Consumer Expenditure

Survey Anthology, 2003

U.S. Department of Labor

Bureau of Labor Statistics

September 2003

Report 967
iii

Preface

T

his is the first in a series of reports presenting both

analytical articles that use data from the Bureau of

Labor Statistics (BLS) Consumer Expenditure (CE)

Survey and methodological articles that discuss ongoing

research and issues pertaining to the survey. In the past, the

CE Survey Division published a biennial report that included

analytical articles, standard tables of the most recent CE Survey data, a discussion of expenditure changes, and a description of the survey and its methods. The most recent of

these was Consumer Expenditure Survey, 1998–99 Report

955, published in November 2001. The biennial report will be

replaced by two separate biennial reports that will be published in alternating years. One will continue the practice of

publishing tables with recent survey data, a brief discussion

of recent changes in expenditures, and a description of the

survey and its methods. The first of this type of report is

Consumer Expenditure Survey, 2000–2001, Report 969, published in September 2003. The other, of which this is the first,

includes both methodological and analytical articles. The

methodological articles are intended to provide data users

with greater insight into both ongoing improvements in the

survey and issues that are faced in collecting, processing,

and publishing information from such a complex survey. The

analytical articles furnish information on topics of interest

pertaining to CE Survey data.

The CE Survey program provides a continuous and comprehensive flow of data on the buying habits of American

consumers for use in a variety of economic analyses and in

support of periodic revisions of the Consumer Price Index.

BLS makes data available in news releases, reports, bulletins, and articles in the Monthly Labor Review, as well as on

CD-ROMs and on the Internet.

This report was prepared in the Office of Prices and Living Conditions (OPLC), Division of Consumer Expenditure

Survey (DCES), under the general direction of Steve

Henderson, Chief of the Branch of Information and Analysis, and was produced and edited by John M. Rogers, Section Chief. Articles on research and methodology were contributed by Sioux Groves, Chief of the DCES, Jeff Blaha and

Sally Reyes-Morales of the Division of Price Statistical Methods, Geoffrey Paulin of the Branch of Information and Analysis, Linda Stinson of the Office of Survey Methods Research

(OSMR), and Nhien To and Jeanette Davis of the Branch of

Research and Program Development. Analytical articles were

contributed by Abby Duly, George Janini, Eric Keil, Laura

Paszkiewicz, and Geoffrey Paulin of the Branch of Information and Analysis and Neil Tseng of the Branch of Production and Control.

The material that follows is divided into two sections:

section 1 contains articles on survey research and methodology, and section 2 presents analyses of topics of interest based

on CE Survey data. An appendix includes a general description of the survey and its methods and a glossary of terms.

Current and historical CE Survey tables classified by standard demographic variables are available at the BLS Internet

site http://www.bls.gov/cex. Other survey information, including answers to frequently asked questions, a glossary

of terms, order forms for survey products, and Monthly Labor Review and other research articles, also is available on

the Internet.

Sensory-impaired individuals may obtain information on

this publications upon request (voice phone: (202) 691–5200,

Federal Relay Service: 1–800–877–8339). The material presented is in the public domain and, with appropriate credit,

may be reproduced without permission. For further information, call (202) 691–6900.
v

Contents

Part I. Survey Research and Methodology ...................................................................................................................... 1

Creating a “User-Friendly” Expenditure Diary ...................................................................................................... 3

Computer-Assisted Personal Interviewing for the Consumer Expenditure Interview Survey ...................... 18

Introducing Brackets: Quality in the Consumer Expenditure Interview Survey .............................................. 21

Characteristics of Complete and Intermittent Responders in the Consumer Expenditure

 Interview Survey ..................................................................................................................................................... 25

Standard Errors in the Consumer Expenditure Survey ........................................................................................ 30

Part II. Analyses Using Survey Data .................................................................................................................................. 33

Consumer Spending for Necessities ...................................................................................................................... 35

Consumer Expenditures for Alcohol in 2000 ......................................................................................................... 39

The Costs and Demographics of Vehicle Acquisition ......................................................................................... 61

Out-of-Pocket Expenditures by Consumer Units with Private Health Insurance ............................................ 67

Expenditures on Entertainment ............................................................................................................................... 73

Travel Expenditures in 2000 ..................................................................................................................................... 79

Appendix A. Description of the Consumer Expenditure Survey .................................................................................... 83

 Page
 Consumer Expenditure Survey Anthology, 2003 1

Part I.

Survey Research and Methodology
 Consumer Expenditure Survey Anthology, 2003 3

Creating a “User-Friendly”

Expenditure Diary

Linda Stinson is a research psychologist formerly in the Office of Survey Methods Research, Bureau of Labor Statistics.

Nhien To is an economist in the Branch of

Research and Program Development, Division of Consumer Expenditure Surveys, Bureau of Labor Statistics.

Jeanette Davis is a senior economist in the

Branch of Research and Program Development, Division of Consumer Expenditure Surveys, Bureau of Labor Statistics.

LINDA STINSON

NHIEN TO

JEANETTE DAVIS I

nterest in American expenditures

has a long history dating back to

 the late 1800s, when the Bureau of

Labor Statistics (BLS) first looked at

the economic welfare of our early immigrants. Today, BLS is mandated to report detailed information on all the

ways in which Americans spend their

money. The Consumer Expenditure Diary (CED, Diary) is the instrument used

to collect information on the many purchases made each week by sampled

households.

When it comes to reporting detailed

expenditure information, not all purchases are equally easy to remember

and record. Some expenditures, such

as daily busfare, are often part of a

“work commute” mental script and may

be readily recalled. Other purchases, like

sodas and snacks from vending machines, tend to be more mundane, buried within the concerns of daily activities, and more easily overlooked. The

diary mode of data collection has long

been recognized as an especially useful tool for collecting daily records of

these types of frequent, low-salience

purchases before they are forgotten.

The diary also makes it possible to collect followup details on purchases that

can be used to produce the weights for

the Consumer Price Index.1 Such information would be difficult, if not impossible, to collect accurately without

some means of recording the purchases

during the week as they occur.

Over time, numerous economic researchers have adopted a diary approach to track household consumption, gauge reactions to new products

appearing on the market, and observe

social trends. Through their work, it

has become abundantly clear that diaries are useful data collection tools. However, in order to attract and keep respondents, a diary must be user friendly and

actively engage the respondents’ interest in the data-reporting task.

Developing a BLS diary

Over the years, BLS created various

expenditure diaries with the hope that

they would produce high response

rates and accurate estimates. But evidence from numerous research studies, expert reviews, and the reports of

interviewers and respondents alike has

indicated that these diaries were not

particularly user friendly. From the perspective of the respondent, the main

problem with the current CED Diary is

that it is difficult to navigate; neither

its logic nor its structure is apparent.

(See exhibit 1.) The respondent must

navigate both vertically and horizontally and must inspect every page thor1

 For example, reports for grocery items

need to include details about the type of packaging and whether the item is fresh or frozen. Detailed information on clothing includes the gender and age range of the recipient. Meals away from home have

followup details about purchase of alcoholic

beverages.
4 Consumer Expenditure Survey Anthology, 2003

oughly in order to determine how to

proceed. In addition, respondents have

reported that Diary instructions are not

easy to read or follow. (See exhibit 2.)

For example, respondents do not understand some of the words, such as

“consumer unit,” used by BLS. Likewise, the pages used as examples in

the current diary have been reported

to be somewhat overwhelming and,

worse, may contribute to, rather than

ameliorate, respondents’ confusion.

Finally, the large size and landscaped

layout (as opposed to the more typical

book format) makes it difficult for some

respondents to read and use the diary.

In response to these concerns, the

Branch for Research and Program Development in the BLS Division of Consumer Expenditure Surveys chartered

the Redesign and Analysis of Diary

(RAD) team to develop a more attractive and appealing CED that would be

less burdensome to complete. The first

step in the process was to identify the

many graphical features that might be

used to guide respondents through a

diary. Color, icons, highlighting, and

shading were all considered as tools

that could clarify the respondents’ task

and help them report information fully

and accurately.

Working with a contractor, the RAD

team developed three prototype diaries

that were ready for evaluation by the

spring of 2001. The prototypes were

distinguished by the color of their covers, their internal structure, and their

length.

Prototype 1 (the peach diary), also

entitled “Your Daily Notebook,” was

identical to the current BLS production

diary, but was reformatted with icons,

color, and a portrait, booklike orientation. It was divided into seven days,

within which were five major expenditure categories. Within each category

were several subcategories identifying

subsets of expenditures that should be

recorded. Because of its peach-colored

cover, Prototype 1 was referred to as

the peach “current” diary. (See exhibit

3.) The copious subcategorization of

expenditures rendered the peach diary

the longest of the three, at 144 pages.

Prototype 2 (the yellow diary), entitled “Track How You Spend Your

Money; also was divided into 7 days.

As with the peach diary, all the expenditure categories and subcategories

were repeated every day, with tabs indicating where each day began. Expenditures were recorded on the day

of purchase and under the correct descriptive category. The difference between this diary and the peach “Current” diary was that in the former there

were fewer subcategories within the

major expenditure categories. Because

of its yellow cover, Prototype 2 was

referred to as the yellow “day” diary.

(See exhibit 4.) With fewer subcategories, it was 132 pages long.

Prototype 3 (the teal diary), entitled

“Your Daily Notebook,” was divided

into four major expenditure categories,

instead of the days of the week. Respondents recorded purchases under

the correct expenditure category, along

with the day on which they were purchased. Because of its teal cover, Prototype 3 was referred to as the teal

“parts” diary. (See exhibit 5.) By eliminating the repetition of the 7 days, it

was the shortest of the prototypes, at

only 36 pages.

The first step in the process of

evaluating the strengths and weaknesses of each version of the diary was

to submit all three to knowledgeable

BLS staff for review.2 The comments

generated by this review process

ranged from the correction of typos to

more profound concerns about missing data elements and the quality of

the data. The initial process of internal

review resulted in the elimination of the

peach “current” diary, which was almost universally disliked because of its

length and complexity. This left the

RAD team with two viable prototypes.

Round 1: Evaluation of Prototypes 2 and 3

Beginning in June, 2001, copies of the

yellow and teal prototype diaries were

distributed to 15 U.S. Census Bureau

interviewers known as field representatives, 90 BLS staff and summer interns from the Office of Prices and Living Conditions and the Office of

Survey Methods Research, and 11 managers and staff from the Census Bureau. The prototype diaries were randomly assigned, with roughly half of

the participants receiving a yellow

“day” diary and half receiving a teal

“parts” diary.

All participants were asked to keep

the assigned diary for their entire consumer unit for 1 week. In addition, the

field representatives completed a short

questionnaire developed by the Census Bureau, which they mailed to the

RAD team at BLS, along with comments written in the margins of their

diaries. All other BLS and Census Bureau participants took part in discussion groups to talk about their experiences using the diary, to identify

potential problems, and to brainstorm

ideas for improvements.

In total, the RAD team conducted

12 discussion sessions with 6 to 13

participants per session and a smallgroup interview with three Census Bureau managers. In each of the groups,

there was a mix of participants, some of

whom kept the yellow diary, and some,

the teal diary. In this way, participants

were able to discuss the relative merits

of the two versions.

The strategy of choosing knowledgeable BLS and Census Bureau staff

as participants in the first round of

study was selected for many reasons.

First, it was a way to generate interest

in the new diary by disseminating information about proposed changes.

Second, it provided BLS subject-matter experts and Census Bureau field

staff with an opportunity to comment

on the prototypes and to help determine the design of the new diary. Third,

it was a chance to draw upon the expertise of those who know what data

the diary should collect and to critique

the prototypes in light of the estimates

they would produce.

While each discussion group had

its own unique flavor and focus of interest, the comments made throughout

were remarkably similar. Unanimity on

2 The first rounds of internal BLS evaluation included reviews by staff in the Consumer Expenditure Program, the Consumer

Price Index Program, and the Office of Survey Methods Research.
 Consumer Expenditure Survey Anthology, 2003 5

certain key points was highly reassuring and made it relatively easy to decipherthe main themes conveyed in many

different ways.

As regards the yellow “day” diary,

participants reported that having the

diary divided by day of the week

helped them to recall their purchases.

However, at 132 pages, this version was

still bulky, repetitive, and somewhat

difficult to navigate and use.

The teal “parts” diary was more

problematic. While it was considerably

shorter and easier to manage, participants reported that they missed the

day-of-purchase structure in attempting to recall their expenses. Apparently,

these memories were not classified internally by expenditure category, but

rather were associated with the activities of the day of the week.

The main results from the first round

of study found their fullest expression

in the following list of recommendations generated by the participants:

• Clarify the instructions, recording rules, and definitions for both

prototype diaries; provide a set

of “frequently asked questions”

(FAQs).

• Eliminate the subcategories and

simplify the recording task in the

yellow “day” diary.

• Expand the examples and avoid

needless repetition of examples

in both diaries; use the pages with

examples to convey as much new

information as possible.

• Organize the teal “parts” diary by

day of the week, as done in the

yellow “day” diary.

• Make the yellow “day” diary as

compact as possible, with a length

similar to that of the 36-page teal

“parts” diary.

• Provide a “mental map”—an overview of all the major categories—

at the beginning of the diary so

that respondents do not have to

study the entire booklet in order

to understand what lies ahead.

• Tell respondents about any expenses that should not be recorded.

• Use check boxes to collect followup details, such as the type

of packaging for groceries or the

type of meal eaten away from

home.

• Make the diary look easy and user

friendly, yet, at the same time,

maintain a professional and official quality.

While these recommendations were

directed specifically toward the development of a new prototype, other comments surfaced that addressed the

overall task of keeping a diary:

1. Keeping a diary is a difficult memory

task.

• It is often difficult to remember to

record expenditures in the diary.

• If expenditures are not recorded

close to the time of purchase, they

generally become increasingly

difficult to report accurately.

• If a diary is not portable, it is sometimes difficult to remember what

was purchased and what the price

was by the time one returns home.

2. Reporting for other people is difficult.

• Family members other than the respondent are less diligent about

tracking their expenses and reporting them than the respondent is.

• Family members other than the respondent may become irritated

and annoyed when asked about

their spending.

• Adolescents are often uncomfortable and uncooperative about reporting their expenditures to their

parents.

• Household members not directly

instructed by the FR tend to make

more reporting errors.

3. Mathematical calculations are difficult.

• It is often difficult to compute

prices (with or without sales tax),

even with the aid of a receipt.

• Many respondents are unable to

figure out the price of a purchase

if a receipt for that purchase does

not clearly specify discounted

coupon amounts and sale prices.

• Rebates also are difficult to compute and record.

Taking into account all of this information, the RAD team turned to expenditure diaries from other countries for

ideas on how to apply what was

learned. Many international diaries had

appealing designs, but the diary used

by the Household Budget Survey Program from the United Kingdom seemed

to fit most closely the needs described

by our study participants and answered many of their objections. The

U.K. diary included check-box-style

columns for followup details, a day-ofthe-week structure with only five major categories each day, and an attractive, yet professional-looking, design.

Consequently, the RAD team designed

a new “Prototype 4” diary in the same

vein as the one from the United Kingdom,3

 but incorporating additional beneficial features specified by BLS participants. (See exhibit 6.) For example,

Prototype 4 included a “mental map”

at the beginning of the diary, explaining its overall structure (exhibit 7), as

well as expanded example pages (exhibit 8) and a series of FAQs addressing the most common recording difficulties that arose during the study

(exhibit 9). Among the last were the following:

• How detailed should my descriptions be?

3

 The major categories in BLS Prototype

4 are (a) “food and drinks from grocery and

other stores,” (b) “catered events and meal

plans,” (c) “food and drinks from grocery

and other stores,” (d) “clothing, shoes, jewelry, and accessories,” and (e) “all other products, services, and expenses.”
6 Consumer Expenditure Survey Anthology, 2003

• How should I record multiple purchases?

• How should I record prepayments,

such as a subway fare card?

• How should I record credit card

purchases?

• Should I record automatic deductions taken from my paycheck or

bank account?

• Should I record typical monthly

bills?

• What should I do when I use coupons, discount cards, or loyalty

cards?

• Can I just give you receipts instead of writing the information

down?

• How should I record items if I don’t

know whether they include tax?

• What if I make a contribution or

a charitable donation?

• What about gift certificates or gift

cards?

• What do I do about returns and

exchanges?

• Should I record subsidized and

reimbursed expenses?

• What should I do about shipping

and handling costs?

• What’s the difference between a

concession stand and a mobile

vendor?

Round 2: Evaluation of Prototype 4

Even though Prototype 4 was developed from information gathered during

the first round of study, the new design still needed to be evaluated to identify both its strengths and weaknesses.

A five-pronged strategy was formulated for a second round of study:

• Eight diaries were posted throughout the Division of Consumer with

a request for review and comment.

• Fifteen diaries were mailed to the

same Census Bureau FRs who

participated from the first round

of study, along with a short questionnaire to target key questions

of interest.

• Fourteen diaries were distributed

to a subgroup of BLS staff who

participated in the first round of

study, so that they could participate in another 2-hour review

session comparing the prototypes.

• Fourteen diaries were distributed

to staff of the Office of Prices

and Living Conditions and the

Office of Survey Methods Research who had not participated

in the first study, so that they

could record their expenditures

for a week and participate in an

interview.

• Twenty diaries were distributed

to members of the public, so that

they could record their expenditures for a week and participate

in an interview.

During the course of the study, the

participants mentioned several features

of the new diary that they especially

liked and found helpful: (a) The division of the diary into days of the week,

(b) the book’s graphical design and layout, (c) the FAQs, (d) the lists of products and services used as examples

within each major category, and (e) the

new example pages with more sample

entries and information boxes used to

highlight reporting details.

Participants also identified concepts

and instructions that still needed to be

clarified:

1. Some participants remained unsure how to record multiple purchases of the same item (for example, five cartons of yogurt). To

resolve this uncertainty, an additional FAQ was included: “How

should I record multiple quantities?”

2. In keeping with the requirements

of the Consumer Price Index, respondents were told in the instructions not to record expenses

incurred when they were away

overnight. However, almost every participant in the study supplied a different interpretation of

what being “away overnight”

meant. To standardize reports, it

was recommended that this instruction be clarified and highlighted in interviewer training

sessions.

3. The diaries instructed respondents to record each meal that

was eaten as “Food & Drinks

from Food Service Places” as either “breakfast, lunch, dinner, or

snack/other.” However, only 72

percent of the meals from food

service places recorded in Prototype 2 and Prototype 3 during

round 1 of the study specified

any one of the four types of meals

listed. Similarly, low percentage

also has been cited as one of

the flaws of the current CED.

One goal of the redesign project was to reduce the amount

of information, including the

number of records having to do

with meals, that needed to be imputed because of missing data.

Because this same error occurred in a number of diaries kept by

Census Bureau field representatives, it was decided that the

place to begin would be with improved interviewer training. In

addition, Prototype 4 was redesigned to include check boxes

for “breakfast, lunch, dinner, or

snack/other” in order to standardize reporting and reduce the

information burden on respondents. (See exhibit 10.)

4. The Consumer Price Index program requires additional information about grocery purchases,

including whether the items are

fresh, frozen, bottled, canned, or

other. An ever-increasing variety of types of packaging, however, makes these distinctions

difficult to describe and burdensome to use. Many of the par-
 Consumer Expenditure Survey Anthology, 2003 7

ticipants in the study requested more

clarification of these distinctions, and

it became clear that two separate questions had become intertwined in the

minds of the respondents:

Question 1—

• How is the food packaged? That

is, does it come in a can, a bottle,

or some other type of packaging?

Question 2—

• Is the food fresh, frozen, or in

some other condition?

To make explicit the twin possibilities

that fresh food may be packaged (for

example, fresh tomatoes may be

wrapped in cellophane) and frozen food

may be canned (for example, frozen orange juice may be sold in a can), the

two followup questions were placed

into two separate columns together with

checkboxes. (See exhibit 6.)

These and other observartions collected during the evaluation phase of

round 2 of the study translated into

many small ideas for correcting minor

flaws and tiny oversights—the traditional “tweaking.” The overwhelming

message, however, was that Prototype

4 is a user-friendly, attractive, and professional-looking data collection instrument.

Next steps

The final steps in the creation of the

user-friendly expenditure diary involve

• transforming Prototype 4 into an

image-scannable document according to Census Bureau specifications,

• updating interviewer training to

mirror design changes in the diary, and

• conducting a field test to assess

the effect of changes to the diary.

Producing an image scannable document. Because the Census Bureau has

updated its system of managing and

processing paper forms, it is now possible to move away from the old procedure of using microfiche to preserve

documents. The goal is to produce paper forms, including diaries, that can

be scanned into an electronic image.

Data would be keyed directly from the

computer image, which would also

serve as the archived document, replacing microfiche.

In order to meet the demands of this

automated process, the user-friendly

diary must also be converted into a processing-friendly document. In other

words, the final formatted diary must

fit the color, font, and size constraints

of the processing system’s specifications. This work has been undertaken

by the Census Bureau’s Forms Design

Office.

Updating interviewer training. As the

new diary prototypes were being developed, it became apparent that certain aspects of the diary-keeping task

needed more emphasis during interviewer training. For instance, BLS suggested that interviewer training needed

to include more explanations and practice (1) identifying which “overnight”

expenses should not be recorded, (2)

specifying the different types of meals,

and (3) explaining why the diary has a

day-of-the-week structure, but the additional overflow pages do not.

Also, because many of the diary’s

new design features would be unfamiliar to the interviewers, a new training manual and procedures for both

self-study and classroom study needed

to be developed. Among the new features that required instructions were

the following:

• FAQs

• example pages with information

boxes

• check boxes

• pockets for receipts

• a daily reminder list

In addition, because the new diary

will incorporate a computerized introductory segment to collect the household demographic details, new training

on the computer will be required.

Conducting a field test. In September

2002, a field test was scheduled to assess the feasibility of using the new

user-friendly diary and to evaluate the

effects upon estimates and response

rates. The redesigned diary will be

placed in nine census regions for 4

months; it is anticipated that 1,600 completed diaries will be collected. These

diaries will be analyzed and compared

with those obtained from a control

group, as well as with the regularly produced diaries.

The four main goals of the field test

are as follows:

• to determine whether the new

user-friendly diary yields higher

response rates than those generated by the current production

diary;

• to test whether there is a statistically significant difference between the estimates produced by

the new diary, and those obtaintained from the curent production diary;

• to evaluate the user friendliness

of the new diary in terms of the

burden it places on respondents

(for example, the length of time

the respondent needs to complete the diary and the difficulty

respondents experience in completing it); and

• to test the operation of the computerized segments of the data

collection and operational control processes.

Only at the end of these final steps

will we know whether BLS has, in fact,

created a user-friendly diary that is at

the same time “processing friendly,”

“image friendly,” and “data quality

friendly.” If the final verdict is affirmative, the new user-friendly diary will be

implemented in 2004.
8 Consumer Expenditure Survey Anthology, 2003

Exhibit 1: Navigation problems in the former BLS expenditure diary
9 Consumer Expenditure Survey Anthology, 2003

Exhibit 2: Instructions from the current BLS diary 
10 Consumer Expenditure Survey Anthology, 2003

Exhibit 3: The peach “Current” diary
11 Consumer Expenditure Survey Anthology, 2003

Exhibit 4: The yellow “Day” diary
12 Consumer Expenditure Survey Anthology, 2003

Exhibit 5: The teal “Parts” diary
13 Consumer Expenditure Survey Anthology, 2003

Exhibit 6: Prototytpe 4 
14 Consumer Expenditure Survey Anthology, 2003

Exhibit 7: The “mental map” in Prototype 4
15 Consumer Expenditure Survey Anthology, 2003

Exhibit 8: An example page in Prototype 4
16 Consumer Expenditure Survey Anthology, 2003

Exhibit 9: The “Frequently Asked Questions” in Prototype 4
17 Consumer Expenditure Survey Anthology, 2003

Exhibit 10: The “food from food service places” page in Prototype 4
18 Consumer Expenditure Survey Anthology, 2003

Computer-Assisted

Personal Interviewing for

the Consumer

Expenditure Interview

Survey

LINDA SIOUX GROVES

Linda Sioux Groves is Chief, Division of Consumer Expenditure Surveys, Bureau of Labor

Statistics.

B

eginning in April 2003, the Consumer Expenditure (CE) Interview survey will be conducted

by computer-assisted personal interviewing (CAPI). The survey will continue to be conducted in person by U.S.

Census Bureau interviewers in the

respondent’s home; however, the interviewer will administer the questions

and record the answers on a laptop

computer in place of the current paperand-pencil questionnaire. This article

describes the process whereby the CE

CAPI questionnaire was designed and

developed and discusses some of the

benefits expected to be realized from

CAPI data collection in the areas of

data quality, operational efficiency, and

opportunities for future improvements.

Design and development of CAPI

The Census Bureau collects the data

for the CE Survey under contract with

the Bureau of Labor Statistics (BLS).

The administration of the survey is

very much a collaborative effort between the two agencies. Discussions

and planning regarding converting the

CE Interview Survey to CAPI began in

1997. The Census Bureau was already

collecting several other surveys in

CAPI mode at that time, including the

Current Population Survey and the

Health Interview Survey. However, all

of the CAPI surveys being collected

by the Census Bureau, as well as all

the peripheral systems that support

collection activities, such as the Case

Management System, had been developed in a DOS computing environment.

The availability of new instrumentauthoring software and more powerful

laptops led to an early decision that CE

CAPI would be developed in a Microsoft

Windows  environment. The authoring

software chosen was Blaise, which was

developed by Statistics Netherlands

and is in wide use in Europe and in other

U.S. survey organizations.

The CE CAPI development project

was an interagency effort, with management representatives from both BLS

and the Census Bureau serving on the

CAPI Steering Group (CSG). The steering group developed the strategic plan

for the project and chartered numerous

working teams that were then assigned

to establish instrument design standards, write specifications, program

and test the CAPI instrument and related systems, develop a new Case

Management System, establish newinterviewer training, and plan a large

“dress rehearsal” to assess the impact

of CAPI on CE estimates. The steering

group approved the project plans for

each of the teams, facilitated communication among teams, and monitored

progress throughout the project.

Among the goals of the Census Bureau

were (1) to use the CE CAPI development process to set Windows stan-
 Consumer Expenditure Survey Anthology, 2003 19

dards for any future CAPI development

of other surveys and (2) to create a

Case Management System that all of

the surveys that the Census Bureau

administers could use. The latter aim

was important because Census Bureau

interviewers typically work on many

different surveys.

A great emphasis was placed on testing during the CE CAPI development

process, with numerous different kinds

of tests designed to accomplish various goals. During the design standards

stage, instrument prototypes were developed and examined by experts from

several survey organizations. Once basic design features, such as colors and

fonts, were established, development

of the CE instrument began. The decision was made to program the questionnaire, which consists of 22 sections

roughly corresponding to different topics, in three modules.

The first module consisted of sections of the questionnaire representing as many different design issues as

possible. For example, it included sections in which the interviewer reads

long lists of items to the respondent,

sections wherein screening questions

are used to skip the respondent to the

correct set of detailed questions, and

sections in which reported data from

the previous interview are used extensively. Functionality testing was done

to ensure that the module met the

specifications. Following this testing,

a panel of Census Bureau CE data collectors was brought in to test the usability of the module. The results from

these two rounds of testing and the

decisions made on program design issues were then applied to the development of the next module, and the process was repeated until the entire data

collection instrument was programmed.

The early input from data collectors

during the development process resulted in a more “interviewer-friendly”

collection instrument.

Concurrently with the development

of the CAPI instrument, the new Case

Management System and postcollection processing system were developed

and tested. Once all of these pieces

were complete, they were integrated

with the CAPI questionnaire and a systems test was performed. The test was

used to make final adjustments to the

CAPI system for the dress rehearsal.

The CE CAPI dress rehearsal began

in January 2002 in all 12 Census Bureau regional offices. Lasting 9 months,

with three quarterly interviews per respondent in a sample of about 3,000

households, the purpose of the dress

rehearsal was to analyze the impact of

CAPI on response rates and on expenditure estimates. A secondary purpose

of the test was to make final adjustments

to training and procedures by involving

a larger pool of data collectors.

The CAPI system was fully implemented in April 2003. Interview cases

that began their five-interview cycle

with the paper questionnaire were converted to CAPI at that time. In an effort

to ease the transition of cases from

paper to CAPI, changes that were anticipated in the CAPI questionnaire

were largely incorporated into the paper questionnaire in advance (during

2001). Thus, the content of the paper

instrument and that of the CAPI instrument are nearly identical.

CAPI and data quality

The CE interview is long and complex

and usually takes from 60 to 90 minutes to complete. In addition to collecting information on expenditures for

a wide range of items, the survey collects detailed demographic and income

data pertaining to consumer unit members, data on assets and liabilities, and

descriptive information about expenditures for classification and bounding purposes. The interviewer is required to navigate correctly through

numerous screening questions and on

to the detailed questions, all the while

skipping inapplicable questions. In

some cases, the interviewer is required

to carry forward information from one

part of the survey to another and make

decisions about which subsequent

parts to administer or questions to ask,

all on the basis of a complex decision

table.

Reviews of the collected data reveal

that, because of the complexity involved, interviewers sometimes make

mistakes in administering the paperand-pencil interview, resulting in inconsistencies or gaps in the reported data.

If these errors are detected early

enough, the interviewer may recontact

the respondent to fill in the missing

data. Otherwise, the errors must be resolved through postcollection editing.

