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Activities and Taxpayer

Noncompliance

by Jeffrey A. Dubin, California Institute of Technology*

he annual tax gap (i.e., the difference between taxes owed and taxes

paid) is estimated to be $200 billion, or about 10 percent of what is

collected each year from individuals and businesses. The Internal Revenue Service (IRS) estimates that three-quarters of this tax gap is attributable

to individual taxpayers. At that rate, individuals currently represent $150 billion of the tax gap, which is more than double the level estimated in 1985.1

While the tax gap has grown, the IRS’s ability to audit and enforce the tax

code has diminished. For instance, in 2002, the IRS had roughly 13,000 revenue and tax agents devoted to examination. This number is down from the

18,000 revenue and tax agents employed in 1995. Meanwhile, the Criminal

Investigation (CI) Division of the IRS is considerably smaller. In 1970, CI had

approximately 2,500 agents. By 1998, the number of CI agents had increased

to approximately 3,000 agents. Due to the increases in the tax gap, it is important to reassess the role played by examination in taxpayers’ voluntary compliance and to ascertain what effect CI investigations play in general deterrence.

The empirical approach used in this paper follows Dubin, Graetz, and

Wilde (1990) (DGW). The DGW method can determine both specific and

general deterrence effects of CI activities, as well as the effects of audit rates

on taxpayer compliance. Although the general deterrence effects provided by

audits have been widely acknowledged, the IRS has never reported the

“spillover” benefits provided by audits. Spillover benefits are the increase in

collections from taxpayers, whether or not they are audited, who report more

taxes in response to an increased likelihood of an audit. DGW’s principal

innovation was to directly estimate taxes due, rather than first attempting to

construct a noncompliance measure and then extrapolating from noncompliance to revenue.

The current study’s purpose is to answer several basic questions. First,

does CI have a measurable effect on voluntary compliance, which includes

both civil and criminal tax laws? Second, if CI does have a measurable effect

on voluntary compliance, what mix of CI investigations has the greatest influence on voluntary compliance? (CI investigates two broad categories of cases:

tax violations and money laundering violations.) A subsidiary inquiry is whether

either or both types of cases have an effect on voluntary compliance with the

tax laws. Third, does media attention and publicity on CI investigations in-

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crease the compliance effect? Fourth, do convictions that result in prison

sentences affect compliance differently from cases that result in probation?

In this paper, I empirically test whether CI’s measurable activities affect

taxpayer compliance. 2 I replicate and extend the original DGW analysis to

include factors that measure CI activity. The time period covered by the new

model is 1988-2001. I reach several conclusions. First, I find that CI activities

have a measurable effect on voluntary compliance. I found statistically significant results from my measure of CI sentenced cases on general tax deterrence. Second, I conclude that the mix of sentenced cases (tax and money

laundering) is not a significant determinant of tax compliance. Third, media

attention shows some weak evidence of increasing compliance, at least among

money laundering cases. However, it is logical to think that media attention

plays an important role in disseminating information to the public. The significant magnitude of general deterrence results implies that media play a large

role in CI cases. Finally, I find that incarceration and probation (rather than

fines) have the most influence on taxpayers.

I also performed simulations to determine the direct revenue (spillover)

effect of audits and CI activities. I find that the direct effect of doubling the

audit rate on assessed tax collections (reported amounts and additional taxes

and penalties) is $18.7 billion. Doubling CI tax and money laundering sentences is forecast to increase assessed collections by $16.7 billion. I estimate

the spillover effects from both audit and CI activities to be approximately 94

percent. Doubling the audit rate or doubling money laundering sentences produced similar increases in total collections.

The remainder of the paper is organized as follows. I review the empirical tax evasion literature and discuss the process of criminal investigations

and potential influences on taxpayer compliance. I then discuss the methodology, data, and results of the econometric models and present the results of

several simulations with conclusions.

Literature Review

Andreoni, Erard, and Feinstein (1998) and Slemrod and Yitzhaki (2002) provide summaries of the tax compliance literature. As discussed by these authors, the IRS has made available to researchers few data sources that can be

used to study tax compliance. With respect to nonexperimental and nonsurvey

data limited to the United States, there continues to be limited data. As discussed in the authors’ review, there are essentially two data sources. The first

data source is the Taxpayer Compliance Measurement Program (TCMP) data.

These data have been analyzed by Dubin and Wilde (1988), Witte and Woodbury

(1985), and Beron, Tauchen, and Witte (1993) for Tax Year 1969. These were

important empirical papers on audit effects and compliance because they dem-

CI Enforcement Activities and Taxpayer Noncompliance

onstrated endogeneity of audit rates and positive compliance effects from

audits on certain audit classes. Subsequently, Dubin, Graetz, Udell, and Wilde

(1992) used 1979 TCMP data to study tax return preparation decisions by

taxpayers. Kamdar (1995) also utilized TCMP data in studying information

return. Recently, Mete (2001) combined TCMP surveys conducted by the

IRS for several tax years in studying the interaction among taxpayers, the

IRS, and political ideology.

The second data source is based on time-series cross-sectional information available by State and year. Measures of audit activity, taxes assessed, and

taxes collected are taken from the Annual Reports of the Commissioner of the

IRS. For instance, DGW (1990) used IRS audit data and taxpayer information

measured at the State level over a 10-year period in analyzing taxpayer noncompliance. Ali, Cecil, and Knoblett (2001) also relied on data taken from the

Annual Reports. Their analysis was based on 1980 through 1995 annual data

(i.e., 16 observations). Their model specification included two equations: reported income (as a function of actual income, audit rates, and other factors)

and an audit rate equation. Ali et al. used filing status categories rather than

geography in a pooled estimation procedure. However, their instrument for

audit rates was insignificant, leading to imprecise estimates of audit effects.

Giles and Caragata (2001) present an aggregate analysis similar to DGW (1990).

Their study analyzed the ratio of the hidden economy to Gross Domestic

Product (GDP) and the ratio of tax revenues to GDP. 3

Plumley (1996) extended the analysis in Dubin, Graetz, and Wilde (1990).

His time-series cross-section analysis covered the period 1982-1991, whereas

the DGW study used data from 1977-1986. Importantly, Plumley was the

first to show that CI activities (measured as criminal convictions obtained per

million people) were significant and positively related to compliance. 4

Background

One of CI’s functions is to investigate alleged violations of the tax and money

laundering statutes. CI has focused its activities for some time on narrowing

the tax gap. Tax gap investigations include both tax and money laundering

cases that involve tax issues. Tax gap investigations normally do not include

illegal activity associated with narcotics investigations. Tax-related investigations encompass all Title 26 violations (tax evasion, failure to file, filing of

false returns, fraudulent returns, or aiding or providing assistance to fraudulent returns), as well as tax violations that fall under Title 18 USC §286, 287,

371 (conspiracy to defraud the Government or commit offense or false claims).

CI also has jurisdiction over Title 31 cases (currency reporting violations). CI

tax investigations are so-called legal source tax crimes because they encompass all cases involving tax violations where income is derived from legal

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activity, including questionable refund schemes, return preparer cases, excise

tax cases, employment tax cases, and frivolous filers and nonfilers. CI also

investigates illegal source financial crimes and narcotics-related financial crimes.

The CI is literally the IRS’s criminal investigation arm. It is the only

Federal agency with the power to investigate potential criminal violations of

the U.S. Tax Code. CI’s tax cases sometimes result from referrals by the

IRS’s civil arm. During an audit or tax investigation, a case might be referred

to the CI for criminal investigation.5 However, audits are not the sole source

for tax-related cases. CI may investigate a tax case initiated by a special agent

in the field, a referral from another agency (FBI, Customs, or the US Attorney

or DOJ), informants (as part of the Grand Jury process), or as a result of

refund fraud-related activity.

While the IRS can investigate and audit tax returns and recommend civil

penalties, CI has the exclusive responsibility and authority to investigate tax

fraud and to recommend prosecution for willful and egregious tax code violations. CI’s role as a tax crimes agency expanded in 1970 under the Bank

Secrecy Act (BSA) and has been further expanded over the last 30 years to

include narcotics investigations and money laundering violations. Money laundering cases often result from the recordkeeping requirements established in

the BSA. 6

Money laundering activity and tax activity can be closely related. Money

laundering activity (i.e., activity involving illegal income sources) is often a

precursor to tax evasion. As such, it is sometimes difficult to determine whether

a case is primarily tax-related or not. CI has been able to classify its cases in

terms of whether they are primarily tax-related or money-laundering-related.

CI has further classified cases according to whether they are both tax and

money laundering cases, tax cases only, money laundering cases only, or

neither. For this study, I treated any case with a tax-related component as a

tax case and any case with a money laundering component as a money laundering case.

CI summarizes its activities in different ways. First, CI reports its cases

by the Title and Section of law for which there is a violation or an alleged

violation. For Fiscal Year 1999, for example, CI reports cases recommended

for prosecution as follows: 1,068 for Title 26 violations; 1,988 for Title 18

violations; and 64 for Title 31 violations. Of these 3,120 cases, CI further

classifies 1,959 cases as fraud-related and 1,161 cases as narcotics-related.

Tax cases, in this study, include all primary and secondary recommended

violations of tax-related offenses (Title 26, 18-287, 18-286, 18-371K). Money

laundering cases, in this study, include all primary and secondary recommended violations of money-laundering-related offenses (Title 18-1956, 18371T, 18-371M, 18-1960 or Title 31).

A criminal investigation case proceeds in several steps. Generally, cases

subject to investigation are either recommended for prosecution or are dropped.

CI Enforcement Activities and Taxpayer Noncompliance

If a case is recommended for prosecution, then the Department of Justice

(DOJ) or U.S. Attorney may proceed with the case, and the U.S. Attorney

either issues an indictment or declines to prosecute. Indicted individuals may

be acquitted, have their cases dismissed, or be convicted. If a conviction is

obtained, then the individual is sentenced. In this study, I analyzed CI activities

from the perspective of cases recommended for prosecution and from the

perspective of successfully prosecuted cases where the defendant was sentenced. Cases recommended for prosecution represent the outcomes of CI

procedures and protocols. Such cases may or may not be processed by the

DOJ depending on the nature of the case or resource constraints at the DOJ.

In most cases where there is an indictment, defendants will be found guilty

and will be sentenced. At this point in the process, the sentence is given and

the media attention paid to the case is measured. The impact on compliance

can be experienced whenever publicity is received. This may include the coverage of an issued search warrant, indictment, plea, or conviction. Media

coverage acts as a form of indirect contact with the general public and provides the greatest amount of exposure for CI activities.

Data and Model Specification

As discussed above, this study’s purpose is to update and extend DGW (1990)

to analyze the role of CI activities on taxpayer noncompliance. The DGW

empirical analysis was based on two models that are both estimated using a

State-level time-series cross-section. One model specified reported taxes per

return filed as a function of audit rates and a variety of socioeconomic factors. The other model specified returns filed per capita as a function of the

same variables.

