A PROFILE OF NON-FILERS 

by 

Jim Cilke

U.S. Department of the Treasury

OTA Paper 78 July, 1998

OTA Papers and Briefs are circulated so that the preliminary

findings of tax research conducted by staff members and others

associated with the Office of Tax Analysis may reach a wider

audience. The views expressed are those of the author, and do

not reflect Treasury policy. Additional copies of this

publication may be purchased from the National Technical

Information Service, 5285 Port Royal Road, Springfield, VA 22161.

Office of Tax Analysis

U.S. Treasury Department

Washington, DC 20220

I wish to thank several people for their valuable comments

and suggestions; Lowell Dworin, Bob Gillette, Janet Holtzblatt,

Karl Scholz, and Roy Wyscarver. 
 In October, 1996 the Office Of Tax Analysis (OTA) received

the CPS-IRS exact match data file produced by the Bureau of the

Census. To produce this file, the Bureau of the Census tried to

exactly match information from 1990 Federal tax returns to every

person on the March 1991 Current Population Survey (CPS).1 In

this paper, I describe how I used this data file to address one

specific problem. I was interested in developing a "profile" of

non-filers. Looking only at the population of CPS persons not

legally required to file a tax return (e.g., with income below

the filing threshold), I compared and contrasted the

characteristics of people that did not file a tax return with the

characteristics of those that did. 

Being legally required to file a tax return is not the same

as having an incentive to file. Even though not legally

required, a person may have an incentive to file to obtain a

refund of any taxes that were withheld from his wages. A person

with children and earned income may have an additional incentive

to file to claim a refundable earned income tax credit (EITC).

Other agencies, such as a state government, may ask for

information normally reported on a tax return. Finally, people

may a file tax return for personal reasons, or for no apparent

reason at all.

1
 The first section of this paper discusses why a profile of

the non-filing population is important. The next two sections

briefly describe the exact match study and the steps I have taken

to determine whether a CPS person was required to file. The last

section contains the results of the comparison. This section

also contains the results of a series of probit econometric

equations. Among persons not required to file, the equations

predict, for each person, the likelihood of being a non-filer.

Before proceeding, let me highlight some topics this paper

does not address. First, this is not a paper about tax

compliance (or non-compliance).2 I only look at persons that,

based on CPS information only, are apparently not required to

file a Federal tax return. Second, this is not a paper about

poverty or the characteristics of persons living in poverty in

the United States. Although the people I examine generally have

incomes below the poverty line, the focus of this paper is simply

to compare and contrast the characteristics of low-income persons

who file a return against those who do not. Third, I do not

attempt to profile the entire non-filing U.S. population; I only

examine those people within the CPS sampling framework who are

not required to file a tax return. Thus, the population under

investigation excludes the institutional population and all

2
persons under the age of 15 years. Fourth, I only present

relative comparisons of subgroups in the population. Estimates

of the actual number of non-filers is a topic of future research. 

Finally, this paper only provides descriptive statistics; I do

not make any policy prescriptions.

WHY WE NEED INFORMATION ON NON-FILERS

Many agencies, including OTA, use data sampled from

individual tax returns to describe certain characteristics of the 

population. Arguably, the best annual source of micro-level 

information on the sources and levels of income in the U.S. is

the large sample of tax returns drawn by the Statistics of Income 

division (SOI) of the Internal Revenue Service.3 This data file

forms the backbone of the individual tax simulation models used 

by the Treasury Department, as well as the Joint Committee on

Taxation, the Congressional Budget Office, and other government

and non-government agencies.

One obvious drawback to the SOI data is that the sample only 

covers the population who file an income tax return. However, 

many tax reform proposals affect individuals who do not currently

file an income tax return; i.e., non-filers. For example, during

3
the health care reform debate, OTA examined ways of using the tax

system to provide subsidies of health insurance costs for lowincome individuals, including non-filers. More concretely, in

1993 Congress expanded the Earned Income Tax Credit so that some

non-filers with low earnings and without children became eligible

to receive a refundable credit if they filed a tax return.4

Further, OTA estimates the distribution of tax burdens from all

Federal taxes including excise taxes on cigarettes and gasoline. 

Significant portions of these additional taxes are borne by

non-filers. 

Here then is the fundamental modeling issue: OTA uses data

from tax returns for many policy analyses. However, for some

analyses, tax data does not cover an important part of the

population. To address this problem, OTA augments the tax data

with microdata information on non-filers. The solution, adopted

by OTA is to create non-filers from low-income persons on the

CPS.

In OTA’s current tax model based on 1989 tax data, nonfilers were drawn almost entirely from CPS persons apparently not

required to file a tax return. Non-filers were largely generated

as the residual group after persons on the CPS were linked to

returns on an SOI file using a constrained statistical match.5 

4
OTA extracted from the CPS, a weighted number of “constructed tax

units” that exactly equaled the tax filing population. Every CPS

person not in this group became a non-filer in the tax model. 

Other researchers are struggling with the same dilemma.6

With the availability of the CPS-IRS exact match study, OTA and

other researchers may be able, in future tax models, to

more-closely define the non-filing population. This paper

assists this process in two ways. First, the paper provides

descriptive statistics comparing low income filers to low income

non-filers on the CPS. Second, the paper presents a set of

probit econometric equations that may be used to impute nonfilers from a CPS file.

SUMMARY OF THE CPS-IRS EXACT MATCH STUDY

The Current Population Survey (CPS), conducted by the Bureau

of the Census, obtains economic, employment, and demographic

information from a sample of the non-institutional population of

the United States. The universe for the survey is the United

States civilian non-institutional population living in housing

units plus members of the Armed Forces living in civilian housing

on or off a military base. The Bureau of the Census used a

5
probability sample to select approximately 60,000 households for

the March 1991 survey. Detailed information is collected for the 

approximately 121,000 persons in the survey aged 15 and over. 

The Bureau of the Census attempted to exactly match all 

persons aged 15 and over on the March 1991 CPS to tax returns on 

the IRS's Individual Returns Transactions File (IRTF) filed for

tax year 1990. A record linkage between these two data files 

involved two major tasks. First, SSNs reported on the CPS file 

needed to be validated. Second, tax return data needed to be 

found and extracted for each valid SSN. 

The Census Bureau worked in cooperation with the Social 

Security Administration (SSA) to validate reported SSNs. Each 

SSN as well as other person-specific information was compared to 

administrative data held by the SSA. Of the 121,000 persons on

the CPS approximately 8,000 CPS respondents refused (or were

unable) to provide an SSN. The SSNs for another 8,000

respondents did not match records from the Social Security

Administration and were declared invalid. In the end,

approximately 106,000 CPS records had validated SSNs. Only these

CPS persons were included in the matching process. 

6
 The next step was to find and extract the tax return 

information for those persons with a valid SSN. The IRS provided 

the Census Bureau with an extract data file from the IRTF 

containing all 1990 tax returns filed as of the end of August

1991.7 A taxpayer claiming an automatic extension had until

August 15, 1991 to file his 1990 tax return. Approximately

86,000 CPS persons were matched to tax returns.8

The Bureau of the Census then created a data file based on

the results of the exact match.9 The file contains 23 variables

for each of the 121,000 persons on the CPS file over the age of

15. One variable indicates whether or not a successful match was

obtained. This variable also indicates whether a match was

possible (i.e., whether the CPS person reported a valid SSN). 

