52 Consumer Expenditure Survey Anthology, 2008

Examining Expenditure 

Patterns of Young

Single Adults in a 

Historical Context:

Two Recent Generations 

Compared

Geoffrey Paulin

Geoffrey Paulin is a senior economist in the 

Division of Consumer Expenditure Surveys, 

U.S. Bureau of Labor Statistics. This article is 

abridged. The complete version appears in the 

December 2008 issue of the Monthly Labor 

Review.

For many Americans, the age 

of 21 is a major point of demarcation in one’s life cycle. 

This age marks the start of full legal 

adulthood—that is, the age at which 

the young person is no longer considered a minor and can freely engage in 

all legal activities, such as renting or 

purchasing a home. By age 21, many 

Americans have completed their formal education, and many more will do 

so during their twenties.1 In addition, 

numerous individuals in this age group 

are starting on their first jobs leading 

to a career, and consequently, they face 

many new challenges. Achieving and 

maintaining financial independence can 

be difficult and has long-term ramifications for young adults and others in 

society. After all, income and spending 

patterns established in youth will affect one’s ability not only to save for 

the purchase of a home, provide for a 

family—including future children’s 

education—and live well in retirement, 

but also to contribute toward programs 

such as Social Security for current retirees. Clearly, then, understanding the 

economic status of young single adults 

is important for society as a whole, 

especially when substantial structural 

changes in the economy occur, as they 

have during the last generation.

Indeed, the changes that have taken 

place may lead to outcomes that differ 

from what has happened in the past. On 

the one hand, there has been a persistent belief, based on experience, that 

the current generation of Americans 

will be better off economically than 

1

 According to data from the 1998 Current 

Population Survey (CPS), 36 percent of 21-yearolds reported graduating from high school as 

the highest level of education attained, while 7 

percent reported completing an associate’s degree 

or higher. Eight years later, in 2006, the CPS 

indicated that 28 percent of 29-year-olds reported 

graduating from high school as the highest level 

of education attained, while 41 percent reported 

completing an associate’s degree or higher level 

of education. In comparison, that same year, 31 

percent of 21-year-olds reported graduating from 

high school as the highest level of education 

attained, while 9 percent reported completing an 

associate’s degree or higher level of education. 

(See “Table 2. Educational Attainment of the 

Population 15 Years and Over, by Single Years 

of Age, Sex, Race, and Hispanic Origin: 2006,” 

on the Internet at www.census.gov/population/

socdemo/education/cps2006/tab02-01.xls; and 

“Table 2. Educational Attainment of Persons 15 

Years Old and Over, by Single Year of Age, Sex, 

Race, and Hispanic Origin: March 1998,” from 

“Educational Attainment in the United States: 

March 1988 (Update)” (U.S. Census Bureau, report 

P20-513, issued October 1998), on the Internet at 

www.census.gov/prod/3/98pubs/p20-512u.pdf 

(visited May 20, 2008). Note that 2006 is the 

last year for which tables showing educational 

attainment by exact age were produced.)

2

 For an example of these changing beliefs, 

see Melinda Crowley, “Generation X Speaks Out 

on Civic Engagement and the Decennial Census: 

An Ethnographic Approach,” Census 2000 

Ethnographic Study, June 17, 2003, especially 

page 2, on the Internet at www.census.gov/

pred/www/rpts/Generation%20X%20Final 

%20Report.pdf (visited Sept. 26, 2007). For 

an example of the changing economic status 

of young single adults, see Geoffrey Paulin 

and Brian Riordon, “Making it on their own: 

the baby boom meets Generation X,” Monthly 

Labor Review, February 1998, pp. 10–21; on 

the Internet at www.bls.gov/opub/mlr/1998/02/

art2full.pdf.
Consumer Expenditure Survey Anthology, 2008 53

the previous generation. On the other 

hand, since the 1990s, much literature 

has suggested that that belief may not 

be true anymore.2 This article examines expenditure and income patterns 

for single, never-married young adults 

(persons aged 21 to 29 years) who were 

interviewed in 2004–05 and compares 

the patterns with those exhibited by 

single young adults 20 years earlier. 

The aim of the comparison is to assess 

the economic status of the two groups 

of singles in each period.

Before starting the analysis, it is 

important to keep in mind that many 

factors describe one’s economic status 

and none by itself can provide a complete answer to the question “Who was 

better off when?” Each measure has its 

own inherent strengths and limitations 

that must be considered before attempting to draw conclusions.

The data

The main source of data used in this article is the Interview Survey, a component of the Consumer Expenditure Survey (CE). The CE is the most detailed 

source of expenditure information collected directly from households by the 

Federal Government. In addition, data 

on income and other demographics 

are collected. Collected periodically 

throughout most of the 20th century, 

consistent data from the Interview 

Survey are available for analysis on a 

quarterly basis from 1984 onward.

Terms and definitions

Expenditures and outlays. Technically, 

this article examines outlays, which are 

similar, but not identical, to expenditures. Both expenditures and outlays 

consist of the transaction costs, including taxes, of goods and services. They 

also include spending for gifts for persons outside the consumer unit, but exclude business purchases. However, expenditures include the full cost of each 

purchase, even though full payment 

may not have been made at the date of 

purchase.3

 Outlays include periodic 

credit or installment payments for major items already acquired, such as automobiles.4

 For example, if a consumer 

purchases a new automobile during the 

3 months prior to the interview (that is, 

the “reference period”), the full cost of 

which is $30,000, then, under the definition of “expenditure,” the consumer 

is taken to have spent $30,000 during 

the reference period. However, if the 

consumer financed the purchase with a 

loan and made payments of $500 each 

month of the reference period, then, 

under the definition of “outlays,” the 

consumer is taken to have spent $1,500 

during the reference period, plus any 

additional amount spent on a downpayment or a similar fee.5

 In addition, 

for homeowners, mortgage principal 

payments, if any, are excluded from the 

expenditure computation; for outlays, 

principal payments are included.6

Although expenditures are useful 

to analyze in many contexts, outlays 

are used in the analysis that follows 

because they provide a better view of 

monetary flows for young consumers, 

who presumably have less in savings or 

investments on which to rely for purchases and who therefore may depend 

on loans for financing more than do 

older consumers.7

Adjustment for expenditures for food at 

home. Prior to 1988, respondents to the 

Interview Survey were asked to report 

usual monthly expenditures for food at 

home during the reference period. Starting in 1988, respondents were asked to 

report usual weekly expenditures instead. 

Due to this change in the questionnaire, 

expenditures for food at home are not 

directly comparable over time. This incomparability is evidenced by a large 

increase in the average for these expenditures for young single adults from 1987 

to 1988 (almost 45 percent), which is 

inconsistent with all other year-to-year 

changes in these expenditures from 1984 

to 2005. Therefore, prior to any analysis, 

1984–85 data on food at home are adjusted to account for this change to the 

extent possible. Outlays that include food 

at home as a component, either directly 

(for example, total food outlays) or indirectly (for example, outlays for all other 

items, which are computed by subtracting several expenditures from total outlays), are recomputed with the use of the 

adjusted expenditures for food at home. 

(Details concerning the change in the 

questionnaire and the computation of the 

adjustment factor are given in “Statistical 

procedures” in the technical notes.)

Group of interest: young single adults. 

In this article, the main analysis is performed using data from young, single, 

never-married adults aged 21 to 29 years 

who constitute their own consumer units.8

4 Ibid. See also “2004 Consumer Expenditure 

Interview Survey Public Use Microdata Documentation,” Oct. 18, 2006, p. 103, on the Internet 

at www.bls.gov/cex/2004/cex/csxintvw.pdf (visited 

Sept. 8, 2008). 5

 In addition to automobiles, major items 

include other vehicles used primarily for transportation (for example, trucks, vans, and motorcycles) 

or entertainment and recreation (such as boats and 

campers). For other items (for instance, apparel) 

that have been financed by other means (say, by 

credit card), the expenditures approach applies. 

That is, the full purchase price is recorded in the 

reference period during which the purchase was 

made, even if the balance is not paid immediately. 

Payments for interest accruing to the balance 

also are collected during each interview, but 

the proportion of the total interest accruing to 

any particular purchase (apparel in the present 

example) that is included in the total balance, which 

may also include amounts from other purchases in 

addition to the amount for the particular purchase, 

is neither collected nor estimated. 6

 This criterion applies to all mortgage principal 

payments, whether for the home of residence, a 

vacation home, or some other property. However, 

regardless of the kind of computation—of 

expenditures or outlays—mortgage interest, but 

not the full purchase price, paid for the owned 

home is included. Nevertheless, information on 

“purchase price of property (owned home)” is 

collected, and is included as a component of “net 

change in total assets” in published tables.

3

 See “BLS Information: Glossary,” on the 

Internet at www.bls.gov/bls/glossary.htm#E, or 

“Consumer Expenditure Survey: Glossary,” on the 

Internet at www.bls.gov/cex/csxgloss.htm#expn, 

both visited Jan. 30, 2007.

7

 However, actual values for assets and 

liabilities are not examined here. See section titled 

“Limitations of the Data” for more information. 8

 Excluded from the analysis are cases in 

which two or more single, never-married adults 

who share living quarters are either financially 

interdependent or sharing responsibility for 

major expenses (or both). By definition, these 

consumer units consist of at least two members, 

who may be described either as “unrelated 

persons” (1984–85 and 2004–05) or “unmarried 

partners” (2004–05), unless they are related by 

blood or some legal arrangement. Such consumer 

units are in contrast to single, never-married 

persons who share living quarters, but who are 

financially independent, and who do not share 

responsibility for more than one major expense. 

These consumer units constitute single-member 

consumer units within the same housing unit. 

(For more information, see the definition of 

“consumer unit” in the glossary.)
54 Consumer Expenditure Survey Anthology, 2008

The group is limited to single-member 

consumer units in order to facilitate 

comparisons across time. For example, 

if all consumer units that include at 

least one 21- to 29-year-old are compared, changes in patterns may be due 

solely to changes in the composition of 

these units: If there are more (or fewer) married couples, single parents, or 

other non-single-member units in the 

later period, expenditure patterns for 

the group as a whole will appear to differ, even if there has been no change 

when only married couples, single parents, or other non-single-member units 

are compared. In addition, the sample 

is limited to never-married singles 

because singles who were previously 

married may have very different expenditure or other patterns based on 

differences in their life experiences or 

differences in income resulting from 

their unions. These patterns may even 

include expenditures for a child who 

lives in a consumer unit different from 

that of the previously married parent. 

Therefore, to remove the potential influence of these factors on the analysis, 

only never-married singles are included, wherever possible.

Quarterly outlays or annualized outlays? In the Interview Survey, data for 

expenditures and outlays are collected 

quarterly in most cases. That is, respondents are usually asked to report values 

for expenditures or outlays that occurred 

during the 3 months prior to the interview. For convenience, the data for expenditures and outlays presented in this 

article are annualized prior to analysis. 

That is, quarterly values are multiplied 

by 4. However, the annualized values do 

not represent calendar-year spending. 

For example, respondents interviewed 

in January 1984 reported outlays that 

occurred between October and December 1983. Similarly, respondents interviewed in February 1984 reported outlays that occurred between November 

1983 and January 1984, thus crossing 

years. Also, multiplying an individual’s 

quarterly outlays by 4 may not accurately represent what that individual actually spent during the 12-month period 

of interest. However, on average, this 

approach provides a reasonable estimate 

of outlays for a 12-month period.

Real dollars or nominal dollars? In performing economic comparisons across 

time, it is essential to control for changes 

in prices, because changing prices affect 

purchasing power. That is, if a person 

spent $1 for apples yesterday, but $2 today, then the person did not buy more apples today if the price of apples doubled 

since yesterday. Price indexes are often 

used to convert nominal (that is, reported) 

dollars into real (that is, price-adjusted) 

dollars, either by converting yesterday’s 

expenditures into today’s dollars or by 

converting today’s expenditures into 

yesterday’s dollars. (For more information on this topic, see “Real or nominal 

expenditures?” in the technical notes.)

Sample or population? In conducting the 

CE, it is impossible to interview every 

consumer unit in the United States (the 

population). Therefore, a representative 

group is interviewed. The members of 

this group constitute the sample. To obtain population estimates, each consumer 

unit in the sample is weighted by the 

number of consumer units it represents. 

In 1984–85, there were 2,359 consumer 

units of interest sampled; as shown in 

table 1, together they are estimated to 

represent nearly 4.9 million consumer 

units in the population. In 2004–05, there 

were 2,158 consumer units of interest 

sampled, representing about 4.6 million 

consumer units in the population.9

Statistical significance. Because data 

compared across groups come from 

samples of each group, rather than entire 

populations, it is important to consider 

the probability that differences in outcomes are the result of actual differences 

in the population and not due to chance. 

Depending on the type of sampling performed, different formulas are available 

to compute the statistical significance

of the outcome—that is, the probability 

that the difference was due to chance 

alone, rather than to a real difference in 

outcomes. In the analysis that follows, 

when results are described as “statistically significant,” the outcome is not likely 

to have been due to chance alone. (Tests 

used to measure statistical significance 

are described in “Measuring statistical 

significance: types and computations of 

t-statistics” in the technical notes.)