Data

The DGW analysis was based on data reported in the Annual Report of the

Commissioner of Internal Revenue for the years 1977-86. These reports include district-level data on IRS collections, number of returns filed, amount

and number of refunds, number of examinations, total additional tax and penalties recommended after examination, and budgets. The data employed in this

study are a compilation of annual tax enforcement, criminal investigation,

socioeconomic, and political statistics for each U.S. State from 1977 to 2001.

The tax collections and examination variables rely on data reported in the

Annual Report of the Commissioner of Internal Revenue, IRS Data Book, and

IRS Statistics of Income Bulletin.

Dependent Variables. The dependent variables are (i) ALR (Assessed

Liability Per Return): reported individual income tax plus additional tax and

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penalty recommended after examination divided by the number of individual

income tax returns filed, in 1972 dollars; (ii) RTR (Reported Taxes Per Return): reported individual income tax divided by the number of individual tax

returns filed, in 1972 dollars; and (iii) RCAP (Returns Per Capita): reported

total individual income tax returns filed divided by total population.

IRS Enforcement Factors. The effect of audit examination on compliance is as important in the current analysis as it was for the original DGW

study. The audit rate is defined as AUDIT (Individual Audit Rate): reported

total individual income tax returns examined divided by total individual income

tax returns filed, and is treated endogenously. 7 The dramatic decline in the

individual audit rate (IAR) between 1977 and 1987 was followed by an equally

staggering decline during the subsequent 15 years. Indeed, audit rates fell

from 1.98 percent in 1977 to 0.59 percent by 1991. The decline continued

through the end of the analysis period, until the individual audit rate was only

0.15 percent in 2001. The IRS indicates that this decline in audit rates has

been partially offset by automated programs like the CP2000 program and

other correspondence audits. I examine this proposition below. Meanwhile,

individual returns filed per capita (RCAP) grew steadily over the 25-year period by 18.44 percent, or 0.74 percent per annum.

As part of this study, the IRS provided several new factors to examine

tax enforcement. These factors refine the individual audit rate used in DGW

but are limited to a subset of the analysis period (from 1993 forward). The

first factor measures examinations of individual tax returns conducted by

revenue agents (AUDR1). Revenue agents are required to have extensive accounting knowledge. Revenue agents typically audit more complex issues that

involve higher income levels or greater deductions. Revenue agents conduct

their audits in person rather than through the mail. As with the audit rate

defined in the DGW study, I express the revenue agent audit rate as a fraction

of individual returns examined. Revenue agent audits have declined significantly between 1994 and the present. The rate of these audits fell from 0.313

percent to 0.065 percent during the period.

The second examination factor represents the tax agents’ audit activity.

Tax auditors or tax agents generally have less tax knowledge than revenue

agents. They typically audit individual nonbusiness returns and Schedule C

returns (sole proprietorships). Relative to revenue agent audits, tax audits are

less complex and involve lower income and expense levels. Expressed as a

fraction of individual returns filed, tax audits (AUDR2) also show a dramatic

decline over the last decade. The rate of these audits fell from roughly 0.428

percent to 0.086 percent by the end of the period.8

Finally, the IRS provided a measure of correspondence audits. These

audits are done through the mail, as the name implies, and represent a modern

extension of the CP2000 program (see DGW (1990)). I attributed service

center audits to the State in which the taxpayer resided. Normalizing by individual returns filed yields the third audit factor (AUDR5). Correspondence

CI Enforcement Activities and Taxpayer Noncompliance

audits have increased from 0.261 percent in 1993 to 0.962 percent in 1996. In

recent years, however, the rate of correspondence audits has declined; the

2001 measurement shows an average rate of just 0.395 percent.

Instrumental Variables

With respect to instrumental variables, I extended the budget per return variable (BPR): reported total IRS budget divided by total returns filed, in 1972

dollars) used in the DGW study and added some new instruments. First, the

IRS budget per individual return filed was estimated and published by the IRS

through 1999. The budget (in real 1972 dollars) reached its peak of $5.29 per

return in 1988. The growth was likely a consequence of the Tax Reform Act

of 1986 (TRA86).9 However, the budget per return subsequently underwent a

significant decline between 1993 and 1999, dropping from $5.18 to $3.69,

ultimately falling to levels lower than those in any of the previous years in the

analysis.10

Next, the IRS provided a measure of the total available resources devoted to examinations [DIR_EXAM (Direct Examination): Percentage of all

examiners’ time allocated to direct examination of the returns.]11 This percentage further refines the budget variable described above; it should be highly

correlated with audit activity but nevertheless exogenously set by the IRS in

any fiscal period as it corresponds to the planned examination activity. 12 Beginning in 1980 with a State average of 64.4 percent, the direct examination

percentage fell to 41.1 percent by 1988. While the percentage of time devoted

to examinations rose somewhat through 1997 (to 54.1 percent), the pattern

from 1997 to 2001 had been to reduce direct examination time (measured at

36.9 percent in 2001).

In some models, I needed additional instruments as I discuss further,

below. Following Mete (2002), I assembled several political factors that could

be used as potential instruments. Based on correlations with the audit rate, I

ultimately focused on four potential instruments: (1) the political party of the

State governor (GOVR); (2) a measure for State government liberalism

(GOVIDO); (3) the ratio of Democrats to Republicans in the House (HRATIO);

and (4) the ratio of Democrats to Republicans in the Senate (SRATIO).13

Based on the empirical results, I ultimately selected the instrument based on

government liberalism and used it in conjunction with IRS budget per return

filed and the direct examination percentage. I then used these instruments in a

subset of models that simultaneously considered three examination factors.

Socioeconomic Factors

I followed DGW and used several socioeconomic explanatory variables, all

reported on a calendar-year basis: STAXR (Average State Income Tax Rate):

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total State individual income tax paid as a percentage of total State personal

income; PERED: percentage of the adult population with at least a high school

education; PER65: percentage of the adult population over age 65; UI: the

unemployment rate; PICAP: income per capita, in 1972 dollars; PMAN: percentage of the workforce employed in manufacturing; PSERV: percentage of

the workforce employed in the service industry; HOUSES: households per

capita; FRMFAM: farms per household; and PWELFAM: the percentage of all

households on welfare.

Most of the explanatory factors appeared to continue the trends first

discussed in DGW. First, the percentage of families on welfare (PWELFAM)

declined slightly during the 1970’s and 1980’s, falling from 4.70 percent to

4.08 percent, before rising to its peak of 5.17 percent in 1994. From 1995

through 2001, the percentage of families on welfare declined to 2.03 percent.

This decline may have been due to welfare reform enacted in the Personal

Responsibility and Work Opportunity Reconciliation Act of 1996 (Personal

Responsibility Act). Welfare cases necessarily fell when fewer individuals qualified for welfare under the PRA.

Next, the number of farms per household (FRMFAM) continued to show

a decline during the analysis period, reflecting fewer farms in the United States

and a larger number of households. The decline was from 3.30 farms per

hundred families to just over 2.06 farms per hundred families. Unemployment

rates (UI) varied notably during the late 1970’s and 1980’s. Reaching a peak in

the early 1980’s (at 9.23 percent), unemployment has generally declined with

the exception of the recession in the early 1990’s and the increase in unemployment that has occurred in recent years.

Personal income in real terms (PICAP) rose steadily from 1977 through

2001. Average real income per capita rose from $5,066 to $8,017.

State tax rates (STAXR) rose on average from 4.06 percent in 1977 to

4.52 percent in 1984. From the mid-1980’s forward, the State tax rate grew

to 4.74 percent and remained fairly steady at this level in the late 1990’s.

The percentage of the population over age 65 (PER65) showed a relatively modest growth during the period. The percentage of employed individuals in manufacturing (PMAN) declined from just over 21.45 percent in 1977

to roughly 12.94 percent by 2001. The percentage of employed individuals in

service industries (PSERV) increased from 16.61 percent in 1977 to nearly

29.49 percent in 2001. This pattern continues the trends described in the

original DGW study. However, as described more fully below, the importance

of manufacturing and service industry employees may have changed as compliance and collections associated with these sectors have shifted since the

original 1977-1986 study of DGW.

CI Enforcement Activities and Taxpayer Noncompliance

CI Enforcement Factors

CI provided detailed information about sentenced cases and cases recommended for prosecution, including media coverage and sentence type (typically probation or prison). The sentence counts were first broken down by

the crime that was committed and then further distinguished by the sentence’s

punishment (prison or probation) and by whether news of the case was released through any form of media (radio, television, print). My analysis begins

by decomposing all CI cases that were sentenced. Sentenced cases can arise

as a result of a pure tax investigation, a pure money laundering investigation, a

combination of both tax and money laundering investigations, or something

not related to either tax or money laundering. The preponderance of CI cases

had either tax or money laundering aspects. In addition, sentenced cases may

or may not have received media coverage. Finally, sentenced cases may have

received recommendations for prison, probation, or some other fine or penalty. There are many ways in which to classify individual CI cases. Finding

the empirical classifications that have significance with respect to tax compliance is one of the goals in this study.

Money laundering cases, in this study, are not considered tax gap cases,

except for a few cases that were both tax- and money-laundering-related. I

allocated these cases to both the tax and money laundering category. It is

natural to consider how such cases can affect taxpayer compliance. The most

plausible mechanism is through publicity. It is possible that a CI-related activity that receives media attention may influence some taxpayers to be more taxcompliant. It is also possible that media coverage of money laundering cases

and the sentences received by the individuals under indictment convey the

mission of the CI division and emphasize its role in tax matters. To the extent

that media variables are measurable for a reasonable time period, analysis of

media attention provides a direct test of the CI message mechanism. Ultimately, it is an empirical question and one that I investigate in this paper.

Total CI cases recommended for prosecution (TOTP) ranged from 2,937

cases per annum in 1988 to 4,126 cases in 1993. TOTP fell to 2,271 cases in

2001. Annual counts of CI tax cases recommended for prosecution (TP)

reached 2,255 in 1993 but then fell dramatically to 991 cases by 2001. Money

laundering cases recommended for prosecution (MP) grew rapidly, from 385

cases in 1988 to 2,042 cases in 1992, nearly equaling the number of tax

prosecutions for the same year (2,047). Interestingly, annual counts of money

laundering prosecutions became greater than tax prosecutions beginning in

1997, and they have remained that way every year since.

Total CI sentenced cases (TOT) ranged between 2,133 and 3,157 during the period from 1988 through 2001. There is some evidence of a recent

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decline in the total cases performed by CI. Tax cases conducted by CI (T)

have declined fairly steadily from 1988 to 2001 and declined from 1,876 cases

per annum in 1998 to 899 cases in 2001. Conversely, money laundering cases

(M) have risen from 132 cases per year in 1998 to a high of 1,170 cases per

annum in 1994. There are approximately 900 such cases conducted per year

at present.

