Consumer Expenditures In The United States:

Survey Description And Distributional Analyses.

Thesia I. Garner and Stephanie Shipp

U.S. Bureau of Labor Statistics.

Household expenditure survey data can be used to provide information concerning the 

economic status and material well-being of families within and across countries. The purpose 

of this paper is two-folds first, to describe the survey which is conducted in the United States 

(U.S.) to collect those data, and second, to present results from recent studies in which these 

data have been used to examine the distribution of household expenditures. We hope this paper 

will stimulate grater interest among researchers internationally to examine household 

expenditure data when addressing distributional issues. The paper is divided into four sections. 

Section one provides a description of the U.S. Consumer Expenditure Survey (CEX) and 

procedures. Section two includes results from analyses of the distribution of expenditures 

across households and the inequality in these expenditures. Section three discusses limitations 

of the CEX data. Section four is the summary and conclusions.

1. The Consumer Expenditure Survey

Expenditure surveys have boon conducted by the U .S. Bureau of Labor Statistics (BLS 

since the late 19th century to provide a profile of consumers' spending patterns. On average, 

these surveys have been conducted at about 10-year intervals. However, since 1980, Consumer 

Expenditure Survey data have been collected continually in the U.S. Brier to 1980, the most 

recent expenditure survey data wore collected in 1973. Microlevel data currently available to 

the public are for the years 1980 to 1987.

The CEX serves two primary purposes. First, it is used to collect data on the
personal spending of consumer units1 on an ongoing basis; these data are used by a wide range 

of public and private organizations for economic analysis and planning. Second, the CEX 

provides the basis for the weights and item selections for the Consumer Price Index (CPI). An 

unique aspect of the survey data is that detailed demographic information is connected about 

each consumer unit so that this can be related to family spending patterns and income. The 

early surveys arose because of the need by U.S. Government policymakers for information 

relevant for tariff negotiations, and concern over the effects of rapidly rising prices on the 

living costs of workers. Expenditure surveys have been broadened over the years to collect 

more than just expenditure data. Additional questions have been added to the survey to obtain 

information about commodities, income, and family socio-demographic characteristics. Data 

that once were collected only for selected population groups are now collected for most of the 

U.S. population and can be used to adress a greater of issues. A wide range of public and 

private agencies use the CEX data for economic analysis and planning. For example, the data 

are used to study the welfare of particular segments of the population, such as the aged or 

low-income families. The U.S. Department of Commerce uses the survey data for revising its 

benchmark estimates of some of the personal consumption expenditure components of the 

Gross National Product. Economic policymakers use the data to study the effects of policy 

changes, such as the impact of various tax proposals on diverse socioeconomic groups. 

Econometricians find the data useful in constructing economic models. Market researchers find 

them valuable in identifying consumer trends for various groups of good and services. 

Businesses often use the data for forecasting.

The CEX data are collected by the Bureau of the Census (Census), U.S. Department of 

Commerce, under contract to the BLS, an agency within the U.S. Department of Labor. To 

those of you from countries with central statistical offices, this contractual arrangement may 

seem unusual. However, in the U.S., government statistics are produced by many departments 

independently. Data are collected for a specific purpose, or they may be by-products of 

administrative functions. The Office of Management and Budget provides limited coordination 

for Federal government agencies collecting survey data. Approval by this office is required 

before surveys that may place an undue burden on the public can be undertaken.

The U.S. Consumer Expenditure Survey has two components, each with its

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1Consumer Unit: A single person or group of persons in a sample household related by blood, 

marriage, adoption, or other legal arrangement, or who share responsibility for at least 2 out of 3 major 

types of expenses-food, housing, and other expenses. The term household or family is used for 

convenience, although there may be more than one consumer unit in a household and one person families 

are included. Each consumer unit has a householder or "reference" person; this person is the first member 

mentioned by the survey respondent when asked by the interviewer to name the person or persons who 

owns or rents the home. It is with respect to that person that consumer units are classified.
own questionnaire and sample: (1) an Interview panel in which 5,000 consumer units are 

visited by an interviewer every 3 months over a five quarter period, and (2) a Diary survey in 

which 5,000 consumer units are asked to maintain a record of expenses for two consecutive 

1-week periods. The Interview survey is used to obtain data on the types of expenditures 

respondents can be expected to recall for a period of 3 months or longer-such as those for 

property, automobiles, major appliances and travel, and expenditures that occur on a regular 

basis such as for rent, insurance, and clothing. The Diary survey is designed to obtain data on 

frequently purchased items that are more difficult to recall over longer time periods. These 

include food and beverages, tobacco, drugs, personal care products, and services. Expenditures 

on trips away from home are not included in the Diary. Business related expenditures are not 

included in either survey.

