SALES TAX COMPLIANCE AND AUDIT SELECTION - …€¦ · · 2016-08-31SALES TAX COMPLIANCE AND...

SALES TAX COMPLIANCE AND AUDIT SELECTION MATTHEW N. MURRAY*

Abstract - Literature on tax compliance has experienced explosive growth in the past 20 years. Yet the vast majority of this work has focused on individuals and compliance with the personal income tax, rather than company compliance and compliance with other taxes. This paper explores the subject of sales tax audit selection and firm underreporting of statutory sales tax liabilities. The analysis relies on sample selection estima tion techniques in identifying systema tic audit selection rules and the determinants of sales tax underreporting. The results support the view that sales tax accounts are chosen for audit nonrandomly. The analysis also provides strong evidence that taxpayer opportunities for underreporting are correlated with the observed behavior of firms.

INTRODUCTION

The general sales tax remains the single- most important revenue source for state governments in the United States, repre- senting 24.7 percent of own-source tax revenues in fiscal year 1993.’ Noncompli- ance with the sales tax is not thought to be as serious a problem as noncompli-

*Department of TN 37996-4170

Economics, Unlverslty of Tennessee, KnoxwIle.

ante with other taxes, for example, the personal Income tax. Yet there is lrttle evidence to support this supposition, with the exception of Due (1975), who estimated the sales tax gap to be on the order of 5 percent of actual collections. In general, both the magnitude and determinants of sales tax noncompliance remain elusive targets.

Despite the importance of the sales tax In state finances, and despite suspicions, if not clear evidence, that marginal audit assessments exceed marginal auditing costs, sales tax audit coverage in the United States has declined over time.* Survey information reported in Due and Mikesell (1994) indicates that between 1979-81 and 1989-92, 25 states reduced sales tax audit coverage, while only 17 states in- creased their coverage of sales tax accounts. A survey of American states and Canadian provinces conducted by the On- tario Ministry of Revenue (1991) found that between 1987 and 1989, sales tax audit coverage slipped from 3.37 percent of all accounts to 3.05 percent.

The decline In sales tax audit coverage likely reflects several factors, including budget shortfalls, growth in the number of sales tax accounts, and the reallocation of audit resources to more complicated accounts.3 As audit coverage declines and as audit resources are reallocated, it is crucial that revenue administrators have

guidance on which sales tax accounts may be most productive. Yet identifying and selecting sales tax accounts for audit is problematic, and progress on developing systematic, statistically based selection systems has been slow (Due and Mikesell, 1994). Most of the problems relate to the data that can be used to identify the determinants of sales tax noncompliance. In practice, the only available data are those obtained from previous audits or from limited experiments with randorn audrts. In the former Instance, data are contami- nated by selection bias and may produce erroneous estimates of the determinants of underreporting. This contamrnation may in turn lead to substantial misalloca- tions of audit resource’; on the part of state, provincial, and local tax administrators.

While a substantial body of literature has evolved in the past 20 years on compllante with the personal income tax, surprisingly little academic research has focused on the broad supject of company tax compliance and the more narrow subject of firm compliancci with the sales tax. For example, Rice (1992) explores company tax compliance, focusing on the U.S. corporate income tax using unique data drawn from the laxpayer Compli- ance Measurement Program (TCMP). Yaniv (1988) provides a conceptual analy- SIS of company and employee tax noncompliance, but the analysis relates to the income tax in a vvithholding regime. Mar- relll (1984) examines theoretically the eva- sion and shifting of indirect taxes, but fo- cuses solely on monopolistic firms. Mikesell (1985) provides a more targeted and insightful discusslon of sales tax compliance, including an aggregate interstate analysis of the impact of sales tax auditing on the sales tax base. Many open questions remain regarding the behav- ioral and empirical aspects of sales tax audit selection and noncompllance

The purpose of this paper is to provide more specific emplncal insights on sys-

ternatic sales tax audit selection and the determinants of sales tax underreporting. The analysis also shedis light on how available data and statistical techniques might be used to develop add evaluate audit selection rules. The procedures employed rely on farniliar statistiical tools to address selection bias, circumventing the need to conduct costly random audits to develop sales tax audit selectidn rules. An illustration of the procedure is provided, using a unique database on sales tax accounts for the state of Tennessee. Two interesting results emerge from the analysis. First, there IS strong evldenre that taxpayer noncornpllance is related to opportunities for noncompliance, a ~problem that can be addressed in part ghrough stronger re- portincl and cross-verification require- ments, and taxpayer awareness cam- patgns A simplificaticin of sales and use tax laws may have g@ater promise, but at the same time may have greater difficul- tie’s being Implemented. A second and somewhat surprising result surfaced in the analysis of sales t+x audit selectlon rules. Despite the potential for selection bias in developing augit flags based solely on audited returns, tlie simple application of ordinary least squaIres (CILS) estimation techniques provides superior out-of-sample predictive power over several rnore general estimation teqhniques that for- mally address systematic audit selection. This result should motivate further inquiry into the dIevelopment of optlmal audit selection rules.

The retnalnder of the paper IS organized as follows. The next sbction provides a conceptual overview Qf a firm’s sales tax compliance strategy @d the sales tax en- forcemenl. strategy ofi the revenue authority The empirical framework is then presented, including database development and the specification of estirnatlng equations. The following section discusses the results and evalu$es competing models of :#ales tax audit yelection tn terms of

I SALES TAX COMPLIANCE AND AUDIT SELECTION

their out-of-sample predictive power. The paper closes with a brief conclusion.

