TAX EVASION BY SMALL BUSINESS 

by 

David Joulfaian and Mark Rider** 

U.S. Department of the Treasury and 

Georgia State University 

OTA Paper 77 June 1998 

OTA Papers is an occasional series of reports on the research, models, and datasets developed 

to inform and improve Treasury’s tax policy analysis. The papers are works in progress and 

subject to revision. Views and opinions expressed are those of the authors and do not 

necessarily represent official Treasury positions or policy. OTA Papers are distributed in order 

to document OTA analytic methods and data and invite discussion and suggestions for revision 

and improvement. Comments are welcome and should be directed to the authors. 

Office of Tax Analysis

U.S. Department of the Treasury

Washington, DC 20220

david.joulfaian@do.treas.gov

prcmwr@langate.gsu.edu

_____________ 

* A revised version of the paper was published under the title “Differential Taxation and Tax 

Evasion by Small Business” in the National Tax Journal, Volume LI, No. 4, December 1998, 

675-687. 

** This paper was written while the author was on the staff of the Office of Tax Analysis. The 

authors would like to thank Robert Carroll, Lowell Dworin, Doug Holtz-Eakin, Karl Scholz, 

and Janet Speyer for useful comments. 
TAX EVASION BY SMALL BUSINESS 

ABSTRACT 

This paper examines the compliance pattern of small businesses. Specifically, it focuses 

on the voluntary reporting of income from proprietorship, farm, and rental real estate activities. 

Using pooled 1985 and 1988 data, we find that differential taxation due to self-employment 

taxes (SECA) plays an important role in explaining the observed pattern of noncompliance. 

This finding supports the notion that under reporting of income is positively correlated with tax 

rates. 
I. Introduction 

It is commonly believed that the self-employed exhibit lower rates of voluntary 

compliance than taxpayers whose primary source of income are wages and salaries. For 

example, the Internal Revenue Service (IRS, 1988) estimates that the voluntary reporting 

percentage for wages and salaries is over 99 percent for filers, while the percentage for all 

other income is 80 percent. In large part this disparity in reporting compliance is attributed to 

the lower probability of detecting unreported self-employment income; or conversely stated, 

the higher cost of detecting unreported self-employment income due to the absence of thirdparty reporting of income and income tax withholding. In contrast, we observe nearly 

complete reporting of wage income which is subject to third-party reporting and withholding. 

There are also substantial differences in the rates of voluntary reporting compliance 

among sources of self-employment income. For example, the IRS (1988) estimates that the 

voluntary reporting percentage for filers ranges from approximately 41 to 84 percent for 

proprietorship, and rent and royalty incomes, respectively. By analogy to the disparity in wage 

and business income reporting compliance, one could conclude that reporting compliance 

disparities among sources of self-employment income are due to differences in detection 

probabilities as well. Although there are certainly some differences in the probability of 

detecting unreported self-employment income by source, it is important to recognize that their 

federal tax treatment varies as well. 

1
2

Specifically, proprietorship and farm income below a certain income threshold is 

subject to the self-employment tax (SECA); while income from rental real estate is not.1 

Therefore, the total tax liability on proprietorship income is likely to be higher than the liability 

on the same level of income from rental real estate. We believe that this differential tax 

treatment contributes to the disparities in the voluntary reporting of income from different selfemployment activities. Indeed for the tax filing universe, the IRS (1988) estimates that only 

about 50 percent of the SECA tax is voluntarily reported, compared to about 84 percent for 

the personal income tax. Also, differential taxation of business income allows us to take 

advantage of variation in the tax rates that is independent of the level of income.2 

In order to isolate the effect of tax rates on business income reporting compliance, we 

focus on a number of self-employment activities that are subject to different tax rates with 

limited third party reporting. Specifically, we distinguish among three sources of income: 

(1) proprietorship, (2) farm, and (3) rental real estate. We use pooled data drawn from the 

1985 and 1988 Taxpayer Compliance Measurement Program (TCMP) files. These data 

consist of detailed information reported on tax returns that have been subject to intensive lineby-line review by experienced auditors of the Internal Revenue Service. Furthermore, the data 

straddle the Tax Reform Act of 1986 (TRA 1986), which provides considerable variation in 

marginal tax rates. 

