Food Expenditures by U.S. Households: Looking Ahead to 2020. By Noel Blisard,

Jayachandran N. Variyam, and John Cromartie, Economic Research Service, U.S.

Department of Agriculture, Agricultural Economic Report No. 821. 

Abstract

By 2020, the effects of demographic changes and income growth will increase per capita

spending on food 7.1 percent. Income growth alone, which will effect spending increases

of almost 10 percent on away-from-home foods and 3 percent on at-home foods, will raise

per capita food spending about 6 percent. Expansion of the Nation’s population will drive

growth in food demand and, combined with rising incomes and other demographic

changes, is projected to boost total U.S. food spending 26.3 percent. On a national level,

the slow but steady growth of the population will result in little variation among expenditure growth levels of individual food groups. The largest projected increase is for fruits, up

27.5 percent, while the smallest is for both beef and beverages, up 21.1 percent. 

Keywords: Household food expenditures, income, demographics, projections, Consumer

Expenditure Survey.

Acknowledgments

The authors thank Nicole Ballenger and James Blaylock of USDA's Economic Research

Service for helpful review of the earlier drafts of this report, John Weber for editorial support, Cynthia Ray for final document layout and charts, and Curtia Taylor for cover design.

1800 M St., NW

Washington, DC 20036 February 2003
ii z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA

Contents

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

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

Theoretical and Empirical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Demand Considerations With Observed Expenditures and Model Considerations . . . . . . . . . . 3

Data Used in the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Characteristics of American Households and Their Food Expenditures . . . . . . . . . . . . . . . . . 5

Model Specification and Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Empirical Results From the 1997-98 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Influence of Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Demographic and Seasonal Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Household Age Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Race . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Population and Demand Projections: Background and Methods . . . . . . . . . . . . . . . . . . . . . . 20

Projected Age Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Projected Regional Population Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Projected Population of the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Projected Racial Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Projected Educational Attainment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Method of Projections Based on Diet-Health Knowledge and Tobit Models . . . . . . . . . . . . . 23

Food Expenditure Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Age Distribution Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Regional Distribution Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Racial Distribution Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Diet-Health Knowledge Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Income Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Combined Demographic and Income Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

National Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z iii

Summary

Projected demographic changes combined with an assumed increase in inflation-adjusted

incomes of 1 percent per year in the United States will increase per capita food spending

7.1 percent between 2000 and 2020. This effect will be due to spending increases of 8.1

percent on food away from home and 5.4 percent on food at home. Among individual athome foods, expenditures for fruits (up 8.1 percent) and vegetables (up 7.2 percent) would

increase the most under this scenario. Beef expenditures (up 2.6 percent) would increase

the least of all categories over the 20-year period. 

Household expenditure data indicate that higher income households spend more per person

than poorer households on most food groups, especially food away from home, fruits, miscellaneous prepared foods, vegetables, and dairy. Americans age 74 or older tend to spend

the most on cereal and bakery goods as well as on fruits. Household food expenditures

vary regionally, with households in the Northeast spending the most on total food and

households in the North Central spending the least. Non-Black households outspend Black

households in every category except meats, poultry, fish, and eggs. 

Projections of household food expenditures to 2020 based on shifts in age, regional, and

racial distribution of the U.S. population, as well as expected changes in diet-health knowledge, income, and population growth show that regional population shifts, racial distribution, and diet-health knowledge will have only small effects on household per capita food

expenditures. Income growth will increase away-from-home food expenditures 9.7 percent

per capita but at-home food expenditures just 3 percent per capita. The shift toward an

older age distribution in the U.S. population is projected to increase total per capita food

expenditures just 1 percent over the 20-year period. Among at-home foods, the rising share

of elderly will have the most effect on expenditures for fruits (up 3.7 percent), vegetables

(up 3.6 percent), and fish and pork (up 3.1 percent).

The most important factor behind the growth in total food demand is the expansion of the

U.S. population. Total U.S. food expenditures are projected to increase 26.3 percent by

2020. Away-from-home food expenditures are projected to increase 27.5 percent, compared

with 24.3 percent for at-home food expenditures. One effect of the slow but steady growth

of the population will be little variation on a national level among expenditure growth levels of food groups. The largest projected increase is for fruits, up 27.5 percent, while the

smallest is for beef and beverages, both up 21.1 percent.

Another way to interpret the projections in this study is to view them as scenarios of what

would have occurred if projected demographic or income changes were already in place.

For example, a relevant question may be as follows: “What would have happened to food

expenditures in our base year if the projected changes in the racial mix of the population

for 2020 were already in place?” This approach to viewing the projections lessens the

potential for misinterpretation by focusing on our underlying assumptions, as detailed in

this report. Although we feel this alternative interpretation is the most appropriate, due to

the nature of the data, we will use the term “projections” and draw comparisons between

the base year, 2000, and a future period as we discuss our results.

This study uses recent Bureau of Census data to project U.S. food expenditures in the years

2000-20. The projections incorporate demographic factors, such as age, race, income,

region of residence, diet-health knowledge, season of the year, and number of persons in a

household. Total growth in U.S. expenditures is based on per capita shifts due to demographic changes plus growth in the total population.
Introduction

By 2020, the U.S. population is projected to grow by

another 50 million, creating a base of 331 million people

to feed. This steady population expansion is expected to

fuel a 26-percent increase in U.S. food expenditures

between 2000 and 2020. With food spending approaching $800 billion per year, annual food sales by supermarkets, restaurants, fast food outlets, and other retail food

establishments will increase $208 billion by 2020. 

Aggregate growth in food expenditures driven by population increase, however, is only one aspect of how

changing consumer demand will affect the future of the

U.S. food system. The demographic profile of the U.S.

population in 2020 will differ from today’s in ways that

have implications for what people will eat, where they

will eat, and the product characteristics that will command the consumer’s food dollar. These future dietary

and food choices will affect not only the health of the

U.S. population but also the organizational structure of

the food industry and the economic well-being of farmers and other participants in the food production and

marketing system.

We can summarize the demographic shifts likely to

occur between 2000 and 2020 as follows: the U.S. population will be somewhat better off economically, older,

better educated, and more ethnically diverse. Population

density will also have shifted somewhat toward the

South and West and, consistent with the aging trend,

households will be smaller. These demographic shifts,

when added together in an economic model, signal

important trends ahead for the food sector.

This report focuses on household expenditure patterns

for 16 food groups. We used a set of comprehensive

behavioral models to isolate the net effect of income

and other socioeconomic characteristics on household

food expenditures. The models were then applied to

explore shifts in consumer food demand that will result

from changes in the socioeconomic characteristics of

the domestic population. This work is particularly timely, as the projections are based on the most recent

(2000) census data. 

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 1

Food Expenditures by U.S. Households:

Looking Ahead to 2020

Noel Blisard

Jayachandran N. Variyam

John Cromartie
Theoretical and Empirical

Considerations

According to Ernst Engel, a pioneer in analyzing family

budgets, “the poorer a family is, the greater is the proportion of the total outgo (total expenditure) which

much be used for food.” Engel’s most important finding,

known as Engel’s law of consumption, states the following: “As income increases, the expenditure on different

items in the budget has changing proportions, and the

proportions devoted to urgent needs (such as food)

decrease, while those devoted to luxuries or semiluxuries increase.”

Many analyses of family budgets conclude that the proportions of income devoted to various groups of commodities not only change with increasing income, as

stated in Engel’s law, but also vary systematically.

Analysts consequently postulate that the expenditure on

a given commodity varies with income in accordance

with some underlying mathematical law. This observation leads analysts to estimate Engel functions by

employing a variety of functional forms to express the

underlying relationship between income and expenditures on a given commodity.

Surveys of individual households generally provide the

information necessary to study the relationships

between commodities, expressed in terms of quantities

or expenditures. The framework used to analyze such

surveys is based on the classical theory of consumer

demand. The theory of the individual is broadened to

encompass the vast heterogeneity in households and the

differing environments in which they live. Cross-sectional surveys provide information on households of

varying sizes, incomes, and consumer-oriented preferences. These households often exist in different economic, social, and regional environments that influence

food purchase decisions. To capture these variable factors and to control for them requires an expanded analytic framework.

A number of household socioeconomic characteristics

other than income have been shown to influence expenditures, including household size, age distribution of

household members, and region of residence (Blisard

and Blaylock). Contemporary statistical representations

of Engel curves usually include these and other characteristics, such as the seasons of the year, as explanatory

variables. 

Because household survey data are collected within a

span of several days or weeks, researchers generally

assume that prices will fluctuate little in such a short

period. Observed price differences are usually assumed

to reflect variation in product content and quality rather

than variation in relative prices for the same product.

The influence of item prices on purchase behavior is,

consequently, modeled differently in household survey

data than in aggregate time series data.

This assumption about prices simplifies the process

involved in estimating Engel relationships. Demand

equations are functions of income and relevant household characteristics only. Food expenditures and budgeting patterns observed in cross-sectional survey data are

snapshots of a wide variety of households in different

circumstances. Analysts usually assume that the different

circumstances reflect what would occur if the circumstances changed for any particular household. If this

assumption is valid, one can then use statistical models

to measure the implied behavioral response parameters.

Hence, the fact that one does not usually observe a particular household under changing circumstances does not

prevent the measurement of these response parameters. 

Household food surveys measure consumption in terms

of quantity (physical weight) or money value. The quantity measure is related to the physical satisfaction of

demand and the need to fulfill certain nutritional

requirements (Wold and Jureen). The money value is a

measure of consumer satisfaction and economic wellbeing obtained through the marketplace, in the sense

that the prices consumers pay reflect the unit value of

the goods. The money value of a purchased product

group, such as red meats, is a price or value-weighted

sum of the physical quantities used. Viewing expenditures as a value-weighted quantity provides a link

between household budget analysis and the traditional

theory of consumer demand. Using prices as weight to

aggregate items into groups has been shown to be consistent with economic theory when relative item prices

are constant (Green). The use of expenditures, or money

value, provides a consistent method for aggregating

many detailed and heterogeneous items into a manageable number of product groups when using cross-sectional data.

Construction of statistical models requires that one

account for those household features that contribute

substantially to differences in consumption among

households. Income, diet-health knowledge, and household composition are the survey response features that

2 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA
account for the primary differences in food spending

among households in any one period. Other determinants of demand, such as geographic region of household residence and season of the year, are included in

the model to improve the measurement and statistical

properties of the equations but are of less economic

concern. Regional and seasonal variables may also represent price variation. Hence, they are not exact measures of regional taste differences. The omission of a relevant explanatory variable that is correlated with an

included variable will bias the estimated parameter of

the corresponding included variable. Therefore, to the

extent feasible, all relevant determinants of household

consumption must be included in the analysis.

Demand Considerations With Observed Zero 

Expenditures and Model Considerations

Household size, the frequency and mix of product use,

and the amount of product consumed per eating occasion influence total household expenditures for various

food items. Most expenditure surveys include a large

number of households that report detailed information

on food spending over 1 or 2 weeks, which is not long

enough to represent the average expenditure pattern for

any particular household. However, by examining a

group of similar households, one can infer how a typical

household within the group would behave over a longer

period. Inferences can be drawn regarding the average

expenditure, the probability of purchasing an item, and

the amount spent per household during a given period.

Many households do not purchase or use certain food

items during the survey period. Thus, zero values are

common in household surveys, and the economic interpretation one should give to these observed values is not

always clear. Survey information is usually insufficient

to determine whether a zero value represents a household that never consumes the item, does not consume

the item given the current values of the household’s

demand determinants (such as prices and income), or

consumes the item infrequently (Maddala).

Assigning a nonconsuming household to one of the

above categories has implications for demand analysis.

How often and whether or not a particular household

uses a given product is not usually reported and, consequently, must be inferred by examining the reported

purchases or nonpurchases by many similar households.

