Abstract

Consumer spending at full-service and fast food restaurants will continue to grow over the

remainder of this decade and the next. However, the larger increase is predicted to occur at

full-service restaurants. Simulations assuming modest growth in household income plus

expected demographic developments show that per capita spending could rise by 18 percent

at full-service restaurants and by 6 percent for fast food between 2000 and 2020. The

assumed increase in income alone causes such spending to rise by almost 15 percent and 7

percent at full-service and fast food restaurants, respectively. The increasing proportion of

households containing a single person or multiple adults without live-at-home children will

cause per person spending to rise by another 1 to 2 percent in each of these segments.

However, the aging of the population will decrease spending on fast food by about 2 percent per capita. 

Keywords: full-service restaurants, fast food restaurants, food spending, household

income.

Acknowledgments

The authors thank Mark Denbaly and Nicole Ballenger for their assistance in preparing

this report. For review comments on earlier drafts, we also thank Oral Capps, Jr., John

Park, Chung Huang, Wen Chern, and Geoffrey Paulin. Thanks too to Dale Simms for

editing and production. Any remaining errors and omissions are solely the the responsibility of the authors.

United States

Department

of Agriculture

www.ers.usda.gov

Electronic Report from the Economic Research Service

January 2004

Agricultural

Economic

Report No. 829 The Demand for 

Food Away From Home

Full-Service or Fast Food? 

Hayden Stewart, Noel Blisard,

Sanjib Bhuyan, and Rodolfo M. Nayga, Jr.
Contents

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

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

Determinants of Consumer Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Effect of Household Characteristics on Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Statistical Model of Away-From-Home Expenditures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Simulating Future Away-From-Home Expenditures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Implications for Market Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

ii ● Demand for Food Away From Home / AER-829 Economic Research Service/USDA
Summary

Consumer spending at full-service and fast food restaurants will continue to grow over the

remainder of this decade and the next. However, the larger increase will likely occur at fullservice restaurants. Simulations assuming modest growth in household income plus

expected demographic developments show that per capita spending could rise by 18 percent

at full-service restaurants and by 6 percent for fast food between 2000 and 2020. The

assumed increase in income alone causes such spending to rise by almost 15 percent and 7

percent at full-service and fast food restaurants, respectively. The increasing proportion of

households containing a single person or multiple adults without live-at-home children will

cause per person spending to rise by another 1 to 2 percent in each of these segments.

However, the aging of the population will decrease per person spending on fast food by

about 2 percent per capita. 

Fast food restaurants had been increasing their share of the growing away-from-home market until the middle of the 1990s. Sales at fast food restaurants briefly surpassed those at

full-service restaurants around the same time. However, the fast food share of the awayfrom-home market has been relatively steady since then. In 2002, full-service restaurants

again accounted for a slightly larger share of total sales.

A household's demand for food away from home depends on its income as well as on its

demographics. A 10-percent increase in a typical household's per capita income would

cause it to spend 6.4 percent and 3.2 percent more per capita at full-service and fast food

restaurants, respectively. Away-from-home expenditures are typically higher for single-person households and households containing multiple adults without live-at-home children.

For instance, a single person spends almost $3 more per person each week at each type of

establishment than an otherwise identical person who is married and has live-at-home children. 

Current and future changes in the away-from-home market could reflect changes in the diet

and health of American consumers. Any shift in market share between fast food and fullservice restaurants may reflect important changes in what people are eating away from

home, because fast food tends to have different quantities of fat and calories than meals prepared at full-service restaurants. 

This study also represents a necessary first step in understanding how and why the structure

of the foodservice industry is changing. As the demand for meals and snacks at full-service

restaurants increases relative to the demand for fast food, restaurant companies will be

motivated to adjust what menu items and services they offer. To be sure, such adjustments

might also alter any projected changes in market share between full-service and fast food

establishments.

Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● iii
Introduction

Americans now spend nearly half of their food dollars

on meals and snacks at foodservice facilities, such as

restaurants, hotels, and schools. Total away-fromhome expenditures, defined to include all food dispensed for immediate consumption outside of the

consumer's home, amounted to $415 billion in 2002.1

That is about 58 percent greater than annual awayfrom-home expenditures in 1992 which totaled $263

billion. Even after accounting for inflation and business cycles (fig. 1), expenditures still increased by 23

percent between 1992 and 2002. We anticipate that

households will continue to increase their spending on

foodservice meals and snacks at an annual rate of

about 1.2 percent in real (inflation-adjusted) terms

(Blisard et al., 2003).2 Rising household incomes and

demographic developments, such as smaller household

sizes, will account for this. However, it is not clear

what types of foodservice facilities will be selling

these meals and snacks.

A diverse array of foodservice firms—full-service

restaurants, fast food establishments, hotels, retail

stores, recreation places, bars, and operators of vending machines—compete for the consumer's awayfrom-home dollar. However, full-service and fast food

restaurants have captured the bulk of the market, with

39.9 percent and 37.9 percent of total sales in 2002

(fig. 2).3 Full-service restaurants, defined as establishments with waitstaff, tend to offer more varied menus

and dining amenities. Fast food establishments tend to

emphasize convenience (table 1). 

The composition of the away-from-home market is

dynamic. The full-service and fast food segments now

command a similar share of the market, but it is

unclear which segment is poised to expand relative to

the other. Until the middle of the 1990s, sales of fast

food were increasing faster, and briefly surpassed

those at full-service restaurants. This upsurge was

buoyed by the strategic location of new fast food outlets. Convenience is a major sales point for fast food

operators. If driving to an outlet takes longer than

cooking at home, then fast food is not truly convenient.4 Thus, as fast food companies open more outlets

per square mile in appropriate locations, consumers

have to travel less for fast food, on average. In turn,

these new store openings have stimulated the demand

for fast food (Jekanowski et al., 2001). The proliferation of fast food restaurants can be seen in a trend

known as “channel-blurring,” whereby gas stations and

retail stores, such as Wal-Mart and Target, are hosting

foodservice chains like Pizza Hut and Taco Bell. 

Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● 1

The Demand for Food Away From Home

Full-Service or Fast Food? 

Hayden Stewart, Noel Blisard,

Sanjib Bhuyan, and Rodolfo M. Nayga, Jr.*

* Sanjib Bhuyan is a member of the faculty at the Department of

Agricultural, Food, and Resource Economics at Rutgers

University. Rodolfo M. Nayga, Jr., is a member of the faculty at

the Department of Agricultural Economics at Texas A&M

University.

1 Figures reported in this study are supplied by the Economic

Research Service (ERS) and do not include alcohol. However,

estimates of total expenditures do include the value of food consumed away from home, even if not purchased, such as the value

of food distributed in some institutional facilities, as well as taxes

and tips. See Manchester (1987) for a more detailed explanation

of how ERS figures are calculated, including the distinction

between sales and expenditures. Other sources report similar estimates of the market's size even though these sources base their

calculations on very different formulas. The National Restaurant

Association, for one, emphasizes the value of sales by restaurant

companies. For 2002, it estimated the value of sales at $407.8 billion.

2 Industry studies have also projected the continued growth of the

market including analyses by the National Restaurant Association

(Restaurant Industry 2010: The Road Ahead) and a consulting

firm (Foodservice 2010). 

3 The National Restaurant Association estimates that, in 2002,

sales by full-service restaurants totaled $146 billion and sales by

fast food restaurants amounted to $116 billion (Restaurant Industry

Forecast 2003). 

