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The Determinants of

Individual Income Tax

Compliance

Estimating The Impacts of

Tax Policy, Enforcement, and IRS Responsiveness

Department of the Treasury

Internal Revenue Service

Publication 1916 (Rev. 11-96)

Catalog Number 22555A

Department

of the

Treasury

Internal

Revenue

Service

The Determinants of

Individual Income Tax

Compliance

Estimating the Impacts of

Tax Policy, Enforcement, and

IRS Responsiveness

November 1996

Alan H. Plumley

IRS Research Division

This is a copy of a Ph.D dissertation prepared for Harvard

University, slightly modified for distribution by the Internal

Revenue Service. The estimates presented herein are subject

to change. In fact, this revision is the second IRS printing,

and includes corrections of a number of typographical errors

contained in the August 1996 printing. Other refinements are

being made continually. Comments and questions are

welcome, and may be directed to Alan Plumley as follows:

Mail: IRS Research Division CP:R:R:AR:E

1111 Constitution Ave. NW, Washington, DC 20224

Phone/VoiceMail: (202) 874-0508

Fax:

(202) 874-0634

E-mail:AHPLUM50@M1.IRS.GOV

Suggested Citation

Internal Revenue Service

The Determinants of Individual Income Tax Compliance:

Estimating The Impacts of Tax Policy, Enforcement, and IRS

Responsiveness

Publication 1916 (Rev. 11-96)

Washington, DC: 1996

ABSTRACT

This paper presents an econometric analysis of the impact of a wide variety of

potential determinants of voluntary compliance with individual income tax filing and

reporting obligations. Based on perhaps the richest dataset yet compiled (by state

and year, from 1982 through 1991), including data on taxpayer behavior, IRS

actions, and other factors, the analysis finds significant compliance effects

attributable to many tax policy and tax administration parameters, including: audits;

the matching of third-party information documents; the issuance of targeted nonfiler

notices; criminal tax convictions; marginal tax rates; the burden associated with

completing the myriad tax forms and schedules; and the preparation of returns by

the IRS Taxpayer Service function.

The Determinants of Individual Income Tax Compliance:

Estimating The Impacts of Tax Policy, Enforcement, and IRS Responsiveness

Contents

1. Introduction . . . . . . . . . . .

1.1 Background . . . . . . . . .

1.2 Previous Research . . . . . . .

1.3 Advances Made By This Research .

2. The Model . . . . . .

2.1 The Data . . . . .

2.2 Estimation Approach

2.3 Model Specification

3. Results . . . . . . . . . . . . . . . . . . . . .

3.1 Filing Compliance . . . . . . . . . . . . . . .

3.2 Reporting Compliance . . . . . . . . . . . . .

3.3 Alternative Definitions of Income and Offsets . . . .

3.4 Other Potential Determinants of Voluntary Compliance .

3.5 Implications for Resource Allocation . . . . . . . .

4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . .

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Appendices

A. Data Sources and Derivations . . . . . . . . . . . . . . . . . .

B. Method for Using the Current Population Survey to Estimate the Number

of Returns Required to be Filed . . . . . . . . . . . . . . . .

C. Three Definitions of Income and Offsets . . . . . . . . . . . . . .

D. Personal Income Compared With Reportable Income . . . . . . . . .

E. Descriptive Statistics . . . . . . . . . . . . . . . . . . . . .

F. Estimation of the Burden Associated With Tax Forms . . . . . . . . .

G. Estimated Revenue Impact of the Decline in Audit Coverage . . . . . .

H. Interactions With the AuditRate Variable . . . . . . . . . . . . . .

I. Derivation of Total and Marginal Indirect Revenue Functions

for Five IRS Activities . . . . . . . . . . . . . . . . . . .

. .

The Determinants of Individual Income Tax Compliance:

Estimating The Impacts of Tax Policy, Enforcement, and IRS Responsiveness

1. Introduction

For many years, the Internal Revenue Service (IRS) has presumed that its activities

promote better income tax compliance in the general population—both through deterrence and

taxpayer service—but it has never been able to quantify this impact, or even verify that it exists.

This paper does both, providing the first empirical basis for choosing the best combination of

major IRS activities to improve voluntary filing and reporting compliance among individuals—a

capability that is increasingly needed in this climate of declining budgets. It also estimates the

compliance impact of important tax policy parameters—most notably marginal tax rates.

The success in estimating these compliance effects is not the result of sophisticated, new

statistical techniques, but rather the creative application of straightforward techniques to the right

data, which took eight years to compile from IRS reports and databases, as well as numerous other

sources. The analysis uses panel data over a ten-year period (1982-91) aggregated to the state level

to estimate one filing compliance equation and three separate reporting compliance equations.

Among the findings: the deterrent effect of audits in the general population is about 11 times as

large as the adjustments proposed by the audits themselves, but nonfiler notices, information

document matching, and return preparation assistance are more cost-effective in boosting revenue.

1.1 Background

The federal income tax system operates on a self-assessment basis. That is, the

government expects taxpayers to determine their own tax obligations and to pay voluntarily

whatever is due—both regularly (through withholding from wages and through estimated tax

payments, if necessary) and at year end (by filing tax returns and paying any additional balances

due). By placing the onus on taxpayers, the government avoids the costly alternative of

determining each individual’s tax liability and doing whatever it must to collect it.

However, one cost of relying so heavily on the voluntary compliance of taxpayers is that

not all tax is voluntarily paid. The IRS estimates that the gross individual income tax gap (the

difference between what taxpayers should pay and what they actually do pay voluntarily and

timely) was about $94 billion for Tax Year 1992—about 20 percent of total individual income tax

receipts and over half the size of the budget deficit!1

Congress has taken several steps to strengthen voluntary compliance with the income tax

laws. These actions fall into two major categories: requirements (e.g., requiring withholding of

tax at the source of income and requiring third party information reporting) and deterrents (e.g.,

giving the IRS certain enforcement powers, and stipulating the penalties that those who are caught

through this enforcement must pay).

Probably the most widely known example of an IRS deterrent is the “audit” of an

individual’s tax return. In Fiscal Year 1992, for example, IRS completed examinations of just

over 1 million tax returns of individuals. Although these returns were for several tax years of

liability, this is approximately the same number of examinations conducted on Tax Year 1992

IRS (1990).

The Determinants of Individual Income Tax Compliance

returns, which numbered about 115 million—implying an audit coverage rate of roughly 0.9

percent. As a result of the examinations completed in FY 1992, IRS recommended that the

taxpayers in question pay additional tax and penalties totaling $6.0 billion.2 The additional

revenue that the government will collect from this “yield” (following all appeals, litigation, and

collection efforts) is the direct revenue effect of those 1 million examinations. However, it is quite

likely that the audits also had an indirect revenue effect—inducing some amount of voluntary

compliance in the population at large through the general deterrent effect of the examinations

(referred to at IRS as the “ripple effect” of the examinations), and (perhaps) by influencing the

voluntary compliance of the contacted taxpayers in subsequent years (referred to as the

“subsequent year effect”). Indeed, one of the purposes of IRS enforcement is establishing a

credible deterrent to noncompliance. It has generally been believed, for example, that many

taxpayers would perceive increased auditing by IRS as an increase in their chances of being

audited, and that they would improve their voluntary compliance as a result.

In addition to the various requirements and deterrents that Congress has implemented

specifically to improve voluntary compliance, other actions or laws may have influenced

compliance indirectly. Examples of this phenomenon may include a change in compliance

behavior resulting from a change in tax policy (e.g., the marginal tax rate structure), or from some

change in the public’s attitude toward the IRS (which may arise from changes in IRS’s

responsiveness to taxpayers’ needs).

The focus of this paper is the indirect behavioral response of taxpayers (as measured by

changes in their voluntary filing of required income tax returns, and their reporting of income and

offsets to income on those returns) to changes in IRS enforcement, IRS’s responsiveness, and

basic tax policies.3 Quantifying these responses could help to shape tax policy and tax

administration for the foreseeable future—especially given the need to reduce the budget deficits

and to make the best use of government resources.

1.2 Previous Research

The indirect revenue effect of audits is beginning to receive attention from researchers, but

little—if any—empirical research has been done to quantify the separate compliance effects of

enforcement, tax policy and IRS responsiveness. What makes such research challenging, of

course, is that the compliance impact of government actions is never observed in isolation; it can

only be estimated. Voluntary income tax compliance is probably determined by a wide variety of

factors that interact differently for each individual. Although many such factors have been

suggested, and several studies have focused on some of them, nothing has emerged to guide

policy-makers concerning the relative merits of alternative approaches to improving compliance.

This may be due in part to the fact that no comprehensive theory exists that explains the compliance

behavior of taxpayers. It may also be because very little data are available to test such theories.

One of the earliest attempts to model taxpayer compliance was done by Allingham and

Sandmo (1972), which applied the utility-maximization approach of Becker (1967) to tax

compliance. Allingham and Sandmo’s simple model predicts the intuitive result that taxpayers will

voluntarily report more income in response to either an increase in the probability of being

detected, or an increase in the penalty imposed on those who are caught. However, the model is

inconclusive in predicting the response to an increase in the tax rate; the net response is the sum of

two terms in their model—one negative (suggesting a decrease in income reported as the tax rate

increases), and another, which is most probably positive, assuming that taxpayers’ risk aversion

IRS (1992).

Strictly speaking, my analysis quantifies such behavioral responses, but it does not address why taxpayers behave

the way they do. Although traditional deterrence mechanisms may be responsible, this study cannot prove it.

The Determinants of Individual Income Tax Compliance

decreases with income. Allingham and Sandmo liken these two terms to a positive income effect

and a negative substitution effect. They reason that the substitution effect means that an increase in

the tax rate makes it more profitable to underreport income at the margin (i.e., the higher the tax

rate, the more money is retained when one underreports a dollar of income). The income effect,

however, is most likely positive because an increase in the tax rate reduces net income, and

assuming decreasing absolute risk aversion, the taxpayer is less willing to underreport income than

before. Which of these two effects is stronger depends in part on the taxpayer’s degree of risk

aversion.

This early model—and virtually everything that has followed—says nothing about IRS

responsiveness to taxpayers, or about nonfiling. Roth, Scholz, and Witte (1989) gives an

excellent overview of the theoretical and empirical work on tax compliance through the 1980’s, but

several empirical studies are worth mentioning here. At least three studies (Clotfelter (1983), Cox

(1984), and Dubin, Graetz and Wilde (1990)) have explored the possibility that the actual marginal

tax rate to which individuals respond is the sum of the federal and state marginal rates, and that

since the federal income tax law is applied uniformly across states, using the state marginal tax rate

alone has equivalent explanatory power. The primary reason for making this assumption arises

from econometric pragmatism: to gain cross-sectional variation in the marginal tax rate variable.

However, for panel studies such as this one (i.e., a time series of cross sections), the difficulties

inherent in creating a state marginal tax rate variable, and the opportunities associated with the

variation in federal tax rates over time, caused me to explore the role of federal tax rates alone.

Four econometric studies that attempted to estimate the indirect effect of audits on

compliance are worth noting. Erard (1992) focused solely on the “subsequent-year effect” of

audits on the reporting compliance of those who were audited, and reports inconclusive results.

Three other studies examined the general deterrent effect of audits. 4 Tauchen, Witte, and Beron

(1989) was a cross-sectional study based on the 1979 Taxpayer Compliance Measurement

Program (TCMP) micro database, and concluded that the impact of audits is weak at best. The

other two studies were aggregate analyses. Dubin, Graetz and Wilde (1990) used panel data

(aggregated at the state level, 1977-1986) to estimate both reported tax per return and returns filed

per capita, and finds a very large and significant deterrent effect of audits. Beron, Tauchen, and

Witte (1992) uses 1969 data aggregated at the 3-digit ZIP Code level to estimate average Adjusted

Gross Income and average tax reported, and finds a weak indirect effect of audits that is limited to

certain taxpayer groups.

1.3 Advances Made By This Research

This study improves upon all of the earlier work in this area. It is based on one of the most

comprehensive datasets ever compiled on the potential determinants of voluntary compliance, and

provides an empirical basis for choosing the best mix of strategies for improving and maintaining

voluntary compliance.

Before describing the details of my model (section 2) and the results (section 3), I provide

in this section an overview of the advances made by this research. These advances are almost

entirely in the realm of improved data and an improved econometric specification; no new

estimating procedures are developed. Since my approach is most similar to that of Dubin, Graetz

and Wilde (1990) (which I will hereafter refer to as DGW), I will describe the advances with

respect to that important work. There are several obvious similarities between this study and

Actually, these studies examined the combined indirect effect of audits on the compliance of those who were not audited

(the “ripple” effect) as well as of those who were audited (the “subsequent-year” effect). But since the methodologies could

not distinguish between these effects, and since the “ripple” effect is presumed to dominate, the studies can be thought to

examine general deterrence.

The Determinants of Individual Income Tax Compliance

DGW. Each attempts to estimate the impact of various factors (including audits and tax rates) on

the voluntary income tax filing and reporting compliance of individuals using a 10-year panel of

data aggregated to the state level. Each accounts for the endogeneity of audit rates. However,

there are many important differences between these studies. I describe these differences—and

explain why they represent improvements—below.

1.3.1 Dependent Variables

DGW estimates three equations: one for reported tax per return (a measure of reporting

compliance), one for returns filed per capita (reflecting filing compliance), and one for “assessed

liability” (reported tax plus additional tax and penalties proposed by audits) per return. These

dependent variables control for cross-sectional and time variations by dividing by the number of

returns filed or by population. My dependent variables use as denominators exogenous surrogates

for reporting and filing obligations, giving them a meaning closer to traditional measures of

voluntary compliance. My estimation of filing obligations by state from Census data, for example,

creates a very powerful measure of the filing rate—obviating the need for such DGW variables as

the number of households per capita, and the percent of households on welfare. Moreover, using

reported tax per return filed as a dependent variable makes it difficult to interpret the results since

the explanatory variables could conceivably influence both the numerator and the denominator.

For example, a positive coefficient could indicate that the explanatory variable increases reporting

compliance, but it is also possible that it increases average tax reported by decreasing filing

compliance among low-income taxpayers. The implications of those two possibilities are

dramatically different. In fact, one may initially view the latter possibility as the correct

interpretation of DGW’s positive coefficient on audit rate in the reporting equation and its negative

coefficient on audits in the filing equation—especially given DGW’s assessment that “one way to

escape audits has been simply not to file.” DGW reports, however, that the magnitudes of these

coefficients is such that the net effect of audits is to increase dollars reported—but extraneous side

calculations are necessary to conclude this, illustrating the cumbersome nature of the specification.

The numerators are also quite different in the two studies. DGW uses the tax reported on

returns as its measure of voluntary reporting compliance. I avoid tax as a measure of reporting

compliance because several of the potential determinants of voluntary compliance (e.g., marginal

tax rates, filing thresholds, marital status, and allowable child exemptions) also have a direct role in

the calculation of tax from gross income, making it difficult to separate their impact on compliance.

I use three more useful measures instead: total income reported, total offsets reported, and net

income reported (income minus offsets). These three equations also have the advantage of

providing insight into the major forms of noncompliance (underreporting income vs. overstating

offsets to income or to tax), and they allow consistency comparisons across equations (since

income minus offsets equals net income). I have also controlled for the extent to which the tax

rules have changed concerning the amount of income that must be reported and the amount that

may be claimed as offsets. I have done this largely by defining income and offsets in three

different ways, and by estimating separate equations for each definition.