THE NATURE OF THE SALES TAX COMPLIANCE GAME

Income tax compliance-tax enforcement is generally viewed as a strategic interactive game between taxpayer and revenue authority.4 Subject to the uncertain pros- pects of audit and penalty, taxpayers seek to minimize reported tax liabilities. Based on taxpayer information reports and subject to available resources, revenue authorities in turn establish audit selection rules so as to maximize audit assess- ments5

Sales tax compliance-tax enforcement is a similar game. Under the general sales tax, firms serve as withholding agents for final consumers. As taxes are collected, the firm’s entrepreneur may choose to remit only a portion of the total statutory tax liability by underreporting gross sales or by abusing use and exempt tax provisions.6 State revenue agencies in turn require that firms submit monthly or quarterly reports detailing sales transactions and taxes withheld. If there is suspicion of noncompliance, and auditing resources are available, the firm may be subjected to audit. If found to be noncompliant, the firm will be subject to fines and interest penalties, depending on the nature of the infraction.

To develop the sales tax compliance-enforcement framework in greater detail, consider first the behavior of the firm. It is assumed that firm behavior reflects that of the risk-averse entrepreneur owner/ manager, whose objective is to maximize the expected utility of profits, consistent with the familiar Von-Neumann-Morgen- stern framework for decision making under uncertainty. The production/sales decision is assumed to have already been made, so the entrepreneur’s choice problem is reduced to choosing the fraction of

total sales tax revenue collected7 that will not be remitted to the revenue authority.*

Profits and expected utility are state dependent in this framework, as revenue authorities may audit and detect noncompliance behavior. If the entrepreneur is not audited, profits l7,,, are

+ tR(1 - B)(V - 1) - c

where t is the ad valorem sales tax rate, R is net-of-tax total revenue, B is the share of taxes not remitted, V is the vendor’s compensation rate on remitted taxes, and C is total production costs. Vendor’s compensation is the rate at which firms are compensated for the costs they incur in collecting and remitting the sales tax. A higher vendor’s compensation rate, cet- eris pa&us, reduces the marginal cost of reporting a dollar of sales tax revenue and provides an incentive for honest reporting.

If noncomplrance activity is detected by the revenue authorities, profits II, are

I7. = I7, - (1 + f)BRt

where f is the interest and penalty charge levied on unremitted sales taxes. In practice the interest charges imposed by the states exceed the market rate of interest, thus adding to the penalty for late pay- ment.

The entrepreneur’s chorce problem is thus

Max, R[U(Z7)] = (1 - r-)U,(n,j + ~‘&(~,)

where ‘8 is the expectations operator and I- represents the probability of audit and

detection. The probability of audit is viewed as exogenous to the entrepreneur as there IS no specific knowledge of the revenue authority’s audit selection rule nor the resources available to conduct audlts.

The optimal level of sales tax underreporting is characterized by the followng first order condition:

u;(qu;+) == [l-I/(1 - 1”)][(1

+ f)l(l -- V) -- I]

Note that due to the proportionality of t and the nature of the penalty function on evaded taxes, the sales tax rate does not appear in the first-order condttions. Nonetheless, it remains a determinant of underreporting through its influence on state-dependent income and expected utility. The primary determinants of underreporting are then r, f; t, and V, as well as the tastes and preferences of the entrepreneur.”

The revenue agency is assumed to maxl- mize audit assessments subject to an ex- ogenously imposed budget constraint. In practice this entails development of audit selection rules (or audit: flags) that, based on experience or intuitson, are believed to be indicative of noncompliance. This audit selection rule, in conjunction with available resources, determines the probability that the firm is subjected to audit, based on taxpayer-provided information reports.

This Index of potential audit productivity can be written

with the vector X’ reflecting the firm’s reporting characteristics, 2 audit assessment weights (or flags), Y audit resources with coefficients Z; and ,U a normally distrib- uted error term that recognizes the white

noise associated with the 6’~ anfe deter- mlnatlon of audit productivlty.lc’

Equations ,4 and 5 provide the structural basis for the emplncal~analysls that IS the primary focus of this paper. Equation 4 indicates the key factors that enter into the compliance decisiqn of the entrepreneur: the penalty rate, the vendor’s com- pensatil3n rate, the prpbability of audit, sales tax rates, and tattes. Equation 5, which (orresponds to the behavior of the revenue agency, inclu4es reported return characteristics and thq level of audit resources. Assuming pr&ommltrnent in the design of audit selectipn rules, this repre sents a sequential equilibrium model of the tax compliance galme.”

EMPIRICAL FRAMEWORK

There are two objectiyes to the empirical analysi:,. The first is to’estirnate the audit selection rule of the principal and the factors that reflect firm uinderreporting of statutory tax liabilities, The second is to use the estimation res~ults to better under- stand the way in which returns might be selected for audit by the revenue authorities, consistent with tt;le objective of maxi- mizing audit assessm$nts. To address these questions, the eimpirical analysis builds on equations 4 and 5, reflecting the behavior of the taxpayer and revenue agency, respectively.

