1

through 3. In the 2SLS model, the entire effect of

being female on HEI occurs through NCK, as evidenced by the significant positive effect of the female

dummy variable on NCK in table 8, column 1. This

result implies that, holding all sociodemographic and

household characteristics constant, a male meal planner and a female meal planner, both possessing the

same level of nutrient content knowledge, do not significantly differ in their HEI’s. However, on average,

female meal planners possess higher nutrient content

knowledge than male meal planners, explaining part of

the positive effect of NCK on HEI.

The informational effects of race and ethnicity are

equally striking. The relatively small or insignificant

effects of these variables in models 1 through 3 change

to large positive effects in model 4. Thus, when the

endogeneity of information is taken into account, all

other things equal, the HEI for Black meal planners is

about three points higher than that for White meal

planners, meal planners of other races have HEI scores

four points higher than White meal planners, and

Hispanic meal planners have HEI scores that are eight

points higher than non-Hispanic meal planners.

However, when nutrition information is allowed to

vary, the higher HEI’s of these groups decline, disappear, or turn negative as in table 7, model 1. As table

8, column 1 estimates show, all other things equal,

Black and other non-White meal planners have significantly lower nutrient content knowledge than White

meal planners and the same holds for Hispanic meal

planners compared with non-Hispanic meal planners.

These results suggest that, while the tastes and preferences of non-White and Hispanic meal planners lead

them to choose a more healthful diet, their relative

lack of nutrition information reduces their ability to

choose a better quality diet.

The 2SLS estimates for gender, race, and ethnicity also

highlight the sensitivity of results to the information

measures used in the analysis. For example, the female

dummy variable is insignificant in the 2SLS model

with NCK, but it is positive and significant in the

2SLS model with DHA. For the Black binary variable,

the result is the opposite, and the Hispanic effect in

model 5 is half of that in model 4 (table 7). The reason

for these differences is that these variables are capturing some of the variation that is specific to the information measure. For example, although the gender

effect is positive on both NCK and DHA, women have

relatively more nutrition information as measured by

NCK than as measured by DHA. At the sample means

of NCK and DHA, the female coefficient estimates in

table 8, columns 1 and 2, suggest that women have 8.3

percent higher NCK scores and 7.8 percent higher

DHA scores than men.5

14 ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA

5

The female coefficient estimates for NCK and DHA in table 8

show that, all else equal, women answer 1.28 more NCK questions

and 0.413 more DHA questions correctly than men. Therefore, at

the sample means of NCK and DHA, 15.4 and 5.3 respectively

(table 6), women have 8.3 percent higher NCK score and 7.8 percent higher DHA score than men.
Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ 15

Table 8—Coefficient estimates from first-stage reduced-form information equations (absolute t-values in parentheses)

2SLS LV

Exogenous variable NCK DHA INFO

(1) (2) (3)

Log income .562* .324* .562*

(6.86) (4.53) (7.23)

High school .642* .284** .596*

(4.49) (2.36) (4.73)

Some college 1.088* .756* 1.132*

(6.37) (5.25) (7.28)

College 1.563* 1.340* 1.735*

(6.33) (7.12) (7.97)

Postgraduate 1.928* 1.710* 2.171*

(9.20) (10.71) (9.59)

Age .000 -.003 -.001

(.04) (.91) (.29)

Female 1.280* .413** 1.103*

(4.73) (2.30) (5.34)

Black -1.030* -.483* -.971*

(5.74) (3.40) (6.25)

Other -1.276** -1.045** -1.400*

(2.21) (2.50) (3.42)

Hispanic -1.574* -.588* -1.403*

(6.73) (3.30) (7.00)

Part-time employed .188 .451* .330**

(1.22) (3.36) (2.10)

Not employed -.004 .246** .072

(.03) (2.13) (.56)

BMI .013 .003 .009

(1.44) (.46) (1.07)

Smoker -.136 -.193*** -.205***

(1.08) (1.76) (1.74)

Quit smoking .121 .066 .100

(.82) (.57) (.76)

Vegetarian 0.222 -0.196 0.083

(.72) (.78) (.31)

FSP -.015 .076 .003

(.07) (.45) (.018)