One of the advantages of a CAPI

collection instrument is that many of

these types of data problems can be

eliminated. The logic programmed into

the instrument forces the interviewer

to stay on the correct path and does

not allow questions to be inadvertently

skipped. For example, in the section of

the paper questionnaire dealing with

properties owned by the respondent,

interviewers ask (1) one set of questions for each new property reported,

(2) different sets of questions, depending on what type of mortgage the respondent has and whether there are

also home equity loans on the property, and (3) yet another set of questions if the property was disposed of

or the mortgage payment amount

changed from what was reported in the

previous interview. The CAPI instrument will ensure a more seamless flow

through all of the applicable questions

for each property. In addition, the instrument is able to keep track of long

lists of items and ensure that the correct set of detailed questions is asked

for each item. As a result, there is much

less postcollection editing and error

resolution with CAPI.

Another way in which CAPI will

improve the quality of the data is by

requiring the interviewer to verify unusually high or low values with the respondent. Range edits are programmed

into the CAPI instrument, based on

previously reported data. When a value

outside of the allowable range for a

particular item is entered, an edit message is triggered, requiring the interviewer to explicitly accept the value or

change it, thus checking for typos. The

interviewer is also allowed, and even

encouraged, to enter textual notes to

explain unusual values. An unusually

high expenditure for dresses or cut

flowers, for example, could be accompanied by the note “Respondent is pre-
20 Consumer Expenditure Survey Anthology, 2003

paring for daughter’s wedding.” The

interviewer might also use the note field

to indicate uncertainty about the classification of an item. A $45,000 expenditure under “Hobbies,” for instance,

might be accompanied by a note “Respondent collects antique cars.” Notes

such as these can prove useful to analysts who examine the data later, because items with notes associated with

them are flagged in the data file and the

text of the notes will be stored with the

data. An outlier detection system will

automatically display the notes to the

data reviewer.

Another feature of the CAPI instrument is that help screens will be made

readily available to interviewers as they

administer the questionnaire, rather

than in a separate collection manual

that might be difficult to consult during an interview. The CE CAPI help

screens include examples, such as what

to include under “small household appliances,” and definitions, such as

those of “PPO” and “IPA” with regard

to the type of health insurance that

each offers.

Finally, another way in which CAPI

may improve the quality of CE data is

by allowing new items to be added to

the questionnaire more quickly as they

enter the marketplace. This feature is

highly important to one major user of

CE data—the Consumer Price Index

program—in terms of keeping the index as current as possible, as well as

being important to CE data users in the

private sector.

Operational advantages of CAPI

From a survey operations perspective, a CAPI instrument has several

advantages over paper-and-pencil

data collection.

Currently, interviewers send their

completed CE paper questionnaires to

the Census Bureau’s National Processing Center in Jeffersonville, IN. There,

the clerical staff checks questionnaires

against a master control list, applies

codes to certain items (for example, on

the basis of the interviewer’s description, the make and model of a vehicle

are coded), and keys in and verifies the

data. With the implementation of CAPI,

the data are input directly into the computer with no separate keying-in step.

Coding is done as the data are entered.

(In the case of a vehicle’s make and

model, the interviewer will select the

correct description from an alphabetical popup list.) Instead of physically

sending paper questionnaires to a central location for processing, the data

are transmitted nightly from the

interviewer’s home via a modem. Consequently, CAPI data collection should

make the data available for tabulation

sooner.

Other survey operations also will

be streamlined by the conversion to

CAPI. Currently, at the National Processing Center, clerical staff transcribes

certain information from each completed paper questionnaire onto the

next quarter’s blank questionnaire and

mails both back to the Census Bureau

regional office, which, in turn, mails

them out to the appropriate interviewer

in time for the next collection cycle. The

transcribed data include inventoried

items, which the interviewer does not

recollect each time, but rather updates

with current information (for example,

on properties owned), as well as expenditure data collected in the previous period and now used for bounding in the current interview. These

bounding procedures minimize telescoping errors that are common in retrospective interviews and result from

a tendency to report past events in the

reference period of the survey. With

CAPI data collection, once these data

are captured in electronic form and

then transmitted, an input file is created for the next quarter’s interview and

is transmitted directly to the interviewer’s laptop.

Certain survey management and

control functions will also improve under CAPI. Field supervisors can easily

reassign cases to a different interviewer, if needed, simply by retransmitting information about the case. Supervisors in the field, as well as

headquarters staff, can get much more

timely reports on the status of data

collection activities than they could

using paper questionnaires.

Future improvements

Respondent burden is a significant issue for the CE Interview survey, likely

contributing to underreporting of expenditures and to refusals by respondents to participate in later waves of

the survey. Unfortunately, CAPI will

probably not make the interview any

less burdensome to the respondent,

and early indications are that the interview may even take slightly longer.

However, future research might permit CAPI’s capabilities to be used to

streamline the interview and reduce respondent burden. More customization

of the interview could be possible,

based, for example, on respondents’

characteristics or previously reported

data. Also, the added flexibility of

CAPI might allow more experimentation with global questions and randomization of topics, so that not all parts

of the questionnaire would need to be

asked during each wave of the survey.

CAPI will certainly afford survey researchers much more quantitative information about the interview process

itself. For example, each CAPI interview

produces an audit trail that allows one

to “replay” the interview. This can be

used to diagnose trouble spots in the

interview, detect whether the interviewer jumped around in the instrument or followed the default path, and

count how many times help screens

were invoked or warning messages

were suppressed. Similarly, timing data

from the CAPI instrument can be used

not only to measure overall interview

length, but also to access how revisions to questions affect timing in individual sections. These are valuable

tools in the CAPI instrument that are

not available in a paper interview.

Through them, investigators can gain

a much better understanding of some

of the difficulties facing interviewers,

and that increased understanding will

lead to further improvements in the

data collection process.
 Consumer Expenditure Survey Anthology, 2003 21

Introducing Brackets:

Quality in the Consumer

Expenditure Interview

Survey

GEOFFREY PAULIN

Geoffrey Paulin is a senior economist in the

Branch of Information and Analysis, Division of Consumer Expenditure Surveys, Bureau of Labor Statistics.

N

onresponse is a problem in

surveys. Some potential participants may refuse to participate at all in a survey, while others may

provide answers to some, but not all,

questions asked. For those who participate at least partially, reasons for not

responding to certain questions may

include the sensitivity of the respondent to the question asked or simply a

lack of knowledge on the part of the

respondent. One situation in which either of these two reasons may be cited

is when respondents are asked about

income levels and sources. Some respondents may refuse to answer questions about income because they consider the matter too personal to

divulge. Others may be willing to answer, but may not be able to do so completely, because they lack specific or

detailed knowledge. This is often the

case in “proxy reporting,” wherein the

respondent reports income information

for another member of the consumer

unit.1 For example, a parent may not

know precisely the amount of income

earned by a teenaged daughter who is

employed after school at a neighborhood fast-food restaurant.

In the case of complete refusal to

participate in the survey, little can be

done to obtain information. By contrast,

as regards sensitive questions or lack

of knowledge, information may be

gained by allowing the respondent to

give an answer that is not precise. For

example, a person earning a salary of

$300,000 may refuse to divulge that information precisely, but may be comfortable saying that the salary is

“greater than $120,000.” Similarly, the

aforementioned parent may not know

the precise salary of his teenaged

daughter, but may know with confidence that it is “less than $5,000” per

year. Prior to the second quarter of

2001, such information was lost in the

Consumer Expenditure (CE) Interview

survey, because the respondent could

only report a value, assert “don’t

know,” or refuse to answer. However,

starting in April 2001, respondents were

given the opportunity to provide an

income range, or “bracket,” when they

were unable or unwilling to give a specific value. This article describes the

collection of income data and the development of income brackets in the

CE Interview survey.

Income data are collected in the second and fifth interviews for those who

participate in those interviews. If the

consumer unit does not complete its

second interview (for example, if the

family is unavailable during the survey

period or if the family originally residing at the address during the second

interview has moved away and the new

1

 See “Glossary” in Appendix A at the end

of this anthology for the definition of a consumer unit.
22 Consumer Expenditure Survey Anthology, 2003

residents are now participating instead), the information is collected at

the earliest possible interview (the

third, fourth, or fifth). In either case,

incomes are collected for the past year,

as determined by the date of the interview. For example, a consumer unit interviewed in July 2002 would have been

asked to recall income received from

July 2001 to June 2002.

Data are collected on several

sources of income. Some of these, such

as data on wages and salaries, are collected for members of the consumer unit

who are at least 14 years old. Others,

such as information on interest income,

are collected for the consumer unit as a

whole. In addition to data on “labor”

(wage and salary or self-employment)

income and “nonlabor” (interest or dividend) income, information on other

sources (such as alimony, child support, Food Stamps, and welfare income)

also is collected. (For a complete listing of sources, see the appendix to this

article.)

History of bracketing in the

Interview survey

In May 1998, a 2-day seminar was held

at Princeton University to discuss the

utility of the CE Survey for measuring

poverty and related issues. During the

course of the seminar, many ideas for

improving the quality of the data were

proposed. One of these was to investigate the use of brackets for collecting

data on income, assets, and liabilities,

because these data are important, but

frequently missing. Katharine G.

Abraham, Commissioner of the Bureau

of Labor Statistics (BLS) at the time,

asked her organization’s Division of

Consumer Expenditure Surveys to

study the feasibility of collecting bracketed data, starting with the 2000 survey.

In September 1998, a team was chartered to investigate and recommend

strategies for the implementation of

bracketing if it was deemed feasible.

The team had two major questions to

answer: first, does bracketing reduce

nonresponse in practice? Second,

which type or types of brackets, if any,

should be used? Starting with a review

of the literature on the subject, the team

discovered that, as expected, bracketing was useful for collecting data, because respondents with imprecise

knowledge could provide at least some

information. However, one unintended

consequence described in the literature

is that bracketing can lead to a loss of

precision, because some respondents

who report bracketed data might have

reported actual values if the interviewer

had probed sufficiently.2 In addition,

the team reasoned that brackets would

increase respondents’ burden, because,

without them, a respondent could simply refuse to answer or respond “I

don’t know,” and the next question

would be asked. With brackets, once

either of these occurs, the interviewer

attempts to collect a bracketed value.

Still, the team concluded that brackets

would be useful despite these concerns. For example, the loss of precision might be outweighed by an increase

in overall response when brackets were

used. Interestingly, the literature also

supported the hypothesis that brackets do not seriously increase respondents’ burden: although it is true that

there is one more question in cases

where the initial response is “I don’t

know” or a refusal, it also is true that a

large number of those who initially respond in either of those ways is subsequently willing and able to provide a

bracketed value.3

Constructing the brackets

Given that brackets are indeed useful

in data collection, the second question

becomes operative. The team discovered that there are at least two types of

brackets used in practice: “conventional” brackets and “unfolding” brackets. With both types, the respondent

is first asked for a specific value. If he

or she is unable to provide one, then,

in a conventional-bracketing framework, the respondent is asked to identify, from a predetermined list, the range

in which the income or asset is likely to

fall (for example, less than $5,000;

$5,000 to $9,999; $10,000 to $19,999; and

so forth). In an unfolding-bracketing

framework, the respondent is asked a

series of questions designed to elicit

ranges of values. For instance, the interviewer might say, “Is it at least

$5,000?” If the response is “No,” then

a range of less than $5,000 would be

recorded. If the response is “Yes,” then

the respondent would be asked, “Is it

at least $10,000?” If “No,” then a range

of $5,000 to $9,999 would be recorded.

If “Yes,” the respondent would be

asked, “Is it at least $20,000?” If “No,”

then a range of $10,000 to $19,999 would

be entered. If “Yes,” then a response

of “at least $20,000” would be recorded,

and the next question in the survey

would be asked. The team recommended that conventional bracketing

be adopted, for a couple of reasons:

first, more precise answers would be

obtained. (For some sources of income,

such as wages and salaries, it is likely

that a large percentage of recipients

could accurately respond that their income from those sources was “at least

$20,000”; narrower ranges, such as

$20,000 to $29,999 and so forth, allow a

more precise estimate of the value of

such income.) Second, conventional

brackets were thought to be less burdensome, because the respondent

could be handed a card with the appropriate ranges and quickly scan it to find

which was appropriate for the source

in question. With unfolding brackets,

the respondent might be asked three

additional questions, instead of one.

Once the type of bracketing was

selected, the next question was what

the ranges of the brackets should be.

One idea was to use standard publication ranges as a guide. For example,

data currently are published for families whose total income is less than

$5,000; $5,000 to $9,999; $10,000 to

$14,999; and so forth. However, the Interview survey collects information

from a variety of sources, some for each

2 Kennickell, Arthur B., “Using Range

Techniques with CAPI in the 1995 Survey

of Consumer Finances,” on the Internet at

http://www.federalreserve.gov/Pubs/oss/

oss2/papers/rangepap0197.pdf, January

1997.

3.Juster F. Thomas and James P. Smith,

“Improving the Quality of Economic Data:

Lessons from the HRS and AHEAD,” Journal of the American Statistical Association,

vol. 92, no. 440, December 1997, pp. 1268–

1278.
 Consumer Expenditure Survey Anthology, 2003 23

member aged 14 and older, some for the

consumer unit as a whole. The publication ranges may be appropriate for

some sources of income (for instance,

wage and salary income), but may not

be appropriate for other sources. For

example, almost all respondents who

reported interest income reported a

value less than $5,000, so, for this

source, the publication range is too

broad to be meaningful. To determine

the most useful ranges, the distribution of each source was analyzed. Then,

through a combination of empirical examination and normative analysis, a

few sets of brackets were developed to

fit the different kinds of data. The empirical examination involved looking at

the percentiles for each source of income and seeing where breaks occurred. Normative analysis involved

finding “reasonable” cutoff values for

the data.

Refining the brackets, using the

BLS cognitive laboratory

The next step in the implementation

process required testing the results in

the BLS cognitive laboratory. At this

stage, a new team was formed that included a member of the Survey Research branch of the Division of Consumer Expenditure Surveys and a

cognitive psychologist from the BLS

Office of Survey Methods Research.

Cognitive psychologists are trained in

how respondents perceive certain

questions. That is, when the interviewer

asks about interest income, does the

respondent correctly perceive what the

interviewer is asking for (such as interest earned on checking and savings

accounts), or might the respondent be

confused and include other sources of

income (such as dividends from

stocks), or might the respondent even

report no income received, when, in

fact, he or she did receive such income,

but thought it was something else? In

the cognitive laboratory, tests are performed in which respondents are asked

for their answers and then are debriefed

by the psychologist. During the testing, the psychologist might ask the respondent to define certain terms, to

make sure that the respondent’s definition matches the interviewer’s; or the

respondent might be asked questions

about the survey in general—were the

questions posed easy or difficult to

understand and answer, for example.

After the brackets were refined on

the basis of findings from the cognitive tests, the brackets were ready to

be implemented. Various steps were involved in their implementation, including revising the survey instrument designed to collect the data, field-testing

the instrument, and obtaining appropriate approvals from offices that regulate Government surveys. Bracketing

finally appeared in the CE Interview

Survey in the second quarter of 2001.

That is, the first respondents to the

survey who were asked to provide

bracketed information began their participation in April 2001.4

 Currently, only

income brackets have been implemented. The original team investigated

the possibility of using brackets for

assets and liabilities as well, but decided to start with income only and

then apply any lessons learned therefrom to the implementation of assets

and liabilities.

Conclusions

At present, the first year (2001) of data

gathered with the use of brackets has

been published, and a new team has

been chartered to study how brackets

have changed the collection of income

data. Among the questions being investigated are the following: are many

“don’t knows” and refusals to answer

being converted to bracketed values?

Have brackets improved the percentage reporting various sources of income? Has average income reported

risen as a result of using brackets? and

Are there any demographic differences

in the propensity to provide bracketed

information? As these issues are analyzed, further research results will be

published documenting the findings.

APPENDIX:

Income Sources and

Bracket Ranges

Data on the following sources of income are collected for each individual

member of the consumer unit who is at

least 14 years old: Wages or salary; income (or loss) from nonfarm business,

partnership, or professional practice;

income (or loss) from own farm; Social

Security or Railroad Retirement Income;

and Supplemental Security Income.

The following sources of income are

collected for the consumer unit as a

whole: Unemployment compensation;

workers’ compensation and veterans’

payments, including education; public

assistance or welfare, including money

received from job training grants such

as Job Corps; Food Stamps and electronic benefits transfers; interest on savings accounts or bonds; regular income

from dividends, royalties, estates, or

trusts; pensions or annuities from private companies, the military, or government; income (or loss) from roomers or boarders; income (or loss) from

payments from other rental units; child

support; regular contributions from alimony or other sources, such as persons outside the consumer unit; and

other money income, including money

received from care of foster children,

cash scholarships, fellowships, or stipends not based on working.

Table 1 shows the brackets applied

by the interviewer to each source of

income.

4

 Although the initial goal was for implementation in 2000, it became apparent that

to implement bracketing properly would require cognitive testing and other processes.

Therefore, the implementation was delayed

until 2001.
24 Consumer Expenditure Survey Anthology, 2003 Table 1. Data collected by source of income and income brackets Wages and salaries; nonfarm business income; .......... Loss

1 0– 5,000– 10,000– 15,000– 20,000– 30,000– 40,000– 50,000– 70,000– 90,000– 120,000

— own-farm income ........................................................ 4,999 9,999 14,999 19,999 29,999 39,999 49,999 69,999 89,999 119,999 or more

Social Security and Railroad Retirement ......................

— Less 300– 400– 500– 600– 700– 800– 900– 1,000– 1,500 or

—

— Income ........................................................................ than 300 999 499 599 699 799 899 999 1,499 more

Supplemental Security ..................................................

— 0– 1,000– 2,000– 3,000– 4,000– 5,000– 10,000– 15,000– 20,000– 30,000– 40,000– 50,000

 Income ........................................................................ 999 1,999 2,999 3,999 4,999 9,999 14,999 19,999 29,999 39,999 49,999 or moreData collected for the .................................................... 0– 1,000– 2,000– 3,000– 4,000– 5,000– 10,000– 15,000– 20,000– 30,000– 40,000– 50,000 consumer unit as a whole ........................................... Loss

2 999 1,999 2,999 3,999 4,999 9,999 14,999 19,999 29,999 39,999 49,999 or more

1Self-employment income only2Rental income onlyNOTE: The foregoing brackets shown for sources collected for the consumer unit as a whole are also collected for the following sources of money, which are not considered income in

the CE Interview survey: lump-sum payments received from estates, trusts, royalties, alimony, prizes, or games of chance or from persons outside the consumer unit; and sales of householdfurnishings, equipment, clothing, jewelry, and pets or other belongings, excluding the sale of vehicles or property.

0

Income source

Range for bracket—

1 2 3 4 12 11 10 5 6 7 8 9
 Consumer Expenditure Survey Anthology, 2003 25

Characteristics of

Complete and

Intermittent Responders

in the Consumer

Expenditure Quarterly

Interview Survey

Sally E. Reyes-Morales is a mathematical statistician in the Division of Price Statistical

Methods, Branch of Consumer Expenditure

Surveys, Bureau of Labor Statistics.

SALLY E. REYES-MORALES T

he Consumer Expenditure (CE)

Quarterly Interview Survey collects data from consumer units

(CUs) across the United States. Some

CUs complete all five interviews, others complete some, but not all, of the

interviews, and some choose not to

participate in the survey at all. These

CUs can be called complete responders, intermittent responders, and nonresponders, respectively. Do the nonresponses of the intermittent responders

and nonresponders affect the published CE estimates? Are the CUs who

stay in the survey for all five interviews

different from those who do not?

To answer these questions, this

study uses the CE Interview Survey

data collected from 1997 to 2000. In

the study, characteristics and expenditures of complete responders and

intermittent responders are compared. Nonresponders are excluded

because very little information about

them is collected.

Background and definitions

The CE Interview Survey is a rotating

panel survey in which a random sample

of residential addresses is selected and

the CUs living at those addresses are

asked to report their expenditures during the previous 3 months. The U.S.

Census Bureau collects these data for

the Bureau of Labor Statistics. The random sample of residential addresses is

selected by means of systematic sampling, and the CUs at those addresses

are interviewed by the Census Bureau

field representatives once per quarter

for five consecutive quarters. After the

fifth quarter, the CU leaves the sample

and a new address is selected to replace it. The CE sample is representative of the total civilian population of the

United States not living in institutions.

In the initial CE Interview, respondents are asked to report all of the expenditures they made during the previous month. This interview is used

only for “bounding” purposes—that

is, to make sure that the expenditures

reported in the second through fifth

interviews reflect the correct periods.

In the second through fifth interviews,

expenditure data are collected for the 3

months prior to the interview. Only the

expenditure data collected in the second through fifth interviews are used

to compute official CE estimates. Data

collected in each quarter are treated independently, so annual estimates do

not depend upon any CUs participating for all five quarters.

Following are some of the terms that

will be used in this article, together with

their definitions:

Household. The people who occupy a

housing unit. A housing unit is a house,

an apartment, a mobile home, a room,

or a group of rooms occupied (or intended to be occupied) as separate living quarters.
26 Consumer Expenditure Survey Anthology, 2003

INTERI. Interview number (1 through

5).

INSTAT. Final interview status (01

through 19):

01 = Interview

Type A noninterviews:

02 = No one home

03 = Temporarily absent

04 = Refused

05 = Other Type A noninterviews

Type B noninterviews:

06 = Vacant (for rent)

07 = Vacant (for sale)

08 = Vacant (other)

09 = Occupied by person whose usual

09 = residence is elsewhere

10 = Under construction (not ready)

11 = Other Type B noninterviews

Type C noninterviews:

12 = Demolished

13 = House or mobile home moved

14 = Converted to nonresidential use

15 = Merged

16 = Condemned

17 = Located on military base

18 = CU moved

19 = Other Type C noninterviews

Interview. An interview is completed

by an eligible CU (INSTAT = 01).

Type A noninterviews. An address is

within the scope of the survey and

eligible for interview, but an interview

is not obtained (INSTAT = 02 through

05).

Type B noninterviews. An address is

within the scope of the survey, but is

not eligible for interview (INSTAT = 06

through 11).

Type C noninterviews. An address is

out of the scope of the survey or permanently ineligible for the CE sample

(INSTAT = 12 through 19).

CU (consumer unit). See “Glossary” in

Appendix A at the end of this anthology.

Reference person. See “Glossary” in Appendix A at the end of this anthology.

Consumer units used

In this study, selected demographic

characteristics of CUs who completed

the last four interviews (INTERI = 2

through 5) were compared with corresponding characteristics of those who

did not. To make these comparisons,

the universe of CUs from which data

were collected was subdivided, using

the following criteria:

• Only CUs scheduled to participate in all five interviews between January 1997 and December 2000 were used, in order to

follow CUs’ history in the survey.

• Only CUs who completed one or

more of the last four interviews

(INTERI = 2 through 5) were

used, because the demographic

characteristics examined in the

study are not collected in the first

interview.

The response rates for the CUs used

in the current study are different from

the CE response rates published in CE

reports, because not all CUs were used

in the study. Table 1 shows the response rates computed from all records

in the CE sample, compared with the

response rates computed from only the

records used in the study. The study’s

CUs had higher response rates and

lower nonresponse rates than the complete universe of CUs had, because the

study excludes CUs who completed

none of the last four interviews (INTERI

= 2 through 5).

Typically, Type B and Type C noninterviews are not used in response rate

calculations, because they are ineligible

or out of the scope of the survey. Response rates usually are computed with

the following formula:

Response Rate =

Table 2 shows response rates for CUs

who completed the third interview; the

third and fourth interviews; and the

third, fourth, and fifth interviews, given

that they completed the second interview. Of the CUs who completed the

second interview, 93.1 percent also

completed the third interview, 88.7 percent completed the third and fourth interviews, and 85.9 percent completed

the third, fourth, and fifth interviews.

Demographic characteristics of

complete and intermittent

responders

Table 3 compares some demographic

characteristics of the CUs who completed all of the last four interviews

(complete responders) with those of

CUs who did not (intermittent responders). The complete responders tend to

have more members and to be older than

the intermittent responders and also

are more likely to be homeowners and

married couples. The average number

of persons in a complete-responder CU

is 2.6, compared with 2.3 for the intermittent responders. Likewise, the average age of the reference person in complete-responder CUs is greater (50.6,

compared with 40.9), the average quarterly expenditure per CU on all items is

greater ($8,981, as opposed to $7,504),

and the average quarterly expenditure

per person is greater ($3,442, as against

$3,212) than in intermittent-responder

CUs. Complete-responder CUs also are

more likely to have both husbands and

wives present in the household (57.2

percent, compared with 39.8 percent),

less likely to be single consumers (25.3

percent versus 37.5 percent), more

likely to be homeowners (73.2 percent,

as opposed to 41.0 percent), and more

likely to be the only CU living in the

household (98.3 percent, compared

with 87.3 percent).

Table 4 shows some of the same CU

characteristics, by type of noninterview. CUs who had one or more Type B

or Type C noninterviews tend to be

relatively young (the average age of

the reference person is 36.0), have few

people in them (2.2 persons, on average), have a low average expenditure

per CU ($6,863), and have a low average expenditure per person ($3,124).

CUs who drop out of the survey

CUs are considered to have dropped

out of the survey permanently when

no more of their interviews are completed with interview status code

INSTAT = 01. These CUs are a subset

of the intermittent responders. The reason they have dropped out of the survey can be identified by the interview

status code of the first noninterview

100

Interviews TypeA

Interviews

×

+

.
 Consumer Expenditure Survey Anthology, 2003 27

tently, and they completed 1.9 interviews, on average. The intermittent responders accounted for only 26.9 percent of all interviews.

The average quarterly expenditure

is higher for CUs who completed all four

interviews than for those who did not

($8,981, compared with $7,504); the

overall average expenditure was $8,584.

To estimate the effect that intermittent

responders have on the CE expenditure estimates, the average quarterly

expenditure per CU can be computed

in two different ways by changing the

weights used for the intermittent respondents. In table 7, the overall average expenditure per CU can be computed by weighting the two sets of CUs

by the actual number of completed

interviews:

If the response rates could be increased so that the intermittent responders completed all four interviews,

then those CUs would have completed

43,280 (= 4 × 10,820) interviews. If, in

addition, their expenditures are independent of their (non)participation in

the CE Survey, the weighted average

would be $8,339, because

The $8,339 figure is a 2.9-percent

after their last completed interview.

Table 5 shows that the most common

reason for dropping out of the survey

is “refusal” (23.7 percent), followed by

“other” unspecified Type C noninterviews (19.5 percent), “vacant, for rent”

(19.4 percent), and “vacant, other” (14.1

percent).

Table 6 shows the percentage of

CUs who came back and participated

in the survey after a refusal. Of the CUs

whose first refusal was in the second

interview, only 30.8 percent completed

one or more of the remaining interviews. Of the CUs whose first refusal

was in the third interview, 52.7 percent

completed one or more of the remaining interviews, and of the CUs whose

first refusal was in the fourth interview,

47.4 percent completed the fifth interview. Overall, there were 5,554 CUs

whose first refusal was in one of interviews 2 through 4, and 36.8 percent of

them eventually came back to participate in the survey.

The effect of intermittent responders on CE expenditure estimates

Table 7 shows the total number of interviews completed by both the complete and intermittent responders.

There were 24,860 CUs used in the

study and 56.5 percent of them completed all four interviews. Those CUs

accounted for 73.1 percent of all interviews. By contrast, 43.5 percent of the

CUs in the study responded intermitdecrease from the $8,584 calculated the

first way, indicating that the effect of

intermittent responders on the overall

average expenditure is relatively small.

Moreover, every CU in the CE Survey

has a weight associated with it, and

the weights include adjustments for

nonresponses. As a result of these adjustments, the 2.9-percent difference

computed here can be viewed as an

upper bound on the true difference;

hence, the effect of intermittent responders on the published CE estimates is probably considerably less

than 2.9 percent.

Conclusions

The study presented in this article

looked at CE data collected from 1997

to 2000 and found that CUs who completed all of the survey’s last four interviews (INTERI = 2 through 5) are different from CUs who responded only

intermittently. CUs who completed all

four interviews are larger and older and

are more likely to be homeowners and

married couples than are CUs who responded only intermittently. The study

also found that the nonresponses of

the intermittent responders appear to

have a relatively small effect on the

published estimates. An upper bound

on this effect was calculated to be 2.9

percent, but, because CU weights in

the CE Survey include adjustments for

nonresponses, the actual effect is probably considerably smaller.

56,160 43,280

(56,160 $8,981) (43,280 $7,504)

$8,339

+

× + ×

= .