On a percentage basis, these patterns are quite dramatic. The number of

CI tax cases as a percentage of total CI cases (T_TOT) fell from 76.9 percent

in 1988 to 42.2 percent in 2001. Meanwhile, money laundering cases rose

from just 5.4 percent of all CI cases (M_TOT) to 41.2 percent by 2001.14

I next turn to tax-only case disposition. Similar to tax and money laundering cases, an individual who is sentenced may receive prison time, probation, both prison and probation, or neither (typically a fine of some kind).

Unlike the situation with tax and money laundering sentences, where few

cases were sentenced for both tax and money laundering violations, most tax

cases have both prison and probation components. For instance, in the 50

States and for the years 1998-2001, there were 21,604 tax sentences. Only

507 cases received neither prison nor probation, while 11,719 cases received

both. There were 11,660 tax sentences resulting in prison sentences, but only

2,941 of these cases were prison-only sentences.15

With respect to the way cases are disposed, tax cases that received

prison sentences (TPRI) averaged 1,037 per annum from 1989 through 1998.

After 1998, there was a decline to 726 cases per annum in 2001. The number

of tax cases that received probation (TPRO) fluctuated to around 1,300 cases

per annum from 1988 to 1998. In 2001, the amount declined to 811 cases per

annum. Money laundering cases receiving prison sentences (MPRI) increased

dramatically from 80 cases per annum in 1988 to 1,041 cases per annum in

1994. There was an average of 863 cases per annum in the subsequent years

from 1995 to 2001, with 785 cases per annum in 2001. Money laundering

cases receiving probation (MPRO) followed a very similar pattern, rising from

68 cases per annum in 1988 to 727 cases per annum by 2001.

Media attention for tax cases rose between 1992 and 1997. It then fell,

starting in 1998, continuing to decline through 2001.16 Media attention for

money laundering cases followed a similar pattern, peaking in 1997. Cases

receiving media attention (MD) rose from 1,102 in 1992 to 2,539 per annum

in 1997. However, more recently, the coverage of CI cases in the media has

declined to 1992 levels (when such figures were first tracked by the CI division).

Tax cases receiving media attention as a percentage of all media cases

(TMD_MD) and money laundering cases receiving media attention as a percentage of all media cases (MMD_MD) show some modest variation, with

money laundering cases receiving a growing percentage of coverage by the

media. These two categories do not exclusively exhaust media attention, but

CI Enforcement Activities and Taxpayer Noncompliance

the residual coverage is very small in percentage terms.

With respect to sentencing, the patterns are more dramatic. The percentage of all money laundering cases where the defendant received a prison

sentence (MPRI_M) has grown from 60.6 percent in 1988 to 87.4 percent in

1992. During the last decade, this rate has grown further to 91.5 percent of

money laundering cases in 1997.17

Similarly, the percentage of CI money laundering cases receiving probation (MPRO_M) grew from 51.5 percent in 1988 to 85.7 percent in 2000. The

CI division has also managed to improve its sentencing rate for prison and

probation among its tax cases. The percentage of CI tax cases receiving prison

sentences (TPRI_T) rose from 56.8 percent in 1988 to 80.8 percent in 2001.

Similarly, the percentage of tax cases receiving probation among all CI tax

cases (TPRO_T) rose from 74.1 percent in 1988 to 90.2 percent in 2001.

This trend is also reflected in the rate of prison sentences received as compared with individual returns filed. Dramatic increases in prison sentences are

evident when comparing the number of cases receiving prison sentences to

the number of returns examined. However, the prison sentence rate is still

more than 100 times smaller than the audit examination rate for individuals.

Model Specification

DGW selected explanatory variables for the “reporting effect” equation based

on two considerations: the size of the tax base and the taxpayers’ compliance

behavior. 18 The variables primarily related to the tax base are PER65, HOUSES,

and WELFARE. The variables related to both the tax base and taxpayers’

compliance behavior are UR, INCOME, and STAXR. The variables primarily

related to the taxpayers’ compliance behavior are PERED, PMAN, PSERV,

FARMS, and AUDIT. 19

Additional compliance factors include variables created as part of this

study to measure the nature and extent of CI activities. I treat CI activities as

exogenous both on theoretical and empirical grounds. First, CI activity is

largely a result of cases discovered and selected for examination that arise

independently of tax gap or noncompliance issues. Second, Hausman specification tests for endogeneity of the CI enforcement factors did not reveal

endogenous behavior.

Econometric Analysis

In principle, the additions to the original DGW study to accommodate criminal

investigation factors are straightforward. In fact, the task is far more complex

than simply creating and matching various factors from CI and then adding

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these factors to the basic model. For example, individuals face a complex

decision process with respect to criminal activity. An individual may be deterred from tax evasion, money laundering, or other criminal acts based on the

likelihood of being caught. This deterrence possibility has been the empirical

paradigm of modern criminal analysis. In this approach, a potential criminal

may be deterred from committing a crime due to a sufficiently high probability

of being caught and receiving a sufficiently severe penalty. Of course, not all

individuals are rational actors with respect to the crimes they commit. However, a rational calculus applied to crime and punishment is a benchmark test

and provides policymakers with justification for increasing enforcement levels or changing the enforcement mix. Ultimately, the manner in which individuals respond is an empirical matter. Thus, in this approach, I assume that

individuals consider the likelihood that they will be detected and punished.

With respect to civil audit examination, a measure such as the audit rate

may be significant to a potential tax evader because it measures the probability

that the taxpayer will be subjected to an audit. In the current setting, the

natural analogue to the audit rate is the rate at which CI investigations begin or

the rate at which prosecutions are recommended. Prosecution rates are, in

fact, quite small for individual taxpayers. As I noted above, these prosecution

rates may be orders of magnitude smaller than the individual audit rate. A

compounding factor is that not all cases recommended for prosecution lead to

indictments, and not all indictments lead to sentencing. In contrast, the audit

rate leads to an audit whether or not a change in the taxpayer’s liability is

recommended. By focusing on cases sentenced, an exposure measure is produced that is closer to the audit rate but results in a factor that, in relative

magnitude to the population at large, is quite small. Additionally, as a matter of

general deterrence, it is believed that individuals respond to the probability of

detection. The question remains as to how they learn the rates at which they

are likely to be caught. Attention by the media would seem to be the most

likely forum by which taxpayers become aware of the likelihood that their

crimes will be detected. Therefore, those cases that are successfully prosecuted

and sentenced and receive some media attention would appear to be most

relevant. Finally, taxpayers may be concerned only with the sentences that

result in incarceration or probation as compared to monetary fines. Thus, the

percentage of sentenced cases that result in nonmonetary fines may be relevant.

Taxpayers may respond to the probability of an audit in a rational calculus that affects their decision to file a tax return or the degree to which they

file an honest and correct return. This theory is known as deterrence theory in

the literature. It has also been persuasively argued that taxpayers may react to

the actions of other taxpayers, especially as those actions concern notions of

fairness and support for their decisions to voluntarily comply with the law.

CI Enforcement Activities and Taxpayer Noncompliance

This theory of taxpayer behavior is known as assurance theory (see, e.g.,

Roth et al. 1989, Scholz 1998, Scholz and Lubell 1998a,b). Models of conformity and social dynamics (see, e.g., Durlauf and Young, 2001) postulate that

the utility of a given decision may in part be determined by the expected

actions of others. Models of social dynamics bridge the deterrence and assurance theories of taxpayer compliance. Importantly, Manski (1993, 1995) has

shown that, for linear models with aggregate data, there is an inherent identification problem that may not allow the theoretical issue to be resolved empirically. 20

As an empirical matter, many nonexclusive approaches could have significance. Among the choices are: (1) separating tax and money laundering

rates; (2) separating media cases from nonmedia cases; and (3) the sentencing

mix. With three types of CI cases (tax, money laundering, and other), media

(Yes versus No, or type of coverage), and at least three sentencing outcomes,

variables that can be used to measure CI activities quickly expand relative to

the available years and geographic locations available for analysis. My approach simplified the relevant set of CI factors as much as possible, while

considering specifications and models that would allow a full picture to emerge.

Empirical Investigation

As discussed, the original DGW model used data for the years 1977-1986.

Adding data for later years more than doubled the observations. However, the

overall explanatory power of the model fell in this full data period. This change,

coupled with changes in the pattern of coefficients for some factors, suggests

that the period after 1987 (and therefore after the time period considered in the

original DGW study) was different from the earlier period in significant ways.

Focusing on the period after 1987, the re-estimated models showed

some sign changes in socioeconomic factors, including a shift in the roles

played by the percentage of employed populations in manufacturing and service industries. Since these effects were previously understood in terms of

the possibility for individual noncompliance and opportunities to evade, it is

more likely that a change in IRS policy to focus attention on service industry

geographies or a change in the relative economic conditions of these two

sectors explains the change in predicted compliance.

Several empirical experiments showed that CI factors have statistical

significance when considered as counts. However, little significance remains

when these counts are expressed as rates. While a theoretical justification may

be made for using rates as estimates of probabilities, and while probabilities

are motivated by the theoretical criminology and economics literature, the

empirical finding is that these rates are just too small to reveal any correlation

with compliance. However, the finding that absolute counts matter is interest-

Dubin

ing and suggests that general deterrence may result from the overall level of

CI activity rather than the rate at which these investigations take place. This

interpretation affirms the assurance theory of CI activity.

In Table 1, I present the estimated econometric models in a logical

progression from the DGW specifications to the final models used in this

paper. The model of DGW (1988) is a steady state equilibrium relationship. It

is assumed that all effects are in longrun equilibrium. However, as audit rates

change, taxpayers are assumed to change behavior and modify their reported

taxes due. At first blush, it is reasonable to assume that reported taxes in a

given year react to audit rates that prevail in that year. However, the typical

IRS audit cycle may not initiate an audit for several years following the filling

of a tax return. Taxpayers, in this situation, must react to their expectations

of future audit rate levels. Alternatively, the additional taxes and penalties

reported in a given tax year may to some degree depend on the audits of tax

returns from previous years. Hence, additional taxes and penalties may be

some function of past audit rate levels. Finally, taxpayers may change their

reported taxes due in a continuous adjustment to a new target level. There

may be the perception, by some taxpayers, that a rapid or discontinuous

(abrupt) change in behavior may be a signal to the IRS of an existing or

current tax problem. Such taxpayers may adjust their reported taxes based

on a mixture of taxes reported in the previous year and the optimal level of

taxes due based on existing or current conditions.

I investigated the dynamic panel specification using methods due to Anderson and Hsiao (1981). See also Anderson and Hsiao (1982), Arellano (1989),

and Arellano and Bond (1991). The empirical results indicate that a short-term

dynamic is most likely at work, with the majority of adjustment occurring

within 2 to 3 years after a change in tax policy. Interestingly, this period of

time for adjustment and audit expectation formation naturally corresponds to

the audit cycle itself.