By assuming that all households will eventually use the

product and that no infrequency-of-purchase or nonuse

problems exist, we can study consumer behavior in a

large sample of households and determine the probability of consumption and relate this probability to a household’s characteristics. 

If the probability of use or nonuse is determined by the

same household characteristics that determine the level

of use, and if one discards observations on households

not purchasing an item during the survey, then traditional regression procedures will yield biased estimates of

behavioral relationships. Thus, valuable information on

the probability of use will have been ignored. The statistical model used in this study (Tobit model) assumes

that the probability of consumption is related to household income and other selected socioeconomic and

demographic features. This estimated probability is

based on the assumption that all households will eventually purchase all items under consideration. This is a

strong assumption, but the available data do not allow

us to determine if zero purchases are due to infrequent

purchases, nonuse, or economic circumstances, such as

prices or income. Furthermore, we employ a traditional

application of the Tobit model without attempting to

correct for any statistical abnormalities that might be

present. Most variations of this model attempt to correct

for a nonnormality in the error term. However, it can be

shown that both the error term and the parameters are

simultaneously estimated in this model for all observations that have zero expenditures. Hence, any misspecification of the error term will cause the estimated coefficients to be inconsistent estimators of the true parameters (Deaton). Given this outcome, one can choose to

use the model we employ, attempt to correct the abnormality of the error term but risk inconsistent parameter

estimates, use another variation of the Tobit model, or

use a completely different statistical model, such as a

median regression. We have chosen to use the traditional Tobit model.

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 3
Data Used in the Analysis

The Consumer Expenditure Survey (CES) of the Bureau

of Labor Statistics (BLS) for calendar years 1997 and

1998 is the source of data used in this analysis. The

CES contains the most recent and comprehensive data

available on food spending in U.S. households at the

time of this study.

The CES comprises two components, each with its own

questionnaire and sample: (1) an interview panel survey

in which each of approximately 5,000 households is

surveyed every 3 months over a 1-year period and (2) a

diary survey of approximately the same sample size in

which households keep an expenditure diary for two

consecutive 1-week periods.

The diary survey obtains data on small, frequently purchased items that are normally difficult to recall, including foods and beverages, tobacco, housekeeping supplies, nonprescription drugs, personal care products,

services, and fuels. The diary survey excludes expenditures incurred while away from home for 1 night or

longer. The diary survey is the source of data for this

report.

The data used in this report are a subset of the 1997-98

CES. Criteria for inclusion are completeness of reporting and consistency across the 2 survey years. Households that did not report complete income or participate

in both weeks of the diary survey were excluded from

the analysis. After eliminating these households, the

analysis sample consisted of 7,709 households over the

2-year period.

4 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA
Characteristics of American

Households and Their Food

Expenditures

Between 1988-89 and 1997-98, American households

decreased their budget share of food away from home

by 2.3 percentage points (table 1). This decline reversed

a trend toward a larger budget share of food expenditures away from home that began in the early 1970s.

One theory behind this decrease is that households

bought more miscellaneous prepared foods, although

expenditures in this category were up just 0.8 percentage points over the decade.

Other at-home food groups that increased in share of

U.S. food expenditures include cereals and bakery products (up 0.5 percentage points), sugars and sweeteners

(up 0.6 percentage points), and fats and oils (up 1.1 percentage points). In contrast, budget shares of both dairy

and nonalcoholic beverages fell 0.4 percentage points.

Likewise, the meats, poultry, fish, and eggs group as a

whole declined 0.3 percentage points over the decade,

mostly as a result of a 0.6 percentage point decline in

beef expenditures. Among other foods in this group, the

budget share increased for pork (0.2 percentage points)

and poultry (0.5 percentage points). Over the same time

span, the budget share for fish was unchanged. U.S.

households also allocated slightly more of their at-home

food budget to both fruits and vegetables. The budget

share for fruit increased 0.1 percentage points while the

share for vegetables increased 0.2 percentage points. 

The inflation-adjusted price of food away from home

fell 3.8 percent from 1989 to 1998, while the real price

of food at home fell 1.4 percent (table 2). Although a

decline in price normally increases consumption, all

other variables constant, expenditures increased only for

at-home foods over the period. Spending on food away

from home declined. Consumers may have cut back on

the number of times they dined out, or perhaps the rising number of restaurants over the 1990s put downward

pressure on prices. At-home foods with the largest price

declines were nonalcoholic beverages (down 9.1 percent), fats and oils (down 7.8 percent), and meat, poultry, fish, and eggs, (down 7.6 percent). In this last category, the price for beef was down 13 percent, poultry

was down 9.9 percent, and fish declined 3.7 percent. In

contrast, the inflation-adjusted price of fruits and vegetables increased 9.3 percent, while cereals and bakery

products increased 4 percent. 

A great diversity in household income and household

size was found across selected characteristics among

sample households (table 3). For example, households

in the West had the highest income and the largest

household size. Non-Black households had about

$14,800 more in household income per year than Black

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 5

Table 1—Trends in the allocation of U.S. food expenditures, 1988-98

Share of food budget

Food group 1988-89 1997-98

Percent

Food away from home 41.1 38.8

Food at home 58.9 61.2

Cereals and bakery products 9.1 9.6

Meat, poultry, fish, and eggs 15.6 15.3

Beef 5.0 4.4

Pork 3.0 3.2

Poultry 2.4 2.9

Fish 2.1 2.1

Dairy products 7.3 6.9

Fruits 5.8 5.9

Vegetables 4.8 5.0

Sugars and sweeteners 2.1 2.7

Nonalcoholic beverages 5.7 5.3

Fats and oils .6 1.7

Miscellaneous prepared foods 8.0 8.8

Source: Economic Research Service, USDA.
6 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA

Table 2—Trends in inflation-adjusted food prices, 1989-98

Relative food prices

Food group 1989 1998 Change

——————Index1—————— Percent

Food away from home 102.7 98.8 -3.8

Food at home 100.2 98.8 -1.4

Cereals and bakery products 106.8 111.1 4.0

Meat, poultry, fish, and eggs 97.8 90.4 -7.6

Beef 96.2 83.7 -13.0

Pork 91.3 91.1 -.02

Poultry 107.0 96.4 -9.9

Fish 115.8 111.5 -3.7

Dairy products 93.2 92.5 -.08

Fruits and vegetables 111.3 121.6 9.3

Sugars and sweeteners 96.3 92.1 -4.4

Nonalcoholic beverages 89.8 81.6 -9.1

Fats and oils 97.7 90.1 -7.8

Miscellaneous prepared foods 101.2 101.5 .03

1Based on the Consumer Price Index (CPI) for individual food groups divided by the CPI for all urban consumers, 1982-84 = 100.

Source: Economic Research Service, USDA.

Table 3—Annual household income and size by selected demographic groups, 1997-98

Demographic group Annual income before taxes Household size

Dollars Number

All groups 43,050 2.53

Season:

Winter 43,407 2.54

Spring 43,788 2.56

Summer 43,030 2.54

Fall 41,855 2.48

Region:

Northeast 44,613 2.42

North Central 43,323 2.45

South 40,359 2.57

West 45,078 2.64

Race:

Non-Black 44,809 2.49

Black 29,994 2.83

Income quintile:

I (lowest) 7,349 1.79

II 17,936 2.27

III 31,290 2.45

IV 49,509 2.88

V (highest) 100,353 3.14

Household size:

1 member 24,183

2 members 46,094

3 members 52,096

4 members 57,602

5 members 57,362

6 or more members 45,803

Source: Economic Research Service, USDA.
households. The mean before-tax income for households in the lowest 20 percent of the income distribution

was $7,349 per year, while the mean income for households in the top 20 percent of the income distribution

was $100,353 per year. This gap narrows marginally if

these figures are adjusted for household size, as lower

income households tend to have fewer members.

Table 4 breaks total food expenditures per person into

at-home and away-from-home components by selected

socioeconomic characteristics, season, and household

size. Care is required in interpreting this table because it

does not isolate the effect of a single socioeconomic

characteristic on expenditures. For example, household

size, income, and other factors are not held constant in

the breakdown by racial group.

While total food expenditures were nearly the same

across the seasons, they were slightly higher in the

spring and lowest in the winter. At-home food expenditures were highest in the fall and lowest in the summer.

Conversely, away-from-home food expenditures were

highest in summer and lowest in fall.

Food spending varied substantially by region, which

may have been caused by relative price differences,

income disparities, and differences in tastes and preferences. Households in the South spent the least on total

food, while those in the Northeast spent the most. The

same relative pattern held for food at home and food

away from home, with households in the South spending the least and those in the Northeast spending the

most.

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 7

Table 4—Weekly food expenditures per capita, at home and away from home,

by selected demographic variables, 1997-98

Share of Share of

Demographic Expenditures food budget, income spent

group Total At home Away from home at-home on food

—————————Dollars——————————— —————Percent—————

All groups 40.32 24.68 15.64 61.2 10.8

Season:

Winter 40.13 24.34 15.78 60.7 10.7

Spring 40.46 24.71 15.75 61.1 10.8

Summer 40.27 23.79 16.48 59.1 10.8

Fall 40.40 25.62 14.77 63.4 10.9

Region:

Northeast 43.32 25.76 17.56 59.5 11.1

North Central 39.38 24.06 15.33 61.1 10.3

South 37.87 23.75 14.12 62.7 10.9

West 42.23 25.72 16.52 60.9 10.9

Race:

Non-Black 41.32 25.06 16.26 60.6 10.6

Black 31.19 21.25 9.94 68.1 12.8

Income quintile:

I (lowest) 32.17 21.84 10.33 67.9 35.5

II 35.13 23.50 11.63 66.9 19.3

III 40.30 24.71 15.59 61.3 13.3

IV 42.27 24.58 17.69 58.1 10.8

V (highest) 50.60 28.38 22.22 56.1 7.2

Household size:

1 member 48.92 27.86 21.05 57.0 10.6

2 members 43.78 27.27 16.51 62.3 9.9

3 members 35.09 21.94 13.15 62.5 10.6

4 members 30.57 20.08 10.49 65.7 11.0

5 members 27.13 18.27 8.86 67.3 12.0

6 or more members 21.32 15.65 5.68 73.4 16.0

Source: Economic Research Service, USDA.
Non-Black households spent substantially more per person on total food, food at home, and food away from

home than Black households, probably due to the

income disparity between non-Blacks and Blacks and

the larger household sizes among Blacks—larger households tend to have lower per capita expenditures.

Higher income households spent more per person for

both at-home food and away-from-home food in 1997-

98 than households at other income levels. Higher

income households also spent a lower share of their

food dollar on food at home. Larger households spent

less per person for both food at home and food away

from home than other households. Smaller households

tend to spend more of their food dollars away from

home. Because economies of size may be realized in

expenditures on food at home but not on food away

from home, these results are understandable.

Almost all households (98.7 percent) had some total

food purchase every week (table 5). Among this share,

96.2 percent purchased food for at-home consumption,

and 86.6 percent purchased food away from home.

Among households purchasing individual categories of

food at home, 91.4 percent of households purchased

cereals and bakery products, and 89.1 percent purchased

dairy products. Only 38 percent of all households purchased fish.

8 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA

Table 5—Percentage of the population purchasing

food items in a week, 1997-98

Share of population 

Food group purchasing food item

Percent

Total food 98.7

Food away from home 86.6

Food at home 96.2

Cereals and bakery products 91.4

Meat, poultry, fish, and eggs 87.2

Beef 59.6

Pork 54.6

Poultry 53.3

Fish 38.0

Dairy 89.1

Fruits 84.0

Vegetables 82.2

Sugars and sweeteners 65.8

Nonalcoholic beverages 79.3

Fats and oils 58.8

Miscellaneous prepared foods 84.0

Source: Economic Research Service, USDA.
Model Specification and Variables 

In this study, we assume that a person’s diet-health

knowledge, such as knowing the benefits of eating a

high-fiber diet or knowing which foods are likely to

contain large amounts of fat, influences his or her

expenditures on different food groups. For example, we

hypothesize that a household headed by a married couple with a college education or higher is likely to buy

more fruits and vegetables than a household whose

inhabitants never finished high school. Further, we

assume that this knowledge can be introduced as a separate factor into the consumer demand equation for each

particular food category. Hence, diet-health knowledge

is estimated as a separate equation and as a variable in

each individual food expenditure equation. 

