United States 

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of Agriculture 

Economic 

Research 

Service 

Economic

Research

Report

Number 40 

Who Has Time To Cook?

Lisa Mancino

Constance Newman

How Family Resources 

Influence Food Preparation

May 2007
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Photo credit: DigitalVision and PhotoDisc.

Mancino, Lisa

 Who has time to cook? : how family resources influence food preparation.

 (Economic research report (United States. Dept. of Agriculture. 

 Economic Research Service) ; no. 40)

 1. Cookery—United States. 

 2. Food consumption—United States. 

 3. Time management—United States.

 4. Socioeconomic status. 

 5. Food relief—United States. 

 I. Newman, Constance.

 II. United States. Dept. of Agriculture. Economic Research Service. 

 III. Title.

HV696.F6
United States 

Department 

of Agriculture 

www.ers.usda.gov 

A Report from the Economic Research Service

Who Has Time To Cook?

How Family Resources Influence

Food Preparation

Lisa Mancino and Constance Newman

Abstract

Households participating in the Food Stamp Program are increasingly headed

by a single parent or two working parents. As this trend continues, more

low-income households may find it difficult to allocate the time needed to

prepare meals that fit within a limited budget and meet dietary requirements. Using Tobit analysis of the 2003-04 American Time Use Survey

(ATUS), this study finds that household time resources significantly affect

how much time is allocated to preparing food. In fact, working full-time and

being a single parent appear to have a larger impact on time allocated to

food preparation than an individual’s earnings or household income do. The

results are relevant for the design of food assistance programs as well as for

improving our understanding of how different family time resources affect

consumption behavior.

Keywords: Food preparation, Tobit analysis, time use, Thrifty Food Plan

Acknowledgments

The authors greatly appreciate the thoughtful review suggestions from

Charlene Kalenkoski, David Ribar, Andrea Carlson, Mark Lino, and

Dean Joliffe. We also thank Linda Hatcher and Anne Pearl for editorial

and design assistance.

Economic

Research

Report

Number 40

May 2007
Contents

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

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

So Many Choices, So Little Time: How Economic and

Sociodemographic Factors Influence the Way We

Spend Our Time.........................................................................................3

Modeling Time Use Decisions: Our Data and Econometric Approach ...5

Data ..............................................................................................................5

Econometric Approach ................................................................................5

Findings: Who’s Cooking and How Much? .............................................10

Conclusions................................................................................................. 17

References....................................................................................................18

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Summary

USDA uses the Thrifty Food Plan (TFP) to show Food Stamp Program

participants how low-cost, healthy meals can be purchased with monthly

food stamp benefits. When the TFP was first created in 1975, most families

had a nonworking adult in the home who was likely to prepare meals from

scratch. Today, however, an increasing number of low-income families have

either a single working parent or two working parents. These households

may spend less time preparing meals than was typical in the past. Recent

efforts have been made to incorporate more convenient and commercially

prepared foods into the TFP market basket. This research supports those

efforts by showing how differences in family time resources can affect food

preparation decisions.

What Is the Issue?

There is little information on how time resources influence time spent in

preparing food. Thus, to understand if and how time use decisions vary with

both time and monetary resources, this study estimates how the amount of

time an individual spends daily in preparing food correlates with individual

and household characteristics. Does the time allocated to preparing food

vary systematically with income, wage rates, marital status, employment

status, employment status of other household adults, and the number of

children in a household?

What Did the Project Find?

Our study shows that characteristics, such as income, employment status,

gender, and family composition, clearly affect food preparation decisions.

This relationship is weakest among men, stronger among women, and

strongest of all among full-time workers and single parents.

The relationship between personal characteristics and how much time men

spend preparing food, especially low-income men, was unclear. Our results

for men also contradict the hypothesis that lower household earnings mean

more time preparing food. For both full-time employed and nonworking

men, those with lower household income spend less time preparing food

than do men in households with higher incomes.

Regardless of income and marital status, women spend more time preparing

food than men do. Among women, time spent preparing food in the home

falls with higher household income and more time working outside the

home. Our estimates suggest that nonworking women spend just over 70

minutes per day preparing food, whereas women who work part-time spend

53-56 minutes per day and full-time working women spend 38-46 minutes

per day preparing food.

Single women spend less time preparing food than do married or partnered

women whether they are working or not. Single working women spend about

15 minutes less per day preparing food than do married or partnered working

women. Single nonworking women spend approximately 30 minutes less per

day cooking than do nonworking women who are married or have partners.

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Among low- and middle-income women, time spent preparing food does

not decrease significantly with higher wage rates. Among higher income

women, however, an increase in weekly earnings of $100 would translate

into 9 fewer minutes spent in preparing food per day.

Having more children who live in the household also increases the time a

woman spends preparing food, suggesting that, among women, household

time resources significantly affect the amount of time allocated to preparing

food. In fact, working full-time and being a single parent appear to affect

the time allocated to preparing food more than an individual’s earnings or

household income do.

Estimates of the time needed to follow recipes from the TFP range from

80 minutes a day to 16 hours a week. We find that many low-income

households—those with two adults or those headed by a single parent that

works less than 35 hours a week—allocate enough time for food preparation.

However, our estimates also say that low-income women who work full-time

spend just over 40 minutes per day and thus may have difficulties meeting

the past plan’s implied time requirements.

How Was the Project Conducted?

We use 2003-04 data from the American Time Use Survey and multivariate

analysis to explore how time allocated to preparing food differs between

low-income and higher income households. A household is defined as lowincome if total income equals 130 percent of the poverty line or less. We

also run separate estimates based on gender and whether an individual

works full-time (more than 35 hours in week), part-time (less than 35 hours

a week, but in the labor force), or is not employed.

The dependent variable, time spent in food preparation, is the total minutes

in a day spent in the following four activities:

• Preparing food and drinks, which includes cooking and in any way getting

food and drink ready for consumption.

• Serving food and drinks, which includes activities like setting the table.

