Economic

Research

Service

Economic

Research 

Report 

Number 178

November 2014

United States Department of Agriculture

The Role of Time in Fast-Food 

Purchasing Behavior in the 

United States

Karen S. Hamrick 

Abigail M. Okrent
Economic Research Service 

www.ers.usda.gov

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Recommended citation format for this publication:

Hamrick, Karen S., and Abigail M. Okrent. The Role of Time in Fast-Food Purchasing 

Behavior in the United States, ERR-178, U.S. Department of Agriculture, Economic 

Research Service, November 2014.

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United States Department of Agriculture

Economic 

Research 

Service

Economic

Research

Report

Number 178

November 2014

Abstract

Meals, snacks, and beverages purchased at fast-food restaurants account for an increasingly large share of a typical American’s food budget and have been blamed for 

Americans’ expanding waistlines and poor diet quality. This study uses data from the 

2003-11 American Time Use Survey to examine the effects of time-use behaviors, 

prices, sociodemographic characteristics, labor force participation, and prices on fastfood purchasing patterns in the United States before and after the Great Recession. Fastfood purchasers spend less time sleeping, doing housework, eating and drinking, and 

watching television than nonpurchasers, and more time traveling from place to place. 

They also tend to have higher incomes and higher education levels. While the time that 

Americans spent eating out at all restaurants declined during and after the 2007-09 

recession, the share of the population eating at fast-food restaurants on a given day 

stayed fairly constant, seemingly unaffected by the economic downturn, but the share 

for sit-down restaurants declined. 

Keywords: Food away from home, FAFH, fast food, time use, American Time Use 

Survey, ATUS, Eating and Health Module, Great Recession, time pressure, eating 

patterns, sleep

Acknowledgments

The authors thank Rachel Krantz-Kent of the U.S. Bureau of Labor Statistics for information on the American Time Use Survey. We also thank Jean Buzby, Ephraim Leibtag, 

Bill Levedahl, and Tim Park of USDA's Economic Research Service (ERS), and four 

anonymous technical reviewers. Thanks also to John Weber and Cynthia A. Ray of ERS 

for the editing and design of the manuscript.

Karen S. Hamrick, khamrick@ers.usda.gov

Abigail M. Okrent, aokrent@ers.usda.gov

The Role of Time in Fast-Food 

Purchasing Behavior in the 

United States
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Contents

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

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

Relevant Literature on Determinants of FAFH Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Data and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Differences Among Gender, Employment Status, and Fast-Food Purchasing Groups. . . . . . . . . 8

Changes Before and After the Business Cycle Peak. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Multivariate Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Analysis of the Entire Sample Period, 2003-11. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Models by Household Type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Analysis of Pre- and Post-Business Cycle Peak. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Discussion.....................................................................30

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Appendix—Data Definitions and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
United States Department of Agriculture

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providing timely information on economic and policy 

issues related to agriculture, 

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rural America. www.ers.usda.gov

Economic

Research

Service

Economic

Research 

Report 

Number 178

November 2014

United States Department of Agriculture

The Role of Time in Fast-Food 

Purchasing Behavior in the 

United States

Karen S. Hamrick 

Abigail M. Okrent

November 2014

Find the full report 

at www.ers.usda.

gov/publications/erreconomic-researchreport/err178

Karen S. Hamrick and Abigail M. Okrent

The Role of Time in Fast-Food Purchasing 

Behavior in the United States

What Is the Issue?

Food away from home (FAFH) is an important part of a typical American’s diet and continues 

to increase as a share of the food budget. Rising consumption of a particular kind of FAFH—

fast food—has been blamed for American’s expanding waistlines and poor diet quality. 

Previous studies have attributed this increase in purchases of fast foods to many factors, 

including budget and time constraints, demographic and health characteristics, and market-level 

forces, but no study has been able to rigorously address the effects of all of these variables on 

fast-food purchasing behavior. This study is the first to extensively examine the effects of timeuse behaviors, prices, sociodemographic characteristics, labor force participation, and prices 

on fast-food purchasing patterns in the United States before and after the Great Recession. 

Because fast food accounts for a large share of U.S. food expenditures and calorie consumption, 

a better understanding of the motivation behind trends in fast-food purchasing behaviors may 

help inform policies designed to improve the diet quality of Americans. This research complements previous studies that used food expenditure and food intake data (but not time-use data) 

to analyze the effects of demographic characteristics, prices, and income on fast-food purchases 

and consumption.

What Did the Study Find?

Americans purchase fast food to save time. Those that purchase fast food on a given day 

spend less time eating and drinking as a primary (main) activity, sleeping, doing housework, 

and watching television than the average for the total population. The difference in sleep time 

is considerable—fast-food purchasers spent 23 fewer minutes sleeping on a given day over 

2003-11 than the average for the total population. In addition to spending less time in primary 

eating and drinking, fast-food purchasers were more likely than the average person to report no 

primary eating/drinking on a given day. Fast-food purchasers spent about the same amount of 

time as others in “secondary” eating; that is, eating while engaged in another, primary activity. 

However, fast-food purchasers were more likely to eat while at work or while driving a vehicle 

than others. To the extent that eating quickly may not be ideal and that eating is done while one 

is engaged in activities that demand focus suggests that fast-food purchasers have different, and 

perhaps poorer, eating habits than others. 

Effects of employment. On a given day in 2003-11, those who were employed were more likely 

to purchase fast food than those not employed. And those who were employed but on their day 

off were even more likely to purchase fast food than those employed and on a workday or those 

not employed.
Effects of household characteristics. Consistent with findings in other studies, household composition, 

income, age, and education play an important role in fast-food purchasing behavior. Relative to married and 

unmarried couple households with no children, a single-person household had a higher probability of fast-food 

purchase by 1.4 percent on a given day in 2003-11. “Other” households with and without children, typically 

multigenerational households, had the same increased probability. Single-parent households, however, had an 

increase in the probability of fast-food purchase of 2.5 percent relative to married households. Married and 

unmarried couple households with children had the same probability of fast-food purchase as couple households 

without children. Households with higher incomes and households with higher education were also more likely 

than other households to purchase fast food.

Effects of the Great Recession and time spent eating out. During and after the Great Recession, the overall 

time that Americans spent eating out declined, which was associated with a drop in the share of the population 

who ate at sit-down restaurants. However, the share of the population that purchased fast food on a given day 

stayed fairly constant during and after the 2007-09 recession, seemingly unaffected by the economic downturn, while those employed were even more likely to purchase fast food than before the recession. In particular, 

employed single parents were 3.9 percent more likely to purchase fast food than others pre-recession and 5.2 

percent more likely during and after the recession—a large increase in fast-food purchase behavior. 

For the total population, there were small but statistically significant changes in time-use patterns during and 

after the recession—less time spent in paid work and in travel, more time spent in meal preparation and in 

watching television. However, time-use patterns of fast-food purchasers stayed faily stable and exhibited less 

change than those of the total population. 

How Was the Study Conducted?

The study used data on time use and demographic and other characteristics from the American Time Use 

Survey (ATUS). The ATUS is a continuous, nationally representative survey of time use by Americans that has 

been conducted by the U.S. Bureau of Labor Statistics since 2003. ATUS data enabled researchers to compare 

time-use patterns of those who purchased fast food with those of the total population and other subgroups. An 

advantage of using these data rather than food intake data (i.e., National Health and Nutrition Examination 

Survey) or expenditure data (i.e., Consumer Expenditure Survey) is that the ATUS provides detailed information on time use by Americans and offers a unique perspective on the relationships between fast-food 

purchasing behavior and time variables that have been previously unexplored in the literature. Information on 

secondary eating was derived from the 2006-08 ATUS Eating & Health Module. 

Descriptive statistics on time and frequency of purchase were estimated for subpopulations (gender, employment status, and fast-food purchaser) between 2003 and 2011 and also before and after the onset of the Great 

Recession in December 2007. Researchers conducted multivariate analysis on the probability of fast-food 

purchase by household type (i.e., single person, single parent, couple no children, couple with children, other 

households no children, and other households with children). 

www.ers.usda.gov
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The Role of Time in Fast-Food 

Purchasing Behavior in the 

United States

Introduction

Meals, snacks, and beverages purchased at fast-food and sit-down restaurants have become a large 

and growing portion of a typical American’s food budget. In 1929, food purchased for away-fromhome consumption constituted only 13.4 percent of the average U.S. household’s food budget, 

while in 2012, this share was 43.1 percent (USDA-ERS, 2013). During the past decade, however, 

the growth in food-away-from-home (FAFH) expenditures by establishment type has been uneven. 

Expenditures at fast-food restaurants decreased from about 4 percent to 3 percent of the total budget 

for all goods and services for an average U.S. household between 1998 and 2010 but seemed to be 

unaffected by the Great Recession (December 2007-June 2009). On the other hand, the share of 

total expenditures for goods and services attributed to sit-down restaurant meals grew from 1998 

until 2006 and fell just before the Great Recession (Okrent and Alston, 2012). While consumers 

have decreased expenditures on fast food, this segment of consumption still accounts for a relatively 

large and growing share of the calorie intake by Americans (Lin and Guthrie, 2012).

The calories consumed from fast-food and sit-down restaurants constitute nearly a third of the energy 

intake by adults in the United States, and study findings suggest that the nutritional quality of these 

foods is lower than foods purchased for at-home consumption (Lin and Guthrie, 2012). In addition, fast 

food has been found to contribute to obesity (Chou et al., 2004; Davis and Carpenter, 2009; Currie et 

al., 2010; Chen et al. 2013; Alviola et al., 2013). On the whole, this evidence suggests that consuming 

FAFH may lower dietary quality and increase body weight and that different types of FAFH may 

affect dietary quality and individual body weight differently. The linkage between food consumed 

at fast-food and sit-down restaurants and poor nutritional outcomes has led to Federal initiatives to 

promote consumption of healthier options and limit marketing and access of these foods to children (White House Task Force on Childhood Obesity, 2010). For example, in response to First Lady 

Michelle Obama’s Let’s Move campaign, several food companies, including McDonald’s, pledged to 

limit the marketing of “unhealthy” products to children (Federal Trade Commission, 2012). 

However, what if purchasing decisions at fast-food and sit-down restaurants are influenced not only 

by relative prices and marketing but by time constraints? In particular, Mincer (1963) argued that 

ignoring the value of time in demand relationships could produce biased results. In the new household economics, the value of time is an important factor affecting consumption, which suggests that 

households may substitute time-saving services for their own time (Becker, 1965).

Harms (2014) found that married women in the rural United States spent an average of 122 minutes 

cooking and an additional 68 minutes in meal cleanup on an average day in the 1920s. Zick and 

Stevens (2009) found that food preparation time for women declined from an average of 92 minutes 

a day in 1975 to 51 minutes in 2006; for men, food preparation time increased by only 3 minutes 

during the same period and still averaged under 20 minutes per day. The authors conclude that 

these are secular trends that persist after controlling for demographic, economic, and household 
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changes that have taken place over the three decades. Hence, households with two wage earners may 

consume more time-saving goods and services than do households with full-time homemakers. 

This is the first study to extensively examine the effects of time-use behaviors, prices, sociodemographic characteristics, labor force participation, and prices on fast-food purchasing patterns in the 

United States before and after the Great Recession using data from the 2003-11 American Time Use 

Survey (ATUS). The advantage of using ATUS data versus food intake or food expenditure data 

(e.g., National Health and Nutrition Examination Survey (NHANES) and Consumer Expenditure 

Survey, respectively) is that time-use data enable researchers to examine the relationship of time 

spent in work, household production, and leisure activities to fast-food purchasing decisions. In addition, time diaries are considered a neutral method of collecting data on time spent in various activities and are less subject to under- and overreporting than surveys that ask for estimates of time spent 

on specific activities (Robinson and Godbey, 1997, chapter 4; Robinson et al., 2011). 

Because the data range from 2003 to 2011, we could also test the impact of the Great Recession on 

the probability of purchasing fast food. Specific research questions addressed in this study include the 

following: (1) What individual- and household-level characteristics, including how individuals spend 

time, are associated with fast-food purchases? and (2) Did fast-food purchasing behavior change over 

the recent business cycle? A better understanding of the motivation behind the trends in fast-food 

purchasing behaviors will help inform policy designed to improve the diet quality of Americans.
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Relevant Literature on Determinants of FAFH Demand

Demand for FAFH is typically modeled as being a function of prices and income—the standard 

utility maximization framework. With the exception of Okrent and Alston (2012) and Richards 

and Mancino (2013), most previous studies have modeled demand for FAFH as a composite good 

and have generally found demand for FAFH to be more responsive to price- and income-induced 

expenditure changes than food at home (FAH) (see Okrent and Alston (2011) for a review of these 

studies). Okrent and Alston (2012) found demand for fast foods to be almost perfectly inelastic to 

changes in prices (-0.13) and demand for meals from sit-down restaurants to be quite price elastic 

(-1.96). On the other hand, Richards and Mancino (2013) found the price elasticity of demand for 

meals at fast-food and various types of sit-down restaurants to be between -0.5 and -0.9. Demand 

for different types of FAFH—in particular, fast foods—may depend on time constraints and convenience, sociodemographic characteristics, and macroeconomic forces like the business cycle rather 

than just relative prices and income.

Becker (1965) developed the theory of time allocation, which accounted for the time input into the 

commodities and activities that a household produces via a household production function, as well 

as the time constraint faced by individual household members. The household’s utility function 

contains the commodities and activities and is maximized subject to the two constraints. One household commodity is meals. An increase in the value of an individual’s time would lead to a substitution toward goods. “For example, an increase in the value of a mother’s time may induce her to 

enter the labour force and spend less time cooking by using pre-cooked foods....” (p. 514). Becker’s 

theory of time allocation spawned an extensive literature on home production (see Gronau (1986) 

and Gronau (1997) for thorough literature reviews). Indeed, Gronau commented that the household 

production function highlights that “one cannot separate the analysis of consumption behavior from 

the analysis of time use” (1997, pg. 199). 

In keeping with the Becker household production model, several studies have argued that increased 

consumption of meals and snacks at fast-food restaurants has largely been driven by opportunity 

cost of time within a typical American household (e.g., Davis, 2013). Most studies account for the 

opportunity cost of time by using wage, labor hours, women’s labor force participation, presence of 

working spouse, and employment status to proxy for the time constraint faced by households making 

food purchasing decisions. With the exception of Huffman (2011), most of these studies use crosssectional data and assume that prices faced by consumers in a particular period are constant. All of 

these studies also include sociodemographic variables, arguing that household size, age, race, and 

other characteristics are important determinants of FAFH consumption. More recently, studies have 

begun to analyze structural changes in food consumption (i.e., substitution out of FAFH and into 

FAH) due to the most recent recession. 

