United States Department of Agriculture

America’s Eating Habits: 

Food Away From Home

Michelle J. Saksena, Abigail M. Okrent, Tobenna D. 

Anekwe, Clare Cho, Christopher Dicken, Anne Effland, 

Howard Elitzak, Joanne Guthrie, Karen S. Hamrick, Jeffrey 

Hyman, Young Jo, Biing-Hwan Lin, Lisa Mancino, Patrick 

W. McLaughlin, Ilya Rahkovsky, Katherine Ralston, Travis A. 

Smith, Hayden Stewart, Jessica Todd, and Charlotte Tuttle

Economic

Research

Service

Economic

Information

Bulletin

Number 196

September 2018
Economic Research Service 

www.ers.usda.gov

United States Department of Agriculture

Recommended citation format for this publication:

Michelle J. Saksena, Abigail M. Okrent, Tobenna D. Anekwe, Clare Cho, Christopher 

Dicken, Anne Effland, Howard Elitzak, Joanne Guthrie, Karen S. Hamrick, Jeffrey 

Hyman, Young Jo, Biing-Hwan Lin, Lisa Mancino, Patrick W. McLaughlin, Ilya Rahkovsky, 

Katherine Ralston, Travis A. Smith, Hayden Stewart, Jessica Todd, and Charlotte 

Tuttle. America’s Eating Habits: Food Away From Home, EIB-196, U.S. Department of 

Agriculture, Economic Research Service, September 2018.

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United States Department of Agriculture

Economic

Research

Service

Economic

Information

Bulletin

Number 196

September 2018

Abstract

Food away from home (FAFH) has become increasingly integral to the American diet. In 2010, 

the share of Americans’ food budget for FAFH—reaching 50 percent (up from 41 percent in 

1984)—surpassed the share for food at home (FAH) for the first time. Likewise, Americans’ 

share of energy intake from FAFH rose from 17 percent in 1977-78 to 34 percent in 2011-12, with 

differences in growth across types of FAFH (e.g., full- and quick-service restaurant foods, school 

meals, etc.). Along with the demand for FAFH, availability of FAFH has also increased, with 

much of the growth in recent years attributable to quick-service restaurants. The growing presence 

of FAFH in Americans’ diets reflects changes in consumer demand and producer behavior and 

affects the health and nutrition of individuals over time. This report takes a comprehensive look at 

the role of FAFH in American diets, exploring nutritional composition of FAFH and key Federal 

programs that may influence FAFH. The report also discusses how FAFH choices and availability 

relate to diet quality, income, age, and other socioeconomic factors. 

Keywords: food away from home (FAFH), food at home (FAH), diet, nutrition, health, food 

assistance, food consumption, school lunch programs, fast food, quick service, limited service, 

full service, food expenditures, the Great Recession, Healthy Eating Index (HEI), Food 

Acquisition and Purchases Survey (FoodAPS), Food Expenditure Series, National Health and 

Nutrition Examination Survey (NHANES), menu labeling, restaurant location

Acknowledgments

The authors would like to thank the following individuals for technical peer reviews: Christian 

Gregory, Rosanna Morrison, Elina Page, Eliana Zellabos and Brandon Restrepo, USDA, ERS; 

James Binkley, Purdue University; Michael Boland, University of Minnesota; Samantha Cakir, 

Macalester College; Oral Capps, Jr., Texas A&M; George Davis, Virginia Tech; Senarath 

Dharmasena, Texas A&M; Betsy Frazao, formerly of ERS; Rachael Griffith, The Institute for 

Fiscal Studies; Andrea Marie Leschewski, Michigan State University; Eliza Mojduszka, USDA; 

America’s Eating Habits: 

Food Away From Home

Michelle J. Saksena, Abigail M. Okrent, Tobenna D. Anekwe, 

Clare Cho, Christopher Dicken, Anne Effland, Howard Elitzak, 

Joanne Guthrie, Karen S. Hamrick, Jeffrey Hyman, Young 

Jo, Biing-Hwan Lin, Lisa Mancino, Patrick W. McLaughlin, 

Ilya Rahkovsky, Katherine Ralston, Travis A. Smith, Hayden 

Stewart, Jessica Todd, and Charlotte Tuttle
Rodolfo Nayga, University of Arkansas; Cynthia Ogden, Centers for Disease Control and Prevention; 

Tina Saitone, University of California, Davis; and Cathleen Zick, University of Utah. Thanks also to 

Maria Williams and Cynthia A. Ray, USDA, ERS, for editorial and design services. 

About the Authors

Michelle J. Saksena, Abigail M. Okrent, Tobenna D. Anekwe, Clare Cho, Young Jo, Biing-Hwan Lin, 

Lisa Mancino, Patrick W. McLaughlin, Ilya Rahkovsky, Katherine Ralston, Hayden Stewart, and 

Jessica Todd are research agricultural economists with USDA, Economic Research Service (ERS). 

Christopher Dicken is a GIS (geographic information system) analyst with ERS and Joanne Guthrie 

is a nutritionist with ERS. Howard Elitzak and Jeffrey Hyman are agricultural economists with ERS. 

Charlotte Tuttle and Karen S. Hamrick were formerly with ERS. Anne Effland is a senior economist 

with USDA, Office of the Chief Economist (OCE). Travis A. Smith is an Assistant Professor with the 

Department of Agricultural and Applied Economics of the University of Georgia.
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Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .v

Chapter 1—Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Trends and Themes Surrounding FAFH ............................................2

Data and Definitional Nuances of FAFH ............................................7

Conclusion ..................................................................13

References ..................................................................15

Chapter 2: A Brief History of Food Away From Home in the United States . . . . . . . . . . . . . .18

Colonial Era Through the 19th Century ........................................... 18

The Turn of the 20th Century ...................................................19

The Changing Eating Culture ...................................................20

The Rise of National Franchise Chain Restaurants ..................................21

Conclusion ..................................................................22

References ..................................................................22

Chapter 3: A Retrospective of Food-Away-From-Home Expenditures 

From 1987 to 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

USDA’s Food Expenditure Series .................................................23

FAFH Over Time .............................................................24

Factors Affecting Spending on FAFH .............................................28

Conclusion ..................................................................32

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

Chapter 4: Food Away From Home During the Great Recession . . . . . . . . . . . . . . . . . . . . . . .35

Data .......................................................................36

Total Food Expenditures for all Households ........................................37

Differences Across Household Types ..............................................40

Conclusion ..................................................................50

References .................................................................. 51

Appendix: Categories by Uniform Commercial Codes ................................53

Chapter 5: Demographics of Food-Away-From-Home Frequency . . . . . . . . . . . . . . . . . . . . . . . 57

Data .......................................................................57

Age ........................................................................58

Race and Gender .............................................................. 59

Socioeconomic Status: Income, Education, Employment, and Food Assistance Receipt ...... 61

Marital and Parental Status .....................................................64

National School Lunch Program .................................................66

Discussion ..................................................................67

References ..................................................................67

Appendix table: Frequency of food-away-from-home (FAFH) events for adults, 

by age and demographic group ........................................................69

Chapter 6: Evolution of the Food-Away-From-Home Industry: 

Recent and Emerging Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75

Data .......................................................................76

Market Size and Structure From 2000 to 2015 ......................................78

Food-Away-From-Home Sales ...................................................91

Conclusion ..................................................................94

References ..................................................................95

Contents
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Chapter 7: Impacts on Nutrient Intakes From Increased Food-Away-From-Home 

Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .96

Data and Methods ............................................................96

30-Year Rise in Food Prepared Away From Home Briefly Reversed in 2007-10 and Then 

Rebounded .................................................................98

Nutrient Differences Between FAH and FAFH ..................................... 101

Conclusion and Discussion ..................................................... 105

References .................................................................106

Chapter 8: How Food Environment and Proximity to Restaurants Affect 

Nutritional Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .109

Food Environments, Diet Quality, and Health ......................................109

Data ...................................................................... 111

Descriptive Statistics ......................................................... 112

Demographic Characteristics ................................................... 114

Food Environment ........................................................... 116

Diet Quality ................................................................ 118

Rural-Urban Divide .......................................................... 118

Conclusion ................................................................. 121

References .................................................................122

Chapter 9: What Role Does Food Away From Home Play in the Diets of Food 

Assistance Recipients? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .125

Previous Related Research ....................................................126

Data and Methodology ........................................................128

2010 Healthy Eating Index Scores for Food Away From Home Versus Food at Home .......129

Households Without Children ..................................................129

HEI-2010 for FAFH in Households With Children .................................. 132

SNAP Households With Working Members ....................................... 132

The Relationship Between Nutrition Awareness and Attitudes and HEI .................. 132

HEI-2010 for FAFH at School Versus Other FAFH .................................. 138

Conclusion ................................................................. 138

References ................................................................. 140

Chapter 10: Menu Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Years of Debate Culminated in Passage of Menu-Labeling Regulations. . . . . . . . . . . . . . . . . . 143

Will Menu Labeling Lead to Changes in Consumer and Restaurant 

Industry Behavior? ........................................................... 146

Conclusion ................................................................. 155

References .................................................................156

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .160
ERS is a primary source 

of economic research and 

analysis from the U.S. 

Department of Agriculture, 

providing timely information on economic and policy 

issues related to agriculture, 

food, the environment, and 

rural America.

United States Department of Agriculture

A report summary from the Economic Research Service September 2018

America’s Eating Habits: 

Food Away From Home

Michelle J. Saksena, Abigail M. Okrent, Tobenna D. Anekwe, Clare 

Cho, Christopher Dicken, Anne Effland, Howard Elitzak, Joanne 

Guthrie, Karen S. Hamrick, Jeffrey Hyman, Young Jo, Biing-Hwan 

Lin, Lisa Mancino, Patrick W. McLaughlin, Ilya Rahkovsky, Katherine 

Ralston, Travis A. Smith, Hayden Stewart, Jessica Todd, and 

Charlotte Tuttle

What Is the Issue?

Over the past several decades, Americans have grown to rely on the convenience of foods 

prepared outside of the home. Unfortunately, food away from home (FAFH) often contains fewer 

fruits and vegetables and have more calories, fat, and sodium than food prepared at home (FAH), 

and consuming FAFH is associated with obesity. Recently passed labeling legislation aims to 

help consumers make healthier FAFH choices and to encourage FAFH suppliers to produce more 

healthful options. To explore Americans’ eating away from home behavior, this report presents 

research on three broad FAFH topics: (1) food choices and availability; (2) nutrition and diet 

quality; and (3) food policies, including menu labeling and food assistance programs.

What Did the Study Find?

Food choices and availability of FAFH. Over the past 30 years, FAFH’s share of U.S. households’ food budgets and total food spending grew steadily. FAFH options also became more 

widely available as growing numbers and types of businesses—including grocery stores—

served prepared foods. Apart from the Great Recession (2007-09), these trends continued uninterrupted from 1987 to 2017, but the changes were not uniform across socioeconomic groups or 

business types. 

•	 Spending on FAFH surpassed spending on FAH for the first time in 2010, increasing its share 

of total food spending from 44 percent (30 years prior) in 1987 to 50.2 percent in 2010. 

•	 Higher income households spent more on FAFH and bought it more frequently than 

lower income households. Households with incomes greater than 300 percent of the 

Federal poverty guidelines obtained FAFH on 5.5 occasions per week, while households 

whose incomes were less than or equal to Federal poverty guidelines obtained FAFH on 

4.2 occasions per week.

•	 For households with an elderly individual (over 64 years old), the share of household food 

spending on FAFH was 8 percent lower than for other households. Also, Americans who 

were 35–44 years old consumed FAFH more often than other Americans.

www.ers.usda.gov

 

America’s Eating Habits: 

Food Away From Home

 

Summary
•	 In 2000–15, quick-service restaurants (QSRs), also referred to as fast-food and limited-service restaurants, drove the industry’s growth both in sales and number of outlets. The fastest-growing segment of 

the QSRs was fast casuals—e.g., Chipotle Mexican Grill and Panera Bread—which combines counter 

service with the perceived ambiance and product quality of full-service restaurants (FSRs).

•	 Much of the growth in foodservice establishments occurred in urban U.S. counties, consistent with 

patterns of urban and rural migration. As rural populations declined, FSRs in rural areas were particularly hard hit, leaving QSRs to dominate.

•	 Spending on FAFH declined during the Great Recession, by $47 billion (18 percent) in real dollars 

from 2006 to 2010, and rebounded thereafter. 

•	 During the Great Recession, households replaced spending at FSRs with unprepared foods purchased at 

retail stores (like grocery stores), but households’ share of spending for QSRs stayed constant. In 2014, 

household expenditures on FAFH had yet to rebound to pre-Recession levels.

•	 Despite the downturn in household spending on FAFH during the Great Recession, the number of chain 

QSRs grew, and consumers spent a greater share of their FAFH dollars at these restaurants.

Nutritional composition and diet quality. The nutritional composition of FAFH across all income levels 

and all FAFH types (except school foods) was consistently lower quality and more caloric than that of FAH. 

Though FAFH is known to have lower diet quality, access to FAFH did not seem to affect FAFH consumption 

and did not correlate with diminished overall diet quality. 

•	 FAFH’s share of total average daily energy intake increased from 17 percent in 1977–78 to 34 percent 

in 2011–12, and consumption of QSR foods was the largest source of this growth. 

•	 On the whole, FAFH contained more saturated fats and sodium, and less calcium, iron, and fiber than 

FAH—however, the nutritional composition of FAFH varied across outlet types. For example, in 2009–

12, the fat content of school lunches (a type of FAFH) was almost identical to that of FAH (33 percent) 

while the fat content of QSR foods averaged 39 percent. 

•	 Although frequent QSR customers purchased less vegetables, fish, and nuts, their overall diet quality 

was no worse than that of QSR nonconsumers.

Policies that affect FAFH. FAFH consumption is influenced by public policy mainly on two fronts. First, 

current food assistance programs with in-kind food benefits affect food choices and diet quality of participating low-income households. For example, new requirements that improve nutrition of school meals 

directly affect children’s diet quality. Second, new menu labeling regulations may help consumers make more 

informed food choices at restaurants. 

•	 The average household Healthy Eating Index (HEI-2010) for FAFH was lower than for FAH, regardless 

of SNAP participation or income. 

•	 School meals provided by the National School Lunch Program and School Breakfast Program 

contained higher levels of calcium than both FAH and other sources of FAFH and adhered better to 

USDA’s Dietary Guidelines for Americans than other sources of FAFH. 

How Was the Study Conducted?

This report uses a variety of data sources and techniques to examine FAFH trends. The analysis was done 

primarily using descriptive statistics (e.g., means, differences, and correlations) and literature review. The main 

data sources were the National Health and Nutrition Examination Survey (NHANES), USDA ERS’s Food 

Expenditure Series, the National Household Food Acquisition and Purchase Survey (FoodAPS), the Consumer 

Expenditure Survey, U.S. Census Bureau’s Monthly Retail Trade and Foodservices series, NPD ReCount, and 

Euromonitor Passport. These data sources include self-reported information and measurable individual characteristics collected by household survey, establishment information, and proprietary industry data. 

www.ers.usda.gov
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America’s Eating Habits: 

Food Away From Home

Chapter 1—Introduction

This chapter gives a comprehensive summary of this report’s findings about FAFH choices 

and availability, nutrient intake from FAFH, associations between FAFH and diet quality, 

and associations between FAFH and sociodemographic characteristics. It shows the potential drivers of trends in demand and supply of FAFH and describes current policies that 

may affect these outcomes. This chapter also discusses the limitations and advantages of 

the various data sources used in each chapter. 

In 2014, fewer than 60 percent of suppers served at home were actually cooked at home, down from 

75 percent in 1984 (Ferdman, 2015). The decreasing time spent on meal preparation and cooking 

reflects the primacy of food away from home (FAFH) in Americans’ routines. Americans aged 

18 and over spent 65 minutes in meal preparation and cleanup in 1965 (Cutler et al., 2003), but by 

2014, prep and cleanup time had fallen to 37 minutes per day (Hamrick and McClelland, 2016). 

This trend of cooking less and consuming more FAFH is not expected to subside for at least as long 

as Millennial consumers1—who have a greater preference for prepared foods and more disposable 

income than older age groups do—continue to enter the workforce (Kuhns and Saksena, 2017). 

Using data from the Economic Census, table 1.1 shows the number of retail establishments and their 

sales (food and beverage stores, and other retail stores) and foodservice industries for two types of 

products—“on-premise foods” and “off-premise foods”—between 1977 and 2012.2 The food retail 

landscape has changed in the past 30 years, both in terms of access to different types of food stores 

and selection of foods they offer. For example, in 1977, there were about 5.9 food stores, of any kind, 

for every 1,000 Americans, and 25 percent of these stores sold food for on-premise consumption.3

At that time, food offerings were more clearly delineated by food store outlet type. Most on-premise 

food purchases were at foodservice establishments, while the majority of off-premise foods were 

purchased in retail establishments like grocery stores. By 2012, the number of foodservice establishments had increased by 77 percent, while the number of retail establishments declined between 25 

to 50 percent. Even though the number of retail establishments decreased, nearly 40 percent of them 

offered food for on-premise consumption, a fivefold increase. 

Because FAFH composes about 33 percent of daily calorie consumption for the average American 

and is associated with overall poor diet quality, policymakers, health practitioners, and researchers 

have suggested several policies to curb the purchase and consumption of prepared foods, especially 

1The “Millennial” generation comprises those born between 1981 and 1996.

2It is important to note that foods are classified by the retailer, and thus the on-premise consumption category may not capture all prepared and ready-to-eat food sold from grocery and other food retail stores. Nonetheless, on-premise foods generally 

refer to meals and snacks that are prepared at the outlet and meant to be consumed there (essentially, FAFH) while off-premise 

foods are those intended to be consumed away from where they were purchased (FAH). 

3Foodservice establishments are commercial brick-and-mortar operations whose primary business model is to sell food 

for onsite consumption. 
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foods from foodservice establishments. Most of these policy ideas center on encouraging consumers 

to make healthier food choices at foodservice establishments and incentivizing prepared food 

suppliers to offer healthier alternatives. Potential policies include restricting quick-service4 television 

advertising from targeting children, serving smaller entree portion sizes, standardizing serving-size 

information, zoning to limit access to restaurant foods, requiring menu labels, reformulating school 

lunches, and providing subconscious cues in school cafeterias to guide children to healthier foods. 

Many of these policies have been tried in local jurisdictions and even nationally, but it is too soon to 

tell if they can improve diet quality.

This report provides a comprehensive, in-depth look at FAFH’s growing role in Americans’ diets 

with a view to informing the affected sectors and policies. Several specific questions are addressed:

1. What kinds of foods are considered “food away from home” (FAFH)?

2. How does FAFH availability affect Americans’ food choice behaviors, energy intake, and diet 

quality?

3. What groups by age, income, and other demographic criteria are most likely to purchase 

FAFH?

4. How has the availability of FAFH changed over time?

5. To what degree is FAFH correlated with lower diet quality?

6. What is the potential for current and proposed policies to affect the healthfulness of FAFH 

purchases and consumption?

Trends and Themes Surrounding FAFH 

To explore Americans’ eating away from home behavior, this report presents research on three broad 

FAFH topics: (1) food choices and availability; (2) nutrition and diet quality; and (3) food policies. Food choices and availability are discussed in chapters 2-6 and analyze FAFH expenditures, 

purchase frequency, location, and access. Nutrition and diet quality are discussed in chapters 7 and 8 

and analyze the nutrient composition of FAFH over time and correlation between diet quality FAFH 

frequency. Chapters 9 and 10 discuss policies related to FAFH, including food assistance programs 

and menu labeling. 

Chapter 2 sets the stage for the current state of FAFH, chronicling the evolution of the FAFH landscape over time. Beginning in the mid- to late-19th century, the precursors to modern FAFH were 

street vendors, lunch wagons, diners, soda fountains, luncheonettes, and cafeterias. By the 1920s and 

’30s, technological innovation in food processing, the invention of the automobile, and increases in 

women’s labor force participation (which generated more household disposable income) increased 

demand for FAFH consumption. To keep up with increasing demand, restaurants adopted more 

permanent structures with seating for families. Chain restaurants designed to accommodate automobile-based customers proliferated across the Nation, offering low-cost, fast, and predictable food. All 

of these factors led to long-term growth in availability and demand for FAFH.

4Throughout the report the terms fast food, limited service and quick service are used interchangeably to denote restaurants that offer counter service, but do not have wait staff that continually tend to customers. Within this outlet type, fastcasual restaurants offer mainly just counter service, but servers do bring food to individual tables. Full-service and sit-down 

denote restaurants where wait staff continually serve seated customers throughout their meal.
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Table 1.1Comparison of prices, sales, and number of establishments for select industries selling foods1

Price index

(100=1982-

84)3

Number of establishments per 

1,000 persons Nominal per capita sales Real per capita sales

Industry2 Total

Selling 

on-premise 

meals and 

snacks

Selling 

off-premise 

food Total

On-premise 

meals and 

snacks

Off-premise 

food Total

On-premise 

meals and 

snacks

Off-premise 

food

1977Total retail and foodservice 65.7 5.92 1.55 1.23 3,177 249 624 48.34 3.78 9.50

Food and beverage stores 66.8 0.94 0.13 0.83 749 2 595 11.21 0.04 8.91

Other retail stores 66.8 3.90 0.34 0.38 2,181 23 28 32.65 0.34 0.42

Eating and drinking places 62.6 1.08 1.08 0.03 247 224 1 3.95 3.57 0.01

2012Total retail and foodservice 234.2 5.29 2.31 1.23 15,070 1,490 2,063 64.34 6.36 8.81

Food and beverage stores 231.8 0.47 0.18 0.42 1,975 42 1,305 8.52 0.18 5.63

Other retail stores 231.8 2.91 0.28 0.73 11,464 57 752 49.46 0.25 3.25

Eating and drinking places 238.0 1.91 1.85 0.08 1,631 1,390 5 6.86 5.84 0.02

Percentage changeTotal retail and foodservice 256.4 -10.66 49.59 -0.03 374.32 499.27 230.29 33.09 68.15 -7.32

Food and beverage stores 247.0 -49.94 41.76 -48.82 163.62 1648.51 119.11 -24.02 403.94 -36.85

Other retail stores 247.0 -25.35 -18.36 93.59 425.60 154.25 2563.57 51.49 -26.72 667.67

Eating and drinking places 280.2 76.60 71.79 164.15 560.31 521.89 635.24 73.69 63.58 93.40

1Industries that sell food that are excluded from the present analysis include recreational places (e.g., movie theaters, museums, sporting events, performances), hotels and motels, civic and social organizations, transportation (e.g., airlines and trains), and food furnished as an ancillary activity (e.g., inpatient meals at hospitals). According to USDA/ERS’s Food Expenditure Series, these sales were on average 11-12 percent of total food sales between 1977 and 2012. 2For the 1977 Economic Census, the relevant Standard Industry Classification (SIC) industry codes used in this analysis are: G (Retail Trade), 54 (Food Stores), 592 (Liquor Stores), and 58 (Eating and Drinking Places). For the 2012 Economic Census, the relevant North American Industry Classification System (NAICS) industry codes used in this analysis are: 44-45 (Retail Trade), 445 (Food and Beverage Stores), and 722 (Food Services and Drinking Places). 3The price index for total retail and foodservice is derived as a weighted average of food-at-home (FAH) and food-away-from-home (FAFH) Consumer Price Indices (CPI), with nominal per capita sales for each outlet type as weights.Source: U.S. Department of Commerce, U.S. Census Bureau, 1977 Economic Census (1979); U.S. Department of Commerce, U.S. Census Bureau, 2012 Economic Census (2016); Consumer Price Indices (CPI) for FAH and FAFH (Bureau of Labor Statistics, 2017); and U.S. Department of Commerce, U.S. Census Bureau, Census population estimates (2000, 2016)
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Over the past century, changes in work and in attitudes toward family, children, and leisure time 

shaped food preferences and eating routines. Rising incomes, more two-earner households, and 

improved affordability and access to fast-food establishments may have all contributed to growing 

FAFH demand. As households became increasingly more time constrained—as a result of greater 

educational attainment and increases in women’s participation in the labor force—it raised the 

opportunity cost of their time, making time-intensive meal preparation less attractive. As a result, 

FAH-preparation times have declined while FAFH-consumption rates have increased over time. 

Tastes and preferences are also shaped by sociodemographic characteristics, including race, 

ethnicity, education, and household composition (i.e., marital/partnered status of household manager, 

age and gender of household manager, number of household members, and number of children). 

Chapter 3 discusses how these factors may have influenced observed macroeconomic growth in 

FAFH expenditures.

As several chapters note, the Great Recession (December 2007 to June 2009) marked a departure 

from the long-term growth trend in consumer spending on FAFH. As unemployment climbed, 

households had more time for leisure and household activities like meal preparation and less disposable income to purchase FAFH (Beatty and Senauer, 2012). Chapter 3 shows that, unlike in previous 

economic downturns since 1987, long-term growth in FAFH spending reversed during the Great 

Recession and only began to rebound in 2011. The decline in total FAFH spending during this 

period largely manifested in decreased spending at full-service restaurants, while spending at quickservice restaurants grew, albeit at a slower rate than before the Great Recession. By 2010, the share 

of food spending by both businesses and households that purchased FAFH surpassed the FAH share 

for the first time. 

Economic downturns likely affect demand for FAFH by constricting consumers’ budgets and relaxing 

time constraints, but their responses are also shaped by differences in income, household structure and 

composition, education, race, and ethnicity, which are all found to influence American tastes and preferences. Chapter 4 examines food expenditure patterns of households by different sociodemographic 

groups before and after the Great Recession, particularly emphasizing substitution among prepared 

foods purchased at full- and quick-service restaurants and at retail stores. During the Great Recession, 

as the share of household food expenditures allocated to FAFH declined, the share of spending on 

prepared and unprepared FAH ingredients increased, and these trends continued through 2014 for most 

household types, suggesting a general shift toward more home-cooked meals.5 Overall, changes in 

spending allocations across FAFH categories between 2005 and 2014 were relatively small, with the 

largest change from 1 year to the next being less than 1 percentage point for all categories, whereas 

spending patterns differed quite substantially across household types. 

Prices of FAFH have generally outpaced those of FAH as shown in table 1.1 (247-percent increase for 

food and beverage stores versus a 280-percent increase for eating and drinking places since 1982-84). 

Thus, inflation may partially explain some of the observed growth in nominal FAFH spending and 

household expenditures discussed in chapters 2 and 3, but data also show that Americans consume 

meals and snacks from FAFH establishments with increasing regularity. In 1977-78, 16 percent of 

meals and snacks were obtained at restaurants, schools, vending machines, mobile vendors, or through 

donations, and this share increased to 24 percent in 1995 (Lin et al., 1999). Meals from restaurant 

5While the 2008 commodity price spike occurred during the Great Recession, prices of FAH spiked, but prices of FAFH 

did not follow this trend. This finding suggests that American consumers would have substituted out of FAH and into FAFH 

because these products are generally found to be net substitutes (Okrent and Alston, 2012; Huffman, 2011). However, the opposite occurs because income declines during the Great Recession dominated the substitution effects.
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purchases alone constituted 23 percent of meals purchased in 2004, though that share dropped 

significantly during the recession and eventually stabilized to 20 percent of meals in 2012, according to 

data from the NPD group (Business Wire, 2004; Sloan, 2016). 

As examined in chapter 5, socioeconomic factors may affect not only how much consumers spend 

on FAFH, but also how often they consume it (measured as number of meals, number of eating 

occasions, etc.). In some cases, these trends in FAFH expenditures and the frequency of consumption may not exactly parallel each other. For example, higher income consumers may spend more 

on FAFH because they are more willing and able to spend for high quality than are lower income 

consumers. However, greater spending by higher income groups on FAFH does not necessarily 

translate into a greater number of FAFH meals. The same logic can be applied for differences 

between expenditures and frequency among groups with different races/ethnicities, household 

composition (with or without children), and education. 

Chapter 5’s analysis supports several key results in the literature that are discussed in chapter 4, 

including the finding that FAFH consumption increases with income, education, and employment 

status and decreases with the number of children. In addition, younger adults tend to consume FAFH 

more frequently than others: the peak years for FAFH are 35-44 years old, which may be due to higher 

labor force participation and higher income in that age range.

As chapter 2 notes, food choices evolved to increasingly favor FAFH, and the supply and availability of 

FAFH grew to meet this demand. Related to this, chapter 6 shows how supply of FAFH grew both in 

terms of sales and number of establishments, with changes in the composition and structure of restaurants. In particular, roughly 57 percent of all FAFH establishments in 2015 were quick-service restaurants, 66 percent of which were chains, which was the fastest growing subsegment of the past 15 years, 

growing even during the Great Recession. Full-service restaurants were particularly hard hit by the 

Great Recession, with the number of establishments declining 5 percent after peaking in 2009. 

While spending at full-service restaurants decreased during the Great Recession, spending at quickservice restaurants grew, albeit at a slower rate than pre-recession. Chapter 6 details that although 

full-service restaurants consistently made up the largest share of FAFH spending, from 2000 to 

2015, quick-service restaurants drove much of the growth in spending on FAFH. The fastest growth 

of the quick-service segment is largely driven by fast-casual restaurants, a hybrid of quick- and fullservice restaurants, which grew at twice the rate of traditional quick-service restaurants between 

2000 and 2015. Fast casuals offer the convenience and prices of quick-service restaurants with the 

perceived high-quality food and ambiance of full-service restaurants. As Americans continued to 

migrate from rural areas to cities in 2000-15, full-service restaurants were particularly affected, 

leaving the FAFH landscape in rural areas dominated by quick-service restaurants, which may have 

implications for healthfulness of purchases and consumption.

Although healthy FAFH is available, Americans tend to select items that have more calories, fat, 

and saturated fat than home-produced foods (Lin and Guthrie, 2012). Chapter 7 examines the 

nutrient and energy intake from foods obtained from restaurants, schools, and places other than 

home in 1977-78 and 2011-12. The share of calories obtained from FAFH rose from 18 percent in 

1977-78 to 34 percent in 2005-06, with consumption of quick-service foods being the largest source 

of this growth. The share of calories obtained away from home briefly dipped to 29 percent in 

2009-10, paralleling the decline in FAFH expenditures and sales between 2007 and 2010 described 

in chapters 3, 4, and 6. However, on a percentage basis, consumption at full-service restaurants 
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declined more than at quick-service restaurants, indicating some economizing within FAFH options, 

which is also consistent with expenditure and sales patterns. 

Post-Great Recession, by 2011-12, chapter 7 notes that FAFH consumption had risen again to 

comprise 34 percent of calories, essentially double the share of intake from 1977-78, and quick 

service grew to 15.8 percent of calories. Lower income individuals consumed more FAFH over time, 

but less than higher income individuals did, which parallels observations on FAFH expenditure 

patterns in chapter 4. Over time, the energy intake of children and youth from FAFH—particularly 

from quick service—grew in parallel to that of adults, but at the same time, the role of school foods 

diminished for all children and youth and diminished more for those of higher income households. 

FAFH is still found to have more total fat, saturated fat, and sodium than FAH, and FAFH contains 

less fiber, iron, and calcium (with the exception of school foods, which have more calcium and iron 

than FAH).

A related concern to the nutrient composition of FAFH is whether greater access to FAFH establishments influences diet quality (i.e., adherence to USDA’s Dietary Guidelines for Americans). 

To understand whether the increased presence of FAFH establishments can potentially affect food 

choices and diet quality, chapter 8 investigates the associations among restaurant density, consumers’ 

proximity to quick- and full-service restaurants, and diet quality of five types of consumers by 

frequency of purchase—nonconsumers (i.e., people who did not purchase from either quick- or fullservice restaurants), frequent quick-service consumers, frequent quick- and full-service consumers, 

occasional quick-service consumers, and occasional quick- and full-service consumers. This analysis 

finds that frequent quick-service consumers live close to quick-service restaurants, but compared 

with nonconsumers, do not tend to live in areas with high concentrations of restaurants, which 

may imply that quick-service consumers respond more to the proximity of restaurants than to the 

density of restaurants around their home. Additionally, although frequent quick-service purchasers 

purchased less vegetables, fish, and nuts, their overall diet quality was no worse than that of nonconsumers. This finding suggests that increases in quick-service density and proximity are not associated with reductions in diet quality. In addition, restaurant density in rural areas is substantially less 

than in urban areas, but rural consumers purchase only slightly fewer restaurant meals.

Several policies have been proposed to help consumers choose healthier FAFH and incentivize 

FAFH suppliers to provide healthier food options. One such option is restricting marketing of 

unhealthy foods to children for quick-service foods, and some studies have found that bans on television advertising targeting unhealthy foods to children have meaningful effects on calorie intake 

and obesity (Chou et al., 2008; Dhar and Baylis, 2011). However, some have argued that the U.S. 

Constitution might not permit any such regulations to be applied in the United States because of 

the First Amendment protection of free commercial speech, which imposes limits on what the 

Government can do to restrict marketing otherwise (Ippolito, 2011). Limiting access to restaurant 

foods is another potential policy intervention. But such restrictions have not been consistently found 

to affect obesity (Sturm and Hattori, 2015; Currie et al., 2010).

Current programs that provide food assistance to combat food insecurity and support low-income 

families in making food choices consistent with USDA’s Dietary Guidelines may also influence 

FAFH purchasing and acquisition decisions directly and indirectly. The National School Lunch 

Program (NSLP) and School Breakfast Program (SBP) directly affect FAFH purchases by providing 

free or low-cost meals to children in schools. (For more details, see U.S. Department of Agriculture, 

Food and Nutrition Service, 2016.) The foods served in these programs must meet certain Federal 
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meal standards (MacEwan and Okrent, 2017). Indirectly but significantly, the Supplemental 

Nutrition Assistance Program (SNAP) can affect FAFH spending by providing benefits that can be 

used only for FAH spending. Most studies indicate that an extra dollar of SNAP benefits is much 

more likely to be spent on FAH than is an extra food dollar of cash. The share of that extra SNAP 

dollar spent on food, referred to as the marginal propensity to spend (MPS) on food from SNAP 

benefits, has been estimated to be $0.17 to $0.53 while the MPS for cash is estimated to be $0.05-

$0.10. The extra dollar in SNAP benefits would all be spent on FAH as required, but it frees up 

household cash to be spent on other things, and the MPS measure takes that total effect into account 

(Tuttle, 2016; MacEwan and Okrent, 2017). For example, Kim (2016) examined SNAP recipients’ nonfood expenditure response to the increase in benefits attributable to American Recovery 

and Reinvestment Act (ARRA) and found an increase in nonfood expenditures attributable to the 

increase in benefits. 

Chapter 9 examines diet quality of children participating in the NSLP and SBP, and the diet quality 

and FAFH frequency of households participating in SNAP. Chapter 9 finds that school meals 

provided a higher Healthy Eating Index (HEI) for children than other sources of FAFH, consistent 

with other studies finding participation in NSLP enhances participants’ nutrient consumption.6

Because SNAP participants are more likely than higher income groups to participate in school 

meals, the higher diet quality of school meals is likely part of the reason that the HEI for FAFH is 

almost as high for SNAP households with children as for SNAP households without children, while 

for SNAP nonparticipants, the HEI for FAFH is lower for households with children compared to 

those without children. 

Menu labeling at restaurants is another policy option that may engender more healthful eating 

away from home by Americans. Debate over mandatory menu labeling grew during the 2000s as it 

became clear that eating out was associated with less healthful food choices (e.g., Variyam, 2005). 

Chapter 10 describes how representatives in the public health, nutrition, and foodservice sectors have 

collaborated with policymakers to craft a menu labeling rule, which requires restaurant chains with 

20 or more establishments to make calorie information publicly available. This rule was developed 

at the Federal level in response to the often confusing and contradictory menu labeling laws in local 

jurisdictions that had been difficult to implement for restaurants in multiple jurisdictions. 

Chapter 10 shows that the current evidence on how menu labeling would affect food choices and 

calorie intake is quite mixed, with some studies finding no effect and others finding a reduction of up 

to 177 calories per order or purchase. One study found that, on average, providing point-of-purchase 

calorie information in chain restaurants reduced body mass index (BMI) by 1.5 percent and lowered 

the risk of obesity by 12 percent in jurisdictions with such laws (Restrepo, 2017). Similarly, the 

response of the foodservice industry to menu labeling regulations in local jurisdictions is mixed, 

with some restaurants increasing the number of low-calorie offerings on the menu and some not. 

Data and Definitional Nuances of FAFH

This report uses several datasets to conduct analysis on FAFH, and the datasets differ in defining 

FAFH and types of foodservice establishments. As a consequence, there are variations in the results 

depending on the datasets used. Generally, FAFH is defined as being obtained, although not exclu6Constructed by USDA’s Center of Nutrition Policy and Promotion and the National Cancer Institute, the HEI is a measure that determines diet quality. For a more indepth explanation, see chapter 8. 
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sively, from restaurants, cafeterias, food trucks, and vending machines. In contrast, foods prepared 

within the home and obtained from retail establishments like grocery stores, warehouse clubs, 

supercenters, and mail order are commonly referred to as food at home (FAH). Prepared foods, 

however, have increasingly become a greater share of foods sold at retail stores as well, and they 

could also be considered FAFH, given how closely they resemble food purchased at foodservice 

establishments. Table 1.2 summarizes the data used and the corresponding terms used in defining 

FAFH in this report. 

Most data sources base the distinction between FAH and FAFH on the type of outlet where food is 

purchased. FAFH establishments include restaurants, schools, vending machines, and mobile food 

vendors, and FAH establishments are retail stores like grocery stores, warehouse clubs, roadside 

stands, and mail order and home delivery. 

These definitions are used in some publicly available data used in this report, including: 

1. Household food expenditure data from the Consumer Expenditure Series (CES);

2. Prices paid by consumers in the Consumer Price Indexes (CPIs);

3. Store counts in the Quarterly Employment and Compensation Survey; and

4. Sales in the Monthly Retail Trade and Foodservices report and the Economic Census.

Except for the CES, which is collected from households, all of these data are collected from foodservice establishments. Foodservice data classify establishments by the North American Industry 

Classification System (NAICS). FAFH establishments under NAICS include full-service restaurants (wait staff), limited-service restaurants (counter service), and “other FAFH” (caterers, mobile 

food vendors, vending machine operators, food contractors). (See chapter 6 for more details.) The 

CES classifies FAFH as full- and limited-service restaurants, vending machines, and caterers. (See 

chapter 4 for more details.) 

Proprietary data sources are also used in this report. The NPD ReCount data collects store location 

data and organizes these data by outlet type and restaurant brand. The Euromonitor data reports 

sales for branded restaurants and menu type (Italian, Mexican, etc.). The NPD ReCount outlet classification system is similar to NAICS, except that it calls restaurants with counter service “quickservice” rather than “limited-service.” Analysis detailed in chapter 6 further breaks down limited- or 

quick-service outlet types—based on brand in the NPD ReCount and Euromonitor data—into a 

smaller subset called “fast casuals.” (See chapter 6 for more details.)

Individuals and households not only purchase foods at different retail and foodservice establishments, 

but they can also obtain foods as gifts, donations, or grow them at home. The following datasets define 

FAFH based on where food is acquired: 2012-13 Food Acquisition and Purchases Survey (FoodAPS), 

1977-78 National Food Consumption Survey (NFCS), 1989-91 and 1994-96 Continuing Survey of 

Food Intakes by Individuals (CSFII), 2001-12 Continuous National Health and Nutrition Examination 

Surveys (NHANES), and the American Time Use Survey (ATUS). In addition to places where food is

purchased, food can be acquired as donations from food banks and commodity food programs, grown 

in a garden, given as a gift, and in other ways that do not require a purchase.
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Table 1.2

Data sources and food-away-from-home definitions

Chapter Data source Description FAFH definition Food outlet types FAFH measure

1 Economic 

Census1

Store-level sales data by NAICS code and product line

Where food is purchased (NAICS) and 

where food is intended to be consumed 

(on- and off-premise 

consumption)

Food stores, other retail 

stores and eating and 

drinking places 

Number of 

establishments 

and sales

3

Annual 

ERS Food 

Expenditure 

Series2

Store-level sales data (i.e., 

5-year Economic Census and 

annual Census survey data) 

augmented with the value of 

food produced at home and 

donated and the value of food 

furnished to employed civilians, military and individuals 

at institutions (e.g., prisons, 

hospitals, nursing homes and 

so on)

Where food is intended to be consumed

Retail stores; limitedservice restaurants; 

full-service restaurants; 

drinking places; hotels and motels; retail 

stores; recreational 

places; schools and 

colleges; other FAFH 

(food furnished as part 

of another service)

Expenditures 

and expenditure 

shares of food

4

2005-14 

Consumer 

Expenditure Series 

(CES)2

Two-week household food 

expenditure diary for stratified sample of individuals with 

sample weights to extrapolate 

to population

Where food is 

purchased

FAH (prepared food, microwavable food, edible 

ingredients, unprepared 

ingredients, nonalcoholic drinks, and 

other purchased at retail 

stores); limited-service 

restaurants, full-service 

restaurants; other FAFH 

(vending machines, 

caterers, schools)

Expenditures 

and expenditure 

shares of food

2005-14 

Monthly 

Consumer 

Price 

Indexes 

(CPI)2

Monthly national market 

basked of prices for prices 

paid by consumers for FAFH

Where food is

purchased

FAH; full-service restaurants, limited-service 

restaurants, other FAFH

5

2012-13 

Food Acquisition and 

Purchase 

Survey 

(FoodAPS)

One-week household food 

expenditure diary for stratified sample of individuals with 

sample weights to extrapolate 

to population

Where food is 

obtained FAH; FAFH; schools Number of food trips per week

6

2000-15 

NPD 

ReCount

Exact location and brand of 

restaurants

Where food is 

purchased

Chain and independent quick-service and 

full-service restaurants 

(includes fast-casual 

chains) and restaurant 

brands

Density of establishments and 

sales

2006-14 

Euromonitor

Sales at highest selling 

branded restaurants and type 

of menu

Where foods is purchased

Restaurant brands and 

menu types (hamburger, 

subs/deli/other sandwich; pizza/Italian; Mexican; other menu type)

Continued—
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Table 1.2

Data sources and food-away-from-home definitions—continued

Chapter Data source Description FAFH definition Food outlet types FAFH measure

6

2001-15 

Quarterly 

Census of 

Employment and 

Wages

Number of establishments Where food is 

purchased Mobile food vendors

Density of establishments and 

sales 2001-15 

Monthly 

Retail 

Trade and 

Foodservice 

Series2

Monthly sales Where food is 

purchased

Full- and limited-service 

restaurants

7

1977-78 

National 

Food 

Consumption Survey 

(NFCS)

One day dietary recall quantities and nutrient intake for 

stratified sample of individuals with sample weights to 

extrapolate to population 

Where food is obtained

FAH; wait-staff restaurants; fast-food restaurants; school/day care; 

other FAFH (vending 

machines, common coffee pot, mobile vendor)

Energy intake 

(calories) and 

nutrient density, 

measured as percent calories from 

fat, saturated fat; 

nutrient intake per 

1,000 calories for 

other nutrients

1989-91 

and 1994-

98 Continuing Survey 

of Food 

Intakes by 

Individuals 

(CSFII)

2003-2012 

Continuous 

National 

Health and 

Nutrition 

Examination 

Surveys 

(NHANES)1

8 2012-13 

FoodAPS

One week household food 

expenditure diary for stratified sample of individuals with 

sample weights to extrapolate 

to population

Where food is obtainedFast-food and full-service restaurantsFrequency and 

diet quality

9 2012-13 

FoodAPS

One week household food 

expenditure diary for stratified sample of individuals with 

sample weights to extrapolate 

to population

Where food is obtained FAH; FAFH Diet quality

1Data are available for other years but not used in the analyses presented in this report. 

2Prior years of NHANES collect dietary recall data on where the food was consumed rather than where it was prepared.

Notes: FAFH = food away from home. FAH = food (prepared) at home.

Source: Various chapters within the report. 
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Among the aforementioned datasets, the NFCS, CFSII, and NHANES are 24-hour dietary recall 

surveys, which record quantities of foods consumed by individuals by outlet type (e.g., grocery 

stores, full-service restaurant, fast-food restaurant) and other sources (e.g., public donation, office 

coffee fund). These data are converted to nutrient intakes (fat, calories, sodium, etc.) and cup equivalents, which allow an interested user to assess the overall diet quality of the individual. (See chapter 

7 for more details.) The FoodAPS is a week-long household diary that records, by acquisition source, 

the quantities, prices, and expenditures of foods acquired by all household members. The FoodAPS 

data also include nutrition intake data derived from the food acquisitions, which are used in chapters 

8 and 9 to assess diet quality. The ATUS is a 24-hour time use diary in which individuals report the 

time they spent eating and drinking out (at either full- or quick-service restaurants) or elsewhere and 

the time spent in food preparation and cleanup.

FoodAPS is similar to the CES except that it collects food acquisitions from all sources, including 

home gardens and government commodity donation programs, besides the typical food establishments. 

Also, FoodAPS differs from CES because it oversamples low-income and SNAP-participating households. These data generally classify FAFH establishments similarly to NAICS (i.e., full service, limited 

service), but with some additional nomenclature to account for nonpurchase acquisitions. This generally affects what is contained in the “other FAFH” category across the chapters. 

FoodAPS differs from NFCS, NHANES, and CFSII in another key way: FoodAPS collects data on 

food acquisitions, and the others collect data on actual food consumption. This data difference could 

cause differences in measured quantities because many FAFH portion sizes (especially for restaurant foods) are often large and individuals may not always consume whole portions. Hence, reported 

quantities in the food acquisition diaries are likely to be higher than the dietary recall data. Also, 

FoodAPS contains nutritional intake information for the entire household while NHANES, NFCS 

and CSFII are based on individual intake. For measuring nutrient and energy intake for distinct 

groups like children or food assistance participants, individual intake information (as used in chapter 

7) would be more useful than household information.

Rather than defining FAH and FAFH by where they are acquired, as is typical, a few data sources, 

such as the Economic Census, define these terms based on where food is intended to be consumed. 

In this context, FAFH is food meant for on-premise consumption—meals and snacks prepared and 

consumed at any type of establishment except for home—and FAH being food meant for off-premise 

consumption—ingredients to meals and snacks purchased at any establishment and prepared at 

home. Foods are defined by type of product because foods that are more similar in form are categorized as the same, regardless of where they are acquired. 

The Economic Census data, discussed in the introduction, presents estimates of sales by NAICS 

industry and specifies whether food is meant for on- or off-premise consumption. Likewise, the 

Food Expenditure Series—an ERS data product that produces annual and monthly national-level 

estimates of the value of the food system—uses the same classification as the Economic Census to 

define FAFH. The Food Expenditure Series additionally disaggregates FAFH by outlet type. (See 

chapter 3 for more details.) 

The Food Expenditure Series differs from other expenditure datasets like FoodAPS and CES by 

what is included in sales. FAFH in the Food Expenditure Series is total household and business 

expenditures, whereas other expenditure datasets capture only household spending. This difference is particularly relevant to measures of foodservice spending because approximately 10 percent 

of foodservice sales were expensed meals by businesses and government in 2012 (Census Bureau, 
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2016c). The effect of excluding business purchases from FAFH is apparent when inspecting the 

magnitude of and variation in the FAFH shares of the food budget by dataset. For example, in 

chapter 3, which utilizes the Food Expenditure Series, FAFH accounts for more than 50 percent 

of the food budget in 2016 while, in chapter 4, which uses CES data, FAFH accounts for only 44 

percent of the food budget that same year. Additionally, with the inclusion of business expenditures 

in the Food Expenditure Series, chapter 3 shows that FAFH expenditures by all purchasers started 

to rebound in 2010. In contrast, based on the CES data which only captures household purchases, 

chapter 4 observes that the rebound in household FAFH spending started later in 2013. Like CES, 

FoodAPS (which is discussed in chapters 5, 8, and 9) captures household spending only, and average 

household spending on FAFH is similar between datasets (Clay et al. 2016).

