1

Adam Safir

safir.adam@bls.gov

Adam Safir is a division chief in the Office of 

Prices and Living Conditions, U.S. Bureau of 

Labor Statistics.

Daniel Dorfman

dorfman.daniel@bls.gov

Daniel Dorfman is an economist in the Office of 

Employment and Unemployment Statistics, U.S. 

Bureau of Labor Statistics. He formerly worked in 

the Office of Prices and Living Conditions.

Assessing Consumer Expenditure Surveys data 

quality through the lens of data use

This article describes the uses of BLS Consumer 

Expenditure Surveys (CE) data at the federal level, 

discusses the CE program’s approach to assessing data 

quality, and reviews the data’s fitness for use from the 

perspective of the data users.

The Consumer Expenditure Surveys (CE) program of the 

U.S. Bureau of Labor Statistics (BLS) sponsors nationwide 

household weekly diary and monthly interview surveys 

each year for the purpose of producing nationally 

representative estimates of expenditures, income, and 

demographics.1 The surveys’ main objective is to measure 

the spending patterns of consumers living in the United 

States. The surveys are the only federal government data 

collection efforts that provide information on the complete 

range of consumers’ expenditures, as well as their income 

and demographic characteristics. Similar to other large 

scale federal survey programs, the BLS CE program 

aggregates the survey data for a primary purpose, which is 

to provide estimates critical to the Consumer Price Index 

(CPI), a Principal Federal Economic Indicator.

BLS also uses the CE data to report on the economic wellbeing of consumers in the United States. In particular, BLS 

produces a regular series of economic analyses based on 

CE data. In addition, many other federal agencies rely on 

CE data, with each individual use increasing both the value 

of the time that respondents give in answering questions 

from field interviewers and the return on investment to U.S. 

taxpayers. Individual organizations also have their own unique set of objectives for data use, sometimes with 

distinct considerations regarding data quality. In particular, factors such as response rates, measurement error, 

aggregation, data level, and timeliness differ in importance for each data-user constituent according to their 

analytic objectives.

November 2021
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This article summarizes the various uses of BLS expenditure data at the federal level, discusses the CE program’s 

approach to defining and assessing data quality, and reviews fitness for use from the data-user perspective.

One survey with many uses

Although one of the primary purposes of CE data is to update the relative importance of goods and services in the 

CPI market basket (in addition to other uses within BLS), CE data are also used by many other federal 

government, nonprofit, and private-sector organizations, as well as individual users, such as policy analysts and 

other researchers. Government and private agencies use the data to assess spending patterns for specific groups 

of people, such as those over age 65 or part of low-income households, as well as to make decisions about issues 

affecting these groups. Policymakers use CE data to gauge the impact of policy changes on different 

socioeconomic groups. Academic researchers use CE data to assess the spending behavior of different types of 

families across various products (including newly introduced goods and services) and to examine people’s giftgiving behavior. Market researchers use CE data to analyze consumers and businesses and their interest in 

various goods and services.2

In this article, we focus on the use of CE data by eight federal government departments or agencies: BLS,3 the 

Census Bureau, the U.S. Department of Agriculture, the U.S. Department of Defense, the U.S. Department of 

State, the U.S. Department of Health and Human Services, the U.S. Bureau of Economic Analysis, and the Internal 

Revenue Service. (See table 1.) The descriptions of CE data uses that follow are based on discussions with 

agency subject-matter experts and on documentation presented in department and agency materials, including 

official reports, methodology papers, and websites.

Department or 

agency

Topic Format Use and frequency

U.S. 

Department of 

Agriculture

Children Published 

tables Determine the cost of raising a child (periodic)

U.S. Bureau of 

Economic 

Analysis

Housing Custom tables

Input for several key components of BEA’s economic statistics, including the 

national income and product accounts, the input–output accounts, the travel and 

tourism satellite accounts, and the new outdoor recreation satellite account 

(annual)

U.S. Bureau of 

Labor Statistics 

(BLS)

Consumer 

Price Index

Internal 

microdata

Estimate lower level spending weights for the Chained Consumer Price Index 

for All Urban Consumers (C-CPI-U, monthly), the CPI-U, and the Consumer 

Price Index for Urban Wage Earners and Clerical Workers (CPI-W, annualized 

from biennial data), as well as spending weights for lower-level index 

calculations. Select item sampling probabilities (annual). Derive outlet sample 

frame and selection probabilities for the CPI Commodities and Services Survey 

(semiannual)

U.S. Census 

Bureau Construction Unprocessed 

microdata Estimate residential construction spending (monthly)

U.S. Census 

Bureau Poverty Custom tables Use BLS-produced thresholds to produce estimates of poverty based on the 

Supplemental Poverty Measure methodology (annual)

U.S. 

Department of 

Defense

Military Custom tables Determine cost-of-living allowances for military personnel living off base 

(annual)

Table 1. Use of Consumer Expenditure Surveys data by department or agency, topic, and format

See footnotes at end of table.
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Source: U.S. Bureau of Labor Statistics and other federal agencies and departments.

The U.S. Bureau of Labor Statistics

The U.S. Bureau of Labor Statistics (BLS), an agency within the U.S. Department of Labor, is responsible for 

measuring labor market activity, working conditions, price changes and productivity in the U.S. economy. BLS 

serves as one of the principal agencies of the U.S. Federal Statistical System.4 The BLS CPI program is 

responsible for measuring the average change over time in the prices paid by urban consumers for a market 

basket of goods and services. The CPI is among the most widely used measures of inflation and serves as an 

indicator of the effectiveness of government efforts to control inflation through its fiscal and monetary policy. In 

addition, business executives, labor leaders, and other private individuals use the index as a guide in making 

economic decisions. The CPI is used to make cost-of-living adjustments to salaries and pensions paid to millions 

of American workers and retirees; it is also used to adjust the federal income tax structure to prevent inflationinduced increases in taxes5 and to adjust income eligibility levels for government programs and assistance.

The CPI program uses CE data to (1) calculate expenditure weights for CPI calculation, (2) determine item 

sampling probabilities, and (3) collect data on outlet point of purchase.6 The CPI program produces three official 

indexes: the Consumer Price Index for All Urban Consumers (CPI-U); the Chained Consumer Price Index for All 

Urban Consumers (C-CPI-U); and the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPIW). Numerous research indexes are also produced, including the Consumer Price Index for Americans 62 years of 

age and older (R-CPI-E).7 The CPI-U, which is often referred to as the “headline index,” measures the average 

monthly change in the price of goods and services paid by urban consumers living in the United States. The CPI-U 

is based on expenditures of urban wage earners and clerical workers; professional, managerial, and technical 

workers; the self-employed; short-term workers; and the unemployed, retirees, and others who are not in the labor 

force. The C-CPI-U is similar, except that it is designed to be a closer approximation to a cost-of-living index in 

that, in its final form, it accounts for any substitution that consumers make across item categories in response to 

changes in relative prices. Similarly, the CPI-W differs from the CPI-U in that it is based only on expenditures made 

by those in wage-earning or clerical occupations. The CPI-E is designed to represent the inflation experience of 

consumers aged 62 years and older currently living in the United States, with expenditure weights derived from the 

spending behavior of consumer units with reference to people aged 62 years or older.8

Department or 

agency

Topic Format Use and frequency

U.S. 