WIC -.252 -.547** -.440***

(.88) (2.19) (1.71)

TV ≥ 5 hours -.235*** -.497* -.216**

(1.68) (4.27) (2.29)

Diet advice .401** .462* .481*

(2.47) (3.34) (3.76)

Compare nutrients always .566* .211 .665*

(3.75) (1.58) (5.30)

Compare nutrients sometimes .720* .079 .537*

(6.42) (.84) (5.77)

Constant 7.706* 1.539** .000

(8.20) (1.94)

R2 .260 .169 .425

Note: *, **, and *** indicate coefficient estimates significant at 1-, 5-, and 10-percent levels, respectively, under two-sided t-test. Each reduced-form information

equation was regressed on all the independent variables in the model. For brevity, only selected first-stage estimates are reported here.
LV Model

A conceptually attractive and statistically meaningful

way to reconcile these differences is to use a latent

variable (LV) model where NCK and DHA serve as

imperfect measures of an unobserved information variable. This unobserved information variable captures

the common variation in NCK and DHA that best predicts HEI. Variation specific to each measure is relegated to error terms. Empirically, this view is supported by the relatively high correlation between NCK and

DHA (correlation=0.462) and the much higher correlation between their predicted values from equation 8

(correlation=0.904). Under the LV model, the equations for the unobserved information variable (INFO)

can be written as:

(9)

where λ’s are unknown coefficients to be estimated

and e’s are uncorrelated measurement errors.

Equations 7 through 9 constitute a latent variable

model where INFO cannot be observed directly but is

measured indirectly by NCK and DHA. If one

assumes the error terms of the three equations to be

jointly normal, the unknown parameters of the equations can be estimated by maximum likelihood

(Bollen, 1989). The estimation procedure is described

in the appendix. During the estimation, all observed

variables are expressed as deviations from their means,

so the intercept coefficients in equations 7 through 9

are zero.

The scale of the latent variable INFO in equation 9 is

undetermined and a restriction is required to identify it

(Bollen, p. 239). We identify the scale of INFO by

imposing the restriction λ1 = 1 so that NCK and INFO

have the same scale, that is, a unit change in INFO

causes a unit change in NCK. The coefficient for DHA

in equation 9, λ2

, is free. The λ2 estimate is 0.61 with a

t-value of 20.98. The R2 for NCK and DHA are 0.6 and

0.4, respectively. Thus, both NCK and DHA contribute

significantly to the measurement of INFO, although, as

previously found, NCK is a better measure of nutrition

information than DHA. The estimates of the HEI equation under the LV model are reported in table 7, column 6 and the estimates of the INFO equation under

the LV model appear in table 8, column 3.

The R2 for the HEI equation is 0.23, nearly double that

of the 2SLS models. The R2 for the INFO equation is

also nearly double that of the first-stage estimates of

NCK and DHA under 2SLS. Thus, the LV model

appears to have a relatively good fit. The estimated

coefficient for the unobserved information variable

(INFO) in the HEI equation is 5.05. This estimate is

larger than both the NCK and DHA estimates in the

2SLS models.

The estimated effects of income and education in the

LV model are negative and significant. This finding

implies that, holding all else constant including information levels, meal planners with higher incomes or

higher education tend to have lower HEI’s. This is in

contrast to the 2SLS results, which suggested that

higher income or education, all other things equal, has

no significant effect on HEI. The LV results, therefore,

suggest an even larger informational role for income

and education than the 2SLS results. The LV results

are not surprising given that similar effects for income

and education, conditional on identical information

levels, have been found for individual HEI components

such as fat, saturated fat, and cholesterol by Carlson

and Gould (1994), Gould and Lin (1994) and Variyam,

Blaylock, and Smallwood (1997).

Under the LV model, the gender effect is purely informational. Conditional on a constant information level,

the HEI’s of women do not significantly differ from

those of men. On the other hand, if information is held

constant, Blacks and respondents of other races as well

as Hispanics have significantly higher HEI’s than

Whites and non-Hispanics. Holding information constant at the sample mean of the HEI’s, Blacks have 5

percent higher HEI’s than Whites, respondents of other

races have 10 percent higher HEI’s than Whites, and

Hispanics have 14 percent higher HEI’s than nonHispanics. 