• Food and kitchen cleanup.

• Storing or putting away food and drinks.

We use a Tobit model because food preparation time in a single day is zero

for many individuals. To account for the sample design, we use sampling

weights to obtain representative parameter estimates and specify strata and

clustering variables to increase their efficiency.

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Introduction

To ensure access to nutritious food by low-income families,

1 the Food

Stamp Program provides the Thrifty Food Plan (TFP) as a guide for how to

purchase low-cost, healthy meals with food stamp benefits. Initially developed in 1975, the TFP is maintained by USDA’s Center for Nutrition Policy

and Promotion. CNPP determines the TFP with a model that uses data on

the dietary intakes of low-income individuals and national average food

prices to produce a theoretical food plan. The plan minimizes deviations

from current dietary patterns while meeting nutrition requirements within a

budget that does not exceed maximum food stamp benefits (Carlson et al.,

2007). Because the data on the food people consume and the food they

purchase are not directly linked, researchers must make assumptions about

where food is purchased, in what form, and at what level of convenience or

preparation. As a practice, they have assumed that all meals and snacks are

prepared at home, often from scratch. To allow for more convenient and

commercially prepared foods, the most recent TFP was created using a

panel of experts to determine when these assumptions could be relaxed

(Carlson et al., 2007).

However, there is little information on how much time people actually

reserve for food preparation or how much time is needed to meet basic

dietary requirements on a limited budget. Using a companion piece to the

1999 TFP that provides daily menus to conform with the TFP, Recipes and

Tips for Healthy, Thrifty Meals (USDA, 2000), Rose (2004) estimates that it

would require 16 hours a week to follow the suggested menus. Another

study estimates that each of these recipes require an average of 40 minutes

(Davis and You, 2006), where a typical weekly menu includes two dishes

made from scratch each day. According to these estimates, the daily time

required to meet dietary requirements within a family’s maximum food

stamp benefits could range from 80 minutes to 21⁄2 hours per day.

A growing number of low-income families have either a single working

parent or two working parents. Overall, wage earners have become more

prevalent in the Food Stamp Program: In fiscal year 1990, 19 percent of

food stamp recipient households had earnings, whereas in fiscal year 2005,

29 percent had earnings (Barrett, 2006). Also, single parents make up more

than a third of all food stamp households. According to USDA’s Food and

Nutrition Service (FNS) (the agency that administers the Food Stamp

Program), in fiscal year 2005, over 62 percent of food stamp households

with children were headed by a single adult, representing 34 percent of all

food stamp households (Barrett, 2006). As such, an increasing number of

low-income families may find it difficult to allocate the time needed to

prepare meals that would fit within the implied time budget of the TFP.

How much time one chooses to spend preparing food is likely to depend on

both monetary and time resources. For example, the total time available to

the household for food preparation will depend heavily on the number of

adults in a household, how much time they all spend working in the labor

force, and the number of children. Explicitly recognizing the importance of

time as another household resource was first proposed by Vickery (1977).

Two adults living in a household with only one person working full-time

1

We use the terms “family” and

“household” interchangeably. Although

food stamp benefits and the TFP are

defined in terms of family-level

resources, much of the data used in

this study are defined at the household

level.

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outside of the home will have more total household time to prepare meals

compared with a single person who works full-time. For simplicity, we refer

to the total number of adult hours available for household activities as

“household time.” In reference to poverty measures, this relationship has

consequences for low-income households with less household time because

the food assistance they receive is calculated according to a uniform TFP

formula (or poverty line in Vickery’s case) that assumes a certain, average

level of time availability. Because time spent in preparing food generally

reduces the monetary cost of food and commercially prepared food costs

relatively more, the food stamp benefit level implied by a uniform TFP may

not fully address the needs of households that are low on both monetary

resources and total household time.

We assess how sensitive time allocated to food preparation is to both monetary and time resources. We use data from the 2003-04 American Time Use

Survey (ATUS) to estimate how the time an individual spends preparing

food varies with labor force participation, wages, the presence of children

and other adult household members, and earnings and labor force participation of other family members. Ideally, we would analyze total household

time, but the time use data are only available for individuals. Thus, we must

infer the effect of total household time by examining differences across

individuals living in different household types.

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So Many Choices, So Little Time:

How Economic and Sociodemographic

Factors Influence the Way

We Spend Our Time

The framework developed in Becker’s household production model (1965)

is often used to analyze individual time-allocation choices.

2 This model

assumes that individuals maximize utility from consumption goods and

leisure time, subject to constraints on their time, budget, and ability to make

consumption goods themselves. The model explicitly recognizes time as a

valued input that is optimally allocated to produce income, consumption

goods, and leisure. It predicts that individuals choose a mix of time and

purchased inputs that maximizes well-being while minimizing the full cost

(time and money) of doing so. The resulting supply of time spent in

preparing food is determined by the price of inputs, wages, household

income, and other individual and household characteristics.

In this framework, increasing wages and payoffs from time spent working will

typically reduce the time spent in producing goods and services for personal

use. Increasing other household assets, such as the earnings of other family

members, may also reduce the time an individual spends in home production

activities like cooking. Indeed, as family incomes rise, so does spending on

convenience foods: A 10-percent increase in income brings about an estimated

4 percent rise in spending on food away from home but only a 1-percent

rise in spending for food at home (Blisard, Variyam, and Cromartie, 2003).

However, higher earnings of other family members may also increase the

individual’s time in food preparation if that individual specializes in food

preparation in response to having lower relative earnings.

In terms of family characteristics, the number of people living in a household should increase the time required to prepare, cook, and clean up after

meals for everyone. Volume discounts and other economies of scale should

also make cooking at home more cost-effective for a larger family. We

expect that households with more children will devote more time to food

preparation, all else equal. Adult individuals who live with other adults may

be better able to share responsibilities for certain household tasks than those

who live alone or as single parents. One single adult will have less total

time to devote to necessary household activities, such as food preparation,

compared with two married or partnered individuals.