The diet-health variable was based on participants’

responses to health and nutrition knowledge questions

in the 1994-96 Diet and Health Knowledge Survey

(DHKS), a followup survey to USDA’s 1994-96

Continuing Survey of Food Intakes by Individuals

(CSFII). Each year of the 3-year CSFII data sets comprises a nationally representative sample of noninstitutionalized persons residing in the United States. From

each CSFII household, a randomly selected participant

who had provided initial (day 1) intake information and

who was age 20 or older was contacted by telephone

approximately 2-3 weeks after the CSFII was recorded.

The DHKS questions covered a wide range of issues,

including self-perceptions of the adequacy of intake levels of nutrients, awareness of diet-health relationships,

perceived importance of following the dietary guidance,

use and perceptions of food labels, and behaviors related to fat intake and food safety. Out of 7,842 households eligible for DHKS, respondents from 5,765

households, or 73.5 percent, completed the survey.

The diet-health knowledge variable used in this study

was constructed from responses to 27 questions in the

DHKS. These questions asked about the sources and

occurrence of various nutrients in foods (“Which has

more saturated fat: butter or margarine?”), the relationship of specific dietary components to specific diseases

(“Have you heard about any health problems caused by

eating too much cholesterol?”), and the number of servings of various food groups in a healthful diet (“How

many servings would you say a person of your age and

sex should eat each day for good health from the vegetable group?”). The number of correct answers to these

questions given by a respondent provided a direct measure of his or her diet-health knowledge.

The range of the diet-health knowledge variable was 0-

27. Based on the estimated proportions using sampling

weights for the actual data, 74 percent of the respondents scored 16 or above on the 27-point test. Less than

1 percent answered three or fewer questions correctly.

The mean score was 17.6.

The prediction equation for the diet-health knowledge

variable in the expenditure equations was estimated

using a linear multiple regression model. The diet-health

knowledge variable from the DHKS was regressed on a

selected set of economic and sociodemographic characteristics of the respondents. These explanatory variables

were chosen to ensure that a consistent set of variables

was also available in the CES data. For example,

detailed racial and ethnic origin information was available in DHKS, but the ethnic origin variable in the CES

had a significant proportion of missing values. Therefore, we included only a dummy variable indicating

Black racial status in the regression model. After eliminating observations with missing values, a DHKS sample of 5,232 observations was available for estimation.

The explanatory variables, their definitions, and means

from the weighted data are reported in table 6. 

The CSFII-DHKS is a complex survey with a stratified,

multistage, probability sample design. Accordingly, the

regression model was estimated using sampling weights

to compensate for probabilities of selection, differential

response rates, and possible deficiencies in the sampling

technique. The standard errors of the parameter estimates were adjusted for sample design.

The diet-health knowledge equation had a reasonable fit

with an R-squared of 0.2. Except for the proportion of

household heads employed and located in a nonMetropolitan Statistical Area, all other variables or their

categories had significant influence on diet-health

knowledge. Among all variables, educational attainment

had the largest effect on diet-health knowledge. Other

variables held constant, those who completed college

scored 3.12 points higher on the diet-health knowledge

test than those who had less than 12 years of education.

Based on a mean test score of 17.58, this translates to

an 18-percent increase in test scores for college-educated respondents, compared with scores for respondents

who did not complete high school.

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 9
Income had a significant influence on knowledge, with an

additional $100 in weekly per capita household income

increasing test scores by 0.18 points. Among respondents

of similar sociodemographics, men scored 1.6 points

lower than women and Blacks scored 1.4 points lower

than Whites. Adults from households with both a male

and female head displayed greater diet-health knowledge

than adults from households with only a male head or

only a female head. Households with a greater proportion

of adults age 75 or older scored lower on the diet-health

knowledge test than households with a greater proportion

of adults age 45-64. 

Estimates of this equation using CES data were similar

to initial estimates, which used a different data set.

When we used the estimated parameters of the model

from the CSFII data with the CES data, we found the

predicted mean score to be 17.7. In addition, 82 percent

of households in the CES data set scored 16.0 or higher.

This score compares with a predicted mean value of

17.2 in the CSFII (this mean is different from the raw

data mean due to the weighting of the model), and 86

percent of CSFII households scored 16.0 or higher. We

feel the diet-health knowledge equation fits the CES

10 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA

Table 6—Definitions and sample means of independent variables for diet-health knowledge equations

Variable Mean Definition

Diet-health knowledge 17.58 Mean value of diet-health knowledge index in CSFII

Region:

Northeast .210 Omitted base region

North Central .240 Equals 1 if household resides in North Central States,

0 otherwise

South .340 Equals 1 if household resides in South, 0 otherwise

West .210 Equals 1 if household resides in West, 0 otherwise

Race:

Non-Black .890 Omitted base

Black .110 Equals 1 if household is Black, 0 otherwise

Income 3.280 Annual household income before taxes measured in

hundreds of dollars per week per household member

Metro area .780 Omitted base

Nonmetro area .220 Nonmetro region

Female .530 Omitted base

Male .470 Household head is male

Female head .160 Single head of household is female

Male head .080 Single head of household is male

Employed .640 Share of household heads employed

No high school .260 Omitted base

High school .350 12 years of schooling or GED

Some college .220 1-3 years of college completed

College .270 4 years or more of college completed

Household age composition:

Proportion under age 5 .060 Proportion of household members under age 5

Proportion age 5-9 .050 Proportion of household members age 5-9

Proportion age 10-14 .050 Proportion of household members age 10-14

Proportion age 15-19 .050 Proportion of household members age 15-19

Proportion age 20-29 .130 Proportion of household members age 20-29

Proportion age 30-44 .230 Proportion of household members age 30-44

Proportion age 45-64 .250 Omitted base group

Proportion age 65-74 .110 Proportion of household members age 65-74

Proportion older than age 74 .070 Proportion of household members older than age 74

Source: Economic Research Service, USDA.
data very well and will provide reliable estimates for

making projections of food expenditures. 

As noted earlier, the Tobit model is the econometric

procedure used to quantify the relationship of household

characteristics and income to the purchase/nonpurchase

decision and to the level of purchase. In addition, the

diet-health knowledge equation is recursively solved to

supply a numerical variable in the expenditure equations. The dependent variable in the food equations is

average weekly food expenditures per person. Table 7

lists the household socioeconomic and demographic

variables that are used to explain the observed expenditure patterns in the Tobit model, together with descriptions of the variables and their sample means. Table 8

presents the food groups analyzed in this study. The

same model specification is applied for each product

category. 

Variations in size and composition across households

are controlled in the model by including the inverse of

household size and the proportion of household members in selected age groups. The inverse of household

size variable captures the effects of economies of size,

while the proportion of members in each age group controls for age composition of the household. Because the

inverse decreases, a positive coefficient on this variable

indicates positive economies of size. That is, larger

households, even after controlling for the age of members, tend to spend less per person than smaller households. A negative coefficient has the opposite effect.

The inverse transformation forces the size of the scale

effect to diminish as households grow larger. Nine age

groups are used to delineate the effects of household

composition. However, to avoid estimation problems,

the 45-65 age group is not entered directly into the

equation.

Income per person, which includes the net value of food

stamps, is entered quadratically. This specification has

been shown to provide a good statistical fit in models

with income and household composition entered in the

model (Tomek). The quadratic form also allows the

marginal propensity to spend and the income elasticity

to vary with the level of income and has been shown to

satisfy the adding-up criterion (that is, total expenditures must sum to total income).

Region of household residence, race, and season of the

year are entered as a series of binary dummy variables.

That is, the variable is assigned the value of 1 if the

household has that characteristic and the value of 0 otherwise. The year in which a household was surveyed is

also entered as a binary variable to account for changes

in expenditures due to a change in relative prices

between the 2 years.

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 11
12 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA

Table 7—Definitions and sample means of independent variables for expenditure equations

Variable Mean Definition

Diet-health knowledge 17.65 Mean value of diet-health knowledge index

Region:

Northeast .182 Omitted base region

North Central .246 Equals 1 if household resides in North Central States, 0 otherwise

South .342 Equals 1 if household resides in South, 0 otherwise

West .229 Equals 1 if household resides in West, 0 otherwise

Race:

Non-Black .891 Omitted base

Black .109 Equals 1 if household is Black, 0 otherwise

Income 3.710 Annual household income before taxes measured in 

hundreds of dollars per week per household member

Income squared 27.137 Income variable raised to the second power

Season:

Winter .253 Equals 1 if winter, 0 otherwise; includes January, February, 

and March

Spring .260 Equals 1 if spring, 0 otherwise; includes April, May, and June

Summer .252 Equals 1, if summer, 0 otherwise; includes July, August, 

and September

Fall .235 Omitted base season; includes October, November, and December

Year:

1997 .502 Omitted base year

1998 .498 Equals 1 if 1998, 0 otherwise

Household size (inverse) .559 Inverse of the number of household members

Household age composition:

Proportion under age 5 .037 Proportion of household members under age 5

Proportion age 5-9 years .047 Proportion of household members age 5-9 

Proportion age 10-14 years .046 Proportion of household members age 10-14 

Proportion age 15-19 years .057 Proportion of household members age 15-19

Proportion age 20-29 years .145 Proportion of household members age 20-29

Proportion age 30-44 years .227 Proportion of household members age 30-44

Proportion age 45-64 years .242 Omitted base group

Proportion age 65-74 years .103 Proportion of household members age 65-74

Proportion older than age 74 .097 Proportion of household members older than age 74 

Source: Economic Research Service, USDA.
Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 13

Table 8—Food product groups and their compositions included in food expenditures

Food group Composition

Total food Food at home and away from home (except food purchased on overnight

trips), excluding alcoholic beverages.

Food away from home Lunch, dinner, breakfast, brunch, snacks, and nonalcoholic beverages at

restaurants, vending machines, and carryouts, including tips, board, meals for 

someone away at school, and catered affairs.

Food at home Food used in the home, excluding alcoholic beverages.

Cereals and bakery products Ready-to-eat and cooked cereals, pasta, prepared flour mixes, other cereal 

products (cornmeal, cornstarch, rice) bakery products (bread, crackers, cookies,

biscuits, rolls, cakes, and other specified frozen and refrigerated bakery products).

Meat, poultry, fish, and eggs Meat, poultry, fish, and eggs.

Beef Ground beef, roasts, steaks, veal, and other cuts, excluding canned beef.

Pork Bacon, porkchops, ham (including canned), roast, sausage, and other cuts.

Poultry Fresh and frozen chicken, duck, turkey, and cornish hens, excluding canned.

Fish Fresh and frozen fish and shellfish.

Dairy products Fresh and processed dairy products.

Fruits Fresh, frozen, and processed fruits, including juices.

Vegetables Fresh, frozen, and processed vegetables, including juices.

Sugar and sweeteners Sugar, candy, chewing gum, artificial sweeteners, jams, jellies, preserves,

fruit butters, syrup, fudge mixes, icings, and other specified sweets.

Nonalcoholic beverages Diet and nondiet carbonated drinks, coffee, tea, chocolate-flavored powder, 

and other specified beverages.

Fats and oils Margarine, shortening, salad dressings, nondairy creamer, peanut butter, and

substitute and imitation milk.

Miscellaneous prepared foods Frozen prepared foods, canned and packaged soups, potato chips, nuts and 

other snacks, condiments, seasonings, olives, pickles, sauces and gravies,

salads, desserts, baby foods, and canned beef and poultry.