Consider first. the audit selectlon behavior of the principal. Note from equation 5 that A*, the index of expected audit pro- ductivily, IS not directly observable to those outsIde the revenue agency. It IS,

however, possible to Observe those instances where A* is glreater than zero, corresponding to audited sales tax accounts This allows sppcification of the In- dicator variable /,,

q 0 I A q I

1 Iff A * > 0 0 otherwise


Equation 6, which captures the essence of the principal’s audit selection rule, could be estimated using maximum likelihood techniques (such as probit or logit).

Let the optimal level of underreporting, the empirical counterpart to equation 4, be represented as

q E* =Z8+ E

where the vector Z includes taxpayer characteristics, 8 represents the parameters to be estimated, and E is the error term. If the taxpayer perceives that the benefits of noncompliance outweigh the costs, she will engage in cheating activi- ties. The propensity for the entrepreneur to underreport-conditional on audit- can be written using the indicator variable I,, as

, E

= 1 iff E* > O,A* > 0 0 otherwise

As with equation 7, the estimation of equation 8 may also rely on maximum likelihood techniques.

Conditional on both audit and underreporting, it is possible to estimate the level of firm underreporting

q 0 E=Z/i+co

where the vector Z again reflects taxpayer and return characteristics, ,4 is a vector of parameters, and m is an error term.

Together, equations 6, 8, and 9 consti- tute the basis for the empirical analysis. Estimation hinges on the treatment of the sequential censoring problem that culmi- nates with equation 9. It is assumed here that the error terms p, E, and w are dis- tributed trivariate normal. As the likelihood function for the more general case

with cov(p, c) # 0 IS ill behaved, estimation relies on a more restrtcted specification with cov(p, c) = O.l* This rules out, for example, the role of tips that would rarse audit odds In the first stage and be indicative of a greater likelihood of underreportrng In the second stage. While this is admittedly a limiting factor, the model stall can accommodate correlation between tips in stage 1 and the level of underreporting observed in stage 3, through inclusion of the sample selection control variable. Univanate probit techniques are applied to the first-stage model of audit selection and the second-stage model of the likelihood of underreporting. While the results for each of these stages of estimation are of Interest in and of themselves, the probit models also allow gen- eration of independent sample selection terms that can be used to control for selection bias in the analysis of the level of sales tax underreporting. Several alternative estimation procedures are employed to gauge the robustness of the findings.

Because the empirical model applies to two sets of economic agents, exclusion restrictions are used to distinguish between the behavior of the revenue authority and the behavior of the taxpayer. A tax department resource variable, reflecting the number of auditors available to conduct audits on a regional basis, is included in the first-stage analysis of the probability of audit. This same variable is then excluded from subsequent stages of the analysis. The taxpayer’s true level of gross sales is included as an explanatory variable in the analysis of the likelihood of underreporting and the level of underreporting, but this same variable is omitted from the first stage.13 Together, these restrictions serve to isolate the behavior of the revenue agency in the first stage, and taxpayers in the second and third stages.

The data used in the empirical analysis are drawn from Department of Revenue (DOR) records in the state of Tennessee.

519

A primary concerr in constructing the database was the ability to address the underlying selection problem, specifically the audit selection procedure of the DOR. That is, the audited returns themselves cannot be directly used to develop audit flags nor identify the determinants of taxpayer underreporting, necessitating an alternative approach. Data were available on audited accounts (corresponding to different taxpayers), but there was no rec- ord of the audit selectlon rule that generated these audits. In order to address this problem, the first step was to choose a well-defined Industry classification and focus exclusively on the audited returns for this sector. This made the task managea- ble and allowed examtnation of individual records. The next step was to re-create the population of accounts from whi& audit selection was initially made. This in- volved the use of historical files to Identify all firms in the chosen industry (not simply those firms that weIre audited), as vvell as firm and reporting characteristics that may have been used by auditing staff to identify potentially productive accounts. By re-creating the population of accounts that existed when returns were Initially selected for audit, standard statistlcal techniques can be used to isolate the audit selection rule of the revenue agencly. This same Information can then be used to control for nonrandom selection in the analysis of taxpayer behavior.

Data frorn three different sources were merged to produce a file of 2,178 sales tax accounts covering the period 1986-8, representlng the population of accounts for the undisclosed sector of the state economy. Of the total nurnber of accounts, 396 were selected for audit between 1986 and 1988, with 372 accounts found to be noncompliant. Information was available on taxpayers through their Initial registration with the DOR, their rnonthly or quarterly remlt- tance of sales tax returns, and their au&t experience with the state. Unfortunately,

while data are availablp on audit assessments and time spent Iconclucting the audit, the state retains ng histoncal information on postaudit measures of return characttzrlstics.14

Descripl ive statistics for the data used In the empirical analysis dre reported in Ta- ble 1. Note that the averages for the taxpayer return data pert&n to the 1986-8 reporting period. The tax assessrnent fig- ures, on the other hanid, encompass the entire 3-year audit window.