By using multiple datasets, with various advantages and disadvantages stemming from their data 

collection methods, the report aims to give a comprehensive overview of FAFH and its implications 

for Americans’ nutrition and health. For example, chapter 3 uses the annual ERS Food Expenditure 

Series, which captures sales from all purchasers and provides a macroeconomic look at FAFH 

spending, whereas chapter 4 uses the CES and CPI, which capture spending only at the household 

level (see table 1.2). 

Although CES covers a narrower survey pool than the Food Expenditure Series, the advantage of 

CES is that food purchasing behavior can be further broken down by sociodemographic group and 

by income level—an option that is unavailable in the Food Expenditure Series. This more refined 

breakdown is crucial for tracking nutrition and health issues for which trends often differ by socioeconomic groups. These measures (Food Expenditure Series and CES), however, may overstate 

the role of FAFH because expenditure trends reflect not only changes in quantities purchased but 

also changes in prices. Because prices of FAFH have outpaced FAH, the observed upward trends in 

FAFH spending shares may have been exaggerated.

The remaining chapters examine FAFH characteristics using measures that are independent of price. 

Chapter 5 measures FAFH using number of trips to FAFH establishments, and similarly, chapter 

8 categorizes households by how often they frequent FAFH establishments. Measuring FAFH by 

frequency, however, does not capture the actual quantities consumed on each outing. Chapter 6 uses 

the number of restaurants in each U.S. county to approximate FAFH supply. Similar to the limits to 

the measures of FAFH spending, there are limits to supply measures because no data are collected 

on how much each store sells. This limitation makes it difficult to discern whether the placement of 

FAFH locations decisions are driven by supply or demand. Chapters 7 and 8 analyze FAFH in terms 

of nutrient composition and diet quality, which are useful for gauging the effects of FAFH in the 

overall diet.

Given that consumer food choices are driven by multiple influences—foods’ contribution to diet 

and health outcomes as well as their price, locations, and other environmental and socioeconomic 

factors—all of the measures used in this report reveal useful aspects of FAFH’s evolving role in 

American consumers’ lifestyles, food budgets, diet, and health. 
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Conclusion

Over the past several decades, foods prepared outside of the home have become increasingly integral to the American diet. Many researchers are interested in the implications of increased FAFH 

consumption for health outcomes and for how food markets will adapt to accommodate changing 

tastes and preferences for FAFH. This report compiles research that investigates current trends in 

food-away-from-home (FAFH) choices and availability, nutrient intake from FAFH, and FAFH’s 

associations with diet quality. 

This report finds that, although FAFH consumption has generally increased in the past several 

decades, this trend was temporarily curbed during the Great Recession as Americans shifted 

a greater share of household resources toward prepared and unprepared food-at-home (FAH) 

ingredients. This countertrend toward more FAH purchases continued through 2014 for most 

household types. 

Evidence shows consistent purchasing patterns across sociodemographic groups. For example, 

higher income households spent more on FAFH and had more FAFH purchase occasions. In addition, households with an elderly individual spent a smaller share of their food budget on FAFH. The 

older a household head was, the less frequently that household head purchased FAFH. Households 

with children spent a smaller share of their food budget on FAFH and had fewer purchase occasions 

of FAFH than their childless counterparts. 

As overall demand for FAFH has grown, so has the supply, albeit heterogeneously by segment. 

Since 2000, the fastest growing segment of FAFH has been chain quick-service restaurants, which 

expanded even during the Great Recession. Within the quick-service segment, fast-casual restaurants, which are a hybrid between quick- and full-service restaurants, show the speediest growth—

twice the rate of traditional quick-service restaurants. Similar to what was observed in the last 

century, much of the recent growth in FAFH establishments has occurred in urban U.S. counties, 

consistent with patterns of urban and rural migration.

Average American energy intake from FAFH consumption has doubled from 17 percent in 1977-78 

to 34 percent in 2013-14. FAFH generally contains more saturated fats and sodium, and less calcium, 

iron, and fiber than FAH does. Related to the concern about FAFH’s nutritional value is the question 

of whether greater access to FAFH establishments influences diet quality (i.e., adherence to USDA’s 

Dietary Guidelines). Although frequent quick-service consumers purchased less vegetables, fish, and 

nuts, their overall diet quality was no worse than that of FAFH nonconsumers, which suggests that 

quick-service density and proximity to consumers are not associated with poor diet. 

Because FAFH constitutes a substantial part of the typical American diet and is associated 

with poor nutrient intake and overall poor diet quality, policymakers, health practioners, and 

researchers have suggested policy solutions to curb the purchase of prepared foods from both 

foodservice and retail establishments. In one policy area, the U.S. Department of Health and 

Human Services’ Food and Drug Administration (FDA) created menu label regulations that 

require chain restaurants with 20 or more locations to make calorie information publically available. This may help consumers make healthier choices and may also encourage restaurants to 

provide healthier options. However, a review of the literature on restaurant menu labeling and its 

effects on calorie consumption turns up mixed evidence. 
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Current food assistance programs that support low-income families in making choices consistent 

with USDA’s Dietary Guidelines may also influence FAFH purchasing decisions. The benefits 

bolster these families’ budgets, allowing them to purchase more food (both FAH and FAFH), as 

well as other goods. Additionally, new requirements for school breakfasts and lunches improve the 

nutrition of school meals, directly affecting children’s diets. Evidence from the analysis in this report 

shows FAFH’s average nutritional quality was consistently lower than that of FAH, and this finding 

held true for households across all income levels including households that participate in SNAP. This 

suggests that FAFH choices are heavily driven by consumer preferences. Alternatively, the National 

School Lunch Program is shown to improve nutrient consumption for participating households.

Several questions still remain to be answered. For example, figuring prominently in many of the 

chapters, the Great Recession marks a time during which household FAFH expenditures and sales 

declined; the number of FAFH establishments decreased; and the nutritional quality of FAFH relative to FAH seemed to improve. Full-service restaurants lost more business than quick-service did, 

and households appeared to replace full- and quick-service options with prepared FAH from grocery 

stores and other retail stores. Although some of the report’s analysis found that FAFH consumption 

rebounded to pre-recession levels, this finding was not consistent with other analyses. Data from 

NPD Group shows that, in 2016, because of the increased interest in cooking among young adults, 

home cooking appeared to stabilize after decades of decline (NPD Group, 2016). Also, although 

sales of on-premise foods at all retail and foodservice establishments somewhat rebounded after the 

Great Recession, sales for the full-service segment did not, and the number of meals purchased by 

households at restaurants had yet to rebound by 2014 (NPD Group, 2014). 

Full-service restaurants may also be slow to rebound after the Great Recession as a result of 

evolving demographics of the labor force. Baby Boomers,7 who are core consumers of full-service 

restaurants are entering retirement and facing reduced income and are increasingly being replaced 

by Millennials in the labor force who prefer the high-quality quick-service option of fast casuals 

(Klara, 2017), at least at this point in their lifecycle. The question remains of how to disentangle 

enduring Great Recession’s effects, if they exist, on FAFH demand. It also remains to be seen how 

generational preferences, which continue to evolve, will affect nutrient intake, overall diet quality, 

and health outcomes.

7The “Millennial” generation comprises those born between 1981 and 1996, and the Baby Boom generation comprises 

those born between 1946 and 1964.
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References

Beatty, T.K. and B. Senauer. 2012. “The New Normal? US Food Expenditure Patterns and the 

Changing Structure of Food Retailing,” American Journal of Agricultural Economics 95(2): 

318-324.

Bureau of Labor Statistics, U.S. Department of Labor. 2017. “CPI—Food at Home in U.S. City 

Average, All Urban Consumers, Not Seasonally Adjusted” (CUUR0000SAF11). Published 

online.

Bureau of Labor Statistics (U.S. Department of Labor). 2017. “CPI—Food Away From Home in U.S. 

City Average, All Urban Consumers, Not Seasonally Adjusted” (CUUR0000SEFV). Available 

online.

Business Wire. 2004. “Americans May Be Finding Balance In Their Eating Patterns; The NPD 

Group Releases Findings From its 19th Annual Report on Eating Patterns in America.” Published 

online.

Chou, S-Y, I. Rashad, and M. Grossman. 2008. “Fast-Food Restaurant Advertising on Television and 

Its Influence on Childhood Obesity,” The Journal of Law and Economics 51(4): 599-618.

Clay, M.C., M. Ver Ploeg, A. Coleman-Jensen, H. Elitzak, C. Gregory, D. Levin, C. Newman and 

M.P. Rabbitt. 2016. Comparing National Household Food Acquisition and Purchase Survey 

(FoodAPS) Data With Other National Food Surveys’ Data. EIB 157. United States Department 

of Agriculture, Economic Research Service.

Currie, J., S. DellaVigna, E. Moretti, and V. Pathania. 2010. “The Effect of Fast Food Restaurants on 

Obesity and Weight Gain,” American Economic Journal: Economic Policy 2(3): 32-63.

Cutler, D.M. E.L. Glaeser, E.L. and J.M. Shapiro. 2003. “Why Have Americans Become so Fat?” 

The Journal of Economic Perspectives 17 (3): 93-118.

Dhar, T. and K. Baylis. 2011. “Fast-Food Consumption and the Ban on Advertising Targeting 

Children: the Quebec Experience,” Journal of Marketing Research 48(5): 799-813.

Federal Trade Commission. 2012. A Review of Food Marketing to Children and Adolescents. 

Federal Trade Commission, Washington, DC.

Ferdman, R.A. 2015. “The Slow Death of Cooking,” The Washington Post, March 5. 

Hamrick, K.S. and K. McClelland. 2016. Americans’ Eating Patterns and Time Spent on Food: 

The 2014 Eating & Health Module Data. EIB-158. United States Department of Agriculture, 

Economic Research Service.

Huffman, W.E. 2011. “Household Production and the Demand for Food and Other Inputs: US 

Evidence,” Journal of Agricultural and Resource Economics 36(3): 465-487. 

Ippolito, P.M. 2011. “Regulation of Advertising,” Chapter 42 in J. Cawley, ed. The Oxford Handbook 

of The Social Science of Obesity Oxford: Oxford University Press.
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Klara, R. 2017. “Why America’s Aging Casual-Dining Chains Are Quietly Building Fast-Casual 

Restaurants,” Adweek, February 15.

Kuhns, A.M. and M. Saksena. 2017. Food Purchase Decisions of Millennial Households Compared 

to Other Generations. EIB-186. United States Department of Agriculture, Economic Research 

Service.

Lin, B.H., E. Frazao, J. Guthrie. 1999. Away-From-Home Foods Increasingly Important to Quality 

of American Diet, AIB-749, U.S. Department of Agriculture, Economic Research Service.

Lin, B. H., and Guthrie, J. 2012. Nutritional quality of food prepared at home and away from home. 

US Department of Agriculture, EIB-105, U.S. Department of Agriculture, Economic Research 

Service.

MacEwan, J.P., and A.M. Okrent. 2017. “A Review of US Food and Nutrition Programs and Their 

Effectiveness in Assuring Adequate Nutrition and Improving Nutritional Choices for Program 

Participants,” in Handbook of International Food and Agricultural Policies, Volume II: Policies 

for Food Safety and Quality, D. Roberts and T. Josling, eds. Singapore: World Scientific 

Publishing.

NPD Group. 2016. “Turns Out Each Generation Approaches The ‘What’s for Dinner?’ Question 

Differently,” August 16.

NPD Group. 2014. “The NPD Group Releases the 29th Annual Eating Patterns in America Report,” 

November 10. 

Okrent, A.M., and J.M. Alston. 2012. The Demand for Disaggregated Food-Away-From-Home and 

Food-at-Home Home Products in the United States, ERR-139, U.S. Department of Agriculture, 

Economic Research Service. 

Quigley, M. 2016. “Good Eats: Millennials Like Fast, Fresh and Fun,” AARP Blog. Available 

online.

Restrepo, B. 2017. “Calorie Labeling in Chain Restaurants and Body Weight: Evidence from New 

York,” Health Economics 26(1): 1191-1209.

Sloan, A.E. 2016. “What, When, and Where American Eats,” Food Technology 70(1). 

Sturm, R., and A. Hattori. 2015. “Diet and Obesity in Los Angeles County 2007-2012: Is There a 

Measurable Effect of the 2008 ‘Fast-Food Ban’?” Social Science & Medicine 133: 205-11.

Todd, J.E., and B. Scharadin. 2016. Where Households Get Food in a Typical Week: Findings From 

USDA’s FoodAPS, EIB-156, U.S. Department of Agriculture, Economic Research Service.

Tuttle, C. 2016. The Stimulus Act of 2009 and Its Effect on Food-At-Home Spending by SNAP 

Participants, ERR-213, U.S. Department of Agriculture, Economic Research Service.

U.S. Department of Agriculture, Food and Nutrition Service (FNS). 2017. “School Meals—Nutrition 

Standards for School Meals,” January 2.
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U.S. Department of Agriculture, Agricultural Research Service. 2014. “Nutrient Intakes from Food 

and Beverages: Mean Amounts Consumed per Individual, by Gender and Age,” in What We Eat 

in America, NHANES 2011-2012.

U.S. Department of Agriculture, Food and Nutrition Service. 2016. “Schools/Child Nutrition USDA 

Food Programs.” Food and Nutrition Service Nutrition Program Fact Sheet. Available online.

U.S. Department of Commerce, U.S. Census Bureau. 1979. “Economic Census, Retail Trade—

Merchandise Line Sales Series: 1977.” Available online.

U.S. Department of Commerce, U.S. Census Bureau. 2000. “Historical National Population 

Estimates: July 1, 1900 to July 1, 1999.” Available online.

U.S. Department of Commerce, U.S. Census Bureau. 2016. “Table 1: Annual Estimates of the 

Resident Population for the United States, Regions, States and Puerto Rico: April 1, 2010 to July 

1, 2016.” Available online.

U.S. Department of Commerce, U.S. Census Bureau. 2016a. “Economic Census, Accommodation 

and Food Services: Subject Series—Product Lines: Product Lines Statistics by Industry for the 

US and States: 2012.” Available on American FactFinder.

U.S. Department of Commerce, U.S. Census Bureau. 2016b. “Economic Census, Retail Trade: 

Subject Series—Product Lines: Product Lines Statistics by Industry for the US and States: 2012.” 

Available on American FactFinder.

U.S. Department of Commerce, U.S. Census Bureau. 2016c. “Economic Census, Accommodation 

and Food Services: Subject Series—Miscellaneous Subjects: Sales by Class of Customer for the 

U.S.: 2012.” Available on American FactFinder.

Variyam, J. 2005. Nutrition Labeling in the Food-Away-From-Home Sector: An Economic 

Assessment, ERR-4, U.S. Department of Agriculture, Economic Research Service. 
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Chapter 2: A Brief History of Food Away From Home in 

the United States8

This chapter provides a historical overview of the growth in the food-away-from-home 

industry and the primary drivers of its growth in the 19th and 20th centuries in the United 

States. With the invention of automobiles, Americans became more mobile, and restaurant franchises grew to accommodate traveling Americans by offering low-cost, fast, and 

predictable food. In addition, increases in women’s labor force participation spurred 

modern foodservice establishments to cater to families. 

At the turn of the 20th century, the majority of Americans ate most of their meals at home, prepared 

from foods both grown at home and purchased from local stores. By the turn of the 21st century, the 

balance had shifted dramatically, with food eaten away from home rising to more than half of all 

food expenditures in the United States by 2014 (see chapter 3 for more details). Across the century, 

U.S. society changed in dramatic ways, transforming American eating habits. This brief historical 

essay surveys how the changing geography of work and urban life and changing attitudes toward 

family, children, and leisure time affected both food preferences and eating routines, moving a large 

share of U.S. eating away from the home and into a varied array of eating places and new foods.9

Colonial Era Through the 19th Century

Prior to the 20th century, many Americans—both rural and urban—ate away from home when 

they attended events like weddings, funerals, fairs, community socials, and shared work activities. 

However, attendees often brought food for these occasions from their homes or were fed with food 

prepared by other participants at the event. Eating food away from home that was prepared by and 

purchased from others began as an accommodation for travelers and transient workers, then developed with the rise of cities to serve resident workers and businesspeople. In the latter half of the 19th 

century, industrialization and urbanization spurred more rapid change. By the mid-20th century, the 

widespread adoption of the automobile, the increasing numbers of women working away from home, 

8Use of commercial and trade names does not imply approval or constitute endorsement by USDA.

9The narrative in this essay appears in similar form in nearly all sources consulted, making the use of standard parenthetical citations awkward with repetitive multiple references. This essay draws from two of the most frequently cited sources, 

Richard Pillsbury’s From Boarding House to Bistro: The American Restaurant Then and Now (Cambridge, MA: Unwin 

Hyman, 1990) and John Mariani’s America Eats Out (New York: Morrow, 1991). Other sources that support and supplement 

the history include John A. Jakle and Keith A. Sculle, Fast Food: Roadside Restaurants in the Automobile Age (Baltimore: 

Johns Hopkins University Press, 1999); Anne Cooper Funderberg, Sundae Best: A History of Soda Fountains (Bowling 

Green: Bowling Green State University Popular Press, 2002); Linda Civitello, Cuisine and Culture (3rd ed., Hoboken, NJ: 

Wiley, 2011); Andrew Hurley, “From Hash House to Family Restaurant: The Transformation of the Diner and Post-World 

War II Consumer Culture,” Journal of American History (Vol. 83, No. 4, 1997, pp. 1282-1308); Warren J. Belasco, “Toward 

a Culinary Common Denominator: The Rise of Howard Johnson’s, 1925-1940,” Journal of American Culture (Vol. 2, No. 3, 

1979, pp. 503-518); and Michael Karl Witzel, The American Diner (Osceolo, WI: Motorbooks International, 1994).

Readers who would like to explore the subject further may also want to consult Richard J. S. Gutman, American Diner: 

Then and Now (Baltimore: Johns Hopkins University Press, 1993); Warren J. Belasco, Appetite for Change: How the 

Counterculture Took on the Food Industry, 1966-1988 (New York: Pantheon, 1989); Michael Karl Witzel, The American 

Drive-In (Osceola, WI: Motorbooks International, 1994); Jim Heimann, Car Hops and Curb Service (San Francisco: 

Chronicle Books, 1996); David Gerard Hogan, Selling ‘em by the Sack: White Castle and the Creation of American Food

(New York: New York University Press, 1997); and Richard Osborn, The American and His Food (2nd ed. Chicago: Univ. 

of Chicago Press, 1941). 
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higher disposable incomes, and expanded leisure time had launched the transformation of the food 

landscape into its modern form. 

Taverns, inns, and boarding houses were the earliest places to provide food for purchase by travelers 

and transient workers. By most accounts, while the food was often passable, the need to eat away 

from home was tolerated as an inconvenience. For the wealthy traveler, early offerings were perhaps 

more sanitary and the setting more private, but not by much. Luxury accommodations for eating 

away from home improved in the 19th century as a growing population of wealthy industrialists and 

their families demanded well-appointed dining at hotels and on trains, steamboats, and ocean liners. 

Taverns could also function as drinking houses for local populations, and boarding houses became 

common as housing for young, single industrial workers by the early 19th century. Coffee houses 

and oyster houses, which began as specialty eateries, quickly became (respectively, although not 

exclusively) gathering places for businesspeople and sources of cheap meals for workers in the cities 

along the eastern seaboard. For the wealthy, hotel restaurants like Delmonico’s in New York and 

Antoine’s in New Orleans became destinations for conspicuous consumption at evening events and 

family gatherings. By the mid-19th century, French cuisine, as well as specialty American restaurants and elegant hotel dining, had appeared in most major U.S. cities.

Changes over the mid- to late-19th century in urban and industrial work patterns necessitated new 

kinds of eating places. The larger geographic spread of cities could make it difficult for boardinghouse lodgers, as well as those with families, to return home for mid-day meals, prompting a variety 

of businesses to serve these emerging needs. Around the same time, better paid office workers began 

to choose lodging without meals over boardinghouses, favoring the “European plan” in their rental 

arrangements and opting out of the meals traditionally included with rooms. As a result of both 

changes, a variety of restaurants grew up to serve this new clientele. Street vendors, lunch wagons, 

diners, soda fountains, luncheonettes, plate houses, lunch rooms, and cafeterias all appeared or 

expanded as places for working men, and eventually women, to get a quick, cheap meal. 

The Turn of the 20th Century

Street vendors were among the earliest to provide meals to industrial shift workers who could 

not meet the meal schedules of their boardinghouses or the taverns and other eating houses that 

offered meals only at set times. Early street vendors, who carried baskets of sandwiches and other 

simple food items for sale, gave way to the lunch wagon, first devised in 1872 by Walter Scott, 

in Providence, Rhode Island. A horse-drawn wagon sheltered Scott from inclement weather and 

enabled him to serve customers continuously without returning home for more stock. By 1887, 

another innovator, Samuel Messer Jones, used a wagon large enough to accommodate customers 

who wanted to come inside and eat their meals. The rapid expansion of these mobile eateries led, 

in the early decades of the 20th century, to city ordinances controlling their hours of operation. In 

response, vendors began to park their wagons, cover the wheels, and hook up to utilities, inventing 

the diner. Originally constructed from surplus trolleys, streetcars, and railroad dining cars, massmanufactured diners came to epitomize this form of eating place by the 1920s.

Neighborhood restaurants that served fast, inexpensive meals to mostly white-collar workers— 

variously known as soda fountains, luncheonettes, and lunch rooms—began to appear after the 

Civil War in nearly every city. Lunch rooms, which developed from the “plate houses” and earlier 

taverns, functioned as a 19th–early 20th century version of the fast-food restaurant—simple, 
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inexpensive food served quickly. Soda fountains, which served soda water flavored with various 

syrups, became a popular addition to drug stores and candy stores, bringing in additional business 

and enabling customers to socialize on stools adjacent to the serving counters. By the 1880s, many 

soda fountains added light meals to their offerings, keeping their counters busy at times of day when 

sweets were less in demand and creating the luncheonette. As temperance took hold, soda fountains 

and luncheonettes substituted for saloons as social gathering places; in fact, during Prohibition in the 

1930s, many saloons transformed themselves into soda fountains and luncheonettes. 

Cafeterias with self-service dining offered yet another innovation in quick, inexpensive eating. 

The earliest cafeteria, the Exchange Buffet in New York, opened in 1885 offering men only a selfservice restaurant that greatly increased speed for office workers in a hurry and reduced costs for 

the restaurant by eliminating the need for table service. The tray was introduced in 1898 at the 

Childs Brothers’ lunch room in New York and transformed the cafeteria from a standup counter 

arrangement into a sit-down table form. In 1902, Joseph Horn and Frank Hardart opened a specialized version of the cafeteria in Philadelphia called the Automat, based on a German design, which 

allowed customers to choose food items from a wall of coin-operated windowed compartments. The 

Automat concept expanded successfully in Philadelphia and New York, but failed to take hold in 

other cities. 

Many early cafeterias were operated by nonprofits in working-class neighborhoods and by factories 

and other large employers to serve their own workers. The style of service caught on particularly in 

Los Angeles in 1905, and cafeterias came to be known as “California-style restaurants” when they 

spread to other areas. They became particularly popular in the South, where restaurants were in 

short supply. Cafeterias opened in 1915 in Washington, DC, and expanded rapidly with the influx of 

office workers to staff an expanding Federal Government during the First World War. 

As eating places changed, so did the food they served. Early boarding houses, restaurants, and lunch 

places offered hot, home-style meals mimicking those that workers would once have enjoyed in their 

homes. The changing workplace, far from residential areas and operating through multiple shifts 

to keep factories running all hours, transformed not just eating venues but offerings as well. Quick 

lunches from street vendors and lunch wagons (and later from lunch rooms and diners) demanded 

simpler foods like hotdogs and hamburgers that turned hot meat dishes into hand-held sandwiches. 

For breakfast, fried eggs, bacon, and pancakes could be easily prepared on the same grills as the 

lunchtime hamburgers.

New equipment and food processing methods—some originating as early as the 1890s, but many not 

widely adopted until the 1920s and 1930s—also contributed to the efficiency and appeal of restaurants. Homogenized milk, sliced bread, milk bottles, coffee percolators, bottle caps, refrigerators, 

automatic toasters, electric mixers, griddles, waffle irons, hotdog steamers, deep-fat fryers, slicing 

machines, stainless steel tableware/cookware, dishwashers, conveyor belts (to move dirty dishes to 

the dishwashers), the jukebox, Formica counters, and air conditioning all contributed to the ease of 

preparing and serving meals quickly and to the comfort and appeal of the new eating places. 

The Changing Eating Culture

As the share of women in the national workforce rose from 15 percent in 1870 to 22 percent by 1930 

(Jakle and Sculle, 1999, p. 95), urban eating options expanded to compensate for meals once eaten in 

homes where women were no longer available to cook. Luncheonettes and diners also expanded their 
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menus and improved their ambiance to appeal to women workers. At the same time, commercial eating 

places added booths and table service to accommodate children, and devised entertaining themes and 

children’s menus to capture the rising disposable incomes that made family meals out a more common 

occurrence. Diners, luncheonettes, soda fountains, drive-ins, and other casual quick-serve restaurants 

became popular venues for meeting friends, dining with family, and celebrating events. 

Despite the rapid development of restaurants in the late 19th to early 20th centuries, they remained 

a small part of the American eating landscape, mostly clustered in the business centers of industrialized cities. Then beginning in the 1930s and increasingly rapidly in the post-World War II years, 

the automobile became an integral part of American life. In the 1930s, 26 million automobiles and 

thousands of miles of new road enabled whole populations to be more footloose in search of work, 

community, and adventure. And all of these travelers needed to eat. 

Roadside restaurants of various shapes and sizes and differing widely in quality and sophistication—

many associated with gasoline filling stations and overnight lodging—grew up to meet the need for 

mobile dining. While many of the longlasting restaurant and fast-food chains began or expanded 

because of their roadside locations, most roadside restaurants (at least through the 1970s) remained 

individually owned or part of small, relatively local chains and served a wide range of foods, 

including breakfast; snacks like donuts and ice cream; sandwiches, including burgers and hotdogs; 

and ethnic/regional specialties. 

Still, the expansion in numbers of restaurants in the latter half of the 20th century owed a great deal 

to the rise of chain restaurants—not just several restaurants with a single owner, which had been 

relatively common since the late 19th century, but restaurants linked by duplication of structure, 

theme, food, service, and amenities. Centralized sourcing of supplies, some food preparation, and 

building design streamlined management tasks and reduced costs, while controlling food processes 

to ensure a predictable quality of food and experience for customers (i.e., branding). As a result, 

entrepreneurs could expand their successful restaurant formats to regional and even national scale. 

Chain restaurants, like so many other restaurant types, had their origins in the industrial changes 

of the 19th century. The Harvey House, a chain developed in towns along the Santa Fe railroad in 

the Southwest, was an early version of the travel-oriented chain restaurant. They became an icon of 

clean dining rooms, good food, and reliable service. Fred Harvey established—through practices 

like careful menu control, high-quality food (by travel standards), and spotless dining rooms—the 

foundations used by later restaurant chains like Howard Johnson’s that traded on a perception of 

cleanliness, predictable food, and family friendliness.

The Rise of National Franchise Chain Restaurants 

In the 1920s and 1930s, some of the most famous chain restaurants—Horn & Hardarts, Howard 

Johnson’s, A&W Root Beer, Bob’s Big Boy, Dairy Queen, White Castle, and Marriott Hot Shoppes 

among them—appeared in urban areas as walk-up lunch rooms, cafeterias, and hamburger stands. 

The earliest of these chains focused on urban working populations. However, as suburban expansion 

and leisure road travel began to increase around midcentury, most chains altered their designs to 

accommodate parking and drive-in services. New chains appeared specifically to lure automobilebased customers. And as chains moved into suburban settings, the notion of eating out for fun rather 

than as a worktime concession gained traction, fully exploiting the potential for increased sales that 

could come from turning eating out into entertainment. 
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The expansion in the 1950s of chains—spurred by the franchise model developed by Howard 

Johnson in the 1930s and used so successfully by McDonald’s, Kentucky Fried Chicken, Domino’s, 

Long John Silver’s, Burger King, and Sonic, among others—established the familiar national landscape of easy-access, low-cost, quickly served, and predictable food. The franchise model was a 

variation on the earlier centralized chain system that solved the problem of effective central management across widely dispersed locations. Individual owners were licensed to operate local restaurants 

using the chain’s common building design, food, and delivery theme under contracts that specified 

quality and consistency. Franchises facilitated operation at the larger regional and national scale 

made profitable by the increased mobility afforded by the automobile.10

The network of restaurants across the United States grew slowly into the 1960s, from 127,000 

restaurants in 1954 to 135,000 in 1967, and then rapidly increased by more than 100 percent by the 

mid-1980s (Pillsbury, 1990, p. 103). Fast-food restaurants accounted for 40 percent of U.S. restaurants by 1983, and Americans ate out an average of 3-4 times a week, spending 40 percent of their 

food budgets on eating out (Mariani, 1991, p. 174). Breakfast food chains like IHOP, Perkins, Waffle 

House, and Dunkin’ Donuts began to proliferate in the 1960s, first serving commuters and shift 

workers traveling to and from work but, like other chain restaurants, quickly adding families and 

teenagers in search of leisure activities. The addition of breakfast sandwiches at traditional lunch 

fast-food chains like Hardee’s and McDonald’s in the 1980s and at coffee chains like Starbucks in 

the 1990s added breakfast to the list of meals regularly eaten out.

Conclusion

Changes in the geography of work, the structure of the family, the availability of disposable income 

and leisure time, and personal mobility offered by cars and good roads have combined to alter what 

and where we eat. These kinds of developments will continue to affect our eating preferences, and 

the future will certainly bring more changes. Some new trends, such as internet ordering of fresh 

prepared meals for home delivery, rekindle older eating patterns like family meals at home. Just 

as surely, however, new forms of personal transportation, new techniques and technologies for 

food preparation, and new cuisines will take us in different directions, creating innovative ways to 

combine work, leisure, and eating. 

References

Jakle, J.A., and Keith A. Sculle. 1999. Fast Food. Baltimore: Johns Hopkins University Press. 

Mariani, J. 1991. America Eats Out. New York: Morrow.

Pillsbury, R. 1990. From Boarding House to Bistro: The American Restaurant Then and Now. 

Boston: Unwin, Hyman.

10See especially Jakle and Sculle, pp. 69-73, for a discussion of the rise of franchising in the restaurant industry.
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Chapter 3: A Retrospective of Food-Away-From-Home 

Expenditures From 1987 to 2017

This chapter examines macroeconomic trends in food-away-from-home expenditures 

(FAFH) by outlet type (e.g., full- and limited-service restaurants, hotels and motels, 

grocery stores, and schools and colleges) using the Food Expenditure Series and relates 

the observed trends to the economics literature on FAFH. Between 1987 and 2017, FAFH 

steadily grew as a percentage of total food expenditures, exceeding food at-home (FAH)

expenditures for the first time in 2010. Of the three recessions that occurred during the past 

three decades, only the Great Recession appears to have induced a temporary reduction in 

FAFH expenditures (December 2007 to June 2009). 

In 2017, Americans spent $13,395.5 billion on personal consumption expenditures (PCE).11 Food 

expenditures were the third highest aggregate spending category of the U.S. economy, surpassed 

only by housing and transportation expenditures (Kuhns, 2018). Of this total, housing comprised 

33.3 percent, transportation was 17.0 percent, and food accounted for 12.6 percent (Kuhns, 2018). 

Changes in aggregate food spending are largely attributable to major economic and demographic 

developments (Stewart et al., 2004). The previous chapter gives a historical account of the evolution 

of FAFH. As a complement, this chapter provides an overview of broad trends in U.S. food-awayfrom-home (FAFH) expenditures during the 30-year period from 1987 through 2017. Because there 

is some variation in the way FAFH is defined, the chapter begins with an overview of the Food 

Expenditure Series, the basis of this chapter’s analysis. The second section explores general trends 

in FAFH expenditure patterns by outlet type and purchaser and their relationship to personal disposable income. The last section discusses how various economic and demographic factors may drive 

observed FAFH expenditure patterns, including an assessment of the effect of recessions. 

USDA’s Food Expenditure Series

The Food Expenditure Series is an annual time series that estimates the value of all food acquired 

in the United States, including total household food sales, and the cost of food provided to institutionalized populations (e.g., inpatients at hospitals and nursing homes) (Manchester and King, 1979; 

Manchester, 1987; Manchester, 1990; Okrent et al., 2018). The Food Expenditure Series allocates 

the value of food acquired into FAH and FAFH categories. FAH expenditures include sales of food 

for off-premise consumption from grocery stores; other retail stores (e.g., warehouse/wholesale 

clubs and supercenters, gas stations and convenience stores, and department stores); home delivery 

and mail order; direct sales by farmers, manufacturers, and wholesalers; and donations and home 

production. FAFH expenditures comprise sales of food for on-premise consumption from eating and 

drinking places, hotels and motels, retail stores and direct-sales establishments, recreational places, 

schools and colleges, and other places (such as military exchanges and institutions such as hospitals 

and prisons). For example, a deli sandwich purchased at a grocery store would be classified as an 

FAFH expenditure because such foods are typically consumed on the premises of the store. 

The Food Expenditure Series further allocates FAFH by outlet type, including sources that are not 

primarily engaged in selling meals and snacks, such as hotels and motels, retail stores and direct11PCE measures consumer spending on goods and services in the U.S. economy and gives an indication of how much 

household income is allocated for current spending versus saved for future consumption.
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sales venues; recreational sites; schools and colleges; military exchanges; railroad dining cars; 

institutions (e.g., prisons, group homes); and supplies to military forces. Eating places include fullservice and limited-service restaurants. Full-service restaurants have wait staff to take orders and 

deliver food, whereas food is ordered at a counter at limited-service restaurants.

The Food Expenditure Series also breaks down FAH and FAFH expenditures by purchaser type, i.e., 

families and individuals, Government, and businesses. FAFH purchased by families and individuals 

includes expenditures for meals and snacks purchased by or provided to them as part of employment 

or another service (e.g., inpatient meals at hospitals). FAFH purchased by Government includes 

foods donated to schools and meals provided to incarcerated individuals and the military. FAFH 

purchased by businesses includes expense account meals. Expenditures by families and individuals 

are expressed as a percent of disposable personal income (DPI).

The Food Expenditure Series was recently revised to incorporate improved data and methods, and 

its data begin in 1997. The comprehensive revision resulted in major revisions to the magnitude 

of the Food Expenditure Series. Because of the extent of the changes, the comprehensive revision 

establishes a break with the previously published Food Expenditure Series, the data of which began 

in the 1800s. However, the revised FAH and FAFH estimates and the previously published estimates 

mostly grew at the same rate from year to year (Okrent et al., 2018). To provide a longer historical 

perspective in this chapter, we use the rate of change in the previously published Food Expenditure 

series to pull the revised 1997 estimates back to 1987. 

FAFH Over Time

FAFH expenditures rose steadily between 1987 and 2017, with a concurrent decline in the share of 

FAH spending. In 2007, the FAFH and FAH shares of total food expenditures were approximately 

equivalent, but in 2008-09, the FAFH share dipped below 50 percent. By 2010, the FAFH market 

share surpassed the FAH market for the first time (fig. 3.1). FAFH expenditures totaled $616.4 

billion in 2010, about 50.2 percent of total U.S. food spending for that year, equal to $332.0 billion in 

1988 dollars (fig. 3.2). 

Food sales at restaurants, including full- and limited-service restaurants, accounted for 71.9 percent 

of FAFH expenditures in 2017; in 1987, their combined share stood at 66.6 percent (fig. 3.3). During 

the past 30 years, full-service restaurants consistently comprised the larger share of FAFH expenditures, rising from 34.0 to 35.8 percent. Meanwhile, the limited-service eating place share increased 

from 32.6 percent of FAFH expenditures in 1987 to 36.1 percent in 2017. Spending at full- and 

limited-service restaurants has consistently risen in a parallel manner, except for a brief period 

during the 1990s (fig. 3.4). Limited-service restaurant sales rose at a faster rate during this interval, 

briefly surpassing the market share of full-service restaurants in 1995 before full-service restaurants 

regained their dominance the following year.
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Figure 3.1

Relative shares of the two major food markets, 1987-2017

Source: USDA, Economic Research Service Food Expenditure Series.

0

10

20

30

40

50

60

70

80

90

100

1987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017

Percent

Food at home Food away from home

Figure 3.2

Constant-dollar food expenditures, 1987-2017 (1988 = 100)

Source: USDA, Economic Research Service Food Expenditure Series. 

Billion dollars

0

50

100

150

200

250

300

350

400

450

1987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017

Food at home Food away from home
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Figure 3.3

Share of FAFH expenditures by outlet type, 1987 and 2017 (percent)

Notes: FAFH = food away from home. NEC = not elsewhere classified. Estimates include sales taxes and tips.

Source: USDA, Economic Research Service Food Expenditure Series. 

1987

2017

Other FAFH sales, NEC, 4.8

Food furnished and donated, 5.4

Full-service 

restaurants, 34.0

Limited-service 

restaurants, 32.6

Full-service 

restaurants, 35.8

Limited-service 

restaurants, 36.1

Recreational places, 2.1

Retail stores, 5.6

Schools and colleges, 8.8

Hotels and motels, 5.9

Drinking places, 0.9

Other FAFH sales, NEC, 2.6

Food furnished and donated, 5.1

Recreational places, 3.6

Retail stores, 4.2

Schools and colleges, 7.9

Hotels and motels, 4.2

Drinking places, 0.6

 

While restaurants are the largest source of FAFH, Americans can purchase foods at sporting events, 

recreational places, hotels and motels, and schools and colleges, as well as from retail stores and 

vending machines. These outlets are similar to their foodservice counterparts in terms of the foods 

being offered and their nutritional composition. However, foodservice at these outlets is a secondary 

activity that could reflect either a demand for food itself or a demand for eating as a complement to the 

primary activity, or both. The only other sector whose share of the total FAFH rose during the 1987-

2017 period was recreational places (which include movie theaters, sports, and other entertainment 

venues), going from 2.1 percent of the nominal FAFH in 1987 to 3.6 percent in 2017. The expenditures for the other types of FAFH—hotels and motels, schools and colleges, retail stores, and vending 

machines—declined as a share of FAFH between 1987 and 2017. The relative shares of spending 

between FAFH and FAH are also reflected in the proportion of expenditures as a percent of DPI; the 

DPI share of FAH expenditures has steadily declined, while FAFH spending has held steady.
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Figure 3.4

Percent of FAFH sales at full- and limited-service restaurants, 1987-2017

Notes: FAFH = food away from home. Estimates include sales taxes and tips.

Source: USDA, Economic Research Service Food Expenditure Series.

Percent

Full-service restaurants Limited-service restaurants

0

5

10

15

20

25

30

35

40

45

50

1987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017

Three major recessions occurred during the period covered by this chapter. Of these, food expenditure patterns were different during only the Great Recession, when Americans of all income levels 

reduced food spending by eating out less (Kumcu and Kaufman, 2011; see chapter 4). According to 

the Food Expenditure series, this reduction was reflected in decreased FAFH spending from $601.6 

billion in 2008 to $596.7 billion in 2009, a decrease of 0.8 percent. Reduced FAFH spending was 

especially pronounced at full-service restaurants and was largely responsible for decreased aggregate FAFH spending during the Great Recession. Spending at limited-service restaurants actually 

increased at a slower rate during the recession. Between 2004 and 2006, food spending at these 

restaurants grew an average of about 7.3 percent; between 2007 and 2009, it grew an average of 3.1 

percent; and between 2010 and 2017, it grew an average of 5.6 percent

Expenditures on FAFH did not decrease during the other two economic downturns that occurred 

in this 30-year period, and there was little change in the relative shares of the aggregate FAH and 

FAFH markets. Similarly, there was little change among the relative market shares of the various 

outlets comprising the FAFH market. There were pronounced slowdowns in the rate of increase 

in FAFH expenditures within a year of the onset of the recessions in the 1990s and early 2000s 

although expenditures on FAH also rose at a slower pace during this period. For example, nominal 

FAFH spending continued to increase in the wake of the 1990-91 recession; however, this rate of 

increase slowed from 7.2 percent in 1990 to 4.6 percent in 1991 and 2.8 percent by 1992. In 1993, 

FAFH spending picked up again, increasing 5.7 percent. 

The 2001 recession lasted 8 months (as did the 1990-91 recession), but unemployment rose less 

during this period than during the Great Recession. The milder impacts of the 2001 recession 

resulted in smaller changes to food expenditures compared to the other recessions. FAFH spending 

rose, but at a smaller pace, increasing just 3.8 percent in 2001 and 4.5 percent the following year, 

while FAH spending increased 4.6 and 1.8 percent, respectively. However, FAFH expenditures rose 
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4.5 percent in 2003, surging 7 percent by 2004. These increases reflected renewed consumer confidence and a faster rate of increase in disposable income.

According to the Food Expenditure series, between 1987 and 2017, the share of disposable personal 

income spent on total food by American households fell from 11.2 to 9.4 percent, as the share of 

income spent on FAH fell (fig. 3.5). This result is consistent with Engel’s law, an empirical observation 

that as income increases the share of income spent on food declines even if actual expenditure on food 

rises. This decline is largely driven by the share of income spent on food purchased in grocery stores 

and other retailers declining from 6.7 percent to 4.7 percent during this period. At the same time, the 

Food Expenditure data show that the percent of income spent on food purchased at restaurants and 

other away-from-home eating places increased slightly, from 4.5 to 4.7 percent. By 2016 and 2017, the 

share of disposable personal income for FAH and FAFH was equivalent, at 4.7 percent. 

Figure 3.5

Household food expenditures as a percent of personal disposable income, 1987-2017

Notes: FAFH = food away from home. Estimates include sales taxes and tips.

Source: USDA, Economic Research Service Food Expenditure Series.

Percent

0

2

4

6

8

10

12

1987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017

FAH FAFH All Food

Factors Affecting Spending on FAFH

The trends above can be explained by looking at economic determinants of FAFH. Consumers 

choose food based on affordability (how much income they have and how much the food costs), time 

constraints, and their tastes and preferences. Income is an important determinant of food choices, 

FAFH in particular. While the proportion of the budget spent of food generally falls as income 

increases (as described above), the composition of the food basket also changes.

It has been observed that the consumption of starchy, staple foods declines with income (Bennet’s 

law). Staple foods are examples of necessities where consumption of the food increases less than 

proportionally with increases in income. Conversely, foods whose consumption increases more 

than proportionally with income are called luxuries. In other words, FAFH is income-responsive 

to the extent that a 1-percentage point increase in income generates a more than 1-percentage-point 
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increase in demand for FAFH. While the consumption of necessities increases less than proportionally with income, the consumption of luxuries must increase. Kamakura and Du (2012) found that 

Engel curves for FAH were downward sloping while Engel curves for FAFH were upward sloping. 

These results imply that income increases the expenditure allocated to FAFH at more than a proportional rate. In addition, demand for FAFH tends to be more responsive to income changes than 

demand for FAH (Seale et al., 2003; Okrent and Alston, 2012; Okrent and Kumcu, 2016). Between 

1987 and 2017, DPI generally increased (except during economic downturns). This trend has likely 

contributed to observed declines in the share of DPI allocated to food, along with increases in the 

share of DPI allocated to FAFH.

Relative price movements are also an important determinant of food choices. If prices of FAH grow 

at a faster rate than FAFH, then consumers have an incentive to subsitute FAFH for FAH. Between 

1987 and 2017, price increases of FAFH outpaced those of FAH, with average annual FAH and 

FAFH price growth of 2.6 and 2.8 percent, respectively. However, price growth varied across outlet 

types. Okrent and Kumcu (2016) found that prices at full-service restaurants generally held pace 

with limited-service restaurants until 2005 but that limited-service restaurants began to outpace fullservice restaurants thereafter. The price effect generally causes the quantity of FAFH demanded to 

decline, depending on the degree of price elasticity and the extent to which FAH serves as a substitute for FAFH. Previous studies have modeled demand for FAFH as a composite good and have 

generally found demand for FAFH to be more responsive to price changes than FAH (see Okrent 

and Alston (2012) for a review of these studies). Okrent and Alston found demand for limitedservice restaurants to be almost perfectly inelastic to changes in prices (-0.13) and demand for meals 

from full-service restaurants to be quite price elastic (-1.96). Okrent and Kumcu also found demand 

for limited-service meals and snacks to be relatively more inelastic than both full-service and FAH 

food. Richards and Mancino (2013) found the price elasticity of demand for meals at limited-service 

restaurants and various types of full-service restaurants to be between -0.5 and -0.9. Given the 

observed growth in FAFH spending over the period, it is likely that the positive income effect has 

dominated any negative price effect. 

Interestingly, Gicheva et al. (2007) show that American consumers reallocate their expenditures 

across and within food-consumption categories in order to offset necessary increases in gasoline 

expenditures when gasoline prices rise. In particular, gasoline expenditures rise one-for-one with 

gasoline prices, and consumers substitute away from FAFH and towards FAH in order to partially 

offset their increased expenditures on fuel. Within FAH, consumers substitute away from regular 

shelf-price products and toward promotional items in order to save money on overall grocery expenditures. On average, consumers are able to decrease the net price paid per grocery item by 5 to 11 

percent in response to a 100-percent increase in gasoline prices.

Households not only consider affordability when making food choices, but also the time it would 

take to prepare the foods. If the primary meal planner in the household gets a job, then that person’s 

time becomes more valuable. Time-intensive meal preparation thus becomes less attractive and 

motivates increased consumption of away-from-home prepared meals. Over the past several 

decades, increases in women’s education and labor-force participation may have led to less time 

spent cooking and higher FAFH expenditures. Many studies have found that the value of time for a 

household manager (sometimes assumed to be the woman) positively affects demand for total FAFH 

(Prochaska and Schrimper, 1973; Sexauer, 1979; Soberon-Ferrer and Dardis, 1991; Yen, 1993; 

Nayga and Capps, 1994; Byrne, Capps, and Saha, 1996; Dong et al., 2000). However, a few studies, 

namely Huffman (2011), Redman (1980), and Kinsey (1983), found the household time constraint 
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to be a less important determinant of demand for FAFH. Kinsey (1983) argued that while this may 

appear to contradict theory, in fact, household managers need not increase FAFH expenditures in 

order to substitute relatively inexpensive goods and services for time if the cost of purchasing certain 

types of FAFH (i.e., limited-service meals) is cheaper than conventional full-service restaurants. 

A handful of studies investigate whether the value of time has a differential effect on FAFH by 

establishment and meal type. McCracken and Brandt (1987) and Stewart et al. (2004) found that 

an increased value of the household meal planner’s time resulted in higher expenditures on meals 

at limited-service restaurants more than on meals from full-service restaurants. Similarly, Byrne et 

al. (1998) and Stewart and Yen (2004) found the effect of household manager hours to have a positive impact on demand for foods from limited-service restaurants but to be negative for full-service 

foods. Contrary to previous findings, Jekanowski et al. (2001) did not find any significant effect of 

this variable on per capita fast-food sales. Jensen and Yen (1996) examined the demand for FAFH 

by meal type—breakfast, lunch, and dinner—and found that the effects of a wife’s employment are 

positive on both the probability and level of expenditures on lunch and dinner in the FAFH market, 

but did not seem to affect breakfast consumed as FAFH. 

Household size may also impact FAFH expenditures. The average size of the American household 

has shrunk from 2.7 members in 1984 to 2.5 members in 2014 (Current Population Survey, U.S. 