Model 1 replicates DGW for the period 1977-1986, using newly collected data. I used instruments and specifications published in the original

DGW article. The next model, Model 2, relies on the time period from 19882001, using IRS source data for the audit rate in later years merged with IRS

Data Book audit rates, where available. Notable in this model is the switch in

time periods covered and instruments employed. As the table shows, the IRS

budget per return filed is a very significant factor in determining the audit rate

(see the reduced form equation reported under Model 2 for the variable, IAR2).

Also, the instrument for exam time devoted to direct examination is significant

and positive in the audit reduced form. This finding implies that, in districts

and time periods with larger resources devoted a priori to examination, the

audit rate is higher. This result is clearly logical and was expected. The revised model shows that audit rates remain statistically significant. I previously

discussed the changes in sign in some previously significant factors, such as

CI Enforcement Activities and Taxpayer Noncompliance

the percentage of employed persons in manufacturing and service industries.

Another very significant change in results concerns the effect of audit rates

on filings. Previously, DGW had found that an increase in audits would lead to

fewer returns filed.

As discussed in DGW, the relationship between socioeconomic, tax

base, and tax compliance factors and the number of returns filed may be

quite complex. With respect to variables that relate to taxpayers’ compliance,

DGW argued that taxpayers confront three options: (1) to file a return and

report honestly; (2) to file a return and underreport taxes; or (3) not to file a

return. Anything that reduces the benefits or increases the costs of filing a

return and underreporting taxes will increase the likelihood that a given taxpayer chooses (1) to file a return and report honestly or (2) not to file a

return. We called this the compliance principle. DGW argued that the compliance principle would apply very strongly to the Federal audit rate because

increases in the Federal audit rate decrease the benefits and increase the costs

of filing a return and underreporting taxes due. DGW expected (and found)

that an increase in the audit rate decreased returns filed per capita. My results

for the post-1987 period seemingly contradict the findings of DGW pre1988. However, the compliance principle predicts that either returns filed

would decline or returns filed would increase with greater compliance. My

results indicate that the latter situation is now in effect—increases in the audit

rate lead to greater levels of compliance and a greater number of honestly

prepared returns.

In Model 3, the audit rate from the IRS Data Books is replaced with the

combined rate for revenue agent and tax agent audits. The results indicate that

the selected instruments are significant factors in the reduced form for the

audit rate and that the estimated audit effect is positive and statistically significant.

In Model 4, I add the factor for correspondence audits to the previous

specification. Interestingly, the significance of AUDR12 (the combined audit

rate for revenue agents and tax agents) and of AUDR5 (the correspondence

audit rate) is now lost. There is a large change in the estimated magnitude of

the coefficients, which suggests that collinearity issues are again present.

Pursuing this set of models, I then split the combined audit rate for revenue

agents and tax agents into separate factors for each type of audit. This model

(Model 5) again reveals general insignificance of these separate factors.21 Additionally, in this specification, the revenue agent audit effect is no longer

positively associated with compliance. Given that the simplest of these specifications showed a significant and positive audit effect (paralleling results from the

longer time periods), the more refined audit models do not provide useful results.

Next, I examine models selected to measure CI effects. In Model 6, I

include factors for tax sentences (T) and money laundering sentences (M).

This model demonstrates that money laundering sentences have a statistically

significant effect on tax compliance.

Dubin

Model 7 investigates the sentencing form of the explanatory factors

from the previous model. Here, I introduce variables for: (1) the percentage

of tax sentences resulting in prison time; (2) the percentage of tax sentences

resulting in probation; (3) the percentage of money laundering sentences resulting in prison time; and (4) the percentage of money laundering sentences

resulting in probation. These factors do not diminish the available degrees of

freedom, and the estimates are performed for the same period 1988 through

2001 as in Model 6. This specification fails to indicate statistical significance

of tax sentences or of the various percentages of such cases that result in

prison or probation. However, money laundering cases remain statistically

significant in their effects on compliance. Further, the percentage of money

laundering cases that result in prison terms raises the compliance level. However, an increase in the percentage of money laundering cases resulting in

probation does not increase compliance. 22

Turning to media, I added factors for the percentage of tax and money

laundering cases that result in any form of media attention (TMD_T and

MMD_M) to the specification that included the total number of tax and money

laundering sentences. The resulting model is Model 8. Since media information was available only after 1992, this resulted in losing 200 observations (50

States, 4 years). In these models, the basic variables for tax and money

laundering sentenced cases become insignificant. These results appear to

contradict the findings in the models with more observations. Therefore, I

reject their significance.

In Table 2, I aggregate prison and probation cases and consider a factor

for the percentage of sentenced cases not receiving prison or probation. The

results of these specifications are presented in Table 2. I modify Model 7 by

replacing the factors for prison and probation rates in tax and money laundering sentences with variables for the percentage of tax and money laundering

sentences receiving neither prison nor probation (Model 9). As was the case in

Model 7, the variable for counts of money laundering sentences is statistically

significant. In addition, the percentage of money laundering cases receiving

neither prison nor probation has a significantly negative effect on compliance.

The audit rate effects are also consistently positive and significant.

Model 10 combines the tax and money laundering sentences into a single

explanatory factor. This variable reveals statistical significance. However, the

percentages of cases that are tax or money laundering cases are statistically

insignificant. (The coefficients indicate that the higher percentage of tax

cases versus money laundering cases is of greater significance.) Model 11

adds the sentencing effect and reveals that sentenced cases that receive neither prison nor probation are negatively associated with compliance. Finally,

Model 12 combines all CI cases (tax, money laundering, and other) into a

single explanatory factor. I find that this factor is also statistically significant

in its effect on compliance.

CI Enforcement Activities and Taxpayer Noncompliance

I conclude from these final specifications that CI activity has a statistically significant and demonstrable effect on tax compliance. However, while

I have found that sentenced cases that do not receive prison time or probation

lead to lower compliance levels, I am not able to find a specific mix of tax and

money laundering cases that would raise compliance over existing levels. The

percentages of these cases with respect to total sentences did not have statistically significant effects on compliance. 23

Simulations

I performed two basic simulations to determine the direct revenue (spillover)

effect of audits. Following the methodology established in DGW, I calculated

a predicted value for the increase in total assessed liability for a particular year

that would have resulted from holding audit rates at their earlier period (higher)

levels. I also calculated the effect of this audit rate change on reported liabilities (excluding additional taxes and penalties resulting from IRS examinations).

The difference between the two estimates represents the direct revenue effect

of the increase in audit rates. DGW estimated that the spillover effects of

audits produce 6 out of every 7 dollars of additional revenue.

In these simulations, a change in the audit rate (and later the levels of CI

activity) leads to two measurable effects. First, the change in audit rate causes

assessed liabilities to increase and reported liabilities to increase. Let dALR

denote the change in assessed liability per return for a change in the audit rate

of dIAR. Similarly, let dRTR denote the change in reported tax liability per

return for the same change in audit rate dIAR. DGW called the change dALR

the total revenue effect (since it includes both reported amounts and additional

taxes and penalties) and dRTR the indirect effect. The direct effect of audits is

defined as dALR-dRTR. Since ALR-RTR is a measure of additional taxes and

penalties, dALR-dRTR is simply the change in additional tax and penalties

resulting from the audit change. Consequently, it is the direct effect. DGW

defined the spillover measure as the ratio dRTR/dALR since it measures the

percentage of the total change from general deterrence as a result of the change

in the audit rate. 24

I considered several experiments. In some cases, I doubled individual

components such as the audit rate. Similarly, I considered doubling the number of tax sentences or doubling the number of money laundering sentences.

In some cases, I doubled both the number of tax and money laundering cases.

For variables measured in percentages (such as the percentage of money

laundering cases that received prison sentences), I increased the percentage

by 25 percent absolutely. Noticing in some cases that certain variables had

statistically insignificant coefficients, I experimented with the same model but

only increased the levels of the significant variables (generally the money laun-

Dubin

dering components).

The simulations are provided in Table 3. For example, consider the

simulation in which audit rates are doubled. The first row in Model 11 of

Table 3 shows that, for Model 11, estimated assessed tax collections would

rise to $959.1 billion from $940.4 billion in 2001. The change of $18.706

billion is the total revenue effect. The estimates also show that reported tax

collections rise by $17.571 billion. This change is the indirect effect of doubling the audit rate. The difference between these two estimated differences

is approximately $1.135 billion and represents the direct revenue effect. This

amount is 93.9 percent of the total revenue effect.

Doubling CI activity (tax and money laundering cases) leads to $15.698

billion in increased reported taxes, $16.68 billion in increased per annum assessed tax revenue, and $0.982 billion in increased direct revenue. Hence, the

spillover effect is measured to be approximately 94 percent. Importantly, doubling CI activity or doubling the IRS audit examination rate leads to similar

revenue increases and implies similar levels of increased general deterrence.

As seen in Table 3, the estimated spillover effects are large but depend to

some degree on the model. The calculation of confidence intervals for the

simulations conducted in our study is complicated for several reasons. First,

total reported taxes due rise as the product of collections per return and returns filed per capita. Audit and enforcement effects are present in both equations for these variables. Further complications arise due to the dynamics in

the models, the conversion from real to nominal terms, and the adjustment

from per capita to total dollars. An alternate procedure is to simulate the audit/

enforcement experiments using estimated coefficients that are one or two

standard errors different from the estimated values. I have followed this

procedure for the main simulation results.

For simulations in which the audit rate is doubled, I find that a 90percent lower bound on the estimated increase in reported taxes is $11.468

billion. A similar lower bound on the estimated increase in assessed tax revenue is $12.578 billion. At the lower bound estimates, the spillover effect is

91.2 percent. For simulations in which CI enforcement levels are doubled, I

find that a 90-percent lower bound on the estimated increase in reported taxes

is $3.348 billion. A similar lower bound on the estimated increase in assessed

tax revenue is $4.309 billion. At the lower bound estimates, the spillover effect

is 77.7 percent. There are two important conclusions from this analysis. First,

the spillover effect of audits and CI enforcement is quite large and generally

estimated to be over 90 percent. Second, an increase in IRS examination

activity could have important fiscal impacts and make a large contribution

toward reducing the tax gap. However, there is no evidence, in our study, that

correspondence audits have made up for the decline in face-to-face tax audits.

This result may be due to the limited time period during which we were able to

measure the correspondence audit rate.

CI Enforcement Activities and Taxpayer Noncompliance

Conclusions

I now summarize my results and answer the basic questions that were posed

in this project. First, I find that CI activities have a measurable effect on

voluntary compliance. I have found statistically significant results from my

measure of CI sentenced cases on general tax deterrence. Second, I conclude

that the mix of sentenced cases (tax and money laundering) is not a significant

determinant of tax compliance (perhaps because the mix has been already

optimally set). Third, media attention shows some weak evidence of increasing compliance, at least among money laundering cases. However, it is logical

to think that media attention plays an important role in disseminating information to the public. The range of media attention or the time span that we

studied may have been too limited or too short to detect the media’s role. At

present, my results are not refined enough to distinguish types of media coverage. Nevertheless, the significant magnitude of general deterrence results

implies that media play a large role in CI cases. Finally, I have found that

incarceration and probation (rather than fines) have the most influence on

taxpayers. It would seem that an emphasis on prison and probation time should

be encouraged based on these results.