The firs’:-stage analysis of sales tax audit selectioN? is esttmated using probit maximum likelihood techniques applied to a zero-one indicator vari,able for the population of sales tax accounts.15 The DOR resource variable AUDjTOR is the number of auditors available tO conduct audits by region (Including out-gf-state regions). More auditors should, all else the same, increase the probability of audit. Inform+ tton from the taxpayee’s registration file and sales tax returns iq used to specify the remainder of the first stage. Ftrst are aver-

TABLF 1 DESCRIPTIVE STATISTICS FOR SALES TAX AUDIT

DATA A ______--. - Continuous Variables Mean

Average gross sales (RSALh) $199,385 High-to-low gross sales rathge $308,615

(RSALER) Average use taxable sales l(RUSf) $1,943 High-to-low use taxable sdles range $15,161

(RUSE!?) Average exempt sales (REXEM) $13,477 High-to-low exempt sales range $24,131

(RfXfMR) True gross sales (TRUSALE) $479,682 Late returns filed (LATE) 0.07 Firm age in years (AGE) 12.2 Affiliated outlets (OUTLE7$) 23.0 Average reported tax $14,224 Assessments $14,874 Average audit hours 8.2 -___-- ----.- -.- Discrete Variables Frequency

Nonstate firm ownership 6

NSOWV) Corporate structure (COR ,DUM) S/CT S/CL?

827 1,181

640 697


age reported gross sales over the 1986-8 period (RSALE) and the range of high-to- low reported gross sales (RSALER) over the same period. l6 Similar variables are constructed for use taxable sales (or products consumed by the firm “off the shelf”) and exempt sales (or sales for re- sale, sales to out-of-jurisdiction consumers, and sales to nonprofit entities), two of the primary avenues for sales tax underreporting. By understating use taxable sales the firm effectrvely reduces its sales tax liability on products it consumes; lib- eral provision of exempt sales increases receipts and profitability. Reported use taxable sales and reported exempt sales are denoted RUSE and REXEM, respectively. The range in high-to-low reporting for the same variables are RUSER and REXEMR.

Several additional variables are included in the model of audit selection. First is the average number of late returns filed by the firm (LATE). More late returns may prompt the revenue agency to audit the firm as a result of the state’s concern over accurate and timely reporting. Older firms (AGE) may have had previous auditing experience, although whether this left a positive or negative impression on the DOR is unknown. Firms with affiliated outlets (OUTLETS) may have higher probabilities of audit if leads are generated from the detection of noncompliance in a single outlet. Out-of-state firm ownership (NSOWV) is included, as the DOR likely has different audit selection strategies for in-state versus out-of-state enterprises. Corporations (CORPDUM) may also re- ceive differential treatment vis-a-vis alternative business structures. Finally, two industry dummy variables (SKI and SIC2) are included to control for the possibility of industry-specific selection rules.

The second and third stages of the empirical analysis focus on taxpayer reporting behavior, including the probability of underreporting and the level of underreport-

ing. The second stage is estimated using the maximum likelihood probit estimation technique applied to the subset of the population subjected to audit (396 taxpayers); the third stage is an OLS regression on audit assessments per hour of auditor time,” confined to the 372 audited accounts where nonzero assessments were assigned to the taxpayer, controlling for audit selection and nonzero underreporting. The specification of these estimating equations differs from the first stage through the exclusion of the DOR resource variable AUDITOR and the inclusion of the postassessment variable true gross sales (TRUSALE). The remainrng variables remain unaltered across equations.

In both the second and third stages, firms with higher levels of true gross sales might be expected to be less compliant due to firm scale and broader opportunities to underreport. Greater variations in reported gross sales (RSALER) over the period 1986-8 may also be indicative of firms seeking to underreport through their irregular reporting practices. In a relatively narrowly defined industry classification such as that considered here, low levels of use taxable sales and high levels of exempt taxable sales should be positively associated with firm noncomplr- ante. Large variations in the range of high-to-low use taxable and exempt sales reported between 1986 and 1988 should also be positively related to noncompliance, as firms in the same industry should have relatively smooth and similar patterns of sales.

Firms submitting a greater frequency of late returns may be under financial stress or be more lackadaisical regarding reporting requtrements, and may accordrngly be less complrant. Older firms should have better compliance patterns due to more experience, previous auditing experience, and a more established reputation in the bustness communrty; at the same time, older firms may have worse compliance

patterns if they believe that their expen- ence raises their odds of winning the compliance game. The let effect of AGE is thus ambiguous. For firms with affiliated outlets, there may be less flexibility and fewer opportunities to underreport, as accounting procedures may be stan- dardized and there may be internal-to- the-firm financial audlts. Firms with nonstate ownership may rely more on centralized accounting and bookkeeping practices, and may be less familiar with the specific nuances of Tennessee sales tax law, aggravating voluntary compliance. Corporations may have better compliance patterns as a result of access toI more extensive internal legal and accounting services. Moreover, there IS less flexibility in the corporate enterprtse to exploit tax cheating opportunities.

ESTIMATION RESULTS. P OBABILITY OF AUDI-I -~-- -J-AZ--

--~-_-M de’L-.--~..~ Variable --f-..-________-

-4-- Model 2

RSALE -7.3 ox IO ‘= -6.8 x IO 7.a ( .87)

RSALER 4.3 ?I (2.68)

10 7.1) 3.4 x IO 7D

RUSE

RUSER

REXEM 5.9 m IO 6.= 6.3 :< 10 Ga ( .OO) (4.12)

REXEMR 4 -2.6,~ IO 6a ~2.7 A 10 6.a ($81) (3.86)

LATE - 3.598 (3.32)

AGf 0.013" (2.85)

OUTLETS O.OOlc (1.75)

NSO WN 1.82" (5.85)

CORPDUM 1 .68d (7.95)

SKI 0 199' (1.72)

SIC.2 - 0.497" (4.00)

AUDITOR 0.025" (3.02)

AUDITOR * NSOWN ,- - 0.052" (5.29)

Constant -‘2.61’ (10.3)

- 3.71" (10.3)

N

RESULTS AND EXTENSIONS

The estimation results for the first-stage model of sales tax audit selection are presented in Table 2. A large array of explanatory variables have a statlstlcally significant Impact on the likelihood of audit, a strong indication of systematic audit selection on the part of the revenue agency. It should be recognized that these same results would not typlcally apply to other sectors, as the state likely uses different selection criteria for firms in different industries.