1 In 1985, for example, the tax rate was 0.141 on self-employment income below $39,600. 

2

 This also helps reduce the identification problem. See Feenberg (1997) and Poterba (1987). 
3

We proceed with a brief review of the tax compliance literature. Next we describe the 

data and construction of the variables used in our analysis. Then we report our empirical 

results. In order to demonstrate the importance of differential tax treatment by source of selfemployment income, we estimate the empirical model using two different measures of the tax 

rate. One measure is just the federal individual income tax rate; while the alternative measure 

also accounts for the SECA rate. We find that the estimated tax rate effect is sensitive to the 

tax rate measure employed. We also discuss a number of alternate specifications to examine 

the robustness of our results. Based on this evidence we conclude that differential taxation of 

self-employment income is an important additional explanation of the disparities in voluntary 

reporting compliance among small businesses. Finally, we conclude with a summary of our 

findings and suggestions for further research. 

II. Review of the Literature 

Since the seminal papers by Allingham and Sandmo (1972) and Yitzhaki (1974), a large 

body of literature has addressed the determinants of individual income tax compliance. In 

theoretical models of tax compliance, standard comparative static analysis shows that reported 

income varies positively with income, detection rate, and penalties, however, the tax rate effect 

is ambiguous. The effects of the latter depend on attitudes toward risk and the penalty 

structure. If the penalty is based on the amount of taxes understated, then lower taxes may 

lead to more evasion (Yitzhaki, 1974) and Graetz and Wilde (1985). Pencavel (1979) and 

Pestieau and Possen (1991) extend this class of models by incorporating labor supply with nonlinear taxes and occupational choice, respectively. 
4

A game-theoretic literature examines several aspects of tax compliance. Erard and 

Feinstein (1994), who extend the work of Reinganum and Wilde (1986a and 1986b), formally 

incorporate honest taxpayers in their analysis. They find that policies designed to promote an 

increase in the proportion of honest taxpayers can have a beneficial impact on voluntary 

compliance. But the resulting increase in tax revenue may be offset to a substantial extent by a 

decline in enforcement yield. 

Alm, Jackson, and McKee (1992) add to a model of taxpayer compliance the provision 

of a public good financed with taxes subject to non-compliance. They show that policies 

designed to increase tax and audit rate uncertainty have a generally ambiguous effect on 

compliance. Using an experimental design with student subjects to simulate taxpayer 

responses, they find that the tax and audit rate response depends upon the presence or absence 

of a public good. When individuals perceive that they receive a public good in exchange for 

their taxes, uncertainty always lowers compliance. Last, but not least, Sansing (1993) 

examines the effect of information that helps the tax enforcement authority predict tax evasion 

on the strategic choices made by the taxpayer and the enforcement authority. He finds that 

such strategic behavior can increase tax evasion. 

In general the theoretical literature shows that tax rates have an ambiguous effect on 

compliance, depending upon taxpayer attitudes toward risk, audit selection criteria, among 

others. Unfortunately the empirical literature reports mixed results on the compliance effect of 

taxes as well. Using 1979 TCMP data, Clotfelter (1983), for instance, finds a negative 
5

correlation between marginal tax rates and income reporting compliance; while Feinstein 

(1991), using pooled 1979 and 1982 TCMP data, finds the opposite.3 

A number of significant differences in their empirical strategies prevent a direct 

comparison of their results. First, Clotfelter (1983) not only accounts for federal individual 

income tax rates, but also includes FICA, SECA and state income tax rates in his measure of 

the marginal tax rate. Feinstein (1991), consistent with much of the literature, restricts the tax 

rate measure to the ordinary federal income tax rate. Second, Clotfelter uses a single TCMP 

cross-section and Feinstein uses pooled TCMP data. Third, Clotfelter provides separate 

estimates for the self-employed; while Feinstein includes a dummy variable for the presence of 

certain sources of income.4 

The most significant difference between the two approaches, however, lies in their 

empirical models. Clotfelter estimates Tobit regressions on a variety of subsamples and 

employs a number of different definitions of the income gap. In an important contribution, 

Feinstein argues that even the most thorough audits, like the TCMP audit, can not uncover 

every dollar of unreported income. If the auditor’s ability to uncover unreported income varies 

with the level and/or source of income then failing to account for partial detection may result in 

biased estimates. In order to eliminate this potential source of bias he estimates a simultaneous 

model of partial detection and income reporting. Thus it is not clear which of these many 

3 The ambiguity of the compliance response to an increase in marginal tax rates is also observed 

for small corporations. Rice (1992) finds taxes have a small effect on income under reporting. 