Much of the research that investigates the impact of the time constraint on household production 

and demand for FAFH treats FAFH as a composite. These studies generally found that the value of 

time for a household manager (the household member responsible for meal preparation, including 

grocery shopping, cooking, baking, assembling, and serving food, and cleanup after the meal, sometimes assumed to be the female) has a positive effect on demand for total FAFH (Prochaska and 

Schrimper, 1973; Sexauer, 1979; Soberon-Ferrer and Dardis, 1991; Yen, 1993; Nayga and Capps, 

1994; Byrne et al., 1996; and Dong et al., 2000). However, a few studies, namely Huffman (2011), 

Redman (1980), and Kinsey (1983), found the household time constraint to be a less important deter-
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minant of demand for FAFH. Kinsey (1983) argued that while this result may appear to contradict 

theory, household managers need not increase FAFH expenditures to substitute relatively inexpensive goods and services for time if the cost of purchasing certain types of FAFH (i.e., fast foods) is 

cheaper than food at conventional sit-down restaurants. 

Several studies investigated 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 food manager’s time increased expenditures on fast food more than on meals 

from sit-down 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 fast foods but negative impact 

on demand for sit-down 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 

wife’s employment are positive on both the probability and level of expenditures on lunch and dinner 

as FAFH, but wife’s employment did not seem to affect breakfast as FAFH. 

Related to opportunity cost of time, the above studies also acknowledge that meal preparation, 

and, hence, demand for FAFH is also determined by household composition, including presence of 

children, family size, martial status, and gender of household heads (primary wage earners in the 

household). 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. The argument is that additional number of 

adults in the household lead to additional FAFH because of time constraints related to employment 

and social activities. Byrne et al. (1996) found household size had a negative effect on demand for 

total FAFH, arguing that there are economies of household size in meal preparation at home. Byrne 

et al. (1998) found that family size was only negatively related to expenditures at sit-down restaurants and positively related to expenditures at fast-food restaurants.

Later studies began investigating “nontraditional” households by including marital status in the 

analysis because of changes in traditional household structure (e.g., from married to single or multigenerational households). Byrne et al. (1998) found that unmarried households spent less on FAFH 

than married households regardless of restaurant type, arguing that fewer people were involved 

in the FAFH occasion. Contrary to this finding, Stewart and Yen (2004) and Stewart et al. (2004) 

found that single-person households spent around $0.5-$3.0 more per week at fast-food and sitdown restaurants than married households but single-parent households spent $0.30-$1.70 less than 

married households at both types of FAFH establishments.

Lastly, gender of the household manager also seems to affect demand for FAFH, but empirical 

evidence is mixed. Byrne et al. (1996) found that households with female household managers had 

lower expenditures than those with male household managers, which they attributed to males having 

less culinary skills than females. Byrne et al. (1998) found that female household managers who 

worked spent less at upscale and midscale sit-down restaurants but more at fast-food restaurants than 

male household managers.1

Only recently have extensive time-use data been applied to time allocation models to examine 

tradeoffs between time and market prices in meal preparation and consumption of prepared foods. 

1According to Byrne et al. (1998), upscale restaurants offer full alcohol service and accept credit cards whereas midscale restaurants do not.
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Hamermesh (2007) paired data from the 1985 Time Use Survey and ATUS with data from the 

Consumer Expenditure Survey for 1985 and 2003 to examine how married households combined 

expenditures on goods and time to prepare or purchase meals and snacks. Hamermesh found large 

decreases in the time inputs into eating over the period, where eating appears to be a relatively 

goods-intensive commodity. Using the 2005 ATUS, Tashiro (2009) found that more time in paid 

work resulted in less time preparing food at home for all respondents; that time spent in purchasing 

prepared food increases with time spent in leisure activities for Whites and Hispanics; that Blacks 

increase their time spent purchasing prepared food with increases in paid work time; and that 

purchases of prepared food tend to increase with a person’s education level. Lastly, Van der Lippe et 

al. (2004) examined outsourcing of domestic tasks in the Netherlands and found that income was the 

most important determinant of a household’s likelihood to outsource. In particular, Van der Lippe et 

al. found that dual-earning households who spent more time in the labor market were more likely to 

outsource—which includes purchasing takeout meals—and that takeout meals were strongly related 

to the presence of children in the household. 

The last thread of literature that investigates the determinants of FAFH demand addresses how 

economic downturns and the business cycle affect demand for different types of foods. In the most 

recent literature, some researchers have begun to analyze the effects of the most recent recession 

(December 2007 to June 2009)2 and recovery on time-use patterns food expenditures, and dietary 

intake. Using 2003-10 ATUS data and analyzing cross-State variation, Aguiar et al. (2013) found 

that the bulk of the foregone market work hours was spent in leisure, and 30 percent of this time 

was reallocated to nonmarket home production, which includes a 12-percent increase in hours spent 

in core home production (cooking, cleaning, and laundry). Beatty and Senauer (2012) documented 

that food spending shifted into FAH from FAFH over the 2007-09 recession and into the recovery 

and also noted that low-income households spent more time on food preparation. In addition, Todd 

(2014) found that dietary quality improved between 2005-06 and 2009-10, which could be partly 

attributable to a 127-calorie decline in daily energy intake from FAFH (of which a 53-calorie decline 

is from fast-food consumption). Looking at how labor force status—employment and unemployment—affects consumption, Dave and Kelly (2010) found that a higher risk of unemployment is 

associated with reduced consumption of fruits and vegetables and increased consumption of snacks 

and fast food.

The literature on the determinants of FAFH purchase behavior in the United States examines 

a number of competing hypotheses as to why demand for FAFH has grown, including market 

prices, income, opportunity cost of time, household composition and demographics, and macroeconomic shocks. However, none of these studies were able to simultaneously explore many of 

these competing hypotheses due to data limitations. This study uses data from the 2003-11 ATUS 

to directly analyze demand for FAFH by type (fast-food and sit-down restaurant) as a function of 

individual- and household-level time-constraining activities (time spent in work, personal care, meal 

preparation, and leisure activities over the day); demographic, labor force, and household characteristics; and market-level characteristics. Also, since these data cover the 2007-09 recession, the 

longest and deepest post-World War II U.S. recession, the study addresses the effects of business 

cycle events on fast-food purchase behavior. 

2See the National Bureau of Economic Research, US Business Cycle Expansions and Contractions, 

http://www.nber.org/cycles/cyclesmain.html
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Data and Definitions

To identify various FAFH activities and estimate frequency and time duration, we used data from 

the pooled 2003-11 ATUS. Although these data do not indicate dollars spent or calories consumed 

on FAFH, they do draw from survey respondents’ time diaries of activities, as well as provide 

extensive demographic and labor-force participation information, information on all members of the 

household, and some geographic information. This enables researchers to identify different FAFH 

behaviors in different subgroups. As discussed in the literature, other data sources exist that can be 

used to model demand for fast food; for example, food intake data from NHANES and food expenditure data from the Consumer Expenditure Survey have been used to look at the effects of fast-food 

consumption on diet quality and the effects of prices and expenditures on fast-food consumption. 

However, these data sets do not contain information on all of the ATUS respondents’ activities, as 

there may be important tradeoffs in time use that affect fast-food purchasing behavior.

The U.S. Bureau of Labor Statistics’ ATUS is a continuous survey that began in 2003. Survey interviews are conducted by the U.S. Census Bureau nearly every day of the year to allow for analysis 

of weekdays versus weekends, holidays, and seasonality. One individual age 15 or older from each 

sampled household is interviewed about his or her activities for the 24-hour period from 4 a.m. the 

day before the interview to 4 a.m. on the interview day.3 Survey respondents are asked to identify 

their primary activity if they were engaged in more than one activity at a time. They are also asked 

to report where they were and whom they were with for most diary activities. 

The pooled 2003-11 ATUS microdata files contain 124,517 completed interviews, yielding a total 

of 2,462,919 activities in the respondents’ time diaries. ERS used the ATUS Respondent, Roster, 

Activity, Activity Summary, Who, ATUS-Current Population Survey, Replicate Weights, and 

Methodology Case files.4 (See references for Web site links.)

Estimation procedures outlined in the ATUS User’s Guide (U.S. Bureau of Labor Statistics, August 

2012) were followed. All estimates presented were weighted to be nationally representative. 

Averages were calculated as the mean. Standard errors were calculated according to Section 7.5 of 

the ATUS User’s Guide, using the balanced repeated replication method and the ATUS Replicate 

Weights file. A 90-percent level of confidence was constructed around each estimate and used to 

determine statistical differences. All differences between estimates discussed in the text are statistically different at the 90-percent level unless indicated as otherwise. SAS 9.2 and Stata 12 were used 

for estimatation. 

3One potential drawback of the ATUS diary data is that information on only 1 time-diary day per person was collected. There may be concern that some activities, such as eating fast food or engaging in sports and exercise, are not 

daily activities and, thus, a 1-day diary lacks intrapersonal variability. However, some activities, such as eating patterns, 

have a large degree of persistency, meaning that day-to-day variation is minimal. Wansink (2007) discusses the myriad 

external influences that result in eating habits. Exercise is also considered to be a habit, and researchers have studied what 

contributes to habitual exercise (Aarts et al., 1997; Finlay et al., 2002). Indeed, much of an individual’s daily activities 

can be classified as habitual repetition (Neal et. al., 2006). Another argument for using the ATUS 1-day time diary data is 

that the ATUS is large and nationally representative, and so intrapersonal variability would not be an issue.

4Supplemental analysis was done using the 2006-08 ATUS Eating & Health Module (EHM). The pooled 2006-08 microdata files contain 37,832 completed interviews. The EHM Respondent and Replicate weights files were used. Estimation procedures outlined in the EHM User’s Guide (Hamrick, 2010) were followed. 
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ERS used the ATUS time diaries to infer whether an individual made a fast-food/carryout purchase 

(hereafter referred to as fast food) or ate at a restaurant (or related venue). ATUS “where” codes 

denoted the respondent’s location during an activity, and researchers used this information in 

conjunction with the sequence of events relating to a fast-food purchase. Generally, if an individual 

reported time to travel to a restaurant and purchase food, the food purchase was classified as fast 

food; if an individual reported time to travel to a restaurant, eat the food, and then pay for the food, 

this purchase was classified as sit down. Note that ATUS data do not indicate the type of food and/

or beverage purchased, so the purchase could have been just a cup of coffee, or the food could have 

been purchased at a convenience store rather than a restaurant. For a small number of respondents, 

researchers applied other rules to identify fast-food and sit-down purchases (see appendix for more 

details and a discussion of the limitations to identifying fast-food purchase).
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Descriptive Statistics

This section describes differences in fast-food and sit-down restaurant purchasing behavior between 

subpopulations and over time. Because of evidence that the Great Recession has had a lasting impact 

on FAFH purchasing behavior, the first subsection discusses differences in time spent eating and 

drinking, and engaging in other household activities, and fast-food and sit-down restaurant participation rates by gender and employment status. The second subsection analyzes changes in fast-food 

and sit-down participation rates over the business cycle. All estimates are weighted to be nationally 

representative, and a 90-percent level of confidence was used to determine whether estimates were 

statistically different between subpopulations. 

Differences Among Gender, Employment Status, and Fast-Food 

Purchasing Groups

In 2003-11, on an average day, Americans age 18 and older spent 67.5 minutes engaged in primary 

eating and drinking (i.e., eating and drinking as an individual’s main activity) (fig. 1; see appendix 

tables 1 and 2 for more detailed time-use estimates, participation rates, and distributions of eating 

behaviors). This total comprises 14.1 minutes eating out, mostly at sit-down restaurants, and 53.4 

Figure 1

Time spent in various activities on an average day, 2003-11, age 18 and older

Notes: N = number of observations. 

*Time spent looking for employment is considered work. 

**Other activities includes all travel time. 

Source: USDA, Economic Research Service estimates calculated from American Time Use Survey data.

Minutes

513.9 508.9 518.7 496.8 547.4 491.0

45.9 38.1 53.2 45.5

46.6

46.3

33.5 18.0

47.9

26.0

48.0

20.0

38.3

26.5

49.3

34.5

45.9

41.6

212.8 255.6

173.0 317.5

8.6* 241.2

162.0 176.3 148.6

128.7

226.8

131.8

17.9 23.3

12.8

17.7

18.1

21.6

348.3 323.7 371.1

307.6

428.0

389.2

14.1 14.5 13.7 14.8 12.7 16.3 53.4 55.1 51.7 51.1 57.7 40.9

0

160

320

480

640

800

960

1,120

1,280

1,440

Total

(N=117,805)

Men

(N=50,751)

Women

(N=67,054)

Employed

(N=76,451)

Not 

employed

(N=41,354)

Fast-food

purchasers

(N=15,122)

Eating and drinking elsewhere

Eating and drinking out

Other activities**

Physical activity

Television

Work

Caring activities

Meal preparation and cleanup

Grooming and other personal

care

Sleep
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minutes eating elsewhere.5 In addition, 19.5 percent of the population ate at a sit-down restaurant on 

an average day in 2003-11, and 13 percent purchased fast food or carryout food (fig. 2). Eating at a 

sit-down restaurant and purchasing fast food are not mutually exclusive, as 1.7 percent of Americans 

did both on an average day during the period (not shown). Only about one-third of those who 

purchased fast food reported eating at the restaurant (not shown).

Time-use patterns of men and women over 2003-11 reveal many of the same differences identified 

by other ATUS researchers; in particular, men spend more time in eating/drinking activities, paid 

work, and watching television, whereas women spend more time in grooming; meal preparation, 

cleanup, and other household tasks; and caring for others. Interestingly, compared with women, 

more men ate at a sit-down restaurant (20.4 percent versus 18.5 percent) and purchased fast food 

(13.5 percent versus 12.5 percent). These findings are in line with Byrne et al. (1996), who argued 

that households with female household managers may purchase less FAFH because they have better 

cooking skills. 

5Totals do not always add up exactly due to rounding, and may be off by ±0.1 minute or ±0.1 percentage point.

Figure 2

Participation rates for FAFH, on an average day, 2003-11, age 18 and older

Note: Travel time is not included in the calculation of participation rates. 

FAFH = Food away from home.

Source: USDA, Economic Research Service estimates calculated from American Time Use Survey data. 

63

65

67

69

71

73

15

16

17

18

19

20

21

Total

Men

Women

Employed

Not employed

Fast-food purchaser

Total

Men

Women

Employed

Not employed

Fast-food purchaser

Minutes spent

Ate at sit-down restaurants

11.0

11.5

12.0

12.5

13.0

0

20

40

60

80

100

Total

Men

Women

Employed

Not employed

Fast-food purchaser

Total

Men

Women

Employed

Not employed

Fast-food purchaser

Purchased fast food or carryout Minutes spent

Percent

Percent
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The population that was not employed spent 8.6 minutes per person on an average day looking for 

work, which is considered a work-related activity. Because the ATUS data span the 7-day week, 

including holidays, and most people work only 5 days a week, employed averages reflect both those 

who are employed and engaged in paid work on an average day and those who are employed but did 

not engage in paid work. Those who were employed spent, on average, less time in eating/drinking 

than those not employed (65.9 minutes versus 70.5 minutes) and also less time in meal preparation 

and cleanup (26 minutes versus 48 minutes), on the average day. 

However, 20.5 percent of those employed ate at a sit-down restaurant on the average day in 2003-11, 

a higher rate than those not employed (17.4 percent), although the average amount of time spent at 

the restaurant was about the same for the two groups, about 71 minutes. Similarly, 15.2 percent of 

those employed also purchased fast food, compared with only 8.8 percent of those not employed. 