Firms with higher levels of reported gross sales confront lower audit odds, while firms with greater variation in reported gross sales over the 1986-8 window confront significantly higher odds of being audited. Together, these results suggest an auditing focus on taxpayers with reporting irregularities over time. Higher values of reported use taxable and exempt sales also raise audit probabilities. In general, these results are consistent with the empirical findings of various states that gross, use taxable, and exempt sales are the primary avenues of taxpayer non-

aSIgnificant at the 1% level i bsignlficant at the 5% level Sgnificant at the 10% level i

compli;lnce. Somewhdt surprisingly, firms that exhibit a wide ra ge in the reporting of exempt sales over t me face lower likelihoods of audit. In sh rp contrast to the reslJlts .l’or variations i

1

gross sales (RSALER), this sugges s that uneven exempt sales patterns m y be consistent with actual sales patt rns for the sector considered here.

A second and somew at surprising result is that firms that h have a propensity to

522


firms would produce little in assessments. For example, these might be firms with independent owners/managers who have encountered financial difficulties, offering little promise of additional assessments for the DOR in the near term. Older firms tend to face higher audit rates. Repeat auditing is a common practice across the states, and auditors may know from experience that certain older firms will have lower compliance patterns. Auditors may also feel more comfortable auditing a firm that they have had experience with in the past, as they may have some famil- iarity with the firm’s bookkeeping practices and its employees. Both corporations and firms with nonstate ownership also have higher chances of being audited. Because of their complicated financial structure and the propensity for interjur- isdictional sales, these firms are viewed as profitable targets for audit. Finally, there is strong evidence of industry-specific audit selection rules.

The result for the auditor variable is par- ticularly interesting, as it suggests that more auditors in a region reduces the likelihood of audit. This could reflect a va- riety of factors, including regional variations in the number of accountsl* and different degrees of caseload complexity across regional offices. Related is the possibility that firms with nonstate ownership give rise to unique problems for auditors, as auditors may more commonly confront complicated bookkeeping practices and business enterprises engaged in sales across state and county borders.

One indirect test of this hypothesis is provided under the second specification reported in Table 2, where the variable NSOWN is interacted with the AUDITOR resource variable. In this application, the coefficient of the resource variable takes on the anticipated positive sign, while the coefficient of the interaction variable is negative. Hence, in-state firms do confront higher audit probabilities where

there are more extensive auditing resources For out-of-state firms, there is an Independent positive impact on audit odds for nonstate ownership but lower audit odds if the region of residence is characterized by a relatively large audit staff. In this latter case, auditors may simply confront greater caseloads and more complrcated sales tax accounts.

The second-stage probit estimation results on the likelihood of taxpayer underreporting are presented In Table 3. The model has limited overall explanatory power and sheds relatively little light on how economic opportunities influence reporting decisions. The only factors to sur- face as Indicators of nonzero reporting

TABLE 3 ESTIMATION RESULTS: DETERMINANTS OF THE

LIKELIHOOD OF UNDERREPORTING

Variable Coefficient

TRUSALE

RSALER

RUSE

RUSER

REXEM

REXEMR

LATE

AGE

OUTLETS

NSOWN

CORPDUM

SIC1

SIC2

Constant

N = 396 x2 = 83.28

1.4 x 10 6 (1.29)

-6.4 x 10 ’ (1.38)

4.1 x 10-S (0.6 13)

-2.3 x 10 6 (0.373)

1.7 x 10 5 (0.182)

6.3 x 10 5 (1.36) - 1.46

(0.284) 0.046b (2.05)

0.057b (2.56) 1.55’

(3.80) 1.07

(0.883) -0.612 (1.35) 6.34

(0.020) - 2.82b (2.09)

Note: Univariate orobit coefficient estimates are reoorted with asymptotic i-statistics in parentheses. “Significant at the 1% level bSignificant at the 5% level

523

are the firm’s age, the number of affiliated outlets, and nonstate ownership, all of which increase the likelihood that the firm underreports. Older firms may simply feel their business and previous auditing experience has improved the odds of wrn- ning the compliance game. Owners/managers within firms with affiliates rnay feel pressures to Improve their financial posi- tion relative to other firms within the organization. At the same time, it is also possible that such managers are themselves ill prepared to deal with sales and1 use tax law complexity, and have been af- forded insuffrcient guidance by the parent firm. Finally, firrns wrth nonstate ownership may simply have lers specific knowledge of sales tax laws in Tennessee and may unintentionally have lower complr- ante rates. Of course, it is exceedingly difficult to distrnguish whether or not tax law complexity is itself the culprit in such instances, or whether tax law complexity simply provides taxpayers with the oppor- tunity to underreport.

