Food and Agricultural Commodity Consumption in the United States:

Looking Ahead to 2020. By Biing-Hwan Lin, Jayachandran N. Variyam,

Jane Allshouse, and John Cromartie, Food and Rural Economics Division,

Economic Research Service, U.S. Department of Agriculture, Agricultural

Economic Report No. 820.

Abstract

U.S. consumption of food commodities is projected to rise through the year 2020,

mainly due to an increase in population. But the mix of commodities is expected to

shift because of an older and more diverse population, rising income, higher

educational attainment, improved diet and health knowledge, and growing popularity of eating out. This study analyzes data from USDA’s food consumption

survey to project the consumption, through the year 2020, of 25 food groups and

22 commodity groups. Per capita consumption of fish, poultry, eggs, yogurt, fruits,

nuts and seeds, lettuce, tomatoes, some other vegetables, grains, and vegetable oils

is predicted to rise, whereas consumption of beef, pork, other meat, milk, cheese,

potatoes, and sugar is expected to fall. The growth of the at-home and away-fromhome markets varies from one commodity to another. Fruit consumption is

expected to lead all commodities in growth in the at-home market, and fish

consumption is expected to lead in growth in the away-from-home market.

Keywords: Eating out, diet and health knowledge, food-commodity translation

database, food consumption projections, commodity consumption projections, and

Continuing Survey of Food Intakes by Individuals, 1994-96 and 1998.

Acknowledgments

The authors thank Chung L. Huang of the University of Georgia, W. Noel Blisard,

Nicole Ballenger, and James Blaylock of the Economic Research Service for

helpful review of the earlier drafts of this report, Lou King for editorial advice,

Wynnice Pointer-Napper for the final document layout and charts, and Curtia

Taylor for cover design.

1800 M Street, NW

Washington, DC 20036-5831 February 2003
Contents

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

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

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Model Specification, Estimation, and Forecasting . . . . . . . . . . . . . . . . . . . . . 4

Indirect and Direct Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Model Structure and Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Estimation Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Eating-Out Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Diet-Health Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Food Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Projections of Food and Commodity Consumption, 2000-2020 . . . . . . . . . . 11

Economic, Social, and Demographic Variables, 2000-2020 . . . . . . . . . . . . . 11

Assumptions Underlying the Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Projections of Food Consumption, 2000-2020 . . . . . . . . . . . . . . . . . . . . . . . 14

Projections of Commodity Consumption, 2000-2020 . . . . . . . . . . . . . . . . . . 15

Decomposition and Sensitivity Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Effects of Economic, Social, and Demographic Factors . . . . . . . . . . . . . . . . 23

Effects of Eating Out and Diet-Health Knowledge on 

Commodity Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Direct and Indirect Effects of Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Sensitivity Analysis of Eating Out and Age . . . . . . . . . . . . . . . . . . . . . . . . . 26

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Appendix tables: Tobit Results of Food Consumption Equations . . . . . . . . 34

ii ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● iii

Summary

As the American population becomes older and more racially and ethnically

diverse, the volumes and types of foods preferred can be expected to shift. Older

folks, for example, tend to eat out less often than younger folks, and, to some

extent, prefer different foods. An increasing proportion of elderly in the population, therefore, will have implications for the types of foods demanded. Likewise,

different ethnic subpopulations have some distinct food preferences.

This report examines the volumes of individual types of foods eaten by Americans in

the years 1994-98, and projects those volumes to the year 2020, taking into account

population and demographic change as well as trends in economics and immigration.

A two-step econometric system was specified to model food consumption. The

first step was to estimate consumers’ eating-out habits and their diet-health knowledge. In the second step, food consumption, both at home and away from home,

was related separately to consumers’ eating-out habits, knowledge, income, and

their social and demographic characteristics. The Tobit procedure was utilized to

deal with the fact that many consumers may not consume certain foods during a

survey period. Using projected values of economic, social, and demographic

factors for 2000-2020, we projected at-home and away-from-home food consumption for the same period. We then developed a food-commodity translation database to convert food consumption to commodity consumption. Twenty-five food

groups and 22 commodity groups were analyzed in this study. 

This study makes several contributions to the literature on food consumption. To 

our knowledge, no research has been conducted to model the effects of both eating

out and diet-health knowledge on food consumption. Likewise, the at-home and

away-from-home separation of food consumption had never been attempted in the

literature. We developed a food-commodity translation database to convert food

consumption to commodity consumption. This translation database contains the

amounts of several hundreds of commodities in each of more than 7,000 food items.

Due to an anticipated population growth of 50 million between 2000 and 2020 in

the United States, total consumption of all 22 commodities is predicted to rise,

even though per capita consumption of many commodities is predicted to fall. The

results suggest that fruits will lead all commodities in terms of growth in both total

and per capita consumption. Certain vegetables, such as lettuce and tomatoes, are

predicted to grow substantially, while per capita potato consumption (fried and

other) is predicted to decline, slowing down the growth in total U.S. potato

consumption. Increases in meat, poultry, and fish consumption vary. Per capita fish

and poultry consumption is predicted to rise while beef, pork, and other meat per

capita consumption is predicted to fall. Per capita consumption of milk and cheese

is predicted to fall, while per capita consumption of yogurt and eggs is predicted to

rise. The consumption of nuts and seeds and grains is also predicted to rise over

the next two decades.

The consumption projections are based on expected shifts in economic, social, and

demographic conditions. In addition, the consumption projections are sensitive to

two underlying assumptions. First, no prices or expenditures on food consumption

were reported in the surveys, so relative prices are assumed to remain constant

during the survey period (1994-96 and 1998) and the projection period (2000-
iv ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

2020). Second, there is an implicit assumption that as an individual moves from

one demographic group to another, his/her preferences immediately take on the

characteristics of the new group. A sensitivity analysis was conducted to examine

the effects of assuming different eating-out habits as adults grow older between

2000 and 2020. The results suggest that the projections of some commodities, such

as potatoes, are quite sensitive to the assumption about eating-out habits.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 1

Introduction

Thanks to an efficient agricultural production system

and a free trade policy, the U.S. food supply is both

bountiful and full of variety. A continuous influx of

immigrants provides U.S. consumers with constant

exposure to new, more exotic foods and preparation

methods. Dining out, once thought to be a luxury, is

now a necessity for many. Constantly changing

economic, social, and demographic conditions have

created an insatiable appetite for new food products,

new packaging, more convenience, new delivery

systems, and safer and more nutritious foods.

How do changing food preferences and diets affect

future consumption and spending on food? Under

consumer-driven agriculture projects conducted by

USDA’s Economic Research Service (ERS), separate

econometric modeling efforts were undertaken to

project food consumption and food expenditures under

changing economic, social, and demographic conditions. Some of the consumption projections were

reported in the Spring 2002 issue of FoodReview

(Blisard et al.). Two reports have been prepared to

document the method and data as well as to provide a

comprehensive set of the projection results and their

interpretations.

This report examines food consumption using data

from the USDA food consumption surveys. ERS has

projected changes in the U.S. economic, social, and

demographic conditions, and these projections are

used to forecast food consumption. As revealed by the

2000 Census, the average age of the U.S. population

has been increasing, as has the population’s racial and

ethnic diversity. In this study, we pay attention to the

effects of the emerging changes in the U.S. demographic landscape on food consumption. 

This study makes several contributions to the literature

on food consumption. To our knowledge, this study

represents the first attempt to model the effects of both

eating out and diet-health knowledge on food

consumption. The at-home and away-from-home separation of food consumption has never been attempted

in the literature. We developed a first-ever foodcommodity translation database to convert food

consumption to commodity consumption. There are

more than 7,000 food items and several hundred

commodities included in the translation database. This

database was made possible by the combined efforts of

ERS and USDA’s Agricultural Research Service

(ARS). The end product of this research is a projection

of commodity consumption, at home and away from

home, over the next two decades. 

This report begins with a discussion of the survey data

used in estimating food consumption, as well as the

databases from ERS and ARS that were used to develop

the food-commodity translation database. The structure

and specification of the econometric models are then

described, followed by a discussion of the estimates.

The reasoning behind the projected economic, social,

and demographic conditions is presented, followed by

our projections of food and commodity consumption,

both at home and away from home. The contributions to

the growth in consumption from economic, social, and

demographic factors are also decomposed. The report

concludes with a discussion of the sensitivity analysis

that was conducted to examine the effects on at-home

and away-from-home commodity consumption under

the assumption that some adults maintain their eatingout habits as they age.

Food and Agricultural Commodity

Consumption in the United States: 

Looking Ahead to 2020

Biing-Hwan Lin, Jayachandran N. Variyam, 

Jane Allshouse, and John Cromartie
2 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Data

USDA has conducted periodic surveys of household and

individual food consumption in the United States since

the 1930s. The most recent surveys, the 1994-96 and

1998 Continuing Survey of Food Intakes by Individuals

(CSFII), conducted by ARS, provide the basis for this

study. Each year of the 1994-96 survey comprises a

nationally representative sample of non-institutionalized

persons residing in the 50 States and Washington, DC.

As a supplemental survey to the 1994-96 CSFII, the

1998 CSFII was conducted to increase the 1994-96

CSFII sample for children. The 1994-96 and 1998

CSFII were conducted using the same methodology and

can be combined for analysis. The CSFII data include a

sample weight for each respondent, indicating the

number of people the sample represents.

In the CSFII, 2 nonconsecutive days of dietary data for

individuals of all ages were collected 3 to 10 days

apart through in-person interviews using 24-hour

recalls. The 1994-96 CSFII data set includes information on the food intakes of 15,303 individuals, while

the 1998 CSFII data set includes 5,559 children up to

age 9. The respondents in the CSFII provided a list of

foods consumed as well as information on where and

how much of each food was eaten. The locations

where the food was purchased and eaten were coded

into several categories. Various economic, social, and

demographic characteristics were also collected for the

respondent and his/her household.

After respondents in 1994-96 CSFII provided the firstday dietary data, an adult age 20 or above was randomly

selected from each household to participate in the 1994-

96 Diet and Health Knowledge Survey (DHKS). 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 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

completed the survey. Since consumers’ knowledge and

attitudes about diet and health affect their food choices

and consumption, the combined CSFII and DHKS data

provide researchers a unique data set to examine the

factors that affect food consumption.

In addition to food intake data, ARS also provides

technical support documents, such as recipes and a

Pyramid Servings Database (PSD), to support data

analysis. Recently, ARS created the Food Commodity

Intake Database (FCID) for the Environmental

Protection Agency (EPA). The FCID provides data on

the edible amount of agricultural food commodities

contained in each food reported eaten in CSFII. FCID

was developed for the purpose of estimating human

exposure to pesticide residues through the consumption of foods and beverages. Therefore, food intakes in

FCID are expressed in terms of EPA-defined agricultural food commodities. For example, a piece of apple

pie is translated quantitatively into the following

commodities: wheat flour, peeled apple, sugar (from

sugar cane or beet), cinnamon, and the specific

vegetable oils comprising shortening. There are over

500 food commodities listed in the FCID.

Some of the EPA-defined food commodities have to be

modified in order to provide useful information to the

agricultural community. All dairy foods are translated

into milk components (fat, nonfat solids, water, and

sugar) in FCID, whereas the dairy industry is more

interested in knowing future consumption of dairy

products, including milk, cheese, and yogurt. ARS

created the PSD to express food consumption in terms

of the number of servings for comparison with dietary

recommendations in USDA’s Food Guide Pyramid.

The PSD shows, for each food consumed, the number

of servings from 30 food groups, including milk,

cheese, and yogurt. In this study, the three dairy

commodities are expressed in terms of servings.

Many EPA-defined food commodities are different

products of a commodity. For example, apple is

expressed in five different product forms: apple with

peel, peeled apple, dried apple, apple juice, and applesauce. In order to project total apple consumption,

these apple products have to be converted to the raw

weight—apple with peel. As a part of this study, a

conversion-factor database was developed to convert

all EPA-defined commodities to the commodities

reported by ERS (Putnam and Allshouse). Using the

aforementioned data sources, we created a foodcommodity translation database that enabled us to

convert food consumption, in edible weight, to

commodity consumption at the farm or retail level.

In this study we employed econometric modeling to

estimate food consumption. As such, we limited the

number of food groups to a manageable size. More than

7,000 foods were reported eaten in the 1994-96 and

1998 CSFII. ARS uses a list of 71 food groups and

subgroups in reporting U.S. food consumption. In this
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 3

study, we collapsed ARS’s list to 25 food groups: beef,

pork, poultry, fish, other meat, meat mixture (e.g.,

hamburger), eggs, milk, cheese, yogurt, fats and oils,

fruit juice, other fruits, fried potatoes, other potatoes,

tomatoes, legumes and nuts, other vegetables, breakfast

cereals, grain mixture (e.g., pizza), other grain products,

sweeteners, coffee and tea, fruit drinks, and soft drinks.

Given projections of food consumption, we used the

food-commodity translation database to derive projections of commodity consumption. For ease in reporting

commodity consumption, we aggregated commodities

into 22 commodity groups: beef, pork, poultry, fish,

other meat, eggs, milk, cheese, yogurt, vegetable oils,

citrus fruits, apples, grapes, other fruits, nuts and

seeds, fried potatoes, other potatoes, tomatoes, lettuce,

other vegetables, grains, and sugar. It should be

mentioned that it is possible to project consumption of

an individual commodity or product form (e.g., apple

juice), as reported in the FCID and PSD.
4 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Model Specification, 

Estimation, and Forecasting

The ultimate objective of the study was to project

commodity consumption, both at home and away

from home, for every 5 years, ending with the year

2020. Because we utilized data from the 1994-96 and

1998 CSFII, our consumption projections began with

the year 2000. 

The analysis involved several tasks. The first task was to

model eating-out behavior and household heads’ diethealth knowledge. Estimated eating-out and knowledge

variables were then combined with economic, social,

and demographic data to estimate the consumption of

25 food groups. Using the projected values for the

economic, social, and demographic characteristics,

future food consumption was predicted for the years

2000, 2005, 2010, 2015, and 2020. 

The EPA’s FCID and ARS’s PSD were used, together

with the ERS conversion-factor database, to create a

food-commodity translation database so that projected

food consumption could be converted to commodity

consumption.

Indirect and Direct Approaches

There are two approaches to estimating commodity

consumption using food consumption data—indirect

and direct. The indirect approach begins with the estimation of food consumption, then derives commodity

consumption using conversion factors. The direct

approach first converts foods to commodities and then

estimates commodity consumption. Under the argument that consumers make food choices rather than

commodity choices, we employed the indirect

approach. The econometric model for ith food

consumed by an individual j can be expressed as:

(1) Fij = f (P1, P2, ..., Pn, Pgs, Yj

, Xj) 

= 1, 2, ... n

where Fij is the amount of ith food consumed by jth

individual; Pi is the price of ith food; Pgs is a price

index for nonfood goods and services, Yj is income,

and Xj is a vector of social and demographic characteristics of the individual j.

Then the projected values of the exogenous variables

(P, X, and Y) are plugged into the estimated food

consumption equations to forecast food consumption.

Using the food-commodity translation database (equation 2), the consumption of n foods can be converted

to the consumption of m commodities (equation 3). 

(2) Qk = Σi Tki Fi

where Tki is a translation coefficient indicating the

units of the commodity Qk contained in each unit of

the food Fi

.

(3) Qkj = g(P1, P2, ..., Pn, Yj

, Xj) 

k = 1, 2, ... m.

The direct approach is to apply the food-commodity

translation database to derive the consumption of

commodity k by individual j, Qkj, from the consumption of food i consumed by individual j, Fij. Then

equation 3 is estimated.

Model Structure and Specification

We specified a two-step econometric system to model

consumers’ food consumption (fig. 1). In the first step,

we estimated consumers’ eating-out habits and their diethealth knowledge. In the second step, food consumption,

both at home and away from home, were related separately to consumers’ eating-out habits, knowledge,

income, and social and demographic characteristics.

Over the past three decades, eating out has become

increasingly popular for Americans. In 2000, Americans

spent 47 percent of their food expenditures away from

home, up from 33 percent in 1970 (fig. 2). Americans

Figure 1

Two-stage econometric modeling

Eating out Knowledge

Food Consumption:

Food at home

Food away from home

Source: Economic Research Service, USDA.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 5

tend to eat different kinds of foods and different

amounts when eating out than eating at home (Lin,

Guthrie, and Frazao). If the upward trend in eating out

continues, we expect food and commodity consumption

will change accordingly. Therefore, we paid special

attention to modeling the decision to eat out and its

effect on the type and amount of food and commodities

consumed at home and away from home. We also

examined the growth of the at-home and away-fromhome segments of the food and commodity markets.

In addition to eating out more frequently, U.S.

consumers have also changed their food consumption

patterns in reaction to the flood of diet and health

information coming out of the Nation’s laboratories

and research institutions (Variyam and Golan). We

therefore tried to model how diet-health knowledge

and attitudes are reshaping food consumption.

To our knowledge, no research has been conducted to

model the effects of both eating out and diet-health

knowledge on food consumption. In this study, eating

out and consumers’ diet-health knowledge were

modeled as two separate equations in the first step of a

two-step recursive econometric system. Eating-out

behavior was hypothesized to be affected by household

characteristics, race and education of the household

head, and individual characteristics, as shown below.

(4) FAFH = f(Y, Hsize, Htype, Tenancy, Region,

Metro, Edu, Race, Sex, Age,

School, Employ, Weekend)

where FAFH measures eating out by the individual

(subscript is dropped for simplicity). Y is per capita

household income. Hsize is the household size. Htype

is the type of household structure (single or dual

heads, with or without children). Tenancy is the household housing arrangement (own, rent, etc.). Region is

the Census region. Metro indicates if the respondent

resides in a city, suburb, or rural area. Edu is the

education level of the respondent. Race is the race and

ethnicity of the household head. Sex is the gender of

the respondent. Age is a vector of age classes for the

respondent. School indicates if the respondent was in

school. Employ indicates the employment status of the

respondent. Weekend indicates if the day of food

intake was a Saturday or Sunday.