Census). Additionally, chapter 4 shows notable differences in food spending patterns across households of different sizes. As a household adds more members, FAH may become more economical for 

several reasons. First, food preparation time per person increases as household size decreases. 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. Second, as household size increases, food preparation and clean up 

can be delegated across more people. Third, the household with more members can also benefit by 

purchasing larger package sizes with lower per unit costs. Byrne et al. (1996) found household size 

had a negative effect on demand for total FAFH, arguing that there are economies of scale in household size in food production at home. However, Byrne et al. (1998) found that family size was only 

negatively related to expenditures at full-service restaurants and positively related to expenditures 

at fast-food restaurants. On the flip side, some argue that single-person households may demand 

less food away from home. For example, they may demand less food from full-service restaurants 

because they do not want to eat alone away from home. Prochaska and Schrimper (1973) and 

Soberon-Ferrer and Dardis (1991) found that even though the presence of children in the household 

negatively affected demand for total FAFH, the size of the household increased demand for total 

FAFH. They argue that the additional number of adults in the household leads to additional FAFH 

purchases because of employment and social activities. 

Studies have also investigated changes to household structure where American households used to 

be primarily headed by married partners and have transitioned more to single and multi-generational 

households (Hamrick and Okrent, 2014). Byrne et al. (1998) found that unmarried households spent 

less on FAFH than married households regardless of restaurant type, arguing that a fewer number 

of people were involved in the FAFH occasion. Contrary to this result, Stewart and Yen (2004) 

and Stewart et al. (2004) found that single-person households spent around $0.50–$3.00 more per 

week at fast-food and full-service restaurants compared to married households. On the other hand, 

single-parent households spent $0.83 less than married households with children at both types of 

FAFH establishments. The gender of the household manager also seems to play an important role 

on demand for FAFH. However, the dominance of one gender over the other is uncertain, based on 

the literature. For example, Byrne et al. (1996) found that female household managers spent less 
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than male household managers on FAFH, which they attributed to males having less culinary skill. 

This result is contrary to that of Dong et al. (2000), who found that female household heads tended 

to purchase more FAFH meals than male household heads, while single households had no effect on 

the number of FAFH meals. By establishment type, Byrne et al. (1998) found that female household 

managers who worked outside the home spent less at upscale and midscale full-service restaurants, 

but more at fast-food restaurants, than male household managers.12

Differences in dining-out preferences across generations may also be important determinants of 

FAFH consumption. In the past, people often spent less away from home as they became older 

(see the chapter 4 for more details about the impact of the elderly on FAFH and FAH consumption 

patterns). It remains to be seen whether this pattern will hold true for Baby Boomers (people born 

between 1946 and 1964). Further, the greater tendency of Millennials—defined as people born after 

1980 (Pew Research Center, 2015) – to eat away from home could explain recent increases in the 

FAFH share of total food expenditures. Indeed, Consumer Expenditure Survey data from 2015 show 

that Millennials had the highest FAFH expenditure share, at 47.0. This share drops with older generations. For example, Baby Boomers had a share of 40. 6 percent. The Greatest Generation (born 

before 1928) had the smallest share at 30.3 percent. By contrast, the share of FAH expenditures 

was greater for each generation. Millennials had the smallest FAH share at 53.0 percent, while the 

Greatest Generation had the largest at 69.7 percent. Over time, this may change as Millennials get 

older, when their preferences may become more similar to current Greatest Generation individuals.

Business cycles can enhance or diminish the impact of the various factors, just discussed, that influence changes in the level of consumer food expenditures. First, higher unemployment levels during 

the Great Recession permitted more time for food preparation such that households substituted away 

from FAFH (Nevo and Wong, 2015; Todd and Morrison, 2014; see Chapters 4 and 7). Second, relatively higher aggregate FAFH prices incentivized people to eat at home more, thereby influencing 

expenditure levels in these markets during the recession (Todd and Morrison, 2014). Third, demand 

for FAFH tends to be more responsive to income changes than demand for FAH (Okrent and Alston, 

2012). Hence, compared to FAH, FAFH was more responsive to the 5.3-percent decline in disposable income from 2008 to 2009, and it is likely that, if FAFH is a luxury, the income effect would 

be amplified. Finally, consumer confidence was very low during the Great Recession and reached its 

lowest recorded level in 2008 (De Nardi et al., 2012). Consumer expectations provide information 

about potential future changes in consumer spending and serve as a leading indicator for the aggregate economy. These expectations can affect consumer preferences, and hence expenditure allocations between the FAH and FAFH sectors.

Advertising likely plays a role in household food sourcing. Advertising expenditures for meals and 

beverages offered by quick-service and full-service restaurants are substantial, with some of the 

top advertisers in the country being McDonald’s, Yum Brands and Darden Restaurants. There is 

also some promotion of commodities commonly consumed at home, such as milk, some fruits, and 

beef and pork, as well as ready-to-cook and ready-to eat products like Campbell’s and Hershey’s. 

However, the promotion of foods for at-home consumption pales in comparison to that for FAFH 

(Okrent and Kumcu, 2016). Some studies have found demand for quick-service foods to be quite 

responsive to advertising. Andreyeva, Kelly, and Harris (2011) found a significant effect of fast-food 

advertising on body weight for overweight and obese (body mass index or BMI ≥ 85th percentile) 

12Byrne et al, (1998) define upscale restaurants as offering full alcohol service and accepting credit card whereas midscale 

restaurants do not.
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children. Grossman, Tekin, and Wada (2012) found that banning television advertisements for fast 

food would reduce youth BMI by 2 percent and youth body fat by 3 percent.

The extent to which increased U.S. racial and ethnic diversity affects FAFH expenditures remains to 

be seen. Many studies find significant differences in eating patterns based on race, including studies 

focusing on FAFH (see chapters 4 and 5, as well as Hamrick and Okrent, 2014, Byrne et al., 1998, 

and Stewart et al., 2004, to name a few). Typically, these findings are attributed to differences in 

tastes and preferences across ethnic groups. The diversity of ethnic restaurants, which grew during 

the past 15 to 20 years, may reflect both increased population diversity and increased demand for 

ethnic foods. 

Conclusion

FAFH expenditures steadily grew as a percentage of total food expenditures between 1987 and 2017, 

and they exceeded FAH expenditures for the first time in 2010. However, these expenditures have 

remained fairly steady as a percent of disposable personal income (DPI). In contrast, this ratio has 

declined for the FAH market. Total food expenditures have also declined as a percent of DPI. 

Three recessions have occurred during the last three decades, and of these, only the Great Recession 

appears to have induced lower FAFH expenditures. However, it should be noted that two of these 

recessions lasted two-thirds of a year, while the Great Recession had a duration of 18 months. 

Limited-service restaurants have gained the most share of the various FAFH outlets during this 

period, but full-service restaurants still account for the highest share of FAFH expenditures. 

Many factors can explain observed changes in FAFH spending. Income is likely one of the most 

important, and the Food Expenditure series shows changes consistent with two empirical laws—

Engel’s Law and Bennett’s Law. While the proportion of the budget spent on food generally falls as 

income increases (Engel’s Law), foods such as FAFH that are considered luxuries will increase more 

than proportionally with income (Bennett’s Law). Prices changes are important as well, but the cost 

of gasoline may be a more important factor in influencing FAFH spending than the cost of FAH. 

Household time constraints have also become an important factor affecting FAFH expenditures. 

Over the past several decades, increases in women’s education and labor-force participation may 

have translated into less time cooking and higher FAFH expenditures. Changes to household composition and structure have also likely played a role in the upward trend in FAFH spending. Increased 

advertising for FAFH also may have played a major role. It is difficult to pinpoint any one factor as 

being the most important because there are currently insufficient data to allow for a simultaneous 

analysis of the various factors impacting FAFH expenditures. 
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Chapter 4: Food Away From Home During the Great 

Recession

This chapter examines changes in food spending during and after the 2007-09 Great 

Recession using the Consumer Expenditure Survey from 2005 to 2014. Households 

decreased the share of total food expenditures allocated to FAFH, which had not returned 

to pre-recession levels by 2014. Households shifted some expenditures from FAFH to FAH 

during the Great Recession and through the recovery, increasing spending on edible and 

unprepared ingredients and decreasing spending on full-service restaurants. 

Unlike prior economic downturns in the past 30 years, during the most recent recession, food expenditure patterns changed substantially as Americans spent less of their food budget on food away 

from home (FAFH) and more on food at home (FAH) (Kumcu and Kaufman, 2011). Known as the 

Great Recession, the most recent economic downturn lasted from December 2007 to June 2009 

and was the most severe since the Great Depression.13 Unemployment continued to rise during the 

initial recovery, with over 8 million people losing their jobs between December 2007 and February 

2010—a 6-percent decline.14 Labor market conditions were slow to improve thereafter. As a result, 

employment did not reach pre-recession levels until May 2014; mean and median household income 

remained below pre-recession levels in 2014.15

As the economy slowed and unemployment rose, many U.S. households experienced financial 

hardship and decreased consumption; the decline in aggregate spending was the most severe and 

persistent since World War II (De Nardi et al., 2012). Food spending was no different, falling 5 

percent—from $726 billion in 2006 to $690 billion in 2009—largely due to an 11.5-percent decline 

in FAFH expenditures (Kumcu and Kaufman, 2011). This change was reflected in food consumption 

patterns as well, with daily calories from FAFH declining 20 percent (166 calories) among workingage adults between 2005 and 2010 (Todd, 2014). Nevertheless, the number of quick-service restaurants continued to grow during this period, while the number of full-service restaurants remained 

relatively constant (see chapter 6).16

Although economic downturns can have a negative effect on households by reducing their income, 

studies show mixed effects of downturns on work hours and health outcomes. While some studies 

find that reduced work hours during the recession led to healthier lifestyles, including an increase 

in physical activity and a decline in severe obesity (e.g., Ruhm, 2005), others find negative health 

effects, particularly among individuals who had unhealthy behaviors prior to the recession (e.g., 

Charles and DeCicca, 2008). Similarly, changes in food purchasing patterns during the Great 

Recession reflect changes in financial and time constraints faced by households. Nevo and Wong 

(2015) find that between 2008 and 2010, individuals went grocery shopping more often but spent 

less money by taking advantage of coupons and discounts. Using data from the American Time 

13These are the dates set by the National Bureau of Economic Research, which is considered to determine the official 

dates of a recession based on various economic indicators. 

14This percentage was calculated using Current Employment Statistics from the U.S. Department of Labor, Bureau of 

Labor Statistics. February 2010 was the cutoff date because this period marked the lowest employment level during the recession and recovery.

15The mean and median income were obtained from the Census Bureau’s Current Population Survey. 

16Quick-service restaurants typically offer counter service for ordering while full-service are sit-down restaurants with a 

wait-staff.
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Use Survey, Aguiar et al. (2013) find that 30 percent of the foregone market work hours during 

the Great Recession were reallocated to nonmarket work—including food preparation.17 However, 

other studies find that the recession did not have a significant effect on the amount of time spent 

cooking at home or eating away from home (Smith et al., 2014). Nevertheless, it remains unclear 

how households adjusted the types of food they purchased throughout the Great Recession and the 

prolonged recovery. 

This chapter explores fluctuations in food expenditures during the Great Recession and the 

prolonged recovery, examining total food spending as well as spending on FAH and FAFH. In addition, the analysis breaks apart FAFH expenditures by level of service and FAH expenditures by level 

of preparation needed for consumption to gain insight into the factors affecting spending allocations. 

This chapter also examines average per capita expenditures among households separated by socioeconomic status and composition, which may affect how households adjusted their food spending 

patterns. These analyses provide insight on how households adjust their spending in response to 

changes to financial and time constraints during economic downturns, and the effect it may have 

on their diets. This information can be used by policymakers to help mitigate the possible health 

impacts of recessions. 

Data

This study uses data from the 2005-2014 Consumer Expenditure Survey (CES) conducted by the 

Bureau of Labor Statistics (BLS).18 The period covered by the survey begins nearly 3 years before 

the recession began and ends at the point for which we have most recent data available, and when 

employment also recovered to its pre-recession level. The CES is a diary survey sent to about 

7,000 households each year. Participant households record all of their purchases for 2 consecutive 

1-week periods, including Uniform Commercial Codes (UCC) of each product purchased. Nonfood 

purchases are removed, and food spending that is representative of a full calendar year is estimated 

using sampling weights and estimation adjustments provided by BLS.19

Reported expenditures in the CES are adjusted using the FAH and FAFH Consumer Price Index 

(CPI) from BLS to account for inflation and any fluctuations in food prices, such as the global spike 

of 2007-08.20 All of the food expenditures in this chapter are reported in 2005 FAH and FAFHdollars; total food expenditures are calculated by aggregating the deflated FAH and FAFH expenditures.21 It is important to note that the CES data only provide information on the total amount spent 

17Market work hours consist of any hours spent working at a job, including any time spent commuting to work and overtime. Nonmarket work hours consist of any time spent working to take care of a home (e.g., vacuuming), obtain goods (e.g., 

grocery shopping), and take care of other adults.

18This chapter’s estimations of FAFH and FAH may differ from estimations in chapter 3 for two reasons. First, the ERS 

Food Expenditures series includes business expenditures. Second, unlike the ERS food expenditure survey, BLS classifies its 

data according to where the product was purchased. For example, carryout from a full-service restaurant would be considered 

“Lunch/Dinner at Full-Service,” or FAFH.

19See BLS website for details.

20The 2007-08 spike in food prices has been attributed to increasing grain prices resulting from droughts as well as government policies and international demand. 

21Although a few of the individual items have their own CPIs, most of the product categories in this analysis do not. Thus, 

the FAH CPI is used for all FAH categories, even though they may have had different price changes. For example, in 2008, 

the percentage change in the CPI for FAH was 6.4, although pork prices increased by only 2.3 percent, while egg prices 

increased by 14.0 percent.
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by each household for each UCC code during the week; the data do not include any information on 

the prices of the products or the quantities purchased.

Total Food Expenditures for all Households

During the Great Recession, overall food spending declined and households allocated a greater 

share of their food budgets to FAH in place of FAFH. Total food expenditures adjusted for inflation 

fell 7 percent ($42 billion) between 2007 and 2010 (fig. 4.1).22 This drop was primarily driven by the 

steady decline in spending on FAFH, which fell by 14 percent ($35 billion) during this period; its 

share of total food expenditures fell by 4 percentage points during this period. Although the Great 

Recession officially ended in June 2009, food expenditures did not begin to increase until 2010, 

coinciding with employment trends. The increase in FAFH spending was slower than the decline 

during the Great Recession, reflecting the slow labor market recovery. As a result, real (inflationadjusted) food expenditures remained 2 percent ($13 billion) below pre-recession levels in 2014. 

The large differences between total, FAH, and FAFH expenditures makes it difficult to examine 

their relative fluctuations. The percent change in spending levels, relative to 2005, shows the relative fluctuations more clearly, and statistically significant differences are noted with stars (fig. 4.2). 

Real spending on FAFH declined by 18 percent ($47 billion) from 2006 to 2010 and remained below 

its 2005 level through 2014. In contrast, FAH expenditures during 2005-14 exceeded their 2005 

level in every year except 2010, reaching a peak in 2013 at 5 percent ($19 billion) above the initial 

level. These divergent trends suggest that households were replacing FAFH with FAH, or they were 

purchasing more groceries rather than eating out at restaurants.

 

22The dollar values are lower than those presented in Kumcu and Kaufman (2011) because we do not include alcoholic 

beverages.
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Total Food Expenditures by Category

Examining changes in aggregate FAH and FAFH expenditures provides a broad view of changes in 

household food spending in response to the Great Recession. However, there are a number of ways 

households could have adjusted their food spending within FAH and FAFH as well. For example, 

households could have reduced their FAFH expenditures by patronizing full-service restaurants 

less frequently but maintaining their spending at fast-food restaurants. Conversely, they could have 

increased their FAH spending by purchasing prepared foods and ready-to-heat meals or by cooking 

more meals with cheaper, raw ingredients, particularly if their leisure time had grown and they were 

financially constrained. Although the data limit analysis on whether households purchased fewer or 

cheaper items, the data do allow examination of changes in FAH expenditures according to preparation time and FAFH expenditures based on level of service. 

FAH spending consists of six categories: prepared food; ready-to-heat food; edible ingredients; 

unprepared ingredients; nonalcoholic drinks; and other.23 Similar to categories used in Okrent and 

Kumcu (2016), the first four categories used in this study are distinguished by the level of preparation required to consume the food.24 Prepared foods are those that are eaten only in their current 

form, such as cake or prepared salads. Ready-to-heat foods, such as canned soup and frozen meals, 

only need to be heated. Edible ingredients are foods that may be eaten in their current form but 

that could also be used as ingredients in other dishes that require more preparation. These include 

23There were a total of 139 UCC codes. See appendix to see which UCC codes are included in each category. 

24The categories and their names are not identical to those in Okrent and Kumcu (2016) because the UCCs provided by 

CES are less detailed than the UPCs used in their dataset. 
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canned meats, fruits, and vegetables, and fresh fruits and vegetables.25 Finally, unprepared ingredients are foods that require preparation prior to consumption, such as eggs, rice, or raw meat. 

Nonalcoholic drinks are self-explanatory. The other FAH food category includes candy and chewing 

gum, baby food, and vitamin supplements. FAFH spending on meals and snacks is separated into 

three categories: fast food, full service, and other (e.g., catered affairs and vending machines).

Households allocated the largest shares of food expenditures to fast food (FAFH) and unprepared 

ingredients (FAH) throughout all of the years examined, with each category accounting for almost 

one-fifth of the total food budget (fig. 4.3). Changes in expenditure shares for each category during 

the period studied ranged from 0.5 percentage points (prepared foods) to 1.9 percentage points 

(edible ingredients and full-service restaurants). From 2006 to 2010, the 4-percentage-point decline 

in the share of FAFH expenditures was largely due to the 1.7-percentage-point decline in spending 

at full-service restaurants, whereas the increase in FAH expenditures mostly stemmed from the 

1.5-percentage-point increase in spending on edible ingredients.

 

25This category includes salad dressings because it does not need to be cooked, and is generally used with other items in 

this section, particularly fresh vegetables.
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Examination of the percent change in expenditure shares for each category (excluding other FAH 

and FAFH), relative to the 2005 shares, provides a more clear picture (fig. 4.4). The categories 

with the greatest food expenditure shares had the greatest percent change from 2005 to 2010: 

edible ingredients (12-percentage-point increase) and full-service restaurants (9-percentage-point 

decrease). The percent change from 2005 was statistically significant beginning in 2008 for both of 

these categories and for unprepared ingredients. FAFH expenditures rebounded beginning in 2010, 

but the shares of food expenditures spent at full-service restaurants remained 4 percentage points 

below pre-recession shares in 2014. Although spending on FAFH substitutes—prepared and readyto-heat foods—increased during the recession, both declined by 2014, with prepared food returning 

to the 2005 level and ready-to-heat food falling even further below the 2005 level. The consistently 

higher budget shares on edible and unprepared ingredients after the recession suggest that while the 

recession may have pushed households to prepare more food at home, there may also have been a 

general shift in preferences for home-cooked meals.

Figure 4.4

Percent change in share of real food expenditures by type, 2005-14

Note: FAH = food at home. FAFH = food away from home. The stars indicate that the shares are statistically different from 

2005 (p<0.05).

Source: USDA, Economic Research Service calculations from U.S. Department of Labor, Bureau of Labor Statistics 

Consumer Expenditure Survey data.

Percent

-20

-10

0

10

20

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

FAH - prepared FAH - ready-to-heat

FAH - edible ingredients FAH - unprepared ingredients

FAFH - fast food FAFH - full service

Differences Across Household Types

Studies find that income, prices, and time constraints can affect total food expenditures, particularly for convenient FAH (i.e., prepared and ready-to-heat food) and fast food (e.g., Jabs and Devine, 

2006; Okrent and Kumcu, 2016). This next section explores changes in food expenditure allocations during the Great Recession across various household types—by income and participation in 
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USDA’s Supplemental Nutrition Assistance Program (SNAP),26 presence of at least one elderly individual or child in the household, and age of the oldest child for households with children. To ensure 

that differences in household size do not overstate differences across these various socioeconomic 

groups, the analysis compares average per capita expenditures within each group.27

Comparison by Income Quintiles 

Households in the lowest, middle, and highest income quintiles had average incomes of $11,467, 

$48,351, and $149,498, respectively, in 2005.28 As expected, households in the highest income quintile had the highest average per capita food expenditures in all years examined, while households 

in the lowest income quintile had the lowest (table 4.1). Middle-income households decreased their 

average per capita food expenditures during the Great Recession, which reduced the gap between the 

lowest and middle quintile by $279 from 2005 to 2014. 

Similar to total food expenditures, the share of food expenditures allocated to FAFH was highest 

for the highest income quintile and lowest for the lowest income quintile but declined during the 

Great Recession for all households, regardless of income (table 4.1). The lowest income quintile 

had the sharpest decline in the share of FAFH between 2007 and 2009 (3 percentage points, or 10 

percent). Households in the middle-income quintile had the slowest recovery following the recession; in 2014, the share allocated to FAFH for this group remained 3 percentage points (7 percent) 

below the 2005 share. 

Table 4.1

Food expenditures by income quintiles, 2005-14

Average food expenditures

(per capita, 2005 dollars) Percent of food expenditures on FAFH

Lowest 

quintile

Middle 

quintile

Highest 

quintile

Lowest 

quintile

Middle 

quintile

Highest 

quintile

2005 $2,255 $2,766 $3,293 32.2 40.9 44.6

2006 $2,281 $2,763 $3,521** 31.6 39.8 44.7

2007 $2,256 $2,751 $3,451** 33.1 39.5 44.9

2008 $2,340 $2,691 $3,378 29.8** 37.9** 44.0**

2009 $2,309 $2631** $3,260 29.8** 37.9** 41.8**

2010 $2,227 $2557** $3,290 30.9 36.8** 41.3**

2011 $2,229 $2,489 $3,390 30.2** 36.0** 42.1**

2012 $2,304 $2,532 $3,296 29.8** 37.4** 42.2**

2013 $2,304 $2,549 $3,190 30.0** 36.6** 41.2**

2014 $2,313 $2,545 $3,324 31.0 38.1** 43.7

Note: ** indicates that the expenditures are statistically different from 2005 (p<0.05).

Source: USDA, Economic Research Service estimates using U.S. Department of Labor, Bureau of Labor Statistics Consumer 

Expenditure Survey data.

26The Food, Conservation, and Energy Act of 2008 (farm bill) changed the name of the Food Stamp Program to SNAP as 

of October 1, 2008.

27Annual averages for each group are calculated on a per capita basis—i.e., household expenditures divided by household 

size. See BLS website for details.

28Income quintiles are based on pre-tax income. This includes all potential income sources, such as unemployment insurance, workers’ compensation, and alimony, in addition to wages received. In 2005, the income quintiles are separated by the 

following thresholds: $20,000, $37,710, $60,000, and $91,240.
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Changes in the share of FAFH expenditures over time for all three income quintiles were driven 

largely by changes in spending at full-service restaurants (fig. 4.5). The share spent at full-service 

restaurants is positively related to income, with the highest income quintile allocating at least 10 

percentage points more than the lowest income quintile. Between 2005 and 2010, middle- and highincome households decreased their share of expenditures at full-service restaurants. In comparison, 

over the same period, only households in the highest income quintile decreased fast-food expenditures. Throughout all the years examined, all income quintiles maintained similar shares of expenditures at fast-food restaurants (between 17.1 and 19.7 percent).

Over all 9 years examined, households in the lowest income quintile spent the most on unprepared ingredients and had a larger share of their food expenditures in every FAH category than 

households in the middle and highest income quintiles. From 2005 to 2010, middle- and highincome households increased their share of expenditures on edible and unprepared ingredients; 

middle-income households maintained these higher shares through 2014 for both categories, while 

high-income households only did so for edible ingredients. Low-income households increased 

their share of edible ingredients in 2010, relative to 2005, and increased their share of unprepared 

ingredients in 2014. 

FAH - prepared FAH - ready-to-heat FAH - edible ingredients

FAH - unprepared ingredients FAH - other FAFH - fast food

FAFH - full service FAFH - other

Figure 4.5

Percent change in share of real food expenditures by type, 2005-14

Note: FAH = food at home. FAFH = food away from home. FAH – Other includes all beverages. A solid color for 2010 and 

2014 indicates statistically significant change from 2005 (p<0.05), whereas the lighter shading indicates an insignificant 

change.

Source: USDA, Economic Research Service calculations from U.S. Department of Labor, Bureau of Labor Statistics 

Consumer Expenditure Survey data.

0

10

20

30

40

50

60

70

80

90

100

Percent

Lowest quintile Middle quintile Highest quintile

2005 2010 2014 2005 2010 2014 2005 2010 2014

11.8

4.3

15.3

20.8

17.5

11.4

11.1 10.7 11.9 11.3 11.0 12.0 10.8 10.2

4.2 3.6 4.4 4.0 3.8 4.2 3.6 3.4

13.0 13.0 16.2 14.6 14.8 15.4 14.7 14.8

18.0 17.4

21.6 20.1 18.4 22.2 20.4 17.8

19.4 18.7

18.0

19.6

17.4

17.6

19.7

18.6

10.4 14.7 21.0

11.7 16.0 17.5 22.5 22.6

3.3 4.0 3.4 2.5 2.5 2.9 1.7 2.3 2.6

15.6

12.8 10.8

15.0 13.1 10.6

15.2

12.4 10.1
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Comparison by SNAP Participation and Eligibility

SNAP provides households with a cash-like benefit that can be used to purchase groceries that will 

be taken home; benefits cannot be used to purchase hot foods and food that will be eaten in the 

store.29 SNAP caseloads increased by 56 percent during the Great Recession. This change stemmed 

partially from increasing need, as the number of people in poverty increased by 26 percent, but 

also from changes in SNAP policies that made the application process easier and the benefits 

more generous (Andrews and Smallwood, 2012). For example, the 2009 American Recovery and 

Reinvestment Act (ARRA) expanded eligibility by temporarily suspending the work requirement for 

able-bodied adults without dependents. ARRA also increased SNAP benefit amounts, which helped 

reduce food insecurity and increased the SNAP budget share allocated to food by a greater amount 

than a similar increase in income (Nord and Prell, 2011; Beatty and Tuttle, 2015). Thus, changes in 

food expenditures during the Great Recession among low-income households likely vary by SNAP 

participation status. In the next section, we compare food expenditures among SNAP participants 

with those of income-eligible and income-ineligible nonparticipants.30

From 2005 to 2014, income-ineligible nonparticipants had the highest average per capita food expenditures, while SNAP participants had the lowest (table 4.2). Over the same period, SNAP participants and eligible nonparticipants had insignificant changes in their food expenditures. Ineligible 

nonparticipants increased per capita spending in 2006 and 2007 relative to 2005. The difference 

in food expenditures between SNAP participants and income-eligible nonparticipants was driven 

largely by differences in household size. The lower per capita food expenditures for SNAP households may be partially attributable to economies of scale. In this dataset, SNAP households tend 

to be larger than income-eligible nonparticipant households because a higher proportion of SNAP 

households have children. When food expenditures are compared for the entire household rather 

than for each household member, the average for SNAP participant households is nearly three times 

higher than that for income-eligible nonparticipant households.31

Eligible nonparticipants also had a higher share of their food budget allocated to FAFH than did 

SNAP participants, which is not surprising given that SNAP supports the purchase of food for home 

preparation and consumption (table 4.1).32 While income-ineligible households decreased their 

share of food spending on FAFH in 2008-14 relative to 2005, SNAP households had no statistically 

significant changes in FAFH share throughout the entire 2005-14 period. However, relative to 2005, 

income-eligible nonparticipants had a 2-percentage-point decline in the share of FAFH in 2008 and 

2012 (8- and 9-percent decline, respectively). 

29Restaurants can also be authorized to accept SNAP benefits from qualified homeless, elderly, or disabled individuals for 

low-cost meals in some areas. See USDA Food and Nutrition Service website on SNAP for more details.

30A household is eligible to participate in SNAP if its monthly gross income is below 130 percent of the poverty line 

and its monthly net income is below 100 percent of the poverty line. However, categorical eligibility raises or eliminates 

these limits in some States. Furthermore, the Consumer Expenditure Survey (CES) reports annual income, and low-income 

households can experience income volatility over the year, making them eligible during some months but not others. Thus, 

the income limit is set at 150 percent of the poverty line according to household size. 

31Some of the eligible nonparticipant households may also be misclassified SNAP participants, as studies find that survey 

respondents underreport SNAP participation (e.g., Meyer et al., 2009). 

32SNAP benefits can only be redeemed at authorized food retailers (see FNS website for details). 
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Table 4.2

Food expenditures by participation in and eligibility for the Supplemental Nutrition 

Assistance Program, 2005-14

Average food expenditures 

(per capita, 2005 dollars) Percent of food expenditures on FAFH

SNAP 

participants

Eligible nonparticipantsIneligible nonparticipantsSNAP 

participants

Eligible nonparticipantsIneligible nonparticipants

2005 $1,685 $2,136 $2,917 26.9 33.5 41.6

2006 $1,685 $2,113 $3,023** 26.6 32.1 41.3

2007 $1,627 $2,173 $2,994** 26.3 34.8 40.9

2008 $1,745 $2,236 $2,962 27.3 30.8** 39.5**

2009 $1,761 $2,215 $2,895 25.0 31.4 38.7**

2010 $1,647 $2,138 $2,850 24.9 32.5 38.9**

2011 $1,695 $2,180 $2,891 25.9 31.6 38.7**

2012 $1,652 $2,261 $2,888 25.3 31.2** 39.1**

2013 $1,816 $2,197 $2,881 26.9 31.5 38.5**

2014 $1,667 $2,257 $2,849 26.3 33.2 39.9**

Note: SNAP = Supplemental Nutrition Assistance Program. ** indicates that the expenditures are statistically different from 

2005 (p<0.05).

Source: USDA, Economic Research Service estimates using U.S. Department of Labor, Bureau of Labor Statistics Consumer 

Expenditure Survey data.

The decline in the share of FAFH expenditures from 2005 to 2010 for ineligible nonparticipants 

was primarily driven by the 1-percentage-point (6 percent) decline in the share of expenditures at 

full-service restaurants (fig. 4.6). Similar to the differences when viewed by income quintile, SNAP 

participants allocated at least 11 fewer percentage points in food expenditures to full-service restaurants than did income-ineligible nonparticipants for the entire period examined; eligible nonparticipants allocated at least 5 fewer percentage points than income-ineligible nonparticipants. In contrast, 

the share of food expenditures allocated to fast food was relatively similar across all three groups 

and exhibited no significant changes in response to the Great Recession.

All three household groups increased the share of spending allocated to FAH edible ingredients during the Great Recession; for SNAP participants and ineligible nonparticipants, the share 

remained higher in 2014. The share allocated to unprepared ingredients increased by a little over 

1 percentage point in 2010 for both nonparticipant groups and remained higher than 2005 in 2014. 

However, both groups consistently spent a lower share of expenditures on unprepared ingredients 

than SNAP participants, which may partially explain why the share did not increase for SNAP 

participants in response to the recession. Ineligible households also decreased the share of expenditures on ready-to-heat and prepared foods in 2014.
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Comparison by Presence of an Elderly Person in the Household

On average, per capita expenditures did not change between 2005 and 2014 among households with an 

elderly individual (table 4.3).33 In contrast, expenditures for households without an elderly individual were 

significantly different from their 2005 expenditures for all but 2 of the years examined, decreasing by 

$230 (8 percent) from 2006 to 2010 and remaining relatively stable thereafter. Thus, by 2014, households 

without an elderly individual spent $133 (5 percent) less per person than they did in 2005. 

The FAFH share of total food spending was at least 8 percentage points lower for households with 

an elderly individual than for households without older individuals for all 9 years examined (table 

4.3). During the Great Recession, the share of expenditures allocated to FAFH by households 

without an elderly member declined 3 percentage points (8 percent) from 2005 to 2010 and remained 

below 2005 levels through 2014. In contrast, households with an elderly individual increased their 

share of spending on FAFH by 2 percentage points (8 percent) in 2014 relative to 2005. This difference suggests that elderly individual(s) may have a less volatile income, coinciding with studies that 

find that economic downturns affect elderly poverty rates much less than poverty rates for other age 

groups (e.g., Bitler and Hoynes, 2015). Furthermore, findings in this analysis indicate that the recession did not have long-term effects on elderly households, which recovered more quickly than other 

households, consistent with findings by Todd (2014).

33The CES identifies an individual older than age 64 as elderly. Elderly households are those with at least one elderly 

individual, regardless of the composition of the remainder of the household (e.g., whether there are any children).
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Table 4.3

Food expenditures by presence of elderly, 2005-14

Average food expenditures 

(per capita, 2005 dollars) Percent of food expenditures on FAFH

Elderly No elderly Elderly No elderly

2005 $2,713 $2,722 29.9 42.1

2006 $2,705 $2,838** 30.6 41.4

2007 $2,787 $2,799** 30.6 41.6

2008 $2,753 $2,784 29.4 39.8**

2009 $2,819 $2,670 28.7 39.0**

2010 $2,673 $2,608** 28.8 39.0**

2011 $2,835 $2,618** 29.4 38.7**

2012 $2,781 $2,616** 29.8 38.6**

2013 $2,787 $2,604** 30.4 38.2**

2014 $2,763 $2,589** 32.2** 39.5**

Note: **indicates that the expenditures are statistically different from 2005 (p<0.05).

Source: USDA, Economic Research Service estimates using U.S. Department of Labor, Bureau of Labor Statistics Consumer 

Expenditure Survey data.

The higher share of FAFH expenditures among households without an elderly individual was mostly 

due to their higher share of fast-food expenditures, which was at least 8 percentage points higher 

than that among households with elderly individuals over all of the years examined (fig. 4.7). Both 

household types had small, but significant, increases in the share of food spending allocated to 

unprepared and edible ingredients between 2005 and 2010; these increases persisted through 2014 

for households without an elderly individual. In 2014, households with an elderly person had a small 

decline in expenditures on prepared and ready-to-heat foods—0.7 and 0.6 percentage points (5 and 

13 percent), respectively.

Comparison by Presence of Children in the Household

Households with at least one child spent about $1,000 less per capita per year than households 

without children from 2005 to 2014 (table 4.4).34 This is not surprising given that households with 

children are generally larger, allowing for economies of scale in food purchases. Furthermore, young 

children generally require fewer calories than adults. Households with children had a more rapid 

decline in average per capita food expenditures in response to the Great Recession, falling by $83 

(4 percent) from 2005 to 2009; spending among households without children decreased by $91 (3 

percent) from 2005 to 2010. Both household types remained below their respective 2005 spending 

level in 2014: expenditures dropped $148 (7 percent) for households with at least one child and $128 

(4 percent) for those without a child.

Throughout the period, the share of food expenditures allocated to FAFH was consistently higher 

for households without children, suggesting that it could be more difficult, financially and logistically, for households with children to eat out (table 4.4). Nevertheless, both groups responded similarly to the Great Recession. From 2005 to 2010, the share allocated to FAFH decreased by about 4 

percentage points (9 percent) for households with children and by 3 percentage points (7 percent) for 

those without children. By 2014, the shares remained 3 and 2 percentage points (7 and 4 percent), 

respectively, below 2005 levels. 

34The CES identifies a child as an individual under age 18.
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Table 4.4

Food expenditures by presence of at least one child, 2005-2014

Average food expenditures 

(per capita, 2005 dollars) Percent of food expenditures on FAFH

Child No child Child No child

2005 $2,043 $3,114 38.6 40.0

2006 $2,052 $3,231* 38.3 39.5

2007 $2,011 $3,202* 37.7 40.0

2008 $1,992 $3,183 36.8* 37.8*

2009 $1,960* $3,086 35.4* 37.2*

2010 $1,840* $3,022* 35.1* 37.3*

2011 $1,885* $3,069 35.6* 36.9*

2012 $1,886* $3,049 34.8* 37.2*

2013 $1,883* $3,012* 35.1* 36.6*

2014 $1,895* $2,986* 35.8* 38.3*

Note: The stars indicate that the expenditures are statistically different from 2005 (p<0.05).

Source: USDA, Economic Research Service estimates using U.S. Department of Labor, Bureau of Labor Statistics Consumer 

Expenditure Survey data.
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Although households without children allocated a greater share of food expenditures to full-service 

restaurants (at least 4 percentage points) than households with children for the entire sample period, 

they allocated a smaller share (less than 3 percentage points) to fast-food restaurants (fig. 4.8). 

Nevertheless, both household types reduced the share allocated to full-service expenditures in 2010 

relative to 2005. Households with children also reduced the share of food spending at fast-food 

restaurants, although the shares allocated to both FAFH categories returned to pre-recession levels 

by 2014. Households without children maintained a lower share of expenditures at full-service 

restaurants from 2010 to 2014 relative to 2005 but increased spending at fast-food restaurants. Both 

household types increased the share of food expenditures on edible and unprepared ingredients from 

2005 to 2010 and maintained these higher shares through 2014.

There are clear differences in FAFH expenditure patterns for households with children by age—

younger than age 6, age 6 to 11, and age 12 to 17. As expected, households whose oldest child was 

age 12 to 17 had the highest average per capita food expenditures throughout the period, likely 

because older children generally require more calories (table 4.5). Food expenditures for households 

in the two categories with children younger than age 12 were relatively similar to each other, which 

could be an indication that the dietary needs among children are relatively similar until their teenage 

years, when their calorie requirement rises.

Figure 4.8

Share of food expenditures by presence of at least one child—2005, 2010, 2014

Note: FAH = food at home. FAFH = food away from home. FAH – other includes all beverages. A solid color for 2010 and 

2014 indicates statistically significant change from 2005 (p<0.05), whereas the lighter shading indicates an insignificant 

change.

Source: USDA, Economic Research Service calculations from U.S. Department of Labor, Bureau of Labor Statistics 

Consumer Expenditure Survey data.

FAH - prepared FAH - ready-to-heat FAH - edible ingredients

FAH - unprepared ingredients FAH - other FAFH - fast food

FAFH - full service FAFH - other

Percent

11.5 11.8 11.5 11.0 11.2 10.8

4.1 4.2 3.4 4.0 4.2 3.9

12.4 14.5 14.8 14.0 15.4 15.2

19.5 20.7 21.1 18.4 19.7 19.8

20.4 19.1 20.2

17.8 18.0 18.6

13.3 12.4 13.0 19.1 17.1 17.8

0

10

20

30

40

50

60

70

80

90

100

2005 2010 2014 2005 2010 2014

Child No child

4.9 3.6 2.7 3.0 2.2 1.9

13.8 13.8 13.3 12.7 12.3 12.1
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Table 4.5

Food expenditures by children’s age, 2005-14

Average food expenditures 

(per capita, 2005 dollars) Percent of food expenditures on FAFH

Oldest 

younger

than 6 Oldest 6-11 Oldest 12-17

Oldest 

younger 

than 6 Oldest 6-11 Oldest 12-17

2005 $1,917 $1,992 $2,194 40.2 39.9 38.8

2006 $1,984 $2,049 $2,173 39.9 38.5 39.2

2007 $1,897 $1,994 $2,184 37.3 38.9 38.2

2008 $1,903 $1,924 $2,197 37.3* 37.4* 37.7

2009 $2,008 $1,916 $2,082* 33.6* 37.5 37.3

2010 $1,778* $1,795* $2,019* 35.8* 36.3* 36.4*

2011 $1,890 $1,874 $2,012* 37.2* 37.3* 35.7*

2012 $1,830 $1,911 $2,070* 36.1* 36.7* 34.8*

2013 $1,914 $1,807* $2,030* 35.7* 35.7* 35.1*

2014 $1,869 $1,925 $2,023* 37.1* 37.2* 35.6*

Note: The stars indicate that the expenditures are statistically different from 2005 (p<0.05).

Source: USDA Economic Research Service estimates using U.S. Department of Labor, Bureau of Labor Statistics Consumer 

Expenditure Survey data.

Average per capita food expenditures declined for all groups of households with children between 

2005 and 2010. During this period, by ascending age of the oldest child, household expenditures 

declined by $139, $197, and $174 (7, 10, and 8 percent), respectively. Although households whose 

oldest child was age 6 to 11 had the greatest decline, by 2014, average per capita food expenditures 

were not statistically different from those in 2005. In contrast, per capita food expenditures for 

households whose oldest child was age 12 to 17 did not recover by 2014, remaining $171 (8 percent) 

below 2005 levels.

Despite the differences in total food expenditures, all households with children, regardless of the 

oldest child’s age, allocated a similar share to FAFH (table 4.5). Between 2005 and 2010, all three 

household types had a significant decline in the share of FAFH expenditures: 4 percentage points for 

those whose oldest child was younger than age 6 or age 6 to 11 (11 and 9 percent, respectively), and 

2 percentage points (6 percent) for those whose child was age 12 to 17. By 2014, FAFH shares for all 

three household types remained 3 percentage points (8, 7, and 8 percent by ascending age of oldest 

child) below the 2005 shares. 

Despite the statistically significant decline in the overall share of FAFH expenditures for all three 

household types, only the share allocated to fast food declined significantly between 2005 and 2010 

among households whose oldest child was age 11 or younger—it dropped 2.4 percentage points (11 

percent) for households whose oldest child younger than age 6 and 0.6 percentage points (3 percent) 

for those whose oldest child was age 6 to 11 (fig. 4.9). For these households, the share spent at fastfood restaurants returned to pre-recession shares by 2014. The change in the share allocated to 

full-service restaurants was not statistically significant for any of the household groups in 2010 and 

2014. With respect to FAH spending, the share allocated to edible ingredients increased for all three 

household types by 2010: 1.9 percentage points for households with an oldest child younger than age 

6 or age 12 to 17 (15 and 16 percent, respectively), and 2.2 percentage points (18 percent) for those 

with an oldest child age 6 to 11. By 2014, the share of these expenditures had grown even more. 
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Figure 4.9

Share of food expenditures by children’s age—2005, 2010, 2014

Note: FAH = food at home. FAFH = food away from home. FAH – Other includes all beverages. A solid color for 2010 and 

2014 indicates statistically significant change from 2005, whereas the lighter shading indicates an insignificant change 

(p<0.05).

Source: USDA, Economic Research calculations from U.S. Department of Labor, Bureau of Labor Statistics Consumer 

Expenditure Survey data.

FAH - prepared FAH - ready-to-heat FAH - edible ingredients

FAH - unprepared ingredients FAH - other FAFH - fast food

FAFH - full-service FAFH - other

Percent

0

20

40

60

80

100

2005 2010 2014 2005 2010 2014 2005 2010 2014

Oldest < 6 Oldest 6-11 Oldest 12-17

10.9 10.7 10.4 11.3 11.9 11.4 12.1 12.1 12.1

3.9 4.2 3.1 4.1 4.1 3.6 4.2 4.3 3.6

12.3 14.2 14.4 12.3 14.5 15.0 12.1 14.0 14.9

16.8 18.7 18.8 19.3 20.3 20.6 20.0 20.4 21.3

22.9 20.5 22.4 21.0 20.4 21.3 19.7 18.7 19.4

14.3 12.7 12.6 13.8 12.4 13.1 13.1 13.3 13.3

15.8

16.4 16.2 13.1 12.9 12.2 12.7 12.7 12.5

3.0 2.6 2.1 5.1 3.5 2.7 6.0 4.5 2.9

In addition, for those with an oldest child age 12 to 17, the share of food expenditures allocated to 

unprepared ingredients increased by 1.3 percentage points (6 percent) relative to 2005.

Conclusion

This analysis finds that households replaced a declining share of FAFH expenditures with FAH 

expenditures during the Great Recession and through the recovery period until 2014. Disaggregating 

food expenditures by the level of preparation needed for at-home consumption (FAH) or by the level 

of service when dining out (FAFH) illustrates that the main substitution across categories was an 

increase in the share of expenditures allocated to edible and unprepared ingredients in place of fullservice restaurants. These results are consistent with findings that service industries were hit the 

hardest during the Great Recession (e.g., Petev et al., 2012; De Nardi et al., 2012). In contrast, most 

households maintained their share of expenditures at fast-food restaurants throughout the period, 

which coincided with the continued growth in the number of quick-service restaurants throughout 

the recession (chapter 6) and the observed increase in the share of calories from fast foods among 

working age adults in 2013-14, relative to 2005-06 (Todd, 2017). Overall, changes in food spending 

allocations across categories were relatively small, with the largest change from one year to the next 

being less than 1 percentage point for all categories. This suggests that households have strong preferences for their food choices, trying to find ways to lower spending within categories rather than 

making changes across categories. 
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Spending levels and patterns differ across household types. Higher income households spent more 

per capita and allocated a larger share of their food spending to FAFH, while the lowest income 

households, particularly those that participate in SNAP, had the lowest share of FAFH expenditures. 

These differences in FAFH shares are mostly attributed to differences in expenditures at full-service 

restaurants. The shares of expenditures allocated to fast-food restaurants are relatively similar across 

household types, which may be partially due to fast-food restaurants being cheaper on average than 

quick-service and full-service restaurants. 

Households with an elderly individual or with children spent a smaller share of their food budgets 

on FAFH than households without such members. It could be that cooking is more cost-effective 

for these larger household or that children and elderly individual(s) can help prepare meals at home, 

although some elderly individuals are less mobile. It could also be an indication that there are generational differences in preferred types of meals or consumption patterns, or that dietary restrictions 

make it difficult for these households to purchase FAFH. Households with an elderly individual allocated a lower share of their food budgets to fast-food restaurants than did households without elderly 

members, which could suggest that elderly individuals are less time-constrained (a main appeal of 

fast food). Households with children spent less per capita on food than households without children, 

likely due to economies of scale. Lower prices for children’s meals, on average, may also contribute 

to lower per capita food expenditures for these household types. 

Differences in spending patterns across household types were greater than their changes within each 

household type from 2005 to 2014. Across household types, the share of food expenditures allocated 

to FAFH decreased during the Great Recession and the share of spending on edible and unprepared 

FAH ingredients increased. These trends continued through 2014 for most household types, perhaps 

suggesting a general shift toward home-cooked meals, particularly if these trends persist. However, 

most households maintained their spending on fast food throughout the period, which suggests that 

time constraints remain or that fast food plays an important role in most household diets. It could 

also be that there are cheaper items available at fast-food restaurants, although data limitations 

prevent us from exploring how prices or quantities of food purchased changed during this period. 

References

Aguiar, M., E. Hurst, and L. Karabarbounis. 2013. “Time Use During the Great Recession,” 

American Economic Review 103(5): 1664–96. 

Andrews, M., and D. Smallwood. March 2012. “What’s Behind the Rise in SNAP Participation?” 

Amber Waves, U.S. Department of Agriculture, Economic Research Service.

Beatty, T., and C. Tuttle. 2015. “Expenditure Response to Increases in In-Kind Transfers: Evidence 

from the Supplemental Nutrition Assistance Program,” American Journal of Agricultural 

Economics 97(2): 390–404.

Bitler, M., and H. Hoynes. 2015. “Heterogeneity in the Impact of Economic Cycles and the Great 

Recession,” American Economic Review: Papers and Proceedings, 105(5): 154–60.

Charles, K., and P. DeCicca. 2008. “Local Labor Market Fluctuations and Health: Is There a 

Connection and for Whom?” Journal of Health Economics 27(6): 1532–50.
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De Nardi, M., E. French, and D. Benson. 2012. “Consumption and the Great Recession,” Federal 

Reserve Bank of Chicago Economic Perspectives 36(1): 1–16. 

Jabs, J., and C. Devine. 2006. “Time Scarcity and Food Choices: An Overview,” Appetite, 47(2): 

196–204.

Kumcu, A., and P. Kaufman. September 2011. “Food Spending Adjustments During Recessionary 

Times,” Amber Waves, U.S. Department of Agriculture, Economic Research Service.

