Nur_Tegin 2008

The B.E. Journal of EconomicAnalysis & Policy

TopicsVolume 8, Issue 1 2008 Article 18

Determinants of Business Tax Compliance

Kanybek D. Nur-tegin∗

∗Florida Atlantic University, [email protected]

Recommended CitationKanybek D. Nur-tegin (2008) “Determinants of Business Tax Compliance,” The B.E. Journal ofEconomic Analysis & Policy: Vol. 8: Iss. 1 (Topics), Article 18.

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Determinants of Business Tax Compliance∗

Kanybek D. Nur-tegin

Abstract

This paper provides empirical evaluation of a number of determinants of tax evasion by firms.The analysis includes both standard determinants, such as tax rates and probability of detection,and non-traditional factors, such as trust in government, compliance costs, and corruption. Firm-level survey data from 4,538 firms in 23 transition economies are analyzed. One of the mainfindings is that fighting corruption is more important in deterring tax evasion than conventionalmeasures.

KEYWORDS: tax compliance, tax evasion, determinants, transition economies

∗This paper has benefited from review and suggestions by Hendrik Van den Berg, James Schmidt,and Keith Jakee, as well as from comments by the participants of the seminar given at the Univer-sity of Nebraska-Lincoln, Department of Economics, Spring 2006. Special thanks to Hans JorgCzap for valuable ideas and continuous discussions. Finally, I wish to thank an anonymous refereefor very useful comments on the earlier version of this paper.



1. Introduction Tax evasion has long been recognized as a serious social malady. The extent of this problem is staggering. Franzoni (2000) cites studies that estimate evasion in the Western industrialized countries to be between five and 25 percent of the potential tax revenue and up to 30-40 percent for the less developed countries. Crocker and Slemrod (2004) note that in 1998 the IRS figure for corporate underreporting alone in the United States was $37.5 billion. The problem of such magnitude entails numerous harmful consequences. First, evasion directly affects the fairness of the distribution of the tax burden by reallocating it from evaders to honest taxpayers. Evasion may require higher and more distortionary taxes on reported income (Andreoni et al., 1998). Franzoni (2000) argues that effective taxation due to evasion may be regressive because higher income taxpayers are likely to have better opportunities to evade without being detected. Evasion also erodes government revenues, which leads to a decline in provision of public goods and weakens the government’s ability to reach its goals. Further, tax evasion diverts resources to unproductive activities, such as establishing financial subsidiaries to camouflage evasion (Slemrod, 2004). Moreover, evasion causes production inefficiencies when firms’ output and evasion decisions are not separable (see Goerke and Runkel, 2006, for conditions when non-separability may hold). Evasion is also frequently associated with firms moving into the underground economy, which exacerbates inefficiencies. For example, these firms can suffer from unenforceable contracts or inability to take advantage of economies of scale due to the need to stay small and invisible. Finally, according to Johnson et al. (2000), evasion tends to impede economic growth because it prevents the government from providing adequate market supporting institutions, infrastructure, growth of human capital, research and development, and so on. Dealing with the problem of tax evasion requires thorough understanding of the factors that affect it. To date, the literature in this area is relatively well-developed. However, according to Crocker and Slemrod (2004, p. 21), “nearly all of it pertains to [tax evasion by] individuals.” The lack of research on tax compliance by businesses is unfortunate, especially given the fact that the bulk of taxes in most countries is remitted by firms (McCaffery and Slemrod, 2004). Chang and Lai (2004, p. 344) lament that theoretical modeling in some areas of tax evasion by firms “is sterile and lacking in the literature.” Considering the scant theoretical literature on business tax evasion, especially on its determinants, the void in empirical research in this area is even greater (Chen and Chu, 2005; Cowell, 2003; Franzoni, 2000; Joulfaian, 2000; Slemrod and Yitzhaki, 2002). Among the few papers that focus on this area is Rice (1992), who examines a large sample of medium-sized U.S. corporations.

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Nur-tegin: Determinants of Business Tax Compliance



His results reveal a positive but insignificant relationship between evasion and the marginal tax rate. In addition, he discovers two contrasting effects of corporate profits on evasion. First, those firms whose profits are below the industry average may resort to evasion to improve performance. Second, firms with higher than average profits find it easier to underreport income without being detected. He also finds that tax evasion is lower in industries that are heavily regulated or publicly traded.

Kamdar (1997) relies on time-series data to uncover the effects of audits, penalties, and marginal tax rates on corporate compliance. He reports that corporate compliance responds positively to audits, but finds no evidence for the usual expectations that compliance is positively related to penalties and negatively related to marginal tax rates. Johnson et al. (2000) use firm-level survey data from five Eastern European countries and find a significant association between bureaucratic corruption and the firms’ underreporting of sales. Joulfaian and Rider (1998) also study tax evasion by small businesses. Their main conclusions are that evasion is higher with lower audit rates, higher incomes, and higher tax rates. Joulfaian (2000) examines corporate tax evasion behavior and finds that corporate compliance correlates with lower marginal tax rates, higher audit rates, and larger firm size. One interesting result is that corporations are more likely to evade taxes if their managers also evade personal income taxes.