Source: Economic Research Service, USDA.
Empirical Results From 

the 1997-98 Data

The estimated models for the 16 food groups plus the

diet-health knowledge equation allow us to evaluate the

proportion of consumers purchasing the relevant item as

well as the level of consumer expenditures with a specified set of household characteristics (appendix). For

convenience, we present the estimated responses in per

capita weekly expenditures associated with changes or

differences in household demand factors. The estimated

responses are evaluated at the sample means for all variables except the variable examined in the particular

table. In other words, all variables in both the diethealth knowledge equation and the food expenditure

equation are set to their observed mean values, except

for the variable of interest. The variable of interest is set

to its actual value if continuous, or to 1 if it is a binary

variable.

Influence of Income

Table 9 shows the per capita effect of a 10-percent

increase in weekly per capita income, as well as a 10-

percent increase in diet-health knowledge. These effects

represent only the so-called direct effects, or direct elasticities. The elasticity is simply the percent change in

the dependent variable—in our case, the food group

expenditure—divided by the percent change in income

or diet-health knowledge. As such, they ignore the

effects that occur in the diet-health knowledge equation,

the so-called indirect effect. Hence, the direct effects in

table 9 may understate or overstate the magnitude of the

elasticities.

Income is an important determinant of food expenditures, and all income variables were jointly significant

at acceptable statistical levels for all 16 food groups.

Also, all calculated income elasticities are positive in

table 9, which indicates that food expenditures increase

as income rises.

Food groups most responsive to an increase in income

are food away from home, miscellaneous prepared food,

fruits, dairy products, and sugars and sweeteners. Given

a 10-percent increase in income, expenditures rise 4.56

percent for food away from home, 1.63 percent for miscellaneous prepared foods, 1.62 percent for fruits, and

1.14 percent for both dairy products and sugars and

sweeteners.

A 10-percent change in diet-health knowledge would be

truly extraordinary. However, if diet-health knowledge

increased 10 percent, expenditures would rise 12.50 percent for fish, 11.72 percent for fruits, and 8.79 percent

for vegetables. In contrast, pork expenditures would

decrease 1.12 percent and beef expenditures would

decrease 7.84 percent if diet-health knowledge increased

10 percent.

As noted earlier, the market entry response comprises

several components that are distinctly different but

impossible to identify with our data. Correct interpretation of the market entry response requires an understanding of these components as well as the data. Three

points deserve emphasis. First, the CES data are an

expenditure, not a use, survey. Consequently, some

households did not report any food expenditures during

their survey period, but they undoubtedly consumed

food from current supplies. Second, sampling units at

which occupants were temporarily absent are included

in the sample. These two factors will tend to cause the

market entry response to be overestimated and possibly

misinterpreted, especially for total food and food at

home. Third, it is not possible to discern whether zero

expenditures may represent nonuse of the commodity or

infrequency-of-purchase behavior, as all households

reported only for a 2-week period during the survey.

Table 9 also shows changes in expenditures due to market entry by consumers who did not previously purchase

the good as well as changes due to the expenditure

effect (the effect of those who already purchase the

good increasing or decreasing expenditures). For example, if income increased by 10 percent, expenditures on

vegetables would increase 1.03 percent. Of this amount,

0.48 percent would be due to households entering the

market to make a vegetable purchase, and 0.55 percent

would be due to increased expenditures by households

that already purchase vegetables.

In terms of an income increase only, products with over

50 percent of the total income response due to market

entry include beef, pork, poultry, fish, and sugars and

sweeteners. In addition, both nonalcoholic beverages

and miscellaneous prepared food are close to 50 percent. Hence, increases in income will benefit these food

groups more than others in terms of market entry. Food

companies could develop advertising strategies to attract

these consumers.

To help understand the effects of income on food expenditures, we simulate average per capita expenditures on

14 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA
the 16 food groups at selected per capita income levels

using the estimated Tobit equations evaluated for an

average sample household (table 10). The per capita

income levels we chose were the mean ($19,721) and

25, 50, 125, and 150 percent of the mean. This exercise

took into account both the diet-health knowledge equation and the expenditure equation.

Expenditures in all categories increase as income

increased. Expenditures with the highest income elasticities, such as food away from home and miscellaneous

prepared food, generally increase the most as income

rises and, conversely, fall the most as income declines

from the mean. Expenditures on food away from home

fall to about 69 percent of average expenditures when

income declines to 25 percent of the mean.

These responses are larger than would be predicted

using the elasticities because, as noted, these results

include not only the direct effect but the indirect effect

of the diet-knowledge equation. Close study indicates

that the indirect effect tends to increase expenditures

above what would be predicted by the direct income

effect. For example, raising per capita income from

$19,721 to $24,652 (25 percent) increases average total

food expenditures about 8.2 percent, whereas the

income elasticity from table 9 would increase expenditures just 6.9 percent.

The most revealing data in table 10 are the small

increases in food expenditure, which are exactly in line

with our calculated elasticities. If average income

increased 50 percent, total food expenditures would

increase just 14.8 percent while away-from-home food

expenditures would increase 23.6 percent. The reason:

American households are already well off and well fed.

In fact, it should be noted that these expenditure simulations embody both a quantity and a quality effect. In

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 15

Table 9—Per capita direct effects of 10-percent increases in income and diet knowledge on weekly food 

expenditures, 1997-98

Response to income increase Response to diet-knowledge increase 

Total Market Total Market

increase in entry Expenditure increase in entry Expenditure

Food group expenditures effect1 effect expenditures effect effect

Percent

Total food 2.76 .69 2.07 2.07 .51 1.56

Food away from home 4.56 2.15 2.41 2.37 1.12 1.25

Food at home 1.25 .38 .87 3.60 1.09 2.51

Cereals and bakery products 1.06 .44 .62 4.85 2.01 2.84

Meats, poultry, fish, and eggs .72 .34 .38 -2.23 -1.04 -1.19

Beef .68 .42 .26 -7.84 -4.89 -2.95

Pork .56 .36 .20 -1.12 -.72 -.40

Poultry .76 .49 .27 3.05 1.95 1.10

Fish .65 .47 .18 12.50 9.06 3.44

Dairy products 1.14 .53 .61 5.05 2.36 2.69

Fruits 1.62 .79 .83 11.72 5.73 5.99

Vegetables 1.03 .48 .55 8.79 4.12 4.67

Sugars and sweeteners 1.14 .70 .44 7.70 4.70 3.00

Nonalcoholic beverages 1.05 .52 .53 .95 .47 .48

Fats and oils .50 .31 .19 3.52 2.16 1.36

Miscellaneous prepared food 1.63 .79 .84 6.22 3.01 3.21

1Entry refers to how much of the total effect is due to new market entry or exit by consumers.

Source: Economic Research Service, USDA.
other words, a 10-percent increase in expenditures does

not translate to a 10-percent increase in quantities

demanded. Rather, the increase in food expenditures

goes in large part to increased demand for quality factors, such as convenience, packaging, and the substitution of products (for example, steak for hamburger).

Because our data are an expenditure survey, separating

an expenditure elasticity into its quality and quantity

components is not possible. For a discussion of changes

in the quantities consumed, see Lin, 2003, the companion report to this study.

Demographic and Seasonal Effects 

Household characteristics and factors other than income

that influence consumer demand for food include household age composition, region of residence, race, and

season. Differences in per capita expenditures associated with these factors are simulated using the estimated

Tobit and diet-health knowledge equations evaluated at

alternative levels of the particular factor being examined, while other factors are held constant at their

respective sample averages. For example, households

are grouped into four categories according to their

region of residence: Northeast, North Central, South,

and West. To simulate expenditures in a region, we

determined the overall mean expenditure using the sample mean for all variables in the model, including the

mean for all dummy variables. This mean expenditure

was compared with the computed expenditure retaining

all mean values but including the appropriate dummy

variable for the variable of interest. 

Household Age Composition

The age composition of a given household tends to have

a dynamic effect on household food expenditures. In

table 11, the base age group is made up of adults age

45-64. In general, as household age composition

increased, expenditures on food at home increased. As

household age composition decreased, expenditures on

food away from home increased. Households in which

the members are age 45 or older tended to spend more

on food at home, while those composed mainly of 

16 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA

Table 10—Simulated weekly expenditure per capita by weekly income level, 1997-98 

Expenditure change

Base 25% of base 50% of base 125% of base 150% of base

Food group $19,721 $4,930 $9,861 $24,652 $29,582

Dollars ————————————Percent————————————

Total food 41.12 79.9 87.3 108.2 114.8

Food away from home 16.62 68.7 80.3 113.3 123.6

Food at home 25.34 90.3 93.8 103.9 107.1

Cereal and bakery products 4.13 92.3 95.2 103.4 106.1

Meat, poultry, fish, and eggs 6.73 95.5 96.7 100.6 102.2

Beef 1.97 97.0 97.5 100.0 100.5

Pork 1.39 100.0 100.0 100.7 100.7

Poultry 1.23 93.5 95.9 101.6 104.1

Fish 1.03 92.2 94.2 101.0 102.9

Dairy 3.12 91.7 94.9 103.8 106.7

Fruits 2.61 86.2 91.6 105.7 110.3

Vegetables 2.10 90.5 93.8 103.8 107.1

Sugars and sweeteners 1.31 91.6 95.4 103.8 106.9

Beverages 2.29 92.6 95.6 103.5 106.1

Fats and oils .74 95.9 97.3 102.7 104.1

Miscellaneous prepared foods 3.89 87.9 92.8 105.7 109.8

Source: Economic Research Service, USDA.
Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 17 Table 11—Simulated weekly food expenditures per capita by age group, 1997-98 Share of mean weekly expenditures by age (years) Base Under Over Food group 45-64 51 5-9 10-14 15-19 20-29 30-44 65-74 74 Dollars ———————————————————————Percent—————————————————————— Total food 45.21 59.2 68.8 78.4 69.2 86.5 97.2 102.7 88.7 Food away from home 15.30 77.1 83.9 108.1 113.9 134.7 128.2 105.5 75.2 Food at home 30.82 54.5 63.5 65.2 47.2 62.8 81.2 100.5 94.3 Cereals and bakery products 4.70 52.6 80.6 76.6 51.7 74.3 84.0 108.3 112.3 Meats, poultry, fish, and eggs 8.64 45.7 53.6 51.6 31.7 55.7 81.0 95.8 85.5 Beef 2.50 59.2 65.6 65.6 35.2 60.4 76.8 89.2 86.0 Pork 1.93 46.1 44.6 37.3 27.5 37.3 72.0 92.2 94.3 Poultry 1.48 50.0 62.8 67.6 40.5 62.1 92.6 95.3 89.2 Fish 1.30 46.9 68.5 39.2 39.2 45.4 83.1 107.7 91.5 Dairy 3.51 85.8 82.3 81.5 55.3 69.8 87.7 103.7 101.4 Fruits 3.09 80.9 79.0 64.4 42.4 60.2 69.3 110.4 120.7 Vegetables 2.74 48.5 48.5 47.4 26.6 49.3 72.6 108.0 97.1 Sugars and sweeteners 1.46 71.9 93.2 92.5 68.5 65.8 82.2 117.8 100.7 Nonalcoholic beverages 2.73 52.0 52.0 72.5 61.2 73.6 95.2 87.5 73.6 Fats and oils .98 36.7 58.2 56.1 32.7 44.9 71.4 96.9 100.0 Miscellaneous prepared foods 4.52 70.6 71.5 79.2 56.2 70.1 89.2 93.6 92.0

1For example, total food expenditures for a child under age 5 would be 59.2 percent of the total food expenditures for an adult age 45-64.

Source: Economic Research Service, USDA.
members under age 45 tended to spend more on food

away from home.

In the under-45 age group, households with children

under age 14 as well as adults age 30-44 spent more on

dairy items. The 30-44 age group also spent more on

miscellaneous prepared foods than younger age groups.