Limitations of the data

A complete description of economic 

well-being includes measures that are 

not available in the data analyzed. For 

example, the CE does not collect information about expectations of the 

future. Presumably, the anticipation of 

a particular event or outcome in the 

future influences expenditure patterns 

in the present. For example, if one expects to make a major purchase (for instance, a home or a car) soon, one may 

save more in the present than someone 

who does not expect to do so for some 

time; or, as discussed subsequently, the 

more one expects to earn in the future 

as the result of obtaining a college degree, the more one is willing to pay for 

it. As another example, rapid changes 

in technology, such as those which occurred during the period under study, 

presumably have ramifications for economic well-being that are impossible 

9

 Publications of the 2005 CE data use 

information from consumer units that were 

selected for interview under a sample design 

different from that of those selected for interview 

in 2004. For technical reasons, only consumer units 

participating from February through December 

2005 were eligible to be selected for interview 

under the new sample design. Therefore, only 

information from these consumer units is used in 

this article when results from 2005 are described. To 

ensure a proper computation of population counts, 

the weight of each consumer unit interviewed in 

2005 is multiplied by 12/11 before any additional 

computation is performed. The reason is that 

11 months of sample are used to represent 12 

months of population. This adjustment does not 

affect the means or variances of outlays or other 

characteristics that would have been obtained 

from the sample of interviews occurring in 2005 

and that are used in this study had the adjustment 

not been made. However, it corrects the population counts, thereby changing the weight of 

the 2005 interviews in the total sample (that is, 

interviews occurring in 2004 and 2005) when 

the means and variances for the 2-year period are 

computed. For interviews occurring in 2004, no 

additional adjustment is necessary. Although the 

sample design used to select consumer units for 

interview in 2004 is different from the one used 

in 2005, the same design is used consistently from 

January through December 2004. Therefore, no 

adjustment to weights is necessary for consumer 

units interviewed anytime during that period.
Consumer Expenditure Survey Anthology, 2008 55

Table 1. Demographic characteristics of never-married young adults (aged 21 to 29 

years), 1984–85 and 2004–05

 Characteristic 1984–85 2004–05

Estimated population (rounded) ......................... 4,854,000 4,610,000

Percent distribution:

Educational status:

Highest level attained:

 High school diploma or less..................... 26.2 17.8

 College experience.................................. 73.8 82.2

 Attended college................................. 140.1 245.3

 Graduated college.............................. 333.7 436.9

Currently enrolled in college:

 Full time ................................................... 25.6 35.7

 Part time .................................................. 7.0 7.4

 Not at all................................................... 64.7 53.4

 Not eligible............................................... 2.7 3.5

Housing tenure:

Homeowner................................................... 8.0 15.8

Renter ...................................................... 92.0 84.2

Race and ethnic origin

Hispanic ...................................................... 3.5 7.1

Non-Hispanic................................................. 96.5 92.9

 Black ...................................................... 8.3 10.1

 White and other ....................................... 88.2 82.8

Men..................................................................... 57.6 59.3

Women ............................................................... 42.4 40.7

Size of dwelling:

Homeowners.................................................

 Rooms, other than bathrooms ................. 5.0 5.3

 Bedrooms........................................... 2.4 2.5

 Bathrooms ............................................... 1.2 1.5

 Half baths................................................. .2 .2

Renters

 Rooms, other than bathrooms ................. 4.1 4.2

 Bedrooms........................................... 1.8 2.1

 Bathrooms ............................................... 1.2 1.3

 Half baths................................................. .1 .1

1

 Includes those who report attending or 

completing 1 to 3 years of college and those 

who report attending, but not completing, 4 

years of college. 2

 Includes those who report some college, 

but no degree, and those who report receiving 

an associate’s degree (occupational/vocational 

or academic). 3

 Includes those who report completing 4 

years of college or attending graduate school. 4

 Includes those who report receiving 

a bachelor’s degree, master’s degree, professional school degree, or doctoral degree.

to measure by examining expenditures 

alone. 10

In addition, a consideration of assets and liabilities is excluded from 

this analysis. Although the CE collects information on assets and liabilities, the information is not detailed 

enough for purposes of analysis. For 

example, some information about levels of debt and to whom it is owed is 

collected; however, information about 

many sources of debt, including school 

loans, is not collected separately from 

information about other debt.11 Furthermore, the CE data on assets and liabilities are not considered as reliable as 

expenditure data, due to nonresponse.12

Finally, unlike expenditure data, which 

are collected during each interview, 

data on assets and liabilities are collected only during the fifth interview. 

Therefore, not all consumer units that 

are interviewed have an opportunity to 

provide information about assets and 

liabilities.13 Despite these data limitations, young singles presumably make 

expenditure decisions with the preceding factors in mind. Consequently, 

those factors are implicitly included in 

the analysis that follows.

Demographic analysis

Before comparing groups, it is important 

to understand their basic demographic 

characteristics. Changes in demographics, such as educational attainment, may 

explain differences in economic attainment. For example, a higher percentage 

attending college may indicate a better 

trained workforce whose members are 

more able to enter professional or skilled 

careers. At the same time, changes in 

demographics may be associated with 

changes in tastes and preferences that 

would change expenditure patterns.

10 Paulin and Riordon, “Making it on their 

own,” pp. 16, 18. 11 In 2004, school loans began to be cited as an 

example when the respondent is asked to report the 

amount owed for “other credit, such as school loans, 

personal loans or loans from retirement plans.” (See 

“Consumer Expenditure Survey: Section 21, Part 

A—Credit Liability—Credit Balances—Second 

Quarter Only” (U.S. Bureau of Labor Statistics, 

Nov. 20, 2005), on the Internet at www.bls.gov/

cex/capi/2004/csxsection21a1.htm (visited Apr. 

9, 2008).) Nevertheless, the proportion of the 

total amount owed for any of these types of credit 

separately is neither collected nor estimated. 12 See “Consumer Expenditure Survey: Frequently Asked Questions (FAQ’s)” (U.S. Bureau 

of Labor Statistics, Mar. 4, 2008), on the Internet at 

www.bls.gov/cex/csxfaqs.htm#q8 (visited Mar. 25, 

2008). 13 Like asset and liability data, income data are 

collected less frequently than expenditure data. 

However, in contrast to asset and liability data, 

income data are collected not only during the fifth 

interview, but also during the second interview 

(or during the earliest interview, in the event that 

either no respondent was available in time to 

complete the second interview or the consumer unit 

originally at the address visited has been replaced 

by a new consumer unit). Income information 

from the second (or the earliest) interview is then 

carried forward to subsequent interviews until it is 

replaced with information collected during the fifth 

interview. However, values for assets and liabilities 

are considered validly blank for records pertaining 

to all but the fifth interview; that is, no attempt is 

made to carry the information backward to records 

pertaining to earlier interviews. Therefore, although 

information on income is at least potentially 

available for each consumer unit in the sample, 

regardless of which particular interview is under 

consideration (even for those who participate 

only once), information on assets and liabilities is 

available only for consumer units participating in 

the fifth interview, thus limiting its contribution to 

the analyses conducted herein.
56 Consumer Expenditure Survey Anthology, 2008

Population share. The data indicate 

that, despite growth in the U.S. civilian 

noninstitutional population, the number 

of young adults (of any marital status, 

living alone or with others) in that population has decreased over time. For example, the number of consumer units 

in the U.S. population increased from 

more than 90.5 million in 1984–85 to 

more than 116.6 million in 2004–05. At 

the same time, the approximate number 

of 21- to 29-year-olds who lived in consumer units of any size decreased from 

37.5 million in 1984–85 to 34.3 million 

in 2004–05. As a result, the number of 

consumer units reporting at least one 

member between the ages of 21 and 29 

fell from nearly 27.7 million (almost 31 

percent) to 25.7 million (22 percent).

Nevertheless, despite the overall decrease in the number of young adults 

over this time span, the estimated number of young single (never-married) 

adults increased from about 17.2 million to 20.3 million. In addition, the 

number of consumer units that included at least one young single increased 

from 14.5 million to 16.7 million, and, 

the values increased dramatically for 

consumer units with at least one young 

adult of any marital status. For example, in 1984–85, more than half (53 

percent) of these consumer units included at least one young single adult, 

with an average of 0.6 per consumer 

unit. In 2004–05, nearly two-thirds (65 

percent) included at least one young 

single adult, with an average of nearly 

0.8 per consumer unit.

Presumably, these findings indicate 

that although, due to demographic 

shifts, there were fewer young adults 

in the population, they were marrying 

later in life in 2004–05 than they were 

in 1984–85.14 If so, whether this trend 

indicates an improvement or a deterioration in that age group’s economic 

status is not clear. On the one hand, the 

decision to wait may reflect the desire 

to complete a degree or establish a career before undertaking such an important commitment as marriage. On the 

other hand, it may be that young persons still want to marry early, but find it 

too difficult financially. At any rate, as 

evidenced by this discussion, the trend 

toward later marriage again underscores the importance of narrowing the 

subject of study to young singles. Attempting to include marriage, and even 

children, into the analysis introduces 

comparisons that are too complex to 

complete meaningfully.

Education. According to table 1, in 

2004–05 young singles reported higher 

levels of educational attainment than 

they did in 1984–85.15 From the earlier survey period to the later one, the 

percentage reporting a high school 

diploma or less dropped substantially 

(from 26 percent to 18 percent), while 

the percentage reporting at least some 

college experience increased notably 

(from 74 percent to 82 percent).16 In 

addition, those enrolled in college full 

time increased their share from a little 

more than 1 in 4 (26 percent) to well 

over 1 in 3 (36 percent). 17

Higher education is usually considered to be a benefit, leading to higher 

pay for professional or skilled workers. 

This is especially true as changes in 

technology and communications dur14 Indeed, the following tabulation from the 

U.S. Census Bureau shows that the median age 

at first marriage has risen by about 2 years from 

1984–85 to 2004–05 for both men (25 to 27 

years) and women (23 to 25 years): 

Year Men Women

1984................ 25.4 23.0

1985................ 25.5 23.3

2004................ 27.4 25.3

2005................ 27.1 25.3

Source: Table MS-2, “Estimated Median 

Age at First Marriage, by Sex: 1890 to the 

Present” (U.S. Census Bureau, Mar. 27, 2007), 

on the Internet at www.census.gov/population/

socdemo/hh-fam/ms2.xls (visited May 21, 2008.) 15 In the 1984–85 data, educational attainment 

is described by the highest grade attended and 

whether or not that grade was completed. For 

the data from this period, college graduates 

are defined as those who reported completing 

the fourth year of college or its equivalent and 

those who reported attending at least 1 year of 

graduate school. Those who reported attending, 

but not completing, 4 years of college are defined 

as having attended college, as are those who 

reported attending for 1 to 3 years, even if they 

reported completing the final year they attended. 

In the 2004–05 data, educational attainment 

is described by degree received, including 

associate’s degree (occupational/vocational or 

academic), bachelor’s degree, master’s degree, 

professional school degree, and doctoral degree. 

For consistency with the 1984–85 data, those who 

reported receiving a bachelor’s degree or higher 

are defined as college graduates in the 2004–05 

data. In addition, those who reported receiving an 

associate’s degree or attending college, but not 

receiving any degree, are defined in the 2004–05 

data as having attended college.

16 Data from the CPS also show increased 

levels of educational attainment for young 

adults. In 1985, 41.4 percent of those aged 20 to 

24 years and 43.7 percent of those aged 25 to 29 

years had completed at least 1 year of college. In 

2005, 55.3 percent of those aged 20 to 24 years 

and 56.8 percent of those aged 25 to 29 years 

had completed at least some college. Note that 

CPS data underwent a change in the definition 

of educational attainment similar to the change 

undergone by CE data. In 1985, data are shown 

by highest level of grade or year of school 

completed. In 2005, for those who attended 

college, data are shown for some college, but 

no degree, and for degree received: Associate’s 

degree, occupational/vocational or academic 

degree; bachelor’s degree; master’s degree; 

professional school degree; and doctoral degree. 

Sources of data are as follows: “Educational 

Attainment in the United States: March 1982 to 

1985 (P20-415) Issued November 1987: Table 2, 

Years of School Completed by Persons 15 Years 

Old and Over, by Single Years of Age, Sex, Race, 

and Spanish Origin: March 1985” (U.S. Census 

Bureau, November 1987), on the Internet at www.

census.gov/population/socdemo/education/p20-

415/tab-02.pdf (visited May 20, 2008); Table 

1, “Educational Attainment of the Population 

15 Years and Over, by Age, Sex, Race, and 

Hispanic Origin: 2005” (U.S. Census Bureau, 

Oct. 26, 2006), on the Internet at www.census.

gov/population/socdemo/education/cps2005/

tab01-01.xls (visited May 20, 2008). 17 Although not measuring an identical 

sample, data from the National Center for 

Education Statistics show that college enrollment 

has increased over time for students graduating 

from high school. In 1984, 55.2 percent of high 

school completers were enrolled in college in 

the October immediately following high school 

completion. By 2005, the figure had increased to 

68.6 percent. Note that these data do not separate 

enrollment rates for full- and part-time students, 

nor do they take age into account—presumably, 

most high school completers in this group are 

younger than 21, and some are older than 29. 