4 Making a meal convenient includes building outlets near where

consumers live, work, and shop. Convenience also means speedy

service. For example, when it comes to drive-thru service, it

appears that a goal among fast food chains is to serve customers in

under 3 minutes. In 2002, the average service time—from when a

car reaches the speaker to the car's driver receiving his or her

food—at 25 major chains was about 187 seconds (Tutor, 2003). 
The relative expansion of the fast food market segment

appears to have stalled in recent years. In 2002, sales

at full-service restaurants accounted for a slightly

larger share of total away-from-home spending, while,

in 2003, McDonald's reduced its estimates for nearterm sales growth from 15 percent to 2 percent (Gogoi

and Arndt, 2003). Meanwhile, Burger King was sold

to a group of investors in December 2002 for $1.5 billion. Five months earlier, these same investors had

offered $2.26 billion for the company. Reasons cited

by the investors for scaling back their offer included

the competitive environment in which Burger King

and other fast food chains now operate.

The full-service restaurant segment is seemingly

poised to expand its share of the away-from-home

market (Restaurant Industry 2010: The Road Ahead).5

The National Restaurant Association believes that the

overall growth of the market will depend on rising

incomes and demographic changes. However, it also

argues that these demand forces will have the greatest

impact on the kinds of services and types of menus

typically available at full-service restaurants.

Any shift in market share between fast food and fullservice restaurants may further affect what foods and

services restaurants of both types offer. If trends in

demand favor full-service restaurants, the market could

soon include more full-service restaurants offering

more varied menus and a wider range of dining amenities. Fast food restaurants might also introduce many

of these same foods and services themselves. 

Developments in the demand for foodservice meals

and snacks could also reflect important changes in the

diet and health of the American population. Fast food

tends to have different quantities of fat and calories

than meals prepared at full-service restaurants (Lin

and Frazao, 1999). Thus, to the extent that any shift in

expenditures reflects changes in consumption, it is

likely that changes also are occurring in the healthfulness of what consumers are eating away from home.

This study examines how the demand for food away

from home is changing, how these changes could

affect the size of the full-service and fast food segments, and the implications for how restaurants might

respond. In particular, we focus on how developments

in the economic and demographic characteristics of

the Nation's population may be leading to changes in

demand for the foods and services supplied by restaurants. Important trends in the U.S. population include

rising incomes, an aging population, smaller household sizes, and an increasingly high proportion of

households containing single people or multiple adults

without live-at-home children. These developments

could prompt changes in the demand for food away

from home that will affect both the supply of restaurant foods and services as well as the diet and health

of the consumers who demand those items. 

2 ● Demand for Food Away From Home / AER-829 Economic Research Service/USDA

Table 1—Segments of the market at a glance

Item Full-service Fast food

Share of sales in

2002 (percent)1 39.9 37.9

Number of outlets2 191,245 214,774

Number of paid

employees (million)2 3.6 3.3 

Sales points and dining

amenities Variety Convenience

1 CPI, Prices, and Expenditures: Expenditure Tables. USDA/ERS. The

remainder, 22.2 percent, is for sales at hotels/motels, schools and colleges,

retail stores, recreational places, and other foodservice establishments.

21997 Economic Census. Accommodation and Foodservice. Bureau of the

Census.

Figure 1

Annual away-from-home expenditures

Expenditures ($bil.)

Real

Source: Food CPI, Prices, and Expenditures: Expenditure Tables.

Economic Research Service.

1980 1985 1990 1995 2000 2005

0

100

200

300

400

500

Nominal

Figure 2

The away-from-home market by outlet type

Percent of sales

Source: Food CPI, Prices, and Expenditures: Expenditure Tables.

Economic Research Service.

1980 1985 1990 1995 2000 2005

28

32

36

40 Full-service

Fast food

5 Foodservice 2010, a report by a consulting company, McKinsey

& Co., also expresses this same view.

Real
Determinants of 

Consumer Demand 

The theory of household production, outlined by

Becker (1965), extends classical demand theory to

consider how prices, income, demographics, and time

constraints can all influence a household's purchases

of items like food. This economic model of household

behavior holds that the costs of consumption can

include prices as well as time spent eating food,

preparing food, and cleaning up after a meal or snack.

A household must therefore decide whether to spend

time on all aspects of the activity of eating a meal (i.e.,

prepare food at home) or outsource some aspects like

preparation and cleaning up (i.e., purchase food away

from home). The optimal decision depends on many

factors, including the household's finances, the opportunity cost of its manager's time, and how well the

household manager can cook. In the context of

Becker's model, a household manager can be defined

as the person primarily responsible for shopping,

cooking, cleaning, and other household chores. 

Empirical analyses have further shown how specific

economic and demographic characteristics of a household can influence its demand for food away from

home by market segment. Four such studies use

household survey data from the 1970s and 1980s.

McCracken and Brandt (1987) and Byrne et al. (1998)

analyzed the relationship between some key household

characteristics and expenditures at each type of restaurant. Nayga and Capps (1994) studied the relationship

between a household's characteristics and its frequency

of dining at each type of facility. Also, Hiemstra and

Kim (1995) analyzed the impact of household characteristics on expenditure by eating occasion and market

segment.6 Characteristics found to be important in

these studies include the household's income, time

constraints faced by the household manager, the

household manager's age, number of people in the

household, education level of the household manager,

the household's region of residence, and the household's race and ethnicity. 

Households with higher incomes tend to spend more

on products and services, including leisure, variety,

and dining amenities like waitstaff, ambience, and

alcohol service. Food away from home is a form of

leisure where leisure is defined as time spent outside

of both the labor force and household production.

Both fast food and full-service restaurants can provide

leisure for a household manager who is freed from

cooking, cleaning, and shopping. Moreover, along

with the additional leisure, households with more

income may also buy more variety and other dining

amenities. Thus, households with higher incomes

have been shown to have higher expenditures for both

fast food and full-service meals and snacks, but spending at full-service restaurants is most responsive to any

changes in income (e.g., McCracken and Brandt,

1987; Byrne et al., 1998). 

Households also may demand more food away from

home as their manager works longer hours outside the

home. In particular, fast food may come to represent a

convenient meal option, if such a restaurant is reasonably accessible. Spending for fast food has been

shown to increase along with the number of hours

worked by a household manager in the labor force

(e.g., Byrne et al., 1998). By contrast, dining at a fullservice restaurant can take as long as preparing, eating,

and cleaning up after a meal at home. Thus, there is

neither a clear theoretical nor empirical relationship

between a household's demand for food at full-service

restaurants and its time constraints. 

The number of people living in a household also may

influence its demand for meals and snacks away from

home. In particular, as a household adds more members, food prepared at home may become more economical. For example, it might take 20 minutes to

prepare a meal for one person at home, but just 30

minutes to prepare a meal for four people. When

cooking at home, the household with more members

can also benefit by purchasing larger package sizes

with lower per unit costs. In total, single-person

households will likely have the highest time and monetary costs per person for eating at home, while larger

households will incur lower costs per capita.

Empirical studies do find that larger households tend

to spend less money per capita away from home (e.g.,

McCracken and Brandt, 1987).

A household's demand for food away from home also

may depend on the ages of its members. One reason

is that tastes may change as people age. For example,

if the sensitivity of taste buds diminishes with age,

older people may demand foods with bolder flavors

(Friddle et al., 2001). Also, older and younger people

may have different opportunities to socialize, so if they

Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● 3

6 Eating occasion was defined to include, for example, breakfast,

lunch, and dinner.
eat out for different reasons, they may logically go to

different kinds of establishments. On balance, empirical studies find that households with younger members

tend to spend more money on fast food, while households with older people tend to spend more money on

full-service dining (Byrne et al., 1998). 