Department of 

Health and 

Human 

Services

Health Custom tables Estimate health expenditures for National Health Expenditure Accounts (annual)

Internal 

Revenue 

Service

Taxes Custom tables Calculate alternate sales tax standard deduction tables (annual)

U.S. 

Department of 

State

Foreign 

Service costof-living 

allowances

Published 

tables Determine cost-of-living allowances for diplomats living overseas (annual)

Table 1. Use of Consumer Expenditure Surveys data by department or agency, topic, and format
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•

Expenditure weights for CPI index calculation

The CPI uses CE data to update the expenditure weights across CPI products and processes. This includes (1) 

determining lower-level weights used to calculate basic-level indexes from underlying prices collected, (2) updating 

monthly weights for aggregating basic-level index components into the final estimate of the C-CPI-U, and (3) 

providing biennial weights for aggregating basic-level index components to calculate the CPI-U, the CPI-W, and 

the CPI-E.

Lower-level weights for basic indexes. All CPI products are calculated in two stages. In the first stage, 

basic indexes are calculated for each of the 7,776 item-area combinations. For instance, a basic index is 

calculated for bananas in Boston as a weighted average of the change in the price of bananas at sampled 

stores across the Boston area. The weights for the first stage are usually derived from the sampling frame 

for the category in the area.9 Prior to 2018, the CE was only used to calculate lower-level weights for item 

categories whose outlet samples were derived from sources other than the Telephone Point of Purchase 

Survey (TPOPS).10 TPOPS data collection ended in September 2019 and the last TPOPS-sourced sample 

initiated in the commodities and services survey occurred in August 2020. The first CE-sourced sample 

initiation in the commodities and services survey began in August 2021. Beginning with this sample, CE 

data are used to estimate all of the lower-level weights in the basic-level indexes. The first CE-sourced 

sample initiation in the commodities and services survey began in August 2021. Beginning with this sample, 

CE data are used to estimate all of the lower-level weights in the basic-level indexes.

Monthly weights for C-CPI-U. The C-CPI-U differs from the CPI-U in the formula and weights used to 

combine basic indexes. The formula used in the C-CPI-U accounts for consumers’ ability to achieve the 

same standard of living from alternative sets of consumer goods and services.11 This formula requires 

consumer spending data that are not immediately available. Consequently, the C-CPI-U, unlike the other 

two official CPI data series, is published first in preliminary form and is subject to scheduled revisions using 

updated expenditure data.12 In publishing the final C-CPI-U, the CPI program uses monthly expenditure 

estimates from each elementary item-area combination to create aggregation weights. The monthly 

expenditure estimates for an item are summed across 32 areas to obtain a U.S. monthly item expenditure 

and then allocated across these areas according to each area’s relative expenditure share for the prior 12 

months. The CE program delivers microdata on a quarterly basis for the monthly weights in the C-CPI-U.13

Aggregation weights for published indexes. In calculating the CPI-U, the CPI-W and the R-CPI-E, CE 

data are used to derive the reference period aggregation weights at the second stage of index calculation. 

Aggregate indexes are produced by averaging across two or more of the 7,776 CPI item-area combinations, 

such as the all-items index for New York, which is the average of its 243 basic indexes. The aggregation 

weights in the second stage assign each elementary index a relative importance to the aggregate index, 

corresponding to consumer expenditure choices (from the CE) among the 243 elementary items in the 32 

elementary areas in the CPI sample for the reference period.14 Lastly, these weights are updated biennially 

and enable the CPI program to keep pace with relatively current spending patterns.

Item-sampling probabilities
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The CPI’s classification is composed of approximately 70 expenditure classes divided into 243 item strata. Each 

item strata consists of entry-level items (ELIs). These ELIs are the first-stage sampling units for consumer goods 

and services selected within each sample outlet. In other words, ELIs represent the items from which CPI data 

collectors sample within in each sampled outlet. CE data are used to probabilistically select which ELIs should be 

priced in each sample. Every year, the CPI program uses the most recent 2 years of CE data to select new item 

samples because of the changes in the consumer marketplace.15

Commodities and services outlet sample frame

Since 2019, the CE program has collected information on retail stores and service providers for CPI program 

production use for items purchased by consumers living in the United States. This step replaces the TPOPS, 

which suffered from rising costs, low response rates, and sample frame issues related to mobile phones. 

Collecting these data forms the basis for the CPI’s commodities and services sample frame, which allows the CPI 

to then select the retail establishments consumers frequent most and monitor the prices of a sample of goods and 

services.

Other federal departments and agencies

U.S. Department of Agriculture (USDA)

The USDA is the federal executive department responsible for developing and executing federal laws related to 

farming, forestry, rural economic development, and food and nutrition programs (distribution, nutrition assistance 

and school lunch programs, etc.). The USDA Food and Nutrition Service uses CE data to produce its report 

Expenditures on Children by Families, which is also known as “The Cost of Raising a Child.”16

Expenditures on children. The USDA, which has been tracking the cost of raising a child since 1960, uses CE 

data to examine child-related expenses by the age of a child or children, household income, budgetary component 

(e.g., housing, transportation, education, healthcare, clothing, food, etc.), and geographic region.17 This report 

fulfills the USDA’s mission in supporting the financial health and well-being of American families. These estimates 

give families a greater awareness of the expenses they are likely to face while raising children, and they provide 

valuable information to people planning to start a family. In particular, this report provides insights regarding how 

child-rearing expenses can reflect economies of scale.18 Families may also use this information to identify 

financial-health resolutions by understanding the costs of feeding a child and plan for both anticipated and 

unexpected life events. Additionally, courts and state governments use these data to inform their decisions about 

child support guidelines and foster care payments. Lastly, financial planners may use this information to provide 

advice to their clients for family financial planning.19

U.S. Bureau of Economic Analysis (BEA)

BEA is the federal agency responsible for providing official macroeconomic and industry statistics. The cornerstone 

of BEA’s statistics are the national income and product accounts (NIPAs), which feature estimates of gross 

domestic product (GDP) and related measures. In a letter to BLS management about the Consumer Expenditure 

Surveys, BEA Chief Economist Dennis J. Fixler wrote, “This important survey is our only data source for several 

key components of BEA’s economic statistics.” Fixler continued:
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Information from the CE surveys is critical in preparing the national income and product accounts (NIPA’s), the 

input–output (I–O) accounts, the travel and tourism satellite accounts, and the new outdoor recreation satellite 

account. Also, beginning with the 2013 Benchmark Revision, BEA uses the CE Surveys’ data to estimate the 

diesel fuel portion of the personal consumption expenditure (PCE) gasoline and other motor fuel estimate. 