My data also reflect two important qualitative improvements. First, DGW uses dollars of

tax reported on returns as tabulated from IRS’s Statistics of Income (SOI) samples. I use the same

source, but since these samples are not designed to be accurate at the state level, I have adjusted the

sample weights (by state and tax form type) so that they conform to actual return filings by state.

Second, I have restricted the number of returns filed and the dollars reported on those returns to

include only those returns that were required to be filed—excluding those with no tax liability, but

were filed to claim a refund of withheld tax or to claim the Earned Income Tax Credit.

The Determinants of Individual Income Tax Compliance

Finally, I do not estimate anything like “Assessed Liability per Return” (ALR) because it is

too misleading. Not only does it suffer from all of the disadvantages of reported tax as a

dependent variable, but the proposed audit adjustments component of ALR is an incorrect measure

of the direct revenue effect of audits. This is because the proposed audit adjustments

(“recommended additional tax and penalties”), which DGW compiled from IRS’s Commissioner’s

Annual Reports, are not the amounts assessed through enforcement; a large portion of these

“recommended” adjustments is never assessed, due to successful taxpayer appeals and litigation,

and the rate at which these recommendations are ultimately assessed has varied greatly over time

and across states. Moreover, even the proposed adjustments are endogenous with audit rates;

since IRS allocates its audit resources so as to audit only those returns it perceives to be most

noncompliant, the average audit “yield” declines with audit rate. Even though DGW recognizes

that ALR is endogenous with audit rates, and that as voluntary compliance increases (say, due to

increased audit rates) average audit yield is likely to decrease proportionately, the paper does not

recognize that audit yield decreases with audit rates by design. This complicates the choice of

instruments for audit rate, since an effective instrument now has to be unrelated to both taxpayer

compliance and overall resource levels. A final source of confusion introduced by the inclusion of

proposed audit adjustments with the tax voluntarily reported arises from the fact that they relate to

very different time frames, which could be important in a longitudinal study such as this.

Recommended audit adjustments are typically made more than a year after the return is filed; the

larger the adjustment, the greater the likelihood that the time lag is more than one year. Therefore,

the impact of variables whose effect on tax obligations varies over time may be difficult to estimate

reliably with this dependent variable.

1.3.2 Independent Variables

A more obvious improvement in this study is the inclusion of a much richer set of

explanatory variables—richer both in quantity and in quality. This allowed the specification of

each equation to be uniquely suited to the differences in the dependent variables; DGW employed

the same specification for each equation. I have grouped my variables into the following

categories: Tax Policy, Burden/Opportunity, IRS Enforcement, IRS Responsiveness, and

Demographics/Economics. I have constructed federal marginal tax rate variables in lieu of the

average state tax rate used in DGW, and have included a variety of other tax policy variables, as

well: the filing threshold, a state amnesty indicator, allowed child exemptions, and state and local

taxes that are deductible federally. I include the prevalence of sole proprietors rather than of

farmers, since they are a more prevalent (and, arguably, more important) indicator of the

opportunity to avoid (and, perhaps, to evade) taxes. Other Burden/Opportunity variables I have

included are the burden (in hours) needed to complete and file all required returns and schedules,

and the percentage of returns prepared by a paid practitioner.

The only IRS activity DGW includes is the audit rate. This study also includes the audit

rate, but it is the first to use the audit start rate instead of the audit closure rate. Since audits are

typically closed several years after the returns are filed, and closures in any given year relate to

many different prior tax years, the start rate better represents the percentage of returns filed in a

given year that are audited. I have also included variables for four additional enforcement

activities: the information return matching program, nonfiler notices, refund offsets, and criminal

tax convictions. This is also the first study of its kind to include variables related to IRS’s nonenforcement activities. Two included variables relate to IRS’s Taxpayer Service telephone

assistance and return preparation services. Variables considered, but not found to have a

significant impact on compliance include other Taxpayer Service activities (correspondence and

educational outreaches), and the speed with which refunds are processed and sent to taxpayers.

The Determinants of Individual Income Tax Compliance

1.3.3 Estimation Technique

DGW actually employs a more sophisticated econometric procedure, but I do not believe

that it is appropriate in this context. That study uses a “random effects” model to control for

unobservable state-specific factors that do not vary over time, whereas I employ a “fixed effects”

model to account for this common peculiarity of panel data. (I explain in section 2.2.1 why this is

a preferable approach in this context.)

1.3.4 Identification

One of the most critical features of any study of this kind is the choice of instrument(s) for

audit rates. DGW uses two: Budget per Return (BPR), and information documents (other than

W-2s) filed divided by the number of tax returns filed. Although these variables reflect a very

creative use of available data, each has serious drawbacks as an instrument for the audit rate. (The

reasons for this are given in section 2.2.2.) This paper introduces for the first time two

instruments that help to explain the audit rate, but which are unrelated to compliance: the

percentage of auditor time directly devoted to audits (Direct Examination Time, or DET), and the

average DET per audit. As productivity-related measures, these variables cause the audit rate to

increase or decrease quite independent of taxpayer compliance. Strong evidence that my reporting

equations are identified is given in section 3.2.1.

1.3.5 Functional Form

The DGW specification is strictly linear. In contrast, I use the logarithms of those

independent variables that are likely to have a non-linear effect on compliance. This is especially

important among the enforcement variables, which almost certainly achieve diminishing indirect

marginal returns to effort, much like their direct revenue effects.

2. The Model

It is tempting for economists to develop theoretical models of individual (micro) tax

compliance behavior. However, there are two significant reasons—one theoretical and one

practical—why these models may be inadequate. The theoretical reason is that much of that

behavior is governed by what is called in the literature “general deterrence.” 5 As Nagin (1978)

correctly observes, “general deterrence is inherently an aggregate phenomenon since it is reflected

in the behavior of the entire population.”6 That is why analyses of criminal sanctions have

generally been aggregate studies. Many studies of tax compliance have been aggregate, also, but

this has typically been due to a lack of access to micro-level compliance data, such as IRS develops

in its Taxpayer Compliance Measurement Program (TCMP). The aggregate studies, therefore,

have tended to start with micro-level economic models, and attempted to estimate these using the

lowest level of aggregation possible—such as at the 3-digit, or 5-digit ZIP Code level. (See, for

example, Beron, Tauchen, and Witte (1992) ). The lower the level of aggregation, however, the

less realistic is the model. That is because such models implicitly assume that the general deterrent

operates only within the strict confines of each unit of observation (e.g., a ZIP Code boundary),

We may think of general deterrence as including both negative influences, such as IRS enforcement actions, as well as

potentially positive influences, such as IRS responsiveness to taxpayers’ needs. Although the latter may not intuitively be

considered a deterrent, it undoubtedly influences the general population just like a deterrent. For example, many of those

who have good or bad experiences with IRS efforts to help them presumably share their experiences and perceptions with

their friends, who may change their own compliance accordingly. This is completely analogous to the way in which

perceptions about IRS enforcement are developed in the general population.

Nagin (1978), p. 99.

The Determinants of Individual Income Tax Compliance

and it seems obvious that people will develop their compliance perceptions and propensities based

on the information they get from a wide variety of sources from many locations. In fact, many

people today interact more with people from outside their ZIP Code (such as at work) than they do

with others in the immediate vicinity of their residence.

The practical reason why micro models may be inadequate is that it is virtually impossible

to quantify deterrence-type activities (like audit rates and Taxpayer Service phone calls) in any

meaningful way for each individual observation. Inevitably, these variables are aggregated in

some way, then imputed to individual observations; they are therefore subject to the same

limitations as the low-level aggregate studies, and are poor substitutes for the individual

perceptions called for in the theoretical models.

In order to avoid or minimize these problems, I have aggregated all data to the state level.

It would be nice to have been able to aggregate to the IRS district level,7 but it would have been

extremely difficult to have aggregated most of the non-IRS data in this way. Fortunately, the IRS

data could easily be combined to derive state-level aggregations. Such an aggregate analysis suits

the IRS from the perspective of its usefulness, as well; IRS is not as interested in individual

behavior as it is in aggregate behavior. Although the aggregate behavior is certainly the sum of the

behaviors of individuals, IRS is interested in the bottom line: how could we allocate resources

differently to improve voluntary compliance, and hence, net revenues? Aggregation bias cuts both

ways: it makes it hard to use aggregate results to estimate a micro model of individual behavior,

but by the same token, it is equally problematic to estimate a micro model using micro data, and

then to generalize the results to make aggregate calculations. It is best to make aggregate

calculations based on aggregate data. Although it may not have the sophistication of some micro

models, and it cannot model individual motivations, it seems appropriate in this context.

2.1 The Data

The data collected for this study form one of the most comprehensive datasets ever

compiled of potential determinants of voluntary filing and reporting compliance, and took over

eight years to assemble in usable form. Many of the IRS variables had never before been

assembled for any study, and were available only on paper or microfiche tables—often in a form

requiring some manipulation to derive the desired concept. Some IRS and external data were

available as representative samples of individuals; certain IRS variables were available at the district

level; and certain external data were available at the state level. All variables were aggregated to the

state level,8 and were compiled for a ten-year period: 1982-1991. The panel nature of the data

increased the number of observations, and also captured important variations in both compliance

and in its determinants over time. Appendix A contains a detailed summary of the sources and

derivations of the raw data used to create the variables included in this study.

2.1.1 Measures of Voluntary Compliance

The IRS recognizes three types of voluntary compliance: filing compliance (the timely

filing of any required return); reportingcompliance (the accurate reporting of income and of tax

liability); and payment compliance (the timely payment of all tax obligations). This study focuses

on both filing compliance and reporting compliance (both income reporting and offset reporting—

subtractions such as deductions, exemptions, adjustments, and credits). The most basic measure

Until just recently, IRS had 63 districts, each of which was a single state or a portion of a single state.

The District of Columbia (DC) is included in Maryland both because of its small size and because most IRS data are not

available for DC separately, since IRS’s Baltimore District includes DC with all of Maryland. Moreover, Alaska is excluded

from the data for reasons explained below.

The Determinants of Individual Income Tax Compliance

of voluntary compliance is what the taxpayers actually did: how many returns were filed, and how

much income and offsets they reported on those returns. However, while analyzing the possible

determinants of this taxpaying behavior, one clearly has to control for the corresponding “true”

obligations—returns required to be filed, income required to be reported, and offsets allowed to be

claimed. One could control for these true obligations either by including them among the

explanatory variables, or by dividing the basic voluntary measures by them to derive appropriate

compliance ratios. I have adopted the ratio alternative. Either way, these true obligations are never

observed; they can only be estimated.

It is tempting to use TCMP’s compliance data—thorough audits on a representative sample

of taxpayers—to estimate true obligations, but even these data suffer greatly from a general lack of

good information on the true filing and reporting obligations of the individuals in the samples.9

Moreover, TCMP data on individual income tax reporting compliance are currently available for

only four years (1979, 1982, 1985, and 1988), and only one survey on filing compliance is

available (1988), making it difficult to construct a useful panel of data from the TCMP. I therefore

controlled for true filing and reporting obligations using non-TCMP data.10

Filing Compliance

My measure of filing compliance [FilingRate] is the ratio of the number of required returns

actually filed to the total number required to be filed, expressed as a percentage. The number of

required returns actually filed was aggregated by state from IRS’s Statistics of Income (SOI)

samples of individual returns for each year.11 All three basic compliance measures (the number of

returns, and the amount of income and offsets reported on those returns) correspond to returns

required to be filed. This includes all returns having a positive tax liability or net losses. This

definition excludes returns filed “unnecessarily,” as well as those having no tax liability, but are

filed to claim a refund of any withholding, or solely to claim the refundable Earned Income Tax

Credit (EITC). I have excluded returns filed solely to claim the EITC since EITC noncompliance

tends to increase filing, whereas usual filing noncompliance decreases filing.

I have defined the denominator—the number of returns required to be filed—in the same

way, and have estimated it by state for each year from the micro data files of the Current

Population Survey (CPS) compiled by the Bureau of the Census.12 The CPS is the most

comprehensive annual U.S. census of individuals, and recognizes families and households, as

well. In order to structure the data to reflect potential tax returns instead of individuals, I first

combined the information about spouses into combined records, approximating a jointly-filed tax

Since IRS auditors are not omniscient, TCMP inevitably falls short of identifying all unreported income and the

corresponding tax liability. (Alexander and Feinstein (1987) attempted to estimate the undetected unreported income using

a sophisticated detection-controlled econometric technique, but while this would be useful for making aggregate

adjustments to estimates of noncompliance, using it to impute specific amounts of undetected unreported income to the

individuals in the sample is likely to make inferences about taxpayer behavior very sensitive to any errors introduced by

the imputation.) Estimating true filing obligations is even more troublesome at the micro level, since nonfilers, by

definition, tend not to be identified. (Erard and Ho (1995), for example, implemented a sophisticated analysis of the recent

nonfiler TCMP micro database to estimate both the number of nonfilers and the tax gap associated with them.)

It is somewhat ironic that I have chosen not to use TCMP data—even though I have access to them—while others have

used aggregate data only because they had no access to TCMP. This seems to be the right choice for this study, however.

State-by-state aggregations of filing data are available from IRS’s Individual Masterfile (IMF) of all returns, but

comprehensive reporting data are much too difficult to produce from the IMF—making it impossible to restrict our counts

to required returns. The SOI samples are the only alternative—both for filing data and for reported data. However, even

though the SOI samples of 100,000 or so individuals are relatively easy to access and manipulate, they are not designed to

be representative of state populations. Therefore, I adjusted the SOI sample weights for each year to make these files

conform to the actual number of returns filed in each state.

The “March Supplement” of the CPS is compiled each year, and typically has a sample size of 80,000 to 90,000

records. Each file reflects income received in the prior calendar year, so I compared the SOI estimates for a given tax year

with CPS estimates based on data compiled the following March.

The Determinants of Individual Income Tax Compliance

FilingRate (%) *

return.13 I then estimated which records corresponded to dependents in the tax sense, since

special filing rules apply to them.14 Next, I estimated which individuals might have been eligible

to claim the Head of Household filing status, making it a simple matter, then, to divide the

remaining records into the Single and Married-Filing-Jointly statuses. Finally, I calculated the

correct standard deduction and exemption values based on the estimated filing status and the tax

law for the year in question. Using the amounts of various types of income reported on the CPS, I

was then able to estimate whether the “return” had income over the filing threshold for that year.

Any record that did, or which showed negative income, I counted as a required return. Appendix

B contains detailed information about the logic of this estimation process, as well as a summary of

the CPS variables used and the relevant tax parameters for each year. Figure 1 illustrates the

resulting national trend in the FilingRate variable.

100

* FilingRate is the number of required

returns filed, as a percent of the total

number of returns required to be filed.

Tax Year

Figure 1. Filing Compliance Measure: The number of required individual income tax returns filed (per

SOI), as a percent of the total number of returns required (estimated from CPS), Tax Years 1979-1991

As Figure 1 illustrates, the national FilingRate has remained in the low 90’s, with a

general downward trend until 1988, and a significant improvement thereafter. (Average

FilingRates by state are tabulated in Appendix E.)

Reporting Compliance

Reporting noncompliance takes two forms: underreporting income and overstating

offsets to income (i.e., exemptions, adjustments, and deductions) or to tax (i.e., credits). Since

I had to assume that all couples filed jointly, since there is no conclusive information on the CPS to suggest otherwise.

This is a fairly small approximation, however, since very few couples file separately in reality.