Households with children age 10-19 tended to spend

more on food away from home, as did the 20-29 age

group. Also, households with children age 9 or under

spent relatively more on fruits than households with

older children.

The over-45 age group spent more on food at home than

the younger age groups and spent more on cereal and

bakery products, meats, dairy, fruits, vegetables, and

sugar and sweeteners. The over-74 age group spent the

most on cereal and bakery products and fruits and spent

the least on food away from home.

Each figure in table 11 approximates the per capita

effect that a household member of a given age would

have on total household expenditures. Hence, the weekly expenditure of a household composed of a particular

combination of members may be readily calculated. For

example, a household composed of a child age 7 and

two adults age 25 and 32 would have weekly total food

expenditures of $114.16 (calculated as 45.21 X (0.688 +

0.865 + 0.972)). This approach enables us to compare

expenditures for households of different sizes and/or

age composition.

Region

Households in the Northeast generally spent the most on

total food, including food at home and food away from

home (table 12). While the South spent the least on food

away from home, the North Central spent the least on

food at home. Households in the Northeast spent the

most on cereals and bakery products; meats, poultry,

fish, and eggs; dairy; fruits; and vegetables. At the same

time, the Northeast spent the least on sugars and sweeteners, nonalcoholic beverages, fats and oils, and miscellaneous prepared foods.

Households in the West spent the most on fats and oils,

and miscellaneous prepared foods. Households in the

South spent the most on pork, while households in the

North Central spent the most on sugars and sweeteners

and nonalcoholic beverages. 

Race 

Non-Black households outspent Black households by 6

percentage points on food at home and by about 25 percentage points on food away from home, all other factors held constant (table 13). Non-Black households had

higher expenditures in every major category except

meat, poultry, fish, and eggs. In this category, Black

households outspent non-Black households by about 15

percentage points. Black households spent substantially

more on pork, poultry, and fish and slightly more (about

3 percentage points) on fruits. Both types of households

spent the same on fats and oils.

18 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA
Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 19

Table 12—Simulated weekly food expenditures per capita by region, 1997-98

Mean Share of mean weekly expenditures, by region

Food group base Northeast North Central South West

Dollars ————————————Percent————————————

Total food 41.12 104.8 94.5 94.7 98.5

Food away from home 16.62 106.1 95.2 93.5 96.1

Food at home 25.34 102.6 94.5 96.5 101.0

Cereals and bakery products 4.13 108.7 93.7 90.6 93.5

Meats, poultry, fish, and eggs 6.73 104.9 92.9 97.0 97.3

Beef 1.97 103.6 93.9 98.0 95.9

Pork 1.39 96.4 105.0 107.9 97.1

Poultry 1.23 113.8 84.6 88.6 89.4

Fish 1.03 111.7 108.7 90.3 102.9

Dairy products 3.12 104.2 94.2 96.2 98.7

Fruits 2.61 104.6 93.1 92.3 102.7

Vegetables 2.10 104.8 89.0 95.2 102.4

Sugars and sweeteners 1.31 99.2 103.1 100.0 100.0

Nonalcoholic beverages 2.29 94.8 105.2 104.8 103.9

Fats and oils .74 97.3 98.6 102.7 104.1

Miscellaneous prepared foods 3.89 88.9 110.0 105.7 115.2

Source: Economic Research Service, USDA.

Table 13—Simulated weekly food expenditures per capita by race, 1997-98

Share of mean expenditures

Food group Mean Black Non-Black

Dollars ——————Percent——————

Total food 41.12 88.7 101.2

Food away from home 16.62 77.6 102.5

Food at home 25.34 94.6 100.6

Cereals and bakery products 4.13 88.9 101.2

Meats, poultry, fish, and eggs 6.73 113.8 98.5

Beef 1.97 99.0 100.0

Pork 1.39 120.9 97.8

Poultry 1.23 129.3 96.7

Fish 1.03 130.1 96.1

Dairy 3.12 76.9 102.6

Fruits 2.61 103.1 99.6

Vegetables 2.10 95.2 100.5

Sugars and sweeteners 1.31 89.3 101.5

Nonalcoholic beverages 2.29 87.3 101.3

Fats and oils .74 100.0 100.0

Miscellaneous prepared food 3.89 80.5 102.3

Source: Economic Research Service, USDA.
Population and Demand Projections:

Background and Methods

If we combine the the U.S. Census Bureau’s projections

of demographic change over the next two decades with

the variations found in food expenditures by household

income, age composition, region of residence, race, and

diet-health knowledge, the results will show that household food expenditures are also likely to change.

Several assumptions and limitations underlying the projected expenditures should be noted. First, the following

analysis assumes that the relationships of income and

demographics to food expenditures stay the same as

those found in the statistical analysis of the 1997-98

CES data, implying that relative prices and alternative

opportunities for food choices, as well as tastes and

preferences, remain unchanged. Second, as their economic and demographic circumstances change, consumers are assumed to acquire the expenditure patterns

of individuals already observed in those circumstances.

That is, a household that migrates from the Northeast to

the West will acquire the expenditure characteristics of

households in the West. Likewise, a 5-year-old in 2020

is assumed to have the same food expenditure pattern as

a 5-year-old in 1997-98. Third, the models are driven by

projected changes in demographics and projected

income growth. Hence, deviations from these projections will result in different expenditure patterns. These

assumptions may appear unduly restrictive, but the

information required to relax them is either unavailable

or unreliable.

Another way to interpret the projections is to view them

as scenarios of what would have occurred in 1997-98 if

projected demographic changes had already been in

place. For example, a relevant question may be as follows: “What would have happened to food expenditures

in 1997-98 if the projected changes in the racial mix of

the population for 2020 were already in place?” This

approach to viewing the projections lessens the potential

for misinterpretation by focusing attention on the underlying assumptions noted earlier. Although we feel this

alternative interpretation is the most appropriate, we

will instead use the term “projections” and draw comparisons between the base year, 2000, and a future period as we discuss our results.

Projections of food expenditures in this report are based

on projected changes in the age distribution of the population, future regional population shifts, changes in the

racial mix of the population, changes in educational

attainment (diet-health knowledge), population growth,

and inflation-adjusted income growth. Both the isolated

and the combined effects on food expenditures are projected for each of these socioeconomic factors. As

noted, food expenditure projections are based on census

population projections and an assumed annual income

growth of 1 percent and are presented on both a per person and a national basis. 

Population, household, and education projections used

in this analysis come from the census bureau (Hollman

et al.; Day, 1996; Day, 2000). The population series

includes projections by single year of age, sex, race,

Hispanic origin, and nativity (foreign-born or native)

out to the year 2100. Educational attainment projections

by sex and race are available to 2028. These projections

are not intended as forecasts or predictions but represent

the results of assumptions about future trends in population change, household formation, schooling, and the

economy at large. In the population series, projections

are based on assumptions about fertility, mortality, and

immigration. Differing assumptions are made to provide

three different projection series, representing high, middle, and low alternatives. Our projections are based on

the middle series.

Several modifications and extensions were applied to

census projections to enable us to obtain the projections

for 2020 used in this report. Census projections for education were published for 2003 and 2028, so our numbers represent interpolations between these two dates. 

The current population projections provided by the census bureau are based on the 1990 census, as enumerated, and postcensal estimates up through 1999. The number of people counted in the 2000 census was 6 million

more than anticipated by pre-census estimates (281 versus 275 million). Various factors undoubtedly contributed to the higher count, including a more complete

census count in 2000 than in 1990 and a likelihood of

more duplications in 2000. Any statement about the relative importance of different factors at this point is

speculative. It is likely that the level of unauthorized

immigration, clearly the most difficult component of the

population to tabulate, was significantly higher than

expected. To account for these discrepancies, population

was multiplied by the ratio of the 2000 census result

and the 2000 projection. For example, the 2000 census

counted 39.9 million people age 24-29 and the projection was 37.4 million, for a ratio of 1.06. We multiplied

projections for that age group to 2020 by 1.06.

20 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA
For the first time, census filers in 2000 were allowed to

identify themselves as members of more than one race,

and over 6.8 million Americans chose to do so. This

segment represents only 2.5 percent of the total population but a much higher share among the overall minority

population. Therefore, an additional modification had to

be made to assign these people to one race category. We

chose to use ratios derived by research into primary and

secondary race identification that allow for a fractional

assignment of a multiracial group into its component

races (Allen and Turner). Research showed, for example, that 61 percent of those self-identified as Black and

White would likely choose Black as their primary race.

We divided the total number into the two races according to that percentage.

Projected Age Distribution

The proportion of the U.S. population over age 45 is

expected to increase, while the proportion under age 45

is expected to decrease (table 14). In 2000, 34.4 percent

of the population was over age 45. By 2020, this age

group is expected to account for 41.1 percent of the

population. In contrast, the under-45 age group represented 65.6 percent of the total population in 2000. By

2020, this group is expected to represent about 59 percent of the Nation’s population.

Projected Regional Population Distribution

Based on census projections, the Northeast and North

Central are expected to lose population while the South

and the West are expected to grow (table 15). The

Northeast is expected to decline from 19.0 percent to 

17.4 percent from 2000 through 2020. At the same time,

the share of the population residing in the North Central

is expected to decline from 22.9 percent to 21.1 percent.

The South is expected to increase from 35.6 percent to

36.3 percent during 2000-2020, while the West is

expected to increase from 22.5 percent to 25.2 percent.

Projected Population of the United States

The U.S. population is expected to increase about 18

percent from 2000 to 2020, from 281.4 million to 331.9

million (table 16).

Projected Racial Distribution

The proportion of the U.S. population that is Black will

increase from 12.8 percent of the total population in

2000 to 13.8 percent in 2020.

Projected Educational Attainment

Changes in educational attainment will be applied to the

diet-health knowledge equation to determine the effect

of these changes on diet-health knowledge and food

expenditures. The proportion of the U.S. population

with a high school diploma is expected to decline

between 2000 and 2020, from 35.2 percent to 32.6 percent. At the same time, the proportion of the population

with some college education will increase from 24.1

percent to 27.0 percent, while the share with a college

degree or higher will increase from 23.5 percent to 26.4

percent.

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 21

Table 14—Projected percentage of population by age group

Share of population

Age group (years) 2000 2005 2010 2015 2020

Percent

0-4 6.8 6.6 6.7 6.7 6.7

5-9 7.3 6.8 6.6 6.6 6.7

10-14 7.3 7.2 6.7 6.5 6.6

15-19 7.2 7.3 7.2 6.7 6.5

20-29 13.5 13.7 14.0 14.0 13.3

30-44 23.5 21.3 19.4 18.9 19.2

45-64 22.0 24.6 26.2 25.9 24.6

65-74 6.5 6.3 7.0 8.3 9.6

Over 74 5.9 6.0 6.0 6.1 6.9

Source: U.S. Department of Commerce, U.S. Census Bureau.
22 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA

Table 15—Projected percentage of population by region

Share of population

Year Northeast North Central South West

Percent

2000 19.0 22.9 35.6 22.5

2005 18.8 22.3 35.8 23.1

2010 18.4 21.9 36.0 23.7

2015 18.1 21.4 36.1 24.4

2020 17.4 21.1 36.3 25.2

Source: U.S. Department of Commerce, U.S. Census Bureau.

Table 16—Projected U.S. population, percentage of population that is Black, and educational attainment

Educational attainment

Age group (years) Population Blacks in population High school Some college College

Millions ——————————————Percent—————————————

2000 281.4 12.8 35.2 24.1 23.5

2005 294.0 13.1 34.5 24.8 24.2

2010 306.4 13.3 33.9 25.5 24.9

2015 319.1 13.6 33.2 26.3 25.7

2020 331.9 13.8 32.6 27.0 26.4

Source: U.S. Department of Commerce, U.S. Census Bureau.
Method of Projections Based on DietHealth Knowledge and Tobit Models

Consumer demand for food commodities was projected

to the year 2020 at 5-year intervals, beginning with the

base year 2000. The projections were developed using

knowledge about the existing structure of demand from

the estimated Tobit expenditure models and the diethealth knowledge equation described earlier. The projections combine the projections of demographic characteristics of the U.S. population and projected income

growth with the demand response parameters estimated

for the per capita food expenditure and diet-health

knowledge models.