The impact of aging on demand is complicated by

uncertainty about whether generations will retain their

distinctive eating habits as they age. For example, will

an elderly person in 2020 have the same expenditure

patterns as an elderly person now with similar characteristics? Perhaps not. Younger generations know less

about cooking than earlier generations did at the same

point in their life (Foodservice 2010). However, even

if this argument is true, younger generations may still

evolve like older generations. Younger generations

may compensate for their lack of skills by taking

advantage of the growing array of prepared foods and

convenience appliances. In fact, Blisard (2001) finds

that members of different generations tend to have

similar behavior away from home at the same points in

their lives. 

Does the structure of a household also influence its

demand for meals and snacks away from home? For

instance, a married couple with children is likely to

have different preferences and preparation capabilities

than a single-parent family, a single-person household,

and multiple adults living together without children.

Even after controlling for hours worked in the labor

force and income, members of each of these types of

household may not share the same opportunities to

socialize or face the same time constraints. This is a

subject area not taken up by previous research.

Effect of Household

Characteristics on Demand

Our first step in this analysis is to identify the characteristics of a household that are potential determinants

of its demand for food away from home. In addition

to characteristics identified in past studies, we include

the structure of a household, whether it is comprised

of a married couple with children, a single parent with

children, a single person, or multiple adults without

live-at-home children. A data set must also be identified to empirically examine the relationships between

the characteristics of a household and that household's

demand for meals and snacks at both fast food and

full-service restaurants. 

Changing Structure of Households

The increasing incidence of alternative types of household in the U.S. has been much publicized (e.g.,

Kinsey, 1990). In this study, we define a traditional

household as a married couple with live-at-home children. Traditional households accounted for 30.2 percent of all households in 1980, but just 23.5 percent in

2000 (Cromartie, 2002). Single-person households,

single-parent families, and households of multiple

adults without a live-at-home child are on the rise (see

box, “Changing Structure of American Households”). 

Differences are likely to exist in the preferences and

household production capabilities of diverse types of

households. Members of single-person households

may be more likely to socialize and date than members

of a traditional family. But do these pursuits inflate

one's expenditures at full-service or fast food establishments? For example, dating might lend itself to fullservice restaurants promising a leisurely dining

experience, while fast food establishments with play

facilities may appeal more to families with children. 

Single-parent households also may differ from traditional households in that they are more likely to contend with limited social opportunities, financial

insecurity, and greater time constraints. These factors

could influence a single-parent household's demand

for convenience or other amenities associated with

dining away from home.

A household with multiple adults and no child rearing

responsibilities could also be very different. Having

no children to raise could increase the household's

ability to finance dining away from home, and expand

its set of social opportunities. Greater financial

resources and fewer time constraints might encourage

the household to spend more money at full-service

restaurants in particular.

Data Used in the Analysis

To test hypotheses about how a household's demand

for food away from home is affected by its structure

and other characteristics, we need a data set with

information on households, their characteristics, and

how much they spend in each market segment. The

ideal set of data for this study would include information on at least several thousand households, the characteristics of each household, and how much each

4 ● Demand for Food Away From Home / AER-829 Economic Research Service/USDA
Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● 5

Changing Structure of American Households

The structure of the American household is changing. The average age is higher, people are better

educated, and there are fewer members per households. More Americans are also living outside of a

traditional family (a married couple with live-athome children). 

A household's structure can have significant implications for how it buys and prepares food. For

example, families with three or more children are

considered a prime market for the basic food ingredients and volume discounts traditionally provided

by grocery stores (Kinsey, 1990). 

Demographic changes are behind the increasing frequency of nontraditional households. There are

more "empty nest" adults living together after their

children have grown up, as well as more unmarried

people who are perhaps waiting longer to get married or who have been widowed (Cromartie, 2002). 

This report seeks to determine whether nontraditional households eat out more or less often than

their traditional counterparts, and where they tend

to spend their money. For example, as compared

with a married couple engaged in child rearing, single people may have more social opportunities to

dine out at full-service restaurants. 

2000

Single 

person 

(25.8%)

Single 

parent 

(9.2%)

Traditional 

 Family 

 (23.5%)

Multiple 

adults, no 

Children 

(41.5%)

Single 

person 

(28.6%)

Single 

parent 

 (8.7%)

Traditional 

 Family

 (16.7%)

Multiple 

adults, no 

children 

 (46%)

2020 (Projected)

Single 

person 

(22.7%)

Single 

parent 

(7.3%)

Traditional Family (30.2%)

 Multiple 

 adults, no 

 children 

 (39.8%)

1980

Source: Derived from Cromartie, 2002, who provides projections

for traditional, single-person, and single-parent households. He

also provides a projection for married couples without children.

However, these four categories are not all encompassing. Some

household types do not belong to any group, e.g., unmarried,

cohabitating adults without children. Thus, in this study, we

derived projections for households comprised of multiple adults

without children by determining the percentage of households not

belonging to any one of the other three groups. It follows that this

fourth group includes all households with multiple adults and no

children.
household spent in each market segment. Moreover, it

would follow this sample of households over 20 to 50

years, and report on how each household's characteristics and expenditures have changed. By witnessing

how each household's food spending changed with its

characteristics, we might project how spending is

likely to further evolve as each current household

becomes wealthier, older, or different in structure.

Unfortunately, these data are not available. Still,

employing some assumptions, we can adapt existing

sources of data to undertake the same sort of analysis.

The Bureau of Labor Statistics (BLS) provides the

only public survey of household characteristics and

household expenditures.7 The BLS Consumer

Expenditure Survey (CES) is an annual representative

sample of spending by American households.8 In the

diary section of the survey, each household reports its

expenditures on food away from home and other

goods for 2 weeks. These data can also be matched

with information about each household, such as its

income, number of members, region, and race. 

The CES does not follow the same households over

time, and it does not classify household expenditures

away from home prior to 1998. The BLS surveys a

household for one 2-week period, and then drops this

household from its survey. Thus, each annual survey

contains a completely different set of households.

Moreover, because the BLS did not break down awayfrom-home spending on fast food versus food at fullservice restaurants prior to 1998, we can only use data

on household characteristics and their spending patterns for 1998, 1999, and 2000.9

An additional limitation of household surveys in general, including the CES, is that they do not include

expenditures by businesses or for people in institutions. It follows that the analysis in this study does

not capture all of the away-from-home market. In

order to determine how much of the market is captured

by the CES, we undertook a “back of the envelope”

calculation. In 2000, among households completing

the survey, we find that per capita away-from-home

spending averaged $19.21 each week, not including

alcohol. It follows that households in the United

States spent about $1,000 per person per year. Thus,

since the U.S. population equaled 281 million in 2000,

it can be further estimated that spending by all households was around $281 billion. We estimate that the

CES captures about 75 percent of the total market,

since the size of the away-from-home market was

approximately $385 billion in 2000 (fig. 1).

Statistical Model of AwayFrom-Home Expenditures

The statistical model used in this report relates a

household's pattern of spending away from home to its

economic and demographic characteristics, but not to

prices. We recognize that prices are an important

determinant of demand. However, since the CES does

not contain information on prices and our data were

collected over a short period of time, we assume that

there was little variation in the price of fast food relative to the price of food at full-service restaurants over

the period when the data were collected. In other

words, households are assumed to have faced similar

relative prices.10 This assumption allows us to view a

household's expenditures on meals and snacks as

value-weighted quantities. For example, a meal at a

full-service restaurant may be more costly than a meal

at a fast food restaurant. It is therefore possible that a

household eats fast food more often than full-service

meals, but reports similar expenditures in both market

segments. In this case, price differences serve to

6 ● Demand for Food Away From Home / AER-829 Economic Research Service/USDA

7 The National Panel Diary Group (NPD) also undertakes such a

survey, Consumer Reports on Eating Share Trends (CREST).