Data from the CE surveys is also used for comparison purposes [as they were] during the 2012 NIPA/

Benchmark I–O reconciliation [when] these data were used as a check on final demand for personal 

consumption expenditures.20 [See table A-1.]

U.S. Census Bureau

The Census Bureau is the principal agency of the U.S. Federal Statistical System responsible for producing data 

about the American people and economy.21 The Census Bureau relies on CE data for the calculation of residential 

housing expenditures and the production of Supplemental Poverty Measure thresholds.

Residential housing expenditures. The Census Bureau uses CE data collected on homeowners’ alterations and 

repairs to produce the monthly Value of Construction Put in Place report, which is a principal federal economic 

indicator. This measure provides monthly estimates of the total dollar value of construction work done in the United 

States each month on new, private residential and nonresidential construction, public construction, and 

improvements to existing buildings and infrastructure. CE data are used as a source for estimating the monthly 

value in place data for residential improvements to owner-occupied housing units.22 In addition, regular revisions 

to the owner-occupied residential improvements estimates are benchmarked to the annual total using CE data.23

Supplemental poverty measure (SPM). The Census Bureau, in cooperation with BLS, produces the SPM.24 The 

SPM acts as an experimental poverty measure that defines thresholds and resources in a manner different from 

the official poverty measure. As a result, it is not used to determine eligibility for government programs, but instead 

is used to evaluate the impact of benefit programs on poverty. In determining the SPM, the CE serves as the 

source of the expenditure-based poverty thresholds. More specifically, the BLS Division of Price and Index Number 

Research (DPINR) is responsible for conducting research and producing the SPM thresholds that are based on 

CE data. The thresholds are experimental and thus not subject to the same review as official BLS projects. The 

thresholds are posted on the DPINR webpage and sent to the Census Bureau for use in producing experimental 

poverty estimates.25 Currently, SPM thresholds are based on 5 years of quarterly CE Interview Survey data on 

out-of-pocket expenditures for food, clothing, shelter, and utilities (FCSU), by consumer units with two children, 

with values converted to those for a reference unit composed of two adults and two children. A multiplier is applied 

to FCSU expenditures to account for other basic goods and services (e.g., household supplies, personal care, and 

nonwork-related transportation) in the thresholds. The Census Bureau applies equivalence scales to the BLSproduced reference-unit poverty thresholds to derive thresholds for consumer units with differing numbers of adults 

and children. The Census Bureau also adjusts the housing portion of the thresholds for geographic differences in 

the cost of housing.

U.S. Department of Defense (DOD)

The DOD is the federal executive department that coordinates and supervises all agencies and functions related to 

national security and the U.S. Armed Forces. The DOD uses CE data to calculate cost-of-living allowances for 

military personnel who live off base. The DOD uses CE Interview Survey data to calculate the Overseas Cost of 
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Living Allowance (OCOLA), the continental United States (CONUS) Cost of Living Allowance (COLA), and the 

spendable income table for military personnel.

The DOD regularly uses specially prepared data from BLS on expenditures by military families from the prior 3 

years by family size and income bracket to develop the spendable income tables used in the CONUS weights. The 

OCOLA is a nontaxable allowance paid to U.S. military personnel stationed overseas to partially offset higher 

overseas prices on nonhousing goods and services.26 OCOLA helps maintain the purchasing power of overseas 

U.S. military personnel so they can purchase goods and services comparable to those of their CONUS-based 

counterparts. The DOD computes an OCOLA index by comparing the cost of a specific market basket of goods 

and services overseas to the cost of the same market basket of goods and services in the CONUS, on average. 

Holding all else constant, if market-basket costs rise in an overseas location compared with average CONUS 

costs, then the DOD will increase the COLA for service members stationed in that overseas location.

To compute the OCOLA Index, the DOD uses a CE Interview Survey data to construct a weighted system that 

places a greater significance on frequently purchased goods and services, such as car insurance, gasoline, and 

day care. The Armed Forces extract of the CE Interview Survey details how U.S. military families allocate their 

spendable income across all OCOLA types of goods and services. This extract is used to determine the market 

basket items and the appropriate weights for each item based on relative expenditures. The overseas weightedaverage cost for each market basket item (determined by using location-specific data from the Living Pattern 

Survey and the Retail Price Schedule survey) is compared with the CONUS weighted-average cost for the same 

item to produce an index for each item.27

CONUS COLA compensates for nonhousing expenses incurred in areas that exceed average costs in CONUS by 

more than 8 percent. By statute, the CONUS COLA index must be comparable to the Consumer Price Index. 

Therefore, the DOD derives a CONUS COLA index by using data on typical expenditures from the CE.28 OCOLA 

and CONUS COLA payments are based on the amount of average spendable income that is applicable for each 

regular military compensation level. Spendable income is determined using the expenditures by income level 

generated by the CE.29

U.S. Department of Health and Human Services (HHS)

The HHS is the federal executive department whose mission “is to enhance the health and well-being of all 

Americans, by providing for effective health and human services and by fostering sound, sustained advances in 

the sciences underlying medicine, public health, and social services.”30 The Centers for Medicare & Medicaid 

Services (CMS), part of HHS, is responsible for publishing the National Health Expenditure Accounts (NHEA), 

which consists of national health expenditures (historical and projections), state health expenditures, and age and 

gender estimates. The NHEA provide official estimates of total healthcare spending in the United States by type of 

good or service delivered (hospital care, physician and clinical services, retail prescription drugs, etc.), as well as 

the source of funding for those services (private health insurance, Medicare, Medicaid, out-of-pocket spending, 

etc.) and the sponsors (businesses, households, and governments).31

Measure out-of-pocket healthcare expenditures. The CMS uses CE data specifically to measure out-of-pocket 

spending for healthcare goods and services not covered by insurance. This includes the amount of coinsurance 

payments or deductibles required by private insurance plans, health savings accounts (HSAs), as well as public 

programs such as Medicare and Medicaid.32 The CMS uses CE data in conjunction with several other sources of 
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data (e.g., the Census Bureau’s Service Annual Survey, the Medical Expenditures Panel Survey, and the Medicare 

Current Beneficiary Survey)33 that serve as a baseline for comparing the accuracy of the CMS estimates of private 

health insurance spending and benefits.34

In order to publish the NHEA, the CMS requires timely and detailed expenditure data. The CMS utilizes both 

annual aggregate summaries (using integrated Interview and Diary surveys) and microdata for various income, 

asset, and healthcare expenditure categories (including the number of policies). In particular, the CMS requires 

income and asset characteristics at the aggregate level to account for households with older members (who earn 

less income and hold more assets). The CMS also uses special tabulations of annual data by age prepared by 

BLS to produce estimates of individually purchased health insurance expenditures.35

Internal Revenue Service (IRS)

The IRS is tasked with administering the Internal Revenue Code and collecting federal taxes. The IRS uses CE 

data to (1) calculate optional state and local sales tax deductions tables, and (2) determine typical household 

expenditures in investigating tax repayment issues.