The purpose of the present article is to complement the scant existing literature by empirically evaluating the factors that affect tax compliance of business enterprises. We use a number of theoretical models, reviewed below, to guide our estimation, because according to Cowell (2003, p. 1), “[n]o overall modeling framework can be expected to offer an all-encompassing story of the compliance problem.” These models are discussed in the section that immediately follows. Because our data comes from transition economies, section 3 is devoted to the tax evasion environment in these countries. Section 4 provides an overview of the methodology of estimation and section 5 offers a brief description of the data. Section 6 discusses the results, while limitations are addressed in section 7 and conclusions drawn in section 8.

2. Determinants of Tax Evasion in Theoretical Models

Traditional determinants

Myles (1995) presents an overview of the key formal models of tax compliance, the earliest of which were developed by Allingham and Sandmo (1972), Srinivasan (1973), and Yitzhaki (1974). The latter articles concentrate on tax compliance by individuals, while our task is to focus on the determinants of tax compliance by business enterprises. In one of the papers that analyze tax

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The B.E. Journal of Economic Analysis & Policy, Vol. 8 [2008], Iss. 1 (Topics), Art. 18



compliance decisions by competitive firms, Virmani (1989) augments the standard expected profit maximization problem for a risk-neutral firm by adding a cost of evasion and arrives at a surprising result that evasion may increase with the rise in penalties. He posits that firms respond to harsher penalties by lowering output in order to reduce the probability of detection, which may outweigh the higher cost of evasion due to the increase in the penalty rate, and thus lead to more evasion. He also suggests that when evasion is costless, small-scale firms choose to evade completely, regardless of the tax rate. Comparative statics by Cremer and Gahvari (1993) yield standard results that competitive firms reduce the proportion of their reported sales in response to higher taxes and lower audit probabilities.

Marrelli (1984) examines how the optimal level of total sales revenue reported for tax purposes by a monopoly responds to the changes in the government policy variables. He finds that while, as expected, the firm evades less with higher probability of detection and larger fines, the effect of higher tax rates is ambiguous. In addition, he shows that large firms evade proportionately less than small firms and that, under decreasing risk aversion, an indirect tax is evaded less than a profit tax of equal yield.

Wang and Conant (1988) also model a monopolist’s tax evasion under uncertainty and conclude that higher tax rates on profits, higher penalty rates, and higher probability of detection all lower the optimal level of tax evasion. Gordon (1990) notes that firms can evade taxes by selling their goods for cash and finds that a monopolist would charge a higher cash price, i.e. evade less, in response to higher penalties and audit probabilities. Higher tax rates, however, do not produce an unambiguous result in his model. “This reflects the interaction of two conflicting forces in the setting of the cash price – the desire to shift a higher tax forward … and the greater incentive to evade a higher tax by substituting towards cash sales …” Gordon (1990, p. 252).

Tax compliance behavior of oligopolies is investigated theoretically by Marrelli and Martina (1988). Here again, standard expectations that increased probability of detection and higher penalties reduce evasion are confirmed. They also find that firms that have a large enough market share tend to evade more with increasing collusion. Finally, they demonstrate that if absolute risk aversion is assumed to decline with income, the effect of tax rates on evasion is unambiguously negative for a profit tax and may also be negative for unit tax and ad valorem tax. Slemrod and Yitzhaki (2002) explain this counter-intuitive result by the absence of a substitution effect. They point out that increasing the tax rate has both income and substitution effects when the penalty for evasion is related to the amount of underreported income. For a taxpayer with decreasing absolute risk aversion, higher tax rates reduce income and make a less risky choice preferred. Thus, the income effect is always negative. The substitution effect

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from higher rates of taxes raises the relative price of consumption in the honest (non-evading) world, thereby giving incentives for taxpayers to evade more. If the penalty is instead related to tax understatement, then the substitution effect vanishes, and we are left with only the negative income effect.

In contrast to the above findings that higher tax rates may lead to lower tax evasion, Cowell (2003) predicts that higher tax rates have unambiguously positive effect on evasion. His conclusion rests on the supposition of risk-neutral behavior usually assumed for corporations whose shareholders normally hold diversified portfolios. Risk-neutrality assumption for firms, however, may not be always applicable, particularly when the decisions on evasion depend more on the managers, rather than the owners, and when firms are small enough to have individual taxpayer characteristics. Sandmo (2005, p. 659) states that the results depend “crucially on who is penalized, the corporation or the manager,” and notes that the effect of marginal tax rates on evasion is still unclear.

Non-traditional determinants

In addition to the standard deterrents of tax evasion, namely the probability of detection, size of penalty, and tax rates, a number of more recent theoretical inquiries have been devoted to the study of alternative factors, such as trust in government, taxpayer morality, social norms, corruption, and supply of tax evasion opportunities. Here again, the greater part of this literature focuses on individual tax compliance. While it is unsurprising that such non-traditional factors, as trust and morality, are examined in the context of an individual taxpayer, we would argue that their relevance should be extended to at least sole proprietorships and partnerships.

McCaffery and Slemrod (2004) note that real world data suggest that tax evasion is much lower than what is predicted by traditional models. They cite several papers that may explain this divergence by looking at non-traditional determinants of tax compliance. For example, Andreoni, Erard, and Feinstein (1998) distinguish three classes of factors in this context: moral rules and sentiments, taxpayers’ perception of fairness of the tax code, and evaluation of government’s effectiveness in the provision of public goods and services. Frey (1997) maintains that harsher tax enforcement leads to lower voluntary compliance. According to Falkinger (1995), evasion declines with an increase in perceived equity. In Bordignon’s (1993) model, taxpayers weigh their private consumption against publicly provided goods and services. Taxpayers evade if they think that the system is unfair due to an insufficient level of public goods, evasion by other taxpayers, or an unfair tax schedule.