Two variables are used to exactly link each record on the match

file to the corresponding record on the full 1991 March CPS. The

remaining 20 variables each contain tax information. The Census

Bureau "masked" all of the tax information to remove the

possibility of uniquely identifying any person on the file.10

For purposes of this exercise, the only variables used from 

the Exact Match file were the two file linking variables and the 

matching indicator variable. Further, I assumed that persons who 

did not report a valid SSN are randomly distributed among filers 

7
and non-filers. I also assumed that the number of people 

mis-identified as a filer or as a non-filer is very small.

FORMING RETURNS AND DETERMINING FILING REQUIREMENTS

The next step is to identify, using CPS information only,

those persons who were not required to file a Federal tax return. 

All of the analyses presented in this paper are based on this

particular population. In general, a filing requirement can be

determined with three pieces of information; filing status

(single, joint, or head-of-house), dependency status (dependent

or non-dependent), and adjusted gross income (AGI). The Office

of Tax Analysis has well-established procedures for determining

these three pieces of information for each person on the CPS.11 

These procedures attempt to follow the IRS rules as closely as

possible. 

Determining Filing Status

Married persons (except separated) are assigned a joint

filing status. A person deemed to have a head-of-house filing

status had the following characteristics. First, the person was

unmarried or married but separated. Second, the person was the

“reference” person in the household. In general, the housing

8
unit is owned or rented in this person’s name. Third, the

household had at least one of the following: a) had an unmarried

child, grandchild, or foster child, b) a married child or

grandchild that could be claimed as a dependent, or c) a nonchild relative that could be claimed as a dependent. I assumed

all other unmarried persons had a single filing status.12

Determining Dependency Status

A person could be a dependent or a non-dependent. A person

was a dependent if he passed a “relationship test,” a “support

test,” and for certain people, a “gross income test.” In

general, a person met the relationship test if he was a relative

of the primary person or spouse in the family. The support test

was met if the person’s total income was less than the average

total income among all family members. The gross income test was

met if the person’s AGI was less than the value of a personal

exemption ($2050 in 1990.) The gross income test did not apply

to children under 19 years of age. Nor did the test apply to

children or foster children who were going to school and were at

least 19 years old.13

Determining Adjusted Gross Income

An estimate of individual’s adjusted gross income (AGI) is

simply the sum of the following income sources reported on the

9
CPS. For joint returns, the amounts for each spouse were

combined. 

o Wages, 

o Net non-farm business income, 

o Net farm income, 

o Unemployment compensation, 

o Pensions, 

o Interest, 

o Dividends, 

o Net rental income, 

o Alimony,

o Survivors benefits except from workers compensation,

o Disability income except from workers compensation and

from military retirement benefits,

o Certain educational assistance,

o Up to one half of Social Security and Railroad Retirement

benefits, according to the taxation of benefits rules.

Determining a filing requirement

As a general rule a person (or joint couple) is required to 

file a tax return if the sum of his (their) AGI exceeds a

specified threshold amount. The threshold amount is equal to the

sum of the return's standard deduction plus personal exemptions

for self and spouse.14 In 1990, the threshold amount for

non-dependent single returns was $5,300 plus an additional $800

if the person was aged 65 or older. The threshold for dependent

single returns was the greater of $500 or earned income up to the

non-dependent single's threshold. For joint returns the

threshold was $9,550 plus an additional $650 for each spouse aged

65 or older. For head-of-house returns, the threshold was $6,800

plus $800 if aged 65 or older. 

10
In addition to this threshold test, any person with net

self-employment earnings greater than $400 is required to file. 

For purposes of this study, I further assumed any person with

negative total income, which could occur with business losses,

was required to file.

Several important differences between tax return information

and CPS data are worth noting. These differences make it

impossible to exactly determine whether a low-income CPS person

was required to file a tax return. First, demographic

information on the CPS reflects each person's status in March of

1991 and may be legitimately different from what was reported on

a 1990 tax return.

Second, the CPS does not collect information on certain

types of income included in AGI. In particular, CPS does not

collect realized capital gains. Further, the definitions of

individual income items on the CPS may differ somewhat from the

definitions used by the IRS. For example, wages on the CPS may

include tax-deferred contributions to 401(k) or other retirement

plans. Interest income on the CPS may include tax-exempt

interest. However, such differences are likely to be negligible

among low-income persons.

11
 Third, the CPS does not collect certain pieces of

information that would indicate a person is required to file a

tax return, regardless of the level of his AGI. For example, the

CPS does not capture whether a person had a penalty tax from an

early retirement plan distribution. In addition, the tax rules

for determining filing status or dependency status are much more

complicated than modeled here.15

Fourth, the Bureau of the Census recognizes that income 

nonresponse is an important problem. So, the CPS imputes sources 

and amounts of income to "non-respondents." Such imputations are 

unlikely to exactly hit the true amount of income for any given 

person.

As noted earlier, even though they are not legally required,

many people still file a tax return. A person with income below

the filing requirement may file to obtain a refund of any taxes

that were withheld from his or her wages. A person with children

and earned income may have an incentive to file a return to claim

a refundable earned income tax credit (EITC).16 The CPS does not

indicate whether a person made estimated tax payments or had tax

withheld from his or her wages. But, as I shall show, the

presence of earned income substantially increases the likelihood

a person will be a filer.

12
RESULTS

To reiterate, the population examined here are people in the

CPS who: a) reported a valid SSN (or, in some cases, was the

spouse of someone with a valid SSN), and b) were apparently not

required to file a tax return (using CPS information only).

The population was further divided into nine unique groups:

1) Unmarried dependents, 

2) Unmarried, without children, and under 62 years of age,

3) Unmarried, without children, and aged 62 and over,

4) Married, without children, and age under 62 years,

5) Married, without children, and aged 62 and over, 

6) Married, with children, and age under 62 years,

7) Married, with children, and age 62 and over.

8) Unmarried, with children, and under 62 years of age,

9) Unmarried, with children, and aged 62 and over,

The characteristics examined are: 

a) Gender, 

b) Primary activity (in the previous week of the survey), 

c) Highest education grade completed, 

d) Head of a household status, 

e) Race class (Black, Asian+Indian+Other, Hispanic, and White), 

f) Type of living quarters (home or apartment vs. any other 

living quarters including mobile homes), 

g) Public or assisted housing status,

h) Presence of means tested transfer income (general 

assistance including AFDC, SSI, and food stamps). 

i) Presence of non-taxable, non-means tested transfer income 

(veteran's benefits, workers compensation, and other 

non-taxable transfers except Social Security).

j) Presence of Social Security income

k) Presence of earned income (wages, farm, and non-farm

business income), 

l) Presence of taxable unearned income (dividends, interest,

13
 net rent, and alimony),

m) Presence of taxable transfers income (pensions and 

annuities, taxable survivors income, taxable disability 

income, and unemployment compensation)

n) Presence of Adjusted Gross Income. 

For purposes of this study, I assumed that incomes of 

married persons are shared. So, for example, a person without 

earned income would be classified as having earnings if his 

spouse had earnings. For all persons, certain household 

characteristics are assumed to be shared. For example, if the 

household receives public housing assistance, then each person in 

the household receives public housing assistance.

Table 1 shows three columns of data. The first column shows

the percent distribution, across the nine groups, of all persons

on the CPS who apparently are not required to file a tax return.

The second column presents the percent distribution of actual

non-filers as determined by the exact match data file. The third

column shows the percent of each group who are non-filers. That

is, the third column shows, for each group, the ratio of actual

non-filers to the total population not required to file. For

example, among unmarried dependents not required to file, 64.8%

do not file a tax return.