Of particular interest is time use among those who purchased fast food. Compared with averages for 

the total population, fast-food purchasers spent less time in primary eating/drinking, sleeping,6 meal 

preparation and cleanup and other household activities, and watching television, and more time in 

caring for household and nonhousehold members and in paid work and accompanying travel. Fastfood purchasers were considerably more likely than the total population to report no primary eating/

drinking (8.4 percent versus 4.8 percent) and more likely to report that all their eating/drinking was 

at an eating-out establishment (7.5 percent versus 5.4 percent) (fig. 3). 

Because fast-food purchasers are more likely to report no primary eating/drinking, there was the 

possibility that they engage in more secondary eating, that is, eating while doing another “main” 

activity. Based on data from the 2006-08 ATUS Eating & Health Module, fast-food purchasers 

spent about the same amount of time in secondary eating on an average day as the total population (23.1 minutes versus 23.9 minutes) but more time in secondary drinking (76.9 minutes versus 

65.4 minutes). In addition, fast-food purchasers were more likely to engage in secondary eating or 

drinking while at work or driving a vehicle than non-fast-food purchasers.

6Fast-food purchasers spent, on average, 3.0 minutes in sleeplessness and 488.0 minutes actually sleeping, for a total of 

491.0 minutes. The total population spent, on average, 3.5 minutes in sleeplessness and 510.5 minutes actually sleeping, 

totaling 513.9 minutes.
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Figure 3

Distribution of eating behaviors, 2003-11, age 18 and older

Source: USDA, Economic Research Service estimates calculated from American Time Use Survey data.

Percentage

14.8 15.4 14.1 15.2 13.9 18.3

5.4 5.7 5.2 6.0 4.2

7.5

75.0 74.2 75.9 73.6 77.9 65.9

4.8 4.8 4.9 5.2 4.0 8.4

0

10

20

30

40

50

60

70

80

90

100

Total Men Women Employed Not employed Fast-food

purchasers

% who ate out and elsewhere % who ate out only

% who ate elsewhere only % who reported no primary eating/drinking
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Changes Before and After the Business Cycle Peak

The National Bureau of Economic Research dates the last U.S. business cycle peak as December 

2007, the end of an expansion, and the beginning of a recession that would last until the business cycle trough June 2009, ending the Great Recession. Although a new expansion started at the 

trough, economic recovery was slow post-recession, and, in particular, the labor market was slow to 

recover. It was not until May 2014 that the economy recovered the 8.7 million jobs lost during the 

recession, fully 5 years after the trough. Although time-use patterns change little if any year to year, 

the economic impact of a recession, especially one with the depth of the 2007-09 recession, would 

be expected to cause changes in Americans’ activities due to higher unemployment, lower household income, lower consumer confidence, and other factors, such as a decreased rate of household 

formation.7 

In this study, we analyzed the time-use patterns of Americans during January 2003-November 2007 

(the ATUS data available for the 2001-07 expansion) and during December 2007-December 2011 

(post-Great Recession peak)8 (table 1). For the total population, time-use patterns reveal small but 

systematic changes in Americans’ time-use patterns from one period to the next—less time, on 

average, spent in paid work and in travel; more time spent in meal preparation; less time spent in 

making purchases as households’ discretionary income decreased or households’ uncertainty about 

future income caused them to cut back on spending; and more time watching television. However, 

average time spent in primary eating/drinking stayed essentially the same in both periods, but the 

amount of time spent eating out decreased and the amount of time spent eating elsewhere (e.g., at 

home, at work) increased. 

The share of the population who reported no primary eating/drinking on an average day dropped 

from 5.1 percent pre-peak to 4.5 percent post-peak (table 2). Although the difference in these estimates is statistically significant, this drop is likely due to a statistical artifact—the ATUS had a large 

number of respondents (9 percent) who reported no primary eating/drinking in the first year of the 

survey (2003). As a result, changes were made in the survey instrument in 2004. The share of the 

population with no primary eating/drinking has been consistently lower in the years that followed. 

Among the differences in pre- and post-peak changes between men and women, the most notable is 

the increase in men’s meal preparation time from 17.0 to 19.2 minutes. This finding is consistent with 

Berik and Kongar (2013), who found that gender difference in housework hours narrowed during the 

recession from a small increase in fathers’ unpaid work and a decrease in mothers’ unpaid work. 

Time-use patterns of fast-food purchasers were fairly stable between the two periods and exhibited 

less change than those of the total population. Particularly striking is that the share of fast-food 

purchasers who ate at a sit-down or fast-food restaurant stayed about the same, while the share of the 

total population who ate out declined.

7The rate of household formation—people setting up new households—dropped considerably over 2007-11 as families 

and unrelated individuals “doubled up” in the same household (Elliott et al., 2011; Paciorek, 2013).

8We also analyzed data for the time periods January 2003-November 2007, December 2007-June 2009, and July 

2009-December 2011 and found little difference between time-use patterns for December 2007-June 2009 and for July 

2009-December 2011. This is likely due to the soft labor market during the recovery.
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The rate of eating out at sit-down restaurants declined from 20.4 percent of the population on an 

average day pre-peak to 18.4 percent post-peak (table 2). With the rate of fast-food purchase staying 

essentially the same, 13.2 to 12.8 percent (not a statistically significant difference), Americans maintained their pattern of purchasing fast food, despite or because of the recession. 

The share of the population age 18 and older purchasing fast food on an average day appears to have 

trended upward between 2003 and 2005 but then leveled off and declined slightly in 2011 (fig. 4). 

The rate at which these individuals purchased food from sit-down restaurants, however, appears to 

be in secular decline since before the recession began, at the start of the financial crisis in February 

Table 1

Time spent in various activities on an average day, 2003-11, age 18 and older, by time period, in minutes

Total Men Women

Pre-BCP Post-BCP Pre-BCP Post-BCP Pre-BCP Post-BCP

Number of observations 67,746 50,059 29,017 21,734 38,729 28,325

Eating and drinking 67.4 [±.37] 67.5 [±.54] 69.5 [±.60] 69.7 [±.74] 65.6 [±.49] 65.3 [±.73]

 Eating/drinking out 14.7 [±.30] 13.4 [±.32] 15.0 [±.48] 13.9 [±.47] 14.4 [±.40] 12.9 [±.46]

 Sit-down restaurant 14.4 [±.30] 13.0 [±.31] 14.8 [±.47] 13.5 [±.46] 14.1 [±.39] 12.5 [±.45]

 Fast food/carryout 0.3 [±.03] 0.4 [±.04] 0.3 [±.04] 0.4 [±.07] 0.3 [±.05] 0.4 [±.07]

 Eating/drinking elsewhere 52.7 [±.37] 54.1 [±.47] 54.4 [±.57] 55.8 [±.66] 51.2 [±.44] 52.4 [±.64]

Personal care:

 Sleep 511.1 [±1.04] 517.2 [±1.22] 505.9 [±1.70] 512.2 [±1.74] 515.9 [±1.44] 521.8 [±1.62]

 Other personal care 45.8 [±.50] 46.0 [±.59] 38.2 [±.75] 38.0 [±.86] 52.9 [±.68] 53.5 [±.90]

HH activities:

 Meal prep and cleanup 32.8 [±.39] 34.2 [±.47] 17.0 [±.43] 19.2 [±.55] 47.5 [±.60] 48.3 [±.74]

 Other HH activities 80.0 [±.88] 75.4 [±1.08] 66.4 [±1.44] 63.9 [±1.50] 92.6 [±1.21] 86.1 [±1.48]

Caring activities:

 Caring for HH members 29.2 [±.51] 27.8 [±.61] 18.0 [±.52] 18.2 [±.78] 39.6 [±.78] 36.8 [±.83]

 Caring for non-HH 

 members 10.2 [±.40] 9.2 [±.50] 8.7 [±.59] 8.0 [±.63] 11.6 [±.57] 10.3 [±.63]

Work-related activities 216.8 [±1.71] 208.2 [±2.28] 262.1 [±2.90] 248.1 [±3.44] 174.8 [±1.94] 170.8 [±2.93]

Educational activities 13.9 [±.70] 14.3 [±.97] 12.8 [±1.05] 13.1 [±1.33] 14.9 [±.86] 15.4 [±1.26]

Purchases:

 Grocery purchases 6.4 [±.15] 6.5 [±.19] 4.4 [±.19] 4.8 [±.23] 8.3 [±.21] 8.1 [±.28]

 Other purchases 25.2 [±.45] 23.1 [±.45] 20.0 [±.64] 18.6 [±.65] 30.0 [±.67] 27.3 [±.72]

 Fast-food/carryout 

 purchases 1.6 [±.04] 1.5 [±.04] 1.6 [±.06] 1.6 [±.07] 1.5 [±.06] 1.5 [±.06]

Volunteer activities 16.1 [±.42] 17.7 [±.56] 13.9 [±.62] 15.5 [±.75] 18.0 [±.56] 19.9 [±.86]

Leisure activities:

 Watching television 156.7 [±1.25] 167.9 [±1.52] 170.2 [±2.06] 183.3 [±2.33] 144.2 [±1.36] 153.6 [±2.04]

 Other leisure 118.7 [±1.13] 114.6 [±1.36] 118.4 [±1.97] 112.1 [±2.03] 119.0 [±1.43] 117.0 [±1.76]

Sports and excercise 17.9 [±.50] 17.8 [±.54] 23.2 [±.82] 23.4 [±.96] 12.9 [±.53] 12.7 [±.49]

Communication 6.3 [±.21] 6.2 [±.22] 3.4 [±.18] 3.7 [±.26] 9.0 [±.32] 8.5 [±.34]

Travel 76.2 [±.70] 72.3 [±.70] 79.2 [±1.16] 75.0 [±1.09] 73.4 [±.86] 69.9 [±.94]

Other activities NEC 9.2 [±.33] 14.0 [±.48] 8.7 [±.53] 13.3 [±.75] 9.6 [±.42] 14.7 [±.62]

Continued—
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2007 (Federal Reserve Bank of St. Louis, 2014). So, despite the decline in 2011, it appears that the 

fast-food rate weathered the recession better than the sit-down rate.

For individuals who are employed, the moving average fast-food participation rate stayed fairly 

level since the start of the recession whereas the sit-down rate declined (figs. 5a and 5b). Both rates 

appear to have declined for those not employed. Not surprisingly, both sit-down and fast-food rates 

are lower for those not employed than for those employed.

Table 1

Time spent in various activities on an average day, 2003-11, age 18 and older, by time period, in minutes

Fast-food purchasers* Employed Not employed

Pre-BCP Post-BCP Pre-BCP Post-BCP Pre-BCP Post-BCP

Number of observations 8,739 6,383 44,685 31,766 23,061 18,293

Eating and drinking 56.9 [±1.17] 57.6 [±1.22] 65.8 [±.43] 66.0 [±.68] 70.9 [±.72] 70.1 [±.86]

 Eating/drinking out 16.4 [±.88] 16.3 [±.93] 15.2 [±.38] 14.3 [±.41] 13.7 [±.49] 11.7 [±.44]

 Sit-down restaurant 14.1 [±.85] 13.2 [±.89] 14.9 [±.38] 13.9 [±.41] 13.4 [±.48] 11.3 [±.44]

 Fast food/carryout 2.2 [±.25] 3.1 [±.33] 0.3 [±.04] 0.4 [±.05] 0.4 [±.07] 0.4 [±.08]

 Eating/drinking elsewhere 40.5 [±.92] 41.3 [±1.04] 50.6 [±.43] 51.7 [±.56] 57.1 [±.70] 58.4 [±.77]

Personal care:

 Sleep 488.7 [±2.97] 493.7 [±3.24] 494.8 [±1.36] 499.1 [±1.55] 544.6 [±1.85] 550.4 [±2.24]

 Other personal care 45.5 [±.83] 47.3 [±1.21] 45.4 [±.48] 45.7 [±.64] 46.8 [±1.08] 46.5 [±1.28]

HH activities:

 Meal prep and cleanup 19.7 [±.84] 20.4 [±1.01] 25.2 [±.40] 27.0 [±.45] 48.5 [±.90] 47.5 [±1.06]

 Other HH activities 62.9 [±2.25] 57.7 [±2.49] 65.5 [±.95] 62.0 [±1.06] 109.9 [±1.94] 99.9 [±2.25]

Caring activities:

 Caring for HH members 30.4 [±1.31] 30.2 [±1.78] 27.0 [±.53] 26.1 [±.68] 33.8 [±1.20] 31.0 [±1.12]

 Caring for non-HH 

 members 11.5 [±1.17] 11.0 [±1.10] 8.3 [±.46] 7.4 [±.46] 14.2 [±.75] 12.6 [±1.04]

Work-related activities 247.5 [±5.89] 233.8 [±7.11] 319.2 [±2.31] 315.6 [±2.96] 6.8 [±.71] 10.6 [±.94]

Educational activities 19.0 [±2.46] 18.3 [±2.81] 11.5 [±.71] 11.7 [±1.09] 18.8 [±1.59] 19.1 [±1.73]

Purchases:

 Grocery purchases 5.3 [±.35] 5.7 [±.43] 5.5 [±.17] 5.5 [±.21] 8.3 [±.31] 8.3 [±.38]

 Other purchases 43.8 [±1.43] 41.7 [±1.47] 23.3 [±.46] 21.3 [±.51] 29.1 [±.84] 26.4 [±.85]

 Fast food/carryout 

 purchases 11.6 [±.18] 11.7 [±.22] 1.8 [±.06] 1.8 [±.06] 1.1 [±.07] 1.1 [±.06]

Volunteer activities 17.8 [±1.65] 18.3 [±1.74] 13.4 [±.47] 14.6 [±.57] 21.5 [±.87] 23.5 [±1.04]

Leisure activities:

 Watching television 128.7 [±2.96] 135.5 [±3.94] 125.8 [±1.18] 132.1 [±1.60] 220.2 [±2.85] 233.9 [±2.98]

 Other leisure 113.3 [±3.17] 116.8 [±3.84] 96.1 [±1.19] 91.9 [±1.45] 165.1 [±2.45] 156.4 [±2.66]

Sports and excercise 22.2 [±1.49] 20.9 [±1.59] 17.7 [±.62] 17.8 [±.71] 18.2 [±.80] 18.0 [±.88]

Communication 7.4 [±.61] 7.5 [±.74] 5.0 [±.21] 4.7 [±.23] 9.0 [±.39] 9.0 [±.48]

Travel 110.4 [±2.29] 111.1 [±2.27] 83.4 [±.78] 80.5 [±.83] 61.4 [±1.29] 57.3 [±1.17]

Other activities NEC 9.0 [±1.21] 12.8 [±1.23] 7.3 [±.39] 11.0 [±.52] 13.1 [±.64] 19.5 [±1.06]

Notes: NEC = not elsewhere classified. HH = household. BCP = business cycle peak. 90-percent confidence interval in brackets. Eating out is 

defined as primary eating/drinking at the ATUS locations restaurant or bar, grocery store, other store/mall, and other place. Eating elsewhere 

is any other place, including at home or at the workplace. The pre-BCP time period is January 2003 to November 2007, and the post-BCP time 

period is December 2007 to December 2011. *Fast-food purchasers are individuals with at least one fast-food purchase. 