Alm et al. (1992) set up laboratory experiments to study how compliance responds to uncertainty about tax policies and enforcement, which may stem from

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the lack of information, frequent changes and ambiguity of the tax code, as well as non-uniform training of tax auditors. They argue that uncertainty induces taxpayers to report more income. Franzoni (2000, p. 65) argues that “[h]igh compliance costs … not only tilt the ‘cost-benefit analysis’ towards evasion, but may also generate resentment, weakening taxpayers’ moral conscience or even prompting them to evade as a form of ‘punishment’ for the tax administration.”

Collaborative tax evasion between a seller and a buyer is analyzed by Chang and Lai (2004). They contend that higher penalties not only have the straightforward effect of deterring tax evasion, but may also create incentives for the seller to persuade buyers to collude in tax evasion by offering price discounts. They further note that (p. 344) “[o]nce this perverse effect outweighs the usual deterrent effect associated with raising fines for tax evasion, a more severe fine may result in higher rather than lower tax evasion.” Boadway et al. (2002) arrive at the same result by modeling the interaction between a seller and a buyer as a prisoner’s dilemma. They also find that a higher tax rate can lead to more evasion. Bilotkach (2006) is a game theoretic approach showing how corruption affects tax evasion. In his Nash equilibrium, firms may underreport profits when the government officials’ best actions are to take bribes. Chen and Chu (2005) model corporate tax compliance and show that corporate owners may exhibit better compliance because sanctioning tax evasion may result in an excessive transfer of control to the managers of the corporation.

For the reader’s convenience, Table 1 summarizes the effects of various determinants of tax evasion predicted in the foregoing theoretical literature. These factors are used to help guide the ensuing empirical analysis.

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Table 1. Summary of Predicted Effects of Various Determinants on Tax Evasion in Theoretical Models. Traditional determinants Higher Effect on evasion Source Audit rate Negative Cremer & Gahvari (1993), Marrelli (1984), Wang &

Conant (1988), Gordon (1990), Marrelli & Martina (1988).

Penalty rate Negative

Positive

Marrelli (1984), Wang & Conant (1988), Gordon (1990), Marrelli & Martina (1988). Virmani (1989).

Tax rate Ambiguous Various. Firm size Negative

Positive Virmani (1989), Marrelli (1984). Marrelli & Martina (1988).

Non-traditional determinants Higher Effect on evasion Source Equity/fairness Negative Falkinger (1995), Bordignon (1993). Uncertainty Negative Alm et al. (1992). Compliance costs

Positive Franzoni (2000).

Corruption Positive Bilotkach (2006). Corporate control

Positive Chen & Chu (2005).

3. Tax Compliance in Transition Economies

The empirical analysis in this paper is based on micro-level data from the countries of the former Soviet Union and Eastern Europe, often called transition economies. The choice of the region is primarily data driven as the study became possible when firm-level survey data that spans thousands of firms over 27 countries was made available in 2002 by the European Bank for Reconstruction and Development (EBRD) and the World Bank. For more accurate analysis, features specific to transition economies need to be taken into account. We briefly highlight such features.

First, certain non-traditional determinants of business tax compliance are particularly relevant for transition economies. For example, social norms and trust in government in transition economies may exert an especially powerful effect on the general attitude towards paying taxes. According to Kornai (1990, p. 118-119)1:

People in general consider it a laudable act, rather than something to be ashamed of, if someone defrauds the state, appropriates its wealth, or

1 Quoted in Pirttilä (1999).

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shuns its obligations. Those who refrain from this kind of behaviour are seen as dupes… Consequently, when we contemplate budget revenues we should be prepared to face the fact that many citizens will try hard to dodge taxes.

Complexity of the tax system may also be a particularly important determinant of tax evasion in these countries because, as indicated by Martinez-Vazquez and McNab (2000), one of the legacies of communism was that people and firms were unfamiliar with the new market concept of “paying taxes.” Furthermore, the effect of corruption on tax compliance in transition economies is likely to be significant. According to Friedman et al. (2000), businesses go underground not necessarily to evade taxes but to escape from bureaucracy and corruption. The authors claim that firms might accept paying taxes at reasonable levels, but have less tolerance with constant arbitrary and extortionate demands. To illustrate, they quote a Western businessperson who comments on doing business in Russia:2

It doesn’t matter who it is: fire inspector, zoning committee member, mayor for that region, anybody can come and shut you down in five minutes. The fire guy could come, find fire hazards, and demand $50,000 into his overseas account. They know that if you shut down production for a few days, you’re going to lose a lot more. Thus, Friedman et al. (2000) argue that foreign businesses that encounter

such hostile environment decide to locate operations elsewhere in the world, while local firms have no choice but to go underground. Pirttilä (1999, p.7) asserts that “fighting corruption may in fact be one of the most important measures in reducing tax evasion in transition economies.” An additional factor that may improve tax compliance in transition economies is better tax administration (Mitra and Stern, 2003, and Schaffer and Turley, 2000). Tanzi and Tsibouris (2000, p. 4) note that prior to the transition “there was no budget office, budget law, or treasury in these economies.” In other words, market reforms introduced an unprecedented number and diversity of economic agents, and the state suddenly found itself inadequate to the task of administering collection of tax revenues. Finally, varying rates of progress with tax reforms across transition economies can be expected to affect compliance.