14
------- --------- 

 TABLE 1

PERCENT DISTRIBUTION OF PEOPLE NOT REQUIRED TO FILE A TAX RETURN

BY TYPE OF PERSON 

(all values in percentages)

Persons Not Required to File 

-----------------------------

ratio of

actual actual 

all non- non-filers 

people filers to total 

--------

Unmarried Dependents 31.1 36.4 64.8

Unmarried, No Children, Age < 62 13.2 10.4 43.7

Unmarried, No Children, Age >=62 16.0 19.5 67.3

Married, No Children, Age < 62 4.1 2.6 35.6

Married, No Children, Age >=62 16.7 12.9 43.0

Married, With Children, Age < 62 6.2 4.0 36.1

Married, With Children, Age >=62 1.3 1.2 51.3

Unmarried, With Children, Age < 62 8.8 9.9 62.2

Unmarried, With Children, Age >=62 2.5 3.1 69.7

All Persons 100.0 100.0 55.5

-----------------------------------------------------------------------

15
Table 2 compares, for several assorted demographic and

economic characteristics, actual non-filers with the total

population not required to file. Each row in Table 2 classifies

a person by a particular demographic or economic characteristic.

Table 2 shows two columns of data for each of the nine groups

under investigation. The first column shows the percent of

actual non-filers with a particular attribute; the second column

shows the same statistic as it applies to all persons not

required to file. By comparing these two columns, one can see

how actual non-filers compare to persons not required to file a

return. 

Many of the results of this study are not surprising. Some 

highlights of important results seen in Tables 1 and 2 are as 

follows (in bullet form). 

Results relating to population subgroups.

S The largest group of non-filers, comprising 36.4% of the 

non-filing population, are single dependents. Not 

surprisingly, most people in this class are going to school. 

Recall that the stringent gross income test does not apply 

persons age of 19 and over who going to school. 

S The second and third largest classes of non-filers are 

unmarried and married persons without children and aged 62 

and over. These groups, respectfully accounting for 19.5% 

and 12.9% of the population of non-filers, can largely be 

characterized as retired or semi-retired persons living 

primarily on Social Security income. Interestingly, almost 

79% of the unmarried non-filers are female. 

16
S The next largest class of non-filers, accounting for 10.4% 

of the non-filing population, are unmarried persons without 

children and under 62 years of age. This is probably the 

most difficult group to characterize. The group is fairly 

evenly split between male and female. About 60% of this 

group have no or little taxable income and about 32% have 

some earned income. About 53% are the primary person in the 

household. Most have at least a high-school education. 

S Unmarried persons with children comprise 13.0% of the nonfiling population. An overwhelming majority of these 

persons are female; 94% of persons under 62 years of age and 

86% of persons aged 62 and over are female. 

S As seen in column 3 of Table 1, married people who are not 

required to file are more likely to file a return than 

unmarried people. Similarly, non-dependents under the age 

of 62 are more likely to file a return than non-dependents 

aged 62 and over. 

Results relating to sources of income included in AGI

S The presence of earned income increases the likelihood of 

filing a return. Table 2 shows that 33.5% of all persons in 

the examined population have some earnings. However, among 

non-filing persons, only 17.3% have earnings. The presence 

of earnings is often associated with wage withholding. 

Interestingly, this approximate 2-to-1 ratio applies to 

people with children as well as people without children. 

S Although not as dramatic, the presence of unearned income 

included in AGI also increases the likelihood of filing a 

return. Here, 32% of all examined persons have some taxable 

unearned income, while 26% of non-filing returns have 

unearned income. 

S Following a similar pattern, the presence of taxable 

transfer income (pensions and annuities + unemployment + 

certain types of disability and survivors income) included 

in AGI slightly increases the likelihood of filing a return. 

Here, 14.5% of all examined persons have some taxable 

transfer, while just over 10% of non-filers have a taxable 

transfer. 

17
Results relating to sources of government transfer income.

S People with means-tested government transfer income (food 

stamps, SSI, or AFDC) are less likely to file a return. 

Here, nearly 25% of persons not required to file have some 

means-tested transfer income. However, 32% of non-filers 

have some means-tested transfer. Among unmarried persons 

with children and under 62 years old, the corresponding 

percentages are 73% and 83%. The corresponding percentages 

for unmarried persons without children and under 62 years of 

age are 30% and 50%. 

S Interestingly, the pattern is reversed for persons with 

selected non-taxable non-means tested transfers (veteran’s 

benefits, workers compensation, and other benefits.) 

Non-filers are slightly less likely to receive these types 

of benefits. 

Results relating to gender, education, and race

S An overwhelming majority of unmarried persons with children 

who are not required to file are female. Further, the 

percent of filers who are female is roughly the same as the 

percent of non-filers who are female. 

S Interestingly, among married persons without children and 

under 62 years, 62.5% are female. Correspondingly, among 

married without children and over 62, 47.5% are female. The 

obvious explanation is that many females are married to an 

older, retired male. (Obviously, the number of married male 

and female non-filers must be equal.) 

S Persons with an education level at or below the 10th grade 

are less likely to file. Persons with some college 

education are likely to be filers. 

S Non-filers who are household heads occur at approximately 

the same percentage as all persons below the filing 

threshold who are household heads. 

S Minorities are somewhat less likely to file than whites. 

18
------------------ ------------------ ------------------ -------------------

-------- -------- -------- -------- -------- -------- ------- --------

TABLE 02 (continued) 

PERCENT OF ACTUAL NON-FILERS AND PERSONS NOT REQUIRED TO FILE: 