In the 1994-96 CSFII, an adult age 20 or above who

provided the first-day intake data was randomly

chosen from each household to provide information

related to his/her diet-health knowledge and attitude.

The data were used to construct a knowledge variable

(KNOW) for household heads. This knowledge variable was hypothesized to be affected by the characteristics of the household and the household head

(Variyam et al.), as shown below:

(5) KNOW = f(Y, Htype, Region, Metro, Edu,

Race, Sex, Hhage, Hemploy)

where Hhage is the age of the household head and

Hemploy is the percent of household heads employed;

other variables were already discussed in equation 4.

The fitted values of the two dependent variables

(FAFH and KNOW) from the first step were treated as

exogenous variables in the second step, in which a

system of food consumption was estimated. We separated food at home and food away from home and estimated two sets of food consumption equations: one set

for 25 food groups at home and the other for 25 food

groups away from home. By doing so, we were able to

estimate the differing effects of exogenous variables

(including eating out, knowledge, income, social, and

demographic variables) on food consumption at home

and away from home. For example, as consumers eat

out more, they are likely to eat more french fries away

from home but fewer french fries at home. Another

advantage of separating food consumed at home and

food consumed away from home is to enable us to

forecast the growth in food and commodity consumption in these two market segments.

Figure 2

U.S. expenditures for food away from home as a

share of total food expenditures

Percent of total food expenditures

Source: Economic Research Service, USDA.

1970 75 80 85 90 95 2000

25

30

35

40

45

50

Food away from home
6 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Because CSFII respondents only reported the amount

of foods they ate but not the price they paid, price variables in equation 1 were dropped from equation 6.

(6) F = f(FAFH, KNOW, Y, Hsize, Htype, Season,

Region, Metro, Race, Sex, Age)

where Season is the season during which the individual’s food intake occurred; other variables were

already discussed. 

For detailed definitions of all variables as well as their

descriptive statistics (weighted means and standard

deviations) see table 1.

Estimation Procedure

Food intake data in CSFII were collected for sample

persons of all ages, while DHKS data were collected

for those age 20 and above who completed the firstday intake recall. Therefore, the DHKS sample is a

subset of the CSFII sample. The knowledge variable is

included in the food consumption equation 6, which

was estimated using the CSFII sample. So, the knowledge variable had to be generated for some CSFII

respondents. This was accomplished by fitting the

knowledge equation 5 for DHKS respondents who

were also household heads. Because personal data for

household heads were collected for each CSFII

respondent, the data and the fitted knowledge equation

were used to generate household heads’ knowledge for

the whole CSFII sample. The knowledge equation was

estimated with the weighted ordinary-least-squares

estimator, using the DHKS sample weight.

On any given day, not everyone consumed all the 25

food groups, either at home or away from home.

Therefore, a cluster of zero consumption values for

eating out and for various food groups was observed in

the data—making it necessary to estimate a censored

regression model. Any statistical procedure that does

not account for zero observations produces inconsistent parameter estimates. 

Tobin was the first to propose a censored normal

regression model (Tobit model) to deal with censored

data in regression. Since the development of Tobit,

many censored regression estimators have been

proposed, including the maximum-likelihood procedure of Amemiya, Lee, Wales and Wood, and the twostep procedures of Heien and Wessells, Shonkwiler

and Yen, and Perali and Chavas. 

In this study, we chose the Tobit procedure for its ease

of estimation. The Tobit model can be expressed as:

(7) qi = x′βi

+εi if x′βi+εi > 0

= 0 if x′βi

+εi ≤ 0, i = 1, 2, ..., n

where qi denotes the endogenous variable, x is the

vector of exogenous variables, and εi is the error term.

The Tobit procedure was used to estimate 50 censored

consumption equations (25 food groups, at home and

away from home) as well as a separate censored

eating-out equation, using the CSFII sample.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 7

Table 1—Variables used in the two-step recursive system and their descriptive statistics

Standard

Variable Definition Mean deviation

Eating out Percent of food consumption, in quantity terms, that was prepared away from home (%) 22.6 22.8

Knowledge Scores of diet-health knowledge of the household head 17.2 4.1

Income Household income, per capita (in $1,000) 14.7 11.6

Employment The respondent's employment status (0, 1) 0.47 0.50

Hemploy Percent of household heads employed (0, 0.5, 1) 0.57 0.44

Student The respondent was attending school (0, 1) 0.18 0.39

Weekend Number of intake days that fell on weekend (0, 1, 2) 0.57 0.57

High school The respondent completed high school education but did not go to college (0, 1) 0.27 0.44

Base = respondents who did not finish high school

Some college The respondent went to college but did not graduate with a degree (0, 1) 0.31 0.46

College The respondent completed a college degree (0, 1) 0.22 0.41

Male The respondent is male (0, 1) 0.49 0.50

Age 0-4 The respondent is age 0-4 (0, 1) 0.08 0.27

Age 5-9 The respondent is age 5-9 (0, 1) 0.07 0.26

Age 10-14 The respondent is age 10-14 (0, 1) 0.08 0.26

Age 15-19 The respondent is age 15-19 (0, 1) 0.07 0.25

Age 20-29 The respondent is age 20-29 (0, 1) 0.14 0.35

Age 30-44 The respondent is age 30-44 (0, 1) 0.24 0.43

Age 45-54 The respondent is age 45-54 (0, 1) 0.12 0.33

Age 55-64 The respondent is age 55-64 (0, 1) 0.08 0.27

Age 65-74 The respondent is age 65-74 (0, 1). Base = respondents age 75 and over 0.07 0.25

HH age 20-34 The household head is age 20-34 (0, 1) 0.26 0.44

HH age 35-54 The household head is age 35-54 (0, 1) 0.48 0.50

HH age 55-69 The household head is age 55-69 (0, 1). Base = respondents age 70 and over 0.15 0.35

Black The respondent is non-Hispanic Black (0, 1). Base = non-Hispanic White 0.13 0.33

Hispanic The respondent is Hispanic (0, 1) 0.11 0.31

Asian The respondent is Asian/Pacific Islander (0, 1) 0.03 0.17

Other The respondent's race/ethnicity is none of the above nor White (0, 1) 0.01 0.12

HH type1 The household is dual-headed, with children (0, 1) 0.48 0.50

HH type2 The household is dual-headed, without children (0, 1) 0.27 0.44

HH type3 The household is single-headed (either male or female), with children (0, 1) 0.10 0.31

Base = single-headed households without children 

Midwest The respondent resides in the Midwestern States (0, 1) 0.23 0.42

South The respondent resides in the Southern States (0, 1) 0.35 0.48

West The respondent resides in the Western States (0, 1). Base = Northeast 0.22 0.41

Nonmetro The respondent resides in a rural area (0, 1) 0.21 0.41

Suburb The respondent resides in a suburb (0, 1). Base = central city 0.47 0.50

HH size Number of household members 3.42 1.67

Tenancy The respondents own their homes (0, 1) 0.67 0.47

Quarter 1 The first day of intake falls in January - March (0, 1) 0.25 0.43

Quarter 2 The first day of intake falls in April - June (0, 1) 0.25 0.43

Quarter 3 The first day of intake falls in July - September (0, 1). Base = October - December 0.25 0.50
8 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Estimation Results

The eating-out equation was fitted with the 1994-96

and 1998 CSFII data, and the knowledge equation was

fitted with the 1994-96 CSFII and DHKS data. The

results are reported in table 2.

Eating-Out Equation

Many economic, social, and demographic variables

were found to influence an individual’s eating-out

behavior. In this study, eating out was measured by the

percent of food consumption, in terms of physical

amounts, prepared away from home. Among the

20,487 individuals included in the analysis, 15,202 of

them (74 percent) reported consuming foods that were

prepared away from home.

As expected, eating out rose with household income.

Also, an individual ate out more when he/she was

employed or attending school. Eating out was found to

exhibit an inverted U-shape relationship with the education level—those who did not complete high school

were treated as the base. Those who went to college but

did not complete a college degree ate out the most, and

those who completed a college degree ate out the least. It

is important to point out that eating out in this study is

represented by the percent of food prepared away from

home, not by the frequency of eating out or the percent

of food expenditures spent away from home. The main

purpose of the study is to project the consumption of

food and commodities at home and away from home.

Those age 75 and above were treated as the base category for age. Eating out rose initially with age, peaked

among those age 15-19, and then declined with

age—an inverted U-shape relationship between eating

out and age. Men ate out more than women. Compared

with Whites, Blacks ate out more, and Hispanics and

Asians ate out less. Dual-headed households, with or

without children, ate out less than other households.

Eating out declined as household sizes increased.

Diet-Health Knowledge

In the 1994-96 CSFII, 5,765 individuals provided

information about their knowledge and attitude toward

diet and health. After excluding those with missing

information and those who were not household heads,

5,169 respondents were included in the analysis of

diet-health knowledge. 

Table 2—Estimated models for eating out and diethealth knowledge of household head

Eating out Diet knowledge

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -5.35 1.31*** 14.06 0.22***

Income 0.25 0.02*** 0.03 0.00***

Employment 12.45 0.55*** ... ...

Hemploy ... ... 0.22 0.15

Student 3.82 1.25*** ... ....

Weekend -0.63 0.34* ... ....

High school 0.34 0.61 1.73 0.15***

Some college 2.56 0.61*** 2.57 0.17***

College -2.00 0.69*** 3.25 0.17***

Male 2.71 0.40*** -1.68 0.11***

Age 0-4 7.44 1.33*** ... ....

Age 5-9 20.36 1.72*** ... ...

Age 10-14 21.02 1.78*** ... ...

Age 15-19 24.36 1.51*** ... ...

Age 20-29 21.86 1.19*** ... ...

Age 30-44 14.42 1.17*** ... ...

Age 45-54 9.83 1.20*** ... ...

Age 55-64 5.24 1.22*** . ...

Age 65-74 3.40 1.23*** ... ...

HH age 20-34 ... ... 0.86 0.21***

HH age 35-54 ... ... 1.16 0.19***

HH age 55+ ... ... 1.15 0.17***

Black 1.50 0.67** -1.39 0.18***

Hispanic -1.81 0.71** -1.82 0.21***

Asian -1.75 1.20 -2.71 0.44***

Other race -0.55 1.67 -1.26 0.49***

HH type1 -3.76 0.84*** 0.81 0.16***

HH type2 -2.68 0.68*** 0.71 0.13***

HH type 3 0.37 0.96 -0.03 0.24

Midwest 3.52 0.61*** 0.68 0.16***

South 3.68 0.56*** 0.10 0.15

West 1.21 0.63* 0.01 0.17

Nonmetro -0.74 0.58 0.21 0.14

Suburb -0.27 0.48 0.18 0.13

HH size -0.48 0.18*** ... ...

Tenure 0.78 0.48 ... ...

Quarter 1 -0.48 0.55 ... ...

Quarter 2 0.07 0.55 ... ...

Quarter 3 -1.87 0.55*** ... ...

Scale 26.85 0.16 ... ...

N 20,487 5,169

Note: Significance levels are denoted by *** for 1%, ** for 5%, and 

* for 10%.

... = variable not included.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 9

The diet-health knowledge variable was constructed

from responses to 27 questions in the DHKS. The

questions tested the respondents’ knowledge of the

sources and occurrences of various nutrients in foods,

the relationship of specific dietary components to

specific diseases, and the number of servings of

various food groups in a healthful diet. The number of

correct answers to these questions given by a respondent provided a direct measure of his/her diet-health

knowledge. The range of the diet-health knowledge

scores was 0-27, with a mean of 17.2.

The diet-health knowledge equation has a reasonable

fit with an R-squared of 0.23, with most variables

being statistically significant at the 5-percent level. As

expected, educational attainment had the largest effect

among the explanatory variables, and diet-health

knowledge rose with education attainment and household income. Men scored lower than women of

comparable characteristics. Blacks and Hispanics

scored lower than Whites.

Adults from dual-headed households (with or without

children) displayed greater diet-health knowledge,

compared with adults from single-headed households.

Age was a significant determinant of diet-health

knowledge: household heads age 70 or above (the base

category) scored lower on the diet-health knowledge

than younger household heads. The employment status

of household heads (measured as percent of household

heads being employed) had no influence on the diethealth knowledge.

Food Consumption

There are 25 food groups classified in this study, separated into at-home and away-from-home consumption.

The means and standard deviations for the percent of

population consuming and the amount consumed of the

25 food groups at home and away from home are

reported in table 3. The estimation involved 50 food

consumption equations, using the Tobit procedure.

There are 20,487 individuals included in the estimation.

Tobit results are summarized in the 25 appendix tables

(see pp. 34-58).

As expected, an increase in eating out (that is, a higher

portion of all foods eaten out) resulted in a higher

consumption of each individual food group away from

home. Potatoes were the only exception (app. tables 14

and 15). While increased eating out resulted in higher

consumption of fried potatoes and other potatoes away

from home, it had no effect on at-home consumption

of these two types of potatoes. The effect of diethealth knowledge on food consumption varied by food

and source. For example, as diet-health knowledge

increased, beef consumption at home and away from

home declined (app. table 1), legumes and nut

consumption rose both at home and away from home

(app. table 17), poultry consumption rose at home but

declined away from home (app. table 3), and milk

consumption rose at home but stayed the same away

from home (app. table 8).

In general, food consumption rose with age among

children, peaked among young adults, and declined

with age. There are notable exceptions. Compared

with other consumers, children age 9 and under drank

more milk (app. table 8) and children age 4 and under

drank more juices (app. table 12). Seniors showed a

strong preference for other potatoes—potatoes other

than fried (app. table 15).

Race and ethnicity appear to affect food choices. For

example, compared with other groups, Whites drank

more milk at home and away from home (app. table

8), Blacks showed a preference for poultry, especially

away from home (app. table 3), Asians liked to eat fish

at home and away from home (app. table 4), and

Hispanics ate more tomatoes at home (app. table 16).
10 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Table 3—Descriptive statistics of food consumption, at home and away from home, 1994-96 and 1998 CSFII

Percent of consuming population Consumption quantity

Food group At home Away from home At home Away from home

Standard Standard Standard Standard

Mean deviation Mean deviation Mean deviation Mean deviation

--------------- Percent --------------- ------------- Grams per capita per day -------------- Meats

Beef 27 44 12 32 17.3 44.3 6.7 22.8

Pork 21 41 6 24 8.5 25.5 1.5 9.5

Poultry 27 45 15 36 16.8 38.2 9.3 28.4

Fish 10 29 6 23 6.3 26.8 4.1 21.6

Other meat 43 50 13 34 21.1 37.5 5.3 18.5

Meat mixtures 37 48 28 45 61.4 119.9 38.7 85.5

Eggs 24 43 7 26 12.5 29.2 4.6 20.7

Dairy

Milk 64 48 10 30 167.3 220.2 15.3 61.1

Cheese 40 49 15 36 12.0 27.5 3.6 13.0

Other dairy products 40 49 18 39 51.2 123.7 19.4 63.5

Fats and oils 62 48 25 43 9.6 15.7 3.9 10.9

Fruit

Fruit juice 35 48 7 25 76.1 148.5 9.1 42.1

Other fruit 50 50 11 31 75.0 126.9 7.3 30.9

Vegetables

Fried potatoes 22 42 27 44 9.3 29.1 13.8 30.1

Other potatoes 29 45 9 29 28.9 65.8 7.4 29.3

Tomatoes 41 49 27 44 20.9 48.7 6.9 19.0

Legumes and nuts 30 46 7 26 23.5 80.5 3.8 23.3

Other vegetables 71 45 33 47 76.6 105.1 21.3 52.3

Grains

Breakfast cereals 40 49 2 13 15.2 26.3 0.4 3.2

Grain mixtures 38 49 28 45 71.9 134.6 35.8 82.7

Other grain products 95 22 46 50 144.6 131.9 28.4 51.8

Sweeteners 62 48 18 39 19.7 39.2 5.1 22.8

Coffee and tea 54 50 22 42 301.7 492.9 77.3 220.5

Fruit drinks 26 44 5 22 78.5 199.9 10.9 61.0

Soft drinks 51 50 33 47 204.7 337.3 118.4 243.9

Number of observations 20,487
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 11

Projections of Food and 

Commodity Consumption, 

2000-2020

The first step in forecasting food and commodity

consumption is to project the values of the exogenous

economic, social, and demographic variables that

affect food consumption. The projected values of some

variables are reported in table 4. Values of other

exogenous variables that are not in table 4 are assumed

to remain constant at the mean values of the sample

data, the 1994-96 and 1998 CSFII. These projected

exogenous variables were first used to project future

values of eating out and diet-health knowledge, and

then all the projected variables were used to forecast

food consumption in 2000-2020. The food-commodity

translation database was used to convert food

consumption to commodity consumption. 

Economic, Social, and Demographic

Variables, 2000-2020

We assumed that real household income would grow by

1 percent annually between 2000 and 2020, using the

mean household income in the sample data, 1994-98.

This growth level is conservative, compared with an

observed average increase of 1.8 percent per year during

1978-88 and 1.2 percent annually during 1988-98. 

The design of the 1994-96 and 1998 CSFII was based on

the 1990 Census results, which were used by the U.S.

Census Bureau project the current population and households. However, the 2000 Census results show the

United States has been undergoing rapid demographic

expansion. The prospect of a dynamic demographic

future, setting the United States apart from most other

industrialized countries, is the result of a high tide of

immigration that began rising in the 1960s and shows no

signs of diminishing in the near future. Therefore, we

modified population, household, and education projections used in this study from Census projections (see

box, “Modification of Census Population, Household,

and Education Projections,” p. 13). Over the 2000-2020 

period, the proportions of Blacks, Asians, and, especially, Hispanics in the U.S. population are expected to

increase, while the proportion of Whites declines (fig. 3). 