Source: USDA, Economic Research Service estimates using American Time Use Survey (ATUS) data.

— continued
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Overall, the summary statistics show fast-food and sit-down purchasing behavior varied by gender 

and employment status in that men and those employed purchased more fast food and sit-down food 

than women and those not employed. Also, fast-food purchasers had very different time-use patterns 

than the total population, with fast-food purchasers spending less time in eating and drinking, meal 

preparation and other household activities, and watching television but more time in caring for household members and in paid work. Over the business cycle, fast-food purchase behavior in terms of the 

share of the population purchasing fast food on an average day held fairly steady through and after the 

recession, while the share eating at sit-down restaurants declined. While there may seem to be certain 

associations between fast-food purchasing behavior and gender, employment, and time use, many other 

factors changed over the study period that may confound any associations discussed here.

Table 2

Participation rates and distribution of eating behaviors, 2003-11, age 18 and older, by time period

Total Men Women

Pre-BCP Post-BCP Pre-BCP Post-BCP Pre-BCP Post-BCP

Participation rates (percent) and time spent by 

participants (minutes):

% who ate out, sit-down 20.4 [±.34] 18.4 [±.37] 21.2 [±.54] 19.5 [±.57] 19.6 [±.46] 17.4 [±.53]

Ave. time spent eating at sit down 70.7 [±.76] 70.5 [±.92] 69.6 [±1.21] 69.0 [±1.33] 71.9 [±.97] 72.0 [±1.29]

% who purchased fast food/carryout 13.2 [±.30] 12.8 [±.33] 13.8 [±.47] 13.0 [±.51] 12.5 [±.36] 12.6 [±.43]

Ave. time spent in purchasing FF 11.8 [±.18] 11.9 [±.22] 11.7 [±.24] 12.0 [±.33] 11.9 [±.24] 11.8 [±.28]

% who ate out, FF restaurant 0.8 [±.08] 1.0 [±.10] 0.7 [±.10] 1.0 [±.16] 0.8 [±.10] 1.0 [±.13]

Ave. time spent eating at FF 41.2 [±2.04] 42.7 [±2.40] 38.8 [±2.81] 41.4 [±3.16] 43.1 [±2.97] 43.8 [±3.63]

Distribution of eating behaviors:

% who ate out and elsewhere 15.3 [±.31] 14.1 [±.34] 15.9 [±.48] 14.9 [±.52] 14.8 [±.39] 13.3 [±.50]

% who ate out only 5.7 [±.19] 5.1 [±.19] 5.9 [±.32] 5.3 [±.30] 5.4 [±.25] 4.9 [±.28]

% who ate elsewhere only 74.0 [±.38] 76.3 [±.41] 73.2 [±.57] 75.3 [±.62] 74.7 [±.50] 77.2 [±.60]

% who reported no primary E/D 5.1 [±.18] 4.5 [±.22] 5.1 [±.25] 4.4 [±.30] 5.1 [±.25] 4.7 [±.31]

Fast-food purchasers Employed Not employed

Pre-BCP Post-BCP Pre-BCP Post-BCP Pre-BCP Post-BCP

Participation rates (percent) and time 

spent by participants (minutes):

% who ate out, sit-down 21.0 [±.96] 20.2 [±1.16] 21.3 [±.42] 19.6 [±.47] 18.6 [±.54] 16.2 [±.53]

Ave. time spent eating at sit-down 67.4 [±2.21] 65.0 [±2.25] 70.2 [±.95] 70.9 [±1.16] 72.1 [±1.39] 69.6 [±1.36]

% who purchased fast food/carryout 98.0* [±.32] 97.7* [±.34] 15.2 [±.39] 15.1 [±.46] 8.9 [±.45] 8.5 [±.41]

Ave. time spent in purchasing FF 11.8 [±.18] 11.9 [±.22] 11.5 [±.21] 11.7 [±.25] 12.8 [±.39] 12.6 [±.45]

% who ate out, FF restaurant 5.4 [±.56] 7.3 [±.74] 0.7 [±.09] 1.0 [±.13] 0.8 [±.15] 0.9 [±.13]

Ave. time spent eating at FF 41.5 [±2.11] 42.9 [±2.44] 39.7 [±2.35] 40.4 [±2.17] 44.0 [±3.89] 47.7 [±5.83]

Distribution of eating behaviors: 

% who ate out and elsewhere 17.7 [±.96] 19.0 [±1.16] 15.6 [±.39] 14.6 [±.44] 14.7 [±.48] 13.2 [±.51]

% who ate out only 7.6 [±.66] 7.3 [±.67] 6.2 [±.26] 5.8 [±.25] 4.6 [±.31] 3.7 [±.30]

% who ate elsewhere only 66.1 [±1.20] 65.6 [±1.27] 72.8 [±.47] 74.6 [±.48] 76.4 [±.62] 79.4 [±.65]

% who reported no primary E/D 8.6 [±.70] 8.1 [±.83] 5.4 [±.21] 5.0 [±.29] 4.3 [±.31] 3.7 [±.29]

Notes: FF = fast food. E/D = eating/drinking. BCP = business cycle peak. Confidence interval in brackets. Eating out is defined as primary 

eating/drinking at the ATUS locations restaurant or bar, grocery store, other store/mall, and other place. Eating elsewhere is any other place, 

such as at home or at the workplace. The pre-recession period is January 2003 to November 2007, and the mid/post-recession period is 

December 2007 to December 2011. *Percent who purchased fast food/carryout is not 100 percent because some individuals do not report 

purchase time, only eating/drinking time. Here, the category “Only those who purchased fast food” includes those with zero purchase time 

but some fast-food eating/drinking time. 

Source: USDA, Economic Research Service estimates using American Time Use Survey (ATUS) data.
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Figure 4

Participation rates of fast-food and sit-down purchasers, age 18 and older

Source: USDA, Economic Research Service estimates calculated from American Time Use Survey data.

Percent

0

5

10

15

20

25

30

200320042005200620072008200920102011

Recession Beginning of financial crisis

Fast-food purchase rate 12-month moving average, fast-food rate

Sit-down purchase rate 12-month moving average, sit-down rate
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Figure 5

Participation rates of fast-food and sit-down purchasers, age 18 and older, 

by employment status

Source: USDA, Economic Research Service estimates calculated from American Time Use Survey data.

Percent

0

5

10

15

20

25

30

200320042005200620072008200920102011

a. Employed

Recession Beginning of financial crisis

Fast-food participation rate 12-month moving average, fast-food rate

Sit-down participation rate 12-month moving average, sit-down rate

0

5

10

15

20

25

30

200320042005200620072008200920102011

b. Not employed
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Multivariate Analysis

The descriptive results provide a motivation for multivariate treatment because many of the explanatory factors are correlated, which makes it difficult to determine the direct associations of these 

factors. For example, fast-food purchase appears to be related to employment and also to time spent 

in travel. However, those who are employed tend to spend more time in travel on an average day than 

those not employed, as travel includes their work commute. In addition, fast-food purchasers tend to 

spend less time than others in certain activities, such as household tasks and watching television, but 

spend more time in paid work. Lastly, men are more likely than women to eat out and are also more 

likely to be employed. As a consequence, time patterns are interrelated with labor force status and 

demographic characteristics, and with fast-food purchasing behavior as well. 

We modeled whether individual j purchased fast food, yj, as a function of a matrix of demographic, 

employment status, and household composition characteristics of individual j (Dj), the value of time 

as proxied by time spent in market labor, household, and leisure activities (Wj), geographic covariates including price for market m (Mmt

), which vary over time, t, and household income (Vj).9 Note 

that we modeled whether the individual, not the household, purchased fast food. Specifically, we 

adopted a probit model such that

(1) ' '' Pr[ 1 | , , , ] ( 1 2 34 ) j j j j mt j j j mt j py V = = DWM D W M =Φ β + β + β + β V ,

where Φ(⋅) is the cumulative distribution function for the standard normal distribution. We used a 

probit model as the vast majority of fast-food purchasers made one purchase on an average day.10

The marginal effect of the kth explanatory variable, xk, on the probability of fast-food purchase is 

calculated as

(2) / ( ') jk k dp dx =φ ββ X ,

where X is the matrix of Dj, Wj, Mmt, and Vj, and φ(⋅) is the density of the standard normal distribution. We evaluated the marginal effects in (2) at the means of the data11 and used the Delta method 

to calculate the standard errors of the marginal effects.

9We considered modeling demand for fast food as hurdle model such that the hurdle would be the probability of 

purchasing fast food, and then the second model would be predicting the time spent in purchasing and/or eating fast food 

conditional upon fast-food purchase. However, since the distribution of the time duration of purchasing fast food is fairly 

narrow, and the time spent eating fast food is not straightforward, we concluded that a double-hurdle model would not be 

useful. First, almost half of fast-food purchasers do not report eating fast food as a primary activity within 90 minutes 

of purchasing fast food, and those that do report eating fast food typically do so in a small window of time (between 15 

and 30 minutes). Looking at the 2006-08 ATUS Eating & Health Module data, which included data on secondary eating 

and drinking (i.e., eating/drinking while doing something else), we found that of those who reported no primary eating/

drinking time after fast-food purchase, only 22.5 percent reported secondary eating and only 9 percent reported secondary drinking after fast-food purchase. Consequently, we concluded that estimating the amount of time spent eating/drinking fast food would be difficult, likely to contain measurement error, and not particularly revealing because fast-food 

purchasers reported relatively short durations of eating/drinking occurrences, if any at all.

10Results show 85.3 percent of fast-food purchasers had only 1 purchase, 13.0 percent had 2 purchases, 1.4 percent 

had 3 purchases, and the remainder had 4-6 purchases. As a robustness check, we also modeled the number of fast-food 

purchases within a day by an individual as a zero-inflated Poisson model. The results of the zero-inflated Poisson models 

were consistent with the findings presented in this study.

11Wooldridge (2002) stated that “Putting in the averages for the binary variables means that the effect does not really 

correspond to a particular individual. But often the results are similar, and the choice is really based on taste” (p. 466). 
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We proxied the value of time by including indicators for employment and part-time schedule and the 

amount of time spent at various “necessary” and “committed” activities. Following Kalenkoski et al. 

(2011), “necessary” activities are sleeping and grooming, whereas “committed” activities are household activities (includes meal preparation), caring for household members, paid work and workrelated activities, and travel associated with these activities. These definitions are in line with the 

time poverty literature.12 If individuals have greater necessary and committed activities, they may 

be more likely to purchase fast food due to time pressures. We included time-use variables such as 

work time (1-12 hours and more than 12 hours), personal care time (sleep and grooming), household 

activities, care of household children and adults, and total travel time. Indeed, results show that fastfood purchasers spent less time in personal care activities due to less sleep and spent more time in 

paid work and travel than the total population. This indicates that time constraints are likely a factor 

in the fast-food purchase decision. We included the recreational activity watching television, which 

is by far the dominant recreational activity with the longest average time duration (2.7 hours a day) 

over the total population and usual subgroups.13 We also included primary eating and drinking time 

because in table 2 we found that fast-food purchasers were more likely to have no primary eating/

drinking time over the day, and we wanted to determine if a relationship exists between primary 

eating/drinking time and fast-food purchases while controlling for other factors. 

A problem with several of our time-use variables that proxy for the opportunity cost of time is that 

they may be endogenous. Endogeneity can arise if the household decision to prepare meals or go 

out to eat and the time associated with this decision is correlated with omitted variables that capture 

household production patterns not observed in equation (1).14 However, it is very difficult to find 

appropriate instruments for these time-use patterns (Cawley and Liu, 2012). To address the endogeneity that may arise from unobserved differences in household production patterns, we modeled fastfood purchasing behavior by household type (i.e., single person, single parent, couple no children, 

couple with children, other households no children, and other household with children).15

Endogeneity of the time-use variables can arise from division of labor decisions in household 

production for the different types of households, as they face different preferences and pressures 

and have different decisionmaking processes. For example, a single-person household would have 

12See Bittman (2002) and Burchardt (2008).

13Looking at total population (age 15 and older) averages, watching television has been the third-longest duration activity each year since the ATUS started. For 2011, sleeping was the longest duration activity at 8.71 hours on an average day, 

paid work was 3.20 hours, and watching television was 2.75 hours. Watching television dominates the combined major 

categories of leisure and sports, which have an average time of 5.21 hours on an average day. The next-longest duration 

activity was eating and drinking, at 1.24 hours (U.S. Bureau of Labor Statistics, American Time Use Survey—2011 

Results News Release, USDL-12-1246, June 22, 2012). 

14We performed the Smith-Blundell endogeneity test on time spent in household activities (including meal production 

time) for our probit model of fast-food purchase. The instrument used was female (=1 if respondent was female). Based 

on this test, we could not reject the null hypothesis that the model is specified properly and, hence, concluded endogeneity was not a problem. 

15These categories are based on the ATUS respondent and not the totality of the household, as we have information 

only on the individual who did or did not purchase fast food. The “other household” categories occur because of this 

focus on the respondent. For example, a household with a married couple with two children, one age 20 and the other age 

15, would have different categorizations depending on which family member was the respondent. If either parent was 

the respondent, then the category would be “married couple with children.” If either of the children was the respondent, 

then the category would be “other household with children.” Another example is a household that included just a married 

couple and a child age 20. If either parent was the respondent, then the household category would be “married couple 

without children” (because the child is age 18 or older), or if the 20-year-old child was the respondent then the household 

category would be “other household without children.”
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different meal and snack behavior than a married couple with children. In particular, the individual 

in the single-person household would make time-use decisions independent of others, as there are 

no others in the household. An individual in a married or unmarried couple may have meal and 

snack patterns without meal preparation if the other household member does those tasks. Hence, we 

estimate the model for the different household types to control for differences in division of labor. 

In these models, we include proxies for the division of labor decision, the employment status of 

the spouse/partner, the spouse/partner’s usual weekly work hours, or whether the spouse/partner’s 

usual weekly work hours vary. These proxies are in line with the home production literature.16 We 

acknowledge that estimation by household type may not completely eliminate the endogeneity issue, 

and, therefore, we only discuss results as associations between the time-use variables and fast-food 

purchase behavior in the analysis that follows.

The demographic characteristics include gender, age group, education level, citizenship status, race, 

and Hispanic ethnicity. Employment status of the respondent is also included to account for potential 

changes in employment during the Great Recession and its effect on fast-food purchasing status. In 

the “All” model with all individuals, we included household type and then estimated models for each 

household type. 

We also included several market-level and time-varying variables. We included indicators for 

Census region and metro/nonmetropolitan area of residence because previous studies found that 

nonmetro residence would be expected to have a negative relationship with the probability of fastfood purchase due to less access to fast-food establishments. While such regional indicators proxy 

for price effects, we also included the price of a limited-service meal.17 This is the average quarterly 

price of meals and entrees in a Census division at an establishment that does not have table service 

(Kumcu and Okrent, 2014). We inflated the price to 2011 dollars using the Census region Consumer 

Price Index for all items (BLS, 2013a). Indicators for quarter and year were included to account 

for seasonality in eating-out behavior. Also, an indicator of day of week was included—weekday 

day (Monday through Friday, and not a holiday) and weekend day or holiday (Saturday, Sunday, or 

holiday). We also included time-trend variables (1-9) because we used pooled cross-section data. 