The current Consumer Expenditure Interview Survey is an ongoing survey with panels of 

respondents interviewed on a continuous basis; rotating panels of respondents are interviewed 

quarterly. Each quarter, one-fifth of the units are new to the survey. After being interviewed for 

five consecutive quarters, each panel is rotated out of the survey. For the initial interview, 

information is collected on demographic and family characteristics and on an inventory of 

major durable goods of each consumer unit. Expenditure information is also collected in this 

interview, using a one-month recall. This information is used for bounding purposes to 

minimize telescoping errors. These errors, common in retrospective interviews, result from a 

tendency to report events prior to the reference period of the survey. In the second through fifth 

interviews, uniform questionnaires with a three-month recall are used to collect out-of-pocket 

expenditures and expenditures which are made using credit. Expenditures that are reimbursed, 

such as medical expenses, are subtracted from the out-of-pocket expenditure reported for a 

good or service. Detailed wage, salary, and employment information is collected in the second 

and fifth interviews. The fifth interview is used to obtain a financial profile of the unit, 

including information on assets and liabilities.

For the Diary, as with the Interview, information on the demographic and family 

characteristics of the consumer unit is collected from a member of the consumer unit by an 

interviewer during an initial visit to the consumer unit's home. Each consumer unit then 

receives a Diary that is to be completed over a one-week period. At the end of the week, the 

interviewer collects this Diary, reviews the entries, and leaves a second diary. Finally, the 

interviewer returns a third time to collect the second Diary.

For the current survey, data are being collected in 91 urban areas and 18 rural areas of the 

country. These areas were selected to produce a sample representative of the U.S. population2. 

The sampling frame, the list from which housing units

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2 The population of interest is the total U.S. civilian population. Within this framework, the 

eligible population is composed of all civilian noninstitutional persons (e.g., those living in 

houses, condominiums, or apartments and all people residing in the following group quarters: 

boarding
were chosen, was generated from the 1980. Census 100-percent-detail file. Data on new 

construction permits augmented this basic list; in areas where new construction permits were 

inadequate or not available, area probability samples were selected. Average response rates 

computed on a quarterly basis have been fairly consistent since 1980, fluctuating between 86 

and 91 percent for the Diary and between 83 and 86 percent for the Interview survey. These 

rates are computed on a quarterly basis and do not represent participation in the survey over the 

entire 2-week or 5-quarter period.

Once the data are collected and reviewed, data imputation routines are used to account for 

missing or inconsistent expenditure entries on a quarterly basis. Imputation includes the use of 

hot deck procedures, weighting class procedures, and percent distributions. Currently missing 

income dare are not imputed. Allocation routines are applied where necessary to transform 

reports of combined expenditures into more detailed categories of expenditures. The data are 

also "time adjusted" and this results in monthly income and expenditure data. 3

During processing, weights are created for consumer unit included in the survey sample. 

The weight is the inverse of the probability of selecting the consumer unit adjusted to account 

for field subsampling and nonresponse. The weight is designed to reflect the U.S. population 

distribution by selected consumer unit characteristics, for example, age, sex, and race.

As noted previously, the data from the CEX surveys are made available to the public in 

various forms. News releases provide a first look at the data with a few tables showing 

expenditures by socio-demographic characteristics, such as income, age, and region. These are 

followed by bulletins which contain more extensive text and tables, more characteristics, and 

more detailed expenditures. Computer printouts are provided when special requests are made 

for characteristics not usually published or for more detailed expenditure levels. These requests 

are easily filled with the aid of an online data base management system. Diskettes containing 

macro level data are provided to those who want to use a personal computer to perform 

analyses such as percent changes, percent distributions, or comparisons of expenditures of 

different groups. Microlevel data are available on public-use computer tapes. These tapes 

contain information on the characteristics of each consumer unit member (for example, age, 

employment, and earnings), the expenditure data at the lowest level of classification in our 

system, ownership of durable

_______________________________

houses; housing facilities for students and workers; staff units in hospitals and homes for the 

aged, infirm, or needy; permanent type living quarters in hotels and motels; and mobile home 

parks. Patients and inmates are eliminated before sampling. The remaining ineligible persons 

in group quarters and housing units on military reservations are eliminated by later screening. 