State and local sales tax tables. The IRS uses CE data to develop optional state and local sales tax tables. 

These tables are based on the Internal Revenue Code, which provides taxpayers with an option to deduct state 

and local general sales tax instead of state and local income tax.36 This option is especially important to taxpayers 

residing in states with no income tax. Taxpayers can either deduct their actual sales tax amounts or estimate the 

deduction using these optional state and local sales tax tables. In addition, taxpayers can use the tables to deduct 

sales taxes paid on certain specified items, such as motor vehicles, aircraft, or boats. For this purpose, BLS 

produces special tabulations on the purchase of taxable items, which the IRS uses to prepare the optional state 

and local tax tables.37 The IRS publishes these tax tables in its annual publication, “Instructions for Schedule 

A.”38 The IRS also provides an online sales tax deduction calculator, which taxpayers can use to estimate their 

general sales tax deduction.39

In creating these tables, the IRS uses BLS-produced custom tabulations of integrated CE Interview and Diary 

surveys data to estimate state and local general sales tax amounts by family size and income bracket. The IRS 

provides both state and local taxability data at Universal Classification Code levels to BLS, which in turn uses 

these taxability files to estimate the average sales tax amounts along with household income, family size, and 

other variables by state and locality.40 Upon receiving these data, the IRS estimates state and local general sales 

tax amounts by family size and income bracket.41

Collection financial standards. The IRS also uses typical consumer unit expenditures, as reported in the CE, 

when investigating tax repayment cases. Specifically, the IRS uses CE data to derive its Collection Financial 

Standards, which help to assess a taxpayer’s ability to pay a delinquent tax liability. These standards measure 

allowable living expenses for taxpayers regarding healthcare, food, clothing, housing, utilities, and 

transportation.42 Taxpayers may use the standard monthly amount, which is determined by family size. The IRS 

specifically derives the national standards on food, clothing, and other items, as well as local standards on 

transportation, from the CE data:
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National standards: food, clothing, and other items. The IRS breaks down the national standards into 

five categories of necessary expenses: food, housekeeping supplies, apparel and services, personal care 

products and services, and miscellaneous items. The IRS requires detailed data on aggregate expenditures 

by income level and family size to determine the national standards on food, clothing, and other items.43

Local standards: transportation. The local transportation standards for taxpayers include vehicle 

ownership and public transportation by Census Region and Metropolitan Statistical Area (MSA).44 The IRS 

computes standards for vehicle ownership based on monthly loan or lease payments. The public 

transportation standards include a single nationwide allowance derived from CE expenditure data on mass 

transit fares for a train, bus, taxi, ferry, etc.45
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U.S. Department of State (DOS)

The DOS is the federal executive department responsible for carrying out U.S. foreign policy and conducting 

international relations. The State Department’s Office of Allowances uses data from the CE in determining 

spendable-income tables, a key measure for revising the post-allowance (COLA) payment tables. The postallowance tables provide COLAs supporting the entire federal civilian overseas workforce.46 Post allowances 

make it possible for the federal civilian overseas workforce to spend the same portion of their basic compensation 

for living expenses in a foreign country (where they are “posted”) as they would if they lived in Washington, D.C. 

The post allowances enable employees to avoid a reduction in their standard of living because of higher costs of 

goods and services in the location where they are posted.47

Cost-of-living adjustments for the federal civilian overseas workforce. Data from the CE Interview Survey are 

used to calculate the post allowance payment tables. The information in the tables represents a percentage 

increase over the cost of living in Washington, D.C., and it is applied to “spendable income”—that is, the amount of 

money that households have available for spending after deductions for taxes, gifts and contributions, savings 

(including insurance and retirement), and U.S. shelter and household utility expenses.48

Unlike the DOD allowance, the DOS post allowance for the federal civilian overseas workforce is developed using 

a slightly different approach. Price data from overseas are collected and compared with those in the Washington, 

D.C., areas to determine if the federal overseas civilian workforce qualifies for a post (COLA) allowance. The State 

Department’s Office of Allowances develops a cost index to evaluate expenditure patterns between foreign 

locations and Washington D.C. The expenditure patterns (index weights) for Washington, D.C., are updated using 

data from the CE.49 The State Department compares costs in the foreign location for goods and services in 11 

categories—food (whether purchased at grocery stores, restaurants, or other venues), tobacco and alcohol, 

clothing, personal care items, furnishings, household goods, medical services, recreation, public transportation, 

vehicle-related expenses, and household help—to the cost for those same goods and services in Washington, 

D.C. If the overall costs of goods and services at a foreign post—after considering expenditure patterns—are at 

least 3 percent above the cost of the same goods and services in the Washington, D.C., area, then the State 

Department establishes a post allowance for that location.50

Approach to assessing data quality

In keeping with “Statistical Policy Directive No. 1: Fundamental Responsibilities of Federal Statistical Agencies and 

Recognized Statistical Units,” BLS is committed to producing data that consistently are of the highest statistical 

quality; in other words, the agency strives to produce data that are relevant, accurate, coherent, timely, accessible, 

and interpretable.51 Monitoring data quality in the CE includes procedures conducted by the Census Bureau during 

data collection and by BLS during data processing and analysis.52 “Fitness for use” is an important component of 

data quality; as such, the CE is also committed to helping data users assess the fitness for use of the CE data for 

their purposes. BLS has historically provided a variety of metrics for data users to evaluate the overall quality of its 

products. Official tables provide standard errors, the public-use microdata user documentation provides response 

rates, and the datasets contained in the public-use microdata provide all the variables and flags necessary for 

users to create their own quality measures.53
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The CE program defines data quality in a manner that allows for objective measurement, provides an assessment 

framework, and addresses the fitness-for-use concerns of individual program stakeholders. Based on the Total 

Quality Management and Total Survey Error paradigms, the definition includes six dimensions: relevance, 

accuracy, coherence, timeliness, accessibility, and interpretability.54 (See table 2.)