Lastly, we included a monthly unemployment rate variable (BLS, 2013b) as well as an indicator 

of whether the ATUS respondent’s diary day occurred during the recession (December 2007-June 

2009) or after to capture the effects of the Great Recession. 

For household income, we include a fairly blunt variable to define “high-income” for 2003-11, indicating whether the household had income greater than 200 percent of the poverty threshold for a 

family of four. A more precise indicator was not used because of the difficulty working with the 

ATUS income variable.18

16See Davis (2013) for a comprehensive review.

17We also tried the ratio of the price of limited-service meals to the price of full-service meals, but that variable was 

not successful. In some Census divisions, the prices would move in lockstep, resulting in a constant ratio over the time 

period; in other divisions, the ratio varied considerably. With both these price patterns in the same variable, the net result 

was that the ratio variable was not significant. 

18The income survey question—household income over the previous 12 months—was asked of the respondent’s 

household in the first month of the Current Population Survey, which would have been 18-21 months before the ATUS interview. In addition, the CPS income variable has a large amount of nonresponse, which is why we included an “income 

value missing” variable in the model. To complicate the variable, imputation of missing income values began in January 

2010. These income imputations start appearing in the ATUS in March 2010.
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We present the marginal effects at the mean of the data for the probit model in two sections: (1) the 

entire ATUS sample for the period 2003-11 (table 3, coefficient estimates in appendix table 4); and 

(2) a subsample of the ATUS for the pre-recession period (i.e., January 2003-November 2007) and a 

subsample for the recession and post-recession period (i.e., December 2007–December 2011) (table 

4). All estimates are weighted to be nationally representative (see appendix table 3 for the mean 

values of the variables used in the models).19

Analysis of the Entire Sample Period, 2003-11

Looking at the “All” model with individuals from all household types and controlling for other 

covariates shows many of the demographic variables to be associated with the probability of fastfood purchase. Specifically, individuals age 18-24 were 3.5 percentage points (marginal probability 

of 0.035) more likely to make a fast-food purchase on an average day than the excluded group of 

individuals age 25-64, and those age 65 or older were 3.7 percentage points less likely to purchase 

fast food. Individuals with more education were more likely to purchase fast food than lesser 

educated individuals, and the probablity that noncitizens would purchase fast food, with a marginal 

probability of -0.059, was lower than that of citizens. Although we found a statistically significant 

difference in the rate of fast-food purchase between men and women in the descriptive statistics, the 

gender difference was not statistically different from zero after controlling for other factors. 

In terms of employment status, those employed were 2.4 percentage points more likely to purchase 

fast food than those unemployed, although having a part-time work schedule did not seem to make 

a difference. However, the analysis finds that the association between time spent in paid work on 

an average day and probability of fast-food purchase was nonlinear and negative. Compared with 

the excluded group (individuals with 0 minutes of paid work), those who engaged in paid work or 

work-related activities (such as job search) for some positive amount of time (up to and including 

720 minutes, or 12 hours) reduced the marginal probability of purchasing fast food by 1.8 percentage 

points. However, the net effect (2.4 - 1.8 = 0.6) was that those who were employed and who worked 

up to 12 hours on the average day still had a higher probability of purchasing fast food than others. 

Working more than 12 hours20 was associated with a reduction of the marginal probability of fastfood purchase of 8.8 percentage points, one of the larger model results. Closer investigation shows 

that the individuals who reported working more than 12 hours were more likely to own a business 

or farm (30.4 percent) and were concentrated in the management, health care, or transportation 

fields. For these individuals, the long hours indicate that it was difficult for them to get away from 

their jobs, due to either the nature of the work or the isolation of the work location—so they perhaps 

brought their own food or had food provided for them. Although this result may seem counterintuitive, note that it is those who were employed who did not work on the average day (i.e., on their day 

off) that had the largest marginal probability of fast-food purchase among the labor force and worktime groups (marginal probability of employment is 0.024). This finding may suggest that the time 

pressure of the work week spills into days off, making fast food a desirable option. 

19The “All” model and the household-type models include gender as an independent variable. We did not find evidence in either the descriptive statistics or the multivariate analysis to warrant dividing our analysis into more detailed 

subgroups, such as estimating the household-type models by gender.

20We analyzed the behavior for paid work hour by hour to determine whether a break point existed and found that the 

time-use patterns were noticeably different for those with more than 12 hours of paid work versus those with 1-12 hours 

of paid work.
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Continued—

Table 3

Marginal estimates at the mean based on probit model of fast-food purchase, 2003-11, by household type—continued

All Single person Single parent

Couple, 

no children

Couple with 

children

Other HH, 

no children

Other HH with 

children

Demographic 

characteristics:

 Female 0.001 -0.023*** 0.022 -0.002 0.011 0.006 0.011**

 Age 18-24 0.035*** 0.036*** -0.020 0.035** 0.019 0.055*** 0.022***

 Age 65+ -0.037*** -0.031*** -0.017** -0.044*** -0.038**

 Education, less

 than high school -0.019*** -0.022** 0.028 -0.022** -0.017 -0.007 -0.030***

 Education, some

 college 0.013*** 0.010 0.017 0.013** 0.011 0.018* 0.008

 Education, 

 college degree 0.015*** 0.003 0.034** 0.021*** 0.005 0.024** 0.006

 Education, 

 advanced degree 0.005 0.005 -0.017 0.001 -0.007 0.002 0.011

 Noncitizen -0.059*** -0.038*** -0.074** -0.028** -0.071*** -0.060*** -0.075***

 African-American/

Black -0.002 0.004 -0.011 -0.001 0.002 -0.003 -0.008

 Asian -0.028*** -0.033 -0.021 -0.004 -0.022 -0.061** -0.027**

 Mixed race 0.001 0.010 -0.118** -0.002 -0.005 0.025 -0.020

 Hispanic -0.011** -0.013 -0.024 0.007 0.005 -0.019 -0.014**

Geographic 

characteristics:

 Midwest region -0.059 -0.059 -0.007 0.005 0.015 -0.024* 0.006

 South region -0.002* -0.002 -0.001 0.004 0.015 -0.001 0.014**

 West region -0.028 -0.028 -0.018 -0.001 0.012 -0.001 0.016**

 Nonmetro 

 residence 0.001** 0.001 0.010 -0.001 0.013 -0.013 -0.010*

Labor force 

characteristics:

 Employed 0.024*** 0.021** 0.043** 0.017** 0.010 0.025** 0.027***

 Employed part-

 time schedule: -0.005 -0.011 -0.024 -0.010 0.053 -0.014 0.001

Household type:

 Single person HH 0.014***

 Single parent HH 0.025***

 Couple with 

 child(ren) HH 0.008

 Other type of HH, 

 with children 0.018***

 Other type of HH, 

 no children 0.012***

Household 

characteristics:

 Number in HH -0.015 0.013 -0.028 -0.006

 Number in HH 

 squared -0.001* -0.001 -0.002 0.003 0.001
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Table 3

Marginal estimates at the mean based on probit model of fast-food purchase, 2003-11, by household type—continued

All Single person Single parent

Couple, 

no children

Couple with 

children

Other HH, 

no children

Other HH with 

children

 Child less than

 age 6 in HH 0.001 -0.003 0.017 0.003

 Spouse 

 employed 0.017 -0.027

 Spouse’s usual

 weekly work hrs -0.001 0.001

 Spouse’s weekly

 work hrs vary -0.028 0.058*

 HH income> 

 200% poverty 

 threshold

0.020*** 0.007 0.014 0.010 0.055*** 0.027*** 0.025***

 HH income value

 missing 0.018*** 0.012 0.007 -0.005 0.049** 0.055*** 0.017**

Time use: 

 Work and work 

 related,1-12 hrs -0.018*** 0.002 -0.040** -0.009 -0.010 -0.025** -0.027***

 Work and work 

 related, >12 hrs -0.088*** -0.051*** -0.135*** -0.047** -0.082** -0.120*** -0.112***

 Primary eating/

 drinking, hours -0.034*** -0.029*** -0.032*** -0.028*** -0.028*** -0.033*** -0.044***

 Personal care 

 time, hours -0.007*** -0.006*** -0.008*** -0.006*** -0.006* -0.009*** -0.006***

 Housework, 

 hours -0.011*** -0.011*** -0.011*** -0.010*** -0.011*** -0.012*** -0.011***

 HH child/adult

 care, hours -0.001 -0.001 -0.001 -0.003 0.014** -0.002

 Watching TV, 

 hours -0.004*** -0.006*** -0.003 -0.003*** -0.004 -0.006*** -0.004***

 Travel time, hours 0.027*** 0.024*** 0.035*** 0.023*** 0.026*** 0.025*** 0.034***

Calendar variables:

 Weekend/holiday -0.001 -0.003 0.002 0.005 0.016 -0.010 -0.001

 Quarter 2 0.015*** 0.012* -0.008 0.006 0.014 0.032*** 0.018***

 Quarter 3 0.008** 0.005 0.023 0.001 0.011 0.019* 0.007

 Quarter 4 -0.002 -0.006 -0.007 -0.012* 0.002 0.008 0.004

 Time trend 1-9 0.002** -0.001 0.001 -0.001 -0.003 0.011*** 0.001

Unemployment rate -0.002** -0.003* -0.002 -0.001 0.004 0.001 -0.006***

Fast-food meal price 

(2011 $) -0.007** -0.010 -0.017 0.002 0.004 -0.018* -0.009

Post-business cycle 

peak (12/07) -0.001 0.019* 0.002 0.010 0.001 -0.057*** 0.013

Notes: HH = household. Alaska and Hawaii are not included due to missing fast-food meal prices. Other variables have missing values due 

to “Don’t know” and “Refused” responses. See appendix table 1 for means and more discussion of missing values. For the household types 

single parent and couple with children, analysis excludes those age 65 and older. Excluded group is male, age 25-64, high school diploma, 

not employed with zero hours worked on average day, citizen, White, non-Hispanic, Northeast and metro residence, couple without children 

household, household income less than 200 percent of the poverty threshold, weekday day, pre-December 2007, and first quarter (January, 

February, March). See appendix table 4 for coefficient estimates and model statistics. *indicates significance at the 90-percent level; 

**indicates significance at the 95-percent level; and ***indicates significance at the 99-percent level. Shading = not applicable.

Source: USDA, Economic Research Service estimates using American Time Use Survey and other data discussed in text.
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Similar to findings in the literature, the results of this analysis show that household composition and 

income had a significant effect on fast-food purchasing behavior. All households with the exception of couples with children were more likely to purchase fast food than the control of couples 

with no children. The coefficient for number in household squared was negative as expected and 

significant at the 90-percent level, although the effect was very small. This finding is consistent with 

McCracken and Brandt (1987), who found that more people in the household increased the probability of fast-food purchase except for larger families, who had a lower probability.

Despite the bluntness of the income indicator variables, having household income greater than 200 

percent above the poverty threshold for a family of four or having missing income values had positive, significant coefficients, meaning increased probability of fast-food purchase. A positive coefficient was expected for missing income value because higher income households tend to be less 

likely to report income.21 This is consistent with other studies that found that higher household 

incomes are associated with fast-food purchase (Stewart et al., 2004; Kim and Leigh, 2011).

Personal care time, which is mostly sleep, was negatively related to fast-food purchase. An increase 

of an hour of personal care time was associated with a decrease in the probability of fast-food 

purchase of about 1 percentage point (-0.007 marginal probability). This finding is consistent with 

the descriptive statistics earlier. Personal care activities are considered necessary activities, and the 

less time spent in personal care and committed activities, the more discretionary time that an individual has. However, with the finding of fast-food purchase being associated with less sleep, it is 

possible that these individuals who sleep less may be so time pressured that they do not have enough 

time to tend to their necessary tasks so they sacrifice sleep time.22 This finding is also consistent 

with other research that found less sleep is associated with poorer food choices (Buxton et al., 2009; 

Chaput et al., 2011; Nishiura et al., 2010).

Time spent in household tasks was negatively related to fast-food purchase, as expected. Meal preparation is part of household tasks, so by definition, fast-food purchasers would not be expected to spend as 

much time on meal preparation. Although the descriptive statistics showed fast-food purchasers spent 

slightly more time in caring for household children and adults than the total population, after controlling for other factors, time spent in caring activities does not seem to contribute to the probability of 

fast-food purchase. Greater travel time was associated with a greater probability of fast-food purchase 

as expected because of the need for individuals “on the go” to find fast and convenient food and 

because of the time restraints on eating typically associated with more hours of travel.23

21The authors’ analysis of missing income observations in the Current Population Survey Food Security Supplement 

found that the prevalence of food insecurity of the unknown income group was about the same as that for households 

with income 185 percent of the poverty threshold (Nord et al., 2007). Indeed, for other measures of food security in the 

report, the estimates of the missing income group were similar to those for households with income 185 percent of the 

poverty threshold. The authors concluded that the households with missing income were disproportionately households 

with income above 185 percent of the poverty threshold. 

22In a variation of the model presented, we included a variable for time poverty of the individual (not shown) and 

found that time poverty was associated with a lower probability of fast-food purchase, which is consistent with Kalenkoski and Hamrick (2013). In this analysis, we explore the components of time poverty and their association with fastfood purchase because we speculated that fast-food purchasers may be time pressured in a way that is not captured by a 

time-poverty threshold. Time-poor individuals have shorter average sleep time than the total population, which may be 

the result of time pressure and appears to give these individuals more discretionary time. In particular, fast-food purchasers had an average total discretionary time of 516.0 minutes, compared with 498.1 minutes for the total population, and 

the difference, 17.9 minutes, is similar to the difference between the average sleep time for fast-food purchasers (491.0 

minutes) and that for the total population (513.9 minutes).

23Using the 2006-08 ATUS Eating & Health Module data, we found that fast-food purchasers were more likely to 

engage in secondary eating or secondary drinking while driving a vehicle than other individuals.
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Watching televison, the one recreational time variable in the model, was negatively related to fastfood purchase. This was expected because watching TV is a discretionary activity, and fast-food 

purchase would be more likely to occur with time-pressured individuals. However, the effect is 

small—an additional hour of TV watching is associated with a 0.4-percentage-point decline in the 

probability of fast-food purchase. Perhaps this small result reflects the dominance of television as 

Americans’ main recreational activity.

Time spent in primary eating and drinking, the other discretionary activity variable in the model, 

was negative and significant—an additional hour of primary eating/drinking time is associated with 

a reduction of 3.4 percentage points in the probability of fast-food purchase. This is one of the larger 

marginal effects estimated. There is no inherent reason that actual eating/drinking time is less for 

a fast-food meal or snack than for other food, although there would be less waiting time to eat fast 

food, such as between courses at a sit-down restaurant.24 However, this result could also indicate 

different eating patterns, as several studies found an association between speed of eating and energy 

intake, satiety, and/or body mass index (Andrade et al., 2008; Leong et al., 2011; Greene et al., 2008; 

Sasaki et al., 2003). 