Individual differences in time-allocation decisions are also likely to depend

on the households’ overall ability to substitute time for money. All else

equal, individuals living in homes with higher household incomes will be

more able to afford foods prepared away from home and, therefore, more

likely to substitute prepared foods for home-cooked foods. For example, we

expect the inverse relationship between an individual’s wage rate and time

spent cooking to be less pronounced among individuals with lower household income. Similarly, because lower income households are less able to

substitute time for money, we expect that some aspects of family composition, such as the number of children and working adults, will have a more

pronounced effect on the amount of time they allocate to preparing food.

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2

Although modeling the joint supply

of all household members’ time would

be more precise, the ATUS collects time

use data only from a single individual

within a household. Thus, modeling

these decisions as a household would

complicate the conceptual framework

and, ultimately, not be directly applicable to the empirical analysis.
In addition to wages, household wealth, and the time availability of other

household members, certain sociodemographic characteristics will likely

affect how much time an individual allocates to preparing food. For one, we

expect to find that women spend more time preparing food than men do

even when taking into account other factors that determine food preparation

time. As women move into the labor force in greater proportions, the traditional pattern of women doing all of the food preparation is changing.

Nonetheless, the tradition persists and women tend to do more household

work than men do. Women still spent over twice as much time as men did

in child care activities in 2005 and nearly three times more time in food

preparation (Bureau of Labor Statistics, 2006). Harnack et al. (1998) also

found that men are less likely to be involved in planning or preparing family

meals. In households with both female and male household heads, they

found that less than 30 percent of male respondents reported any involvement in either planning or preparing family meals, whereas at least 90

percent of females reported involvement with these same tasks.

Although women still tend to do most of the meal planning and preparing in

married and partnered homes, as women’s hours in paid work increases,

their time in food preparation decreases (Zick, 1996). In fact, the increased

availability of convenience foods and food away from home has often been

attributed to women’s increased participation in the labor force. For that

reason, we expect that changes in family resources, such as wages, household income, and family composition, will have more prominent effects on

women’s food-preparation decisions than on men’s. Gender roles have also

changed over time and differ across cultures. As such, the effects of age,

culture, and other factors are likely to differ across gender as well.

In summary, we expect that the amount of time an individual spends preparing

food each day to be a function of his or her earnings; the presence, earnings,

and labor force participation of other family members; the number of children living in the household, and the individual’s sociodemographic characteristics, such as education, ethnicity, and gender.

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Modeling Time Use Decisions:

Our Data and Econometric Approach

Data

We use data from the 2003-04 American Time Use Survey (ATUS), which

is administered by the Bureau of Labor Statistics and the Census Bureau.

The ATUS is a stratified, three-stage, random and nationally representative

sample of households completing their final month of interviews for the

Current Population Survey (CPS) (Bureau of Labor Statistics, 2006). To

account for the sample design, we used STATA 9.0 and sampling weights to

obtain representative parameter estimates. We also specify strata and clustering variables to increase the efficiency of our estimates.

The ATUS interviews one person per household. On average, ATUS interviewed about 1,725 individuals per month in 2003 and 1,100 individuals per

month in 2004. Respondents were asked about their activities on the day

before their interview, covering a 24-hour period beginning at 4 a.m. Through

conversational interviews, each respondent provides a description of the type

and duration of each activity in which he or she participated during the

previous 24 hours. Survey respondents were asked to identify their primary

activity if they engaged in two or more activities simultaneously. Except for

time spent caring for children, data on time spent in secondary activities were

not included in the 2003-04 ATUS, which may have led to an underestimate

of the total amount of time spent in any single activity. For example, someone

who cleaned the refrigerator while cooking dinner and reported cleaning as

his or her primary activity would have reported fewer minutes in food

preparation than someone who spent the same amount of time cooking, but

reported no other primary activity.

In addition to detailed activity descriptions, ATUS data also contain demographic information, such as age, gender, ethnicity, and household income

as well as information about an individual’s labor force participation and the

labor force participation and earnings of other household members. All

household members ages 15 and older are eligible for the ATUS. Some of

these respondents, such as teenage children and elderly parents living with

other family members, may not be as involved in preparing meals. If these

individuals are also more likely to be single, which we believe they are, then

including them in our sample could bias downward the effect of being single.

We therefore limit our final sample to include only individuals who identify

themselves as the male or female head of household in the CPS interview.

Our final sample includes observations from 30,058 adult respondents who

are identified as a household head in the CPS interview.

Econometric Approach

Our goal is to estimate if and how much time use decisions vary with differences in specific family and individual characteristics, such as whether or not

an individual has a partner or spouse, while holding all other individual and

family characteristics constant. For this application, we use multivariate

analysis, which allows us to determine the net effect that a single variable

has on how much time one spends in food preparation, while holding all

other household and individual characteristics constant. Other techniques,

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such as cross tabulations, would not allow us to look at differences in food

preparation by marital status while simultaneously controlling for the

employment status of other household members, the number of children in

that household, and one’s level of education.

Theoretically, the optimal allocation of each household member’s time in all

possible pursuits is determined simultaneously as a household decision. Due

to data limitations, however, we cannot estimate all of these separate decisions

and include their impacts on how much time an individual allocates to food

preparation, as would be ideal. Consequently, including a measure of labor

force participation directly in our model to estimate how much time one

spends preparing food will lead to biased estimates.

However, the decision to work outside the home is so important to the decision

to allocate time for food preparation that omitting this variable would also

bias our results. Working outside the home requires that a large block of one’s

time be devoted to it—typically 8 or more hours per day. As such, it will

greatly reduce the amount of time one can dedicate to other activities like

food preparation. Unfortunately, estimating these decisions simultaneously

using a two- or three-stage estimator becomes difficult because both food

preparation and hours worked outside the home are censored, or take on a

zero value, for a large portion of the sample. Among women, 36 percent of

our final sample reported no time in food preparation and 41 percent were

not in the labor force. Among men, 57 percent reported no time in food

preparation and 26 percent were not in the labor force. To circumvent this

problem, we run separate estimates of time spent preparing food by those

who work full-time (more than 35 hours per week), those who work parttime (less than 35 per week, but employed), and those who are not

employed.