Nevertheless, these data are consistent with the 

findings presented in table 1, namely, that college 

enrollment has increased for young adults over 

time. Source of data is “Student Effort and 

Educational Progress, Table 25-1, Percentage 

of high school completers who were enrolled 

in college the October immediately following 

high school completion, by family income and 

race/ethnicity: 1972–2005” (National Center for 

Education Statistics, 2006), on the Internet at 

nces.ed.gov/programs/coe/2007/section3/table.

asp?tableID=702 (visited May 21, 2008).)
Consumer Expenditure Survey Anthology, 2008 57

ing the intervening years have created 

jobs, such as computer technicians and 

administrators, that may require at least 

some college education for a jobseeker 

to qualify for employment. However, 

at the same time, the Consumer Price 

Index (CPI), which measures changes 

in prices for goods and services that 

urban U.S. consumers purchase, shows 

that the cost of college tuition and fees 

more than quadrupled—rising 365.3 

percent—from January 1984 to December 2005.18 This increase is in contrast to one of 93.1 percent—less than 

double—for all goods and services over 

the same period. Thus, young singles in 

the later period may have been receiving education in larger numbers, but 

they were facing considerably higher 

prices than their historical counterparts. 

In order to benefit from their education, 

at least in a purely financial way, expected wages and salaries or other income would have to rise substantially 

to compensate for the increased cost of 

education.

Housing status. In recent years, there 

has been much discussion regarding 

students moving back into their parents’ homes after college, rather than 

into their own dwellings. Many reasons 

for this development have been posited, and some would suggest that it is 

due to a decrease in economic well-being—for example, because nowadays 

students are unable to afford housing 

on their own. However, others suggest 

that moving back with parents is a benefit to young adults, as it allows them 

to forego rent and spend savings therefrom on consumer goods.19 It could 

also be that young adults who choose 

to live with parents do so in order to 

save for a downpayment on a nicer 

home than they could have afforded if 

they had to pay housing expenses while 

saving.

Whatever the case, the CE data do 

not support this conclusion. To demonstrate, the sample is expanded to include all consumer units consisting of 

at least one never-married adult aged 

21 to 29 years. Expanding the sample 

to take these individuals into account 

ensures that young singles who live 

with their parents, as well as those who 

live with others but who do not pay rent 

or are otherwise not financially independent, are included in the analysis. In 

this new sample, 35 percent of young 

singles were reported to be the child 

of the reference person20 in 2004–05, 

compared with 48 percent in 1984–85. 

In addition, the percent reporting that 

they were the reference person increased from 39 percent in 1984–85 to 

43 percent in 2004–05.21

Another key factor in considering 

well-being is that, despite a sharp increase in home prices in many U.S. cities in recent years, young single adults 

in 2004–05 were more likely to own 

their homes than they were in 1984–

85. The percentage of homeowners 

doubled from 8 percent to 16 percent 

during that time. Usually, homeownership is considered to indicate higher 

economic status than renting. Owning 

a home provides the purchaser with not 

only living quarters, but a valuable asset against which to borrow in case of 

emergency. Of course, if young adults 

in the later period were buying homes 

with riskier, more exotic mortgages that 

were not available in the earlier period, 

that could have led to worse outcomes 

than renting. However, the answer to 

that question is beyond the scope of the 

CE data.

Economic analysis

Macroeconomic factors. One indicator 

of economic conditions is the real value of gross domestic product (GDP). 

GDP measures the value of all goods 

and services produced in an economy.22 

According to this measure, both groups 

look like they were about equally well 

off. Each group lived and worked during a period of economic growth. Real 

GDP expanded both from 1983 to 1985 

(by 11.6 percent) and from 2003 to 

2005 (by 6.8 percent).23 Interestingly, 

the two groups also grew up in similar 

historical contexts as far as economic 

growth is concerned. In this regard, 

real GDP grew at an average annual 

rate of about 3.3 percent from 1964–

65 to 1984–85 and 3.0 percent from 

1984–85 to 2004–05,24 while the population grew at an average annual rate 

of about 1 percent over each of the 

two periods.25 Therefore, each group 

experienced periods in which real GDP 

grew faster than population growth, 

indicating that there were more goods 

and services per person available to 

be consumed or otherwise used in the 

economy.

Though important, the GDP values 

reflect changes for the economy as a 

18 Data are from tables that were created with 

online tools (“Create Customized Tables”), on the 

Internet at www.bls.gov/cpi/home.htm (visited 

Dec. 5, 2000). Data are for “All Urban Consumers 

(Current Series)” and are not seasonally adjusted. 19 See “Echoboomerang—number of adult 

children moving back home—Statistical Data 

Included,” American Demographics, June 1, 

2001, on the Internet at www.findarticles.

com/p/articles/mi_m4021/is_2001_June_1/ai_

76579415 (visited July 17, 2007).

20 See glossary for definition.

21 Data from the U.S. Census Bureau are 

consistent with these findings. Specifically, one 

Census Bureau table shows separately the percentages 

of men and women 18 to 24 years old, presumably 

of any marital status, who are classified as “child 

of householder” in various years. For women 

aged 18 to 24 years, there is not much change 

between 1984 (47 percent) and 2005 (46 percent). 

However, men in that age group exhibit a decline 

from 62 percent to 53 percent. The reason for this 

decline is not clear. One possibility is that young 

men used to live at home during their college 

years and then moved out after graduation, 

whereas now they move to campus for their 

college years and return home after graduation. 

Whatever the cause, a thorough investigation is 

beyond the scope of this article. (Source: Table 

CH-1, “Young Adults Living At Home: 1960 to 

Present” (U.S. Census Bureau, Mar. 27, 2007), 

on the Internet at www.census.gov/population/

socdemo/hh-fam/ad1.xls (visited May 21, 2008). 

22 See the Bureau of Economic Analysis (BEA) 

glossary at bea.gov/bea/glossary/glossary.cfm?key_

word=GDP&letter=G#GDP (visited Jan. 30, 2007). 23 Growth rates for real GDP were derived 

from data listed in the Excel file titled “Currentdollar and ‘real’ GDP” (Bureau of Economic 

Analysis, Oct. 31, 2007), on the Internet at bea.

gov/national/index.htm#gdp (visited Nov. 8, 

2007). 24 Ibid. 25 Percentages are derived from Statistical 

Abstract of the United States: 2007, 126th ed. 

(U.S. Census Bureau, 2006), table 2, “Population: 

1960 to 2005.”
58 Consumer Expenditure Survey Anthology, 2008

whole—not necessarily for the group 

of interest. Therefore, other macroeconomic indicators are also useful to 

examine. One of these is the unemployment rate. This measure describes the 

ratio of persons actively seeking work, 

but unable to find it, to all persons in the 

labor force, which includes the former 

group as well as those who currently 

hold jobs.26 Although the available 

measures are not precise or specific to 

the group in question, there are historical data readily available to describe 

outcomes.27 Using such data enables 

rates for young (never-married) singles 

to be computed for those aged 20 to 24 

years. Data also are available for adults 

aged 25 to 29 years, but no data are 

available for never-married persons in 

this age group.

Both sets of data show a decline 

of nearly 2 percentage points in unemployment rates for young adults in 

each age group. Although they experienced higher rates of unemployment 

than the general population (all adults 

aged 20 years and older) did in each period (about 6.5 percent in 1984–85 and 

4.7 percent in 2004–05), the decline 

in rates for young adults indicates that 

they were better off in the later period 

than the earlier one.28 The following 

tabulation shows unemployment rates 

for young singles and for all young 

adults for 1984–85 and 2004–05:

Young 

singles only

(20 to 24 

years)

All young 

adults

aged 25 to 

29 years

1984

–85

2004

–05

1984

–85

2004

–05

 Total 11.7 9.6 7.8 6.0

Men 12.8 10.6 7.6 5.8

Women 10.2 8.3 8.0 6.2

In addition to these unemployment 

figures, certain related macroeconomic 

factors may have affected economic 

well-being differently for young adults 

in the two periods. If so, these factors 

also support the hypothesis that young 

adults were better off in the second 

period. For example, the first group 

experienced several serious economic 

recessions from the mid-1970s to the 

early 1980s that were marked by historically high levels of unemployment. 

By contrast, there were only two recessions from 1984–85 to 2004–05 (in 

1990–91 and 2001), each with peak 

unemployment rates lower than in the 

earlier downturns.29 Although 1984–

85 and 2004–05 were each periods of 

growth in real GDP, the differences in 

economic outcomes in the preceding 

years may have affected the abilities 

of the young adults to secure jobs or 

savings prior to the years of study or 

may have affected the finances of those 

on whom they would normally rely for 

support, such as parents or other family members.30 These experiences also 

may have affected the group’s expectations about the future and therefore affected its members’ planning.

Microeconomic factors: measures using outlays. In defining the economic 

status of a particular group, many persons would probably immediately think 

of income as the appropriate measure. 

However, outlays are used in this article, for both theoretical and practical 

reasons.

From a theoretical viewpoint, total outlays reflect not only income received today (that is, current income), 

but expectations of future income. For 

26 For definitions of the unemployment rate and 

the labor force, visit www.bls.gov/bls/glossary.

htm (visited Jan. 30, 2007).

27 These data are from computations that were 

made with annual data obtained with the use of 

online tools (“Create Customized Tables”) that 

were found on the Internet at www.bls.gov/cps/

home.htm (visited Jan. 30, 2007). 28 These statistics exclude marginally attached 

workers—those who are available and willing to 

work and who have sought employment in the past 

12 months, but not during the past 4 weeks. (For 

a precise definition of marginally attached workers, 

visit the Web site www.bls.gov/bls/glossary.htm#M

(visited Nov. 6, 2007).) The statistics also exclude 

discouraged workers, a subset of marginally attached 

workers, namely, those who have looked for work 

in the past 12 months, but are not currently looking 

because they believe that there are no jobs available 

for which they qualify. (For a precise definition of 

discouraged workers, visit the Web site www.bls.

gov/bls/glossary.htm#D (visited Nov. 6, 2007).) 

However, no data on either marginally attached 

or discouraged workers were found for any age 

group prior to 1994 when the BLS Web site (www.

bls.gov/cps/home.htm) was last visited (Nov. 6, 

2007).

29 In 1975, the annual unemployment rate for 

the entire civilian noninstitutional population 

(that is, a population not limited to young single 

adults) peaked at 8.5 percent, the highest annual 

unemployment rate between 1970 and 1979. In 

1982, the annual unemployment rate reached 9.7 

percent. By contrast, in 1990–91, annual unemployment rose to only 6.8 percent (in 1991) and 

was 4.7 percent in 2001. These figures were 

obtained with online tools (“Create Customized 

Tables”), on the Internet at www.bls.gov/cps/

home.htm (visited July 17, 2007).

30 This is especially true for the group in 

the earlier period. Many of those aged 21 to 29 

years in 1984 would have been members of the 

labor force in 1981. In July 1981, the seasonally 

adjusted civilian unemployment rate fell to its 

lowest point for that year: 7.2 percent. One year 

later, it reached 9.8 percent. In November and 

December 1982, it peaked at 10.8 percent. The rate 

did not return to its 1981 minimum until almost 3 

years later, in June 1984. (See “Most Requested 

Statistics: Labor Force Statistics from the 

Current Population Survey: Unemployment Rate 

—Civilian Labor Force—LNS14000000,” on the 

Internet at data.bls.gov/cgi-bin/surveymost?ln

(U.S. Bureau of Labor Statistics, no date) (visited 

Nov. 29, 2007).) Although the actual rates are 

different for 20- to 24-year-olds and 25- to 29-

year-olds during these periods, the patterns they 

follow are similar to those for the labor force as 

a whole. (See “Labor Force Statistics from the 

Current Population Survey” (U.S. Bureau of 

Labor Statistics, no date), on the Internet at data.

bls.gov/PDQ/outside.jsp?survey=ln (visited Nov. 

29, 2007), accessible by using “One-screen data 

search” for the database named “Labor Force 

Statistics including the National Unemployment 

Rate (Current Population Survey—CPS)”) at 

www.bls.gov/cps/home.htm#data (visited Sept. 

18, 2008). Seasonally adjusted rates for the 25- 

to 29-year-old group are not available at this 

link, but unadjusted rates are.) For many of the 

younger members of this group (that is, the 20- to 

24-year-olds), who, as shown in the tabulation on 

this page, have higher unemployment rates than 

the older members of the group (that is, the 25- to 

29-year-olds), finding a first job was presumably 

quite difficult; even for those older members 

who held jobs prior to 1981, the situation was 

likely precarious. Undoubtedly, many of them 

lost jobs due to the recession or had difficulty 

changing jobs if they desired to. Those who 

were unemployed not only lacked the ability to 

add to their savings from the wages or salaries 

they earned, but also may have had to use their 

savings to pay for basic goods and services, such 

as food and housing. By contrast, during the 

analogous timeframe for the second group, the 

unemployment rate for the entire civilian labor 

force was lowest in January and February 2001 

(4.2 percent) and eventually peaked in June 2003 

(at 6.3 percent). Although never matching the 

2001 minimum during the second period, the 

rate declined from March 2004 (5.8 percent) 

through December 2005 (4.8 percent). Again, 

these figures support the hypothesis that young 

adults in the later period were economically better 

off than those in the earlier period both during and 

immediately prior to the years under study.