In this report, we first simulated age, region, race, diethealth knowledge, and income separately. We then combined age, region, race, diet-health knowledge, and

income to make a sixth projection. All six of these projections are per capita. We derived a seventh projection

by taking the combined per capita projection and adjusting it by the projected U.S. population estimates.

Income is assumed to increase 1 percent per year, which

is slightly below historical growth rates but implies an

increase in purchasing power of about 23 percent

between 2000 and 2020.

We developed per capita projections by evaluating the

estimated Tobit models using the average projected

demographic characteristics of the population and the

assumed annual growth rate for income. Several variables in the Tobit models do not directly enter into the

projections and therefore were held constant in all projection scenarios. The dummy variable for 1998 was set

equal to its mean value of 0.498. In addition, dummy

variables for season were also set to their mean values.

Household size was also held constant at its sample

average. All variables in the diet-health knowledge

equation were set equal to their mean values except

those for educational attainment.

Because the Tobit model is nonlinear, the best measure

of average expenditures would be developed by projecting expenditures for each possible household type (size,

age composition, race, region, and income level) and

computing a weighted average of expenditures using

weights proportional to the number of households of

each type. However, the detailed data required for such

a procedure far exceed what is available. As a pragmatic

alternative, we estimated average expenditures by evaluating the model for a typical consumer. Like the empirical simulations reported earlier, the diet-knowledge

equation was first evaluated and an index value derived.

This value was then recursively entered into the appropriate food expenditure equation. The projections are

expressed as a percentage of the base year (2000) for

ease of interpretation and to minimize any bias introduced by using population averages rather than the

entire distribution.

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 23
Food Expenditure Projections 

This section presents projected per capita effects of

individual and combined demographic and income

changes on weekly food expenditures per person as well

as national effects.

Age Distribution Changes

The projections assume that all demographic factors

(except age distribution), relative prices, and income

remain constant at 1997-98 levels. As noted before,

another way to interpret the projections is to imagine

how expenditures in 1997-98 would have changed if the

projected age distributions for 2000-20 had already been

in place in 1997-98.

All food categories, except for food away from home,

show slight increases in food expenditures due to the

changing age distribution of the population (table 17).

Total food expenditures would be expected to increase

just 0.8 percent from 2000 to 2020. Over this time,

food-at-home expenditures would be expected to

increase 2.2 percent, while food-away-from-home

expenditures would decline 1.3 percent.

In the major food categories, expenditures are projected

to increase 3.7 percent for fruits and 3.6 percent for

vegetables. Fats and oils expenditures would increase

2.9 percent due to changing age distribution while sugars and sweeteners expenditures would increase 2.4 percent. Expenditures on meat, poultry, fish, and eggs

would increase 2.5 percent between 2000 and 2020.

Within this category, both pork and fish would increase

3.1 percent over the 20-year period. Miscellaneous prepared food would increase the least—just 1.1 percent—

due to the changing age distribution. 

Regional Distribution Changes

The projections assume that all other factors influencing

consumer demand remain constant at 1997-98 levels

and that the new residents of a region will assume the

expenditure patterns of the present population. Given

this assumption, food expenditures would be basically

24 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA

Table 17—Projected per capita effects of changing age distribution on weekly food expenditures

Change in expenditures

Food group 2000 base 2005 2010 2015 2020

Percent

Total food 100.0 100.3 100.5 100.8 100.8

Food away from home 100.0 99.6 99.2 99.0 98.7

Food at home 100.0 100.7 101.4 102.0 102.2

Cereals and bakery products 100.0 100.5 101.0 101.5 102.0

Meats, poultry, fish, and eggs 100.0 100.9 101.6 102.2 102.5

Beef 100.0 101.1 101.6 102.1 102.1

Pork 100.0 100.8 102.3 103.1 103.1

Poultry 100.0 100.0 100.0 100.8 100.8

Fish 100.0 101.0 102.1 102.1 103.1

Dairy 100.0 100.3 100.7 101.0 101.3

Fruits 100.0 101.2 102.0 102.8 103.7

Vegetables 100.0 101.0 102.1 103.1 103.6

Sugars and sweeteners 100.0 100.8 100.8 101.6 102.4

Nonalcoholic beverages 100.0 100.5 100.9 100.9 100.5

Fats and oils 100.0 101.4 101.4 102.9 102.9

Miscellaneous prepared food 100.0 100.3 100.5 100.8 101.1

Source: Economic Research Service, USDA.
unchanged from the base year (table 18). The three

main aggregates, total food, food at home, and food

away from home, are unchanged over the 20-year period. In fact, expenditures only change for miscellaneous

prepared foods and meats, poultry, fish (up 0.5 percent),

and eggs (up 0.1 percent) over the 20-year period.

Racial Distribution Changes

Changes in the racial mix of the population, all other

variables held constant, are also expected to have minor

effects on food spending (table 19). Total food and food

at home spending would be expected to decline about

0.1 percent from 2000 to 2020. Expenditures on food

away from home would decline 0.2 percent over the

same period. Both dairy and miscellaneous prepared

food would have the largest spending declines: just 0.3

percent between 2000 and 2020. More significant shifts

in spending might be seen by looking at a more disaggregated racial breakdown or by looking at food quantities rather than food expenditures. For an alternative

approach using quantities, see Lin, 2003. 

Diet-health Knowledge Changes

The diet-health knowledge equation is a function of

many variables, including region, income, household

type, age distribution, and educational attainment. In

this simulation, all variables in this equation were held

at mean 1997-98 levels, except for educational attainment. Using the projected level of educational attainment, we derived a value for diet-health knowledge.

This value was then inserted into the Tobit expenditure

equations, with all other variables set at mean levels.

Changes in diet-health knowledge are projected to have

little effect on food expenditures (table 20). Total food

expenditures would increase just 0.3 percent over 20

years, with food away from home rising 0.3 percent and

food at home rising 0.4 percent. Among individual athome categories, fruits would have the largest increase

in expenditures, 0.8 percent, while expenditures for

meats, poultry, fish, and eggs would decline 0.1 percent.

In this category, pork expenditures would decline 0.7

percent while beef expenditures would decline 0.5 percent. Expenditures on fish would increase 1 percent

over the 20-year period. Spending on sugars and sweetEconomic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 25

Table 18—Projected per capita effects of changing regional population distribution on weekly food 

expenditures

Change in expenditures

Food group 2000 base 2005 2010 2015 2020

Percent

Total food 100.0 100.0 100.0 100.0 100.0

Food away from home 100.0 100.0 100.0 100.0 100.0

Food at home 100.0 100.0 100.0 100.0 100.0

Cereals and bakery products 100.0 100.0 100.0 100.0 100.0

Meats, poultry, fish, and eggs 100.0 100.0 100.1 100.1 100.1

Beef 100.0 100.0 100.0 100.0 100.0

Pork 100.0 100.0 100.0 100.0 100.0

Poultry 100.0 100.0 100.0 100.0 100.0

Fish 100.0 100.0 100.0 100.0 100.0

Dairy 100.0 100.0 100.0 100.0 100.0

Fruits 100.0 100.0 100.0 100.0 100.0

Vegetables 100.0 100.0 100.0 100.0 100.0

Sugars and sweeteners 100.0 100.0 100.0 100.0 100.0

Nonalcoholic beverages 100.0 100.0 100.0 100.0 100.0

Fats and oils 100.0 100.0 100.0 100.0 100.0

Miscellaneous prepared food 100.0 100.3 100.3 100.3 100.5

Source: Economic Research Service, USDA.
26 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA

Table 19—Projected per capita effects of changing racial distribution on weekly food expenditures

Change in expenditures

Food group 2000 base 2005 2010 2015 2020

Percent

Total food 100.0 100.0 100.0 99.9 99.9

Food away from home 100.0 99.9 99.9 99.8 99.8

Food at home 100.0 100.0 100.0 100.0 99.9

Cereals and bakery products 100.0 100.0 100.0 100.0 100.0

Meats, poultry, fish, and eggs 100.0 100.1 100.1 100.1 100.1

Beef 100.0 100.0 100.0 100.0 100.0

Pork 100.0 100.0 100.0 100.0 100.0

Poultry 100.0 100.0 100.0 100.0 100.0

Fish 100.0 100.0 100.0 100.0 100.0

Dairy 100.0 100.0 99.7 99.7 99.7

Fruits 100.0 100.0 100.0 100.0 100.0

Vegetables 100.0 100.0 100.0 100.0 100.0

Sugars and sweeteners 100.0 100.0 100.0 100.0 100.0

Nonalcoholic beverages 100.0 100.0 100.0 100.0 100.0

Fats and oils 100.0 100.0 100.0 100.0 100.0

Miscellaneous prepared food 100.0 100.0 100.0 99.7 99.7

Source: Economic Research Service, USDA.

Table 20—Projected per capita effects of changing diet-health knowledge on weekly food expenditures

Change in expenditures

Food group 2000 base 2005 2010 2015 2020

Percent

Total food 100.0 100.1 100.1 100.2 100.3

Food away from home 100.0 100.1 100.2 100.2 100.3

Food at home 100.0 100.1 100.2 100.3 100.4

Cereals and bakery products 100.0 100.2 100.2 100.5 100.5

Meats, poultry, fish, and eggs 100.0 100.0 100.0 100.0 99.9

Beef 100.0 100.0 100.0 100.0 99.5

Pork 100.0 100.0 100.0 99.3 99.3

Poultry 100.0 100.0 100.0 100.0 100.0

Fish 100.0 100.0 100.0 101.0 101.0

Dairy 100.0 100.0 100.3 100.3 100.3

Fruits 100.0 100.0 100.4 100.4 100.8

Vegetables 100.0 100.0 100.0 100.5 100.5

Sugars and sweeteners 100.0 100.0 100.0 100.0 100.0

Nonalcoholic beverages 100.0 100.0 100.0 100.0 100.0

Fats and oils 100.0 100.0 100.0 100.0 100.0

Miscellaneous prepared food 100.0 100.3 100.3 100.3 100.5

Source: Economic Research Service, USDA.
eners, nonalcoholic beverages, and fats and oils would

remain the same between 2000 and 2020. 

Income Changes

Changes in income are projected to have a relatively

large effect on food expenditure patterns, particularly

food away from home. Under this scenario, food-awayfrom-home spending would increase 9.7 percent, while

food-at-home expenditures would increase 3.0 percent

(table 21). Among individual at-home foods, fruit would

have the largest percentage increase in expenditures

from 2000 (up 4.2 percent). Expenditures on miscellaneous foods would be expected to increase approximately 3.8 percent, while spending on vegetables would

increase about 3.3 percent. Expenditures for cereals and

bakery products, dairy, sugars and sweeteners, and nonalcoholic beverages would all increase about 2.5 percent

between 2000 and 2020. The smallest spending increase

occurs for meats, poultry, fish, and eggs—up just 1.3

percent over the 20-year period. The largest spending

increase within this category occurs for fish, up 1.9 percent, while the smallest increase is for beef, up just 0.5

percent, over the same period. These expenditure

changes tend to be larger than the corresponding quantity changes because consumers purchase better quality

products as income rises, not more of each product.

Combined Demographic and Income Changes 

Projected per capita expenditures based on all demographic projections (changing age, regional, diet-health

knowledge, and racial distributions) combined with an

assumed 1-percent growth in annual income show

increases in all food categories (table 22). Total food

expenditures are projected to increase 7.1 percent.

Food-away-from-home spending is projected to increase

8.1 percent, while food-at-home spending is projected to

increase 5.4 percent between 2000 and 2020.