3 Note, however, that a potential sample selection bias remains

because we are splitting the sample along endogenous choice variables.

Because we expect the effect of some individual and household characteristics to differ by gender and income, we also run separate estimates for

women and for low-income households. We define a household as lowincome if that household’s income relative to the poverty guideline for a

household of the same size is 1.3 or below. This income relative to poverty

ratio (IRP) is a common standard for comparing income across households

of different sizes. It is used to determine whether an individual or household

can qualify for certain food assistance and welfare programs. For example,

an IRP of 1.3 or below is one of the first qualifications a household must

meet to qualify for food stamp benefits. Researchers also use this same

cutoff to calculate the typical diet of low-income consumers when creating

the TFP (USDA, 1999). Because the ATUS reports income as a categorical,

rather than continuous, variable, we use the midpoint value from each of the

income categories and the top code value of $150,000 for the highest

income category to create the IRP values.

Our dependent variable, the amount of time spent each day in food preparation (TF), is zero for many individuals. Thus, estimating the relationship

between this dependent variable and the independent variables via ordinary

least squares would result in inconsistent parameter estimates. We therefore

use a Tobit model for our analysis. This model assumes a latent unobservable variable,TF*

, and an observable variable, TF, that is defined to equal

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3

See Jenkins and O’Leary (1995) for

analysis of how paid market work

should be treated in models of domestic

work. They review early literature,

some of which included paid work as a

regressor and some of which did not.
this latent variable whenever it takes on a value greater than zero and is

defined equal to zero otherwise. The econometric model estimated for the

Tobit can then be written as:

And the latent variable, , is defined as:

The subscript i refers to each individual, and the subscript g refers to his or

her gender, income (IRP at or below 1.3 or IRP above 1.3), and employment

(working full- or part-time or nonworking) grouping, which leaves a total of

12 subgroups. TFig

is measured as the total minutes spent in preparing food,

wigis an individual’s wage rate, Aigis the a vector representing the presence

of other household adults, wAigis a vector of their earnings, TFAigis a

vector of their household time, Cigis the number of children living in a

household, Dig

is a vector of individual sociodemographic characteristics,

and εig

is a normally distributed error term.

Dependent Variable—Time Spent Preparing Food

Definitions and summary statistics for the variables used in the econometric

analysis are found in table 1. The ATUS data contain six activity codes

related to food preparation and acquisition. We combine the following four

food preparation activities into one because they are all part of the main

activity of preparing a meal:

1. Preparing food and drinks, which includes cooking and in any way

getting food and drinks ready for consumption.

2. Serving food and drinks, which includes activities like setting the table.

3. Food and kitchen cleanup.

4. Storing or putting away food and drinks.

We refer to this general combination variable as “food preparation.”

Explanatory Variables

For individuals in the work force, we use total weekly earnings and the number

of hours worked at all jobs to calculate an individual’s average hourly wage

rate. However, we do not have such information for individuals who are not

in the labor force. One way to correct for any possible bias caused from omitting a variable that represents an individual’s opportunity cost of time would

be to estimate the wage rate an individual would have earned if he or she were

employed. Because these estimates are typically determined by the same variables that influence time-allocation decisions, however, using predicted values

will also bias estimates. Thus, we opt to use reported wage rates for our

entire sample, which means that our estimates among unemployed individuals,

whose reported wage rate is zero, will suffer from an omitted-variable bias.

4

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4

We ran all of our estimates using

predicted wages and found that the

predicted values of time spent in food

preparation were extremely similar

when using either reported or predicted wage rates. Also, there were

few changes in terms of magnitude

or significance of our parameter

estimates. In using predicted wages,

the biggest changes in significance

were among variables meant to capture sociodemographic variables—

education and urbanization—which is

not surprising because these variables

are most likely positively correlated

with wage rates. Thus, our estimates

that do not include wages for unemployed individuals may be downward

biased for these variables. Conversely,

any variables that are negatively correlated with either the probability of

working or an opportunity cost of time

wage may have an upward bias.
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Table 1

Summary statistics for how much time family and individual characteristics affect time spent preparing food

Standard

Variable Definition and units Mean deviation

Dependent variable:

Food time Daily sum of minutes spent in food preparation 39.59 58.00

Separating variables:

Full-time 1 if individual works more than 35 hours per week .53 NA

Part-time 1 if individual works but less than 35 hours per week .12 NA

Nonworking 1 if individual is neither part-time nor full-time employed .35 NA

Low-income 1 if midpoint of household income category divided by poverty

guideline for family size is 1.3 or below .17 NA

Female 1 if individual is female .57 NA

Monetary and time resources:

Reported wage Individual’s reported hourly wage rate 16.53 15.79

Other earnings Midpoint of weekly household income range less individual’s

weekly contribution 486.12 571.94

Partner 1 if individual has a spouse or unmarried partner .66

Partner’s household time Maximum number of hours one’s partner has for household work.