Meyer, B., W. Mok, and J. Sullivan. 2009. “The Under-Reporting of Transfers in Household Surveys: 

Its Nature and Consequences,” Working Paper No. 15181, National Bureau of Economic Research 

(NBER), Cambridge, MA.

Nevo, A., and A. Wong. 2015. “The Elasticity of Substitution Between Time and Market Goods: 

Evidence from the Great Recession,” Working Paper No. 21318, National Bureau of Economic 

Research, Cambridge, MA.

Nord, M., and M. Prell. 2011. Food Security Improved Following the 2009 ARRA Increase in SNAP 

Benefits, ERR-116, U.S. Department of Agriculture, Economic Research Service. 

Okrent, A., and A. Kumcu. 2016. U.S. Households’ Demand for Convenience Foods, ERR-211, U.S. 

Department of Agriculture, Economic Research Service.

Petev, I., L. Pistaferri, and I. Saporta-Eksten. 2012. “ An Analysis of Trends, Perceptions, and 

Distributional Effects in Consumption,” in The Great Recession, D. Grusky, B. Western, and C. 

Wimer, eds. New York, NY: The Russell Sage Foundation.

Ruhm, C. 2005. “Healthy Living in Hard Times,” Journal of Health Economics, 24(2): 341–63

Smith, L., S. Ng, and B. Popkin. 2014. “Resistant to the Recession: Low-Income Adults’ 

Maintenance of Cooking and Away-From-Home Eating Behaviors During Times of Economic 

Turbulence,” American Journal of Public Health, 104(5): 840–46.

Todd, J.E. 2014. Changes in Eating Patterns and Diet Quality Among Working-Age Adults, 2005–

2010, ERR-161, U.S. Department of Agriculture, Economic Research Service.

Todd, J. E. 2017. “Changes in consumption of food away from home and intakes of energy and other 

nutrients among US working-age adults, 2005–2014,” Public Health Nutrition 20(18):3,238-3,246. 

doi:10.1017/S1368980017002403.
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Appendix: Categories by Uniform Commercial Codes

FAH – PREPARED

010210 CEREAL

020310 FRESH BISCUITS, ROLLS, MUFFINS

020410 CAKES AND CUPCAKES

020510 COOKIES 

020610 CRACKERS

020620 BREAD AND CRACKER PRODUCTS

020710 DOUGHNUTS, SWEETROLLS, COFFECAKE

020820 FRESH PIES, TARTS, TURNOVERS

100410 ICE CREAM AND RELATED PRODUCTS

180310 POTATO CHIPS AND OTHER SNACKS

180611 PREPARED SALADS

180612 PREPARED DESSERTS

180710 MISC. PREPARED FOODS

FAH – PREPARED (READY-TO-HEAT)

020810 FROZEN & REFRIG. BAKERY PROD.

130121 FROZEN FRUITS

140110 FROZEN VEGETABLES

180110 SOUP

180210 FROZEN MEALS

180220 FROZ/PREP. FOOD OTH THAN MEALS

FAH – UNPREPARED (EDIBLE INGREDIENTS)

020110 WHITE BREAD

020210 BREAD OTHER THAN WHITE

040610 CANNED HAM

070110 CANNED FISH AND SEAFOOD

100210 CHEESE

110110 APPLES

110210 BANANAS

110310 ORANGES

110410 OTHER FRESH FRUITS

110510 CITRUS FRUITS EXCL. ORANGES

120210 LETTUCE

120310 TOMATOES

120410 OTHER FRESH VEGETABLES

130310 CANNED FRUITS

130320 DRIED FRUITS

140210 CANNED BEANS

140220 CANNED CORN

140230 CANNED VEGETABLES MISC

140310 OTHER PROCESSED VEGETABLES

140320 OTHER PEAS
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140330 OTHER BEANS

140340 OTHER VEGETABLES MISC

160212 SALAD DRESSINGS

160320 PEANUT BUTTER

180320 NUTS

180420 OLIVES, PICKLES, RELISHES

FAH – UNPREPARED (INGREDIENTS)

010110 FLOUR

010120 PREPARED FLOUR MIXES

010310 RICE

010320 PASTA CORNMEAL OTH CEREAL PRODS

030110 GROUND BEEF EXCLUDE CANNED

030210 CHUCK ROAST

030310 ROUND ROAST

030410 OTHER ROAST

030510 ROUND STEAK

030610 SIRLOIN STEAK

030710 OTHER STEAK

030810 OTHER BEEF (EXCLUDE CANNED)

040110 BACON

040210 PORK CHOPS

040310 HAM (EXCLUDE CANNED)

040410 OTHER PORK

040510 PORK SAUSAGE

050110 FRANKFURTERS

050210 BOLOGNA, LIVERWURST, SALAMI

050310 OTHER LUNCHMEAT

050410 LAMB AND ORGAN MEATS

050900 MUTTON, GOAT, GAME

060110 FRESH & FROZEN WHOLE CHICKEN

060210 FRESH OR FROZEN CHICKEN PARTS

060310 OTHER POULTRY

070230 FRESH FISH & SHELLFISH

070240 FROZEN FISH & SHELLFISH

080110 EGGS

090210 CREAM

100110 BUTTER

100510 OTHER DAIRY PRODUCTS

120110 POTATOES

150211 SUGAR

150212 ARTIFICIAL SWEETENERS

150310 OTHER SWEETS

160110 MARGARINE

160211 FATS & OILS

160310 NONDAIRY CREAM SUBSTITUTES

180410 SALT/OTHER SEASONINGS & SPICES
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180510 SAUCES AND GRAVIES

180520 OTHER CONDIMENTS

DRINKS 

090110 FRESH MILK ALL TYPES

130110 FROZEN ORANGE JUICE

130122 FROZEN FRUIT JUICES

130211 FRESH FRUIT JUICE

130212 CANNED/BOTTLE FRUIT JUICE

140410 FROZEN VEGETABLE JUICES

140420 FRESH & CANNED VEGETABLE JUICES

170110 COLA DRINKS

170210 OTHER CARBONATED DRINKS

170310 ROASTED COFFEE

170410 INSTANT/FREEZE DRIED COFFEE

170510 NONCARB FRUT FLAV/LEMADE NONFROZ

170520 TEA

170530 OTHER NONCARB. BEVERAGES/ICE

FAH – OTHER

150110 CANDY AND CHEWING GUM

180620 BABY FOOD

180720 VITAMIN SUPPLEMENT

FAFH – FAST FOOD

190111 LUNCH AT FAST FOOD

190211 DINNER AT FAST FOOD

190311 SNACKS AT FAST FOOD

190321 BREAKFAST AT FAST FOOD

FAFH – FULL SERVICE

190112 LUNCH AT FULL SERVICE

190212 DINNER AT FULL SERVICE

190312 SNACKS AT FULL SERVICE

190322 BREAKFAST AT FULL SERVICE

FAFH – OTHER 

190113 LUNCH AT VENDING MACHINE

190114 LUNCH AT EMPLOYER

190115 LUNCH AT BOARD

190116 LUNCH AT CATERED AFFAIRS

190213 DINNER AT VENDING MACHINE

190214 DINNER AT EMPLOYER

190215 DINNER AT BOARD

190216 DINNER AT CATERED AFFAIRS

190313 SNACKS AT VENDING MACHINE
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190314 SNACKS AT EMPLOYER

190315 SNACKS AT BOARD

190316 SNACKS AT CATERED AFFAIRS

190323 BREAKFAST AT VENDING MACHINE

190324 BREAKFAST AT EMPLOYER

190325 BREAKFAST AT BOARD

190326 BREAKFAST AT CATERED AFFAIRS

190911 BOARD AT FAST FOOD

190912 BOARD AT FULL SERVICE

190913 BOARD AT VENDING MACHINE

190914 BOARD AT EMPLOYER

190915 BOARD AT BOARD

190916 BOARD AT CATERED AFFAIRS

190921 CATERED AFF AT FAST FOOD

190922 CATERED AFF AT FULL SERVICE

190923 CATERED AFF AT VEND MACHINE

190924 CATERED AFF AT EMPLOYER

190925 CATERED AFF AT BOARD

190926 CATERED AFF AT CATERED AFF 
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Chapter 5: Demographics of Food-Away-From-Home 

Frequency

This chapter focuses on the frequency of FAFH acquisitions per week. It uses data from 

the National Household Food Acquisition and Purchase Survey (FoodAPS) to examine 

whether the observed increases in expenditure shares on FAFH are driven by increases in 

FAFH acquisitions and variation in acquisitions by demographic group. Findings indicate 

a positive correlation between socioeconomic status and the number of FAFH acquisitions 

per week. 

While the household budget share for FAFH has increased (chapter 3) with variation across socioeconomic groups (chapter 4), it is unclear whether these changes are related to increases in FAFH 

prices (chapter 1) or increases in quantities of FAFH purchased. Studying quantities gives additional 

information about the purchase behavior of households. This chapter examines whether the observed 

increase in expenditures on FAFH is a result of more frequent trips to FAFH outlets. Otherwise, 

expenditure increases may reflect larger purchases per FAFH event.35 It also compares the average 

weekly frequency of FAFH acquisitions by demographic groups using data from USDA’s National 

Household Food Acquisition Survey (FoodAPS) (see box “FoodAPS and Food Away From Home”). 

This analysis complements the analysis in chapter 8, which delves into the relationship between 

proximity to food outlets and diet quality by demographic group. 

Data

Using FoodAPS data, frequencies of FAFH acquisitions were calculated per week per capita by 

counting the number of FAFH events, identified by a unique code, divided by the number of household members. No distinctions were made by where the FAFH was acquired. Thus, visits to fastfood and full-service restaurants were treated as commensurate events. To get average frequencies, 

weekly frequencies were grouped by the relevant demographic characteristic of the household head. 

Each adult group was split into 10-year age cohorts since evidence suggests that FAFH purchases 

vary across age groups (Kuhns and Saksena, 2017). 

Appendix 5.1 shows the average number of times an adult purchased FAFH by 10-year age cohorts 

and corresponding demographic variables. Because of small sample sizes, households were topcoded at 75 years old and above for the household head. Similarly, appendix table 5.2 provides statistics for households with children, defined as individuals under the age of 19, grouped by the ages 

that correspond to grade, middle, and high school attendance. The data are disaggregated by age to 

highlight any age effects that may be driving FAFH acquisition frequency. As noted by Kuhns and 

Saksena (2017), food purchase behavior can vary by the age of the shopper. Although conducting 

the analysis at the individual level admittedly adds noise to the statistics reported here because the 

acquisition data are reported at the household level, this method allows the number of FAFH acquisitions of children to be analyzed.

35While chapter 3 analyzes food expenditures over time, this chapter’s analysis is cross-sectional due to the data limitations of FoodAPS, which currently has only one round of data. This chapter highlights frequency of FAFH acquisitions, 

rather than the quantity purchased (e.g., grams or pounds) at each FAFH acquisition with an awareness that food expenditures 

have been increasing for the past 30 years.
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FoodAPS and Food Away From Home

The National Household Food Acquisition and Purchase Survey (FoodAPS) is a dataset jointly 

sponsored by USDA’s Economic Research Service and Food and Nutrition Service. The data 

contain comprehensive information on household food acquisitions for both food-at-home 

(FAH) and food-away-from-home (FAFH) consumption, collected between April 2012 and 

January 2013, for 4,826 sample households, with sampling weights to generalize to the U.S. 

population. Data collected include information about individual and household characteristics, 

food assistance participation, purchase prices, and nutritional content. As defined by the survey, 

the youngest adults in the survey were 19 years old, and the oldest were over 90. Children were 

defined as 18 years of age and younger. 

In FoodAPS, FAFH and FAH are defined by where the food is acquired. FAFH include food 

acquisitions from restaurants (e.g., bakery/bagel/donut shops; buffet restaurants; burger/hot 

dog restaurants; cafés and bakery-cafes; chicken restaurants; coffee shops and teahouses; dairy 

dessert shops; drinking places; miscellaneous specialty shops such as candy, cheese, juice, 

pretzel, or popcorn; pizza restaurants; American restaurants; Asian restaurants; European 

restaurants; Mexican/Tex-Mex/Latin American restaurants; seafood restaurants; steak house 

restaurants; other restaurants; sandwich/deli/salad shops); travel places; vending machines and 

food trucks; recreational places (e.g., gyms; bowling alleys; casinos; colleges; country clubs; 

fairs, concerts, and amusement parks); fraternal organizations; hospitals; institutions; movie 

theaters; municipal offices; nonfood retailers; parks and community centers; multiple places; 

and unknown. Food that is acquired from a restaurant and brought home for consumption is 

also considered FAFH. For example, delivery pizza is considered FAFH, while an apple, which 

is also ready to eat but does not require preparation for it to be consumed, is considered FAH. 

FoodAPS considers food assistance programs that feed individuals outside the home as FAFH. 

Such programs include the National School Lunch Program. 

To get nationally representative results, FoodAPS provides replicate weights created by using a 

jackknife resampling technique. Jackknife resampling samples without replacement, dropping 

one observation for each permutation and is used to search for overly influential observations.

Age

Frequency of adult FAFH acquisitions is higher among younger cohorts, usually peaking with 

35-44-year-olds and steadily decreasing with older cohorts (fig. 5.1). According to the U.S. Census 

Bureau, 25-34-year-olds have the highest labor force participation, while 45-54-year-olds have 

the highest average monthly earnings; 35-44-year-olds are between these two age groups in both 

categories. This hump-shaped pattern is consistent with lifecycle expenditure patterns of goods 

that are associated with labor participation (Aguiar and Hurst, 2013). Peak FAFH acquisitions 

among younger adults may also be attributable to more active social networks typical of this age 

(Bhattacharya et al., 2016), which might encourage eating out more frequently (Higgs and Thomas, 

2016). Furst et al. (1996) note that convenience and social influences are important factors in making 

food choices. This finding is consistent with previous research that also indicates that younger adults 

eat out more often (Dave et al., 2009; Anderson et al., 2011; Van der Horst et al., 2011; Bezzerra et 

al., 2013).
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Race and Gender

Both men and women on average make five FAFH acquisitions per week. There do not seem to be 

statistical differences between men and women in terms of frequency of FAFH acquisitions within 

each age group, (fig. 5.2), a result confirmed by t-tests.36 This is contrary to previous research that 

similarly compares FAFH by gender and finds that men are more frequent consumers of FAFH 

(Dave et al., 2009; Anderson et al., 2011; Van der Horst et al., 2011; Bezzerra et al., 2013; Seguin et 

al., 2016). Similarly, across all age groups, non-Hispanic Whites eat FAFH the most often, though 

the differences by race are small and were found not to be statistically significant (fig.5.3). It appears 

that the driving factor for frequency of FAFH acquisitions is age. 

36Two-sample t-tests were performed when comparing means of demographic groups. A t-test is a statistical 

hypothesis test used to determine if the means of two groups, in this case frequency of FAFH acquisitions by demographic group, are equal.
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Figure 5.2

Weekly frequency of FAFH by adults by gender

Note: FAFH = food away from home. Vertical error bars represent the standard error of the mean at the 95th percentile 

and the spread of data for each statistic.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and 

Purchase Survey.

Number of FAFH events / week

2

3

3

4

4

5

5

6

6

7

Under 25 25-34 35-44 45-54 55-64 65-74 75+

Age cohort

Male Female

Figure 5.3

Weekly frequency of FAFH by adults by race

Note: FAFH = food away from home. Vertical error bars represent the standard error of the mean at the 95th percentile 

and the spread of data for each statistic.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and 

Purchase Survey.

Number of FAFH events / week

Under 25 25-34 35-44 45-54 55-64 65-74 75+

Age cohort

Non-Hispanic White Non-Hispanic Black/African American Hispanic 

0

1

2

3

4

5

6

7
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Socioeconomic Status: Income, Education, Employment, and 

Food Assistance Receipt

Frequency of FAFH acquisitions appears to be positively correlated with income within all age 

groups (fig. 5.4). Individuals whose incomes were less than 100 percent of the Federal poverty line 

acquired food from FAFH places much less frequently than those whose incomes were equal to 

or greater than 300 percent above the poverty line for the under 25, 25-34, 45-54, and 55-64 age 

groups. The general upward income trend (though not always statistically different across age groups) 

is consistent with other findings in this report, particularly that SNAP participants purchase FAFH 

less frequently (chapter 4) and that lower income individuals obtain an increasing number of calories 

from FAFH, but to a lesser extent than their higher income peers (chapter 7). In the FoodAPS survey, 

individuals with household income equal to or greater than 300 percent of the poverty line showed 

the highest number of FAFH acquisitions.37

Chapter 8 further delves into the frequency and nutritional content of households’ food purchases 

by FAFH outlet type, finding that among household groups delineated by outlet-type patronage, 

“occasional fast food” households purchased the fewest number of calories on a weekly basis. In 

fact, “nonconsumers” of fast food purchased more calories. Along with the results in this chapter, 

this suggests that the increase in FAFH expenditures may not necessarily be the result of more 

frequent trips to FAFH outlets, but that individuals, especially those in households that do not 

patronize fast-food restaurants, may be purchasing larger quantities at each FAFH event. 

 

37Poverty thresholds are determined by the U. S. Census Bureau (2017). 
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Education is another indicator of socioeconomic status (SES). Highly educated individuals are 

more likely to have higher earnings and thus more discretionary income to spend on FAFH, 

possibly leading to more FAFH consumption (BLS, 2017). There is a consistent upward trend in the 

frequency of FAFH acquisitions from the least educated adults to the most, across all age groups 

except the 75+ group (fig. 5.5). The difference in acquisition rates between adults with a high school 

education or less and adults who are college graduates or higher is statistically significant for all but 

the 45-54 and 75+ age groups. A similar upward trend is found for employed versus unemployed 

adults (fig. 5.6). T-tests determined the differences were statistically significant for all age groups 

except 65-74 and 75+.

3

Finally, when comparing adult participants in the Supplemental Nutrition Assistance Program 

(SNAP) with nonparticipants, SNAP participants acquire FAFH with far less frequency across 

all age groups (fig. 5.7). This result is consistent with the findings for education level and employment status that also indicate that adults with lower SES eat FAFH less frequently. The differences 

between SNAP participants and nonparticipants were statistically significant for all but the under 25 

and 65-74 age cohorts. 

Figure 5.5

Weekly frequency of FAFH by adults by education

Number of FAFH events / week

Note: FAFH = food away from home. Education levels are based on the individual’s response to his or her highest 

attained level of education. Vertical error bars represent the standard error of the mean at the 95th percentile and the 

spread of data for each statistic.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and 

Purchase Survey.

High school or less 2-year degree & some college College graduate or higher

Under 25 25-34 35-44 45-54 55-64 65-74 75+

Age cohort

0

1

2

3

4

5

6

7

8

9
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Figure 5.6

Weekly frequency of FAFH by adults by employment status

Note: FAFH = food away from home. Employment status is determined for the primary responder and corresponds to 

individual-level employment status, not household-level. Vertical error bars represent the standard error of the mean at 

the 95th percentile and the spread of data for each statistic.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and 

Purchase Survey.

Under 25 25-34 35-44 45-54 55-64 65-74 75+

Age cohort

Not employed Employed

0

1

2

3

4

5

6

7

Number of FAFH events/week

Figure 5.7

Weekly frequency of FAFH by adults by SNAP participation

Note: FAFH = food away from home. SNAP = Supplemental Nutrition Assistance Program. Vertical error bars 

represent the standard error of the mean at the 95th percentile and the spread of data for each statistic.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and 

Purchase Survey.

Number of FAFH events/ week

Under 25 25-34 35-44 45-54 55-64 65-74 75+

Age cohort

SNAP No SNAP

0

1

2

3

4

5

6

7

8

9
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Marital and Parental Status

Single adults age 44 and younger acquire FAFH with more frequency than their married/partnered 

counterparts, but this reverses beginning with the 45-54 age group (fig. 5.8). This analysis shows that 

adults with children in their household acquired FAFH less frequently than those without children 

cross all age groups (fig. 5.8). However, after testing for statistically significant differences, first 

between married and single adults and then between adults with and without children, no statistically significant differences between them within age groups are found. 

Differences in FAFH Frequency for Households with Children 0-18 

Years Old Across Demographic Groups

Instead of 10-year age cohorts, households with children are clustered according to the age of the 

children in the household: child (0-l0 years of age), youth (11-13), and teen (14-18). At first glance, 

a general trend can be seen across the three age subgroups, showing that households with youths 

appear to acquire FAFH with the most frequency (figs. 5.9-5.11).38

Much like the adults, there was little variation in the frequency of FAFH acquisitions when disaggregating households with children by race. African-American and Hispanic youth and teens acquire 

FAFH with greater frequency than non-Hispanic White youth and teens, but these differences were 

found to be statistically insignificant.

 

38Since children below 4 years of age are generally not yet attending school, we were asked for a separate designation for 

children not of school age.
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Figure 5.9

Weekly frequency of FAFH by children by race/ethnicity

Note: FAFH = food away from home. Vertical error bars represent the standard error of the mean at the 95th percentile 

and the spread of data for each statistic.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and 

Purchase Survey .

Number of FAFH events/ week 

0

2

4

6

8

10

Age group

Non-Hispanic White Non-Hispanic Black/African American Hispanic

Child (0-10) Youth (11-13) Teen (14-18)

0

1

2

3

4

5

6

7

8

9

Child (0-10) Youth (11-13) Teen (14-18)

Age group

Figure 5.10a

Weekly frequency of FAFH by children participating in the National School Lunch 

Program (NSLP), including NSLP meals

Number of FAFH events / week

Note: FAFH = food away from home. Vertical error bars represent the standard error of the mean at the 95th percentile 

and the spread of data for each statistic.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and 

Purchase Survey.

Child does not receive meals from NSLP Child receives meals from NSLP
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0

1

2

3

4

5

6

Child (0-10) Youth (11-13) Teen (14-18)

Age group

Figure 5.10b

Weekly frequency of FAFH by children participating in the National School Lunch 

Program (NSLP), including NSLP meals

Number of FAFH events / week

Note: FAFH = food away from home. Vertical error bars represent the standard error of the mean at the 95th percentile 

and the spread of data for each statistic.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and 

Purchase Survey.

Child does not receive meals from NSLP Child receives meals from NSLP

National School Lunch Program

Previous research has analyzed the effect of Federal food assistance programs on shopping behaviors. Tuttle (2016) found that participation in the Supplemental Nutrition Assistance Program (SNAP) 

reduces expenditures on FAFH. Using the FoodAPS data, ERS researchers examine the correlation between FAFH acquisitions and participation in another Federal food assistance program, the 

National School Lunch Program (NSLP), among households with children. 

Because school lunches must adhere to certain nutritional standards, they may not necessarily be 

considered typical FAFH fare. To control for this variation, frequency of FAFH acquisitions is calculated for households with children that do and do not receive lunches from the NSLP. Children who 

receive school lunches from NSLP acquire FAFH much more frequently than children who do not 

receive lunches from NSLP (fig. 5.11a). T-tests find the differences within each age group statistically significant. However, when NSLP acquisitions are excluded, the differences are negated and, in 

fact, children who do not receive lunches from NSLP acquire FAFH slightly more frequently, though 

the differences were statistically significant only for youth (fig. 5.11b). Nonetheless, the results are 

consistent with the findings of Tuttle (2016) and chapter 4 that households that participate in SNAP 

allocate a smaller share of their budget to FAFH and consume FAFH less frequently than nonparticipants. The parents of children who do not participate in NSLP are likely to be wealthier and able 

to afford to consume FAFH on a more regular basis. And since SNAP benefits cannot be used to 

purchase FAFH, and NSLP offers free or reduced-price lunches, participating households have less 

incentive than nonparticipants to consume FAFH. 
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Discussion

Americans are spending more on FAFH than ever before; however, the relationship between 

increased expenditures and food purchasing behavior is still unclear. Using FoodAPS data, this 

chapter explores whether and how observed increases in expenditure share (see chapter 3) on FAFH 

translate into differences in the frequency of FAFH acquisitions among various demographic groups 

within age cohorts. This analysis shows that 35-44-year-olds acquire FAFH with greater frequency 

than other adult age cohorts. However, after performing t-tests, these differences were found to be 

largely statistically insignificant. In addition, there were virtually no differences in frequency of 

FAFH acquisitions between men and women across all 10-year adult age groups, as well as when 

disaggregating the data by race, marital, and parental status. Similar insignificance was observed 

when analyzing FAFH frequency for households with children; there do not seem to be statistically 

significant differences when dividing the data by race.

The most compelling results were frequency differences by various measures of socioeconomic 

status (SES). When disaggregating the data by household income as a percentage of the poverty 

level, education level, employment status, and SNAP participation, adults with measurably higher 

SES acquired FAFH more frequently compared to their lower SES counterparts. A similar result 

was found with households with children. After controlling for NSLP meals, households with children who did not receive NSLP meals displayed more frequent FAFH acquisitions, though these 

differences were statistically insignificant except within the youth age group. 

References

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Economy,121(3): 437-92.

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Higgs, S., and J. Thomas. 2016. “Social Influences on Eating,” Current Opinion in Behavioral 

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 Kuhns, A., and M. Saksena. 2017. Food Purchase Decisions of Millennial Households Compared to 

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Seguin, R.A., A. Aggarwal, F. Vermeylen, and A. Drewnowski. 2016. “Consumption Frequency of 

Foods Away from Home Linked with Higher Body Mass Index and Lower Fruit and Vegetable 

Intake among Adults: A Cross-Sectional Study,” Journal of Environmental and Public Health.
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Tuttle, C. 2016. The Stimulus Act of 2009 and Its Effect on Food-At-Home Spending by SNAP 

Participants, ERR-213, U.S. Department of Agriculture, Economic Research Service.

U.S. Bureau of Labor Statistics. April 20, 2017. “EP News Releases.”

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Mean Income.” 

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2020 Dietary Guidelines for Americans. 8th Edition. 

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With Weight Status and Cooking Skills,” Public Health Nutrition 14(2): 239-45.
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Appendix table 5.1

Frequency of food-away-from-home (FAFH) events for adults, by age and demographic group

Cohort group by age Under 25 95% 

CI 25-34 95% 

CI 35-44 95% 

CI 45-54 95% 

CI 55-64 95% 

CI

Number of Individuals 1,093 1,716 1,419 1,396 1,049

Demographic variables

Age 5.01 4.60 5.41 5.31 4.78 5.84 5.45 4.86 6.04 5.05 4.50 5.60 4.96 4.45 5.48

Race

Non-Hispanic 

White 5.14 4.61 5.64 5.55 4.92 6.23 5.42 4.66 6.20 5.23 4.53 5.87 5.20 4.63 5.77

Non-Hispanic 

Black/African 

American

4.56 3.77 5.36 4.50 3.40 5.69 5.19 3.63 6.70 4.13 3.22 5.13 4.00 3.29 4.68

American Indian 

or Alaska Native 7.63 3.70 9.29 7.05 1.10 11.35 7.76 2.91 8.61 4.82 2.15 6.89 3.63 2.37 7.38

Asian 4.83 3.48 6.26 4.32 3.22 5.42 5.67 3.18 7.95 5.70 3.57 8.66 2.63 1.82 3.45

Hispanic 4.28 3.55 5.00 4.78 4.04 5.52 5.40 4.15 6.66 4.41 3.67 5.14 4.67 3.56 5.79

Sex

Male 4.92 4.19 5.65 5.45 4.71 6.20 5.41 4.64 6.19 4.98 4.47 5.49 4.87 4.41 5.33

Female 5.08 4.65 5.52 5.19 4.57 5.81 5.49 4.82 6.15 5.12 4.28 5.96 5.04 4.30 5.79

Race-sex

White, male 5.11 4.06 6.16 5.77 4.86 6.68 5.35 4.42 6.28 5.04 4.38 5.70 4.96 4.30 5.61

Black, male 4.24 3.06 5.43 4.33 2.55 6.11 4.54 3.14 5.94 4.45 2.91 6.00 3.91 2.95 4.86

White, female 5.17 4.27 6.07 5.35 4.57 6.14 5.48 4.66 6.30 5.42 4.42 6.41 5.40 4.53 6.27

Black, female 4.80 3.75 5.85 4.63 3.44 5.82 5.64 3.75 7.53 3.86 2.82 4.90 4.08 2.91 5.25

Body weight classification

Not overweight 4.91 4.23 5.59 5.45 4.57 6.34 5.64 4.60 6.69 5.37 4.12 6.61 4.94 3.66 6.22

Overweight 5.11 4.44 5.77 4.99 4.19 5.78 5.10 4.36 5.84 5.11 4.51 5.71 5.05 4.11 6.00

Obese 5.32 4.12 6.53 5.51 4.54 6.48 5.73 4.90 6.55 4.78 4.01 5.54 4.91 4.35 5.46

Socioeconomic variables

Income (All races and genders)

< 100% of 

poverty

line

3.90 3.34 4.46 4.36 3.50 5.23 4.84 4.12 5.56 4.17 3.44 4.89 4.06 3.14 4.98

100-130% of 

poverty line 4.65 3.36 5.95 4.68 3.50 5.86 4.75 3.77 5.73 4.21 2.96 5.46 3.91 2.66 5.17

131-167% of 

poverty line 4.17 3.25 5.08 4.65 3.66 5.64 4.78 3.72 5.84 4.96 3.35 6.57 3.31 1.90 4.71

168-299% of 

poverty line 5.36 4.38 6.35 4.86 4.13 5.59 5.28 3.87 6.68 4.81 3.85 5.77 4.90 3.87 5.93

≥ 300% of 

poverty line 5.62 4.82 6.42 6.11 5.16 7.06 5.89 4.89 6.88 5.47 4.65 6.29 5.45 4.68 6.22

Income by race and gender

< 100% of poverty 

line 3.33 2.22 4.43 4.68 2.96 6.39 4.62 3.07 6.16 3.71 2.63 4.79 4.23 2.26 6.20

White, male 2.97 1.72 4.22 3.89 1.32 6.47 4.62 1.80 7.43 3.64 2.36 4.93 4.29 1.52 7.06

Black, male 4.80 3.18 6.43 4.19 3.00 5.38 4.85 3.21 6.49 4.79 3.93 5.65 3.89 2.57 5.20

White, female 3.78 2.44 5.13 4.22 3.30 5.13 6.10 3.84 8.36 4.10 2.60 5.60 3.92 1.15 6.69

Black, female

100-130% of poverty line

White, male 4.39 2.51 6.26 3.20 2.22 4.18 4.47 2.22 6.72 3.46 1.51 5.41 3.12 2.15 4.09

Black, male 3.64 1.15 6.12 4.91 -0.05 9.87 5.49 0.22 10.76 6.90 4.72 9.07 4.51 0.52 8.49

White, female 4.59 2.26 6.92 4.07 2.31 5.83 4.63 2.99 6.28 3.76 2.10 5.42 4.72 2.44 7.00

Black, female 5.71 3.32 8.09 6.52 4.00 9.04 5.88 2.90 8.86 2.72 1.25 4.18 3.88 2.70 5.05

131-167% of poverty line

White, male 4.25 2.91 5.59 4.61 3.34 5.88 4.41 2.09 6.73 4.85 2.08 7.62 2.85 0.77 4.92

Black, male 4.08 2.35 5.80 6.38 0.84 11.91 3.46 1.81 5.11 5.81 1.69 9.92 1.41 -0.38 3.19

White, female 4.62 3.04 6.21 4.64 3.28 6.00 4.99 3.62 6.35 4.57 2.81 6.32 3.87 1.60 6.14

Black, female 3.82 0.05 7.58 5.64 1.34 9.95 6.16 3.10 9.22 4.24 -1.88 10.36 3.74 0.74 6.74

Continued—
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Cohort group by age Under 25 95% 

CI 25-34 95% 

CI 35-44 95% 

CI 45-54 95% 

CI 55-64 95% 

CI

168-299% of poverty line

White, male 5.60 4.26 6.94 5.35 4.20 6.49 5.17 3.28 7.05 5.19 3.32 7.07 4.64 2.60 6.67

Black, male 5.07 2.41 7.72 3.52 0.50 6.54 3.88 2.24 5.52 4.64 1.15 8.13 4.52 -0.05 9.09

White, female 5.01 3.17 6.86 4.65 3.73 5.57 5.07 3.27 6.86 5.20 3.99 6.42 4.95 3.76 6.14

Black, female 6.17 3.62 8.71 3.46 0.61 6.32 4.78 2.00 7.55 3.97 1.60 6.34 6.53 1.03 12.03

≥ 300% of poverty line

White, male 5.76 3.74 7.78 6.69 5.23 8.15 5.78 4.24 7.31 5.40 4.65 6.16 5.44 4.61 6.27

Black, male 4.54 3.39 5.70 4.14 2.12 6.16 4.70 0.72 8.68 4.64 1.84 7.44 3.19 1.55 4.82

White, female 5.61 3.63 7.60 6.50 5.04 7.96 6.08 4.72 7.44 5.78 4.33 7.23 6.06 4.68 7.44

Black, female 4.67 1.29 8.04 4.94 1.65 8.22 5.47 1.35 9.60 3.83 1.79 5.87 3.68 2.32 5.03

Not employed 3.76 3.13 4.40 4.42 3.75 5.10 4.74 4.16 5.33 3.86 3.27 4.45 3.93 3.50 4.37

Employed 5.65 5.18 6.12 5.68 5.10 6.26 5.69 5.00 6.38 5.43 4.81 6.06 5.73 5.00 6.46

No SNAP 5.19 4.68 5.71 5.54 4.91 6.16 5.62 4.97 6.27 5.24 4.64 5.85 5.10 4.55 5.65

SNAP 4.14 3.25 5.03 4.25 3.72 4.78 4.27 3.48 5.07 3.56 3.06 4.07 3.69 2.82 4.56

Education

High school 

or less 4.39 3.77 5.01 4.43 3.93 4.93 4.82 4.15 5.50 4.85 3.58 6.11 4.09 3.57 4.60

2-year degree 

and some college 5.05 4.39 5.71 4.92 4.28 5.56 5.57 4.83 6.31 4.98 4.04 5.92 4.87 4.15 5.59

College graduate

 or higher 6.62 5.20 8.03 6.33 5.34 7.32 6.00 4.98 7.01 5.38 4.39 6.37 5.79 4.52 7.06

Household characteristics

Single 5.02 4.56 5.48 5.56 4.71 6.41 5.63 4.70 6.57 5.08 4.39 5.77 4.57 3.57 5.56

Married/partnered 4.92 3.97 5.88 5.04 4.43 5.64 5.34 4.62 6.06 5.03 4.31 5.76 5.21 4.76 5.66

No children 5.33 4.74 5.91 5.82 4.81 6.84 5.83 4.90 6.77 5.23 4.24 6.21 4.99 4.44 5.54

Children 4.56 3.77 5.36 4.91 4.43 5.39 5.30 4.61 6.00 4.83 4.32 5.34 4.81 3.47 6.15

Lifestyle variables

< Median commuter

time (15 minutes) 5.61 5.14 6.08 5.66 5.05 6.26 5.77 5.09 6.46 5.43 4.80 6.07 5.64 4.88 6.40

≥ Median commuter

time (15 minutes) 3.76 3.13 4.39 4.21 3.49 4.92 4.39 3.86 4.93 3.78 3.28 4.29 3.96 3.47 4.45

Not vegetarian 5.03 4.65 5.41 5.31 4.78 5.83 5.42 4.86 5.97 5.04 4.47 5.61 5.04 4.57 5.51

Vegetarian 4.44 1.52 7.35 5.44 2.16 8.73 7.00 2.28 11.71 5.74 0.96 10.53 2.42 1.11 3.73

Not dieting 5.08 4.55 5.61 5.25 4.74 5.77 5.38 4.72 6.03 5.20 4.50 5.90 4.86 4.35 5.37

Dieting 4.43 2.69 6.17 5.70 3.88 7.51 5.76 4.87 6.65 4.55 3.49 5.60 5.19 3.91 6.47

Health status

Excellent 4.81 3.44 6.18 5.88 4.00 7.76 5.40 4.12 6.68 4.84 3.88 5.80 4.75 3.58 5.93

Fair 4.84 3.53 6.15 5.80 3.88 7.72 5.53 4.63 6.44 5.08 4.14 6.01 4.05 3.40 4.70

Nonsmoker 5.10 4.68 5.52 5.27 4.72 5.83 5.47 4.79 6.15 5.18 4.62 5.75 5.15 4.53 5.77

Smoker 4.73 3.83 5.63 5.42 4.44 6.40 5.39 4.25 6.53 4.59 3.80 5.37 4.09 3.20 4.97

Census region

Northeast (CT, ME,

MA, NH, RI, VT, 

NJ, NY, PA) 5.07 4.15 5.98 4.79 3.54 6.04 4.74 3.71 5.77 4.74 4.12 5.35 5.06 3.75 6.37

Midwest (IL, IN, MI, 

OH, WI, IA, KS, MN, 

MO, NE, ND, SD) 5.11 4.17 6.04 5.40 4.31 6.49 5.36 4.15 6.57 5.69 4.87 6.51 4.63 4.13 5.12

South (DE, DC, FL, 

GA, MD, NC, SC, 

VA, WV, AL, KY, 

MS, TN, AK, LA, 

OK, TX) 5.17 4.34 6.00 5.64 4.68 6.60 6.02 5.35 6.70 4.58 3.62 5.55 5.50 4.29 6.71

West (AZ, CO, ID, 

MT, NV, NM, UT, 

WY, AK, CA, HI, 

OR, WA) 4.53 3.78 5.29 5.05 4.60 5.50 5.28 4.02 6.55 5.06 3.53 6.58 4.35 3.39 5.32

Continued—
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Cohort group by age 65-74 95% CI 75+ 95% CI N 95% CI

Number of individuals 629 337 6,673

Demographic variables

Age 4.28 3.48 5.08 3.44 2.77 4.12 4.96 4.72 5.20

Race

Non-Hispanic White 4.39 3.45 5.32 3.55 2.82 4.29 5.07 4.78 5.36

Non-Hispanic Black/African American 3.20 1.75 4.57 2.19 1.36 2.93 4.36 3.79 4.92

American Indian or Alaska Native 4.00 0.89 9.83 1.00 -1.71 9.05 6.37 4.39 8.36

Asian 3.09 0.00 6.17 2.79 -2.00 7.59 4.47 3.61 5.33

Hispanic 2.55 1.54 3.56 2.90 1.59 4.22 4.56 4.17 4.94

Sex 4.83 3.53 6.14 3.85 2.87 4.84 5.03 4.71 5.36

Male 3.78 3.16 4.41 3.09 2.38 3.80 4.90 4.64 5.16

Female

Race-sex

White, male 5.07 3.57 6.57 3.97 2.93 5.01 5.13 4.71 5.55

Black, male 3.54 1.29 5.78 2.40 1.26 3.55 4.23 3.62 4.85

White, female 3.78 3.10 4.45 3.17 2.39 3.95 5.01 4.75 5.28

Black, female 2.82 1.08 4.56 2.10 1.14 3.06 4.45 3.72 5.18

Weight classification

Not overweight 4.87 3.11 6.63 3.55 2.71 4.38 5.12 4.68 5.56

Overweight 4.07 3.11 5.02 3.06 2.37 3.75 4.81 4.44 5.19

Obese 4.01 3.13 4.89 3.91 2.25 5.57 4.98 4.72 5.25

Socioeconomic variables

Income (all races and genders)

< 100% of poverty line 4.22 2.14 6.29 2.83 1.56 4.10 4.16 3.75 4.58

100-130% of poverty line 3.25 1.92 4.58 2.91 1.20 4.62 4.16 3.58 4.75

131-167% of poverty line 3.20 2.18 4.21 3.38 0.65 6.11 4.21 3.61 4.82

168-299% of poverty line 3.42 2.67 4.17 3.82 2.52 5.13 4.76 4.30 5.23

≥ 300% of poverty line 4.89 3.55 6.23 3.60 2.76 4.45 5.50 5.11 5.89

Income by race and gender

< 100% of poverty line 7.37 1.77 12.96 2.76 0.98 4.53 4.31 3.49 5.12

White, male 3.19 0.92 5.47 2.77 -2.73 8.27 4.05 3.06 4.72

Black, male 2.87 1.84 3.90 2.96 1.26 4.66 4.32 3.61 4.52

White, female 2.02 0.08 3.95 1.46 0.20 2.72 3.89 3.33 5.24

Black, female

100-130% of poverty line

White, male 3.66 1.14 6.19 3.43 -1.68 8.54 4.75 2.65 4.58

Black, male 1.00 -0.99 2.99 4.10 2.80 7.50

White, female 3.16 1.87 4.46 3.03 1.74 4.31 3.61 3.42 4.68

Black, female 1.05 -0.84 2.94 2.17 -1.87 6.21 5.13 2.82 6.27

131-167% of poverty line

White, male 2.64 1.80 3.47 3.17 -0.71 7.06 4.56 2.99 4.97

Black, male 3.08 -1.17 7.33 1.00 -0.99 2.99 4.27 1.91 6.91

White, female 3.41 2.40 4.42 3.65 0.02 7.28 3.99 3.64 4.94

Black, female 3.16 -0.15 6.46 1.00 1.00 1.00 4.44 2.42 6.58

168-299% of poverty line

White, male 3.87 2.42 5.32 4.39 2.06 6.72 4.56 4.33 5.52

Black, male 4.31 0.99 7.63 2.29 -0.76 5.33 4.64 2.88 5.63

White, female 3.07 2.64 3.51 3.13 2.45 3.82 4.94 3.96 5.21

Black, female 2.96 -1.36 7.29 4.18 0.30 8.06 4.19 3.56 5.54

≥ 300% of poverty line

White, male 5.51 3.07 7.95 4.11 3.09 5.14 5.63 4.95 6.30

Black, male 4.45 -3.60 12.50 2.88 -0.13 5.89 4.28 3.03 5.52

White, female 4.47 3.34 5.60 3.21 1.93 4.49 5.69 5.29 6.09

Black, female 6.38 3.26 9.49 1.91 0.09 3.73 4.51 2.66 6.36

Not employed 3.97 3.25 4.69 3.31 2.64 3.98 4.00 3.76 4.24

Employed 5.31 2.54 8.07 5.40 3.11 7.68 5.60 5.26 5.94

No SNAP 4.34 3.49 5.18 3.56 2.84 4.27 5.11 4.84 5.38

SNAP 3.44 2.65 4.22 2.12 1.02 3.21 3.91 3.57 4.25

Continued—
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Cohort group by age 65-74 95% CI 75+ 95% CI N 95% CI

Education

High school or less 3.62 2.46 4.78 2.81 2.11 3.52 4.29 3.91 4.67

2-year degree and some college 3.98 2.97 5.00 5.19 3.01 7.37 4.96 4.58 5.33

College graduate or higher 5.15 3.67 6.62 3.61 2.90 4.33 7.00 5.18 6.21

Household characteristics

Single 4.11 3.23 5.00 3.32 2.54 4.10 4.92 4.66 5.19

Married/partnered 4.40 3.08 5.71 3.64 2.63 4.65 5.00 4.65 5.36

No children 4.32 3.47 5.17 3.47 2.77 4.16 4.98 4.63 5.32

Children 3.36 1.97 4.75 2.81 0.81 4.82 4.94 4.69 5.20

Lifestyle variables

< Median commuter time (15 minutes) 5.34 2.46 8.22 5.40 3.11 7.68 5.59 5.25 5.94

≥ Median commuter time (15 minutes) 3.90 3.17 4.63 3.31 2.64 3.98 3.90 3.65 4.15

Not vegetarian 4.26 3.46 5.06 3.50 2.79 4.22 4.98 4.73 5.22

Vegetarian 4.82 -3.18 12.82 2.20 0.83 3.57 4.57 3.46 5.69

Not dieting 4.50 3.56 5.43 3.29 2.69 3.90 5.01 4.71 5.30

Dieting 3.82 2.85 4.79 3.74 2.37 5.11 4.82 4.35 5.28

Health status

Excellent 6.13 2.68 9.58 2.84 1.60 4.08 5.12 4.35 5.90

Fair 3.12 2.42 3.82 3.39 0.70 6.08 4.76 4.35 5.17

Nonsmoker 4.20 3.38 5.02 3.39 2.68 4.11 4.99 4.74 5.25

Smoker 4.79 2.52 7.05 3.86 2.54 5.17 4.86 4.37 5.34

Census region

Northeast (CT, ME, MA, NH, RI, VT, NJ, NY, PA) 4.62 3.27 5.97 3.23 1.96 4.50 4.74 4.39 5.10

Midwest (IL, IN, MI, OH, WI, IA, KS, MN, MO, NE, 

ND, SD) 4.44 2.94 5.94 3.83 2.60 5.07 5.07 4.73 5.41

South (DE, DC, FL, GA, MD, NC, SC, VA, WV, AL, 

KY, MS, TN, AK, LA, OK, TX) 4.05 2.27 5.83 3.48 2.28 4.68 5.11 4.48 5.75

West (AZ, CO, ID, MT, NV, NM, UT, WY, AK, CA, 

HI, OR, WA) 4.00 2.85 5.16 2.63 1.33 3.93 4.73 4.26 5.19

Note: SNAP = Supplemental Nutrition Assistance Program.