These include: camps, communes; convents; halfway houses; home for the aged, infirm, or 

needy; transient quarters in hotels or motels; and missions. The collection of data for the rural 

population was discontinued in 1982 and 1983 for budgetary reasons.

3 All expenditures are mapped (assigned) to a standard uniform classification code (UCC). 

The UCC system is used by both CEX and CPI.
289

goods and vehicles, and selected other nonexpenditure information (for example, the 

characteristics of housing units, estimated values of homes, assets, and liabilities). A detailed 

review process is used to insure the confidentiality of respondents. Identifiers such as 

household name and address and unusual values which might be revealing, such as very high 

incomes or very high values of homes, are not included in the data files. Release of geographic 

detail is limited. Since 1984, flags have appeared on the public-use tapes to indicate whether 

and adjustment, (for example, allocation, imputation, computation, or some combination of 

these three) was made to the expenditure data.

In addition to actively producing data for public users, economic and statistical research is 

conducted within the Bureau using CEX data. In the following section, results are present from 

studies in which published aggregate data and micro-level data have been used.

2. Distributional Analysis of Expenditures

In this section, results are presented from two studies in which the distribution of household 

expenditures have been examined. The distribution of expenditures is first presented in terms 

of aggregate shares4. Gini coefficients, based on microlevel expenditure data, are presented in 

the second part of this section5. Data for both of these studies are weighted to represent the 

U.S. population. Results from each of these studies have implications for social , and economic 

policy related to the distribution of household expenditures across diverse population groups.

A. Aggregate Expenditure Shares

Aggregate expenditure shares are computed for several categories of expenditure items and 

are compared across subgroups of the population. An aggregate item expenditure share is the 

ratio of item expenditures for a particular subgroup of consumer units compared to the item 

expenditures for the total population. In Tables 1 and 2, aggregate expenditure shares for 198? 

axe presented by income quintiles6and then by age groups, respectively. Consumer units in the 

lowest income quintile group account for 8 percent of the 1987 total aggregate expenditures, 

less than half of the proportion of the population they represent. The highest income quintile's 

aggregate expenditure share for all items is 38 percent, which is

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4Results are from ongoing research in the Division of Consumer Expenditure Survey. For a 

description of the method and earlier results see: Kaneer, Kirk (1986)

 

5

see: Garner (1989)

 6 Across-the-board zero income is considered an invalid response; in this case the consumer 

unit would be identified as an incomplete income reporter. It is important to note that even 

complete income reporters may not have provided a full accounting of all income from all 

sources. Thus, it is possible for consumer units not reporting income completely to be 

considered complete income reporters.


almost twice their 20-percent proportion of the population. For consumer units in the lowest 

income quintile, which includes many consumer units headed by a student, the only items for 

which aggregate expenditure shares approximate their population share are rent and education.

When evaluating the data in terms of the expenditure shares of different age groups, 

distributional and expenditure level differences also emerge. More than a third of aggregate 

expenditures for rented dwellings are made by consumer units

in wick the reference person is in the 25 to 34 years age group. Only 45 percent of these 

families are homeowners. In the next age group (35 to 44), homeownership increases to 68 

percent. As a result, those age 35 to 44 account for 30 percent of the aggregate for owned 

dwellings; this one age group spends the largest share for owned dwellings. These families are 

more likely to have newer and hence larger mortgages as well as higher interest rates than are 

families in the other age groups.

Consumer units with reference persons younger than 25 years have the lowest aggregate 

expenditure share for transportation (6 percent) while the 35 to 44 years age group has the 

largest (26 percent). Seventy percent of all consumer unite in the younger age group own 

vehicles, while 92 percent of the consumer units in the 35 to 44 years age group own vehicles.