Over the two decades, the Hispanic population is

expected to grow by 1.2 million annually, compared

with an annual growth of 500,000 among nonHispanic Whites and 400,000 each among Blacks and

Asians. Growth among Whites, Blacks, and Native

Americans is expected to come largely from natural

increase (births minus deaths), while growth among

the Hispanic and Asian populations is expected to

come from a combination of natural increase and

immigration. Higher birth, death, and immigration

rates all contribute to a younger age structure among

minority populations and, consequently, a built-in

growth momentum. We assumed that the U.S. population will grow by about 50 million, from 281.4 million

in 2000 to 331.9 million in 2020.

The regional population distribution will also change.

The Northeast will decline from 19 percent of the

Table 4—Projected economic, social, and demographic variables, 2000-2020

Variables 2000 2005 2010 2015 2020

Exogenous variables

Income 15.166 16.125 17.216 18.685 20.216

High school 0.352 0.345 0.339 0.332 0.326

Some college 0.241 0.248 0.255 0.263 0.270

College 0.235 0.242 0.249 0.257 0.264

No high school 0.173 0.165 0.157 0.148 0.140

Age 0-4 0.068 0.066 0.067 0.067 0.067

Age 5-9 0.073 0.068 0.066 0.066 0.067

Age 10-14 0.073 0.072 0.067 0.065 0.066

Age 15-19 0.072 0.073 0.072 0.067 0.065

Age 20-29 0.135 0.137 0.140 0.140 0.133

Age 30-44 0.235 0.213 0.194 0.189 0.192

Age 45-54 0.134 0.144 0.146 0.133 0.119

Age 55-64 0.086 0.102 0.116 0.126 0.129

Age 65-74 0.065 0.063 0.070 0.083 0.096

Age 75+ 0.059 0.060 0.060 0.061 0.069

Midwest 0.229 0.223 0.219 0.214 0.211

South 0.356 0.358 0.360 0.361 0.363

West 0.225 0.231 0.237 0.244 0.252

Northeast 0.190 0.188 0.184 0.181 0.174

Nonmetro 0.179 0.171 0.164 0.158 0.151

Suburb 0.493 0.504 0.514 0.523 0.532

City 0.328 0.325 0.322 0.319 0.317

Black 0.124 0.125 0.127 0.128 0.129

White 0.704 0.683 0.662 0.643 0.625

Hispanic 0.126 0.141 0.155 0.167 0.180

Asian 0.039 0.044 0.049 0.053 0.058

Other race 0.007 0.007 0.007 0.009 0.008

HH type1 0.235 0.218 0.198 0.183 0.167

HH type2 0.281 0.290 0.297 0.306 0.314

HH type3 0.092 0.091 0.090 0.089 0.087

Predicted eating out and knowledge

Eating out 22.939 22.978 23.033 23.148 23.336

Knowledge 16.954 16.963 16.974 17.006 17.041

Note: See table 1 for units of measurement.
12 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

population in 2000 to 17.4 percent in 2020. Likewise,

the North Central will decline from 22.9 percent in

2000 to 21.1 percent in 2020. Over the same period,

the South will increase from 35.6 to 36.3 percent,

while the West will increase from 22.5 to 25.2 percent. 

Age distribution, expressed as a share of the total

population, will change to reflect the aging of U.S.

population (fig. 4). The proportion of the population

age 20-29 will decline from 13.5 to 13.3 percent

between 2000 and 2020, while the proportion of the

population age 30-44 will decline from 23.5 to 19.2

percent. With the aging of the baby boom generation,

whose members currently range in age from 37 to 55,

the proportion of the population age 45-64 will

increase from 22 to 24.8 percent over the next two

decades. Likewise, the proportion of the population

age 65-74 will increase from 6.5 to 9.6 percent and the

population over age 74 will increase from 5.9 percent

of the total population in 2000 to 6.9 percent in 2020.

Without another baby boom, the U.S. population under

age 18 will increase by 7 million by 2020 but decline

as a percentage of the total. 

The rate of household formation in the United States

has exceeded population growth for decades, resulting

in a steady decrease in the average household size. At

the same time, the share of U.S. households consisting

of a married couple with children has declined, while

the share of single-person households has risen.

Average household size will continue to shrink over

the next two decades, dropping below 2.4 persons by

2020 as the number of “empty nest” households rises

from 28 to 31 percent. The aging of the U.S. population will also contribute to a higher proportion of

single-person households.

Educational progress in the United States has been one

of the demographic hallmarks of the past century, as

the share of the population completing high school

rose from 40 to 83 percent and the share of those with

college degrees rose from 10 to 23 percent. Average

educational attainment has advanced over the past

Figure 3

Racial and ethnic distribution of the U.S.

population, 2000-2020

Percent of population

Source: U.S. Census Bureau, adjusted by ERS.

Hispanic Black Asian White

12.6

18.0

12.4 12.9

3.9 5.8

70.4

62.4

2000

2020

Figure 4

Aging of the U.S. population, 2000-2020

Percent of population

Source: U.S. Census Bureau, adjusted by ERS.

2000

2020

0 to 9 10 to 19 20 to 29 30 to 44 45 to 54 55 to 64 65 to 74 75+

14.1 13.4

14.8

13.1 13.5 13.3

23.5

19.2

13.4

11.9

8.6

12.9

6.5

9.6

5.9

6.9

Age
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 13

several decades, in part, because older, less educated

generations have been replaced by more educated

younger generations. Generational replacement effects

have diminished, but it is also reasonable to assume

that college attendance will continue to rise, especially

among women, and that overall education levels

among the rapidly increasing foreign-born population

will rise toward the higher levels seen in native-born

population groups of the same race and ethnic background. Thus, by 2020, a projected 86 percent of the

U.S. population will have a high school degree and 26

percent will have completed college (fig. 5).

Assumptions Underlying the Forecasts

We made two assumptions for projection purposes.

First, the analysis is based on a cross-section of data

collected over a short period of time. Given no price

information from the survey, we had to drop prices

from the consumption equations. As such, relative

prices are assumed the same for all households. Thus,

the observed consumption behavior is for a fixed set of

food and nonfood prices. As supply and demand

conditions change over time, relative prices will

change and the consumption patterns suggested here

could be quite different. However, with the inclusion

of regional and seasonal dummy variables in the

model, systematic price variations by region and

season should have been captured.

Second, there is an implicit assumption that as any

individual moves from one demographic group to

Figure 5

Educational attainment of the U.S.

population, 2000-2020

Percent of population

Source: U.S. Census Bureau, adjusted by ERS.

2000

2020

Less than

high school

High school Some college College+

17

14

35

33

24

27

24

26

Population, household, and education projections

used in this study are modified versions of projections from the U.S. Census Bureau (Hollman et al.,

Day, and Day and Bauman). The Census population

series includes projections by single year of age, sex,

race, Hispanic origin, and nativity (foreign-born or

native) out to the year 2100. The number of households by type (family/non-family; with and without

children; married/not married; living alone) was

projected out to 2010. 

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 case of the population

series, projections are based on assumptions about

fertility, mortality, and immigration.

The current population and household projections

provided by the U.S. 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 the estimates (281 million versus

275 million). Various factors undoubtedly

contributed to the higher count, including a more

complete count in 2000 than in 1990 and a possibility 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 equation to keep tabs on, was significantly

higher than expected. To account for these discrepancies, population and household projections were

multiplied by the ratio of the 2000 Census result and

the 2000 projection. For instance, the 2000 Census

showed 39.9 million people age 24 to 29, and the

projection was 37.4 million, for a ratio of 1.06.

Projections for that age group for 2010 and 2020

were multiplied by 1.06.

Modification of Census Population, Household, and Education Projections
14 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

another, his/her preferences immediately take on the

characteristics of the new group. For example, the

eating habits of those living in the South in the year

2000 are assumed to continue into the year 2020.

Similarly, younger age groups will assume the eating

habits of older age groups as they age.

We made these two assumptions in forecasting food and

commodity consumption for the five periods between

2000 and 2020—the base scenario. However, a sensitivity analysis can be conducted to examine the effect of

relaxing the second assumption. There are many demographic characteristics incorporated in the analysis,

so there are many alternative scenarios possible for

conducting sensitivity analyses. In this study, we

focused on the relationship between eating out and age.

As shown earlier, eating out exhibits an inverted U shape

with respect to age. The popularity in eating out rises

with age among children, peaks among those age 14-19,

and declines with age among adults. In the sensitivity

analysis, we assumed that in 2020, consumers age 45

and over will eat out as much as those in the 30-44 age

group—the alternative scenario. Differences in the

predicted consumption between the base and alternative

scenarios would indicate how sensitive predicted

consumption is to the assumption about eating out.

Projections of Food Consumption,

2000-2020

The response of the endogenous variable in a Tobit

model to changes in the exogenous variables can be

decomposed into two components (McDonald and

Moffitt). Using the eating-out model as an example, a

change in exogenous variables will affect the probability that a respondent will eat out as well as the total

amount of eating out. The forecast values of eating

out after incorporating the two components are

reported in table 4. The projected exogenous

economic, social, and demographic variables together

with the forecast eating out and diet-health knowledge

were then fitted into the 50 Tobit equations to forecast

food consumption at home and away from home, on a

per capita basis. The projected per capita food

consumption at home and away from home, in grams,

is given in table 5. 

To show the projected changes in per capita food

consumption, we indexed the projections, treating the

2000 consumption as the base (table 6). The per capita

consumption of fish, fruit juice, other fruit, tomatoes,

legumes and nuts, other vegetables, and other grain

products is projected to increase both at home and

away from home. But, the per capita consumption of

milk, cheese, fried potatoes, and soft drinks is

projected to decline both at home and away from

home. The per capita consumption of beef, pork, eggs,

fats and oils, other potatoes, and grain mixtures is

projected to decline at home but rise away from home. 

Even though the per capita consumption of some foods

is projected to decline, the total U.S. consumption of all

25 food groups is projected to increase both at home and

away from home. This is because the U.S. population is

projected to grow from 281 million in 2000 to 332

million in 2020. Figures in table 7 show the growth of

the two market components, with the 2000 figures as the

base. Take the meat group as an example: the growth of

the away-from-home markets for beef, pork, fish, and

other meat was predicted to outpace the growth of the athome markets (fig. 6). But, the growth of the at-home

markets for poultry and meat mixtures was predicted to

exceed their away-from-home counterparts. 

We also report shares of the at-home and away-fromhome market components for all food groups (table 8),

and these shares indicate the relative growth of these 

two markets. For example, the growth of the away-fromhome fried-potato market was predicted to outpace 

its at-home counterpart, so the dominance of the awayfrom-home market will be reinforced over time. As

shown in figure 7, the market shares for the meat group

were predicted to be quite stable over time, with the

Figure 6

Total U.S. consumption of meat groups

Percent change, 2000-2020

Source: Economic Research Service, USDA.

Food at home Food away from home

Beef Pork Poultry Fish Meat mixtures

10

22

14

32

22

18

26

37

19 18
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 15

exception of fish, which was predicted to experience a

surge in the share of the away-from-home market.

The at-home and away-from-home figures were aggregated into total food consumption, which was then

indexed with the 2000 level as the base (table 9). Fish

and fruits are predicted to lead the increase in total

consumption, with about 30-percent growth over the

next two decades. The consumption of fried potatoes,

other meat, fruit drinks, and soft drinks is predicted to

experience the smallest growth, no more than 10

percent over the next two decades.

Projections of Commodity 

Consumption, 2000-2020

The projections of food consumption are measured in

terms of edible weights, and represent the aggregated

Food at home

Figure 7

Market share of meat group, 2000-2020: 

At home versus away from home

Percent

Source: Economic Research Service, USDA.

Food away from home

2000 2020 2000 2020 2000 2020 2000 2020 2000 2020

74.7

25.3

72.7

27.3

84.8

15.2

82.9

17.1

65.3

34.7

66.0

34.0

63.3

36.7

61.4

38.6

61.9

38.1

62.1

37.9

Beef Pork Poultry Fish Meat mixtures

Table 5—Projected per capita daily food consumption, at home and away from home, 2000-2020

2000 2005 2010 2015 2020

Food group At home Away At home Away At home Away At home Away At home Away

Grams

Meats

Beef 16.62 5.63 16.42 5.67 16.13 5.72 15.82 5.76 15.49 5.81

Pork 7.83 1.40 7.81 1.44 7.77 1.48 7.70 1.52 7.60 1.57

Poultry 16.15 8.58 16.29 8.55 16.42 8.53 16.54 8.54 16.66 8.58

Fish 6.16 3.57 6.26 3.71 6.37 3.85 6.49 4.00 6.58 4.14

Other meat 20.92 5.02 20.51 4.96 20.06 4.90 19.62 4.87 19.17 4.85

Meat mixtures 58.54 36.11 58.85 36.02 58.98 35.89 59.00 35.91 59.02 36.02

Eggs 11.22 3.87 11.29 3.91 11.31 3.95 11.21 3.99 11.05 4.03

Dairy

Milk 170.03 11.74 168.60 11.58 167.48 11.42 167.05 11.32 167.22 11.32

Cheese 12.86 3.45 12.79 3.40 12.74 3.35 12.76 3.32 12.77 3.32

Other dairy products 55.07 20.15 55.00 19.94 55.18 19.73 55.69 19.64 56.28 19.79

Fats and oils 10.42 3.78 10.38 3.82 10.32 3.87 10.29 3.94 10.25 4.00

Fruit

Fruit juice 75.58 7.89 77.02 7.91 78.69 7.94 80.59 7.95 82.34 8.02

Other fruit 73.71 7.30 75.74 7.35 78.17 7.44 81.26 7.57 84.18 7.75

Vegetables

Fried potatoes 8.59 11.77 8.37 11.56 8.13 11.34 7.90 11.11 7.68 10.99

Other potatoes 26.39 5.95 26.13 5.97 25.79 6.01 25.48 6.05 25.09 6.07

Tomatoes 23.58 7.05 23.85 7.10 24.12 7.15 24.46 7.23 24.72 7.33

Legumes and nuts 27.65 3.50 27.98 3.57 28.34 3.67 28.66 3.79 28.98 3.94

Other vegetables 81.97 22.83 82.73 23.26 83.46 23.72 84.33 24.30 85.00 24.92

Grains

Breakfast cereals 14.64 0.20 14.58 0.20 14.56 0.20 14.65 0.20 14.81 0.20

Grain mixtures 70.17 31.38 69.32 31.34 68.45 31.24 67.39 31.28 66.54 31.57

Other grain products 152.65 30.34 153.77 30.67 154.58 31.03 155.18 31.52 155.81 32.12

Sweeteners 22.96 6.13 22.78 6.12 22.58 6.11 22.45 6.12 22.34 6.14

Coffee and tea 248.17 50.78 249.83 52.26 250.08 53.58 249.86 55.13 247.16 56.20

Fruit drinks 73.02 7.77 71.79 7.79 70.60 7.78 69.07 7.74 67.91 7.75

Soft drinks 195.40 90.43 193.00 89.24 189.36 87.70 185.56 86.24 182.25 85.32
16 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

weights of various foods classified under the same

group. For example, the “meat mixtures” food group

includes about 1,000 different foods reported eaten in

the 1994-96 and 1998 CSFII. The “beef” food group

includes different cuts of beef and beef prepared by

different cooking methods. Furthermore, beef is an

ingredient in many foods that are classified under meat

mixtures, a group separate from the beef group.

Clearly, the projections of food consumption by food

groups offer limited use to those who are interested in

commodity consumption. 

In this study, we translated food consumption into

commodity consumption, which is expressed in terms

of farm- or retail-level weight. We developed the

translation database using EPA’s Food Commodity

Intake Database, USDA’s Pyramid Servings Database,

and a conversion-factor database developed for this

study. The translation database can be used to derive

commodity consumption, commodity by commodity.

In this study, commodities having minor shares of

total farm receipts were aggregated into groups,

resulting in 22 commodity groups. For each physical

unit of a food group, we calculated the mean amount

of each commodity included. The commodity content

of food may vary by its source—at home and away

from home. In an attempt to improve forecasting

accuracy, we derived two sets of mean commodity

Table 6—Index of projected per capita daily food consumption, at home and away from home, 2000-2020

2000 2005 2010 2015 2020

Food group At home Away At home Away At home Away At home Away At home Away

2000=100

Meats

Beef 100 100 99 101 97 102 95 102 93 103

Pork 100 100 100 103 99 106 98 109 97 112

Poultry 100 100 101 100 102 99 102 100 103 100

Fish 100 100 102 104 103 108 105 112 107 116

Other meat 100 100 98 99 96 98 94 97 92 97

Meat mixtures 100 100 101 100 101 99 101 99 101 100

Eggs 100 100 101 101 101 102 100 103 98 104

Dairy

Milk 100 100 99 99 99 97 98 96 98 96

Cheese 100 100 99 99 99 97 99 96 99 96

Other dairy products 100 100 100 99 100 98 101 97 102 98

Fats and oils 100 100 100 101 99 102 99 104 98 106

Fruit

Fruit juice 100 100 102 100 104 101 107 101 109 102

Other fruit 100 100 103 101 106 102 110 104 114 106

Vegetables

Fried potatoes 100 100 97 98 95 96 92 94 89 93

Other potatoes 100 100 99 100 98 101 97 102 95 102

Tomatoes 100 100 101 101 102 101 104 103 105 104

Legumes and nuts 100 100 101 102 102 105 104 108 105 113

Other vegetables 100 100 101 102 102 104 103 106 104 109

Grains

Breakfast cereals 100 100 100 100 99 100 100 100 101 100

Grain mixtures 100 100 99 100 98 100 96 100 95 101

Other grain products 100 100 101 101 101 102 102 104 102 106

Sweeteners 100 100 99 100 98 100 98 100 97 100

Coffee and tea 100 100 101 103 101 106 101 109 100 111

Fruit drinks 100 100 98 100 97 100 95 100 93 100

Soft drinks 100 100 99 99 97 97 95 95 93 94
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 17

content, one for at home and the other for away from

home. Each 100 grams of at-home grain mixture, for

example, contains an average of 28 grams of tomatoes, 22 grams of grain, 8 grams of other vegetables, 4

grams of beef, 4 grams of nuts and seeds, and some

amounts of 17 other commodities. The mean

commodity contents for all food groups are not

reported here, but are available upon request.