Among major at-home food categories, expenditures

increase the most for fruits (up 8.1 percent), vegetables

(up 7.2 percent), and miscellaneous prepared food (up

5.3 percent). Expenditures for several categories of food

are expected to grow between 4 and 5 percent over the

20-year period, including cereals and bakery products

(up 4.3 percent), fats and oils (also up 4.3 percent),

meats, poultry, fish, and eggs (up 4.1 percent), and dairy

(up 4.0 percent). Within the meats, poultry, fish, and

eggs category, expenditures will increase 6.2 percent for

fish and just 2.6 percent for beef.

National Effects 

To derive the total effect on the Nation’s food expenditures of a 1-percent growth rate in income coupled with

the effects of age, regional, diet-health knowledge, and

racial distributions, we multiplied the total projected per

capita expenditure in table 22 by the expected change in

the U.S. population. The most important factor driving

growth in total food demand between 2000 and 2020 is

the expansion of the U.S. population. The census bureau

projects a U.S. population increase of nearly 50 million

over the 20-year period. 

Total food expenditures are projected to increase 26.3

percent (table 23). Food-away-from-home expenditures

are projected to increase 27.5 percent, compared with

24.3 percent for food-at-home expenditures. Because

the individual food groups represent at-home food

expenditures only, these projections understate total

food expenditure growth for the individual food groups

to the extent that the away-from-home market grows for

particular foods. One effect of the slow but steady

growth of the population is that the variation of growth

levels between food groups is less than that exhibited by

the per capita projections. The largest projected increase

in expenditures is for fruits, up 27.5 percent, while the

smallest is for both beef and nonalcoholic beverages, up

21.1 percent. Expenditures for meats, poultry, fish, and

eggs are projected to increase 22.8 percent. Within this

category, beef spending will increase 21.1 percent while

fish will increase 25.2 percent. Clearly, the biggest

boost to food demand in the future will come from population growth. 

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 27
28 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA

Table 21—Projected per capita effects of a 1-percent increase in annual income on weekly food 

expenditures

Change in expenditures

Food group 2000 base 2005 2010 2015 2020

Percent

Total food 100.0 101.4 102.9 104.5 106.2

Food away from home 100.0 102.2 104.6 107.0 109.7

Food at home 100.0 100.7 101.4 102.2 103.0

Cereals and bakery products 100.0 100.5 101.0 101.7 102.4

Meats, poultry, fish, and eggs 100.0 100.3 100.6 100.9 101.3

Beef 100.0 100.0 100.0 100.5 100.5

Pork 100.0 100.0 100.0 100.0 100.7

Poultry 100.0 100.8 100.8 101.6 101.6

Fish 100.0 100.0 101.0 101.0 101.9

Dairy 100.0 100.6 101.3 101.9 102.6

Fruits 100.0 100.8 101.9 103.1 104.2

Vegetables 100.0 101.0 101.4 102.4 103.3

Sugars and sweeteners 100.0 100.0 100.8 101.5 102.5

Nonalcoholic beverages 100.0 100.0 101.3 101.7 102.6

Fats and oils 100.0 100.0 101.4 101.4 101.4

Miscellaneous prepared food 100.0 100.8 101.8 102.8 103.8

Source: Economic Research Service, USDA.

Table 22—Projected per capita effects of combined demographic changes and a 1-percent increase in

annual income on weekly food expenditures

Change in expenditures

Food group 2000 base 2005 2010 2015 2020

Percent

Total food 100.0 101.7 103.5 105.4 107.1

Food away from home 100.0 101.7 103.6 105.8 108.1

Food at home 100.0 101.5 102.9 104.3 105.4

Cereals and bakery products 100.0 101.0 102.0 103.3 104.3

Meats, poultry, fish, and eggs 100.0 101.3 102.4 103.5 104.1

Beef 100.0 101.1 101.6 102.1 102.6

Pork 100.0 101.5 102.3 103.1 103.8

Poultry 100.0 100.8 101.7 102.5 103.4

Fish 100.0 102.1 103.1 105.2 106.2

Dairy 100.0 101.0 102.0 103.0 104.0

Fruits 100.0 102.0 104.0 106.1 108.1

Vegetables 100.0 101.5 103.6 105.7 107.2

Sugars and sweeteners 100.0 100.8 102.4 103.1 104.7

Nonalcoholic beverages 100.0 100.9 101.8 102.3 102.7

Fats and oils 100.0 101.4 102.9 104.3 104.3

Miscellaneous prepared food 100.0 101.3 102.7 104.0 105.3

Source: Economic Research Service, USDA.
Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 29

Table 23—Projected national effects of combined demographic changes and a 1-percent increase in annual

income on weekly food expenditures

Change in expenditures

Food group 2000 base 2005 2010 2015 2020

Percent

Total food 100.0 106.3 112.7 119.5 126.3

Food away from home 100.0 106.3 112.8 120.0 127.5

Food at home 100.0 106.0 112.0 118.3 124.3

Cereals and bakery products 100.0 105.5 111.1 117.1 123.0

Meats, poultry, fish, and eggs 100.0 105.8 111.5 117.3 122.8

Beef 100.0 105.6 110.6 115.8 121.1

Pork 100.0 106.1 111.4 116.9 122.5

Poultry 100.0 105.4 110.7 116.3 121.9

Fish 100.0 106.6 112.3 119.2 125.2

Dairy 100.0 105.5 111.0 116.8 122.6

Fruits 100.0 106.6 113.3 120.3 127.5

Vegetables 100.0 106.1 112.8 119.8 126.5

Sugars and sweeteners 100.0 105.3 111.5 117.0 123.5

Nonalcoholic beverages 100.0 105.4 110.8 116.0 121.1

Fats and oils 100.0 106.0 112.0 118.3 123.1

Miscellaneous prepared food 100.0 105.9 111.8 117.9 124.2

Source: Economic Research Service, USDA.
Conclusions

Our food expenditure projections are based on several

assumptions. First, the relationships of income and

demographics to food expenditures will stay the same as

those found in the statistical analysis of the 1997-98

CES data. Second, as economic and demographic circumstances change, consumers will acquire the expenditure patterns of individuals already observed in those

circumstances. Third, the estimated models are driven

by projected changes in demographics and projected

income growth. These assumptions are extremely

restrictive, but the information required to relax them is

either unavailable or unreliable.

It is important to stress an alternative way to view the

projections: as scenarios of what would have occurred

in 1997-98 if projected demographic changes had

already been in place. For example, a relevant question

may be as follows: “What would have happened to food

expenditures in 1997-98 if the projected changes in the

racial mix of the population for 2020 were already in

place?” This approach to viewing the projections

lessens the potential for misinterpretation by focusing

attention on the underlying assumptions described earlier in this report. 

Based on our projections, changes in regional makeup,

diet-health knowledge (educational attainment), and

racial distribution will have little effect on U.S. per

capita food expenditures. The changing age distribution

of the U.S. population between 2000 and 2020 will also

have minimal impact. Under this scenario, total food

expenditures will increase just 0.8 percent. This projected increase is due to food-away-from-home expenditures declining 1.3 percent and food-at-home expenditures increasing 2.2 percent. The isolated impact of

income growth had a larger effect on spending than projected demographic variables. A 1-percent increase in

inflation-adjusted income would increase total food

expenditures 6.2 percent between 2000 and 2020. This

effect is due to a 9.7-percent increase in food expenditures away from home and a 3-percent increase in food

expenditures at home.

When demographic changes and income growth were

combined in a composite projection, we found that total

per capita food expenditures were expected to increase

7.1 percent. This effect is due to an 8.1-percent increase

in food-away-from-home expenditures and a 5.4-percent

increase in food-at-home expenditures. By taking into

account projected increases in the U.S. population

between 2000 and 2020, we used our composite projections to derive a total national effect. Total national food

expenditures are projected to increase 26.3 percent over

the 20-year period. This effect is due to a 27.5-percent

increase in expenditures for food away from home and a

24.3-percent increase in expenditures for food at home. 

The effect of demographic and income changes on

demand for food can be separated into two possible

components—demand for quantity and demand for

quality. The demand for quantity typically describes the

demand for undifferentiated basic commodities, while

the demand for quality describes the demand for a wide

array of food characteristics, such as taste, nutritional

content, safety, and convenience.

Increased demand for quality can be manifested through

purchases of higher valued items within a food group or

through purchases of new food types. For example,

within the red meat food group, more affluent consumers may choose steaks instead of hamburgers. More

affluent consumers may also expand their food choices

to include luxury items, such as lobster or truffles, or

new convenience foods, including away-from-home

foods. As incomes rise, consumers may also increase

their demand for processed foods that meet particular

safety requirements, such as pasteurized eggs, or foods

with preferred nutrition attributes, such as leaner meats. 

Our analysis supports the hypothesis that consumers

may demand quality over quantity in the future, especially as real incomes increase. Among the major food

groups, the net effect of income growth and demographic change is projected to have its largest percentage

effect on per capita expenditures for fruits, vegetables,

miscellaneous prepared foods—a category that captures

a vast array of processed foods—and food away from

home. This effect suggests that processors may want to

continue developing new products that are convenient,

safe, nutritious, and easy to prepare.

30 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA
References

Allen, James P., and Eugene Turner. “Bridging 1990 and

2000 Census Race Data; Fractional Assignment of

Multiracial Populations,” Population Research and

Policy Review, forthcoming.

Blaylock, James, and David Smallwood. U.S. Demand

for Food: Household Expenditures, Demographics,

and Projections, Technical Bulletin No. 1713, U.S.

Department of Agriculture, Economic Research

Service, February 1986.

Blisard, Noel, and James Blaylock. U.S. Demand for

Food: Household Expenditures, Demographics, and

Projections for 1990-2010, Technical Bulletin No.

1818, U.S. Department of Agriculture, Economic

Research Service, December 1993.

Day, Jennifer Cheeseman. Projections of the Number of

Households and Families in the United States: 1995

to 2010, Current Population Reports No. P25-1129,

U.S. Census Bureau, 1996.

Day, Jennifer Cheeseman, and Kurt J. Bauman. Have

We Reached the Top? Educational Attainment

Projections of the U.S. Population, Population

Division Working Paper No. 43, U.S. Census Bureau,

2000.

Deaton, Angus. The Analysis of Household Surveys: A

Microeconometric Approach to Development Policy,

The Johns Hopkins University Press, 1997.

Green, H. Aggregation in Economic Analysis, Princeton

University Press, 1964.

Hollman, Frederick W., Tammany J. Mulder, and Jeffery

E. Kallan. Methodology and Assumptions for the

Population Projections of the United States: 1999 to

2100, Population Division Working Paper No. 38,

U.S. Census Bureau, 2000.

Lin, Biing-Hwan. Food and Agricultural Commodity

Consumption in the United States: Looking Ahead to

2020, Agricultural Economics Report No. 820, U.S.

Department of Agriculture, Economic Research

Service, February 2003.

Maddala, George S. Limited-Dependent Qualitative

Variables in Econometrics, Cambridge University

Press, 1983.

Tomek, W. “Empirical Analyses of the Demand for

Food: A Review,” Food Demand and Consumption

Behavior, Agricultural Experiment Station,

University of Georgia, 1977.

U.S. Department of Agriculture, Economic Research

Service. FoodReview, Volume 25, Issue 1, Spring

2002.

Wold, H., and L. Jureen. Demand Analysis, Wiley and

Sons, 1953. 

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 31
Appendix 

The statistical model used in this analysis uses information from both consuming and nonconsuming households. The censored normal regression model, commonly referred to as the Tobit model, is used to obtain

expenditure estimates when some households purchase

and others do not purchase in a given time period.

The Tobit model can be expressed, for a typical household, as:

Yi = Xi

B + εi if XiB + εi > 0;

Yi = 0 if Χi

B + εi ≤ 0.