Category
Consumer Expenditure Survey Anthology, 2008 59

example, an applicant seeking a student loan almost certainly knows that 

his or her current savings and income 

are inadequate to cover tuition, but has 

the expectation that future earnings 

(enhanced by the degree sought) will 

more than repay the loan. The sum of 

current income and expected future income is known as permanent income; 

the idea that consumers spend money 

on the basis of their permanent income 

levels is known as the “permanent-income hypothesis.”31 Because outlays 

are hypothesized to be based on permanent income, they are used as a proxy 

thereof in this analysis. 

Among the practical reasons for using outlays rather than (current) income 

with CE data is that, prior to 2004, income before taxes was published only 

for “complete income reporters.” In 

general, complete reporters were those 

for whom at least one member of the 

consumer unit (usually, the reference 

person) reported a value for a major 

source of income, such as wages and 

salaries. However, even complete income reporters did not necessarily provide a full accounting of income from 

all sources. For example, the respondent 

might have provided a value for wage 

and salary income, but not known or 

refused to provide the value for interest 

income. Relying on complete reporters 

only, then, reduced available information in two ways: Not all respondents 

were complete reporters, and not all 

complete reporters provided full income information for analysis. Using 

total outlays as a proxy for permanent 

income solves both problems, because 

values for outlays are either reported 

or, where appropriate, estimated by 

various methods.32

Using outlays to assess economic status. Perhaps the first answer to come to 

mind to the question, “Which group is 

economically better off?” is the answer 

to another question: “Which group has 

more income?” As has already been 

demonstrated, even answering this 

question is not as straightforward as 

it might seem. A simple comparison 

of permanent incomes would make it 

seem as if the young adults in 2004–05 

were better off than those in 1984–85: 

Total annualized outlays for the average young single adult studied rose 

from $13,145 to $22,744 over the period between the two surveys, an increase of 73 percent! However, in the 

United States, total annualized outlays 

would probably be observed to increase 

during any 20-year period since World 

War II, simply because of inflation, 

which is defined as a rise in prices for 

goods and services when other factors 

(such as size and quality) remain essentially constant. Given this situation, it 

is more accurate to compare real outlays (those adjusted for price change 

with the use of the CPI for all goods 

and services) than nominal outlays (unadjusted figures, as cited earlier). The 

2-year average of the annual CPI for all 

goods and services rose nearly 82 percent from its base in 1984–85 (105.8) 

to its value in 2004–05 (192.1). That 

means that the $13,145 spent in 1984–

85 would purchase about the same 

amount of goods and services as would 

$23,867 in 2004–05. By this measure, 

young adults in 2004–05 were worse

off than their earlier counterparts, experiencing a decrease of almost 5 percent ($23,867, compared with $22,744) 

in their real outlays. However, caution 

must be used in interpreting this finding, because the difference in means is 

not statistically significant.

Of course, the preceding finding 

relies on certain assumptions, namely, 

that the same goods and services are 

purchased in each year by each group, 

that qualities remain unchanged, and 

so forth. Even so, by this measure, 

young adults in the later period appear 

to be worse off than they were in the 

earlier period. But perhaps the same is 

true of all other consumers. If so, is the 

decrease in purchasing power experienced by young singles larger, smaller, 

or about the same as that experienced 

by others? In other words, how are 

young adults faring compared with the 

rest of the population?

Comparing the changes in real total outlays from 1984–85 to 2004–05 

for young singles with those of other 

single, never-married adults who also 

were surveyed during those periods is 

one way to attempt to answer this question. Before proceeding, it is useful to 

remove outlays for food at home from 

both groups, because of the change in 

questionnaire occurring in 1988. As 

noted earlier, young, single, nevermarried adults exhibit a large change 

(almost 45 percent) in food at home 

expenditures from 1987 to 1988 that 

is inconsistent with annual changes in 

these expenditures for this group in 

other years. Other single, never-married adults exhibit a similarly large 

(more than 38 percent) and inconsistent change in these expenditures. 

However, the factors required to adjust 

their expenditures are almost certainly 

different from those required for young 

single adults. Performing this adjustment would therefore add one more 

element of uncertainty to the comparison: If differences are found in the rates 

of change of total outlays for these 

groups, how much will be due to actual 

differences in expenditure patterns and 

how much to qualitative differences in 

the estimated factor for adjustment of 

food-at-home expenditures for each 

group? Therefore, for simplicity, outlays less food at home are compared.

For young singles, real total outlays less food at home fell 3.8 percent 

over time, from $21,613 in 1984–85 to 

$20,795 in 2004–05. For other singles, 

real total outlays less food at home 

increased 6.1 percent over the same 

period, from $24,415 to $25,906. Although this finding appears to indicate 

that young singles are falling behind 

in permanent income while others are 

gaining, it is not conclusive. First, neither change is statistically significant, 

indicating that the differences in means 

observed for each group across time 

may be due to chance alone. Second, 

31 Milton Friedman, A Theory of the Consumption Function (Princeton, NJ, Princeton 

University Press for National Bureau of Economic 

Research, 1957); on the Internet at www.nber.

org/books/frie 57-1 (visited Aug. 6, 2008). 32 Starting with the publication of data 

collected in 2004, multiple imputation began to 

be used to fill in blanks for income. It will be 

interesting to use the data obtained therefrom for 

future cross-generational analyses.
60 Consumer Expenditure Survey Anthology, 2008

the increase in outlays for other singles 

may be due to changing demographics within this group. For example, 

the proportion of singles aged 35 to 

54 years increased from 39 percent in 

1984–85 to 56 percent in 2004–05. In 

each year during the period examined, 

never-married adults in both age groups 

had the highest levels of average total 

outlays. Therefore, even if average real 

total outlays for singles aged 35 to 54 

years have not changed over time, the 

fact that there are more members of 

that group in the sample will increase 

the mean for the entire sample of other 

singles.

Using shares to measure 

outcomes

Another useful tool for comparing 

the economic well-being of different 

groups is derived from a finding known 

as Engel’s proposition. In 1857, Prussian economist Ernst Engel reported 

that, as income increases, the share of 

total expenditures allocated to food decreases.33 The assumption in the analysis presented in this article is that the 

smaller the share of total expenditures 

a consumer allocates to expenditures 

for basic needs like food, the larger is 

the share available to allocate to other 

items. Therefore, understanding the 

allocation of shares of total outlays 

provides insight into the economic 

well-being of the groups studied. (For 

more information on analyzing shares, 

including caveats associated with 

this type of analysis, see “Analyzing 

shares” in the technical notes.) Table 2 

shows shares of total outlays that young 

adults allocated to selected goods and 

services in 1984–85 and 2004–05.

Several findings are of note. First, 

the share of outlays allocated to food 

has declined over time—by more than 2 

percentage points, in fact. Taken alone, 

this may indicate an increase in economic well-being. However, food outlays can be decomposed into two parts: 

outlays for food at home (for example, 

food purchased at grocery stores) and 

outlays for food away from home (for 

instance, food purchased at restaurants). Analyzing these components 

separately is useful, because they represent two different types of spending. 

Because of the convenience, change 

in ambience, and typically higher cost 

associated with meals at restaurants, 

these meals are considered to be a treat 

for many consumers; therefore, it is 

reasonable to suppose that an increased 

share for food away from home indicates an increase in well-being, while 

an increased share for food at home indicates a decrease in well-being. Over 

the period examined, the shares for 

food at home and for food away from 

home both decreased. Each of these 

changes is statistically significant, as 

are many of the other changes in share 

shown in the table. However, the directions of the changes in the components 

of food spending are contradictory, one 

indicating an increase, and the other 

a decrease, in economic well-being. 

Resolving this apparently paradoxical 

outcome is the topic of the next section. (See also “Analyzing shares” in 

the technical notes.) 

Other measures using outlays

Although analyzing shares of outlays 

provides an easy, intuitive way to compare economic statuses, it has its limitations. In historical comparisons, one 

major limitation is, once again, price 

change. When outlays within a certain 

period are compared, it is usually assumed that all groups face roughly the 

same prices. However, across different 

periods, prices for some goods and services may have risen, perhaps rapidly, 

while others stayed the same or even 

dropped. When prices are not changing 

at a uniform rate, the shares can be affected in ways that do not accurately 

reflect the underlying idea of analysis 

using a framework based on Engel’s 

proposition. (See “Analyzing shares” 

in the technical notes.) Therefore, comparing real (price-adjusted), rather than 

nominal (contemporaneous), outlays 

for specific items is a useful way of 

seeing whether a decrease in share is 

due to less consumption or a change in 

prices.

The CPI for food at home rose more 

than 81 percent from 1984–85 (103.6) 

to 2004–05 (188.0). Therefore, the realdollar expenditure for food at home in 

1984–85 was about $2,252, which is 

more than the $1,950 spent in 2004–

05. Similarly, the CPI for food away 

from home rose about 79 percent from 

1984–85 (106.3) to 2004–05 (190.5). 

Therefore, the real-dollar expenditure 

for food away from home in 1984–85 

was about $1,437, which is more than 

the $1,073 spent in 2004–05. In each 

case, the real-dollar expenditure in 

1984–85 is statistically significantly 

different from the value observed in 

2004–05. Consequently, these findings 

are consistent with the Engel analysis, 

which indicates a higher economic 

well-being in the second period than in 

the first due to a decrease in expenditures for food at home, but a lower economic well-being in the second period 

due to a decrease in expenditures for 

food away from home.

Further analysis reveals another 

interesting finding: Although the percentage of respondents reporting expenditures for food at home remained 

unchanged (almost 97 percent in each 

period), the percentage reporting expenditures for food away from home 

fell nearly 5 percentage points (from 

90.8 percent to 86.3 percent). This finding supports a diminution in economic 

well-being, given the smaller percentage of young singles who report expenditures for food away from home.

However supportive, by themselves 

these numbers do not conclusively indicate that the second group was worse 

off than the first. For example, an increased variety of frozen and prepared 

foods in the second period may mean 

that consumers can enjoy, at home, the 

convenience of food away from home 

at lower, grocery store prices. In addition, the consumer can make one trip 

to the grocery store each week and 

purchase all meals at once, rather than 

visiting a fast-food establishment every 

day, thus saving time. If all this is true, 

33 Louis Phlips, Applied Consumption Analysis

(Amsterdam, Elsevier Science Publishers B.V., 

rev. ed. 1983; distributed in the U.S. and Canada 

by Elsevier Science Publishing Company, Inc., 

of New York, NY), p. 103.
Consumer Expenditure Survey Anthology, 2008 61

then the decreased share for food away 

from home may indicate an increase in 

well-being. Yet, if it is true, it is inconsistent with the fact that real expenditures for food at home fell between the 

two periods; that is, given that the price 

index for food at home rose between 

the two periods, purchasing more food 

at home and less food away from home 

should lead to higher, not lower, realdollar expenditures for food at home in 

the second period. Still, this outcome 

is not implausible. The price index for 

food at home is based on what all consumers purchase, and not solely on what 

young singles purchase. If young singles are purchasing more food at home, 

and the prices of the foods they tend to 

purchase have increased less than the 

prices of other types of food at home, 

then the preceding findings are consistent with the hypothesis described here 

(that is, that young singles are substituting lower priced foods from grocery or 

other stores for food from restaurants). 

In fact, the CPI for frozen and freezedried prepared foods increased less 

than 48 percent (from 103.8 to 153.2) 

from January 1984 to December 2005, 

substantially less than the 81-percent 

increase in prices already reported for 

food at home in general.34 However, to 

investigate this hypothesis fully requires 

both further investigation into price increases for specific foods and an examination of data from the CE’s Diary 

component, or Diary Survey, which, unlike the Interview Survey, is designed to 

collect detailed information on food expenditures. Such an investigation, while 

Table 2. Average annualized outlays and shares, young single adults, 1984–85 and 2004–05

 

Average annualized outlay 

1984–85

Total outlays1 ...................................................................... $13,145 $23,866 $22,744 100.0 100.0 ...