56,160 20,702

(56,160 $8,981) (20,702 $7,504)

$8,584

+

× + ×

= .
28 Consumer Expenditure Survey Anthology, 2003

Table 1. Response and nonresponse rates for all records, compared with those

for records from CUs in this study

Number Percent Number Percent

Interviews (I) .................................................... 135,383 65.6 76,862 84.3

Type A noninterviews (A) ................................ 32,982 16.0 6,992 7.7

Refusals (R) ............................................... 27,095 13.1 5,272 5.8

Other Type A noninterviews ....................... 5,887 2.9 1,720 1.9

Type B noninterviews ...................................... 29,980 14.5 4,852 5.3

Type C noninterviews ...................................... 7,994 3.9 2,442 2.7

Total ................................................................. 206,339 100.0 91,148 100.0

Response rate of the total sample (I/Total) ..... 65.6 84.3

Response rate of the eligible units

 (I/(I + A)) ....................................................... 80.4 91.7

Refusal rate of the eligible

 units (R/(I + A)) ......................................... 16.1 6.3

Records from CUs

in this study

All records

(INTERI = 1–5)

2 2,3 2,3,4 2,3,4,5

CUs who completed the interviews ................. 19,310 16,819 15,145 14,040

CUs with at least one Type A noninterview ..... 1,242 1,921 2,309

CUs with only Type B or Type C noninterviews 1,249 2,244 2,961

Total Interview + Type A ................................... 18,061 17,066 16,349

Probability of completing interview ................... 3 3,4 3,4,5

Response rate (I/(I + A)) (percent) ................. 93.1 88.7 85.9

Table 3. Demographic characteristics of complete responders and intermittent

responders

Yes No

Average size of CU ........................................ 2.6 2.3

Average age of reference person .................. 50.6 40.9

Average quarterly expenditure per CU .......... $8,981 $7,504

Average quarterly expenditure per person .... $3,442 $3,212

Husband-and-wife families ............................. 57.2 39.8

Husband and wife only .............................. 23.7 15.6

Husband and wife with children ................. 29.0 20.9

Other husband-and-wife families .............. 4.5 3.3

One parent, own children ............................... 5.5 8.3

Single consumers ........................................... 25.3 37.5

Other families .................................................. 12.0 14.4

Homeowner .................................................... 73.2 41.0

Renter and other ............................................. 26.9 59.0

Single-CU household ...................................... 98.3 87.3

Multiple-CU household .................................... 1.7 12.7

Demographic characteristics

Did the CU complete all four interviews

(INTERI = 2–5)?

Item

Table 2. CU response rates, given that the second interview was completed

Item

Interviews

Percent distributions

Type of family:

Housing tenure:

Multiplicity household:
 Consumer Expenditure Survey Anthology, 2003 29

Table 5. Reasons for dropping out of the survey

Refusal .................................................................................... 23.7

Other unspecified Type C noninterviews (INSTAT = 19) ........ 19.5

Vacant, for rent (INSTAT = 06) ............................................... 19.4

Vacant, other (INSTAT = 08) ................................................... 14.1

Other Type A (INSTAT = 02,03,05) ......................................... 9.4

Other Type C (INSTAT = 12–18) ............................................ 7.3

Other Type B (INSTAT = 07,09–11) ........................................ 6.6

Number of CUs ................................................. 14,040 10,820 24,860

Percent of CUs ................................................. 56.5 43.5 100.0

Number of interviews ........................................ 56,160 20,702 76,862

Percent of interviews ........................................ 73.1 26.9 100.0

Average quarterly expenditure per CU ............ $8,981 $7,504 $8,584

Table 4. CU characteristics by type of interview

Total ...................................................................... 76,862 48.0 2.5 $8,584 $3,385

Completed all interviews (2–5) ............................. 56,160 50.6 2.6 8,981 3,442

At least one noninterview ...................................... 20,702 40.9 2.3 7,504 3,212

At least one Type A noninterview .................... 9,084 47.2 2.5 8,324 3,309

No refusals ................................................. 2,462 46.0 2.4 8,991 3,811

At least one refusal .................................... 6,622 47.6 2.6 8,077 3,138

At least one Type B or Type C noninterview

 (no Type A noninterview) ................................ 11,618 36.0 2.2 6,863 3,124

Characteristic

Number of

completed

interviews

Means, 1997–2000

Age of

reference

person

Number of

persons

in CU

Quarterly

expenditure per

CU on all items

Quarterly

expenditure per

person on all

items

Table 6. CUs who came back after a refusal in the Interview survey

2 1,186 2,665 3,851 30.8 69.2 100.0

3 523 469 992 52.7 47.3 100.0

4 337 374 711 47.4 52.6 100.0

Total 2,046 3,508 5,554 36.8 63.2 100.0

1These CUs were excluded from the study because they completed none of the last

four interviews.

First

refusal

Came

back

Did not

come back Total Came back

(percent)

Did not

come back

(percent)

Total

(percent)

1

Category

Did the CU complete all

four interviews

(INTERI = 2–5)? Total

Yes No

Reason Percent

Table 7. The effect of intermittent responders on consumer expenditure

estimates
30 Consumer Expenditure Survey Anthology, 2003

Standard Errors in the

Consumer Expenditure

Survey

JEFFREY L. BLAHA

Jeffrey L. Blaha is a mathematical statistician in the Survey Methods Division, Consumer Expenditure Survey, Bureau of Labor

Statistics.

D

ata for the Consumer Expenditure (CE) Survey are collected

from a sample of all the consumer units (CUs) in the United States.

Estimates of the average (mean) annual

expenditure per CU in the CE tables for

the year 2000 were based on a sample

of about 30,000 CUs, out of a total of

about 109 million in the Nation. These

mean estimates differ from the true

population values because a subset,

rather than the whole population, is

observed. Sampling error is the difference between the survey estimate and

the true population value. The most

common measure of the magnitude of

the sampling error is the standard error of the estimate. The standard error

provides data users with information

about the variability associated with

the estimate.

Prior to the publication of the 2000

data, the CE program made available

separate tables of standard errors for

the Interview and Diary components

of the CE Survey. Starting with the 2000

data, the CE program began using

tables with integrated data from both

surveys. Integrated data provide a complete accounting of consumer expenditures and income, which neither survey component alone is designed to

do. The tables, which correspond to

standard integrated tables of CU expenditures published in the CE reports and

on the CE Web site, are provided by

standard demographic characteristics

(except for region).1 This article gives

a summary description of the halfsample replication method used to calculate the standard error statistics and

demonstrates the proper interpretation

of these statistics.

Methodology

Standard textbook formulas for calculating standard errors assume simple

random sampling and do not apply to

the CE Survey, because it does not use

a simple random sample. Instead, the

Survey uses stratified random sampling, with systematic sampling within

the strata. Hence, a different method

for calculating standard errors is

needed. Replication methods make up

a class of techniques that provides a

way to produce unbiased and designconsistent estimates of standard error

for complex survey designs when the

usual assumptions are not satisfied.

The fundamental idea behind replication methods is to select

subsamples repeatedly from the full

sample, calculate the statistic of interest for each subsample, and use

the variability among the subsamples

to estimate the standard error of the

full-sample statistic.

1 The replication methodology used to

calculate the standard errors is designed to

work at the national level and is not applicable to regional estimates.
 Consumer Expenditure Survey Anthology, 2003 31

the square root of the variance:

The coefficient of variation (CV)is

the standard error expressed as a percentage of the sample mean estimate

and thus is independent of the scale of

measurement. The CV is calculated as

These formulas apply to aggregated

categories as well as individual component items. In producing a table that

uses integrated data from both surveys,

the aggregated categories may be composed of items from one survey or the

other, or they can be based on integrated data from both surveys.

Interpretation of the statistics

The primary purpose of calculating

standard errors for the mean estimates

is to provide data users with a measure

of the variability associated with the

estimates. This variability measures

how close different estimates would be

to each other if it were possible to repeat the survey over and over, using

different samples of CUs. While it is

not feasible to repeat the survey over

and over, statistical theory allows the

standard error to be estimated anyway.

A small standard error indicates that

multiple independent samples would

produce values that are consistently

very close to each other, whereas a large

standard error indicates that multiple

independent samples would produce

values that are consistently not very

close to each other.

The balanced repeated replication

method is used to estimate the standard error in the CE Survey. In this

method, sampled geographic locations

are divided into 40 groups (called

strata). The CUs within each stratum

are randomly divided into two half

samples. Half of the CUs are assigned

to one half sample, and the other half

are assigned to the other half sample.

Because there are 40 strata and 2 groups

of CUs in each stratum, we can compute 240 (approximately 18 trillion) different estimates of expenditure in which

we use exactly half of the collected

data. With this information, we can estimate the standard error of CE estimates by examining how the different

estimates compare with the full-sample

estimate. In the balanced repeated replication method, we use a 44 × 44

Hadamard matrix to choose the 44

“best” combinations of groups out of

the 18 trillion possible combinations.

A variance estimate for each category of item is obtained by first computing the mean estimate of the item

for each replicate, then summing the

squared deviations of the replicate

mean estimates from the full-sample

mean estimate, and then dividing by

the number of replicates. Thus,

where xi r is a calendar-period estimate

of the mean expenditure for item i, using the rth replicate data and xi

 is the

calendar-period estimate of the mean

expenditure for item i, using the fullsample data.

The standard error is calculated as

( ) ( ) ,

44

1 2

44

1

i r i

r

i V x = ∑ x − x

−

SE( ) ( ).

i i

x = V x

( )

( )

100.

SE CV = ×

i

i

i

x

x

x

Table 1 is an extract from one of the

standard published CE demographic

tables. The table shows the mean estimate, standard error, and coefficient of

variation for a list of expenditure items

and categories, using integrated data

from both the Interview and Diary Surveys in 2000. For example, the table

shows that the average annual expenditure by all CUs on personal care products and services for 2000 was $563.62,

with a standard error of $7.94. Because

it was impossible to ask all CUs in the

country how much they spent on personal care products and services, the

$563.62 mean figure is an estimate, and

we have a margin of error, usually defined as ± 2 standard errors. In this example, the average annual expenditure

on personal care products and services

has a margin of error of ± $15.88. Thus,

we can say that the average CU probably spent between $547.74 and $579.50

($563.62 ± $15.88) annually on personal

care products and services.

Because the CV is the standard error as a proportion of the mean estimate, it provides an indication of the

spread of the data around the mean.

The smaller the CV, the smaller is the

spread of the data around the mean.

The CV also makes possible comparisons of the spread of data around the

mean of different items. For example, in

the 2000 integrated survey, the CV for

education is 4.55 percent and the CV

for personal care products and services

is 1.41 percent. Comparing the CVs for

the two items, we can say that the

spread of the data around the mean for

education expenditure is larger than the

spread of the data around the mean for

personal care products and services.
32 Consumer Expenditure Survey Anthology, 2003

Table 1. Quintiles of income before taxes, annual means, standard errors, and coefficients of variation,

Consumer Expenditure, 2000

Mean ................................................... $44,649 $44,649 $7,683 $19,071 $32,910 $53,295 $110,118 (

1

)

SE ....................................................... 517.9 517.9 137.6 87.1 84.6 193.3 1613.4 (

1

)

CV(percent) .......................................... 1.2 1.2 1.8 0.5 0.3 0.4 1.5 (

1

)

Average annual expenditures

Mean ................................................... $38,041.03 $40,234.86 $17,939.45 $26,547.37 $34,713.42 $46,791.00 $75,093.08 $32,059.31

SE ....................................................... 336.7 356.8 399.9 622.2 412.3 626.7 850.8 713.0

CV(percent) .......................................... 0.9 0.9 2.2 2.3 1.2 1.3 1.1 2.2

Food

Mean ................................................... $5,157.88 5,434.76 2,673.31 4,178.21 5,183.19 6,451.56 8,679.37 4,516.56

SE ....................................................... 65.8 77.4 97.0 106.8 143.6 127.7 184.0 93.3

CV(percent) .......................................... 1.3 1.4 3.6 2.6 2.8 2.0 2.1 2.1

Alcoholic beverages

Mean ................................................... 371.81 422.87 206.4 247.93 366.12 512.9 780.2 253.67

SE ....................................................... 15.6 21.2 32.7 24.7 22.3 53.7 75.4 17.9

CV(percent) .......................................... 4.2 5.02 15.9 10.0 6.1 10.5 9.7 7.1

Housing

Mean ................................................... 12,318.51 12,527.38 6,508.78 8,482.33 10,857.48 14,151.75 22,610.61 11,788.92

SE ....................................................... 148.8 172.1 202.2 125.9 144.9 267.4 326.7 267.7

CV(percent) .......................................... 1.2 1.4 3.1 1.5 1.3 1.9 1.5 2.3

Apparel and services

Mean. .................................................. 1,852.53 2,000.22 843.47 1,298.60 1,612.83 2,261.46 3,980.12 1,500.88

SE ....................................................... 38.9 54.3 61.3 72.3 86.6 79.1 198.4 62.1

CV(percent) .......................................... 2.1 2.7 7.3 5.6 5.4 3.5 5.0 4.1

Transportation

Mean ................................................... 7,417.36 7,567.51 3,211.97 5,042.68 7,028.41 9,223.30 13,315.32 6,985.40

SE ....................................................... 101.2 110.7 133.4 222.5 234.9 211.0 322.1 211.2

CV(percent) .......................................... 1.4 1.5 4.2 4.4 3.3 2.3 2.4 3.0

Health

Mean ................................................... 2,065.67 2,120.04 1,469.87 1,987.62 1,964.07 2,312.36 2,864.12 1,919.38

SE ....................................................... 30.1 31.6 71.7 63.7 50.6 61.7 64.0 60.3

CV(percent) .......................................... 1.5 1.5 4.9 3.2 2.6 2.7 2.2 3.1

Entertainment

Mean ................................................... 1,863.50 1,957.63 836.92 1,146.62 1,609.18 2,324.39 3,866.21 1,602.97

SE ....................................................... 35.6 39.1 66.4 50.7 64.0 74.7 117.7 70.3

CV(percent) .......................................... 1.9 2.0 8.0 4.4 4.0 3.2 3.0 4.4

Personal care products and services

Mean ................................................... 563.62 595.33 318.28 441.98 533.59 698.91 982.91 490.56

SE ....................................................... 8.0 9.8 13.1 14.8 22.7 23.0 23.0 14.3

CV(percent) .......................................... 1.4 1.7 4.1 3.4 4.3 3.3 2.4 3.0

Reading

Mean ................................................... 146.47 156.11 73.04 105.17 135.61 174.92 291.47 118.38

SE ....................................................... 2.2 2.3 3.2 4.3 3.9 5.9 7.0 5.0

CV(percent) .......................................... 1.5 1.5 4.4 4.1 2.9 3.4 2.4 4.3

Education

Mean ................................................... 631.93 635.52 430.25 290.47 393.09 600.05 1,461.94 625.79

SE ....................................................... 28.8 35.7 59.5 41.8 48.9 53.5 107.4 56.3

CV(percent) .......................................... 4.6 5.6 13.8 14.4 12.4 8.9 7.4 9.0

Tobacco products and smoking supplies

Mean ................................................... 318.62 333.3 257.24 316.91 366.31 390.04 335.94 275.75

SE ....................................................... 8.1 11.1 13.0 18.3 19.6 16.9 17.4 10.4

CV(percent) .......................................... 2.5 3.3 5.1 5.8 5.3 4.3 5.2 3.8

Miscellaneous

Mean ................................................... 775.78 831.81 364.53 594.94 832.71 1,047.19 1,318.23 619.2

SE ....................................................... 19.6 22.8 50.1 53.4 55.0 67.1 55.8 49.6

CV(percent) .......................................... 2.5 2.7 13.7 9.0 6.6 6.4 4.2 8.0

Cash contributions

Mean ................................................... 1,192.44 1,344.06 332.27 1,162.95 953.01 1,217.29 3,050.11 749.99

SE ....................................................... 96.8 116.2 39.0 452.9 125.6 117.0 285.9 128.1

CV(percent) .......................................... 8.1 8.6 11.7 38.9 13.2 9.6 9.4 17.1

Personal insurance and pensions

Mean ................................................... 3,364.92 4,308.33 413.14 1,250.97 2,877.80 5,424.88 11,556.55 611.86

SE ....................................................... 54.7 58.2 28.0 34.0 76.5 110.3 229.4 30.5

CV(percent) .......................................... 1.6 1.4 6.8 2.7 2.7 2.0 2.0 5.0

Item All

consumer

units

Complete

income

reporters

Lowest 20

percent

Secondlowest 20

percent

Thirdlowest 20

percent

Fourthlowest 20

percent

Incomplete

income

reporters

Highest 20

percent

Complete reporting of income

1

 Components of income and taxes are derived from complete income reporters only; see glossary.
 Consumer Expenditure Survey Anthology, 2003 33

Part II.

Analyses Using Survey Data
 Consumer Expenditure Survey Anthology, 2003 35

Consumer Spending for

Necessities

Abby Duly is an economist in the Branch of

Information and Analysis, Division of Consumer Expenditure Surveys, Bureau of Labor

Statistics.

ABBY DULY T

he proportion of household1

spending used to purchase basic necessities is of interest to

policymakers and social researchers as

an elementary indicator of economic

well-being. There are several complexities, however, in this application of the

data; for example, the definition of

“well-being” itself is not necessarily

universal, and, even when the term is

defined, the criteria upon which to

evaluate well-being also are subjective

and debatable. This article does not

attempt to address these complexities;

rather, data on consumer spending for

necessities are presented in a manner

that may be interpreted by a variety of

readers for a variety of uses.

The discussion that follows is organized into three main sections. The

first is a description of the data, including the definition of “necessities” used

in this study and the demographic variables chosen for comparison. The second section is an evaluation of the Prussian mathematical statistician Ernst

Engel’s proposition, using data from

the 2000 Consumer Expenditure (CE)

Survey to determine whether the relationship between income and the proportion of expenditures spent on necessities that Engel observed in 1857

still holds true. In the third part of the

text, spending on necessities as a share

of total spending is presented for various additional demographic groups.

Study methodology

The study uses the expenditure shares

tables published in the CE Survey.

These tables provide the proportions

of average annual expenditures (or total spending) allocated to various categories of items. The categories of interest here are those designated to be

necessities: Food, housing, and apparel. These three types of expenses

are chosen to be consistent with the

work done by Engel, which, as previously mentioned, is used as a basis for

analyzing spending for necessities by

households of differing income levels.

For consistency, the same definition of

necessities is used in the comparisons

among demographic groups. It is important to note that, while food, housing, and apparel are certainly reasonable candidates for necessities in 2000,

there have been changes to these

spending categories over time. For example, within the necessity category

of food, the allocation among subcomponents has shifted such that the share

of the food dollar spent on food away

from home (including meals at restaurants or fast food, carryout, and home

delivery) has grown from 3.0 percent in

1 The basic unit of measurement in the

Consumer Expenditure Survey is the consumer unit. (See the glossary at the end of

this anthology for the definition of a consumer unit. For convenience, consumer unit

and household are used interchangeably

throughout this article.)
36 Consumer Expenditure Survey Anthology, 2003

First quintile Second quintile Third quintile Fourth quintile Fifth quintile

0

5

10

15

20

25

30

0

5

10

15

20

25

30

Housing Food Apparel

Percent Percent

1909, to 29.0 percent in 1987,2 to 41.0

percent in 2000.3

While data on food and apparel presented here are taken directly from the

published CE tables, the housing category is constructed specifically out

of two main subcomponents: Shelter4

and utilities. This is an important deviation from the published data. The

reason is that, arguably, shelter and

utilities are the actual necessities of

housing and that other components

collected in the CE Survey, such as

household furnishings and equipment,

are not, in fact, basic goods.

In the next section, necessity shares

are compared across income quintiles,

using Engel’s proposition as a guide.

The final analyses presented here provide a broad overview of necessity

spending by additional demographic

groups: Homeowners and renters, urban consumers and rural consumers,

black households and white and other

households, Hispanic and non-Hispanic households, consumer units living in different regions, and consumer

units of varying compositions.

Spending on necessities

by income group

In 1857, Engel observed a relationship

between household income and the

proportion of total expenditures used

to purchase food, housing, and apparel.

He found that, as income increases, the

proportion of spending devoted to food

decreases, while the shares of expenditures used to provide housing and

apparel remain stable.5 Are the same

patterns visible in the most recent CE

Survey? Chart 1 illustrates the shares

of total spending allocated to each of

the three categories of necessity.

In support of Engel’s proposition,

the share of average annual expenditures used to purchase food declines

from 14.9 percent to 11.6 percent as income increases from the third quintile

to the fifth quintile. (See table 1.) However, consumer units in the first quintile

allocate a smaller proportion of total

spending to food (14.9 percent) than

do consumer units in the second quintile (15.7 percent), which would seem

to violate Engel’s proposition. But, as

published by the CE Survey in 2000,

the average income before taxes of the

lowest income quintile is $7,683,

whereas the average annual (total) expenditures for the same quintile are

$17,940.6 Although this sounds contradictory, there are some explanations

for the discrepancy. One is the effect

of missing income: even though the

responses of complete income reporters7

 are used, the respondents may not

have provided a complete accounting

of all income from all sources. Also,

some consumer units in the lowest

quintile—retirees and full-time students, for example—may be able to

spend beyond their apparent means by

using loans or cashing in on investments that are not included as income

in the CE Survey. Therefore, caution

should be used in interpreting the food

share of the first income quintile as a

violation of Engel’s proposition.

Expenditure shares for housing

clearly decline across income quintiles,

as shown in chart 1. While consumer

units in the highest income quintile

devote 22 percent of their total spending to shelter and utility costs, those in

the lowest income quintile spend almost

30 percent. This pattern is not the same

one observed by Engel in 1857, and it

may be related to rather large differences

in housing tenure. In 2000, 57 percent

of consumer units in the first income

quintile are renters, while 88 percent of

consumer units in the fifth quintile are

homeowners.

The shares of average annual expenditures allocated to apparel are barely

discernible in chart 1, supporting Engel’s

observation that spending on apparel

remains stable across income levels. In

fact, the range of apparel shares is less

than 1 percentage point, from 4.7 percent spent by those in the lowest income quintile to 5.3 percent spent by

those in the highest income quintile.

Spending on necessities

by selected demographic

characteristics

As mentioned previously, the share of

total spending allocated to housing is

much greater for lower income households, and those households are also

more likely to be renters. Looking at

2

 Eva Jacobs and Stephanie Shipp, “How

family spending has changed in the U.S.,”

Monthly Labor Review, March 1990, pp. 20–

27.

3

“Table 1. Quintiles of income before taxes:

Average annual expenditures and characteristics,

Consumer Expenditure Survey, 2000,” http://

www.bls.gov/cexann00.pdf, January 2003.

4

 Shelter includes out-of-pocket expenditures for mortgage interest and charges, property taxes, rent, and maintenance and repair

services and commodities.

5

 Louis Philips, Applied Consumption

Analysis: Revised and Enlarged Edition

(Amsterdam, Elsevier Science Publishers,

B.V., 1990), p. 103.

6

 “Table 1. Quintiles of income before

taxes: Average annual expenditures and characteristics, Consumer Expenditure Survey,

2000.”

7 See “Glossary” in Appendix A at the end

of this anthology for the definition of a complete income reporter.

Chart 1. Shares of average annual expenditures allocated to necessities

by income quintile, Consumer Expenditure Survey, 2000
 Consumer Expenditure Survey Anthology, 2003 37

the data classified by housing tenure,

one readily sees that consumer units

who rent their homes also devote a

greater share of their total expenditures

to food (15.0 percent) and apparel (5.4

percent) than do their homeowning

counterparts (13.1 percent and 4.7 percent, respectively).

Urban consumers spend a higher

proportion of their total expenditures

on housing (25.7 percent, as opposed

to the 21.2 percent spent by consumers living in rural areas) and on apparel

(5.0 percent, compared with 4.2 percent,

respectively). Food, however, makes up

a slightly greater proportion of total

spending among rural households (14.1

percent) than among urban households

(13.5 percent).

Race and Hispanic origin, which are

based on the reference person8 of the

consumer unit, are the next demographic characteristics listed in the

table. Black consumer units spend

higher shares of total expenditures on

all three of the necessity categories

than do white and other9 consumer

units. The same is true for Hispanic

compared with non-Hispanic households, although the relevant housing

shares are not very different, with Hispanic consumer units allocating 26.3

percent of total spending to housing and

Table 1. Shares of average annual expenditures allocated to necessities, by

selected demographic characteristics, Consumer Expenditure Survey, 2000

Characteristic Food Housing1 Apparel

 All consumer units ................................................... 13.5 25.2 4.9

 Income quintile2

 First............................................................................... 14.9 29.9 4.7

 Second .......................................................................... 15.7 25.7 4.9

 Third .............................................................................. 14.9 25.0 4.7

 Fourth ........................................................................... 13.8 22.9 4.8

 Fifth ............................................................................... 11.6 22.0 5.3

 Housing tenure

 Homeowner .................................................................. 13.1 24.2 4.7

 Renter ........................................................................... 15.0 28.5 5.4

 Type of area

 Urban ............................................................................ 13.5 25.7 5.0

 Rural ............................................................................. 14.1 21.2 4.2

 Race of reference person

 White and other ............................................................. 13.5 24.8 4.8

 Black ............................................................................. 14.5 29.3 6.0

 Hispanic or non-Hispanic origin of reference person

 Hispanic ........................................................................ 16.4 26.3 6.3

 Non-Hispanic ................................................................ 13.3 25.2 4.8

 Region of residence

 Northeast ...................................................................... 13.8 27.7 5.4

 Midwest......................................................................... 13.4 23.3 4.9

 South ............................................................................. 13.6 24.3 4.7

 West.............................................................................. 13.4 26.4 4.7

 Composition of consumer unit

 Husband and wife only ................................................. 13.2 23.3 4.1

 Husband and wife with oldest child under 6 ................. 11.5 26.0 5.1

 Husband and wife with oldest child 6 to 17 ................... 13.9 24.4 5.2

 Husband and wife with oldest child 18 or older ............ 14.4 22.0 4.9

 One parent with at least one child under 18 ................. 14.7 30.0 6.6

 Single-person and other consumer units ..................... 13.4 27.9 4.9

1

 Shelter plus utilities.

2

 Complete income reporters only.

non-Hispanics allocating 25.2 percent.

There is little variation in the necessity shares of consumer units living in

different regions. For example, the

range of expenditure shares used to

purchase food is from 13.4 percent in

the West and Midwest to 13.8 percent

in the Northeast. (Households in the

South spend a comparable 13.6 percent

on food). Housing shares across regions are more variable, with consumer

units in the Midwest having the lowest share (23.3 percent) of total spending and consumer units in the Northeast region having the highest share

(27.7 percent).

Chart 2 depicts the shares of average annual expenditures allocated to

necessities by the composition of the

consumer unit.The household types

selected for this analysis are husband

and wife only, husband and wife with

the oldest child under 6 years of age,

husband and wife with the oldest child

between the ages of 6 and 17, husband

and wife with the oldest child aged 18

or older, and single parents with at least

one child under the age of 18 years.

(Table 1 also provides data for singleperson and other consumer units.) The

chart indicates that single parents devote greater proportions of their total

spending to food (14.7 percent), housing (30.0 percent), and apparel (6.6 percent) than do other types of household.

Also, the age of the oldest child in the

household is inversely related to the

share of total spending allocated to

housing and directly related to the

share allocated to food. Interestingly,

the expenditure share for food is greater

for husband-and-wife-only consumer

units (13.2 percent) than for those with

young children (11.5 percent). This difference is attributable to a decline in

food away from home, as parents of

young children may not eat outside of

the home as often, or in restaurants as

expensive, as do couples without children.10

8 See the glossary at the end of this anthology for the definition of reference person.

9

 The “other” race group includes Native

Americans, Alaska Natives, Asians, and Pacific Islanders.

10 The expenditure shares for food at home

are roughly equivalent for husband-and-wife

consumer units (7.5 percent) and households

with children under 6 years of age (7.2 percent). However, the former allocate 5.7 percent of total spending to food away from home

while the latter allocate just 4.3 percent.
38 Consumer Expenditure Survey Anthology, 2003

0

5

10

15

20

25

30

35

0

5

10

15

20

25

30

35

Food Housing Apparel

Percent

Oldest child 

between ages 6 

and 17, inclusive

Oldest child 

age 6 or 

younger

Husband and 

wife only

Oldest child Single parent

18 or older

Percent

In sum, this article has presented a

variety of data on spending for necessities as a proportion of total expenditures, from the 2000 Consumer Expenditure Survey. With respect to Engel’s

proposition, the expected trends are

observed for food and apparel, while a

contradictory decrease in housing

shares occurs as income increases.

Necessity spending also varies among

consumer units with different demographic characteristics.

Chart 2. Shares of average annual expenditures allocated to necessities

by consumer unit composition, 2000
 Consumer Expenditure Survey Anthology, 2003 39

Consumer Expenditures

for Alcohol in 2000

GEOFFREY PAULIN

Geoffrey Paulin is a senior economist in the

Branch of Information and Analysis, Division of Consumer Expenditure Surveys, Bureau of Labor Statistics.

I

n 2000, per capita consumption of

alcoholic beverages was 24.9 gallons, mostly in the form of beer (21.7

gallons).1 That same year, according to

the Consumer Expenditure (CE) Survey,

the average consumer unit 2reported

expenditures of $372 for alcoholic beverages; that is, about $1 was spent on

alcohol for every $8 spent on food at

home.3 Other recent studies have cited

similar figures, as well as health and

social concerns, as reasons for studying the consumption of alcoholic beverages.4 These studies examine either

the consumption of a specific beverage

by a specific group or the consumption

of alcohol in countries other than the

United States. By contrast, this article

focuses on U.S. domestic consumer expenditures on alcohol in 2000—specifically, the demographic patterns involved,

the mean weekly expenditure on alcohol, the probability of purchase of alcohol either at home or away from home

(such as a drink at a restaurant or bar),

and the type of alcohol purchased

(beer, wine, or other alcohol, such as

whiskey).