Unfortunately, the CPS files compiled for me excluded records for children under the age of 15. This resulted in a slight

underestimation of filing obligations—and a corresponding overestimation of the FilingRate variable—for all states, but

this was especially fatal for Alaska. The state of Alaska pays from its oil holdings a “Permanent Fund Dividend” on the

order of $1,000 each year to every permanent state resident—including children—and this dividend is taxable federally.

Virtually every child in Alaska, therefore, has an obligation to file a federal income tax return—especially after the Tax

Reform Act of 1986, when the filing threshold for dependent children was drastically reduced in most cases. Because of

this, the number of Alaska returns filed doubled for tax year 1987. Since my CPS files could not reflect this, I chose to drop

Alaska from my analysis, leaving me with 49 states rather than 50.

The Determinants of Individual Income Tax Compliance

the various determinants of compliance presumably influence income reporting and offset reporting

differently, I have measured reporting compliance by means of three amounts that taxpayers

voluntarily report on filed returns: the total amount of income that they report, the amount of

offsets15 they report, and the corresponding net income—income minus offsets. It is tempting to

monitor the tax reported on the returns, but the impact of tax policy parameters (e.g., marginal tax

rates and filing thresholds) on the tax voluntarily reported is twofold—influencing the tax

calculation directly, and potentially influencing compliance propensities, as well. By focusing on

income and offsets separately—before the tax calculation—we can isolate the impact of tax policy

parameters on reporting compliance. I estimate the net income equation both because it is close to a

tax concept, and because it is a useful check on the results from the separate income and offsets

equations.

The amounts of income and offsets reported were aggregated by state from the SOI files for

each year. However, during the 1982-1991 time period, the rules that govern what income must

be reported and what amount of offsets may be claimed changed significantly for many items (see

Appendix C for complete details). Unless we can control for these rule changes by modifying the

data to reflect constant-law amounts, or by including adequate explanatory variables, we may

misinterpret the effects of tax policy variables (like marginal tax rates) that are highly correlated

with these other tax law changes.16 I handled this problem by defining the three reporting

compliance measures (income, offsets, and net income) in three separate ways: (A) excluding all

components whose reporting rules changed during the period (except for ones that could be

controlled for by creating constant-law data or by including appropriate explanatory variables; this

definition included on the order of 97 percent of total income, 30 to 60 percent of adjustments, 94

percent of itemized deductions, and 30 to 60 percent of credits); (B) making all of those

adjustments, but including income and offset components whose rules were changed only by the

Tax Reform Act of 1986 (TRA86); and (C) including all income and offset items regardless of rule

changes. Comparing the results using these three sets of definitions gives us insight into the

impact of changes in the rules governing what must (or can) be reported.

As with filing compliance, each of the reporting concepts was expressed as a ratio.

However, unlike for filing compliance, it is not possible to develop comprehensive exogenous

estimates of the amount of income or offsets that should be reported. Therefore, to control for

true reporting obligations, each of these reported amounts was divided by the amount of Personal

Income estimated for the National Accounts by the Bureau of Economic Analysis (BEA) for the

appropriate state and year. Although Personal Income does not correspond exactly to the amount

of “true” income that must be reported on federal income tax returns (it includes the income of all

those who do not need to file returns, for example; see Appendix D for a detailed comparison), and

it is certainly not the same as “true” offsets allowable, it is a very effective control for these

concepts. This is because it is probably the most comprehensive individual income variable

available annually at the state level, and because it is derived substantially independent of tax return

data. That is, it is reasonably exogenous to income tax compliance and income tax administration

decisions. Table 1 summarizes the data sources for each of the major components of Personal

Income. As the table shows, four major components are based (at least in part) on individual

income tax returns. However, BEA adjusts these amounts to account for underreporting using

data from the IRS Taxpayer Compliance Measurement Program, the Information Returns Program,

audits, and other data (see Parker (1984)).

All of these offsets are subtractions from income, except credits, which are subtractions from tax. In order to combine

these amounts into a single offsets concept, I converted the credit amounts on a given return to the equivalent income offset

amounts by dividing them by the marginal tax rate faced by the return.

I am indebted to Brian Erard for making this observation.

The Determinants of Individual Income Tax Compliance

Table 1. Data Sources for Major Components of Personal Income

Component of Personal Income

Wages and salaries

Other labor income

Non-farm proprietor income

Farm proprietor income

Rental income of persons

Royalty income of persons

Personal dividend income

Personal interest income

Transfer payments

Data Source

State Unemployment Insurance data (Form ES 202)*

Forms 5500 submitted by employers and plan managers

Forms 1040*

U.S. Department of Agriculture

Census of Housing, Bureau of the Census

Forms 1040*

Forms 1120 submitted by payers of dividends

Forms 1120 submitted by payers of interest*

Federal budget, Social Security Administration, etc.

Source: Thae Park, Bureau of Economic Analysis, Department of Commerce; see also BEA (1994).

* These data are adjusted by TCMP, IRP, audit , and other data; see Parker (1984).

The resulting ratios [IncomePct, OffsetsPct, and NetIncomePct] must not be interpreted

as compliance rates in the same sense that the FilingRate ratios can be, but in the context of the

regression analysis described below, dividing by Personal Income (and including additional

control variables among the explanatory variables) is an effective way to control for variations in

true obligations over time and across states.

The national trends in the income and offset reporting variables are illustrated in Figures 2

and 3, respectively. (Average values by state are tabulated in Appendix E.) One of the most

striking things about Figure 2 is to see that reported income as a percent of Personal Income

generally declined through 1986, and then rose sharply. A decomposition of total reported income

into the separate types of income reveals that most of the increase after 1986 (presumably in

response to TRA86), can be attributed to Schedule E income (consisting mainly of rents, royalties,

and income from partnerships, S-Corporations, estates and trusts), and to wage and salary income.

Of the Schedule E income types, S-Corporation income is not included in Personal income, and yet

increased after TRA86, so this may have contributed to the overall rise in IncomePct. However,

excluding all Schedule E income from IncomePct does not change the results appreciably;

IncomePct still rises after 1986, and the econometric parameter estimates are similar. The reason

for the rise in wage and salary reporting is less clear. The biggest difference in the three definitions

of income reported is that the unadjusted measure of total income reported declines after 1988.

This is due primarily to the decline in capital gains reported during that period. Given that capital

gains are not included in Personal Income, and that the timing of gains realizations is often

influenced by tax law changes, I excluded it from income definitions A and B.

The trend of offsets under definition A follows a pattern quite similar to that of income

reported. The main reason for the sharp rise after TRA86, however, is the large increases in

standard deductions and exemptions. Although these increases imply larger filing thresholds

(resulting in fewer required returns), their net effect is to increase offsets reported as a percent of

Personal Income. I control for the increase in standard deductions and exemptions by including

several explanatory variables. The large differences between the amount of offsets claimed under

The Determinants of Individual Income Tax Compliance

Income Reported as a Percent of Personal Income

Tax Year

A. Excluding capital gains and all income components with rule changes in 1983-91

B. Excluding capital gains and all income components with non-TRA86 rule changes

C. Total Income (unadjusted)

Figure 2. Income Reporting Compliance Measures: The amount of income reported on returns (per

SOI), as a percent of the amount of Personal Income (per BEA), for the three definitions of income,

Tax Years 1979-1991

definitions B and C compared with definition A reflect the large number of offset items excluded

from definition A because the rules for what can and cannot be claimed on these items changed

during the period—especially due to TRA86.

2.1.2 Potential Determinants of Voluntary Compliance

I have included over twenty variables likely to influence the four measures of voluntary

compliance described above—one filing equation and three reporting equations (income, offsets,

and net income). Apart from FilingRate, which I include in the reporting equations (since the

more returns that are filed, the more income and offsets will be reported overall), these explanatory

variables fall into five categories: Tax Policy, Burden/Opportunity, IRS Enforcement, IRS

Responsiveness, and Demographics/Economics. These variables are defined in Table 2, and are

described in greater detail below—including a discussion of my a priori judgment of the direction

(sign) of each one’s impact on compliance. Descriptive statistics (and units) for all the variables

are provided in Appendix E.

Offsets Reported as a Percent of Personal Income

The Determinants of Individual Income Tax Compliance

Tax Year

A. Excluding all offset components with rule changes in 1983-91 *

B. Excluding all offset components with non-TRA86 rule changes *

C. Total Offsets (unadjusted)

* Except standard deductions, exemptions, and state & local taxes

Figure 3. Offsets Reporting Compliance Measures: The amount of offsets reported on returns (per SOI),

as a percent of the amount of Personal Income (per BEA), for the three definitions of offsets, Tax Years

1979-1991

Tax Policy

I control for changes in the values of standard deductions and exemptions through the use

of two variables: FThresholdPct, which is the aggregate filing threshold value17 among all

required returns (estimated from the CPS), expressed as a percentage of Personal Income; and

ChildExemptsPct, which is the aggregate exemption value for all children among all required

returns (estimated from the CPS), also expressed as a percentage of Personal Income. Increased

filing thresholds will decrease the number of returns filed, but it is not clear whether increased

thresholds would affect the FilingRate one way or the other; it may depend on whether those who

no longer need to file have a higher filing rate than those who are still required to file. If filing

thresholds and dependent exemptions have any impact on how much income is reported

(controlling for the filing rate), that impact is likely to be positive, since the larger these offsets are,

the more income that can safely be reported without paying more tax. FThresholdPct and

ChildExemptsPct should both have a positive impact on OffsetsPct, since these are two of the

most important income offsets.

The filing threshold for a given return is the amount of total income below which the taxpayer generally does not need

to file a return. In most cases, this is defined as the sum of the standard deduction applicable to the taxpayer plus the value

of personal exemptions to which he is entitled. Personal exemptions include those for the taxpayer and spouse (if filing

jointly), and exclude exemptions claimed for dependents; claiming dependent exemptions requires filing a return.

The Determinants of Individual Income Tax Compliance

Table 2. Definitions of Explanatory Variables

Variable

Numerator

Tax Policy

FThresholdPct

Amnesty5

MargTaxRate@$15K

MargTaxRate@$57K

ChildExemptsPct

StateTaxPct

Burden / Opportunity

AvgBurden

SoleProps

SolePropTFS

Filing threshold on required returns (CPS)

Dummy indicating whether state has had

an amnesty in the last 5 years

Marginal tax rate at $15K taxable income

(weighted for married-single mix by state and year)

Marginal tax rate at $57K taxable income

(weighted for married-single mix by state and year)

Value of exemptions for children (CPS)

State income, property & sales tax revenues

Personal Income (BEA)

Total tax form burden on required returns (CPS)

Number of sole proprietors (CPS)

SoleProps x percentage of non-farm employment

in Trade, Finance & Service sectors

No. of returns prepared by paid practitioner (SOI)

Number of potential returns (CPS)

PaidPrep

IRS Enforcement

AuditRate

Number of district audits started in fiscal year (AIMS)

IRP_DocRate

No. of IRP documents matched against returns

TDI_TotRate

Total number of TDI notices issued

RefOffRate

Number of refunds offset for outstanding debts

CID_ConvRate

Criminal convictions

IRS Responsiveness

TPS_CallsPC

Number of telephone calls handled by TPS

TPS_RetPrepPC

Number of returns prepared by TPS

Demographics / Economics

Singles

Number of singles among potential returns (CPS)

Under30

Number of potential returns under age 30 (CPS)

Over64

Number of potential returns over age 64 (CPS)

PCBirths

Number of births (HHS)

AvgPI

Personal Income (BEA)

AvgPIgrowth

Annual growth in AvgPI

ExclIncomePct

Income on potential returns that is not taxable

UnemplRate

Unemployment rate (among those 16 and older)

Abbreviations:

AIMS

BEA

CID

CPS

HHS

IRP

SOI

TDI

TPS

Denominator

Personal Income (BEA)

Number of returns filed (SOI)

Returns filed in prior tax year (SOI)

Number of potential returns (CPS)

Number of refunds

Population, in millions (Census)

Population, in thousands (Census)

Number of potential returns (CPS)

Population, in thousands (Census)

Number of potential returns (CPS)

Personal Income (BEA)

Audit Information Management System (IRS Examination function)

Bureau of Economic Analysis, national accounts (Commerce Department)

Criminal Investigation Division (IRS)

Current Population Survey (Census Bureau)

U.S. Department of Health & Human Services

Information Returns Program, document matching (IRS)

Statistics of Income (IRS)

Taxpayer Delinquency Investigation, nonfiler program (IRS Collection function)

Taxpayer Service function (IRS)

The Determinants of Individual Income Tax Compliance

Taxable Income of $57,000 (in 1982 $)

Marginal Tax Rate (%)

Taxable Income of $15,000 (in 1982 $)

Tax Year

Single

Married

Weighted Average

Figure 4. Marginal Tax Rates: The national trend for two taxable income levels, Singles and Marrieds Filing

Jointly, and the weighted average of the two, Tax Years 1979-1991

Marginal tax rates are difficult to reflect in an aggregate analysis—especially since the tax

rate schedules apply to everyone equally in a given year. Also, since the average marginal tax rate

for each state population does not by itself reflect the progressivity of the tax rate schedules, or the

possibility that marginal rates have different impacts on the reporting compliance of taxpayers at

different income levels, and since any average marginal rate calculated from filed returns is

endogenous, I developed an alternative way to estimate the impact of marginal rates. This involved

determining the marginal tax rate for each year from the tax rate schedules (included in the Form

1040 tax package) at two separate levels of taxable income: $15,000 and $57,000, expressed in

constant 1982 dollars.18 These marginal rates saw significant changes over time (see Figure 4),

and varied across states each year due to the widely variable mix of single and married taxpayers

from state to state.19 I constructed a composite marginal tax rate variable for each income level by

weighting the single and married rates by the relative mix of singles and marrieds among potential

$15,000 was chosen as a fairly modest income, and $57,000 was chosen because it is the highest level of taxable

income (in 1982 dollars) for which the Single and Married-Joint marginal tax rates were different for each year. If the

marginal tax rate were the same for these two rate schedules in any year, then we would not observe any variation in the

variable across states for that year.

The ratio of marrieds to singles in a given state typically ranged from 0.6 to 1.2.

The Determinants of Individual Income Tax Compliance

returns (which I estimated from the CPS). (I also included the percent of singles in the state

population among the demographic explanatory variables to ensure that these marginal tax rate

variables are not simply surrogates for the married-single mix.) As Figure 4 shows, the marginal

tax rate at $15,000 of taxable income remained relatively stable, whereas the rate at $57,000 of

taxable income dropped significantly over the 1982-1991 period of my analysis. It is not

completely clear a priori whether the effect of marginal tax rates is positive or negative; it most

likely depends on how risk-averse the typical taxpayer is relative to the magnitude of other

important tax parameters. Since low-income taxpayers are most likely more risk averse than higher

income taxpayers, however, marginal rates are likely to have more of a positive effect on those

with low income.

Two additional tax policy variables relate to state taxes. StateTaxPct is the amount of state

income, property, and sales tax revenues that were deductible federally, expressed as a percentage

of Personal Income. We should expect this to have a positive impact on OffsetsPct. Amnesty5

is a dummy variable that indicates whether the state has had a tax amnesty20 within the five years

up to and including the year in question. Since state and federal tax administration is linked in

many important ways, it is conceivable that the federal government realizes some of the short-term

gains that the states have enjoyed following their amnesties. However, it is also possible that

taxpayers reduce their compliance following an amnesty—either because they expect another one

will follow, or because they feel they deserve to receive some of the government’s leniency from

which their less compliant neighbors have benefitted. Therefore, it is not clear a priori whether

state amnesties have a positive or a negative impact on federal compliance.