Number of hours in week, less 8 hours for sleep each day and

usual hours worked per week 55.92 44.37

Extra adults—healthy Number of adults in household who have not identified themselves

as sick or disabled .05 .25

Extra adults—unhealthy Number of adults in household who are not in the labor force

because of disability or illness .01 .07

Extra adults’ household time Maximum number of hours other healthy household adults have for

household work 4.27 23.27

Number of children Total number of children younger than 18 living in household .82 1.13

Young child 1 if one or more children are younger than 5 .17

Sociodemographic shifters:

High school only 1 if high school or GED only .29 NA

Some college 1 if attended but did not complete a college degree .18 NA

Bachelor’s or

associate degree 1 if received a bachelor’s or associate degree .29 NA

Graduate work or

professional degree 1 if received a graduate or professional degree .12 NA

Age Age of respondent in years 48.81 15.71

Black (non-Hispanic) 1 if respondent is non-Hispanic Black .11 NA

Hispanic 1 if respondent is Hispanic .10 NA

Asian (non-Hispanic) 1 if respondent is Asian .02 NA

Other race (non-Hispanic) 1 if other ethnicity .01 NA

Foreign born 1 if respondent was born outside the United States .11 NA

Regional shifters:

Northeast 1 if respondent lives in the Northeast .20 NA

West 1 if respondent lives in West .35 NA

South 1 if respondent lives in the South .20 NA

Metropolitan 1 if respondent lives in a metropolitan area .79 NA

Other shifters:

Weekend 1 if time diary was recorded for a weekend day .51 NA

Holiday 1 if time diary was recorded for a holiday .02 NA

NA = Not applicable.

Note: Sample includes 30,058 adult respondents who identify themselves as household head (self or spouse) in Current Population Survey

interview.

Source: 2003-04 American Time Use Survey.
In addition to an individual’s own wage, other family members’ earnings

will also influence the entire household’s monetary constraints. To approximate the earnings of other household members, we use the midpoint value

of the categorical household income variable, convert it to a weekly value,

and subtract an individual’s weekly contribution.

To estimate how food preparation varies with family time resources, we

include the number of adults present in the household and their availability of

time for household chores like cooking. We create a categorical variable that

takes on a zero or one value if the individual has a partner (married or unmarried) that was present in the household during the time use diary. We also

create variables to indicate the number of other adults living in the household

during the time use survey. We further classify these individuals as “healthy”

or “unhealthy.” Although these distinctions may be a bit crude, an individual

is classified as unhealthy if he or she indicated the reason for not being in the

labor force was due to ill health or disability. All other adults were classified

as healthy. We anticipate that each unhealthy adult in a household will increase

the time needed for food preparation more than other household adults.

We also anticipate that how much time an individual spends cooking

depends on the time availability of other household adults. Thus, we create

a variable to measure the maximum amount of time an individual’s partner

has for household production activities. This variable is simply the total

number of hours in a week, less 8 hours a day for sleeping, and the total

number of hours the partner or spouse works, on average. We create a

similar variable for all healthy adults in a household.

The number of children in a household should increase the time spent

preparing food. We also expect that households with young children spend

more time preparing food than those with older children. The presence of

young children may increase the opportunity cost of eating outside the

home. Individuals with children in their care may find that eating food

outside the home entails foregoing time that could have been spent reading

to children or helping with their homework. For some, it may also reduce

the enjoyment of eating outside the home. Thus, we include a dummy variable to indicate whether or not a nonschool-aged child (ages 5 or younger)

lives in the household.

Many of the variables we include as individual characteristics, such as age,

gender, and education, are self-explanatory. We also include age as a quadratic

term because an individual’s caloric requirements and health tend to decrease

with age. We may therefore see them spending less time preparing meals. To

proxy for cultural influences on diet and cooking, we include whether an individual is non-Hispanic White, non-Hispanic Black, Hispanic, Asian, or any

other ethnicity and whether that individual was born outside the United States.

To control for possible price variations across regions, we include four

regional dummy variables—Northeast, Midwest, West, and South. We

include a variable to indicate whether an individual lives in a metropolitan

area. We control for whether an individual’s time diary was recorded on a

weekend because past studies on time use have found very different patterns

between weekdays and weekends. We also include a dummy variable to

indicate if the diary day was a holiday.

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Findings: Who’s Cooking

and How Much?

Estimation results for women and men are reported in tables 2-4, where the

reported coefficients indicate the combined effect (in minutes) that each

explanatory variable has on both the probability that one spends any time in

food preparation and the amount of time he or she spends doing so in a

single day. As expected, the results differed greatly for men and women and

for whether they were employed or not. Using estimated coefficients and

sample means within each subgroup, we estimated the predicted amount of

time per day women and men spent preparing food, by income category and

labor force participation (tables 2-5 and figs. 1 and 2).

We found very few explanatory variables to be significantly associated with

how much time men spend preparing food, especially low-income men. Our

results for men also contradict the hypothesis that there is an inverse relationship between household earnings and time spent in food preparation. For

both full-time employed and nonworking men, those with lower household

income spend less time preparing food than do men in households with higher

incomes. Moreover, among men with higher incomes, the relationship

between time spent in paid work and time spent preparing food was not clear.

One possible reason for this lack of systematic variation among men is that

most men still generally spend very little time preparing food compared with

women. Our results suggest that men spend about a third less time preparing

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Figure 1

Time that women spend per day 

preparing food, by income category

and labor force participation

Women spend less time preparing food 

as time requirements of paid work 

increase, an effect that is less

pronounced among low-income women1

Minutes2

1

Low income = Income-to-poverty ratio of 

1.3 or below. 2

Estimates are calculated using the mean 

value by income category and working status.

Source: Economic Research Service, USDA.

Low-income

women

All other

women

0

10

20

30

40

50

60

70

80

56

71

38

53

71

46

Working full-time Working part-time

Nonworking

Figure 2

Time that men spend per day 

preparing food, by income category

and labor force participation

Low-income men also spend less time 

preparing food as time requirements of 

paid work increases, but rising income 

increases the time spent preparing food1

Minutes2

1

Low income = Income-to-poverty ratio of 

1.3 or below. 2

Estimates are calculated using the mean 

value by income category and working status.

Source: Economic Research Service, USDA.

Low-income

men

All other

men

Working full-time Working part-time

Nonworking

0

10

20

30

40

50

60

70

80

13 16

26

17 15

30
food than women do—between 12 and 30 minutes per day. Because our

models did not work well for men, we do not discuss the results further.

All of the results for women that we discuss here are estimated to be statistically significant in the regressions. The amount of time women allocate to

preparing food appears to conform with standard economic theory: Time

spent preparing food in the home decreases with household income. Also,

working women spend less time preparing food than nonworking women.