Source: U.S Bureau of Labor Statistics

The CE program continuously evaluates these six dimensions of data quality, in part or in whole, through the 

assessment of internal indicators of data quality, external indicators of data quality, nonresponse bias study results, 

and measurement-error study results:

Internal indicators. The CE program supports a systematic and integrated approach for monitoring and 

reporting on internal data quality indicators. Maintaining a consistent, well-defined set of metrics establishes 

baselines for monitoring trends in the quality of routine survey production activities over time. For external 

users, this set of metrics serves as an indication of data quality; for internal users, the metrics are actionable 

and provide a basis for survey improvements. Because the quality of survey estimates is affected by errors 

that occur anywhere in the survey cycle, it is expected that these internal metrics will evolve over time as the 

CE continually researches methods to monitor and improve data quality.55 As of 2019, eight internal 

indicators are being tracked: final disposition rates of eligible units; records use; expenditure edit rates; 

income imputation rates; respondent burden; information book use; survey mode; and survey time.56

External indicators. From an external-indicator standpoint, the CE program compares CE data with other 

data sources that measure the same or similar items. Although every household survey has its own unique 

set of errors, the monitoring of ratios between sources and any associated change over time can help 

identify potential underreporting or overreporting of expenditure items. The CE program routinely compares 

its results to various external sources, including the following: Personal Consumption Expenditures, the 

Residential Energy Consumption Survey, National Health Expenditure Accounts, the Medical Expenditure 

Panel Survey, the Current Population Survey, the American Community Survey, the Panel Study of Income 

Dynamics, the Survey of Consumer Finances, and the American Housing Survey.57

Dimension Description

Relevance The degree to which the survey products meets the user's specific needs in terms of both content and coverage.

Accuracy

The degree to which the estimate is similar or dissimilar to the true value of the population parameter. This 

dimension gives consideration to survey errors stemming from coverage, sampling, nonresponse, construct validity, 

measurement, and post-collection processing and adjustments.

Coherence The degree to which different sources or methods on the same phenomenon are similar.

Timeliness The interval between the time data are made available to users and the event or the phenomena the data describe.

Accessibility The ease with which statistical information and appropriate documentation describing that information can be 

obtained from the statistical organization.

Interpretability The availability of adequate information to allow users to properly use and interpret the survey products.

Table 2. Data quality dimensions
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•

•

Nonresponse bias. Nonresponse bias is the systematic error that occurs when results collected from 

respondents differ in meaningful ways from those that would be collected, but by definition are not 

collectable, from nonrespondents.58 The Office of Management and Budget (OMB) encourages all federal 

survey programs to study their nonresponse bias, and OMB requires all federal surveys whose response 

rates are below 80 percent to conduct such a study.59 Both the CE Interview and Diary surveys have 

response rates below 80 percent and therefore are subject to the OMB requirement.60 The CE program 

assesses potential nonresponse bias through the continuous monitoring of response rates (both for 

collection and estimation rates) and with nonresponse-bias studies. The most recently completed 

nonresponse-bias study concluded that the data in both the CE Interview survey and Diary survey were not 

missing completely at random.61 This conclusion is similar to that of other studies, which also find the data 

are not missing completely at random. However, these other studies find that the amount of nonresponse 

bias is not substantial.62

Measurement error. Measurement error is the difference between the reported value of a variable of 

interest and the true value of that variable.63 Optimally, measurement error is 0 (i.e., the respondent reports 

the true value), but it is generally not directly observable in survey data, which, by definition, consist of 

reported values that may or may not be correct. Therefore, in addition to the external data comparisons 

mentioned previously, the CE program assesses measurement error through (1) comparisons with other 

administrative data sources,64 (2) comparisons to respondent expenditure records (e.g., bank statements, 

credit card statements, etc.), and (3) within-survey subgroup comparisons (such as respondents completing 

the survey in person or on the telephone or using the information booklet or not). As reported by Roger 

Tourangeau et al., measurement error evaluation results suggest that overestimation is just as common as 

underestimation in the CE, and that the degree and direction of measurement error varies considerably by 

expenditure category and respondent characteristics—therefore, analytic objectives play a large role in 

determining the data’s fitness for use.65
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•

•

•

•

•

Fitness for use

Although the regular revision of the CPI remains the primary purpose of the CE, other uses of CE data at the 

federal level have developed over time. As a result, the CE program continuously endeavors to understand each 

user’s unique preferences with respect to data quality, as well as the data’s fitness for use. This approach includes 

addressing concerns of other federal users regarding the six dimensions of data quality mentioned previously: 

relevance, accuracy, coherence, timeliness, accessibility, and interpretability. It also involves frequently evaluating 

the survey quality in terms of each federal data user and adjusting production processes as necessary. In recent 

years, the CE program has also begun an initiative to address specific data quality concerns through the annual 

release of a data quality profile and more accessible methods research results through an online research 

library.66

From the CE program perspective, select data quality factors that are critical to major data users include the 

following:

Cost of raising a child (USDA): relevance. In estimating the cost of raising a child, detailed expenditures 

by consumer unit are critical to the U.S. Department of Agriculture’s work in estimating the cost of raising a 

child.

CPI (BLS): relevance, accuracy, and timeliness. Although CE response rates have been declining in 

recent years, the downward trend does not appear to have affected CPI weights, and the weights for 

consumer units are quite robust in terms of declining response rates. However, ultimately, the CPI 

production schedule is highly dependent on receiving CE data on a timely basis.

Out-of-pocket healthcare expenditures (CMS): relevance, accuracy, and timeliness. The Centers for 

Medicare and Medicaid Services is most concerned with underreporting of healthcare expenditures. 

Understanding the impact of policies such as the Affordable Care Act on out-of-pocket healthcare spending 

while also reconciling CE data with other data sources is a major concern of the CMS. Also, because the 

CMS produces annual estimates, it is imperative that data be available early in the year. The CE program 

began publishing midyear tables in 2013, and that has helped provide more timely and readily available 

data. Ultimately, the CE’s level of detail, timeliness, and continuous publication enable the CMS to fulfill its 

mission of producing national health expenditure estimates.

Residential Housing Expenditures (Census): timeliness. Timely and continuous dissemination of CE 

data are critical to the Census Bureau’s work in estimating and revising key economic indicators.

Supplemental Poverty Measure (Census): coherence, timeliness, interpretability. For the SPM, data 

sources that are consistent between threshold and resource definitions, in terms of poverty concepts, are 

essential. Therefore, priorities include data that are timely, provide simplicity in estimation, stability in the 

measure over time, and ease of explaining the methodology.

Overall, results from assessments of internal indicators, external indicators, nonresponse bias studies, and 

measurement error studies show that CE data are generally of high quality for their intended uses.67 However, 

concerns persist about the impact of declining response rates, measurement error, and a diminishing 

correspondence to national account aggregates.68 Using observable characteristics, CE samples are designed to 
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be representative of the population, although there is evidence of underrepresentation at the top of the income 

distribution as well as underreporting of income and expenditures.69

Conclusion

BLS collects, processes, and disseminates expenditures data for numerous stakeholders, each of whom maintains 

a particular set of preferences with respect to data quality factors related to their intended use of the estimates. 

Establishing a framework for continually assessing data quality and viewing results through the lens of fitness for 

use enables the Consumer Expenditure Surveys program to provide high-quality data. These data present an 

unbiased statistical picture of consumer expenditures—which are used for the Consumer Price Index, by various 

government agencies and by other data users—in an effort to improve understanding of consumer economic 

behavior.