The Data

Data for the CE Survey are derived from

two sources: The Interview survey,

which is a rotating-panel quarterly recall survey, and the Diary survey, in

which respondents record all their expenditures during the 2-week survey

period. Data from the two sources are

integrated into tables for analysis and

subsequent publication. The data for

this article are taken from the Diary component of the 2000 CE Survey. In the

published CE Survey, one item—alcoholic beverages purchased on trips—

is taken from the Interview component.

However, this item (which is collected

solely in the Interview survey) accounts for only about $34, or less than

10 percent of average total expenditures

for alcohol in 2000, so it is safe to exclude it from the current analysis. Using only Diary data also allows the regression results (described later) to be

compared with the expenditure data

examined herein.

Caution should be exercised in attempting to interpret some of the data

shown. Expenditures for alcohol are

subject to a great deal of “allocation”

during the publication process. That

is, when a respondent records “expenditures for alcohol” or “meal at restau1 Statistical Abstract of the United States,

2002 (U.S. Census Bureau, 2002), p. 130,

table 197, “Per Capita Consumption of Selected Beverages by Type: 1980 to 2000.”

2 See the glossary at the end of this anthology for the definition of a consumer unit.

3 Consumer Expenditure Survey, 1999–

2001, Report 966 (Bureau of Labor Statistics, April 2003), table A, “Average annual

expenditures of all consumer units and percent changes,” p. 3.

4 J. R. Blaylock and W. N. Blisard, “Wine

Consumption by U.S. Men,” Applied Economics, May 1993, pp. 645–51; and Mohamed

Abdel-Ghany and J. Lew Silver, “Economic

and Demographic Determinants of Canadian

Households’ Use of and Spending on Alcohol,” Family and Consumer Research Journal, September 1998, pp. 62–90.
40 Consumer Expenditure Survey Anthology, 2003

rant, including alcohol,” but provides

no details on the type or amount of alcohol purchased, the expenditure is estimated on the basis of the total expenditure reported by the respondent for

alcohol or the meal at the restaurant,

together with an allocation factor that

is in turn based on responses from

those who record specifically what was

purchased. At the aggregate level, this

technique presumably has little impact

on total expenditures for alcohol, but it

could cause a larger share of those expenditures to be accounted for by either beer, wine, or other alcoholic beverages than is actually the case; in

addition, at the individual-record level,

a consumer unit might show expenditures for beer, wine, and other alcohol,

even though that consumer unit purchased only one of those items. For

example, suppose a respondent purchases beer for $10 and records a $10

expenditure for alcohol. Then, because

the fact that all $10 went for beer is not

recorded, the consumer unit might

show expenditures of $7 for beer, $2 for

wine, and $1 for other alcohol, assuming allocation factors of 70 percent for

beer, 20 percent for wine, and 10 percent for other alcohol. The actual number of records created through allocation as opposed to reporting varies by

the type of alcohol purchased. (For example, 43 percent of beer-at-home reports 5

 are the result of allocation, compared with 76 percent of wine-at-home

reports and 92 percent of other-alcohol-at-home reports.) Overall, about 46

percent of expenditures reported for

specific types of alcohol are created by

allocation from general reports of alcohol either at home or away from home.

Methodology

This article investigates expenditures

in several ways. First, expenditure values and the percent of consumer units

that report purchasing alcohol (that is,

the percent reporting) are examined for

three demographic categories: Income

quintile, age of reference person, and

sex of reference person for single consumers only.6 The analysis is extended

through the use of logistic regression,

or “logit,” a technique that enables one

to predict the probability that an event

(in this case, the purchase of alcohol)

will occur, given that certain conditions

(in this case, demographic characteristics) are held constant. By means of

regression analysis, it is possible to

isolate relationships between these

characteristics and the probability of

purchase of some kind of alcohol. For

example, the probability of purchasing

wine rises steadily with income and increases with age until the reference

person is 45 to 54, after which it decreases with age. Given that income

also increases with age until the reference person is 45 to 54 and starts to

decrease with age thereafter, it is difficult, in the absence of regression analysis, to say which characteristic—age

or income—is more strongly related to

the purchase of alcohol. Logit is used

to estimate the probability of purchasing alcohol in general, as well as that of

purchasing alcohol at home, away from

home, or both. Logit also is used to

predict the probability of purchasing

beer, wine, or some other alcoholic beverage. (The appendix to this article describes the use of logit in more detail.)

Except for the data in the logit analyses, the data used in this article are

weighted to reflect the population. (The

reasons why the data employed in the

logit analyses are not weighted will be

presented shortly.) The data used in

the article also are limited to consumer

units whose reference person is at least

21 years old—that is, the legal age to

purchase alcohol in the United States.

(Those under the legal age may be more

likely than those who are at least 21

years old to omit expenditures for alcohol from their diaries.) Specific income

data (such as mean values and quintile

assignments) are derived from complete income reporters only, unless otherwise specified.7 For best results, families that reported income losses (for

example, through self-employed business loss or rental property loss) also

are excluded from the sample.8

5 The CE Survey uses the terminology “at

home” and “away from home” to describe

places at which goods are purchased, rather

than where they are ultimately consumed.

For example, when an expenditure is reported

for “food at home,” it means that the food

was purchased at a grocery store or similar

vendor, rather than at a restaurant, cafeteria, or bar. The food purchased may have

been consumed elsewhere—for example, a

person buys fruit and takes some to the office for lunch or packs a sandwich for the

child’s lunch at school. Even though the food

was not eaten in the home, the food was

purchased at a grocery store and is therefore

7 See “Glossary” in Appendix A at the end

of this anthology for the definition of complete income reporter.

8 The income used in the CE Survey results is found by summing the value of all

sources of income reported. When losses

occur, the negative income is added to the

total (or the loss is subtracted, depending on

how one looks at it), which has the result of

artificially lowering total income. Sometimes,

the losses are large enough to cause total income to be negative. Losses make comparison across consumer units difficult. For example, a family in which one member receives $50,000 in salary appears to have the

same income as another family in which one

member receives $75,000 in income, but in

which another member incurs a loss of

$25,000. Both consumer units have $50,000

in income, according to the survey results,

but each may have different spending patterns; the losses may be temporary and anticipated, for example, causing the consumer

unit incurring the losses to spend differently

than the unit that regularly receives $50,000

in income. Including the loss could substantially increase the variance for the income

data and could also bias parameter estimates

in the regression section. For these reasons,

consumer units reporting losses are omitted

from the sample.

designated as “food at home.” Similarly, when

a person has a pizza delivered from a local

restaurant, the amount paid is classified as an

expenditure for “food away from home,”

despite the fact that the pizza was eaten in

front of the living room television. The reason is that the vendor was a restaurant. With

alcoholic beverages, the same rules apply. An

expenditure for beer, wine, or other alcohol

that is purchased from a grocery, liquor, or

convenience store is considered an expenditure for “alcohol at home,” even though the

purchaser may have taken the bottle of wine

to a dinner party or taken the beer to a local

park to drink at a picnic or while watching a

softball game. In the case of alcohol, however, it is not likely that alcohol classified as

“away from home” would have been consumed inside the home, because restaurants

and bars usually restrict alcohol purchased to

be consumed on the premises. For consistency with the classifications used in the CE

Survey, the terms “at home” and “away from

home” will be used in this article to describe

expenditures for alcohol, regardless of where

the alcohol was actually consumed.

6 See “Glossary” in Appendix A at the end

of this anthology for the definitions of reference person and income quintile.
 Consumer Expenditure Survey Anthology, 2003 41

Demographic analysis

By any measure shown in table 1, beer

is the most popular form of alcohol

purchased by the average consumer

unit. Whether one looks at percent reporting or mean weekly expenditure,

beer is at the top of the list, both at

home and away from home. However,

this ranking changes when one looks

at the mean weekly expenditure of only

those consumer units reporting purchases of alcohol, a figure that can be

calculated by dividing mean weekly

expenditure by percent reporting. In

this case, the largest average expenditure for all consumer units is for wine

at home ($23.29). Other alcohol at home

is second ($19.36), with beer at home a

distant third ($16.39). In contrast, the

largest expenditure for alcohol away

from home is for other alcohol ($12.08).

The smallest expenditure obtained by

using this measure is that for wine away

from home ($9.73).

Income. As one might expect, expenditures for alcohol increase with income.

(See table 2.) This statement holds true

regardless of the type of alcohol purchased and regardless of whether it is

alcohol at home or away from home.

What is more interesting is the rate of

increase with income. For example,

while the fifth income quintile spends

about 3.5 times as much for alcohol as

does the first income quintile, it spends

only 2.7 times as much for alcohol at

home, compared with more than 7.1

times as much for alcohol away from

home. When the types of alcohol purchases are analyzed, the ratios of the

fifth to the first income quintile range

from 1.6 (for beer at home) to 9.2 (for

other alcohol away from home).

The percent reporting follows a similar pattern. For alcohol at home, the

percent reporting for the fifth quintile

(29.1 percent) is more than double the

percent reporting for the first quintile

(11.9 percent). For alcohol away from

home, the differences across quintiles

are even more dramatic, ranging from

6.9 percent for quintile 1 to 25.6 percent for quintile 5. The smallest range

is for other types of alcohol at home,

which only doubles from the lowest to

the highest quintile (2.3 percent to 5.1

percent). The largest range in absolute

terms is for beer away from home (6.1

percent to 22.6 percent). However, the

percent reporting other alcohol away

from home is still more than 6 times

higher for the fifth quintile (11.8 percent) than it is for the first (1.8 percent).

Age. In all cases, expenditures for alcohol away from home rise with age up to

a point and then decline. (See table 2.)

The pivotal age group is the one whose

reference persons are 35 to 44 years

old. For alcohol at home, wine follows

the pattern, except that expenditures

peak for those aged 45 to 54. However,

expenditures for beer and other (that

is, nonwine) alcohol at home actually

decline with age. For beer at home, expenditures range from a high of $5.48

for the under-25 group to a low of $0.65

for the 75-and-older group, a decrease

of 88 percent over that entire age range.

Stated another way, the youngest

group spends 8.4 times as much for beer

at home as does the oldest group. The

percent of those reporting expenditures

for beer at home follows a similar pattern: nearly 1 in 4 consumer units in the

youngest group report such expenditures, compared with fewer than 1 in 20

consumer units in the oldest group.

Most other expenditures for alcoholic

beverages follow the same pattern for

percent reporting, peaking either for the

under-25 group or the 25- to 34-yearold group. The lone exception is wine:

the percent reporting expenditures for

wine peaks with the 45- to 54 year-old

group (13 percent), and the group with

the lowest percent reporting is again

the 75-and-older group (6 percent). The

percent reporting wine away from home

is only about 4 to 5 percent for those

under 65, but decreases for those aged

65 and older (of whom less than 2 percent report such expenditures).

Singles. Single individuals spend their

money differently than do nonsingles.

(See table 3.) Interestingly, though,

when the data are classified by the sex

of the reference person, it becomes

clear that single men spend more, on

average, than do nonsingles (of both

sexes) for all alcoholic beverages, except wine at home, while single women

spend less than non-singles on all alcoholic beverages (including wine at

home). The same pattern holds for the

percent of consumer units reporting

expenditures on alcohol. That is, except

in the case of wine at home, single men

have the largest percent reporting, followed by nonsingles and then single

women. The difference also affects the

total percent reporting expenditures for

wine generally, but here single men run

a close second (10.3 percent reporting)

to nonsingles (10.7 percent reporting),

with fewer single women reporting purchases (6.2 percent).

Predicted probabilities

Given the similarity in trends for expenditures for alcoholic beverages at home

and for those away from home (for example, percent reporting increases

steadily with income for both types of

purchase), logit is used only to analyze total purchases of beer, wine, and

other alcohol once the probability of

purchase for alcohol in general is examined by type of purchase. Accordingly, the first set of analyses to follow

examines the probability of purchasing

alcohol in general. The rest of the analyses examine probabilities of purchasing specific beverages. In other words,

what is the probability of purchasing

alcohol at home as opposed to the probability of purchasing alcohol away from

home? What is the probability that a

consumer will purchase both alcohol

at home and alcohol away from home,

rather than one or the other? What is

the probability of purchasing beer,

wine, or other alcohol? The results of

the logits, used to answer these questions, should be interpreted with caution. Those who did not purchase alcohol may have chosen not to do so

for any number of reasons, including

the fact that they had enough liquor in

the cabinet to last for the week during

which they filled out the diary or that

they may be persons who choose not

to consume alcohol on any occasion

at all. Because it is not possible to distinguish “potential” purchasers from

“nondemanders” in the Diary survey,
42 Consumer Expenditure Survey Anthology, 2003

the answers can be interpreted to predict only the probability of actual purchase during the previous week, rather

than the probability of actual use (or

nonuse) of alcohol by the consumer

unit over longer periods.

Also, unlike the data in the previous section, the logit results here are

not weighted to reflect the population.

Previous experience has shown that

weighting logistic regressions for that

purpose yields parameter estimates

similar to the unweighted results, but

with much smaller standard errors. This

has the effect of making every parameter estimate appear to be statistically

significant. Therefore, to be conservative in the estimates, unweighted regressions are used to estimate probabilities of purchase in this article.

In using regression analysis, a “control group” is standardly identified to

serve as a reference point for the analysis. In this article, parameter estimates

that have negative coefficients are predicted to have lower probabilities of

purchase than the control group, while

those with positive coefficients have a

higher predicted probability of purchase than the control group. Here, the

control group consists of consumer

units whose reference person (1) is 35

to 44 years old; (2) reports income in

the middle quintile; (3) is a single, white,

non-Hispanic male employed as a manager or professional receiving a wage

or salary; (4) owns his home, but pays

a mortgage; and (5) is living in the urban South. Comparisons with the control group are made by changing one

characteristic at a time; for example, in

attempting to find the relationship of region of residence to purchases of alcohol, one assumes that all characteristics

of the members of the group to be tested

are identical to those of the members of

the control group (that is,every member

of each group is a single, white, nonHispanic male, aged 35 to 44 years old,

with an income in the middle quintile,

and so forth), except that the members

of the group to be tested live in the Northeast instead of the South. Such comparisons are known as “ceteris paribus” comparisons in economics—comparisons in

which “all else is held equal.”

General purchases of alcohol. The

probability of purchasing alcohol for

the general adult population appears

to follow the trends already described,

at least with respect to age, income, and

sex of the reference person. That is, the

predicted probability of purchase,

which is about 38 percent for the control group, is highest for the youngest

group (46 percent) and lowest for the

oldest group (22 percent). Similarly, the

probability of purchase is lowest for

the first income quintile (29 percent)

and highest for the fifth (50 percent).

Single women are less likely to purchase (23 percent) than are single men

(38 percent).

The logit regressions also allow

comparisons across a variety of other

characteristics. For example, ethnicity

appears to have little relationship to the

probability of purchasing alcohol in

general: the parameter estimate for “Hispanic” is small in magnitude (–0.0628)

and is not statistically significant. Race,

by contrast, appears to play a role in

probability of purchase: black and

Asian consumers have much lower

probabilities of purchase than do white

consumers, and those of other races

appear to be similar to Asians in their

purchasing behavior. (The coefficient

associated with “other race” is nearly

equal to that of Asians, while it is not

statistically significant.) Occupation

has a less strong relationship: although

persons in technical, sales, or service

positions and those in agricultural

fields (forestry and farming) have positive, statistically significant coefficients, no other working group is predicted to be statistically significantly

different from salaried (or wage-earning) managers and professionals in their

purchases of alcohol in general. Of

those who do not work, retirees have a

fairly small coefficient that is not statistically significant. The long-term unemployed9 have a large, but not statistically significant, negative coefficient,

indicating that they are a lot less likely

to purchase than are managers and

professionals. The sample size for this

group is small, so it is difficult to say

whether the negative relationship is

indicative of the general population in

the group. However, those who are not

working for reasons other than that

they are a member of the long-term unemployed (for example, they may be

attending school, working without pay,

too ill to work, or doing something else)

also have a large negative coefficient

that, this time, is statistically significant.

The predicted probability of purchase

for this group is 31 percent, compared

with 38 percent for managers and professionals. Finally, the South appears

to be the region with the lowest probability of purchasing alcohol (38 percent); persons in other regions have

predicted probabilities ranging from 44

percent to 46 percent. Rural men are

about 9 percent less likely than their

urban counterparts to purchase alcohol. (That is, their predicted probability of doing so is 29 percent, about 9

percentage points lower than that of

urban single men.)

Probabilities for specific purchases of

alcohol. The remaining sets of regression results are for specific types of

alcohol purchase—at home, away, or

both; and for beer, wine, or other alcohol. Once again, several demographic

characteristics appear to be related to

the probability of purchase. For example, the probability of purchasing

alcohol at home is negatively related

to age, as is the purchase of alcohol in

general. The youngest age group has

a 30-percent predicted probability of

purchase at home compared with a 12-

percent probability for the oldest

group. The coefficients for each of

these groups are statistically significant

at the 99-percent confidence level, as

are all of the age coefficients, with the

exception of the 25- to 34-year-old age

group (significant at the 95-percent

level) and the 45- to 54-year-old age

group (not statistically significant). Income, by contrast, is positively related

to the purchase of alcohol at home,

9

 The survey question on occupation asks

at what profession the person earned the

most money in the previous year. If the reference person received unemployment insurance and then did not work or worked only

sporadically, the person could be reported to

have “earned” the most through unemployment.
 Consumer Expenditure Survey Anthology, 2003 43

ranging from 18 percent for the lowest

quintile to 29 percent for the highest.

Interestingly, the presence of children

or a single adult woman in the home

appears to lower the probability of purchasing alcohol at home. Single men

(the control group) have a predicted

probability of purchase of 24 percent,

while single women have only an 11

percent probability. Single mothers

have an even lower predicted probability: 9 percent. Husband-and-wife families with children have a lower probability of purchasing alcohol at home

(20 percent) than the 24-percent probability of single men. Families with a

husband and wife only, however, with

a 23-percent probability of purchasing

alcohol at home and a coefficient that

is not statistically significant, are similar to single men in that type of purchase. Like husband-and-wife-only

families, other-husband-and-wife families in which children are present have

a predicted probability of purchase of

alcohol at home of 23 percent, with a

coefficient that is not statistically significant.) Here, too, ethnicity appears

to play no role in the probability of purchase, but race does: both black and

Asian families have a lower predicted

probability of purchase (18 percent)

than that of the control group, and both

coefficients are statistically significant

at the 99-percent confidence level. Families of other nonwhite races appear to

have a similarly lower probability (17

percent), but their coefficient is not statistically significant. Occupation also

appears to play a role: technical, sales,

and service workers (29 percent), as

well as blue-collar workers (28 percent),

have slightly higher probabilities of

purchasing alcohol at home than do

managers and professionals (24 percent); however, agricultural workers (40

percent) and armed-service workers (41

percent) have substantially higher

probabilities of purchase. Work status,

by contrast, plays less of a role: the

self-employed, with a probability of

purchase of 24 percent, are not statistically significantly different from wage

or salaried families, and, although retirees are predicted to have a higher probability of purchase (29 percent) than

wage or salaried families, those who are

unemployed or who are not working

for another reason are not statistically

different from wage or salaried families.

Region plays a role (the Northeast has

the highest predicted probability of

purchasing alcohol at home, 28 percent), as does degree of urbanization

(with rural “control” families 7 percent

less likely than similar urban families to

purchase). Finally, the purchase of alcohol away from home is also positively

related to the purchase of alcohol at

home. The coefficient is positive and

significant at the 99-percent level.

However, it is so small (0.0173), that it

is economically not significant in its relationship to the probability of purchase.

For purchases of alcohol away from

home, the findings are similar, but not

identical. First, the probability of purchase is lower (21 percent) for the control group in this case than it is for the

probability of purchase of alcohol at

home (24 percent). Second, the probability of purchase of alcohol away from

home is higher for 25- to 34-year-olds

than for those under 25, but it peaks

for the former (at 27 percent) and declines with age thereafter. It is also positively related to income, but the range

of predicted probabilities is wider (from

14 percent to 33 percent) than it is for

alcohol purchased at home. Although

husband-and-wife-only families are not

statistically significantly different from

single men in respect of purchasing alcohol away from home, all other types

of family are. Single women have a 16-

percent predicted probability of purchase, compared with 21 percent for

single men. The presence of children

also appears to be related to the probability of purchase, with single fathers,

single mothers, and husband-and-wife

families with their own children only all

having a lower probability of purchasing alcohol away from home (12 percent) than single men without children.

Other husband-and-wife families with

children have a higher probability of

purchase (16 percent), but it is still

lower than that for single men. Perhaps

this is because the other members of

the consumer unit also are likely to be

adults (such as the parent or sibling of

one of the spouses), and, therefore, the

additional adults contribute to the total probability of purchasing alcohol

away from home. Unlike its weak relationship to alcohol purchased at home,

ethnicity now is strongly related to the

probability of purchase. Hispanics (15

percent) have a much lower probability of purchase than do non-Hispanics

(21 percent); the same is true for Asians

(16 percent) and, especially, blacks (11

percent). Region makes a difference,

but now the Midwest is the region with

the highest predicted probability of

purchase (26 percent). Rural families are

still less likely to purchase (18 percent),

and the purchase of alcohol at home

also makes a statistically, but not economically, significant difference in the

probability of purchasing alcohol away

from home.

The probability of purchasing both

alcohol at home and alcohol away from

home is only about 12 percent. The

probability of purchasing both appears

to be negatively related to age: the

youngest group (those under 25) has

the largest coefficient, but it is not quite

significant at the 95-percent confidence

level. Taken at “face value,” though

(that is, without regard to statistical significance), the predicted probability for

the youngest group is 16 percent, compared with 5 percent for the oldest

group (75 and older). The positive relationship to income still holds, with the

predicted probability of purchase ranging from 7 percent to 20 percent. Again,

the presence of children or a single

woman appears to lower the probability of purchasing alcohol for both purposes. Single women have a predicted

probability of purchase of 5 percent,

while single mothers have an even

lower 3-percent probability. The lowest probability of all, however, is that

for single fathers: 2 percent. Married

couples whose children are biologically

related to both parents or have been

jointly adopted by them have a 6-percent probability of purchasing both alcohol at home and alcohol away from

home. This probability, although larger

than that for single parents, is still only

about half the predicted probability for
44 Consumer Expenditure Survey Anthology, 2003

single men (12 percent). Hispanics also

have a lower predicted probability of

purchase (9 percent) than do non-Hispanics (12 percent), but race lowers the

predicted probability even more: both

blacks and Asians are about half as

likely (6 percent) as whites to purchase

both alcohol at home and alcohol away

from home. Finally, neither occupation

nor region plays a major role in the predicted probability of purchase. Rural

consumers (9 percent) appear to be less

likely than urban consumers (12 percent) to purchase alcohol for both purposes, but the coefficient is significant

only at the 10-percent confidence level.

It is also interesting to examine predicted probabilities for purchasing specific types of alcohol. Although, in

these regressions, the same variables

are retained as predictors of probability, three new independent variables are

added to each equation. The first two

are binary variables and indicate that

the purchaser purchased some other

type of alcohol than the type under

study. For example, in predicting the

probability of purchasing beer, the first

binary variable describes whether the

consumer unit did or did not purchase

wine, and the second variable describes whether the consumer unit did

or did not purchase other alcohol. In

predicting the probability of purchasing wine, the first binary variable describes whether the consumer unit did

or did not purchase beer, and the second describes whether the consumer

unit did or did not purchase other alcohol. And in predicting the probability

of purchasing other alcohol, the first

binary variable describes whether the

consumer unit did or did not purchase

beer, and the second describes whether

the consumer unit did or did not purchase wine. The third term is an “interaction term” indicating that the consumer unit purchased both remaining

types of alcohol, given the particular

dependent-variable alcohol. (For example, if the probability of purchasing

beer is being predicted, the interaction

term will be equal to unity if the consumer unit purchased both wine and

other alcohol, but will be equal to zero

if the consumer unit bought only wine

or other alcohol or bought neither wine

nor other alcohol.) These variables are

added to the analysis to see whether

different types of alcohol are “substitutes” or “complements,” at least in

terms of their probability of purchase.

Once again, the total sample includes

all consumers who purchased at least

some type of alcohol during the week

they filled in the diary.

Beer. As mentioned earlier, beer is the

most popular alcoholic beverage. The

parameter estimate associated with the

intercept is –1.1944, indicating that the

control group’s predicted probability

of purchasing beer is 23 percent. The

probability of purchase is strongly related to age, declining from 29 percent

for the youngest group (under 25) to

10 percent for the oldest group (75 and

older). The probability of purchase

also is related to income, although only

the lowest and highest quintiles have

statistically significant coefficients.

The probability for the lowest quintile

is 17 percent, compared with 27 percent for the highest quintile. Single men

are again the most likely to purchase

beer (23 percent), single women (12 percent) and single mothers (9 percent)

the least likely. Married couples without children are not different from

single men to a statistically significant

degree, but when children are added

to the family, the probability of purchase drops slightly, to 17 percent.

When ethnicity and race are considered, only blacks (16 percent) are significantly different from the control

group. Among salaried workers, occupation makes a difference, with technical, sales, and service workers (28 percent), blue-collar workers (30 percent),

agricultural workers (35 percent), and

members of the armed services (38 percent) all having higher predicted probabilities of purchasing beer than do

managers or professionals (23 percent). Neither the self-employed nor

nonworkers are significantly different

from wage and salaried workers, although retirees appear to have a higher

probability of purchasing beer (28 percent) than do wage and salaried workers. (The coefficient is positive, but

statistically significant only at the 90-

percent level.) The Midwest has the

highest probability of purchase (29

percent), and the purchase of wine (57

percent) or of some other alcohol (65

percent) strongly increases the probability of the purchase of beer. However, the purchase of both wine and

another alcohol does not significantly

increase the probability beyond what

is predicted when the coefficient for

purchasing wine alone and that for

purchasing another alcohol alone are

incorporated into the equation. (That

is, without including the interaction

effect, a member of the control group

who purchases both wine and another

alcohol has a predicted probability of

purchasing beer of 89 percent. When

the interaction term is incorporated, the

probability rises to 91 percent. This 2-

percent difference is not statistically

significant, because the coefficient for

the interaction term is not statistically

significant.)

Wine. The probability of purchasing

wine is much lower than the probability of purchasing beer: only 1 in 20 consumer units (5 percent) in the control

group is predicted to buy wine during

the week its respondent fills out the

diary. Age does not appear to be

strongly related to the purchase of

wine, although 45- to 54-year-olds have

the only statistically significant coefficient and thus the highest predicted

probability of purchase of any age

group. However, at 6 percent, this difference is not economically significant.

The probability of purchasing wine increases with income, although only the

highest quintile has a statistically significant coefficient associated with it.

Once again, without regard to statistical significance, the lowest quintile has

a predicted probability of purchase of

4 percent, compared with a predicted

probability of purchase of 7 percent for

the highest quintile. Family type is not

related to the purchase of wine to a statistically significant degree, while

ethnicity is perhaps weakly related: the

predicted probability for Hispanics (4

percent) is different from the probability for non-Hispanics (5 percent) only
 Consumer Expenditure Survey Anthology, 2003 45

at the 10-percent confidence level.

However, blacks (4 percent) and Asians

(3 percent) do have statistically significant coefficients at the 95-percent confidence level. (The coefficient for

Asians actually is significant at the 99-

percent confidence level.) Occupation

plays little role; although blue-collar

workers have the lowest predicted

probability of purchasing wine (3 percent) of all working consumers. Similarly, those who are not working for reasons other than retirement or

unemployment have a lower probability than other groups (3 percent). Region plays little role in predicting the

probability of purchasing wine, but rural consumers also are less likely (3

percent) than urban consumers (5 percent) to purchase. However, both the

purchase of beer (18-percent probability) and the purchase of other alcohol

(17-percent probability) substantially

increase the probability of purchasing

wine. Nevertheless, purchasing both

beer and some other alcohol adds little

to the probability of purchasing above

what purchasing beer or another alcohol alone adds.

Other alcohol. As with wine, the predicted probability of purchasing other

alcohol is low—only 4 percent for the

control group. However, demographics play a larger role in predicting the

probability of purchasing some other

alcohol than wine, in that more coefficients are statistically significant.

Although age does not have a statistically significant relationship to the

probability of purchasing some alcohol other than wine or beer, both the

fourth and fifth income quintiles (6 percent) are more likely to purchase than

is the control group. Family type plays

a role as well, with female-headed consumer units having lower predicted

probabilities (3 percent for single

women and 2 percent for single mothers) than do single-male households.

In addition, husband-and-wife couples

with their own children only have a

lower predicted probability of purchasing some other alcohol (2 percent) than

have single men. Hispanics and Asians

both have lower predicted probabilities

(2 percent) than do white non-Hispanics (4 percent). In respect of occupation, only blue-collar workers have a

statistically significant coefficient, with

a predicted probability of purchase of

3 percent. By region, only the Midwest

has a statistically significant coefficient, raising its probability of purchasing some other alcohol to 5 percent.

Once again, the predicted probability

of purchase rises sharply when either

beer (21 percent) or wine (16 percent)

is purchased, but purchasing both beer

and wine has no additional effect on

the probability of purchasing some

other alcohol than is accounted for by

including the coefficients for purchasing beer and wine separately. (That is,

the expenditures on alcohol of those

who purchase beer, but not wine, or

wine, but not beer, are not statistically

significantly different from those who

purchase both beer and wine.)