As expected, consumer units in the top two age groups combined (65-74 and 75 and over) 

have the largest share of aggregate out-of-pocket health and medical expenditures. These 

families represent 21 percent of the population; however, they account for almost one-third of 

the aggregate share for out-of-pocket health care expenditures. Although most of these 

consumer units participate in the federal

Medicare program, they are frequently saddled with large medical expenditures since our 

federal health care system is not totally comprehensive.

The largest aggregate expenditures share for education is made by families with reference 

persons age 45 to 54 years. This group spends 29 percent of the education expenditures for the 

total population. They are followed closely by families age 35 to 44 who account for 27 

percent of the aggregate education expenditures. Families in these two age groups are likely to 

be paying for their children's college education, which can be expensive in the U.S.

Results such as these can be produced from the data BLS publishes. For certain types of 

policy questions, aggregate shares may be the most appropriate form in which to present the 

distribution of expenditures for demographic groups.

B. Consumer Expenditures and Inequality:

Gini coefficients can be used to examine the inequality in the distribution of household 

consumption expenditures. The Gini coefficient is a measure of the inequality of a distribution; 

it is a summary statistic of inequality derived from the Lorenz Curve and is equal to one minus 

twice the area under the Curve. The lower the value of the Gini, the less the inequality in the 

distribution. For example, as expenditures become more equally distributed across consumer 

units, the value
of the of the Gini declines. A Gini of zero would represent absolute equality in expenditures. 

Garner7 used a method developed by Lerman and Yitzhaki8to produce Gini coefficients for 

consumption expenditures; some of these coefficients are reproduced in this discussion.

Three sets of Gini coefficients are presented. First, Gini coefficients based on total annual 

consumption expenditures are produced and compared to Gini coefficients based on annual 

income before and after taxes for the total population. Second, expenditure Gini coefficients are 

produced for demographic subgroups of the population. The demographic subgroups are 

defined in terms of consumer unit size, household composition, the race and age of the 

reference person, and region and degree of urbanization of residence. Consumer unit size is 

defined in terms of five groups, from one person to five or more persons. Household 

composition is defined in terms of six categories: (1) single, (2) one parent with children, (3) 

husband and wife only, (4) husband and wife with their own children, (5) other husband and 

wife consumer units, and (6) other consumer units. Race is defined as black or non-black. Age 

of the reference person is divided into two groups: less than 65 years of age and greater than or 

equal to 65 years of age. Region refers to the U.S. Census regions: Northeast, Midwest, South, 

and West. Degree of urbanization refers to one of the following: a large urban area, a small 

urban area, or a rural area (farm and non-farm). Third, the overall Gini coefficient is 

decomposed and individual budget component Gini coefficients are produced. The effects of 

the inequality within expenditure budget components on overall inequality are identified by 

decomposing the Gini. To study the decomposition effects, total expenditure are divided into 

ten budget components: food, shelter, fuel and utilities, household operations, apparel and 

services, private transportation, public transportation, medical care and services, entertainment, 

and other expenditures. (See the Lerman and Yitzhaki papers and the Garner paper for a 

description of the decomposition method).

Microlevel data from the U.S. Consumer Expenditure Interview Survey, collected in 

January 1987 through March 1988, are used. Since an objective of the study is to produce Gini 

coefficients based upon income, the sample is limited to those consumer units who were 

interviewed for four consecutive quarters during the survey period. Thus, Gini coefficients 

based upon annual expenditures and annual income could be compared for the same time 

period. Expenditure items are aggregated into the ten budget components noted previously, 

with expenditure values of zero assigned to items for with consumer units did not report 

expenditures. Although collected in the Interview, expenditures for life insurance, endowments, 

annuities, other personal insurance, retirement, pensions, and Social Security are not included 

as part of total expenditures for the study since expenditures for these items are not considered 

to be for current consumption. This excluded group of

7

see: Garner (1989).

8

see: Lerman-Yitzhaki (1984), Yitzkaki (1983) and Lerman-Yitzhaki

(1985)
items accounts for approximately 9 percent of total expenditures reported by the BLS for the 

U.S. population for 1987 9. For the expenditure-income comparison, a subsample of the primary 

sample is used. This subsample is restricted to include only complete income reporters. 