Food, total less trips1..................................................... 2,043 3,710 3,022 15.5 13.3 2–4.49

 Food at home, less trips1 ......................................... 1,241 2,254 1,950 9.4 8.6 2–2.55

 Food away from home, less trips............................. 802 1,456 1,073 6.1 4.7 2–4.75

Shelter and utilities........................................................ 3,113 5,652 7,249 23.7 31.9 29.88

 Owned dwellings...................................................... 353 641 1,326 2.7 5.8 24.53

 Rented dwellings ..................................................... 2,039 3,702 4,602 15.5 20.2 25.99

 Utilities ..................................................................... 722 1,312 1,322 5.5 5.8 1.21

Apparel and services..................................................... 821 1,490 757 6.2 3.3 2–8.84

Transportation ............................................................... 2,320 4,213 3,494 17.7 15.4 2–2.44

 Cars and trucks (new).............................................. 606 1,100 457 4.6 2.0 2–4.74

 Cars and trucks (used) ............................................ 462 840 853 3.5 3.7 .32

 Other vehicles.......................................................... 31 57 33 .2 .1 –1.10

 Gasoline and motor oil............................................. 583 280 969 4.4 4.3 –.86

 Maintenance and repair........................................... 304 1,058 398 2.3 1.7 2

–2.37

 Vehicle insurance..................................................... 211 552 487 1.6 2.1 23.40

 Public transportation................................................ 49 383 76 .4 .3 -.62

 Vehicle rental ........................................................... 74 89 223 .6 1.0 23.10

Health care.................................................................... 256 466 478 2.0 2.1 .55

Entertainment................................................................ 703 1,277 1,129 5.4 5.0 –.79

Travel and trips ............................................................. 631 1,146 668 4.8 2.9 2–5.47

Education ...................................................................... 558 1,012 1,760 4.2 7.7 22.55

All other outlays1 ........................................................... 2,699 4,900 4,186 20.5 18.4 2–2.45

Nominal 

dollars

Real 

2004–05 

dollars

2004–05

nominal

(real) 

dollars

1984–85 2004–05

t-statistic

Outlay category

Share (percent)

1 Item or subcomponent computed with the use of adjusted values 

for food at home in 1984–85; see “Adjusting expenditures for food at 

home” and “Computing adjustment factors for food at home,” in the 

technical notes, for details. 2

 Indicates statistically significant difference in shares when 

periods are compared.

NOTE: To convert to real 2004–05 dollars, nominal 1984–85 

dollars are multiplied by 192.1 (the average CPI for 2004–05) and 

divided by 105.8 (the average CPI for 1984–85). Components may 

not add to aggregate values due to rounding.

34 To better understand this chain of reasoning, 

suppose that young singles purchase only frozen 

and freeze-dried prepared foods in both periods, 

while other consumers purchase different foods. 

Then adjusting food-at-home expenditures 

for young singles will overestimate their real 

expenditures for food at home purchased in 

1984–85. If the overestimate is large enough, 

it will make it appear that young singles have 

lower expenditures for food at home in 2004–05 

than they did in 1984–85. Now, as seen from the 

values presented in table 2, real expenditures 

for food at home decrease for young singles 

when the CPI for all food at home is used to 

adjust these expenditures. But if young single 

consumers really did purchase only frozen and 

freeze-dried prepared foods in each period, 

then the $1,241 nominal expenditure shown in 

that table should be adjusted to $1,832 [1,241 

× (153.2/103.8)]. Then, because $1,832 is less 

than the value reported in 2004–05 ($1,950), it 

follows that young singles actually purchased 

more food at home in the second period than the 

first, and they may have done so because they 

purchased less food away from home, just as the 

hypothesis purports.
62 Consumer Expenditure Survey Anthology, 2008

interesting for future work, is beyond 

the scope of this study.

Regardless, expenditures on other 

goods and services are also useful to examine. First, consider the case of shelter 

and utilities.35 The share allocated to 

these outlays has increased substantially, 

from less than one-fourth to nearly onethird of total outlays. Again, it is possible that housing attributes account for 

this change. Now, if outlays for shelter 

and utilities have risen because young 

singles are purchasing or renting larger 

homes, the change in share may be due to 

an increase in their well-being. However, evidence to suggest such purchases is 

limited. For example, only the increase 

in number of bathrooms (see table 1) is 

statistically significant for both owners 

and renters. The changes in the numbers 

of bedrooms and half baths for renters, 

while statistically significant, are not 

necessarily economically significant. 

(For example, the number of bedrooms 

for those who rent increased from less 

than 1.8 to less than 2.1.) Neither homeowners nor renters experienced a statistically significant change in “rooms, 

other than bathrooms.” Although other 

factors, not measured in the CE, also 

affect these outlays—for example, the 

quality of the neighborhood in which 

the housing exists—the substantial 

change in these shares, coupled with 

the considerable increase in housing 

prices noted in recent years, may be 

evidence of a diminution of well-being for this group, or at least that the 

increase in well-being from slightly 

larger dwellings is more than offset 

by the increase in outlays. However, 

these data do not tell the full story. The 

numbers of rooms, bedrooms, bathrooms, and half baths are all described 

for the consumer unit. However, many 

of the consumer units sampled actually reside in the same household. It is 

quite possible that numbers of rooms 

per consumer unit have not changed, 

but that the number of households in 

which these consumer units reside has 

changed; if the number has increased, 

it could indicate an increase in well-being. To illustrate, consider two young 

singles sharing a one-bedroom apartment (that is, two separate consumer 

units sharing one household). Suppose 

that each roommate is interviewed and 

reports that the apartment has one bedroom. Then the data would show two 

separate consumer units, each with one 

bedroom. Now suppose that one roommate moves into a new apartment, also 

containing one bedroom. Then, assuming that each of the former roommates 

still lives alone, the data still show two 

separate consumer units with one bedroom. Yet, if they prefer to live alone, 

the constant number of rooms per consumer unit would not reflect the hypothetical increase in their well-being. 

Fortunately, the data provide information that allows the analyst to distinguish these two cases. That is, it is possible to count the number of consumer 

units per household to see whether two 

roommates are sharing one household 

with one bedroom or two young singles live alone in separate households, 

each of which contains one bedroom. 

Analyzed in this way, the results tell a 

different story: First, in 1984–85, more 

than one-third (nearly 36 percent) of the 

Table 3. Housing attributes of young singles, households including at least one young single person, 1984–85 and 2004–05

Household includes only young single person Household includes at least one other person

1984–85 2004–05 t-statistic1 1984–85 2004–05 t-statistic1

 1 Based on test of proportions when percentages are compared 

and difference in means when number of rooms are compared. (See 

“Measuring statistical significance: types and computations of tstatistics,” in the technical notes, for details.) 2

 Results are not computed for multiple-member households. The 

problem is that, within the household, there can be a mix of owners 

and renters. For example, the homeowner may rent a room or 

part of the house to at least one young single person. In addition, 

in this case the consumer unit that owns the home may be of any 

composition. That is, the owner may be a young, single person, as 

defined throughout this study, or may be of a different age or marital 

status. 3

 These households include at least one young single person, as 

defined in this study, who constitutes a unique consumer unit within 

Sample size.......................................... 1,252 1,401 ... 701 410 ...

Percent of households with at 

 least one young single person........... 64.1 77.4 8.91 35.9 22.6 –8.91

Percent owners..................................... 10.5 21.1 7.42 (

2

) (

2

) ...

Per-capita number of: 3

 Rooms, other than bedrooms........... 3.7 3.9 4.62 2.0 2.0 1.68

 Bedrooms .......................................... 1.4 1.7 8.31 .9 1.1 5.99

 Bathrooms ......................................... 1.1 1.2 10.96 .5 .6 4.64

 Half baths .......................................... .1 .1 3.44 (

4

) .1 1.60

Characteristic

the household. However, the remaining members may constitute 

any number of consumer units from one to the total number of other 

members of the household. For example, if a husband and wife with 

two children rent a room to a young single, the household size is five, 

but the number of consumer units is two. In this case, the per-capita 

number of rooms is still computed to be the total number of rooms 

in the household divided by the household size, whether or not the 

renter has full use of other rooms in the house. 4 Less than .05.

 NOTE: Values presented are for the sample and are not 

weighted to reflect the population. Weights computed in the survey 

are designed for use with consumer units, not households.

35 Because rent includes utilities in some 

cases, comparing only expenditures for rent 

with outlays for a mortgage does not provide an 

accurate comparison of basic housing costs.

36 The other person or persons could be 

roommates, the landlord, or anyone else not 

related by blood, marriage, or some other legal 

arrangement and from whom the young single is 

financially independent. If any of these conditions 

is violated, the young single would no longer 

constitute a single-member consumer unit.
Consumer Expenditure Survey Anthology, 2008 63

young singles studied lived in a household with at least one other person;36 

then, in 2004–05, less than one-fourth 

(under 23 percent) did. (See table 3.)

Of course, some caution must be 

used in interpreting these numbers. 

The data are not edited for consistency, 

for example. Therefore, it is possible 

that, due to differences in the way respondents interpret their situations (for 

instance, one housemate reports the 

second bedroom, which is being used 

as a den, as a room other than a bedroom, while the other reports it as a 

second bedroom), data entry error, or 

another reason, different numbers of 

rooms or bedrooms are reported for the 

same household within or across interviews. Also, some of the information 

is missing due to nonresponse or some 

other reason. But assuming that these 

factors are random each year, the data 

obtained provide useful information to 

help measure changes in numbers of 

rooms available to young single adults. 

Analyzed in this way, the data show 

that, regardless of household composition—at least, whether one or more 

than one person lives in the household—the number of rooms per capita 

has increased over time. Although the 

increases are small, they are statistically significant in most cases. Especially because more young singles are 

the sole occupants of their households, 

it is more difficult to argue that the increased expenditures for housing noted 

at the consumer-unit level clearly indicate a diminution of well-being. Those 

who are the sole occupants of their 

households may value privacy enough 

to pay the extra dollars, and if they can 

afford to do so in larger numbers in the 

later period than in the earlier period, 

then they are arguably better off in the 

later period, or at least any diminution 

in well-being due to higher housing 

prices is offset at least partially by an 

increase in privacy or in the number of 

bedrooms and bathrooms per capita.

In contrast to housing expenditures, 

which are necessary for at least a minimal level of economic well-being, travel expenditures are purely discretionary for most consumers. Therefore, an 

increase in the frequency of purchasing 

goods or services related to travel or in 

dollars allocated toward trips presumably indicates an increase in economic 

well-being. However, for young singles, the share of total outlays allocated 

to travel has fallen substantially, from 5 

percent to 3 percent. At the same time, 

the percentage of respondents reporting travel expenditures has decreased 

sharply, from more than half (53 percent) to more than one-third (35 percent). The percentage reporting many 

of the components of travel expenditures (such as food, lodging, transportation, and entertainment on trips) 

also has declined. Therefore, the drop 

in share is not the result of decreased 

prices, nor is it likely that members of 

this group are making different lodging 

arrangements than before (for example, 

staying with friends or relatives instead 

of in hotels). Young singles simply appear to be traveling less. However, they 

are not unique in this regard: The percentage reporting travel expenditures 

(including the components previously 

described) has decreased for all other 

consumer units as well during the period 

examined. (See chart 1.) Accordingly, 

rather than decreased prices, increased 

prices may play a role.37 In addition, 

these changes in travel expenditures 

may be explicable by changes in technology. For example, the percentage 

reporting travel expenditures decreased 

as e-mail, cellular telephones, and instant messaging became more available. Therefore, consumers in general 

(and young singles specifically) may 

be substituting new forms of communication for travel, which would indicate 

an increase in their economic well-being. That is, young singles in the later 

period enjoy choices not available to 

those in the earlier period.38 However, 

there is still no perfect substitute for 

the personal visit. From this perspective, the availability of new technology 

mitigates the decrease in well-being resulting from less frequent travel, whatever its cause (for example, increased 

prices), but does not necessarily negate 

(or outweigh) the decrease entirely.

Of particular interest is the change in 

shares for educational expenses, which 

nearly doubled over the period examined. This change is challenging to interpret. The proportion of young single 

adults enrolled in college full time rose 

sharply—from just above one-fourth 

(26 percent) to more than one-third (36 

percent); the proportion of part-time 

students remained unchanged at about 

7 percent, while the proportion not 

enrolled (including those not eligible) 

declined almost 11 percentage points. 

(See table 1.) However, those reporting educational expenditures actually 

dropped slightly—from 26 percent to 

24 percent. Of course, not all of the 37 The CPIs for at least three categories of goods 

and services directly related to travel are readily 

available on the Internet (data.bls.gov/PDQ/outside.

jsp?survey=cu (visited Dec. 5, 2007), accessible 

by using “One-screen data search” for the database 

named “All Urban Consumers (Current Series) 

(Consumer Price Index—CPI)” at www.bls.gov/

cpi/home.htm#data (visited Sept. 18, 2008)). In 

each case, the increase in the CPI for these categories 

is higher than the increase in the CPI for all goods 

and services from 1984 to 2005 (88 percent). The 

categories are “other lodging away from home, 

including hotels and motels” (157 percent); “gasoline 

(all types)” (99 percent); and “airline fare” (243 

percent). Changes in annual indexes are compared 

in this case, instead of changes from January 1984 

to December 2005, in order to reduce the effects of 

intrayear volatility. Prices for each of these travel 

expenditure categories presumably vary by season 

if not by month, so comparing values for different 

months across years, rather than comparing average 

annual values, may either mitigate or exacerbate 

differences in price changes computed. In addition, 

seasonally adjusted indexes are not available for 

airline fares in years prior to 1989.