2 The original source is Wilson (1996).

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4. Empirical Method

As noted, the main objective of this paper is to determine the effect of various factors on the degree of tax evasion by firms. The relationship can be summarized in a linear model as (1) iii xy εβ += ' , where the dependent variable yi represents the response of firm i to the BEEPS question 58 (see section 5 for the description of the data), x is the vector of explanatory variables, and εi is the disturbance.3 The nature of the dependent variable proxied by the survey question, which asks firms to reveal delicate information on the degree of tax evasion by their peers (and, implicitly, themselves), raises concerns for a possible sample selection bias. It is plausible that at least some observations among those that indicated that exactly 100% of sales were reported for tax purposes,4 i.e. full tax compliance, belong to the firms that know that tax evasion happens, and might even evade themselves, and yet maintain that they are not aware of any tax evasion taking place at all.

Since sample selection models are more frequently described with the dependent variable censored at zero, rather than some other constant, we have transformed BEEPS question 58, from a measure of compliance to a measure of evasion by subtracting the percent of sales reported for tax purposes from 100. Thus, the absence of evasion is represented with a zero. From this perspective, a possibility of sample selection bias may arise when we assume that all zeros observed for the dependent variable are reported by firms truthfully. Of course, there is no direct way to determine whether a firm was honest about the degree of tax evasion. But we can presume that firms’ truthfulness about the degree of tax evasion can be evaluated on the basis of their answers to a question about a phenomenon that is arguably more generally known, i.e. whether these firms were willing to admit the occurrence of corruption of a type related to tax evasion. In other words, if we can determine that the firm was not honest about its view on the existence of bribery to evade taxes by rejecting its existence, then we can say that the firm was not honest about the degree of evasion as well.5 The rest of the firms are “off the hook.” The selection bias becomes an issue when misrepresentation by dishonest firms of their views is systematic in the direction of creating too many non-true zeros. If, however, this misrepresentation is

3 The underscore is used throughout the paper to signify that the variable is a vector. 4 Note that approximately 54% of all observations on the dependent variable indicate full compliance. 5 Of course, the implicit assumption here is that bribery of tax authorities is an omnipresent phenomenon, and everyone knows about it. Thus, denying the existence of this bribery is an indication of dishonesty. We believe that this assumption is not unrealistic for transition economies.

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random, i.e. if dishonest firms are just as likely to name any other number as they are to name a zero, then selection bias is not present (Breen, 1996).

The usual method of estimating regressions with potential sample selection bias is a two-stage estimating procedure proposed by Heckman (1976). The model, often called heckit, can be written as

(2) iii

iii

uzh

xy

+=

+=

γεβ

'*

' ⎟

⎟⎠

⎞⎜⎜⎝

⎛⎥⎦

⎤⎢⎣

⎡⎥⎦

⎤⎢⎣

⎡1

,0~2

ρσρσσε

Nui

i ,

where the bottom expression is called the selection equation.6 Here, the values in vector y are believed to be true representations of firms’ views only when the variable hi* in the selection equation crosses a specified threshold, say zero. In this paper, hi* can be understood as firms’ degree of confidence in not being prosecuted for revealing their true beliefs. In other words, if a firm’s fear of prosecution is not overcome, hi* ≤ 0, then the corresponding yi is false. However, hi* itself is not directly observed. Rather, a dichotomous variable hi is observed with the property that

0*0*

,0,1

≤>

⎩⎨⎧

=i

ii h

hifif

h .

The variable hi, which denotes an acknowledgement by the respondent that bribery to evade taxes occurs, is constructed from BEEPS question 56g (see Table 3). Thus, the observations for the degree of evasion (BEEPS question 58) are taken as true values only when BEEPS question 56g is answered as “seldom” or more.

The model in (2) can be rewritten as (3) iiii uxy ηρσβ ++= ' . The conditional expectation of yi, given that true yi is observed, is

(4) [ ])'()'(

'0*γγφ

ρσβi

iiii z

zxhyE

Φ+=> ,

where )'()'(

γγφ

i

i

zz

Φ = E[ui| hi* > 0] is the so-called inverse Mills ratio.

The algorithm of the two-step heckit model first acquires an estimate of γ by probit from the selection equation in (2). This estimate of γ is then used to calculate the inverse Mills ratio. The latter is then inserted as an additional explanatory variable into the main regression, which assumes the form of equation (4) with an appended residual; this permits estimation of β and σ consistently with ordinary least squares. Stolzenberg and Relles (1997) note that vector zi may contain the same variables as vector xi or may even be identical to it.

6 Baltagi (1998) notes that normalizing the variance of ut to 1 is not restrictive.

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However, Breen (1996) cautions that if zi is equal to xi, then the parameters of the main equation are identified only because of the nonlinearity of the probit part. To avoid relying solely on this source of identification, it is recommended that an exclusion restriction is added; this is a variable that affects the probability of being selected at the probit stage, but has no effect on the main dependent variable, yi. According to Wooldridge (2005), xi must be a strict subset of zi.

Davidson and MacKinnon (1993) note that equation (4) not only provides an estimation technique, but also a test for sample selectivity bias. The coefficient on the inverse Mills ratio in (4) is ρσ. Since σ ≠ 0, the null hypothesis that ρ = 0 can be tested using the ordinary t-statistic of the coefficient. If the null cannot be rejected, then we may reasonably assume that a sample selectivity problem is not present in the given data set and carry on with estimation using the least squares method.