BY ASSORTED DEMOGRAPHIC AND ECONOMIC CHARACTERISTICS

BY TYPE OF PERSON

UNMARRIED UNMARRIED MARRIED MARRIED

DEPENDENTS ONLY NO KIDS, AGE < 62 NO KIDS, AGE >=62 NO KIDS, AGE < 62 NO KIDS, AGE >=62

------------------

NON- NON- NON- NON- NONFILERS ALL NOT FILERS ALL NOT FILERS ALL NOT FILERS ALL NOT FILERS ALL NOT

(NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED

MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE 

-------- --------

LIVING QUARTERS 

HOUSE OR APARTMENT 94.4 95.1 88.3 91.6 91.4 92.5 85.3 86.9 89.6 91.6 

OTHER 5.6 4.9 11.7 8.4 8.6 7.5 14.7 13.1 10.4 8.4 

PUBLIC HOUSING ASSISTANCE 

NO ASSISTANCE 94.7 95.6 83.9 90.1 78.6 83.8 90.4 94.5 94.3 96.8 

WITH ASSISTANCE 5.3 4.4 16.1 9.9 21.4 16.2 9.6 5.5 5.7 3.2 

MEANS TESTED TRANSFERS 

NO TRANSFERS 80.0 83.9 50.0 69.5 75.0 81.9 59.3 76.9 86.5 92.8 

WITH TRANSFERS 20.0 16.1 50.0 30.5 25.0 18.1 40.7 23.1 13.5 7.2 

NON-MEANS TESTED TRANSFERS 

NO TRANSFERS 98.5 98.3 86.4 88.5 92.1 92.8 78.0 81.0 88.6 89.2 

WITH TRANSFERS 1.5 1.7 13.6 11.5 7.9 7.2 22.0 19.0 11.4 10.8 

SOCIAL SECURITY BENEFITS 

NO SOCIAL SECURITY 90.4 92.4 73.4 84.2 6.0 5.0 44.7 54.9 3.7 3.1 

WITH SOCIAL SECURITY 9.6 7.6 26.6 15.8 94.0 95.0 55.3 45.1 96.3 96.9 

COMPONENTS OF AGI 

NO EARNED INCOME 78.4 63.3 68.5 38.3 95.9 93.0 74.1 51.1 93.3 85.3 

WITH EARNED INCOME 21.6 36.7 31.5 61.7 4.1 7.0 25.9 48.9 6.7 14.7 

NO UNEARNED INCOME 87.4 89.2 82.0 72.9 54.2 45.5 77.6 59.3 45.1 34.2 

WITH UNEARNED INCOME 12.6 10.8 18.0 27.1 45.8 54.5 22.4 40.7 54.9 65.8 

NO TRANSFERS IN AGI 99.9 99.9 96.0 94.9 82.6 77.6 87.7 77.6 64.4 56.9 

WITH TRANSFERS IN AGI 0.1 0.1 4.0 5.1 17.4 22.4 12.3 22.4 35.6 43.1 

NO AGI 68.7 55.4 54.2 28.0 44.9 34.9 52.2 23.5 31.4 18.8 

WITH AGI 31.3 44.6 45.8 72.0 55.1 65.1 47.8 76.5 68.6 81.2 

ADDENDUM AGI < $100 77.3 61.9 59.7 30.9 53.7 42.0 56.6 27.9 36.2 22.3 

-----------------------------------------------------------------------------------------------------------------------------

20
-------- -------- -------- -------- -------- -------- -------- --------

 TABLE 2 (continued) 

PERCENT OF ACTUAL NON-FILERS AND PERSONS NOT REQUIRED TO FILE: 

BY ASSORTED DEMOGRAPHIC AND ECONOMIC CHARACTERISTICS

BY TYPE OF PERSON

MARRIED MARRIED UNMARRIED UNMARRIED

W KIDS, AGE < 62 W KIDS, AGE >=62 W KIDS, AGE < 62 W KIDS, AGE >= 62 ALL RETURNS

------------------ -------------------- ------------------- ------------------- -------------------

NON- NON- NON- NON- NONFILERS ALL NOT FILERS ALL NOT FILERS ALL NOT FILERS ALL NOT FILERS ALL NOT

(NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED

MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE 

-------- --------

GENDER 

MALE 45.1 45.9 61.0 60.2 5.6 7.0 14.1 17.8 39.5 41.4 

FEMALE 54.9 54.1 39.0 39.8 94.4 93.0 85.9 82.2 60.5 58.6 

ACTIVITY LAST WEEK 

WORKING 15.7 30.2 3.3 4.3 8.5 21.7 1.5 4.9 5.7 13.0 

WITH JOB NOT AT WORK 0.6 1.3 0.5 0.3 0.1 0.8 0.0 0.0 0.2 0.5 

LOOKING FOR WORK 5.9 5.2 0.0 0.2 2.8 2.6 0.0 0.0 2.0 2.3 

KEEPING HOUSE 40.9 36.4 27.9 29.2 64.7 52.9 45.9 40.9 28.3 24.9 

GOING TO SCHOOL 7.7 5.2 0.0 0.0 8.2 8.0 0.0 0.0 26.7 24.2 

UNABLE TO WORK 10.6 6.6 6.1 4.4 5.6 4.1 13.6 10.4 7.8 5.4 

RETIRED 2.4 1.5 49.3 51.0 0.4 0.4 28.7 34.7 17.3 18.6 

OTHER 16.1 13.6 12.8 10.6 9.8 9.4 10.3 9.0 12.1 11.1 

EDUCATION LEVEL 

EDUC <= 10TH GRADE 41.3 38.4 69.9 65.3 38.6 32.7 69.2 60.3 58.0 46.0 

EDUC 11TH OR 12TH GRADE 44.4 44.8 26.7 28.8 47.6 50.8 27.3 31.6 32.1 38.1 

EDUC 1-3 YRS COL 11.2 11.6 0.5 3.3 11.9 14.0 2.2 4.7 7.6 11.9 

EDUC 4YR COLLAGE+ 3.1 5.3 2.9 2.6 1.8 2.5 1.3 3.5 2.3 4.0 

HOUSEHOLD STATUS 

HOUSEHOLD HEAD 96.4 95.2 100.0 100.0 90.3 89.1 100.0 99.9 54.3 57.0 

HOUSEHOLD MEMBER 3.6 4.8 0.0 0.0 9.7 10.9 0.0 0.1 45.7 43.0 

RACE 

BLACK 17.0 14.9 31.8 24.9 44.1 40.5 35.0 28.5 22.3 18.3 

ASIAN, INDIAN, OTHER 8.4 7.8 3.8 5.0 3.4 3.1 2.7 2.5 4.2 3.8 

HISPANIC 18.4 20.5 9.9 11.5 12.8 11.7 8.6 7.4 9.5 8.5 

WHITE 56.1 56.8 54.5 58.5 39.6 44.7 53.8 61.7 64.1 69.4 

21
-------- -------- -------- -------- -------- -------- -------- -------- --------

 TABLE 2 (continued)

PERCENT OF ACTUAL NON-FILERS AND PERSONS NOT REQUIRED TO FILE: 

BY ASSORTED DEMOGRAPHIC AND ECONOMIC CHARACTERISTICS

BY TYPE OF PERSON

MARRIED MARRIED UNMARRIED UNMARRIED

W KIDS, ALL AGES W KIDS, ALL AGES W KIDS, AGE < 62 W KIDS, AGE >= 62 ALL RETURNS

------------------ -------------------- ------------------- ------------------- -------------------