Our estimates suggest that nonworking women spend just over 70 minutes

per day preparing food, whereas women who work part-time spend 53-56

minutes per day and full-time working women spend 38-46 minutes per day

preparing food.

5

In support of our initial hypothesis, we find evidence that low-income

working women may be less able to substitute money for time than women

with higher income. Our results show that, among low-income women, the

time spent preparing food does not decrease significantly with their wage

rate. However, higher wage rates are significantly and negatively related to

less food preparation time among higher income women who work full-time.

An increase in weekly earnings of $100 would translate into 9 fewer minutes

spent in food preparation per day. Time allocated to food preparation does

not vary significantly with the earnings of other household members.

In all regressions, single women spend less time preparing food than their

partnered counterparts do. The effect of having a partner is among the

highest in magnitude of all parameter estimates. Figure 3 shows how the

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Figure 3

Time that women spend per day preparing food, by marital status, 

labor force participation, and income category

Regardless of employment status, the extra time requirements associated with 

having a spouse or partner are more pronounced among low-income women, 

possibly because they are less able to substitute money for time1

Minutes2

1

Low income = Income-to-poverty ratio of 1.3 or below. 2

These estimates assume that each household has two children and that the spouse or 

partner works full-time. For all other parameters, estimates are calculated using the mean value 

by income category and working status.

Source: Economic Research Service, USDA.

Single Married or partnered

Working

full-time

(more than

35 hours/

week)

Working

part-time

Nonworking

0

20

40

60

80

100

120

43

62 57

77

67

102

36

51 49

64 69

96

Working

full-time

(more than

35 hours/

week)

Working

part-time

Nonworking

Low-income women All other women

5

Although not presented here, we

estimated the amount of time spent

preparing food for men as well. The

results indicate that men spend 13-20

minutes per day preparing food. These

differences do not vary predictably

by income. Also, the differences

between working and nonworking

men are much less pronounced than

the differences between working and

nonworking women.
estimated time allocated to food preparation by married and single women

varies with labor force participation and income category. To control for

household size and time availability of the spouse or partner, we calculate

predicted minutes in food preparation for a household with two children and

a fully employed partner.

As expected, the effect of having a partner varies by household income,

likely because individuals with higher household incomes are better able to

purchase time-saving inputs. Even after controlling for the number of children,

low-income working women with partners spend about 19-20 more minutes

per day preparing food than their single counterparts do. Higher income

working women with partners, however, spend an additional 15-16 minutes

per day. Similarly, among nonworking women, the low-income ones with

partners spend about 35 more minutes per day cooking than single women,

while nonworking, partnered women with higher incomes spend about 28

more minutes cooking per day than their single counterparts do.

Neither the presence of other healthy adults in the household nor their availability of time for household chores correlates significantly with time spent

preparing food. However, among women who work part-time, having an

unhealthy adult in the household was positively correlated with more time

preparing food, regardless of income. Among low-income women who

work full-time, time spent preparing food decreases with the number of

unhealthy adults in the household. We find that the number of children in a

household is another important determinant of food preparation time for

both working and nonworking women. However, this effect was not systematically more pronounced among lower income women as we had expected.

Among low-income women, both part-time employed and nonworking, the

amount of time spent preparing food was significantly lower for more

educated individuals. However, education and time spent preparing food is

not significantly correlated among low-income women who work full-time.

The exact opposite is true for higher income women: The negative correlation between education and food preparation is only significant among

women who work full-time. This odd result may indicate an omitted variable bias resulting from splitting the sample into employment categories. If

the choice to work full-time is positively correlated with education, our estimates on education may be biased downward.

Across income categories, age had a significant, positive effect on the amount

of time spent preparing food. Some of the variables used to proxy for culture

also significantly affect food preparation. For high-income households,

working Asian women spend approximately 11-20 more minutes per day

preparing food compared with non-Hispanic White women. Among lower

income women who work full-time, Hispanic and Asian women spend more

time preparing food. Among higher income women, those born outside the

United States spend more time preparing food, regardless of labor force

participation. These results may reflect differences in cultural norms or

availability of convenient foods that conform to one’s traditional diet.

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Table 2

Estimation results: Determinants of the time low-income women spend preparing food1