The amount of each commodity predicted for at-home

and away-from-home consumption per person per day

is presented in table 10. Consumption of milk, cheese,

and yogurt is expressed in terms of servings, while

consumption of all other commodities is expressed in

grams. Total per capita daily consumption of the 22

commodities is the sum of at-home and away-fromhome consumption (table 11).

To facilitate an easier understanding of the growth of the

at-home, away-from-home, and total commodity markets

over the next two decades, table 12 presents only the

figures for the beginning and ending years—2000 and

2020. Fruits are predicted to lead all commodities in the

growth of the at-home market, with a 24-28 percent

growth (fig. 8), followed by a 23-percent increase for

fish, 22 percent for lettuce, and 21 percent for nuts and

seeds as well as other vegetables. Fried potatoes

consumed at home are predicted to grow the least, by

only 5 percent. Fried potatoes consumed away from

home are also predicted to experience slow growth—

Table 7—Index of projected total U.S. food consumption, at home and away from home, 2000-2020

2000 2005 2010 2015 2020

Food group At home Away At home Away At home Away At home Away At home Away

2000=100

Meats

Beef 100 100 103 105 106 111 108 116 110 122

Pork 100 100 104 107 108 115 111 123 114 132

Poultry 100 100 105 104 111 108 116 113 122 118

Fish 100 100 106 109 113 117 119 127 126 137

Other meat 100 100 102 103 104 106 106 110 108 114

Meat mixtures 100 100 105 104 110 108 114 113 119 118

Eggs 100 100 105 106 110 111 113 117 116 123

Dairy

Milk 100 100 104 103 107 106 111 109 116 114

Cheese 100 100 104 103 108 106 112 109 117 114

Other dairy products 100 100 104 103 109 107 115 111 121 116

Fats and oils 100 100 104 106 108 111 112 118 116 125

Fruit

Fruit juice 100 100 106 105 113 110 121 114 129 120

Other fruit 100 100 107 105 115 111 125 118 135 125

Vegetables

Fried potatoes 100 100 102 103 103 105 104 107 105 110

Other potatoes 100 100 103 105 106 110 109 115 112 120

Tomatoes 100 100 106 105 111 110 118 116 124 123

Legumes and nuts 100 100 106 107 112 114 118 123 124 133

Other vegetables 100 100 105 106 111 113 117 121 122 129

Grains

Breakfast cereals 100 100 104 104 108 109 113 113 119 118

Grain mixtures 100 100 103 104 106 108 109 113 112 119

Other grain products 100 100 105 106 110 111 115 118 120 125

Sweeteners 100 100 99 100 98 100 98 100 97 100

Coffee and tea 100 100 101 103 101 106 101 109 100 111

Fruit drinks 100 100 98 100 97 100 95 100 93 100

Soft drinks 100 100 99 99 97 97 95 95 93 94
18 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

10 percent over the next two decades (fig. 9). Fish is

predicted to lead in the growth of the away-from-home

commodity market, with a 30-percent increase (fig. 10).

Table 12 also shows the shares of at-home and awayfrom-home markets for the 22 commodities for 2000

and 2020. In general, the shifts between at-home and

away-from-home market shares are quite small. For

meats, only poultry is predicted to have a shrinking

away-from-home market, although the decrease is

quite small. A small decline in the away-from-home

market share is also predicted for citrus fruits and

apples. No change in the distribution of market shares

is predicted for all dairy products, grapes, other fruits,

nuts and seeds, other potatoes, tomatoes, other vegetables, grains, and sugar. 

Figure 8

Growth of fruit consumption, 2000-2020,

at home versus away from home

Percent change

Source: Economic Research Service, USDA.

Food at home Food away from home

Citrus Apples Grapes Other fruit

28

20

28

20

24

20

27

22

Table 8—Relative shares of projected food consumption at home and away from home, 2000-2020

2000 2005 2010 2015 2020

Food group At home Away At home Away At home Away At home Away At home Away

Percent

Meats

Beef 74.70 25.30 74.33 25.67 73.84 26.16 73.31 26.69 72.72 27.28

Pork 84.84 15.16 84.47 15.53 84.03 15.97 83.47 16.53 82.89 17.11

Poultry 65.30 34.70 65.58 34.42 65.79 34.21 65.94 34.06 65.99 34.01

Fish 63.33 36.67 62.81 37.19 62.32 37.68 61.87 38.13 61.36 38.64

Other meat 80.64 19.36 80.54 19.46 80.38 19.62 80.12 19.88 79.81 20.19

Meat mixtures 61.85 38.15 62.03 37.97 62.17 37.83 62.16 37.84 62.10 37.90

Eggs 74.36 25.64 74.30 25.70 74.10 25.90 73.73 26.27 73.30 26.70

Dairy

Milk 93.54 6.46 93.57 6.43 93.62 6.38 93.66 6.34 93.66 6.34

Cheese 78.86 21.14 79.02 20.98 79.18 20.82 79.36 20.64 79.38 20.62

Other dairy products 73.21 26.79 73.39 26.61 73.67 26.33 73.93 26.07 73.99 26.01

Fats and oils 73.40 26.60 73.07 26.93 72.71 27.29 72.34 27.66 71.93 28.07

Fruit

Fruit juice 90.54 9.46 90.69 9.31 90.83 9.17 91.02 8.98 91.13 8.87

Other fruit 90.99 9.01 91.15 8.85 91.31 8.69 91.48 8.52 91.57 8.43

Vegetables

Fried potatoes 42.20 57.80 41.99 58.01 41.75 58.25 41.54 58.46 41.14 58.86

Other potatoes 81.61 18.39 81.39 18.61 81.11 18.89 80.81 19.19 80.52 19.48

Tomatoes 76.97 23.03 77.06 22.94 77.14 22.86 77.19 22.81 77.12 22.88

Legumes and nuts 88.77 11.23 88.68 11.32 88.55 11.45 88.33 11.67 88.04 11.96

Other vegetables 78.21 21.79 78.05 21.95 77.87 22.13 77.63 22.37 77.33 22.67

Grains

Breakfast cereals 98.68 1.32 98.67 1.33 98.66 1.34 98.66 1.34 98.66 1.34

Grain mixtures 69.10 30.90 68.87 31.13 68.66 31.34 68.30 31.70 67.82 32.18

Other grain products 83.42 16.58 83.37 16.63 83.28 16.72 83.12 16.88 82.91 17.09

Sweeteners 78.93 21.07 78.82 21.18 78.70 21.30 78.59 21.41 78.43 21.57

Coffee and tea 83.01 16.99 82.70 17.30 82.35 17.65 81.92 18.08 81.47 18.53

Fruit drinks 90.38 9.62 90.21 9.79 90.07 9.93 89.92 10.08 89.76 10.24

Soft drinks 68.36 31.64 68.38 31.62 68.35 31.65 68.27 31.73 68.11 31.89
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 19

Figure 9

Growth of vegetable consumption, 2000-2020,

at home versus away from home

Percent change

Source: Economic Research Service, USDA.

Food at home Food away from home

Fried

potatoes

Other

potatoes

Tomatoes Lettuce Other

5

10

13

20 19 20

22

26

21

26

Figure 10

Growth of meat consumption, 2000-2020,

at home versus away from home

Percent change

Source: Economic Research Service, USDA.

Food at home Food away from home

Beef Pork Poultry Fish Other

13

19

13

21

19 18

23

30

13

18

Table 9—Index of projected total food consumption, 2000-2020

Food group 2000 2005 2010 2015 2020

2000=100

Meats

Beef 100 104 107 110 113

Pork 100 105 109 113 117

Poultry 100 105 110 115 120

Fish 100 107 114 122 130

Other meat 100 103 105 107 109

Meat mixtures 100 105 109 114 118

Eggs 100 105 110 114 118

Dairy

Milk 100 104 107 111 116

Cheese 100 104 107 112 116

Other dairy products 100 104 108 114 119

Fats and oils 100 104 109 114 118

Fruit

Fruit juice 100 106 113 120 128

Other fruit 100 107 115 124 134

Vegetables

Fried potatoes 100 102 104 106 108

Other potatoes 100 104 107 111 114

Tomatoes 100 106 111 117 123

Legumes and nuts 100 106 112 118 125

Other vegetables 100 106 111 118 124

Grains

Breakfast cereals 100 104 108 113 119

Grain mixtures 100 104 107 110 114

Other grain products 100 105 110 116 121

Sweeteners 100 104 107 111 116

Coffee and tea 100 106 111 116 120

Fruit drinks 100 103 106 108 110

Soft drinks 100 103 106 108 110
20 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Table 10—Projected per capita daily commodity consumption, at home and away from home, 2000-2020

2000 2005 2010 2015 2020

Commodity group At home Away At home Away At home Away At home Away At home Away

Meats (grams)1

Beef 33.55 16.88 33.28 16.88 32.89 16.88 32.45 16.92 32.00 17.01

Pork 18.35 4.96 18.19 4.98 17.99 4.99 17.76 5.04 17.51 5.09

Poultry 25.81 11.93 25.89 11.89 25.93 11.86 25.95 11.86 25.97 11.91

Fish 7.96 4.15 8.06 4.26 8.15 4.36 8.25 4.48 8.32 4.59

Other meat 1.02 0.31 1.01 0.31 1.00 0.31 0.99 0.31 0.98 0.31

Eggs (grams) 15.61 4.35 15.69 4.39 15.72 4.44 15.63 4.49 15.49 4.54

Dairy (servings)

Milk 0.88 0.13 0.88 0.13 0.87 0.13 0.87 0.13 0.87 0.13

Cheese 0.29 0.17 0.29 0.17 0.29 0.17 0.29 0.17 0.29 0.17

Yogurt 0.03 2 0.03 2 0.03 2 0.03 2 0.03 2

Vegetable oils (grams) 19.42 9.27 19.43 9.29 19.40 9.31 19.38 9.37 19.35 9.46

Fruit (grams)3

Citrus 924.06 112.20 939.84 112.43 958.26 112.90 979.34 113.02 998.95 113.99

Apples 224.46 21.15 228.41 21.20 233.00 21.30 238.34 21.35 243.31 21.56

Grapes 92.89 9.18 93.91 9.19 95.11 9.22 96.59 9.24 98.00 9.31

Other fruit 161.53 18.33 163.83 18.40 166.63 18.51 170.15 18.66 173.54 18.92

Nuts and seeds (grams) 18.45 4.55 18.56 4.59 18.67 4.63 18.77 4.70 18.88 4.79

Vegetables (grams)3

Fried potatoes4 12.86 13.56 12.53 13.32 12.17 13.06 11.82 12.80 11.49 12.66

Other potatoes 29.95 6.39 29.72 6.41 29.41 6.45 29.12 6.50 28.74 6.52

Tomatoes 74.98 23.85 75.24 23.90 75.42 23.94 75.59 24.09 75.72 24.35

Lettuce 9.71 7.38 9.80 7.48 9.88 7.59 9.98 7.74 10.06 7.90

Other vegetables 113.45 27.85 114.32 28.21 115.11 28.58 116.01 29.10 116.72 29.69

Grains (grams) 122.42 36.53 122.81 36.71 123.06 36.89 123.27 37.22 123.60 37.72

Sugar (grams) 81.18 24.49 80.84 24.42 80.37 24.31 79.93 24.26 79.57 24.33

1Boneless, trimmed equivalent. Includes fat. 2Greater than zero but less than one hundredth of a serving. 3Fresh equivalent. 4Includes frenchfried potatoes, potato chips, potato sticks, hash browns, and other potato-based snacks.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 21

Table 11—Projected per capita daily commodity consumption, 2000-2020

Commodity group 2000 2005 2010 2015 2020

Meats (grams)

Beef 50.43 50.17 49.77 49.38 49.02

Pork 23.31 23.17 22.98 22.80 22.59

Poultry 37.74 37.78 37.78 37.82 37.89

Fish 12.12 12.32 12.51 12.72 12.91

Other meat 1.34 1.32 1.31 1.30 1.29

Eggs (grams) 19.97 20.09 20.16 20.12 20.03

Dairy (servings)

Milk 1.01 1.00 1.00 1.00 1.00

Cheese 0.46 0.46 0.46 0.45 0.45

Yogurt 0.03 0.03 0.03 0.03 0.03

Vegetable oils (grams) 28.69 28.72 28.72 28.75 28.81

Fruit (grams)

Citrus 1,036.27 1,052.27 1,071.17 1,092.36 1,112.95

Apples 245.61 249.61 254.31 259.69 264.87

Grapes 102.07 103.11 104.33 105.83 107.31

Other fruit 179.86 182.24 185.14 188.81 192.46

Nuts and seeds (grams) 22.99 23.14 23.30 23.47 23.67

Vegetables (grams)

Fried potatoes 26.43 25.84 25.23 24.62 24.15

Other potatoes 36.34 36.14 35.86 35.62 35.26

Tomatoes 98.83 99.14 99.36 99.67 100.07

Lettuce 17.09 17.28 17.47 17.72 17.96

Other vegetables 141.31 142.53 143.70 145.12 146.41

Grains (grams) 158.94 159.51 159.94 160.49 161.32

Sugar (grams) 105.66 105.26 104.67 104.19 103.89

See footnotes in table 10.
22 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Table 12—Growth of commodity markets and shares of at-home and away-from-home markets,

2000 and 2020

Commodity market share

Commodity group Market growth: 2000 to 2020 2000 2020

At home Away At home Away At home Away

Percent

Meats

Beef 13 19 67 33 65 35

Pork 13 21 79 21 77 23

Poultry 19 18 68 32 69 31

Fish 23 30 66 34 64 36

Other meat 13 18 77 23 76 24

Eggs 17 23 78 22 77 23

Dairy

Milk 17 18 87 13 87 13

Cheese 18 18 63 37 63 37

Yogurt 18 0 97 3 97 3

Vegetable oils 18 20 68 32 67 33

Fruit

Citrus 28 20 89 11 90 10

Apples 28 20 91 9 92 8

Grapes 24 20 91 9 91 9

Other fruit 27 22 90 10 90 10

Nuts and seeds 21 24 80 20 80 20

Vegetables

Fried potatoes 5 10 49 51 48 52

Other potatoes 13 20 82 18 82 18

Tomatoes 19 20 76 24 76 24

Lettuce 22 26 57 43 56 44

Other vegetables 21 26 80 20 80 20

Grains 19 22 77 23 77 23

Sugar 16 17 77 23 77 23

See footnotes in table 10.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 23

Decomposition and Sensitivity

Analyses

As shown in the econometric results, commodity

consumption is affected by a host of economic, social,

and demographic factors. The change in commodity

consumption between 2000 and 2020 was decomposed

into individual effects. Some of the factors affect

commodity consumption directly and indirectly. For

example, household income affects commodity

consumption directly through the consumption equation, and also affects eating out and diet-health knowledge, which in turn affect commodity consumption.

The income effect can be decomposed into direct and

indirect effects.

In addition to being affected by changes in income,

social, and demographic factors, the consumption projections are also affected by the underlying assumptions

(see “Assumptions Underlying the Forecasts,” p. 13). A

sensitivity analysis was conducted to examine the

changes under an alternative assumption about eatingout behavior.

Effects of Economic, Social, and 

Demographic Factors

We conducted a decomposition analysis on the

expected changes in commodity consumption between

2000 to 2020. The decomposition was also conducted

for food consumption; while those results are not

reported here, they are available from the authors.

Because the Tobit model is nonlinear, the individual

effects have to be adjusted so that they sum to the total

change. The individual and total effects are expressed

in terms of percent change from the 2000 projected

consumption, at home, away from home, and total.

The total change and individual effects are reported in

table 13 for commodity consumption at home, in table

14 for away-from-home consumption, and in table 15

for total consumption. The first column in the three

tables, total effect, shows the percent change between

2000 and 2020 for the total U.S. market, incorporating

projected population growth. Numbers in other columns

measure the percent change from 2000 to 2020 on a per

capita basis, with the column “net” showing net change

in per capita consumption, followed by changes attributable to individual variables.

For the at-home market, per capita consumption is

predicted to increase between 2000 and 2020 for

poultry, fish, yogurt, all fruits, nuts and seeds, tomatoes,

lettuce, other vegetables, and grains. Per capita

consumption of beef, pork, other meat, eggs, milk,

cheese, fried potatoes, other potatoes, and sugar is

predicted to fall. As expected, income plays an important role in commodity consumption. For example,

growth in income dampens at-home beef, pork, and egg

consumption directly and indirectly. As income rises,

Americans eat less at home and accumulate more diethealth knowledge, both of which have negative effects

on at-home consumption of beef, pork, and eggs (see

the Tobit results in the appendix tables, p. 34-58). The

aging of the U.S. population will increase per capita

consumption of fish, eggs, fruits, other potatoes, lettuce,

and other vegetables at home. The changing racial

composition of the U.S. population affects eating out

and diet-health knowledge, and hence directly and indirectly affects consumption of several commodities at

home—positive effects for poultry, fish, eggs, and fruits;

negative effects for dairy and potato products. As household heads become more educated and their diet-health

knowledge improves, per capita at-home consumption

of fruits is expected to rise but consumption of beef,

pork, and eggs is expected to decline.