Summary

This article has examined expenditures

for alcohol from several perspectives,

including mean weekly expenditures,

percent reporting expenditures, and

predicted probability of purchase for

consumers with different demographic

characteristics. Expenditures for alcohol are analyzed both by place of purchase (at home or away) and by type

of alcohol purchased (beer, wine, and

other alcohol, such as whiskey). Consistent with national sales figures, beer

appears to be the most popular form of

alcohol purchased, both at home and

away from home. Beer has the largest

average weekly expenditure for all consumer units and the largest percent of

all consumer units reporting the purchase of alcohol. However, when the

average expenditure for those who actually purchase alcohol is examined,

wine has the largest average expenditure, followed by other alcohols.

Expenditures for alcohol at home rise

substantially with income and decrease

with age. The exception is expenditures

for wine at home, which peak for consumers aged 45 to 54. Expenditures for

alcohol away from home also rise with

income, but, like expenditures for wine

at home, rise with age to a point and

then decline. Regardless, single men

spend more on alcohol than do single

women, with nonsingles in the middle

for expenditures on all alcoholic beverages except wine at home, for which

nonsingles spend the most, on average, followed by single men.

When characteristics are held constant by means of regression analysis,

the trends in the predicted probability

of reporting appear generally to match

those described for the observed percent reporting. Other characteristics

also appear to be related to the purchase of alcohol, including race and

ethnicity, occupation, and region of

residence. However, the parameter estimates associated with these variables

are not always statistically significant,

especially for specific categories of

characteristics. (For example, with regard to the purchase of specific types

of alcohol, Asians are predicted to be

less likely than whites to purchase

wine, but the Asian coefficient for the

predicted purchase of beer is not statistically significant.) Also, the probability of purchasing one type of alcohol is strongly related to the purchase

of another type of alcohol. For instance,

consumers who purchase wine or some

other alcohol are more likely to purchase beer as well, but the coefficient

for the purchase of both wine and another alcohol is not statistically significant, indicating that there is no “additional effect” on the probability of

purchasing beer when both wine and

another alcohol are purchased than is

captured by including the effects of

wine and other purchases of alcohol

separately.

APPENDIX:

The Use of Logistic Regression (LOGIT) as a

Probability Predictor

Logistic regression, or “logit,” is often

used to predict the probability that an

event will occur, based on a series of
46 Consumer Expenditure Survey Anthology, 2003

in general yields

P = exp(–0.4741)/[1 + exp(–0.4741)]

 = 0.384.

However, suppose one wanted to

know the predicted probability for

single women instead of single men.

That probability is

P = exp(–0.4741 – 0.7493)/

[1 + exp(–0.4741 – 0.7493)] = 0.227.

The coefficient for single women

(–0.7493) is simply added into the equation as appropriate.

observed variables. In this approach,

the probability of incurring expenditures for alcoholic beverages away

from home, given a series of demographic characteristics, is examined.

One of the advantages of logit is

that the coefficients are easily converted into probabilities without having to resort to special tables or other

means of calculation. The formula for

such a probability is

Pj = exp(a + b1X1j + ... + bnXnj)/[1 +

exp(a + b1X1j + ... + bnXnj)],

where b1,...,bn are parameter estimates

and X1j,..., Xnj are characteristics for the

jth unit.

In the simplest example in this study,

suppose one wants to calculate the

probability of purchasing alcohol away

from home for the control group described in the text of this article (that is,

single men in the middle-income group,

and so forth). Because all the independent variables in this case are binary,

the only coefficient of concern is that

for the intercept. In other words, using

the results for the purchase of alcohol
 Consumer Expenditure Survey Anthology, 2003 47

Number of consumer units ...................... 80,020,767 13,215,599 14,720,627 14,628,126 14,613,513 14,653,034 8,189,868

Sample size ............................................. 11,276 1,727 2,010 2,063 2,138 2,202 1,136

Income before taxes

 (complete reporters only, except

 where designated otherwise) ................ $48,248 $8,914 $20,191 $34,647 $55,141 $118,611 $7,576

Age of reference person .......................... 49.3 56.4 50.7 45.6 44.5 45.3 57.3

 Percent

Family type: ..............................................

Husband and wife only ....................... 21.5 8.9 22.4 21.8 23.7 25.8 27.4

Husband and wife, all children

 under 18 ........................................... 20.3 4.8 12.0 19.3 30.9 38.1 11.2

Husband and wife, at least one

 child 18 or older ................................ 6.4 1.8 2.9 6.2 8.1 11.4 8.7

Single parent (male) ........................... 7 0.5 0.4 1.3 1.0 0.2 0.3

Single parent (female) ........................ 5.4 8.7 10.5 5.9 2.2 0.6 4.7

Single man .......................................... 12.3 20.0 17.6 13.4 9.4 5.9 5.6

Single woman ..................................... 15.7 42.3 16.2 11.8 6.6 2.1 19.9

Other family ........................................ 17.7 13.0 18.1 20.2 18.2 16.0 22.3

Ethnic origin:

Hispanic .............................................. 9.1 10.9 13.8 11.4 7.5 4.4 5.4

Non-Hispanic ...................................... 90.9 89.1 86.2 88.6 92.5 95.6 94.6

Race:

White ................................................... 83.4 78.3 81.4 84.6 84.7 88.5 82.1

Black ................................................... 12.4 18.0 14.6 12.0 11.2 6.2 13.7

Asian ................................................... 3.4 2.7 2.7 2.8 3.3 5.1 3.4

Other race .......................................... 0.8 1.0 1.3 0.6 0.8 0.2 0.8

Occupation:

Works for wage or salary:.................. 65.1 36.5 57.0 74.7 84.8 85.2 37.9

Managers and professionals ........ 20.3 4.4 8.3 16.8 28.2 48.2 9.9

Teachers ....................................... 3.7 1.3 1.8 4.6 6.4 5.6 1.7

Technicians, sales, and

 services ...................................... 25.6 21.5 29.8 32.2 29.4 20.9 14.2

Blue collar ...................................... 14.2 8.5 15.1 18.9 19.4 9.6 12.0

Agriculture

 (farming, forestry, or fishing) ....... 0.9 0.8 1.9 1.6 0.6 0.2 0.1

Armed services ............................. 0.4 0.0 0.1 0.6 0.8 0.7 0.0

Self-employed ..................................... 5.0 4.1 4.6 5.9 3.1 5.9 7.4

Not working: ....................................... 32.1 59.3 38.6 19.3 12.2 9.0 54.5

Retired ........................................... 19.9 37.9 27.7 12.1 7.7 3.7 41.0

Unemployed .................................. 2.4 1.1 0.0 1 0.2 0.0 0.2

Other not working ......................... 9.8 20.3 10.9 7.2 4.3 5.3 13.3

Housing tenure:

Homeowner: ....................................... 66.8 48.3 56.9 61.4 75.2 86.7 73.2

Has mortgage ............................... 41.8 14.0 24.5 39.2 59.3 75.5 30.5

Owns without mortgage ................ 25.0 34.3 32.4 22.2 15.9 11.2 42.7

Renter ................................................. 33.2 51.7 43.1 38.6 24.8 13.3 26.8

Region of residence:

Northeast............................................ 19.6 17.5 17.6 22.3 18.5 20.3 22.8

Midwest .............................................. 24.1 21.2 22.8 24.3 28.0 23.6 25.2

South .................................................. 35.1 40.3 39.5 32.7 32.0 30.8 35.5

West ................................................... 21.2 21.0 20.1 20.7 21.5 25.3 16.5

Degree of urbanization:

Urban ................................................. 86.9 82.2 85.4 85.5 88.3 91.3 88.8

Rural ................................................... 13.1 17.8 14.6 14.5 11.7 8.7 11.2

All consumer

units

(21 and

older)

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Incomplete

reporters

Table 1. Purchases of alcohol by income quintile and selected demographic characteristics, 2000
48 Consumer Expenditure Survey Anthology, 2003

 Percent reporting

Purchase of alcohol:

Alcohol, total ....................................... 29.0 16.7 22.9 30.9 35.8 43.2 18.6

At home ......................................... 20.4 11.9 16.9 22.3 24.8 29.1 13.4

Away from home ........................... 14.6 6.9 9.2 14.4 19.8 25.6 8.5

Both types purchased2.................. 6.0 2.1 3.2 5.8 8.8 11.5 3.3

Beer: ................................................... 23.7 13.1 19.8 26.1 28.9 35.2 13.7

At home ......................................... 14.4 8.4 13.5 17.3 17.0 18.7 8.5

Away from home ........................... 12.8 6.1 8.1 12.4 17.3 22.6 7.4

Wine: ................................................... 9.9 4.3 5.5 9.0 12.4 19.3 7.3

At home ......................................... 7.0 3.3 3.9 6.1 8.4 13.9 5.5

Away from home ........................... 3.7 1.2 1.9 3.3 5.0 7.7 2.1

Other alcohol: ..................................... 8.3 4.0 4.9 7.4 11.2 15.1 5.8

At home ......................................... 3.7 2.3 2.3 3.6 4.8 5.1 3.9

Away from home ........................... 5.3 1.8 3.0 4.4 7.2 11.4 2.3

 Mean weekly expenditure

Alcohol, total ............................................. $7.05 $3.72 $4.09 $6.49 $9.22 $13.15 $3.94

At home ......................................... 4.71 3.05 3.07 4.51 5.37 8.35 3.01

Away from home ........................... 2.34 0.67 1.02 1.98 3.85 4.80 0.93

Beer: ................................................... 3.70 2.24 2.68 4.18 4.93 5.48 1.70

At home ......................................... 2.36 1.83 2.06 2.92 2.67 2.96 1.15

Away from home ........................... 1.34 0.41 0.62 1.26 2.26 2.52 0.55

Wine: ................................................. 1.98 0.93 0.77 1.12 2.34 5.00 1.37

At home ......................................... 1.63 .83 0.65 0.85 1.76 4.19 1.22

Away from home ........................... 0.36 0.10 0.12 0.27 0.58 0.81 0.15

Other alcohol: ..................................... 1.36 0.55 0.64 1.19 1.95 2.67 0.87

At home ......................................... 0.72 0.39 0.36 0.74 0.94 1.20 0.64

Away from home ........................... 0.64 0.16 0.28 0.45 1.01 1.47 0.23

All consumer units

(21 and

older)

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Incomplete

reporters

Table 1. Purchases of alcohol by income quintile and selected demographic characteristics, 2000

1

 Less than 0.5 percent.

2 This group is included in both alcohol-at-home and alcohol-away-from-home groups. When the figure shown is subtracted from

the at-home and the away-from-home totals, the total percent reporting alcohol is obtained.
 Consumer Expenditure Survey Anthology, 2003 49

Number of consumer units ............................. 80,020,767 4,271,663 14,262,057 18,057,721 14,844,186 10,265,925 9,498,725 8,820,490

Sample size ................................................ 11,276 591 2,073 2,517 2,093 1,477 1,287 1,238

Income before taxes

 (complete reporters only, except where

 designated otherwise) ................................. $48,248 $24,207 $46,818 $60,703 $61,814 $49,729 $33,191 $22,659

Age of reference person ................................ 49.3 22.5 29.8 39.4 49.2 59.4 69.4 80.5

 Percent

Family type:

 Husband and wife only ............................. 21.5 12.2 12.5 8.9 19.1 37.6 42.7 28.3

 Husband and wife, all children under 18 ...... 20.3 12.7 36.9 40.6 17.4 4.6 0.3 3

 Husband and wife, at least one

 child 18 or older .................................... 6.4 0.0 0.2 5.6 15.0 9.5 6.3 3.1

 Single parent (male) ................................. 0.7 0.4 0.7 1.5 0.8 0.2 3 3

 Single parent (female) .............................. 5.4 9.9 8.9 10.1 5.3 0.3 3 3

 Single man ............................................. 12.3 24.2 12.3 10.9 11.9 10.6 10.0 14.9

 Single woman ......................................... 15.7 14.3 9.3 6.1 10.4 18.5 24.9 42.4

 Other family ........................................... 17.7 26.3 19.1 16.3 20.0 18.6 15.9 11.3

Ethnic origin:

 Hispanic ................................................ 9.1 11.1 16.6 9.6 9.6 5.3 4.6 3.7

 Non-Hispanic .......................................... 90.9 88.9 83.4 90.4 90.4 94.7 95.4 96.3

Race: .........................................................

 White .................................................... 83.4 82.2 78.8 82.8 82.1 84.3 86.9 90.0

 Black .................................................... 12.4 12.8 14.0 13.7 12.7 12.9 11.1 7.8

 Asian .................................................... 3.4 4.8 6.2 2.1 4.0 2.8 1.7 2.1

 Other race ............................................. 0.8 0.2 1.0 1.4 1.2 1 0.3 0.1

Occupation:

 Works for wage or salary: ......................... 65.1 89.9 86.7 84.3 79.7 59.5 22.6 4.4

Managers and professionals ................ 2.3 15.7 26.9 27.8 25.3 19.8 8.3 1.4

Teachers .......................................... 3.7 3.4 5.3 3.3 6.2 4.9 0.5 0.1

Technicians, sales, and

 services ......................................... 25.6 50.7 33.4 30.5 31.5 21.0 10.3 2.5

Blue collar ........................................ 14.2 15.7 18.9 21.9 15.9 12.8 3.3 0.4

Agriculture

 (farming, forestry, or fishing) ............. 0.9 3.5 1.4 0.1 0.8 0.6 0.2 3

Armed services ................................. 0.4 0.9 0.8 0.7 3 0.4 3 3

 Self-employed ......................................... 5.0 2.0 3.2 5.3 6.1 6.4 6.1 3.9

 Not working: ........................................... 32.1 8.2 10.2 9.3 14.2 34.0 71.3 91.6

Retired ............................................. 19.9 0.4 0.1 0.2 1.2 18.3 65.1 86.3

Unemployed ...................................... 2.4 3 0.3 0.3 0.3 1 0.5 3

Other not working .............................. 9.8 7.8 9.8 8.8 12.7 15.7 5.7 5.3

Housing tenure:

 Homeowner: .......................................... 66.8 15.5 47.5 66.2 73.8 73.8 83.4 78.1

Has mortgage ................................... 41.8 11.9 42.3 58.2 58.1 58.1 24.9 9.8

Owns without mortgage ...................... 25.0 3.6 5.2 8.0 15.7 15.7 58.5 68.3

Renter ............................................. 33.2 84.5 52.5 33.8 26.2 26.2 16.6 21.9

Region of residence:

 Northeast ............................................... 19.6 9.5 18.9 18.6 19.7 21.8 21.6 23.0

 Midwest ................................................. 24.1 22.8 23.5 25.1 23.9 22.2 24.3 26.3

 South .................................................... 35.1 38.6 32.7 35.2 35.0 36.4 38.2 31.8 West

West ..................................................... 21.2 29.1 24.9 21.1 21.4 19.6 15.9 18.9

Degree of urbanization:

 Urban .................................................... 86.9 91.3 88.2 86.9 88.6 85.4 80.4 88.1

 Rural ..................................................... 13.1 8.7 11.8 13.1 11.4 14.6 19.6 11.9

All

consumer

units (21

 and older)

Under

25 25–34 35–44 45–54 55–64 65–74

75

 and older

Table 2. Purchases of alcohol by age group and other selected demographic characteristics, 2000
50 Consumer Expenditure Survey Anthology, 2003

 Percent reporting

Purchase of alcohol:

 Alcohol, total ........................................... 29.0 34.6 36.5 31.7 31.5 27.4 21.3 14.3

At home ........................................... 20.4 26.4 26.1 22.6 22.5 18.3 14.2 9.2

Away from home ................................ 14.6 14.9 19.4 16.0 15.6 14.9 9.9 7.1

Both types purchased2....................... 6.0 6.7 9.0 6.9 6.6 5.8 2.8 2.0

 Beer: ...................................................... 23.7 31.3 32.1 27.1 24.9 21.3 14.8 9.9

At home ........................................... 14.4 23.2 21.4 17.5 14.4 10.6 7.6 4.6

Away from home ................................ 12.8 13.8 16.7 14.1 14.2 13.0 8.2 6.1

 Wine: ..................................................... 9.9 9.1 11.1 10.4 12.7 10.0 7.0 5.9

At home ........................................... 7.0 5.8 7.5 7.1 9.3 7.1 5.5 4.3

Away from home ................................

 Other alcohol: .......................................... 8.3 8.7 10.3 8.9 8.9 8.6 6.5 4.3

At home ........................................... 3.7 4.8 3.4 3.8 3.2 4.5 4.2 2.6

Away from home ................................ 5.3 4.7 8.0 5.8 6.2 4.9 2.8 2.0

 Mean weekly expenditure

Alcohol, total ............................................... 7.05 9.65 8.18 8.57 7.60 6.69 4.72 2.81

At home ................................................ 4.71 7.46 5.18 5.33 5.10 4.35 3.78 2.12

Away from home .................................... 2.34 2.19 3.00 3.24 2.50 2.34 0.94 0.69

Beer: .................................................... 3.70 6.85 4.95 4.58 3.91 3.09 1.56 1.05

At home ........................................... 2.36 5.48 3.24 2.77 2.50 1.69 0.97 0.65

Away from home ................................ 1.34 1.37 1.71 1.81 1.41 1.40 0.59 0.40

 Wine: .................................................... 1.98 1.34 1.66 2.29 2.40 2.23 2.13 1.05

At home ........................................... 1.63 1.04 1.23 1.79 2.00 1.86 2.00 .92

Away from home ................................ 0.36 0.30 0.43 0.50 0.40 0.37 0.13 0.13

 Other alcohol: ......................................... 1.36 1.46 1.57 1.70 1.29 1.37 1.03 0.71

At home ........................................... 0.72 0.94 0.71 0.77 0.60 0.80 0.81 0.55

Away from home ................................ 0.64 0.52 0.86 0.93 0.69 0.57 0.22 0.16

1 Less than 0.5 percent.

2 This group is included in both alcohol-at-home and alcohol-away-from-home groups. When the figure shown is subtracted from the at-home and the awayfrom-home totals, the total percent reporting alcohol is obtained.

 3 No data reported.

All

consumer

units (21

 and older)

Under

25 25–34 35–44 45–54 55–64 65–74

75

 and older

Table 2. Purchases of alcohol by age group and other selected demographic characteristics, 2000
 Consumer Expenditure Survey Anthology, 2003 51

Number of consumer units ............................................................... 80,020,767 9,882,436 12,584,190 57,554,141

Sample size ...................................................................................... 11,276 1,365 1,708 8,203

Income before taxes

 (complete reporters only, except where designated otherwise) .... $48,248 $35,788 $22,042 $56,108

Age of reference person ................................................................... 49.3 48.2 60.0 47.1

 Percent

Family type:

Husband and wife only ................................................................ 21.5 1 1 29.8

Husband and wife, all children under 18 ..................................... 20.3 1 1 28.2

Husband and wife, at least one child 18 or older ........................ 6.4 1 1 8.9

Single parent (male) .................................................................... 0.7 1 1 0.9

Single parent (female) ................................................................. 5.4 1 1 7.6

Single man ................................................................................... 12.3 100.0 1 1

Single woman .............................................................................. 15.7 1 100.0 1

Other family ................................................................................. 17.7 1 1 24.6

Ethnic origin:

Hispanic ....................................................................................... 9.1 5.7 3.1 11.0

Non-Hispanic ............................................................................... 90.9 94.3 96.9 89.0

Race:

White ............................................................................................ 83.4 85.6 84.8 82.7

Black ............................................................................................ 12.4 10.9 12.0 12.8

Asian ............................................................................................ 3.4 2.9 2.8 3.6

Other race ................................................................................... 0.8 0.6 0.4 0.9

Occupation:

Works for wage or salary:........................................................... 65.1 65.4 45.6 69.4

Managers and professionals ................................................. 20.3 20.7 15.0 21.4

Teachers ................................................................................ 3.7 2.4 4.6 3.8

Technicians, sales, and services .......................................... 25.6 23.7 22.3 26.6

Blue collar ............................................................................... 14.2 16.4 3.4 16.2

 Agriculture (farming, forestry, or fishing) ................................. 0.9 1.7 0.3 0.9

Armed services ...................................................................... 0.4 0.5 3 0.5

Self-employed .............................................................................. 5.0 6.4 2.6 5.3

Not working: ................................................................................ 32.1 28.0 52.0 25.4

Retired .................................................................................... 2.4 0.1 0.2 0.3

Other not working .................................................................. 9.8 8.2 9.2 10.2

Housing tenure:

Homeowner: ................................................................................ 66.8 49.4 58.2 71.6

Has mortgage. ....................................................................... 41.8 26.2 20.5 49.1

Owns without mortgage ......................................................... 25.0 23.2 37.7 22.5

Renter .................................................................................... 33.2 50.6 41.8 28.4

Region of residence:

Northeast..................................................................................... 19.6 19.8 21.3 19.2

Midwest ....................................................................................... 24.1 23.5 27.4 23.5

South ........................................................................................... 35.1 35.0 32.0 35.8

West ............................................................................................ 21.2 21.7 19.3 21.5

Degree of urbanization:

Urban ........................................................................................... 86.9 90.5 89.0 85.8

Rural ............................................................................................ 13.1 9.5 11.0 14.2

Singles only All consumer

units (21 and

older)

Not single

Men Women

Table 3. Purchases of alcohol by marital status and other selected demographic characteristics, 2000
52 Consumer Expenditure Survey Anthology, 2003

 Percent reporting

Purchase of alcohol:

Alcohol, total ................................................................................ 29.0 34.4 16.9 30.7

At home .................................................................................. 20.4 25.0 9.3 22.0

Away from home .................................................................... 14.6 17.4 10.1 15.2

Both types purchased2........................................................... 6.0 8.0 2.5 6.5

Beer: ............................................................................................ 23.7 30.0 12.0 25.2

At home .................................................................................. 14.4 19.8 4.4 15.7

Away from home .................................................................... 12.8 15.3 8.6 13.3

Wine: ............................................................................................ 9.9 10.3 6.2 10.7

At home .................................................................................. 7.0 5.7 4.3 7.8

Away from home .................................................................... 3.7 5.7 2.2 3.7

Other alcohol: .............................................................................. 8.3 11.2 4.6 8.6

At home .................................................................................. 3.7 4.4 2.2 3.8

Away from home .................................................................... 5.3 7.7 2.7 5.4

 Mean weekly expenditure

Alcohol, total ...................................................................................... 7.05 10.44 2.79 7.40

At home ....................................................................................... 4.71 6.08 1.69 5.14

Away from home ......................................................................... 2.34 4.36 1.10 2.26

Beer: ............................................................................................ 3.70 5.80 1.38 3.86

At home .................................................................................. 2.36 3.57 0.67 2.53

Away from home. ................................................................... 1.34 2.23 0.71 1.33

Wine: ............................................................................................ 1.98 2.43 0.80 2.16

At home .................................................................................. 1.63 1.55 0.65 1.85

Away from home .................................................................... 0.36 0.88 0.15 0.31

Other alcohol: .............................................................................. 1.36 2.21 0.61 1.38

At home .................................................................................. 0.72 0.96 0.37 0.76

Away from home .................................................................... 0.64 1.25 0.24 0.62

1 Not available.

2 This group is included in both alcohol-at-home and alcohol-away-from-home groups. When the figure shown is subtracted from the

at-home and the away-from-home totals, the total percent reporting alcohol is obtained.

3

 No data reported.

All consumer

units (21 and

older)

Not single

Men Women

Table 3. Purchases of alcohol by marital status and other selected demographic characteristics, 2000

Singles only
 Consumer Expenditure Survey Anthology, 2003 53

Intercept ........................................................................................... –0.4741 0.1047 20.4963 <0.000

Age of reference person (35 to 44):

Under 25 ..................................................................................... 0.3076 0.1053 8.5411 0.0035

25 to 34 ....................................................................................... 0.2466 0.0659 13.9996 0.0002

45 to 54 ....................................................................................... –0.1135 0.0678 2.7993 0.0943

55 to 64 ....................................................................................... –0.3159 0.0828 14.5675 0.0001

65 to 74 ....................................................................................... –0.4296 0.1100 15.2484 <0.0001

75 and older ................................................................................ –0.8070 0.1300 38.5615 <0.0001

Income quintile (quintile 3):

Quintile 1 ..................................................................................... –0.4375 0.0878 24.8015 <0.0001

Quintile 2 ..................................................................................... –0.2861 0.0739 14.9804 0.0001

Quintile 4 ..................................................................................... 0.1532 0.0685 5.0008 0.0253

Quintile 5 ..................................................................................... 0.4824 0.0727 44.0884 <0.0001

Incomplete income reporters ..................................................... –0.4096 0.0920 19.8067 <0.0001

Family type (single man):

Husband and wife only ............................................................... –0.0925 0.0791 1.3676 0.2422

Husband and wife, own children only ......................................... –0.4797 0.0814 34.6906 <0.0001

Other husband and wife with children ........................................ –0.1951 0.1057 3.4057 0.0650

Single father ................................................................................ –0.2906 0.2414 1.4494 0.2286

Single mother .............................................................................. –1.0723 0.1316 66.4027 <0.0001

Single woman ............................................................................. –07493 0.0905 68.5886 <0.0001

Other family ................................................................................ –0.2896 0.0795 13.2701 0.0003

Ethnic origin of reference person (non-Hispanic):

Hispanic ...................................................................................... –0.0628 0.0779 0.6499 0.4201

Race of reference person (white):

Black ........................................................................................... –0.5253 0.0810 42.0712 <0.0001

Asian ........................................................................................... –0.3847 0.1096 12.3303 0.0004

Other race .................................................................................. –0.3502 0.2669 1.7217 0.1895

Occupation of reference person (manager or professional,

 wage or salaried):

Teacher ...................................................................................... –0.0911 0.1173 0.6039 0.4371

Technical, sales, or services ..................................................... 0.1292 0.0626 4.2587 0.0390

Blue collar ................................................................................... 0.0346 0.0751 0.2122 0.6451

Agricultural .................................................................................. 0.4531 0.2136 4.5014 0.0339

Armed services .......................................................................... 0.2514 0.2837 0.7854 0.3755

Self-employed ............................................................................. 0.0129 0.1073 0.0144 0.9046

Retired ........................................................................................ 0.0723 0.1059 0.4662 0.4948

Unemployed long term ............................................................... –0.6179 0.6308 0.9596 0.3273

Not working, other reason .......................................................... –0.3305 0.0950 12.1095 0.0005

Housing tenure (homeowner with mortgage):

Homeowner no mortgage ........................................................... –0.0477 0.0674 0.4992 0.4798

Renter ......................................................................................... –0.0155 0.0575 0.0723 0.7880

Region of residence (South):

Northeast.................................................................................... 0.2978 0.0639 21.6987 <0.0001

Midwest ...................................................................................... 0.2903 0.0602 23.2703 <0.0001

West ........................................................................................... 0.2285 0.0600 14.5163 0.0001

Degree of urbanization (urban):

Rural ........................................................................................... –0.4238 0.0842 25.3511 <0.0001

Intercept ........................................................................................... –1.1579 0.1168 98.2728 <0.0001

Age of reference person (35 to 44):

Under 25 ..................................................................................... 0.3185 0.1138 7.8368 0.0051

Alcohol, total

Standard error Chi-square Pr > chisquare

Logit results

Characteristic (control group value in parentheses) Parameter

estimate

Table 4. Parameter estimates and other results of the logit regressions on alcohol purchase patterns, 2000

Alcohol at home
54 Consumer Expenditure Survey Anthology, 2003

25 to 34 ....................................................................................... 0.1773 0.0720 6.0620 0.0138

45 to 54 ....................................................................................... –0.0441 0.0745 0.3503 0.5540

55 to 64 ....................................................................................... –0.3031 0.0934 10.5272 0.0012

65 to 74 ....................................................................................... –0.4555 0.1263 13.0060 0.0003

75 and older ................................................................................ –0.8446 0.1518 30.9599 <0.0001

Income quintile (quintile 3):

Quintile 1 ..................................................................................... –0.3573 0.0995 12.8953 0.0003

Quintile 2 ..................................................................................... –0.2210 0.0823 7.2202 0.0072

Quintile 4 ..................................................................................... 0.0545 0.0759 0.5164 0.4724

Quintile 5 ..................................................................................... 0.2399 0.0802 8.9408 0.0028

Incomplete income reporters ..................................................... –0.3521 0.1038 11.5177 0.0007

Family type (single man):

Husband and wife only ............................................................... –0.0521 0.0875 0.3543 0.5517

Husband and wife, own children only ......................................... –0.2185 0.0891 6.0163 0.0142

Other husband and wife with children ........................................ –0.0531 0.1156 0.2113 0.6457

Single father ................................................................................ –0.3249 0.2718 1.4284 0.2320

Single mother .............................................................................. –1.1369 0.1574 52.1439 <0.0001

Single woman ............................................................................. –0.9314 0.1089 73.1263 <0.0001

Other family ................................................................................ –0.1672 0.0872 3.6803 0.0551

Ethnic origin of reference person (non-Hispanic):

Hispanic ...................................................................................... 0.1099 0.0830 1.7526 0.1855

Race of reference person (white):

Black ........................................................................................... –0.3580 0.0899 15.8760 <0.0001

Asian ........................................................................................... –0.3680 0.1246 8.7175 0.0032

Other race .................................................................................. –0.4521 0.3068 2.1715 0.1406

Occupation of reference person (manager or professional,

 wage or salaried):