Working full-time Working part-time Not working

N=1,505 N=866 N=2,986

Variable Coefficient Z-value *** Coefficient Z-value *** Coefficient Z-value ***

Reported wage 0.09 0.36 -0.16 -1.12 — —

Other earnings -.01 -1.10 0 -.27 0 -0.41

Partner 28.63 3.55 ** 34.06 2.26 ** 28.77 2.92 **

Partner’s household time -.19 -1.79 * -.25 -1.36 .01 .14

Extra adults—healthy -43.56 -1.20 25.47 .39 .41 .01

Extra adults—unhealthy -23.70 -1.42 * 73.72 2.51 ** 35.05 1.72 *

Extra adults’ household time .58 1.34 -.28 -.42 -.01 -.04

Number of kids 5.09 3.06 ** 11.18 4.21 ** 10.37 6.76 **

Young child 4.99 .98 1.74 .29 5.76 1.01

High school only .65 .12 -22.36 -2.96 ** -1.13 -.29

Some college -2.49 -.46 -24.17 -3.26 ** -0.97 -.19

Bachelor’s or associate degree -5.71 -1.07 -23.56 -2.48 ** -3.52 -.75

Graduate work or professional degree -2.23 -.35 -13.27 -1.14 -21.71 -3.63 **

Age 1.90 2.14 ** 1.97 1.98 ** 1.34 2.38 **

Age2 -.02 -1.58 -.02 -1.45 -.01 -1.60

Black (non-Hispanic) .55 .13 .54 .09 .74 .17

Hispanic 12.68 2.04 ** -4.17 -.46 6.73 1.20

Asian (non-Hispanic) 25.24 1.94 ** 25.81 1.11 -1.67 -.12

Other race (non-Hispanic) -10.26 -.84 -12.99 -.96 -4.51 -.46

Foreign born 7.44 1.09 4.80 .53 20.00 3.10 **

Northeast -4.19 -.98 -2.93 -.38 -3.22 -.75

West -8.74 -2.28 ** -10.02 -1.87 * 1.66 .44

South -4.51 -.98 -6.73 -1.10 -3.73 -.87

Metropolitan -6.04 -1.30 -1.36 -.26 -5.06 -1.45

Weekend 3.54 1.06 -3.00 -.75 -7.59 -2.63 **

Holiday 4.45 .33 80.98 2.62 ** 7.50 .69

Left censored observations 466 208 667

Uncensored observations 1,039 658 2,319

Predicted minutes preparing food 46.38 56.11 70.59

Pseudo R2 .08 .11 .11

**Parameter estimated to be significant at the 5 percent level.

*Parameter estimated to be significant at the 10 percent level.

†Predicted minutes are calculated from parameter estimates evaluated at the mean value for each employment, gender, and income group.

Note: The predicted excluded dummy variables are (1) less than high school education, (2) White non-Hispanic, (3) unmarried or unpartnered

status, (4) Midwest, (5) nonmetropolitan, (6) weekday, and (7) nonholiday. 1Income-to-poverty ratio is equal to 1.3 or below.

Source: 2003-04 American Time Use Survey.
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Table 3

Estimation results: Determinants of the time all other women spend preparing food1

Working full-time Working part-time Not working

N=5,844 N=1,938 N=4,070

Variable Coefficient Z-value *** Coefficient Z-value *** Coefficient Z-value ***

Reported wage -0.25 -3.26 ** -0.02 -0.36 — —

Other earnings 0 -.12 0 -.13 0.00 -0.37

Partner 21.41 6.27 ** 25.97 3.43 ** 25.55 3.37 **

Partner’s household time -.13 -2.96 ** -.18 -2.17 ** -.02 -.28

Extra adults—healthy 18.52 1.12 -28.49 -1.11 13.16 .46

Extra adults—unhealthy 22.97 1.35 60.36 7.08 ** 9.17 1.10

Extra adults’ household time -.22 -1.29 .33 1.23 -.11 -.38

Number of kids 5.45 6.47 ** 7.19 4.55 ** 11.32 7.18 **

Young child 6.14 2.65 ** 3.39 .87 4.57 1.15

High school only -8.25 -1.93 * 6.10 .87 .40 .09

Some college -7.50 -1.77 * 5.70 .77 .30 .06

Bachelor’s or associate degree -8.13 -1.93 * 3.78 .55 -2.11 -.47

Graduate work or professional degree -10.82 -2.61 ** -4.53 -.67 -3.23 -.53

Age 1.73 3.80 ** 1.19 1.72 * 2.06 3.75 **

Age2 -.01 -2.52 ** -.01 -.75 -.01 -2.65 **

Black (non-Hispanic) -.93 -.36 -5.84 -.89 -1.32 -.28

Hispanic 1.22 .44 7.36 1.11 10.02 1.76 *

Asian (non-Hispanic) 11.27 2.01 ** 20.60 2.02 ** 8.61 1.08

Other race (non-Hispanic) 2.30 .40 -5.75 -.54 -6.77 -1.05

Foreign born 6.91 1.95 * 16.57 2.06 ** 21.23 4.16 **

Northeast 7.70 3.45 ** 3.33 .93 6.32 1.74 *

West 1.25 .74 -3.33 -.89 3.27 1.06

South 2.21 1.14 -1.16 -.31 2.52 .73

Metropolitan .09 .05 -5.64 -1.51 -6.19 -2.03 **

Weekend 10.49 7.55 ** 3.96 1.47 -12.74 -5.80 **

Holiday 21.23 1.84 * 6.63 .53 21.12 1.47

Left censored observations 1,870 464 819

Uncensored observations 3,974 1,474 3,251

Predicted minutes preparing food 38.05 53.11 71.08

Pseudo R2 .08 .05 .07

**Parameter estimated to be significant at the 5 percent level.

*Parameter estimated to be significant at the 10 percent level.

†Predicted minutes are calculated from parameter estimates evaluated at the mean value for each income group.

Note: The predicted excluded dummy variables are (1) less than high school education, (2) White non-Hispanic, (3) couple-headed household,

and (4) Midwest. 1Income-to-poverty ratio is above 1.3.

Source: 2003-04 American Time Use Survey.
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Table 4

Estimation results: Determinants of the time low-income men spend preparing food1