It is not too speculative to suggest that the IRS could double its audit

rate without doubling its organizational size. Clearly, the IRS has not shrunk in

size in the same proportion that audits have declined. Conversely, doubling CI

activities might easily necessitate economically and physically doubling the

resources devoted to CI. CI has never sentenced a number of cases represented by doubling of its current load. According to estimates reported by

Plumley (1996, Table 5, pp. 41), the cost for a CI conviction was nearly 80

times more expensive than an audit in 1991. While these unit costs are unlikely

to apply to doubling CI activity, we can get some idea of the dollar magnitude

of these simulations using Plumley’s reported figures.

In 1991, Plumley reported a unit cost of $1,298 per audit and a unit cost

of $103,064 per CI conviction. These are $1,597 and $126,801 in 2001 after

adjusting for inflation. In the same year, there were approximately 202,244

individual audits performed and only roughly 2,000 tax and money laundering

sentences. Plumley’s estimates of unit costs include overhead, support, and

follow-on costs.

Doubling tax and money laundering sentences would cost $254 million

(at these unit cost estimates), while doubling the audit rate would cost $323

million. However, doubling the audit rates is predicted to lead to an $18.71billion increase in per annum reported collections, while doubling tax and

money laundering cases was predicted to increase reported tax collection by

$16.68 billion per annum. Hence, an additional dollar allocated to audit would

return $58 in general deterrence, 25 while an additional dollar allocated to CI

would result in $66. Thus, there is some evidence that resources between

Dubin

civil and criminal enforcement at the IRS have been misallocated, with CI’s

activities receiving too few resources. This difference is not statistically different from zero. A 90-percent lower bound on additional reported collections

per dollar cost is $39 for the doubled audit rate simulation and $17 for the

doubled CI activities simulation.

However, as I mentioned above, it is unlikely that CI could double its

activity level without incurring substantially greater costs than these marginal

(per unit) estimates imply. Moreover, the larger the increase in CI activity we

simulate through the model, the less reliable the estimates become if we move

away from measurable historical experience. Doubling CI activity is very different from doubling the individual audit rate, since CI has never operated at

twice its current size. Conversely, doubling the individual audit rate is within

the IRS’s historical experience.

Still, an increase in the IRS budget of $25 million allocated to CI for

additional investigations, prosecutions, and sentencing would not appear to

push the envelope of historical experience. Such an amount might be used to

increase tax and money laundering cases by roughly 200 per year. This represents a roughly 10-percent increase in tax and money laundering cases at

2001 levels. But, more important, this increase is within the range of historical

CI experience. According to the simulations, general deterrence would rise by

nearly $1.7 billion as a result of the $25-million allocation to cases processed

by CI. With fixed budgets, a cost savings of this magnitude allocated to prosecutions and sentences could achieve the same result if efficiency and productivity gains could be achieved.

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CI Enforcement Activities and Taxpayer Noncompliance

Endnotes

* The author is Associate Professor of Economics, California Institute of

Technology, Pasadena, California 91125. This research was sponsored in

part by the IRS under the project: IRS Criminal Investigation Research—

Empirical Analysis of the Impact of CI Activities on Taxpayer Compliance,

TIRNO-00-D-0039. James Lin (Pacific Economics Group) provided excellent research assistance. The author thanks Patrick Travers (Operations Research Analyst in CI Research), Peggy Opeka (Program Analyst in CI Research), Debbie King (Director of CI Research), Alan Plumley (Economist

and Technical Advisor in IRS Office of Research), Mark Matthews (IRS Deputy

Commissioner for Services and Enforcement), and Colleen McGuire (Senior

Associate, ICF Consulting), as well as seminar participants at the IRS Research Conference. The useful comments of the discussant, Professor John

Scholz, Florida State University, are gratefully acknowledged.

The tax gap attributable to individual taxpayers has grown from $70

billion in 1985 to $95 billion in 1992 (the date of the last Taxpayer

Compliance Measurement Program (TCMP) measurement).

The Webster Report (Review of the IRS’s Criminal Investigation

Division (William Webster), April 1999, observed that a previous lack of

empirical evidence “makes it impossible to prove that the cases CI has

investigated previously and is currently investigating either do or do not

foster compliance.” In this study, I provide the empirical evidence that

Judge Webster sought.

The similarity to DGW is due to using proxy evasion measures for the

economy rather than direct evasion measures. Another similarity is

using a time-series data source as opposed to a purely cross-sectional

data source, such as the 1969 TCMP. However, DGW (1990) combined

both cross-sectional and time-series information in their empirical analysis.

Plumley modified some of the DGW reporting and compliance equations by using: (i) income and offsets rather than tax collected; and (ii)

tax return filings relative to expected filings rather than to population.

Plumley introduced refinements to the DGW audit rate measure (based

on start rates versus closure rates) and considered new factors for

taxpayer burden and CI enforcement activity.

DGW (1990) noted that fewer and fewer cases were being referred to

CI from audits over the period from 1979 to 1988. The Webster report

also noticed and discussed this same trend.

Dubin

These requirements stipulate that banks must (1) report certain large

currency transactions; (2) disclose foreign bank accounts; and (3)

report currency movements across the border. These regulations

trigger reporting currency transactions involving dollar amounts over

$10,000. In addition, the Money Laundering Control Act established

criminal offenses for engaging in unlawful monetary transactions.

More recently, in 1996, financial institutions were required to report

suspicious financial activity that could indicate loan fraud or money

laundering.

Statistics on examination coverage variables such as numbers of returns

examined, additional taxes and penalties recommended after examination, and costs incurred by the IRS were broken down by district office

and service center in the IRS Data Book and Annual Report. In States

where there were multiple districts, I performed an aggregation to

derive State-level figures for those factors. The IRS Reform Act

reorganized the entire district system and required many district offices

to be responsible for the tax returns filed by multiple States. As a result,

most of the district-level statistics from 1997 to 2001 included services

provided to multiple States. Since only State-level data is used in the

analysis, I took the 1996 allocation of examinations, additional taxes,

and cost incurred for each State among all States in the newly defined

districts and extrapolated the annual figures for 1997-2001 based on the

1996 percentages. For States with multiple districts, the district-level

data is aggregated to the State level.

Statistics on the number of examinations and additional taxes and

penalties were not published in the Data Book after 1999 and 1997,

respectively. In order to have the two variables span the entire period, I

substituted the data obtained directly from the IRS for the published

data in the post-1993 portion of the dataset. The sum of audits performed

by both revenue agents and tax auditors tied out closely to the number

of audits reported in the Data Book. Therefore, I used the factor

(AUDR12) to extend the DGW in later years. I used the same approach

for additional taxes and penalties. In the years where the new data and

published data overlap, the correlation between the original and updated

versions of the audit variable is 0.92. Similarly, the correlation between

the two versions of the additional tax variable is 0.97. The resulting

variable is denoted IAR2 and extends the DGW factor IAR for recent

years where the IRS Data Books no longer report audit rates by State.

The TRA was a major shift in United States tax policy. Tax rates were

cut, the tax base was broadened, IRA rules were changed, and the tax

laws were generally simplified.

CI Enforcement Activities and Taxpayer Noncompliance

Given the importance of this factor as an instrument for IRS audit

levels, I extended this figure for the 2000 and 2001 period at 1999

levels. There is little consequence from this approximation when budget

per returns filed is used as an instrumental variable.

DGW used a measure of information returns filed as an instrument in

some of their models, but this factor was not available at the State level

for the time period covered in this study.

As discussed by Plumley (1999), the direct examination measure is a

reasonably exogenous measure of audit activity.

Mete (2002) provides the rationale behind these factors and discusses

how they are expected to correlate with the audit rate. Mete argues that

Republicans prefer lower levels of enforcement for all forms of regulation than do Democrats. Additionally, Mete argues that Republicans

provide less support for increasing Government spending and enforcement activities than Democrats. Therefore, the undesirable effects of

tax enforcement on citizens may be worse for Republican politicians.

Thus, a higher proportion of Democrats in Congress or a more liberal

ideology score should lead to generally higher audit rates.

As the percentages reveal, a small number of cases conducted by CI

are classified neither as money laundering nor tax cases. Similarly, there

are a few cases that have aspects of both money laundering and tax. I

have included such cases as both money laundering and tax cases. The

amount of double counting is, however, insignificant.

The overlap in money laundering sentenced cases was similar. Of

11,865 sentenced money laundering cases, 164 received neither prison

nor probation, while 7,789 received both sentence types.

The media data provide the number of cases that received media

attention and the type of media coverage given (i.e., newspaper,

television, or radio). However, the data do not reveal the amount of

media attention a case received.

This growth cannot be attributed to mandatory sentencing guidelines

already in place during this period and must reflect an increase in

efficiency of the CI in choosing cases.

The effects of these variables on reported taxes per return are based

on conventional theoretical considerations. For a full discussion, see

DGW (1990).

Dubin

I expected increases in the Federal audit rate (AUDIT) to increase

taxpayer compliance (and thus reported taxes per return), since audit

rates presumably respond to compliance levels. Therefore, I cannot

treat the Federal audit rate as an exogenous factor.

Our finding that CI enforcement levels are significant determinants of

taxpayer compliance would reinforce the assurance theory aspects of

behavior rather than the deterrence theory. Conversely, the empirical

support for significant audit rates found in this study and others suggests

that deterrence theory is equally valid for types of taxpayer behavior.

This model requires the use of a third instrument as discussed above.

The percentage of tax or money laundering cases not resulting in prison

or probation was also not statistically significant in these models.

The shortened time period available to study media effects on the

subcomponent of examinations did not allow me to precisely measure

these effects. Given the large general deterrence effect found for CI

activities, there is indirect evidence of a large media effect, even if the

econometric model did not have sufficient data to isolate this result.

The simulations rely on two simultaneous predicted changes in all

cases. As I discussed, the simulation affects the level of assessed

liabilities per return filed or reported liabilities per return filed. However,

the simulation also affects the estimated number of returns filed per

capita. In conjunction with estimates of population (and after conversion from real to nominal terms), the product of population, predicted

returns per capita, and collections per return filed yields the final dollar

figures in the tables. Hence, in some cases, the sign on a single variable

in a model is not sufficient to understand the overall significance of

increasing one or more components in the model.

Plumley’s (1991) estimate of the return to audits was similar. He found

a marginal indirect revenue to cost ratio of 55.