With respect to effects on away-from-home consumption, income and the aging of the population dominate

other factors (table 14). Per capita consumption of the

22 commodities away from home is expected to rise

with income as rising income drives an increase in

eating out. On the other hand, the aging of the population negatively affects eating out and, hence, the awayfrom-home consumption of all commodities, except

fish and other potatoes.

Changes in the aggregated at-home and away-fromhome commodity consumption are shown in table 15.

The total U.S. consumption of fruits and fish will lead

the commodity market in growth over the next two

decades, while potatoes, beef, pork, other meat, milk,

cheese, and sugar will show the smallest growth. The

growth in fruit consumption comes mainly from the athome segment and is greatly influenced by rising

income, the changing racial composition, and diethealth knowledge from higher educational achievement. Income and educational achievement are also

the main drivers for rising per capita yogurt consumption. Income also affects per capita fish consumption,

but to a lesser degree than the aging of the population

and the changing racial composition. 
24 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Effects of Eating Out and Diet-Health

Knowledge on Commodity Consumption

Tables 13-15 show the effects of exogenous variables

on future commodity consumption. The effects include

those channeled through eating out and diet-health

knowledge. It is also useful to assess the effects of

increases in eating out and diet-health knowledge

between 2000 and 2020 on commodity consumption. 

As eating out rises over the next two decades, the

away-from-home consumption of all 22 commodities

will also rise, whereas the at-home consumption of all

commodities, except fried and other potatoes, will fall

(table 16). As eating out increases, the rise in awayfrom-home beef consumption will outpace the fall in

at-home beef consumption, resulting in an increase in

per capita beef consumption of 0.2 percent. Similarly,

potatoes, cheese, vegetable oils, lettuce, and sugar are

predicted to have positive growth due to an increase in

eating out. It should be noted that the total effects are

the sum of effects at home and away from home,

weighted by their market shares.

As consumers accumulate better diet-health knowledge,

they are predicted to consume, both at home and away

from home, less beef, pork, other meats, and fried potatoes, but consume more grains, tomatoes, and nuts and

seeds (table 16). With increased knowledge, consumers

are predicted to consume less poultry but more fish

Table 13—Changes in at-home commodity consumption under the base scenario, 2000 to 2020

Per capita effects

Household

Commodity group Total Net Income Age Region Urbanization Race type Education

Percent change from 2000 projected consumption

Meats

Beef 12.52 -4.61 -2.49 -0.08 -0.22 -0.33 0.52 -1.27 -0.73

Pork 12.55 -4.58 -2.39 0.73 -0.69 -0.48 -0.20 -0.70 -0.86

Poultry 18.69 0.63 -0.85 0.33 -0.33 0.02 1.95 -0.54 0.04

Fish 23.25 4.49 -0.79 2.17 0.12 0.35 2.32 0.51 -0.19

Other meat 12.51 -4.61 -1.99 -0.16 -0.35 -0.09 -0.80 -0.65 -0.58

Eggs 17.05 -0.77 -3.31 2.08 0.02 -0.34 2.57 -0.82 -0.97

Dairy

Milk 16.67 -1.09 -0.39 -0.17 0.02 0.37 -1.22 -0.27 0.57

Cheese 15.88 -1.76 0.36 -0.50 -0.13 0.09 -1.88 -0.55 0.85

Yogurt 20.55 2.20 1.38 0.57 -0.16 0.50 -1.57 0.41 1.06

Vegetable oils 17.51 -0.38 -0.71 0.65 -0.12 -0.07 -0.09 -0.19 0.16

Fruit

Citrus 27.51 8.10 1.72 1.24 -0.66 0.45 2.64 0.32 2.40

Apples 27.86 8.40 1.84 1.56 -0.57 0.48 2.51 0.23 2.35

Grapes 24.43 5.49 1.07 1.14 -0.48 0.41 1.42 0.09 1.85

Other fruit 26.72 7.43 1.31 2.61 0.05 0.53 1.42 -0.19 1.70

Nuts and seeds 20.72 2.34 -0.61 1.06 0.29 -0.22 1.85 -0.30 0.27

Vegetables

Fried potatoes 5.38 -10.66 -1.26 -3.40 -0.03 -0.78 -2.11 -1.59 -1.49

Other potatoes 13.18 -4.04 -3.09 3.18 -0.77 -0.55 -1.94 -1.16 0.29

Tomatoes 19.12 0.99 -0.22 0.69 0.03 -0.03 1.00 -0.47 -0.01

Lettuce 22.18 3.58 -0.38 2.59 -0.22 0.12 0.48 0.35 0.65

Other vegetables 21.35 2.88 -0.48 2.19 -0.18 0.07 0.61 0.15 0.51

Grains 19.09 0.97 -0.40 0.27 -0.14 0.04 0.93 -0.07 0.34

Sugar 15.61 -1.99 -0.58 -0.56 -0.14 0.08 -0.88 -0.09 0.19

See footnotes in table 10.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 25

away from home and consume fewer eggs but more

fruits and dairy products, mainly at home.

Direct and Indirect Effects 

of Income

Household income affects eating out and diet-health

knowledge, and hence indirectly affects commodity

consumption. The total effects of rising household

income over the next two decades are decomposed into

three components: direct effect, indirect effect through

eating out, and indirect effect through diet-health

knowledge (fig. 11 and table 17, p. 28).

All three income-induced effects will contribute to a

lower per capita consumption of pork and other meat,

but contribute to a higher consumption of cheese and

lettuce. Eating out favors beef consumption, but the

other two effects outweigh eating out and result in

lower per capita beef consumption. On the other hand,

income has a positive direct effect, as well as a positive indirect effect through diet-health knowledge, but

a negative indirect effect through eating out on the

consumption of fish, milk, yogurt, all fruits, nuts and

seeds, tomatoes, other vegetables, and grains. 

Table 14—Changes in away-from-home commodity consumption under the base scenario, 2000 to 2020

Per capita effects

Household

Commodity group Total Net Income Age Region Urbanization Race type Education

Percent change from 2000 projected consumption

Meats

Beef 18.90 0.80 2.94 -3.91 0.25 0.01 0.70 0.80 0.02

Pork 20.92 2.52 3.35 -2.28 0.19 0.34 0.33 0.58 0.01

Poultry 17.79 -0.14 3.42 -4.72 0.04 0.71 -0.13 0.51 0.03

Fish 30.44 10.59 4.76 0.98 0.64 0.24 1.88 0.95 1.14

Other meat 16.80 -0.98 3.14 -3.82 -0.17 0.52 -0.85 0.59 -0.40

Eggs 23.02 4.29 3.24 -0.69 0.46 0.40 -1.19 1.67 0.41

Dairy

Milk 15.69 -1.92 1.49 -4.57 -0.52 0.07 -0.97 2.27 0.31

Cheese 16.90 -0.89 3.91 -6.57 0.25 0.51 -0.53 0.73 0.81

Yogurt 15.84 -1.80 1.71 -5.18 -0.68 0.14 -1.19 3.23 0.19

Vegetable oils 20.34 2.03 3.90 -3.79 0.26 0.42 -0.38 1.07 0.55

Fruit

Citrus 19.83 1.59 3.12 -5.77 -0.33 0.43 1.19 3.01 -0.06

Apples 20.23 1.93 2.90 -5.50 -0.24 0.37 1.43 3.11 -0.13

Grapes 19.64 1.43 2.81 -4.93 -0.11 0.37 0.73 2.48 0.08

Other fruit 21.72 3.20 3.07 -3.74 0.12 0.25 0.52 2.23 0.76

Nuts and seeds 24.29 5.37 4.61 -3.41 0.94 0.10 0.89 0.95 1.30

Vegetables

Fried potatoes 10.11 -6.65 1.56 -8.01 0.14 0.10 -1.35 1.09 -0.19

Other potatoes 20.41 2.08 3.91 3.17 -0.75 -0.26 -3.37 0.08 -0.69

Tomatoes 20.42 2.09 4.30 -5.31 0.35 0.43 0.50 1.07 0.75

Lettuce 26.31 7.08 5.42 -1.83 0.53 0.46 0.21 1.50 0.79

Other vegetables 25.75 6.61 5.29 -2.14 0.51 0.45 0.24 1.44 0.81

Grains 21.80 3.26 4.12 -4.16 0.30 0.56 0.69 0.95 0.80

Sugar 17.18 -0.66 3.41 -4.98 0.22 0.39 -0.58 0.48 0.40

See footnotes in table 10.
26 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Sensitivity Analysis of Eating 

Out and Age

Eating out increases with age among children, peaks

among those age 14-19, then declines with age among

adults (table 2). The parameter estimates suggest that

consumers age 45-54 eat out less than those age 30-44

by 4.6 percentage points (subtracting 9.8 from 14.4),

in terms of the percent of food eaten out (fig. 12). This

eating out-age relationship is maintained in the forecasting analyses discussed so far, and this is termed

the base scenario. 

In the sensitivity analysis, we assumed that those age

45 and over in 2020 will eat out as much as those age

30-44 (fig. 13). Under this alternative scenario, eating

out is predicted to rise much higher than the base

scenario. Under the base scenario, eating out is

predicted to represent 23.3 percent of total food

consumption in 2020, compared with 38 percent under

the alternative scenario. Clearly, the extent to which

young adults will retain their eating-out habits as they

grow older will greatly influence the growth of eating

out, which in turn affects the type and amount of foods

and commodities consumed by Americans.

In the interest of brevity, we report only the change in

total and per capita consumption, combining at-home

and away-from-home consumption. Under the alternative scenario, the consumption of some commodities

Table 15—Changes in total commodity consumption under the base scenario, 2000 to 2020

Per capita effects

Household

Commodity group Total Net Income Age Region Urbanization Race type Education

Percent change from 2000 projected consumption

Meats

Beef 14.65 -2.80 -0.67 -1.36 -0.06 -0.22 0.58 -0.58 -0.48

Pork 14.33 -3.07 -1.17 0.09 -0.50 -0.30 -0.09 -0.42 -0.67

Poultry 18.41 0.38 0.50 -1.26 -0.21 0.24 1.29 -0.21 0.03

Fish 25.71 6.58 1.11 1.76 0.30 0.31 2.17 0.66 0.26

Other meat 13.52 -3.76 -0.79 -1.01 -0.30 0.05 -0.81 -0.36 -0.54

Eggs 18.35 0.33 -1.89 1.48 0.12 -0.18 1.75 -0.28 -0.67

Dairy

Milk 16.54 -1.19 -0.15 -0.73 -0.05 0.34 -1.19 0.05 0.54

Cheese 16.26 -1.44 1.67 -2.73 0.01 0.24 -1.38 -0.08 0.83

Yogurt 20.41 2.08 1.39 0.40 -0.18 0.49 -1.56 0.50 1.04

Vegetable oils 18.42 0.40 0.77 -0.78 0.00 0.09 -0.19 0.21 0.29

Fruit

Citrus 26.68 7.40 1.87 0.48 -0.62 0.45 2.48 0.61 2.13

Apples 27.20 7.84 1.93 0.95 -0.55 0.47 2.42 0.47 2.14

Grapes 24.00 5.13 1.23 0.59 -0.45 0.40 1.35 0.31 1.69

Other fruit 26.21 7.00 1.48 1.96 0.06 0.50 1.33 0.06 1.61

Nuts and seeds 21.43 2.94 0.42 0.18 0.42 -0.16 1.67 -0.05 0.47

Vegetables

Fried potatoes 7.81 -8.60 0.19 -5.76 0.06 -0.33 -1.72 -0.21 -0.82

Other potatoes 14.45 -2.97 -1.86 3.18 -0.76 -0.50 -2.19 -0.94 0.12

Tomatoes 19.43 1.25 0.86 -0.75 0.11 0.08 0.88 -0.10 0.18

Lettuce 23.96 5.09 2.12 0.68 0.10 0.26 0.37 0.84 0.71

Other vegetables 22.21 3.61 0.65 1.34 -0.04 0.14 0.54 0.41 0.57

Grains 19.72 1.49 0.63 -0.74 -0.04 0.16 0.88 0.16 0.45

Sugar 15.98 -1.68 0.34 -1.58 -0.06 0.15 -0.81 0.04 0.24

See footnotes in table 10.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 27

rises while the consumption of other commodities

declines, compared with the base scenario (table 18). 

As expected, the most affected commodity is fried

potatoes (fig. 14 and table 19), the only commodity for

which the away-from-home market exceeds the athome market. Over the next two decades, total

consumption of fried potatoes is predicted to grow by

7.81 percent under the base scenario and by 10.38

percent under the alternative scenario. Total consumption of other potatoes is also noticeably affected by the

eating-out habits of the older age groups over time.

The differences between the base and alternative

scenarios are affected by the relative size of the athome and away-from-home market components. The

market for fried potatoes is a case in point. However,

other factors appear to affect the differences between

the base and alternative scenarios. This is evident from

the changes in beef and pork consumption. In 2000,

at-home consumption was predicted to represent 67

percent of total beef consumption, while 79 percent 

of pork was predicted to be consumed at home. The

rates of change between the two scenarios (the level

under the alternative scenario over the level under 

the base scenario) are quite similar for beef and pork.

Table 16—Changes in per capita commodity consumption due to increases in eating out and diet-health

knowledge, 2000 to 2020

Effects of eating out Effects of diet-health knowledge

Food away Food away

Commodity group Total Food at home from home Total Food at home from home

Percent change from 2000 projected consumption

Meats

Beef 0.20 -0.47 1.52 -0.34 -0.41 -0.22

Pork -0.03 -0.43 1.44 -0.45 -0.50 -0.27

Poultry -0.02 -0.53 1.09 -0.04 0.06 -0.25

Fish -0.21 -0.81 0.93 0.06 -0.14 0.46

Other meat -0.17 -0.47 0.80 -0.38 -0.34 -0.54

Eggs -0.22 -0.73 1.61 -0.33 -0.45 0.12

Dairy

Milk -0.46 -0.74 1.43 0.35 0.39 0.09

Cheese 0.02 -0.69 1.23 0.46 0.56 0.28

Yogurt -0.49 -0.53 1.10 0.67 0.69 -0.02

Vegetable oils 0.10 -0.48 1.33 0.15 0.16 0.13

Fruit

Citrus -0.97 -1.26 1.39 1.34 1.52 -0.13

Apples -0.99 -1.22 1.41 1.34 1.48 -0.17

Grapes -0.77 -1.00 1.58 1.07 1.19 -0.07

Other fruit -0.67 -0.90 1.32 1.00 1.07 0.38

Nuts and seeds -0.35 -0.85 1.70 0.34 0.27 0.60

Vegetables

Fried potatoes 0.73 0.04 1.38 -0.61 -1.00 -0.24

Other potatoes 0.28 0.05 1.35 0.15 0.32 -0.66

Tomatoes -0.01 -0.43 1.34 0.08 0.02 0.27

Lettuce 0.10 -0.66 1.10 0.38 0.48 0.24

Other vegetables -0.26 -0.60 1.14 0.36 0.39 0.25

Grains -0.15 -0.58 1.27 0.28 0.28 0.27

Sugar 0.17 -0.38 1.98 0.13 0.16 0.02

See footnotes in table 10.
28 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Compared with younger adults, older adults tend to eat

more beef and pork when eating out, and they favor

pork over beef (app. tables 1 and 2).

Compared with the base projections, the total consumption of several commodities is predicted to fall under the

alternative scenario. Fruit is the major commodity that

is predicted to fall in total consumption if older age

groups eat out as much in 2020 as they did in 2000.

This alternative assumption about eating out also

dampens the consumption of milk and yogurt, but not

cheese. Among the three dairy commodities, cheese has

the highest portion of consumption away from home-37

percent in 2000, compared with 13 percent for milk and

3 percent for yogurt. 

Figure 11

Income effects—direct and indirect

Source: Economic Research Service, USDA.

Eating out Knowledge

Income

Consumption

Indirect Indirect Direct

Table 17—Total, direct, and indirect effects of rising income on per capita commodity consumption,

2000-2020

Indirect income effect

Commodity group Total income effect Direct income effect Eating out Knowledge

Percent change from 2000 projected consumption

Meats

Beef -0.67 -0.59 0.32 -0.41

Pork -1.17 -0.57 -0.04 -0.55

Poultry 0.50 0.59 -0.02 -0.05

Fish 1.11 1.42 -0.41 0.09

Other meat -0.79 -0.07 -0.27 -0.46

Eggs -1.89 -1.07 -0.40 -0.44

Dairy

Milk -0.15 0.12 -0.60 0.33

Cheese 1.67 1.00 0.03 0.62

Yogurt 1.39 1.38 -0.84 0.85

Vegetable oils 0.77 0.37 0.19 0.20

Fruit

Citrus 1.87 1.85 -1.77 1.83

Apples 1.93 1.95 -1.81 1.81

Grapes 1.23 1.21 -1.42 1.48

Other fruit 1.48 1.36 -1.22 1.36

Nuts and seeds 0.42 0.58 -0.55 0.40

Vegetables

Fried potatoes 0.19 -0.47 1.75 -1.08

Other potatoes -1.86 -2.53 0.49 0.19

Tomatoes 0.86 0.76 -0.01 0.11

Lettuce 2.12 1.42 0.18 0.50

Other vegetables 0.65 0.63 -0.46 0.47

Grains 0.63 0.53 -0.27 0.36

Sugar 0.34 -0.18 0.34 0.19

See footnotes in table 10.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 29

Figure 12

The estimated coefficients for age groups in the eating-out equation—the base scenario

Percent of food eaten out

Source: Economic Research Service, USDA.

0 to 4 5 to 9 10 to 14 15 to 19 20 to 29 30 to 44 45 to 54 55 to 64 65 to 74

7.4

20.4 21.0

24.4

21.9

14.4

9.8

5.2

3.4

Age

Figure 13

The assumed coefficients for age groups in the eating-out equation—the alternative scenario

Percent of food eaten out

Source: Economic Research Service, USDA.