4

 Joulfaian and Rider (1996) also include SECA rates. Although they focus on low income 

filers, their results are qualitatively similar to Clotfelter (1983). 
6

differences in their empirical implementation accounts for the difference in their results on the 

effect of taxes on reported income. 

A number of studies report that differences in the probability of detection account for 

the reporting disparities between wage and salary versus self-employment income. Clotfelter 

(1983) finds that the compliance pattern of sole proprietors (Schedule C filers) is considerably 

different from that of wage earners. Feinstein (1991) also finds that sole proprietors and 

farmers are less compliant than other taxpayers. More recently Erard (1992), using 1982 and 

1985 cross-sectional data, reports higher levels of income under reporting for Schedule C and 

F filers. Focusing on the reporting of incomes from various sources, Klepper and Nagin 

(1989) document that voluntary reporting compliance is lower for self-employment income 

than for wages and salary. In fact, they attribute the lower rate of income reporting by the selfemployed to the difficulty of uncovering unreported self-employment income. 

While there seems to be a consensus that the self-employed have a greater propensity 

to under report income, the literature does not provide much insight into why income reporting 

varies by source of business income. Furthermore, there is little acknowledgment that 

differential taxation of business income may play a role in influencing the observed pattern of 

reporting compliance. 

III. Description of the Data and Construction of the Variables 

The data used in this study are drawn from the IRS's Taxpayer Compliance 
7

Measurement Program (TCMP) pooled over the years 1985 and 1988.5 TCMP data are 

stratified random samples of individual tax returns subject to intensive line-by-line 

examinations. These data provide information as reported by the taxpayer and as corrected 

upon audit for most entries on IRS Form 1040 and the accompanying schedules. The 1985 file 

consists of about 49,000 records and reflect pre-TRA 86 law where the top statutory ordinary 

marginal tax rate is 50 percent. The 1988 TCMP file consists of about 54,000 records and 

reflects post-TRA 86 law where the top statutory marginal tax rate is 28 percent. The data are 

augmented by the age of the taxpayer, obtained from administrative records. 

We focus on noncompliance for three sources of self-employment income: 

proprietorship, farm, and rental real estate. We use two different measures of noncompliance, 

the income gap, defined as the amount of income understated, and the income gap ratio, 

defined as the fraction of income understated. In order to demonstrate the importance of 

differential taxation on compliance, our primary interest, we compute the applicable marginal 

federal individual income tax rate with and without the SECA rate. Two sets of variables are 

used to control for the pattern of noncompliance; a set of economic variables suggested by the 

theory and a set of variables to control for unobserved tastes and preferences for evasion and 

attitudes toward risk. The economic variables consist of income, tax rate, and audit rate. The 

taste variables include age and age-squared; family size; regional dummy variables; and the 

presence of Schedules C and F on a tax return. In addition, we also include a dummy variable 

5 See Long (1992), for a critique of TCMP data. 
8

for 1988 to account for year specific effects. Below is a brief description of the variables and 

their construction. 

Income Gap: The income gap is measured as the difference between corrected and 

reported net business income by source in 1988 dollars.6 Proprietorship income is obtained 

from Schedule C; farm income from Schedule F; and rental income from Schedule E. Ideally, 

we would have included partnership income as well. However, we are unable to distinguish 

between partnership income subject to and exempt from the SECA tax. 

Income: Income is defined as corrected net business income by source. 

Marginal Tax Rate: We compute the ordinary marginal tax rate by adding $100 to 

corrected net business income by source. The computation of the ordinary tax rate, denoted 

J1, incorporates virtually all of the complexities of the tax code including the earned income tax 

credit which produces negative tax rates. We also compute a second variant of the tax rate, J2, 

which accounts for the self-employment tax, or J2 = J1 + SECA.7 

In 1985, the statutory SECA rate was 0.141, partially offset by a credit of 0.023 

resulting in a rate of 0.118, on combined wages and self-employment earnings under $39,600. 