NON- NON- NON- NON- NONFILERS ALL NOT FILERS ALL NOT FILERS ALL NOT FILERS ALL NOT FILERS ALL NOT

(NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED

MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE 

--------

LIVING QUARTERS 

HOUSE OR APARTMENT 89.9 89.3 92.7 94.1 93.2 92.3 96.3 95.9 92.1 92.7 

OTHER 10.1 10.7 7.3 5.9 6.8 7.7 3.7 4.1 7.9 7.3 

PUBLIC HOUSING ASSISTANCE 

NO ASSISTANCE 86.9 89.0 97.3 97.9 64.2 68.7 93.4 95.2 87.0 90.4 

WITH ASSISTANCE 13.1 11.0 2.7 2.1 35.8 31.3 6.6 4.8 13.0 9.6 

MEANS TESTED TRANSFERS 

NO TRANSFERS 35.5 46.9 66.9 77.9 17.7 26.6 68.7 75.6 67.8 75.2 

WITH TRANSFERS 64.5 53.1 33.1 22.1 82.3 73.4 31.3 24.4 32.2 24.8 

NON-MEANS TESTED TRANSFERS 

NO TRANSFERS 80.9 82.0 82.2 83.2 93.4 91.9 94.3 94.0 92.6 92.0 

WITH TRANSFERS 19.1 18.0 17.8 16.8 6.6 8.1 5.7 6.0 7.4 8.0 

SOCIAL SECURITY BENEFITS 

NO SOCIAL SECURITY 76.0 83.4 7.2 6.7 87.8 89.0 9.6 8.5 55.4 56.8 

WITH SOCIAL SECURITY 24.0 16.6 92.8 93.3 12.2 11.0 90.4 91.5 44.6 43.2 

COMPONENTS OF AGI 

NO EARNED INCOME 61.1 31.5 91.6 80.0 79.8 58.8 95.8 90.7 82.7 66.5 

WITH EARNED INCOME 38.9 68.5 8.4 20.0 20.2 41.2 4.2 9.3 17.3 33.5 

NO UNEARNED INCOME 87.7 80.9 70.6 66.4 90.6 86.8 72.0 62.5 74.3 67.9 

WITH UNEARNED INCOME 12.3 19.1 29.4 33.6 8.4 13.2 28.0 37.5 25.7 32.1 

NO TRANSFERS IN AGI 94.4 89.5 63.0 60.2 97.6 95.8 84.0 78.0 89.8 85.5 

WITH TRANSFERS IN AGI 5.6 10.5 37.0 39.8 2.4 4.2 16.0 22.0 10.2 14.5 

NO AGI 50.3 23.6 47.3 35.8 72.6 52.2 59.5 48.1 56.4 38.4 

WITH AGI 49.7 76.4 52.7 64.2 27.4 47.8 40.5 51.9 43.6 61.6 

ADDENDUM AGI < $100 53.7 26.0 51.3 39.5 77.1 55.6 66.9 54.7 63.4 43.3 

------------------------------------------------------------------------------------------------------------------------------

22
THE PROBIT EQUATION

One of the objectives of this paper is to provide a

methodology for obtaining microdata information about non-filers

that could be used in tax modeling. The approach taken here is

to estimate a set of econometric probit equations that predict

whether a person with income below the filing threshold is likely

to be a non-filer. I ran separate, but identically specified,

equations for each of the nine groups of people. (The appendix

at the end of this paper shows the definition of each of the

variables used in the probit equations.) To obtain consistent

results for married couples, I ran the probit equation on only

one spouse.17

Almost all of the independent variables used in the

equations are dummy variables. The only continuous variable is

AGI as a percent of the filing threshold. Note that I eliminated

some variables from some equations because some values of certain

variables were particularly sparse. For example, hardly any

dependents had AFDC benefits. A parameter value of 0.000 shown

in Table 3 indicates that either every observation had the same

value for that variable or that I forced every observation to

have the same value. 

23
 The results of the equations are seen in Table 3. A 

positive value for a parameter means the presence of that 

characteristic increases the likelihood that person will be a 

non-filer. For example, the parameters for people without earned

income are positive for all subgroups. The results of the probit

equations seen in Table 3 largely mirror the descriptive

statistics shown in Table 2. 

As a test, I applied the probit equations to the same set of

observations used to create the equations. This produced, for

each observation, a cumulative probability estimate. I then

predicted whether each observation would be a non-filer based on

whether the person’s probability estimate was below the 

probability of being a non-filer as shown in column 3 of Table 1. 

For example, if the cumulative probability estimate for a

dependent person was less than 64.8%, then I predicted this

person would be a non-filer. Similarly, I predicted that married

persons under 62 years of age and with children who had a

probability estimate less than 36.1% were non-filers. This led

to a 2-by-2 measure-of-association table (Table 4). The rows in

Table 4 show the actual counts of filers and non-filers in the

sample, while the columns show the predicted counts of filers and

non-filers.18 Table 4 shows that the probit equations correctly

predict whether a person is a filer or non-filer 76% of the time. 

24
Further, the total predicted number of non-filers from the sample

(12,973), is approximately equal to the actual number of nonfilers in the sample (13,725).

25
------------------ ------------------ ------------------ -----------------

-------- -------- -------- -------- -------- -------- -------- --------

 TABLE 3

ESTIMATES FROM PROBIT EQUATIONS PREDICTING PERSONS LIKELY TO BE NON-FILERS

BY TYPE OF PERSON

DEPENDENTS ONLY 

------------------

PROBIT STD 

ESTIMATE ERROR 

-------- --------

INTERCEPT 0.7323 0.6279 

GENDER 

MALE -0.0070 0.0334 

EDUCATION LEVEL 

<= 10TH GRADE 1.0964 0.1098 

11TH OR 12TH GRADE 0.5900 0.1107 

1-3 YRS COL 0.1746 0.1176 

HOUSEHOLD STATUS 

HEAD 0.0000 0.0000 

RACE 

BLACK 0.1228 0.0504 

ASIAN, INDIAN 0.1470 0.0694 

HISPANIC 0.2532 0.0506 

LIVING QUARTERS 

HOUSE OR APT -0.2365 0.0798 

ACTIVITY LAST WEEK 

IN LABOR FORCE -0.4220 0.0714 

KEEPING HOUSE OR IN SCHOOL -0.1307 0.0579 

UNABLE TO WORK 0.6875 0.1669 RETIRED -0.1420 0.1352 

PRESENCE OF EARNED INCOME 

NO EARNED INCOME 0.5069 0.0449 

PRESENCE OF UNEARNED INCOME 

NO UNEARNED INCOME -0.0920 0.0541 

PRESENCE OF TAXABLE TRANSFERS 

NO TAXABLE TRANSFERS 0.1280 0.5743 

PUBLIC HOUSING ASSISTANCE 

NO PUBLIC HOUSING 0.0482 0.0864 

UNMARRIED 

NO KIDS, AGE < 62 

PROBIT STD 

ESTIMATE ERROR 

0.4890 0.2830 

0.0268 0.0533 

0.5897 0.1130 

0.2417 0.1049 

0.0453 0.1087 

0.1094 0.0577 

0.2175 0.0730 

0.2070 0.1154 

0.0586 0.0800 

-0.2663 0.0904 

-0.0961 0.0801 

-0.0056 0.0794 

0.2595 0.1203 

-0.5181 0.2016 

0.5297 0.0815 

0.2055 0.0626 

0.0968 0.1159 

-0.1033 0.0918 

UNMARRIED 

NO KIDS, AGE >=62 

PROBIT STD 

ESTIMATE ERROR 

1.7772 0.2648 

-0.0635 0.0604 

0.5789 0.1118 

0.2234 0.1125 

0.0898 0.1307 

-0.2095 0.0723 

0.3121 0.0929 

0.1440 0.1907 

0.1045 0.1151 

-0.3477 0.0905 

-0.0742 0.1611 

0.1144 0.0805 

0.2089 0.1302 

-0.0758 0.0785 

0.4147 0.1053 

0.1397 0.0535 

-0.0266 0.0599 

-0.6259 0.0740 

MARRIED 

NO KIDS, AGE < 62 

PROBIT STD 

ESTIMATE ERROR 

0.9710 0.7372 

0.0000 0.0000 

-0.5424 0.2747 

-0.4140 0.2669 

-0.7199 0.3419 

0.0000 0.0000 

0.5764 0.2441 

0.6184 0.2927 

0.8322 0.2165 

-0.1552 0.2070 

-0.5073 0.2154 

0.0135 0.2185 

0.4684 0.2601 

-0.4119 0.2355 

0.4388 0.2019 

0.4733 0.1658 

0.1371 0.2284 

0.3421 0.3425 

MARRIED

NO KIDS, AGE >=62

PROBIT STD 

ESTIMATE ERROR

1.9457 0.3863

0.0000 0.0000

0.9191 0.1452

0.4561 0.1476

0.3028 0.1731

0.0000 0.0000

0.0685 0.1171

0.0206 0.2244

-0.0682 0.1219

-0.3358 0.1047

-0.3210 0.1998

0.0533 0.1573

0.0535 0.1480

-0.2041 0.0900

0.4424 0.0938

0.1191 0.0631

-0.2047 0.0670

-0.6428 0.1700

26
 TABLE 3 (continued) 

ESTIMATES FROM PROBIT EQUATIONS PREDICTING PERSONS LIKELY TO BE NON-FILERS 

BY TYPE OF PERSON 

UNMARRIED UNMARRIED MARRIED MARRIED DEPENDENTS ONLY NO KIDS, AGE < 62 NO KIDS, AGE >=62 NO KIDS, AGE < 62 NO KIDS, AGE >=62 ------------------ ------------------ ------------------ ------------------ ----------------- PROBIT STD PROBIT STD PROBIT STD PROBIT STD PROBIT STD 