Where i = 1, 2. ..., n; n is the number of households; Yi

is item expenditure; X is a vector of explanatory variables; B is a vector of coefficients; and εi is an independently and normally distributed random disturbance

term with a mean of zero and constant variance, σ2. The

level of expenditures for the ith household is determined by the combination of a nonstochastic component, Xi

β, and a stochastic component, εi. The determinate or nonstochastic portion of the model is a linear

function of household characteristics and their respective response parameters. Expenditures differ among

households due to both the determinate portion of the

model and to the stochastic element, which embodies

the unobserved factors and idiosyncrasies of individual

households. For a more detailed discussion of the Tobit

technique, see Blisard and Blaylock. 

32 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA
Appendix table 1—Tobit model for food expenditures, 1997-98: parameter estimates and statistics

Meat,

Independent Total Food Cereals poultry, fish, 

variables food at home and bakery and eggs Beef Pork Poultry Fish Dairy

Constant 11.30 14.13 1.81 10.86 5.44 5.68 0.83 -4.48 0.54

(4.59) (3.22) (0.70) (1.35) (0.80) (0.65) (0.56) (0.95) (0.63)

Diet knowledge 0.98 0.80 0.16 -0.08 -0.16 -0.20 0.05 0.25 0.15

(0.26) (0.18) (0.04) (0.08) (0.05) (0.04) (0.03) (0.05) (0.04)

North Central -4.03 -2.13 -0.56 -0.80 -0.17 0.32 -0.55 -1.31 -0.43

(1.02) (0.72) (0.16) (0.30) (0.18) (0.15) (0.13) (0.21) (0.14)

South -3.52 -1.51 -0.73 -0.40 -0.08 0.35 -0.42 -0.47 -0.26

(0.96) (0.67) (0.15) (0.28) (0.17) (0.14) (0.12) (0.20) (0.13)

West -0.68 0.55 -0.41 -0.35 -0.22 -0.17 -0.35 0.19 -0.04

(1.00) (0.70) (0.15) (0.30) (0.18) (0.14) (0.12) (0.20) (0.14)

Race -4.16 -0.55 -0.40 1.25 -0.26 0.37 0.88 1.44 -0.89

(1.17) (0.82) (0.18) (0.34) (0.20 (0.16) (0.14) (0.24) (0.16)

Income 3.41 0.95 0.14 0.10 0.03 0.04 0.04 0.01 0.12

(0.19) (0.14) (0.03) (0.06) (0.03) (0.03) (0.02) (0.04) (0.03)

Income squared -0.05 -0.01 -3.39-e3 5.63-e3 3.50-e3 -3.29-e4 3.61-e4 3.00-e3 -2.73-e3

(0.01) (4.75-e3) (1.03-e3) (1.20-e3) (1.15-e3) (9.96-e4) (8.15-e4) (1.28-e3) (9.47-e4)

Winter -0.33 -1.12 0.11 -0.03 0.24 -0.21 -0.15 0.59 -0.37

(0.89) (0.62) (0.14) (0.26) (0.16) (0.13) (0.11) (0.18) (0.12)

Spring 0.22 -0.73 0.02 -0.04 0.48 -0.24 -0.19) 0.13 -0.24

(0.89) (0.62) (0.14) (0.26) (0.16) (0.13) (0.11) (0.18) (0.12)

Summer -0.02 -1.75 -9.99-e3 -0.38 0.18 -0.26 -0.20 -0.02 -0.37

(0.89) (0.63) (0.13) (0.26) (0.16) (0.13) (0.11) (0.19) (0.12)

Year 1.84 -0.43 -0.20 -0.10 -0.03 -0.06 -0.11 0.18 -0.10

(0.64) (0.45) (0.10) (0.19) (0.11) (0.09) (0.08) (0.13) (0.09)

Household size 10.50 0.97 -0.37 -2.65 -2.65 -2.34 -1.69 -2.56 -0.06

(inverse) (1.38) (0.97) (0.21) (0.41) (0.25) (0.21) (0.18) (0.30) (0.19)

Proportion age 0-4 -19.76 -15.54 -2.74 -6.28 -2.00 -2.20 -1.48 -2.07 -0.63

(3.35) (2.35) (0.51) (0.99) (0.58) (0.47) (0.41) (0.69) (0.46)

Proportion age 5-9 -14.85 -12.24 -1.07 -5.39 -1.72 -2.30 -1.09 -1.15 -0.76

(2.97) (2.08) (0.45) (0.87) (0.51) (0.42) (0.36) (0.60) (0.41)

Proportion age 10-14 -10.41 -11.89 -1.35 -5.59 -1.68 -2.52 -0.98 -2.40 -0.84

(3.01) (2.11) (0.46) (0.88) (0.52) (0.42) (0.36) (0.61) (0.41)

Proportion age 15-19 -15.35 -18.45 -2.87 -7.85 -3.05 -2.84 -1.81 -2.51 -2.13

(2.29) (1.63) (0.36) (0.71) (0.43) (0.36) (0.30) (0.52) (0.32)

Proportion age 20-29 -6.13 -12.45 -1.42 -5.14 -1.98 -2.59 -1.13 -2.08 -1.34

(1.31) (0.92) (0.20) (0.39) (0.24) (0.20) (0.17) (0.29) (0.18)

Proportion age 30-44 -0.87 -6.09 -0.85 -2.23 -1.19 -1.20 -0.21 -0.58 -0.49

(1.20) (0.84) (0.18) (0.36) (0.21) (0.18) (0.15) (0.25) (0.17)

Proportion age 65-74 1.22 0.08 0.47 -0.47 -0.51 -0.29 -0.16 0.28 0.16

(1.38) (0.96) (0.21) (0.40) (0.24) (0.20) (0.17) (0.28) (0.19)

Proportion age 75 

and older -3.56 -0.37 0.57 -1.36 -1.01 -0.62 -0.14 0.14 0.37

(1.52) (1.06) (0.23) (0.45) (0.27) (0.22) (0.19) (0.32) (0.21)

Sigma 28.21 19.72 4.29 8.19 4.57 3.67 3.15 4.86 3.82

Direct income elasticity 0.27 0.12 0.09 0.07 0.07 0.06 0.08 0.05 0.10

Probability of purchase 0.92 0.89 0.80 0.75 0.52 0.48 0.49 0.32 0.75

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 33
Appendix table 2—Tobit model for food expenditures, 1997-98: parameter estimates and statistics

Sugars and Nonalcoholic Fats and Miscellaneous Food away

Independent variables Fruits Vegetables sweeteners beverages oils prepared food from home

Constant -2.54 -0.12 -0.62 1.87 0.44 -1.32 -12.58

(0.57) (0.43) (0.49) (0.51) (0.29) (0.83) (3.48)

Diet-health knowledge 0.28 0.17 0.11 0.01 0.03 0.24 0.61

(0.03) (0.02) (0.03) (0.03) (0.02) (0.05) (0.20)

North Central -0.53 -0.53 4.17-e3 0.21 -0.04 0.51 -1.91

(0.13) (0.10) (0.11) (0.11) (0.06) (0.19) (0.77)

South -0.42 -0.20) -0.01 0.22 0.06 0.44 -2.19

(0.12) (0.09) (0.10) (0.11) (0.06) (0.17) (0.72)

West 0.20 0.12 0.03 0.18 0.08 1.12 -1.05

(0.12) (0.09) (0.11) (0.11) (0.06) (0.18) (0.75)

Race 0.53 0.09 -0.13 -0.43 0.04 -0.82 -4.68

(0.15) (0.11) (0.13) (0.13) (0.07) (0.21) (0.90)

Income 0.16 0.07 0.07 0.10 0.02 0.24 2.77

(0.02) (0.02) (0.02) (0.02) (0.01) (0.04) (0.15)

Income squared -3.70-e3 -1.55-e3 -1.81-e3 -2.46-e3 -6.30-e4 -5.57-e3 -0.04

(0.8.57-e4) (6.44-e4) (7.24-e4) (7.67-e4) (4.38-e4) (1.22-e3) (0.01)

Winter -0.02 0.02 -0.42 -0.13 -8.49-e3 -0.18 0.72

(0.11) (0.08) (0.10) (0.10) (0.06) (0.16) (0.67)

Spring 0.27 0.06 -0.61 0.18 0.02 -0.24 1.07

(0.11) (0.08) (0.10) (0.10) (0.06) (0.16) (0.67)

Summer 0.23 -0.05 -0.72 0.02 -0.10 -0.63 1.79

(0.11) (0.08) (0.10) (0.10) (0.06) (0.16) (0.67)

Year -0.03 0.05 9.97-e3 -0.12 -0.07 -0.05 2.76

(0.08) (0.06) (0.07) (0.07) (0.04) (0.12) (0.49)

Household size -0.33 -0.86 -0.98 -0.25 -0.82 -0.05 7.16

(inverse) (0.17) (0.13) (0.15) (0.15) (0.09) (0.25) (1.04)

Proportion age 0-4 -0.74 -1.84 -0.73 -1.82 -1.14 -1.77 -4.56

(0.41) (0.31) (0.36) (0.37) (0.21) (0.60) (2.53)

Proportion age 5-9 -0.76 -1.81 -0.13 -1.81 -0.73 -1.66 -3.03

(0.37) (0.28) (0.31) (0.33) (0.18) (0.54) (2.25)

Proportion age 10-14 -1.46 -1.89 -0.19 -1.04 -0.78 -1.24 1.78

(0.37) (0.28) (0.32) (0.33) (0.19) (0.54) (2.27)

Proportion age 15-19 -2.53 -2.74 -0.87 -1.46 -1.23 -2.78 2.71

(0.30) (0.23) (0.26) (0.26) (0.15) (0.43) (1.72)

Proportion age 20-29 -1.55 -1.77 -0.87 -0.99 -0.98 -1.73 7.45

(0.16) (0.12) (0.14) (0.15) (0.09) (0.24) (0.98)

Proportion age 30-44 -1.17 -0.92 -0.42 -0.17 -0.49 -0.56 6.12

(0.15) (0.11) (0.13) (0.13) (0.08) (0.22) (0.90)

Proportion age 65-74 0.42 0.29 0.46 -0.47 -0.06 -0.42 1.07

(0.17) (0.13) (0.15) (0.15) (0.09) (0.25) (1.04)

Proportion age 75 and older 1.44 0.23 0.23 -0.98 0.07 -0.01 -3.89

(0.19) (0.14) (0.16) (0.17) (0.09) (0.28) (1.16)

Sigma 3.42 2.58 2.85 3.04 1.63 5.01 20.84

Direct income elasticity 0.15 0.10 0.11 0.11 0.05 0.15 0.44

Probability of purchase 0.72 0.75 0.55 0.72 0.54 0.73 0.74

34 z Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 Economic Research Service/USDA
Appendix table 3—Diet-health knowledge equation

Independent variables Diet-health knowledge

Constant 16.19

(0.27)

North Central 0.78

(0.18)

South 0.07

(0.27)

West -0.21

(0.24)

Nonmetro 0.01

(0.17)

Income 0.18

(0.03)

Male -1.57

(0.17)

Black -1.44

(0.36)

Male head -0.64

(0.22)

Female head -0.80

(0.17)

Employed 0.24

(0.19)

High school education 1.46

(0.21)

Some college 2.51

(0.19)

College 3.12

(0.22)

Proportion age 0-4 -0.25

(0.54)

Proportion age 5-9 -0.46

(0.60)

Proportion age 10-14 -0.19

(0.70)

Proportion age 15-19 0.25

(0.65)

Proportion age 20-29 -0.52

(0.31)

Proportion age 30-44 -0.50

(0.31)

Proportion age 65-74 0.10

(0.21)

Proportion age 75 and older -2.00

(0.35)

R-squared = 0.20

F(21,23) = 48.4

N = 5,232

Economic Research Service/USDA Food Expenditures by U.S. Households: Looking Ahead to 2020/AER-821 z 35