In 1988, the SECA rate was 0.1502, or 0.1302 after applying a credit of 0.02, on combined 

wages and self-employment earnings under $45,000. 

6 A distinction should be maintained between corrected income and true income. Even if a 

return is audited, IRS examiners may not be able to uncover every dollar of unreported 

income. 

7

 As discussed below we also estimate a number of specifications that account for state income 

tax rates. 
9 

Probability of Detection: Following Erard (1992) we impute the taxpayer’s objective 

audit probability using a Probit equation estimated from tax year 1987 examination information 

provided in the 1988 TCMP file. The explanatory variables include adjusted gross income 

(AGI) and indicator variables for the presence on the taxpayer’s return of proprietorship, farm, 

partnership, rental income, S-corporations, and capital gains. The estimated Probit equation is 

then used to impute a taxpayer’s audit probability in 1985 and 1988 using the taxpayer’s 

reported attributes. 

There are several potential limitations to this approach. First, in principle we should 

use the taxpayer’s perceived probability of detection which is not known in TCMP data. 

Instead we use an estimate of the taxpayer’s objective audit probability as a proxy, albeit 

imperfect, for the perceived detection rate. This measure is imperfect because audit 

probabilities and detection probabilities are not identical and perceived probabilities may differ 

from objective probabilities. And, second, since the IRS uses non-random audit selection 

criteria, the taxpayer’s objective audit probability depends, in part, on the taxpayer’s income 

compliance. Thus the audit probability is potentially endogenous to the size of income not 

reported. We believe that this problem is mitigated somewhat because the regressors in the 

audit probability model are likely to be exogenous. Finally, we would have preferred to use 

pre-1985 examination history to impute the audit probabilities for the 1985 returns in the 

sample. Unfortunately such data are not available in our version of the 1985 TCMP file. 

Demographic Variables: We employ a number of demographic variables to control 

for otherwise unobservable preferences, such as tastes and attitudes toward risk. These 
10

include age, family size, region, and two dichotomous variables; one for the presence of a 

Schedule C and the other for the presence of Schedule F on the return. Family size is 

computed using filing status and the number of dependents. Regional dummy variables are 

based on the zip code reported with the filer's address. We also include a dummy variable for 

1988 to account for changes in tax administration and TRA 1986. 

Description of the Sample 

We drop all observations with negative corrected income so that we can construct 

variables, such as the income gap ratio and natural logarithm of income. We also exclude joint 

returns to allow for precise measurement of the SECA rate. In order to compute the SECA 

rate for married filing joint returns information is required on the earnings of each spouse, 

which is not available in our data. The resulting sample consists of 8,341 taxpayers for a total 

of 9,023 observations with 5,976 representing proprietorship income, 1,090 farm income, and 

1,957 representing rental income.8 It is important to note that the unit of observation is the 

income gap by source and not the taxpayer. 

Table 1 provides sample means for selected variables. The average amount of income 

understated, reported on line 1, is $5,094 or 25 percent of the average business income of 

$20,123 in the sample. For proprietorship income, the average amount understated is $5,894 

or 26.6 percent of the average reported income, for farm income $7,377, or 30.2 percent, and 

$1,378, or 11.9 percent, for rental income. The average fraction of income understated, 

8

 Note that individuals may report more than one source of business income. The number of 

individuals with only proprietorship income is 5,502, only farm income 826, and 1,345 with 

only rental income, for a total of 7,673 taxpayers. Another 668 taxpayers have 2 to 3 sources 

of income, or an average of 2.02 sources. 
11

reported on line 2, is 82 percent and, except for farm income, is invariant to the source of 

income. The disparity in the average share of income understated simply reflects a number of 

outliers as suggested by the large standard deviation for farmers.9 Under the first measure, 

using the amount of income understated, the same compliance pattern is observed for 

proprietorship and farm income, with the most compliance observed for rental income. In 

contrast, and under the second measure, using the fraction of income understated, 

proprietorship and rental incomes exhibit the same pattern of compliance, with the least 

compliance observed for farm income. 

The average ordinary federal income tax rate is 19.9 percent for the sample. 