Source: USDA, Economic Research Service using data from the National Household Food Acquisition and Purchase Survey 

(FoodAPS).
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Appendix table 5.2

Weekly frequency of food-away-from-home events for children, by age and demographic group

Cohort group by age

Child 

(0-10) 95% CI

Youth 

(11-

13) 95% CI

Teen 

(14-

18) 95% CI

All 

Individuals 95% CI

Number of Individuals 1,994 627 967 3,589

Demographic variables

Race

Non-Hispanic White 4.16 3.74 4.58 5.74 4.81 6.66 5.43 4.69 6.18 4.80 4.34 5.25

Non-Hispanic Black/African American 4.80 3.48 6.11 6.02 4.37 7.67 6.06 4.30 7.81 5.43 4.68 6.18

American Indian or Alaska Native 6.14 4.54 7.75 7.47 4.17 10.78 6.56 2.19 10.93 6.60 4.62 8.57

Asian 4.76 2.03 7.50 5.10 0.77 9.44 5.20 -0.39 10.79 4.91 1.75 8.08

Sex

Boys 4.28 3.77 4.80 5.73 4.51 6.96 5.63 4.71 6.55 4.93 4.41 5.44

Girls 4.34 3.91 4.77 5.95 5.09 6.80 5.65 4.94 6.37 4.98 4.63 5.32

Race-sex

White, male 3.99 3.44 4.53 5.87 4.12 7.62 5.67 4.56 6.77 4.83 4.21 5.46

Black, male 5.04 3.16 6.93 5.72 3.37 8.08 5.58 3.84 7.32 5.35 4.21 6.49

White, female 4.30 3.80 4.81 5.62 4.34 6.90 5.18 4.59 5.78 4.76 4.25 5.27

Black, female 4.51 3.25 5.78 6.52 3.91 9.13 6.55 4.60 8.50 5.52 4.75 6.29

Body weight classification

Not overweight 4.41 3.74 5.08 5.72 4.94 6.51 5.51 4.74 6.27 5.06 4.56 5.55

Overweight 3.84 2.94 4.74 5.84 4.54 7.14 6.22 4.86 7.58 5.11 4.30 5.92

Obese 4.39 3.74 5.05 6.85 4.98 8.72 5.45 4.26 6.64 4.95 4.40 5.49

Body weight classification, male

Not overweight 4.38 3.38 5.38 5.64 4.40 6.88 5.57 4.50 6.64 5.05 4.37 5.73

Overweight 3.99 2.58 5.39 6.25 3.88 8.61 5.74 3.78 7.69 5.10 3.82 6.39

Obese 4.40 3.53 5.27 6.56 3.93 9.19 5.56 3.62 7.49 5.00 4.17 5.82

Body weight classification, female

Not overweight 4.44 3.79 5.09 5.79 4.69 6.89 5.45 4.71 6.18 5.06 4.58 5.54

Overweight 3.70 2.66 4.74 5.51 4.31 6.70 6.80 5.01 8.59 5.13 4.24 6.01

Obese 4.39 3.43 5.35 7.47 3.40 11.54 5.28 4.14 6.42 4.87 3.96 5.79

Socioeconomic variables

Income by race and gender

< 100% of poverty line

White, male 3.79 2.81 4.76 4.92 3.90 5.95 4.91 3.39 6.43 4.34 3.65 5.04

Black, male 3.91 2.43 5.39 7.00 5.00 9.00 5.31 3.98 6.64 5.01 3.73 6.30

White, female 4.32 3.22 5.42 5.09 3.57 6.60 4.23 3.12 5.34 4.47 3.63 5.30

Black, female 5.06 2.93 7.20 5.66 0.53 10.78 4.61 2.09 7.13 5.01 3.67 6.35

100-130% of poverty line

White, male 4.15 2.78 5.51 4.42 2.24 6.61 6.86 4.88 8.84 4.69 3.54 5.85

Black, male 5.11 3.39 6.83 7.03 -0.91 14.96 7.24 3.22 11.25 6.00 3.09 8.90

White, female 4.57 1.61 7.54 6.11 3.26 8.96 6.49 3.79 9.20 5.37 3.65 7.10

Black, female 5.22 2.51 7.93 9.01 1.69 16.33 8.92 6.47 11.38 7.00 4.82 9.17

131-167% of poverty line

White, male 3.86 3.04 4.69 5.88 3.72 8.04 5.47 3.22 7.72 4.73 3.72 5.74

Black, male 4.63 1.55 7.72 6.59 2.15 11.03 6.53 3.30 9.77 5.31 2.62 8.01

White, female 3.88 2.11 5.65 3.71 1.39 6.04 6.61 3.75 9.46 4.66 3.45 5.87

Black, female 4.31 1.05 7.57 6.66 3.28 10.04 7.24 1.64 12.83 5.43 3.43 7.44

168 299% of poverty line

White, male 3.85 3.12 4.58 5.72 2.31 9.12 5.48 3.17 7.79 4.60 3.32 5.87

Black, male 9.34 2.96 15.73 4.20 0.09 8.31 7.40 5.50 9.30 8.09 3.57 12.61

White, female 4.25 3.37 5.14 6.39 4.18 8.60 5.38 3.79 6.97 4.93 4.00 5.85

Black, female 3.82 1.03 6.61 5.73 4.54 6.91 7.14 3.79 10.49 4.76 2.88 6.65

Continued—
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Cohort group by age

Child 

(0-10) 95% CI

Youth 

(11-

13) 95% CI

Teen 

(14-

18) 95% CI

All 

Individuals 95% CI

≥ 300% of poverty line

White, male 4.23 2.64 5.83 6.93 3.91 9.94 6.06 4.59 7.53 5.33 4.25 6.40

Black, male 2.99 0.61 5.38 3.84 -1.89 9.57 5.08 1.55 8.61 4.20 2.34 6.06

White, female 4.37 3.28 5.45 5.66 3.50 7.82 4.91 3.97 5.85 4.69 3.71 5.67

Black, female 3.09 2.35 3.83 7.08 1.29 12.88 7.04 2.03 12.05 6.08 3.42 8.74

No SNAP 4.34 3.95 4.73 5.83 4.90 6.76 5.72 4.90 6.55 5.01 4.61 5.41

SNAP 4.23 3.65 4.80 5.89 4.78 7.01 5.23 4.40 6.06 4.73 4.31 5.15

Schooling

Grade school or less 4.31 3.95 4.67

Middle school 5.84 5.15 6.53

High school 5.64 4.92 6.36

NSLP events included in FAFH tally

Child does not receive meals from 

NSLP 3.08 2.63 3.53 4.67 3.76 5.58 4.22 3.68 4.76 3.61 3.30 3.92

Child receives meals from NSLP 6.86 6.25 7.47 7.01 6.13 7.89 7.68 6.91 8.44 7.14 6.75 7.54

NSLP events not included in FAFH tally

Child does not receive meals from 

NSLP 3.08 2.63 3.53 4.67 3.76 5.58 4.22 3.68 4.76 3.61 3.30 3.92

Child receives meals from NSLP 2.95 2.40 3.50 3.49 2.87 4.11 3.95 3.30 4.60 3.42 3.10 3.73

Lifestyle variables

Not vegetarian 4.31 3.95 4.68 5.89 5.18 6.60 5.64 4.90 6.38 4.96 4.61 5.30

Vegetarian 4.25 2.17 6.34 3.52 0.28 6.76 5.60 2.65 8.55 4.70 3.48 5.91

Not dieting 4.30 3.93 4.67 5.89 5.20 6.59 5.65 4.91 6.39 4.95 4.59 5.31

Dieting 4.77 3.06 6.48 4.64 1.94 7.34 5.43 3.50 7.36 4.97 3.89 6.06

Census region

Northeast (CT, ME, MA, NH, RI, VT, 

NJ, NY, PA) 4.57 3.52 5.62 4.79 3.46 6.12 5.51 4.75 6.26 4.89 4.39 5.40

Midwest (IL, IN, MI, OH, WI, IA, KS, 

MN, MO, NE, ND, SD) 4.46 3.75 5.16 5.94 4.78 7.10 5.16 4.13 6.19 4.90 4.27 5.52

South (DE, DC, FL, GA, MD, NC, SC, 

VA, WV, AL, KY, MS, TN, AK, LA, OK, 

TX)

4.17 3.62 4.72 6.20 5.04 7.37 6.26 4.75 7.76 5.12 4.35 5.88

West (AZ, CO, ID, MT, NV, NM, UT, 

WY, AK, CA, HI, OR, WA) 4.12 3.35 4.89 6.06 4.77 7.36 5.38 4.56 6.21 4.79 4.43 5.16

Note: SNAP = Supplemental Nutrition Assistance Program; NSLP = National School Lunch Program.

Source: USDA, Economic Research Service using data from the National Household Food Acquisition and Purchase Survey 

(FoodAPS).
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Chapter 6: Evolution of the Food-Away-From-Home 

Industry: Recent and Emerging Trends

This chapter uses publicly and commercially available data to characterize changes in 

the supply-side of food away from home (FAFH) from 2000 to 2015. The number of food 

service establishments operating in the United States between 2000 and 2015 has increased 

more than 8 percent, and geographic variation in growth is consistent with patterns of 

rural-urban migration. Roughly 40 percent of these establishments are chains, as defined 

according to criteria set forth by an U.S. Food and Drug Administration (FDA) ruling on 

menu calorie labeling. 

Chapter 2 outlines the evolution of the supply of food away from home (FAFH) to meet American’s 

demand for fast, convenient food options. The increasingly dominant role of FAFH in Americans’ 

food consumption shapes the food industry and has implications for dietary quality. For instance, the 

food service industry’s general growth as well as the proliferation of certain subsegments is partly a 

response to these shifts in consumption patterns. However, the influence may go both ways: just as 

consumer preferences can shape the food industry, so, too, the omnipresence of FAFH outlets may 

sway consumer preferences (chapter 8). With public policy concerns in mind about how FAFH may 

affect Americans’ diets, this chapter segues from a consumer-focused analysis into profiling changes 

in the commercial FAFH industry that occurred from 2000 to 2015. 

By drawing on both publicly available and proprietary commercial sources, this chapter focuses on 

changes in industry size and composition, as well as on new trends in menu offerings. First, the findings from this chapter support previously observed trends (Stewart 2011; Pullman and Wu 2012), 

although this analysis shows more pronounced differences across types of foodservice establishments than earlier research found previously. In 2000-15, the supply of FAFH grew in terms of sales 

and number of establishments, and the composition and structure of firms changed. Over 630,000 

commercial quick-service (QSR) and full-service (FSR) restaurants39 operated in the United States 

as of March 2015, more than an 8-percent increase since 2000. Roughly 54 percent of all FAFH 

establishments in 2015 were QSRs, over 60 percent of which were chains.40 QSR chains were the 

fastest growing subsegment of recent years, even growing during the Great Recession and outpacing 

the number of independent FSRs.41 Across the country, chain restaurants in both segments constitute 

roughly 40 percent of all establishments, although their prevalence varies geographically. 

Second, the analysis in this chapter documents the recent proliferation of the so-called fast-casual 

restaurant. Fast-casual restaurants are generally characterized as QSRs that offer mainly counter 

service, while maintaining an ambiance and food quality similar to FSRs. Industrywide, these 

restaurants have been seen as creative leaders in the trend toward better meeting consumers’ 

sometimes-competing preferences for healthiness, convenience, and value. In part, because of the 

39Throughout the report, the terms fast food, limited service, and quick service are used interchangeably to denote 

restaurants that offer counter service, but do not have wait staff that continually tend to customers. Within this outlet type, 

fast-casual restaurants offer mainly just counter service, but servers may bring food to individual tables. Full-service denotes 

restaurants where wait staff continually serve seated customers throughout their meal.

40The definition of restaurant chain is based on the Food and Drug Administration’s (FDA) ruling on national restaurant 

menu labeling standards stemming from the Affordable Care Act of 2010: that is, a restaurant brand is a chain if it has 20 or 

more establishments. See chapter 10 for a detailed discussion of menu labeling in the United States.

41The Great Recession corresponds to the economic downturn of December 2007 to June 2009. See National Bureau of 

Economic Research for U.S. business cycle dates (online). 
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emphasis on higher quality ingredients, many consumers perceive fast-casual meals to be healthier 

than those of other QSRs, although it is unclear whether or not fast-casual meals tend to be higher in 

calories. Fast casuals were the fastest growing QSR subsegment in terms of numbers of outlets and 

sales. Constituting an estimated 3.6 percent of all QSRs in 2002, fast casuals made up roughly 10.5 

percent of all QSRs nationwide as of 2015.

Third, the analysis shows that, in recent years, much of the growth in FAFH establishments occurred 

in urban U.S. counties in ways consistent with patterns of urban and rural migration. In many locales 

with large population increases, the number of QSRs has likewise risen sharply and the number 

of FSRs, modestly. Rural regions with population declines saw many FSRs close, leaving QSRs to 

dominate their FAFH options.42 This shift may have implications for the healthiness of FAFH environments in these regions, as QSRs may tend to offer less nutritious and more calorie-dense food 

than FSRs do (Binkley, 2008; Lin and Guthrie, 2012; chapter 4).

Data

This chapter draws from several commercial data sources for foodservice location and branded sales 

information, including NPD ReCount and Euromonitor Passport. Retail sales and prices from publically available data sources like U.S. Census Bureau and the Bureau of Labor Statistics are also used 

in the analysis.

NPD ReCount

The primary data source tracking the economic geography of FAFH is NPD ReCount, a commercial 

data set that contains information on nearly all FAFH brick-and-mortar establishments operating in 

the United States from 2000 to 2015. To collect information on food-service locations, NPD employs 

a multi-pronged approach that surveys multiple commercially and publicly available sources, 

including chain directories from company headquarters, restaurants guides and industry magazines, 

various business lists, as well as Internet and phone verifications. For every outlet conducting business in 2000-15, a record is entered in the dataset at the first observed date the outlet is open. From 

that time, data collection for each operational establishment occurs on an ongoing semiannual basis 

and ceases once the establishment closes. NPD ReCount tracks the following relevant variables: 

firm-level characteristics (such as establishment name), exact geographic location, segment (i.e., 

quick-service versus full-service), restaurant type (e.g., hamburger, Mexican, etc.), chain membership, open date, and close date (if applicable).43

The nature of the data collection presents challenges for capturing a yearly snapshot. NPD semiannually publishes new versions of the ReCount data set, accounting for all open restaurants at the end 

of every March and September. One issue that arises is a restaurant that appears, for example, for 

the first time in the data in March 2015 could have opened any time between the end of September 

2014 and the end of March 2015: any opening in this time range would be recorded as a March 2015 

release. Given that a similar issue exists in tracking restaurants closing, characterizing the supply of 

restaurants in a given year poses a challenge. Because the March 2015 release is the latest version of 

42Definitions of rural and urban areas are at the county level and come from the 2003 and 2013 ERS Rural-Urban Continuum Codes.

43Note that ReCount may classify some establishments as quick-service that other data sources, such as the U.S. Census 

County Business Patterns (CPB), classify differently. For example, a specialty food or dessert business that sells items for onsite consumption may be classified as something other than a QSR. The result may be discrepancies in establishment counts 

dues to classification differences. 
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ERS’s available data, March of each year is used to provide annual snapshots in presenting the state 

of the FAFH industry.

Another point of consideration in defining variables is how to assign the designation of “chain” to 

a particular establishment. For this analysis, a chain is defined as a brand of restaurants that operated 20 or more outlets in 2015. This definition is consistent with the language of the FDA ruling 

on calorie labeling for restaurant menus, which is more relevant for diet and nutrition policies than a 

definition based on business structure would be.44 In addition, chains that fit this definition in 2015 

were more likely than chains that fit the definition prior years to be covered by any future labeling 

regulation. Thus, restaurant brands that grew to 20 or more outlets by 2015 (but had fewer prior to 

then) count as chains, but brands that shrank to fewer than 20 outlets by 2015 do not count as chains 

(but had more previously).45 Although the summary of the NPD ReCount data is retrospective, this 

analytical choice allows for the characterization of the future FAFH landscape.

The geographic variables are used to characterize the spatial distribution of FAFH establishments 

over time. In particular, the growth in several key segments of foodservice industry (e.g., QSRs, 

chains) is examined by comparing maps of summary statistics by U.S. counties. The summary 

statistics include the share of a restaurant subset in a county, the number of outlets per county, and 

the percentage growth from 2000 to 2015.

The outlet name reported in the NPD ReCount data is used to integrate secondary data sources 

that include additional information on FAFH establishments. One such source is a binary variable 

indicating whether or not a particular establishment is a fast-casual restaurant. Over 200 unique 

foodservice brands in NPD ReCount are labeled as fast casuals using information from the annually 

published “Top 100 Movers & Shakers” reports from 2009 and from 2011 to 2015 by FastCasual.

com. NPD ReCount considers a restaurant to fit the fast-casual subsegment if it “creates a unique 

value and quality experience, with gourmet-level food or drink, that delivers an interior that wows 

the guest, all at a price that is driven by value” (FastCasual.com, 2009).46 This list is likely not 

exhaustive and excludes non-chains, but it positively identifies many high-profile fast-casual chains. 

Euromonitor Passport

Euromonitor Passport provides historical data on the value of consumer food sales by local brand 

names (e.g., Burger King of Yum! Brands) from 2006 to 2014 for FSRs and QSRs. The dataset 

includes both private and publicly traded companies, which allows for a full composition of industry 

sales. These data are used to compute the sales concentration ratios of FAFH brands over time, which 

involves first constructing an ordinal ranking of firms by sales for a given year. Then, sales for the top 

four firms are summed, for example, and divided by the total value of sales in the segment in that year. 

44The specific language is that a covered outlet has “20 or more locations doing business under the same name (regardless of the type of ownership of the locations) and offering for sale substantially the same menu items.” In addition, an FAFH 

establishment must offer “restaurant-type food,” or essentially food for immediate consumption. All businesses included in 

NPD ReCount fit this criterion. The complete version of the FDA ruling can be found online.

45For example, several present-day leaders in the QSR fast-casual subsegment operated a small number of outlets in the 

early 2000s, and thus, the calorie labeling law would not have applied to them if it was hypothetically enacted then. That 

these businesses are prolific today means that they have a greater effect on the food environment, health, and nutrition of 

today’s consumers than do businesses that closed many locations. Hence, this analysis tracks over time the types of chains 

that are available to consumers today.

46It is worth noting that one of the original contributors of these reports and founder of FastCasual.com is Paul Barron, 

who is credited with pioneering the term “fast casual.”
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In addition, brand names from Passport are merged with unique outlet names in NPD ReCount to 

obtain the restaurant category (e.g., hamburger, subs/deli/other sandwich, pizza/Italian, Mexican, 

and other). The restaurant categories used herein do not exactly reflect those in NPD ReCount, and 

to simplify, some are combined (e.g., “subs” and “other sandwich.”) Much like the individual brands, 

sales concentration ratios for restaurant categories are computed over time. 

Publicly Available Sources

The proprietary data are augmented with a number of publicly available data sources to characterize 

total sales by segment and food costs faced by restaurant operators. The U.S. Census Bureau’s Monthly 

Retail Trade and Foodservices series and the Quarterly Census of Employment and Earnings provide 

(unadjusted for seasonal factors) monthly sales in millions of dollars reported in FSR and QSR 

segments covering 2000 to 2015. Nominal dollars are deflated with the Bureau of Labor Statistics 

Urban Consumer FAFH Consumer Price Index so that all relevant units are in 2015 dollars.

Market Size and Structure From 2000 to 2015

In 2000-15, both the total value of sales and the number of restaurants operating in the United States 

grew significantly, extending long observed industry trends, despite two major recessions. Chain 

QSRs generated most of this growth, while other subsegments stagnated or slightly declined in 

numbers (fig. 6.1). Therefore, QSRs continue to outnumber FSRs as the majority establishment type, 

as they have done for over a decade (Stewart, 2011), accounting for roughly 54 percent of all restaurants in the Nation as of 2015. Because QSRs tend to offer less nutritious yet calorie-dense food at 

low prices (Lin and Guthrie 2012; chapter 3), the trend may mean some food environments saw a 

decline in the healthiness of available foods. 

Figure 6.1

Number of food-away-from-home outlets by segment and chain status, 2000-15

Note: Shaded area indicates recessions. Indep. = independent.

Source: USDA, Economic Research Service calculations from NPD ReCount data.

0

50,000

100,000

150,000

200,000

250,000

300,000

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15

Recessionary 

period

Chain FSR Chain QSR Indep. FSR Indep. QSR
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Between the recession of 2001 and the Great Recession, the FSR segment remained mostly stagnant. 

FSR numbers rose briefly after 2007: their greatest increase in the study period was roughly 4.7 

percent between 2008 and 2009, similar to the QSR segment’s growth during this time. This growth 

may have been driven by increases in FAFH expenditures (especially by higher income Americans), 

which peaked just prior to the Great Recession (chapter 4). 

In contrast with the drop in the number of FSR and QSR establishments in the recession of 2001, 

the number of establishments in both segments grew during the Great Recession, with FSRs leveling 

off just after it ended. The apparent delay in the slowdown of new growth is partially an artifact 

of data collection: the number of restaurants open in 2008 was measured in March of that year, 

potentially including many establishments beginning to operate before the start of the recession. 

QSRs continued to show strong growth, albeit at a seemingly lesser rate, perhaps due to Americans 

shifting their FAFH spending to cheaper fast-food options.47 FSRs’ prevalence dropped over 5 

percent since the peak observed in 2009. A notably slow recovery in general employment delayed 

the return to previous patterns of FAFH consumption for many consumers (chapter 3). Some of the 

FSRs that strived to stay open through the lean time have since shut down.

QSR and FSR growth between 2000 and 2015 was heterogeneous across the United States (figs. 

6.2 and 6.3). The number of QSR establishments expanded in most regions of the country, with the 

sharpest increases—amounting to more than a 30-percent change in 2000-15—concentrated in the 

Mid-Atlantic and Southeast (fig. 6.2). Although the greatest growth in these areas occurred in urban 

counties, QSRs increased in many rural counties as well. The relatively equal number of establishments in both industry segments in 2015, combined with comparatively modest growth in FSRs (fig 

6.3), suggests an FAFH landscape increasingly dominated by QSRs. 

In the urban centers nationwide, QSRs and FSRs showed strong positive growth. For instance, in 

metropolitan counties in the Northeastern and Western regions, each of these restaurant segments 

grew more than 30 percent in 2000-15.48 Sharper positive growth occurred in several urban regions, 

such as the Las Vegas metro area—a hotspot for chain and smaller FSR brands seeking publicity 

by catering to this region’s many tourists.49 Large FSR expansions also took place also in regions 

just outside of city cores, including in the western suburbs of Chicago; the counties contiguous to 

Nashville’s Davidson County; Loudon County, VA, west of Washington, DC; and many of the urban 

counties in eastern Texas. This pattern may reflect significant, recent population increases in these 

regions.

Echoing patterns of rural-urban migration, many rural counties sustained losses in both restaurant 

segments, especially in the central United States (USDA, ERS, 2016). For instance, in many of the 

counties in agriculture-dominated Nebraska and Kansas counties, population decreased in 2010-15. 

47In addition, an observed increase in the diversity (as measured by the predominance of menu categories) of QSRs compared to FSRs in 2000-15 may have helped QSRs retain their attractiveness during this time. The relative number of the top 

three menu categories of QSRs (“hamburger,” “pizza/Italian,” and “frozen sweets” in 2000 or “subs” in 2015) fell from 49.3 

percent in 2000 to 46.1 percent in 2015. FSRs, on the other hand, had an increase in the relative number of the top three menu 

categories of FSRs (“Asian-casual dining,” “bar and grill,” and “family style”) from 41.9 percent in 2000 to 47.4 percent in 

2015. Although this change was observed over a long time period, it was observed between 2007 and 2010 (i.e., both before 

and after the Great Recession) suggesting QSR options were increasingly varied.

48The definitions of rural and urban areas are at the county level and come from the 2003 and 2013 ERS Rural-Urban 

Continuum Codes.

49The Las Vegas region also experienced high growth in QSRs.
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From 2000 to 2015, the total number of restaurants (FSRs and QSRs) fell 30 to 87.5 percent in these 

counties. 

However, migration patterns and restaurant growth rates did not parallel each other in all regions. 

For some regions, such as rural counties in southern Texas and central Tennessee, population and 

restaurant growth rates did mirror each other: both increased. On the other hand (as a counterexample), much of Wyoming and western Montana saw net population increases, but FSR numbers 

dropped steeply in many Wyoming and Montana counties—although some counties did have significant positive growth in QSRs, even while losing restaurants overall. Many of the counties with 

significant population increases were primarily economically engaged in mining, including coal, 

which saw significant increases in employment in 2000-15.50

50ERS county typology codes are used to determine a county’s primary industry. According to Bureau of Labor Statistics’s Quarterly Census of Employment and Wages, employment levels for coal mining (2012 North American Industry Classification System code: 2121) grew by 30 percent in Wyoming and 54.5 percent in Montana, statewide, in 2005-14.
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Figure 6.3

Percent growth in full-service restaurants in 2000-15 by U.S. county

The white areas on the map indicate missing data.

Source: USDA, Economic Research Service calculations from NPD ReCount data.

Percent change full service 

restaurants, 2000-15

 -87.5 - -30

 -29.9 - 0

 0.1 - 30

 30.1 - 600

In 2015, as a result of the past 15 years of growth, the numbers of QSRs and FSRs were roughly 

equal in most counties, especially urban counties, although a significant number of counties did not 

follow this pattern (fig. 6.4). By this time, the restaurant options in many relatively rural counties in 

the Northern and Southern Central U.S. regions consisted mainly of FSRs. For example, only 1 in 

17 restaurants in Buffalo County, WI, was a QSR in 2015. In contrast, QSRs dominated the FAFH 

landscape in rural Ohio, Kentucky, and West Virginia in 2015. One such county—Lawrence County, 

KY—over 80 percent of its 26 observed restaurants were QSRs, which included mostly chain 

hamburger restaurants and both chain and independent pizza restaurants. However, despite the relative dominance of QSRs in rural America, the number of QSRs per capita in rural America is well 

below their prevalence in urban areas (fig. 6.5).
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Figure 6.4

Share of quick-service restaurants among all restaurants in 2015 by U.S. county

The white areas on the map indicate missing data.

Source: USDA, Economic Research Service calculations from NPD ReCount data.

Percent quick service 

restaurants, 2000-15

 5.9 - 36.4

 36.5 - 50.8

 50.9 - 62.2

 > 62.3

Growth of Chain Restaurants

In 2015, chain outlets (defined as operating 20 or more outlets), including FSRs and QSRs, 

accounted for over 250,000 restaurants nationwide, or roughly 40 percent. This represents a 44.7- 

percent increase from 2000, when chains were roughly 30.1 percent of all FAFH outlets. The QSR 

sector was mainly responsible for the rise of chains (fig. 6.1). In recent years, the prevalence of the 

chain-QSR subsegment has approached that of the long-predominant independent-FSR subsegment. 

Chain FSRs, on the other hand, have remained relatively stagnant. A few possible reasons that most 

of the new chain outlets have been QSRs include the following: (1) many QSRs are franchised and 

require significantly less start-up capital than FSRs require; (2) QSRs comprise the quickly growing 

fast-casual subsegment, which contains many large chains; and (3) few FSR brands operate more 

than 1 restaurant, let alone 20.
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In some regions of the country, independents tended to overshadow chains (fig. 6.6). For example, 

in 2015, chains constituted at most 35.4 percent of FAFH establishments in the majority of counties in the Northeast. New York City was a case in point. Given the city’s small share of restaurants 

(relative to the rest of the country) that met the Federal regulatory definition of a chain, New York’s 

support for calorie labeling regulations was somewhat unexpected.51 As of 2015, New York City’s 

share of chain restaurants that would be affected by Federal calorie labeling laws was 14 to 24.4 

percent, depending on the specific borough.52

Yet, in many U.S. regions, the restaurant landscape comprises mostly chains, especially in the South, 

Midwest, and West, where at least 50 percent of the restaurants are chains under the FDA definition. 

51A higher share were affected under current standing New York City law, which considers a brand with 15 outlets to be 

chain, rather than the federally mandated 20.

52The estimated 2015 shares of chain restaurants in all five counties that make up New York City were 14 percent in Kings 

County (Brooklyn), 14.9 percent in New York County (Manhattan), 19.2 percent in Bronx County (Bronx), 19.3 percent in 

Queens County (Queens), and 24.4 percent in Richmond County (Staten Island).
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Figure 6.6

Share of chain restaurants among all restaurants in 2000-15 by U.S. county

The white areas on the map indicate missing data.

Source: USDA, Economic Research Service calculations from NPD ReCount data.

Percent chain 

restaurants, 2015

 0 - 16.8

 16.9 - 34.4

 34.5 - 48.4

 48.5 - 100

This disparity suggest that any impact of the impending FDA menu-labeling regulations (requiring 

calorie counts) may be felt unevenly in consumers from different regions across the country. Notably, 

many of the chain-dominated regions also experience some of the country’s highest rates of obesity. 

Hence, if the new calorie-count menu labels prompt consumers to consume less calorie-dense food, 

then these chain-dominated regions may reap significant improvements in public health outcomes. 

Chapter 10 discusses the evidence to date on how consumers have responded to menu labeling. 

Despite the overall surge in growth of chains in 2000-15, restaurants in the relatively chain-free 

Northeast grew more than many chain-dominated regions grew in that period (fig. 6.7). The relatively high share of independent operators in these areas may be explained by the dense concentrations of people with higher incomes, who are the typical FSR customers (chapter 8) and by the fact 

that most FSRs are not chains. However, the higher FAFH spending of higher income consumers 

across all restaurant segments likely supports the expansion of chains in the Northeast, as well as in 

other urban regions. For example, although QSRs form a small share of New York City restaurants, 

the number of chains per capita is similar to that of most other U.S. regions (fig. 6.8). Nevertheless, 

the effect of Federal calorie labeling laws will be limited by the healthiness of the food offerings of 

the available chains, as well as by regional variations in tastes and eating habits.
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Figure 6.7

Percent growth in chain restaurants in 2000-15 by U.S. county

The white areas on the map indicate missing data.

Source: USDA, Economic Research Service calculations from NPD ReCount data.

Percent chain restaurants, 

2000-15

 -100 - 15

 14.9 - 0

 0.1 - 30

 > 30.1

Emergence of New Foodservice Subsegments

The past decade also saw the emergence of several unconventional restaurant subsegments, 

including one of the most prolific: the “fast casual.” Although no formal industry definition exists, 

a fast-casual restaurant can be loosely defined as a hybrid of QSR and FSR restaurants. QSRs offer 

mainly counter service (instead of servers taking orders), convenience (e.g., food prepared quickly 

in assembly line format), and lower prices than FSRs. However, fast casuals retain FSRs’ perceived 

higher quality of menu offerings and ingredients, as well as FSRs’ ambiance. The fast-casual trend 

synchs with larger consumer-driven trends toward higher quality and potentially healthier menu 

items offered at relatively low prices. Nonetheless, recently, the nutritional value of fast-casual menu 

offerings has been called into question, with evidence that fast-casual meals contain more calories 

than fast-food meals (Schoffman et al., 2016).
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Figure 6.8

Number of chain restaurants per 1,000 persons in 2015 by U.S. county

The white areas on the map indicate missing data.

Source: USDA, Economic Research Service calculations from NPD ReCount data.

Chain restaurants per 

thousand persons, 2015

 0.00 - 0.42

 0.43 - 0.83

 0.84 - 1.58

 1.59 - 4.11

The idea of fast casuals first emerged, primarily as an industry-specific term, some time in the late 1990s 

to early 2000s. Much of the early fast-casual market comprised large chains with menus other than the 

typical QSR fare, including Chipotle Mexican Grill, Noodles & Co., and Panera Bread. Since then, fast 

casuals have continued to offer options different than the typical QSR menus and consistently grown 

more quickly than any other FAFH subsegment. Although the sales of fast-casual chains have not yet 

rivaled those of historic QSR juggernauts (e.g., McDonald’s), this segment saw 13 percent and 16 percent 

in sales growth in 2012 and 2013, respectively (National Restaurant Association, 2015). This growth more 

than doubled that of the QSR segment as a whole in 2012 and 2013. 
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As of 2015, fast-casual restaurants made up 10.5 percent of all QSRs, up from less than 5 percent 10 

years prior (fig. 6.9), but this growth spurt did not manifest itself equally (or at all) in all regions (fig. 

6.10). Urban counties, especially coastal ones, saw a massive expansion of fast casuals, and some 

metropolitan areas, such as in Washington, DC, and San Francisco, CA, underwent more than a fivefold increase in 2000-15. In many urban areas, a significant share of QSRs are fast casuals, and there 

are a high number of fast casuals relative to local population (fig. 6.11); in urban areas, this emergence of fast casuals has driven the growth of the QSR segment.

Rural America, on other hand, appears to have been mostly passed over by the fast-casual expansions. The vast majority of rural counties have few if any restaurants, despite the relative dominance 

of the QSR segment. In fact, in 2015, Los Angeles County contained more than five times as many 

fast-casual restaurants per capita than most rural U.S. counties. One possible explanation: lower 

(and shrinking) incomes and, thus, lower FAFH spending in many rural counties have not been 

able to sustain fast casuals, because despite their purportedly lower prices, fast casuals are not as 

inexpensive as fast food. Nevertheless, fast-casual numbers did rise in some rural counties. For 

example, in 2015, some rural counties of western Colorado had fast-casual shares on par with most 

urban regions. This anomaly may be due to additional revenue from outdoor-recreation tourism in 

this area, as well as the proximity to metropolitan Colorado, the birthplace of many prominent fastcasual chains such as Chipotle.

The number of mobile foodservice (e.g., food trucks) nearly doubled from 2009 to 2015 (fig. 6.12).53

Setting themselves apart from food trucks’ historical reputation for lower quality, many new mobile 

operations not only offer low cost staple items, but also specialize in higher quality or gourmet 

offerings (Crowther, 2013). In this way, while bearing comparatively lower operating and start-up 

costs than fast casuals, mobile foodservice leverages the same demand for higher quality and varied 

meals that gave rise to fast casuals (Gold, 2012). These factors may partially explain why the surge 

in mobile foodservice operators began during the Great Recession, when consumers sought cheaper 

food options and investments in brick-and-mortar operations were either prohibitively expensive or 

risky. The popularity of mobile foodservice endured even as consumer spending recovered along 

with the economy.

53Note that mobile foodservice operations do not appear in NPD ReCount, which collects only data on brick-and-mortar 

commercial establishments.
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Figure 6.9

Number of quick-service restaurants (QSRs) and fast-casual restaurants, 2000-15

Note: Shaded areas indicate recessions.

Source: U.S. Department of Labor, Bureau of Labor Statistics Quarterly Census of Employment and Wages.

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

Recessionary periods QSR Fast casual

Processed food manufacturers that typically market food-at-home (FAH) products are also tapping 

into the FAFH market with brand-driven restaurants aimed at presenting products in new ways. For 

example, Kellogg’s most recently opened a restaurant New York City’s Times Square that serves 

the company’s ready-to-eat breakfast cereals with the addition of fruits, nuts, spices, and herbs 

(Black, 2016). Not only does this foray into FAFH allow FAH manufacturers to directly compete 

with FAFH purveyors in their own marketplace, it also allows FAH companies to test new items. 

The focus of FAH-brand-driven eatery resembles that of fast casuals and traditional FSRs—i.e., as 

much about crafting an experiential good as selling food products. The marketing of these “forward 

integrated” establishments often mimics that of higher quality FSRs, with catchphrases like “artisanal” and “natural” that may soften consumers’ perception of products as being massed-produced 

commodities. 
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Figure 6.10

Percent growth in fast-casual restaurants by U.S. county, 2000-15

The white areas on the map indicate missing data.

Source: USDA, Economic Research Service calculations from NPD ReCount data.

Percent change fast-casual 

restaurants, 2000-15

 -100 - 0

 0.1 - 500

 > 500.1

.
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Figure 6.11

Number of fast-casual restaurants per 1,000 persons in 2015 by U.S. county

The white areas on the map indicate missing data.

Source: USDA, Economic Research Service calculations from NPD ReCount data.

Fast-casual restaurants per 

thousand persons, 2015

 0.00 - 0.05

 0.06 - 0.15

 0.16 - 0.57

 0.58 - 1.42
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Food-Away-From-Home Sales

From 2010 to 2015, the average monthly real value of FAFH sales climbed to over $20 billion each 

for QSRs and FSRs (table 6.1). These real sales were up from $16.8 billion for FSRs in 2000 and up 

from slightly under $16 billion for QSRs 2001, with sales steadily growing in the years preceding the 

Great Recession years. FAFH sales maintained positive growth even during recessionary period of 

2001, albeit than in the 6 years that followed. In addition, growth and levels of FSR sales exceeded 

those of QSRs in 2000-15.

FSRs’ dominance in sales began to wane with the onset of the Great Recession, fulfilling economists’ prediction made of the late 1990s that QSR sales would exceed FSR sales (Stewart et al. 

2004). During the Great Recession, while FSRs lost the real value of all sales gained in 2005-07, 

QSR sales grew slightly. QSRs’ position strengthened as consumers shifted FAFH consumption to 

less costly QSRs and the number of QSR outlets grew steadily. With the economic recovery, both 

sectors returned to previous sales trends with average annual growth over 3 percent each. Both 

segments now have roughly equal shares of all commercial FAFH expenditures in the United States.

Generally, in 2000-15, sales revenue in the FAFH sector remained diffused across brands in line 

with the characteristic competitiveness of this industry, despite several high-profile mergers and 

acquisitions among key brands.54 In 2005-15, FSR sales revenue remained extremely diffused, 

with roughly 7 percent of sales revenue going to the top 4 brands and 16 percent going to the top 20 

brands. The QSR sector, on the other hand, stayed comparatively more concentrated, with 30 percent 

of all sales revenue accruing to the top four brand owners (fig. 6.13).

54For example, Restaurant Brands International owned by the private equity firm 3G Capital acquired Burger King and 

Tim Hortons. Both of these brands consistently rank among the top 10 of all QSRs.
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Table 6.1

Growth rates of total sales for full-service restaurants (FSRs) and quick-service restaurants 

(QSRs) 

Average annualized growth rate Average value of monthly sales 

Years FSR QSR FSR QSR

Percent Percent Billion $ Billion $

2000-01 1.90 1.04 16.827 15.994

2002-04 3.21 3.52 17.999 16.802

2005-07 2.55 1.50 19.535 18.748

2007-10 -2.33 0.07 19.023 18.833

2011-15 3.77 3.18 20.793 20.629

Source: U.S. Census Bureau Monthly Retail Trade and Foodservices.

 
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In general, the relative position of dominant brands remained constant in 2006-14 (fig. 6.13).55 The 

shares of sales to QSR restaurant categories remained the same. One exception is the share of QSR 

sales revenue going to the top 20 QSR brands—a share that grew by nearly 10 percent from 2006 to 

2014. This rise reflects the growing dominance of fast-casual chains in QSRs. For example, the top 

fast-casual chains ranked either near 20th place or below in terms of QSR sales share in 2006, but 

climbed to nearly the top 10 by 2014. For QSR menu categories, the most sales revenue accrued to 

the fast-food burger market throughout 2006 to 2014 (table 6.2). However, the “hamburger” category’s share showed a modest decline, and the shares of the “subs/deli/other sandwich” and “Mexican” 

categories showed small upticks. This change is attributable to the growth of sales by Panera and 

Chipotle, the two largest fast-casual chains.

Table 6.2

Concentrations of quick service restaurant sales among top ranking menu categories, 

2006-14

Category 2006-08 2009-11 2012-14

Percent

Hamburger 44.9 44.4 42.4

Subs/deli/ 

other sandwich

14.8 16.6 17.6

Pizza/Italian 10.6 10.3 10.5

Mexican 6.6 6.9 7.6

Other 23.1 21.8 22.0

Source: USDA Economic Research Service calculations from Euromonitor Passport data.

55That is, one QSR brand (McDonald’s) retained its first-ranked position in share of sales throughout 2006-14.
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Conclusion

The recent increase in the supply of FAFH is partly driven by the rising dominance of chain QSR 

restaurants. Growth in QSRs outpaced that of FSRs for most years in 2000-15, which resulted 

in QSR sales now nearly equaling those of FSRs. As of 2015, well over half of all restaurants 

were either a QSR, a chain, or both. Most U.S. regions, including the independent-restaurantheavy Northeast, saw significantly large growth in chain QSRs. With the chain QSR subsegment, 

hamburger-centric brands accounted for 42.5 to 44.9 percent of all QSR sales in 2006-15. 

Along with the strong growth of QSR chains, the emergence of the fast-casual restaurant and large 

gains in mobile foodservice combined to further expand the QSR market share. Responding to the 

bar set by fast casuals for perceived healthiness, high quality, and competitive prices, many traditional QSRs were incentivized to improve these value markers in their own outlets. During the Great 

Recession and subsequent sluggish recovery, fast casuals and other subsegments such as mobile 

foodservice were able to thrive while many traditional QSRs and FSRs struggled. As of 2015, the 

fast-casual subsegment alone accounted for roughly 10 percent of all QSRs and overtook many 

longstanding QSRs in terms of sales. Two large fast-casual chains have even overtaken longstanding 

traditional QSRs firms in terms of the rankings of sales by chain.

However, these broad industry trends did not manifest uniformly across the country. Growing urban 

population centers of the United States saw the most growth in commercial FAFH establishments 

of all types but especially QSR fast casuals. Yet, the restaurant landscape in rural counties told a 

different story. There, FSRs declined to the point that, by 2015, many rural counties that had been 

served mainly by FSRs in 2000 came to be served mainly by QSRs. Although QSRs’ dominance 

in rural areas mirrored the rest of the Nation, the rise of the fast-casual subsegment, so pronounced 

elsewhere, mostly passed over rural America (with several notable exceptions). The general shift 

from FSRs toward QSRs in rural areas may have health implications insofar as FSRs and QSRs 

differ in the healthiness of their menus.
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References

Binkley, J. 2008. “Calorie and Gram Differences between Meals at Fast Food and Table Service 

Restaurants,” Applied Economic Perspectives and Policy 30(4): 750-63.

Black, J. July 1, 2016. “Kellogg’s Cereal as Dining Event? Welcome to the Experience Economy,” 

The Wall Street Journal. 

Crowther, G. 2013. Eating Culture: An Anthropological Guide to Food. North York, Ontario, 

Canada: The University of Toronto Press.

FastCasual.com. 2009. “FastCasual.com Top 100 2009 Movers & Shakers.” 

Gold, J. March 2012. “How America Became a Food Truck Nation.” Smithsonian Magazine.

Lin, B-H, and J. Guthrie. 2012. “Nutritional Quality of Food Prepared at Home and Away From 

Home, 1977-2008.” EIB-105, U.S. Department of Agriculture, Economic Research Service.

National Restaurant Association. 2015. “Culinary Trends.” Restaurant, Food & Beverage Market 

Research Handbook 2014-2015.

Pullman, M., and Z. Wu. 2012. Food Supply Chain Management: Economic, Social and 

Environmental Perspectives. New York, NY: Routledge.

Schoffman, D.E., C.R. Davidson, S.B. Hales, A.E. Crimarco, A.A. Dahl, and G.M Turner-McGrievy. 

2016. “The Fast-Casual Conundrum: Fast-Casual Restaurant Entrées Are Higher in Calories than 

Fast Food,” Journal of the Academy of Nutrition and Dietetics, 116(10) 1606-12.

Stewart, H., N. Blisard, S. Bhuyan, and R.M. Nayga, Jr. 2004. “The Demand for Food Away From 

Home,” AER- 829, U.S. Department of Agriculture, Economic Research Service. 

Stewart, H. 2011. “Food Away From Home,” in The Oxford Handbook of the Economics of Food 

Consumption and Policy, J.L. Lusk, J. Roosen, and J.F. Shogren, eds., Oxford, United Kingdom: 

Oxford University Press.

U.S. Department of Agriculture, Economic Research Service. 2016. “Food Consumption and 

Demand: Food-Away-From-Home.” 
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Chapter 7: Impacts on Nutrient Intakes From Increased 

Food-Away-From-Home Consumption

This chapter examines the growth in consumption of foods prepared away from home 

(FAFH), the nutritional differences in foods consumed by source, and the implications for 

Federal efforts to improve the diets of Americans. The analysis finds that, on the whole, 

FAFH contained more saturated fats and sodium and less calcium, iron, and fiber than 

FAH, but nutrient composition varied across source of FAFH and over time. 

The shift in consumer preferences toward FAFH is driven by numerous factors, including socioeconomic and lifestyle changes (see chapters 3 and 4) and increased availability of FAFH options (see 

chapters 6 and 8). This chapter examines the nutritional implications of this shift using national 

data from Federal surveys of Americans’ dietary intakes. National food consumption survey data 

collected from 1977-78 to 2014 are used to compare intakes of selected nutrients by sources to 

examine the shift in food consumption from food prepared at home and away from home over time.

Observed changes in dietary patterns over time may come from a variety of sources, including an 

aging U.S. population, the changing racial and ethnic makeup of the U.S. population, and other 

socioeconomic factors (e.g., single-parent households) that may influence food consumption decisions. The report compares mean dietary patterns over time without statistically testing the differences across survey years or adjusting for changing age, racial, and ethnic composition. Further 

research would be needed to identify the role of such factors. Despite these limitations, the findings 

provide information on the role of FAFH in the U.S. diet and the nutritional implications.

The analysis in this chapter finds that the nutritional composition of FAFH across all income levels 

and all FAFH types (except school food) was consistently lower quality and more caloric than that 

of FAH. With the exception of school meals and other foods obtained at school (a type of FAFH), 

FAFH generally contained more saturated fats and sodium, and less calcium, iron, and fiber than 

FAH. In 2009-12, the fat content of school meals was almost identical to that of FAH (33 percent) 

while the fat content of fast foods averaged 39 percent. The FAFH share of total average daily 

energy intake increased from 17 percent in 1977-78 to 34 percent in 2011-12, and consumption of 

QSR (Quick Service Brand) foods was the largest source of this growth. Consistent with FAFH 

expenditure patterns discussed in chapters 3 and 4, calories from FAFH sources declined in the most 

recent economic downturn in 2007-10, but by 2011-12, consumption of FAFH had rebounded. While 

food from FAFH sources is less healthy compared to most FAH sources, it is not clear whether 

FAFH consumption is correlated with diminished overall diet quality of Americans. Chapters 8 and 

9 discuss in more detail how FAFH consumption contributes to diet quality. 

Data and Methods

This analysis uses several federally collected national surveys to track nutrient consumption by source:56

•	 USDA Nationwide Food Consumption Survey (NFCS) 1977-78,

•	 USDA Continuing Survey of Food Intakes by Individuals (CSFII) 1989-91 and 1994-98, and

56Information on USDA surveys can be found at USDA’s Agricultural Research Service website. Information on 

NHANES can be found at the website for the Centers for Disease Control and Prevention.
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•	 The National Health and Nutrition Examination Survey (NHANES) 2003-04, 2005-06, 2007-

08, 2009-10, 2011-12, and 2013-14. 

These surveys collected information on the types and amounts of foods eaten by respondents, as 

well as information on where the food was obtained. In keeping with most analyses using national 

food consumption survey data, respondents under age 2 were excluded. Using this information 

in conjunction with its nutrient databases, USDA estimated amounts of food energy (calories) 

and nutrients consumed by individuals.57 Each survey employed a complex design with stratified 

sampling to efficiently obtain a large national sample of Americans. Sample weights were calculated 

to adjust for variable probability of selection and survey nonresponse to yield nationally representative estimates. However, since participation was voluntary, there may have been self-selection bias 

that was not perfectly corrected by weighting. 

Each survey was conducted using the best methodology available at the time. Although each 

survey drew on the methodology of the previous survey, they were not completely consistent in all 

measures. The authors have recoded data using consistent food source definitions to better investigate longrun trends in consumption of food prepared away from home (Guthrie et al., 2016). 

It should be noted that changes in survey methods—particularly as they pertain to collection 

methods of food intake data from survey participants—create difficulties in comparing statistical 

results across time. In particular, earlier surveys (1977-78 and 1989-91) collected 3 consecutive days 

of dietary intake data—the first day employing the 24-hour recall methodology and the latter days 

obtaining data from food records kept by respondents. Later surveys employed only the 24-hour 

recall method and collected data over 2 nonconsecutive days. 

To minimize differences that could be attributed to these changes in methodology, only day 1 intake 

data were used from each survey, which in all cases was collected via 24-hour recall. Other changes, 

however, may also have had an impact on the data. Most notably, a five-step Automated MultiplePass Method (AMPM) designed to improve the completeness of data collection has been employed in 

NHANES but not in NFCS or CSFII (Raper et al., 2004). Adopting AMPM may have reduced underreporting, resulting in an increase in reported food intake, but the extent of its contribution is unknown. 

This analysis examines changes in share of intake from FAH and FAFH sources and changes in 

nutrient densities associated with those changes over time, rather than changes in absolute amounts, 

which may mitigate differences that are an artifact of more complete reporting. However, improvements in reporting may have affected some categories of intake or nutrients more than others—for 

example, probing may result in survey respondents remembering small items like spreads and condiments that could be disproportionately high in sodium or fat. Therefore, the potential impact of 

underreporting may have been present in the estimates but no attempt was made to adjust for it.

Trends in food energy intake by food source (see box, "Definitions of Food Sources")—FAH and 

FAFH, with FAFH further disaggregated into restaurant, fast food, school/day care, and other—

were estimated using the mean proportion approach (Krebs-Smith et al., 1989). Shares of food 

energy intake by food source were calculated for each respondent, and then the sample weights 

were used to calculate the weighted average shares of food energy intake by food source for the 

U.S. population and for population subgroups, such as children and youth (age 2 to 19) versus adults 

57USDA nutrient databases are regularly updated to include the best available information on foods consumed. Nevertheless, there may be limitations. For example, some schools serve products (e.g., frozen pizzas) specially formulated for sale to 

school foodservices that may differ somewhat from standard products.
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(age 20 and older) or low income (at or below 185 percent of the Federal poverty level) versus high 

income (above 185 percent). The mean proportion approach generated the weighted average shares 

by food source for a representative respondent in the population in question. 