38 Evidence supporting the hypothesis that 

consumers substitute new forms of communication 

for travel is seen in the CE results. The trend line 

for the percentage of those reporting total travel 

expenditures is much steeper downward from 

1997 to 2005 than it is from 1984 to 1996, a 

pivotal year that coincides with a period of rapid 

increase in usage of these technologies. For 

example, the U.S. Census Bureau reports that 

in 1997 less than three-eighths (36.6 percent) of 

all households owned a computer and that about 

half of these households (18.0 percent of all 

households) had Internet access. By 2003, nearly 

five-eighths (61.8 percent) of all households 

owned a computer and nearly eight-ninths of 

these households (54.7 percent of all households) 

had Internet access. See Jennifer Cheeseman 

Day, Alex Janus, and Jessica Davis, “Computer 

and Internet Use in the United States: 2003,” 

Current Population Reports, P23-208, October 

2005, pp. 1–14, especially Figure 1, p. 1, on the 

Internet at www.census.gov/population/www/

socdemo/computer.html, item 1, CPS, October 

2003, “Report” (visited Dec. 5, 2007). 
64 Consumer Expenditure Survey Anthology, 2008

expenditures included in the CE definition of educational expenditures are 

for college tuition; however, the tuition 

expenditure accounts for a substantial 

portion.39 Although many of these students may be receiving scholarships, 

participating in deferred payment 

plans, or working for payment of tuition instead of working for other pay, 

or may be children of parents who pay 

their tuition directly to the school, it 

is likely that those who do make payments were paying much more for their 

education in 2004–05 than those who 

did in 1984–85, even after adjustment 

for general price changes. In support of 

this claim, recall the increase in college 

tuition and fees described earlier. The 

fact that more young adults are attending college either because of a greater 

opportunity to do so or because of the 

changing nature of the general economy probably reflects an improvement 

in economic well-being. However, the 

fact that the price of going to college 

has escalated so much means that the 

expected gains from a college education would have to rise substantially for 

current students to “break even” with 

their older counterparts.40 

Demographic differences among 

young singles

So far, the analyses presented have 

focused on young single adults as a 

group. However, as noted earlier, there 

are demographic differences within 

this segment of the population that 

either may account for changes in the 

group overall or may be obscured when 

the group is examined as a whole. For 

example, an increase in total outlays 

may be observed because one group 

has “caught up” to another or because 

both subgroups have experienced an 

increase in total outlays but one group 

has experienced a larger increase than 

the other. To examine these outcomes, 

total outlays for selected demographic 

groups within the young singles sample 

are compared.

Table 4 shows that, consistent with 

the larger population of young single 

adults, no subgroup tested experienced 

a statistically significant change (increase or decrease) in real total outlays. However, within each subgroup, 

substantial differences appear in each 

period observed. For example, total 

outlays for single men substantially 

exceed total outlays for single women 

in each period. Although the gap is 

larger in 1984–85 (18.5 percent) than 

in 2004–05 (12.6 percent), the decrease 

is due to a decrease in total outlays for 

men, rather than an increase in outlays 

40 The increase in education expenditures 

presumably also affects the allocation of shares 

for those who pay them. That is, given the same 

amount of funds available for spending, the 

person who allocates more to education has less 

to allocate to food, housing, and all other goods 

and services. However, separating out those 

who make these expenditures from those who 

do not and comparing the differences in their 

share allocations, both within and across various 

periods, is beyond the scope of this discussion.

39 For all consumer units, college tuition 

accounted for 58 percent of educational expenditures 

in 1984–85 and 64 percent in 2004–05.

60

50

40

30

20

10

0

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 

Chart 1. Percent reporting expenditures for travel, 1984–2005

Percent

All other consumer units

Young singles
Consumer Expenditure Survey Anthology, 2008 65

Table 4. Real total outlays, by demographic group, 1984–85 and 2004–05

 

All young single adults........................... $23,866 663.03 $22,744 531.85 –4.7 –1.32

Men .................................................. 25,585 844.92 23,838 722.68 –6.8 –1.57

Women............................................. 21,536 717.51 21,151 637.39 –1.8 –.40

Non-Hispanic:

White........................................... 24,122 557.19 22,977 638.19 –4.7 –1.35

Black........................................... 23,416 1,975.59 21,644 1,456.91 –7.6 –.72

Hispanic ........................................... 18,508 4,047.05 21,585 1,400.21 16.6 .72

High school or less........................... 21,617 1,126.33 19,316 877.42 –10.6 –1.61

Some college ................................... 21,283 808.08 19,846 765.83 –6.8 –1.29

College graduate.............................. 28,685 1,209.18 27,962 848.94 –2.5 –.49

Demographic characteristic

Real total 

outlays,

1984–85

Standard error

Real total 

outlays,

2004–05

Standard error

Percent change 

in real total 

outlays

t-statistic 

(change in 

mean of real 

total outlays)

for women. Nevertheless, the decrease 

is not statistically significant and therefore reveals nothing about the change 

in relative well-being between young 

single men and women in this study.

Similarly, Hispanics appear to have 

the smallest total outlays, on average, 

in each period, but regardless of the 

interval studied, the difference in average total outlays is not statistically significant when Hispanics are compared 

with either group of non-Hispanics. 

The results—both within 1984–85 and 

across the time span examined—are 

more difficult to interpret, though, because of the relatively large variance of 

total outlays for Hispanics in the earlier 

period. At the same time, for non-Hispanics, the gap in real total outlays between Whites and Blacks nearly doubled from 1984–85 ($706) to 2004–05 

($1,333). In this case, both groups experienced decreases in average real total outlays, but the decrease for young 

Black singles ($1,772) was larger than 

the decrease for young White singles 

($1,145). Nonetheless, neither the 

difference within, nor the difference 

across, periods was statistically significant for either of these groups.

By contrast, there are clear differences by education level: Those with 

a college degree have significantly—in 

both economic and statistical terms—

higher total outlays in each period than 

those who have not earned a college degree. However, there are no statistically 

significant differences between the two 

groups of non-college graduates (that 

is, those with a high school diploma 

or less and those who attended, but did 

not graduate from, college).

Conclusion

Both demographic and spending patterns changed for young, never-married adults from 1984–85 to 2004–05. 

Whether these changes indicate an increase or decrease in economic status 

is unclear. By some measures, such as 

the rate of economic growth and unemployment rates, the more recent group 

is at least as well off—if not better 

off—than the earlier group. The more 

recent group also enjoys higher educational attainment and higher rates of 

homeownership, both of which are generally considered positive attributes.

However, other results indicate that 

there has been little discernible change 

over time. When average real total 

outlays for subgroups of young single 

adults, such as men and women, are 

compared, differences across groups 

within each period are apparent, but 

changes within groups across time are 

not generally observed.41 Although it 

may be interesting to perform Engel or 

some other, similar analysis on the demographic subgroups, this task is left 

for future work.

Finally, the evidence that young 

singles are worse off today is inconclusive. For example, young singles experienced a decrease in real total outlays 

from 1984–85 to 2004–05, while other 

singles experienced an increase during 

that time. However, neither change was 

statistically significant. In addition, 

young singles today allocate smaller 

shares of total outlays to food away 

from home and to travel, and larger 

shares to food at home and to housing. 

Each of these changes would appear 

to indicate a diminution in economic 

well-being, yet they are consistent with 

increased economic well-being as described earlier: The increased share for 

food at home may be due to the greater 

availability of convenience foods, allowing young singles to save time and 

money by “stocking up” rather than 

frequenting restaurants; and the housing share may have increased because 

more young singles are living alone, 

presumably by choice, and also because they are more likely to be homeowners.

Taken together, the results described 

in this study do not indicate that young 

singles were clearly better off in the 

second period than the first, a finding 

that is consistent with the belief among 

young adults that it is harder for them 

to gain economically than it was for 

41 These findings are confirmed by regression 

analysis, which estimates changes in real total 

outlays over time when demographic differences 

are held constant. An explanation of the technique, 

together with some results obtained, is included in 

the complete version of this article in the December 

2008 issue of the Monthly Labor Review.
66 Consumer Expenditure Survey Anthology, 2008

well-being for young adults would be 

useful in order to provide a fuller perspective on what changes have occurred 

and when they did so. In the meantime, 

it is valuable to continue to monitor expenditure patterns for young singles to 

better understand the challenges they 

face and how such challenges may affect them and others in the future.

their parents.42 Still, the results do not 

provide strong evidence that young 

singles are worse off than their predecessors, as has been found in previous 

work.43 Given that previous work compared young adults in the mid-1990s 

with those in the mid-1980s and found 

a decrease in economic well-being, the 

current results may indicate that the 

fortunes of young adults are improving 

after a period of decline. This finding 

suggests that future work examining 

trends in outlays and other measures of 

42 Crowley, “Generation X Speaks Out,” p. 

2; based on interviews conducted in 2000–01 of 

young adults born from 1968 to 1979. 43 Paulin and Riordon, “Making it on their 

own,” especially p. 18.
Consumer Expenditure Survey Anthology, 2008 67

Technical notes 

Analyzing shares

In analyzing shares, the allocations of 

total outlays for two different groups 

are compared to find out which group 

is better off. To understand this idea, 

consider two single persons, each of 

whom purchases the same amount of 

food each week for $20. Suppose the 

first person has the lower income and 

spends $100 per week on all purchases; the second person spends $200 per 

week. The share of expenditures allocated to food is 20 percent for the first 

person, but only 10 percent for the second, even though the same amount of 

food is purchased. Even if the second 

person buys more, or higher quality, 

food for $30, the share increases only 

to 15 percent. In each case, the second 

person has a larger portion of spendable dollars left over to purchase goods 

and services other than food than does 

the first person; therefore, the second person is considered to be better off.

Although analyzing shares is particularly useful for comparing groups 

within the same period, there are some 

caveats to consider in analyzing changes in shares over time. For example, important information can be masked by 

price changes. To see this effect, consider a person who enjoys apples as an 

occasional snack and budgets $10 per 

month for their purchase. If the price 

of apples is $1 per pound, this person 

can afford 10 pounds per month. If the 

price rises to $2 per pound, the person 

can afford only 5 pounds per month. 

If no other prices change, and the 

person’s expenditure pattern remains 

the same in all other respects, then the 

share of total outlays allocated to apple 

purchases remains the same each period, yet the person is enjoying fewer 

pounds of apples. 

If, then, the change in the price of 

apples is known, expenditures can be 

adjusted, and it becomes clear that the 

person is purchasing fewer pounds of 

apples. In the current example, the price 

of apples has doubled. Therefore, if the 

person bought the apples in the first period at the price of the second period, 

then the expenditure in the first period 

would be double the value observed. 

(That is, 10 pounds of apples purchased 

at the price of the second period would 

cost $20, not $10.) Because the priceadjusted outlay for the first period ($20) 

is larger than the observed outlay for 

the second one ($10), it is clear that the 

number of pounds of apples purchased 

has declined in the second period. This 

relationship (higher price-adjusted expenditures mean a larger quantity purchased) holds even when the actual 

number of pounds of apples (or quantity 

of other goods and services) purchased 

is unknown, as it is for the values shown 

in table 2 in the text.1 

In addition, the allocation of total 

outlays changes with tastes and preferences, which in turn can change over 

time for individuals or groups. In cases 

such as these, in which both kinds of 

change occur, changes in shares are not 

so easy to interpret. For example, as 

discussed in the text, the share for food 

away from home has been decreasing 

over time, while the share allocated 

to food at home has been increasing. 

1

 In general, the Consumer Expenditure 

Survey (CE) collects information on expenditures 

made, but not on amounts or quantities purchased. 

For example, a person may report having spent 

$20 for movie tickets in the past 3 months, but 

data on whether that person went to the movies 

twice and spent $10 each time or went 10 times 

to a discount movie theater are not collected.
68 Consumer Expenditure Survey Anthology, 2008

Assuming that food away from home 

is preferred to food at home, this outcome reflects a decrease in well-being. 

However, if young adults in the second 

period have a higher preference for 

education than they did in the first period, they may forego some of the expenditures for food away from home in 

order to purchase education, even if the 

costs of education remain stable. In that 

case, if the increase in well-being due 

to purchasing more education is larger 

than the decrease due to purchasing 

less food away from home, then young 

adults in the second period are better 

off than they would be if they did not 

make such a tradeoff.

Finally, changes in technology and 

in the availability of products can influence the allocation of total outlays. 

As noted in the text, the availability of 

new types of food at home may lead to 

changes in purchases such that the increased share for food at home and decreased share for food away from home 

reflect an increase in well-being. Similarly, changes in technology or in the 

availability of products may lead less 

directly to changes in certain shares. 

For example, young adults in the first 

period may have purchased food away 

from home in conjunction with entertainment away from home (as when 

they go out for dinner and a movie). 

Although they still may do so in the 

second period, new products or services may have been developed that allow 

young adults to enjoy similar forms of 

entertainment at home (for instance, 

joining a movie-by-mail rental club or 

viewing movies over the Internet). In 

this case, the share for food away from 

home could decrease while both the 

share for food at home and well-being 

increase, because young adults in the 

second period could still choose to purchase the same amount of food and entertainment away from home as those 

in the first period did, but they are also 

able to choose an allocation that was 

not available in the first period.

Because no data on tastes, preferences, technological change, or the 

availability of products are collected 

directly in the Consumer Expenditure 

Survey, it is impossible to identify precisely how these factors change and 

how expenditure patterns change as 

a result. Nevertheless, despite these 

caveats, analyzing shares in a historical context is useful as long as the assumptions underlying the analysis are 

reasonable and explicitly stated as 

needed.