Burden / Opportunity

It seems reasonable to presume that the more complex the tax system becomes, and the

harder it becomes to comply with one’s tax obligations, the more likely people will become

noncompliant—either unintentionally due to confusion, or willfully out of frustration. On the other

hand, the fewer opportunities there are to be noncompliant (e.g., with the introduction of

requirements that payers of certain types of income report this information both to the recipient and

to the IRS), the less noncompliance we should expect. These two factors are actually much the

same: the complexity of the tax system (and therefore the burden associated with complying with

it) arises to a large extent from the various opportunities left open for noncompliance (e.g.,

underreporting business income), and the many mechanisms in the tax system to minimize those

opportunities (e.g., detailed forms and schedules, complicated rules, and lengthy instructions).

Moreover, what is complexity to one (e.g., itemized deductions) may be opportunity to another.

Recognizing that business income among individuals presents both tax-paying complexities

and opportunities, I have included two variables: SoleProps, the percent of potential returns

having non-farm sole proprietor income (per the CPS); and SolePropTFS, an interaction term

between SoleProps and the percent of nonfarm employment in the Trade, Finance, and Services

sectors. Sole proprietors generally keep a fairly visible public profile, and thus may find it difficult

to hide from the IRS’s notice. However, dealing largely in cash and “moonlighting” may provide

many entrepreneurs—especially in the Trade, Finance, and Services sectors—the opportunities to

evade taxes by not filing or by not reporting all of their income. It is not clear whether SoleProps

should have a positive or negative impact on compliance, but we should expect SolePropTFS to

have a less positive, or even negative, impact compared with SoleProps.

By 1991, 33 states had conducted some form of amnesty. Virtually all of these waived some or all penalties associated

with nonfiling if a delinquent return were filed within a specific time period. Many amnesties also focused on accounts

receivable.

The Determinants of Individual Income Tax Compliance

The extent to which taxpayers pay others to prepare their returns has been related to

compliance before, and it seems appropriate to control for it. It is not obvious, however, whether

paid tax practitioners improve reporting compliance (say, by asking their clients if they had any

extra income from easy-to-forget sources, or by avoiding misunderstanding as to what offsets are

available to their clients, and how to calculate them), or whether they reduce the amount of net

income reported (say, by pointing out legal tax avoidance strategies open to their clients).

A variable that has not been related to compliance before is the burden associated with

getting, learning how to use, completing, and filing the various required tax forms and schedules.

This “burden” was estimated by an IRS study conducted in response to the Paperwork Reduction

Act. Estimates of the burden (measured in hours) associated with each form and schedule have

been published in the Form 1040 tax package in the most recent years, and I have applied the same

methodology to develop estimates for the earlier years. (These estimates are tabulated in

Appendix F.) I then multiplied these burden estimates for each form and schedule by the number

of corresponding forms and schedules required to be filed, which I estimated from the CPS (see

Appendix F for details on the logic employed). I then derived AvgBurden by dividing this total

burden estimate for the state and year by the corresponding number of potential returns indicated

by the CPS. (I use potential returns instead of filed or required returns because an increase in

the filing threshold could easily increase the average burden among the remaining required returns,

but should be considered a burden reducer.) We should expect this burden to decrease filing

compliance, as well as the reporting of offsets, since the hassle associated with maintaining the

correct records and completing the paperwork can only diminish taxpayers’ willingness to file

returns and to claim offsets to which they might be entitled. It is not clear a priori what impact

burden might have on income reporting compliance, though the “hassle factor” could have a

negative effect.

IRS Enforcement

The primary enforcement variable thought to influence voluntary compliance is the audit

coverage rate—the percentage of returns audited. Presumably, if taxpayers respond to the

deterrent effect of audits, they (subjectively, at least) try to estimate their chances of being audited.

This probability depends both on what they report on their returns (making the audit rate

endogenous with compliance) and on the prevailing level of audit resources in their area.

Traditionally (in IRS reports, and therefore in academic research), the latter concept has been

expressed as an average audit coverage rate, which is usually defined as the number of audits

closed in a given fiscal year divided by the corresponding number of returns filed in the prior

calendar year. Since the length of audits varies widely, and is often longer than one year, the

traditional coverage rate concept does not accurately reflect the average percentage of returns filed

in a given year that are eventually audited. That is better captured by the percentage of audits

started in a given year, which has never been used in an analysis of taxpayer compliance. It is

possible, however, that audits send different signals to the general population when they are started

compared with when they are closed. For example, the message that gets “rippled” to friends and

neighbors when an audit begins presumably focuses on the fact of the audit, and may shape their

perception of their own likelihood of getting audited. In contrast, the message communicated

when an audit ends probably has more to do with the quality of the audit, and may shape others’

perceptions more of the consequences of the audit (good or bad) than of its likelihood. Including

both audit measures—which are obviously highly correlated—however, introduces the problem

of multicollinearity into the analysis, so I have included just the audit start rate alone, since it

displayed the greater predictive power.21

My definition includes only the person-to-person audits conducted by the district offices; it excludes the simple

correspondence audits conducted by the service centers.

The Determinants of Individual Income Tax Compliance

Many have observed that over the last two decades voluntary compliance seems to have

fallen concurrent with a decline in the audit coverage rate. However, as shown in Figure 5, much

of the decline in conventional, labor-intensive audits has been accompanied by a very significant

rise in IRS’s ability to detect noncompliance through the use of automated matching of third-party

information documents with tax returns in its Information Returns Program (IRP). We ought to

control for this shifting of enforcement resources, since taxpayers may not perceive much of a

difference between getting caught by a person and getting caught by a computer. For this reason,

some recent analysis22 has included the number of computer-generated notices to taxpayers (called

CP-2000’s) arising from such mismatches. I do not think that that is the correct variable,

however. The deterrent effect of the IRP document matching program is achieved when taxpayers

believe that virtually every mismatch will be detected and pursued. It is not the number of

mismatches found that reflects the level of enforcement, but rather the number of documents

actuallymatched. In fact, in recent years, the reporting of wage, salary, interest and dividend

income has steadily improved while the number of mismatches has generally declined.

Compliance has improved in these areas because taxpayers have increasingly understood that

virtually all mismatches will be detected (a “coverage rate” approaching 100 percent); the number

of mismatches has fallen as a result. Therefore, I have included the average number of IRP

documents processed per potential return [IRP_DocRate] as an explanatory variable. (As with

AvgBurden, this is divided by the number of potential returns; since IRP documents are

submitted even for those who have no filing obligation, we should not compare the number of IRP

documents with only the required returns.) Since one of the uses of these documents is to identify

nonfilers, the more IRP documents that are processed, the greater the likelihood that a potential

nonfiler will choose to file. Although the overall impact of IRP has been to improve the reporting

of income, as well, it is not clear what impact new types of IRP documents have had in the recent

past. Their positive deterrent may be mitigated somewhat by a “What the IRS doesn’t know won’t

hurt them” type of mentality. That is, taxpayers could improve their reporting of IRP-covered

income types, but reason that income not reported to IRS is easy to conceal. The more that

taxpayers are aware of the limits of IRS knowledge, the more opportunity they have to underreport

their income.

I am not aware of any other study that has attempted to measure the impact of three other

IRS enforcement programs. The first, the Taxpayer Delinquency Investigations (TDI) program, is

specifically targeted toward nonfilers. Based on either the presence of sufficient IRP-detected

income without a return having been filed (known as the “IRP-Nonfiler” program), or on the

absence of a return from someone who had filed the previous year (known as the “Stopfiler”

program), IRS issues up to four TDI notices to potential nonfilers. If the notices do not yield the

required returns, a more-intensive investigation may be conducted by the IRS Collection function.

TDI_TotRate includes all such notices issued by state and year, and we should expect that this has

a positive effect on the FilingRate.

Most of the effort of IRS’s Criminal Investigation Division (CID) is intended to improve

voluntary compliance by catching and prosecuting tax fraud cases. This can improve compliance

in two ways: either as a deterrent among those tempted to defraud the government, or as an

encouragement to the general population (to the extent that they don’t want to see criminals go

scott-free). It is possible, however, that the latter effect primarily works in reverse; that is,

criminal convictions might not improve compliance, but the lack of them might erode compliance.

Since one of the primary mechanisms for influencing the general population by CID activities is the

publicity surrounding the cases—especially if convictions result—I have included

CID_ConvRate, which I have defined as the number of criminal convictions obtained per million

people in the population.

For example, an early draft of Dubin, Graetz and Wilde (1990).

The Determinants of Individual Income Tax Compliance

IRP_DocRate

(information documents

processed per potential return)

AuditRate

(district audits started per

100 returns filed in prior year)

Year

Figure 5. IRP and Audit Coverage Trends: The number of IRP documents processed per potential return,

and the audit start rate, 1980-1991

The third IRS enforcement activity unique to this study involves not paying some or all of a

refund to a taxpayer in order to satisfy delinquent child support payments, or some other debt. I

have included RefOffRate, which is the percentage of refunds offset in this way. It seems likely

that if a taxpayer has such a debt, he is likely to find ways to avoid being offset in the future (such

as by adjusting his withholding, not filing altogether, or both) if he has been offset once, or if he

hears of others getting smaller refunds than they had claimed.

IRS Responsiveness

One of the most significant contributions of this study is a better understanding of the

extent to which IRS responsiveness to taxpayer needs influences voluntary compliance. To my

knowledge, this has not been studied before. Unfortunately, reliable and consistent measures of

such responsiveness have generally not been maintained by IRS for very long. In most cases,

only one or two years worth of data are available. However, I have compiled data for two

important types of Taxpayer Service (TPS) activities: the number of telephone calls handled per

thousand people in the population [TPS_CallsPC], and the number of returns TPS helps to

prepare, also per thousand people in the population [TPS_RetPrepPC]. We should expect

TPS_RetPrepPC to contribute positively to both filing and reporting compliance. The impact of

the telephone calls is somewhat ambiguous, however. Since these calls are almost always initiated

by taxpayers, the ones who choose to call are are probably not representative of the overall

population. Taxpayers call for two major reasons: seeking information about the administrative

progress or status of their account, or seeking clarity on some substantive tax law issue that they

face. Generally, a pleasant experience with the IRS (e.g., getting the correct answers in a

reasonable amount of time) ought to contribute to higher voluntary compliance, but since the

The Determinants of Individual Income Tax Compliance

quantity of phone calls is not always related to their quality, the likely impact of the calls is not

straightforward. There are two additional factors that add uncertainty: (1) the people who ask the

IRS substantive tax law questions are likely to call because they are generally compliant, and will

tend to seek the correct answer rather than not call and take a risk, and the answers to their

questions might either be in their favor or not; and (2) the number of calls answered does not

always relate to the number attempted, but not answered, or to the number of times a taxpayer had

to try before being served. Including both of these TPS variables, however, could shed important

light on the impact that these activities have.

Demographics and Economics

It is probable that a variety of demographic and economic factors help to shape an

individual’s tax compliance behavior. Other factors help to control for fluctuations in the

dependent variables (specifically Personal Income in the denominator of the reporting compliance

measures) that are not related to compliance.

The demographic variables include the prevalence of singles as percent of potential returns

[Singles], the percentage of potential returns in young and old age categories [Under30 and

Over64], and the number of births per thousand in the population [PCBirths]. Marital status has

important tax implications, and is likely to be negatively correlated with compliance. The two age

categories included tend to have less income, but it is not obvious whether they contribute to higher

filing compliance. The per capita birthrate may be related to people’s present satisfaction level and

to their optimism about the future. If so, it may be related to better compliance.

The economic variables include: the level and rate of growth of Personal Income [AvgPI

and AvgPIgrowth, respectively]; a measure of the income included in Personal Income, but

excluded from taxable income [ExclIncomePct]; and the unemployment rate among those age 16

and older [UnemplRate]. AvgPI and AvgPIgrowth control for income differences across states,

which must be done through the inclusion of independent variables in the FilingRate equation.

(Recall that the three reporting compliance measures are divided by Personal Income, so including

Personal Income on the right-hand side of these equations would introduce non-linearity with

respect to income, which may or may not be realistic.) ExclIncomePct controls for fluctuations in

Personal Income (and therefore in the income reporting compliance measures) related to sources of

income not reported on income tax returns—such as the income of those not required to file,

veterans’ benefits, and child support payments—and should therefore have a negative sign.

Finally, a high unemployment rate is likely to cause taxpayers to become less compliant; to the

extent that they have less disposable income, they are more likely to cut corners on their taxes—or

maybe not even file at all.

2.2 Estimation Approach

Three important complexities had to be addressed in order to estimate successfully the

impact of these various potential determinants on voluntary compliance. The first arose from the

panel structure of the data (a time series of cross sections). The second dealt with the endogeneity

of audit rates. The third involved the problem of accounting for changes in the rules as to what

income should be reported and what offsets could be claimed.

The Determinants of Individual Income Tax Compliance

2.2.1 Panel Data

In the standard regression model, the unexplained error term is assumed to vary randomly

across all observations. With a cross-sectional and time-series panel structure, however, certain

left out (perhaps unmeasurable) variables may vary cross-sectionally (i.e., across states in this

study), but not over time (such as cultural factors), while other variables may vary over time, but

not across states (such as features of the tax law). The effects of these left-out variables are

captured in the error term, which is no longer random across all observations. The accepted

econometric approach in such cases is to assume that the error term is made up of components:

one varying only across individuals (i.e., states), reflecting the “individual effect;” sometimes

another component that varies over time, but not across individuals, reflecting a “time effect;” and

then the usual error term, varying randomly across all observations. Pioneering work applying

this technique to economic problems include Kuh (1959) in estimating investment equations,

Mundlak (1961) and Hoch (1962) estimating production functions, and Balestra and Nerlove

(1966) on demand functions.

The existence of such individual and time effects can be determined from a simple analysis

of variance examination of the residuals obtained from the standard regression that assumes no

error components. Such a test on my data reveals a very significant presence of both individual

(i.e., state) and time effects.

There are two principal ways to model these error components. One is to view them as

“fixed effects” for each individual (state) and time period (year). Under this assumption, the

effects are not only unique to each state or time period, but they are constant, or “fixed.” If a

separate regression of the same equation were estimated for each state on its own, all of the

estimated parameters would be identical for each state, except for the constant term; the “individual

effect” would appear in combination with the overall constant term, making the constant vary

across states. The other possibility is that the individual effect is random rather than constant.

Under the “random effects” assumption, all components of the error term (the individual effect, the

time effect, and the remaining disturbance) are distributed with a mean of zero and a constant

variance. However, the variance of the individual effect is unique to each individual (state), and

the variance of the time effect is unique to each time period (year), while the variance of the

remaining disturbance is common across all individuals and time periods.

Fixed Effects vs. Random Effects

Which is the best way to model the error term? Two considerations have led me to choose

the fixed effects model. First, the random effects approach is best suited when the panel consists

of individuals tracked over time who are representative of a much larger population. But if the

individuals are the population of interest, then the individual effects would most appropriately be

fixed.23 In this problem, the “individuals” are states, and are the population of interest; they are

not a sample drawn from a larger population, so the fixed effects model would seem the most

appropriate. Second, the random effects model may lead to biased parameter estimates if the

individual effects include unobserved factors that are correlated with the included explanatory

variables.24 The usual example of such an unobserved factor is an individual’s inherent “ability”

(or intelligence) which is undoubtedly correlated with education (say, in an equation to explain

income differences). In this study, the unobservable state effects undoubtedly include factors

having to do with people’s attitudes toward and perceptions about the federal government

generally, and tax compliance specifically. If so, then these effects are almost by definition

See Hsiao (1986), pp. 42-43, and Baltagi (1995), p. 13.