Proprietorship and farm incomes face an average marginal rate of approximately 19 percent 

which is lower than that faced by recipients of rental income (24.2 percent). In part, the lower 

tax rates reflect the effect of the earned income tax credit which creates negative rates for 

some proprietors and farmers. The average SECA rate is 8 percent for the sample; and 

approximately 10 percent for proprietorship and farm income, and zero for rental income. The 

average combined ordinary and SECA tax rates are 28 percent for the sample. 

The predicted probability of an audit is 4.6 percent for the sample and ranges from 4.3 

to 5.7 percent for the separate sources of income. Average business income is about $20,000. 

Finally, the average age is about 45 years old; and rental income recipients are older than 

proprietorship and farm income recipients. 

9

 The share of income understated may exceed 100 percent if an individual reports a loss 

instead of business profits. For example, the share of income understated for an individual 

with a profit of $100 who reports a loss of $100 is 200 percent. 
12

IV. Empirical Results 

Basic Statistics 

Table 2 provides the frequency and relative frequency of misreporting income for four 

classes of tax rates. Two measures of the tax rate are used in the analysis. The first panel of 

Table 2 provides the distribution of observations in our sample using a federal marginal income 

tax that excludes the SECA rate. In the second panel of Table 2 the marginal tax rate includes 

the SECA rate. 

Beginning with the first panel of Table 2, there is no clear pattern in the relative 

frequency of evasion. If we exclude the J # 0 group, however, there is a negative relationship 

between the relative frequency and the tax rate. In contrast, a clear pattern is evident in the 

second panel, where the relative frequency increases with the tax rate. For example, focusing 

on the second column of the first panel, we see that 57 percent of the taxpayers with J # 0 

under report income, which is almost equal to the relative frequency for the J > 0.28 range. 

But when the definition of the tax rate is modified to include the SECA rate, as in the second 

column of the first panel of Table 2, 39 percent of the taxpayers in the J # 0 range under report 

income. This is substantially less than the relative frequency of 0.72 observed in the J > 0.28 

range. 

By including SECA in the measurement of the tax rate, a clear pattern of income 

reporting emerges: the probability of evasion is positively correlated with the tax rate. By 

ignoring the SECA rate one invariably misclassifies individuals. Referring to the second 

column of the first panel, which excludes the SECA rate, in the J # 0 we see that 13.5 percent, 

or 812 of the 5,931 observations in our sample under report self-employment income. In 
13

contrast after adjusting the tax rates to include the SECA rate, the frequency of understating 

income drops to 3.2 percent, or 191 of the 5,931 observations. 

Multivariate Analysis 

Now we turn to the multivariate analysis in order to shed further light on the influence 

of differential taxes on income reporting compliance among sources of self-employment 

income. Because a number of taxpayers in our sample correctly report income, as shown in 

Table 2, we resort to Tobit estimates in modeling the determinants of the size of income under 

reported. 

Table 3 provides Tobit estimates where the dependent variable is the natural logarithm 

of the income gap. Estimates are provided for two measures of the marginal tax rate. In 

equation 1 the marginal tax rate is the ordinary federal individual income tax rate: it does not 

account for the SECA rate. In equation 2, the marginal tax rate includes the SECA rate as 

well as the federal income tax rate. Otherwise the regressors are identical in equations 1 and 2. 

The means of the regressors are provided in the first column. 

Beginning with equation 1, which is reported in column 2 of Table 3, we find that the 

income gap varies with age, region, income, tax rate, and audit rate. The estimated audit rate 

coefficient is negative and statistically significant. The corresponding elasticity evaluated at the 

mean is approximately -0.70, implying that a 10 percent increase in the probability of an audit 

reduces the income gap by 7 percent.10 This result is qualitatively consistent with the 

10 All reported elasticities are calculated at the mean. 
14

theoretical and empirical findings on the audit rate effect. The estimated income coefficient is 

positive and statistically significant with an estimated elasticity of 0.80. 

Turning to the tax rate effect, the estimated coefficient is negative and statistically 

significant, with a value of -1.029 and standard error (S.E.) of 0.511. The implied elasticity is 

-0.16, which means that an increase in the tax rate leads to a decrease in the amount of income 

under reported. An inverse relationship between the tax rate and income reporting does not 

contradict the predictions of theory, which generally report an ambiguous tax rate effect (see, 

for example, Allingham and Sandmo, 1982). This finding is also consistent with some 

empirical findings (Feinstein, 1991), but not all. 