Appendix. Uses of Consumer Expenditure Surveys data by the U.S. 

Bureau of Economic Analysis (BEA)

Note: CPI = Consumer Price Index; I–O = Input–Output Accounts; NIPA = National Income and Product Accounts; PCE = Personal Consumption 

Expenditures; TTSA = Travel and Tourism Satellite Account; ORSA = Outdoor Recreation Satellite Account.

Source: U.S. Bureau of Labor Statistics.

SUGGESTED CITATION

Item Use Program area

Food Cross-check and comparison with PCE-comparable items NIPA

Vehicle renting and leasing PCE auto and truck leasing; PCE other vehicle leasing I–O, NIPA, 

TTSA

Travel (out-of-town trips) Travel expenditures TTSA

Babysitting or other child care in 

someone else’s home PCE child care NIPA

Day-care centers, nursery, and 

preschools PCE nursery schools NIPA

Taxicabs Total receipts for taxicabs; PCE for taxicabs I–O, NIPA, 

TTSA

All items Cross-check and comparison with PCE-comparable items NIPA

BEA use of various Consumer Price Indexes (CPIs) based on Consumer Expenditure Surveys data

CPI for household fuels PCE housing services for tenant-occupied nonfarm housing and 

gross housing output NIPA

CPI for owners’ equivalent rent and 

residential rent

PCE housing services for owner- and tenant-occupied nonfarm 

housing and gross housing output NIPA

CPI for housekeeping services Real private household compensation NIPA

CPI for ship fare Travel expenditures TTSA

CPI for intercity train fare Travel expenditures TTSA

CPI for recreational equipment Consumer expenditures ORSA

CPI for travel (local trips) Travel expenditures ORSA

Table A-1. BEA use of Consumer Expenditure Surveys data
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Adam Safir and Daniel Dorfman, "Assessing Consumer Expenditure Surveys data quality through the lens of data 

use," Monthly Labor Review, U.S. Bureau of Labor Statistics, November 2021, https://doi.org/10.21916/mlr.

2021.24.

NOTES

1 “Sponsors” are federal or state agencies on behalf of whom the Census Bureau manages surveys. The funds for these surveys are 

appropriated to the sponsoring agency, and the work is conducted on a cost reimbursable basis under the auspices of an interagency 

agreement.

2 See “Consumer Expenditure Surveys: value of the Consumer Expenditure Survey” (U.S. Bureau of Labor Statistics, last modified 

August 26, 2014), https://www.bls.gov/respondents/cex/cevalue.htm.

3 This article looks only at the secondary uses of the Consumer Expenditures Surveys (CE) data by BLS; the analyses and reports 

that BLS produces on the CE itself are outside the scope of the article.

4 This section discusses the ways that BLS uses the CE data to produce other BLS products and does not include the CE products 

and publications themselves, which are not discussed at length in this article.

5 See BLS, “Consumer Price Index: Consumer Price Index overview” (U.S. Bureau of Labor Statistics, last modified January 10, 

2020), https://www.bls.gov/cpi/overview.htm.

6 See “Consumer expenditures and income,” Handbook of Methods (U.S. Bureau of Labor Statistics, last modified March 28, 2018), 

https://www.bls.gov/opub/hom/cex/pdf/cex.pdf.

7 For a complete list of research products available, see “Consumer Price Index: CPI Research Series,” (U.S. Bureau of Labor 

Statistics, last modified August 5, 2020), https://www.bls.gov/cpi/research-series/.

8 For more information, see “Chapter 17: The Consumer Price Index,” Handbook of Methods (U.S. Bureau of Labor Statistics, 

updated February 14, 2018), https://www.bls.gov/opub/hom/pdf/cpi-20180214.pdf.

9 Ibid., pp. 4, 18.

10 The Telephone Point of Purchase Survey (TPOPS), conducted by the U.S. Census Bureau, was formerly the source of data on 

where certain items were purchased by U.S. consumers. Suffering from low response rates and data quality concerns, TPOPS was 

replaced by the CE in 2018. For more information, see “Telephone Point of Purchase Survey (TPOPS)” (U.S. Census Bureau, last 

revised October 8, 2021), https://www.census.gov/programs-surveys/tpops.html.

11 The Chained CPI-U uses a superlative index formula designed to reflect consumers’ behavior in response to changes in relative 

prices. For more information, see “Chapter 17. The Consumer Price Index.”

12 See “Chapter 17. The Consumer Price Index,” p. 4.

13 Ibid., p. 32.

14 Ibid., p. 29.

15 Ibid., p. 12.

16 See Mark Lino, Kevin Kuczynski, Nestor Rodriguez, TusaRebecca Schap, Expenditures on Children by Families, 2015, 

miscellaneous report no. 1528–2015 (U.S. Department of Agriculture, Center for Nutrition Policy and Promotion, January 2017, 

revised March 2017), https://fns-prod.azureedge.net/sites/default/files/crc2015_March2017_0.pdf. See also Mark Lino, “The cost of 

raising a child” (U.S. Department of Agriculture, Center for Nutrition Policy and Promotion, February 18, 2020), https://www.usda.gov/

media/blog/2017/01/13/cost-raising-child.
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17 For access to all of the past reports, see “Expenditures on children by families reports—all years” (U.S. Department of Agriculture, 

Center for Nutrition Policy and Promotion, March 26, 2019), https://www.fns.usda.gov/resource/expenditures-children-families-reportsall-years.

18 Economies of scale are achieved when increasing inputs in production by x percent increases average costs by less than x

percent. In the present context, consider “inputs” as members of a family, and “costs” as expenditures. A married couple renting a 

one-bedroom apartment does not spend twice as much for rent on that apartment as a single person would; similarly, their costs for 

food and other expenses may be less than twice what a single person would spend. If that couple is living in a larger house, and has 

a child, family size increases by 50 percent. But if there is already a bedroom in the house available for use of the child, the basic 

housing cost (i.e., rent or mortgage, taxes, and other similar ownership costs) does not change, let alone rise by 50 percent.

19 See Families Projected to Spend an Average of $233,610 Raising a Child Born in 2015, release no. 0004.17 (U.S. Department of 

Agriculture, January 9, 2016), https://www.usda.gov/media/press-releases/2017/01/09/families-projected-spend-average-233610-

raising-child-born-2015.

20 See letter from Dennis J. Fixler to Nora Kincaid of the BLS Office of Administration, dated July 22, 2020, https://www.reginfo.gov/

public/do/DownloadDocument?objectID=103672300. The letter was written in response to requests for comments in the Federal 

Register, vol. 85, no. 113 (U.S. Office of Management and Budget, June 11, 2020), pp. 35665–67, https://www.govinfo.gov/content/

pkg/FR-2020-06-11/pdf/FR-2020-06-11.pdf. For a full list of the ways that the U.S. Bureau of Economic Analysis uses CE data, see 

appendix table A-1.