See Mundlak (1978), and Hsiao (1986), pp. 43-46.

The Determinants of Individual Income Tax Compliance

correlated with the included explanatory variables, since these variables are included for the very

reason that they are thought to influence taxpayer attitudes and perceptions. For example, if IRS is

not responsive to taxpayer needs (e.g., by answering telephone enquiries correctly and efficiently),

then those taxpayers—and perhaps others influenced by them—may develop unobserved attitudes

toward taxpaying (built on frustration, or perceived unfairness, for example) that cause them to be

less compliant next time around. So, by both considerations, the fixed effects model would seem

to be the most appropriate specification.

Estimation Procedure

The most straightforward way to estimate a fixed effects model is to include an overall

constant term as well as dummy variables for all but one of the states and for all but one of the

years. This captures the fixed effects explicitly, leaving only one error component: the standard

random disturbance. I employed this procedure, which is sometimes called a Least SquaresDummy Variable (LSDV) approach (or 2SLSDV for Two-Stage Least Squares), since it gives

insight into the magnitude and distribution of the effects.

2.2.2 Endogeneity of Audit Rates

By carefully constructing my dependent and explanatory variables (for example, using the

CPS to estimate filing obligations and certain economic and demographic characteristics of the

potential filing population, and using marginal tax rates taken directly from the tax rate schedules),

I have avoided a number of potential endogeneity problems. However, the problem of

endogenous audit rates remains, and is one of the most significant estimating challenges in any

comprehensive study of IRS’s impact on voluntary compliance. The problem arises because not

only do audit rates presumably influence taxpayers’ perceptions of their chances of getting

audited—and, therefore, their compliance decisions—but IRS allocates its audit resources based on

its perceptions of taxpayers’ noncompliance. That is, audit rates and voluntary compliance are

jointly (simultaneously) determined. Since these audit resources are allocated on a district-bydistrict basis (where most districts encompass an entire state), this endogeneity can be expected to

manifest itself in state-level data. All of the most recent attempts to estimate the determinants of

voluntary compliance have included audit rates, and have recognized this endogeneity. However, I

am not confident that these earlier studies have successfully identified the compliance equations in

which audit rates appear. Dubin, Graetz, and Wilde (1990) employs an instrumental variables

approach in which IRS budget per return filed and the the number of information returns per tax

return filed are used as instruments for audit rate. Since district budgets vary in large part due to

IRS perceptions of the variations in taxpayer compliance, since information documents have their

own impact on voluntary compliance (particularly on filing compliance), since the number of

returns filed (the denominator for each instrument) is itself endogenous, and since data on

information documents were not compiled by state, these would seem to be poor instruments for

the audit rate. Beron, Tauchen, and Witte (1992) uses a similar approach, including as an

instrument the number of returns filed per IRS employee in a given district—in essence, the

inverse of the budget per return instrument described above. This is intended to reflect the fact that

district audit staffing levels are often dictated by constraints unrelated to compliance, but this

variable includes all types of district staffing, and it is unclear whether the constraints on optimal

resource allocation are significant enough to say that this variable is an effective instrument for

audit rates. Since the paper does not include, for the sake of comparison, results using the

endogenous audit rate in the reporting equations, we can only speculate whether the predicted audit

rates based on this instrument corrected the bias of the parameter estimate, or exacerbated it.

The Determinants of Individual Income Tax Compliance

Direction of Bias

To understand the presence and direction of this bias, consider the following generic

income reporting and audit rate equations:

IncomePct = α0 + α1AuditRate + ε

AuditRate = β0 – β1IncomePct + ν

We would expect the impact of AuditRate on IncomePct (i.e., α1) to be positive, and the

impact of IncomePct on AuditRate (β1) to be negative. If the endogeneity is not controlled for,

what is the relationship between the desired parameter estimate (α1) and the error term? A positive

ε implies a larger value of IncomePct. The second equation, then, because the coefficient on

IncomePct is negative, suggests that the larger value of IncomePct is associated with a lower

value of AuditRate. So, a positive ε is associated with a smaller AuditRate. This means that the

coefficient on AuditRate (α1) would be biased downward if we estimated the equation using the

endogenous AuditRate directly, with no correction for the endogeneity. This negative bias can be

seen in state level data; many states with high audit coverage also have low compliance. This does

not mean that audits reduce compliance; rather, it simply reflects that IRS intentionally allocates its

audit resources where they are needed most. Using similar logic, one would expect a positive

bias of the AuditRate parameter in the OffsetsPct equation—on the assumption that OffsetsPct

has a positive impact on AuditRate. I examine these biases, and the extent to which they are

corrected, in the discussion of results, section 3.2.1.

Instruments for AuditRate

The ideal instrument for AuditRate would be something that helps to predict AuditRate,

but is not related to compliance. I have chosen two audit productivity-related measures to fulfill

this purpose. Presumably, the more productive the auditors are in a given year, the more audits

they can start in that year. And, to a lesser extent, perhaps, the more time applied per audit last

year, the greater the likelihood that those audits will be completed (and new audits started) this

year. Audit productivity is routinely measured in several ways, but the best definition for this

purpose is probably the percent of all examiner time available that is applied to the direct

examination of returns. Direct Examination Time (DET) is generally on the order of 50 percent of

total time, and varies widely across districts and by year. Non-direct activities include vacations,

training, travel, and an assortment of administrative duties. I have used as instruments both the

DET percent and the one-year lag of the average DET per audit.

2.2.3 Alternate Definitions of Income and Offsets

As discussed earlier, the amount of income that taxpayers report is often influenced by

changing rules that dictate what must be reported. For example, prior to the Tax Reform Act of

1986 (TRA86), taxpayers could exclude from total income the first $100 of dividends received

($200, on married-joint returns). After 1986, there was no such dividend exclusion. Unless this

is controlled for, we might interpret the increase in reported income to be an improvement in

voluntary compliance instead of a straightforward response to the new rule. This would be

especially detrimental if the elimination of the dividend exclusion were correlated with other policy

parameters included as explanatory variables (like marginal tax rates). Many more such rule

changes have occurred over the 1982-1991 timeframe of this study—especially in the rules

governing offsets.

The Determinants of Individual Income Tax Compliance

No single approach to this problem is ideal, so I have employed a combination of

techniques. Where possible, I have included explanatory variables that control for certain rule

changes—such as FThresholdPct, ChildExemptsPct, and StateTaxPct. In the case of dividends,

I was able to construct a constant-law dividend variable by applying the dividend exclusion to the

post TRA86 micro data before aggregating to the state level. But more often than not, neither of

these two approaches could control for specific rule changes. Therefore, I have constructed the

reporting compliance variables according to three different definitions to test the sensitivity of my

results to these rule changes. The basic approach (Definition A) was to exclude from total income

or total offsets any component whose rules changed during the period, and neither of the other

forms of controlling for the changes was available. The other extreme (Definition C) included all

income and offsets components regardless of rule changes. A hybrid of these two (Definition B)

excluded only those components of total income or total offsets whose rules changed in years other

than 1986. Definition A has the advantage of being purged of such rule changes, allowing a

straightforward interpretation of the parameter estimates. However, it has the drawback that it is

incomplete; it says nothing about the influence of the explanatory variables on the excluded

components of income and offsets. By comparing the results from all three alternatives, however,

it should be possible to conclude something more definitive about the sensitivity of the results to

unaccounted-for tax rule changes.

2.3 Model Specification

The model consists of four compliance equations (FilingRate, IncomePct, OffsetsPct,

and NetIncomePct), and one first-stage AuditRate equation. They are all estimated using singleequation procedures (LSDV and 2SLSDV) to avoid the likelihood of introducing omitted variable

bias across equations. The equations are estimated from the panel of 49 states (i.e., excluding

Alaska, as discussed earlier) over ten years (1982-1991), giving 490 observations for each

variable.

2.3.1 Functional Form

Many of the explanatory variables can be expected to have a non-linear effect on

compliance, reflecting diminishing returns to IRS effort, for example. When this is plausible, I

have expressed the independent variables as logarithms.25 For such variables that frequently take

on values between zero and one, or values near one, I have used the logarithm of one plus the

variable. Otherwise, all variables are modeled linearly.

2.3.2 First-Stage AuditRate Equation

It is both difficult and unnecessary to estimate a structural equation for AuditRate. We

need only estimate a first-stage AuditRate equation using appropriate instruments, and then use the

predicted AuditRate in the structural compliance equations. My specification for the AuditRate

equation, therefore, was as follows:

AuditRate =

δ0 + Σ σ 1iStatei +Σ τ1tYeart+ δ1DET% + δ2Ln(AvgDET-1 +1) + ε1

As with all of the specifications that follow, there are 48 State dummies (i = 1 to 48) and 9

Year dummies (t = 1 to 9), and all of the state and time subscripts on the other variables are

omitted for simplicity.

I have used natural logarithms. However, since the instruction for natural logs in my econometric software is simply

“log,” I inadvertently thought I had been using base 10 logs in prior versions of this report. Although this did not affect

the econometric results, it does change any calculations based on those results. This version of the report includes the

corrected nomenclature and calculations. For example, see Figure 6, Table 5, and Appendices G, H and I.

The Determinants of Individual Income Tax Compliance

2.3.3 FilingRate Equation

Many of the determinants of reporting compliance are not relevant to filing compliance, and

vice-versa. I used the following specification for the FilingRate equation:

FilingRate =

φ0 + Σ σ 2iStatei +Σ τ2tYeart+ φ1FThresholdPct + φ2Amnesty5

+ φ3Ln(AvgBurden) + φ4SoleProps + φ5SolePropTFS

+ φ6IRP_DocRate + φ7Ln(TDI+1) + φ8Ln(RefOffRate+1)

+ φ9TPS_RetPrepPC

+ φ10Singles + φ11Under30 + φ12Over64

+ φ13AvgPI + φ14AvgPIgrowth + φ15UnemplRate + ε2

The explanatory variables unique to the FilingRate equation are: Amnesty5, since the

common characteristic of all the various state amnesties was that they targeted nonfilers;

Ln(TDI+1), since the TDI program is only targeted to nonfilers; Ln(RefOffRate+1), since the

incidence of refund offsets is likely to induce some nonfiling, but is likely to manifest itself in

reporting compliance (if at all) through smaller amounts of tax withheld, which is not reflected in

any of my reporting compliance variables; and AvgPI and AvgPIgrowth, in order to control for

income variations across states and over time (this is handled in the reporting compliance equations

by dividing the dependent variables by Personal Income). In addition, IRP_DocRate is included

as a linear variable here, whereas it enters the income reporting equations in logarithm form. This

is because the filing decision is an either-or choice; more IRP documents would induce more

people to file—not greater filing compliance among those who already file.

The other variables of primary interest in this equation are: FThresholdPct, which should

indicate whether raising the filing threshold increases or decreases filing compliance;

Ln(AvgBurden), which is likely to diminish filing compliance; and TPS_RetPrepPC, which we

would expect to improve filing compliance.

2.3.4 IncomePct Equation

The income and offset reporting equations include FilingRate as an explanatory variable,

but this is not endogenous. This is because, although we would expect that greater filing

compliance (i.e., more people filing) would increase the aggregate amounts of income and of

offsets reported, the amounts reported on filed returns do not affect whether or not people file

returns. The specification of the IncomePct equation, then, is as follows:

IncomePct =

α0 + Σ σ 3iStatei +Σ τ3tYeart+ α1FilingRate

+ α2FThresholdPct + α3MargTaxRate@$15K + α4MargTaxRate@$57K

+ α5ChildExemptsPct

+ α6Ln(AvgBurden) + α7SoleProps + α8SolePropTFS + α9PaidPrep

+ α10Ln(pAuditRate+1) + α11Ln(IRP+1) + α12Ln(CID+1)

+ α13TPS_CallsPC + α14TPS_RetPrepPC

+ α15Singles + α16Under30 + α17Over64

+ α18PCBirths + α19ExclIncomePct + α20UnemplRate + ε3

The Determinants of Individual Income Tax Compliance

Like the other two reporting compliance equations, this equation includes several

variables—in addition to the FilingRate variable itself—that are not in the FilingRate equation.

Additional enforcement variables include Ln(pAuditRate+1) (the predicted results from the firststage AuditRate equation), and Ln(CID+1); additional tax policy variables include

MargTaxRate@$15K, MargTaxRate@$57K, and ChildExemptsPct (which controls for the

changing value of dependent exemptions); PaidPrep is an additional Burden/Opportunity variable,

which is relevant only to filers; and TPS_CallsPC is an additional IRS Responsiveness variable,

which seems to be mostly relevant to filers. The reporting compliance equations also include

PCBirths, which does not appear to affect FilingRate significantly. Finally, as pointed out

earlier, Ln(IRP+1) is used for the income reporting equations instead of IRP_DocRate in linear

form.

The IncomePct equation has two variables not included in the OffsetsPct equation:

Ln(IRP+1), since virtually all information documents report income rather than offsets; and

ExclIncomePct, which accounts for types of income included in Personal Income that do not need

to be reported on tax returns—either because the income falls below the filing threshold (and no

return is required), or the type of income is not taxable.

The explanatory variables of primary interest in the IncomePct equation are:

Ln(pAuditRate+1), FThresholdPct, the two MargTaxRate variables, Ln(AvgBurden),

Ln(IRP+1), Ln(CID+1), and the two TPS variables. These represent the most important tax

policy and tax administration variables that could conceivably be manipulated so as to foster better

voluntary compliance.

2.3.5 OffsetsPct Equation

This equation is much like the income reporting equation, but with some important

differences. The specification is as follows:

OffsetsPct =

β0 + Σ σ 4iStatei +Σ τ4tYeart+ β1FilingRate

+ β2FThresholdPct + β3MargTaxRate@$15K + β4MargTaxRate@$57K

+ β5ChildExemptsPct + β6StateTaxPct

+ β7Ln(AvgBurden) + β8SoleProps + β9SolePropTFS + β10PaidPrep

+ β11Ln(pAuditRate+1) + β12Ln(CID+1)

+ β13TPS_CallsPC + β14TPS_RetPrepPC

+ β15Singles + β16Under30 + β17Over64

+ β18PCBirths + β19UnemplRate + ε4

As discussed above, the OffsetsPct equation does not include two of the variables included

in the IncomePct equation: Ln(IRP+1), and ExclIncomePct. However, it includes one other

variable not in the IncomePct equation: StateTaxPct, which controls for variations in the amount

of state and local taxes that are deductible federally (and therefore contribute to offsets).

The same tax policy and tax administration variables are of primary interest in the

OffsetsPct equation (to the extent that they are included). However, some of these variables are

likely to affect offsets and income in opposite ways, while others have the same kind of impact.

The Determinants of Individual Income Tax Compliance

2.3.6 NetIncomePct Equation

The final reporting equation represents income minus offsets, and so includes all of the

explanatory variables included in either of the two separate reporting equations, as follows:

NetIncomePct = γ 0 + Σ σ 5iStatei +Σ τ5tYeart+ γ 1FilingRate

+ γ 2FThresholdPct + γ 3MargTaxRate@$15K + γ 4MargTaxRate@$57K

+ γ 5ChildExemptsPct + γ 6StateTaxPct

+ γ 7Ln(AvgBurden) + γ 8SoleProps + γ 9SolePropTFS + γ 10PaidPrep

+ γ 11Ln(pAuditRate+1) + γ 12Ln(IRP+1) + γ 13Ln(CID+1)

+ γ 14TPS_CallsPC + γ 15TPS_RetPrepPC

+ γ 16Singles + γ 17Under30 + γ 18Over64

+ γ 19PCBirths + γ 20ExclIncomePct + γ 21UnemplRate + ε5

This equation represents the “bottom line” of reporting compliance; it differs from tax

reporting compliance only because the amount of tax reported reflects the application of the tax rate

schedule to net income. (Recall that I have defined offsets—and therefore net income—to include

the income-offset value of credits, which are tax offsets. Therefore, this net income is not

synonymous with taxable income, as reported on tax returns.)