Age

0 to 4 5 to 9 10 to 14 15 to 19 20 to 29 30 to 44 45 to 54 55 to 64 65 to 74

7.4

20.4 21.0

24.4

21.9

14.4 14.4 14.4 14.4
30 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Table 18—Changes in total commodity consumption under alternative scenario, 2000-2020

Per capita effects

Household

Commodity group Total Net Income Age Region Urbanization Race type Education

Percent change from 2000 projected consumption

Meats

Beef 16.82 -0.96 0.22 -1.10 0.02 -0.18 0.67 -0.23 -0.36

Pork 16.48 -1.25 -0.44 0.40 -0.36 -0.23 0.18 -0.28 -0.52

Poultry 18.63 0.58 0.96 -1.55 -0.15 0.31 1.08 -0.10 0.04

Fish 26.04 6.86 1.73 1.49 0.36 0.27 2.03 0.60 0.38

Other meat 13.89 -3.45 -0.44 -1.32 -0.26 0.10 -0.71 -0.30 -0.51

Eggs 19.34 1.17 -1.00 1.28 0.18 -0.07 1.18 0.08 -0.47

Dairy

Milk 15.77 -1.85 -0.12 -1.47 -0.11 0.32 -1.23 0.21 0.54

Cheese 16.91 -0.88 2.09 -3.13 0.07 0.30 -1.14 0.10 0.82

Yogurt 19.45 1.27 1.30 -0.26 -0.21 0.49 -1.62 0.52 1.06

Vegetable oils 19.17 1.03 1.30 -0.89 0.05 0.14 -0.24 0.35 0.32

Fruit

Citrus 24.76 5.77 1.83 -1.21 -0.65 0.45 2.50 0.86 1.99

Apples 25.35 6.27 1.84 -0.63 -0.57 0.47 2.48 0.66 2.03

Grapes 22.06 3.48 1.08 -0.63 -0.43 0.39 1.15 0.41 1.52

Other fruit 24.79 5.79 1.40 0.90 0.07 0.48 1.22 0.17 1.54

Nuts and seeds 22.02 3.45 1.12 -0.34 0.54 -0.13 1.51 0.10 0.66

Vegetables

Fried potatoes 10.38 -6.42 0.64 -4.82 0.09 -0.21 -1.55 0.07 -0.64

Other potatoes 15.59 -2.00 -1.33 3.56 -0.74 -0.47 -2.26 -0.83 0.07

Tomatoes 19.77 1.54 1.27 -1.11 0.14 0.12 0.84 0.03 0.24

Lettuce 24.80 5.80 2.74 0.53 0.19 0.31 0.34 0.97 0.73

Other vegetables 22.34 3.72 1.04 0.87 0.01 0.17 0.54 0.49 0.60

Grains 19.96 1.70 0.97 -1.13 0.00 0.20 0.96 0.23 0.48

Sugar 17.10 -0.72 0.92 -1.39 0.01 0.19 -0.78 0.09 0.24

See footnotes in table 10.

Figure 14

Change in per capita commodity consumption between the base and alternative scenarios

Percent change

Source: Economic Research Service, USDA.

Beef Pork Poultry Fish

Milk

Cheese

Citrus Apples

Fried

potatoes

Other

potatoes

2.2 2.2

0.2 0.3

-0.8

0.7

-1.9 -1.9

2.6

1.1
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 31

Table 19—Comparison of growth in commodity markets under the base and alternative scenarios,

2000-2020

Commodity group Base scenario (A) Alternative scenario (B) (B)-(A)

Percent change from 2000 projected consumption

Meats

Beef 14.65 16.82 2.17

Pork 14.33 16.48 2.15

Poultry 18.41 18.63 0.22

Fish 25.71 26.04 0.33

Other meat 13.52 13.89 0.37

Eggs 18.35 19.34 0.99

Dairy

Milk 16.54 15.77 -0.77

Cheese 16.26 16.91 0.65

Yogurt 20.41 19.45 -0.96

Vegetable oils 18.42 19.17 0.75

Fruit

Citrus 26.68 24.76 -1.92

Apples 27.20 25.35 -1.85

Grapes 24.00 22.06 -1.94

Other fruit 26.21 24.79 -1.42

Nuts and seeds 21.43 22.02 0.59

Vegetables

Fried potatoes 7.81 10.38 2.57

Other potatoes 14.45 15.59 1.14

Tomatoes 19.43 19.77 0.34

Lettuce 23.96 24.80 0.84

Other vegetables 22.21 22.34 0.13

Grains 19.72 19.96 0.24

Sugar 15.98 17.10 1.12

Base scenario = eating out declines with age according to table 2. Alternative scenario = increased eating out for adults age 45 and over.

See footnotes in table 10.
32 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Conclusions

The objective of this study was to project U.S.

commodity consumption for the period 2000-2020,

using data from USDA’s 1994-96 and 1998 Continuing

Survey of Food Intakes by Individuals. An econometric system was estimated (1) to explain consumers’

eating-out habits as well as their diet-health knowledge, and (2) to relate food consumption, both at home

and away from home separately, to consumers’ eatingout habits, diet-knowledge knowledge, income, and

their social and demographic characteristics. Using

projected values of economic, social, and demographic

factors for 2000-2020, we projected at-home and

away-from-home food consumption for the same

period. Then we used a unique food-commodity translation database to convert food consumption to

commodity consumption. Twenty-five food groups and

22 commodity groups were analyzed in this study. 

The total consumption of all 22 commodities is

predicted to rise during the period 2000-2020, mainly

due to the projected addition of 50 million consumers in

the Nation. But, the per capita consumption of many

commodities is predicted to fall. The results suggest that

fruits will lead all commodities in terms of growth in

both total and per capita consumption. Certain vegetables, such as lettuce and tomatoes, are predicted to grow

substantially, while per capita potato consumption (fried

and other) is predicted to decline, retarding the growth

in total U.S. potato consumption. The increase in meat,

poultry, and fish consumption varies. Per capita fish and

poultry consumption is predicted to rise while per capita

consumption of beef, pork, and other meat is predicted

to fall. Per capita consumption of milk and cheese is

predicted to fall, while per capita consumption of yogurt

and eggs is predicted to rise. The consumption of nuts

and seeds and grains is also predicted to rise over the

next two decades.

Both at-home and away-from-home fruit consumption

are predicted to grow substantially over the 2000-2020

period, with at-home growth ranging between 24 and

28 percent and away-from-home growth between 20

and 22 percent. Fish consumption is expected to grow

by 30 percent away from home and 23 percent at

home. The growth of away-from-home fish consumption exceeds the growth of at-home consumption for

other commodities, including beef, pork, other meat,

eggs, milk, vegetable oils, nuts and seeds, all vegetables, grains, and sugar. The separation of at-home and

away-from-home consumption in our analysis points

out that food and commodity consumption is affected

by a host of economic, social, and demographic

factors. Some of the factors, such as income, have

opposite effects on these two market segments. In

addition to its direct, positive effect on fruit consumption, for example, rising income boosts eating out and

hence dampens fruit consumption away from home.

But, rising income also contributes to improved diethealth knowledge and hence results in more fruit

consumption at home. 
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 33

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34 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 1—Tobit results of beef consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -18.56 14.90 -165.22 18.29***

Eating out -1.04 0.27*** 2.74 0.33***

Knowledge -4.52 0.93*** -3.11 1.10***

Income -0.33 0.15 ** 0.01 0.16

Male 15.59 2.69*** 18.24 3.23***

Age 0-4 -28.42 6.81*** -23.80 9.58**

Age 5-9 10.85 7.68 -0.03 9.84

Age 10-14 29.91 7.70*** 0.76 9.92

Age 15-19 33.17 8.67*** 2.34 11.04

Age 20-29 25.29 7.91*** 3.47 10.11

Age 30-44 43.86 6.78*** 11.15 8.89

Age 45-54 34.91 6.44*** 17.04 8.44**

Age 55-64 36.08 6.09*** 25.62 8.10***

Age 65-74 15.47 6.02 ** 18.42 8.14**

Black 3.69 3.66 -4.85 4.39

Hispanic 6.57 4.07 6.75 4.91

Asian -27.49 6.79*** 12.59 7.47*

Other 20.55 8.09 ** 14.54 9.81

HH type1 19.01 4.54*** -0.15 5.28

HH type2 6.93 3.64 * 5.76 4.17

HH type 3 3.65 4.95 -4.37 5.81

Midwest 12.64 3.25*** 9.21 3.93**

South -0.38 2.97 4.72 3.61

West 2.38 3.21 -0.63 3.96

Nonmetro 12.76 2.91*** 20.44 3.40***

Suburb 3.06 2.45 -1.97 2.91

HH size -1.71 0.89 * 0.85 1.09

Quarter 1 1.83 2.85 1.05 3.33

Quarter 2 9.19 2.83*** -4.27 3.36

Quarter 3 14.68 2.82*** 5.26 3.36

Sigma 110.75 1.21 104.56 1.84

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.

Appendix tables: Tobit Results of Food Consumption Equations
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 35

Appendix table 2—Tobit results of pork consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -6.59 10.72 -113.05 13.03***

Eating out -0.25 0.20 1.81 0.24***

Knowledge -3.67 0.67*** -0.50 0.79

Income -0.13 0.10 -0.07 0.11

Male 7.00 1.97*** 3.29 2.30

Age 0-4 -29.44 4.74*** -39.30 6.41***

Age 5-9 -18.86 5.49*** -41.88 6.76***

Age 10-14 -9.38 5.51* -47.30 6.93***

Age 15-19 -18.91 6.32*** -42.01 7.60***

Age 20-29 -13.94 5.66** -38.65 6.87***

Age 30-44 -4.01 4.78 -25.49 5.78***

Age 45-54 1.90 4.45 -17.29 5.34***

Age 55-64 9.40 4.11** -4.64 4.92

Age 65-74 -0.43 4.03 -3.71 4.85

Black 25.12 2.53*** -0.79 3.16

Hispanic -1.59 3.07 9.23 3.56***

Asian -2.54 4.89 14.42 5.23***

Other 8.52 6.21 16.11 6.81**

HH type1 13.28 3.36*** 6.47 3.77*

HH type2 16.18 2.61*** 3.93 2.85

HH type 3 4.08 3.63 -1.13 4.29

Midwest 16.01 2.37*** 7.84 2.84***

South 3.31 2.18 6.84 2.63***

West -7.91 2.44*** 3.95 2.88

Nonmetro 12.87 2.10*** 7.84 2.43***

Suburb -0.04 1.79 2.53 2.06

HH size -0.71 0.66 -0.93 0.82

Quarter 1 -2.28 2.06 -1.75 2.38

Quarter 2 0.31 2.05 -0.17 2.36

Quarter 3 -0.33 2.05 2.41 2.38

Sigma 75.32 0.95 60.09 1.45

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
36 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 3—Tobit results of poultry consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -79.91 13.24*** -134.17 18.11***

Eating out -1.03 0.24*** 1.50 0.32***

Knowledge 2.05 0.82** -2.22 1.10**

Income 0.08 0.13 0.36 0.17**

Male 15.69 2.38*** -2.12 3.21

Age 0-4 -17.16 5.73*** 26.11 8.43***

Age 5-9 -4.98 6.71 40.79 9.42***

Age 10-14 -3.11 6.81 22.36 9.67**

Age 15-19 4.85 7.65 34.63 10.74***

Age 20-29 4.62 6.91 31.20 9.81***

Age 30-44 11.59 5.91** 19.60 8.59**

Age 45-54 9.78 5.58* 17.53 8.20**

Age 55-64 -0.93 5.30 0.23 8.04

Age 65-74 7.13 5.12 -3.84 8.09

Black 40.68 3.11*** 26.72 4.10***

Hispanic 27.60 3.61*** -9.29 5.07*

Asian 23.66 5.55*** -11.52 7.99

Other 12.98 7.43* 1.01 10.20

HH type1 5.39 4.05 5.77 5.29

HH type2 13.16 3.17*** 5.69 4.21

HH type 3 13.53 4.31*** -7.20 5.72

Midwest -9.89 2.84*** 10.68 3.86***

South -10.62 2.56*** 6.94 3.52**

West -13.37 2.79*** -7.11 3.93*

Nonmetro -9.02 2.61*** -7.07 3.54**

Suburb -4.08 2.11* 1.25 2.85

HH size -0.63 0.79 -2.65 1.08**

Quarter 1 -8.70 2.47*** -2.95 3.30

Quarter 2 -5.72 2.46** -9.19 3.33***

Quarter 3 -9.13 2.48*** -5.19 3.35

Sigma 98.41 1.08 112.40 1.74

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 37

Appendix table 4—Tobit results of fish consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -108.58 24.28*** -361.57 35.45***

Eating out -1.91 0.45*** 1.21 0.60**

Knowledge -0.53 1.53 5.24 2.12**

Income 0.22 0.23 0.63 0.29**

Male 9.04 4.42** 24.97 6.02***

Age 0-4 -24.36 10.64** -91.45 16.43***

Age 5-9 8.30 12.29 -80.03 17.71***

Age 10-14 2.10 12.55 -66.90 17.45***

Age 15-19 1.73 14.21 -71.37 19.84***

Age 20-29 3.60 12.53 -44.23 17.27**

Age 30-44 22.52 10.50** -34.82 14.68**

Age 45-54 14.18 9.93 -6.30 13.54

Age 55-64 25.95 9.14*** -4.86 12.67

Age 65-74 13.51 8.90 3.52 12.35

Black 44.78 5.73*** 19.53 8.21**

Hispanic -13.65 7.23* 14.62 9.76

Asian 98.82 8.76*** 78.18 12.66***

Other 33.16 13.00** -13.22 22.63

HH type1 -16.22 7.42** -3.76 9.99

HH type2 -3.62 5.57 7.48 7.42

HH type 3 -10.40 7.98 -1.06 11.38

Midwest -41.23 5.34*** -17.10 7.35**

South -30.71 4.66*** 11.33 6.44*

West -19.94 4.99*** -7.66 7.21

Nonmetro -17.03 5.03*** 4.73 6.51

Suburb -3.32 3.90 -3.47 5.35

HH size -3.79 1.52** 0.29 2.13

Quarter 1 5.48 4.71 6.64 6.24

Quarter 2 14.12 4.64*** 1.57 6.29

Quarter 3 -1.75 4.76 4.19 6.31

Sigma 135.66 2.63 156.13 3.98

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
38 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 5—Tobit results of other meat consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept 27.10 8.16*** -33.32 12.71***

Eating out -0.78 0.15*** 0.28 0.23

Knowledge -2.62 0.51*** -4.94 0.78***

Income -0.18 0.08** 0.38 0.12***

Male 18.00 1.48*** 11.34 2.29***

Age 0-4 -0.44 3.59 12.91 6.31**

Age 5-9 20.73 4.14*** 40.42 6.81***

Age 10-14 25.45 4.19*** 38.49 6.90***

Age 15-19 20.30 4.76*** 32.90 7.75***

Age 20-29 19.73 4.30*** 38.59 7.02***

Age 30-44 26.99 3.67*** 41.68 6.14***

Age 45-54 20.32 3.48*** 29.20 5.87***

Age 55-64 16.69 3.30*** 32.72 5.59***

Age 65-74 7.58 3.23** 15.07 5.64***

Black 10.20 1.97*** 1.79 3.03

Hispanic -20.50 2.33*** -21.89 3.71***

Asian -35.02 3.86*** -28.22 6.02***

Other -6.82 4.66 -30.23 8.54***

HH type1 4.63 2.50* 2.14 3.79

HH type2 5.29 1.96*** 0.04 2.97

HH type 3 5.53 2.70** -11.81 4.21***

Midwest 4.02 1.76** 8.52 2.71***

South -3.06 1.61* -6.60 2.51***

West -7.07 1.76*** -9.75 2.78***

Nonmetro 9.94 1.60*** 1.38 2.49

Suburb 7.57 1.34*** 0.93 2.05

HH size -1.64 0.50*** -2.60 0.80***

Quarter 1 -6.47 1.55*** -7.03 2.36***

Quarter 2 0.83 1.53 -4.66 2.33**

Quarter 3 -4.08 1.55*** -12.56 2.42***

Sigma 67.78 0.57 77.07 1.27

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 39

Appendix table 6—Tobit results of meat mixture consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept 11.60 30.39 -264.46 28.75***

Eating out -0.39 0.56 4.28 0.50***

Knowledge -6.23 1.89*** -0.61 1.75

Income 0.03 0.29 -0.01 0.26

Male 15.87 5.49*** 42.59 5.08***

Age 0-4 -92.09 13.34*** -8.42 13.84

Age 5-9 -69.70 15.50*** 10.77 15.18

Age 10-14 -38.15 15.63** 26.01 15.30*

Age 15-19 -20.86 17.63 56.71 16.90***

Age 20-29 -25.86 15.95 81.39 15.42***

Age 30-44 -6.78 13.66 79.27 13.54***

Age 45-54 1.97 12.90 52.62 12.98***

Age 55-64 15.60 12.13 24.64 12.66*

Age 65-74 13.90 11.81 7.97 12.76

Black -30.94 7.57*** 16.53 6.76**

Hispanic -0.32 8.40 -12.02 7.94

Asian 109.51 12.33*** 40.47 11.84***

Other -57.38 18.00*** 26.20 15.63*

HH type1 35.66 9.28*** 10.58 8.30

HH type2 23.81 7.28*** 2.51 6.64

HH type 3 40.04 10.00*** -0.57 9.05

Midwest 4.93 6.59 14.42 6.19**

South -13.60 6.03** 17.10 5.62***

West 20.49 6.42*** 15.97 6.13***

Nonmetro 19.15 5.95*** -13.70 5.51**

Suburb 10.86 4.92** -7.42 4.49*

HH size 3.30 1.81* -8.27 1.74***

Quarter 1 3.35 5.62 -5.68 5.27

Quarter 2 -37.18 5.72*** 1.04 5.23

Quarter 3 -25.82 5.74*** -1.99 5.32

Sigma 243.77 2.23 208.27 2.25

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
40 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 7—Tobit results of egg consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept 29.05 11.07*** -252.32 27.09***