CI Enforcement Activities and Taxpayer Noncompliance

TABLE 1

ALR

0.59

(2.94)

MODEL 1

RTR

RCAP

0.55

303.24

(2.77)

(10.18)

-0.00023

(-0.03)

-0.0144

(-1.72)

0.254

(16.88)

-0.690

(-1.26)

-3.98

(-4.46)

-0.16

(-1.08)

-0.27

(-0.56)

0.08

(0.49)

-1.72

(-6.32)

-0.95

(-3.49)

0.069

(5.14)

0.12

(5.05)

0.00089

(0.13)

-0.0149

(-1.79)

0.250

(16.79)

-0.679

(-1.23)

-3.97

(-4.55)

-0.09

(-0.59)

-0.20

(-0.41)

0.11

(0.66)

-1.77

(-6.62)

-0.97

(-3.5)

0.063

(4.57)

0.11

(4.59)

VARIABLE

Constant

Percent of Families on

Welfare

State Tax Rate

Personal Income Per Capita

Family Size

Farms Per Household

Percent of Adults with High

School Diploma

Percent of Pop Over 65

Percent of Employed Persons

in Manufacturing

Percent of Employed Persons

in Service

Unemployment Rate

Dummy (Year>1980)

Audit Rate

0.67656

(0.69)

-3.4992

(-2.87)

15.149

(6.76)

-105.775

(-1.38)

-270.12

(-1.92)

143.64

(6.11)

-129.67

(-1.76)

47.40

(1.81)

211.93

(4.9)

-311.01

(-8.67)

-0.853

(-0.5)

-14.25

(-4.22)

IAR

0.89

(1.57)

ALR

0.24

(0.70)

MODEL 2

RTR

RCAP

0.27

180.34

(0.79)

(5.13)

IAR2

2.14

(3.37)

ALR

1.50

(3.55)

MODEL 3

RTR

RCAP

1.53

169.02

(3.65)

(3.49)

0.03000

(1.47)

-0.047

(-1.95)

-0.244

(-5.9)

-2.308

(-1.42)

-5.19

(-1.97)

3.12

(7.87)

-0.85

(-0.61)

0.81

(1.68)

0.88

(1.06)

-2.88

(-3.44)

0.010

(0.25)

0.01515

(1.85)

-0.0042

(-0.66)

0.338

(18.19)

-2.220

(-2.18)

0.63

(0.31)

-0.68

(-4.93)

2.58

(2.32)

-1.00

(-2.98)

0.36

(0.88)

-3.11

(-5.51)

0.01430

(1.76)

-0.0041

(-0.64)

0.339

(18.31)

-2.318

(-2.29)

0.82

(0.4)

-0.69

(-5.04)

2.62

(2.36)

-1.00

(-2.99)

0.37

(0.92)

-3.13

(-5.59)

-0.47104

(-0.66)

-2.2240

(-2.84)

11.608

(6.07)

578.597

(5.9)

-191.74

(-0.59)

30.17

(2.5)

-397.95

(-2.87)

211.38

(5.56)

8.76

(0.22)

-185.50

(-4.02)

0.05970

(4.16)

-0.0120

(-0.98)

-0.134

(-4.24)

1.131

(0.6)

3.86

(1.01)

-1.19

(-4.56)

-7.81

(-4)

-0.55

(-0.86)

0.75

(0.99)

-0.44

(-0.42)

0.00780

(0.75)

-0.0250

(-3.02)

0.314

(16.54)

-0.355

(-0.35)

-3.96

(-1.62)

-1.93

(-6.56)

0.57

(0.45)

-0.25

(-0.6)

-0.43

(-0.84)

-6.83

(-9.33)

0.00750

(0.72)

-0.0260

(-3.05)

0.315

(16.65)

-0.454

(-0.45)

-3.79

(-1.55)

-1.94

(-6.63)

0.63

(0.5)

-0.25

(-0.6)

-0.43

(-0.85)

-6.84

(-9.39)

-1.65160

(-1.49)

-0.1850

(-0.14)

16.149

(7.38)

349.885

(3.13)

-87.30

(-0.23)

90.58

(2.85)

-327.82

(-1.84)

141.15

(2.52)

-1.81

(-0.03)

-171.18

(-2.31)

0.09530

(7.97)

-0.0180

(-1.45)

-0.084

(-3.17)

1.061

(0.72)

7.12

(1.99)

0.28

(0.64)

-4.15

(-2.27)

-0.28

(-0.44)

0.69

(0.92)

1.94

(1.74)

-0.15

(3.42)

-0.13

(3.13)

-16.15

(4.44)

AUDR12

(0.21)

(-0.33)

Audit Rate (Revenue Agents)

Audit Rate (Revenue

Agents+Tax Auditors)

-0.117

(2.16)

-0.097

(1.81)

-35.625

(6.42)

---

Audit Rate (Tax Auditors)

Audit Rate (Service Centers)

Total Sentences

Tax Sentences

Percent of Tax Sentences in

Media

Percent of Money Laundering

Sentences in Media

Percent of Tax Sentences

Resulting in Prison

Percent of Tax Sentences

Resulting in Probation

Percent of Money Laun Sent

Resulting in Prison

Percent of Money Laun Sent

Resulting in Probation

--0.133

(-9.45)

-258.945

(10.89)

-1.019

(5.26)

24.447

(10.19)

-1.229

(8.1)

16.094

(7.97)

Money Laundering Sentences

Info returns not W2 filed / tot

number of info returns filed

Direct Examination Time

Budget Per Return

State Government Ideology

(100 is most liberal)

DGW2: obsno=500 years: 1977-1986

DGW_IAR2A: obsno=700 years: 1988-2001

DGW93_12,5: obsno=450 years: 1993-2001

Dubin

TABLE 1 (cont.)

VARIABLE

Constant

Percent of Families on

Welfare

State Tax Rate

Personal Income Per Capita

Family Size

Farms Per Household

Percent of Adults with High

School Diploma

Percent of Pop Over 65

Percent of Employed

Persons in Manufacturing

Percent of Employed

Persons in Service

Unemployment Rate

MODEL 4

ALR

RTR

RCAP AUDR12 AUDR5

9.91

9.80 1817.36

(0.33)

(10.99)

(0.26)

(0.22)

(-0.46)

(-1.72)

MODEL 5

ALR

RTR

RCAP AUDR1 AUDR2 AUDR5

0.90

0.95 201.06

0.09

(0.40)

(9.25)

(0.57) (0.61)

(0.82)

(0.23)

(-0.88)

(-1.51)

ALR

0.20

(0.6)

MODEL 6

RTR

RCAP

IAR2

0.24

188.76

2.13

(0.69)

(5.42)

(3.4)

-0.233 -0.227 -68.993

(-0.21) (-0.21)

(-0.21)

-0.1246 -0.1211

-36.35

(-0.28) (-0.28)

(-0.2)

0.326 0.325

30.70

(1.4) (1.45)

(0.36)

-16.755 -16.577 -2582.63

(-0.22) (-0.22)

(-0.17)

-5.63

-5.12 -660.72

(-0.12) (-0.11)

(-0.08)

-0.79

-0.80 141.47

(-0.12) (-0.12)

(0.14)

-7.58

-7.60 -1168.83

(-0.19) (-0.19)

(-0.19)

7.60

7.44 2146.70

(0.21)

-9.49

-9.29 -2332.85

(-0.22) (-0.23)

(-0.21)

-13.01 -12.92 -1447.67

(-0.46) (-0.46)

(-0.21)

0.067 0.065

(1.07) (1.06)

-0.0219 -0.0225

(-0.81) (-0.86)

0.255 0.258

(4.38) (4.52)

0.451 0.330

(0.14)

(0.1)

-3.58

-3.25

(-0.59) (-0.54)

-1.10

-1.15

(-1.19) (-1.27)

1.88

1.94

(0.58) (0.61)

-1.47

-1.44

(-0.81) (-0.81)

-0.41

(-0.23) (-0.23)

-6.15

-6.20

(-3.53) (-3.64)

0.098

(7.6)

-0.0210

(-1.12)

-0.079

(-2.58)

1.830

(1.14)

10.70

(1.93)

0.30

(0.63)

-5.61

(-2.21)

0.07

(0.09)

0.37

(0.43)

1.79

(1.58)

0.274

(2.37)

0.1250

(0.75)

0.024

(0.09)

20.874

(1.45)

4.03

(0.08)

-1.66

(-0.4)

12.34

(0.54)

-10.29

(-1.4)

11.58

(1.5)

7.82

(0.77)

7.407

(0.78)

-1.2784

(-0.31)

2.485

(0.28)

139.855

(0.28)

-656.14

(-0.67)

353.58

(2.54)

-64.36

(-0.13)

-26.51

(-0.1)

-254.88

(-0.94)

-105.68

(-0.4)

0.043

(6.27)

-0.0070

(-0.78)

-0.055

(-3.4)

-0.047

(-0.06)

1.01

(0.39)

0.57

(2.26)

-0.80

(-0.64)

-0.15

(-0.37)

-0.29

(-0.63)

0.26

(0.42)

0.054

(6.48)

-0.0130

(-1.2)

-0.027

(-1.4)

1.630

(1.58)

8.15

(2.6)

-0.27

(-0.88)

-4.35

(-2.87)

0.14

(0.27)

0.82

(1.5)

1.52

(2.04)

0.266

(2.35)

0.1430

(0.98)

0.043

(0.16)

18.579

(1.32)

0.02

(0)

-1.66

(-0.4)

11.00

(0.53)

-10.44

(-1.53)

9.67

(1.29)

7.88

(0.78)

0.01087

(1.31)

-0.0034

(-0.54)

0.335

(18.35)

-1.754

(-1.74)

0.37

(0.19)

-0.73

(-5.11)

2.24

(2.07)

-0.90

(-2.75)

0.15

(0.37)

-3.13

(-5.55)

0.01026

(1.24)

-0.0032

(-0.52)

0.336

(18.46)

-1.854

(-1.85)

0.54

(0.27)

-0.74

(-5.24)

2.30

(2.13)

-0.90

(-2.77)

0.16

(0.4)

-3.15

(-5.63)

-0.24503

(-0.34)

-2.3815

(-3.09)

11.874

(6.26)

529.818

(5.33)

-130.62

(-0.42)

35.84

(2.91)

-388.86

(-2.86)

192.35

(5.12)

26.20

(0.65)

-184.41

(-3.98)

0.05970

(4.09)

-0.0120

(-1.08)

-0.131

(-4.21)

1.005

(0.54)

3.81

(1.02)

-1.18

(-4.4)

-7.52

(-3.94)

-0.60

(-0.96)

0.75

(0.99)

-0.42

(-0.39)

--339.36

(-1.95)

-0.13

(3.19)

-16.17

(4.44)

---1.3842 -1.3557

(-1.19) (-1.2)

-0.15

(3.48)

-0.52

(1.48)

-0.08

(-0.56)

-156.77

(2.9)

-0.60

(-0.03)