Even though the NetIncomePct equation alone cannot provide the insight into the method

of noncompliance (i.e., underreporting income or overstating offsets) that the other two equations

provide, it does serve two useful purposes. First, when we are interested in tax compliance,

NetIncomePct is the most direct way to estimate it. Second, it provides a useful check on the

other two reporting equations; we should expect that when a variable is included in all three

equations, the coefficient in the NetIncomePct equation should be roughly equal to the coefficient

in the IncomePct equation minus the coefficient in the OffsetsPct equation. This should

especially be true when the coefficients are all significant, and can help to evaluate the results when

one of the coefficients is not significant.

3. Results

The principal results for all four compliance equations are summarized in Table 3. These

results pertain to the most restricted definition of the dependent variables IncomePct, OffsetsPct,

and NetIncomePct (Definition A), which excludes all income and offset components for which the

rules changed during the 1982-1991 period as to what should be reported—unless the rule change

could be reflected in the data (as with the dividend exclusion), or the change could be controlled for

with explanatory variables (as with the value of standard deductions and exemptions, and the

deductibility of state and local income taxes). These results are discussed below, first with respect

to filing compliance, and then with respect to reporting compliance. In the case of reporting

compliance, all three equations are discussed concurrently. For convenience, the explanatory

variables in Table 3 are grouped in the five major categories (Tax Policy, Burden/Opportunity,

Enforcement, IRS Responsiveness, and Demographics/Economics) used in the discussion.

Following this discussion of the principal results is a section that compares the results for

all three definitions of the reporting compliance variables (Definitions A, B, and C), focusing on

the sensitivity of the results to rule changes in the 1982-1991 time period. The fourth section

describes a number of additional variables that were tested as potential determinants of voluntary

The Determinants of Individual Income Tax Compliance

compliance, but were found not to have any significant impact. Finally, section 3.5 discusses the

estimated relative merits of expanding the five IRS activities found to have a positive impact on

voluntary compliance.

3.1 Filing Compliance

3.1.1 Impact of Tax Policy Parameters

Of the two policy variables included, only the filing threshold (the sum of one’s standard

deduction and personal exemptions) significantly influences the FilingRate. The impact is

strongly negative, which probably reflects two phenomena: first, as the filing threshold increases,

some people who are still required to file stop doing so (perhaps out of confusion); and second,

low-income people (who are most affected by a change in the filing threshold) may exhibit higher

filing compliance than those with higher incomes—raising the overall FilingRate while they are

required to file, but causing a drop in that rate when they no longer need to file. The latter

possibility is consistent with the strongly negative impact of AvgPI on FilingRate. State tax

amnesties apparently have no significant impact on federal filing compliance, but the results

suggest that they may have a weak positive influence.

3.1.2 Impact of Burden / Opportunity

All of the included variables significantly affect filing compliance—at least at the 10 percent

level of significance. The burden (in hours) associated with the various tax forms and schedules

seems to reduce the FilingRate, as one might expect. This seems to confirm IRS concerns that as

the forms get more numerous and complex, requiring more time to maintain records and to

complete the paperwork, more people decide to forget about filing altogether. The coefficients on

the two sole proprietor variables suggest that sole proprietors, generally, improve the

FilingRate—except for those within the Trade, Finance, and Service sectors, who have a strong

negative impact on filing compliance.26 This may reflect the fact that businesses typically have

multiple “paper trails,” making it hard for them to hide from the IRS, but that these three special

sectors tend to be associated with the “underground economy,” including cash-based businesses

and “moonlighting.”

3.1.3 Impact of Enforcement Activities

Both the matching of third-party information documents (IRPDocRate) and the issuance of

TDI nonfiler notices (Ln(TDI+1)) provide a fairly strong deterrent against nonfiling, as we would

expect. The more information that is provided to the IRS by the payers of income, the more people

will file required returns, since it is harder for them to hide. Furthermore, when IRS uses this

information (and prior filing patterns) to issue TDI notices to presumed nonfilers, the general

population seems to respond with a higher FilingRate than it would otherwise. The two programs

apparently complement each other nicely to promote the filing of required returns.

Refund offsets seem to have a negative impact on filing compliance, but it is too weak to be

considered significant. This suggests that some taxpayers may stop filing in order to avoid paying

the debts being addressed by these offsets, but the most likely response—if any—seems to be to

adjust their withholding to minimize their refunds (a phenomenon that I cannot verify with a model

focused solely on filing and reporting compliance).

Note that SolePropTFS is actually an interaction term, the product of SoleProps (the number of proprietors as a

percentage of all potential returns) times TFSEmplPct (Trade, Finance, and Service employment as a percent of total

nonfarm employment). It seems reasonable to view the interaction term, though, as representing the relative concentration

of proprietors in these three sectors.

The Determinants of Individual Income Tax Compliance

Table 3. Determinants of Voluntary Filing and Reporting Compliance, Definition A *

Equation

Explanatory Variables

FilingRate

IncomePct

OffsetsPct

NetIncomePct

0.345586

0.137683

0.207853

1.182627

0.857427

0.339758

MargTaxRate@$15K

1.221297

-0.663976

1.921272

MargTaxRate@$57K

-1.978458

0.530545

-2.442911

ChildExemptsPct

1.475395

0.457696

1.000080

0.145114

-0.101522

FilingRate

(7.64)

FThresholdPct

-3.569438

Amnesty5

0.207335

(-8.31)

(4.04)

(8.72)

(8.33)

(5.57)

(1.40)

(0.67)

(1.17)

(-1.00)

(1.86)

StateTaxPct

(-1.80)

(0.76)

(1.63)

(1.99)

(2.22)

(-1.50)

(1.52)

(-0.59)

Ln(AvgBurden)

-11.929189

3.383676

-3.550471

4.888900

SoleProps

1.953925

1.428688

0.274123

1.169128

SolePropTFS

-3.414896

-2.925128

-0.527449

-2.399908

PaidPrep

-0.166282

-0.014858

-0.153009

Ln(pAuditRate+1)

16.158539

3.313904

13.892113

IRP_DocRate

(-1.78)

(2.44)

(-2.30)

(0.55)

(1.84)

(-2.02)

(-4.81)

(3.37)

(-1.77)

(0.98)

(-1.01)

(-1.23)

(2.00)

(0.96)

(1.77)

(-1.95)

(-5.36)

(3.46)

1.565057

(2.71)

-1.121633

Ln(IRP+1)

0.675205

(-0.23)

Ln(TDI+1)

3.850765

Ln(RefOffRate+1)

-0.873704

(0.16)

(1.81)

(-1.13)

Ln(CID+1)

0.932191

0.314909

0.593380

TPS_CallsPC

-0.003994

-0.000742

-0.003378

(3.08)

(-1.34)

(2.96)

(-0.71)

(2.37)

(-1.36)

TPS_RetPrepPC

0.146118

0.130914

-0.007756

0.136453

Singles

-0.551763

0.266954

0.072872

0.190927

Under30

0.186049

-0.098600

-0.008269

-0.091372

Over64

0.242260

-0.075873

-0.042744

-0.022223

0.991262

0.253864

0.734990

(2.18)

(-5.77)

(1.95)

(2.40)

PCBirths

(2.07)

(1.46)

(-1.10)

(-0.76)

(4.55)

AvgPI

-0.920930

AvgPIgrowth

0.192294

(-0.35)

(1.14)

(-0.26)

(-1.23)

(3.58)

(2.61)

(1.26)

(-1.23)

(-0.27)

(4.02)

(-4.32)

(3.68)

ExclIncomePct

-0.642278

-0.917979

(-1.60)

(-2.77)

UnemplRate

-0.200099

-0.370384

0.078118

-0.428625

Adj. R-Squared

0.627007

0.757573

0.919223

0.801260

(-1.50)

(-2.97)

(1.84)

* 2SLSDV estimates (just LSDV for the FilingRate equation) from state-level panel data for 1982-1991;

t-statistics in parentheses; variables in bold are the primary tax policy and tax administration parameters of interest.

(-4.14)

The Determinants of Individual Income Tax Compliance

3.1.4 Impact of IRS Responsiveness

The effort of IRS’s Taxpayer Service (TPS) function to help taxpayers prepare their returns

(TPS_RetPrepPC) seems to have a significant, positive impact on the overall FilingRate.

Specifically, we can interpret the estimated coefficient to mean that for every additional seven

returns prepared by TPS per thousand of population,27 the FilingRate will increase by one

percentage point. This suggests that some of the induced returns may be ones prepared by TPS,

but also that some of the new filers are influenced indirectly by the TPS outreach.

3.1.5 Impact of Demographic and Economic Variables

Five of the six demographic and economic control variables included have significant and

intuitive effects on the FilingRate. A greater concentration of singles within a state is strongly

associated with lower FilingRates. This may reflect less of a requirement to file among singles, a

misunderstanding as to when dependents need to file their own returns (especially now with

relatively low filing thresholds for dependents), or—like auto insurance companies have learned—

that singles are typically less careful than their married counterparts. However, higher

concentrations of the population in the under-30 and over-64 age categories each seem to improve

filing compliance. This may reflect fewer opportunities among the young and the old to avoid IRS

notice.

As noted earlier, income seems to be strongly and negatively associated with filing

compliance. Specifically, every additional thousand (1992) dollars of average Personal Income

(AvgPI) is associated with a drop in the FilingRate of almost one percentage point. This

suggests that those who are able to hide from IRS entirely can conceal significant amounts of

income—and as long as it works, they’ll find ways to make more such income—while those who

contribute little to aggregate Personal Income either have no requirement to file, or derive most of

their income from wages and interest, which are hard to hide from IRS.

Of the two variables reflecting the state of the economy, only the rate of real income growth

(AvgPIgrowth) has a strongly positive impact on FilingRate, which we would expect. As real

income increases in the general population, fewer people are tempted to cut corners by not filing a

tax return. By the same token, as the unemployment rate increases, filing compliance seems to

decline. While this result is intuitive, however, it does not appear to be significant.

3.2 Reporting Compliance

Before discussing the impact that specific variables have on voluntary reporting

compliance, it is necessary to ensure that these equations are appropriately identified, given the

endogeneity of audit rates.

3.2.1 Identification

As mentioned earlier, the first-stage AuditRate equation is a function of two audit variables

related to productivity: DET% and AvgDET, where DET (Direct Examination Time) is the time

that auditors apply directly to the examination of returns, as opposed to the time they spend on

leave, in training, or performing various administrative duties. The specification for this equation

is as follows:

Note that the coefficient (0.146) is approximately one-seventh.

The Determinants of Individual Income Tax Compliance

AuditRate =

δ0 + Σ σ 1iStatei +Σ τ1tYeart+ δ1DET% + δ2Ln(AvgDET-1 +1) + ε1

In words, the percentage of returns subject to a started audit this year is a function of the

percentage of their time that auditors apply directly to such audits (DET_Pct) and the logarithm of

one plus the average amount of time spent directly on audits last year. The more time that auditors

are able to apply to examinations this year ought to increase the number of examinations

conducted, suggesting that δ1 ought to be positive. Similarly, if more time was applied to the

average audit last year, more audits should be closer to being completed—and new audits started—

this year, suggesting that δ2 also ought to be positive. The actual results for this regression (with

t-statistics in parentheses) are as follows:

δ1 = 0.007570,

(1.84)

δ2 = 0.057984,

Adj. R2 = 0.766978

(0.58)

Except for the fact that the impact of the prior year’s AvgDET is not statistically significant, these

results seem encouraging.

There are two indications that the reporting equations estimated using AuditRate predicted

from this first-stage regression (pAuditRate) are identified. First, when we compare the

coefficient on the predicted (and presumed exogenous) Ln(pAuditRate+1) from these reporting

equations with what is obtained using same specifications, but using the actual (endogenous)

AuditRate (i.e., Ln(AuditRate+1)), we see that the two-stage approach corrects the downward

bias anticipated in the coefficients in both the IncomePct equation and the NetIncomePct equation

(see section 2.2.2 above).

These comparisons are given in Table 4 for each of the reporting

Table 4. Comparison of AuditRate Coefficients in the Three Reporting Compliance

Equations, Estimated Using the Endogenous and Exogenous AuditRate Variables

Definition A

Definition B

Definition C

-0.188470

-0.234193

0.747762

(-0.17)

(-0.21)

(0.54)

16.158539

15.823423

17.100229

(3.37)

(3.30)

(2.79)

-0.014032

0.400095

0.468282

(-0.04)

(0.91)

(0.93)

3.313904

0.838515

-0.525384

(2.00)

(0.44)

(-0.24)

-0.164486

-0.600534

0.505934

(-0.18)

(-0.69)

(0.38)

13.892113

15.751865

17.449324

(3.46)

(4.11)

(2.97)

IncomePct Equation

Ln(AuditRate+1) [endogenous]

Ln(pAuditRate+1) [exogenous]

OffsetsPct Equation

Ln(AuditRate+1) [endogenous]

Ln(pAuditRate+1) [exogenous]

NetIncomePct Equation

Ln(AuditRate+1) [endogenous]

Ln(pAuditRate+1) [exogenous]

t-statistics in parentheses. All equations specified as in Table 3. For a complete tabulation of these results, see Appendix C.

The Determinants of Individual Income Tax Compliance

compliance equations for all three definitions of the dependent variables. For both the IncomePct

and NetIncomePct equations, assuming that the AuditRate is exogenous yields parameter

estimates that are very small and very insignificant. Accounting for the endogeneity of AuditRate,

however, yields parameter estimates that are larger (as anticipated) and highly significant.

Notice, however, that the same pattern is true with the OffsetsPct equation—even though

we anticipated that the coefficient on AuditRate would be biased upward, and that the corrected

coefficient would be negative. Understanding this counter-intuitive result first requires making

two observations: first, the estimated impact of Ln(pAuditRate+1) on OffsetsPct is much less

than its impact on IncomePct; and second, the estimated impact of Ln(pAuditRate+1) on

NetIncomePct (shown as about 13.89 in Table 4) is very significant, and is the logical

combination of the separately-estimated coefficients in the IncomePct and OffsetsPct equations

(shown as 16.16 and 3.31, respectively in Table 4—their difference being 12.85). All the results

are significant and internally consistent, but why the unanticipated sign on the AuditRate

parameter in the OffsetsPct equation? The logical explanation seems to lie in the fact that the

claiming of offsets is not a simple matter. For example, some offsets, such as medical expenses

and miscellaneous deductions, are specifically limited by the amount of Adjusted Gross Income

reported on the return; as more income is reported, we should expect more to be claimed for these

types of offsets. Likewise, most credits are not refundable; they are limited by the amount of tax

due. If more income (and, therefore, more tax) is reported, we should expect more to be claimed

as credits. It may also be true that if taxpayers feel compelled to report more income (e.g., in

response to an increased AuditRate), they may seek to find additional offsets to reduce the bite

somewhat. This may be especially true if (as one might expect) taxpayers perceive that they may

become audit targets if the offsets they claim seem out of line to the IRS with respect to their

income; so, if they do not report all of their income, they may consciously avoid claiming all of

their potential offsets. The fact that the impact of AuditRate on OffsetsPct is much smaller than

its impact on IncomePct may also mean that audits have some negative impact on the amount of

offsets claimed, after all, but that this effect is more than compensated for by the phenomena just

described. In any event, if the net impact of OffsetsPct on the AuditRate is actually negative (as

is the impact of IncomePct), then the coefficient on AuditRate in the OffsetsPct equation is

biased downward, rather than upward, and the 2SLS estimates correct this bias. This reasoning

suggests that OffsetsPct is dependent, in part, on IncomePct—a possibility not controlled for in

this specification. Not controlling for this does not affect the results of either the IncomePct

equation or the NetIncomePct equation (which represents the “bottom line” results); in fact,

estimating the three separate reporting equations allows us to identify such dependencies. As long

as we are able to interpret the results for the OffsetsPct equation with this dependency in mind, we

can make the correct inferences.