In equation 2 of Table 3, in which the tax rate variable accounts for the SECA rate, the 

estimated coefficients are similar to those obtained with equation 1 with one notable exception. 

The tax coefficient is positive and significant rather than negative and significant. The 

estimated coefficient is 2.222 (S.E. = 0.527), which suggests that higher marginal tax rates 

increase the under reporting of self-employment income. The corresponding elasticity is 0.48. 

This sign reversal is consistent with the descriptive statistics reported in Table 2.11 In 

the first panel of Table 2 the tax rate measure omits the SECA tax. Comparing the upper half 

of the first panel with the lower half implies that under reporting of income is higher for 

11 One would expect that a progressive income tax, like the federal individual income tax, 

would give rise to a high degree of linear correlation between income and the tax rate. The 

resulting multicollinearity may lead to unstable estimates that switch sign and significance. To 

address this concern we estimate the simple correlation coefficients for the tax rate with 

respect to income. They are rln(y), t1 = 0.32, rln(y), t2 = 0.42, ry, t1 = 0.28, and ry, t2 = 0.19; where 

the subscripts y, ln(y), t1, and t2 indicate self-employment income; the log of self-employment 

income; ordinary individual income tax rate; and the ordinary federal income tax rate plus the 

SECA rate. Not surprisingly, they are positively correlated, but the correlation is low. 
15

taxpayers facing lower ordinary tax rates than for those facing higher rates. This could lead 

one to suppose that tax rates and under reporting are negatively correlated. In the second 

panel of Table 2 we include the SECA rate and then we see a clear pattern between higher 

marginal tax rates and the incidence of income reporting. 

The relative frequency distributions in Table 2 and the estimated tax effects in Table 3 

are telling the same story: the effect of taxes on income reporting by small businesses is 

influenced by differential taxation of self-employment income. 

Alternative Specifications 

Next we explore the robustness of this finding to alternative specifications of the 

empirical model. We discuss, in turn, the effect of using an alternative measure of reporting 

compliance as the dependent variable, estimating the model with weighted data, accounting for 

state tax rates in both tax rate measures, and controlling for the balance due (refund due or 

amount owed) effect. 

Rather than using the log of the income gap as the measure of reporting compliance on 

the left-hand-side, we use the income gap ratio, or the fraction of income understated, by type 

of activity. This specification resembles the one employed by Klepper and Nagin (1989) using 

aggregate data. The estimates for this specification are reported in Table 4. In general the 

results are similar to those reported in Table 3. When the tax rate measure does not account 

for the SECA rate, the estimated tax rate coefficient is 1.698 (S.E. = 1.089), as reported in 

column 2 of Table 4. In this case the estimated tax rate coefficient is not statistically 

significantly different than zero. When we account for the SECA rate in column 3 of Table 4, 

the estimated tax rate coefficient is 5.646 (S.E. = 1.810) and the implied tax elasticity is 0.75. 
16

In summary, when we account for the SECA tax the estimated tax effect is positive and 

statistically, as well as economically significant. 

In order to account for potential bias from the sampling design, we re-estimate the 

canonical specifications reported in Tables 3 and 4 using weighted data.12 The estimated tax 

rate coefficients are 0.127 (S.E. = 0.569) and 1.963 (S.E. = 0.581) when we use weighted data 

to re-estimate equations 1 and 2 of Table 3, respectively. Similarly for equations 1 and 2 in 

Table 4, the estimated tax rate coefficients are 0.408 (S.E.=0.844) and 1.735 (S.E.=0.824), 

respectively. Using weighted data renders the estimated tax rate coefficients on the ordinary 

tax rate insignificant, but the specifications that account for the SECA rate remain positive and 

significant. 