In short, although it appears that fast-food purchasers have more discretionary time than others—they 

spend less time sleeping, doing household tasks, and caring for others—they work more hours and 

spend more time traveling from place to place. They spend less time eating/drinking and are more 

likely to report no primary eating/drinking. It appears that fast-food purchasers are time pressed but 

not in a way that is easily measurable. Their time-use patterns stand out from those of other groups, 

and the differences suggest that fast-food purchasers have distinctly different eating patterns.

The market-level variables had mixed effects on the probability of fast-food purchase. Regional 

factors were not particularily strong. It is not surprising that the regional dummies are mostly insignificant because the model included the price of fast foods in each region, and previous studies used 

region to proxy for regional price differences (e.g., Jensen and Yen, 1996). Indeed, the analysis found 

that the real price of fast food was also negatively related, as expected. A dollar increase in the price 

(in 2011 $) of a fast-food meal was associated with a decline of about 1 percentage point of the probability of fast-food purchase, which is consistent with demand theory. The unemployment rate was 

negatively related to the probability of fast-food purchase, as expected. An increase in unemployment would lead to both lower household incomes for some and overall increased economic uncertainty that may curtail purchases. 

Looking at behavior over the business cycle, being in the post-business cycle peak period did not 

seem to affect the probability of fast-food purchase. The coefficient is negative, although very small 

and not statistically significant. This is consistent with the visual inspection of the fast-food purchase 

participation rates during 2003-11 (see fig. 3), findings based on expenditure data (e.g., Okrent and 

Alston, 2012), and data that show sales at fast-food restaurants did not change much between 2008 

and 2009 (USDA-ERS, 2013). In addition, the time trend variable is positive and significant, indicating that there may be a secular trend toward more individuals purchasing fast food on a given day. 

This could be due to the increased number of fast-food establishments and expanded food/beverage 

choices during the 2000s. 

24Waiting to eat time (activities 1102xx) is infrequently reported in the ATUS diaries. Fast-food purchasers and those 

who ate at sit-down restaurants both reported average “waiting associated with eating time” as less than 1 minute. It is 

likely that respondents instead report their wait time as socializing, grooming (restroom visit), reading, or cell phone use.
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Models by Household Type

The models of fast-food purchase behavior by household type included spouse’s labor force participation information as a means of proxying the household division of labor decision (table 3). These 

models were estimated to account for endogeneity. The household-type marginal effects are generally the same as the “All” model in direction and significance with some exceptions. Interestingly, a 

woman living alone was 2.3 percentage points less likely to purchase fast food, but a woman in an 

“Other household with children” was 1.1 percentage points more likely to purchase fast food. The 

proxies for household division of labor had mixed results. For a couple with no children, the spouse 

being employed increased the probability of fast-food purchase of 1.7 percentage points, although 

not significant. For a couple with children, the only significant variable in this group is work hours 

vary, with a marginal effect of 5.8 percentage points, one of the larger marginal effects. This means 

that individuals with children and with a spouse who works varied hours had a larger probability 

of fast-food purchase than others. Perhaps these individuals chose fast food because of the spouse’s 

nonstandard and irregular work hours, making it harder to plan meals.

Although the post-recession peak indicator was not significant in the “All” model, it is significant and 

positive in the single-person household—these individuals were 1.9 percentage points more likely 

to purchase fast food on the average day during or after December 2007 than before, whereas other 

households with no children were 5.7 percentage points less likely to purchase fast food after the business cycle peak. At the same time, these households had a positive and significant time trend, indicating that the secular upward trend of fast-food purchase mitigated the lower probability of fast-food 

purchase post peak. An increase in the unemployment rate was associated with a lower probability of 

fast-food purchase, as expected, but only for single-person households and other households with children. The remaining households did not have behavior associated with the unemployment rate. 

Results of the household-type models, acknowledging the endogeneity of household decisons, 

support the “All” model but also reveal associations not apparent when all household types are 

aggregated. However, about half of individuals are in other households, typically multigenerational 

families. Households categorized as other are a growing segment of the U.S. population, and in 2011, 

over 20 percent of all U.S. households had one additional adult, frequently an adult child between 

ages 18 and 34 (U.S. Census, 2011). Consequently, the model results for this growing segment are 

relevant and not just a catchall for those who do not fit the traditional household categories. 

Analysis of Pre- and Post-Business Cycle Peak

The effects of the business cycle on fast-food purchasing behavior were investigated by estimating 

the model for the time periods January 2003 through November 2007 and December 2007 through 

December 2011. First, likelihood ratio tests for structural change were conducted on a probit model 

using the Chow test. Chi-squared value for the probability of fast-food purchase was found to be 

greater than the upper tail critical value of chi-square at the 0.1 level (i.e., probability less than the 

critical value is 0.999), indicating a structural break in the data when evaluating 2003-07 and 2007-11. 

Even though figure 4 shows that the participation rates for fast-food purchase seemed unaffected by the 

recession and that the indicator for post-business cycle peak in the probit model of all data (table 4) was 

statistically insignificant, the Chow test indicated that the slope and intercept coefficients for the period 

before the recession were statistically different from those during and after the recession. 
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Table 4

Selected marginal estimates at the mean based on probit model, pre- and post-BCP, by household type—continued

All Single person Single parent

Couple no 

children

Couple with 

children

Other HH, no 

children

Other HH 

with children

Labor force and HH 

characteristics: Pre-BCP (January 2003 – November 2007)

 Employed 0.015*** 0.007 0.039* 0.014 -0.018 0.018 0.016*

 Employed part-time

 schedule -0.030** 0.002 -0.076 -0.018 0.057 -0.072* -0.026

 HH income > 200% 

 poverty thresh 0.022*** 0.011 0.001 0.011 0.036* 0.025** 0.035***

 HH income value 

 missing 0.022*** 0.015 0.030 -0.005 0.036 0.064*** 0.022**

Time use:

 Work and work 

 related,1-12 hours -0.011** 0.008 -0.042* -0.007 0.011 -0.019 -0.015*

 Work and work related, 

 >12 hours -0.086*** -0.048** -0.100* -0.075*** -0.057 -0.126*** -0.090***

 Primary eating/drinking, 

 hours -0.036*** -0.027*** -0.041*** -0.028*** -0.028*** -0.036*** -0.050***

 Personal care time, 

 hours -0.007*** -0.005*** -0.009** -0.006*** -0.011** -0.009*** -0.006***

 Housework, hours -0.010*** -0.011*** -0.006 -0.009*** -0.010*** -0.010*** -0.011***

 HH child/adult care, 

 hours -0.002 -0.002 -0.008 -0.006 0.014 -0.002

 Watching TV, hours -0.004*** -0.007*** -0.006* -0.003** -0.002 -0.006*** -0.002*

 Travel time, hours 0.026*** 0.022*** 0.041*** 0.021*** 0.024*** 0.022*** 0.035***

 Time trend 1-9 -0.003 -0.014** 0.009 -0.014* 0.006 0.002 0.005

Business cycle and 

price variables:

 Unemployment rate -0.018* -0.040** 0.035 -0.036* 0.029 -0.032 0.004

 Fast-food meal price 

 (2011 $) -0.009* -0.006 0.017 0.005 -0.013 -0.029* -0.014

Labor force 

characteristics: Post-BCP (December 2007 – December 2011)

 Employed 0.034*** 0.034*** 0.052** 0.021** 0.044** 0.032** 0.040***

 Employed part-time 

 schedule 0.026 -0.024 0.042 -0.002 0.061 0.046 0.043

 HH income > 200% 

 poverty threshold 0.017*** 0.002 0.030 0.008 0.071*** 0.028** 0.012*

 HH income value 

 missing 0.009 0.006 -0.054 -0.009 0.068** 0.033* 0.010

Time use: 

 Work and work 

 related,1-12 hours -0.027*** -0.004 -0.034 -0.011 -0.031 -0.033** -0.043***

 Work and work related, 

 >12 hours -0.089*** -0.051** -0.198*** -0.020 -0.106 -0.115** -0.142***

 Primary eating/drinking, 

 hours -0.032*** -0.031*** -0.025** -0.027*** -0.028** -0.030*** -0.038***

 Personal care time, 

 hours -0.007*** -0.007*** -0.008** -0.006*** 0.001 -0.009*** -0.006***

Continued—
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Table 4 presents estimates for the two periods for all household types. Results are similar to those 

for the model for 2003-11, with small changes post-business cycle peak. Several of the variables have 

slightly smaller marginal effects for the post-peak period, reflecting the decline in jobs, income, and 

consumer confidence due to the recession and the recovery’s soft labor market. The biggest change 

pre- and post-peak is for those employed—being employed added 1.5 percentage points to the probability of fast-food purchase pre-peak but added 3.4 percentage points post-peak. Perhaps those who 

were employed shifted expenditure from sit-down restaurants to fast food during the recession and 

afterward. This increase in probability of fast-food purchase by those employed seems to hold up the 

participation rate (see figs. 4 and 5), as many of the post-peak changes decreased the probability or 

increased the probability by very small amounts. 

Results for the household-type models show that individuals who were single parents had a large 

change in behavior following the business cyle peak (BCP). For this group, the probability of 

purchasing fast food pre-BCP for those employed was 3.9 percentage points greater than that for 

those not employed but then increased to 5.2 percentage points post-BCP. Single parents’ fast-food 

purchase was not associated with the meal’s real price pre-peak, but post-peak, a $1 increase (2011$) 

was associated with a reduced probability of 5.1 percent.

Table 4

Selected marginal estimates at the mean based on probit model, pre- and post-BCP, by household type—continued

All Single person Single parent

Couple no 

children

Couple with 

children

Other HH, no 

children

Other HH 

with children

 Housework, hours -0.012 *** -0.011 *** -0.016 *** -0.012 *** -0.012 *** -0.014 *** -0.010 ***

 HH child/adult care, 

 hours

0.001 0.002 0.006 -0.001 0.014 -0.002

 Watching TV, hours -0.005 *** -0.005 *** -0.001 -0.002 -0.005 -0.005 ** -0.006 ***

 Travel time, hours 0.029 *** 0.027 *** 0.029 *** 0.026 *** 0.026 *** 0.027 *** 0.034 ***

 Time trend 1-9 -0.002 -0.004 0.009 -0.005 0.007 0.001 -0.002

Business cycle and 

price variables:

 Unemployment rate 0.001 -0.001 -0.010 0.004 -0.002 0.006 -0.004

 Fast-food meal price 

 (2011 $)

-0.009 * -0.013 -0.051 ** -0.004 0.027 -0.017 -0.009

Notes: HH = household. BCP = business cycle peak. Alaska and Hawaii are not included due to missing fast-food meal prices. Other variables 

have missing values due to “Don’t know” and “Refused” responses. See appendix table 1 for means and more discussion of missing values. 

For the household types single parent and couple with children, analysis excludes those age 65 and older. Excluded group is male, age 25-

64, high school diploma, not employed with zero hours worked on average day, citizen, White, non-Hispanic, Northeast and metro residence, 

couple without children household, household income less than 200 percent of the poverty threshold, weekday day, pre-December 2007, and 

first quarter (January, February, March).* indicates significance at the 90-percent level; ** indicates significance at the 95-percent level; and *** 

indicates significance at the 99-percent level. Shading = not applicable.

Source: USDA, Economic Research Service estimates using American Time Use Survey and other data discussed in text.
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Discussion

The analysis addressed questions on fast-food purchasing behavior that have not been addressed 

in the literature due to data limitations. Specifically, what characteristics are related to fast-food 

purchase? And, did fast-food purchase behavior change during or after the Great Recession?

First, findings show that Americans purchase fast food as a means of saving time. Supporting this 

claim, the probability of fast-food purchase is negatively correlated with time in primary eating 

and drinking (and, in some cases, no primary eating and drinking at all), in personal care activities 

(primarily sleep), and in meal preparation and was positively correlated with employment and time 

spent in travel from place to place. 

Findings also show that the greater the number of hours a person engaged in the labor market on 

an average day, the less likely he or she was to purchase fast food, even after controlling for income 

and education. This is somewhat surprising given that McCracken and Brandt (1987), Byrne et 

al. (1998), Stewart and Yen (2004), and Stewart et al. (2004) found that an increased value of and 

hours employed in a week by the household food manager increased demand for fast food. However, 

previous studies used weekly hours spent in work, whereas this study used daily hours spent in work 

and work-related activities, and the weekly hours worked may be a proxy for whether a person was 

employed full-time or part-time. Hence, these previous studies might actually be picking up the 

positive relationship between full-time work and demand for fast food, which this study also found. 

Also, the previous studies do not control for time spent in leisure, travel, and household activities, 

which this study found to have statistically significant relationships with demand for fast food. Lastly 

findings show that those employed but on their day off were more likely than others to purchase fast 

food, indicating that the time pressure from work days spills over into nonwork days.

Second, fast-food purchasers have different eating patterns than others. They spend less time eating 

meals and snacks on an average day and are more likely to report no primary eating and/or drinking 

beverages time. Although they spend about the same amount of time in secondary eating and 

drinking (i.e., eating and drinking during another “main” activity), they are more likely to engage 

in eating while at work and while driving. To the extent that eating quickly may not be ideal, that 

eating/drinking activities may be so inconsequental to an individual that they are not remembered as 

part of the day, and that eating is done while one is engaged in activities that demand focus—work 

and driving—it appears that fast-food purchasers may have poor eating habits.

Third, youth and household composition (single person, single parent, and households with children) 

were found to have a relatively large positive and statistically significant relationship with probability 

of fast-food purchase. Given that average ages and household composition have changed significantly 

over the last several decades, these factors will become important determinants of fast-food industry 

growth (Stewart et al., 2004). Also, income was found to be associated with an increase in probability of fast-food purchase by 1.7 percent, which is consistent with Stewart et al. (2004), Byrne et al. 

(1998), and McCracken and Brandt (1987), suggesting that households with more income purchase 

more leisure as well as dining amenities. Note that this effect may be attributable to the bluntness 

of the income variable, and usage of an income variable with finer income gradients or that was 

continuous may lead to a more complete interpretation of the association of income and fast-food 

purchasing behavior over all income levels.
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Fourth, fast-food purchase behavior in terms of the share of the population purchasing fast food 

on a given day was found to stay fairly constant during and after the Great Recession. Indeed, 

those employed were even more likely to purchase fast food during the recession than before the 

recession. Purchasing fast food has become an established part of Americans’ meal and snack 

habits, seemingly unaffected by an economic downturn. Another plausible explanation is that the 

downturn fueled the demand for low-priced fast food as Americans spent less money at sit-down 

restaurants. This is consistent with findings that average household expenditures on fast food 

remained unchanged or even increased slightly during the recession (Okrent and Alston, 2012). 

Also, according to ERS’s Food Expenditure Series (USDA-ERS, 2013), sales of meals and snacks at 

sit-down restaurants declined between 2008 and 2009 from $750 to $725 per capita, while sales of 

meals and snacks at fast-food restaurants increased from $264 to $275. 

Todd (2014) found a 166-calorie per day decline from FAFH for adults age 25-64 between 2005-06 

and 2009-10, with fast-food consumption accounting for just over half of this decline (84 calories). 