ESTIMATE ERROR ESTIMATE ERROR ESTIMATE ERROR ESTIMATE ERROR ESTIMATE ERROR -------- -------- -------- -------- -------- -------- -------- -------- -------- --------

-0.2088 0.0569 -0.1031 0.0734 -0.4368 0.1118 -0.3034 0.2114 -0.9412 0.2051 

-0.0588 0.0730 -0.1155 0.0813 -0.2043 0.1208 -0.1780 0.1791 -0.1680 0.1666 

-0.4089 0.1233 -0.4346 0.0945 -0.5456 0.1098 -0.6743 0.2551 -0.6727 0.1700 

0.0000 0.0000 -0.2119 0.1074 0.0000 0.0000 -0.7647 0.3177 0.0000 0.0000 

-0.3840 0.1083 0.1185 0.0807 0.1133 0.0879 0.1679 0.1659 0.0182 0.0881 

-2.0285 0.1374 -1.2196 0.1134 -1.1628 0.0944 -0.8699 0.2805 -1.4177 0.1194 

8469 3544 4379 462 2590 

PRESENCE OF FOOD STAMPS 

NO FOOD STAMPS 

PRESENCE OF SOCIAL SECURITY 

NO SOCIAL SECURITY 

PRESENCE OF SSI 

NO SSI 

PRESENCE OF AFDC 

NO AFDC 

PRESENCE OF "OTHER" BENEFITS 

NO "OTHER" BENEFITS 

AGI DIVIDED BY

FILING THRESHOLD 

SAMPLE SIZE 

---------------------------------------------------------------------------------------------------------------------------------

27
 TABLE 3 (Continued) 

ESTIMATES FROM PROBIT EQUATIONS PREDICTING PERSONS LIKELY TO BE NON-FILERS 

BY TYPE OF PERSON 

INTERCEPT 

GENDER MALE 

MARRIED W KIDS, AGE < 62 

------------------

PROBIT STD 

ESTIMATE ERROR -------- --------

-0.3651 0.4599 

0.0000 0.0000 

EDUCATION LEVEL 

<= 10TH GRADE 0.1664 0.2146 

11TH OR 12TH GRADE 0.2584 0.2092 

1-3 YRS COL 

HOUSEHOLD STATUS HEAD RACE 

BLACK 

ASIAN, INDIAN 

HISPANIC 

LIVING QUARTERS 

HOUSE OR APT 

ACTIVITY LAST WEEK 

IN LABOR FORCE 

0.1219 0.2402 

0.0000 0.0000 

0.1702 0.1557 

0.1579 0.1775 

0.1091 0.1192 

-0.0495 0.1506 

0.0395 0.1364 

KEEPING HOUSE OR IN SCHOOL 0.1641 0.1430 

UNABLE TO WORK 0.0458 0.1928 RETIRED 0.1335 0.3201 

PRESENCE OF EARNED INCOME 

NO EARNED INCOME 0.8002 0.1425 

PRESENCE OF UNEARNED INCOME 

NO UNEARNED INCOME 0.3852 0.1332 

PRESENCE OF TAXABLE TRANSFERS 

NO TAXABLE TRANSFERS 0.4000 0.1781 

PUBLIC HOUSING ASSISTANCE 

NO PUBLIC HOUSING -0.0082 0.1544 

MARRIED UNMARRIED UNMARRIED 

W KIDS, AGE >= 62 W KIDS, AGE < 62 W KIDS, AGE >= 62 ------------------ ------------------ ------------------ PROBIT STD PROBIT STD PROBIT STD 

ESTIMATE ERROR ESTIMATE ERROR ESTIMATE ERROR -------- -------- -------- -------- -------- --------

4.8799 1.4199 0.2442 0.3705 0.1617 0.8032 

0.0000 0.0000 0.1672 0.1193 -0.4765 0.1692 

-0.5725 0.5891 0.3784 0.1813 0.8919 0.3389 

-0.5808 0.5904 0.1594 0.1758 0.4659 0.3408 

-2.4300 1.1376 0.1689 0.1862 0.0943 0.4148 

0.0000 0.0000 -0.0690 0.1033 0.0000 0.0000 

0.3390 0.2722 0.0321 0.0737 0.5219 0.1700 

-0.7021 0.4189 0.0876 0.1456 0.6209 0.3974 

0.0429 0.2908 -0.0714 0.0907 0.3161 0.2246 

0.0995 0.4362 0.1327 0.1192 -0.2261 0.3075 

0.5470 0.5628 -0.2528 0.1132 -0.6626 0.4453 

-0.6947 0.5050 0.0842 0.1013 -0.2316 0.2460 

0.1920 0.4932 0.1216 0.1962 -0.0070 0.3346 

-0.3255 0.3555 -0.5402 0.4261 -0.7095 0.2445 

0.7567 0.3273 0.9030 0.0861 0.9598 0.2911 

-0.3639 0.2319 0.0569 0.0936 0.2927 0.1425 

-0.8199 0.2759 0.1512 0.1491 0.0027 0.1712 

-1.3613 0.6994 -0.1331 0.0734 -0.3004 0.3870 

28
 TABLE 3 (Continued)

ESTIMATES FROM PROBIT EQUATIONS PREDICTING PERSONS LIKELY TO BE NON-FILERS

BY TYPE OF PERSON

MARRIED W KIDS, AGE < 62 

------------------

PROBIT STD 

ESTIMATE ERROR -------- --------

0.0295 0.1170 

-0.4712 0.1552 

-0.4057 0.1855 

-0.3949 0.1326 

MARRIED UNMARRIED UNMARRIED 

W KIDS, AGE >= 62 W KIDS, AGE < 62 W KIDS, AGE >= 62 ------------------ ------------------ ------------------ PROBIT STD PROBIT STD PROBIT STD 

ESTIMATE ERROR ESTIMATE ERROR ESTIMATE ERROR -------- -------- -------- -------- -------- --------

-0.8264 0.3585 -0.1031 0.0846 0.1796 0.2131 

-0.1318 0.4855 -0.0573 0.1042 -0.1649 0.2354 

-1.1241 0.4757 -0.3349 0.1320 -0.6717 0.2538 

-0.2289 0.4773 -0.1808 0.0855 0.0000 0.0000 

-0.1430 0.2602 -0.1034 0.1063 0.1227 0.2814 

-2.2047 0.5024 -0.9825 0.1484 -0.9898 0.3107 

233 2413 692 

PRESENCE OF FOOD STAMPS 

NO FOOD STAMPS 

PRESENCE OF SOCIAL SECURITY 

NO SOCIAL SECURITY 

PRESENCE OF SSI 

NO SSI 

PRESENCE OF AFDC 

NO AFDC 

PRESENCE OF "OTHER" TRANSFERS 

NO "OTHER" TRANSFERS 0.0323 0.1263 

AGI DIVIDED BY 

FILING THRESHOLD -0.5652 0.2029 

SAMPLE SIZE 940 

---------------------------------------------------------------------------------------------------------------

29
 Table 4

CROSS-TABULATION OF ACTUAL AND PREDICTED FILERS AND NON-FILERS

(Values are sample number of people, percent in parenthesis)

Predicted from Probit Equation 

------------------------------------

Non-filer Filer Total 

Actual ---------- ----------- ----------

Non-filer 10422 3303 13725 

(43.93) (13.92) (57.86) 

Filer 2551 7446 9997 

(10.75) (31.39) (42.14) 

Total 12973 10749 23722 

(54.69) (45.31) (100.0) 

--------------------------------------------------------------------

Chi-squared statistic with 1 degree of freedom is 5933.