Since many states tie their tax policy and administration to the federal system, federal 

reporting compliance may be influenced by state rates.13 Therefore, we include state income 

tax rates in both tax rate measures. After accounting for state income tax rates the estimated 

tax coefficients corresponding to equations 1 and 2 of Table 3, are slightly smaller with 

reported values of -1.205 (S.E. = 0.477) and 1.609 (S.E. = 0.494), respectively. Likewise, the 

estimated coefficients are 1.247 (S.E. = 1.688) and 4.707 (S.E. = 1.696) for equations 1 and 2 

12 The probability of being included in the sample depends on income. If the income gap and 

tax rates are correlated with income, then using unweighted data may lead to biased estimates. 

13 After accounting for the state rates, the means of the two tax rate measures are 0.222 (S.D. 

= 0.141) and 0.302 (S.D. = 0.132), respectively. In addition to the SECA rate, and using 

pooled data which straddle TRA86, state tax rates may also help reduce any identification 

problem. 
17

of Table 4, respectively. Including state tax rates has a small effect on the estimated 

coefficients. 

Finally, there is some evidence (Jackson and Spicer, 1986; Yaniv, 1988; Chang and 

Schultz, Jr., 1990; and Martinez-Vazquez et al., 1992) that tax evasion is influenced, at least in 

part by the amount of the final payment or refund, which is referred to as the balance due 

effect. Accounting for this potential effect is particularly important in this context because the 

SECA tax is not due until April 15th, the deadline for making final payments for federal 

individual income taxes. Thus taxpayers subject to the SECA tax may be more likely to have a 

balance due when they file their federal income tax. In this case, the SECA rate may be merely 

picking up the balance due effect. In order to distinguish between the tax rate effect and the 

balance due effect we include a balance due variable in our four canonical specifications. We 

define this variable as the difference between the taxpayer’s corrected tax liability less any 

estimated tax payments, withheld taxes, and payments with filing extensions. 

The estimated coefficients on the balance due variable are positive and significant in the 

canonical specifications. In the specification corresponding to equation 2 of Table 3, for 

example, the estimated balance due coefficient is 0.97@10-5 (S.E. = 0.26@10-5). In other words, 

the greater the balance due the lower the rate of voluntary reporting compliance. Nevertheless, 

the estimated tax rate coefficients are invariant to the inclusion of the balance due variable. The 

estimated tax rate coefficients corresponding to the two specifications reported in Table 3 are -

1.306 (S.E. = 0.511) and 2.027 (S.E. = 0.527) for equations 1 and 2, respectively. Likewise 

for Table 4, the estimated tax rate coefficients are 1.314 (S.E. = 1.817) and 5.313 (S.E. = 
18

1.817), respectively. Accounting for the balance due effect does not appear to change our 

substantive findings. 

V. Conclusion 

This paper examines the pattern of income reporting by small businesses. Using pooled 

TCMP data for tax years 1985 and 1988, and taking advantage of the SECA tax, we focus on 

the effect of tax rates on the misreporting of proprietorship, farm, and rental income. 

Both the frequency distribution, reported in Table 2, and multivariate analysis 

demonstrate that taxes play an important role in explaining differences in voluntary reporting 

compliance among sources of self-employment income. We also demonstrate the importance 

of correctly measuring major features of the tax system. In particular, failure to incorporate 

the SECA rate risks biasing the estimated effects of tax rates. Our finding supports the notion 

that under reporting of income is positively correlated with tax rates. 
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20

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Table 1 

Sample Means for Selected Variables 

(standard deviations in parentheses) 

Variable All Source of Income 

Proprietorship Farm Rental 

Income gap 5,094 

(28,636) 

5,894 

(24,368) 

7,377 

(54,402) 

1,378 

(12,049) 

Income gap ratio 0.817 

(14.547) 

0.700 

(6.712) 

1.719 

(38.011) 

0.672 

(5.795) 

Ordinary tax rate (J1) 0.199 

(0.132) 

0.186 

(0.123) 

0.192 

(0.134) 

0.242 

(0.143) 

Ordinary + SECA tax 

rates (J2) 

0.279 

(0.123) 

0.288 

(0.111) 

0.293 

(0.134) 

0.242 

(0.143) 

SECA tax rate 0.080 

(0.060) 

0.102 

(0.049) 

0.101 

(0.051) 

0 

0 

Audit rate 0.046 

(0.035) 

0.043 

(0.032) 

0.047 

(0.035) 

0.057 

(0.041) 

Business income 20,123 

(52,360) 