The second fact consistent with the proposition that the reporting equations are identified is

the result of specification tests for endogeneity based on Hausman (1978). This procedure tests

for statistically significant differences between a regression that assumes endogeneity and an

otherwise identical one that does not. The results of this test are given for each pair of such

specifications in the tables in Appendix C. In every important case,28 the test statistic is strongly

significant, which is consistent with notion that AuditRate is endogenous, and that using

pAuditRate corrects for that endogeneity. Moreover, experimentation with alternate specifications

for the first-stage AuditRate equation yielded no alternatives that produced Hausman test statistics

as strongly significant as these. This, together with the observed bias correction, suggests that the

reporting equations are identified.

The only exceptions to this are the OffsetsPct equation under Definitions B and C.

drawbacks of these two definitions.

This is consistent with the

The Determinants of Individual Income Tax Compliance

3.2.2 Impact of Tax Policy Parameters

Now let us turn to discussing the the impact of the various determinants of reporting

compliance, as summarized in Table 3. As expected, FilingRate positively and significantly

influences the reporting of both income and offsets; as more people file returns, they obviously

report some of each.

More interesting are the effects of the five tax policy parameters included in the three

reporting equations. First, having controlled for the FilingRate, an increase in the filing threshold

seems to increase the reporting of both income and offsets significantly. However, since

FThresholdPct has a strongly negative impact on FilingRate, which is included in the reporting

equations, the net impact of FThresholdPct must take this into account. For example, a one

percentage point increase in FThresholdPct decreases the FilingRate by roughly 3.57 percentage

points, which, in turn, decreases IncomePct by about 1.23 percentage points (-3.57 times 0.35,

the coefficient on FilingRate in the IncomePct equation). So, the increase in FThresholdPct has

virtually no net impact on IncomePct; the apparent increase of 1.18 is just offset by the reduction

of 1.23 due to a decline in the FilingRate. Similar arithmetic reveals that the net impact of a one

percentage point increase in FThresholdPct is an increase in OffsetsPct of 0.37 and a decrease in

NetIncomePct of 0.40. (The fact that these two numbers are opposite and roughly equal is

consistent with the net impact on IncomePct being zero. That is: 0.00 – 0.37 ≈ -0.40). So, an

increase in the filing threshold—controlling for its impact on FilingRate—increases the amount of

offsets claimed (as we would expect), but has no effect on income reported.

The influence of marginal tax rates on the voluntary reporting of income and offsets is even

more interesting. An increase in the marginal tax rate for those with $15,000 of taxable income

increases IncomePct by 1.22 and decreases OffsetsPct by 0.66, resulting in an increase in

NetIncomePct of 1.92, which is strongly significant. In other words, low-income taxpayers seem

to respond to an increase in marginal rates by becoming more compliant. This is consistent with

the Allingham and Sandmo (1972) prediction when the positive “income effect” is stronger than the

negative “substitution effect” (see section 1.2 above). This is most likely to be true for low-income

people, since they are typically more risk-averse than those with higher incomes, and since the

penalties for underreporting their income would represent a much larger share of their net income.

The impact of marginal tax rates on those with $57,000 of taxable income appears to be in the

opposite direction (i.e., decreasing the reporting of income and net income, and increasing the

amount of offsets claimed), but the effect is only marginally significant. A significant impact

would have meant that the positive “income effect” is weaker than the negative “substitution

effect,” meaning that these taxpayers would, on balance, increase the amount of income they

underreport if the marginal tax rate increased, since each dollar of underreported income is now

worth more (in terms of tax savings) than before. The fact that the results are not very significant

suggests that the “income” and “substitution” effects almost evenly balance each other at this

income level. It would be nice to be able to estimate the impact of marginal rates at some larger

income (hypothesizing that they would have a significant negative impact on reporting

compliance), but the changes in marginal rates over the 1982-1991 time period and the panel

structure of my data preclude testing this hypothesis at larger incomes.29 However, even without

Recall that the two MargTaxRate variables were constructed as weighted averages of the Married and Single tax rates

for the income level in question, where the weights are the percentage of marrieds and singles in the potential filing

population by state and year. This only works if the Married and Single tax rates are different; if they are the same in any

year, then there will be no variation in the tax rate variable across states for that year, making interpretation of the results

much more difficult. Unfortunately (for this purpose, at least), the Tax Reform Act of 1986 drastically reduced the number of

marginal rate brackets, so that incomes over the $57,000 (in 1982 dollars) used here typically face the same marginal rate

regardless of marital status; a marginal tax rate variable defined in this way at a much larger income level would exhibit

virtually no variation after 1986, and would essentially be a dummy variable for all those post-TRA86 years. Since I

already include dummy variables for each of those years separately to account for the fixed time effects, it would be difficult

to interpret the results of such a specification.

The Determinants of Individual Income Tax Compliance

being able to test that hypothesis fully, these results suggest that the impact of marginal tax rates is

different at different income levels, and that reporting compliance might be improved with a flatter

rate schedule.

Both of the variables included to control for changes over time in the value of important

offsets seem to affect OffsetsPct, as expected. The impact of ChildExemptsPct is larger than that

of StateTaxPct, but is not as significant. The smaller coefficient on StateTaxPct makes sense,

since not all state taxes are claimed as offsets; only taxpayers who itemize deductions claim them.

Somewhat surprising, however, is the finding that ChildExemptsPct significantly affects

IncomePct also.30 This may simply suggest that taxpayers with children tend to have more

income, but it may also mean that they report more of their income. In any case, the coefficient of

1.00 on ChildExemptsPct in the NetIncomePct equation suggests that the net effect of dependent

exemptions is to increase the reporting of net income dollar for dollar.

3.2.3 Impact of Burden / Opportunity

As with its impact on filing compliance, AvgBurden seems to be related to lower amounts

of offsets being reported, although this impact is not strongly significant. Apparently, taxpayers

do not claim some offsets because they view it as too burdensome (in terms of the time required) to

do so. The impact of the forms burden on income reporting—even NetIncomePct—does not

appear to be significant, though it tends to be positively related. This may be because higherincome taxpayers—especially those with business income—typically have to fill out more forms

and schedules, and thus face a higher burden.

The three opportunity variables all seem to influence income reporting significantly, but not

offset reporting. The impact of SoleProps on IncomePct is much like its impact on the

FilingRate, probably for much the same reasons; a higher concentration of proprietors generally is

associated with more income reported, but that effect is offset by the negative impact of higher

concentrations of employment in the Trade, Finance, and Service sectors. The opportunity

variable with the most significant impact on income reporting is PaidPrep, the percentage of

returns prepared with the help of a paid practitioner. Interestingly, controlling for other important

determinants of income reporting, preparers seem to reduce the amount of income reported.

Clearly, many people seek the help of professional tax preparers for the express purpose of

reducing their tax liability. But one would suppose that the primary method for doing this would

be by finding additional offsets that can be claimed. The results in Table 3, however, indicate that,

if anything, the impact of paid preparers is to reduce the amount claimed as offsets (although this

result is not significant). The very strong negative impact of preparers on income reporting is

probably a reflection of their preparation of returns with primarily business-source income, which

is net of business expenses; it seems reasonable to assume that professional tax preparers routinely

reduce the amount of business income needing to be reported on tax returns by maximizing the

business expenses that are claimed.

I had not intended to include ChildExemptsPct in the IncomePct equation, but concluded that it belonged when I

noticed that its coefficient in the NetIncomePct equation was fairly significant, but not close to zero minus the coefficient

in the OffsetsPct equation (in fact, it also has a positive sign)—suggesting that ChildExemptsPct also influenced income

reporting.

The Determinants of Individual Income Tax Compliance

160

120

100

FY91 Estimated

Additional Tax Reported, TY91 ($B)

140

FY91 Actual

Additional Net Income Reported

As a Percent of Personal Income

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Audit Start Rate (%)

TY91 Summary

Audit Start Rate:

Average Direct Yield Per Examination:

Estimated Average Indirect Revenue Per Examination:

Ratio of Indirect Effect to Direct Effect:

0.65%

$7,986

$93,217

11.67

Estimated Additional Tax Revenue at 0.65% Audit Start Rate:

Estimated Additional Tax Revenue at 1.65% Audit Start Rate:

Difference:

$59.0 B

$115.2 B

$56.2 B

Figure 6. The Indirect Effect of the Audit Start Rate: NetIncomePct and additional tax reported (in TY91

dollars) as a function of the audit start rate, based on Table 3 results (i.e., Definition A).

3.2.4 Impact of Enforcement Activities

One of the most important findings of this analysis is that audits have a strong, positive

impact on reporting compliance. As discussed above in the context of identification issues, the

impact of audits seems to be to increase the reporting of income and, to a lesser extent, offsets.

The estimated impact of AuditRate on NetIncomePct is illustrated in Figure 6, which also

expresses the magnitude of this “ripple effect” in terms of the additional dollars of tax induced as a

function of AuditRate (using Tax Year 1991 as an example). As the details at the bottom of

Figure 6 indicate, the average indirect effect of the audits started in 1991 was over 11.6 times as

large as the average adjustment directly proposed by audits closed that year. Moreover, if the

The Determinants of Individual Income Tax Compliance

AuditRate were to have been 1.65 percent in 1991 instead of the actual 0.65 percent, an additional

$56 billion of additional tax would have been reported voluntarily.31 Similarly, these results

suggest that if the AuditRate had remained constant at its 1982 level of 1.62 percent, the

cumulative impact through 1991 would have been that an additional $257 billion of tax would have

been reported voluntarily (see Appendix G for the detailed calculations). This is strong evidence

that audits are a potent tool to foster voluntary compliance. In fact, since the effect is significantly

larger than the direct revenue effect of the audits, these results suggest that the allocation of audit

resources (which is currently based almost solely on their direct revenue potential) ought to be

modified to give more weight to this indirect effect on voluntary compliance.32

Perhaps one of the most surprising findings is that IRP document matching does not have

any significant impact on reporting compliance. This seems inconsistent with the familiar

observation (and common intuition) that those types of income covered by third-party information

reporting (such as wages, interest, and dividends) exhibit a much greater degree of reporting

compliance than income types not subject to IRP (see, for example, IRS (1988)). There are two

important reasons for this counter-intuitive finding. First, by the time of the ten-year period

studied in this analysis (1982-1991), most of the IRP-generated improvements in voluntary

reporting compliance had already been realized. Although it was not until the mid-1980’s that IRS

actually expanded its matching program to encompass virtually all IRP documents received, most

taxpayers apparently assumed that the matching was always in place, judging by the earlier

compliance statistics. Therefore, there was little more compliance improvement to be realized by

1982. The second reason for this finding is that one of the effects of the IRP program is to make it

clearer to taxpayers how much IRS knows about them. The more people are aware of what

information is given to IRS and what is not, the more they may be tempted to hide some of what is

not reported. In other words, they might take the attitude, “What the IRS doesn’t know won’t hurt

them!” The fact that the estimated coefficients are very insignificant suggests that this kind of

response cannot be very strong or widespread, which may be because of the strong deterrent effect

of audits.

The final enforcement variable in the reporting equations—criminal convictions arising

from the work of the IRS Criminal Investigation Division (CID)—has a highly significant and

positive impact on income reporting, and an equally significant, but smaller, positive impact on

offsets reporting. These convictions seem to have the same kind of impact on reporting as do

audits (increasing both income and offsets), but perhaps for entirely different reasons. As

mentioned earlier, the most important influence that these convictions have on the general

population may be to satisfy the typical taxpayer that criminals are not going scott-free, thus

encouraging him to pay his “fair share.” When the opposite perception prevails (i.e., that

scofflaws get away with flagrant tax violations), it could very well cause some otherwise lawabiding taxpayers to underreport some of their own tax obligation in protest. It is reasonable to

assume that the smaller, but positive, impact on OffsetsPct is attributable to the same phenomena

causing the positive impact of audits on OffsetsPct—such as the fact that many offsets are made

possible by larger reported income, and the likelihood that many taxpayers try to keep their offsets

fairly proportional to their income so as to avoid an audit.

These estimated tax effects are based on the average marginal tax rate in 1991.

Since this specification uses the overall average audit rate for all individual income tax returns, however, these results

do not help us to allocate audit resources cost-effectively to the different classes of individual returns (e.g., business vs.

non-business, or low-income vs. high income). If (as is likely) the indirect effect of audits varies according to which

classes of returns are audited, then we should allocate audit resources at the margin according to the combination of the

direct plus indirect revenue-to-cost ratio. The best way to estimate the extent to which the indirect effect varies across audit

classes is to include AuditRate data for the separate classes. Lacking such data (for now), I tested interactions of AuditRate

with both SoleProps and AvgPI (see Appendix H). The results were inconclusive, but that may be because the mix of

returns audited is more important than the mix of returns filed.

The Determinants of Individual Income Tax Compliance

3.2.5 Impact of IRS Responsiveness

The impact of Taxpayer Service’s (TPS’s) return preparation efforts on income reporting

compliance is much like its impact on filing compliance—significant and positive, as one might

expect. This type of activity seems to have no impact on offset reporting compliance, however—

perhaps indicating that the typical return prepared by TPS claims few offsets, or that the TPS

assistance results in some taxpayers claiming more in offsets than they otherwise would have, and

other taxpayers claiming less, with little significant net impact on the population.

The telephone calls that TPS handles seem to have a weakly significant negative impact on

income reporting, however. This may indicate that many taxpayers who call IRS find out that they

do not need to report certain income (which they perhaps thought might be the case, but wanted

confirmation). It seems reasonable to assume that those who believe there is a good chance that

their questionable income must be reported do not even call. However, it is quite possible that this

negative impact of TPS telephone calls is due more to the quality of service that taxpayers receive.

If they have a hard time getting through, or need to spend a long time getting a response, or if they

are treated rudely or incompetently, the taxpayers who call—and, perhaps others influenced by

them—may underreport some of their income in protest. Whatever the reason, although the effect

is only marginally significant, it does suggest the need for further study, and perhaps some

improvement in the way these calls are handled.

3.2.6 Impact of Demographic and Economic Variables

In contrast with their impact on filing compliance, the concentration in the state population

of singles, those under 30 years old, and those over 64 years old do not seem to have a significant

impact on either income reporting or offset reporting. However, the signs on these coefficients in

all three reporting equations are opposite their signs in the FilingRate equation. That is, a higher

concentration of singles seems to be associated with lower filing compliance, but higher levels of

income and offset reporting, while higher concentrations of both young and old taxpayers tend to

strengthen filing compliance, but diminish income and offset reporting. This may mean that

singles who file tax returns are marginally more compliant in reporting their income than are

married taxpayers, and that the extreme age groupings have less income (and offsets) to report.