--0.00004

(-0.11)

0.00111

(2.86)

-0.03478

(0.98)

-0.08360

(-2.47)

-0.00000

(0.12)

0.00000

(-0.24)

Dummy (Year>1980)

Audit Rate

Audit Rate (Revenue

Agents)

Audit Rate (Revenue

Agents+Tax Auditors)

-0.36

(0.28)

-0.34

(0.27)

-124.53

(0.27)

-0.78

(0.22)

-0.76

(0.22)

-254.28

(0.2)

-0.56

(1.54)

-0.08

(-0.56)

Audit Rate (Tax Auditors)

Audit Rate (Service

Centers)

Total Sentences

Money Laundering

Sentences

-0.00000

(-0.01)

0.00112

(2.87)

Percent of Tax Sentences in

Media

Tax Sentences

Percent of Money

Laundering Sentences in

Media

Percent of Tax Sentences

Resulting in Prison

Percent of Tax Sentences

Resulting in Probation

Percent of Money Laun

Sent Resulting in Prison

Percent of Money Laun

Sent Resulting in Probation

--0.15

(-0.11)

-8.307

(-0.46)

-0.189

(2.24)

5.302

(4.84)

(0.65)

-1.015

(9.99)

10.783

(8.19)

(0.29)

--0.339

(-0.25)

-8.088

(-0.45)

-0.005

(-1.08)

Info returns not W2 filed /

tot number of info returns

filed

Direct Examination Time

-1.182

(7.68)

16.104

(8.09)

Budget Per Return

State Government Ideology

(100 is most liberal)

DGW2: obsno=500 years: 1977-1986

DGW_IAR2A: obsno=700 years: 1988-2001

DGW93_12,5: obsno=450 years: 1993-2001

-1.028

(5.29)

24.494

(10.16)

CI Enforcement Activities and Taxpayer Noncompliance

TABLE 1 (cont.)

VARIABLE

Constant

Percent of Families on Welfare

State Tax Rate

Personal Income Per Capita

Family Size

Farms Per Household

Percent of Adults with High School

Diploma

Percent of Pop Over 65

Percent of Employed Persons in

Manufacturing

Percent of Employed Persons in

Service

Unemployment Rate

MODEL 7

RTR

RCAP

ALR

IAR2

MODEL 8

RTR

RCAP

ALR

IAR2

0.23

(0.7)

0.25

(0.77)

204.67

(5.86)

1.55

(2.6)

1.43

(3.6)

1.47

(3.7)

147.95

(3.4)

-0.001

(0)

0.00825

(0.98)

-0.0055

(-0.95)

0.329

(18.99)

-1.527

(-1.57)

-0.24

(-0.13)

-0.72

(-5.12)

1.90

(1.89)

-0.78

(-2.55)

0.13

(0.33)

-3.13

(-5.55)

0.00775

(0.93)

-0.0054

(-0.93)

0.330

(19.1)

-1.604

(-1.66)

-0.09

(-0.05)

-0.74

(-5.22)

1.95

(1.94)

-0.78

(-2.54)

0.15

(0.38)

-3.15

(-5.62)

-0.48990

(-0.65)

-2.4248

(-3.16)

12.019

(6.38)

510.856

(5.1)

-134.29

(-0.44)

31.86

(2.55)

-401.06

(-2.98)

180.60

(4.8)

-0.42

(-0.01)

-193.25

(-4.14)

0.06330

(4.46)

-0.0120

(-1.16)

-0.119

(-4.09)

1.419

(0.8)

4.53

(1.34)

-1.06

(-4.01)

-7.04

(-4.04)

-0.51

(-0.9)

1.30

(1.8)

0.08

(0.08)

0.00786

(0.8)

-0.0253

(-3.09)

0.311

(17.32)

-0.954

(-1)

-2.92

(-1.22)

-1.71

(-6.91)

1.28

(1.06)

-0.33

(-0.82)

-0.32

(-0.68)

-6.53

(-10.46)

0.00765

(0.78)

-0.0254

(-3.1)

0.312

(17.41)

-1.052

(-1.1)

-2.71

(-1.14)

-1.73

(-7.00)

1.32

(1.10)

-0.33

(-0.82)

-0.30

(-0.64)

-6.55

(-10.53)

-1.56726

(-1.58)

-0.2042

(-0.17)

16.453

(8.32)

407.125

(4.05)

-23.80

(-0.07)

89.30

(3.54)

-321.31

(-2.04)

144.80

(2.9)

-41.06

(-0.8)

-89.36

(-1.46)

0.09710

(8.3)

-0.0170

(-1.38)

-0.080

(-3.1)

0.279

(0.19)

7.16

(2.01)

0.29

(0.77)

-4.27

(-2.4)

-0.08

(-0.14)

0.91

(1.29)

1.31

(1.36)

-0.14

(3.28)

-0.13

(2.98)

-17.33

(4.75)

-0.09

(1.71)

-31.07

(6.31)

-0.10

(2.06)

--0.00010

(-0.27)

0.00121

(3.15)

--0.00013

(-0.37)

0.00120

(3.14)

-0.03410

(0.95)

-0.08378

(-2.46)

-0.00000

(0.27)

0.00000

(-0.37)

--0.023

(-0.93)

0.00046

(0.02)

-0.038

(-1.89)

0.049

(2.31)

--0.022

(-0.88)

-0.00059

(-0.02)

-0.037

(-1.86)

0.047

(2.2)

-3.354

(1.67)

-1.53718

(-0.65)

0.200

(0.12)

3.896

(2.25)

--0.017

(-0.38)

0.082

(1.53)

-0.150

(-3.95)

-0.012

(-0.31)

-0.00048

(1.17)

0.00000

(0.00)

-0.044

(-2.22)

0.025

(1.54)

-0.00047

(1.15)

-0.00003

(-0.07)

-0.044

(-2.23)

0.024

(1.49)

-0.01078

(0.26)

-0.04440

(-0.92)

4.248

(2.25)

3.933

(2.51)

--0.001

(-1.49)

0.001

(1.25)

0.026

(0.87)

-0.024

(-0.95)

Dummy (Year>1980)

Audit Rate

Audit Rate (Revenue Agents)

Audit Rate (Revenue Agents+Tax

Auditors)

Audit Rate (Tax Auditors)

Audit Rate (Service Centers)

Total Sentences

Tax Sentences

Money Laundering Sentences

Percent of Tax Sentences in Media

Percent of Money Laundering

Sentences in Media

Percent of Tax Sentences

Resulting in Prison

Percent of Tax Sentences

Resulting in Probation

Percent of Money Laun Sent

Resulting in Prison

Percent of Money Laun Sent

Resulting in Probation

Info returns not W2 filed / tot

number of info returns filed

-1.142

(5.95)

23.862

(9.98)

Direct Examination Time

Budget Per Return

State Government Ideology (100 is

most liberal)

DGW2: obsno=500 years: 1977-1986

DGW_IAR2A: obsno=700 years: 1988-2001

DGW93_12,5: obsno=450 years: 1993-2001

-1.238

(8.15)

15.579

(8.5)

Dubin

TABLE 2

MODEL 9

VARIABLE

ALR

Constant

0.19

RTR

0.22

MODEL 10

RCAP

IAR2

0.19

1.91

ALR

0.14

RTR

0.17

RCAP

0.19

IAR2

1.87

(0.57)

(0.66)

(5.42)

(3.24)

(0.43)

(0.52)

(5.33)

(3.06)

Percent of Families on Welfare

0.00559

0.00521

-0.00056

0.06501

0.00926

0.00866

-0.00027

0.06129

(0.65)

(0.61)

(-0.73)

(4.71)

(1.12)

(1.05)

(-0.37)

(4.29)

Family Size

-1.7768

-1.8732

0.5175

1.0760

-1.7869

-1.8774

0.5684

1.4303

(-1.78)

(-1.88)

(5.15)

(0.61)

(-1.84)

(-1.93)

(5.72)

(0.8)

-0.369

-0.172

-0.190

5.361

0.511

0.679

-0.213

4.183

(-0.19)

(-0.09)

(-0.63)

(1.53)

(0.28)

(0.37)

(-0.71)

(1.22)

-0.711

-0.726

0.038

-1.160

-0.602

-0.615

0.029

-1.124

(-5.02)

(-5.15)

(3)

(-4.57)

(-4.3)

(-4.42)

(2.3)

(-4.25)

-3.10

-3.13

-0.19

-0.10

-3.06

-3.08

-0.19

-0.03

(-5.49)

(-5.57)

(-3.97)

(-0.09)

(-5.4)

(-5.46)

(-4.03)

(-0.03)

Farms Per Household

Percent of Adults with High School

Diploma

Unemployment Rate

Personal Income Per Capita

Percent of Employed Persons in

Percent of Employed Persons in Service

Percent of Pop Over 65

State Tax Rate

0.33

0.34

0.01

-0.11

0.32

0.33

0.01

-0.12

(18.57)

(18.68)

(6.4)

(-3.86)

(18.53)

(18.63)

(6.33)

(-4.13)

-0.75

-0.76

0.19

-0.91

-0.75

0.19

-0.64

(-2.3)

(-2.33)

(5.12)

(-1.57)

(-2.42)

(-2.43)

(5.09)

(-1.1)

0.13

0.14

0.02

0.70

0.37

0.39

0.00

0.98

(0.32)

(0.35)

(0.54)

(0.98)

(0.94)

(0.99)

(-0.03)

(1.34)

2.40

2.45

-0.37

-7.35

1.88

1.93

-0.39

-7.03

(2.23)

(2.29)

(-2.71)

(-4.12)

(1.85)

(1.9)

(-2.88)

(-3.96)

-0.006

-0.005

-0.003

-0.006

-0.0054

-0.0053

-0.0023

-0.0133

(-0.93)

(-0.89)

(-3.39)

(-0.59)

(-0.93)

(-0.91)

(-2.94)

(-1.24)

0.0005

0.0000

0.0001

(2.23)

(2.14)

(-1.08)

(0.17)

0.03242

0.03075

-0.00004

-0.14580

(0.89)

(0.85)

(-0.01)

(-2.17)

1.14E-02

9.47E-03

1.43E-03

-2.41E-01

(0.27)

(0.23)

(0.42)

(-3.23)

Total Sentences

Total Sentences Either Tax or Money

Laundering

Tax Sentences

Money Laun Sentences

0.00001

-0.00003

0.00003

0.00012

(0.02)

(-0.08)

(0.84)

(0.19)

0.00111

0.00110

-0.00008

-0.00024

(2.84)

(2.83)

(-2.47)

(-0.34)

Tax Sentences / Total Sentences

Money Laun Sentences / Total Sentences

Tax Sentences Neither Pris nor Prob /

Total Tax Sentences

3.33E-02

(0.37)

(0.31)

(1.31)

(-2.71)

