Jeff Larrimore 

Federal Reserve Board

Jacob Mortenson

Joint Committee on Taxation

David Splinter

Joint Committee on Taxation

The analysis and conclusions set forth in this presentation are those of the authors and do not indicate 

concurrence of the Federal Reserve Board, the Federal Reserve Banks, or their staff. This paper 

embodies work undertaken for the staff of the Joint Committee on Taxation, but as members of both 

parties and both houses of Congress comprise the Joint Committee on Taxation, this work should not 

be construed to represent the position of any member of the Committee. 

Household Incomes in Tax Data: 

Using Addresses to Move From 

Tax Unit to Household Income 

Distributions
Larrimore, Mortenson, and Splinter 2

• IRS tax return data increasingly 

common for measuring income 

distributions

• Income inequality and income mobility (Piketty and 

Saez, 2003; Chetty, Hendren, Kline, and Saez, 2014)

• Tax liabilities (JCT 2012, Tax Policy Center 2017)

• Two major limitations

• 15% of adults do not file a tax return

• No links to others in household 

(Atkinson, Rainwater, Smeeding 1995; CBO 2016)

Background
Larrimore, Mortenson, and Splinter 3

1. Overcome these two limitations of tax data

• Incorporate non-filers 

• Combine tax records into households

2. Determine how the limitation of not 

observing households affects income 

inequality measurement

(Focus on 2010 to match Decennial Census)

Goals of this paper
Larrimore, Mortenson, and Splinter 4

All individual tax forms received by the IRS

•Annual tax returns (e.g. Form 1040)

•Information returns 

•W-2

•SSA-1099

•1099-INT, 1099-DIV, etc

•1099-Misc

Data: IRS Compliance Data Warehouse
Larrimore, Mortenson, and Splinter 5

0.0

1.0

2.0

3.0

4.0

5.0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Millions of persons

Age

Decennial Census: 

Resident population

Tax returns:

Resident filing 

population 

Decennial Census: 308.7 million

IRS (resident filing population only): 281.3 million

Population coverage of tax data (2010)
Larrimore, Mortenson, and Splinter 6

Decennial Census: 308.7 million

IRS (resident filing population only): 281.3 million

IRS (all residents): 307.9 million

Population coverage of tax data (2010)

0.0

1.0

2.0

3.0

4.0

5.0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Millions of persons

Age

Decennial Census: 

Resident population

Tax records: 

Resident population 

on any tax form

Tax returns:

Resident filing 

population 
Larrimore, Mortenson, and Splinter 7

State level population
Larrimore, Mortenson, and Splinter 8

All tax forms contain a mailing address

• Clean addresses to a uniform style 

• Link all returns with the same mailing 

address and ZIP code

Creating Households
Larrimore, Mortenson, and Splinter 9

Check validity of unmatched 1-person addresses with 

a master list of valid street-ZIP code combinations

• Replace invalid street name with neighboring 

year if similar and valid

• Replace remaining invalid street names with 

most similar valid street (if any) in ZIP code

Similarity of text strings defined using Levenshtein

distance method

• Count number of replacements, insertions, and 

deletions between text strings

• Example: [Suoth Street] to [South Street] = 2

Creating Households (continued)
Larrimore, Mortenson, and Splinter 10

Household Counts

March CPS: 117.5 million 

Decennial Census: 116.7 million

Tax Data: 113.5 million

About 2 million households would be added if 

dependents with different addresses were 

counted independently rather than with claimant
Larrimore, Mortenson, and Splinter 11

Note: dependent non-filers are included as part of the tax-unit who claimed them on their return

Households vs. Tax Units

Non-filing individuals

0 1 2+

Filing 

tax units

0 --- 10% 2%

1 59% 4% 1%

2+ 23% 2% <1%

Percent of households with each 

combination of filers and non-filers:
Larrimore, Mortenson, and Splinter 12

Income distributions

$0

$50,000

$100,000

$150,000

$200,000

$250,000

0 10 20 30 40 50 60 70 80 90 100

Mean income by centile

Income centile

Households (March CPS)

Tax units (tax data)
Larrimore, Mortenson, and Splinter 13

$0

$50,000

$100,000

$150,000

$200,000

$250,000

0 10 20 30 40 50 60 70 80 90 100

Mean income by centile

Income centile

Households (March CPS)

Tax units (tax data)

Households (tax data)

Income distributions
Larrimore, Mortenson, and Splinter 14

Top 5% distribution

$0

$200,000

$400,000

$600,000

$800,000

$1,000,000

95 96 97 98 99 100

Mean income by centile

Income Centile

Households (CPS)

Tax units (tax data)

Households (tax data)
Larrimore, Mortenson, and Splinter 15

Inequality Statistics

Tax 

data 

(HH)

Tax 

data 

(TU)

March 

CPS 

(HH)

% 

difference 

using

tax units

% 

difference 

using 

March CPS

Gini 0.516 0.570 0.483 +10% -6%

P90/P10 13.2 18.8 13.7 +42% +4%

Top 20% share 54.6 59.5 51.0 +9% -7%

Top 5% share 27.9 31.4 21.8 +13% -22%

Top 1% share 14.0 16.2 --- +16% ---
Larrimore, Mortenson, and Splinter 16

• Using universe of tax-record data, possible to:

• Incorporate non-filers

• Observe complete households

• CPS understates inequality (greatly understates 

incomes of the top 2%)

• Tax-units are not a sufficient proxy for 

households in tax data – assuming equivalence 

overstates inequality

Conclusions