This report used the population proportion approach, the recommended method for this analysis, 

to calculating and comparing nutrient density, defined as nutrients per 1,000 kilocalories, by food 

source for two periods—1977-78 and 2011-14 (Freedman et al., 2008). Following this method, the 

weighted total intakes for a given nutrient and kilocalories were calculated for a food source (e.g., 

FAH, FAFH) among the total population as well as population subgroups. The nutrient densities for 

total diet, FAH, FAFH, and the four subcategories of FAFH—for the total population as well as for 

population subgroups—were expressed per 1,000 kilocalories, except for total fat and saturated fat, 

which were expressed as percent of calories, following the convention of the Dietary Guidelines for 

Americans (USDHHS and USDA, 2015). 

To demonstrate, the population proportion approach can be applied in calculating the calcium 

density of foods obtained at school cafeterias among children. Using the mean proportion method, 

the weighted sums of calcium and calorie intakes obtained at school cafeterias among children are 

first calculated; second, the calcium density is calculated as the ratio of total calcium to total calories. The mean proportion method is chosen because, on a given day, some individuals may obtain 

foods exclusively from either FAH or FAFH. As a result, the mean proportion method (i.e., calculating nutrient density based on the intake of each individual and then averaging intake densities) 

may generate nutrient density values for the total diet that fall outside the densities for FAH and 

FAFH. This seemingly counterintuitive result can be prevented by using the population proportion 

approach (Freedman et al., 2008; Lin et al., 2016).

30-Year Rise in Food Prepared Away From Home Briefly 

Reversed in 2007-10 and Then Rebounded 

The share of calories obtained from FAFH rose from 17.8 percent in 1977-78 to 33.7 percent in 

2005-06 (figure 7.1). Increased consumption of fast food had the strongest influence on this trend, 

with the share of calories obtained from fast food increasing from 5.7 percent in 1977-78 to 15.6 

percent in 2005-06. The increase in share of calories from full-service restaurant food, the secondhighest source of FAFH, was from 3.2 percent to 9.9 percent. 

Between 2007 and 2010, the share of calories obtained away from home briefly dipped to 29.1 

percent in 2009-10, while calories obtained from fast food, the leading FAFH source, dropped to 

13.2 percent. This period roughly corresponds to the 2007-09 recession in the United States—the 

most severe recession since the 1930s—where the share of household food expenditures on FAFH 

declined for the first time in several decades (chapters 3 and 4).58 These findings demonstrate that 

Americans economized by eating less FAFH. The larger decline in consumption at full-service 

restaurants (on a percentage basis) than at fast-food establishments indicates some economizing 

within FAFH options, consistent with expenditure patterns discussed in chapter 4. But, by 2011-12, 

FAFH rose again to 34 percent of calories and fast food grew to 15.8 percent of calories, and this 

resurgence continued through 2013-14 with total FAFH at 33.7 percent and fast food at 15.9 percent 

of calories, respectively. This quick rebound suggests that FAFH is now an ingrained preference that 

Americans quickly return to when economic conditions permit.

58The National Bureau of Economic Research’s Business Cycle Dating Committee determined that the recession began in 

December 2007 and ended in June 2009. 
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Definitions of Food Sources

Across all surveys, food sources are classified into two main categories, defined by where the 

food was purchased. Food from supermarkets, smaller grocery stores, supercenters, or other 

retailers is defined as food prepared at home (FAH) although it could include prepared or semiprepared items such as rotisserie chicken or bagged salad. Food prepared away from home 

(FAFH) includes foods obtained from full-service restaurants with wait staff, fast-food establishments with no wait staff, food obtained at school or day care, and a catchall “other” subcategory that includes vending machines, common coffee pot/snack tray, Meals on Wheels, from 

someone else, street vendor, etc. 

The food source coding scheme differs between USDA's Nationwide Food Consumption Survey 

(NFCS), Continuing Survey of Food Intakes by Individuals (CSFII), and What We Eat in 

America (WWEIA)/National Health and Nutrition Examination Survey, but many sources are 

common in all surveys, such as grocery store, restaurant with waiter/waitress service, fast food, 

and school cafeteria. In this report, food sources are aggregated into two broad categories—

FAH and FAFH—and FAFH is further disaggregated into restaurant, fast food, school, and 

other FAFH. 

The definitions of FAH and FAFH are anchored on where the food was obtained. FAH food can 

be eaten away from home and FAFH food can be eaten at home. For example, FAH includes 

breads and peanut butter purchased at grocery stores and eaten as a peanut butter sandwich at 

home, school, or work. Meanwhile, home delivery or takeout from a pizza parlor is classified as 

FAFH even if it is eaten at home. 

The restaurant category includes restaurants with waiter service. Fast-food establishments 

include restaurants without waiter service, fast food, pizza, and cafeterias at work or residential 

facilities. Several eating places—including bar, tavern, lounge, sport, recreation, and entertainment facilities—are in categories separated from “restaurant with waiter service” in all surveys, 

but not identified in the 1977-78 NFCS. There is no cut-and-dried rule as to whether they should 

be included in restaurant or fast food; in this study, these eating places (as well as vending 

machines) are included in the fast-food category. The school category includes school cafeterias 

(meals and a la carte), daycare, and summer camp. 

The FAH category includes foods purchased from grocery and other stores (e.g., convenience 

and drug stores), mail order, and foods grown or caught by the respondent or someone else. 

Foods obtained at a soup kitchen, food pantry, or community feeding program are classified as 

either FAH (if eaten at home) or other FAFH (if eaten away from home). 
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The shift to more FAFH was broad-based, although the extent of change varied across income and 

age groups, as did the importance of specific FAFH sources (Guthrie et al., 2016). Comparing individuals living in higher income households (above 185 percent of the Federal poverty level59) with 

those in lower income households (at or below 185 percent), lower income individuals participated 

in the trend toward consuming more FAFH, but to a lesser extent (figure 7.2). Choices within the 

FAFH sectors also varied: both higher and lower income individuals increased fast-food consumption, but consuming food from restaurants with wait staff was less common for lower income individuals, which is not surprising given its typically higher price. 

In 1977-78, FAFH made up 18.8 percent of the diets of children and youth ages 2-19. Over time, their 

intake of FAFH—particularly from fast food—grew in parallel to that of adults (figure 7.3). In 1977-

78, fast food provided less than 4 percent of the mean daily energy intake of children and youth. 

Their intake of calories from fast food peaked at 16.5 percent of total calories in 2003-04, declined 

to 12.6 percent in 2009-10, and rose again to 16 percent in 2013-14. At the same time, the importance of school foods diminished. In 1977-78, school foods provided 8.9 percent of total calories, but 

just 6-7 percent of calories for those age 2-19 since 2003. Lower income children and youth ate more 

school foods at all time periods, probably because they are eligible for free or reduced-price USDA 

school meals (figure 7.4). In earlier time periods, lower income children and youth ate less fast food 

than their higher income counterparts. In 2011-12, their FAFH intakes were very similar—14.2 

59The 185 percent of poverty threshold is the cutoff for income eligibility to such public food assistance as the Special 

Supplemental Program for Women, Infants, and Children (WIC) and reduced-price USDA school meals, and is therefore 

frequently used to group households by income.
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Figure 7.2

Share of mean daily energy intake from food away from home sources, by income, 

U.S. population age 2 and older

Notes: FAFH = food away from home. Restaurant = full-service restaurant (with wait staff). Fast food = restaurant with 

counter service only. Higher income defined as household income above 185 percent of the Federal poverty level. Lower 

income defined as household income at or below 185 percent of the Federal poverty level. 

Source: USDA, Economic Research Service using data from the USDA Nationwide Food Consumption Survey (NFCS), 

1977-78; USDA Continuing Survey of Food Intakes by Individuals (CSFII), 1989-91 and 1994-98; and the National Health 

and Nutrition Examination Survey (NHANES), 2003-04, 2005-06, 2007-08, 2009-10, 2011-12, 2013-14.

Percent of total daily calories (kcals)

0

5

10

15

20

25

30

35

40

1977-78 89-91 94-98 03-04 05-06 07-08 09-10 11-12 13-14

Higher income total FAFH

Lower income total FAFH

Higher income fast food

Lower income fast food

Higher income restaurant

Lower income restaurant

percent of calories for lower income children and youth and 14.4 percent for higher income children 

and youth. However, in 2013-14, the disparity widened once more, with higher income children 

consuming 16.8 percent of calories from fast food, compared to lower-income children at 15.2 

percent of calories.

Nutrient Differences Between FAH and FAFH

In the 1990s, the nutritional quality of FAFH was inferior to FAH (Guthrie et al., 2002). In recent 

years, the nutritional quality of restaurant and fast-food meals has been subject to more scrutiny, 

with several major chains offering healthier options. At the same time, grocers and supermarkets are 

offering more prepared options. These changes may have narrowed the differences in caloric intake 

and nutritional density between FAH and FAFH.

However, changes in nutrient databases make it difficult to assess changes in the nutritional composition of FAH and FAFH over time. For perspective, the focus of this analysis is on changes in the fat 

composition of FAH and FAFH at two time points: 1977-78 and 2011-14. There are two reasons for 

focusing on fat. First, unlike some other nutritionally important components, such as saturated fat, 

dietary fat totals are available at both time points. Second, during the time period in question (1977-

2014), reduction in the fat content of diets was a major focus of attention from both nutritionists and 

the general public. For example, the Nutrition Facts panel on packaged goods, as implemented in 

1994, required information on both grams of fat in the product and calories from fat. (The most recent 

edition of the Federal Dietary Guidelines for Americans (USDHHS and USDA, 2015) shifted focus to 

the type of fat consumed, recommending replacing saturated fats with oils.) 
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Figure 7.3

Share of mean daily energy intake from food at home, total food away from home, 

and FAFH-fast food, children ages 2-19 and adults

Notes: FAFH = food away from home. 

Source: USDA, Economic Research Service using data from the USDA Nationwide Food Consumption Survey (NFCS), 

1977-78; USDA Continuing Survey of Food Intakes by Individuals (CSFII), 1989-91 and 1994-98; and the National Health 

and Nutrition Examination Survey (NHANES), 2003-04, 2005-06, 2007-08, 2009-10, 2011-12, 2013-14.

Percent of total daily calories (kcals)

1977-78 89-91 94-98 2003-04 05-06 07-08 09-10 11-12 13-14

Adult - 

home

Children -

home

Adult - 

total FAFH

Children - 

total FAFH

Adult - 

fast food

Children -

fast food

0

10

20

30

40

50

60

70

80

90

100

Figure 7.4

Calorie intake by U.S. lower income households with children and youth ages 2-19 

from consuming fast food versus school meals 

Notes: Higher income defined as household income above 185 percent of the Federal poverty level. Lower income defined 

as household income at or below 185 percent of the Federal poverty level. 

Source: USDA, Economic Research Service using data from the USDA Nationwide Food Consumption Survey (NFCS), 

1977-78; USDA Continuing Survey of Food Intakes by Individuals (CSFII), 1989-91 and 1994-98; and the National Health 

and Nutrition Examination Survey (NHANES), 2003-04, 2005-06, 2007-08, 2009-10, 2011-12, 2013-14.

Percent of total daily calories (kcals)

1977-78 89-91 94-98 2003-04 05-06 07-08 09-10 11-12 13-14

Higher income

children - fast food

Lower income

children - fast food

Lower income

children - school

Higher income

children - school

0

2

4

6

8

10

12

14

16

18
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If FAH and FAFH changed in response to public interest in nutritional quality, change in the fat 

composition of these sources would be most likely to be apparent. These changes could occur 

because of different choices being made by consumers (e.g., choosing a lean grilled chicken sandwich instead of fried chicken), changes in product formulation (e.g., food manufacturers developing 

a lower fat lasagna), or both. 

In addition to fat composition, the nutrient density of five additional dietary components is examined—saturated fat, sodium, calcium, iron, and dietary fiber—using 2011-12 data. For all components except dietary fat and saturated fat, density is defined as amount per 1,000 calories; for dietary 

fat and saturated fat, it is defined as percent of total calories.

Change in Fat Content of FAH and FAFH: 1977-78 and 2009-12

Total fat content of FAH and FAFH as a percent of total calories was essentially identical in 1977-78, 

at 41 and 41.2 percent of calories, respectively (figure 7.5). By 2011-14, the fat content of FAH was 

significantly lower than that of FAFH. Fat in FAH had dropped to 32.1 percent of calories, below the 

upper limit of 35 percent recommended by the Food and Nutrition Board of the National Academies 

of Sciences (FNB, 2002). For FAFH, the drop was less precipitous, going from 41.2 percent to 37.4 

percent. Moreover, that drop masked considerable variation within FAFH categories. The fat content 

of school meals dropped almost as much as FAH, going from 40.1 percent to 32.6 percent of calories. The fat content of fast food, on the other hand, changed very little, going from 41.1 percent in 

1977-78 to 39.1 percent in 2011-14.

Figure 7.5

Fat density of all food sources, food at home and food away from home 

Notes: FAH = food at home. FAFH = food away from home.

Source: USDA, Economic Research Service using data from the USDA Nationwide Food Consumption Survey (NFCS), 

1977-78 and the National Health and Nutrition Examination Survey (NHANES), 20011-14. 

Percent of daily calories (kcals)

41.0 41.0 41.2

46.1

41.1 40.1 39.0

33.9

32.1

37.4 37.1

39.1

32.6

35.0

0

5

10

15

20

25

30

35

40

45

50

Total FAH Total

FAFH

FAFHrestaurantFAFHfast foodFAFHschool FAFHother

1977-78 2011-14
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Nutrient Densities of FAH and FAFH and Differences in Contribution to 

Total Intake

Table 7.1 presents differences in the nutrient density of FAH and FAFH in 2011-14. The selected 

dietary components can be subdivided into those for which a lower density diet would be optimal—

saturated fats and sodium—and those for which a more dense diet would be optimal—calcium, iron, 

and dietary fiber.

The most recent Federal Dietary Guidelines for Americans recommends that Americans consume 

less than 10 percent of calories per day from saturated fats. Both FAH and FAFH density exceeded 

that standard in 2011-14, but FAH, at 10.6 percent of calories, is significantly lower than FAFH at 12.2 

percent. The saturated fat density of fast food is particularly high at 13 percent of calories (table 7.1).

The Healthy Eating Index-2010—developed by USDA’s Center for Nutrition Policy and Promotion 

in collaboration with the National Cancer Institute (Guenther et al., 2013)—is used to measure 

how well diet quality conforms to Federal dietary recommendations. Its sodium standard sets the 

maximum sodium density of the diet at no more than 1,100 milligrams per 1,000 calories (see data 

box in chapter 8). Both FAFH and FAH exceed that standard, but again FAFH was significantly 

higher in 2011-14—1,796 mg/1,000 calories versus 1,535 mg/1,000 calories for FAH. Wait-staff 

restaurant and fast-food sources are both high in sodium density at 1,962 mg and 1,833 mg per 1,000 

calories, respectively (table 7.1).

Calcium density is significantly higher for FAH than for FAFH sources, with one notable exception. 

School food, at 725 mg/1,000 calories, has significantly higher calcium density than any source. 

Milk is served with USDA-funded school breakfasts and lunches, and cheese is an ingredient in 

popular school foods such as pizza. Since dairy foods are the largest contributors to calcium intake, 

school foods are particularly calcium-rich. 

Table 7.1

Nutrient density by food source, 2011-14

Food sources 

Fullservice

restaurant

Fast

food

Other

 Nutrient Total FAH Total FAFH School FAFH

Percent of calories

Saturated fats 11.16 10.63 12.16 11.28 12.98 11.83 11.58

(11.05-

11.27)

(10.51-

10.76)

(12.03-

12.30)

(11.01-

11.55)

(12.80-

13.16)

(11.55-

12.12)

(11.26-

11.91)

Per 1,000 calories

Sodium (mg) 1,625 1,535 1,796 1,962 1,833 1,651 1,532

(1,614-

1,635)

(1,523-

1,547)

(1,777-

1,815)

(1,923-

2,001)

(1,812-

1,853)

(1,608-

1,693)

(1,489-

1,575)

Calcium (mg) 466 503 394 336 419 725 342

(460-471) (496-511) (387-400) (325-346) (409-429) (704-746) (329-354)

Iron (mg) 7.03 7.53 6.07 5.97 6.07 6.99 6.02

(6.97-7.09) (7.45-7.61) (6.01-6.13) (5.86-6.07) (5.99-6.15) (6.81-7.17) (5.88-6.15)

Fiber (g) 7.77 8.38 6.60 6.81 6.31 8.13 6.62

(7.65-7.89) (8.21-8.55) (6.50-6.69) (6.56-7.06) (6.19-6.43) (7.89-8.37) (6.40-6.85)

Note: FAH = food at home. FAFH = food away from home. Numbers in parentheses indicate the 95% confidence interval.

Source: USDA, Economic Research Service using data from the National Health and Nutrition Examination Survey 

(NHANES) 2011-14. 
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Iron and dietary fiber density of FAH are both significantly higher than for total FAFH. However, iron 

density of school foods is significantly higher than for other FAFH sources, while dietary fiber densities of FAH and school foods are not significantly different (table 7.1). School breakfasts and lunches 

provided through USDA-funded meal programs are expected to meet nutrition standards established 

by USDA, resulting in those meals having a different nutrient profile than other FAFH sources.

Conclusion and Discussion

This chapter presents data from large, nationally representative food consumption surveys that 

provide insights into how the shift from FAH to FAFH may have affected the nutritional quality of 

diets. The analysis is not without weaknesses. Changes in survey methodology, especially methods 

of collecting food intake data, may affect the comparability of data collected by the individual 

surveys, as may the problems of underreporting and self-selection that plague surveys of dietary 

intake. In addition, descriptive studies cannot draw causal interpretations. More sophisticated multivariate analysis is needed to fully assess the determinants of this trend. Nevertheless, these descriptive findings provide information that can guide discussion and inform future analyses.

Over the past four decades, food prepared away from home has almost doubled as a share of the 

average caloric intake of Americans. The strongest economic downturn since the 1930s resulted in a 

decline in FAFH in 2007-10, but by 2011-12, consumption of FAFH had rebounded (consistent with 

expenditure patterns discussed in chapters 3 and 5). This indicates the strength of consumers’ desire 

to include FAFH in their diets. 

Lower income Americans participated in the shift to more FAFH, though to a lesser extent than 

higher income consumers, and chose proportionately more of the lower cost, fast-food option. 

Compared to their higher income peers, lower income children also obtained a larger share of daily 

calorie intake from school, where they are income-eligible for free or reduced-price meals through 

USDA’s National School Lunch Program.

Of particular interest is FAFH consumption by children. School foods in this analysis include both 

foods provided as part of USDA-funded school meals (breakfast and lunch) and other foods obtained 

at school. Non-USDA school foods are sometimes termed “competitive foods” because their sale can 

be seen as competition with USDA school meals (Guthrie et al., 2013). Although FAFH as a whole 

is less nutritious than FAH, school foods are more calcium-dense than home foods, similar to home 

foods in dietary fiber content, and more iron-dense than other FAFH. 

In 2011-12, USDA-sponsored school meals had nutrition standards specifying that the meals would 

contain minimum amounts of calcium, iron, and other dietary essentials, and limiting fat and saturated fat levels. In the school year of 2012-13, USDA required schools participating in its National 

School Lunch Program (NSLP) to begin serving lunches that met updated nutrition standards, 

requiring lowfat milk, fruits, a healthier mix of vegetables, and more whole grains (Guthrie and 

Newman, 2013). In 2013-14, new standards for school breakfasts were implemented requiring lowfat 

milk, more whole fruit, and whole grains. These updated standards could be expected to impact 

school foods consumed by children in 2012-14. However, either in anticipation of new standards or 

because of their own desire to offer the most nutritious meals possible, some school foodservices 

began offering healthier meals even before the required dates of implementation. In addition, some 

vendors serving the school foodservice market had begun offering healthier products, such as pizzas 

with whole grain-rich crusts. These changes had already led to some improvement in the nutritional 

quality of foods consumed (Newman, 2013; Fox et al., 2010). 
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During the data collection time period, few limits were placed on competitive foods, and the most 

commonly purchased items were desserts, sweetened beverages, and salty snacks (Guthrie et al., 

2013). In 2014-15, new standards for competitive foods were implemented, requiring limits on 

sodium, fat, and sugars (Guthrie and Newman, 2013). These changes would be expected to further 

improve the nutritional profile of school foods. A number of small studies suggest that at least in 

some schools, students are reacting positively to the changes and eating the healthy foods offered to 

them (Ralston and Newman, 2015; Johnson et al., 2016).

There are some encouraging signs of change in nonschool FAFH, particularly related to children’s 

options. With growing social awareness of the problem of childhood obesity, more parents have 

become concerned about the FAFH choices offered to children. Recent years have seen an increase 

in healthier options as part of children’s meals served at restaurants and fast-food establishments 

(CBS News, 2011). Some establishments have even begun offering them as the default—that is, 

the standard children’s meal comes with a healthier beverage or side item, such as fruit, possibly 

with other options, such as fried potatoes, available upon request (Wootan, 2012). Some fast-food 

companies have agreed to voluntary standards limiting advertising of less nutritious foods to children (Kolish et al., 2015). Some studies have reported positive changes in the nutritional content of 

children’s meal orders, indicating these efforts may be paying off (Wansink and Hanks, 2014; Peters 

et al., 2016). Some restaurants may brand themselves as promoting healthful options for children and 

families, further shifting norms (Peters et al., 2016). 

Looking forward, the visibility and ubiquity of healthful FAFH options may make healthful choices 

more normative. Ideally, this could create a virtuous circle in which changes in social attitudes lead 

to increased demand for healthier options, generating a supply-side response that makes it ever easier 

to prioritize health. Such changes are unlikely overnight and may be more pronounced in certain 

segments of the population. Parents may be quicker to make changes for their children. Among 

adults, menu labeling likely will appeal disproportionately to health-conscious consumers (see 

chapter 10). Less health-conscious consumers might experience the spillover benefit of availability 

of healthier versions of popular items. Or, given a dynamic marketplace, some FAFH establishments 

might specialize in more indulgent items, appeasing less health-conscious consumers. 

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Chapter 8: How Food Environment and Proximity to 

Restaurants Affect Nutritional Quality

Using data from USDA’s National Household Food Acquisition and Purchase Survey 

(FoodAPS), this chapter examines the food environments, diet quality, and demographic 

characteristics of the survey respondents who dine out frequently versus those who do not 

do so. This analysis finds a weak association between fast-food purchases and diet quality 

and that consumers at full-service restaurants tend to have a better diet. This finding is 

partly explained by the fact that those who frequent full-service restaurants are of higher 

socioeconomic status and tend to purchase a more nutritious diet.

In chapter 7, food away from home (FAFH) is found to have inferior nutritional quality to homeprepared meals, with the notable exception of school meals. For several reasons, restaurant and supermarket location may nudge consumers to consume more FAFH. This analysis builds on chapter 7 by 

examining the premise that closer proximity to restaurants is associated with greater frequency of 

restaurant meals, consequentially deteriorating overall diet quality and health. Additionally, chapter 

5 finds associations between frequency of FAFH acquisitions and demographic characteristics using 

the FoodAPS. In this chapter, FoodAPS is again used to investigate (1) if local food environments are 

associated with the frequency of FAFH purchases, particularly fast-food and full-service restaurant 

meals; and (2) if the frequency of FAFH purchases is associated with diet quality.

Food Environments, Diet Quality, and Health

The location of restaurants and stores can influence how often people consume restaurant meals, 

which in turn can affect their diet quality and health outcomes, such as obesity. Existing studies 

examine the relationship between local food environments and residents’ diet quality and health 

outcomes. However, the observed relationship does not necessarily imply causality because individuals with poor diet quality and health may be more likely to live in areas with adverse food environments. Moreover, fast-food restaurants and grocery stores are likely to be located in areas with high 

demand. A growing number of studies apply a range of econometric methods to identify causality 

(Alviola et al., 2014; Currie et al., 2010; Dunn, 2010; Dunn et al., 2012; Lhila, 2011). However, these 

studies are limited to how the food environments affect body weight rather than diet quality, potentially because of the lack of appropriate datasets. 

Food prepared away from home generally contains more calories, is served in larger portion sizes, 

and is less nutritious (e.g., high in fat and sodium) than food prepared at home (Guthrie et al., 2002; 

Mancino et al., 2010; Nielsen and Popkin, 2003; chapter 7). Fast food (FF), in particular, is associated with high energy intake, low intake of essential micronutrients, and inferior metabolic outcomes 

(Duffey et al., 2009; French et al., 2000 and 2001; Fulkerson et al., 2011; Gillis and Bar-Or, 2003; 

Larson et al., 2011). Using the Continuing Survey of Food Intakes by Individuals (CSFII 1994-96), 

Bowman and Vinyard (2004) note that adults who consumed fast food (FF) during the survey period 

had substantially higher intakes of energy, total fat, saturated fat, carbohydrates, added sugars, and 

protein than non-FF consumers. The FF consumers also had lower intakes of vitamin A, carotenes, 

vitamin C, calcium, and magnesium. 
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There are two strands of literature looking at the effects of local food environments (including 

restaurants), on the diet quality and health outcomes of residents. One body of literature focuses on 

the relationship between lack of grocery stores offering healthy food options, commonly known as 

food deserts, and poor diet quality and health outcomes. Another body of literature focuses on the 

relationship between an abundance of restaurants selling unhealthy foods—such as fast-food restaurants—and poor diet quality. The underlying assumption in these studies is that proximity to stores 

or restaurants lowers the cost of consumption, resulting in frequent consumption of particular types 

of food, which subsequently affects diet quality. For example, the cost of consuming a restaurant 

meal consists of accessibility (travel time) and affordability (price). Therefore, given some fixed 

price, an individual is more likely to consume a restaurant meal if travel time declines.

The findings on food environments and diet quality are mixed. Some researchers find that easy 

access to stores selling healthy foods is associated with better diet quality and improved health 

(Carroll-Scott et al., 2013; Harrison et al., 2011; Laraia et al., 2004; Rose and Richards, 2004; Zick 

et al., 2009); others find that physical proximity to a grocery store is not strongly associated with 

diet quality since most people do not buy all of their food from the nearest grocery store (Aggarwal 

et al., 2014; Ver Ploeg et al., 2015). The overall availability of healthy foods, rather than physical 

distance, seems to be what matters in influencing people’s diet quality (Bodor et al., 2008; Cheadle 

et al., 1991; Poti et al., 2014). 

One of the main shortcomings of the aforementioned studies is that the observed relationship does 

not necessarily imply causation. People with poor diet quality and health may prefer to live in areas 

with easy access to stores and restaurants selling unhealthy foods. In addition, business owners 

are likely to open stores and restaurants in areas where they expect a strong demand. For instance, 

chapter 6 finds that most FAFH growth has been in urban areas. In these cases, the local food environments would be associated with diet quality and health, but the relationship cannot be deemed 

causal. Therefore, to identify the causal effect of access on diet quality, one must partition overall 

change in diet quality into supply and demand factors.

Recent studies attempt to identify the causal effect of food environments on health outcomes. Dunn 

(2010) finds that an increased availability of restaurants raises the Body Mass Index (BMI) of 

females and non-Whites in counties with medium population density (90 to 400 people per square 

mile and fewer than 25 interstate exits).60 On the contrary, Anderson and Matsa (2011) find no 

causal effect of restaurants—both fast-food and full-service—on obesity. In their study, the prevalence of obesity in communities immediately adjacent to the interstate highways (0 to 5 miles) is 

similar to that in communities slightly farther from highways (5 to 10 miles) despite the lower accessibility cost of the former. The authors argue that although proximity to a restaurant induces frequent 

consumption of restaurant meals, people also offset extra calories from restaurants by eating less 

60Dunn (2010) uses the number of interstate highway exits in a county to proxy for the supply of fast-food restaurants. 

The underlying assumption is that interstate highway exits attract fast-food restaurants without a strong demand from residents nearby, thereby effectively separating supply from the demand effect. Implicit in the assumption is that people with a 

strong preference for fast food do not choose to reside near highway exits.
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during other meals. Anderson and Matsa also provide evidence suggesting that obese individuals 

consume more nutritionally deficient foods regardless of where they consume them.61

The analysis in this chapter examines the assumptions implicit in many studies of how local food 

environments affect diet quality or health. It investigates if the local food environments are indeed 

associated with the frequency of dining at fast-food and full-service restaurants. Furthermore, the 

analysis examines if the frequencies of restaurant meal purchases are associated with diet quality. 

Data

The main source of data for this analysis is USDA’s 2012 National Household Food Acquisition and 

Purchase Survey (FoodAPS) (see data box in chapter 5). FoodAPS allows the linking of an individual’s diet quality with the food environment and thus provides a unique opportunity to test the 

assumptions underlying the current research. Studies on restaurant locations often rely on datasets 

with limited information on a respondent’s diet. Most publicly available datasets contain information on self-reported frequency of restaurant meal consumption but not on the nutritional quality or 

types of meals consumed. FoodAPS uniquely provides information on the types and amount of food 

households acquired, as well as the source of acquisition (i.e., supermarket, fast-food restaurant, etc.) 

and the source’s proximity to the respondent’s home. 

While FoodAPS provides detailed food acquisition and geographic information not commonly 

available in other datasets, there are several limitations. First, the survey collects information on 

the amount of food purchased or acquired instead of consumed. Because of spoilage and waste, 

we expect the amount consumed to be less than the amount purchased. Nevertheless, we assume 

that individuals consume all of the food they purchase in this analysis.62 Second, because of the 

short sample collection period (participants report 7 days of food acquisition information), the 

food acquisition data may not be representative of what households typically purchase throughout 

a year. For instance, some households may have eaten FAFH more or less often or bought larger 

or smaller quantities of supermarket food during the particular week that the FoodAPS sample 

was collected.63,64 Finally, the food acquisition data likely suffer from measurement error since 

they are self-reported. Despite these limitations, FoodAPS is the best available dataset for the 

present analysis.

61The inconsistent findings between Dunn (2010) and Anderson and Matsa (2011) can be due to a number of reasons. 

First, the estimation sample for Anderson and Matsa consists primarily of White rural residents. Consistent with Anderson 

and Matsa, Dunn (2010) and Dunn et al. (2012) find that fast-food availability has no effect on White residents in rural areas. 

Second, the proxy used in the model and the measure of fast-food availability differs slightly. Dunn (2010) uses the number 

of highway exits as a proxy for the number of fast-food restaurants, whereas Anderson and Matsa use the distance to highway 

as a proxy for the distance to all types of restaurants. Finally, the geographic level of the sample differs. Although both studies 

use the Behavioral Risk Factor Surveillance System as their main source of obesity data, Dunn’s analysis is at the county 

level whereas Anderson and Matsa’s (2011) is at the ZIP Code level.

62A particular group of people (e.g., low-income households) may consume a larger share of purchased food than other 

groups. The analysis in this chapter assumes that the proportion of consumed to purchased amount does not vary across different groups. 

63Some households may have purchased nothing, while others may have purchased food planned for several weeks during 

the survey week. The analysis excludes 3 percent of households that purchased food products with caloric content in excess 

of 500 percent of the recommended weekly caloric consumption for a household.

64About 30 percent of the products purchased at food stores lacked information on product weight. For this missing 

information, product weight is imputed based on the package prices, food groups, and types of stores the product was purchased at. 
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FoodAPS records restaurant purchases for the household, and does not provide information on how 

food was distributed among household members. In order to compare households of different sizes, 

the food purchases are normalized by the expected food consumption amount, based on the number 

of standard adult-equivalent (SAE) individuals with 2,000 kcal recommended intake. First, each 

individual in the household is assigned a weight according to the recommended caloric intake for 

sedentary individuals by gender and age from the Dietary Guidelines for Americans (USDA and 

HHS, 2010). For instance, if a household consists of a 35-year old man (2,400 kcal), a 35-year old 

woman (1,800 kcal), and 5- and 10-year old girls (1,200 and 1,400 kcal), then the recommended 

daily caloric intake for the household (at 3.4 SAEs) is 6,800 calories. 

The primary measure of diet quality is the 2010 Healthy Eating Index (HEI)(Guenther et al., 2013) 

(see box “2010 Healthy Eating Index”). Because FoodAPS reports food acquisition at the household 

level only, HEI in this analysis represents the healthfulness of food that a household acquired during 

the survey week. The component scores for each of the food groups that make up the HEI are also 

presented. 

Descriptive Statistics

Households in the sample consist of five distinct, similarly sized clusters based on the frequency of 

meals purchased at fast-food and full-service restaurants. The size of the “frequency” clusters and their 

definitions are presented in table 8.1.

65 The first cluster is “restaurant nonconsumers,” where households did not purchase meals from either fast-food or full-service restaurants during the survey week. 

Nineteen percent of the sample households belong to the first cluster. The second cluster is “occasional 

FF consumers,” who purchased less than 1.5 fast-food meals and no full-service restaurant meals (19 

percent). The third cluster, “frequent FF consumers,” purchased more than 1.5 fast-food meals per 

week and no full-service restaurant meals (18 percent). The fourth cluster, “occasional FF and FS 

consumers,” (22 percent),66 purchased less than 1.5 fast-food meals and some full-service restaurant 

meals per week. The last cluster, “frequent FF and FS consumers,” purchased more than 1.5 fast-food 

meals and some full-service restaurant meals per week (22 percent of households). Figure 8.1 shows 

FoodAPS respondents clustered by frequency of fast-food and full-service restaurant dining.

Table 8.1

Classification of households by frequency of fast-food and full-service meals per week

Restaurant

nonconsumers

Occasional FF 

consumers

Frequent FF 

consumers

Occasional FF 

and FS 

consumers

Frequent 

FF and FS 

consumers

Fast-food (FF) meals 

per person per week 0 0-1.5 >1.5 0-1.5 >1.5

Full-service (FS) meals 

per person per week 0 0 0 >0 >0

Share of the population 0.19 0.19 0.18 0.22 0.22

Notes: The statistics are calculated using population weights and stratification information available in the National Household 

Food Acquisition and Purchase Survey (FoodAPS).

Source: FoodAPS data and authors’ calculations.

65Clusters are constructed to clarify both the difference between consumers and non-consumers of FF and FS foods as 

well as the difference between heavy and light consumers of FAFH foods. The clusters may be different from the clusters 

constructed using a formal cluster analysis.

66While some households purchased one full-service meal per person per week and no fast-food meals (“moderate fullservice consumers”), they made up only 6 percent of the sampled households, and thus are lumped into this cluster. 
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2010 Healthy Eating Index 

The Healthy Eating Index (HEI) was developed by the U.S. Department of Agriculture’s Center 

for Nutrition Policy and Promotion (CNPP) as a means to quantitatively measure dietary compliance of the U.S. population with the Dietary Guidelines for Americans (DGA). Currently, 

updates are made to the HEI by CNPP and the National Cancer Institute every 5 years, corresponding to 5-year updates to the DGA. The most recent iteration of HEI is the HEI-2015 with 

past versions including HEI-2005 and HEI-2010.

The HEI is calculated based on 12 total food groups which include 9 standards for adequacy 

(total vegetables, dark green vegetables and legumes, total fruits, whole fruits, dairy, whole 

grains, total protein, seafood and plant protein, fatty acids) and 3 standards for moderation 

(empty calories, refined grains, and sodium). The standards for adequacy are targets for food 

groups recommended for increased consumption by the Dietary Guidelines, while moderation standards are targets for food groups recommended for decreased consumption. Scores for 

moderation components are derived so that higher scores reflect closer adherence to the applicable standard. Total HEI is a measure of the quality of food consumed per 1,000 calories and 

does not reflect quantity consumed. 

Total HEI scores are the aggregation of component scores for each of the 12 nutrient groups. 

Each component has a maximum and a minimum score. For example, the recommended 

consumption of fruits is 0.8 cup equivalent per 1,000 kcal. Any consumption larger than the 

recommended amount will result in a maximum score of five and any consumption less than 

the amount will result in a lower score, with a minimum score of zero. The total HEI ranges 

from 0 to 100. Scores closer to 100 are considered healthier. As a reference, the mean HEI for 

Americans over age 2 was 49.9 in 2003-04 (Guenther et al., 2014). 

Components and scoring of the 2010 Healthy Eating Index

Maximum 

score

Standard for 

maximum score1

Standard for minimum score of 

zero

Adequacy :

Total fruits 5 ≥0.8 cup No fruits

Whole fruits 5 ≥0.4 cup No whole fruits

Total vegetables 5 ≥1.1 cup No vegetables

Greens and beans 5 ≥0.2 cup No dark green vegetables or 

legumes

Whole grains 10 ≥1.5 oz No whole grains

Dairy 10 ≥1.3 cup No dairy

Total protein foods 5 ≥2.5 oz No protein foods

Seafood and plant 

proteins 

5 ≥0.8 oz No seafood or plant proteins

Fatty acids 10 (PUFAs + MUFAs)/SFAs 

≥2.52

(PUFAs + MUFAs)/SFAs ≤1.2

Moderation:

Refined grains 10 ≤1.8 oz ≥4.3 oz

Sodium 10 ≤1.1 gram ≥2.0 grams

Empty calories 20 ≤19% of energy ≥ 50% of energy

1All standards, except for fatty acids, represent amounts per 1,000 kilocalories. 

2PUFAs = polyunsaturated fatty acids. MUFAs = monounsaturated fatty acids. 

SFAs = saturated fatty acids. 

Source: Guenther et al (2013).
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The statistics are calculated using population weights and stratification information available in 

FoodAPS. Bivariate t-tests are used to test if a cluster mean is statistically different from the average 

of other households. The statistically significant differences are denoted with bold fonts. Some 

caution should be used in interpreting the statistical associations presented in this chapter. The 

results consider only unconditional correlations between restaurant locations, restaurant consumption, and consumers’ diets. The analysis of these correlations in a regression setting, or using exogenous variation in the supply and demand of restaurants, could produce different results. 

 

Demographic Characteristics

Table 8.2 presents demographic characteristics of consumers by frequency of visits to different types 

and combinations of restaurants.67 Frequent FF and FS consumers have the highest socioeconomic 

status (monthly income), while restaurant nonconsumers have the lowest. Occasional and frequent 

FF and FS consumers have higher income, employment, education, and home and car ownership 

rates than average households. Occasional and frequent FF and FS consumers are also less likely 

than average consumers to participate in Supplemental Nutritional Assistance Program (SNAP) and 

the Special Supplemental Nutrition Program for Women, Infants and Children (WIC).

67For a more indepth analysis of the demographics of FAFH consumers, see chapter 5. 
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Table 8.2

Demographic characteristics of consumers by frequency of visits to different types and 

combinations of restaurants

Restaurant

nonconsumers

Occasional 

fast-food (FF) 

consumers

Frequent FF 

consumers

Occasional FF 

and full-service 

(FS) consumers

Frequent FF 

and FS 

consumers

Female 0.55 0.54 0.51 0.52 0.53

(0.34) (0.29) (0.32) (0.29) (0.28)

Age 54.4** 47.3** 45.0** 51.9** 46.1**

(16.3) (14.7) (14.8) (16.3) (14.4)

Number of children 0.4** 0.9** 0.6 0.5 0.6

(1.3) (1.4) (1.2) (1.2) (1.1)

Household size 2.0** 2.9** 2.4 2.4 2.6**

(1.7) (1.9) (1.7) (1.6) (1.5)

Single parent 0.04 0.05 0.05 0.03 0.05

(0.27) (0.29) (0.27) (0.24) (0.25)

Hispanic 0.12 0.19** 0.14 0.13 0.09**

(0.37) (0.42) (0.39) (0.36) (0.35)

Black 0.16 0.17** 0.18** 0.08** 0.08**

(0.37) (0.37) (0.39) (0.29) (0.30)

Asian 0.03 0.04 0.04 0.03 0.04

(0.19) (0.20) (0.20) (0.18) (0.20)

Education (years) 20.0** 20.1** 20.9 21.2** 21.5**

(2.7) (2.5) (2.3) (2.4) (2.2)

Work commute time (minutes) 22.2 22.1 18.1** 20.1 19.7

(22.9) (21.4) (16.9) (33.5) (16.0)

Employed 0.44** 0.57 0.62 0.61 0.70**

(0.4) (0.4) (0.4) (0.4) (0.4)

Married 0.27** 0.51 0.41** 0.51 0.54**

(0.46) (0.5) (0.49) (0.5) (0.5)

BMI children (age 6-17) 20.3 20.9 20.9 19.8** 21.0

(7.1) (5.6) (5.2) (4.9) (6.9)

BMI adults 27.3 27.6 27.9 27.5 28.0

(5.8) (5.2) (6.1) (5.3) (6.1)

Monthly household Income ($) 3,406** 4,772 4,656** 6,325** 6,711**

(4,123) (3,793) (3,678) (4,979) (4,432)

Own house 0.58 0.56** 0.56** 0.67 0.68**

(0.50) (0.49) (0.50) (0.50) (0.49)

Household has a car 0.84** 0.89** 0.95 1.00** 0.98**

(0.52) (0.47) (0.45) (0.39) (0.45)

Have access to car if does not own a car 0.37 0.34 0.55 0.51 0.71**

(0.84) (0.89) (0.95) (1.00) (0.98)

Food expenditures per single-adult 

equivalent (SAE) 61.0** 56.3** 73.5** 97.2** 110.0**

(46.8) (38.7) (52.0) (60.5) (63.6)

Public transport expenditures ($/month) 14.9 10.9 15.2 42.8 20.9

(490) (48) (71) (412) (100)

Child care expenditures ($/month) 36.5 35.3 27.9 25.8 41.3

(204.9) (115.7) (110.3) (122.6) (149.6)

SNAP participation verified 0.22** 0.21** 0.12 0.07** 0.06**

(0.50) (0.49) (0.46) (0.41) (0.38)

SNAP participation reported 0.21** 0.19** 0.12 0.06** 0.05**

(0.49) (0.49) (0.45) (0.39) (0.36)

Anyone in household receiving 

benefits from WIC 0.28 0.35 0.36 0.22 0.17**

(0.50) (0.50) (0.50) (0.49) (0.48)

Share of population 0.19 0.19 0.18 0.22 0.22

Notes: The statistics are calculated using population weights and stratification information available in the National Household Food Acquisition 

and Purchase Survey (FoodAPS). ** indicates a statistically significant difference from the average (5 percent). Standard errors are in parenthesis.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and Purchase Survey (FoodAPS), 2012. 
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Relative to the restaurant nonconsumers, frequent FF and FS consumers are younger, more educated, 

more likely to be employed, and have higher monthly incomes. They are also less likely to participate in Federal food assistance programs. Frequent FF and FS consumers live in areas with a shorter 

commute time to work, and are more likely to own a car. Occasional FF consumers are more likely 

to be racial minorities and have children, whereas restaurant nonconsumers are the oldest group and 

are the least likely to have children (table 8.2). 

Since eating FAFH, especially in full-service restaurants, is generally more expensive than eating 

at home, consumers of full-service restaurant meals have the highest total food expenditures ($97 

and $110 per single adult equivalent (SAE) a month), while restaurant nonconsumers ($61) and 

occasional FF consumers ($56) have the lowest expenditures. The BMIs of children and adults do 

not differ significantly across the FAFH clusters. Only the children from occasional FF and FS 

consumers have slightly lower BMI than the rest, although the difference is small.

Food Environment

Table 8.3 presents descriptive statistics of the food environment for the five types of restaurant 

consumers. Both frequent FF consumers and frequent FF and FS consumers tend to live in rich food 

environments close to all types of stores, including their primary grocery store. They also tend to 

live close to FS and FF restaurants. Density of restaurants around the residence is mostly statistically 

insignificant, except for frequent FF consumers, who tend to have more fast-food restaurants within 

a 1-mile radius of their home. Interestingly, occasional FF consumers have the most fast-food restaurants within a 1-mile radius of their home. 

Restaurant consumers spend less time traveling and go fewer miles to their primary store (i.e., the 

store where consumers primarily shop for food) than restaurant nonconsumers. Restaurant nonconsumers travel 6.3 miles, or 11.1 minutes on average, to their primary store, whereas occasional FF 

consumers drive only 4.4 miles or 8.8 minutes. Distances to other types of food stores are also 

slightly farther for restaurant nonconsumers. For instance, the average distance to the nearest super 

store is 4.2 miles for restaurant nonconsumers and 2.5 miles for occasional FF consumers.

The distance to the nearest fast-food restaurant is strongly associated with the frequency of fast-food 

meals purchased. Among occasional FF consumers, the distance to the nearest fast-food restaurant 

is 1.7 miles; among restaurant nonconsumers, it is 3.3 miles. Moreover, the distance to the nearest 

McDonald’s is 2.7 miles for occasional FF consumers compared to 5.4 miles for restaurant nonconsumers (table 8.3). Occasional FF and FS consumers also have a fast-food restaurant nearby (2 

miles). In short, people who consume fast food frequently tend to live near fast-food restaurants. By 

contrast, the distance to the nearest full-service restaurant shows little correlation to the frequency of 

full-service restaurant meals, despite some statistically significant differences across clusters. 
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Table 8.3

Descriptive statistics of the food environment for the five types of restaurant consumers

Restaurant

nonconsumers 

Occasional 

FF 

Frequent 

FF

Occasional 

FF & FS

Frequent 

FF & FS

Distance to nearest super store (miles) 4.2** 2.5** 3.2 2.7** 2.7**

(4.5) (3.0) (3.9) (2.9) (2.9)

Distance to nearest supermarket (miles) 3.9** 2.7** 3.7 2.6** 2.6**

(5.1) (3.6) (4.8) (3.1) (3.1)

Distance to nearest grocery/other store (miles) 2.4** 1.5** 2.2 1.7 1.7

(2.6) (1.7) (2.7) (1.9) (1.9)

Distance to nearest convenience store (miles) 2.0 1.5 1.8 1.7 1.7

(2.3) (1.8) (2.0) (1.7) (1.7)

Distance to nearest larger grocery store (miles) 5.4** 4.5 4.6 4.5 4.5

(5.7) (5.1) (5.1) (4.6) (4.6)

Distance to nearest Walmart (miles) 7.0** 4.1** 5.8 4.9 4.9

(6.5) (3.9) (5.4) (4.1) (4.1)

# fast-food restaurants within 0.5 mile 1.5 1.8 1.5 1.5 1.5

(3.2) (3.4) (3.0) (3.1) (3.1)

# fast-food restaurants within 1 mile 4.8 6.3** 4.6** 5.3 5.3

(6.6) (7.0) (6.3) (7.6) (7.6)

# fast-food restaurants within 10 miles 118 154 126 137 137

(166) (185) (160) (169) (169)

# full-service restaurants within 0.5 mile 9.4 10.3 8.1 9.4 9.4

(23.4) (24.6) (20.3) (26.2) (26.2)

 # full-service restaurants within 1 mile 26.8 30.5 24.2 30.1 30.1

(56.9) (60.1) (54.3) (69.0) (69.0)

 # full-service restaurants within 10 miles 548 665 551 617 617

(959) (940) (855) (923) (923)

 #McDonald’s restaurants within 0.5 mile 0.17 0.18 0.15 0.15 0.15

(0.50) (0.52) (0.41) (0.48) (0.48)

 # McDonald’s restaurants within 1 mile 0.45 0.58 0.45 0.55 0.55

(0.81) (0.80) (0.75) (0.87) (0.87)

 # McDonald’s restaurants within 10 mile 11.8 14.7 12.4 13.3 13.3

(16.8) (17.3) (15.8) (16.4) (16.4)

Distance to the nearest fast-food restaurant (miles) 3.3** 1.7** 2.4 2.0** 2.0**

(4.4) (2.3) (3.6) (2.6) (2.6)

Distance to the nearest full-service restaurant (miles) 1.3 0.8** 1.2** 1.0 1.0

(1.6) (1.1) (1.4) (1.3) (1.3)

Distance to the nearest McDonald’s (miles) 5.4** 2.7** 3.6 3.1 3.1

(6.3) (3.6) (4.8) (3.9) (3.9)

Rural area indicator 0.42** 0.28 0.34 0.33 0.33

(0.47) (0.42) (0.46) (0.44) (0.44)

Driving distance to the primary store (miles) 6.3** 4.4 5.9 4.3** 4.3**

(6.3) (6.2) (8.7) (4.7) (4.7)

Driving time to the primary store (minutes) 11.1** 8.8** 10.8 8.8** 8.8**

(8.1) (7.8) (10.2) (6.5) (6.5)

Share of population 0.19** 0.19** 0.18** 0.22** 0.22**

Notes: The statistics are calculated using population weights and stratification information available in the National Household Food Acquisition 

and Purchase Survey (FoodAPS). FS = full-service restaurant meals. FF = fast-food restaurant meals. ** indicates a statistically significant difference from the average (5 percent). Standard errors are in parenthesis.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and Purchase Survey (FoodAPS), 

2012.
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Diet Quality

Table 8.4 compares the diet quality of consumers who patronize restaurants at different frequencies. 