However, once the analysis controlled for demographic and employment characteristics and income, 

energy from FAFH was found to decline by 134 calories per day, with a little over a third of this 

amount attributable to fast-food consumption. This may seem contradictory to this study’s finding 

that the rate of fast-food purchase appeared to be unaffected by the recession. However, it may be 

the case that individuals purchased fast food at the same frequency during the recession but were 

choosing items of lesser calorie content. In fact, many fast-food restaurants have started offering 

“healthier” menu items, especially in States and municipalities that have implemented regulations 

requiring provision of nutrition information at chain restaurants (e.g., Bruemmer et al., 2012). Also, 

Todd (2014) found evidence that between 2005-06 and 2009-10, the quality of at-home food and 

away-from-home food increased, partly due to individuals demanding more nutritional foods.

A better understanding of fast-food purchase behavior can inform nutrition programs and education, 

especially since fast food may be higher in calories and less nutritious than some other foods. To the 

extent that fast-food purchasers appear to be more time pressured than others, the challenge for policymakers interested in improving the dietary quality of Americans is to develop policies that make 

lower calorie nutritious meals fast and convenient.
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Appendix—Data Definitions and Limitations

This study used data from the 2003-11 American Time Use Survey (ATUS). The survey sample 

included respondents age 18 and older with “good” diaries, resulting in 117,805 respondents. “Bad” 

diaries are those with variable TUDQUAL2, “Collected from interviewer after interview: why 

do you think the data should not be used?,” equal to 1 (Respondent intentionally providing wrong 

answer), 2 (Respondent trying to provide correct answer, but could not correctly remember his/her 

activities), 3 (Respondent deliberately reported very long duration activities), or 4 (Other). There 

were 861 of these cases.

Primary eating and drinking occurrences were ATUS activities 11xxxx (ATUS major activity group 

“Eating and Drinking”) and 050202 (“Eating and drinking as part of job, which includes eating/

drinking with clients, coworkers”).

We defined eating out as primary eating/drinking activities that took place at restaurant or related, 

such that 

TEWHERE = 4 (restaurant or bar),

 6 (grocery store),

 7 (other store/mall), or

 11 (other place).

Eating/drinking at TEWHERE=11, other place, is usually an entertainment venue when eating/

drinking is involved.25 We include these food purchases, as concessions at movie theaters, sports 

venues, and other entertainment venues are essentially counter-service restaurants. We classified 

all other primary eating/drinking as eating/drinking elsewhere, which includes meals purchased 

at schools.

Diary entries were classified as miscode if the respondent reported primary eating/drinking at the 

same time as traveling26 (i.e., activity is 11xxxx or 050202 and 

TEWHERE = 12 (vehicle, driver), 13 (vehicle, passenger), 14 (walking),

 15 (bus), 16 (subway/train), 17 (bicycle), 18 (boat/ferry), or

 19 (taxi/limo), 20 (airplane), 21 (other mode), or 99 (unspecified mode).

It is unclear if the respondent’s intention was to report primary eating/drinking while traveling, or if 

the interviewer recorded the activity on the wrong line. A total of 624 respondents have at least one 

travel miscode in their diaries, and the number of miscodes range from one to three in a diary. We 

reclassified these miscodes as travel time.

25Secondary eating and secondary drinking (survey questions in the 2006-08 ATUS Eating & Health Module) that 

were coded as “Other place” mostly took place at a stadium, arena, ball field, or a movie theater. Personal correspondence 

with Dorinda Allard, U.S. Bureau of Labor Statistics, and Jennifer Montcalm, U.S. Census Bureau, via email on February 28 and 29, 2008.

26Email correspondence with Rachel Krantz-Kent dated May 16, 2014, and ATUS Coding Rules 2011, version 

05/28/08, Miscellaneous rule #6. 
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Because some individuals reporting long-duration eating/drinking activities (11xxxx and 

050202) at a restaurant or related location, we identify those eating/drinking activities greater than 

180 minutes (3 hours) as “events” that are likely to be catered, such as receptions. Or, because 

TEWHERE=4 includes both restaurants and bars, long-duration activities could be watching sports 

at the bar or spending time at a nightclub. It is unlikely that these activities are regular sit-down 

dinners at a restaurant. For those who reported an eating activity that was over 120 minutes in 

their diaries, 180 minutes was the 75th percentile value. A total of 317 respondents reported eating/

drinking occasions at a restaurant (or related) of 180 minutes or more, and these cases were reassigned from eating out to primary eating/drinking elsewhere.

We identified carryout/fast-food purchases as activity 070103, food (not grocery) purchase that 

immediately follows a travel activity (180782) in the time diary. This identifies food purchased at a 

counter or from a delivery person where the respondent reported paying first. The examples given 

in the ATUS 2011 lexicon for activity 070103 are: paying the pizza delivery person, paying for 

meal at restaurant, paying check for a meal/drink/snack, picking up takeout food, buying fast food, 

placing order at a deli/fast food place, paying for fast food at drive-through, and talking to fast-food 

cashier/talking to the waiter. So, we excluded cases of 070103 that are paying for food or talking to 

a waiter at a sit-down restaurant. After the food is purchased, the individual may travel away from 

the restaurant (carry out the food) or may eat the food at the restaurant. Hence, we define fast-food/

carryout purchase as the sequence (1) activity 18xxxx (travel) and TEWHERE=mode of transport, 

and (2) activity 070103 (purchase food not groceries) and TEWHERE=4,6,7, or 11. A total of 11,908 

respondents have at least one reported carryout/fast-food purchase in their diaries over the sample 

period, and the number of carryout/fast-food purchases over the day range from one to six. 

A number of reported eating activities at restaurants and related locations are of short duration with 

no reported purchase of food. We identify short-duration restaurant visits as fast food if they 

are 20 minutes or fewer of eating/drinking (110101). Waiting time (110201) is not included in the 

20-minute limit. The median time of those who purchased carryout/fast food and then reported 

eating/drinking within the next two activities was 20 minutes. As a consequence, we categorize 

restaurant visits of short duration of 20 or fewer minutes as carryout/fast food. The respondent may 

have neglected to report paying for the food, or the respondent may not have been the person who 

paid for the food. A total of 4,035 respondents reported eating at a restaurant (or related) for a duration of 20 minutes or less, and the number of such occurrences in a diary ranged from one to four. 

The time reported as eating/drinking at a restaurant for these short-duration restaurant visits was 

reassigned from eating/drinking to purchasing food (070103), and an indicator was added for fast 

food/carryout for these respondents. We analyzed eating/drinking times of fast-food purchasers with 

these short-duration eating activities classified as eating/drinking time and not fast-food purchase 

time, and the results do not change—fast-food purchasers still had less primary eating/drinking time 

and were still more likely to report no primary eating/drinking time than others. Including the shortduration restaurant eating times as primary eating/drinking activities reduces the gap between the 

average time spent by fast-food purchasers in eating/drinking and others, but the difference is still 

statistically different.

There are a number of reported long-duration purchasing food activities (070103). These are 

frequently not followed by an eating/drinking activity in the time diary. Above, we identify shortduration restaurant visits as fast food if they are 20 minutes or fewer. Here, we identify these 

long-duration 070103 activities as purchasing and eating/drinking food if the reported activity is 

more than 20 minutes. There are 458 respondents who reported purchasing carry out/fast food 
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for durations greater than 20 minutes, with two respondents reporting two such occurrences. The 

time spent in these long-duration food purchasing activities was reassigned from purchasing food 

(070103) time to eating/drinking time (11xxxx and 050202). Although we reassigned the time, these 

cases were still counted as fast-food purchases. 

The adjustments on short-duration restaurant eating/drinking times and long-duration food 

purchases are a result of the ambiguity of respondents’ reporting on food purchases and 

consuming of these food purchases. Many of the respondents who reported a food purchase did 

not report eating/drinking activities within 90 minutes of the purchase. The short-duration restaurant eating/drinking activities with no food purchase reported had to include a food purchase, 

either by the respondent or someone else. Our goal in doing the reclassifications was to best identify fast-food purchase. 

Table A.1 defines each major activity group by ATUS activity code and TEWHERE.

A time diary is a narrative—the story of a person’s day. Because of the way a respondent may report 

his/her day, and the way in which interviewers code the reported activities, the nuances of fast-food 

purchase versus eating a meal at a sit-down restaurant may be lost. A respondent may not report 

every step of the activity and, for example, may not report paying for the food first before eating the 

meal at a fast-food restaurant. Or, it may be the case that the respondent was at a fast-food restaurant, 

but someone else paid for the meal so the respondent did not engage in the activity of purchasing 

food. A respondent is unlikely to report a purchase at a vending machine due to the short amount of 

time involved. Because of the lack of this specific information pertaining to food away from home, 

we made the adjustments described above. In some cases, we needed to infer the type of restaurant 

and specific type of activity. For example, a respondent reporting eating/drinking at a restaurant for 

a 10-minute duration is unlikely to be at a sit-down restaurant. Also, a respondent reporting being 

at a restaurant or bar from 10:00 p.m. to 2:00 a.m. is unlikely to be eating dinner and more likely to 

be at a bar or nightclub. There is the possibility that we inferred incorrectly in making these adjustments. In addition, we cannot tell the specific type of fast-food/carryout restaurant that the respondent visited. It could be a national chain limited-service restaurant, or it could be carryout from a 

sit-down restaurant.

There is also the limitation that we do not know what foods or beverages the respondent ate/drank 

and, consequently, the caloric intake. An occurrence of fast-food purchase could be just for coffee 

and not a meal or snack. We also do not know how much money the respondent spent (or someone 

else spent) on the food/beverages. 

More general limitations are that the ATUS asks the respondent to report his/her primary activity 

only, and this may miss eating as a secondary activity. Consequently, while the data contain the 

purchase of fast food, they may not contain the consumption of fast food. 

Another possible limitation is that we use a blunt income variable to indicate if the household income is 

“high income”—above 200 percent of the poverty threshold. Were a continuous income variable available that was timely and consistent over 2003-11, we may have been able to better identify probabilities 

of fast-food purchase for various income groups before and after the business cycle peak. 

A discussion of the reliability of the ATUS estimates, including sampling and nonsampling error, is 

in the Technical Note in each ATUS News Release. 
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Appendix table 1

Time spent in various activites on an average day, 2003-11, age 18 and older—continued

ATUS activity 

codes Total Men Women Employed Not employed

Fast-food 

purchasers

Number of 

observations 117,805 50,751 67,054 76,451 41,354 15,122

Minutes

Eating and 

drinking 11xxxx, 050202 67.5 [±.32] 69.6 [±.47] 65.5 [±.45] 65.9 [±.39] 70.5 [±.57] 57.2 [±.80]

Eating/

drinking out

11xxxx, 050202 

and TEWHERE=

4,6,7,11 14.1 [±.23] 14.5 [±.36] 13.7 [±.31] 14.8 [±.28] 12.7 [±.34] 16.3 [±.64]

Sit-down 

restaurant

as defined in 

text above 13.7 [±.22] 14.2 [±.35] 13.3 [±.31] 14.4 [±.27] 12.3 [±.33] 13.7 [±.61]

Fast food/

carryout

as defined in 

text above 0.4 [±.03] 0.3 [±.04] 0.4 [±.04] 0.3 [±.03] 0.4 [±.05] 2.6 [±.20]

Eating/

drinking 

elsewhere*

11xxxx, 050202 

and TEWHERE 

not 4,6,7,11 53.4 [±.29] 55.1 [±.41] 51.7 [±.41] 51.1 [±.33] 57.7 [±.53] 40.9 [±.65]

Personal care:

Sleep 0101xx 513.9 [±.84] 508.9 [±1.23] 518.7 [±1.12] 496.8 [±1.06] 547.4 [±1.48] 491.0 [±2.13]

Other 

personal 

care

0102xx, 

0103xx, 

0104xx, 

0105xx, and 

019999 45.9 [±.38] 38.1 [±.55] 53.2 [±.49] 45.5 [±.40] 46.6 [±.80] 46.3 [±.75]

HH activities:

Meal prep 

and cleanup 0202xx 33.5 [±.30] 18.0 [±.35] 47.9 [±.47] 26.0 [±.28] 48.0 [±.70] 20.0 [±.67]

Other HH 

activities

all 02xxxx 

activities 

(except 0202xx, 

020904) 77.8 [±.73] 65.2 [±1.03] 89.6 [±.99] 63.9 [±.75] 105.0 [±1.53] 60.5 [±1.78]

Caring activities:

Caring for 

HH members 03xxxx 28.6 [±.39] 18.1 [±.46] 38.3 [±.57] 26.6 [±.43] 32.5 [±.80] 30.3 [±1.08]

Caring for 

non-HH 

members 04xxxx 9.7 [±.31] 8.4 [±.42] 11.0 [±.39] 7.9 [±.32] 13.4 [±.62] 11.3 [±.82]

Work, work related, and educational activities:

Work-related 

activities

all 05xxxx 

activities 

except 050202 212.8 [±1.38] 255.6 [±2.18] 173.0 [±1.72] 317.5 [±1.95] 8.6** [±.62] 241.2 [±4.55]

Educational 

activities 

purchases 06xxxx 14.1 [±.56] 12.9 [±.87] 15.1 [±.70] 11.6 [±.62] 18.9 [±1.20] 18.7 [±1.92]

Purchasing activities:

Grocery 

purchases 70101 6.5 [±.11] 4.6 [±.14] 8.2 [±.16] 5.5 [±.13] 8.3 [±.25] 5.4 [±.29]

Other 

purchases

All 07xxxx 

(except 

080101), 

08xxxx, and 

09xxxx 24.2 [±.32] 19.3 [±.48] 28.7 [±.51] 22.4 [±.33] 27.8 [±.67] 42.8 [±1.02]

Fast-food/

carryout 

purchases

070103, 

specified in text 

above 1.5 [±.03] 1.6 [±.05] 1.5 [±.04] 1.8 [±.04] 1.1 [±.05] 11.6 [±.13]

Continued—
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Appendix table 1

Time spent in various activites on an average day, 2003-11, age 18 and older—continued

ATUS activity 

codes Total Men Women Employed Not employed

Fast-food 

purchasers

Government, civic, 

religious, 

volunteer 

activities

10xxxx, 14xxxx, 

15xxxx except 

150101 16.8 [±.36] 14.7 [±.48] 18.9 [±.55] 14.0 [±.37] 22.5 [±.69] 18.0 [±1.17]

Leisure and sports activities:

Watching 

television

120303, and 

120304 162.0 [±.95] 176.3 [±1.49] 148.6 [±1.26] 128.7 [±.94] 226.8 [±2.10] 131.8 [±2.27]

Other 

socializing, 

leisure, and 

screentime

all 12xxxx 

(except 120303 

and 120304), 

020904, 

120307, 

120308, and 

150101 116.8 [±.85] 115.4 [±1.36] 118.1 [±1.20] 94.2 [±.97] 160.9 [±1.79] 114.9 [±2.40]

Sports and 

exercise 13xxxx 17.9 [±.38] 23.3 [±.65] 12.8 [±.37] 17.7 [±.50] 18.1 [±.53] 21.6 [±1.13]

Communication 16xxxx 6.3 [±.16] 3.5 [±.17] 8.8 [±.24] 4.9 [±.16] 9.0 [±.33] 7.4 [±.49]

Travel 18xxxx 74.4 [±.53] 77.2 [±.82] 71.8 [±.68] 82.1 [±.60] 59.4 [±.90] 110.7 [±1.71]

Other 

activities 

NEC 50xxxx 11.4 [±.29] 10.9 [±.46] 11.9 [±.37] 9.0 [±.32] 16.2 [±.61] 10.8 [±.89]

Total time 1440 1440 1440 1440 1440 1440

Note: HH=household. NEC = Not elsewhere classified. 90-percent confidence intervals in brackets. Eating out is defined as primary eating/

drinking at the ATUS locations restaurant or bar, grocery store, other store/mall, and other places. Eating elsewhere is any other place. The main 

elsewhere locations are at home or at the workplace. *Eating elsewhere is anywhere that is not in a restaurant. **Work activities for those not 

employed are job search activities. 