30
CONCLUSIONS AND FURTHER RESEARCH

This study, as it should, raises more questions and suggests

additional avenues of research that I or others may pursue. How

sensitive are my results to alternative working assumptions? How

does the population examined here compare to the population of

“low income” persons who are required to file? How are the

results presented here likely to have changed since 1990? What,

if any, is the relationship between poverty, the tax filing

requirement, and tax liability in the U.S.? What is the overlap

between the Earned Income Tax Credit and other means-tested

government transfer programs?

One area I specifically avoided in this paper was an

estimate of the actual number of non-filers. This question has

its own estimating issues; and many of these issues are outside

the context of this paper. 

31
APPENDIX - DEFINITION OF VARIABLES

The definition of variables used in the probit equation are 

presented below. The CPS variable name is shown in parentheses. 

The first variable, INON, is the dependent variable. 

are independent variables. 

INON

0 = NON-FILER

1 = FILER

GENDER (A-SEX)

1 = Male

2 = Female

EDUCATION LEVEL - HIGHEST GRADE ATTENDED (A-HGA) 

1 = 3 years of high school or less (A-HGA <=11)

2 = 1 year of college (A-HGA =12 or =13)

3 = 4 years of college (A-HGA >=14 and <=16)

All others

4 = 5 or more years of college (A-HGA =17 or =18)

HOUSEHOLD STATUS (HHDREL)

1 = Head (HHDREL =1)

2 = Non-Head (HHDREL >=2)

RACE (A-RACE and A-REORGN)

1 = Black (A-RACE =2)

2 = Indian, Asian, or Other (A-RACE >=3)

3 = White Hispanic (A-RACE =1 and A-REORGN <=7)

4 = White (A-RACE =1 and A-REORGN >=8)

LIVING QUARTERS (H-LIVQRT)

1 = House, apartment, flat (H-LIVQRT =1)

2 = Other (H-LIVQRT >=2)

ACTIVITY LAST WEEK (A-MAJACT)

1 = In the labor force (A-MAJACT >=1 and <=3)

2 = Keeping house or going to school (A-MAJACT =4 or =5)

3 = Unable to work (A-MAJACT =6)

4 = Retired (A-MAJACT =7) 

5 = Other (A-MAJACT =0 or =8) 

PRESENCE OF EARNED INCOME (WSAL-VAL, SEMP-VAL, and FRSE-VAL)

1 = No earned income

2 = With earned income 

PRESENCE OF UNEARNED INCOME (INT-VAL,DIV-VAL,RNT-VAL,ALM-VAL (a))

1 = No unearned income

2 = With unearned income 

PRESENCE OF TAXABLE TRANSFERS (RTM-VAL,UC-VAL,SRVS-VAL,DSAB-VAL (b))

1 = No taxable transfers 

2 = With taxable transfers

PUBLIC HOUSING ASSISTANCE (HPUBLIC and HLORENT) 

1 = No public housing assistance (HPUBLIC and HLORENT ^=1)

2 = With public housing assistance (HPUBLIC or HLORENT =1)

32
APPENDIX - DEFINITION OF VARIABLES (continued)

PRESENCE OF FOOD STAMPS (HFOODSP)

1 = No food stamps assistance (HFOODSP ^=1)

2 = With food stamps assistance (HFOODSP =1)

PRESENCE OF SOCIAL SECURITY (SS-VAL (a))

1 = No Social Security 

2 = With Social Security 

PRESENCE OF SSI (SSI-VAL)

1 = No Supplemental Security income 

2 = With Supplemental Security income 

PRESENCE OF AFDC (PAW-VAL (a)) 

1 = No public assistance including AFDC

2 = With public assistance including AFDC

PRESENCE OF “OTHER” BENEFITS (c))

1 = No other non-taxable, non-means tested transfer income

2 = With other non-taxable, non-means tested transfer income

AGI DIVIDED BY FILING THRESHOLD

= MAX(0., AGI / THRESHOLD)

a = May include some money listed in other income fields such as

survivor's income, disability income and/or other income.

b = Survivors income, disability income, and other income are

allocated between taxable and non-taxable, depending on the

values of accompanying codes. 

c = All other transfer income includes veteran’s benefits, nontaxable workers compensation, educational assistance and

financial assistance, plus unallocated portions of survivors'

income, disability income, and other income. 

33
 ENDNOTES

1. For a description of the CPS sample used in this study, see

Current Population Survey, March 1991 Tape Technical

Documentation, (Bureau of the Census, 1991). The Census Bureau

also maintains an informative web page, see

www.bls.census.gov/cps/cpsmain.htm.

2. For a discussion of non-compliant non-filers, see Brian Erard

and Chin-Chin Ho, “Searching for Ghosts: Who are the Nonfilers

and How Much Tax Do They Owe?” Unpublished paper presented at

the Allied Social Science Associations Meetings, (1995). See

also, Internal Revenue Service, Federal Tax Compliance

Research: Individual Income Tax Gap Estimates for 1985, 1988,

and 1992. Publication 1415. Washington DC, 1996.

3. For a description of the SOI sample, see Statistics of Income

division of the Internal Revenue Service, Individual Income Tax

Returns. Note that tax return data does not include

information on certain non-taxable sources of income such as

AFDC, SSI, food stamps, and certain other government transfer

programs. Tax data is not strictly limited to information

reported on individual tax returns. OTA augments the SOI

sample with additional information from both tax and non-tax

sources. For example, OTA exactly links each tax return on the

SOI sample to a file of “information returns” held by the IRS. 

The most important type of information return is the W-2, which

provides additional information on wages, employment taxes, and

retirement plan participation. A second important information

return is the SSA-RRB 1099. Here, amounts of Social Security

and Railroad Retirement benefits are exactly linked to

individuals on the SOI sample. As an example of non-tax data,

the year of birth of taxpayers on the SOI sample is obtained

from an exact match with Social Security Administration data.

4. Some low-income workers who do not reside with their children

may be eligible for the new “childless” Earned Income Tax

Credit. For a description of the Earned Income Tax Credit

eligibility rules for any given tax year, see the Internal

Revenue Service’s annual publication, Publication 17, Your

Federal Income Tax.

5. James Cilke, The Treasury Individual Income Tax Simulation

Model. (Department of the Treasury, Office of Tax Analysis,

1994).

34
6. For a recent example, see Daniel Feenberg, Andrew Mitrusi, and

James Poterba, “Distributional Effects of Adopting a National

Sales Tax.” NBER Working Paper Series no. 5885. National

Bureau of Economic Research, 1997.