Restaurant nonconsumers and frequent FF consumers tend to have the worst diets (relatively low 

HEI scores), whereas occasional FF and FS consumers tend to have the best diets or diets that are 

closest to the recommendation. All measures in table 8.4 are weekly totals, normalized by expected 

caloric intake of a household. The recommended caloric intake per adult equivalent is 14,000 kcal 

per week (2,000 kcal x 7). However, consumer clusters report more purchased calories per week, 

ranging from 16,803 kcal purchased by occasional FF consumers to 19,890 kcal purchased by 

frequent FF and FS consumers. The Healthy Eating Index-2010, which measures dietary quality per 

1,000 calories, shows that occasional FF and FS consumers have the best diet with a score of 55.5. 

Households that did not go to any restaurants had the worst diets, with a HEI score of 50.9. Thus, 

contrary to expectations, the lack of FAFH meals is not associated with healthier diet.

The relatively high HEI score of occasional FF and FS consumers compared to restaurant nonconsumers is due to frequent purchases of vegetables (16.6 cups a week), dark green vegetables (2.8 

cups), and purchase of recommended fish and plant proteins (15.4 oz). The diets of occasional FF 

consumers also have low HEI scores, due to small amounts of vegetables (12.8 cups) and fish/plant 

proteins (8.8 ounces), as well as excess saturated fats (221 grams). At the same time, occasional FF 

consumers purchased the least amount of alcohol (23 grams) out of all groups.

Why do the restaurant nonconsumers have the lowest HEI score? These consumers purchase 

relatively small amounts of whole grains, and large amounts of meat, saturated fat, and sodium 

compared with other consumer groups. None of their food group purchases or nutrient amounts 

are statistically different from the average. They tend to purchase slightly fewer foods that increase 

HEI score and slightly more foods that reduce HEI score than occasional and frequent FF and FS 

consumers. Nevertheless, the magnitude of HEI differences between restaurant nonconsumers and 

occasional FF consumers is just 1.5 points. 

Rural-Urban Divide

No significant difference is found in the tendency to purchase meals from FS restaurants between 

rural and urban consumers. However, urban consumers purchase slightly more FF meals (1.85 versus 

1.54 meals per person per week). The diets of rural consumers are slightly healthier (HEI of 42.47 

versus 40.82 for urban consumers), but this difference is not statistically significant. Also, food 

expenditures of rural and urban consumers are not statistically different. 

Predictably, rural consumers live in less dense food environments. They have almost 10 times less 

restaurants around them than do urban consumers (table 8.5). Rural consumers have only 0.73 FF 

restaurant within 1 mile of their home, whereas urban consumers have 7.6. If rural consumers have 

to drive 6.0 miles to the nearest fast-food restaurant, urban consumers can find one by driving just 

0.6 mile. The distance to the nearest supermarket is 6.6 miles for rural consumers and 1.4 miles for 

urban ones. The large differences in distance do not always translate into large differences in travel 

time. For example, average distance to the primary store is 10 miles for rural consumers and 2.6 

miles for urban consumers (3.8 times as far), whereas the driving time to the primary store is 16 

minutes for rural consumers and 6.6 minutes for urban consumers (just 2.4 times as lengthy). 
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Table 8.4

Diet quality of consumers who patronize restaurants at different frequencies 

Restaurant

nonconsumers

Occasional 

fast-food (FF) 

consumers

Frequent FF 

consumers

Occasional FF 

and full-service 

(FS) consumers

Frequent FF 

and FS 

consumers

Full-service restaurant meals 0.00** 0.00** 0.00** 1.51** 1.46**

(0.00) (0.00) (0.00) (1.04) (0.96)

Fast-food meals 0.00** 0.88** 3.28** 0.66** 3.31**

(0.00) (0.37) (1.62) (0.52) (1.68)

Fruits (cups) 7.3 7.5 7.2 9.4 8.0

(8.7) (7.5) (7.8) (11.0) (8.1)

Vegetables (cups) 15.4 12.8** 13.1** 16.6** 15.8

(17.4) (15.5) (11.1) (15.8) (12.8)

Vegetables, dark green (cups) 1.8 1.6 1.7 2.8** 2.4

(4.0) (8.9) (3.7) (8.6) (4.9)

Refined grains (oz) 45.8 43.4 48.3 47.5 54.9**

(45.1) (41.3) (40.1) (43.9) (40.2)

Whole grains (oz) 7.2 7.2 8.0 7.3 8.3

(12.4) (13.4) (11.6) (12.1) (10.3)

Fish, plant protein (oz) 10.1 8.8** 8.8 15.4** 10.8

(30.9) (12.8) (14.7) (20.3) (17.2)

Meats (oz) 47.9 38.4** 46.4 54.4 50.2

(57.8) (35.7) (44.8) (49.7) (36.6)

Energy (kcal) 18,101 16,803 18,200 18,896 19,890

( 13,692) ( 11,993) ( 12,140) ( 12,093) ( 10,856)

Carbohydrates (gram) 2,222 2,081** 2,272 2,297 2,399**

( 1,704) ( 1,561) ( 1,592) ( 1,580) ( 1,403)

Saturated fat (gram) 246 221** 233 243 265**

( 229) ( 178) ( 182) ( 187) ( 174)

Alcohol (gram) 40 23** 30** 49 63**

( 102) ( 69) ( 83) ( 96) ( 127)

Added sugars (gram) 165 154 171 163 168

( 191) ( 173) ( 186) ( 168) ( 135)

Sodium (mg) 42,433 30,280 30,915 38,881 33,700

( 58,808) ( 40,634) ( 43,376) ( 47,550) ( 45,593)

Healthy Eating Index 2010 50.9** 52.4 52.0 55.5** 53.4

(15.1) (13.4) (13.0) (13.2) (12.6)

Shares 0.19** 0.19** 0.18** 0.22** 0.22**

Notes: The statistics are calculated using population weights and stratification information available in FoodAPS. ** indicates a statistically significant difference from the average (5 percent). Standard errors are in parenthesis.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and Purchase Survey 

(FoodAPS), 2012. 
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Table 8.5

Urban-rural differences in frequency of food away from home, diet, food expenditures, and 

distance to restaurants

Rural Urban

Full-service restaurant meals 0.75 0.65

S.E (0.89) (0.81)

Fast-food Meals 1.54** 1.85**

S.E (1.48) (1.76)

Total expenditures, dollars 175.10 175.94

S.E (134.42) (140.40)

Healthy Eating Index, 2010 42.47 40.82

S.E (16.17) (16.09)

 # fast-food restaurants within 1 mile 0.73** 7.55**

S.E (2.30) (6.98)

# fast-food restaurants within 10 miles 22.13** 191.79**

S.E (32.89) (179.31)

# full-service restaurants within 1 mile 2.71** 39.83**

S.E (5.16) (64.07)

# non-fast-food restaurants within 10 miles 86.76** 872.12**

S.E (132.50) (993.71)

Distance to the nearest fast-food restaurants (miles) 6.04** 0.60**

S.E (5.17) (0.61)

Distance to the nearest full-service restaurants (miles) 2.20** 0.38**

S.E (1.98) (0.44)

Distance to nearest supermarket (miles) 6.64** 1.36**

S.E (6.09) (2.45)

Driving distance to the primary store (miles) 9.96** 2.61**

S.E (9.94) (3.20)

Driving time to the primary store (minutes) 15.99** 6.57**

S.E (11.68) (4.69)

Notes: The statistics are calculated using population weights and stratification information available in FoodAPS. 

S.E. = standard error. ** indicates a statistically significant difference from the average (5 percent). Standard errors are in 

parenthesis.

Source: USDA, Economic Research Service using data from USDA’s National Household Food Acquisition and Purchase 

Survey (FoodAPS), 2012. 
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Conclusion

Occasional fast-food (FF) and full-service (FS) restaurant consumers tend to live near grocery stores 

and occasional FF consumers tend to live close to fast-food restaurants. Although some evidence 

shows that restaurant consumers of all kinds (four clusters) are more likely to live in areas with a 

high concentration of restaurants compared to restaurant nonconsumers, the differences are not 

statistically significant in most cases. Furthermore, consumers in rural areas have far fewer restaurants nearby, but they consume only slightly fewer restaurant meals than urban consumers.

Assuming that those who prefer fast food are only slightly more likely to live near fast-food restaurants than those who do not, the results suggest that fast-food consumers respond more strongly to 

convenience (short distance) than to a variety (density) of restaurants.68 In addition, full-service 

consumers are not as sensitive to the proximity of restaurants as are fast-food consumers. 

The results from the diet quality analysis are mixed. Frequent FF and FS consumers purchase the 

most calories in a week, which is consistent with previous research. Occasional FF consumers 

purchase the least calories, significantly less than restaurant nonconsumers. Considering the association between high calorie content and FAFH, this result is somewhat unexpected, but could, in part, 

be explained by how FoodAPS collects data. FoodAPS gives information on food acquisitions, and 

so it is likely that nonconsumers are purchasing more food than they actually consume on a weekly 

basis. In other words, when calorie content is matched to purchases, it may give an inflated approximation of calories consumed, while those who purchase FF are more likely consuming all of the 

food they purchase in that week. 

There is little evidence that supports claims that fast-food meals are associated with poorer diet 

quality. For example, the Healthy Eating Index (HEI) of nonconsumers tend to be slightly lower 

than FF consumers. This is perhaps because nonconsumers purchase more saturated fat, sodium, 

and alcohol than FF consumers even though nonconsumers purchase more vegetables, meats, and 

fish/plant proteins. Additionally, this analysis finds no evidence that frequency of restaurant meal 

purchases is associated with BMI.

People from higher socioeconomic status tend to eat healthy (Darmon and Drewnowski, 2008), 

which partly explains the lack of correlation between frequency of restaurant meal purchases and 

diet quality. Restaurant consumers—especially those who frequent full-service restaurants—have 

much higher socioeconomic status than those who do not dine out often. They are more educated, 

have higher incomes, are more likely to be married, and more likely to be employed. Therefore, 

restaurant consumers who tend to have high socioeconomic status may be consuming healthy meals 

at restaurants, resulting in no statistically significant differences in diet quality and BMI compared 

to restaurant nonconsumers. 

No causal inference can be drawn from these observational findings without a more rigorous econometric analysis. This report shows that demographic characteristics are strong correlates of frequent 

restaurant meal consumption and that food environments are not. Future research can examine 

whether these associations are causal. 

68The distance measure is arguably more exogenous than the density measure, since consumers who prefer eating FAFH 

may choose to live in areas with a high density of restaurants but are less likely to move close to a particular restaurant.
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Chapter 9: What Role Does Food Away From Home Play in 

the Diets of Food Assistance Recipients?

Using data from the National Household Food Acquisition and Purchase Survey 

(FoodAPS), this chapter examines the diet quality of food away from home (FAFH) 

consumed by food assistance recipients. The average 2010 Healthy Eating Index (HEI2010) score for FAFH was lower than for food at home (FAH) for all households by income 

and participation in the Supplemental Nutrition Assistance Program (SNAP).

Several policies have been proposed to help consumers opt for healthier food choices. Food assistance is intended to increase food security and support healthy diet quality for the low-income 

elderly, individuals with disabilities, and children. USDA’s Supplemental Nutrition Assistance 

Program (SNAP) is a Federal program designed to increase the food purchasing power of lowincome participants. Households qualify for the program if their income is below 130 percent of the 

Federal poverty threshold;69 households are also able to categorically qualify for the program if they 

participate in other Federal assistance programs. As of June 2017, 41 million individuals participated 

in SNAP, which is a decrease from a high of 47 million in the aftermath of the Great Recession. 

The role of food away from home (FAFH) in the diets of SNAP recipient households is of concern, 

as previous studies have found that FAFH reduces diet quality70 (Todd et al., 2010), and overall diet 

quality is lower for SNAP recipient households than for nonrecipients (Condon et al., 2015). While 

SNAP benefits cannot be used directly to purchase FAFH in most circumstances, SNAP recipient 

households do purchase FAFH with other resources. Yet little is known about the relationship 

between FAFH, dietary quality, and SNAP participation.

Chapter 8 discusses the associations between purchase frequency of FAFH and diet quality. This 

chapter focuses on the importance of FAFH in the diets of a subset of individuals who participate 

in food assistance programs, using the Healthy Eating Index -2010 (HEI-2010). The HEI-2010 was 

developed to measure how well individuals meet the 2010 Dietary Guidelines for Americans; it can 

also be used to measure the nutritional quality of the food supply, foods available in a grocery store, 

or two different market baskets (Strasser et al., 2015). The National Household Food Acquisition 

and Purchase Survey (FoodAPS) collects data on all foods purchased or otherwise acquired for all 

household members, so the HEI can be used to measure how closely the reported household-level 

acquisitions over a week match dietary recommendations. 

FoodAPS is used to calculate Healthy Eating Index (HEI-2010) scores for SNAP recipient households, as well as for low-income and higher income nonparticipants (see data box in chapter 5 

69The U.S. Census Bureau calculates the poverty threshold annually based on a minimum income level for a given number of household members in order to provide an income measure of poverty for the current year. Household income can then 

be reported relative to the threshold, i.e., 130 percent of the threshold, 180 percent of the threshold, etc. The poverty threshold 

for a family of 4 in 2016 was $24,300.

70The literature on food away from home varies somewhat in what is included. Todd’s study of adults counted a meal as 

FAFH if the majority of calories were obtained from fast food, table-service restaurants, cafeterias, or taverns. This study includes the following categories in FAFH: eating places (restaurants, fast-food outlet, carry out, coffee shop, vending machine, 

etc.); schools; noncommercial places (family, friends, parties, and places of worship); work (any event reported at work); and 

food banks and Meals on Wheels. The nutritional quality of these sources would be expected to differ, but in the FoodAPS 

survey all FAFH sources have lower HEI-2010 scores than FAH, and diet quality for food from family, friends, parties and 

places of worship was similar to that of food from restaurants and other eating places (Mancino et al., 2018a). 
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for details on FoodAPS). Diet quality of FAFH for SNAP recipient households is compared to 

nonparticipants at different income levels, as well as potential factors that could affect this outcome. 

Because diet quality of children is a concern and because households with children may receive food 

assistance through school meals, adult-only households are analyzed separately from those with 

children. This chapter addresses the following questions:

1. How does diet quality for FAFH compare to diet quality for FAH among SNAP recipient 

households and nonparticipant households at different income levels? 

2. How does diet quality for FAFH among SNAP recipient households with adults only compare 

to other segments of the population? 

3. How does diet quality for FAFH among SNAP recipient households with children compare to 

other segments of the population? 

4. Do SNAP recipient households with working members obtain higher diet quality from FAFH? 

5. Do SNAP recipient households with a higher level of nutrition knowledge acquire FAFH with 

higher diet quality? 

6. How does the diet quality of school meals compare to other FAFH acquired by households 

with children?

Previous Related Research 

Per capita FAFH expenditures are significantly less for SNAP recipient households than for eligible 

nonparticipant households and considerably less than for households above 185 percent of the 

Federal poverty threshold. Tiehen and colleagues (2017) found that weekly expenditures on restaurants and other eating places for SNAP recipient households were about $11 per adult-male equivalent71 (AME) compared to $21 per AME for eligible nonparticipant households and $30 per AME 

for households above 185 percent of the Federal poverty threshold.

On a nutrient basis, however, the differences are much narrower and reveal the importance of FAFH 

in the diets of individuals across the income spectrum. Other studies using FoodAPS found that 

FAFH accounted for 31 percent of calories in SNAP recipient households’ acquisitions, only slightly 

less than the 34-percent share for households above 185 percent of the poverty threshold (Mancino et 

al., 2018).72 The greater similarity in share of calories than in total expenditures suggests that SNAP 

households likely choose lower cost FAFH options. Average daily calories per person in SNAP 

households (in FoodAPS) totaled 3,055 kcal per AME, almost as many as for households above 

71An AME is a normalized consumption unit requiring 2,200 calories per day, similar to the standard adult equivalent 

requiring 2,000 calories per day, discussed in Chapter 8. To compare households of different sizes and age compositions, the 

number of normalized units is calculated by weighting household members by their daily calorie requirements as a fraction of 

the standard.

72These calorie shares are lower than overall share of calories from FAFH reported by Lin and Guthrie (2012) based 

on the 2005-08 National Health and Nutrition Examination Survey (NHANES) (32 percent), and calorie totals are higher 

than the average daily per capita calorie intake from NHANES (2,002). The difference in results may reflect the fact that 

NHANES measures food consumption using two nonconsecutive 24-hour dietary recalls, while FoodAPS measures food 

acquisition rather than consumption. If the gap between acquisition and consumption is smaller for FAFH than for FAH, that 

could partially account for the higher share of calories from FAFH estimated from NHANES. Note also that the calories from 

FAH are based on food acquired that may not be consumed in the same week, while calories from FAFH are based on food 

purchases that are likely consumed immediately. 
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185 percent of poverty (3,209), and more than for nonparticipant households at 100-185 percent of 

poverty (2,800).73

Davis (2014) examined why FAFH accounts for a substantial portion of food intake by SNAP recipient households even though their incomes are low and benefits cannot be used for FAFH directly. 

Using a theoretical model of household decisions, Davis explained that households’ decisions about 

how much FAH and FAFH used in meeting food needs are influenced by the opportunity cost of 

time, the share of time cost in the total cost of meeting household food needs, and the elasticity of 

substitution between time and goods in meeting household food needs. Davis reviewed the available 

literature on these factors to show that the cost of time in food acquisition and preparation is high 

and that the elasticity of substitution between time and goods in meeting household food needs is 

low. These conditions, Davis argued, help explain the use of FAFH for a substantial portion of food 

needs on a calorie basis in households facing time pressure, with SNAP benefits freeing up resources 

for use in purchasing FAFH, even when the benefits cannot be used directly. 

You and colleagues (2009) showed that including moderate FAFH spending in the Thrifty Food 

Plan (TFP) calculation can result in nutrient consumption similar to the original TFP, while allowing 

for convenience and practicality in feeding a family. Still, FAFH has been found to degrade diet 

quality, on average. Todd and colleagues (2010) found that every meal eaten away from home in 

2003-04 lowered intake of fruit, dark green/orange vegetables, and whole grains, while increasing 

consumption of saturated fat. Lin and Guthrie (2012) examined data from 2005 to 2008 and found 

that FAFH was higher in sodium and lower in fiber than food prepared at home. Guthrie, Lin, and 

Smith (Chapter 7) similarly found FAH is typically nutritionally superior to FAFH—richer in underconsumed nutrients and less dense in overconsumed nutrients. Foods obtained at school, however, 

were found to be more calcium-rich than FAH and more nutritionally similar to FAH. Mancino and 

colleagues (2010) also found FAFH to degrade children’s diets.

Todd (2014) recently found the nutritional profile of FAFH chosen by consumers has improved, 

with lower levels of cholesterol and sodium per 1,000 calories, and higher levels of fiber in 2009-10 

compared to 2005-06. Todd also found that FAFH consumption declined over 2005-10 due to the 

2007-09 Great Recession, but concluded that the recession itself did not cause a change in the nutrients consumed. Todd documented improvements in nutrition awareness over the same period, and 

cited these improvements as a likely explanation for the change in foods chosen. Increasing nutrition 

awareness may also prompt vendors to offer more healthy alternatives. 

Improvements in diet quality for FAFH would benefit all consumers, but especially SNAP recipient 

households whose diet quality is inferior to SNAP nonparticipants. Using data from the National 

Health and Nutrition Examination Survey (NHANES) 2007-10, Condon and colleagues (2015) 

found that—compared to both income-eligible nonparticipants and higher income nonparticipants—

adult SNAP participants consumed less whole fruit, total vegetables, dark green vegetables, orange 

vegetables, and legumes; and more empty calories (solid fats added sugars, and alcohol).74 Similarly, 

consumption of dark green/orange vegetables and legumes was lower for children in SNAP households than for income-eligible and higher income nonparticipants.

73Calorie intake at different income levels may also reflect differences in job-related energy expenditures for workers 

involved in heavy manual labor.

74Condon’s analysis was based on the 2005 Healthy Eating Index, an earlier version of the measure used in this report.
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Data and Methodology

Data from FoodAPS collected in 2012 and 2013 (see data box in chapter 5 for more details on 

FoodAPS) are used in this analysis. FoodAPS oversampled SNAP recipient households and other 

low-income households, providing improved information on food acquisitions for this group. 

FoodAPS is the first survey to provide detailed information on food obtained from all sources for 

all members of the household, providing a clearer picture of diet quality at the household level for 

SNAP recipient households. Further, because SNAP reporting status in FoodAPS was confirmed 

through matching with program administrative data in 22 out of the 27 States covered in the survey, 

FoodAPS allows for greater accuracy in comparing diet quality of SNAP recipient households and 

nonrecipient households, as other surveys that include dietary intake are thought to underreport 

SNAP participation (Kreider et al., 2012). 

FoodAPS converts all foods acquired to equivalent amounts of food groups targeted by the 2010 

Dietary Guidelines for Americans, making it possible to assess the diet quality of FAFH using the 

2010 Healthy Eating Index (HEI-2010), a scoring system that indicates relative adherence to the 

Guidelines. The total HEI-2010 score is calculated as the sum of subscores based on adherence to 

nine targets for foods encouraged by the Guidelines (total vegetables, green vegetables and legumes, 

total fruit, whole fruit, dairy, whole grains, total protein, seafood and plant protein, and fatty acids 

as a share of calories) and three targets for moderation (empty calories, or calories from solid fats, 

added sugars and alcohol; refined grains; and sodium). (See box in chapter 8 for more information 

about HEI.) 

There remain some limitations in using the HEI-2010, versus food intake data, to measure diet 

quality from food acquisition data. First, the HEI-2010 calculated here is based on food acquisitions over a week but does not take into account food stored by a household before the observation 

week or foods acquired and reported during the data collection week that may be stored for later 

use. While this could introduce some variance into a measure of diet quality for an individual, this 

variance may be diminished for an HEI-2010 score averaged over a population group. Second, the 

HEI-2010 measure from FoodAPS counts food acquired even if it is not consumed. This is a more 

serious limitation, since food waste is not evenly distributed across the diet, but rather is higher for 

fruits and vegetables that are underconsumed. Overall, however, Mancino and colleagues (2018b) 

found that the HEI-2010 scores and component densities from FoodAPS were similar to HEI-2010 

scores based on dietary recall data from the NHANES for 2011-12.
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FoodAPS uses detailed data collected on income together with household size to construct an estimate of the household’s income as a percent of the Federal poverty line. FoodAPS also collected 

responses on nutrition knowledge and attitudes from a primary respondent in each sampled household. This analysis investigates the healthfulness of FAFH purchases by SNAP and non-SNAP 

households by income group (less than 130 percent of the poverty threshold, which is the cutoff 

for SNAP eligibility; 130-185 percent of the poverty threshold, since 185 percent confers income 

eligibility in other food assistance programs; and above 185 percent of the poverty threshold); by 

household type (with and without children); and by employment status. Differences in HEI-2010 for 

FAFH between households with higher levels of nutrition awareness and those with less awareness 

are also explored. To adjust for the stratified sample design of FoodAPS, jackknife weights were 

used (USDA/ERS, 2016) to calculate average estimates and variance; estimates that are statistically 

significant at 1, 5, or 10 percent are discussed. 

2010 Healthy Eating Index Scores for Food Away From Home 

Versus Food at Home

Looking at HEI-2010 scores for all households, the average HEI-2010 score for FAFH is significantly lower than for FAH in all non-SNAP income categories, but not for SNAP recipient households (fig. 9.1). The difference between scores for FAFH and FAH increased with income, as the 

spread across income and SNAP participation groups was smaller for FAFH than for FAH. While 

HEI-2010 scores for FAH ranged from 46 to 54 across income and SNAP participation groups, 

scores for FAFH ranged from 42 to 45. Only households above 185 percent of the poverty level had 

significantly higher HEI scores than SNAP recipient households for FAFH, while all income groups 

had significantly higher FAH scores than SNAP households. The results suggest that food choices 

away from home are more similar across the income spectrum than food choices at home, which 

could reflect consumer preferences as well as the offerings made available by vendors in response to 

those preferences. `

The FoodAPS data do not allow for attribution of food acquisition events to either adults or children, 

but they do allow for examination of the HEI-2010 score for FAFH acquired by households with 

only adults and for those with children. The results, then, are indicative of the nutritional quality 

of foods in households with different age ranges rather than a comparison of nutritional quality 

between adults and children.

Households Without Children

For households with adults only, average HEI-2010 scores for FAFH (HEI-FAFH) are also narrowly 

distributed across income and SNAP participation groups, ranging from 40 to 45 (fig. 9.2),

suggesting low levels of adherence to the 2010 Dietary Guidelines across the income distribution, 

similar to the results for all households. Differences in total HEI-FAFH scores compared to SNAP 

recipient households were statistically significant at the 10-percent level for households at 130-185 

percent of the poverty line and statistically significant at the 1-percent level for households above 185 

percent of the poverty level. The HEI-FAFH score for nonrecipient households below 130 percent of 

the poverty level was not statistically different from SNAP recipient households. 
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Figure 9.1

HEI-2010 scores for food at home and food away from home, by income and participation in 

USDA’s Supplemental Nutrition Assistance Program (SNAP)

Average HEI-2010 score

### = Significantly different from food at home at the 1-percent level. ** = Significantly different from SNAP households 

at the 5-percent level, *** = Significantly different from SNAP households at the 1-percent level.

Note: 2010 Healthy Eating Index (HEI-2010) scores presented here provide a profile of foods, as purchased or 

acquired, relative to the 2010 Dietary Guidelines for Americans, without adjustment for what portion of foods acquired 

during the observation week were consumed during the week. 

Source: USDA, Economic Research Service estimates using National Household Food Acquisition and Purchase 

Survey, collected 2012 and 2013.

Food away from home Food at home

### ### ###, *** ** *** ***

0

10

20

30

40

50

60

SNAP households Non-SNAP, household

income < 130% 

of poverty

Non-SNAP, household

income 130-185%

of poverty

Non-SNAP, household

income > 185%

of poverty

HEI-FAFH subscores for each component, expressed as a percent of the maximum score for the 

component, were also not significantly different (from SNAP recipient households) in any category 

for nonrecipient adult households below 130 percent of the poverty level. The scores for total protein, 

as a percent of the target, were significantly higher for adult households above 185 percent of the 

poverty level than for adult SNAP recipient households, perhaps reflecting higher meat content in 

FAFH for higher income households. Scores for total vegetables and dark green vegetables/legumes 

were also significantly higher for households above 185 percent of the poverty level than for SNAP 

recipients. However, HEI-FAFH subcomponent scores for total fruit, whole fruit, and whole grains 

for adult SNAP recipient households were significantly higher than for adult households above 185 

percent and 130-185 percent of the poverty level. 
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Figure 9.2

Food away from home: HEI-2010 score and subcomponents, as a percent of maximum, 

for households with adults only, by income and participation in USDA’s Supplemental 

Nutrition Assistance Program (SNAP)

* = Significantly different from SNAP recipients at the 10-percent level. ** = Significantly different from SNAP recipients at

the 5-percent level.*** = Significantly different from SNAP recipients at the 1-percent level. 

Note: 2010 Healthy Eating Index (HEI-2010) scores presented here provide a profile of foods, as purchased or acquired, 

relative to the 2010 Dietary Guidelines for Americans, without adjustment for what portion of foods acquired during the 

observation week were consumed during the week. 

Source: USDA, Economic Research Service estimates using National Household Food Acquisition and Purchase Survey, 

collected in 2012 and 2013.

***

*

***

**

***

***

***

*

**

*

*

***

**

**

0 10 20 30 40 50 60 70 80 90 100

Healthy eating index

Avoidance of sodium

Avoidance of refined grains

Avoidance of empty calories

Fatty acids

Seafood/plant protein

Total protein

Dairy

Whole grains

Whole fruit

Total fruit

Dark green vegetables/legumes

Total vegetables

Percent

SNAP

households

Non-SNAP, household

income < 130% 

of poverty

Non-SNAP, household

income 130-185%

of poverty

Non-SNAP, household

income > 185%

of poverty
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HEI-2010 for FAFH in Households With Children

Total HEI-FAFH scores in households with children are even more narrowly distributed, with 

values ranging from 43 to 45 (fig. 9.3) across income groups; total HEI-FAFH was not significantly 

different for any group. Compared to households above 185 percent of the poverty level, SNAP 

households have significantly higher HEI-FAFH subscores for whole fruit, but lower subscores for 

dark green vegetables/legumes and total vegetables.75 Households at 130-185 percent of poverty 

have significantly lower scores for whole grains and (avoidance of) empty calories than SNAP 

households. Non-SNAP households below 130 percent of poverty have significantly lower scores for 

(avoidance of) refined grains but significantly higher scores for fatty acids and total vegetables.

Among SNAP recipient households, HEI-FAFH is significantly higher for households with children 

(44) than for adult households (41), but this is not the case for any other income or SNAP participation group.76 The higher scores for households with children among lower income groups may reflect 

participation in school meals.

SNAP Households With Working Members

SNAP households with working members may be more reliant on FAFH to meet food needs under 

more binding time constraints, even though SNAP benefits cannot be used for FAFH. In FY 2012, 

when FoodAPS was conducted, this group accounted for 31 percent of SNAP households and 51 

percent of SNAP households with children (Gray and Eslami, 2014), similar to the latest available 

figures for FY 2016—32 percent and 55 percent, respectively (Lauffer, 2018). Tiehen and colleagues 

(2017) found that SNAP recipient households with employed members spent significantly more on 

FAFH ($14 a week per adult male equivalent, or AME) than those with no employed members ($10 

per AME). 

The total HEI-FAFH score for SNAP recipient households with working members (43) is higher 

than for SNAP recipient households with no workers (42), though the difference is not statistically 

significant (fig. 9.4). The dairy subscore is significantly higher for working SNAP households than 

for nonworking SNAP households, but seafood, whole grain, whole fruit, dark green vegetable/

legume, and total vegetable subscores were significantly lower for working SNAP households. 

Since SNAP households with working members may have other characteristics that differ from 

nonworking SNAP households, further research is needed to explore what factors explain observed 

differences in FAFH choices for these households under time pressure.

The Relationship Between Nutrition Awareness and Attitudes and HEI

Mancino and Kinsey (2004) examined the relationship between nutrition knowledge and HEI total 

scores and found that respondents who indicated greater nutrition knowledge had higher HEI scores. 

This pattern does not appear to hold for FAFH among SNAP recipient households (based on the 

FoodAPS data), though it does hold for other groups.

75Changes in USDA school meal standards implemented in school year 2012-13 required more fruits and vegetables and 

more whole grains. FoodAPS was collected in the first year that changes in meal requirements were implemented. 

76Significance test results were not shown in figure 3 for the comparison between HEI-FAFH for households with children 

and households without children. T-statistics were 0.61 for SNAP households, 1.15 for non-SNAP households below 130 

percent of poverty, 1.03 for households at 131-185 percent of poverty, and 0.62 for households above 185 percent of poverty. 
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Figure 9.3

Food away from home: the HEI-2010 score and subcomponents, as a percent of 

maximum, for households with children by income and participation in USDA’s 

Supplemental Nutrition Assistance Program (SNAP)

* = Significantly different from SNAP recipients at the 10-percent level. ** = Significantly different from SNAP recipients at

the 5-percent level. *** = Significantly different from SNAP recipients at the 1-percent level. 

Note: 2010 Healthy Eating Index (HEI-2010) scores presented here provide a profile of foods, as purchased or acquired, 

relative to the 2010 Dietary Guidelines for Americans, without adjustment for what portion of foods acquired during the 

observation week were consumed during the week.

Source: USDA, Economic Research Service estimates using National Household Food Acquisition and Purchase Survey, 

collected 2012 and 2013.

SNAP

households

Non-SNAP, household

income < 130% 

of poverty

Non-SNAP, household

income 130-185%

of poverty

Non-SNAP, household

income > 185%

of poverty

**

***

**

**

**

**

**

**

*

**

*

0 10 20 30 40 50 60 70 80 90 100

Healthy eating index

Avoidance of sodium

Avoidance of refined grains

Avoidance of empty calories

Fatty acids

Seafood/plant protein

Protein

Dairy

Whole grains

Whole fruit

Total fruit

Dark green vegetables/legumes

Total vegetables

Percent
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Figure 9.4

Food away from home: HEI-2010 score and subcomponents, as a percent of 

maximum, by work status of households participating in USDA’s Supplemental 

Nutrition Assistance Program (SNAP)

* = Significantly different from SNAP recipients at the 10-percent level. ** = Significantly different from SNAP recipients at

the 5-percent level. *** = Significantly different from SNAP recipients at the 1-percent level. 

Note: 2010 Healthy Eating Index (HEI-2010) scores presented here provide a profile of foods, as purchased or acquired, 

relative to the 2010 Dietary Guidelines for Americans, without adjustment for what portion of foods acquired during the 

observation week were consumed during the week.

Source: USDA, Economic Research Service estimates using National Household Food Acquisition and Purchase Survey, 

collected 2012 and 2013.

Percent

SNAP households with 

working members

SNAP households with 

no workers

***

***

***

**

***

**

0 10 20 30 40 50 60 70 80 90 100

Healthy eating index

Avoidance of sodium

Avoidance of refined grains

Avoidance of empty calories

Fatty acids

Seafood/plant protein

Total protein

Dairy

Whole grains

Whole fruit

Total fruit

Dark green vegetables/legumes

Total vegetables
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To examine the role of nutrition awareness and attitudes in FAFH diet quality, HEI-FAFH scores 

are compared between respondents who indicated a higher priority placed on nutrition and those 

who indicated a lower priority, based on responses to questions about nutrition awareness (see box, 

“Measuring Nutrition Awareness and Attitudes in FoodAPS”). 

For each statement or question, the difference in average HEI-FAFH score is calculated between 

respondents whose answers indicated a higher priority placed on nutrition and those whose answers 

indicated a lower priority. A positive difference indicates that the average HEI-FAFH was higher 

for “higher nutrition priority” respondents compared to “lower nutrition priority” respondents. 

Differences are calculated for SNAP households and each nonrecipient cohort.77

Among SNAP recipient households, the difference in HEI-FAFH scores is significantly different 

from zero for only two statements dealing with MyPlate (fig. 9.5). SNAP recipient respondents who 

reported they had heard of MyPlate have higher HEI-FAFH scores (a difference of 2.3). However, 

SNAP recipient respondents who reported that they had tried to follow the MyPlate guidelines have 

significantly lower HEI-FAFH scores (a difference of -5.7). Further research is needed to interpret 

these findings.

Among non-SNAP households, higher HEI-FAFH scores are observed for a number of statements, especially for respondents with incomes above 185 percent of the poverty line. In that group, 

those who reported they had heard of MyPlate have HEI-FAFH scores 2 points higher than those 

in the same income group who had not heard of MyPlate, a statistically significant difference at 

the 5-percent level. Similar differences are observed for that income group who reported they use 

Nutrition Facts panels and those who think they should eat more fruits and vegetables. Those who 

disagreed that it costs too much to eat healthy food have HEI scores 1.9 points higher than those who 

agreed, and those who rated the healthfulness of their diets as fair, good, very good or excellent have 

HEI scores 2.6 points higher than those who rated their diets as poor. For respondents at 130-185 

percent of the poverty level, HEI-FAFH is significantly different only for those who had tried to 

follow the MyPlate guidelines; that difference is large (8.3 points) but only weakly significant at the 

10-percent level. For nonrecipient respondents below 130 percent of the poverty level, differences are 

weakly statistically significant at the 10-percent level for respondents who had heard of MyPlate (3.2 

points) and for those who disagreed that healthy food tastes good (5.8 points). 

These results suggest that while the priority placed on nutrition is associated with higher nutritional 

intake for some consumers, other factors are more important for food assistance households and 

low-income nonrecipient households. Further research is needed to explore the role of differences 

in the level of nutrition awareness across income and SNAP participation groups versus the binding 

constraint of income in choosing healthier food away from home. 

77Significance tests indicate whether the difference in HEI-FAFH between “higher nutrition priority” respondents and 

“lower nutrition priority” respondents was significantly different from zero.
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Measuring Nutrition Awareness and Attitudes in FoodAPS 

FoodAPS includes several questions about respondents’ attitudes toward healthy food and their 

awareness of the Dietary Guidelines for Americans, as represented by graphics developed to 

symbolize the structure of a healthy diet. The Food Guide Pyramid was introduced with the 1995 

Dietary Guidelines, depicting a triangle with grain products at the base to indicate the number 

of servings recommended for this group; fruits and vegetables at the next level up; followed 

by protein and dairy foods; and topped by fats, oils, and sweets, signifying that those should 

be used sparingly. The MyPyramid graphic developed as part of the 2005 Dietary Guidelines

depicted the number of recommended servings by vertical stripes and added a figure climbing 

stairs up the pyramid to signify the importance of adequate exercise. Most recently, the MyPlate 

graphic developed in 2011 by USDA depicts the recommendations as a plate split into areas 

representing healthy portions of each food group in a single meal. Because consumers have been 

exposed to multiple graphic representations of the Dietary Guidelines, FoodAPS included questions about each of the recent versions, along with other questions related to food preferences, 

time constraints, and cost concerns. The questions are listed below, with the answers counted as 

“higher nutrition priority” and “lower nutrition priority” indicated. 

Question Responses counted 

as lower nutrition 

priority 

Responses counted 

as higher nutrition 

priority

Thinking only about yourself, in general, how healthy 

is your overall diet?

Poor Fair, Good, Very Good, 

Excellent

In general, how healthy is your family’s overall diet? Poor Fair, Good, Very Good, 

Excellent

It costs too much for (me/my family) to eat healthy 

foods.

Agree Disagree

I’m too busy to take the time to prepare healthy foods. Agree Disagree

I don’t think healthy foods taste good. Agree Disagree

People in my family don’t think healthy foods taste 

good.

Agree Disagree

The things that (I/my family) eat and drink now are 

healthy so there is no reason for (me/us) to make 

changes.

Disagree Agree

Have you heard of MyPlate? No Yes

Have you heard of MyPyramid? No Yes

Have you tried to follow the MyPyramid Plan or Pyramid plan recommended for you?No Yes

Do you think you eat the right amount of fruits and 

vegetables now, or do you think you should eat 

more? 

Right amount, eat less Eat more

When choosing between different food items at the 

grocery store, how often do you use the Nutrition 

Facts panel to help you decide which item to buy?

Never seen Sometimes, Always
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Figure 9.5

Difference in average HEI-2010 scores for food away from home, higher nutrition priority 

responses versus lower nutrition priority responses, by SNAP participation status and 

income level

SNAP

households

Non-SNAP, household

income < 130% 

of poverty

Non-SNAP, household

income 130 -185%

of poverty

Non-SNAP, household

income > 185%

of poverty

Note: Statistical tests are for whether the difference in HEI scores for “higher nutrition priority” and “lower 

nutrition priority” are different from zero, for each SNAP/income group. * = Significant at the 10-percent level. 

** = Significant at the 5-percent level. *** = Significant at the 1-percent level. SNAP = USDA’s Supplemental 

Nutrition Assistance Program. (1)Difference in average HEI-2010 for respondents who gave responses 

indicating “higher nutrition priority” versus “lower nutrition priority.” See box, “Measuring Nutrition Awareness 

and Attitudes in FoodAPS,” for classification of responses. 2010 Healthy Eating Index (HEI-2010) scores 

presented here provide a profile of foods, as purchased or acquired, relative to the 2010 Dietary Guidelines 

for Americans, without adjustment for what portion of foods acquired during the observation week were 

consumed during the week.

Source: USDA, Economic Research Service estimates using National Household Food Acquisition and 

Purchase Survey, collected 2012 and 2013.

**

**

**

**

**

***

*

*

*

***

**

-8 -6 -4 -2 0 2 4 6 8 10

How often do you use the Nutrition Facts panel to help you

decide which item to buy?

Do you think you eat the right amount of fruits and vegetables

now, or do you think you should eat more?

Have you tried to follow the MyPyramid Plan or Pyramid plan

recommended for you?

Have you heard of MyPyramid?

Have you tried to follow the MyPlate guidelines?

Have you heard of MyPlate?

The things my family eats and drinks now are healthy so there is

no reason for us to make changes.

People in my family don't think healthy foods taste tood.

I don't think healthy food tastes good.

I'm too busy to take the time to prepare healthy foods.

It costs too much for my family to eat healthy foods.

How healthy is your familiy's overall diet?

How healthy is your overall diet? (1)

FoodAPS nutrition awareness and

attitudes questions
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HEI-2010 for FAFH at School Versus Other FAFH

Food acquired at school had a significantly higher overall HEI-2010 score than did other FAFH 

(fig. 9.6). On average, total HEI-2010 is 49 for school-acquired food, compared to other food away 

from home, which is 46. This difference may be a conservative estimate, since the nutrient and 

food group databases used in FoodAPS may not completely capture special formulations for the 

school food market, such as whole-grain pizza crusts. Further, food acquired at school is most likely 

consumed by children, while other FAFH acquired by households with children could be consumed 

by adults or children.78 Thus, the comparison cannot tell us how school food compares to other 

FAFH consumed by children, but it does shed light on other FAFH that children may be exposed to 

in households at each income level. 

The higher HEI-2010 for school food is driven by significantly higher component scores for dairy, 

whole grains, whole fruit, and total fruit, which counterbalanced significantly lower scores for 

several other components (sodium, refined grains, fatty acids, seafood/plant protein, green vegetables/legumes, and total vegetables).79 The findings in general are consistent with previous studies 

showing that participation in the NSLP (USDA’s National School Lunch Program) increases participants’ nutrient consumption and consumption of many, but not all, underconsumed foods (Gordon 

et al., 1995; Jaime and Lock, 2009). Mancino and colleagues (2018a) find that for SNAP households 

(but not for non-SNAP households), HEI scores for foods obtained at school are higher than for 

foods obtained at larger grocery stores. 

Conclusion

Food away from home (FAFH) is of consistently lower nutritional quality than food at home (FAH) 

for households receiving SNAP—as well as for income-eligible nonparticipant households and 

higher income households. This suggests that the nutrition quality of food choices away from home 

are similar across the income spectrum, reflecting consumer preferences as well as the offerings 

made available by vendors in response to those preferences. HEI-2010 scores for FAFH are lower 

for SNAP recipient households, both with and without children, while the gap between nutritional 

quality of FAFH compared to FAH is lowest for SNAP recipient households, as nonparticipants had 

higher HEI-2010 scores for FAH. Also, school meals score contribute to a higher HEI for households 

with children than other types of FAFH, consistent with other studies finding participation in NSLP 

enhances participants’ nutrient consumption. 

Some components of FAFH diet quality, such as saturated fat and fiber content, have improved over 

time, reflecting healthier choices by consumers and more nutritious options offered by vendors. 

Total fat content of FAFH decreased between 1977-78 and 2009-12 (see chapter 7). Menu labeling 

rules could be encouraging this trend as well (see chapter 10) as dining out is increasingly perceived 

as more of a staple for busy households instead of an occasional indulgence. The growth in higher 

quality FAFH options with the growth of fast-casual restaurants (see chapter 6) could improve the 

nutritional profiles of SNAP recipient households who do consume food away from home, even 

though they are unable to use SNAP benefits to do so. 

78Food acquired at school includes all foods regardless of whether it was part of the school meal programs.

79Changes in school meal standards implemented in school year 2012-13, toward the latter part of FoodAPS data collection, stipulate low-fat or nonfat milk only, more fruits and vegetables, and more whole grains. HEI subcomponent scores for 

green vegetables/legumes and total vegetables would be expected to improve over time as student acceptance of healthier 

meals increases.
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Figure 9.6

Food acquired at school and other food away from home: HEI-2010 and its 

subcomponents, as a percent of maximum, for households with children participating in 

USDA’s Supplemental Nutrition Assistance Program (SNAP)

*= Significant at the 10 percent level. ** = Significant at the 5-percent level. *** = Significant at the 1-percent 

level.

Note: 2010 Healthy Eating Index (HEI-2010) scores presented here provide a profile of foods, as purchased 

or acquired, relative to the 2010 Dietary Guidelines for Americans, without adjustment for what portion of 

foods acquired during the observation week were consumed during the week.

Source: USDA, Economic Research Service estimates using National Household Food Acquisition and 

Purchase Survey, collected 2012 and 2013.

Percent

Other food away from home School meals

***

*

***

**

**

***

***

***

***

***

***

0 10 20 30 40 50 60 70 80 90 100

Healthy Eating Index

Avoidance of sodium

Avoidance of refined grains

Avoidance of empty calories

Fatty acids

Seafood/plant protein

Protein

Dairy

Whole grains

Whole fruit

Total fruit

Dark green vegetables/legumes

Total vegetables
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References

Condon, E., S. Drilea, K. Jowers, C. Lichtenstein, J. Mabli, E. Madden, and K. Niland. 2015. 

Diet Quality of Americans by SNAP Participation Status: Data from the National Health and 

Nutrition Examination Survey, 2007–2010. Prepared by Walter R. McDonald & Associates, Inc. 

and Mathematica Policy Research for USDA’s Food and Nutrition Service.

Davis, G.C. 2014. “Food at Home Production and Consumption: Implications for Nutrition Quality 

and policy,” Rev. Econ. Household, 2:565-588.

Gordon, A.R., B.L. Devaney, and J.A. Burghardt. 1995. “Dietary Effects of the National School 

Lunch Program and the School Breakfast Program,” The American Journal of Clinical Nutrition

61(1): 2215-2315.

Gray, K. Farson, and E. Eslami. 2014. Characteristics of Supplemental Nutrition Assistance 

Program Households: Fiscal Year 2012. U.S. Department of Agriculture, Food and Nutrition 

Service, Office of Policy Support. Project Officer, Jenny Genser. 

Jaime, P.J., and K. Lock. 2009. “Do School-Based Food and Nutrition Policies Improve Diet and 

Reduce Obesity?” Preventive Medicine 48(1): 45-53.

Kreider, B., J.V. Pepper, C. Gundersen, and D. Jolliffe. 2012. “Identifying the Effects of SNAP 

(Food Stamps) on Child Health Outcomes When Participation Is Endogenous and Misreported,” 

Journal of the American Statistical Association 107: 958-75.

Lauffer, S. 2018. Characteristics of Supplemental Nutrition Assistance Program Households: Fiscal 

Year 2016. U.S. Department of Agriculture, Food and Nutrition Service, Office of Policy Support. 

Project Officer, Jenny Genser. 

Lin, B.H., and J. Guthrie. 2012. Nutritional Quality of Food Prepared at Home and Away From 

Home, 1977-2008, EIB-105, U.S. Department of Agriculture, Economic Research Service.