From the LV model estimates, a year of age adds

about one-fifth of a point to the HEI, an additional 

unit of BMI reduces HEI by a similar amount, and

smokers’ HEI’s are about 3.5 points lower than nonsmokers’. In contrast to income, schooling, or gender

effects, however, the effects of age, body mass, and

smoking are almost entirely due to the different tastes

and preferences associated with these characteristics

and not due to any informational differences. The firststage coefficient estimates for age and BMI are

insignificant, while smoking has a small but significant

negative effect on information at the 10-percent level

(table 8). The absence of strong informational effects

explains the relative stability of the coefficient estimates for these variables under OLS, 2SLS, and LV in

table 7. The results for age agree with similar effects

found for fat, saturated fat, and cholesterol by Carlson

and Gould (1994) and by Variyam, Blaylock, and

Smallwood (1997). For BMI, a negative diet quality

effect has also been obtained by Variyam, Blaylock,

and Smallwood (1997) with respect to cholesterol; by

Yen, Jensen, and Wang (1996) for eggs; and by

Guthrie and Fulton (1995) for the consumption of food

16 ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA

NCK INFO e

DHA INFO e

= +

= +

λ

λ

1 1

2 2
groups conforming to the Food Guide Pyramid. The

results for smoking confirm previous findings by

McPhillips, Eaton, and Gans (1994) that smokers eat a

less healthful diet than nonsmokers. Given the weak

informational effect related to smoking, such dietary

behavior on the part of smokers may be related to their

tendency to underestimate health risks more so than

nonsmokers do (Viscusi, 1990). This view is supported

by the lack of significance of the quit-smoking variable, since those who have quit smoking are likely to

have done so due to a higher perceived health risk

from smoking compared with current smokers. 

The estimated household food program participation

and time and resource allocation effects are either

insignificant or inconclusive. The presence of children,

household size, and sex of the head of the household

have no significant effects on HEI or information

(from results not reported in table 8). Similarly, both

the FSP and WIC coefficients are insignificant in the

HEI equation. Interestingly, the WIC coefficient is relatively large (2.004 in table 7) in the LV model. Its

lack of significance may be related to the small proportion of WIC households, about 4 percent of the

sample. WIC participation also has a small but negative effect on nutrition information. These WIC effects

clearly need further investigation because of the

implied benefits of targeted nutrition education.

The evidence on employment status is mixed, with a

largely insignificant direct effect on HEI, but some

positive informational effects indicated by the significant coefficient for part-time employment on INFO

under the LV model. The insignificant effects for those

not employed outside the home is surprising given that

the expected effect is through greater time available for

shopping and food preparation (Horton and Campbell,

1991). Also surprising are the insignificant effects of

being a vegetarian, since strong beneficial diet quality

effects have been found previously by Variyam,

Blaylock, and Smallwood (1996) for dietary fiber; and

by Variyam, Blaylock, and Smallwood (1997) for fat,

saturated fat, and cholesterol.

The first-stage effects of nutrition information sources

are as expected. Excess television watching (TV5) has

a significant negative effect on nutrition information,

while the effects of receiving diet advice from a physician or a dietitian and the use of nutrition labels are all

positive. These results are similar to the findings of

Carlson and Gould (1994) and Gould and Lin (1994).

There is virtually no effect for the degree of urbanization on HEI. Region variables and the year variable

show some effect, indicating some regional and temporal variations in prices, tastes, or preferences.

Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ 17
Our results suggest that nutrition information affects

overall diet quality, even after controlling for individual differences in a host of personal and household

characteristics including income, education, age, gender, race, ethnicity, smoking behavior, and body mass.

This evidence adds to previous findings for individual

nutrients such as fat, saturated fat, and cholesterol

(Carlson and Gould, 1994; Gould and Lin, 1994;

Variyam, Blaylock and Smallwood, 1997); dietary

fiber (Variyam, Blaylock, and Smallwood, 1996); 

and for food groups (Guthrie and Fulton, 1995; Yen,

Jensen, and Wang, 1996). This micro-level evidence

complements evidence of informational effects at the

aggregate level found by Brown and Schrader, 1990;

Capps and Schmitz, 1991; and Chern, Loehman, and

Yen, 1995. Together, these findings show how consumers have absorbed and used the information linking diet and health in their gradual shift toward more

healthful diets.