Eating out -1.06 0.21*** 2.72 0.48***

Knowledge -4.36 0.70*** -0.29 1.62

Income -0.27 0.11** -0.15 0.23

Male 6.27 2.05*** 14.01 4.69***

Age 0-4 -21.08 4.77*** -54.69 13.00***

Age 5-9 -19.21 5.69*** -61.13 14.00***

Age 10-14 -12.10 5.75** -85.90 14.87***

Age 15-19 -16.38 6.54** -54.08 15.55***

Age 20-29 -0.82 5.81 -33.61 13.98**

Age 30-44 -8.63 4.98* -7.52 11.80

Age 45-54 5.19 4.66 -6.29 11.06

Age 55-64 4.55 4.35 15.67 10.27

Age 65-74 -0.09 4.23 2.89 10.32

Black 15.86 2.66*** 1.00 6.32

Hispanic 14.86 3.03*** -18.03 7.81**

Asian 5.66 4.71 -20.08 12.36

Other 1.61 6.31 -28.40 17.94

HH type1 7.79 3.45** -8.42 7.60

HH type2 6.81 2.72** 4.03 5.74

HH type 3 9.85 3.70*** -26.91 8.96***

Midwest 7.19 2.51*** 7.98 5.76

South 8.87 2.26*** 16.70 5.23***

West 10.10 2.43*** 11.90 5.79**

Nonmetro -1.15 2.21 12.90 5.08**

Suburb -1.41 1.83 12.32 4.22***

HH size 0.93 0.65 2.47 1.63

Quarter 1 -1.76 2.14 -2.36 4.87

Quarter 2 -1.28 2.14 -0.23 4.82

Quarter 3 -0.12 2.14 2.11 4.89

Sigma 82.20 0.97 130.69 2.96

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 41

Appendix table 8—Tobit results of milk consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept 13.72 32.87 -461.72 54.31***

Eating out -5.55 0.60*** 6.04 1.07***

Knowledge 11.09 2.05*** 3.59 3.29

Income 0.23 0.31 -1.13 0.52**

Male 81.03 5.92*** 12.77 9.43

Age 0-4 152.60 14.13*** 131.20 23.93***

Age 5-9 190.19 16.57*** 207.91 27.78***

Age 10-14 167.15 16.84*** 146.60 28.35***

Age 15-19 121.38 19.15*** -11.55 33.14

Age 20-29 35.73 17.26** -126.24 31.23***

Age 30-44 12.06 14.79 -84.68 26.65***

Age 45-54 -26.18 13.99* -51.34 25.05**

Age 55-64 -36.45 13.20*** -58.19 24.24**

Age 65-74 -0.90 12.78 -61.09 24.28**

Black -104.05 8.28*** -30.41 13.13**

Hispanic -9.87 9.14 1.72 14.46

Asian -63.17 14.43*** 2.17 23.39

Other -56.60 18.75*** 50.18 25.95*

HH type1 -11.85 10.03 -36.48 16.50**

HH type2 -24.44 7.85*** -32.15 13.47**

HH type 3 -5.03 10.84 22.18 16.91

Midwest 21.73 7.05*** -77.61 11.01***

South -22.62 6.45*** -94.97 10.25***

West 29.18 6.93*** -56.03 10.68***

Nonmetro -52.79 6.47*** 7.41 10.27

Suburb -10.89 5.28** -1.78 8.43

HH size -7.49 1.98*** 0.49 3.07

Quarter 1 -2.76 6.14 -6.75 9.62

Quarter 2 -16.67 6.17*** -8.96 9.63

Quarter 3 -25.95 6.22*** -33.66 10.06***

Sigma 292.35 1.93 285.94 5.34

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
42 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 9—Tobit results of cheese consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -53.98 6.45*** -81.22 8.03***

Eating out -0.73 0.12*** 0.60 0.13***

Knowledge 2.79 0.40*** 0.35 0.47

Income 0.20 0.06*** 0.12 0.07*

Male 9.76 1.14*** 3.46 1.35**

Age 0-4 -1.15 2.79 17.05 4.30***

Age 5-9 9.73 3.24*** 29.19 4.52***

Age 10-14 10.98 3.29*** 37.06 4.52***

Age 15-19 17.31 3.70*** 38.89 4.94***

Age 20-29 16.55 3.35*** 36.52 4.59***

Age 30-44 12.99 2.88*** 34.01 4.16***

Age 45-54 5.20 2.73* 25.10 4.04***

Age 55-64 3.70 2.59 18.07 4.01***

Age 65-74 5.66 2.52** 13.78 4.05***

Black -18.38 1.67*** -1.89 1.85

Hispanic 0.51 1.75 -7.08 2.14***

Asian -34.09 3.23*** -10.99 3.47***

Other -1.78 3.55 -26.16 5.54***

HH type1 -2.24 1.91 -2.09 2.19

HH type2 -5.57 1.51*** -5.51 1.77***

HH type 3 1.50 2.09 -8.66 2.41***

Midwest -1.61 1.34 -3.37 1.60**

South -8.61 1.24*** -8.57 1.47***

West -1.35 1.32 -3.53 1.58**

Nonmetro -0.68 1.24 -2.16 1.45

Suburb 0.07 1.02 -3.73 1.19***

HH size -0.72 0.39* -1.94 0.47***

Quarter 1 -1.58 1.19 -1.14 1.37

Quarter 2 0.72 1.19 -2.81 1.38**

Quarter 3 1.32 1.19 -5.99 1.43***

Sigma 51.01 0.44 46.53 0.69

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 43

Appendix table 10—Tobit results of other dairy consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -217.77 28.83*** -235.28 31.25***

Eating out -2.09 0.53*** 3.02 0.57***

Knowledge 12.2 1.79*** -0.21 1.90

Income 0.63 0.27** -0.09 0.28

Male 24.55 5.12*** 2.97 5.49

Age 0-4 135.52 11.98*** 25.65 14.98*

Age 5-9 21.69 14.34 177.25 15.95***

Age 10-14 -1.30 14.68 166.50 16.19***

Age 15-19 -40.03 16.81** 92.35 18.55***

Age 20-29 -43.75 15.11*** 3.24 17.21

Age 30-44 -12.17 12.74 39.90 14.69***

Age 45-54 -31.67 12.04*** 31.76 13.94**

Age 55-64 -33.35 11.29*** 13.14 13.42

Age 65-74 -11.66 10.88 20.62 13.16

Black -30.84 7.20*** -8.88 7.58

Hispanic -2.02 8.06 -12.81 8.66

Asian -28.56 12.78** -26.19 13.90*

Other -5.52 16.16 -44.26 19.01**

HH type1 -13.55 8.75 -40.22 9.37***

HH type2 4.95 6.78 -1.91 7.26

HH type 3 -19.94 9.58*** -42.85 10.00***

Midwest -25.37 6.04*** -12.45 6.40*

South -32.58 5.53*** -49.63 5.98***

West -16.74 5.94*** -30.84 6.42***

Nonmetro -27.00 5.62*** -10.87 6.01*

Suburb -2.19 4.55 -10.09 4.91**

HH size 0.72 1.73 -2.89 1.90

Quarter 1 -3.40 5.32 0.71 5.70

Quarter 2 -11.99 5.35** 0.98 5.67

Quarter 3 0.99 5.34 -7.84 5.81

Sigma 227.83 1.97 200.94 2.72

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
44 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 11—Tobit results of fat and oil consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -2.83 2.41 -43.70 4.04***

Eating out -0.26 0.04*** 0.66 0.07***

Knowledge 1.01 0.15*** 0.52 0.25**

Income -0.01 0.02 0.11 0.04***

Male 3.93 0.43*** 1.81 0.71**

Age 0-4 -13.76 1.06*** -14.77 2.03***

Age 5-9 -5.14 1.21*** -9.51 2.11***

Age 10-14 -3.85 1.23*** -10.03 2.14***

Age 15-19 -3.28 1.39** -4.13 2.35*

Age 20-29 -2.99 1.26** 0.96 2.12

Age 30-44 2.90 1.07*** 2.29 1.84

Age 45-54 1.75 1.01* 3.21 1.74*

Age 55-64 3.25 0.95*** 5.07 1.65***

Age 65-74 2.07 0.92** 2.91 1.65*

Black -2.80 0.60*** -2.49 0.99**

Hispanic -6.18 0.69*** -3.98 1.16***

Asian -13.45 1.15*** -5.04 1.82***

Other -4.84 1.39*** -7.43 2.54***

HH type1 -2.40 0.73*** -1.69 1.16

HH type2 -1.35 0.56** -1.44 0.90

HH type 3 -1.69 0.79** -0.31 1.28

Midwest -1.49 0.52*** 0.98 0.85

South -4.23 0.47*** -1.94 0.78**

West 0.43 0.51 0.62 0.85

Nonmetro -0.53 0.47 0.05 0.77

Suburb 0.06 0.39 0.05 0.64

HH size -0.20 0.15 -0.09 0.25

Quarter 1 -0.66 0.45 1.23 0.73*

Quarter 2 -0.41 0.45 -1.09 0.74

Quarter 3 -0.63 0.45 -0.21 0.75

Sigma 21.17 0.14 28.11 0.32

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 45

Appendix table 12—Tobit results of fruit juice consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -572.38 41.02*** -435.84 60.07***

Eating out -7.21 0.75*** 4.48 1.15***

Knowledge 40.01 2.55*** -4.87 3.63

Income 1.29 0.38*** 0.29 0.56

Male 91.37 7.28*** 13.15 10.61

Age 0-4 104.71 16.96*** 160.55 28.41***

Age 5-9 70.40 20.37*** 168.24 32.29***

Age 10-14 77.43 20.68*** 129.24 32.86***

Age 15-19 75.97 23.62*** 88.83 37.02**

Age 20-29 56.13 21.18*** 23.44 34.67

Age 30-44 -41.64 18.26** -2.43 30.36

Age 45-54 -40.33 17.19** 19.41 29.05

Age 55-64 -66.23 16.20*** -30.07 29.37

Age 65-74 -13.19 15.48 -12.81 29.00

Black 74.25 9.94*** 79.64 13.24***

Hispanic 153.67 11.13*** 24.02 15.93

Asian 130.31 17.18*** -1.56 25.95

Other 122.29 21.73*** 5.64 32.45

HH type1 -66.39 12.31*** -66.89 17.68***

HH type2 -65.93 9.61*** -40.84 14.64***

HH type 3 -22.36 13.25* -30.15 18.22*

Midwest -91.15 8.52*** -61.85 12.78***

South -93.59 7.75*** -58.64 11.44***

West -100.06 8.40*** -46.09 12.32***

Nonmetro -72.59 8.04*** -18.72 11.88

Suburb -37.93 6.42*** -3.54 9.37

HH size -6.41 2.43*** 5.18 3.27

Quarter 1 1.27 7.55 -12.41 10.76

Quarter 2 -1.24 7.55 -32.06 11.02***

Quarter 3 -23.92 7.63*** -38.69 11.27***

Sigma 316.00 3.02 287.14 6.90

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
46 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 13—Tobit results of other fruit consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -241.80 24.24*** -302.37 25.92***

Eating out -3.60 0.44*** 1.84 0.48***

Knowledge 20.84 1.51*** 5.14 1.55***

Income 1.35 0.22*** -0.03 0.23

Male 24.18 4.31*** 6.23 4.42

Age 0-4 -77.20 10.03*** 25.95 11.27**

Age 5-9 -70.40 11.98*** 79.18 12.80***

Age 10-14 -107.59 12.27*** 38.54 13.15***

Age 15-19 -133.26 14.10*** -36.27 15.57**

Age 20-29 -116.08 12.58*** -34.54 14.00**

Age 30-44 -98.53 10.65*** -22.60 12.00*

Age 45-54 -80.01 9.99*** -13.41 11.31

Age 55-64 -37.69 9.22*** -15.09 10.77

Age 65-74 7.02 8.86 -5.96 10.58

Black 2.76 6.06 -7.64 6.27

Hispanic 62.19 6.70*** 23.56 6.80***

Asian 106.64 10.05*** 25.31 10.45**

Other 38.87 13.42*** 39.46 12.52***

HH type1 -0.83 7.33 -33.38 7.55***

HH type2 -18.71 5.63*** -14.41 5.94**

HH type 3 -15.28 8.06 * -8.16 8.00

Midwest -10.42 5.11** 6.04 5.42

South -25.00 4.70*** 1.52 4.99

West 35.27 4.97*** 14.19 5.26***

Nonmetro -36.60 4.74*** -1.14 4.81

Suburb -2.05 3.82 -4.85 3.96

HH size -2.98 1.46** 2.38 1.46

Quarter 1 12.50 4.52*** 8.63 4.57*

Quarter 2 26.57 4.50*** 3.15 4.62

Quarter 3 34.65 4.52*** -3.38 4.77

Sigma 201.20 1.53 141.06 2.53

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 47

Appendix table 14—Tobit results of fried potato consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -45.21 11.84*** -117.96 10.95***

Eating out 0.05 0.22 1.59 0.19***

Knowledge -5.20 0.73*** -1.28 0.65*

Income -0.01 0.11 -0.08 0.10

Male 1.49 2.11 15.55 1.89***

Age 0-4 37.47 5.58*** 43.42 5.42***

Age 5-9 52.45 6.33*** 47.94 5.99***

Age 10-14 55.71 6.41*** 57.52 6.03***

Age 15-19 35.38 7.22*** 63.42 6.65***

Age 20-29 40.56 6.63*** 51.21 6.18***

Age 30-44 41.98 5.81*** 42.92 5.51***

Age 45-54 36.98 5.56*** 29.08 5.32***

Age 55-64 33.79 5.33*** 16.47 5.27***

Age 65-74 15.43 5.38*** 3.85 5.44

Black -6.32 2.84** 4.81 2.50*

Hispanic -22.64 3.30*** -7.04 2.92**

Asian -26.94 5.29*** -19.75 4.82***

Other 4.68 6.25 -5.63 6.03

HH type1 16.67 3.59*** -2.35 3.13

HH type2 10.81 2.95*** 0.22 2.58

HH type 3 19.76 3.81*** -4.94 3.37

Midwest 13.34 2.53*** 18.46 2.33***

South 1.02 2.34 14.70 2.13***

West 6.48 2.53** 8.15 2.33***

Nonmetro 23.97 2.26*** 7.22 2.04***

Suburb 8.75 1.93*** 4.41 1.69***

HH size -0.02 0.69 -0.28 0.62

Quarter 1 7.53 2.21*** -4.31 1.95**

Quarter 2 7.44 2.21*** -5.14 1.95***

Quarter 3 7.99 2.23*** -1.12 1.96

Sigma 81.53 0.98 76.06 0.85

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
48 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 15—Tobit results of other potato consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -118.70 21.67*** -140.85 29.95***

Eating out 0.24 0.40 2.52 0.56***

Knowledge 4.48 1.35*** -6.22 1.87***

Income -1.18 0.21*** 0.51 0.27*

Male 19.91 3.89*** -0.68 5.43

Age 0-4 -96.62 9.29*** -96.45 14.20***

Age 5-9 -97.42 10.89*** -82.37 15.36***

Age 10-14 -75.99 10.94*** -102.45 15.99***

Age 15-19 -81.53 12.51*** -107.70 18.11***

Age 20-29 -81.33 11.25*** -64.95 15.73***

Age 30-44 -47.91 9.45*** -51.60 13.27***

Age 45-54 -44.76 8.86*** -26.95 12.25**

Age 55-64 -11.00 8.16 -2.76 11.16

Age 65-74 -16.61 7.93** -10.99 10.97

Black -46.39 5.58*** -40.96 7.78***

Hispanic -11.88 6.14* -35.73 9.19***

Asian -36.25 10.22*** -80.05 17.31***

Other 1.87 12.37 -42.57 20.77**

HH type1 18.77 6.61*** 14.26 9.04

HH type2 13.80 5.08*** 9.95 6.79

HH type 3 17.91 7.24** 6.99 10.24

Midwest 20.74 4.62*** 24.05 6.53***

South 6.92 4.26 20.04 6.03***

West -12.25 4.71*** -21.63 7.04***

Nonmetro 22.90 4.18*** 21.03 5.82***

Suburb 5.33 3.53 4.10 4.99

HH size -1.72 1.35 0.92 1.94

Quarter 1 -9.75 4.02** -0.80 5.60

Quarter 2 -10.80 4.02*** -2.60 5.59

Quarter 3 -9.07 4.05** -16.19 5.83***

Sigma 161.60 1.72 163.47 3.34

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 49

Appendix table 16—Tobit results of tomato consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -11.30 10.91 -72.42 6.60***

