by

Geoffrey Paulin, Ph.D.

Senior Economist

Consumer Expenditure (CE) Survey Program

CE Survey Methodology Symposium

July 14, 2015

Washington, DC

Investigating the Imputation 

of Assets and Liabilities in 

the CE Interview Survey
The Consumer Expenditure Survey 

(CE) collects information on:

 Expenditures

 Income

 Taxes (income and other)

 Assets and Liabilities
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
Assets and 

Liabilities

Assets and Liabilities are currently under 

investigation.
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
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?
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
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.
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.
The team considered 

several methods:

 Survey of Consumer Finance method 

(multiple imputation, iterative process)

 Regression trees

 Hotdeck

...But none is feasible.
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:
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.)
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
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.?
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.)
Comments/Suggestions 

are welcome!
Contact Information

Geoffrey Paulin, Ph.D.

Senior Economist

Consumer Expenditure Survey Program

www.bls.gov/cex

202-691-5132

Paulin.Geoffrey@bls.gov