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Investigating the Imputation 

of Assets and Liabilities in 

the CE Interview Survey

Geoffrey Paulin, Ph.D

Senior Economist

Consumer Expenditure Surveys (CE) Program

CE Microdata Users’ Workshop

July 20, 2023

Washington, DC
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The Consumer Expenditure Survey (CE) collects 

information on:

◼ Expenditures

◼ Income

◼ Taxes (income and other)

◼ Assets and Liabilities
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Nonresponse is a problem for each. However, corrections 

are in place for most of these items:

◼ Expenditures: Since the 1980s

◼ Income: Since 2004

◼ Taxes (income only): Since 2013
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Assets and 

Liabilities

Assets and Liabilities are currently under 

investigation.
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Project Overview

◼ “The purpose of this team is to initiate and 

conduct a research project designed to impute 

missing Interview asset and liability data, 

leveraging models from income imputation 

and other relevant procedures.”

◼ “The goal is to implement this into production 

with 2017 Quarter 2 data.”

Source: Charter for the Asset and Liability Imputation Team, 9-9-2014
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This presentation describes three aspects 

of the project:

◼ What asset and liability data are collected?

◼ What processes have been considered for 

imputation of missing values?

◼ What are the next steps in the investigation?
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Asset/Liability Data

Assets:

◼ Retirement accounts

◼ Stocks, bonds, mutual 

funds

◼ Checking, savings, 

money market, CDs

◼ Whole life insurance

◼ Other, including 

annuities, trusts, 

royalties

Liabilities:

◼ Credit cards

◼ Student loans

◼ Other loans, including 

medical and personal
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Collection

◼ Questions are asked in the final survey (4th

interview)

◼ Most are asked in two parts: Did you have 

____? If yes, how much?

◼ For some items, only a total value is collected. 

In these cases, it is not clear whether $0 

means:

No, I did not have such an account or

Yes, I had an account, but it is empty.
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Collection, continued:

◼ For each asset/liability, the total 

value/balance/amount owed is collected:

As of today

As of one year ago today

◼ Bracket questions are asked when the 

respondent cannot provide a specific value.
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The team considered several 

methods:

◼ Survey of Consumer Finance method (multiple 

imputation, iterative process)

◼ Regression trees

◼ Hotdeck

...But none is feasible.
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Going back to the original 

motivations (charter):

A system based on income imputation processing is 

being investigated.

Regression-based, multiple imputation of each 

component asset/liability, from which “total 

change in” values can be derived.

For each component, separate models are run for 

demographic groups across which large variation 

in parameter estimates is observed or expected.

For example:
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Consider IRAX.

◼ Amount reported when asked: “What is the 

total value of all retirement accounts such as 

401(k)s, IRAs, and Thrift Savings Plans that you 

or your household own/owns?”

◼ Expected to vary considerably by age

◼ Preliminary tests support use of one model for 

each age group (group 1: age<A; group 2: 

A<=age<B, etc.)
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Bracket imputation will also be used:

◼ Respondent identifies range in which 

asset/liability falls (e.g., less than $X; $X to $Y; 

etc.)

◼ Five values are selected based on current 

methods used in income imputation; each 

falls within the specified bracket range.

◼ Open-ended brackets ($Z or over) also are 

treated in income imputation
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Related Challenges:

◼ How to distinguish $0 meaning no balance 

from $0 meaning no account.

◼ How to identify groups upon which to base 

models. That is:

Grouping variable: Is IRAX (e.g.) based on age, 

occupation, or something else?

Variable range: If age, where do the breaks 

occur—under 35, 35 to 64, 65 and older, or under 

25, 25 to 34, etc.?
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Work in progress:

◼ Identifying groups, and selecting variables to 

include within each model.

ANOVA/Chow tests have been used so far to test 

differences/pooling potential across groups.

Variables used in income imputation are 

considered the “starter group,” with some to be 

added, deleted, or redesigned. (Example: Age 

ranges used in binary variables could be widened 

or narrowed.)
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Comments/Suggestions are 

welcome!
Contact Information

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Geoffrey Paulin, Ph.D.

Senior Economist

Consumer Expenditure Survey Program

www.bls.gov/cex

paulin.geoffrey@bls.gov