7. As provided under Section 6103 of the Internal Revenue Code,

the Census Bureau annually receives from the Internal Revenue

Service, a micro-level data file of selected information from

all individual tax returns posted on IRS’s Individual Returns

Transactions File (IRTF) by the end of August. However, only

infrequently will the Census Bureau link these tax returns to

persons on the Current Population Survey (CPS). The last

previous public use file of this type occurred when the March

1973 CPS file was linked to tax returns. 

8. In the process of matching married CPS persons to tax returns,

the Census Bureau attempted to match to both the primary and

secondary SSNs. Husbands and wives matching to the same tax

return had the same tax information linked to their individual

records on the CPS. The absence of a secondary SSN on tax

returns was a common problem for joint returns. In the case

where a joint tax return with only the primary SSN present and

that SSN matched to a married CPS individual, then the return

was assumed to have matched to both spouses on the CPS.

9. The Office of Tax Analysis as well as a few other government

agencies have received copies of the file. I am not sure

whether the Census Bureau has made this file available to the

public. 

10. Employees of the Census Bureau have published several technical

papers regarding masking confidential microdata. In

particular, see Jay Kim and William Winkler, “Masking Microdata

Files.” in Proceedings of the Survey Research Methods Section,

American Statistical Association, 1995.

11. J. Scott Turner, “Program Documentation for the UNI-TRIM

Federal Income Taxes Module (FEDTAX).” May, 1976.

12. I ignored the possibility of someone on the CPS with a

married-filing-separate or a qualified widow/widower filing

status. I have not explicitly tried to measure the household

maintenance test for purposes of determining whether a person

can claim a head-of-house filing status. However, the effect

of these assumptions on my results are likely to be negligible. 

It simply meant that I may have used an incorrect threshold

amount for determining whether a person is required to file a

tax return. So, for example, if I mis-identified a person as

35
having a head-of-house filing status instead of single, I would

presume the person was not required to file if his income from

taxable sources was under $6800 instead of $5300.

13. The relationship test includes some unmarried people who are

related to someone in the household except the primary

householder. The definition of a child broadly includes

grandchildren and foster children. The support test does not

include certain education benefits.

14. Being eligible for an additional standard deduction because of

blindness does not affect the filing requirement. Further,

personal exemptions besides those allowed for self and spouse

do not affect the filing requirement. So, some people are

required to file a Federal tax return, even if they have no tax

liability. 

15. There are a number of other circumstances that mandate a person

file a tax return. These circumstances cannot be determined

from CPS information. In particular, a person is required to

file if he owes a special tax such as: a) the Alternative

Minimum Tax, b) a lump-sum tax or penalty tax on a retirement

plan distribution, c) Social Security taxes on tips that were

not reported to an employer, or d) a recapture tax from a

previously claimed credit. In addition, a person must file if

he received an Advanced Earned Income Tax Credit payment, or if

gross business income (business income before expenses) exceeds

the regular gross income thresholds, or if the person worked

for a qualified church-controlled organization that elected to

exempt wage payments from Social Security taxes. For a more

complete list of these circumstances that applied in 1990, see

Internal Revenue Service, Publication 17, Your Federal Income

Tax (1990).

16. Recall that data for this study was collected before people

without children were eligible to receive the EITC. Further,

approximately 96% of all EITC recipients would have a reason to

file even in the absence of the EITC. See Statement of John

Karl Scholz, Deputy Assistant Secretary (Tax Analysis),

Department of the Treasury, Before the Committee on Ways and

Means, United States House of Representative. May 8, 1997. 

17. I did not want to create a situation where one spouse was

likely to be a filer and the other spouse a non-filer. Recall

that most of the dependent variables in the equation are shared

variables and would be the same for either spouse. The spouse

included in the probit equation was simply the first spouse I

encountered.

36
------------------ ------------------ ------------------ -------------------

-------- -------- -------- -------- -------- -------- -------- --------

 TABLE 2

PERCENT OF ACTUAL NON-FILERS AND PERSONS NOT REQUIRED TO FILE: 

BY ASSORTED DEMOGRAPHIC AND ECONOMIC CHARACTERISTICS

BY TYPE OF PERSON

UNMARRIED UNMARRIED MARRIED MARRIED

DEPENDENTS ONLY NO KIDS, AGE < 62 NO KIDS, AGE >=62 NO KIDS, AGE < 62 NO KIDS, AGE >=62

------------------

NON- NON- NON- NON- NONFILERS ALL NOT FILERS ALL NOT FILERS ALL NOT FILERS ALL NOT FILERS ALL NOT

(NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED (NO IRTF REQUIRED

MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE MATCH) TO FILE 

-------- --------

GENDER 

MALE 50.9 50.6 53.4 53.3 21.3 21.7 37.5 37.7 52.5 52.9 

FEMALE 49.1 49.4 46.6 46.7 78.7 78.3 62.5 62.3 47.5 47.1 

ACTIVITY LAST WEEK 

WORKING 4.8 9.5 16.2 31.3 1.8 3.0 7.1 21.1 1.2 3.6 

WITH JOB NOT AT WORK 0.1 0.3 0.7 0.7 0.1 0.2 0.4 1.3 0.0 0.3 

LOOKING FOR WORK 2.4 2.8 4.8 5.4 0.0 0.0 2.6 3.6 0.0 0.1 

KEEPING HOUSE 9.1 7.8 19.4 11.7 41.0 38.5 43.1 37.2 31.7 29.7 

GOING TO SCHOOL 65.0 63.9 17.5 23.4 0.0 0.0 3.5 3.0 0.1 0.1 

UNABLE TO WORK 4.6 3.2 19.1 9.7 7.2 5.9 15.5 8.0 6.4 4.6 

RETIRED 2.6 2.2 1.8 1.6 39.4 42.0 8.3 10.7 51.1 53.3 

OTHER 11.3 10.3 20.5 16.3 10.4 10.3 19.6 15.1 9.6 8.4 

EDUCATION LEVEL 

EDUC <= 10TH GRADE 65.9 54.4 41.9 26.3 61.1 52.7 51.6 38.5 61.1 47.4 

EDUC 11TH OR 12TH GRADE 26.2 31.8 38.5 41.1 30.9 35.9 34.8 45.4 31.1 40.1 

EDUC 1-3 YEARS COLLEGE 6.3 11.3 15.7 25.5 5.2 7.4 7.1 9.0 5.8 8.1 

EDUC 4+ YEARS COLLEGE 1.5 2.5 4.0 7.0 2.7 3.9 6.5 7.0 2.0 4.4 

HOUSEHOLD STATUS 

HOUSEHOLD HEAD 0.0 0.0 53.4 40.1 85.0 86.3 93.5 93.1 98.0 98.8 

HOUSEHOLD MEMBER 100.0 100.0 46.6 59.9 15.0 13.7 6.5 6.9 2.0 1.2 

RACE 

BLACK 21.9 19.9 29.5 22.0 15.5 12.1 15.7 10.9 10.4 7.9 

ASIAN, INDIAN, OTHER 5.9 5.3 4.4 3.7 1.7 1.5 5.1 4.9 2.3 1.8 

HISPANIC 11.8 10.0 9.5 8.6 4.2 3.5 11.8 9.8 5.2 4.0 

WHITE 60.5 64.8 56.6 65.7 78.5 82.9 67.3 74.4 82.2 86.3 

19
18. Table 4 shows that the sample of non-filers makes up 57.9% of

the total sample not required to file a tax return. However,

Table 1 shows that 55.5% of the population under investigation

is a non-filer. This difference is caused by two factors. 

First, for the probit equations, one spouse from each married

couple was dropped. Second, Table 1, reflects population

estimates, where each observation is adjusted by its relative

weight in the population. Table 4 uses unweighted observations.

37