The third-stage model of sales tax underreporting is presented ir Table 4 under three alternative specifications of the model. The ftrst specification (model 1) corresponds to an OLS selectrvrty bras corrected regression with two sample selection terms,, LAMBDA1 alId LAMBDA21g; the second (model 2) is a treatment effects framework; and the third (model 3) IS a two-stage rnodel of audit selection and the level of underreporting. The three alternatives are discussed In turn below. In all cases, the dependent variable is audit assessments per hour of auditor time. Note that with few exceptions, the results are consistent across the alternatives.

The larger the scale of trre firm’s opera- tion, as measured by true gross sales (TRUSALE), the greater IS the degree of taxpayer underreporting. Surprtsingly, greater deviations in reported gross sales

over the monthly/quarterly reporting pe- nod 1986-8 are associated with less underreportrng. *O Higher levels of reported exempt sales (REXfM) reflect a lower degree of taxpayer noncompliance, as hy- pothesized.

Greater j/arration in reported use taxable sales (RUSER), holding the level of sales constant, reflects higheir levels of taxpayer underreportrng. At least for the industry considered here, only a stable pattern of use taxa:lle sales is consistent with complrant ta.cpayers. A similar story holds for tirms that display wide variation In reported exempt sales over trrne (REXEMR). As with I-lse taxable sales, the level of exempt sales IS not as critical as the regular- ity of sales across the fi,rm’s reporting pep nods. In general, reportrng irregularittes appear to be a strong ihdrcator of firm noncomplrance. Controlling for audit selection and other factors, firms with non state onnershwp and firlms with a corporate structure tend to be more compliant. The lattctr result is consistent with fewer opportunities for exploiting noncompliance and more resources to properly comply wrth state-specific tax laws within the (corporate enterprise.“’

Of the two selection terms Included in the model, only LAMBDA I--corresponding to tt-le first-stage model of audit selection-is statistically significant. This result illustrates the importance of controlling for nonrandom selection and indicates that uncibservables associated wrth audit selectiorl are negatively correlated with unobservables associated with the level of underreporting. As an illustration, it IS im- possible to observe the accounting costs Incurred by those firms that choose to keep two sets of books. Such firms may be able to rnask their potenttal for audit recovery through erroneous InformatIon reports, thus reducrng the Ilkelrhood of audit. Yet these same firms rnay have


TABLE 4 ESTIMATION RESULTS: DETERMINANTS OF SALES TAX UNDERREPORTING

Variable Model 1 Model 2 Model 3

TRUSALE 0.005= 0.004a 0.005" (24.1) (49.2) (17.2)

RSALER -0.005a - O.OOla - 0.005" (8.71) (8.76) (5.08)

RUSE - 0.066 -0.017 - 0.062 (1.41) (1.57) (0.785)

RUSER 0.019a o.oosa 0.018" (3.40) (4.84) (1.96)

REXEM - 0.099= - 0.036= - 0.098= (7.73) (12.5) (4.45)

REXEMR 0.057a 0.017" 0.05& (5.97) (12.9) (3.26)

LATE 3.2 x IO“,' 7.90 2.9 x 104 (1.89) (0.019) (0.507)

AGE - 74.3 22.2= - 73.6 (I .27) (2.72) (0.737)

OUTLETS - 92.4 -11.8 - 100.0 (1.19) (1.46) (0.768)

NSO WN -5.3 x 103.b - 300.8 -5.6 x IO3 (2.31) (1.30) (I .48)

CORPDUM -2.0 x 104.b 255.6 -1.7 x lo4 (2.10) (1.15) (0.608)

SIC1 -2.8 x 103,‘ -221.6 -2.9 x 103 (1.75) (0.930) (0.960)

SIC2 7.9 x 103.a - 14.6 6.0 x lo3 (2.75) (0.078) (1.25)

AUDIT - 652.6 - (0.864)

LAMBDA 1 -1.3 x 104.d -1.1 x 104.b -1.2 x IO4 (2.92) (2.38) (1.53)

LAMBDA2 3.6 x lo3 - - (1.09)

Constant 4.2 x 104.b 62.7 3.9 x 104 (2.50) (0.301) (I .08)

R2 0.62 0.54 0.62 N 372 2,178 396

Note: The dependent variable is sales tax assessments per auditor hour. OLS coefficient estimates are reported with asymptotic t-statistics in parentheses; standard errors have been corrected for selection. LAMBDA1 is the inverse Mills ratio generated from a univariate probit on the likelihood of audit; LAMBDA2 is the inverse Mills ratio derived from the second stage likelihood of underreporting. The variable AUDlT is a generated regressor of the probability of audit. C4gnificant at the 1% level 3ignificant at the 5% level <Significant at the 10% level

higher levels of noncompliance if they were subjected to audit. The results also suggest that unobservables used by auditors to select audit cases-rncluding their intuition-is associated with relatively lower audit yields. This is a rather important finding that calls into question the role of nonsystematic techniques to sales tax audit selection.

Model 2, the treatment effects specification of audit selection and the level of underreporting, is applied to the complete

set of 2,178 observations.22 This application collapses the second two stages of the model into a single OLS regression, thus ignoring the censoring of the (underreporting) dependent variable. A strength of the model is that it allows in- clusron of the “treatment” variable-the probability of audit-in the analysis of the degree of taxpayer underreporting. More- over, this procedure explicitly incorporates nonrandom selection through inclusion of a single Heckman sample selection control corresponding to the audit stage.