A much less commonly used alternative to the heckit model for estimating regressions with sample selection bias is the method of maximum likelihood. Wales and Woodland (1980), who consider competing methods of estimation of models with possible selection bias, suggest that the maximum likelihood method is preferable because it is not only consistent, but also more efficient than Heckman’s two-stage estimation.7

To derive the likelihood function we note that the probability that a particular observation on the main dependent variable, yi, is true can be described as (5) )'Pr()'Pr()0*Pr( γγ iiiii zuzuh ≤=−>=> .

Assuming normal distribution for the errors in the selection equation with mean equal to zero and σu = 1 (see footnote 6), the above expression can be written as the cumulative standard normal distribution function, )'( γizΦ . Then, the probability that an observation is false, hi* ≤ 0, is equal to )'(1 γizΦ− .

The likelihood function of this model is (6) )0*()'())'(1( 1

10>ΦΠΦ−Π= −

iiii hyzzL φγσγ ,

where 0

Π and 1Π denote the products over the observations for which hi* ≤ 0 and

hi* > 0, respectively, and )0*( >ii hyφ is the conditional density function of yi

given hi* > 0. Finally, according to Breen (1996), the log-likelihood function can be expressed as

(7) 221210

)'(2

12

1log))'(1log(log βσπσ

γ iii xyzL −Σ−Σ+Φ−Σ=

7 We still use the heckit model in our estimation for comparison and because it offers a convenient test for sample selectivity bias.

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⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

−

⎟⎟⎠

⎞⎜⎜⎝

⎛ −+

ΦΣ+2

121 )1(

')'(

logρ

σβ

ργ iii

xyz

.

Davidson and MacKinnon (1993) and Breen (1996) point out that the residuals in this context are likely to be heteroskedastic and therefore suggest using a heteroskedasticity-consistent covariance matrix estimator.

Estimation of the above models was performed using Stata 9 and EViews 6. 5. Data The empirical analysis was carried out using data from a 2002 Business Environment and Enterprise Performance Survey (BEEPS II). The data spans 6,367 firms in 27 transition economies and was gathered by the EBRD and the World Bank. Table 2 provides a comprehensive list of countries included in the BEEPS II survey. Table 2. List of Countries Represented in BEEPS II. Albania Georgia Romania Armenia Hungary Russia Azerbaijan Kazakhstan Serbia and Montenegro Belarus Kyrgyz Republic Slovakia Bosnia and Herzegovina Latvia Slovenia Bulgaria Lithuania Tajikistan Croatia Macedonia Turkey Czech Republic Moldova Ukraine Estonia Poland Uzbekistan

Bosnia and Herzegovina, Macedonia, Serbia and Montenegro, and Turkey

were excluded from consideration in our analysis, primarily due to the lack of data on some key non-survey independent variables. Observations that had no entries for the dependent variable were also left out. Thus, the final sample was comprised of 4,538 firms in 23 countries. Missing entries in explanatory variables were substituted with the means of the remaining observations (Greene 2003, p. 60). A similar survey, BEEPS I, was conducted in 1999. Unfortunately, the data on the potential dependent variable in BEEPS I turned out flawed and unusable. Statistical information from other sources was used as well (see Table 3).

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Table 3 provides a description of the variables used in estimation, as well as their sources. The dependent variable, BEEPS II question 58, was modified to represent tax evasion instead of tax compliance by subtracting the percent of sales reported for tax purposes from 100. The list of regressors in Table 3 was compiled on the basis of Table 1, as well as the factors specific to tax compliance behavior in Eastern Europe and the former Soviet Union.

The choice of taxes used in this paper, which includes social security contributions by employers (SST), standard value added tax (VAT), and corporate income tax (CIT), was motivated primarily by two considerations: the share of these taxes in the government budget revenues and the availability of reliable cross-country data. According to the tax structure figures presented in Table 4, these three taxes made up about two-thirds of the total tax revenues in transition economies. The tax rates used in estimation are presented in Table 5.

6. Results

Referring to the above-described test for sample selection bias based on the t-statistic of the coefficient on the inverse Mills ratio, Wooldridge (2005, p. 621) remarks that “[i]t often makes sense to test for sample selection using the previous procedure…” Following this recommendation we run the two-step heckit and present the results in Table 6. BEEPS II question 53a (see Table 3 for description) was used as an exclusion restriction. It serves as a proxy to measure firms’ self-confidence, which is thought to affect their willingness to tell the truth about tax related corruption.

The item of primary interest in Table 6 is the coefficient on the inverse Mills ratio (IMR), which is not significant at the five percent level. Hence, we tentatively conclude that sample selection bias is not an issue because there is no evidence of systematic differences between the observations that contain zeros and the rest of the observations. However, the evidence against sample selection bias is not particularly strong since the IMR coefficient is significant, only at the ten percent level. For this reason, we show the results of estimating the model by all three methods: heckit, maximum likelihood, and least squares. The results estimated by the latter two methods are presented in Table 7 and Table 8, respectively. We are particularly interested in comparing the maximum likelihood and the least squares results because if we are concerned about a possibility of incorrectly rejecting the presence of a sample selection bias, then the maximum likelihood estimation is preferred to the two-step heckit. There are four noteworthy differences among the estimates in Table 7 and Table 8: the least squares coefficient on Comp is smaller, but slightly more significant; the coefficients on Enforce and Fair become larger in magnitude and more significant under the least squares estimation; and the coefficient on Owner becomes