Per capita births has a strongly significant, positive impact on both income and offset

reporting. This may confirm the hypothesis raised earlier—that the birthrate reflects the general

level of optimism and, therefore, taxpayers’ willingness to comply with their tax obligations—but

we cannot be certain. If the value of dependent exemptions were not already controlled for, we

might view PCBirths as a surrogate for ChildExemptsPct, but this does not seem probable under

the circumstances.

The ExclIncomePct variable controls for income included in Personal Income that did not

need to be reported on tax returns—either because certain individuals were not required to file

returns, or because of the types of income that are not taxable. As expected, this has a negative

impact on income reporting; the more that does not need to be reported on returns, the less will be

reported on returns.

Finally, the unemployment rate appears to have a strongly significant, negative impact on

reporting compliance—much like its impact on filing compliance. For example, at higher

unemployment rates, more people might save money by cutting back on their taxes—both by

reducing the income they report and by increasing the offsets that they claim. However, these

results might not reflect a deterioration of reporting compliance at all; they may simply reflect the

The Determinants of Individual Income Tax Compliance

fact that unemployment reduces the amount of income that needs to be reported on tax returns (due

to fairly high filing thresholds) more than it reduces Personal Income (the denominator of

IncomePct). On the other hand, unemployment may not influence the reporting of offsets (if the

unemployed tended to have few offsets anyway) as much as it decreases Personal Income—

resulting in an increase in OffsetsPct. Although this is not a variable that can be manipulated by

the government for tax purposes, these highly significant results suggest that the role of

unemployment on reporting behavior must not be underestimated.

3.3 Alternate Definitions of Income and Offsets

All of the preceding results pertain to income and offsets defined by Definition A. That is,

the dependent variables were constructed to omit many components of income and offsets—those

subject to changing rules during the 1982-1991 period. Although this definition included on the

order of 97 percent of total income, 30 to 60 percent of adjustments, 94 percent of itemized

deductions, and 30 to 60 percent of credits each year, and although using this definition seems

necessary in order to control for the many rule changes, it leaves open the question of what impact

these various determinants have had on the income and offset components excluded entirely from

Definition A. It may well be that some of these components are the avenue of noncompliance,

perhaps because of the changing rules.

To check this possibility, I estimated the same equations using Definition B (which

includes components whose rules changed only in the Tax Reform Act of 1986) and Definition C

(which includes all components, regardless of rule changes). The results for the three definitions

are compared in Appendix C for all three reporting equations. Generally, the results are similar for

all three definitions (with the greatest differences typically observed among the tax policy

variables), suggesting that most of the various determinants have much the same impact on the

excluded components of income and offsets that they do on those included in Definition A, or that

the aggregate effect of any different impacts is not large. This allows us to generalize the results

for Definition A more confidently.

3.4 Other Potential Determinants of Voluntary Compliance

A number of other variables were excluded from the final analysis because they were found

to have no significant impact on compliance. Since that is a useful finding in and of itself, it seems

appropriate to mention these variables briefly here.

One of the big surprises had to do with a tax policy variable. Starting in 1987, taxpayers

have been required to supply the Social Security Number (SSN) of any dependents they claim as

exemptions who are above some specified age. That age has progressively declined in the years

that followed. I was able to use detailed Census population data to quantify by state and year the

percent of the under-18 population for whom SSNs were required, but found that this had no

significant impact—even on OffsetsPct. One would assume it would have been possible to detect

the seemingly significant reduction in the number of dependent exemptions claimed shortly after

this rule went into effect. It may have been captured already in the fixed year effects, however,

making the SSN variable somewhat collinear with the year dummies—especially since it was zero

for all observations prior to 1987.

The only additional Burden/Opportunity variable tested represented the concentration of

farmers, and was also insignificant. There are too many potential reasons for this result to

speculate which one(s) might be responsible.

The Determinants of Individual Income Tax Compliance

Three additional enforcement variables were tested—all of them related to other variables

that have been included. In addition to the number of CID convictions obtained, I tested the

number of CID investigations started; in addition to the percentage of refunds that were offset, I

checked the average dollar amount of the refund offsets; and instead of the total number of TDI

nonfiler notices issued, I tried just the number of TDI first notices (out of a series of four possible

notices). None of these additional variables or alternatives was useful, but this is not too

surprising. Since the most common and most effective means of communicating CID’s activities

to the general population is the media coverage of arrests and convictions, it seems reasonable to

find that the start of new investigations is not an important determinant. It also makes sense that

the dollar value of refund offsets has little impact on compliance; the decision of whether or not to

file is more likely influenced by the prevalence of refund offsets than on the amount, and the

response of filers to the prospect of getting a partial (or zero) refund is most likely to be to change

their withholding, which is not reflected in my reporting compliance variables. Finally, since

nonfilers are often so hard to find, it is not surprising that the first notice alone is not as important a

determinant of filing compliance as the combined effect of all of the TDI notices.

I also tested three additional measures of IRS Responsiveness: the speed in issuing

refunds; the volume of Taxpayer Service (TPS) correspondence with taxpayers; and TPS

educational efforts. Refund speed (measured as the percent of refunds taking longer than 35 or 45

days to process) could conceivably influence taxpayer attitudes about the IRS, and, therefore,

subsequent compliance, but I found no evidence for this—even though the variable has exhibited

much variation cross-sectionally and over time. The two additional TPS variables represent

somewhat less important activities than the phone calls handled and returns prepared, and it is not

surprising that they did not significantly influence compliance.33

Several demographic/economic variables did not exhibit a significant impact on compliance,

as well. These include the concentration of males (among potential Single and Head of Household

returns), the percentage of the population with at least some college education, population density,

the concentration of employment in the Trade/Finance/Service sectors (although this was

significant when included as an interaction term with SoleProps), and the concentration of

employment in the Construction/Mining/Manufacturing sectors. It may be that these (or other)

demographic/economic variables might exhibit more significance in slightly different

specifications, but it is likely that many of the included variables have picked up whatever influence

might be attributed to these.

3.5 Implications for Resource Allocation

Given that several IRS activities seem to have a positive impact on voluntary filing and/or

reporting compliance, it is natural to want to compare these activities with respect to their relative

effectiveness in improving compliance. Such an awareness could be used to improve the allocation

of current IRS resources (e.g., by expanding some activities at the expense of others), and to

identify which activities to concentrate on in any expansion of IRS resources intended specifically

to increase revenue or to promote voluntary compliance. These results give IRS this kind of

insight for the first time—at a time when shrinking budgets are forcing tough decisions.

A useful way to compare IRS activities is according to how well they help achieve the

objective of maximizing net revenue—total revenue net of costs. Maximum net revenue is

achieved—at any constrained budget level—when the ratio of marginal revenue to marginal cost is

the same for all activities. If this condition is not true, then additional revenue can be obtained with

the same budget by reallocating resources from the low revenue-to-cost programs to the high

I have been able to compile data on the accuracy of the phone calls, as well, but since these quality checks began only

in 1984, I have not included this variable in the analysis.

The Determinants of Individual Income Tax Compliance

revenue-to-cost programs.

For many IRS activities (e.g., audits), “revenue” may include both the amounts paid

directly by the taxpayers who are contacted by the activity as well as amounts paid by the general

population as an indirect response to the deterrent effect of the activity. Other activities (e.g.,

offsetting refunds to help settle non-tax debts) may have just an indirect effect on tax compliance.

In every case, the marginal revenue must reflect only the dollars collected (i.e., not the amounts

that are proposed or assessed, but are never collected), and the marginal cost must include all

additional overhead and support costs necessary to collect the marginal revenue.

Table 5 compares the cost-effectiveness in 1991 of the five IRS activities found in this

study to have a significant and positive impact on voluntary compliance. Although these

comparisons include only the indirect effect of the activities on compliance (since their marginal

direct effects are not available), the results are very informative. Marginal indirect revenue

functions were derived for each activity based on the FilingRate and NetIncomePct equations

estimated for Definition A (see Appendix I). The first panel summarizes the current level of

activity in 1991, and shows that TDI notices are by far the most cost-effective at the margin in

producing revenue (recall that they do this by promoting better filing compliance). The second

panel shows how much each activity would have had to be expanded (and the corresponding cost)

in 1991 in order for that activity to induce the voluntary reporting of an additional $10 billion of

tax. Notice that an increase in the number of TDI notices would again be the cheapest alternative

(if it were feasible to issue over 460 percent more of these notices), followed by an increase in the

number of returns prepared by TPS. The problem with the second panel is that it may not be

feasible to expand some of these activities to the rate required to induce an additional $10 billion of

tax. The third panel of Table 5 illustrates a more realistic expansion of each activity; it assumes that

the nationwide level of the activity is increased to the largest rate observed within any state during

the 1982-1991 period. In this scenario, audits produce the most revenue by far, followed by the

return preparation assistance of TPS.

In each panel, the least cost-effective activity is criminal convictions. However, these

results support the belief within CID that their principal role is to foster voluntary compliance. In

fact, the marginal indirect revenue-to-cost ratio of 16.3 at their current level of activity is far greater

than the average direct effect of audits. Also, Panel C of Table 5 highlights the fact that a realistic

expansion of CID activities may produce more indirect revenue than the largest realistic expansion

of TDI notices—even though TDI notices are the most cost-effective in producing indirect

revenue. These results emphasize that using estimates of indirect effects to guide IRS resource

allocations must take into account the practical constraints imposed on the expansion of most IRS

activities. However, they also illustrate that the potential benefits for resource allocation and for

revenue generation are immense.

The Determinants of Individual Income Tax Compliance

Table 5. Indirect Revenue-to-Cost Comparisons for Five IRS Activities, 1991 a

A. Actual Level of Activity

IRS Activity

Rate

Number

of units

Audit start rate (%)

TPS- Returns prepared/thousand

IRP documents/potential return

TDI notices/potential return (%)

CID convictions/million

0.647

3.33

7.56

3.43

10.51

632,819

840,126

978,512,924

4,436,942

2,650

Cost b

per unit ($)

Marginal indirect

Revenue/cost

Ratio

1,298

13.74

0.031

0.305

103,064

54.6

395.9

668.0

3,766.1

16.3

B. Rate Required to Induce an Additional $10 Billion of Tax

IRS Activity

Rate

Increase

in units

% Increase

Cost ($M)

Audit start rate (%)

TPS- Returns prepared/thousand

IRP documents/potential return

TDI notices/potential return (%)

CID convictions/million

0.797

7.29

11.30

19.25

88.41

147,469

998,414

483,860,602

20,469,781

19,640

23.3%

118.8%

49.4%

461.3%

741.1%

191.4

13.7

15.0

6.2

2,024.2

C. Nationwide Rate Increased to Highest Rate Observed Within Any State

IRS Activity

Rate

Increase

in units

Audit start rate (%)

TPS- Returns prepared/thousand

IRP documents/potential return

TDI notices/potential return (%)

CID convictions/million

3.510

23.02

10.51

9.29

51.52

2,802,462

4,963,934

381,705,783

7,582,359

10,340

Additional

Marginal indirect

Indirect tax

Revenue/cost

Revenue ($M)

Ratio

115,072

27,001

7,889

5,545

7,405

19.9

395.9

668.0

1,621.4

3.6

a Revenues exclude amounts collected directly from the taxpayers contacted; the appropriate marginal revenue-

to-cost comparison includes both the direct and the indirect effects. Audits, returns prepared by TPS, and CID

convictions typically result in direct revenue at no additional cost, while IRP matching and TDI notices typically

require additional contact with the taxpayers (at additional cost) to generate direct enforcement revenue (in fact,

some level of such direct enforcement contacts are probably necessary to ensure that the matching and notices

are credible deterrents).

b Source: IRS Compliance Planning & Finance: Budget & Resource Allocation Group; includes all appropriate

overhead, support, and follow-on costs.

The Determinants of Individual Income Tax Compliance

4. Conclusions

This study breaks new ground in answering the important question of what the government

can do to foster better voluntary compliance with the individual income tax. The quality and

breadth of the data, together with a simple, yet powerful, econometric specification, have provided

new insights into several important—yet previously unexplored—determinants of filing and

reporting compliance, and have yielded perhaps the most believable estimates yet of the compliance

effects of factors that have been studied before.

Although at least one previous study of this sort has been based on state-level panel data,

this analysis has broken from the usual preference for cross-sectional micro data, arguing that a

state-level panel is the mostappropriate data structure with which to quantify what are inherently

aggregate phenomena. (Micro models may provide insight into the mechanisms of taxpayer

behavior, however.) Data innovations include: the estimation of the required filing population

using the Current Population Survey; the use of Personal Income from the National Accounts in

the denominator of the reporting compliance measures (together with additional explanatory

variables to control for variations in Personal Income not related to changes in actual tax

obligations); the use of two productivity-related variables to estimate audit rates; the use of the audit

start rate, instead of the widely-used audit closure rate; the use of two exogenous federal marginal

tax rate variables, capturing the variation of marginal rates at both low and moderate incomes

separately; and the introduction of several determinants of compliance that had received little or no

quantitative attention until now, including the burden associated with the time to complete the

myriad tax forms and schedules, three new enforcement variables (TDI nonfiler notices, refund

offsets, and CID convictions), and several measures of IRS responsiveness to taxpayers

(telephone calls handled and returns prepared by Taxpayer Service, and the speed with which

refunds are issued).

Most previous studies of income tax reporting compliance have estimated equations for tax

reporting; some have also estimated an equation for some income concept (e.g., Adjusted Gross

Income). This analysis avoids the interpretive problems associated with estimating a tax equation

(in which the compliance effect of important tax policy parameters cannot be separated from the

direct role of these parameters in calculating tax from reported income), and estimates separate

equations for the amount of total income reported and total offsets claimed (including the incomeoffset value of tax credits). Such a specification provides direct insight into the separate problems

of underreported income and overstated offsets. It also allows the estimation of a third reporting

compliance equation—net income, which is total income minus total offsets. This third equation is

not only the “bottom line” with respect to reporting compliance, but it is a useful check on the other

two reporting equations.

Another major innovation of this study is the use of three different definitions of income

and offsets to account for the fact that several income and offset components have been subject to

changing rules as to what should (or can) be reported in any given year. Estimating separate

equations for each definition has prevented inadvertently attributing to some of the explanatory

variables changes in reporting caused entirely by the changed rules.

Perhaps the greatest need suggested by this research follows from the large, strongly

significant, and positive deterrent effect of audits on the general population. If the indirect effect of

audits is, on average, about eleven times as large as their direct effect on revenue, then we should

seek to understand this “ripple effect” in much greater detail. For example, it is reasonable to

assume that this indirect effect is different among different groups of taxpayers (e.g., business vs.

The Determinants of Individual Income Tax Compliance

non-business, or high-income vs. low-income). If so, then, given the relative magnitude of the

indirect effect, an understanding of these differences is probably much more crucial to the optimal

allocation of audit resources than our current understanding of the different direct yields to be

expected within these groups.

This study provides the first quantitative basis for allocating IRS resources optimally across

its various enforcement and non-enforcement activities. Perhaps it can also lead to a method for

evaluating the effectiveness of those activities, since it controls for a wide variety of other

determinants of voluntary compliance as well.

The Determinants of Individual Income Tax Compliance

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The Determinants of Individual Income Tax Compliance

Appendix A

Data Sources and Derivations

The following information describes where the raw data used in this study came from, and what

had to be done to derive the components (typically the numerators and denominators) necessary to

form the variables used in the analysis. The 30 variables actually used in the analysis (and several

more t

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