Money Laun Sentences Neither Pris nor

Prob / Total Money Laun Sentences

-3.55E-01

-3.39E-01

-1.82E-02

9.41E-01

(-4.48)

(-4.3)

(-2.76)

(8.61)

2.76E-02

9.69E-03

-4.31E-01

Money Laun Pris Sentences / Money Laun

Sentences

Tax Pris Sentences / Tax Sentences

Money Laun Prob Sentences / Money

Laun Sentences

Tax Prob Sentences / Tax Sentences

Audit Rate

Budget Per Return

Direct Examination Time

Number of observations: 700

Years of analysis: 1988-2001

0.18

0.17

0.02

1.35E-01

1.22E-01

1.66E-02

(3.93)

(3.64)

(4.69)

(3.19)

(2.89)

(4.5)

22.06

24.51

(9.49)

1.05145

(10.22)

(5.71)

(5.43)

1.05

CI Enforcement Activities and Taxpayer Noncompliance

TABLE 2 (Continued)

MODEL 11

MODEL 12

VARIABLE

ALR

Constant

Percent of Families on Welfare

Family Size

Farms Per Household

Percent of Adults with High School

Diploma

Unemployment Rate

Personal Income Per Capita

Percent of Employed Persons in

Manufacturing

Percent of Employed Persons in Service

Percent of Pop Over 65

RTR

RCAP

IAR2

ALR

RTR

RCAP

IAR2

0.12

0.15

0.19

1.61

0.21

0.23

0.20

1.55

(0.38)

(0.46)

(5.44)

(2.85)

(0.64)

(0.7)

(5.75)

(2.61)

0.00352

0.00316

-0.00058

0.06608

0.00732

0.00680

-0.00047

0.06329

(0.41)

(0.37)

(-0.73)

(4.93)

(0.88)

(0.82)

(-0.62)

(4.48)

-1.7491

-1.8335

0.5490

1.5028

-1.7046

-1.7863

0.5508

1.4841

(-1.83)

(-1.92)

(5.45)

(0.91)

(-1.78)

(-1.87)

(5.57)

(0.85)

-0.341

-0.143

-0.268

5.642

0.226

0.399

-0.218

4.353

(-0.19)

(-0.08)

(-0.92)

(1.8)

(0.13)

(0.22)

(-0.73)

(1.32)

-0.589

-0.602

0.030

-1.076

-0.633

-0.643

0.026

-1.092

(-4.24)

(-4.35)

(2.37)

(-4.33)

(-4.61)

(-4.7)

(2.08)

(-4.27)

-3.01

-3.03

-0.20

0.35

-3.03

-3.05

-0.20

0.07

(-5.3)

(-5.36)

(-4.1)

(0.35)

(-5.37)

(-5.44)

(-4.25)

(0.07)

0.32

0.01

-0.11

0.32

0.33

0.01

-0.12

(18.93)

(19.02)

(6.52)

(-3.82)

(18.95)

(19.05)

(6.39)

(-4.09)

-0.57

-0.58

0.19

-0.92

-0.75

0.18

-0.53

(-1.88)

(-1.9)

(5.02)

(-1.72)

(-2.47)

(4.87)

(-0.94)

0.34

0.35

-0.01

0.96

0.29

0.31

-0.02

1.24

(0.86)

(0.91)

(-0.2)

(1.42)

(0.74)

(0.8)

(-0.47)

(1.75)

1.98

2.02

-0.37

-6.79

1.79

1.84

-0.40

-6.95

(2)

(2.04)

(-2.75)

(-4.17)

(1.81)

(1.85)

(-2.98)

(-4.05)

-0.008

-0.002

-0.008

-0.006

-0.002

-0.012

(-1.37)

(-1.33)

(-3.24)

(-0.86)

(-1.05)

(-1.04)

(-3.05)

(-1.17)

0.00046

0.00045

-0.00003

0.00000

(2.32)

(2.24)

(-1.27)

(0.01)

Money Laun Pris Sentences / Money

Laun Sentences

0.04609

0.04346

0.00418

-0.15003

(2.16)

(2.05)

(2.41)

(-3.94)

Tax Pris Sentences / Tax Sentences

-1.97E-02

-1.82E-02

3.11E-03

-1.94E-02

(-0.79)

(-0.73)

(1.54)

(-0.42)

Money Laun Prob Sentences / Money

Laun Sentences

-3.48E-02

-3.40E-02

1.87E-05

-1.25E-02

(-1.74)

(-1.71)

(0.01)

(-0.34)

Tax Prob Sentences / Tax Sentences

0.003

0.002

-0.002

0.081

(0.1)

(0.07)

(-0.68)

(1.52)

0.13

0.12

0.02

(3.15)

(2.84)

(4.77)

State Tax Rate

Total Sentences

Total Sentences Either Tax or Money

Laundering

0.00055

0.00053

-0.00003

0.00006

(2.29)

(2.19)

(-1.23)

(0.15)

Tax Sentences

Money Laun Sentences

Tax Sentences / Total Sentences

0.03836

(1.05)

(1)

(0.15)

(-2.35)

Money Laun Sentences / Total

Sentences

2.06E-02

1.80E-02

2.58E-03

-2.64E-01

(0.49)

(0.43)

(0.73)

(-3.73)

0.006

-0.001

0.012

-0.468

(0.06)

(-0.01)

(1.59)

(-2.95)

-3.55E-01 - 3 . 3 8 E - 0 1 -1.85E-02

9.48E-01

Tax Sentences Neither Pris nor Prob /

Total Tax Sentences

Money Laun Sentences Neither Pris nor

Prob / Total Money Laun Sentences

Audit Rate

Budget Per Return

Direct Examination Time

Number of observations: 700

Years of analysis: 1988-2001

(-4.44)

0.03642

(-4.26)

0.00047

(-2.75)

0.17

0.15

0.02

(3.66)

(3.35)

(4.73)

-0.14956

(8.72)

22.11

23.95

(9.57)

(10.04)

1.08

1.14

(5.89)

(5.97)

Dubin

Table 3

CI Enforcement Activities and Taxpayer Noncompliance

Table 3 (Continued)

LOG FILE

VARIABLE SUBSTITUTION

SIGNIFICANT

VARIBLES

(A)

(B)

D-C

C/D

$18,500,000,000

$19,609,000,000

$1,109,000,000

94.3%

$2,170,000,000

$15,347,000,000

$2,921,000,000

$15,571,000,000

$751,000,000

$224,000,000

74.3%

98.6%

T-->2 * T

M-->2 * M

$17,640,000,000

$18,605,000,000

$965,000,000

94.8%

TNEI_T = 0

MNEI_M = 0

MNEI_M

$1,056,000,000

$1,091,000,000

$35,000,000

96.8%

-$128,000,000

-$138,000,000

-$10,000,000

92.8%

M-->2 * M

TNEI_T = 0

MNEI_M = 0

MNEI_M

$1,184,000,000

$1,230,000,000

$46,000,000

96.3%

T-->2 * T

M-->2 * M

TNEI_T = 0

MNEI_M = 0

M, MNEI_M

$18,696,000,000

$19,696,000,000

$1,000,000,000

94.9%

IAR2 --> 2 * IAR2

IAR2

$14,436,000,000

$15,457,000,000

$1,021,000,000

93.4%

TOTTM--> 2 * TOTTM

TOTTM

$16,421,000,000

$17,402,000,000

$981,000,000

94.4%

-$1,930,000,000

-$1,890,000,000

$40,000,000

102.1%

$1,936,000,000

$1,903,000,000

-$33,000,000

101.7%

M_TOT--> M_TOT + 0.25

T_TOT--> T_TOT - 0.25

MODEL 10

T_TOT--> T_TOT + 0.25

M_TOT--> M_TOT - 0.25

TOTTM--> 2 * TOTTM

M_TOT--> M_TOT + 0.25

T_TOT--> T_TOT - 0.25

TOTTM

$14,521,000,000

$15,542,000,000

$1,021,000,000

93.4%

IAR2 --> 2 * IAR2

IAR2

$17,571,000,000

$18,706,000,000

$1,135,000,000

93.9%

TOTTM--> 2 * TOTTM

TOTTM

$15,698,000,000

$16,680,000,000

$982,000,000

94.1%

-$1,259,000,000

-$1,176,000,000

$83,000,000

107.1%

$1,311,000,000

$1,237,000,000

-$74,000,000

106.0%

$14,476,000,000

$15,541,000,000

$1,065,000,000

93.1%

-$94,000,000

-$106,000,000

-$12,000,000

88.7%

M_TOT--> M_TOT + 0.25

T_TOT--> T_TOT - 0.25

T_TOT--> T_TOT + 0.25

M_TOT--> M_TOT - 0.25

TOTTM--> 2 * TOTTM

M_TOT--> M_TOT + 0.25

T_TOT--> T_TOT - 0.25

TOTTM

TNEI_T = 0

MNEI_M = 0

MNEI_M

$1,182,000,000

$1,227,000,000

$45,000,000

96.3%

TNEI_T = 0

MNEI_M = 0

MNEI_M

$1,088,000,000

$1,121,000,000

$33,000,000

97.1%

MNEI_M = 0

T_TOT--> T_TOT + 0.25

M_TOT--> M_TOT - 0.25

MNEI_M

$2,493,000,000

$2,464,000,000

-$29,000,000

101.2%

IAR2 --> 2 * IAR2

IAR2

$14,488,000,000

$15,521,000,000

$1,033,000,000

93.3%

TOT--> 2 * TOT

TOT

$16,682,000,000

$17,529,000,000

$847,000,000

95.2%

TPRI_T-> TPRI_T+.25;

TPRO_T-> TPRO_T+.25;

MPRI_M-> MPRI_M+.25;

MPRO_M-> MPRO_M+.25

MODEL 12

(D)

INDIRECT

REVENUE

EFFECT / TOTAL

REVENUE

EFFECT

T-->2 * T

MODEL 11

(C)

DIFFERENCE

(Direct Revenue Effect)

IAR2

IAR2 --> 2 * IAR2

MODEL 9

ESTIMATED REPORTED TAX

ESTIMATED ASSESSED TAX

REVENUE INCREASE

RESULTING FROM CHANGE

(Indirect Revenue Effect)

MPRI_M

TPRI_T-> TPRI_T+.25;

TPRO_T-> TPRO_T+.25

MPRI_M-> MPRI_M+.25;

MPRO_M-> MPRO_M+.25

TOT--> 2 * TOT

TPRI_T-> TPRI_T+.25;

TPRO_T-> TPRO_T+.25;

MPRI_M-> MPRI_M+.25;

MPRO_M-> MPRO_M+.25

MPRI_M

TOT, MPRI_M

$92,000,000

$72,000,000

-$20,000,000

127.8%

-$254,000,000

-$305,000,000

-$51,000,000

83.3%

$346,000,000

$378,000,000

$32,000,000

91.5%

$16,799,000,000

$17,628,000,000

$829,000,000

95.3%

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