Completeness of income reporting is based upon whether the respondent to the Interview 

provided values for various sources of income, primarily for major sources such as wages and 

salaries, self-employment income, and Social Security income.

The primary sample included 2071 consumer units participating in four consecutive 

interviews; the subsample of complete income reporters included 1797 consumer units. The 

weighted primary sample represents approximately 27,000,000 consumer units. The greatest 

percentage of sample consumer units are composed of two persons (.30), although the most 

prevalent household composition type, given the groupings noted previously, is the one 

composed of a husband, wife, and their own children (.32). The majority of consumer units 

include a non-black reference pension (.89), and the reference person is most likely to be less 

than 65 years of age (.75). The largest percentage of consumer units live in the South (.31), 

while the urbanization category most descriptive of the area of residence is large urban (.42). 

This distribution is quite similar to the distribution of all consumer units from which the 

Interview data are collected, although the weighted sample represents less than the total U.S. 

population.

The overall Gini coefficient based on annual expenditures for the total weighted sample in 

1987 is .33. For complete income reporters, the total expenditures Gini coefficient is .327, 

while the Gini coefficients based on income are .448 for before tax income and .435 for after 

tax income. The Gini coefficient for 1987 based on income from the Current Population Survey 

(CPS) is .392 10. The difference in the two survey income Gini coefficients is likely to be 

related to the fact that income is imputed for the CPS but not for the CEX. The lower value for 

the total expenditures estimate, compared to the income estimates, could be due to the stability 

of consumption expenditures relative to income. Or, as noted by Kakwani, the difference may 

be explained in terms of the inequality of savings. "Since income is the sum of expenditures 

and savings, and since households with higher incomes tend to save a greater proportion of the 

incomes, inequality of saving will be high. This is an important reason why the inequality of 

income is higher than that of expenditures11. Kakwani, using Australian data, reported a total 

expenditures Gini to be .256 compared to .331 for disposable income for 1975-76.

Pooling all consumer units with different characteristics into a single distribution could 

hide significant differences in inequality trends if substantial differences among inequality 

estimates of consumer units with different demographic profiles

________________________________________________

9

see: U.S. Department of Labor (1989)

10see: U.S. Census (1987) 

11see: Kakwani (1986)
exist. Thus, when evaluating the effect on inequality of changes in expenditures, it would be 

better to produce separate Gini estimates for demographic subgroups to identify which 

consumer units will be most affected. Results from this study reveal such differences. Within 

the consumer unit size groups, the greatest degree of inequality is among one person consumer 

units (G=.345), the lowest is among four person households (G=.265). Among the household 

composition groups, one parent with children households experience the most inequality of 

expenditures (G=.348); inequality is lowest among husband and wife with children households 

(G=.265). Inequality in expenditures is slightly greater for consumer units in which the 

reference person is 65 years of age or older (G=.344) than it is for those in which the reference 

person is younger (G=.301). Consumer units living in the West appear to have total 

expenditures which are more equally distributed (G=.292) than do consumer units living in 

other regions. Inequality in expenditures is also lower for rural consumer units (G=.309) 

relative to consumer units living in other areas. Inequality did not differ much by race. These 

results substantiate the claim made by others that inequality in economic well-being differs by 

the demographic characteristics of the units 12.

Decomposition of the Gini provides specific information concerning the concentration or 

inequality in consumption expenditures by budget component, the contribution of the budget 

component to overall inequality, and information about how marginal changes in particular 

expenditure can affect overall inequality. Within budget component Gini coefficients are 

expected to be higher, for the most part, than the overall Gini because, with the exception of 

food, not all consumer units have expenditures for each of the budget components. For example, 

approximately 49 percent of the sample does not have expenditures for public transportation,

which is reflected in the high within budget component Gini coefficient of .852. This indicates 

that public transportation expenditures are highly concentrated among consumer units in the 

sample. In contrast, expenditures for food and for fuel and utilities are the most equally 

distributed of au budget components, with Gini coefficients of .324 and .286, respectively. 

These low Gini estimates result from the high proportion of consumer units with these 

expenditures as well as from their relatively even distribution among those consumer units 

which do have these expenditures.