Teacher ...................................................................................... –0.1204 0.1363 0.7798 0.3772

Technical, sales, or services ..................................................... 0.2559 0.0697 13.4980 0.0002

Blue collar ................................................................................... 0.2208 0.0823 7.1906 0.0073

Agricultural .................................................................................. 0.7617 0.2191 12.0920 0.0005

Armed services .......................................................................... 0.7945 0.2840 7.8288 0.0051

Self-employed ............................................................................. –0.0083 0.1229 0.0045 0.9464

Retired ........................................................................................ 0.2674 0.1217 4.8284 0.0280

Unemployed long term ............................................................... –0.1551 0.6299 0.0606 0.8055

Not working, other reason .......................................................... –0.1162 0.1054 1.2149 0.2704

Housing tenure (homeowner with mortgage):

Homeowner no mortgage ........................................................... –0.0192 0.0761 0.0636 0.8009

Renter ......................................................................................... 0.0035 0.0635 0.0030 0.9567

Region of residence (South):

Northeast.................................................................................... 0.2379 0.0710 11.2192 0.0008

Midwest ...................................................................................... 0.1854 0.0674 7.5557 0.0060

West ........................................................................................... 0.1813 0.0665 7.4371 0.0064

Degree of urbanization (urban):

Rural ........................................................................................... –0.4292 0.0964 19.8406 <0.0001

Type of alcohol purchased:

Alcohol for consumption away from home........................................ 0.0173 0.0022 61.4922 <0.0001

Intercept ........................................................................................... –1.3053 0.1314 98.6242 <.0001

Age of reference person (35 to 44):

Under 25 ..................................................................................... 0.1957 0.1380 2.0110 0.1562

25 to 34 ....................................................................................... 0.3044 0.0824 13.6458 0.0002

Alcohol away from home

Standard error Chi-square Pr > chisquare

Logit results

Characteristic (control group value in parentheses) Parameter

estimate

Table 4. Parameter estimates and other results of the logit regressions on alcohol purchase patterns, 2000

Alcohol at home—Continued
 Consumer Expenditure Survey Anthology, 2003 55

45 to 54 ....................................................................................... –0.1808 0.0864 4.3757 0.0365

55 to 64 ....................................................................................... –0.2171 0.1045 4.3120 0.0378

65 to 74 ....................................................................................... –0.3970 0.1441 7.5897 0.0059

75 and older ................................................................................ –0.5690 0.1716 10.9909 0.0009

Income quintile (quintile 3):

Quintile 1 ..................................................................................... –0.4909 0.1225 16.0642 <0.0001

Quintile 2 ..................................................................................... –0.3016 0.1011 8.8954 0.0029

Quintile 4 ..................................................................................... 0.2862 0.0873 10.7341 0.0011

Quintile 5 ..................................................................................... 0.6007 0.0910 43.5773 <0.0001

Incomplete income reporters ..................................................... –0.3696 0.1280 8.3396 0.0039

Family type (single man):

Husband and wife only ............................................................... –0.0995 0.0984 1.0234 0.3117

Husband and wife, own children only ......................................... –0.6775 0.1035 42.8473 <0.0001

Other husband and wife with children ........................................ –0.3196 0.1351 5.5944 0.0180

Single father ................................................................................ –0.6523 0.3300 3.9073 0.0481

Single mother .............................................................................. –07275 0.1724 17.7999 <0.0001

Single woman ............................................................................. –0.3575 0.1131 9.9967 0.0016

Other family ................................................................................ –0.3951 0.1023 14.9156 0.0001

Ethnic origin of reference person (non-Hispanic):

Hispanic ...................................................................................... –0.4688 0.1153 16.5228 <0.0001

Race of reference person (white):

Black ........................................................................................... –0.7365 0.1202 37.5312 <0.0001

Asian ........................................................................................... –0.3744 0.1436 6.7928 0.0092

Other race .................................................................................. –0.1918 0.3554 0.2914 0.5893

Occupation of reference person (manager or professional,

 wage or salaried):

Teacher ...................................................................................... –0.0630 0.1384 0.2073 0.6489

Technical, sales, or services ..................................................... –0.0965 0.0757 1.6263 0.2022

Blue collar ................................................................................... –0.3175 0.0966 10.8138 0.0010

Agricultural .................................................................................. –0.4291 0.3224 1.7721 0.1831

Armed services .......................................................................... –0.4104 0.3782 1.1774 0.2779

Self-employed ............................................................................. –0.0111 0.1305 0.0073 0.9319

Retired ........................................................................................ –0.2224 0.1377 2.6092 0.1062

Unemployed long term ............................................................... –11.5682 201.4000 0.0033 0.9542

Not working, other reason .......................................................... –0.6136 0.1330 21.2917 <0.0001

Housing tenure (homeowner with mortgage):

Homeowner no mortgage ........................................................... –0.1362 0.0886 2.3609 0.1244

Renter ......................................................................................... –0.0819 0.0740 1.2256 0.2683

Region of residence (South):

Northeast.................................................................................... 0.2340 0.0824 8.0587 0.0045

Midwest ...................................................................................... 0.2829 0.0769 13.5133 0.0002

West ........................................................................................... 0.1778 0.0779 5.2164 0.0224

Degree of urbanization (urban): ......................................................

Rural ........................................................................................... –0.2208 0.1095 4.0682 0.0437

Type of alcohol purchased

Alcohol for consumption away from home........................................ 0.0181 0.0016 125.3391 <0.0001

Intercept ........................................................................................... –1.9918 0.1829 118.5713 <0.0001

Age of reference person (35 to 44):

Under 25 ..................................................................................... 0.3589 0.1880 3.6434 0.0563

25 to 34 ....................................................................................... 0.3085 0.1132 7.4340 0.0064

45 to 54 ....................................................................................... –0.1963 0.1205 2.6563 0.1031

Standard error Chi-square Pr > chisquare

Logit results

Characteristic (control group value in parentheses) Parameter

estimate

Table 4. Parameter estimates and other results of the logit regressions on alcohol purchase patterns, 2000

Alcohol away from home—Continued

Alcohol at home and alcohol away from home
56 Consumer Expenditure Survey Anthology, 2003

Alcohol at home and alcohol away from home

—Continued

55 to 64 ....................................................................................... –0.3268 0.1507 4.7008 0.0301

65 to 74 ....................................................................................... –0.8113 0.2330 12.1242 0.0005

75 and older ................................................................................ –0.8939 0.2811 10.1150 0.0015

Income quintile (quintile 3):

Quintile 1 ..................................................................................... –0.6653 0.2013 10.9182 0.0010

Quintile 2 ..................................................................................... –0.3272 0.1535 4.5440 0.0330

Quintile 4 ..................................................................................... 0.3343 0.1249 7.1651 0.0074

Quintile 5 ..................................................................................... 0.5858 0.1295 20.4516 <0.0001

Incomplete income reporters ..................................................... –0.3699 0.1991 3.4501 0.0632

Family type (single man):

Husband and wife only ............................................................... –0.1450 0.1363 1.1324 0.2873

Husband and wife, own children only ......................................... –0.7011 0.1415 24.5382 <0.0001

Other husband and wife with children................................................. –0.2276 0.1837 1.5344 0.2154

Single father ................................................................................ –1.7053 0.7267 5.5062 0.0189

Single mother .............................................................................. –1.3269 0.3028 19.2059 <0.0001

Single woman ............................................................................. –0.8691 0.1854 21.9819 <0.0001

Other family ................................................................................ –0.4285 0.1423 9.0715 0.0026

Ethnic origin of reference person (non-Hispanic):

Hispanic ...................................................................................... –0.3771 0.1595 5.5871 0.0181

Race of reference person (white): .............................................

Black ........................................................................................... –0.8317 0.1873 19.7237 <0.0001

Asian ........................................................................................... –0.7441 0.2331 10.1877 0.0014

Other race .................................................................................. –0.3315 0.5238 0.4007 0.5268

Occupation of reference person (manager or professional,

 wage or salaried):

Teacher ...................................................................................... –0.3262 0.2108 2.3936 0.1218

Technical, sales, or services ..................................................... –0.0185 0.1044 0.0315 0.8591

 Blue collar ................................................................................... –0.2494 0.1349 3.4196 0.0644

Agricultural .................................................................................. –0.0917 0.4091 0.0503 0.8226

Armed services .......................................................................... 0.3739 0.3985 0.8807 0.3480

Self-employed ............................................................................. –0.0689 0.1903 0.1310 0.7174

Retired ........................................................................................ –0.0978 0.2171 0.2032 0.6522

Unemployed long term ............................................................... –11.2583 288.2000 .0015 0.9688

Not working, other reason .......................................................... –0.6011 0.1988 9.1399 0.0025

Housing tenure (homeowner with mortgage):

Homeowner no mortgage ........................................................... –0.2426 0.1356 3.2030 0.0735

Renter ......................................................................................... –0.1114 0.1052 1.1221 0.2895

Region of residence (South):

Northeast.................................................................................... 0.1459 0.1183 1.5218 0.2174

Midwest ...................................................................................... 0.1402 0.1107 1.6050 0.2052

West ........................................................................................... 0.1514 0.1095 1.9114 0.1668

Degree of urbanization (urban):

Rural ........................................................................................... –0.2788 0.1624 2.9457 0.0861

Intercept ........................................................................................... –1.1944 0.1202 98.7746 <0.0001

Age of reference person (35 to 44):

Under 25 ..................................................................................... 0.2794 0.1160 5.8050 0.0160

25 to 34 ....................................................................................... 0.2044 0.0733 7.7706 0.0053

45 to 54 ....................................................................................... –0.2487 0.0776 10.2743 0.0013

55 to 64 ....................................................................................... –0.4444 0.0965 21.2240 <0.0001

65 to 74 ....................................................................................... –0.6851 0.1323 26.8169 <0.0001

75 and older ................................................................................ –1.0011 0.1579 4.1924 <0.0001

Standard error Chi-square Pr > chisquare

Logit results

Characteristic (control group value in parentheses) Parameter

estimate

Table 4. Parameter estimates and other results of the logit regressions on alcohol purchase patterns, 2000

Beer
 Consumer Expenditure Survey Anthology, 2003 57

Income quintile (quintile 3):

Quintile 1 ..................................................................................... –0.3618 0.1002 13.0291 0.0003

Quintile 2 ..................................................................................... –0.1609 0.0830 3.7578 0.0526

Quintile 4 ..................................................................................... –0.0241 0.0784 0.0944 0.7587

Quintile 5 ..................................................................................... 0.1772 0.0832 4.5310 0.0333

Incomplete income reporters ..................................................... –0.5580 0.1101 25.6931 <0.0001

Family type (single man):

Husband and wife only ............................................................... –0.1248 0.0908 1.8898 0.1692

Husband and wife, own children only ......................................... –0.3699 0.0919 16.1972 <0.0001

Other husband and wife with children ........................................ –0.2430 0.1226 3.9307 0.0474

Single father ................................................................................ –0.6450 0.2922 4.8720 0.0273

Single mother .............................................................................. –1.0858 0.1514 51.4280 <0.0001

Single woman ............................................................................. –0.8272 0.1072 59.5817 <0.0001

Other family ................................................................................ –0.2681 0.0905 8.7658 0.0031

Ethnic origin of reference person (non-Hispanic):

Hispanic ...................................................................................... 0.1383 0.0862 2.5710 0.1088

Race of reference person (white):

Black ........................................................................................... –0.4405 0.0934 22.2337 <0.0001

Asian ........................................................................................... –0.0272 0.1217 0.0500 0.8231

Other race .................................................................................. –0.4034 0.3142 1.6487 0.1991

Occupation of reference person (manager or professional,

 wage or salaried):

Teacher ...................................................................................... –0.0312 0.1386 0.0506 0.8220

Technical, sales, or services ..................................................... 0.2266 0.0724 9.7906 0.0018

Blue collar ................................................................................... 0.3630 0.0846 18.3947 <0.0001

Agricultural .................................................................................. 0.5969 0.2294 6.7688 0.0093

Armed services .......................................................................... 0.6854 0.3022 5.1442 0.0233

Self-employed ............................................................................. 0.0699 0.1261 0.3075 0.5792

Retired ........................................................................................ 0.2404 0.1279 3.5300 0.0603

Unemployed long term ............................................................... –0.4230 0.6913 0.3744 0.5406

Not working, other reason .......................................................... –0.0920 0.1084 0.7199 0.3962

Housing tenure (homeowner with mortgage):

Homeowner no mortgage ........................................................... –0.0777 0.0791 0.9634 0.3263

Renter ......................................................................................... 0.0446 0.0654 0.4655 0.4951

Region of residence (South):

Northeast.................................................................................... 0.2833 0.0738 14.7555 0.0001

Midwest ...................................................................................... 0.3047 0.0693 19.3346 <0.0001

West ........................................................................................... 0.1180 0.0693 2.9012 0.0885

Degree of urbanization (urban):

Rural ........................................................................................... –0.1413 0.0933 2.2920 0.1300

Type of alcohol purchased:

Purchased wine .......................................................................... 1.4732 0.0826 318.0120 <0.0001

Purchased another alcohol ........................................................ 1.8090 0.0953 360.5648 <0.0001

Purchased wine and another alcohol ......................................... 0.2146 0.1938 1.2265 0.2681

Intercept ........................................................................................... –2.9972 0.1783 282.7000 <0.0001

Age of reference person (35 to 44):

Under 25 ..................................................................................... 0.2933 0.1799 2.6567 0.1031

25 to 34 ....................................................................................... 0.0643 0.1094 0.3451 0.5569

 45 to 54 ....................................................................................... 0.3258 0.1072 9.2425 0.0024

55 to 64 ....................................................................................... 0.2068 0.1334 2.4022 0.1212

65 to 74 ....................................................................................... 0.0941 0.1800 0.2729 0.6014

75 and older ................................................................................ 0.0200 0.2115 0.0090 0.9246

Standard error Chi-square Pr > chisquare

Logit results

Characteristic (control group value in parentheses) Parameter

estimate

Table 4. Parameter estimates and other results of the logit regressions on alcohol purchase patterns, 2000

Beer—Continued

Wine
58 Consumer Expenditure Survey Anthology, 2003

Income quintile (quintile 3):

Quintile 1 ..................................................................................... –0.3014 0.1582 3.6306 0.0567

Quintile 2 ..................................................................................... –0.2527 0.1330 3.6102 0.0574

Quintile 4 ..................................................................................... 0.1887 0.1137 2.7517 0.0972

Quintile 5 ..................................................................................... 0.4614 0.1158 15.8821 <0.0001

Incomplete income reporters ..................................................... 0.1291 0.1490 0.7513 0.3861

Family type (single man):

Husband and wife only ............................................................... 0.0787 0.1300 0.3666 0.5449

Husband and wife, own children only ......................................... 0.1395 0.1345 1.0765 0.2995

 Other husband and wife with children ........................................ 0.1119 0.1690 0.4380 0.5081

Single father ................................................................................ 0.2328 0.3951 0.3472 0.5557

Single mother .............................................................................. –0.0507 0.2258 0.0504 0.8224

Single woman ............................................................................. 0.0302 0.1515 0.0397 0.8420

Other family ................................................................................ –0.0376 0.1345 0.0781 0.7798

Ethnic origin of reference person (non-Hispanic):

Hispanic ...................................................................................... –0.2494 0.1394 3.2015 0.0736

Race of reference person (white):

Black ........................................................................................... –0.2846 0.1397 4.1493 0.0417

Asian ........................................................................................... –0.6004 0.1989 9.1125 0.0025

Other race .................................................................................. –0.1145 0.4709 0.0591 0.8079

Occupation of reference person (manager or professional,

 wage or salaried):

Teacher ...................................................................................... –0.1149 0.1780 0.4167 0.5186

Technical, sales, or services ..................................................... –0.1094 0.0955 1.3115 0.2521

Blue collar ................................................................................... –0.5779 0.1294 19.9599 <0.0001

Agricultural .................................................................................. –0.6064 0.4275 2.0118 0.1561

Armed services .......................................................................... –0.7662 0.5178 2.1895 0.1390

Self-employed ............................................................................. –0.1024 0.1652 0.3845 0.5352

Retired ........................................................................................ –0.1846 0.1684 1.2029 0.2727

Unemployed long term ............................................................... –0.9262 1.1455 0.6537 0.4188

Not working, other reason .......................................................... –0.6230 .1689 13.6054 0.0002

Housing tenure (homeowner with mortgage):

Homeowner no mortgage ........................................................... 0.0162 0.1098 0.0218 0.8827

Renter ......................................................................................... –0.2146 0.0967 4.9199 0.0265

Region of residence (South):

Northeast.................................................................................... 0.1805 0.1014 3.1686 0.0751

Midwest ...................................................................................... –0.1860 0.1003 3.4416 0.0636

West ........................................................................................... 0.1555 0.0960 2.6234 0.1053

Degree of urbanization (urban):

Rural ........................................................................................... –0.5842 0.1591 13.4874 0.0002

Type of alcohol purchased:

Purchased beer .......................................................................... 1.4781 0.0823 322.3686 <0.0001

Purchased another alcohol ........................................................ 1.4416 0.1669 74.6139 <0.0001

Purchased beer and another alcohol ......................................... 0.2136 0.1931 1.2230 0.2688

Intercept ........................................................................................... –3.1624 0.1913 273.2494 <0.0001

Age of reference person (35 to 44):

Under 25 ..................................................................................... –0.0362 0.1899 0.0363 0.8489

25 to 34 ....................................................................................... 0.1603 0.1150 1.9423 0.1634

45 to 54 ....................................................................................... –0.1186 0.1200 0.9763 0.3231

55 to 64 ....................................................................................... –0.0669 0.1460 0.2098 0.6469

65 to 74 ....................................................................................... 0.1273 0.1915 0.4417 0.5063

75 and older ................................................................................ –0.0402 0.2326 0.0299 0.8628

Another alcohol

Standard error Chi-square Pr > chisquare

Logit results

Characteristic (control group value in parentheses) Parameter

estimate

Table 4. Parameter estimates and other results of the logit regressions on alcohol purchase patterns, 2000

Wine—Continued
 Consumer Expenditure Survey Anthology, 2003 59

Income quintile (quintile 3):

Quintile 1 ..................................................................................... –0.2224 0.1700 1.7113 0.1908

Quintile 2 ..................................................................................... –0.1768 0.1425 1.5397 0.2147

Quintile 4 ..................................................................................... 0.3201 0.1233 6.7399 0.0094

Quintile 5 ..................................................................................... 0.4278 0.1287 11.0468 0.0009

Incomplete income reporters ..................................................... 0.0696 0.1686 0.1703 0.6799

Family type (single man):

Husband and wife only ............................................................... –0.1649 0.1327 1.5440 0.2140

Husband and wife, own children only.................................................. –0.7449 0.1433 27.0278 <0.0001

Other husband and wife with children................................................. –0.1470 0.1794 0.6718 0.4124

Single father ................................................................................ 0.2641 0.3785 0.4868 0.4854

Single mother .............................................................................. –0.5883 0.2501 5.5334 0.0187

Single woman ............................................................................. –0.4545 0.1621 7.8643 0.0050

Other family ................................................................................ –0.2688 0.1363 3.8901 0.0486

Ethnic origin of reference person (non-Hispanic):

Hispanic ...................................................................................... –0.5377 0.1629 1.8983 0.0010

Race of reference person (white):

Black ........................................................................................... –0.1530 0.1496 1.0460 0.3064

Asian ........................................................................................... –0.5739 0.2180 6.9304 0.0085

Other race .................................................................................. 0.2898 0.4528 0.4098 0.5221

Occupation of reference person (manager or professional,

 wage or salaried):

Teacher ...................................................................................... –0.1699 0.2024 0.7042 0.4014

Technical, sales, or services ..................................................... –0.1351 0.1050 1.6558 0.1982

Blue collar ................................................................................... –0.4466 0.1387 1.3688 0.0013

Agricultural .................................................................................. –0.1272 0.4046 0.0988 0.7533

Armed services .......................................................................... –0.1766 0.4906 0.1296 0.7189

Self-employed ............................................................................. 0.0741 0.1778 0.1735 0.6770

Retired ............................................................................................. –02470 0.1856 1.7713 0.1832

Unemployed long term ............................................................... 0.6229 0.8580 0.5272 0.4678

Not working, other reason .......................................................... –03382 0.1806 3.5049 0.0612

Housing tenure (homeowner with mortgage):

Homeowner no mortgage ........................................................... –0.0551 0.1242 0.1971 0.6570

Renter ......................................................................................... 0.0717 0.1034 0.4814 0.4878

Region of residence (South):

Northeast.................................................................................... –0.1086 0.1173 0.8579 0.3543

Midwest ...................................................................................... 0.2482 0.1059 5.4901 0.0191

West ........................................................................................... 0.1782 0.1063 2.8119 0.0936

Degree of urbanization (urban):

Rural ........................................................................................... –0.4489 0.1700 6.9713 0.0083

Type of alcohol purchased:

Purchased beer .......................................................................... 1.8210 0.0950 367.7814 <0.0001

Purchased wine .......................................................................... 1.4733 0.1662 78.6136 <0.0001

Purchased beer and wine .......................................................... 0.1604 0.1918 0.6989 0.4032

Standard error Chi-square Pr > chisquare

Logit results

Characteristic (control group value in parentheses) Parameter

estimate

Table 4. Parameter estimates and other results of the logit regressions on alcohol purchase patterns, 2000

Another alcohol—Continued
 Consumer Expenditure Survey Anthology, 2003 61

The Cost and

Demographics of Vehicle

Acquisition

LAURA PASZKIEWICZ

Laura Paszkiewicz is an economist in the

Branch of Information Analysis, Division

of Consumer Expenditure Surveys, Bureau

of Labor Statistics.

T

ransportation costs make up a

large part of a consumer’s budget. Consumer Expenditure (CE)

Survey data for 2000 indicate that 88

percent of all consumer units 1 either

owned or leased a vehicle,2 and expenditures for leasing and purchasing (the

latter defined as a net outlay) vehicles

made up almost 10 percent of the average consumer unit’s total expenditures.

In April 1991, the CE Survey began

to ask for detailed information on the

leasing of vehicles. Since that time, the

incidence of leasing a vehicle increased

steadily before tapering off in recent

years. With the introduction of the more

detailed data, it is possible to investigate the factors that contribute to a

consumer’s decision to lease a vehicle,

as opposed to purchasing it. The main

factor contributing to this decision is

the varying cost of each option. Using

recent CE Survey data, this article examines the initial and monthly costs

involved in leasing a vehicle, purchasing a new vehicle, and purchasing a

used vehicle. The article presents details on the demographic breakdown of

consumers who lease, buy new, or buy

used vehicles.

Methodology

The sample used for this article includes

all Interview survey participants from

1999 or 2000 who reported a new lease3

or purchase of a vehicle in the year in

which the interview took place. (In

other words, the sample consists of all

participants in the 2000 Survey who

leased or purchased a vehicle in 2000,

as well as all participants in the 1999

Survey who leased or purchased a vehicle in 1999.) Respondents who reported using the vehicle for business,

or, alternatively, receiving complete or

partial payment for the vehicle by an

employer are excluded from the sample.

Costs involved in leasing versus buying are investigated. Average downpayments and monthly payments are

compared, as are the average durations

over which payments are made. The

investigation further includes analyses

of leasing and buying by the following demographic characteristics: Age,

race, gender, income quintile, geographical region, and type of area (urban vs. rural).

Background

The increase in the frequency of leasing vehicles in recent years has been

captured in a new section of the CE

Survey added in April 1991. Leases

1

 See the glossary at the end of this anthology for the definition of consumer unit.

2

 In the published CE data, vehicles are

defined as cars, trucks (including minivans,

vans, sports utility vehicles (SUVs), and

jeeps), and other vehicles (motorcycles and

aircrafts). Henceforth, the term vehicle will

encompass only cars and trucks.

3

 The time at which a lease is started is

determined by the year in which the first

payment was made on the lease.
62 Consumer Expenditure Survey Anthology, 2003

made up 2.7 percent of all new acquisitions of vehicles4

 in 1991. By 1997, leasing had reached 10.0 percent of all recent car acquisitions. (See chart 1, top

panel.) After 1997, the incidence of leasing began to decline, falling to 7.2 percent of vehicle acquisitions in 2000.

With the increasing popularity of leasing during 1991–97, new-vehicle purchases decreased as an overall percentage of vehicle acquisitions, dropping

from 27 percent in 1991 to 21 percent in

1997. After 1997, the incidence of newvehicle purchases began to rise, reaching 25.6 percent of vehicles acquired in

2000. Used-vehicle purchases made up

70.4 percent of all acquisitions of vehicles in 1991. From 1991 to 1996, the

percentage rose to 72.7 percent, after

which it began to fall. In 2000, usedvehicle purchases made up 67.2 percent of all vehicle acquisitions.

In 1999 and 2000, the total percentage of consumer units acquiring a vehicle was just under 4 percent of the

entire population. Of those who reported a recent acquisition, 66 percent

bought a used vehicle, 26 percent

bought a new one, and the remaining 8

percent leased a vehicle. (See table 1.)

Costs

One of the factors involved in choosing a method of acquisition is cost.

Among the costs incurred in acquiring

a vehicle are downpayments and monthly payments for leasing and purchasing.

Overall, 81 percent of new-vehicle

purchasers financed their purchases,

compared with 56 percent of used-vehicle purchasers. The CE Survey asks

questions regarding the amount of

downpayments and monthly payments

for purchased vehicles of those respondents in the sample who financed

the vehicle and have monthly payments remaining. Of those purchasers

who financed, 87 percent of new-vehicle purchasers and 79 percent of

used-vehicle purchasers had payments

remaining.

Downpayments are a good indicator of upfront costs for acquiring a vehicle—costs that could dictate whether

to lease a vehicle, buy a used vehicle,

or buy a new vehicle. Lessees paid

$868,5 on average, as a downpayment,

about 76 percent of the amount that a

used-vehicle purchaser paid as a

downpayment ($1,147) and only 30 percent of the amount that a new-vehicle

purchaser put for a downpayment

($2,914). (See table 2.) The maximum

downpayment was $8,500 for lessees,

$37,000 for new-vehicle purchasers,

and $19,000 for used-vehicle purchasers. These data suggest that the initial

costs for leasing an automobile are

lower than the costs for purchasing either a new or used vehicle. The difference in downpayments can be partially

explained by the main difference between leases and purchases: with leases,

the downpayment is for a service; with

purchases, the downpayment is for an

asset.

Monthly costs also could be a factor in deciding whether to lease, buy

new, or buy used. The average monthly

payment was $353 for lessees, $399 for

new-vehicle purchasers, and $273 for

used-vehicle purchasers. Thus, although lessees have a lower monthly

payment than do new-vehicle purchasers, they still have a higher monthly

payment than do used-vehicle purchasers.

The amount of time it takes to pay

off a loan or to complete a lease also

could have an effect both on a person’s

decision to lease, buy new, or buy used

and on the total cost of the vehicle. On

average, new-vehicle buyers made 54

monthly payments, used-vehicle buyers 43, and lessees 39. The most common term for leasing was 3 years, and

50 percent of lessees chose that term.

For new-vehicle purchasers, the most

common term for financing was 5 years,

and 55 percent of new-car purchasers

chose that term. For used-vehicle purchasers, a number of terms were common, but the top two were 5-year terms

(chosen by 27 percent) and 4-year terms

(selected by 24 percent).

Demographic analysis

The demographic analysis in this section examines the entire sample of those

acquiring a vehicle in 1999 or 2000, including consumers who financed their

vehicles and those who did not. (See

table 1.)

Income. Consumers who purchased

used vehicles had the least income, on

average. The average annual income

(based on complete income reporters 6)

of someone who bought a used vehicle

was $48,004, compared with $72,992 for

lessees and $69,875 for new-vehicle

purchasers. Overall, nearly 30 percent

of those who recently acquired a vehicle were in the highest income

quintile; the 30-percent figure was more

than that for the first and second income quintiles combined.7

 The percentage of used-vehicle purchases decreases and the percentages of both

new-vehicle purchases and leasings of

vehicles increases as one proceeds

from a lower income quintile to a higher

income quintile.

Among the consumer units that

bought or leased a vehicle, those in the

lowest income quintile were the most

likely to buy a used car (80.9 percent).

In comparison, only 54.1 percent of auto

purchasers in the fifth quintile bought

used vehicles. Almost 36 percent of

those leasing or buying in the highest

income quintile bought a new car; the

figure was 10 percentage points higher

than that of the fourth income quintile

and more than 20 percentage points

higher than that of the lowest income

quintile.

Age.8 Twenty-eight percent of those

acquiring vehicles in 1999 and 2000

4 New acquisitions of vehicles include purchases of new vehicles, purchases of used vehicles, and leases of vehicles.

5 In computing these averages, those who

recently acquired a vehicle and reported no

downpayment were counted as having zero

dollars for a downpayment.

6 See “Glossary” in Appendix A at the end

of this anthology for the definition of complete income reporter.

7

 See “Glossary” in Appendix A at the end

of this anthology for the definition of

quintiles of income before taxes.

8

 Both the age and race variables refer to

the age or race of the reference person—the

person first mentioned when the respondent

is asked, “Start with the name of the person

or one of the persons who owns or rents the

home.”
 Consumer Expenditure Survey Anthology, 2003 63

were in the 35-to 44-year-old age

bracket, although that age group made

up just 22 percent of the population.