Real or nominal expenditures? In performing economic comparisons across 

time, it is essential to control for changes in prices. To demonstrate, consider 

a person who spends $10 for apples in 

the first period and $20 in the second. It 

may be that the person purchased twice 

as many pounds of apples in the second 

period. But it also may be that the price 

changed (rose or fell) and the person 

purchased a different amount each period. For example, if the price of apples 

is $1 per pound in the first period, but 

$4 per pound in the second, it is clear 

that the person bought a greater amount 

of apples (10 pounds) in the first period 

than in the second (5 pounds). Usually, expenditures can be adjusted to 

reflect these changes by converting 

nominal expenditures to real expenditures through the mechanism of a price 

index. After adjustment, real expenditures can be compared to provide a better idea of whether changes in expenditures are due to changes in quantities 

purchased or changes in prices.

Price indexes are computed by comparing changes in price for a standard 

market basket of goods. In this case, 

the basket consists only of apples. 

Once the basket is defined, the index is 

computed by dividing the price of the 

basket in the period of interest by the 

price of the basket in the base period 

and multiplying the result by 100.0. 

In the base period, the period of interest and the base period are the same. 

Therefore, the index in the base period 

is always 100.0. However, if prices are 

different in the period of interest, the 

index will take on a higher or lower 

value, depending on the direction of 

the price change. For example, if the 

first period is selected as the base period and the basket is defined as consisting of 1 pound of apples, then the 

base-period index is computed to be 

($1/$1) × 100.0 = 100.0. The index for 

the second period is ($4/$1) × 100, or 

400.0.

Once the indexes are computed, 

they can be used to convert nominal 

expenditures to real expenditures. In 

the current case, suppose the analyst 

wants to convert the nominal value 

of expenditures reported in the first 

period to real-dollar values for comparison with expenditures occurring in 

the second period. In other words, the 

analyst wants to know how much the 

market basket purchased in the first period would have cost if it had been purchased in the second period. The result 

is obtained by dividing the price index 

for the second period by the price index 

for the first period and multiplying the 

result by the expenditures reported in 

the first period. In this example, then, 

the equation is (400.0/100.0) × $10 = 

$40. In other words, in the second period it costs $40 to purchase the same 

amount of apples that was purchased 

in the first period. Even if the quantity 

of apples purchased is unknown to the 

analyst, it is clear that the purchaser 

must have purchased fewer pounds of 

apples in the second period than in the 

first period, because the value of real 

expenditures reported in the first period 

(that is, $40) exceeds the value of real 

expenditures reported in the second period (that is, $20).2

Note that this adjustment works 

because expenditures are defined as 

2

 Note that similar comparisons can be made 

even when neither period of interest is the base 

year for the index. For example, suppose that 

the analyst wants to compare expenditures that 

took place before the base year with those in the 

second period. Suppose also that the price index 

for the pre-base-year period in question is 80.0 

and the expenditures for that period are $3. To 

convert these expenditures to second-period 

values, the analyst once again multiplies the 

expenditures from the pre-base-year period by 

the ratio of the second-period index to the index 

for the pre-base-year period (that is, [400.0/80.0] 

× $3 = $15). In other words, real expenditures in 

the pre-base-year period are less than the value 

of expenditures reported in the second period. 

Therefore, the purchaser must have purchased 

more pounds of apples in the second period than 

in the pre-base-year period, even though the price 

of apples has increased. 
Consumer Expenditure Survey Anthology, 2008 69

price (P) times quantity purchased (Q). 

Therefore, if P1Q1 (that is, the expenditure in the first period) differs from 

P2Q2, it is not clear whether the difference is a result of changes in P or in Q. 

However, adjusting first-period expenditures in the manner just described has 

the effect of comparing P2Q1 with P2Q2. 

Therefore, any difference in expenditure is due to a change in quantity.

However, the comparison is not always so precise. In this case, the analyst is literally comparing apples with 

apples. Suppose, however, the consumer purchases both apples and oranges. 

This purchase leads to a potential comparison of two different baskets of fruit. 

That is, suppose that the initial basket 

consists of 1 pound of apples and 1 

pound of oranges. Suppose also that 

the price of apples remains unchanged, 

but the price of oranges rises. Then the 

price index for fruit will rise, because it 

reflects the change in the total price of a 

basket of fruit consisting of 1 pound of 

apples and 1 pound of oranges. However, in response to the price change, 

the consumer may choose to purchase 

fewer pounds of oranges and continue 

to purchase 1 pound of apples. Alternatively, the consumer may substitute apples for oranges (that is, purchase more 

than 1 pound of apples and less than 1 

pound of oranges) or may indeed purchase less than 1 pound of each fruit. 

Only if the consumer continues to purchase 1 pound of apples and 1 pound of 

oranges after the price change will the 

index perfectly adjust nominal expenditures in the first period to values that 

are to be compared with those observed 

in the second period.3

 

Nevertheless, using the price index 

to convert nominal expenditures to real 

expenditures is important. Although 

the results may not provide a perfect 

 3 These comments pertain to the Laspeyres 

index, upon which the Consumer Price Index 

(CPI) is based. [See BLS Handbook of Methods 

(U.S. Bureau of Labor Statistics, June 2007), 

Chapter 17, “The Consumer Price Index,” 

especially p. 3, on the Internet at www.bls.gov/

opub/hom/pdf/homch17.pdf (visited Mar. 25, 

2008).] Although other price indexes exist that 

attempt to adjust for these kinds of substitutions, 

a complete discussion is beyond the scope of this 

article.

adjustment to the first-period expenditures for comparison over time, they 

still provide better information for analysis than a comparison of unadjusted 

values. Like any tool, a price index has 

to be used cautiously and correctly, and 

the analyst has to be aware of both its 

uses and its limitations before drawing 

analytical conclusions.

Statistical procedures

Adjusting expenditures for food at 

home. In the Interview component, or 

Interview Survey, of the CE, data on 

expenditures for food at home are collected by means of two questions. Prior 

to 1988, the first question asked about 

monthly expenditures for food at grocery stores and the second asked about 

monthly expenditures for food at other 

stores, such as convenience stores. In 

1988, each question was changed to 

ask about weekly expenditures for 

these items. From 1987 to 1988, average expenditures for food at home for 

young single adults rose 44.8 percent. 

By contrast, from 1984 to 1987 the average annual increase (2.5 percent) was 

similar to the average annual increase 

from 1988 to 2005 (1.9 percent).4

 Because the change in these expenditures 

in any single year other than from 1987 

to 1988 ranged between –9.8 percent 

(from 1992 to 1993) and 8.6 percent 

(from 2003 to 2004), the large change 

from 1987 to 1988 is presumably due 

to the change in the two questions.

Some of the change may be due to 

the way in which respondents think 

about the questions, as well as the 

way in which the processing of the 

data changed starting in 1988. When 

asked to report monthly expenditures, 

respondents may have thought about 

weekly expenditures, which they then 

multiplied by 4 before reporting. For 

example, a respondent with $50 in 

usual weekly expenditures would have 

reported $200 per month. During processing, these monthly reported expenditures were multiplied by 3 to produce 

quarterly estimates, because there are 

3 months per quarter. In this example, 

$600 would be the resulting quarterly 

expenditure estimate. However, when 

weekly expenditures are collected directly, they are multiplied by 13 to obtain quarterly estimates, because there 

are 13 weeks per quarter. Thus, the 

quarterly estimate would be $650, not 

$600. However, if the hypothesis presented here is correct, then quarterly 

expenditures are expected to rise about 

8 percent due to the change in the questionnaire, because, essentially, reported expenditures are being increased 

by about one-twelfth. (That is, when 

monthly expenditures are multiplied by 

3, only 12 weeks of expenditures compose the quarterly estimate, whereas, 

since 1988, an extra week is included 

in the composition of the quarterly estimate). Of course, even if this hypothesis is correct, expenditures for 1988 

could increase by more or less than 

8 percent, due to changes in prices or 

other exogenous factors that contribute 

to the natural variation in expenditures 

for food at home from year to year. 

Still, the increase of nearly 45 percent 

strongly reduces the credibility of the 

aforementioned hypothesis, especially 

because data on expenditures for food 

at home (excluding food prepared by 

the consumer unit on out-of-town trips) 

published in standard tables, which are 

derived from the Diary component, or 

Diary Survey, of the CE, do not show 

such a change from 1987 to 1988.5

 

Therefore, to account for the 

change—whatever its cause—requires 

an adjustment more complicated than 

adding 8 percent to reported expenditures in order to make expenditures in 

1984–85 more comparable to those reported in 2004–05.

To start, it is important to note that 

in the Interview Survey, as mentioned, 

4

 The food-at-home figure is computed by 

comparing the value in the final year of interest 

with the value in the first year of interest and 

computing the percentage by which expenditures 

would have to increase each year to reach the 

value in the final year. The formula is described 

subsequently in this section of the appendix.

5

 For all consumer units, average annual 

expenditures reported in the Diary Survey for 

food at home excluding food prepared by the 

consumer unit on out-of-town trips increased by 

1.8 percent from 1987 to 1988; at the same time, 

these expenditures increased by 16.2 percent 

according to results from the Interview Survey.
70 Consumer Expenditure Survey Anthology, 2008

information on expenditures for food at 

home excluding food prepared on trips 

consists of data collected from two 

questions: one on food purchased from 

grocery stores, the other on food purchased from other stores, such as convenience stores. Both questions changed in 

1988 to request usual weekly, rather 

than monthly, expenditures. Each question was affected by the change in the 

magnitude of the response to it: For 

those reporting expenditures at grocery 

stores, the expenditure increased more 

than one-third (37 percent); however, 

for those reporting expenditures at 

other stores, the expenditure more than 

doubled (rising almost 104 percent). 

Nevertheless, the change in the questions does not appear to have affected 

the rate of response to them: From 

1986 to 1989 (that is, the last 2 years 

of the monthly question and the first 2 

years of the weekly question), the percentage of respondents reporting purchases at grocery stores ranged from 

95.9 percent (1986) to 96.8 percent 

(1989), while the percentage reporting 

purchases at other stores ranged from 

40.4 percent (1988) to 42.0 percent 

(1987).

The next step is to estimate the 

values that would have been reported 

in 1984 and 1985 had the questions 

asked about usual weekly, rather than 

monthly, expenditures. One method 

is simply to adjust the 1984 and 1985 

expenditures by the percent change 

reported from 1987 to 1988. Consider, for example, expenditures at grocery stores. As mentioned earlier, the 

change in the mean for young singles 

who report these expenditures was 37 

percent. Therefore, multiplying these 

expenditures, as reported in 1984 and 

1985, by 1.37 would increase them by 

the appropriate amount. However, this 

method is too simplistic, for when the 

1987–88 change is excluded, the percent change in expenditures at grocery 

stores ranges from –9.8 percent (from 

1992 to 1993) to 9.1 percent (from 

1991 to 1992). Even excluding this period of volatility (from 1991 to 1993), 

the percent change ranges from –2.8 

percent (from 1988 to 1989) to 7.5 percent (from 1999 to 2000). Therefore, it 

is difficult to say how much of the 37-

percent change is due to the change in 

the questionnaire and how much is due 

to natural variation in reported expenditures. Simply multiplying expenditures 

reported in 1984 and 1985 by 1.37 may 

substantially over- or underestimate the 

values that would have been reported 

if usual weekly expenditures had been 

collected then.

Instead, regression is used to estimate the adjustment factor. In each 

regression (run separately for grocery 

store expenditures and other store expenditures), for those reporting expenditures in each year, the natural 

logarithm of the mean value of their 

expenditures is regressed on certain 

variables (described subsequently), 

the values of which depend on the period. The purpose of this logarithmic 

model is to use a formula that is well 

known in finance, namely, At = A0

ert, 

where A0 is the initial amount invested 

in an account, r is the rate of growth 

(for example, the interest rate) of the 

investment, t is the number of periods, 

e is a transcendental number equivalent to approximately 2.718, and At

 is 

the amount in the account in the final 

period. In the study of expenditures, 

r is the average annual rate of change 

of expenditures and can be calculated 

when other variables in the equation 

have known values. In the present case, 

the mean value for young singles who 

reported grocery store expenditures 

in 1984 was A0 . $216. In 1987, the 

value was At . $229. Accordingly, by 

what rate would expenditures have to 

have increased each year to meet these 

conditions? To find out, the natural 

logarithm of both sides of the earlier 

equation is taken, or ln(At) = ln(A0) + 

rt. From this point forward, r can be 

found with standard algebra, given that 

t is 3 (because the initial $216 grew for 

3 years after 1984—that is, from 1984 

to 1985, from 1985 to 1986, and from 

1986 to 1987).