The results for Model 2 are very much consistent with alternative specrftcatrons of the model. Interestrngly, the vanable AUDlT has no Impact whatsoever on taxpayer compliance rates, suggesting taxpayer uncertainty over the audit selection rule of the principal. One impllcatlon, at least for the sectors consrdered here, is that audit coverage ltsef does little to promote voluntary taxp,ayer compliance. This result questions the use of costly random audits and other technrques to ex. pand coverage with the hope of IndIrectly fostering compliance.

In lrght of the fact that nost of the audited accounts were found to be In noncompliance, model 3 ccllapses the fina’ two stages of analysis into a single OLS regression on the level of underreporting. Whrle this specificatron Ignores the cen- sonng problem for the small number of audited but compliant taxpayers, It still controls for nonrandom audit selection Note that while the results are conslstent with other specrfication; of the model, the sample selection control for the audit stage is now statistically instgnlficant.23

Together, these results reveal a sales tax compliance game characterized by a revenue agency practicing systematic audit selection and taxpayers exploiting opportunities to underreport. Perhaps the most difficult problem encountered by the revenue agency in playing the game IS the design of audtt selection rules that can provide accurate out-of -sample predrc- tions regarding expected audit productlv- ity. That is, how can accounts be selected to maximize assessmems, subject to available audit resources? The absence of statistically based sales tax audit selectron programs across the states suggests that designing formal rules with this character- istic is no easy task. The models estimated and presented above have not only dem- onstrated the nature of the sales tax compliance process, but also provide the basis

for ldevelopment of au@tt selection rules. The question IS whether these models have strong predrctrve powers VIS-A-VIS actual practice, or whether sirnpler statrstrcal applrcatlons are superror methods of de- velcgpincl audit flags.

An dea experiment would be to com- pare the state’s actual audit selectron rule with various statistically based alternatives to determrne which has the best capacity to predict assessments. Unfortunately, such an expenrnent cannot be conducted In the absence of specrflc knowledge of how the state selects returns for audit. An alternative that IS pursued here com- pares the abilrty of the various selectlon models to predict audit recoveries vvrth simple appllcatrons of OLS. The rnotrva- tlon is that states have experimented with OLS rn the design of audit flags, drsre- gardrng the contamtnaitlon of audited ac- count data with selectron bias. The slmu- latrons presented below can then shed light on the empincal magnitude of the selection bias problem and allow compar- rsons of competing models of audit selection.

The specific procedure employed began with drawrng a random sample of 100 audited accounts from; the total population of 396 audited cases. The 100 audited accounts for which assessment data are avar able can then provide the basis for ernprncally comparing predictive power across equations. Next, eacht of the models reported in Tables 2 and 3 was, re-estimated without the 100 randomly selected accounts to identify factors lndlcatlve of noncomplrance. In addl- tror,, OLS was applied to the subset of 296 audit cases, as well as to the 2,078 accounts remaining in the overall data- basle. In the latter case, which includes both audited and nonaudlted accounts, those observations that were not audited were assigned an asse$srnent value of zero. Note that each of the OLS applica-


trons Ignores the two stages of selectron, as has typically been the case when states have sought to use statistical methods to develop audit selection rules. The final step was to use the revised estimation results to make predictions of expected audit productivity for the 100 randomly chosen accounts for which assessment data were available.24 In doing so, the alternative estimators can be directly compared to determine which provides the best out- of-sample predictive power.

The results of this experiment are reported in Table 5. Note that for the 100 randomly selected audited accounts, average assessments per auditor hour are $1,124. The OLS specificatron confined to the subset of 296 audited accounts surprisingly has the best predictive power of the alternatives considered, at $1,589 per auditor hour, despite the fact that this model ignores both the first-stage and second-stage selection hurdles. The specification with a single selection control and the model with two selection controls overstate assessments by $797 and $828, respectively. The treatment effects model and the OLS application on the full set of 2,078 accounts have the poorest explanatory power of the alternatives considered here.

An important caveat is in order regarding these simulation results. The lesson here is not that selection bias can be ignored when examining the determinants of sales tax noncompliance or when design-

TABLE 5 ACTUAL AND PREDICTED SALES TAX AUDIT

ASSESSMENTS PER HOUR

Assessments Model Framework per Hour

Actual $1,124 OLS prediction (full sample) $3,071 OLS prediction (audited returns only) $1,589 Model 1 (two selection hurdles) $1,952 Model 2 (treatment effects) $2,181 Model 3 (one selection hurdle) $1,921

rng sales tax audit selectron rules. While simple OLS provides the best explanatory power In this specific case, there is ample evidence in the broader economics Irtera- ture that selectron should not be Ignored. In general, there IS no basis to assume this conclusron would hold In other con- texts.

Conclusions

Despite considerable Interest in the tax compliance game, the sales tax has re- ceived surprising little attention, even though it is the most important revenue source for state governments in the United States. As with other taxes, estr- mating the magnitude and determinants of sales tax noncompliance has been sen- ously hampered by data limitations. This paper has illustrated how established statistical techniques can be applied to data available in most states to estimate the determinants of sales tax noncompliance and aid in the design audit selection rules.