22,142 

(54,165) 

24,397 

(59,944) 

11,577 

(39,838) 

Age 45.3 

(16.7) 

41.1 

(13.9) 

46.3 

(18.6) 

57.3 

(17.6) 

Observations 9,023 5,976 1,090 1,957 
Table 2

Frequency and Relative Frequency of Income Reporting by Tax Rate

Tax Rate excluding SECA Tax Rate including SECA 

Tax Rate (J) Correct 

Income 

Income 

Understated 

Correct 

Income 

Income 

Understated 

J # 0 

0 < J # 0.15 

0.15 < J # 0.28 

J > 0.28 

Total Observations 

610 

(0.43) 

827 

(0.28) 

858 

(0.33) 

797 

(0.40) 

3,092 

812 

(0.57) 

2,167 

(0.72) 

1,867 

(0.66) 

1,085 

(0.60) 

5,931 

301 

(0.61) 

566 

(0.40) 

739 

(0.39) 

1,486 

(0.28) 

3,092 

191 

(0.39) 

833 

(0.60) 

1,167 

(0.61) 

3,740 

(0.72) 

5,931 
--

--

Table 3 

Tobit Estimates of the ln of the Business Income Gap 

(standard errors are provided in parentheses) 

Variable Mean Equation 1 Equation 2 

Constant 1.000 -8.626 

(0.534) 

Dummy 1988 0.569 -0.411 

(0.116) 

Age*10-2 0.453 15.342 

(1.917) 

Age2*10-4 0.233 -18.054 

(1.928) 

Family size 1.245 -0.134 

(0.091) 

South 0.314 -0.130 

(0.154) 

West 0.251 0.217 

(0.163) 

North 0.183 0.053 

(0.179) 

Ordinary tax rate (J1) 0.199 -1.029 

(0.511) 

Ordinary + SECA tax 0.279 --

rates (J2) --

Audit rate 0.046 -28.380 

(1.856) 

ln Income 8.764 1.118 

(0.040) 

Schedule F present 0.181 0.917 

(0.200) 

Schedule C present 0.735 2.349 

(0.185) 

F N.A. 5.060 

(0.051) 

Log likelihood -20,782 

Observations 9,023 

Positive Observations 5,931 

-8.515 

(0.531) 

-0.332 

(0.115) 

14.892 

(1.912) 

-17.657 

(1.924) 

-0.115 

(0.090) 

-0.119 

(0.154) 

0.247 

(0.163) 

0.032 

(0.179) 

2.222 

(0.527) 

-30.302 

(1.795) 

1.012 

(0.040) 

1.053 

(0.197) 

2.481 

(0.179) 

5.053 

(0.051) 

-20,775 

9,023 

5,931 
--

--

--

--

Table 4 

Tobit Estimates of the Business Income Gap Ratio 

(standard errors are provided in parentheses) 

Variable Mean Equation 1 Equation 2 

Constant 1.000 -12.171 -13.217 

(1.745) (1.775) 

Dummy 1988 0.569 0.131 

(0.428) 

0.168 

(0.427)

Age*10-2 0.453 30.154 

(7.183) 

29.607 

(7.182)

Age2*10-4 0.233 -37.504 

(7.253) 

-36.864 

(7.255)

Family size 1.245 -0.379 

(0.335) 

-0.352 

(0.336)

South 0.314 0.313 

(0.571) 

0.327 

(0.571)

West 0.251 1.034 

(0.604) 

1.056

(0.604)

North 0.183 0.484 

(0.663) 

0.444

(0.663)

Ordinary tax rate (J1) 0.199 1.698 

(1.809) 

Ordinary + SECA tax 

rates (J2) 

0.279 5.646

(1.810)

Audit rate 0.046 -44.174 

(7.057) 

-45.109 

(6.892)

Income*10-3 20.123 84.378 

(43.047) 

72.358

(42.647)

Schedule F present 0.181 2.971 

(0.743) 

2.935

(0.735)

Schedule C present 0.735 4.309 

(0.679) 

4.181

(0.668)

F N.A. 18.149 

(0.168) 

18.158 

(0.168)

Log likelihood -27,328 -27,324 

Observations 9,023 9,023 

Positive Observations 5,931 5,931