Both the 25- to 34-year-old age group

and the 45- to 54-year-old age group

also made up large portions of those

acquiring vehicles. Each of the two

groups accounted for more than 20

percent of all acquisitions, yet made up

less than 20 percent of the population.

The oldest group (75 and older) acquired the fewest vehicles, with only

2.6 percent of acquisitions, much less

than their 9.6-percent share of the population.

The average age was 44 for consumers leasing vehicles, 47 for consumers buying new vehicles, and 42 for

consumers buying used vehicles. As

these data imply, the probability of

choosing a used car over a new car decreases with age. The incidence of leasing ranges from 7.9 percent to 8.6 percent for those 25 to 64, peaking in the

age range from 55 to 64. Consumers

under the age of 25 or over the age of

75 who acquired a vehicle were the least

likely to lease, with 5.4 percent and 4.9

percent, respectively, doing so.

Gender. In order to examine the statistics on vehicle acquisitions by gender,

the sample was divided into subsets

that include only single-member consumer units. The CE Interview survey

collects expenditure data for all members within the consumer unit combined, not for each member separately.

By using single consumer units instead,

a differentiation can be made between

the expenditures of men and those of

women.

Men acquired a slightly larger percentage of vehicles than their share of

the single population in 1999–2000. The

figures were 58 percent and 54 percent,

respectively.

Results of this portion of the study

suggest that men and women acquire

vehicles differently. A total of 9.6 percent of single men in the sample leased

vehicles, 20.6 percent bought new vehicles, and 69.9 percent purchased used

vehicles. By contrast, 11.5 percent of

single women leased vehicles, 36.9 percent bought new vehicles, and 51.5

percent bought used vehicles.

Even though single women acquired

a smaller percentage of vehicles than

their share of the population, they purchased a greater percentage of new vehicles and leased a greater percentage

of vehicles than their population share.

In particular, women made up 46 percent of the singles population, yet purchased 56.9 percent of all new vehicles,

and leased 46.9 percent of all leased

vehicles, among singles.

Region. Acquisitions of vehicles vary

by region. With 31 percent and 16 percent, respectively, of total acquisitions,

consumer units in the South and the

Northeast acquired smaller percentages of vehicles than their population

shares in 1999–2000: 35 percent and 19

percent of the total U.S. population. By

contrast, with 27 percent and 25 percent, respectively, of vehicle acquisitions, consumer units in the Midwest

and the West acquired higher percentages of vehicles than their population

shares of 24 percent and 22 percent of

the total U.S. population.

Consumers acquiring vehicles in the

Northeast were more likely to lease

than were those in the West, at 12.6

percent, in contrast to 4.8 percent. Consumer units in the West were more

likely to buy a used vehicle, with 66

percent of those acquiring vehicles

doing so, compared with 58 percent of

those in the Northeast. The Northeast

and the West both had about 30 percent of their vehicle-acquiring population reporting a purchase of a new vehicle. The Midwest and the South

varied only slightly in the three kinds

of acquisitions: in the Midwest, 9 percent of those who acquired vehicles

leased them, 23 percent bought them

new, and 69 percent purchased them

used; in the South, 8 percent leased

their vehicles, 25 percent purchased

them new, and 67 percent bought them

used.

Type of area (urban vs. rural). Consumers in urban and rural areas each

acquired roughly the same percentage

of vehicles as their population share.

The methods of acquisition that consumers in the two areas chose, however, were considerably different.

Consumer units in urban areas were

more likely to lease or buy a new vehicle than were those in rural areas.

Among consumer units acquiring vehicles, 3.3 percent of those living in rural areas leased their vehicles, whereas

8.5 percent of those living in urban areas did so. Almost 27 percent of consumer units in urban areas bought new

vehicles, compared with 22.6 percent

of those in rural areas. Someone living

in a rural area was more likely to buy a

used car (71.4 percent) than was someone in an urban area (64.8 percent).

Race.

9 The CE Survey has four race

categories: White; black; Asian or Pacific Islander; and American Indian,

Aleut, or Eskimo.

Persons of Asian or Pacific Islander

heritage accounted for just 3.1 percent

of the population acquiring vehicles

and were the most differentiated in

terms of the three ways of acquiring

them, compared with the other races. A

little more than half of their population

acquiring vehicles bought a used vehicle, 42 percent purchased a new vehicle, and the remaining 7 percent leased

a vehicle. Among the remaining racial

groups, the most similar in terms of

vehicle acquisition method was the

white population, which accounted for

most (88 percent) of the population

acquiring vehicles: among whites, 65.5

percent bought used vehicles, 26.5 percent purchased new ones, and 8 percent leased vehicles.

The black population and the American Indian, Aleut, and Eskimo population were most different from the group

of Asian and Pacific Islander descent

in their distribution over the three kinds

of arrangements for acquiring a vehicle.

The two populations were similar to

each other in having the lowest percentage of leases and new-vehicle purchases and the highest percentage of

9

 Both the age and race variables refer to

the age or race of the reference person, the

person who was first mentioned when the

respondent is asked, “Start with the name of

the person or one of the persons who owns

or rents the home.”
64 Consumer Expenditure Survey Anthology, 2003

used-vehicle purchases. Among black

consumer units acquiring vehicles, 5.3

percent leased, 19.6 percent purchased

a new vehicle, and 75.2 percent bought

a used vehicle. Among American Indians, Aleuts, and Eskimos, 4.2 percent

leased a vehicle, 16.5 percent purchased

a new vehicle, and 79.4 percent purchased a used one.

Conclusion

The 1999–2000 CE Survey data on vehicle acquisition indicates that, overall, purchasing used vehicles is the

most common method of acquiring a

vehicle. This is because it is typically

less expensive to purchase a used vehicle than it is to buy a new vehicle or

lease a vehicle. By contrast, cost is not

the predominant factor in choosing to

purchase a new vehicle over leasing

one. Even though leasing a vehicle is

financially less of a burden compared

with purchasing a new vehicle, the next

most common method of acquiring a

vehicle is purchasing new vehicles.

Leasing remains the least common

method.

The 1999–2000 data also suggest

that the choice of a vehicle acquisition

method varies by age, race, gender, income level, region, and degree of urbanization. The largest differences occur with respect to income levels,

gender, and race.

In addition to demographic differences and various expenses involved

in the decision to lease a vehicle, purchase a new vehicle, or purchase a

used vehicle, several other factors enter into the decision as well. The availability of leases or new vehicles in different regions may affect the frequency

with which one can obtain a lease or

find a suitable vehicle to purchase.

Further, the desirability of owning an

asset may spur an individual to purchase rather than lease. If, instead, the

vehicle’s intended use is most important to a person, then leasing might be

preferred. Finally, the types of vehicles

available under a lease may impel a

consumer to lease rather than buy: if a

consumer can drive a luxury car by leasing it for the same cost as purchasing

a standard car, he or she may prefer to

lease.
 Consumer Expenditure Survey Anthology, 2003 65

Table 1. Percent of consumer units reporting vehicle acquisitions, by type of acquisition, selected consumer unit

characteristics, 1999–2000

All ......................................................................................... 100.0 100.0 7.73 26.15 66.12

Income:1

Quintile 1 ......................................................................... 20.0 9.3 4.7 14.4 80.9

Quintile 2 ......................................................................... 20.0 15.3 4.0 19.9 76.0

Quintile 3 ......................................................................... 20.0 20.4 5.5 20.0 74.5

Quintile 4 ......................................................................... 20.0 26.0 7.2 26.2 66.6

Quintile 5 ......................................................................... 20.0 29.0 10.2 35.8 54.1

Region:

Northeast........................................................................ 19.3 17.2 12.3 29.0 58.7

Midwest .......................................................................... 23.6 26.8 8.1 21.9 70.0

West .............................................................................. 34.9 34.2 4.7 28.5 66.8

South .............................................................................. 22.2 21.8 8.5 25.5 66.1

Degree of urbanization:

Urban .............................................................................. 87.6 85.6 8.5 26.8 64.8

Rural .............................................................................. 12.4 14.5 3.3 22.6 74.1

Race:

White .............................................................................. 83.8 87.4 8.0 26.5 65.5

Black .............................................................................. 12.1 8.9 5.3 19.6 75.2

American Indian, Aleut, Eskimo ...................................... 1.0 1.1 4.2 16.4 79.4

Asian or Pacific Islander ................................................. 3.1 2.7 7.4 42.3 50.3

Age:

Under 25 ......................................................................... 7.5 7.2 5.4 16.9 77.7

25–34 .............................................................................. 17.6 21.5 7.9 22.5 69.6

35–44 .............................................................................. 22.3 27.5 8.1 22.4 69.4

45–54 .............................................................................. 19.6 23.4 8.2 26.1 65.7

55–64 .............................................................................. 12.7 11.4 8.6 37.1 54.3

65–74 .............................................................................. 10.7 6.4 5.8 39.1 55.1

75 and older .................................................................... 9.6 2.6 4.9 41.6 53.5

Gender:

Male ................................................................................ 54.3 57.7 9.6 20.6 69.9

Female ............................................................................ 45.7 42.4 11.5 36.9 51.5

1

 Percentage represents the percent of complete reporters.

Among groups Within groups

Percent of

general

population

Percent

of all

acquisitions

Leased Bought new Bought used

Table 2. Costs and term of vehicle acquisitions, by type of acquisition, 1999–

2000

Leased .............................................. $353 $ 868 39

Bought new1...................................... $399 $2,914 54

Bought used ...................................... $273 $1,147 43

1 The bought new and bought used categories represent vehicles that were financed

and still had payments remaining.

Average

monthly

payment

Average length

of term

(months)

Average

downpayment
66 Consumer Expenditure Survey Anthology, 2003

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

64

66

68

70

72

74

76

78

Percent of all acquisitions

64

66

68

70

72

74

76

78

Percent of all acquisitions

Purchase of used vehicle

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

20

22

24

26

28

30

Percent of all acquisitions

20

22

24

26

28

30

Percent of all acquisitions

Purchase of new vehicle

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

0

2

4

6

8

10

12

Percent of all acquisitions

0

2

4

6

8

10

12

Percent of all acquisitions

Lease of vehicle

Chart 1. Trends in vehicle acquisition methods, 1991-2000
 Consumer Expenditure Survey Anthology, 2003 67

Out-of-Pocket

Expenditures by

Consumer Units with

Private Health Insurance

Eric J. Keil is an economist in the Branch of

Information and Analysis, Division of Consumer Expenditure Surveys, Bureau of Labor

Statistics.

ERIC J. KEIL A

lthough managed-care health

plans have been around for

 quite some time, rising medical costs in the 1970s, along with

changes in Federal law, set the stage

for increased interest in such plans. As

a result, health maintenance organizations (HMOs) have grown steadily in

popularity since the 1970s, while the

popularity of more traditional fee-forservice health plans has declined.1

 Increases in health care costs continue

to stir national debate and have prompted much criticism of current methods

of dealing with high-cost health care.

Although many solutions to the problem have been proposed, no significant

changes have occurred.

In this article, data from the 1999 and

2000 Consumer Expenditure (CE) Interview surveys are used to show that

there are differences in certain out-ofpocket medical expenditures between

consumer units insured through HMOs

and those insured through fee-for-service plans. Demographic characteristics of consumer units are examined as

well, to aid in our understanding of

spending patterns with regard to health

insurance.

Study methodology

The sample for this study was restricted

to those consumer units who completed all four quarterly interviews. All

interviews must have occurred between

January 1999 and December 2000. In

addition, these consumer units must

have had private health insurance for

at least one quarter during the period

in which they were interviewed. Because the CE Interview survey does

not match medical expenditures with

the health plans responsible for covering them, the sample was further restricted either to those consumer units

who had one private health plan or to

those whose multiple plans were all of

one type, either HMO or fee for service. This strategy allowed consumer

units to be grouped into two separate

categories: Those with HMO coverage

or those with fee-for-service coverage.

In either case, it was possible for a consumer unit to have a member who was

also covered by Medicare or Medicaid.

Health care expenditures from the

CE Interview survey are out-of-pocket

expenditures. They consist of expenditures paid for medical services, prod1

 Consumer expenditure data show increasing expenditure levels and percentages

reporting for HMO insurance. (In 1984, average annual expenditures were at $15 with 3

percent reporting; by 1993, they stood at

$110 with 10 percent reporting; and in 2000,

expenditures reached $254 with more than

20 percent reporting.) The data also show

decreasing expenditure levels and percentages reporting for fee-for-service insurance:

in 1997, expenditures were $100 with 8 percent reporting; by 2000, they reached $77

with 5 percent reporting. Due to changes

made to the Interview survey in 1996, it is

not practical to show fee-for-service expenditure levels or percentage reporting prior to

1977.
68 Consumer Expenditure Survey Anthology, 2003

ucts, and supplies that are net of any

payments or reimbursements from

health insurance plans, government

programs, or any other third-party payers.2

Definitions

Two definitions are essential to an understanding of the material presented

in this article:

Health maintenance organization.

There are two basic types of HMOs.

The first is the group or staff type, in

which the participant goes to a central

facility (a group health center) to receive care. The second type is the independent practice association (IPA),

in which providers work from their individual offices and are referred to as

primary care physicians. Expenses in

this type of plan are usually covered in

full, or there is a modest copayment at

the time of the visit.

Fee-for-service plan. Commercial

health insurance plans encompass

both traditional fee-for-service plans

and preferred provider organizations.

In these plans, a fee is charged for each

medical service rendered or for all medical equipment purchased. In traditional

fee-for-service plans, participants receive medical care from the providers

they choose. The plan reimburses either the provider or the individual for

some or all of the cost of care received.

Participants in a preferred provider organization are given a list of doctors

from which they may choose. If they

choose to go to one of the doctors on

the list, the amount of expenses covered is higher than if they had gone to

a doctor who is not on the list.

The impact of health insurance on

medical expenditures

As with most products and services,

health care expenditures are affected

by interactions between prices and

quantities demanded. One major difference is that health insurance acts as a

third-party payer for health-related

products and services. This aspect can

alter expenditure levels both directly

and indirectly. In brief, the presence of

health insurance can affect medical expenditures in the following ways:

1. Differences in payment and benefit structures between the two

types of health plans can lead to

direct differences in the out-ofpocket component of health care

spending. In other words, given

consumer units with identical

medical consumption, those with

HMO insurance may pay less for

each medical bill in comparison

with those with fee-for-service insurance. This difference effectively lowers the out-of-pocket

price of heath care to HMO members, which, in turn, tends to

lower expenditure levels for healthcare-related items or services.

2. The aforementioned differences

in payment and benefit structures

can lead indirectly to different

spending patterns between participants in the two types of health

insurance plans. A consumer unit

who expects to have high medical bills might decide to select insurance that will cover more of

the costs. In addition, lower outof-pocket costs may have an effect on the quantity of medical

items and services demanded.

Because HMO insurance plans

cover a larger proportion of the

bill, one might expect higher usage by those consumer units

with that type of insurance. The

different spending patterns translate into a higher quantity demanded by consumer units in

HMO plans, which, in turn, tends

to raise expenditure levels, all else

held constant.

3. Administrative differences affect

the selection of a health plan. A

consumer unit who anticipates

using medical services with greater frequency might seek an insurance plan with a low administrative burden or one that allows

more flexibility in choosing providers. These considerations

may tend to counteract each

other. A common assumption is

that HMOs tend to require less

paperwork, whereas fee-for-service health plans offer greater

flexibility in choosing physicians

or other health care services. The

overall effect on expenditures is

difficult to determine.

Health care expenditures by type

of insurance

The CE Survey collects comprehensive

spending data for medical goods and

services as well as detailed information

regarding insurance coverage, including the type of health plan and the outof-pocket costs for premiums. The Survey classifies these expenditures into

17 categories. (See table 1.) Summing

up the medical expenditure components

reveals that total out-of-pocket medical spending was significantly higher,

on average, for those who had fee-forservice insurance, than for those who

had HMO coverage ($2,315 per year,

as opposed to $1,789). Of the 17 categories, 6 were found to be significantly

different between the two groups of

consumer units.

Differences were noted for health

care insurance, physicians’ services,

laboratory tests and x rays, hospital

services other than room, prescription

drugs and medicine, and dental care.

In each case, expenditures were greater

for consumer units in fee-for-service

health plans.3 Table 1 shows that the

largest difference in annual out-ofpocket spending, in absolute terms,

was for health care insurance ($159):4

consumer units with fee-for-service insurance paid $1,029 per year, on aver2

 Cash reimbursements paid directly to

the consumer unit are reported only infrequently in the CE Survey.

3

 The following medical expenditure items

were found not to be statistically different

between the two types of health plans: Purchase of eyeglasses and accessories, including

insurance; purchase of medical or surgical

equipment for general use; purchase of supportive or convalescent medical equipment;

hearing aids; eye exams, treatment, or surgery; services by other medical professionals; hospital room and meals; care in a convalescent or nursing home; other medical care

services; rental of medical or surgical equipment for general use; and rental of supportive or convalescent equipment.

4

 Health insurance expenditures include

those captured by payroll deductions.
 Consumer Expenditure Survey Anthology, 2003 69

age, while those with HMO insurance

paid $870. Other significant differences

in spending included physicians’ services ($210 for fee-for-service plans,

$129 for HMOs), laboratory tests and x

rays ($38, compared with $15), hospital

services other than room ($68 and $37),

prescriptions drugs and medicines

($329 and $236), and dental services

($311, as opposed to $265).

A similar analysis shows that consumer units with fee-for-service insurance had a higher percentage reporting for several medical expenditure

categories. In this article, percent reporting is defined as the percentage of

consumer units having at least one, but

possibly more, expenditures during the

year they were interviewed. Table 2

shows that there were significant differences in percent reporting for laboratory tests and x rays (23 percent for

fee-for-service plans, 13 percent for

HMOs), hospital services other than

room (16 percent and 13 percent), prescription drugs and medicines (80 percent and 75 percent), dental care (51

percent, compared with 48 percent),

purchases of medical or surgical equipment (4 percent, as opposed to 2 percent), and eye exams, treatment, or surgery (32 percent and 28 percent).

Although the percentage reporting

for all medical expenditures was higher

for the fee-for-service group, the number of reported expenditures per medical expenditure item was generally

higher for the HMO group. Significant

differences in reported expenditure

were noted for physicians’ services

(13,113 for HMO plans, 11,176 for feefor-service arrangements),5

 prescription drugs (26,871, compared with

24,088), dental care (6,449 and 5,748),

and eyeglasses and accessories (2,445

and 1,909). The number of reported expenditures was higher for the fee-forservice group only for lab tests and x

rays (1,451, as against 940).6

Demographic differences between

the two insured groups

A demographic analysis shows that the

two groups of insured were similar with

respect to age, income, family size, and

the number of children living in the

consumer unit. There was no statistically significant difference between incomes ($43,226 for those in HMO plans,

$43,728 for fee-for-service participants), but there were slight differences with respect to age, family size,

and number of children. Although

there was a statistical difference in age,

it was small, with an average age of 50

for the fee-for-service group and 48 for

the HMO group. Similarly, consumer

units with fee-for-service plans, on

average, were composed of 2.6 persons, of which 0.80 were children; consumer units with HMO insurance comprised 2.7 persons, of which 0.91 were

children. The demographic differences

between these two groups may not be

large enough to be considered a contributing factor in expenditure differences.

Looking at distributions of insured

consumer units by age of the reference

person, one can see that there were

more units with HMO insurance in the

group aged 25 to 54, but more consumer

units in fee-for-service plans in the

upper age categories. 7 (See chart 1.)

The distributions of insured consumer

units with respect to their size do not

show much difference (chart 2), but the

distributions with respect to numbers

of children in the unit indicate that there

were more fee-for-service consumer

units with no children than HMO units

with no children. (See chart 3.)

In sum, out-of-pocket expenditures

and spending patterns vary between

fee-for-service and HMO health plans.

Significant expenditure differences exist for health care insurance, physicians’ services, lab tests and x rays,

hospital services other than room, prescription drugs, and dental care. In

each case, consumer units with HMO

insurance had lower out-of-pocket expenditures for these items. They also

had a lower percentage reporting for

many of the items, but a higher number

of actual reported expenditures within

item categories. The higher frequencies

for reported expenditures may be a result of perceived lower costs. Consumers who have HMO insurance generally incur lower out-of-pocket medical

costs despite a higher number of reported expenditures. Their lower medical expenditures may be more the result of differences in plan benefits. The

demographic makeup of the two

groups of insured is similar with respect

to income, age, family size, and the number of children in the consumer unit.

Although some of the differences

found are statistically significant, they

are nonetheless small.

6 Care must be taken in evaluating percentages of consumer units reporting a medical expenditure, as well as the total volume

of expenditures, because medical goods and

services that are completely paid for by a

third party are not recorded in the CE Interview survey..

5 All figures in parentheses in this paragraph are in millions.

7

 Consumer units whose reference person

is eligible for Medicare also can have members who are insured through a fee-for-service or HMO plan.
70 Consumer Expenditure Survey Anthology, 2003

Under 25 25 to 34 35 to 44 44 to 54 55 to 64 65 to 74 75 and older

Age of reference person

0

5

10

15

20

25

30

Percent

0

5

10

15

20

25

30

Percent

HMO

FFS

One to three Four to five Six and up

Number of persons in consumer unit

0

10

20

30

40

50

60

70

80

Percent

0

10

20

30

40

50

60

70

80

Percent

HMO

FFS

None One Two to three Four and up

Number of children in consumer unit

0

10

20

30

40

50

60

Percent

0

10

20

30

40

50

60

Percent

HMO

FFS

Percentages of consumer units participating in health maintenance organizations

(HMO) and fee-for-service (FFS) health care plans, by age of reference person,

Consumer Expenditure Survey, 1999-2000

Chart 1.

Chart 3.

Chart 2. Percentages of consumer units participating in health maintenance organizations

(HMO) and fee-for-service (FFS) health care plans, by number of persons in the

consumer unit, Consumer Expenditure Survey, 1999-2000

Percentages of consumer units participating in health maintenance organizations

(HMO) and fee-for-service (FFS) health care plans, by number of children in the

consumer unit, Consumer Expenditure Survey, 1999-2000
 Consumer Expenditure Survey Anthology, 2003 71

Table 1. Average annual health care expenditures by type of insurance, Consumer Expenditure Interview Survey, 1999–2000

Total medical expenditures .......................................................................... $2,314.71 $1,789.24 1 $525.47

Health insurance.......................................................................................... 1,028.67 870.45 1 158.22

Physicians’ services .................................................................................... 210.14 128.65 1 81.49

Lab tests and x rays .................................................................................... 37.88 14.63 1 23.25

Hospital services other than room .............................................................. 67.64 37.34 1 30.30

Prescription drugs and medicine ................................................................. 329.02 236.47 1 92.55

Dental care .................................................................................................. 310.96 265.42 1 45.55

Eyeglasses and accessories, vision insurance.......................................... 73.09 77.72 – 4.63

Purchase of medical or surgical equipment for general use ....................... 2.93 2.16 0.77

Purchase of supportive or convalescent medical equipment ..................... 3.52 6.21 – 2.69

Hearing aid ................................................................................................... 21.77 13.68 8.09

Eye examinations, treatment, or surgery .................................................... 42.75 44.94 – 2.19

Services by other medical professionals .................................................... 58.49 38.91 19.58

Hospital room and meals ............................................................................. 56.42 31.24 25.18

Care in a convalescent or nursing home .................................................... 58.70 9.05 49.66

Other medical care services ....................................................................... 11.56 11.28 .28

Rental of medical or surgical equipment for general use ............................ .50 .62 – .11

Rental of supportive or convalescent equipment ........................................ .69 .51 .18

1

 Significantly different at the 95-percent confidence level.

Medical expenditure item Fee-for-service

insurance

Health maintenance

organization

Difference

 in means

Table 2. Percentage reporting medical expenditures, Consumer Expenditure Interview Survey, 1999–2000

Health insurance.......................................................................................... 73 72 1

Physicians’ services .................................................................................... 70 67 3

Lab tests and x rays .................................................................................... 23 13 1 10

Hospital services other than room .............................................................. 16 13 1 3

Prescription drugs and medicine ................................................................. 80 75 1 5

Dental care .................................................................................................. 51 48 1 3

Eyeglasses and accessories, vision insurance.......................................... 34 35 – 1

Purchase of medical or surgical equipment for general use ....................... 4 2

1 2

Purchase of supportive or convalescent medical equipment ..................... 4 3 1

Hearing aid ................................................................................................... 3 3 0

Eye examinations, treatment, or surgery .................................................... 32 28 14

Services by other medical professionals .................................................... 16 15 1

Hospital room and meals ............................................................................. 9 8 1

Care in a convalescent or nursing home .................................................... 1 1 0

Other medical care services ....................................................................... 6 4 2

Rental of medical or surgical equipment for general use ............................ 1 1 0

Rental of supportive or convalescent equipment ........................................ 1 1 0

1

 Significantly different at the 95-percent confidence level.

Medical expenditure item

Percent reporting

fee-for-service

plan

Percent reporting

health maintenance

organization

Difference in

percent reporting
72 Consumer Expenditure Survey Anthology, 2003

Table 3. Frequencies of health care expenditures, Consumer Expenditure Interview Survey, 1999–2000

Health insurance....................................................................................... 34,612 41,852 

1 – 7,240

Physicians’ services ................................................................................. 5,748 13,113 1 – 1,937

Lab tests and x rays ................................................................................. 1,451 940 1 511

Hospital services other than room ........................................................... 1,124 1,062 62

Prescription drugs and medicine .............................................................. 24,088 26,871 1 – 2,783

Dental care ............................................................................................... 5,748 6,449 1 – 701

Eyeglasses and accessories, vision insurance....................................... 1,909 2,445 1 – 536

Purchase of medical or surgical equipment for general use .................... 225 186 39

Purchase of supportive or convalescent medical equipment .................. 195 234 – 39

Hearing aid ................................................................................................ 212 216 –4

Eye examinations, treatment, or surgery ................................................. 1,771 1,924 – 153

Services by other medical professionals ................................................. 1,836 1,857 – 21

Hospital room and meals .......................................................................... 544 628 – 84

Care in a convalescent or nursing home ................................................. 127 81 46

Other medical care services .................................................................... 344 345 –1

Rental of medical or surgical equipment for general use ......................... 76 90 – 14

Rental of supportive or convalescent equipment ..................................... 80 97 – 17

1

 Significantly different at the 95-percent level.

Medical expenditure item

Frequency of

reporting, fee-forservice plans

(in milions)

Frequency of

reporting, health

maintenance

organizations

(in millions)

Difference in

frequency of

reporting
 Consumer Expenditure Survey Anthology, 2003 73

Expenditures on

Entertainment

Neil Tseng is an economist in the Branch of

Production and Control, Division of Consumer Expenditure Surveys, Bureau of Labor

Statistics.

NEIL TSENG O

ver the past half-century, the

 increase in incomes and de-

 cline in hours worked have allowed American consumers to enjoy

more leisure time and increase their

spending on entertainment. In 2000,

spending on entertainment by American consumers totaled approximately

$203 billion (see table 1), almost 3 times

the amount that Americans spent on

education. Using data from the Consumer Expenditure (CE) Survey, this

article looks at the level of expenditures

on entertainment, its share of national

aggregate expenditures, and the ways

in which selected demographic groups

allocate these expenditures. The article

highlights entertainment expenditures

by consumer units 1 in 2000, classified

by age of the reference person,2 income quintiles 3

 of complete income

reporters,4 and education of the reference person.

The CE Survey divides entertainment expenditures into four categories:

Fees and admissions; televisions, radios, and sound equipment; pets, toys,

and playground equipment; and other

entertainment supplies, equipment, and

services. Fees and admissions, which

accounted for 28 percent of entertainment expenditures in 2000, include expenses for out-of-town trips, fees for

recreational lessons, and the cost of

admission to sporting events, cultural

and theatrical events, the movies, and

special events, such as live musical

performances. Television, radios, and

sound equipment accounted for 33 percent of entertainment spending and

include color televisions, DVD players,

VCRs, CD players, video game consoles and software, videotapes and

discs, and speakers and various other

home theater sound systems. Pets,

toys, and playground equipment accounted for 18 percent of entertainment

spending and includes toys, games,

and playground equipment; hobbies

and tricycles; and pet food, veterinarian services and pet services. Other

entertainment supplies, equipment, and

services accounted for 21 percent of

entertainment spending and includes

“volatile” expenditures, such as the

rental or purchase of recreational vehicles and the purchase of boats. Expenditures on many of the items in the

category tend to fluctuate from year to

year, chiefly because, each year, relatively few consumers purchase these

expensive items (such as a boat with a

motor or a motorized camper) and increases or decreases in the percentage

of consumers purchasing the items can

1

 See “Glossary” in Appendix A at the end

of this anthology for the definition of a consumer unit.

2

 See “Glossary” in Appendix A at the end

of this anthology for the definition of reference person.

3 See “Glossary” in Appendix A at the end

of this anthology for the definition of quintiles

of income before taxes.

4 See the glossary at the end of this anthology for the definition of complete income reporter.
74 Consumer Expenditure Survey Anthology, 2003

have a large effect on the mean expenditure. For example, consumer units

who reported no expenditures on motorized recreational vehicles are

counted as spending $0.00. In 1999, 0.33

percent of consumer units reported

purchasing a motorized recreational

vehicle, and they spent an average of

$171, whereas, in 2000, the percent reporting was 0.24 percent, and the average amount spent was $82.