Mancino, L., J.E. Todd, J. Guthrie, and B.H. Lin. 2010. How Food Away From Home Affects 

Children’s Diet Quality ERR-104, U.S. Department of Agriculture, Economic Research Service. 

Mancino, L., J. Guthrie, M. Ver Ploeg, and B.H. Lin. 2018a. Nutritional Quality of Foods Purchased 

and Acquired by Americans: Findings from USDA’s National Household Food Acquisition and 

Purchase Survey, EIB-188. U.S. Department of Agriculture, Economic Research Service. 

Mancino, L., J.E. Todd, and B. Scharadin. 2018b. USDA’s FoodAPS: Methodology for Imputing 

Missing Quantities To Calculate HEI-2010 Scores and Sorting Foods Into ERS Food Groups, 

Technical Bulletin-1947, U.S. Department of Agriculture, Economic Research Service.

Mancino, L., and J. Kinsey. 2004. “Diet Quality and Calories Consumed: The Impact of Being 

Hungrier, Busier, and Eating Out,” Working Paper 04-02, The Food Industry Center, University 

of Minnesota. March.

Strasser, J.H., J. Reedy, and S. Krebs-Smith. 2015. “Visualizing Diet Quality at Multiple Levels of 

the Food Stream,” White paper, National Collaborative on Childhood Obesity Research.
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Tiehen, L., C. Newman, and J.A. Kirlin. 2017. The Food-Spending Patterns of Households 

Participating in the Supplemental Nutrition Assistance Program: Findings From USDA’s 

FoodAPS, EIB-176, U.S. Department of Agriculture, Economic Research Service. 

Todd, J.E. 2014. Changes in Eating Patterns and Diet Quality Among Working-Age Adults, 2005-10, 

ERR-161, U.S. Department of Agriculture, Economic Research Service.

Todd, J.E., L. Mancino, and B.H. Lin. 2010. The Impact of Food Away From Home on Adult Diet 

Quality, ERR-90, U.S. Department of Agriculture, Economic Research Service.

United States Department of Agriculture, Economic Research Service. 2016. National Household 

Food Acquisition and Purchase Survey (FoodAPS) User’s Guide to Survey Design, Data 

Collection, and Overview of Datasets. 

You, W., G. Zhang, B.M. Davy, A. Carlson, and B.H. Lin. 2009. “Food Consumed Away from Home 

Can Be a Part of a Healthy and Affordable Diet,” Journal of Nutrition 139: 1994–1999.
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Chapter 10: Menu Labeling

Federal regulations implemented in 2018 require chain restaurants with 20 or more locations 

operating under the same name to make calorie information publicly available. To gauge how 

consumers and the restaurant industry may adapt to the new regulations, this chapter summarizes 

research that has examined consumer behavior in response to real-world and laboratory interventions that resemble aspects of the new regulations. Researchers have been unable to reach a 

consensus regarding how consumers and restaurants respond to menu labeling. 

In chapter 9, purchases of food away from home (FAFH) were found to be associated with lower diet 

quality among food assistance recipients as well as higher income households. A new Federal policy 

that requires some restaurants to make calorie information publicly available is hoped to improve the 

healthfulness of foods consumed away from home by giving consumers information and nudging 

foodservice providers to offer more healthful food options. Chapter 10 details the evolution of this 

menu labeling requirement and its potential to affect FAFH.

Because of menu-labeling regulations, consumers can better identify lower calorie foods when 

eating out. After New York City in 2008 mandated that chain restaurants provide information about 

the caloric content of foods at the point of sale, several State, county, and municipal governments 

(including Philadelphia, PA; King County, WA; and Montgomery County, MD) followed suit with 

menu-labeling regulations of their own. In New York City, fast-food restaurants were required to 

post calorie information on menu boards, and full-service restaurants were required to print calorie 

information in their menus. Now, the U.S. Food and Drug Administration (FDA) has implemented 

Federal regulations that require chain restaurants and similar retail food establishments (such as 

convenience stores that sell FAFH) to make calorie information publicly available nationwide. These 

regulations apply to establishments that are part of a chain with 20 or more locations doing business 

under the same name. 

The underlying premise of menu-labeling regulations is that giving consumers nutrition information will enable them to make informed choices, which may, in turn, lead to higher quality diets and 

healthier body weights. With the new information, consumers may order lower calorie meals at restaurants or reduce their caloric intake elsewhere to compensate for their restaurant calories. Menu labeling 

may also motivate the restaurant industry to improve the nutritional quality of their offerings.

Previous literature reviews and meta-analyses tend to focus more on consumers’ response to menu 

labeling than on restaurants’ response (VanEpps et al., 2016; Fernandes et al., 2016; Littlewood et 

al., 2015; Long et al., 2015; Krieger and Saelens, 2013; Burton and Kees, 2012). By contrast, the 

analysis presented in this chapter uses economic theory to better anticipate and understand the 

response of both consumers and restaurants to Federal menu-labeling regulations. Although interest 

generally pertains to nutrition-related menu labeling, the discussion presented here centers specifically on calorie labeling because that is what U.S. menu-labeling policy has focused on up to now.80

Calories are just one component of human nutrition, as human nutrition encompasses a range of 

other components such as macronutrients (carbohydrates, proteins, fats) and micronutrients (vitamins, minerals).

80Menu-labeling regulations in the city of Philadelphia were one exception. Chain restaurants there had to post the total 

number of calories (rounded to the nearest 10 calories), grams of saturated fat, grams of trans fat, grams of carbohydrates, and 

milligrams of sodium per menu item as usually prepared and offered for sale.
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Years of Debate Culminated in Passage of Menu-Labeling Regulations

Menu labeling represents the most significant overhaul of U.S. food-labeling laws since the Nutrition 

Labeling and Education Act (NLEA) of 1990. Under FDA regulations that are authorized by the 

NLEA, food manufacturers must place a Nutrition Facts panel on most packaged products. Included 

on this panel are the size of a serving, the number of servings in a package, and calories per serving, 

among other information. Variyam (2008) found that nutrition labels on packaged food products increased the fiber and iron intakes of label users compared with label nonusers. Though the 

problem of obesity in the United States worsened during the 1990s and 2000s despite implementation of the NLEA, Variyam and Cawley (2006) further find that label users gained less weight than 

nonusers.81

Unlike most food at home (FAH), however, when eating out, consumers were not guaranteed access 

to explicit nutrition information for all menu items. Prior to menu-labeling regulations, Federal law 

required restaurants only to support any nutrient content or health claims they made (FDA, 2006). 

For example, if a menu listed an entrée as “low fat,” information about its fat content had to be available on request. Otherwise, barring any nutrient or health claims, disclosure of nutrient and calorie 

information was voluntary and did not have to appear on menus or menu boards.

Some restaurants did opt to provide calorie data prior to menu-labeling laws. According to Wootan 

et al. (2006), 72 percent of McDonald’s outlets in Washington, DC, provided in-store nutrition information, and 59 percent provided it for a majority of items. This information appeared on tray liners, 

in pamphlets, on posters, or on one-page charts. However, it did not appear on menu boards, so to be 

informed, consumers had to research before ordering their food, which few consumers did. Roberto 

et al. (2009) observed consumers at eight outlets that were part of four major fast-food chains and 

found that less than 1 percent of all customers examined nutrition information that was available in 

the restaurant but positioned somewhere other than the point of sale. In 2004 and 2005, Saelens et al. 

(2007) inspected 217 restaurants in Atlanta, GA, and found that only 6.9 percent of fast-food outlets 

placed nutrition information directly on menu boards and only 5.2 percent of full-service restaurants 

printed nutrition information on menus. 

When asked whether restaurants should provide more information about the caloric content of their 

foods, a majority of consumers have supported menu labeling, though their level of enthusiasm 

varies widely. On the one hand, five national polls conducted between 2003 and 2008 found that 

support for menu labeling ranged between 67 percent and 83 percent (Friedman, 2008). On the 

other hand, when Krukowski et al. (2006) surveyed consumers about whether they would use calorie 

information at restaurants if it were available, only about half reported they would do so. Moreover, 

using other national surveys conducted between 2007 and 2010, Gregory et al. (2014) found that 

willingness to use nutrition information at restaurants if such information “were readily available” 

was greater among people who already had healthier diets. Specifically, willingness to use nutrition 

information at restaurants was strongly correlated with a person’s Healthy Eating Index (HEI) score, 

a measure of diet quality that assesses conformance to USDA’s Dietary Guidelines for Americans. 

People who said that they would use nutrition information “often” in both fast-food and full-service 

settings had the highest average HEI scores, followed by those who said they would use it “sometimes.” Both of these groups had higher scores than those who said that they would “never” use 

81The result was statistically significant among only non-Hispanic white women. However, according to Variyam and 

Cawley’s (2006) calculations, the benefits to this subset of the population alone were sufficiently large to outweigh the total 

cost of the NLEA.
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nutrition information. (See box, “Americans Who Have Dark Green Vegetables Available at Home 

Are More Likely To Use Nutrition Information at Full-Service Restaurants.”) 

Proponents of menu labeling argued that providing explicit nutrition information at the point of 

sale would help consumers to choose healthier food options. In a study supported by the Center for 

Science in the Public Interest (CSPI), Backstrand et al. (1997) asked 256 dieticians to estimate the 

calories in five entrées sold by chain restaurants. The dieticians underestimated the calories in these 

foods by anywhere between 220 and 680 calories. Since dieticians guessed inaccurately, Backstrand 

et al. (1997) concluded that “the average consumer has little chance of accurately assessing the 

healthfulness of meals served in restaurants” (p. 2). The American Heart Association (AHA) also 

endorsed menu labeling as “an important part of a comprehensive approach to addressing our 

nation’s obesity epidemic” (AHA, 2009). 

Opponents of menu labeling countered with several arguments (e.g., Armstrong, 2008; Farley et 

al., 2009). Many feared that the costs to industry would be high. Critics also pointed out that many 

restaurants already provide nutrition information in-store or online. Moreover, doubt was cast on the 

helpfulness of menu labeling as a tool for reducing the incidence of overweight and obesity, since (it 

was argued) consumers can already identify more and less healthy meals, if they want. For example, 

the Center for Consumer Freedom (2007) stated, “We don’t need government to tell us the difference between salad and a 12-piece bucket of chicken.”

By the late 2000s, a number of State, county, and municipal governments were debating local 

menu-labeling policies. New York City was the first to implement regulations in 2008. According 

to Farley et al. (2009), who chronicled New York City’s experiences implementing menu-labeling 

regulations, policymakers initially considered mandating information on calories, total fat, saturated 

fat, and sodium but, in the end, mandated only information on calorie counts because providing 

additional information “risked reducing the impact of the calorie information on obesity” (p. 5). 

Moreover, they reasoned that calories tend to be positively correlated with other nutrients, including 

fat and sodium. Over the next few years, the city of Philadelphia, Washington State’s King County, 

Maryland’s Montgomery County, and the State of California, among other jurisdictions, followed 

suit with menu-labeling regulations of their own. Using the 2007-08 and 2009-10 National Health 

and Nutrition Examination Survey, Gregory et al. (2014) found that 21 percent of fast-food patrons 

and 17 percent of full-service restaurant patrons saw nutrition information when they ate out—statistics that likely reflected data on establishments subject to menu-labeling laws as well as those that 

voluntarily provided nutrition information.

The fact that State and local governments were pursuing their own menu-labeling requirements 

was problematic for the restaurant industry. The National Restaurant Association (NRA), which 

advocates on behalf of foodservice providers, reported that requiring “nutrition information on a 

city-by-city or state-by-state basis creates a patchwork quilt of confusing and contradictory local 

regulations” (NRA, 2008). A coalition including the NRA, over 30 restaurant companies, and other 

organizations called for Federal legislation to replace State and local requirements with a uniform, 

national standard (NRA, 2008).
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Americans Who Have Dark Green Vegetables Available at Home Are 

More Likely To Use Nutrition Information at Full-Service Restaurants

ERS researchers used data from the 2007-08 and 2009-10 National Health and Nutrition 

Examination Survey to examine who uses nutrition information in full-service restaurants 

(generally defined as restaurants with wait staff). They found that healthy-diet behaviors are 

correlated with use of nutrition information in full-service restaurants (FSRs). For example, 

people who consult nutrition facts labels on grocery store foods and who store healthy foods at 

home are more likely to use nutrition information on restaurant menus. Although people who 

reported “rarely” or “never” having dark green vegetables at home were as likely to go to FSRs 

as people who “always” or “most of the time” have these vegetables at home, the first group was 

less likely to see and use nutrition information on menus (box fig. 10.1). Among people who 

saw the nutrition information, just 19.1 percent of the group that “rarely” or “never” had dark 

green vegetables available at home said they used it, compared to 54.9 percent of those who said 

they “always” or “most of the time” had dark green vegetables available at home (Gregory et 

al., 2014). 

 
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Authority for developing Federal menu-labeling regulations as mandated in the ACA was given to 

FDA. In 2011, FDA issued a set of proposed rules. After considering approximately 900 comments 

from consumers, consumer groups, industry, and other organizations, FDA published its final regulations in the Federal Register in December 2014 (FDA, 2014).82 Covered establishments have been 

required to comply with these regulations since May 7, 2018 (FDA, 2018). 

Will Menu Labeling Lead to Changes in Consumer and Restaurant 

Industry Behavior?

As debate over the need for menu-labeling regulations heated up in the 2000s and as laws started 

taking effect in 2008, a growing number of researchers became interested in how consumers and 

restaurants respond to menu labeling. Much research now compares consumer behavior at restaurants that list calorie information on their menus versus restaurants that do not. Other studies investigate whether menu labeling influences restaurants to replace higher calorie menu items with lower 

calorie options. Before examining these studies, we briefly consider how consumers and restaurants 

might respond to menu labeling in theory. These theoretical results may prove useful for understanding the available evidence to date on how consumers and restaurants respond in practice and 

for guiding future research on the topic.

Theoretical Evidence on How Consumers and Restaurants Might Respond 

to Menu Labeling 

In theory, providing nutrition information at the point of sale helps consumers to choose foods that 

better align with their overall preferences for taste, nutrition, weight control, and other attributes of 

a meal. Each of these attributes could affect a consumer’s ultimate choice. In one study, Glanz et al. 

(1998) asked 2,967 people to rate the level of importance they place on different food characteristics 

using a scale of 1 (not at all important) to 5 (very important). Respondents placed the most importance on taste (4.7) followed by cost (4.1), nutrition (3.9), convenience (3.8), and weight control (3.4), 

on average. Consistent with this result, when Jones (2010) studied how customers make choices at 

restaurants, many participants in her focus group reported their intention to order tasty foods. One 

participant stated that it was okay to indulge at restaurants; from a menu with 11 items, she would 

order either a T-bone steak or chicken alfredo pasta, even when told that 6 other items were lower in 

calories. However, because the steak had fewer calories than the pasta, she would choose the steak: 

“Both of these things I never make at home, and it would really feel like an indulgence for me. But 

even among my indulgences, it might impact which indulgence I take” (p. 460).

Menu labeling may have the greatest effect on food choices when consumers had previously 

misjudged a food’s true caloric content (e.g., Burton et al., 2006; Bates et al., 2009; Bollinger et 

al., 2011). In one study, Bollinger et al. (2011) analyzed transactions data provided by Starbucks, 

including sales data for New York City stores before and after the implementation of New York’s 

menu-labeling laws. The researchers also surveyed customers at Starbucks restaurants to gauge their 

knowledge of the caloric content of various menu items. The “common presumption,” according to 

Bollinger et al. (2011), “is that consumers will be surprised to learn how many calories are in the 

beverage and food items offered at chain restaurants” (p. 92). However, when the researchers examined how menu labeling affected sales trends for the 60 most popular menu items, they found that 

82These regulations can be accessed online.
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“the direction of the policy’s effect depends on the direction of the surprise” (p. 92). Menu labeling 

had no major effect on beverage choices since consumers had previously overestimated the caloric 

content of these menu items. “For instance, a 16 oz vanilla latte has a relatively high 250 calories, 

and we estimate calorie posting causes a small relative increase in its sales—which seems counterintuitive. But consumers may have previously believed a vanilla latte had even higher calories, and 

were thus surprised to learn it had only 250,” reported Bollinger et al. (2011, p. 106). By contrast, 

menu labeling did have a negative effect on sales of food items for which consumers had underestimated caloric content. (Food items at Starbucks included cookies, muffins, bagels, scones, brownies, 

and doughnuts.)

The “direction of surprise” idea is echoed by Krieger and Saelens (2013), who summarize the state 

of research on menu labeling and conclude that the effect of labeling may vary across menu items. 

They explain: “For example, if deli sandwiches are perceived as generally lower in calories than 

burger items, but are actually similar calorically, the evidence suggests that sandwich purchases 

would decrease more relative to burger purchases after menu labeling that shows their similar caloric 

content” (Krieger and Saelens, 2013, p. 5).

Given that prior nutrition knowledge affects a consumer’s response to menu labeling, Stewart et 

al. (2014, 2015) investigated what consumers could likely figure out on their own in the absence of 

menu labeling. Building on research in behavioral economics, Stewart et al. (2014, 2015) assumed 

that consumers use “rules of thumb” to make inferences about products when those products do not 

come with explicit nutrition information. For example, a consumer may know to seek out meals rich 

in fruits and vegetables and avoid fat-laden side dishes, like French fries and onion rings, in order to 

reduce the number of calories consumed. Detailed information was collected on 361 meals sold by 

two fast-food chains and 5,752 meals sold by six full-service restaurant chains. Results showed that 

consumers who understand some basic rules-of-thumb nutrition knowledge outlined by the AHA 

and the National Heart, Lung, and Blood Institute (NHLBI) can discriminate fairly well between 

meal options with substantial differences in the number of calories.83 However, if faced with two 

meals that differ by less than 200 calories, the same consumers have only about a 50 percent chance 

of correctly identifying the lower calorie option (i.e., equivalent to guessing), suggesting that the 

rules of thumb identified by the AHA and the NHLBI have little discriminatory value in this range. 

Stewart et al. (2014, 2015) concluded that, while many Americans may be able to make crude 

choices between low- and high-calorie foods based on their own nutrition knowledge, menu labeling 

will allow them to refine their choices. 

In addition to helping consumers identify lower calorie meals at restaurants, menu labeling may help 

consumers eat healthier diets in other ways. For example, the succinct statement about an individual’s daily energy needs required by the ACA may help consumers better understand how a particular 

restaurant meal fits within their daily caloric needs. Some people may be motivated to order lower 

calorie foods or, perhaps, order fewer (sugar-filled and therefore caloric) soda refills. Others may 

decide to compensate for calories consumed at a restaurant by eating less throughout the day. Still 

others may consume only a portion of their meal at the restaurant and bring the rest home in a 

“doggie bag” to eat in place of another meal. 

83The AHA and the NHLBI identify a number of characteristics of restaurant foods likely associated with caloric content. 

See AHA (2013) and NHLBI (1998).
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In theory, menu labeling could also motivate restaurants to offer lower calorie foods. Based on the 

findings of Burton et al. (2006), Bates et al. (2009), and Bollinger et al. (2011), lower calorie choices 

may represent reformulations or replacements of menu items for which consumers had previously 

underestimated caloric content. For example, Starbucks may not reformulate its beverages (whose 

caloric contents consumers tended to overestimate (Bollinger et al., 2011)), but the company might 

change its food offerings (some of whose caloric contents consumers tended to underestimate).

However, in other ways, menu labeling could actually serve to increase calories consumed. 

Economists have long recognized that health and nutrition policies, like menu labeling, that aim to 

reduce the rates of obesity and overweight can have surprising, unintended consequences (Kuchler 

et al., 2005; Variyam, 2005; Burton and Kees, 2012). In the case of menu labeling, some unintended 

consequences that might occur include the following:

1. Some people might be underestimating their daily energy needs, and exposure to a statement recommending 2,000 calories per day for a representative consumer might inadvertently 

promote overconsumption (e.g., VanEpps et al., 2016). 

2. Some people may seek out high-calorie menu items, believing that those foods will taste better 

(e.g., Raghunathan et al., 2006). 

3. Restaurants may reduce the price of less healthful foods in order to keep sales strong (a possibility mentioned in Variyam (2005)). 

4. Restaurants may use extra-high-calorie menu items for “anchoring.” That is, restaurants could 

offer some very high-calorie options in order to make their other menu offerings seem lowcalorie by comparison. 

These and other potential mechanisms that produce unintended consequences could offset some of 

the positive effect that menu labeling is theorized to have on consumer health.

The Empirical Evidence to Date on How Consumers Respond to Menu 

Labeling 

In practice, it remains unclear how consumer behavior changes in response to menu labeling. This 

is because—even though much research has been done—published studies reach mixed conclusions. According to Long et al. (2015), who conducted a meta-analysis of 19 published studies, menu 

labeling “may lead consumers to purchase slightly fewer calories” (p. e11). VanEpps et al. (2016) 

conducted a review of 16 published studies and similarly concluded that “...the evidence regarding 

menu labeling is mixed, showing that labels may reduce the energy content of food purchased in 

some contexts, but have little effect in other contexts.” The following subchapters review the results 

of (1) experimental studies conducted in laboratory settings, (2) observational studies conducted in 

fast-food restaurants, and (3) observational studies conducted in full-service restaurants. Challenges 

and directions for future menu-labeling research are discussed. 

Laboratory Experiments

To gauge the effect of restaurant menu labeling on consumer behavior, several researchers have 

designed laboratory experiments in which they attempted to simulate a real-world environment 

while also controlling for potentially confounding factors. Among these studies, Roberto et al. 
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(2010) served meals to 303 participants in a university classroom. Each participant received one of 

three possible treatments: (1) a menu with no calorie information; (2) a menu with numeric calorie 

information; or (3) a menu with numeric calorie information and a succinct statement that read, 

‘‘The recommended daily caloric intake for an average adult is 2000 calories.’’ Menu items included 

salads, sandwiches, wraps, mozzarella sticks, French fries, pizza, hamburgers, desserts, and beverages, among other things. Researchers then observed the number of calories each participant ordered 

and consumed. Participants also returned the following day for a dietary recall interview during 

which they reported all foods consumed during the evening hours immediately after the study. 

Participants consumed 177 fewer calories during the experiment, on average, if they had received 

either type of menu with calorie information. However, consumers who received only numeric 

calorie information consumed more calories later in the day, so their total daily energy intake was 

similar to that of study participants who had received no calorie information. Only participants who 

had received both numeric calorie and supplementary information about their daily energy needs 

consumed fewer calories over the course of the day.

In another experimental study, Harnack et al. (2008) served meals to 594 participants. Each participant received a paper menu similar in format to menu boards at fast food restaurants. Menu items 

included hamburgers, chicken entrées, fish entrées, salads, French fries, beverages, and desserts. 

Some, but not all, menus included calorie information. For each participant, study staff then 

recorded whether the menu included calorie information as well as the foods ordered and consumed. 

Harnack et al. (2008) found no significant difference in the energy composition of meals consumed 

by participants who received menus with and without calorie information. The study did not investigate whether providing study participants with a succinct statement about their daily energy needs 

could influence results.

Studies Conducted in Fast-Food Restaurants

Other studies have observed the food choices of patrons at fast-food establishments with and without 

menu labeling. Many of these studies were natural experiments conducted in parts of the country 

like New York City and King County, WA, before and after the implementation of local menulabeling laws. 

Among the earliest and largest studies to focus on consumer behavior at fast-food restaurants, 

Bollinger et al. (2011) analyzed transactions data provided by Starbucks, including sales data for 

New York City stores before and after the implementation of menu-labeling laws there. As described 

earlier, menu labeling had a negative effect on sales of food items for which consumers had previously underestimated caloric content. Sales of beverages were unaffected. Overall, Bollinger et 

al. (2011) found that customers ordered 14 fewer calories per transaction as a result of calorie 

information. 

Downs et al. (2013) and Wisdom et al. (2010) are the only analyses of customer behavior at fast-food 

restaurants to investigate the effects of providing both numeric calorie information and a succinct 

statement about daily energy needs. Wisdom et al. (2010) find that participants in their study who 

received both types of information ordered almost 100 fewer calories, on average—but they also 

found that menu labeling had a greater effect on non-overweight than overweight individuals. 

In another analysis of customers at fast-food restaurants, Downs et al. (2013) studied the patrons 

at two McDonald’s restaurants located in New York City—one in Manhattan and another in 
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Brooklyn—including 624 patrons 2 months before and another 497 patrons 2 months after the city 

implemented its local menu-labeling regulations. Customers approaching the restaurants at lunchtime were randomly assigned to one of three treatments: (1) handed a slip with information about 

an individual’s daily energy needs (2,000 calories for women or 2,400 for men); (2) handed a slip 

with recommended per-meal calories (650 calories per meal for women or 800 for men); or (3) given 

no recommendation (control condition). Study staff then asked exiting customers to provide their 

receipt and participate in a survey. Downs et al. (2013) found no evidence that consumers ordered 

fewer calories after the implementation of menu-labeling laws in New York City in 2008 than before. 

Moreover, they found no evidence that providing consumers with a succinct statement about daily 

energy needs affected food choices in either time period (i.e., in the time periods in which there was 

an absence or presence of calorie counts on menu boards). 

Similar to Downs et al. (2013), both Cantor et al. (2015) and Elbel et al. (2009) found no evidence 

that menu labeling at fast-food restaurants reduces calories purchased. 

Studies Conducted in Full-Service Restaurants

A smaller number of studies gauged the effect of restaurant menu labeling on consumer behavior 

at full-service restaurants. Among these studies, Auchincloss et al. (2013) found that consumers at 

a full-service restaurant ordered 155 fewer calories. Pulos and Leng (2010) found that consumers 

at six different full-service restaurants chose entrées with 15 fewer calories. However, Ellison et al. 

(2013, 2014) found no evidence that providing calorie information at the point of sale alone reduced 

the caloric content of consumers’ orders.

Heuristic Devices for Delivering Calorie Information

Because of the mixed findings of studies that evaluate the effect of menu labeling on consumer 

behavior, health and nutrition researchers have considered alternative strategies for delivering calorie 

information (e.g., Dowray et al., 2013; Ellison et al., 2013; Ellison et al., 2014; Larrivee et al., 2015). 

At a full-service restaurant on the Oklahoma State University campus, Ellison et al. (2013, 2014) 

presented menus to customers. Some customers received no calorie information. Others received 

menus with numeric calorie information as mandated in the ACA. Finally, a third group received 

menus on which green, yellow, and red symbols were positioned next to numeric calorie information for low-, mid-, and high-calorie entrées, respectively. The researchers also asked customers 

to complete a survey, which revealed that adding the color-coded “traffic light” signals may have 

reduced some consumers’ caloric intake, relative to the other menu formats. However, not all 

customers appreciated the added symbols. According to Ellison et al. (2013, p. 7) “...diners may 

want more information on their menus (the number of calories) but do not want to be told what they 

should or should not consume (i.e., green = good, red = bad).” 

Future Research

A challenge for future research will be to control for the large number of factors that can cause 

researchers to arrive at vastly different conclusions, including the likelihood that menu labeling 

may affect different segments of the population differently. On the one hand, Gregory et al. (2014) 

found that willingness to use nutrition information at restaurants has been greater among people 

who already have healthier diets. Block and Roberto (2014) similarly questioned whether “calorie 

labeling increases disparities among those with lower numeracy and health literacy” (p. 888). On 
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the other hand, Ellison et al. (2013) found that more-informed consumers may stand to learn less 

new information. Some people may already understand fairly well which foods are low and high in 

calories. Such people are less likely to be surprised by menu labels and, in turn, are also less likely 

to make different choices. Moreover, whether demand increases or decreases for a menu item may 

depend on the direction of any surprise (Bollinger et al., 2011). 

Instead of focusing on Americans’ restaurant eating behavior, another possible approach would be to 

investigate the effect of menu labeling on consumers’ health status (Deb and Vargas, 2016; Restrepo, 

2016; Restrepo, 2017). This approach echoes that of Variyam and Cawley’s (2006) study of individuals’ body mass index (BMI) before and after implementation of the NLEA. For example, Restrepo 

(2016, 2017) used data from the nationwide Behavioral Risk Factor Surveillance System (BRFSS). 

Participants in this survey report their height and weight along with information about their income 

and demographic characteristics. For his study, Restrepo (2016, 2017) used data on 103,220 individuals living in 62 counties in New York, New Jersey, and Pennsylvania between 2004 and 2012. New 

York City and six surrounding counties implemented menu-labeling regulations between 2008 and 

2010. Restrepo (2016, 2017) combined this BRFSS data with county-level information on the timing 

of those laws. He then compared changes over time in the BMI of individuals living in jurisdictions 

with and without menu-labeling regulations. For his main empirical analysis, Restrepo (2016, 2017) 

modeled an individual’s weight status as a function of his or her income, demographic characteristics, and menu-labeling policies in the individual’s county of residence, among other potential weight 

determinants. The analysis concluded that, on average, providing point-of-purchase calorie information in chain restaurants reduced BMI by 1.5 percent and lowered the risk of obesity by 12 percent in 

jurisdictions with such laws.

Future research investigating the link between menu labeling and the health status of consumers 

may benefit from the National Health and Nutrition Examination Survey (NHANES), conducted by 

the Centers for Disease Control and Prevention. NHANES consists of a series of initial interviews, 

usually conducted at the respondent’s home, along with a subsequent health examination completed 

at an NHANES Mobile Examination Center (MEC). Since 2007, the Economic Research Service 

has fielded the Flexible Consumer Behavior Survey module (FCBS) as part of NHANES. FCBS 

questions appear within the NHANES household interview as well as within a telephone interview 

that is administered 3 to 10 days after the MEC appointment. The FCBS asks about respondents’ 

food shopping and spending habits as well as the frequency with which respondents cook meals at 

home and eat fast food and food at full-service restaurants, among other topics. Questions asked of 

respondents include84:

•	 Whether they have eaten away from home (at fast-food/pizza or full-service restaurants) in the 

last 12 months;

•	 If so, whether they saw nutrition information on the menu on the last visit to one of these 

places;

•	 If they saw nutrition information, whether they used it in deciding which foods to buy; and

84The questions listed here were added to the FCBS in the 2007-08 NHANES cycle, but they have not appeared in every 

single cycle since. NHANES cycles run continuously, in nonoverlapping 2-year periods—for example, 2007-08, 2009-10, 

2011-12, and so on.
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•	 How likely would they be to use nutrition information when eating out in the future if such 

information were readily available.

Using these data, researchers can compare the characteristics of consumers who do and do not use 

nutrition information when they eat out, as was done by Gregory et al. (2014). 

The Empirical Evidence to Date on How Restaurants Respond to 

Menu Labeling 

As with analyses of consumers’ responses to menu labeling, a large body of research investigates 

the restaurant industry’s response (Bollinger et al., 2011; Bruemmer et al., 2012; Saelens et al., 2012; 

Bauer et al., 2012; Namba et al., 2013; Wu and Sturm, 2014; Deierlein et al., 2015; Bleich et al., 

2015; Bleich et al., 2016), though most of these studies appeared several years after similar research 

on consumers. In theory, if consumers demand more lower calorie choices after the advent of menu 

labeling, restaurants may choose to reformulate or replace existing menu items.

In one of the first studies in this area, Bruemmer et al. (2012) investigated the menus of 37 chain 

restaurants in King County, WA, which enforced local regulations in 2009. Detailed information 

about menu items was collected 6 months after and 18 months after the local law’s implementation. Both fast-food establishments and full-service restaurants were included in the study. The 

authors first examined entrées that were offered in both time periods, and found that the average 

energy content of surviving menu items was 41 calories lower at the time of the 18-month followup 

across all 37 chains, presumably due to menu item reformulations. However, when the researchers 

expanded the scope of their analysis to consider new and discontinued foods along with surviving 

menu items, they found that only full-service restaurants were offering less calorie-dense foods 

overall. No change was detected for fast-food restaurants, so the effect of menu labeling might 

have been less pronounced at fast-food restaurants. Nonetheless, Bruemmer et al. (2012) concluded 

that menu labeling may ultimately benefit all consumers. They reasoned that lower calorie entrées 

and side dishes offer everybody—not just consumers who read and use nutrition labels—healthier 

options when dining at full-service restaurants.

In another study, Namba et al. (2013) identified changes over time in the menus of nine fast-food 

chain restaurants between 2005 and 2011, investigating whether each restaurant chain offered lower 

calorie foods in 2011 than in 2005. They also tested whether a greater proportion of each restaurant’s 

menu items could be considered “healthier” in 2011 than in 2005. The authors defined “healthier” 

menu items based on the foods’ calories, cholesterol, sodium, saturated fat, and fiber in the context 

of USDA’s dietary guidance for individuals on a 2,000-calorie reference diet. Among the nine chain 

restaurants examined for the study, five operated in jurisdictions subject to menu-labeling requirements before 2011 and served as the experimental group. Four chains with no outlets in affected 

areas served as the control group. On the one hand, Namba et al. (2013) find that healthier food 

options increased from 13 percent to 20 percent of all menu items at experimental locations while 

remaining static at control locations. On the other hand, they find no improvement in the average 

caloric content of menu items at either experimental or control restaurants. Two of five chains in the 

experimental group reduced the average caloric content of their offerings. However, a few chains 

launched new options, such as bacon cheeseburgers, that increased average calories on their menus. 

Similar to the previously discussed research on consumer behavior, results have been mixed on the 

question of whether menu labeling prompts restaurants to offer lower calorie foods. One reason for 
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the mixed results may be that researchers examined different samples of restaurants. Indeed, none 

of these studies could claim to have examined a nationally representative sample of the foodservice 

establishments that will be affected by menu labeling. In addition, studies used different survey 

methodologies. For example, some researchers have based their analysis on a comparison of only 

entrées without accounting for the fact that entrées are generally sold with other menu items like 

side dishes. In addition to the studies cited above, for example, Bruemmer et al. (2012) and Wu and 

Sturm (2014) investigated full-service restaurants, but focused on individual menu items without 

fully accounting for how entrées and side dishes were combined to produce meals. Thus, their 

approach did not fully reflect the array of choices consumers face. 

To better account for the array of choices that a consumer faces when ordering foods at a restaurant, 

the analysis conducted in this chapter is based on information collected on the meals available at six 

restaurants by visiting company websites and brick-and-mortar venues (all data were publicly available85, 86) in spring 2012 and again in fall 2015 (Stewart et al., 2014, 2015). The empirical analysis 

identified all dinner entrées available at all six restaurants along with all possible combinations of 

standard side dishes for both time periods.87 A steak entrée, for example, may come with a choice of 

2 side dishes, and if customers can select those 2 sides from a list of 5 possibilities, then there would 

be 10 different combinations of 2 side dishes and the steak entrée. For the present chapter, each of 

these 10 combinations is treated as a separate meal, and each combination’s caloric content is estimated. Previous studies have not followed this method. Finally, as other studies in the literature have 

done, the caloric content of meals at the six restaurants are compared during the two different time 

periods. Defining meals broadly to include dinner entrées with all possible combinations of standard 

side dishes, no evidence was found that these six restaurants were collectively offering less caloriedense meals overall in 2015 than in 2012. (See box, “Restaurants May Be Offering Lower Calorie 

Meals, But the Evidence Is Mixed.”) 

While this comparison of spring 2012 and fall 2015 menus from six full-service restaurants more accurately accounts for the array of food choices a consumer faces when ordering a restaurant meal, it, like 

other studies, has significant limitations. Seasonal differences in menu items, for one thing, could affect 

our results. For another thing, our results were drawn from a small sample of restaurants that is not 

representative of all full-service establishments (let alone all fast-food restaurants) that will be covered 

by Federal menu-labeling regulations mandated by the ACA and being developed by FDA. 

85The data were collected in Montgomery County, Maryland. Complete calorie information was obtained for all standard 

menu items, as compliance with local labeling laws has been mandatory there since January 1, 2011.

86One of the six full-service restaurants examined for this chapter had gone out of business since Spring 2012, so instead, 

calorie information was collected for another restaurant belonging to the same chain located in neighboring Frederick County, 

MD. However, the key results were unaffected if the analysis included only the menus of the five restaurants that were in 

business in Montgomery County, MD, during both time periods.

87The analysis included only side dishes that came with an entrée at no additional cost. Add-on items for which customers 

had to pay extra were excluded.
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Restaurants May Be Offering Lower Calorie Meals, But the Evidence Is Mixed

A growing body of research investigates the restaurant industry’s response to menu labeling. In theory, if 

consumers demand more lower calorie choices, then restaurants might reformulate or replace higher calorie menu 

items. However, the empirical evidence is mixed. Bruemmer et al. (2012) found that full-service restaurants have 

been offering less calorie-dense foods overall, but found no change for fast-food restaurants. In another study, 

Namba et al. (2013) found that “menu labeling has thus far not affected the average nutritional content of fast-food 

menu items, but it may motivate restaurants to increase the availability of healthier options” (p. 1).

 

For chapter 10, the analysis investigated the restaurant industry’s response to menu labeling by focusing on the 

caloric content of meals sold by six full-service restaurants (FSRs) in Montgomery County, MD, in spring of 

2012 and fall of 2015. We collected data on the meals at these chain restaurants by visiting company websites 

and stores in Montgomery County, MD—all data were publicly available.88 ERS researchers identified each 

dinner entrée along with all possible combinations of standard side dishes. Each of these combinations was 

treated as a separate meal, and each meal’s caloric content was estimated. This method accounts for the array 

of choices a consumer might face at an FSR. However, the method gives greater weight to restaurants that 

offered more menu items and more flexibility in customizing. Indeed, among the 6 FSRs examined, 1 allowed 

customers to create their own cheeseburger meal by choosing—at no additional charge—1 of 7 types of cheese, 

1 of 6 types of spread (e.g. mayonnaise, relish, etc.), and 2 of 10 different toppings (e.g., bacon, avocado, etc.). 

88Complete calorie information was obtained for all standard menu items since compliance with local labeling laws has been mandatory in Montgomery County, MD, since January 1, 2011.
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Excluding create-your-own menu options like the cheeseburger meals described above, the 

analysis identified 5,227 meals sold by the six FSR chains in spring 2012 and 3,661 meals sold 

by the same six chains in fall 2015. Following previous researchers, distribution of calories in 

the meals available in both time periods is examined. In spring 2012 (box fig. 10.2), 20 percent 

of all meals had fewer than 769 calories (versus 19.2 percent of all meals in 2015) and 20 percent 

had 1,410 or more calories (versus 20.6 percent in 2015). Thus, there is no evidence that lowcalorie meals represented a greater share or high-calorie meals represented a smaller share of all 

meals collectively sold by these six restaurants. Moreover, when the scope of meals under study 

is expanded to include create-your-own options (more of which became available after spring 

2012), 33.1 percent of menu items have 1,410 or more calories in fall 2015 versus 20 percent in 

spring 2012. This result primarily reflects the aforementioned create-your-own cheeseburger 

option, which was available at one of the six FSRs in fall 2015, but not in spring 2012. 

Ongoing research in this area will likely benefit from the wealth of information now available online 

and in-store about the caloric content of restaurant foods. However, in order to identify whether 

restaurants are adjusting their menus, it may also be necessary to account for how menu labeling 

may affect (or not affect) sales of menu items, depending on consumer expectations. For example, if 

a bacon cheeseburger meal is high in calories, but consumers were already aware of this fact, then 

menu labeling may not reduce demand for such items nor deter restaurants from offering them. In 

future research that builds on Stewart et al. (2014, 2015), researchers might proceed by investigating 

how well “rule of thumb” nutrition knowledge predicts the caloric content of meals. Or, following 

Bollinger et al. (2011), researchers might survey restaurant patrons, asking them to estimate the 

number of calories they expect various menu items to contain. It would then be possible to test 

whether restaurants were more likely to reformulate or replace menu items with substantially more 

calories than patrons expected. 

Conclusion

After years of debate over menu labeling, Federal regulations require chain restaurants and similar 

retail food establishments with 20 or more outlets to make calorie information publicly available. 

The underlying premise of these regulations is that providing consumers with nutrition information 

will enable them to make informed choices, which may, in turn, lead to higher quality diets and 

healthier body weights. However, researchers have yet to reach a consensus on how consumers and 

restaurants are responding to menu labeling, and further analysis is needed. Until more is known, 

researchers, health policy advocates, and policymakers alike may want to exercise caution when 

drawing conclusions from existing research about the effect of menu labeling.
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Glossary

American Recovery 

and Reinvestment Act 

of 2009 (ARRA)

A Federal stimulus package enacted in February 2009 in response to the 

Great Recession (2007-2009). Its primary purpose was to promote job 

creation and provide temporary relief for constituents most affected by 

the recession and to invest in infrastructure, education, health, and renewable energy.

Baby Boomers A demographic cohort that is defined by the Pew Charitable Trust as being born between 1946 and 1964. 

Bennett’s law A principle stating that, as an individual’s income rises, the proportion of 

starchy foods he or she consumes falls. 

body mass index 

(BMI)

A measure used to indicate an individual’s body composition as a function of his or her weight and height. BMI is equal to mass (kg)/height (m 

squared). 

calorie A unit of energy typically used to measure the amount of energy in food. 

One calorie is the amount of energy needed to raise the temperature of 1 

cubic centimeter of water 1 degree Celsius. 

chain (restaurant) A restaurant business with multiple locations.

cup equivalent A unit of measure that standardizes the amounts of foods from each food 

group with similar nutritional content, because foods vary in their caloric 

density. 

diet quality The nutritional quality of one’s overall diet, which is typically measured 

by a diet quality index such as the Health Eating Index (HEI) (see definition).

Dietary Guidelines 

for Americans (DGA)

Guidelines put forth every 5 years by the U.S. Department of Health and 

Human Services and the U.S. Department of Agriculture that provide 

nutritional advice for Americans. The guidelines are based on scientific 

research and are used to inform nutritional policies and programs. 

disposal personal 

income (DPI)

Amount of money households have available for spending and saving 

after income taxes are deducted.

economic downturn A sustained period of sluggish economic activity characterized by are rising unemployment, declining housing prices, and declining investment. 

economies of scale The reduction in per-unit cost as a result of an increase in total production 

of an output. 

Engel curve A graphical depiction of expenditure of a good or service with respect to 

income. 

Engel’s law The observation that as income increases the proportion of income spent 

on food decreases. 

fast casual As a subgroup of quick-service restaurants, fast-casual restaurants offer 

mainly just counter service. However, they approximate qualities of ambiance and food typical of full-service restaurants. 
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fast food Used interchangeably with quick service and limited service to denote restaurants that offer counter service but do not have wait staff that continually tend to customers.

Federal Poverty 

Income Levels (FPL)

Also known as the Federal Poverty Guidelines, FPL are thresholds of annual household income adjusted yearly by the Department of Health and 

Human Services to determine poverty status. FPL are used to determine 

eligibility for certain Federal programs. FPL charts show thresholds by 

number of family members and by region (i.e., 48 contiguous States and 

Washington, DC; Alaska; and Hawaii).

food acquisition Refers to the procurement of food through monetary or nonmonetary 

means. 

Food at home (FAH) Foods that are prepared at home, to be consumed at home. 

Food away from home 

(FAFH)

Food that is prepared and consumed away from home. From a research 

perspective, there are definitional nuances to the categorization of food 

away from home by dataset. Please refer to table 1.2 for further details.

food basket A collection of consumer market prices of a selection of food items. 

food insecurity A state of having unpredictable access to food. Depending on the severity 

of insecurity, it may lead to change in diet quality and food intake.

full service A full-service restaurant has wait staff who cater to patrons while they are 

dining. 

Great Recession A period of worldwide contraction in the business cycle that largely 

resulted from a decline in the U.S. real estate market because of subprime 

mortgage lending practices. The National Bureau of Economic Research 

declared the official timespan of the recession as December 2007 to June 

2009. 

Healthy Eating Index 

(HEI)

A measure originally created by USDA, Center of Nutrition Policy and 

Promotion (CNPP) with subsequent collaborative updates to the measure 

from the National Cancer Institute. The HEI is updated every 5 years and 

is used to measure diet quality and adherence to USDA’s Dietary Guidelines for Americans (see definition). 

insulin resistance A condition in which cells respond abnormally to the hormone insulin. 

The condition is highly associated with obesity, low diet quality, and a 

sedentary lifestyle.

limited service Used interchangeably with fast food and quick service to denote restaurants that offer counter service but do not have wait staff who continually 

tend to customers.

Millennials A demographic cohort that is defined by the Council of Economic Advisers as being born between 1980 and 2004. 

North American 

Industry Classification System (NAICS)

A classification system used to categorize businesses for the purpose of 

collecting, analyzing, and publishing statistical data related to the U.S. 

business economy.
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National School 

Lunch Program 

(NSLP)

USDA, Food and Nutrition Service’s meal program operating in public 

and nonprofit private schools and residential child care institutions that 

provides nutritionally balanced, low-cost or free lunches to children each 

school day. 

nonmarket work Typically, household production activities such as meal preparation but 

broadly refers to any activities or work that is not compensated by monetary wages.

obesity A classification of adiposity, which is generally determined by one’s body 

mass index (BMI) (see definition). A BMI of 30 or higher is considered 

obese.

off-premise foods Foods that are intended to be consumed at home and considered food at 

home (FAH) in the Food Expenditure Series.

on-premise foods A retailer-determined classification, on-premise foods are those intended 

to be consumed where they are prepared and acquired, whether at a restaurant, store, school, or home of family or friends. In the Food Expenditure Series, on-premise foods are the same as FAFH. 

overweight A classification of adiposity. Generally, overweight is determined by 

one’s body mass index (BMI). A BMI between 24 and 29.9 is considered 

overweight. 

personal consumption 

expenditure (PCE)

A measurement of consumer spending on goods and services in the U.S. 

economy that gives an indication of how much household income is allocated for current spending versus savings for future consumption.

perfectly inelastic An economic situation in which the demand for a good or service is 

unchanged by any change in price. 

prepared foods Foods that require only heating or thawing before consumption.

price elasticity A measure used to show the responsiveness (or elasticity) of the demand 

for a good to a change in its price when nothing but the price changes. 

quick service Used interchangeably with fast food and limited service, quick service denotes restaurants that offer counter service but do not have wait staff that 

continually tend to customers.

raw ingredient A minimally processed, often single-ingredient food, generally used to 

produce a meal or snack. 

ready-to-heat foods A type of prepared food that requires only heating prior to consumption. 

School Breakfast 

Program (SBP)

Administered by USDA, Food and Nutrition Service, SBP provides 

monetary assistance to States to operate nonprofit breakfast programs in 

schools and residential childcare institutions. 

Special Supplemental 

Nutrition Program for 

Women, Infants and 

Children (WIC)

Administered by USDA, Food and Nutrition Service, WIC provides 

Federal grants to States for supplemental foods, health care referrals, and 

nutrition education for low-income pregnant, breastfeeding, and nonbreastfeeding postpartum women, and to infants and children up to age 5 

who are found to be at nutritional risk.
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Supplemental 

Nutrition Assistance 

Program (SNAP)

Administered by USDA, Food and Nutrition Service, SNAP offers nutrition assistance to millions of eligible, low-income individuals and families and provides economic benefits to communities. SNAP is the largest 

program in the domestic hunger safety net. 

Socioeconomic status 

(SES)

A hierarchical measure of an individual’s or household’s economic and 

social position in society. Major determinants of SES are income, education, and occupation. 

The Greatest Generation

A demographic cohort that is defined by the Pew Charitable Trust as being born before 1928. 

Thrifty Food Plan 

(TFP)

Formulated by USDA, Center for Nutrition Policy and Promotion, TFP 

details a nutritious diet that can be achieved with limited money and time 

resources. TFP forms the basis for maximum Supplemental Nutrition Assistance Program (SNAP) allotments.

unemployment rate The share of individuals who are not employed and/or are actively seeking employment.

unprepared ingredients

Foods that are basic ingredients to other foods or have little to no processing other than for preserving.