However, these data have not been available for use by outside

researchers in recent years. 

8 It includes only noninstitutional households. An institutional

household would include people living in institutions, such as prisons or military facilities.

9 We also removed households providing incomplete information

on key characteristics and/or reporting negative incomes from the

sample. The CES designates households as "complete" or "incomplete" income reporters, depending on their response to income

questions. The distinction between complete and incomplete

reporters is based, in general, on whether or not the respondent

provided values for major sources of income, such as wages and

salaries, self-employment income, and Social Security income.

However, even complete income reporters may not have provided a

full accounting of all income from all sources. It is also possible 

for complete reporters to report negative incomes due to selfemployment or other income losses. In this study, incomplete

income reporters and complete reporters with negative incomes are

excluded. In each year, the final sample includes about 5,000

households. 

10 Our data were collected over 3 years. We allow prices to vary

from year to year. We also allow prices to depend upon the season

of the year when the survey was administered as well as upon the

region of the country in which the household resides. Households

are assumed to face similar prices otherwise. Studies of the awayfrom-home market commonly make this same assumption, including McCracken and Brandt (1987) and Byrne et al. (1998).
weight the value of purchases to the household. In

fact, a similarity of expenditures in the two segments

would suggest that the household receives similar levels of satisfaction from its total purchases of both

types of food away from home. Viewing prices as

weights for aggregating purchases in this way is consistent with classical demand theory (Green, 1964). 

The Statistical Model 

The statistical model will provide more accurate estimates of the relationships between a household's characteristics and its spending away from home, if we

simultaneously estimate the equations for spending on

fast food and spending at full-service restaurants. For

instance, because of variation in how much household

managers enjoy (or dislike) cooking, some households

may eat out relatively infrequently (or frequently) at

both types of facility. If so, a correlation is said to

exist between a household's spending at full-service

restaurants and the same household's spending on fast

food. Including this correlation in the model will

improve its accuracy, which can be accomplished

using existing procedures for simultaneously estimating models with multiple equations.11

Obtaining accurate estimates of the relationship

between household characteristics and away-fromhome expenditures requires a special statistical procedure to account for households that do not have any

such expenditures. During the 2-week survey period,

21 percent of households completing the CES spent no

money on fast food, and 45 percent spent no money at

a full-service restaurant. This lack of purchases is

known as zero-censoring, and raises some estimation

problems. If the data contain many zero-expenditure

observations, results based on usual methods of estimation could be biased. 

Models that allow a researcher to estimate multiple

equations simultaneously and to account for zero-censoring include those developed by Heien and Wessells

(1990) and Shonkwiler and Yen (1999). Here, we

apply the latter model because it appears to be the

most accurate and is “state-of-the-art.”12 A brief

description of this model follows, and a more detailed

description is supplied in the appendix.

The Shonkwiler and Yen method proceeds in two steps

to correct for the problem of zero-censoring. In our

study, the first step analyzes whether each household

completing the CES had non-zero expenditures in each

market segment. In particular, the probability that a

household spends some money on fast food is estimated as a function of the household's income, time

constraints, and demographic characteristics. The

same equation is also estimated for each household's

decision about whether to spend some money at fullservice restaurants. These results are then used in the

second step. At this point, we derive equations relating a household's income and demographic characteristics to its expenditures in both market segments.

These equations contain an adjustment to correct for

the fact that many households spent nothing, which is

based on the results of estimating the aforementioned

probabilities in the first step. The adjusted equations

for spending at fast food and at full-service restaurants

can then be estimated using ordinary techniques for

the simultaneous estimation of multiple equations. 

Definition of Variables

Data in the CES must be prepared for use in the statistical model before the analysis can be conducted. In

particular, variables must be calculated from the raw

data in the CES. We specify and create several variables, such as measures of household expenditures,

household income, hours worked by household managers, household structure, the age of a household

manager, and the number of people living in a household (table 2).

To calculate the values of per capita expenditures at

fast food and full-service restaurants, we divided a

household's weekly expenditures at each type of facility by the number of members in the household.

Inflation-adjusted spending was then determined by

dividing expenditures by the Consumer Price Index

(CPI)13 for all items. 

Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● 7

11 This procedure is known as a seemingly unrelated regression.

12 The method of Heien and Wessells (1990) has been widely

applied over the past decade, including by Byrne et al. (1998).

However, Shonkwiler and Yen (1999) have found a shortcoming of

this model and present an alternative specification. Furthermore,

they use Monte Carlo techniques to demonstrate that their proposed specification is statistically more accurate. The method of

Shonkwiler and Yen (1999) has been recently applied in several

studies (e.g., Su and Yen, 2000 ; Yen et al., 2002).

13 Fourth quarter of 2000 = 100.
Since income is a key variable that explains spending,

we calculated this variable from data in the CES as

well. To do so, we divided a household's total income

by the number of household members to obtain per

capita income. Per capita income was then made

weekly (divided by 52) and stated in real terms

(divided by the CPI). 

Data in the CES were also used to calculate hours

worked each week outside of the home by the household manager. However, the CES does not identify the

household manager—the person primarily responsible

for household chores. Yen (1993), who also used the

CES, circumvented this issue by studying the impact

of hours worked by married women. However, this

study takes a slightly different approach. Each household's manager is defined as the survey respondent if

the person was single. For married respondents, the

household manager is assumed to be the spouse who

works the fewest hours outside the home.14

Three binary variables were also created to capture

household structure. Each variable corresponds to one

of the three nontraditional types of household identified in this report. These variables equal “one” if the

household belongs to a certain type, and “zero” otherwise. For example, one variable identifies whether a

household includes only a single person. It equals

one for the 28 percent of households in our sample

who are single, and zero for the other 72 percent. All

households were classified as belonging to either one

9 ● Demand for Food Away From Home / AER-829 Economic Research Service/USDA

14 This approach is straightforward for households with either only

one adult or a married couple. However, it may be less clear when

applied to households with multiple unmarried adults, e.g., samesex couples. In such a case, the household manager is always the

survey respondent. There are two reasons for this default

approach. First, the CES does not include information on adults in

a household who are not married to the survey respondent.

Second, it is arguable that the action of responding to the CES is

itself a domestic chore. If so, it is further likely that the person

who maintains the diary section of the CES is the primary household manager. 

Table 2—Variables measuring expenditures and household characteristics, calculated from CES

Variable Mean Definition

Expenditures:

Full-service restaurant $8.43 Per capita, average weekly spending at full-service restaurants

Fast food $8.15 Per capita, average weekly spending on fast food

Household characteristics:

Income $422.00 Household's per capita, average weekly, real, before-tax income 

Hours worked

by manager 24.2 Hours spent in the labor force by the household manager

Age of manager 47.55 Age of the household manager

College-educated manager 0.25 Indicator variable of whether household manager has a college education

Size of household 2.56 Number of members reported to be living in the household

Race:

Asian 0.047 Indicator variable of whether respondent or spouse identified themselves as Asian

Black 0.09 Indicator variable of whether respondent or spouse identified themselves as Black

Hispanic 0.11 Indicator variable of whether respondent or spouse identified themselves as Hispanic

Household type:

Traditional 0.27 Indicator variable of whether respondent is married with live-at-home children

Single 0.28 Indicator variable of whether respondent lives alone

Multiple adults

without children 0.35 Indicator variable of whether respondent lives with at least one other adult but no children

Single parent 0.10 Indicator variable of whether respondent is an unmarried adult with live-at-home children
of the three types of nontraditional household or as

being a traditional household.15

Other household characteristics in our model include

the age of the household manager; number of people

living in the household; whether the household manager had completed college or attained a higher level

of education; household region; year the survey was

completed; season in which the survey was completed;

and whether a member of the household described

himself or herself as belonging to a minority group

including Black, Asian, or Hispanic. 

