1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Considerations for using 

the Public Use Microdata 

(PUMD)

Aaron E. Cobet

Consumer Expenditure Surveys

Microdata Users Workshop

July 18, 2023
2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

CE’s primary goal: 

Provision of annual 

national and subnational data 

of U.S. consumer expenditures 

to the CPI and other users
3 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Presentation outline

◼ Using CE’s non-expenditure data

◼ How CE methods affect PUMD

◼ Analyzing individual Consumer 

Units (CU)
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Geographic data

◼ Limited geographic data 

 4 Regions 

 9 Census divisions

 4 selected states

 23 selected MSAs

 Population size of area

◼ Consider:

 No or few respondents in some areas

 No zip codes or Census tracks data published
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Available geographic areas

Geographic area Number 

areas

PUMD Tables Databases

National 1 ✔ ✔ ✔

Census Regions 4 ✔ ✔ ✔

Census Divisions 9 ✔ ✔

Selected States 4 ✔ ✔

Selected MSA 23 ✔ ✔ ✔

Population Size of Area NA ✔ ✔ ✔
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MSA by region

Region Metropolitan statistical area

Midwest Chicago, Detroit, Minneapolis St. 

Paul, St. Louis

Northeastern New York, Philadelphia, Boston

Southern Washington DC, Baltimore, Atlanta, 

Miami, Dallas Fort Worth, Houston, 

Tampa

Western Los Angeles, San Francisco, San 

Diego, Seattle, Phoenix, Denver, 

Honolulu, Anchorage
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Quality and quantity data

◼ CE mainly collects spending data 

◼ CE selectively collects data on 

quantity and quality

◼ Consider:

 Vehicles: Make and year 

 Houses: Number of 

houses bought and 

selected aspects
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Purchaser and consumer data

◼ CE does not collect data on 

purchasers or consumers 

◼ Users may infer these 

data sometimes 

◼ Consider:

 Single member CU best case

 Multi-member CU depends on item, e.g. baby food

 CUs may gift expenditures
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Financial data

◼ CE collects data on income and assets 

◼ Consider: 

 Use imputed income data (ITBI files)

 Use financial data for categorizing CU and 

expenditure data and be careful using it for financial 

estimates

 Income data may underrepresent affluent CUs

 Income data may misrepresent economic well-being

See hidden slide for articles on the last two consideration
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Financial data

◼ Financial data may underrepresent affluent CUs: 

A Nonresponse Bias Study of the Consumer 

Expenditure Survey for the Ten-Year Period 

2010-2019

◼ CE financial data may misrepresent economic 

well-being: Interpreting CE income group data
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Income tax data

◼ CE provides income tax liabilities

◼ Consider: 

 Break in series between 2012 and 2013 due to 

CE switch from collecting to estimating data

 TAXSIM estimates tax liabilities that may not 

match actual taxes filed, especially for States
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Income tax background material

◼ NBER TAXSIM description: 

http://users.nber.org/~taxsim/feenberg

-coutts.pdf

◼ New estimates of personal taxes in the 

Consumer Expenditure Surveys: 

https://www.bls.gov/spotlight/2015/co

nsumer-expenditures-tax-estimates/
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Impact of survey design on PUMD

◼ Interview and Diary Surveys have different 

coverages

 Interview Survey does not collect expenditures on 

nonprescription drugs or detailed food and clothing 

expenditures. 

 Diary Survey does not collect overnight travel, insurance 

reimbursements and detailed financial information

◼ Consider: 

 To identify what each survey covers, see the CE profile. 
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Impact of survey design on PUMD