Source: USDA, Economic Research Service estimates calculated from American Time Use Survey (ATUS) data.
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Appendix table 2

Participation rates and distribution of eating behaviors, 2003-11, age 18 and older

Total Men Women Employed

Not 

employed

Fast-food 

purchasers

Participation rates (percent) and average time spent (minutes) by participants: 

% who ate out, sit-down 19.5 [±.26] 20.4 [±.41] 18.5 [±.36] 20.5 [±.33] 17.4 [±.39] 20.6 [±.75]

Ave. time spent by those who ate out, 

sit-down 70.6 [±.62] 69.3 [±.91] 71.9 [±.81] 70.5 [±.77] 71.0 [±.98] 66.3 [±1.62]

% who purchased fast food/carryout 13.0 [±.22] 13.5 [±.37] 12.5 [±.28] 15.2 [±.31] 8.8 [±.30] 97.9* [±.20]

Ave. time spent in purchasing FF, those 

who purchased FF 11.9 [±.14] 11.8 [±.20] 11.9 [±.17] 11.6 [±.16] 12.7 [±.31] 11.9 [±.14]

Distribution of eating behaviors:

% who ate out and elsewhere 14.8 [±.23] 15.4 [±.37] 14.1 [±.31] 15.2 [±.30] 13.9 [±.34] 18.3 [±.69]

% who ate out only 5.4 [±.13] 5.7 [±.23] 5.2 [±.18] 6.0 [±.17] 4.2 [±.22] 7.5 [±.49]

% who ate elsewhere only 75.0 [±.30] 74.2 [±.43] 75.9 [±.41] 73.6 [±.35] 77.9 [±.44] 65.9 [±.89]

% who reported no primary eating/

drinking 4.8 [±.14] 4.8 [±.20] 4.9 [±.18] 5.2 [±.17] 4.0 [±.21] 8.4 [±.51]

Total 100.00 100.00 100.00 100.00 100.00 100.00

*Percent who purchased fast food/carryout is not 100 percent because some individuals do not report purchase time, only eating/drinking time. 

Here, the category “Only those who purchased fast food” includes those with zero purchase time but some fast-food eating/drinking time.

Notes: 90-percent confidence intervals in brackets. FF=fast food.

Source: USDA, Economic Research Service estimates calculated from American Time Use Survey data.
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Appendix table 3

Mean values of variables used in probit models—continued

N Mean

Min 

value

MaxvalueStandard 

error

90% CI

Min Max

Fast-food purchase (1=FF purchase, 2=no) 117,214 1.87 1 2 0.0014 1.865 1.870

Female (1=female, 0=male) 117,214 0.52 0 1 0.0004 0.517 0.519

Age 18-24 117,214 0.12 0 1 0.0006 0.124 0.125

Age 65+ 117,214 0.17 0 1 0.0002 0.165 0.165

Education, less than high school diploma 117,214 0.14 0 1 0.0013 0.134 0.139

Education, some college 117,214 0.26 0 1 0.0014 0.262 0.267

Education, college degree 117,214 0.18 0 1 0.0014 0.180 0.184

Education, advanced degree 117,214 0.10 0 1 0.0011 0.096 0.100

Employed 117,214 0.66 0 1 0.0014 0.658 0.663

Employed part-time schedule 117,214 0.02 0 1 0.0005 0.014 0.016

Noncitizen 117,214 0.08 0 1 0.0011 0.082 0.085

African-American/Black 117,214 0.12 0 1 0.0005 0.115 0.116

Asian 117,214 0.04 0 1 0.0010 0.038 0.041

Mixed race 117,214 0.01 0 1 0.0005 0.012 0.013

Hispanic 117,214 0.13 0 1 0.0004 0.131 0.132

Midwest region 117,214 0.25 0 1 0.0020 0.242 0.248

South region 117,214 0.36 0 1 0.0022 0.353 0.360

West region 117,214 0.21 0 1 0.0018 0.212 0.218

Nonmetro residence 117,214 0.19 0 1 0.0045 0.178 0.193

Single-person household 117,214 0.15 0 1 0.0019 0.147 0.154

Single-parent household 117,214 0.03 0 1 0.0005 0.028 0.029

Couple with child(ren) household 117,214 0.05 0 1 0.0009 0.052 0.055

Other type of household, with children 117,214 0.30 0 1 0.0011 0.297 0.301

Other type of household, no children 117,214 0.21 0 1 0.0020 0.205 0.211

Number in household, squared 117,214 10.90 1 256 0.0663 10.794 11.012

Child less than age 6 in household 117,214 0.79 0 1 0.0012 0.791 0.794

Household income > 200% poverty threshold 117,214 0.54 0 1 0.0021 0.538 0.545

Household income value missing 117,214 0.12 0 1 0.0014 0.114 0.119

Work time>0 and ≤12 hours 117,214 0.45 0 1 0.0016 0.452 0.457

Work time>12 hours 117,214 0.02 0 1 0.0007 0.023 0.025

Primary eating/drinking time, hours 117,214 1.12 0 14.9 0.0033 1.119 1.130

Personal care time, hours 117,214 9.33 0 24.0 0.0092 9.316 9.346

Housework, hours 117,214 1.85 0 21.8 0.0083 1.840 1.868

Household child/adult care, hours 117,214 0.48 0 19.2 0.0040 0.469 0.482

Watching TV, hours 117,214 2.70 0 23.8 0.0097 2.686 2.717

Travel time, hours 117,214 1.24 0 24 0.0054 1.231 1.249

Weekend/holiday day 117,214 0.30 0 1 0.0005 0.297 0.298

Time trend 1-9 (2003=1,...,2011=9) 117,214 5.08 1 9 0.0017 5.073 5.079

Unemployment rate 117,214 6.65 4.4 10 0.0015 6.651 6.656

Fast-food meal price (2011 $) 117,214 3.68 2.431 5.798 0.0017 3.677 3.683

Continued—
44

The Role of Time in Fast-Food Purchasing Behavior in the United States, ERR-178

Economic Research Service/USDA

Appendix table 3

Mean values of variables used in probit models—continued

N Mean

Min 

value

MaxvalueStandard 

error

90% CI

Min Max

Post business cycle peak (12/07) 117,214 0.47 0 1 0.0005 0.465 0.467

Quarter 2 (April, May, June) 117,214 0.25 0 1 0.0003 0.249 0.250

Quarter 3 (July, August, September) 117,214 0.25 0 1 0.0003 0.251 0.253

Quarter 4 (October, November, December) 117,214 0.25 0 1 0.0003 0.253 0.254

Notes: FF = fast food. The estimates above exclude all respondents who have missing values for any of the variables included in the probit 

models. Fast-food meal prices were unavailable for Alaska and Hawaii, so those respondents have missing values for prices. Variables from 

the household surveys (Current Population Survey and the American Time Use Survey) will have some missing values for every survey 

question except sex/gender, as some respondents may not know the answer (“Don’t know” response) or refuse to answer the question. The 

time-use estimates for 2003-11 match those in table 1, indicating that there is likely not bias due to item nonresponse.

Source: USDA, Economic Research Service estimates using data from the American Time Use Survey with the appropriate sample weights 

for nationally representative means, and other data sources discussed in text. 
45

The Role of Time in Fast-Food Purchasing Behavior in the United States, ERR-178

Economic Research Service/USDA

Appendix table 4

Probit model coefficients, 2003-11—continued

All

Single 

person

Single 

parent

Couple, no 

children

Couple with 

children

Other HH, 

no children

Other HH 

with children

Intercept -0.6713*** -0.3813** -0.2611 -0.9271*** -1.4241*** -0.2949 -0.5006***

Demographic characteristics:

 Female 0.0059 -0.1290*** 0.0997 -0.0090 0.0621 0.0292 0.0510**

 Age 18-24 0.1819*** 0.2029*** -0.0898 0.2070** 0.1066 0.2660*** 0.1006***

 Age 65+ -0.1925*** -0.1726*** -0.1010** -0.2161*** -0.1787**

 Education, less than high school -0.0986*** -0.1216** 0.1255 -0.1323** -0.0924 -0.0361 -0.1384***

 Education, some college 0.0683*** 0.0549 0.0752 0.0759** 0.0620 0.0847* 0.0386

 Education, college degree 0.0776*** 0.0157 0.1529** 0.1227*** 0.0298 0.1166** 0.0268

 Education, advanced degree 0.0242 0.0296 -0.0770 0.0028 -0.0363 0.0086 0.0496

 Noncitizen -0.3049*** -0.2150*** -0.3269** -0.1640** -0.3941*** -0.2893*** -0.3519***

 African-American/Black -0.0109 0.0205 -0.0469 -0.0073 0.0110 -0.0127 -0.0392

 Asian -0.1446*** -0.1843 -0.0944 -0.0224 -0.1245 -0.2942** -0.1254**

 Mixed race 0.0026 0.0556 -0.5201** -0.0098 -0.0258 0.1234 -0.0942

 Hispanic -0.0580** -0.0734 -0.1085 0.0402 0.0271 -0.0905 -0.0681**

Geographic characteristics:

 Midwest region -0.0132 -0.0568 -0.0317 0.0276 0.0847 -0.1149* 0.0279

 South region 0.0373* 0.0598 -0.0018 0.0217 0.0823 -0.0028 0.0637**

 West region 0.0277 0.0343 -0.0810 -0.0036 0.0667 -0.0069 0.0759**

 Nonmetro residence -0.0313** -0.0200 0.0462 -0.0041 0.0699 -0.0626 -0.0475*

Labor force characteristics:

 Employed 0.1236 *** 0.1171 ** 0.1910** 0.0987** 0.0582 0.1210 ** 0.1244***

 Employed part-time schedule -0.0256 -0.0598 -0.1075 -0.0613 0.2944 -0.0673 0.0065

HH type:

 Single-person HH 0.0724 ***

 Single-parent HH 0.1297 ***

 Couple with child(ren) HH 0.0394

 Other type of HH, with children 0.0930 ***

 Other type of HH, no children 0.0627 ***

HH characteristics:

 Number in HH -0.0647 0.0732 -0.1340 -0.0261

 Number in HH squared -0.0018 * -0.0012 -0.0084 0.0133 0.0016

 Child under age 6 in HH 0.0036 -0.0138 0.0923 0.0139

 Spouse employed 0.1011 -0.1501

 Spouse’s usual wkly work hours -0.0003 0.0033

 Spouse’s weekly work hours vary -0.1680 0.3183*

 HH income>200% poverty 

 threshold 0.1009 *** 0.0385 0.0610 0.0614 0.3017*** 0.1329 *** 0.1176***

 HH income value missing 0.0938 *** 0.0660 0.0309 -0.0306 0.2713** 0.2681 *** 0.0802**

Time use: 

 Work and work related,1-12 hrs -0.0930 *** 0.0125 -0.1751** -0.0510 -0.0541 -0.1231 ** -0.1268***

 Work and work related, >12 hrs -0.4501 *** -0.2849*** -0.5989*** -0.2808** -0.4518** -0.5798 *** -0.5227***

 Primary eating/drinking, hours -0.1747 *** -0.1639*** -0.1409*** -0.1663*** -0.1570*** -0.1592 *** -0.2079***

 Personal care time, hours -0.0349 *** -0.0333*** -0.0377*** -0.0358*** -0.0323* -0.0448 *** -0.0268***

 Housework, hours -0.0561 *** -0.0624*** -0.0492*** -0.0601*** -0.0616*** -0.0578 *** -0.0498***

 HH child/adult care, hours -0.0019 -0.0006 -0.0033 -0.0182 0.0658 ** -0.0095

 Watching TV, hours -0.0233 *** -0.0318*** -0.0144 -0.0164*** -0.0194 -0.0270 *** -0.0209***

 Travel time, hours 0.1406 *** 0.1351*** 0.1569*** 0.1381*** 0.1415*** 0.1215 *** 0.1614***

Continued—
46

The Role of Time in Fast-Food Purchasing Behavior in the United States, ERR-178

Economic Research Service/USDA

Appendix table 4

Probit model coefficients, 2003-11—continued

All

Single 

person

Single 

parent

Couple, no 

children

Couple with 

children

Other HH, 

no children

Other HH 

with children

Calendar variables:

 Weekend/holiday -0.0048 -0.0185 0.0074 0.0306 0.0865 -0.0488 -0.0049

 Quarter 2 0.0793*** 0.0662* -0.0376 0.0384 0.0763 0.1542*** 0.0829***

 Quarter 3 0.0432** 0.0305 0.1024 0.0054 0.0615 0.0936* 0.0348

 Quarter 4 -0.0102 -0.0359 -0.0317 -0.0703* 0.0103 0.0408 0.0162

 Time trend 1-9 0.0115** -0.0021 0.0021 -0.0012 -0.0166 0.0536*** 0.0040

Unemployment rate -0.0118** -0.0188* -0.0112 -0.0022 0.0225 0.0019 -0.0264***

Fast-food meal price (2011 $) -0.0365** -0.0563 -0.0735 0.0125 0.0195 -0.0854* -0.0413

Post-business cycle peak (12/07) -0.0066 0.1044* 0.0080 0.0594 0.0050 -0.2784*** 0.0626

Number of observations 117,214 28,380 6,938 22,978 5,044 13,206 40,615

Percent of observations that are 

fast-food purchasers 12.8 11.1 16.0 10.4 13.0 12.8 14.9

Likelihood ratio, Pr>ChiSq <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Score, Pr>ChiSq <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Wald, Pr>ChiSq <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Association of predicted and 

observed:

 Concordant 70.2 71.3 67.3 70.5 67.9 71.2 69.3

 Discordant 29.1 28.0 32.0 28.7 31.4 28.2 30.0

 Tied 0.7 0.7 0.7 0.8 0.7 0.6 0.7

Notes: HH = household. Alaska and Hawaii are not included due to missing fast-food meal prices. Other variables have missing values due 

to “Don’t know” and “Refused” responses. See appendix table 3 for means and more discussion of missing values. For the household types 

single parent and couple with children, analysis excludes those age 65 and older. Excluded group is male, age 25-64, high school diploma, not 

employed with zero hours worked on average day, citizen, White, non-Hispanic, Northeast and metro residence, couple without children household, household income less than 200 percent of the poverty threshold, weekday day, pre-December 2007, and first quarter (January, February, 

March). * indicates significance at the 90-percent level; ** indicates significance at the 95-percent level; and *** indicates significance at the 

99-percent level. Shading = not applicable.

Source: USDA, Economic Research Service estimates using American Time Use Survey and other data discussed in text.