Results of Model Estimation

The results of our statistical analysis agree with both

economic theory and past studies. Household structure, a variable not considered in past studies, also is

found to have a statistically significant impact on how

much a household spends in each segment of the market. Estimated relationships are evaluated at the sample means shown in table 2. The results describe how

an average household could be expected to adjust its

expenditures in response to a change in a variable,

such as its income or household type (table 3). Other

statistical results are supplied in the appendix.

Spending in both market segments responds positively

to an increase in per capita income. However, a 10-

percent increase in per capita income would cause a

typical household to augment its per capita expenditures on fast food by about 3.2 percent, versus 6.4 percent for full-service restaurants. Like past studies,

including Byrne et al. (1998) and McCracken and

Brandt (1987), our analysis suggests that households

with more income buy more leisure as well as more of

other dining amenities.

Time spent by the household manager in the labor

force also has significant implications for how much a

household spends away from home. Spending for fast

food is especially sensitive. A typical household

increases its per capita spending on fast food by about

1.4 percent following a 10-percent increase in the

number of hours worked outside the home by its manager. By contrast, this same household would increase

its per person spending at full-service restaurants by

only about 0.5 percent. The link between time constraints and spending for fast food—but not for fullservice restaurants—has been established. 

The impact of aging also varies by market segment.

Households with older managers dine at fast food

establishments less frequently and, as a consequence,

spend less money. An increase of 10 percent in the

age of a household manager reduces the same household's per capita expenditures on fast food by about 6

percent. However, the same increase in age does not

negatively affect spending at full-service restaurants

(table 3). As other studies have found, people's preferences for food and services may tend to favor fullservice restaurants as they age. 

Larger households spend less money per capita in both

market segments. This finding supports prior research

arguing that economies exist in purchasing and preparing

meals at home. A typical household can be expected to

reduce such spending in both market segments about 2

Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● 10

Table 3—Relationship between household characteristics and expected expenditures

Characteristic Full-service Fast food

Change in expenditures due to a 10-percent

increase in the variable:

Percent

Income +6.4 +3.2 

Hours worked by manager +0.53 +1.44

Size of household -2.25 -1.74

Age of manager +1.05 -5.99

Absolute change in expenditure due to household

taking on the characteristic:

Dollars

College-educated manager +2.15 +0.24 

Single-person household +2.92 +2.68 

Single-parent family -0.83 -0.83 

Multiple adults

without children +1.98 +0.89 

Asian household +0.81 +0.39 

Black household -2.87 +0.01

Hispanic household -0.93 +0.14 

15 For this reason, we did not include a variable to account for

whether a household was traditional. Since each household in the

data is classified as belonging to one type and only one type, a

proper statistical analysis requires that we omit one category of

household from the analysis. This omission creates the orthogonal

relationship among predictor variables that is required for estimating a covariance matrix and conducting hypothesis tests. The consequence is that the identified relationships between the three

variables in the model and expenditures must be interpreted as a

measure of the difference in per capita weekly spending by these

households and traditional households.
percent following a 10-percent increase in the number of

people living in the household (table 3).

Household structure also is important. However,

because of the way variables capturing this structure

are defined, we must be careful to interpret our results.

The most appropriate interpretation of variables capturing household structure is to consider how a nontraditional household with otherwise typical

characteristics would likely adjust its spending away

from home if it became a traditional household. For

example, as compared with a traditional household,

higher per capita expenditures are typical of singleperson and childless households. Indeed, a single person spends almost $3 more per person at each type of

establishment. Thus, a single person could be

expected to reduce his or her per capita spending away

from home by $3 (for both fast food and full-service

food) if he or she married and had a child. 

Single parents and their children are the only type of

household tending to spend less per capita than traditional households. Single parents spend about 83

cents less per person at each type of establishment

than do their married counterparts. It follows that a

married person with children and otherwise typical

characteristics can be expected to reduce spending on

fast food by 83 cents per person per week should he or

she divorce or become widowed. 

Other variables, like race and education, are also significant determinants of how much a household spends

away from home. For instance, between 1998 and

2000, when all other variables are set at their mean

value, a Black household still spent $2.87 less per person at full-service restaurants than did other households (table 3). This finding is consistent with past

studies, and may reflect differences in tastes, or possibly more limited access to foodservice establishments.

Simulating Future Away-FromHome Expenditures 

Future changes in demand can be simulated by incorporating into our statistical model expectations about

how key variables may change. These projected

changes are based on modest growth in income, no

change in hours worked by household managers, and

the likely evolution of demographic variables, such as

age of household managers, between 2000 and 2020.

This same procedure has been used by Blaylock and

Smallwood (1986), Blisard and Blaylock (1993), and

Blisard et al. (2003). 

One way to interpret our simulation is as a snapshot of

how people would have behaved in 2000, if the projected changes in the population for 2020 were already

in place in 2000. For instance, we might ask how

spending on fast food would have been different in

2000 if household types assumed the same proportions

as we expect in 2020. This interpretation is the best

one because of a number of assumptions we have to

make. First, we assume there will be no change in the

price of fast food relative to the price of food at fullservice restaurants. If such a change were to occur, it

could cause households to spend more or less than the

simulated amount. Second, we assume that household

characteristics will continue to influence consumer

behavior in the same way. For example, as a consumer moves from one demographic group to another,

his or her preferences will take on the characteristics

of the new group. Thus, an elderly person in 2020 is

assumed to have the same expenditure patterns as an

elderly person in 2000 with similar characteristics

(some evidence to justify this latter assumption is provided by Blisard (2001) for the case of spending away

from home). Third, our simulation holds constant factors like the number and location of restaurants as well

as the mix of food and services supplied by restaurants. For instance, it is assumed that fast food

restaurants will continue to supply the same types of

food and the same dining amenities as they did in

2000. We will later consider the significance of relaxing this assumption, i.e., fast food restaurants offering

more varied menus and heightened services. 

Projected changes in the U.S. population include modest growth in household incomes. Real per capita disposable income increased by 1.2 percent per year on

average between 1988 and 1998 (Saunders and Su,

1999). Thus, we assume that per capita incomes will

rise by 1 percent per year on an inflation-adjusted

basis between 2000 and 2020.16

No change is projected in the time constraints faced by

household managers, as we have found no compelling

evidence to suggest that such changes will occur. In

recent years, the growth in labor force participation

among adult women has slowed. The BLS reports that

participation was 51.6 percent in January 1980, 57.7

percent in January 1990, and 60.3 percent in January

11 ● Demand for Food Away From Home / AER-829 Economic Research Service/USDA

16 This same assumption was made in Blisard et al. (2003).
2000. It then fell back to 59.8 percent in December

2002. We assume that, between 2000 and 2020, there

will be no further changes in labor force participation,

nor in how much a typical household manager works

outside of the home. 

The future demographic characteristics of households

are derived from Cromartie (2002).17 Population,

household, and education projections used here are

derived from reports by the U.S. Census Bureau. The

Census Bureau population series includes projections

by single year of age, sex, race, Hispanic origin, and

nativity (foreign-born or native) out to the year 2100.

By contrast, educational attainment projections by sex

and race are available for the years 2003 and 2028, so

our numbers represent interpolations between these

two dates. 