21 For more information, see “What we do” (U.S. Census Bureau, May 2021), https://www.census.gov/about/

what.html#par_textimage.

22 See “Value of construction put in place” (U.S. Census Bureau), https://www.census.gov/econ/overview/co0300.html.

23 See “Construction spending: construction methodology” (U.S. Census Bureau), https://www.census.gov/construction/c30/

methodology.html.

24 See “Supplemental Poverty Measure” (U.S. Census Bureau, last revised October 8, 2021), https://www.census.gov/topics/incomepoverty/supplemental-poverty-measure.html; see also Liana Fox, The Supplemental Poverty Measure: 2019, report no. P60-272 (U.S. 

Census Bureau, September 15, 2020), https://www.census.gov/library/publications/2020/demo/p60-272.html.

25 See “Price and index number research: research experimental poverty thresholds” (U.S. Bureau of Labor Statistics, last modified 

September 3, 2021), https://www.bls.gov/pir/spmhome.htm.

26 See “Overseas cost of living allowances (COLA) frequently asked questions” (U.S. Department of Defense, Defense Travel 

Management Office, updated April 20, 2020), https://www.defensetravel.dod.mil/site/faqcola.cfm.

27 See “Calculation of overseas COLA index” (U.S. Department of Defense, Defense Travel Management Office), https://

www.defensetravel.dod.mil/site/colaIndexCalc.cfm.

28 See U.S. Code, Title 37–Pay and Allowances of the Uniformed Services, section 403b, “Cost-of-living allowance in the continental 

United States,” pp. 116–117, https://www.govinfo.gov/content/pkg/USCODE-2001-title37/pdf/USCODE-2001-title37.pdf.

29 See “Overseas cost of living allowances (COLA) frequently asked questions.”

30 See the U.S. Department of Health and Human Services (HHS) mission statement, “About HHS,” at https://www.hhs.gov/about/

index.html.

31 See “National Health Expenditure Data” (U.S. Centers for Medicare and Medicaid Services, December 17, 2019), https://

www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/index.

32 See “National Health Expenditure Accounts: methodology paper, 2017; definitions, sources, and methods” (U.S. Centers for 

Medicare and Medicaid Services, 2017), p. 15, https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-andReports/NationalHealthExpendData/Downloads/DSM-17.pdf.
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33 Ibid., p. 16.

34 Ibid., p. 26.

35 Don A. Dillman and Carol C. House, eds., Measuring What We Spend: Toward a New Consumer Expenditure Survey (Washington, 

DC: National Academies Press, 2013), pp. 25–26, https://www.bls.gov/cex/cnstat.pdf.

36 See U.S. Code, Title 26–Internal Revenue Code, section 164(b)(5), “General sales taxes,” pp. 664–65, https://www.govinfo.gov/

app/details/USCODE-2011-title26/&collectionCode=USCODE.

37 Wu-Lang Lee, “BLS Consumer Expenditure Survey data usage: IRS experience” (presentation at the BLS CE Data Users’ Needs 

Forum, June 21, 2010), pp. 4–5, https://www.bls.gov/cex/duf2010lee1.pdf.

38 Schedule A is the tax form on which filers claim deductions for various items, such as state and local income or sales taxes, 

property taxes, mortgage interest, charitable contributions, etc. For more information, see “2020 Instructions for Schedule A” (U.S. 

Department of the Treasury, Internal Revenue Service, January 2021), https://www.irs.gov/pub/irs-pdf/i1040sca.pdf.

39 See “Use the Sales Tax Deduction Calculator” (U.S. Department of the Treasury, Internal Revenue Service, updated October 1, 

2021), https://www.irs.gov/credits-deductions/individuals/sales-tax-deduction-calculator.

40 A Universal Classification Code (UCC) is a special code used in the CE program to identify detailed expenditure items or 

categories. For example, UCC 010110 identifies flour; UCC 010120 identifies prepared flour mixes; UCC 010310 identifies rice; and 

UCC 020110 identifies white bread. Some items are grouped together in one UCC because sparse data prevent them from being 

grouped separately. In such cases, data users cannot identify expenditures on the separate items contained in the bundled UCC. See 

“Consumer Expenditure Surveys public use microdata getting started guide” (U.S. Bureau of Labor Statistics, last modified September 

9, 2021), section 7.5, https://www.bls.gov/cex/pumd-getting-started-guide.htm#section7.

41 Lee, “BLS Consumer Expenditure Survey data usage,” p. 5.

42 “Collection Financial Standards” (U.S. Department of the Treasury, Internal Revenue Service, March 25, 2019), https://

www.irs.gov/businesses/small-businesses-self-employed/collection-financial-standards.

43 “National standards: food, clothing and other items” (U.S. Department of the Treasury, Internal Revenue Service, updated April 29, 

2021), https://www.irs.gov/businesses/small-businesses-self-employed/national-standards-food-clothing-and-other-items.

44 “Local standards: transportation” (U.S. Department of the Treasury, Internal Revenue Service, updated April 5, 2021), https://

www.irs.gov/businesses/small-businesses-self-employed/local-standards-transportation.

45 Ibid.

46 Post allowances, which refer to the place where a State Department employee is “posted” (or stationed) are equivalent to cost-ofliving allowances.

47 “Office of Allowances: Department of State Standardized Regulations” (U.S. Department of State, updated January 10, 2016), 

https://aoprals.state.gov/content.asp?content_id=245&menu_id=75.

48 Ibid.

49 “Office of Allowances: frequently asked questions—post (cost of living) allowance” (U.S. Department of State, effective October 11, 

2020), https://aoprals.state.gov/content.asp?content_id=166&menu_id=75#06.

50 “Office of Allowances: special notice for post allowance (COLA) changes” (U.S. Department of State, effective October 11, 2020), 

https://aoprals.state.gov/content.asp?content_id=156&menu_id=75.

51 See “Statistical Policy Directive No. 1: fundamental responsibilities of federal statistical agencies and recognized statistical units,” 

Federal Register, vol. 79, no. 231, pp. 71610–16 (U.S. Office of Management and Budget, December 2, 2014), https://

www.govinfo.gov/content/pkg/FR-2014-12-02/pdf/2014-28326.pdf.
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52 “Consumer Expenditure Surveys: data quality in the Consumer Expenditure Surveys” (U.S. Bureau of Labor Statistics, last 

modified October 19, 2021), https://www.bls.gov/cex/cecomparison.htm.

53 Evan Hubener, Clayton Knappenberger, Yezzi Lee, and Lucilla Tan, “The CE 2017 data quality profile,” Consumer Expenditure 

Surveys Program Report Series (U.S. Bureau of Labor Statistics, September 7, 2018), p. 4, https://www.bls.gov/cex/dqreport2017.pdf.