◼ Survey design affects your ability to interpret 

the data

 Bundle individual items into broad categories, i.e., 

tomatoes into fresh vegetables

 Allocate bundled items to individual items, i.e., 

internet, TV, and telephone, i.e., UTA file has 40.8 % 

allocated and imputed in 2021

 Impute or estimate missing items, i.e., 

income and income tax liabilities 
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Impact of allocation and imputation 

on EXPN files, 2021

File Percent File Percent File Percent

UTA 40.85 TRF 17.14 MDC 08.91 

UTC 29.20 HHM 17.90 CRB 08.77

OPI 28.60 FRA 13.99 OVC 06.78

VEQ 22.47 OPD 13.86 CRA 06.53

OPH 19.56 EDA 13.72 LSD 06.11

INB 18.92 TRB 12.40 OVB 05.24

VLR 18.71 APA 11.92 APB 05.24

OPB 18.38 MDB 11.29
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List of EXPN files

File Description

APA Appliances, Household Equipment, and Other Selected Items, major appliances

APB Appliances, Household Equipment, and Other Selected Items, minor appliances

CRA Construction, Repairs, Alterations, and Maintenance of Property - Screening 

Questions

CRB Construction, Repairs, Alterations, and Maintenance of Owned and Rented 

Property - Job Description

EDA Educational Expenses

FRA Home Furnishings and Related Household Items - Purchases 

HHM Hospitalization and Health Insurance 

INB Insurance Other Than Health – Detailed Questions

LSD Rented and Leased Vehicles

MDB Medical and Health Expenditures – Payments For Medical Expenses

MDC Medical and Health Expenditures – Reimbursements For Medical Expenses 

OVB Owned Vehicles – Detailed Questions

OVC Owned Vehicles – Disposal of Vehicles
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List of EXPN files

File Description

OPB/D/H/I Owned Living Quarters and Other Owned Real Estate

TRB Trips and Vacations – Detailed Questions

TRF Trips and Vacations – Local Overnight Stays 

UTA Utilities and Fuels for Owned and Rented Properties – Telephone Expenses

UTC Utilities and Fuels for Owned and Rented Properties – Additional Telephone 

Expenses

VEQ Vehicle Operating Expenses – Vehicle Maintenance and Repair

VLR Vehicle Operating Expenses – Licensing, Registration, and Inspection of Vehicles
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Impact of topcoding on PUMD

◼ Topcoding affects your ability to interpret the 

data

Adjust data to protect respondents’ privacy, i.e., 

geographic data and high income 

◼ Impact ranges widely by data type

 Geographic data: PSUs over 50% topcoded 

 Income data: 90th percentile over 50% topcoded
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Impact of topcoding on 

geographic data, 2021

Geography Topcoded

PSU 56%

State 9%

Division 7%

Region 2%

Geography Topcoded

PSU 53%

State 8%

Division 6%

Region 2%

Interview Survey Diary Survey 
20 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Impact of top-coding on income data, 2021

Decile Topcoded

10th 50%

9

th 8%

8

th 5%

7

th 2%

6

th 2%

5

th 0%

4

th 1%

3

rd 0%

2

nd 0%

1

st 1%

Interview Survey Diary Survey 

Decile Topcoded

10th 53%

9

th 10%

8

th 6%

7

th 3%

6

th 2%

5

th 1%

4

th 2%

3

rd 1%

2

nd 0%

1

st 0%
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Survey materials

◼ Questionnaires and forms for Interview and 

Diary Survey ask respondents about 

expenditures and income. Both surveys use a 

computer assisted personal interviewing (CAPI) 

instrument. Diary Survey also uses a paper 

form. 

◼ Information Booklets provide response options 

to survey questions, conceptual definitions, 

examples and privacy statements, and 

administrative assistance. 
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Effects of data variability

◼ Survey data are subject to 

variability, which may reduce the 

data’s usefulness 

◼ Consider: 

 Whether your data have sufficient 

precision for your research needs 

 How does variability of Consumer 

Expenditure data impact your analysis?
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Analysis of individual CUs over time

◼ Interview Survey: 

 Expenditure data for up to 4 quarters

 Income data covers the entire year

◼ Diary Survey for up to 2 consecutive weeks

◼ Consider:

 Limit research to available timeframes 

 Analyze data by groups to expand the time frame 

(To identify which survey CE sources data from, 

see source selection file)
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Analysis of individual CUs over time