Projections derived from Cromartie (2002) are not

intended as forecasts or predictions; rather, they represent assumptions about future trends in population,

household formation, schooling, and the economy at

large. For instance, in the population series, projections are based on assumptions about fertility, mortality, and immigration. In fact, differing assumptions

were presented to provide three different projection

series, representing high, middle, and low alternatives.

This study uses projections based upon the middle

series. Despite uncertainty about the extent of

changes, the finding in Cromartie (2002) is that the

Nation's future population will be older, better educated, live in smaller households, be racially and ethnically more diverse, and live in more nontraditional

types of households (table 4). 

Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● 12

Table 4—Current and projected future population characteristics, used in simulation

Characteristic 2000 2020

Based on BLS reports:

Income1 $422.10 $514.98 

Hours worked by manager2 24.2 hours 24.2 hours

Based on Census projections:

Size of household 2.5 members 2.4 members

Age of manager3 47.33 years 49.4 years

College-educated manager 23.5% of households 26.4% of households

Single-person household 25.8% of households 28.6% of households 

Single-parent household 9.2% of households 8.7% of households 

Multiple adults, no children4 41.5% of households 46% of households 

Asian household 3.9% of households 5% of households 

Black household 12.3% of households 12.9% of households 

Hispanic household 12.6% of households 18% of households 

1 Future income is calculated assuming a 1-percent rate of growth in per capita, real income. In particular, we used the formula for future value and continuous compounding, i.e., Income2020 = Income2000(1+0.01)20

2 No change is assumed in hours worked by household managers. Our assumption is based on the observation that measures of the working status of adult

Americans, such as the female labor force rate, have been relatively stable over the past 10 years.

3 The age of a household manager is derived from projections in Cromartie (2002). It is the average age of all people older than 19 years. 4 Derived from Cromartie, 2002, who provides projections for traditional, single-person, and single-parent households. He also provides a projection for married couples without children. However, these four categories are not all encompassing. Some household types do not belong to any group, e.g., unmarried,

cohabitating adults without children. Thus, in this study, we derived projections for households comprised of multiple adults by determining the percentage of

households who could not be classified as belonging to any one of the other three groups. It follows that this fourth group includes all households with multiple adults and no children.

17 Further information on how the projections in Cromartie (2002)

are calculated can be found in Blisard et al. (2003).
Future Spending at Full-Service

Restaurants

The simulated net effect of all developments in the

population is to lift per capita spending by about 18

percent at full-service restaurants between 2000 and

2020.18 Rising incomes, changing household structures, and decreasing household sizes contribute significantly to this growth (table 5). Aging and other

projected developments in the population have only a

small impact on spending in this market segment.19

Rising incomes have the greatest impact of all projected developments in the population on per capita

spending at full-service restaurants. According to our

simulation, if incomes rise by 1 percent annually

between 2000 and 2020 and all other variables remain

unchanged, spending at full-service restaurants will

increase by just under 15 percent per capita by 2020.

In the near future, households with more income will

be buying more leisure, variety, and dining amenities.

Expected developments in household structure have

the next largest impact on spending at full-service

restaurants. As the proportion of households characterized as traditional decreases, spending at full-service

restaurants will increase by about 2 percent per person.

Though small relative to the effect of changes in

income, this impact is still significant given the dollar

size of this market. 

Decreasing household sizes will further increase per

person spending at full-service restaurants by just

under 1 percent. Larger households spend less away

from home (per capita) because of economies in purchasing and preparing meals and snacks at home. 

Future Spending on Fast Food

The simulated net effect of all developments in the

U.S. population is to lift per capita spending by about

6 percent on fast food between 2000 and 2020.20

Rising incomes, changing household structures,

decreasing household sizes, and aging will all be influential. However, unlike the other developments, the

aging of the population will subdue growth in per

capita spending on fast food (table 5). 

Per capita spending on fast food increases with

incomes. If all other variables remain unchanged and

incomes increase by 1 percent annually, such spending

would increase by just under 7 percent by 2020.

Households with more income are likely to buy more

leisure for their manager.

Changes in household structure and decreasing household sizes should lead to increased spending for fast

food. The net effect of these developments is to

increase per capita spending for fast food by about 2

percent. For example, as smaller households find

13 ● Demand for Food Away From Home / AER-829 Economic Research Service/USDA

Table 5—Simulated change in per capita spending due to

economic and demographic trends

Trend Full-service Fast food

Percent

1-percent annual

growth in real income +14.86 +6.67

Increasing proportion of

alternative household types +2.17 +1.33

Aging +0.48 -2.44

Decreasing household sizes +0.97 +0.67

Increased levels of education +0.72 +0.11

Racial and ethnic diversity -0.72 +0.22

Net effect of all

anticipated developments +18 +6

20 Once again, simulated growth is in real terms, i.e, inflationadjusted dollars (base = fourth quarter of 2000). Moreover, this

simulation is for per capita spending. Total growth in the market is

simulated by considering the projected growth in the size of the

population from 281 million to 332 million people. According to

our simulation, the total spending by all households on fast food

will grow by 26 percent. 

18 Simulated growth is in real terms, i.e, inflation-adjusted dollars

(base = fourth quarter of 2000). Moreover, this simulation is for

per capita spending. It does not reflect likely growth in total

spending at full-service restaurants. This result is simulated by

further considering the impact of increased growth in the size of

the population. In total, assuming the population will expand from

281 million to 332 million people (Cromartie, 2002), expenditures

at full-service restaurants could rise by 40 percent. 

19 Some of these other variables may very important determinants

of demand. However, between 2000 and 2020, they are not

expected to change in ways that significantly alters expenditures

away from home. For example, as shown in table 2, Black households spend much less at full-service restaurants than non-Black

households.
Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● 14

The Changing Face of Fast Food 

Many fast food restaurant companies are offering an increasingly wide range of goods and services. In fact, a

new kind of restaurant concept is also emerging, fast-casual, which tries to combine the convenience of fast

food with the kinds of menus more typically found in a full-service restaurant. This changing face of fast food

can be seen in the history of one of America's best-known restaurant chains, McDonald's. 

McDonald's began as a fast food concept in 1948, when the McDonald brothers eliminated many of the menu

items and dining amenities previously available at their full-service restaurant. The remaining core menu had

six products - hamburgers, cheeseburgers, fries, soft drinks, coffee, and shakes. The brothers also ceased to

employ waitstaff, and replaced their short-order cooks with workers who specialized at specific tasks like

grilling burgers. Says the company, "this limited menu concept triggered the 'fast food' concept, because focusing on just a few items that were prepared with standardized procedures made food service a model of efficiency" (McDonald's Corporation, media website).

The menu at McDonald's has gradually expanded to again include a wider variety of menu items. The first

addition to McDonald's original menu was the Filet-O-FishTM in 1963. A handful of other now well-known

products were then promoted over the next few decades including the Big MacTM (1968), apple pie (1968),

Egg McMuffinTM (1973), cookies (1974), and Chicken McNuggetsTM (1983). However, according to

Consortium Members, a group representing about 350 McDonald's franchisees, more recent new product

introductions have been the most "ambitious" in the company's history (Zuber, 2001). 

Efforts to improve the atmosphere at McDonald's stores have accompanied efforts to expand menu items. In

fact, the first McDonald's restaurant built to accommodate indoor seating was opened in 1962. However, the

most noticeable efforts appear to be aimed at families with children. Ray Kroc, who became the company's

franchising agent in 1954 and later purchased the McDonald's chain in 1961, is credited with focusing the

company's marketing strategy on children through characters like Ronald McDonald. "A child who loves our

TV commercials," Kroc is quoted as saying, "and brings her grandparents to a McDonald's gives us two more

customers" (Schlosser, 2001). Notable child-oriented goods and services include packaging meals for children

with toys, known as Happy MealsTM (1979), and installing play equipment in selected restaurants, known as a

PlaylandTM (1971).