Eating out -1.02 0.20*** 0.90 0.12***

Knowledge 0.23 0.68 0.45 0.40

Income 0.46 0.10*** 0.20 0.06***

Male 7.75 1.97*** 5.59 1.16***

Age 0-4 -37.50 4.86*** -2.79 3.29

Age 5-9 -8.21 5.54 8.18 3.52**

Age 10-14 -6.85 5.62 14.12 3.54***

Age 15-19 5.55 6.30 19.40 3.92***

Age 20-29 10.48 5.68* 18.58 3.60***

Age 30-44 12.11 4.85** 15.50 3.18***

Age 45-54 8.15 4.60* 10.49 3.06***

Age 55-64 13.87 4.33*** 8.03 2.96***

Age 65-74 12.68 4.22*** 6.97 2.97**

Black -16.08 2.73*** -2.53 1.58

Hispanic 12.10 3.01*** 3.80 1.78**

Asian -12.30 4.84** -5.63 2.89*

Other 10.46 6.17* 8.86 3.57**

HH type1 -3.30 3.28 -5.43 1.89***

HH type2 -3.52 2.56 -2.83 1.51*

HH type 3 -4.50 3.59 -3.54 2.06*

Midwest -5.52 2.34** -0.90 1.40

South -12.06 2.14*** 1.19 1.27

West -3.56 2.29 -1.62 1.39

Nonmetro -7.22 2.14*** -1.38 1.25

Suburb -4.22 1.76*** -3.20 1.03***

HH size -0.62 0.67 0.19 0.39

Quarter 1 -4.48 2.08** -1.77 1.20

Quarter 2 2.40 2.06 -0.55 1.20

Quarter 3 16.11 2.05*** -0.59 1.21

Sigma 88.72 0.75 46.76 0.51

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
50 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 17—Tobit results of legume and nut consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -157.47 23.47*** -456.54 30.21***

Eating out -3.20 0.43*** 3.51 0.49***

Knowledge 3.58 1.45** 10.09 1.71***

Income 0.39 0.22* -0.15 0.23

Male 31.91 4.21*** 10.40 4.70**

Age 0-4 14.79 9.93 -8.85 13.67

Age 5-9 23.26 11.75** -13.23 14.87

Age 10-14 6.21 12.01 -28.75 15.22*

Age 15-19 -8.58 13.75 -48.86 17.20***

Age 20-29 10.21 12.27 -24.73 15.55

Age 30-44 11.42 10.51 -7.31 13.69

Age 45-54 15.50 9.87 0.71 13.05

Age 55-64 0.23 9.36 -1.70 12.75

Age 65-74 15.98 9.00* 12.04 12.74

Black -10.52 5.93* 13.96 6.46**

Hispanic 62.78 6.28*** 47.41 7.05***

Asian 45.84 9.53*** 40.57 10.60***

Other 19.62 12.68 -18.63 17.05

HH type1 -4.91 7.12 0.50 7.75

HH type2 0.08 5.61 0.22 6.07

HH type 3 -8.98 7.83 -0.87 8.52

Midwest 14.50 5.17*** 9.23 6.14

South 23.81 4.67*** 20.98 5.61***

West 36.49 4.93*** 48.11 5.79***

Nonmetro -6.19 4.54 0.54 4.84

Suburb -12.01 3.73*** -22.42 4.11***

HH size 3.82 1.38*** -0.60 1.62

Quarter 1 -3.07 4.36 5.59 4.73

Quarter 2 1.50 4.36 -5.52 4.85

Quarter 3 -8.84 4.40** -0.48 4.91

Sigma 175.01 1.73 128.33 2.78

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 51

Appendix table 18—Tobit results of other vegetable consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept 10.63 14.39 -134.35 14.45***

Eating out -2.11 0.26*** 1.86 0.26***

Knowledge 7.04 0.90*** 2.26 0.89**

Income 0.07 0.14 0.75 0.13***

Male 21.84 2.59*** 10.12 2.57***

Age 0-4 -88.72 6.23*** -59.30 6.89***

Age 5-9 -68.13 7.29*** -25.28 7.38***

Age 10-14 -56.70 7.38*** -27.12 7.48***

Age 15-19 -58.20 8.35*** -26.18 8.41***

Age 20-29 -42.68 7.52*** -7.56 7.56

Age 30-44 -4.98 6.41 -0.33 6.52

Age 45-54 -6.91 6.05 3.87 6.17

Age 55-64 -2.53 5.69 -2.32 5.89

Age 65-74 8.22 5.55 -1.73 5.82

Black 22.13 3.52*** 6.35 3.49*

Hispanic 6.08 4.04 -1.20 4.07

Asian 57.75 6.20*** 17.43 6.17***

Other 19.19 8.17** 9.45 8.17

HH type1 -11.58 4.34*** -12.94 4.25***

HH type2 4.04 3.39 -3.56 3.29

HH type 3 -15.77 4.74*** -7.11 4.64

Midwest -14.08 3.10*** 3.76 3.10

South -9.66 2.81*** 6.23 2.83**

West -13.09 3.05*** 4.02 3.09

Nonmetro -11.30 2.81*** -1.91 2.78

Suburb -2.77 2.31 -2.26 2.29

HH size 0.04 0.87 -0.08 0.88

Quarter 1 -8.22 2.70*** -0.47 2.66

Quarter 2 -6.76 2.69** -5.75 2.66**

Quarter 3 -1.65 2.71 -5.00 2.70*

Sigma 129.46 0.80 109.92 1.05

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
52 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 19—Tobit results of breakfast cereal consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -52.43 6.47*** -124.93 18.46***

Eating out -1.04 0.12*** -0.11 0.34

Knowledge 3.57 0.40*** -0.41 1.03

Income 0.26 0.06*** 0.34 0.16**

Male 12.75 1.15*** -0.35 2.98

Age 0-4 7.56 2.73*** 42.19 9.26***

Age 5-9 31.49 3.21*** 54.18 10.51***

Age 10-14 30.26 3.27*** 49.17 10.60***

Age 15-19 21.32 3.74*** 30.32 11.75***

Age 20-29 6.54 3.37* 19.95 11.04*

Age 30-44 -3.83 2.90 6.67 10.00

Age 45-54 -10.08 2.75*** 5.57 9.69

Age 55-64 -11.62 2.58*** 13.83 9.04

Age 65-74 -0.20 2.45 -2.39 10.07

Black -9.06 1.62*** 7.86 3.84**

Hispanic -5.64 1.81*** 5.38 4.23

Asian -16.61 2.98*** -50.18 19.25***

Other -8.82 3.71** -1.82 8.54

HH type1 -8.08 1.97*** -3.57 5.75

HH type2 -4.19 1.54*** 2.84 4.82

HH type 3 0.67 2.11 9.54 5.71*

Midwest -0.09 1.37 -5.29 3.71

South -2.53 1.25** -2.95 3.27

West 0.59 1.35 3.34 3.37

Nonmetro -11.79 1.27*** 9.19 3.17***

Suburb -4.10 1.02*** 2.65 2.74

HH size -0.38 0.39 0.17 0.91

Quarter 1 0.20 1.20 -3.27 2.98

Quarter 2 -0.15 1.20 -5.28 3.08*

Quarter 3 -1.46 1.21 -6.35 3.12**

Sigma 51.68 0.46 49.37 2.33

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 53

Appendix table 20—Tobit results of grain mixture consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -227.89 34.15*** -472.91 29.40***

Eating out -1.82 0.61*** 4.18 0.50***

Knowledge 7.00 2.10*** 7.58 1.71***

Income -1.16 0.33*** 0.32 0.26

Male 43.70 6.06*** 25.52 4.92***

Age 0-4 89.47 15.06*** 91.86 15.01***

Age 5-9 112.54 17.46*** 130.43 16.27***

Age 10-14 118.03 17.70*** 161.69 16.35***

Age 15-19 147.27 19.89*** 163.79 17.92***

Age 20-29 125.92 18.10*** 142.65 16.72***

Age 30-44 98.12 15.72*** 119.62 15.11***

Age 45-54 57.99 15.05*** 83.26 14.67***

Age 55-64 32.27 14.53** 47.48 14.57***

Age 65-74 34.06 14.21** 34.33 14.86**

Black 5.03 8.27 -16.33 6.76**

Hispanic 19.33 9.25** 32.08 7.36***

Asian 57.47 14.14*** 41.96 11.42***

Other -52.52 19.53*** 39.84 14.77***

HH type1 -7.77 10.14 -2.12 8.15

HH type2 -34.44 8.21*** 4.28 6.73

HH type 3 -3.63 10.96 14.74 8.73*

Midwest -0.72 7.25 -6.05 5.94

South -40.51 6.68*** -3.92 5.40

West 17.34 7.08** 0.84 5.78

Nonmetro -12.34 6.59* -26.99 5.40***

Suburb -7.34 5.41 -3.43 4.31

HH size 0.65 1.98 -0.32 1.63

Quarter 1 13.60 6.25** 15.18 5.04***

Quarter 2 -13.79 6.32** 5.99 5.07

Quarter 3 -16.95 6.38*** -11.41 5.22**

Sigma 271.00 2.45 199.68 2.15

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
54 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 21—Tobit results of other grain consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept 118.42 14.00*** -95.80 10.69***

Eating out -2.36 0.26*** 2.01 0.19***

Knowledge 4.18 0.87*** 2.00 0.65***

Income 0.17 0.13 0.37 0.10***

Male 48.85 2.53*** 15.00 1.89***

Age 0-4 -84.70 6.12*** -9.68 4.99*

Age 5-9 -10.43 7.13 18.26 5.51***

Age 10-14 3.83 7.23 14.32 5.59**

Age 15-19 8.09 8.16 17.63 6.28***

Age 20-29 -2.93 7.38 13.76 5.72**

Age 30-44 5.12 6.33 17.66 4.97***

Age 45-54 6.52 5.99 20.02 4.72***

Age 55-64 -1.34 5.67 12.04 4.53***

Age 65-74 0.70 5.54 12.62 4.47***

Black 10.43 3.43*** 2.82 2.58

Hispanic 18.79 3.91*** 5.62 2.95*

Asian 133.56 6.08*** 21.85 4.56***

Other 16.69 7.96** 10.50 5.97*

HH type1 -11.86 4.24*** -7.78 3.13**

HH type2 -9.73 3.33*** -7.75 2.47***

HH type 3 -9.80 4.59** -14.03 3.42***

Midwest -12.32 3.04*** -1.28 2.26

South -15.53 2.76*** -5.81 2.07***

West -10.48 2.99*** -4.50 2.25**

Nonmetro -14.14 2.74*** -6.11 2.05***

Suburb -6.86 2.26*** -4.09 1.68**

HH size 2.19 0.84*** -2.39 0.65***

Quarter 1 -5.62 2.63** -1.05 1.95

Quarter 2 -5.68 2.63** -6.55 1.95***

Quarter 3 -13.18 2.65*** -7.95 1.98***

Sigma 132.09 0.68 87.18 0.69

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 55

Appendix table 22—Tobit results of sugar and sweets consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -19.92 6.09*** -98.48 10.71***

Eating out -0.37 0.11*** 1.89 0.19***

Knowledge 2.17 0.38*** 0.53 0.65

Income -0.16 0.06*** -0.19 0.10**

Male 5.84 1.10*** -1.61 1.88

Age 0-4 -11.22 2.64*** -6.06 5.09

Age 5-9 15.88 3.06*** 6.76 5.50

Age 10-14 12.92 3.11*** -3.02 5.63

Age 15-19 -4.99 3.57 -6.86 6.35

Age 20-29 -9.08 3.21*** -15.47 5.83***

Age 30-44 -4.59 2.73* 0.64 4.99

Age 45-54 -5.11 2.57** 8.67 4.72*

Age 55-64 -1.71 2.42 4.74 4.55

Age 65-74 -1.53 2.35 0.95 4.57

Black -4.80 1.50*** 7.49 2.52***

Hispanic -4.81 1.70*** -3.47 2.97

Asian -12.78 2.74*** -3.56 4.59

Other -4.35 3.43 -1.69 5.98

HH type1 0.01 1.86 0.02 3.12

HH type2 -1.28 1.45 -1.90 2.43

HH type 3 -2.24 2.01 -13.50 3.40***

Midwest 6.16 1.30*** -3.29 2.22

South -2.85 1.20** -10.74 2.05***

West 2.43 1.29* 0.52 2.19

Nonmetro -4.88 1.20*** -1.60 2.03

Suburb -0.08 0.98 -1.15 1.67

HH size -0.16 0.37 -1.53 0.66**

Quarter 1 -0.14 1.14 -3.19 1.91*

Quarter 2 -0.75 1.14 -5.54 1.92***

Quarter 3 -1.46 1.15 -7.17 1.96***

Sigma 53.48 0.35 68.18 0.89

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
56 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 23—Tobit results of coffee and tea consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept 772.37 80.50*** -871.39 88.61***

Eating out 4.18 1.45*** 30.23 1.59***

Knowledge -28.63 5.05*** 9.15 5.50*

Income -2.01 0.74*** -2.17 0.76***

Male -60.54 14.72*** 50.38 15.76***

Age 0-4 -1219.90 41.04*** -892.31 61.83***

Age 5-9 -1105.30 43.65*** -1079.20 57.64***

Age 10-14 -953.50 42.69*** -943.63 52.11***

Age 15-19 -839.95 47.15*** -871.08 53.94***

Age 20-29 -467.60 40.97*** -567.41 46.13***

Age 30-44 -53.83 34.43 -222.87 38.58***

Age 45-54 113.25 32.13*** -65.50 35.84*

Age 55-64 161.79 29.94*** 36.13 33.44

Age 65-74 144.10 28.97*** 32.02 32.96

Black -408.61 21.15*** -126.11 22.24***

Hispanic -203.82 23.14*** -39.84 25.89

Asian -141.31 35.48*** 115.75 37.26***

Other -99.90 48.32** -12.12 54.78

HH type1 99.73 24.23*** 70.79 25.67***

HH type2 70.71 18.22*** 4.00 18.79

HH type 3 -2.74 27.40 -14.58 29.62

Midwest -63.04 17.47*** -106.62 18.55***

South -46.74 15.80*** -88.60 16.87***

West -87.46 17.32*** -77.95 18.56***

Nonmetro 121.87 15.86*** 19.76 16.97

Suburb 16.30 13.23 -8.81 13.98

HH size -0.38 5.17 -9.35 5.92

Quarter 1 11.95 15.36 -37.25 16.10**

Quarter 2 37.43 15.30** -58.06 16.10***

Quarter 3 57.30 15.38*** -2.09 16.18

Sigma 674.77 4.83 574.97 6.84

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
Economic Research Service, USDA Food and Agricultural Commodity Consumption in the United States / AER-820 ● 57

Appendix table 24—Tobit results of fruit drink consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -705.22 70.63*** -1691.30 135.85***

Eating out -4.00 1.28*** 5.51 2.11***

Knowledge 10.48 4.33** 27.66 7.39***

Income -2.54 0.70*** 1.16 1.07

Male 78.20 12.50*** 58.32 20.76***

Age 0-4 330.28 31.72*** 287.31 67.03***

Age 5-9 509.34 36.39*** 270.49 73.34***

Age 10-14 463.65 36.99*** 337.43 73.65***

Age 15-19 451.17 41.44*** 336.52 80.18***

Age 20-29 317.86 38.27*** 242.92 76.06***

Age 30-44 170.09 33.41*** 103.41 69.75

Age 45-54 133.87 32.16*** 127.78 67.48*

Age 55-64 54.91 31.44* 68.45 67.42

Age 65-74 54.33 30.89* -4.40 71.93

Black 262.15 16.00*** 201.55 26.08***

Hispanic 87.56 18.75*** 65.35 32.44**

Asian -180.33 33.77*** -228.74 76.52***

Other -0.57 39.16 -35.78 73.96

HH type1 -58.29 21.10*** -37.22 34.58

HH type2 -27.45 17.41 -31.51 29.23

HH type 3 -56.88 22.35** -2.16 36.52

Midwest 52.50 15.06*** -33.49 23.96

South -11.11 13.84 -75.79 22.21***

West 64.69 14.77*** -66.50 24.67***

Nonmetro -13.98 13.55 9.37 23.16

Suburb -5.46 11.12 35.07 18.54*

HH size -4.46 4.01 6.70 6.62

Quarter 1 -21.80 13.30 -24.16 22.24

Quarter 2 62.26 12.97*** 51.84 21.07**

Quarter 3 59.38 13.04*** 21.36 21.78

Sigma 500.10 5.55 496.96 13.97

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.
58 ● Food and Agricultural Commodity Consumption in the United States / AER-820 Economic Research Service, USDA

Appendix table 25—Tobit results of soft drink consumption at home and away from home

At home Away from home

Estimated Standard Estimated Standard

Variables coefficient error coefficient error

Intercept -191.25 62.46*** -906.91 70.74***

Eating out -1.24 1.10 19.84 1.17***

Knowledge -20.60 3.82*** -12.25 4.07***

Income 1.44 0.58** -0.64 0.61

Male 40.77 11.12*** 40.87 11.77***

Age 0-4 -18.15 30.32 173.67 39.22***

Age 5-9 234.85 33.18*** 144.16 41.63***

Age 10-14 506.22 33.05*** 293.02 41.54***

Age 15-19 599.94 36.78*** 417.59 44.75***

Age 20-29 567.42 33.76*** 421.35 42.17***

Age 30-44 526.20 29.50*** 375.83 38.66***

Age 45-54 413.09 28.24*** 310.65 37.63***

Age 55-64 337.66 27.14*** 228.53 37.39***

Age 65-74 164.76 27.19*** 131.95 38.72***

Black -125.77 15.26*** -100.74 16.03***

Hispanic -85.37 17.09*** -10.67 17.85

Asian -349.85 28.88*** -92.85 28.83***

Other -22.46 35.04 9.91 36.42

HH type1 45.51 18.45** 116.91 19.23***

HH type2 39.10 14.73*** 31.39 15.95**

HH type 3 55.90 20.01*** 90.87 20.81***

Midwest 159.90 13.45*** 76.23 14.40***

South 99.20 12.27*** 65.09 13.11***

West 46.05 13.38*** 54.47 14.23***

Nonmetro 50.04 12.04*** 10.38 12.72

Suburb 43.26 9.98*** 9.05 10.43

HH size -4.95 3.73 -8.73 3.92**

Quarter 1 14.57 11.60 10.28 12.27

Quarter 2 35.36 11.58*** 38.21 12.15***

Quarter 3 42.59 11.68*** 59.90 12.35***

Sigma 521.92 3.93 492.38 4.75

Note: Significance levels are denoted by *** for 1%, ** for 5%, and * for 10%.