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significant and reverses its sign under the least squares estimation. As for the rest of the results, the magnitude and statistical significance of parameter estimates are very similar. Therefore, most of the discussion below is based on the results obtained by least squares estimation with heteroskedasticity-consistent standard errors (Breen, 1996). Among the coefficients on tax rates, the only one that is significantly different from zero is the coefficient on the social security contribution by employers (SST). The result indicates that a ten percent increase in SST would lead to about two percent more revenues reported for tax purposes. This finding is consistent with a number of theoretical models discussed above that assume that agents are risk-averse, but contradicts other models. In general, the ambiguity of the effect of tax rates on compliance remains both theoretically (Sandmo, 2005) and empirically (Myles, 1995) unresolved.

The signs of the remaining estimated coefficients conform to expectations, and most exhibit a high degree of statistical significance. The coefficient on the complexity of the tax system or the cost of compliance, Comp, is statistically significantly different from zero at the one percent level. This finding is in agreement with the predictions suggested in sections 2 and 3. Admittedly, the coefficient is relatively small, indicating that a ten percent reduction in time spent by senior management in dealing with public officials and interpreting laws and regulations leads to about one percent more revenues reported for tax purposes.

The next two variables represent corruption. CorrTax is the type of corruption that is directly related to tax collection. Its coefficient is large and highly significant. It shows that a one-step increase in the level of bribery related to taxes results in about a three and one-half percent decline in revenues reported for taxes. CorrGen stands for corruption of a more general type. Its coefficient is also relatively large and significant at the one percent level. It suggests that for every additional percent of annual sales that firms have to pay in bribes to public officials, firms conceal almost as much in revenues that must be reported for tax purposes. These results clearly point to the strong effect of corruption on tax compliance. Together with the coefficients on tax rates, these findings give support to the assertion by Friedman et al. (2000) that firms are much more burdened by corruption than by statutory tax liabilities per se.

One might be inclined to interpret the strong effect of tax-related corruption (CorrTax) on tax evasion as an attempt of firms to bribe their way out of high tax rates, as opposed to the effort by firms to go underground as a result of harassment by corrupt public officials. However, tax rates by themselves do not seem to cause firms to underreport their revenues (given the coefficients on tax rates). Instead, it is more likely that firms are trying to escape from the extortion of additional and arbitrary “bribe taxes” by underpaid tax officials. Harassment of firms in many transition economies by frequent and unwarranted visits from

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tax authorities is a widely documented phenomenon (Johnson et al., 2000). Unlike official taxes, bribery and corruption are erratic in nature, which introduces a great deal of uncertainty in firms’ investments stemming from reasonable expectations of new predatory regulations and the impossibility to enforce “bribe contracts.” In addition, dealing with corrupt officials takes up substantial amount of time investment by the firms’ management (Kaufmann, 1997).

The degree of success with tax reforms seems to have a slight, but statistically significant positive effect on tax compliance. The t-ratio of the coefficient on the reform progress, RefProg, indicates significance at the one percent level. Hence, a four-step improvement in RefProg makes firms report one percent more of their revenues for taxes.

In agreement with theoretical predictions, more tax enforcement by auditing reduces the amount of tax evasion by firms. The coefficient on Enforceis significant at the five percent level, but not very large in magnitude.

The better tax administration variable, TaxAdm, does not have a statistically significant coefficient. It is likely that its proxy was not able to properly reflect the quality of tax administration, which is rather difficult to measure in general.

The estimation results provide grounds to assert that smaller firms tend to comply with taxes to a lesser degree. The coefficient of the dummy for smaller firms is positive, sizable in magnitude, and statistically significant at the one percent level. Apparently, this result reflects the fact that it is easier for small firms to become “invisible.” Wallace (2002) maintains that tax administrators are more likely to go after large enterprises due to a larger potential revenue payback, which effectively relaxes audit pressure for small firms. In addition, Wallace states that even if small start-up firms act in good faith, compliance with a complex tax system might be too expensive for them.

The coefficient on the firms’ trust in their government (Fair) is statistically significant at the one percent level, and it indicates that firms tend to evade taxes less if they are more likely to believe that the legal system in their country is fair and impartial.

Finally, judging from the coefficient on Owner, we can infer that privately-owned businesses are likely to evade more than state-owned enterprises, at the one percent level of significance. A reasonable explanation is the fact that the benefits from concealed revenues are more direct when businesses are in private ownership. The reader is reminded, however, that this result is found only with the least squares estimation.