The greatest amount of inequality in total consumption expenditures is contributed by 

shelter expenditures; 24 percent of the total inequality in expenditures for the population 

sample is accounted for by these expenditures. Expenditures for shelter are highly correlated 

with total expenditures (R = .765), they seem to be somewhat concentrated (G = .463), and 

they represent approximately 23 percent of total consumption expenditures. Public 

transportation expenditures contribute the least to the inequality in total expenditures (2.6 

percent), followed

12 see: Dagum-Grenier (1984), Jorgenson-Dale-Slesnick (1984), Ray (1985), Silber (1989), 

Kakwani (1986).
by medical care and services expenditures (3.7 percent). For most components, the 

contribution to inequality is similar to their share of total expenditures.

The relative effects on overall inequality of a marginal increase in a budget component can 

be determined by comparing its expenditure share to its share of expenditure inequality. 

Changes in expenditures which would lead to reductions in inequality are associated with 

expenditure components for which the expenditure shares are greater than are the shares of 

expenditure inequality. The direction of the marginal relationship indicates the effect at the 

margin of an increase in a component expenditure on overall inequality. This means that a 

component would exert a negative effect on inequality if the relative marginal effect is 

negative. For example, in this study a negative value for food results; this means that a one 

percent increase in food expenditures, holding all else constant (e.g., prices, taxes), would 

decrease the inequality in total consumption expenditures at the margin by 0.04 percent. 

Increases in expenditures for fuel and utilities, private transportation, and medical care and 

services would also decrease overall inequality at the margin.

3. Data Limitations

Thus far in this paper, we have described the U.S. Consumer Expenditure Survey and have 

highlighted results from distributional analyses of the CEX data conducted by researchers 

within the BLS. However, results based on household survey data must be considered with 

some caution. One must keep in mind the fact that studies based up on household survey data 

are subject to both sampling and nonsampling errors. These errors can affect the validity and 

reliability of the data. For the CEX data we have been producing measures of the sampling 

error for some time, with coefficients of variation ranging from two to five percent for most 

published expenditure items. More recently we have been actively dedicating greater resources 

to the study of nonsampling errors. Nonsampling errors can be attributed to many sources, such 

as definitional difficulties, differences in the interpretation of questions, inability or 

unwillingness of the respondent to provide correct or complete information, mistakes in 

conding or recording the data obtained, and other errors of collection, response, processing, 

coverage, estimation for missing data, and interviewer variability. Nonsampling errors affect 

the data, and subsequently the results based on these data. For example, underreporting affects 

the data in two ways. There may be differential underreporting by demographic groups. 

Younger families, for example, may be less likely to report all of their expenditures than are 

older families. There may also be differential underreporting by expenditure component; 

households are more likely to remember large purchases or routine payments but are less likely 

to remember infrequent and/or small
purchases.

Within the BLS, research is being conducted to identify and quantify some of the 

nonsampling errors for the purpose of developing new data collection methodologies. Various 

types of evaluation are being utilized, including comparisons of CEX data with data from other 

sources, statistical analysis of the data to identify the reasons for underreporting, and research 

on the cognitive processes respondents use as they answer survey questions.

The CEX data are regularly compared to the Personal Consumption Expenditure (PCE) 

Component of the National Income Accounts13. After accounting for definitional differences, 

the levels obtained from the Interview component of the survey are generally lower than the 

PCE, indicating that the expenditure data may be somewhat underreported. A more recent 

comparison project begun at the BLS is to evaluate and compare the income, assets, and 

liabilities data from the CEX and data collected using the Federal Reserve Board's Survey of 

Consumer Finance.

As noted, statistical analyses of the CEX data have been conducted to investigate possible 

reasons for underreporting and the effects on the data of this underreporting. Researchers 

within BLS have examined time-in-sample and recall effects in two studies using aggregate 

data from 1982-1983 and microlevel data from 198414. Recall bias, telescoping, and 

time-in-sample have been identified as potential concerns in data collection for the Interview. 

Recall bias results from the selective recollection of past events and is common in retrospective 

interviews. It is related to memory and tends to increase with longer recall periods and for less 

salient events. The incorrect reporting of events' time of occurrence, or "telescoping" interferes 

with an analyses of recall bias since it often affects reporting in a forward direction. Thus, a 

combination of "recall effects" results. Time-in-sample may result in underreporting since 

consumer units are potentially visited five times for the Interview and three times for the Diary. 