Offering more goods and services has required McDonald's to rethink how its operates. In fact, in 1998, the

company replaced its much-touted kitchens with the "Made for You" production system. According to the

company, "Food is prepared to order for each customer. Somebody doesn't want pickles on a Big Mac or

wants mustard on a grilled chicken sandwich? No problem...What's especially exciting is that this is far more

than just an operating system. It provides a platform for food innovation because it makes it easier to serve a

greater variety of products" (McDonald's Corporation, 1998 Annual Report).

Many companies are promoting a newer concept, fast-casual, which strives to combine the food and atmosphere of full-service restaurants with the convenience of fast food. Examples include Boston Market, Chili's

Express, and Schlotzsky's Deli. As of 2003, the McDonald's Corporation continues to own Boston Market.
cooking at home relatively less economical than larger

ones, spending on fast food will grow.

The aging of the population will subdue any increase

in spending due to changes in household structure and

size. Our simulation suggests that per person spending

on fast food may decrease by over 2 percent with the

aging of the population. One possibility is that older

people derive less satisfaction from the foods and services traditionally offered at these establishments. 

Implications for Market

Composition

Changes in demand are driving changes in the relative

sizes of each segment of the away-from-home market.

Rising incomes, the growing incidence of nontraditional households, and other developments in the U.S.

population will allow for growth in both of the two

largest market segments. However, population trends

seem to favor increased spending at full-service restaurants relative to fast food. 

How might restaurant companies adjust their businesses in response to the identified shift in demand?

Our simulation has made some strict assumptions

about prices and the behavior of consumers and firms.

We now relax the assumption about firm behavior. 

One plausible response by fast food companies would

be to introduce more of the foods and services traditionally offered by full-service restaurants. In fact,

among some companies, such a response appears to be

underway. For example, many Subway restaurants

accept debit and credit cards, and McDonald's has

announced the same—despite reservations about its

effect on the speed of its service. However, in tests

using high-speed connections, McDonald's found that

electronic payments can now be processed in only 5

seconds, versus 8 to 10 seconds for cash payments

(CNNMoney, November 2002). 

Many fast food restaurants are also expanding the variety of their menus. A study by the National

Restaurant Association estimated that more than 75

percent of fast food restaurants introduced new menu

items in 2000, while 66 percent intended to add new

food items in 2001 (Operations Report 2001). At

McDonald's restaurants, for example, Big MacsTM are

now sold alongside newer products like breakfast

bagels, salads, fruit and yogurt parfaits, and soft-serve

ice cream with candy mix-ins. In 2003, the company

was further considering an increase in its scope of

healthy menu items, including sliced fruits and vegetables (see box, “The Changing Face of Fast Food”).

The response of fast food restaurant companies varies

by firm, and the ability of many such restaurant companies to adapt may be limited. Marketing and logistics will likely prevent many fast food chains from

aggressively expanding their menus and/or scope of

services. First, some chains appear to worry about

confusing their brand identity. Chick-fil-A, for one,

added its first new category in 6 years in 2001, a

portable salad line called “Cool Wraps.” The vice

president of brand development conceded that “We are

kind of a slow poke for development because we

believe in continuance of the menu” (Yee, 2001).21

Second, fast food chains may jeopardize the speed of

their service in offering too many services or menu

items. 

There is also the behavior of full-service restaurant

companies to consider. These companies could both

open more outlets and offer more variety/dining

amenities at each establishment. In fact, in 2001, fullservice restaurants were offering 31.6 percent more

items on their menu than in 1997 (Yee, 2001). They

were also increasing the scope of their services,

including new options for takeout. In short, full-service restaurants may try to capture the growing demand

for varied menu items among consumers who also

remain time-starved.

In conclusion, the relative growth of the fast food segment appears to have stalled. Trends in demand now

favor full-service dining. However, any changes in

market share between the two segments will also

depend on other factors, such as how firms in both

market segments change the mix of foods and services

supplied to their customers. Future research is needed

to better understand these later changes as well as their

implications for industry structure and the health of

the American population.22

15 ● Demand for Food Away From Home / AER-829 Economic Research Service/USDA

21This survey did not include fine dining establishments (i.e.,

restaurants with white tablecloths and a maitre'd).

22ERS is currently undertaking a study of restaurants to determine

how they are adapting their menus and services. Evidence on this

subject admittedly is anecdotal at this point in time.
References

Becker, G. “A Theory of the Allocation of Time,”

Economic Journal 75(1965): 493-517. 

Blaylock, J., and D. Smallwood. U.S. Demand for

Food: Household Expenditures, Demographics, and

Projections, U.S. Department of Agriculture,

Economic Research Service, TB-1713, 1986.

Blisard, N., and J. Blaylock. U.S. Demand for Food:

Household Expenditures, Demographics, and

Projections for 1990-2010, U.S. Department of

Agriculture, Economic Research Service, TB-1818,

1993.

Blisard, N. Income and Food Expenditures

Decomposed by Cohort, Age, and Time Effects, U.S.

Department of Agriculture, Economic Research

Service, TB-1896, 2001.

Blisard, N., J. Variyam, and J. Cromartie. Food

Expenditures by U.S. Households: Looking Ahead

to 2020, U.S. Department of Agriculture, Economic

Research Service, AER-821, 2003.

Byrne, P., O. Capps, Jr., and A. Saha. “Analysis of

Food-Away-from-Home Expenditure Patterns for

U.S. Households, 1982-89,” American Journal of

Agricultural Economics 78(1996): 614-627.

Byrne, P., O. Capps Jr., and A. Saha. “Analysis of

Quick-serve, Mid-scale, and Up-scale Food Away

from Home Expenditures,” The International Food

and Agribusiness Management Review 1(1998): 51-

72.

CNNMoney. “McDonald's to accept plastic,”

11/26/2002. [Online]: www.cnn.com. 

Consumer Expenditure Survey. Bureau of Labor

Statistics. 1998, 1999, and 2000.

Cromartie, J. “Population Growth and Demographic

Change, 1980-2020,” FoodReview 25,1 (2002): 10-

12.

Food CPI, Prices, and Expenditures: Expenditure

Tables. Economic Research Service. 04/30/03.

[Online]: www.ers.usda.gov/briefing/CPIFoodAnd

Expenditures. 

Foodservice 2010. McKinsey & Company, 2001.

Friddle, C., S. Mangaraj, and J. Kinsey. “The Food

Service Industry: Trends and Changing Structure in

the New Millenium,” Working Paper #01-02, The

Retail Food Industry Center, University of

Minnesota, 2001.

Gogoi, P., and M. Arndt. “Hamburger Hell,” Business

Week, 3/3/2003, pp. 104-108.

Green, H. Aggregation in Economic Analysis,

Princeton University Press, 1964.

Heien, D., and C. Wessells. “Demand Systems

Estimation with Microdata: A Censored Regression

Approach,” Journal of Business and Economic

Statistics 8(1990): 365-71.

Hiemstra, S., and W.G. Kim. “Factors Affecting

Expenditures for Food Away From Home in

Commercial Establishment by Type of Eating Place

and Meal Occasion,” Hospitality Research Journal

19(1995): 15-31.

Jekanowski, M., J. Binkley, and J. Eales.

“Convenience, Accessibility, and the Demand for

Fast Food,” Journal of Agricultural and Resource

Economics 26(2001): 58-74.

Kinsey, J. “A graphic look at key economic figures.