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7. Limitations Several shortcomings should be noted. First, the quality of some of the proxies is not completely satisfactory. For example, the proxy on the enforcement of taxes, BEEPS Q74, may not be adequate. A conceivably better proxy would be constructed from data on the number and amounts of fines actually charged and prison sentences served for tax evasion, but such data for a large cross-section of countries are not readily available. Also, a more recent index of fiscal reform progress would be advantageous. The cumulative tax reform index supplied by Martinez-Vazquez and McNab (1997) covers only the initial years of transition between 1989 and 1996 and may not be suitable for the 2002 survey.8 Another important limitation emerges from the fact that the empirical analysis in this paper was based on survey data. In the words of Fries et al. (2003, p. 2), “[t]he data produced by the survey are based on perceptions of managers and are, therefore, subjective. Because they are subjective, the survey data are inherently ‘noisy’ in the sense of being subject to measurement errors.” Hellman et al. (2000, p. 6) also caution against perception bias, which they characterize as the difference between the answers of a respondent and an objective observer to a survey question. They clarify by saying that “[s]ome respondents could be said to have an inherent tendency to kvetch (i.e. to ‘complain, gripe, grunt, or sigh’) or to kvell (i.e. to ‘beam with immense pride and pleasure’).” This individual perception bias, they argue, raises standard errors of survey estimates.9 The presence of individual perception bias may help explain the low coefficient of determination in our estimation. As an experiment, many different specifications of the regression model, as well as artificial inclusion of a number of additional independent variables, were examined, but without much effect on the degree of fit. Supplementary research based on strictly non-survey data would be highly desirable.

Finally, there may be an unobserved heterogeneity problem as the error term is likely to be correlated across firms within a single country.10 This concern is partly alleviated since the country-constant variables capture a good part of this heterogeneity. One may add dummy variables for each country, but this could lead to a serious collinearity and the dummies would interfere with identification

8 Ebrill and Havrylyshyn (1999) also calculate a tax reform index, but only for the period of 1992 to 1998 and only for the former Soviet republics. 9 Note that Hellman et al. (2000) distinguish the individual-perception bias discussed here from the country-perception bias, which emerges from correlation of individual biases within a country due to some country-specific elements. They run a test for the BEEPS I data and find no evidence of country-perception bias. 10 I thank the anonymous referee for pointing this out.

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of the effect of some key country-constant variables, such as tax rates and reform progress.

8. Conclusion

The empirical results in this paper largely agree with the theoretical predictions summarized in Table 1 and suggestions in the literature on transition economies reviewed in section 3. Among the more interesting results is the finding that the degree of business tax evasion is not likely to be lessened by lower tax rates. This outcome is similar to the findings of Friedman et al. (2000), who analyze data from 69 countries and report that there is no evidence that higher tax rates are associated with larger unofficial economies. The parallel is clear given that the concealment of firms’ revenues partly constitutes the definition of an underground economy. Friedman et al. (2000, p. 17) offer an explanation of this phenomenon by arguing that “higher tax rates generate revenue that provides productivity enhancing public goods and a strong legal environment.” Thus, fewer firms are forced to migrate into the shadow economy.11

Another principal result is the strong empirical evidence that corruption has a negative effect on tax compliance. We find clear support for Friedman et al.’s contention that the primary reason firms go underground is uncontrolled bureaucracy and rampant corruption rather than the tax rates. In the context of transition economies, a frequently expressed opinion is that business tax rates are already reasonably low (Martinez-Vazquez and McNab, 1997, Engelschalk, 2003). However, in terms of corruption, these countries receive low ratings from Transparency International. Therefore, our policy recommendation, at least for transition economies, is that policymakers should devote a greater proportion of their energy and resources on fighting corruption, rather than on recurrent modifications of the tax system. Consistent with increasingly abundant evidence that reduced corruption entails significant economic gains, our paper provides confirmation of additional benefits from fighting corruption in the form of improved tax compliance.

11 But see Kolodko (1999), Mitra and Stern (2003), and Schaffer and Turley (2000) for research that shows that compliance can be improved by reducing tax rates.

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Table 3. Description of Variables and Their Sources. Variables Proxies Description Measurement yi Tax evasion (dependent variable)

Q58 BEEPS: “Recognizing the difficulties that many firms face in fully complying with taxes and regulations, what per cent of total annual sales would you estimate the typical firm in your area of business reports for tax purposes?”

0% to 100%.

hi Probit dep. variable

Q56g01 Constructed from BEEPS question 56g (see description below). A dummy variable with 0 entered for “never” and 1 otherwise.

SST VAT CIT Tax rates

SST VAT CIT

Social Security Tax – employer contribution (Martinez-Vazquez and McNab 2000), Value Added Tax (Mitra & Stern 2003), & Corporate Income Tax (Heritage Foundation)

%

Comp Complexity of tax systm/compliance cost

Q50 BEEPS: “What per cent of senior management’s time in 2001 was spent in dealing with public officials about the application and interpretation of laws and regulations and to get or to maintain access to public services?”

%

CorrTax CorrGen Corruption

Q56g Q55

BEEPS: Q56g. “Thinking now of unofficial payments/gifts that a firm like yours would make in a given year, could you please tell me how often would they make payments/gifts for the following purpose – to deal with taxes and tax collection.” Q55. “On average, what percent of total annual sales do firms like yours typically pay in unofficial payments/gifts to public officials?”

Q56g ranges from 1 (“never”) to 6 (“always”); Q55: 0% to 100%.

RefProg Reform progress

CRI Cumulative Tax Reform Index (Martinez-Vazquez and McNab 1997) CRI ranges from 3 (most reformed) to 17 (least reformed).

Enforce Enforcement

Q74 BEEPS: “Does your establishment have its annual financial statement reviewed by an external auditor?”

A dummy variable with 1 recorded for “yes” & 0 otherwise

TaxAdm Tax administr-n

Q80h BEEPS: “Can you tell me how problematic are these different factors for the operation and growth of your business – h) tax administration.”

A dummy with 0 for “minor obstacle” or less and 1 for “moderate obstacle” or more.