Over time, consumer units may lose interest in the survey and thus may subsequently not report 

their expenditures fully. Research results revealed that recall, more than panel conditioning, 

contributed to the underreporting of expenditures15. The researchers noted that internal 

telescoping, or erroneous reporting of the expenditure month, would not account solely for the 

systematic variations in mean expenditures by recall month. When studying the importance of 

the relationship of consumer unit (CU) characteristics and recall bias BLS researchers reported 

the most important variables were the size, composition, and income level of the consumer 

unit16. Age and education of the survey respondent were important respondent characteristics. 

They also noted sensitivity of reporting patterns to changes in interview

___________________________________________

13 see: Gieseman (1987) 

14 Silberstein-Jacobs (1989), Silberstein (1989)

15 see: Silberstein Jacobs (1989). 

16 Silberstein (1989).
week. Reporting patterns were influenced by the length of the reporting period and the 

respondent's perception of it.

Related research, focusing on the Diary component, examined the record-keeping behavior 

of Diary respondents. Data collected in a 1984 supplemental research survey indicated that 

almost 20 percent of the respondents completed at least one of the two diaries by total recall, an 

obvious threat to the quality of the data received for the Diary Survey 17.

Specific research on the underreporting of income is also being conducted. A comparison of 

the CEX income data with Current Population Survey data reveals that underreporting of 

income, particularly for property and non-wage income, may be a problem in the CEX. 

Analysis of data in the lower income groups shows that a large percentage of consumer units 

report a level of expenditures that greatly exceeds their reported income. Research on income 

reporting and the characteristics of consumer units reveals that the age, education, and 

occupation of the reference person, housing tenure, and region in which the consumer unit 

resides are significant variables in determining the probability that a consumer unit wall be a 

complete income reporter18. Studies such as these have important implications for developing 

methods that can be used to reduce the underreporting of income and for developing procedures 

that can be used to impute income for nonreporters. Implementing an income imputation 

procedure should improve the data even if it does not entirely correct the problem at the source.

Cognitive research related to the data collection instruments and interview procedures began 

in the mid-1980's. During this time the BLS received funding from Congress to create a Survey 

Design Research Center. Shortly thereafter, in January 1987, a (questionnaire Design Advisory 

Conference was convened at the BLS19. As a result of the recommendations received by the 

Bureau at this conference, combined with results from the underreporting studies, research on 

the cognitive aspects of the data collection process increased. Emphasis in the past year has 

been on the use of cognitive laboratory techniques for investigating respondents' thought 

processes in response to survey questions, and developing alternative questionnaire formats and 

question phrasing. Attention has also been focused on the demands placed upon respondents in 

terms of burden. Major form redesigns are planned for 1991 and 1996 based on findings from 

the cognitive research studies.

4. Summary and Conclusions

The U.S. has been conducting expenditure surveys for several decades, begin-

__________________________________________________

17see: Tucker (1986) 

18see: Garner (1987) 

19see: Biennia-Dippo-Palmisano (1987)
ning in the late 19th century. These data have been used for a variety of purposes over this 

time, including the production of weights for the CPI and for economic and trend analyses. 

Included in this paper are results from distributional studies of 1987 expenditure data. First, 

aggregate expenditure data were used to identify the proportion of expenditures by item that 

various demographic subgroups in the population spend. Second, microlevel data were used to 

produce Gini coefficients based on total annual consumption expenditures for the total sample 

population and for demographic subgroups.

These studies represent only two ways of examining distributions. In the future we plan to 

add to this foundation by examining the CEX data using other distribution measures, for 

example, coefficients of variation, a general entropy measure, and Atkinson's measure of 

inequality. We also anticipate expanding our work to include studies of the economic status 

and materiel well-being of families cross-nationally, as similar data from other countries 

become more available. Throughout this work, we will continue to be aware of data limitations 

as we compare results from the CEX with those from other data sources.

REFERENCES

Biennia, J., Dippo, C. and Palmisano, M. (1987), Questionnaire Design: Report on the 1987 

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