These findings also suggest a continued role for nutrition education efforts to close the persistent gap

between actual and healthful diets. In this regard, our

study has placed a special focus on isolating the role

of nutrition information on overall diet quality from

the role of various consumer characteristics that affect

both information and diet quality. Because sociodemographic variables and nutrition information affect diet

quality separately, previous studies linking diet quality

to sociodemographic characteristics without taking

nutrition information into account may be questionable. For example, without controlling for nutrition

information, females tend to have higher HEI scores

(table 5; table 7, column 1). They also tend to possess

more nutrition information than men, other things

equal (table 8). When the effects of nutrition information are properly accounted for, men and women of

similar characteristics have similar HEI scores (table 7,

column 6). Thus, increasing male meal planners’ stock

of nutrition information should result in increased

overall diet quality. Similarly, the results for race and

ethnicity show that main meal planners who are Black,

of other race, or Hispanic will benefit greatly from

additional nutrition information. Targeting these main

meal planner groups for nutrition education and promotion efforts should result in a significant improvement of their overall diet quality. Given the limited

resources available for nutrition education and promotion, our results suggest guidelines for the efficient

allocation of these resources. 

18 ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA

Conclusions
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20 ✼ USDA’s Healthy Eating Index and Nutrition Information/TB-1866 Economic Research Service/USDA
Let y(3×1) = (HEI, NCK, DHA) ́, η(2×1) = (HEI, INFO) ́

and let x(R×1) be a vector of all the independent variables X

p

and X

q

. Then, assuming that all variables are

mean-centered, equations 7 through 9 can be written in

the general latent variable model form as

(10)

(11)

where

such that α(1×R) contains the α

p parameters of equation

7 and γ(1×R) contains the γq parameters of equation 8.

Note α that γ and will contain zero restrictions so as to

exclude those variables in x that do not enter the righthand side of equations 7 and 8 respectively.

The maximum likelihood (ML) estimation of equations 10-11 is based on the idea that for multivariate

normally distributed y, the variance-covariance matrix

of the observables is a sufficient statistic. The variance-covariance matrix of the observables (y x) can be

written as a function of the unknown parameters of

equations 10-11. This can be seen by substituting

equation 11 into equation 10:

(12)

so that,

(13)

where E(xx') = Sxx, E(ζζ') = Ψ, and E(∈∈') = Θ. Note

that E(xζ') = 0, E(x∈') = 0, and E(ζ∈') = 0. Similarly,

(14)

Let E[(y x) (y x')] = Σ(δ) where δ is a vector of the

unknown parameters. Therefore, δ can be estimated by

fitting the variance-covariance matrix implied by the

model, Σ, to the empirical variance-covariance matrix S.

This is achieved by minimizing the ML fitting function

(15)

where m is the number of variables in y (Bollen, 1989,

pp. 319-338).

Economic Research Service/USDA USDA’s Healthy Eating Index and Nutrition Information/TB-1866 ✼ 21

Appendix

y = + Λη ∈

η = + B x η Γ + ζ

Λ Γ

α

γ () () ( )

( )

( )

,, , 32 1

2

22 2

1

1

1 0

0

0

0

0 0 x x xR

xR

xR

=

L

N

M

M

M

O

Q

P

P

P = L

N

M O

Q

P = L

N

M O

Q

λ P

λ

β B

∈ ζ ( ( , , ', ', 3 1) 1 2 2 1) x x = = b0 e e u,v g a f

y I B x IB =− + + − − (- ) Λ() , Γ Λ Γζ ∈ 1 1

E yy B S B

B B

′ = − ′ − ′

+− − ′ +

− −

− −

a f a f a f

a f a f

Λ Γ Γ Λ

Λ Ψ Λ Θ

I I

I I

xx

1 1

1 1

E yx B S ′ = − a f ΛaI f Γ xx - 1 .

F n tr n m R ML = + − −+ − 1 1 1 Σaδf S S Σ aδf a f