Size Firm size

S4s BEEPS screening question on the number of full-time employees. A dummy variable with 1 if the firm is small & 0 otherwise.

Fair Perceived fairness & trust in government

Q41a BEEPS: “How often do you associate the following descriptions with the court system in resolving business disputes? – a) fair and impartial.”

Q41a ranges from 1 (“never”) to 6 (“always”).

Owner Ownership

S2b BEEPS screening question on the legal organization of the company. A dummy with 1 if the firm is state-owned and 0 otherwise.

Excl Exclusion restriction

Q53a BEEPS: “How much influence do you think the following groups actually had on recently enacted national laws and regulations that have a substantial impact on your business? – a) you firm.”

Q53a ranges from 1 (“no impact”) to 5 (decisive influence”).12

12 Here and in Q41a, a few entries with “don’t know” were replaced with the average of the remaining observations.

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Table 4: Tax Structure in Transition Economies. Taxes on Income, Profits, & Capital Gains

Domestic Taxes on Goods & Services International Trade Taxes

of which: of which: of which:

Total Indiv. Corp.

Social Security & Payroll Total

Sales Turnover VAT Excises Total

Import duties

Export duties

Wealth & Property Taxes Other

23.2 12.9 9.3 25.7 40.8 28.5 10.8 5.1 4.8 0.2 2.3 2.9 Source: Table 5 from Mitra and Stern (2003).

Table 5: Tax Rates. Table 5: Tax Rates (continued). SST VAT CIT SST VAT CIT

Albania 32.5 20 25 Latvia 29 18 15 Armenia 32 20 20 Lithuania 30 18 15 Azerbaijan 37 18 25 Moldova 30 20 20 Belarus 53.8 20 30 Poland 23.65 22 19 Bulgaria 48.1 20 19.5 Romania 35 19 25 Croatia 20.6 22 20 Russia 38.5 20 24

Czech R. 35 22 28 Slovak R. 38 23 19 Estonia 33 18 0 Slovenia 15.9 20 25 Georgia 28 20 20 Tajikistan 38 20 30 Hungary 36 25 16 Ukraine 37.5 20 25 Kazakhstan 32 16 30 Uzbekistan 40 20 20 Kyrgyzstan 33 20 30

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Table 6. Two-stage Heckit Estimates of Coefficients.

Number of observations = 4538 Censored observations = 2420 Uncensored observations = 2118 Variable Coefficient t-Statistic*Selection Probit Equation. Dependent Variable: hi = Q56g01 Constant -0.3518 -1.25SST -0.0034 -1.17VAT -0.0282 -2.38CIT 0.0217 5.33Comp -0.0013 -0.74CorrGen 0.1250 16.17RefProg 0.0269 4.24Enforce 0.0505 1.19TaxAdm 0.4711 11.65Size 0.0129 0.28Fair -0.0983 -6.29Owner -0.3821 -6.16Excl 0.0649 2.68Corrected Tax Evasion Model. Dependent Variable: yi = Q58 Constant 40.5810 3.64IMR -12.2287 -1.88SST -0.2819 -3.13VAT -0.4318 -1.14CIT -0.1742 -1.14Comp 0.1315 2.71CorrTax 2.2093 4.77CorrGen 0.2209 0.62RefProg 0.3621 1.70Enforce -1.9233 -1.61TaxAdm -2.8568 -1.29Size 6.0720 4.62Fair -0.4953 -0.81Owner 2.6339 1.01

Wald statistic (23) 781.88

* Computed with heteroskedasticity-consistent standard errors.

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Table 7. Maximum Likelihood Estimates of Coefficients.

Number of observations = 4538 Censored observations = 2420 Uncensored observations = 2118 Variable Coefficient t-Statistic*Selection Probit Equation. Dependent Variable: hi = Q56g01 Constant -0.3384 -1.21SST -0.0034 -1.17VAT -0.0288 -2.44CIT 0.0216 5.36Comp -0.0014 -0.74CorrGen 0.1264 12.13RefProg 0.0269 4.32Enforce 0.0503 1.19TaxAdm 0.4690 11.65Size 0.0136 0.29Fair -0.0986 -6.41Owner -0.3834 -6.30Excl 0.0648 2.71Corrected Tax Evasion Model. Dependent Variable: yi = Q58 Constant 34.42366 4.11SST -0.2937 -3.53VAT -0.5245 -1.57CIT -0.0941 -0.81Comp 0.1292 2.56CorrTax 2.2570 4.61CorrGen 0.5158 2.93RefProg 0.4625 2.78Enforce -1.7379 -1.48TaxAdm -1.1919 -0.98Size 6.1258 4.78Fair -0.8482 -1.85Owner 1.0970 0.53

Log-likelihood -12,511.04 Wald statistic (12) 125.07

* Computed with heteroskedasticity-consistent standard errors.

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Table 8. Least Squares Estimates of Coefficients.

Dependent Variable: yi = Q58 Number of observations = 4538

Variable Coefficient t-Statistic*

Constant 17.2572 4.06SST -0.2251 -4.80VAT -0.2179 -1.20CIT 0.0360 0.61Comp 0.0865 2.68CorrTax 3.6015 12.38CorrGen 0.8139 5.97RefProg 0.2722 2.75Enforce -1.8642 -2.57TaxAdm 0.4477 0.67Size 3.8263 5.12Fair -1.4824 -5.95Owner -2.9067 -3.29

R-squared 0.1339F-statistic (12, 4525) 54.91

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