Working full-time Working part-time Not working

N=1,594 N=273 N=1,270

Variable Coefficient Z-value *** Coefficient Z-value *** Coefficient Z-value ***

Reported wage 0.08 1.22 -0.25 -1.52 — —

Other earnings 0 -.91 .01 1.32 0.02 0.84

Partner 10.49 2.66 ** 23.49 1.92 * 6.77 .48

Partner’s household time -.20 -3.90 ** -.26 -1.75 * -.20 -1.43

Extra adults—healthy -6.34 -.52 -12.61 -.47 -13.51 -.49

Extra adults—unhealthy -15.29 -1.25 -173.56 -5.79 ** -15.11 -1.34

Extra adults’ household time .08 .59 .11 .41 0 -.01

Number of kids .57 .73 .72 .33 6.57 2.21 **

Young child 6.71 2.09 ** 2.28 .32 -2.02 -.27

High school only -.33 -.13 -3.81 -.77 10.45 2.23 **

Some college .12 .04 -6.14 -1.21 -.66 -.14

Bachelor’s or associate degree 3.30 1.14 -8.85 -1.73 * 15.92 1.92 *

Graduate work or professional degree 1.58 .43 -7.84 -1.55 3.99 .53

Age -.21 -.51 .85 1.03 .81 1.35

Age2 0 .81 -.01 -.83 -.01 -.92

Black (non-Hispanic) .13 .04 .60 .11 -2.17 -.52

Hispanic -2.37 -.84 -1.54 -.22 -11.12 -1.93 *

Asian (non-Hispanic) -1.74 -.43 12.49 .77 -23.26 -2.97 **

Other race (non-Hispanic) 1.98 .27 -4.76 -.52 -13.79 -1.89 *

Foreign born -1.12 -.39 -6.10 -1.03 17.11 1.88 *

Northeast -.30 -.13 5.57 .91 -5.84 -1.10

West -2.21 -1.10 1.83 .36 .68 .16

South -.10 -.04 -1.22 -.24 5.20 .99

Metropolitan -2.35 -.96 -3.82 -.78 3.94 1.08

Weekend 5.19 3.10 ** -7.34 -2.16 ** .40 .13

Holiday 1.93 .34 -8.38 -.90 -11.53 -1.29

Left censored observations 1,028 162 669

Uncensored observations 566 111 601

Predicted minutes preparing food 12.73 16.48 25.63

Pseudo R2 .03 .03 .03

**Parameter estimated to be significant at the 5 percent level.

*Parameter estimated to be significant at the 10 percent level.

Note: The predicted excluded dummy variables are (1) less than high school education, (2) White non-Hispanic, (3) couple-headed household,

and (4) Midwest. 1Income-to-poverty ratio is equal to 1.3 or below.

Source: 2003-04 American Time Use Survey.
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Table 5

Estimation results: Determinants of the time all other men spend preparing food1

Working full-time Working part-time Not working

N=7,086 N=535 N=2,091

Variable Coefficient Z-value *** Coefficient Z-value *** Coefficient Z-value ***

Reported wage -0.02 -0.62 -0.10 -2.56 ** — —

Other earnings 0 -1.78 * 0 -1.40 0 1.37

Partner 9.89 5.01 ** 22.08 2.79 ** 26.10 3.21 **

Partner’s household time -.17 -7.11 ** -.23 -2.34 ** -.36 -4.19 **

Extra adults—healthy -4.53 -.60 -.02 0 -13.54 -.45

Extra adults—unhealthy 1.16 .24 14.06 1.43 -23.31 -1.13

Extra adults’ household time .05 .56 .02 .05 .10 .31

Number of kids 2.00 4.16 ** 4.45 2.13 ** 5.26 2.24 **

Young child 2.94 2.43 ** -4.69 -1.11 .99 .16

High school only 1.07 .51 -5.14 -.97 -.17 -.04

Some college 1.45 .65 -5.09 -1.04 3.84 .79

Bachelor’s or associate degree 2.28 1.05 -.03 -.01 1.16 .25

Graduate work or professional degree 3.00 1.25 -5.37 -1.12 -2.26 -.45

Age .84 3.23 ** .94 1.55 .07 .11

Age2 -.01 -2.79 ** -.01 -1.12 0 .10

Black (non-Hispanic) -1.16 -.71 -5.39 -1.31 -4.79 -1.35

Hispanic -3.59 -2.27 ** -4.42 -.84 2.99 .34

Asian (non-Hispanic) -4.62 -1.90 * -8.51 -1.43 -13.37 -2.04 **

Other race (non-Hispanic) 5.14 1.22 5.93 .36 11.78 .93

Foreign born 2.01 1.08 .12 .02 7.46 .99

Northeast -1.01 -.90 -3.81 -1.08 2.74 .78

West -2.20 -2.14 ** .69 .20 2.84 .90

South 1.74 1.48 3.02 .79 -1.69 -.47

Metropolitan 1.20 1.18 7.35 2.55 ** 2.69 .96

Weekend 4.42 5.36 ** 1.19 .41 -3.21 -1.26

Holiday 9.12 2.01 ** -18.22 -6.65 ** 26.90 1.84 *

Left censored observations 4,072 298 1,060

Uncensored observations 3,014 237 1,031

Predicted minutes preparing food 16.82 15.33 30.45

Pseudo R2 .03 .02 .03

**Parameter estimated to be significant at the 5 percent level.

*Parameter estimated to be significant at the 10 percent level.

†Predicted minutes are calculated from parameter estimates evaluated at the mean value for each income group.

Note: The predicted excluded dummy variables are (1) less than high school education, (2) White non-Hispanic, (3) couple-headed household,

and (4) Midwest. 1Income-to-poverty ratio is above 1.3.

Source: 2003-04 American Time Use Survey.
Conclusions

The goal of this study was to estimate if and how time allocated to cooking

varies with both monetary and time resources. We find that how much time

one chooses to spend preparing food does appear to correlate systematically

with differences in variables meant to proxy family time resources. We find

that working women spend less time cooking than those who do not work

outside the home. Being married and having more children living in the

household are separately estimated to increase the amount of time a woman

spends in food preparation. Also, among low-income women, wages do not

significantly affect their food preparation decisions.

These results suggest that, among women, household time resources significantly affect how much time is allocated to preparing food. In fact, we find

these effects are greater than household monetary resources: Separately,

both working full-time and being a single parent each have a greater impact

on time allocated to food preparation than an individual’s earnings or

household income.

Recent estimates of the time needed to follow TFP recipes range from

80 minutes a day to 16 hours a week. We find that many low-income

households—those with two adults or those headed by a single parent that

works less than 35 hours a week—allocate enough time for food preparation.

However, our estimates also say that low-income women who work full-time

spend just over 40 minutes per day and thus may have difficulties meeting

the plan’s implied time requirements. The food assistance they receive is

calculated according to a uniform TFP formula predicated on the assumption

of an average level of time availability. Because time spent in food preparation generally reduces the monetary cost of food and commercially prepared

food costs relatively more, the food stamp benefit level implied by a uniform

TFP is less likely to meet the needs of households that are low on both time

and money. Thus, a worthwhile endeavor would be to investigate whether a

greater number of healthful, convenient foods could be included in the TFP

market basket without requiring higher benefit levels.

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