54 For more information on how BLS defines CE data quality, see Jeffrey Gonzalez, Catherine Hackett, Nhien To, and Lucilla Tan, 

“Definition of data quality for the Consumer Expenditure Survey: a proposal,” (U.S. Bureau of Labor Statistics, 2009), p. 2, https://

www.bls.gov/cex/research_papers/pdf/ovrvwdataqualityrpt.pdf. See also Gordon Brackstone, “Managing data quality in a statistical 

agency,” Survey Methodology, vol. 25, no. 2. (Statistics Canada, December 1999) https://www150.statcan.gc.ca/n1/pub/12-001-x/

1999002/article/4877-eng.pdf; and Robert M. Groves, Floyd J. Fowler Jr., Mick P. Couper, James M. Lepkowski, Eleanor Singer, 

Roger Tourangeau, Survey Methodology, 2nd ed. (Hoboken, NJ: John Wiley & Sons, Inc., 2009), https://www.wiley.com/en-us/

Survey+Methodology%2C+2nd+Edition-p-9780470465462.

55 See Scott Fricker and Lucilla Tan, “A proposal for a preliminary framework for monitoring and reporting on data quality for the 

Consumer Expenditure Survey” (U.S. Bureau of Labor Statistics, 2012) https://www.bls.gov/cex/research_papers/pdf/

cesrvmeth_quality.pdf.

56 Clayton Knappenberger, Yezzi Lee, and Lucilla Tan, “CE data quality profile reference guide,” Consumer Expenditure Surveys 

Program Report Series (U.S. Bureau of Labor Statistics, September 9, 2020), https://www.bls.gov/cex/dqreport2019.pdf.

57 Ibid. See also “Consumer Expenditure Surveys: data quality in the Consumer Expenditure Surveys.”

58 The bias may result from differences in propensity to respond, as it relates to the outcome, or from differences related to 

characteristics related to both propensity to respond and the data collected. In the first case, a mail-in survey asking whether 

respondents agree or disagree with a statement would suffer from nonresponse bias if those who agree are more likely to mail in their 

surveys than those who disagree; in the second case, people with income above the median may be more or less likely to answer a 

survey on income than those with income below the median. Similarly, if the survey regards something correlated with income, such 

as expenditures, and those with income above the median are more or less likely to answer the survey than those with income below 

the median, nonresponse bias could result.

59 See “Standards and guidelines for statistical surveys” (U.S. Office of Management and Budget, September 2006), Guideline 3.2.9, 

p. 16, https://www.ftc.gov/system/files/attachments/data-quality-act/standards_and_guidelines_for_statistical_surveys_-_omb_-

_sept_2006.pdf.

60 See Sharon Krieger, Brett McBride, Brian Nix, Barry Steinberg, Michael Sverchkov, and Daniel Yang, “Estimates of the Consumer 

Expenditure Survey’s nonresponse bias: an updated nonresponse bias study of the Consumer Expenditure Survey for 2007–2016,” 

internal memorandum, (U.S. Bureau of Labor Statistics, 2020).

61 Ibid.

62 See Boriana Chopova, Jennifer Edgar, Jeffrey Gonzalez, Susan King, Dave McGrath, and Lucilla Tan, “Assessing nonresponse 

bias in the CE Interview Survey: A summary of four studies,” internal memorandum (U.S. Bureau of Labor Statistics, 2008).

63 See Daniel Kasprzyk, “Measurement error in household surveys: sources and measurement,” in Household Sample Surveys in 

Developing and Transition Countries, series F, no. 96 (New York: United Nations, 2005), pp. 171–98, https://unstats.un.org/unsd/

hhsurveys/pdf/Household_surveys.pdf.

64 See, for example, Quentin Brummet, Denise Flanagan-Doyle, Joshua Mitchell, John Voorheis, Laura Erhard, and Brett McBride, 

“Investigating the use of administrative records in the Consumer Expenditure Survey,” working paper 2018-01 (U.S. Census Bureau, 

March 2018), https://www.census.gov/content/dam/Census/library/working-papers/2018/adrm/carra-wp-2018.pdf.

65 Roger Tourangeau, Scott Fricker, Brandon Kopp, and Lucilla Tan, “Report on the state of knowledge of measurement error in the 

CE” (U.S. Bureau of Labor Statistics, February 2013), https://www.bls.gov/cex/research_papers/pdf/tourangeau-CE-measureerror.pdf.
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66 See “Consumer Expenditure Surveys: CE library” (U.S. Bureau of Labor Statistics, last modified November 16, 2021), https://

www.bls.gov/cex/research_papers/research-paper-catalog.htm.

67 See the following sources for more information on these issues: Adam Safir and Karen L. Goldenberg, “Mode effects in a survey of 

consumer expenditures” (U.S. Bureau of Labor Statistics, 2008), https://www.bls.gov/cex/research_papers/pdf/st080200.pdf; William 

Passero, Thesia I. Garner, and Clinton McCully, “Understanding the relationship: CE Survey and PCE,” working paper 462 (U.S. 

Bureau of Labor Statistics, 2013), https://www.bls.gov/osmr/research-papers/2013/pdf/ec130020.pdf; Susan L. King, Jennifer Edgar, 

Jeffrey M. Gonzalez, Boriana Chopova, and Lucilla Tan, “Nonresponse bias in the Consumer Expenditure Survey: a case study” (U.S. 

Bureau of Labor Statistics, 2008), https://www.bls.gov/cex/research_papers/pdf/cesrvymethsking.pdf; and Roger Tourangeau, Scott 

Fricker, Brandon Kopp, and Lucilla Tan, “Consumer Expenditure Survey measurement error study phase 1: report on the state of 

knowledge on measurement error in the CE” (U.S. Bureau of Labor Statistics, 2013), https://www.bls.gov/cex/research_papers/pdf/

Tourangeau_CEMeasureError.pdf.

68 Thomas F. Crossley and Joachim K. Winter, “Asking households about expenditures: what have we learned?” NBER Working 

Paper 19543 (Cambridge, MA: National Bureau of Economic Research, October 2013), https://www.nber.org/papers/w19543.

69 For more on these issues, see Adam Bee, Bruce D. Meyer, and James X. Sullivan, “The validity of consumption data: are the 

Consumer Expenditure Interview and Diary Surveys informative?” in Christopher Carroll, Thomas Crossley, and John Sabelhaus, 

eds., Improving the Measurement of Consumer Expenditures (Chicago: University of Chicago Press, 2015), pp. 204–40, https://

www.nber.org/system/files/chapters/c12662/c12662.pdf. For more information on the quality of CE survey estimates, see Dillman and 

House, eds., Measuring What We Spend: Toward a New Consumer Expenditure Survey, pp. 25–26; and Bee, Meyer, and Sullivan, 

“The validity of consumption data.”

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