Age

In 2000, the share of total aggregate

entertainment spending accounted

for by consumer units with reference

persons in two age groups—those

under 35 and those 55 and older—

was smaller than their population

share. The under-35 group accounted

for 25 percent of the total population,

but spent 22 percent of the total of

$203 billion that U.S. consumers allocated to entertainment in 2000,

whereas those 55 and older had a

population share of 33 percent and

spent 25 percent of the total amount

allocated to entertainment. Consumer units with reference persons

in the age group from 35 to 54 had a

population share of 42 percent, but

accounted for more than half of the

total of $203 billion dollars spent on

entertainment.

As regards the individual categories of entertainment, persons under the

age of 35 and those 55 and older spent

less on entertainment than their population share in all four categories of entertainment, whereas those between the

ages of 35 and 54 spent more than their

population share on each of the categories. Although those under 35 made

up 42 percent of the population, their

share of spending on the four subcategories of entertainment was as follows:

Fees and admissions, 55 percent of the

aggregate entertainment share; TVs,

radios, and sound equipment, 50 percent; pets, toys, and playground equipment, 53 percent; and other entertainment supplies, equipment, and

services, 55 percent.

Education

This section examines consumer units

in two broad categories of educational

attainment.The first, those who did

not graduate from college, comprises

four classes: Those who did not

graduate from high school, high

school graduates, high school graduates with some college, and those

with an associate’s degree. The second category, college graduates,

consists of two classes: Those with

a bachelor’s degree and those with a

master’s, professional, or doctoral

degree. Consumer units with reference persons who did not graduate

from college had a population share

of 74 percent and accounted for 60.5

percent of the aggregate expenditures

on entertainment, whereas college

graduates had a population share of

26 percent, yet accounted for 39.5

percent of the aggregate expenditure

on entertainment.

Of the 4 subclasses making up the

group who did not graduate from college, 3 had an aggregate expenditure

share that was lower than their population share: Those who did not graduate high school, 8 percent expenditure

share, compared with 17 percent population share; high school graduates, 24

percent expenditure share, as opposed

to 29 percent population share; and

high school graduates with some college, 20 percent expenditure share and

21 percent population share. Only those

with associate’s degrees had a spending share exceeding their population

share (9 percent, compared with 8 percent). These statistics are evidence

that an increase in education level leads

to an increase in average income, enabling the more educated to spend more

on leisure and recreation. Average incomes for the four classes were as follows: Those who did not graduate high

school, $23,329; high school graduates,

$36,134; high school graduates with

some college, $38,837; associate’s degree, $50,060). Among the college

graduates, those with a bachelor’s

degree and those with advanced degrees had aggregate expenditure

shares of 25 percent and 15 percent,

respectively, and population shares

of 17 percent and 9 percent. These

figures are likely attributable to the

fact that, as their education levels increased, so did their incomes, providing them with more discretionary

income to spend on entertainment.

Income quintiles

An examination of spending on entertainment by income quintile reveals that

the proportion of aggregate expenditures allocated to entertainment ranged

from 9 percent by the lowest quintile to

40 percent by the highest quintile. The

aggregate amount spent on entertainment by complete income reporters was

$158 billion. Not surprisingly, consumer

units in the highest quintile contributed

the most to each of the four categories

of entertainment expenditure. These

consumer units spent more than $22

billion on fees and admissions; approximately $17 billion on televisions, radios,

and sound equipment; $10 billion on

pets, toys, and playground equipment;

and $13 billion on other entertainment

supplies, equipment, and services. To

put the figures in perspective, the $22

billion spent on fees and admissions

was more than twice the amount spent

by consumers in the fourth income

quintile and almost 7 times the amount

spent by those in the first quintile.

The proportion of total aggregate

entertainment expenditures allocated to

fees and admissions ranged from nearly

7 percent for those in the lowest quintile to more than 50 percent for those in

the highest quintile. For pets, toys, and

playground equipment, expenditures

ranged from 7 percent for those in the

lowest quintile to 37 percent for those

in the highest quintile. Total entertainment expenditures allocated to other

entertainment supplies, equipment, and

services ranged from 8 percent for those

in the lowest quintile to almost 38 percent for those in the highest. Although

the lowest quintile contributed only 11

percent toward televisions, radios, and

sound equipment, while the highest

contributed 33 percent, the 11-percent

figure accounted for the largest share

of the bottom quintile’s expenditures

on entertainment. Apparently, the category may be the main form of entertainment for those in the lowest income

quintile.
 Consumer Expenditure Survey Anthology, 2003 75

In sum, consumers spent approximately $203 billion on entertainment in

2000, with about $56 billion going to

fees and admissions; $68 billion to television, radios, and sound equipment;

$36 billion to pets, toys, and playground equipment; and $43 billion to

other entertainment supplies, equipment,

and services. Those with associate’s or

higher degrees accounted for 49 percent

of the aggregate expenditure on entertainment, well above their population

share of 34 percent. Consumer units

with reference persons between the

ages of 35 and 54 had a population share

of 42 percent, but accounted for 53 percent of the aggregate expenditure on

entertainment. Finally, consumer units

with reference persons in the highest

income quintile had a population share

of 20 percent, but accounted for 40 percent of the aggregate expenditure on

entertainment.

Table 1. Average annual entertainment expenditures and aggregate expenditures, by age of reference person, Consumer

Expenditure Survey, 2000

 Aggregate

 Total entertainment ................................................................... $10,687 $203,712 100.0 100.0

 Fees and admissions ............................................................ 2,911 56,308 100.0 100.0

 Televisions, radios, and sound equipment ........................... 3,618 67,999 100.0 100.0

 Pets, toys, and playground equipment .................................. 1,904 36,452 100.0 100.0

 Other entertainment supplies, equipment, and services ...... 2,254 42,910 100.0 100.0

 Under age 35

 Total entertainment ................................................................... 1,485 44,530 21.9 24.9

 Fees and admissions ............................................................ 366 10,923 19.4 24.9

 Televisions, radios, and sound equipment ............................ 577 16,796 24.7 24.9

 Pets, toys, and playground equipment .................................. 262 8,092 22.2 24.9

 Other entertainment supplies, equipment, and services ....... 280 8,719 20.3 24.9

 Aged 35 to 54

 Total entertainment ................................................................... 4,695 107,837 52.9 41.9

 Fees and admissions ............................................................ 1,352 31,082 55.2 41.9

 Televisions, radios, and sound equipment ........................... 1,485 34,135 50.2 41.9

 Pets, toys, and playground equipment .................................. 835 19,210 52.7 41.9

 Other entertainment supplies, equipment, and services ...... 1,023 23,410 54.5 41.9

 Aged 55 and older

 Total entertainment ................................................................... 3,024 51,300 25.2 33.2

 Fees and admissions ............................................................ 828 14,302 25.4 33.2

 Televisions, radios, and sound equipment ............................ 980 17,068 25.1 33.2

 Pets, toys, and playground equipment .................................. 545 9,149 25.1 33.2

 Other entertainment supplies, equipment, and services ...... 671 10,781 25.1 33.2

Average

annual

expenditure

Aggregate

expenditure

(in millions)

Aggregate

share

(in percent)

Population

share

(in percent)

Age of reference person and type of expenditure
76 Consumer Expenditure Survey Anthology, 2003

Table 2. Average annual entertainment expenditures and aggregate expenditures, by education of reference person,

Consumer Expenditure Survey, 2000

 Total entertainment ....................................................................... $896 $15,948 7.8 16.5

 Fees and admissions. .............................................................. 132 2,365 4.2 16.5

 Televisions, radios, and sound equipment ............................... 418 7,480 11.0 16.5

 Pets, toys, and playground equipment ..................................... 192 3,354 9.2 16.5

 Other entertainment supplies, equipment, and services .......... 54 2,749 6.4 16.5

 Total entertainment ....................................................................... 1,519 48,475 23.8 29.2

 Fees and admissions ............................................................... 298 9,516 16.9 29.2

 Televisions, radios, and sound equipment ................................ 566 18,088 26.6 29.2

 Pets, toys, and playground equipment ..................................... 303 9,660 26.5 29.2

 Other entertainment supplies, equipment, and services .......... 351 11,211 26.1 29.2

 Total entertainment ....................................................................... 1,775 39,735 19.5 20.6

 Fees and admissions ............................................................... 438 9,854 17.5 20.6

 Televisions, radios, and sound equipment ................................ 624 14,008 20.6 20.6

 Pets, toys, and playground equipment ..................................... 308 6,853 18.8 20.6

 Other entertainment supplies, equipment, and services .......... 405 9,020 21.0 20.6

 Total entertainment ....................................................................... 2,118 21,296 9.4 8.1

 Fees and admissions ............................................................... 529 4,730 8.4 8.1

 Televisions, radios, and sound equipment ................................ 678 6,052 8.9 8.1

 Pets, toys, and playground equipment ..................................... 376 3,499 9.6 8.1

 Other entertainment supplies, equipment, and services .......... 535 4,897 11.4 8.1

 Total entertainment ....................................................................... 2,780 50,946 25.0 16.8

 Fees and admissions ............................................................... 977 17,906 31.8 16.8

 Televisions, radios, and sound equipment ................................ 802 14,688 21.6 16.8

 Pets, toys, and playground equipment ..................................... 444 8,129 22.3 16.8

 Other entertainment supplies, equipment, and services .......... 557 10,223 23.8 16.8

Total entertainment ............................................................................. 3,011 29,476 14.5 8.9

 Fees and admissions ............................................................... 1,227 11,937 21.2 8.9

 Televisions, radios, and sound equipment ................................ 787 7,684 11.3 8.9

 Pets, toys, and playground equipment ..................................... 500 4,958 13.6 8.9

 Other entertainment supplies, equipment, and services ......... 498 4,897 11.4 8.9

Average

annual

expenditure

Aggregate

expenditure

(in millions)

Aggregate

share

(in percent)

Population

share

(in percent)

Did not graduate high school

 (Income before taxes = $23,329)

High school graduate

 (Income before taxes = $36,134)

High school graduate with some college

(Income before taxes = $38,837)

Associate’s degree

 (Income before taxes = $50,060)

Bachelor’s degree

 (Income before taxes = $64,201)

Master’s, professional, or doctoral degree

 (Income before taxes = $84,438)

Education of reference person and type of expenditure
 Consumer Expenditure Survey Anthology, 2003 77

Average

annual

expenditure

Aggregate

expenditure

(in millions)

Aggregate

share

(in percent)

Population

share

(in percent)

Table 3. Average annual entertainment expenditures and aggregate expenditures, by quintiles of income before taxes,

Consumer Expenditure Survey, 2000

 

 Lowest quintile

 Total entertainment ................................................................... $837 $13,545 8.6 20.0

 Fees and admissions ............................................................ 198 3,227 7.4 20.0

 Televisions, radios, and sound equipment ............................ 363 5,902 11.1 20.0

 Pets, toys, and playground equipment .................................. 122 1,946 6.9 20.0

 Other entertainment supplies, equipment, and services ...... 154 2,470 7.5 20.0

 Second quintile

 Total entertainment ................................................................... 1,147 18,527 11.7 20.0

 Fees and admissions ............................................................ 250 4,100 9.4 20.0

 Televisions, radios, and sound equipment ............................ 465 7,551 14.2 20.0

 Pets, toys, and playground equipment .................................. 239 3,780 13.4 20.0

 Other entertainment supplies, equipment, and services ...... 192 3,096 9.4 20.0

 Third quintile

 Total entertainment ................................................................... 1,609 25,986 16.4 20.0

 Fees and admissions ............................................................ 331 5,408 12.4 20.0

 Televisions, radios, and sound equipment ............................ 590 9,571 18.0 20.0

 Pets, toys, and playground equipment .................................. 337 5,532 18.9 20.0

 Other entertainment supplies, equipment, and services ...... 351 5,475 16.9 20.0

 Fourth quintile

 Total entertainment ................................................................... 2,324 37,476 23.7 20.0

 Fees and admissions ............................................................ 547 8,897 20.4 20.0

 Televisions, radios, and sound equipment ............................ 782 12,709 23.9 20.0

 Pets, toys, and playground equipment .................................. 422 6,714 23.8 20.0

 Other entertainment supplies, equipment, and services ...... 573 9,156 27.8 20.0

 

 Highest quintile

 Total entertainment ................................................................... 3,866 62,579 39.6 20.0

 Fees and admissions ............................................................ 1,349 22,025 50.5 20.0

 Televisions, radios, and sound equipment ............................ 1,071 17,441 32.8 20.0

 Pets, toys, and playground equipment ................................. 656 10,466 37.1 20.0

 Other entertainment supplies, equipment, and services ...... 790 12,647 38.4 20.0

Quintile of income and type of expenditure
78 Consumer Expenditure Survey Anthology, 2003

Table 4. Average annual expenditures of different demographic groups and shares spent on entertainment, by

education, age, and income quintile, Consumer Expenditure Survey, 2000

 

 Education

Did not graduate high school ...................................................... $23,386 3.8

High school graduate ................................................................. 32,447 4.7

High school graduate with some college .................................... 35,999 4.9

Bachelor’s degree ....................................................................... 50,785 5.5

Master’s, professional, or doctoral degree ................................. 60,527 5.0

 Age

Under 25 ..................................................................................... 22,543 4.8

25 to 34 ....................................................................................... 38,945 4.8

35 to 54 ....................................................................................... 45,655 5.1

55 and older ................................................................................ 32,937 4.5

 Income quintile

Lowest ........................................................................................ 17,940 4.7

Second ....................................................................................... 26,550 4.3

Third ........................................................................................... 34,716 4.6

Fourth ......................................................................................... 46,794 5.0

Highest ....................................................................................... 75,102 5.1

Average share of

expenditure spent on

entertainment

(in percent)

Average

annual

expenditure

Characteristic
 Consumer Expenditure Survey Anthology, 2003 79

Travel Expenditures

in 2000

GEORGE JANINI A

lthough most of the average

consumer’s spending budget

 is devoted to food, housing,

and transportation, consumer units

that went on trips in the year 2000 spent

an average of $875 in travel expenses.

The total amount spent on travel by all

consumers was roughly $32 billion.

This article uses data from the 2000

Consumer Expenditure (CE) Interview

survey to look at spending on trips and

vacations by various demographic

groups.

Methodology

Travel expenditures in the CE Survey

are broken down into five main groups:

Transportation, food, lodging, entertainment, and the purchase of gifts.

Transportation expenditures include all

costs of traveling to and from the destination, as well as transportation costs

incurred on the trip. All modes of transportation, such as plane, boat, ship, car,

taxi, truck, motorcycle, and camper, are

considered. Food expenditures include

all food and alcohol consumed on the

trip. Lodging expenses encompass the

costs for hotels, motels, cottages, trailer

camps, and other lodging on the trip.

Entertainment expenditures include all

types of entertainment, such as admission to sporting events, parks, museums, and tours, as well as any type of

fee related to these events. Gift expenditures are the total cost of all gifts

purchased on the trip for persons outside the consumer unit.

George Janini is an economist in the Branch

of Information and Analysis, Division of

Consumer Expenditure Surveys, Bureau of

Labor Statistics.

Age, income, and the composition

of the consumer unit are the characteristics used in the comparison. The data

are reported as both average annual

expenditures and aggregate expenditures, for each of the spending groups.

The data are annual average amounts

spent during the year 2000 on all trips

and not the amount spent per trip. Average and aggregate expenditures are

given only for those consumer units

that actually reported a trip in 2000. All

aggregate amounts were estimated with

weights derived from the CE Survey.

Excluded from the survey are all business-related expenditures for which the

consumer unit is reimbursed.

Expenditures on travel

Overall, consumer units that went on

trips in 2000 spent an average of $352

on transportation, $204 on food, $66

on entertainment, $76 on gifts, and $177

on lodging. These figures aggregated

to about $13 billion spent on transportation, $7.6 billion on food, $2.4 billion

on entertainment, $ 2.8 billion on gifts,

and $6.5 billion on lodging. Out of approximately 109 million consumer units,

34 percent, or 37 million units, reported

taking a trip or vacation in the year 2000.

Age

The highest percentage of trip takers

was posted by the group aged 45 to 54,

with 38 percent reporting a trip. The

lowest percentage was that of the

group aged 65 and older, 27 percent.
80 Consumer Expenditure Survey Anthology, 2003

This group, however, had the highest

average expenditures on trips of any

of the age groups. It is interesting to

note that the group, consisting mainly

of retirees, spent an average of 4 percent of its total average annual expenditures on trips and vacations, about

twice the share spent by most of the

other age groups. However, the 65-andolder group did not account for the

highest share of aggregate trip expenditures. That distinction went to the

group aged 45 to 54, with 24 percent of

aggregate trip expenditures in 2000.

The group aged 35 to 44 spent almost

as much, followed by the 65-and-older

group at 19 percent, with the groups

aged 25 to 34 and 55 to 64 each accounting for 15 percent. The group

aged 25 and under spent the least, accounting for only 4 percent of total trip

expenditures. Except for consumer units

in the two lowest age groups, aggregate expenditure shares were in proportion to the population share of the

group.

Income

Fully 58 percent of consumer units with

reported incomes 1 over $50,000 took a

trip or vacation in 2000, almost double

the share of consumer units with reported incomes of less than $25,000.

With more discretionary income at their

disposal, higher income consumer

units would be expected to spend more

on travel and trips than lower income

groups. Consumer units in the highest

income bracket, $75,000 or more, significantly outspent those in all other

income groups and almost doubled the

average spending on trips and vacations of the next highest income

bracket, those reporting income ranging from $50,000 to $75,000. Not surprisingly, consumer units with reported

incomes of $75,000 or more accounted

for 41 percent of aggregate trip expenditures in 2000, whereas the travel expenditure of all of the other reported

income groups combined was 52 percent. The classifications by income are

based on complete reporters only,

which account for 74 percent of all of

consumer units.

Composition of the consumer

unit

A few selected types of consumer units

are included in this article: Husbandand-wife-only consumer units, husbands and wives with children younger

than 17, single-persons, and one-parent consumer units. Forty-two percent

of husband-and-wife-only consumer

units reported taking a trip, compared

with 20 percent of single-person units.

Thirty-six percent of husband-and-wife

consumer units with children younger

than 17 reported taking a trip, as did 24

percent of one-parent consumer units.

In all, husband-and-wife consumer

units (with and without children) made

about half of aggregate trip expenditures in 2000.

Overall, consumer units reporting

incomes of $35,000 or more accounted

for 76 percent of total travel expenditures, while making up only 35 percent

of the population. Looking at the data

by age reveals that the highest spenders, on average, were aged 65 and older,

while the lowest were under the age of

25. The youngest group did not spend

much, on average, on trips, but did have

a relatively high percentage of trip takers. By comparison, the group aged 65

and older had the lowest percentage of

trip takers, but spent the most money,

on average, on trips. As regards a comparison of expenditure shares with

population shares, the age groups older

than 35 had similar overall travel expenditures and habits. The age groups 35

and under had far lower expenditure

shares compared with their population

shares. Even though single consumer

units made up 43 percent of the population, they accounted for just 22 percent of aggregate expenditures. By contrast, husband-and-wife consumer

units and single consumer units accounted for 40 percent of the population, but 58 percent of aggregate

expenditures.

Tables 1, 2, and 3 give an annual

summary of travel expenditures, by selected categories.

1 The distinction between complete and

incomplete income reporters is based, in general, on whether the respondent provided

information on his or her major sources of

income, such as wages and salaries, self-employment income, and Social Security income. In the survey, across-the-board zero

income reporting was designated as invalid,

and the consumer unit thus reporting was

categorized as an incomplete reporter.
 Consumer Expenditure Survey Anthology, 2003 81

Table 3. Average annual travel expenditures, selected types of consumer unit, Consumer Expenditure Interview survey,

2000

Number of consumer units (thousands) ............................. 109,367 22,805 20,687 46,948 6,132

Population share (percent) .................................................. 100 21 19 43 6

Percent of group that reported a trip .................................... 34 42 36 20 24

Aggregate travel expenditures (billions) .............................. $32.3 $10.1 $7.2 $6.5 $0.9

Share of aggregate expenditures (percent) ........................ 100 34 24 22 3

Average annual expenditures

Total trip expenses ......................................................... $875 $1,049 $957 $675 $642

Transportation ........................................................... 352 425 370 281 244

Food .......................................................................... 204 244 231 149 158

Entertainment ............................................................ 66 75 85 43 56

Gifts ........................................................................... 76 81 64 85 58

Lodging ...................................................................... 177 225 207 118 126

Item

All

consumer

units

Husband

and

wife only

Husband and

wife with

children

younger

than 17

Single

person

One

parent

Table 2. Average annual travel expenditures, by pretax income, Consumer Expenditure Interview survey, 2000

Number of consumer units (thousands) ................................ 109,367 31,543 10,759 12,392 11,337 15,424 28,067

Population share (percent) ..................................................... 100 29 10 11 10 14 26

Percent of group that reported a trip ....................................... 34 22 39 44 64 53 7

Aggregate travel expenditures (billions) ................................. $32.3 $2.8 $2.3 $3.9 $6.5 $12.4 $4.4

Share of aggregate expenditures (percent) ........................... 100 9 8 13 22 41 7

Average annual expenditures

Total trip expenses ............................................................ $875 $404 $557 $718 $886 $1,510 $1,077

Transportation .............................................................. 352 177 221 282 358 585 475

Food ............................................................................. 204 105 139 173 208 342 212

Entertainment ............................................................... 66 26 41 50 69 120 79

Gifts .............................................................................. 76 35 51 71 77 135 66

Lodging ......................................................................... 177 60 106 143 175 328 245

Pretax income

Less

than

$25,000

$25,000

to

$35,000

$35,000

to

$50,000

$50,000

to

$75,000

$75,000

or

more

Incomplete

reporting

of

income

Item

All

consumer

units

Table 1. Average annual travel expenditures, by age of reference person, Consumer Expenditure Interview survey, 2000

Number of consumer units (thousands) .............................. 109,367 8,306 18,887 23,983 21,874 14,161 22,155

Population share (percent) ................................................... 100 8 17 22 20 13 20

Percent of group that reported a trip ..................................... 34 36 34 34 38 36 27

Aggregate travel expenditures (billions) ............................... $32.3 $1.2 $4.7 $7.6 $7.9 $4.9 $6.0

Share of aggregate expenditures (percent) ......................... 100 4 15 23 24 15 19

Average annual expenditures

Total trip expenses .......................................................... $875 $392 $717 $922 $973 $970 $1,025

Transportation ............................................................ 352 170 300 374 365 383 428

Food ........................................................................... 204 106 177 218 233 228 212

Entertainment ............................................................. 66 33 59 74 70 72 67

Gifts ............................................................................ 76 31 54 69 91 88 106

Lodging ....................................................................... 177 52 127 187 214 199 212

Item

All

consumer

units Under

25 25–34 35–44 45–54 55–64 65 andolder

Age of reference person
 Consumer Expenditure Survey Anthology, 2003 83

Appendix A: Description

of the Consumer

Expenditure Survey

T

he current Consumer Expenditure (CE) Survey program began

in 1980. Its principal objective

is to collect information on the buying

habits of American consumers. Consumer expenditure data are used in various types of research by government,

business, labor, and academic analysts. The data are required for periodic revision of the Consumer Price

Index (CPI).

The survey, which is conducted by

the U.S. Census Bureau for the Bureau

of Labor Statistics, consists of two

components: A Diary, or recordkeeping,

survey completed by participating consumer units for two consecutive 1-week

periods; and an Interview survey in

which expenditures of consumer units

are obtained in five interviews conducted at 3-month intervals.

Survey participants record dollar

amounts for goods and services purchased during the reporting period, regardless of whether payment is made

at the time of purchase. Expenditure

amounts include all sales and excise

taxes for all items purchased by the

consumer unit for him- or herself or for

others. Excluded from both surveys

are all business-related expenditures

and expenditures for which the consumer unit is reimbursed.

Each component of the survey queries an independent sample of consumer units that is representative of the

U.S. population. In the Diary survey,

about 7,500 consumer units are sampled

each year. Each consumer unit keeps a

diary for two 1-week periods, yielding

approximately 15,000 diaries a year. The

interview sample is selected on a rotating-panel basis and yields reports for

7,500 consumer units each quarter. Each

consumer unit is interviewed once per

quarter, for five consecutive quarters.

Data are collected on an ongoing basis

in 105 areas of the United States.

The Interview survey is designed

to capture expenditure data that respondents can reasonably recall for a period of 3 months or longer. In general,

the data captured report relatively large

expenditures, such as spending on real

property, automobiles, and major appliances, or expenditures that occur on

a regular basis, such as spending on

rent, utilities, and insurance premiums.

Including global estimates of spending for food, it is estimated that about

95 percent of expenditures are covered

in the Interview survey. Expenditures

on nonprescription drugs, household

supplies, and personal care items are

excluded. The Interview survey also

provides data on expenditures incurred

on leisure trips.

The Diary survey is designed to

capture expenditures on small, frequently purchased items that are normally difficult for respondents to recall.

Detailed records of expenses are kept

for food and beverages—both at home

and in eating places—tobacco, housekeeping supplies, nonprescription

drugs, and personal care products and

services. Expenditures incurred away

from home overnight or longer are ex-
84 Consumer Expenditure Survey Anthology, 2003

cluded from the Diary survey. Although

the diary was designed to collect information on expenditures that could not

be recalled easily over a given period,

respondents are asked to report all expenses (except overnight travel expenses) that the consumer unit incurs

during the survey week.

Integrated data from the BLS Diary

and Interview surveys provide a complete accounting of consumer expenditures and income, which neither survey component alone is designed to

do. Data on some expenditure items are

collected in only one of the components. For example, the Diary survey

does not collect data on expenditures

for overnight travel or information on

third-party reimbursements of consumer expenditures, as the Interview

survey does. Examples of expenditures

for which reimbursements are excluded

are medical care; automobile repair; and

construction, repairs, alterations, and

maintenance of property.

For items that are unique to one or

the other survey, the choice of which

survey to use as the source of data is

obvious. However, there is considerable overlap in coverage between the

surveys. Because of this overlap, integrating the data presents the problem

of determining the appropriate survey

component from which to select expenditure items. When data are available

from both survey sources, the more

reliable of the two (as determined by

statistical methods) is selected. As a

result, some items are selected from the

Interview survey and others, from the

Diary survey.

Population coverage and the definition of components of the CE Survey

differ from those of the CPI. Specifically, consumer expenditure data cover

the total population, whereas the CPI

covers only the urban population. In

addition, home ownership is treated differently in the two surveys. Actual expenditures of homeowners are reported

in the CE Survey, whereas the CPI uses

a rental equivalence approach that attempts to measure the change in the

cost of obtaining, in the rental marketplace, services equivalent to those provided by owner-occupied homes.

Interpreting the data

Expenditures are averages for consumer units with specified characteristics, regardless of whether a particular

unit incurred an expense for a specific

item during the recordkeeping period.

The average expenditure for an item

may be considerably lower than the

expenditure by those consumer units

that actually purchased the item. The

less frequently an item is purchased,

the greater is the difference between

the average for all consumer units and

the average for those purchasing the

item. Also, an individual consumer unit

may spend more or less than the average, depending on its particular characteristics. Factors such as income, the

ages of family members, geographic

location, taste, and personal preference also influence expenditures.

Furthermore, even within groups with

similar characteristics, the distribution of expenditures varies substantially. These points should be considered in relating reported averages

to individual circumstances.

Users of these survey data also

should keep in mind that prices for

many goods and services have risen

since the survey was conducted. For

example, rent, as measured by the CPI,

rose 8.2 percent between 2000 (annual

average index) and September 2002.

In addition, sample surveys are subject to two types of error: Sampling and

nonsampling. Sampling errors occur

because the data are collected from a

representative sample rather than the

entire population. Nonsampling errors

result from the inability or unwillingness of respondents to provide correct

information, differences in interviewers’

abilities, mistakes in recording or coding, or other processing errors.

Glossary

Consumer unit. Members of a household related by blood, marriage, adoption, or some other legal arrangement;

a single person living alone or sharing

a household with others, but who is

financially independent; or two or more

persons living together who share responsibility for at least two out of the

three major types of expenses: Food,

housing, and other expenses. Students

living in university-sponsored housing

are also included in the sample as separate consumer units.

Reference person. The first member

mentioned by the respondent when

asked to “Start with the name of the

person or one of the persons who owns

or rents the home.” It is with respect to

this person that the relationship of

other members of the consumer unit is

determined.

Total expenditures. The transaction

costs, including excise and sales taxes,

of goods and services acquired during

the interview period. Estimates include

expenditures for gifts and contributions

and payments for pensions and personal insurance.

Income. The combined income earned

by all consumer unit members 14 years

or older during the 12 months preceding the interview. The components of

income are wages and salaries; selfemployment income; Social Security

and private and government retirement

income; interest, dividends, and rental

and other property income; unemployment and workers’ compensation

and veterans’ benefits; public assistance, Supplemental Security Income,

and Food Stamps; rent or meals or

both as pay; and regular contributions for support, such as alimony

and child support.

Complete income reporters. In general, a consumer unit who provides information on at least one of the major

sources of its income, such as wages

and salaries, self-employment income,

and Social Security income. Even complete income reporters may not provide

a full accounting of all income from all

sources.

Quintiles of income before taxes. Five

groups with a similar number of complete income reporters, ranked in ascending order of income. Incomplete

income reporters are not ranked and are

shown separately in the quintiles-ofincome tables.