Diverse demographics drive the food industry,”

Choices 5(1990): 22-23.

Lin, B., and E. Frazao. Away-From-Home Foods

Increasingly Important to Quality of American Diet,

U.S. Department of Agriculture, Economic

Research Service, AIB-749, 1999.

Manchester, A. Developing an Integrated Information

System for the Food Sector, U.S. Department of

Agriculture, Economic Research Service, AER-575,

1987.

McCracken, V., and J. Brandt. “Household

Consumption of Food Away from Home: Total

Expenditure and by Type of Food Facility,”

American Journal of Agricultural Economics

69(1987): 274-84. 

Murphy, K., and R. Topel. “Estimation and Inference

in Two-step Econometric Models,” Journal of

Business and Economic Statistics 3(1985): 370-379.

Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● 16
Nayga, Jr., R.M., and O. Capps, Jr. “Impact of SocioEconomic and Demographic Factors on Food Away

from Home Consumption: Number of Meals and by

Type of Facility,” Journal of Restaurant and

Foodservice Marketing 1(1994): 45-69.

Operations Report 2001, National Restaurant

Association, Washington, DC, 2001.

Restaurant Industry 2010: The Road Ahead, National

Restaurant Association, Washington, DC, 1999.

Restaurant Industry Forecast 2003, National

Restaurant Association, Washington, DC, 2003.

Saunders, N., and B. Su. “The U.S. Economy to 2008:

A Decade of Continued Growth,” Monthly Labor

Review. U.S. Dept. Commerce, Bureau of Labor

Statistics, Nov. 1999. 

Schlosser, E. Fast Food Nation. New York: Houghton

Mifflin Company, 2001.

Shonkwiler, J.S., and S. Yen. “Two-Step Estimation of

a Censored System of Equations.” American

Journal of Agricultural Economics 81(1999): 972-

982.

Smallwood, D., and J. Blaylock. Impact of Household

Size and Income on Food Spending Patterns, United

States Department of Agriculture, Economic

Research Service, TB-1650, 1981.

Su, S., and S. Yen. “A Censored System of Cigarette

and Alcohol Consumption,” Applied Economics

32(2000): 729-37.

Yee, L. “Bold New Day,” Restaurants and Institutions,

07/15/2001, pp. 24-32.

Yen, S. “Working Wives and Food away from Home:

The Box-Cox Double Hurdle Model,” American

Journal of Agricultural Economics 75(1993): 884-

95

Yen, S., K. Kan, and S. Su. “Household Demand for

Fats and Oils” Two-Step Estimation of a Censored

Demand System,” Applied Economics 34(2002):

1799-1806.

Zuber, A. “McD president says chain will emphasize

food, not trim menu offerings,” Nation's Restaurant

News, 04/16/2001, p. 1.

17 ● Demand for Food Away From Home / AER-829 Economic Research Service/USDA


Appendix

The first step in the statistical analysis was to model

whether a household purchased some food at a fullservice and/or a fast food establishment. These two

decisions are motivated by the following random utility model:

where Y*

mh is the difference between the benefit and

cost of consumption in market segment m for household h, Wh is a vector of household characteristics, m

is vector of parameters relating Wh to Ymh, and Umh is

a normally distributed error term. Variables included

in Wh are the explanatory variables in table 2, which

are thought to determine a household's likelihood of

purchasing food away from home, as well as variables

to control for the year when the survey was administered, the season when the survey was administered,

and the household's region. It is then assumed that

households buy some food in market m if and only if

Y*

mh > 0 , i.e., the benefits exceed the costs for some

nonzero level of spending. We next denote household

h's observed decision at the first stage as

Finally, given our assumption that Umh is normally

distributed, the probability that household h makes

some positive purchase in market m is represented as 

where (mWh) is the cumulative normal distribution

evaluated at mWh. The statistical analysis of (1) produces the coefficient values reported in appendix table

1, as well as the reported standard errors of these estimates.

In the second step of the model of Shonkwiler and Yen

(1999), we estimate a household's expenditures using

our estimates of the unknown parameters, , from the

first step. In particular, expenditure by the hth household in the mth market is modeled as 

where FAFHmh is h's total expenditure in market m,

 (mWh) is the normal probability distribution evaluated at mWh, Xh is a vector of household characteristics explaining expenditures, m and m are a vector of

unknown parameters, and mh is a normally distributed

error term. 

The variables in Xh also include many of the explanatory variables in table 2, with a notable exception. As

other authors using two-step models have also done,

we omit hours worked by the household manager from

Xh. “Market labor hours constrains the amount of

time available for household production and so is

assumed to have a positive effect on the decision to

consume food-away-from-home,” argue Byrne et al.

(1996). “However, once the decision to consume

food-away-from-home is made, there is little basis to

suggest that the number of hours worked would affect

the expenditure level.”

Maximum likelihood estimates of the unknown parameters are reported in appendix table 2. The standard

errors of these coefficient estimates have been calculated using the method of Murphy and Topel (1985)

and are also reported in the table.

Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● 18

       	 
       
 	 


     

   
 

              

 	         


  



  


 

 

 

 

 



 








	






19 ● Demand for Food Away From Home / AER-829 Economic Research Service/USDA

Appendix table 1—Parameter estimates and standard errors for selected variables at first step 

Full-service Fast food

Constant -0.1866* 1.319*

(0.0744) (0.0864)

Income 0.0011* 0.0004* 

(0.0001) (0.0001)

Income-squared -0.0000002* -0.00000008*

(0.00000002) (0.00000001)

Hours worked by manager 0.0026* 0.0064* 

(0.0007) (0.0008)

Size of household -0.003 0.036* 

(0.0110) (0.0131)

Age of manager 0.0007 -0.0137* 

(0.0007) (0.0008)

College-educated manager 0.2338* 0.1557* 

(0.0275) (0.0327)

Single-person household -0.3301* -0.3827* 

(0.0437) (0.0516)

Single-parent family -0.2603* -0.1924* 

(0.0423) (0.0492)

Multiple adults without children -0.0219 -0.0629 

(0.0329) (0.0399)

Asian household -0.1046* -0.2182* 

(0.0525) (0.0599)

Black household -0.4926* -0.1939* 

(0.0400) (0.0429)

Hispanic household -0.1979* -0.1716* 

(0.0372) (0.0426)

Log-likelihood -9139.236 -6629.114

Likelihood ratio index 0.07767 0.09805

*Denotes statistical significance at the 5 percent level
Economic Research Service/USDA Demand for Food Away From Home / AER-829 ● 20

Appendix table 2—Estimated coefficients and standard errors for selected variables at second step 

Full-service Fast food 

10.90 13.07 

(6.764) (0.8989)

X Income 0.0118* 0.0056* 

(0.0056) (0.0011)

X Income-squared -0.0000007 -0.0000012* 

(0.0000015) (0.0000002)

X Size of household -1.293* -1.024* 

(0.3017) (0.1294)

X Age of manager 0.0247 0.0013 

(0.0206) (0.0211)

X College-educated manager 0.8359 -1.107* 

(1.173) (0.3786)

X Single-person household 9.874* 7.201* 

(1.819) (0.6376)

X Single-parent family 1.152 0.6045 

(1.646) (0.4780)

X Multiple adults w/o children 3.519* 1.574* 

(0.8206) (0.3776)

X Asian household 2.975* 2.776* 

(1.474) (0.7031)

X Black household 1.078 1.959* 

(2.785) (0.5841)

X Hispanic household 0.9039 1.911* 

(1.466) (0.4975)

-4.120 -14.40* 

(7.696) (3.5200)

System-weighted R2 0.3453

*Denotes statistical significance at the 5-percent level.