◼ For more information on the effect of 

non-response and the bias it creates, see 

A Nonresponse Bias Study of the 

Consumer Expenditure Survey for the 

Ten-Year Period 2010-2019
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Analysis of group data over time

◼ CE survey changes 

 Census draws the sample addresses from the Master 

Address File (MAF) every 10 years

 BLS refreshes the sample set of primary sample units 

(PSU) every year

 CE may add, drop, or merge questions or items

◼ Consider:

 Use large aggregates: Fresh fruit instead of apples

 Review underlying UCCs over time for changes 
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Hierarchical grouping files

◼ Hierarchical groupings (zip) includes a description of 

each UCC with its hierarchical standing within each 

expenditure or income category for a given year. For 

available 1996 forward, these three hierarchical 

groupings are available: 

 Integrated groupings lists UCCs that the CE tables use, and identifies the survey source 

for the UCCs. These files use this naming convention: CE-HG-Integ-2017

 Interview groupings lists the UCCs from the Interview Survey. These files use this 

naming convention: CE-HG-Inter-2017. Not available for 1996

 Diary groupings list the UCCs from the Diary Survey. These files use this naming 

convention: CE-HG-Diary-2017. Not available for 1996

◼ See PUMD documentation page
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Supplement CE PUMD 

with non-CE datasets

◼ Consider:

 Ensure the concepts match before using two datasets as 

one

– Variables: Name of data and definition

– Methodology: Collection and aggregation

– Presentation: Time frame, unit, and geography

 Adjust the data to increase compatibility 

 Review the CE Data Comparisons
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Income tax concepts comparison

(Differences in red) 

Concept CE (BLS) IRS CPS (Census) CBO

Name of 

series

Average income 

tax 

Average income 

tax return 

Household

income tax 

Average personal

tax 

Concepts 

included

Individual 

income taxes 

minus tax credits 

Individual 

income revenue 

minus tax credits 

plus trust 

accumulation 

distribution 

Individual 

income taxes 

minus tax credits 

Individual 

income taxes 

plus payroll 

taxes, corporate 

income taxes, 

and excise taxes 

Basic 

unit 

Household Tax return Household Household 

Federal Federal Federal Federal Federal

Method Model Sample of actual 

tax returns

Model Model 
29 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Income tax format comparison

(Differences in red) 

Format CE (BLS) IRS CPS (Census) CBO

People 

counted

People living 

financially 

independent 

People filing one 

tax return

People living in 

one housing unit 

People living in 

one housing unit

Rank by Population 

weighted income

Adjusted gross 

income

No ranking Size-adjusted 

income 

Presentation

Income quintile Income segment No rank 

(mircodata)

Income quintile 

Currency U.S. dollars U.S. dollars U.S. dollars U.S. dollars 

Year 2013 2012 2013 2011
30 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Use the CE tables to familiarize 

yourself with CE data

◼ CE tables and LABSTAT database provide 

means and aggregates for CE data by 

demographic characteristics

◼ Consider:

 Use tables as reference for your estimates 

 Use tables for sense of trends
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CE tables

◼ Topline tables provide 12 months data for all CUs for multiple 

years or in greater detail

◼ Calendar year and midyear means tables by demographic 

characteristics provide 12 months means, shares across all 

items, and variances for two time periods

◼ Calendar year aggregate shares tables by demographic 

characteristics provide 12 months aggregate expenditures and 

shares across demographic groups

◼ Geographic tables provide 24 months means data by state, 

region, Census Divisions, and population size of area of 

residence

◼ Cross-tabulated tables provide 24 months means data by two 

socio-economic characteristics
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Want more information?

◼ Survey materials: How did we ask for the data? 

◼ Data flags: How we adjust the data? 

◼ Protection of respondent confidentiality page: 

How does CE topcode data? 

◼ PUMD Getting Started Guide: What are PUMD 

methods? 

◼ Tables Getting Started Guide: Considerations for 

tables
33 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Aaron Cobet

Senior Economist, Consumer Expenditure Surveys

(202)-691-5018

Cobet.Aaron@bls.gov

Thank you!