Although this method describes 

the average annual growth rate necessary to move from the values observed 

in 1984 to those observed in 1987, 

the rate obtained may be affected by 

random variation in the data. That is, 

suppose that a drought or some other 

event caused prices, and therefore expenditures, to be higher than usual in 

1984, but that they returned to their expected level in 1987. Then the average 

annual growth rate computed in this 

way would underestimate the actual 

underlying long-term growth rate, because expenditures in 1984 would have 

started at a higher level than expected 

and therefore would need to increase 

less swiftly each year to reach the expected 1987 level than they would have 

had observed values equaled expected 

values in both years. To estimate both 

the initial expected starting value and 

the underlying long-term growth rate, 

then, regression is used. Note that when 

the natural logarithm of expenditures is 

regressed on time values, the intercept 

of the equation estimates ln(A0

)—the 

logarithm of the expected value of expenditures when t equals zero—and the 

coefficient of t is the estimated average 

annual growth rate for the long-term 

trend.

Before performing the regression, 

it is important to note that the change 

in question may have affected not only 

the intercept of the equation, but also 

the rate at which reported expenditures change over time. To find out, a 

single regression is run so that the coefficients of the intercept and slope for 

the 1984–87 equation can be compared 

with those for the 1988–2005 equation. 

The equation for the regression is 

ln(At) = c1B1 + c2B2 +r1B1t + r2B2t + u.

In this regression, binary variables 

are used for convenience in place of 

the traditional intercept. The first binary variable (B1

) equals unity for the 

years 1984 through 1987 and zero for 

1988 through 2005. The second binary 

variable (B2) equals zero for the initial 

years (1984 through 1987) and unity 

for the later years (1988 through 2005). 

Next, each year is assigned a value t 

for the period it represents. For 1984, t 

equals zero; for 2005, t equals 21. This 

time variable is not included separately 
Consumer Expenditure Survey Anthology, 2008 71

in the model; however, it is multiplied 

by each of the binary variables just described, and these interaction terms are 

included in the model. The coefficients 

c1 and c2 of the binary variables provide 

the estimated intercept for each of the 

periods, while the coefficients r1 and 

r2 of the interaction terms provide the 

estimated long-term growth rates for 

each model. (The final term, u, is the 

error term.) As expected, the difference 

of the coefficients of the binary variables is statistically significant, indicating that there was a change in reported 

values when the new question was introduced. However, the difference of 

the coefficients of the interaction terms 

is not statistically significant, as shown 

by an F test.6 Therefore, the hypothesis 

that the question had no effect on the 

underlying trend is reasonable on the 

basis of the evidence.

With the regression results computed, the coefficients of the binary variables are used to calculate the adjustment factor. Note that the coefficient of 

the second binary variable provides an 

estimate of what the natural logarithm 

of reported expenditures would have 

been in 1984 had the weekly, rather 

than monthly, question been asked 

then. To find out the estimated actual 

value that would have been reported, 

this coefficient is exponentiated, yielding $212.42. Similarly, the coefficient 

of the first binary variable is exponentiated, yielding the estimated value 

($282.01) for expenditures in 1984 in 

the absence of random variation that 

removed reported values from their underlying trend line. The ratio of these 

two values is about 1.3276; that is, the 

change in the question is estimated to 

have raised expenditures by about 32.8 

percent. Therefore, this ratio is used 

as the adjustment factor for food purchased at grocery stores in 1984 and 

1985. A similar analysis shows that the 

estimated factor for food purchased at 

other stores is about 1.6825.

Once found, expenditures for each 

type of purchase are multiplied by their 

adjustment factor, and food-at-home 

expenditures in 1984–85 are computed 

from these adjusted values. To test the 

adjustment, the unadjusted change in 

average expenditures for food at home 

from 1987 to 1988 is compared with 

the adjusted value. As noted in the 

text, prior to adjustment, expenditures 

for food at home excluding food prepared on trips rise nearly 45 percent 

from 1987 to 1988. However, after the 

adjustment, the percent change is 5.9 

percent, a value that is within the range 

(from –2.8 percent to 7.5 percent) for 

changes in observed (that is, preadjusted) values, even when observations 

from the most volatile period (1991 

to 1993) are excluded. Perhaps more 

important, after adjustment, the components also demonstrate reasonable 

changes in the mean for those reporting from 1987 to 1988.7

 Given that this 

finding is reasonable, the adjustment 

factors are accepted. Finally, as noted 

in the text, other values, such as total 

food expenditures, total outlays, and 

“all other outlays” (that is, total outlays less food, shelter and utilities, and 

other items listed in table 2), are then 

computed from these adjusted values.

An alternative method to that just described is to exponentiate the intercepts 

as described, subtract the 1984–87 value 

from the 1988–2005 value, and add the 

resulting difference to each of the observations in the data set before computing 

results for food at home. Either method 

would result in the same mean for expenditures for food at home excluding 

food prepared on trips. However, in the 

alternative method, the variance of each 

component that would be computed prior 

to the adjustment would be unchanged 

after the adjustment. The result would be 

a larger mean with the same standard error of the mean for each component, thus 

increasing the likelihood that differences 

over time for the aggregate expenditure 

(that is, food at home less trips) would be 

statistically significant.

6 F statistic = 0.16; p-value = 0.6977.

7 The adjusted mean for grocery store 

expenditures rises a modest 3.4 percent during 

this period. The mean for expenditures at other 

stores rises 21.1 percent from 1987 to 1988 after 

adjustment, but this percent change is not out 

of line with figures for other years. The largest 

percent change, from 1995 to 1996, is 28.0 

percent.

In contrast, using the percentage 

adjustment factor allows the variance 

of each component to increase in proportion to the increase in the mean of 

each component. That is, if the mean 

for food purchased at grocery stores 

rises by 37 percent, so will the standard 

error of the mean for that component. 

Similarly, adjusting separately each 

of the components of expenditures for 

food at home excluding food prepared 

on trips allows for a larger variance in 

the recomputed aggregate expenditure 

than performing the regression directly 

on mean expenditures for food at home 

excluding food prepared on trips. The 

reason is that some respondents report 

expenditures only for food at grocery 

stores, some report expenditures only 

for food at other stores, and some report 

both. Because the adjustment factors 

differ for each of the components, the 

percent increase in total expenditures 

for food at home excluding food prepared on trips will differ for each type of 

respondent, which will in turn increase 

the variance among respondents. As 

noted, the larger variance makes the 

analysis of change more conservative. 

That is, the threshold for finding a statistically significant difference is higher 

when the variance is higher, and therefore the analyst can be more confident in 

accepting the results. This conservative 

approach is especially important given 

that the data have undergone adjustments which are themselves based on 

estimates rather than reported values.

Computing adjustment factors for food 

at home. Tables A-1 and A-2 present 

regression results for computing adjustment factors for, respectively, expenditures for food purchased at grocery stores and expenditures for food 

purchased at other stores.

Measuring statistical significance: 

types and computations of t-statistics. 

As noted in the text, a difference in two 

parameters, such as means, is considered to be statistically significant if it is 

not likely to be due to chance alone. A 

common statistic used to measure the 

probability that a difference is due to 
72 Consumer Expenditure Survey Anthology, 2008

the text shows that, in 1984–85, 64 

percent of all households with at least

one young single person were households with only that one young single 

person. (That is, 36 percent of these 

households included at least one other 

person, regardless of age or marital 

status.) In 2004–05, that proportion 

increased to 77 percent. The critical 

value to test whether these proportions 

reflect a change in the composition of 

households is still 1.96; however, the 

formula for computing the t-statistic 

changes to 

where p1 is the proportion of households 

with exactly one young single person 

in 1984–85 (that is, 1,252/1,953); p2 

is the proportion of households with 

exactly one young single person in 

2004–05 (that is, 1,401/1,811); p3 is the 

“pooled” proportion (that is, [1,252 + 

1,401]/[1,953 + 1,811]); n1 is the sample size in 1984–85 (that is, 1,953); and 

n2 is the sample size in 2004–05 (that 

is, 1,811). The outcome of this test is 

similar to that of a chi-square test; in 

fact, the t-statistic equals the square 

root of the chi-square statistic computed by means of a chi-square test.

In addition, there is a special formula for comparing differences in shares 

across groups. A special formula is 

needed for this type of comparison because the value being measured is a ratio of two other variables that not only 

have their own means and standard 

errors, but also are not independent of 

each other. For example, because food 

at home is a component of total outlays, the covariance of mean expenditures for food at home and total outlays 

is expected to be positive. That is, as 

expenditures for food at home rise, so 

do total outlays, assuming that all other 

outlays are held constant. Accordingly, 

in this case, before computing the tstatistic, it is necessary to compute the 

variance of the share for each year. The 

chance alone (and thus is, or is not, statistically significant) is the t-statistic. 

When samples are large, a t-statistic

greater than 1.96 in absolute value indicates that the probability that a difference in parameters is due to chance 

alone is less than 5 percent.

The formula for computing the tstatistic depends on what type of comparison is being performed. Perhaps 

the most common use of the t-statistic 

is for comparing means. In the text, for 

example, average annualized real total 

outlays are compared for young singles 

in two different periods. The samples 

are therefore independent and are assumed to have different variances. In 

this case, the formula for computing 

the t-statistic is 

where xi

 is average annualized real 

total outlays in period i (1984–85 or 

2004–05) and SEi is the standard error 

of the mean in period i.

In table 4 in the text, average annualized real total outlays for all young 

singles is shown to be $23,866 in 

1984–85 and $22,744 in 2004–05. The 

standard errors associated with these 

means are 663.03 and 531.85, respectively. Therefore, the t-statistic is computed to be

Because the absolute value of the t-statistic (1.32) is less than the critical value (1.96), the probability that the difference in means (a decrease of $1,122) is 

due to sampling error or other random 

events is greater than 5 percent; therefore, the difference is not statistically 

significant at the 95-percent confidence 

level.

However, testing differences in 

means is not the only use for t-statistics: They also can be used to detect 

statistically significant differences in 

proportions. For example, table 3 in 

Table A-1. Regression results for computing adjustment factors for expenditures for 

food purchased at grocery stores1

Variable DF Parameter 

estimate

Standard 

error t value Pr > |t|

Year 1984–87 (B1

) 1 5.35857 .02370 226.06 <.0001

Time 1984–87 (B1

t) 1 .01858 .01267 1.47 .1599

Year 1988–2005 (B2

) 1 5.64193 .01742 323.87 <.0001

Time 1988–2005 (B2

t) 1 .02360 .00129 18.34 <.0001

Computation of factor: (exp(5.64193))/(exp(5.35857)) = 1.327583.

1 Dependent variable: Natural logarithm of mean expenditures for food purchased at 

grocery stores.

Table A-2. Regression results for computing adjustment factors for expenditures for 

food purchased at other stores1

Variable DF Parameter 

estimate

 Standard 

error t value Pr > |t|

Year 1984–87 (B1

) 1 4.19795 .06290 66.74 <.0001

Time 1984–87 (B1

t) 1 –.01903 .03362 –.57 .5784

Year 1988–2005 (B2

) 1 4.71821 .04622 102.07 <.0001

Time 1988–2005 (B2

t) 1 .02188 .00342 6.41 <.0001

Computation of factor: (exp(4.71821))/(exp(4.19795)) = 1.682465.

1

 Dependent variable: Natural logarithm of mean expenditures for food purchased at 

other stores.

2 1

2 2

2 1

, SE SE

x x −

+

2 2

22,744 23,866 1.32.

531.85 663.03

− = − +

,

11 )1(

21

3 3

21

















+−

−

nn pp

pp
Consumer Expenditure Survey Anthology, 2008 73

For convenience, this equation simplifies 

to

where F/T is the value of the share (that 

is, the ratio of the averages) undergoing 

testing.

Because V(S) equals the squared 

standard error of the share (and not the 

squared standard deviation of the share), 

the formula for the t-statistic is now

where Si = Fi/Ti. Once again, the critical 

value in this case is 1.96.

outlay; and covF,T is the covariance of 

food at home and total outlays.

Note that V(i) is the variance of the 

observations in the sample, not the 

variance of the mean obtained from the 

sample. That is, V(i) measures how the 

observations vary around the mean of 

the sample, rather than estimating how 

means of similarly sized samples drawn 

from the same population would vary 

around the population mean. In other 

words, V(i) is the square of the sample 

standard deviation, and V(i)/n is equal 

to (SEi

)2. Therefore, the preceding formula can be rewritten as 

formula for the variance of the share in 

a particular year is8

 

where n is the sample size (2,359 for 

1984–85 and 2,158 for 2004–05); F 

is the average expenditure for food at 

home; T is the average of total outlays 

(including food at home); V(i) is the 

sample variance of the expenditure or 

2

4 3 , 2

1 1 ( ) ( )[ ( ) 2( )cov ( ) ( )], F T

F F V S V T V F

n T T T = − +

2

2 2

4 3 , 2

1 1 ( ) [ (SE ) 2( )( )cov ( )(SE ) ]. T F T FF F V S

T n T T = − +

2 1

2 1

, ( ) ( )

S S

V S V S

−

+

2 2

2 ,

1 1 ( ) ( )[(( )SE ) 2( )( )cov (SE ) ], T F T F

F F V S

T T n T = − +

8

 Adapted from SAS online manual, Chapter 

10, “The MIANALYZE Procedure,” p. 216, on 

the Internet at support.sas.com/rnd/app/papers/

mianalyzev802.pdf (visited Nov. 6, 2007); and J. L. 

Schafer, Analysis of Incomplete Multivariate Data

(London, Chapman & Hall, 1997), p. 196.