The results indicate that taxpayers with greater opportunities to reduce their tax liabilities exploit these opportunities to their advantage. Unfortunately, there is no obvrous nor easy-to-implement policy to combat sales tax noncompliance. The problem has far less to do with reporting procedures, tax returns, and cross verification, than with ambiguities in sales and use tax laws themselves. Insofar as exempt and use tax provrsions remain in- tact, ambiguities will remain that will lead to inadvertent and intentional noncompliance activity. Taxpayer awareness programs and other efforts to clarify taxpayer responsibilities can help, as can well-de- veloped audit selection rules and auditing procedures.

The results also rndrcate that, at least in this specific application, OLS estimation technrques applied to underreporting behavior provide superior out-of-sample

527

predrctlve power than more conceptually sound techniques. While this result is dis- turbing, it should not serve as an en- dorsement for ignoring underlylng statlsti- cal problems. Note first that the results presented here apply to a single industiry in a single state; extensions to other sectors or to other states may produce con- trary results. Secoirld and most tmpor- tantly, the potential for selection bias and its attendant problems wll always be present in applicatlonr; couch as that presented here, and the Inappropriate use of audited tax return data bears the risk of substantial misallocatlons of scarce tax admlnistratlon resources.

ENDNOTES

The author woulcl like to thank the Tenncbs- see Department of Revenue for data, Kirk Johnson of the Department of Revenue for assistance and advice, and the edrtor and three referees for their helpful comments on earlier versions of the rnanuscrlpt. All views expressed are those 01 the author U.S. Department of Commerce (1994) The suspicions arise from evtdence on the U.S. personal income tat, see Dubin, Graetz, and Wilde (1990a). For direct evidence that marginal sales tax audit assessments excel?d marginal auditing costs, see State of Tenrtes- see (1988). The Wall Street /ournal’ {September 16, 1991) notes a reallocation of state sales t:3x audit resources to larger, more compllcatc!d accounts. See, e.g, Graetz, Relng<jnum, and Wilde (1986). This is but one objective that a revenue agency might pursue. Aternatlves include maxlmlzlng net assessments, rnlnlrnlzlng the excess burden of taxation (Slemrod and Ylt- zhakl, 1987), or maxlml;lng assessments and voluntary compllante (Mikesell, 1985) State-specific studies have found that unaer- reporting on gross and use taxable sales and overuse of exempt sales are the most proml- nent forms of sales tax noncompliance See State of Callfornla (199:;) and State of New York (1993). Firms may not be able to collect all sales tax llabllltles If, e g , some consumers make pur- chases with bad checks This formul&on of the> rnodel IS consistent

wjth sales tax auditor findings that many firms “back into” their sales tax returns based on final sales data

g In a rrlore general speciiflcatlon of the model that allows the firm to choose both the level of output and the level of underreporting, these jarne factors influence both choices. Unfor un&ely, this more general l’rarnework IS not estlrnable with eklstlng data.

O In practice, states implkment equation 5 in a numbi?r of different w+ys. For an extended discus;ior of the primdry alternatives, see Due and Mikesell (1994).

” See Graetz, Rernganuti, and Wilde (‘1986) aid A m, Bahl, and Murray (1993).

I2 In the more general ca$e, lteratlve blvariate probit estimation techdlques must be applied to the first two sqages of the empirical model These specifrcatrons of the empirlcal model were highly unsfable and typically failed to converge. Forlbackground on the empincal ,appllcatlons, see Tuna11 (1986) arid Catsiaols and RobInsor/ (1982)

3 It IS assumed that the rievenue agency de- tects ~111 noncompllancg activity through Its audit. True gross sales, corresponding to R embedded in equation,3, are thus unknown to the revl?nue authorigy ex ante and are exogenous to the reportibg process

4 The assess,ment data uged here are a11 Im- perfect measure of audit yield, as taxpayer:, may allpeal through administrative (or in some nstance, legal) pt-oceedlngs, arid as some (assessments are $~rnply not collected.

1h The pctnalty rate and the vendor’s compen- satlon rate are omitted from the empIrIcal analys s, as they have no variation across taxpayers A sales tax rbte vanable IS not In- cludec,, as It IS III deflndd for the many firms that ol)er&e across mulltiple state/county/city locations. The probablllity of audit is addressed Indirectly through the use of sample selection tiistimatlon techniques.

lh The use of range variables can be viewed ai a rnultrvariate extension ot the “norms” ap- proact to audiit selection See Adams (1988)

” The In’ent of the third stage IS to Identify taxpayer characteristics lndlcatlve of underreporting, controlllng for the various factors Influerclng underreporting and Its detection. Auditor time IS clearly dne of these factors, but It IS effectively endQgenous In this framehvork. If #data werk available on audl- tars-lncludlng their folrmal training and on- the-jot)-experience-simultaneous equatlor

SALES TAX COMPLIANCE AND AUDIT SELECTION

techniques could conceivably be applied to estimating a broader model. In an effort to deal with regional variations in the number of accounts, the auditor resource variable was scaled by the number of accounts (by region) for the industries included in the database. This did not alter the results reported in Table 2 in any apprecia- ble fashion. A better scale factor would be the total number of accounts by region across all industries, but such data are not available for the historical period covered here. The selection bias controls are derived from the probit results in Table 2 (model 1) and from Table 3. Alternative specifications of the model where the true tax liability and the range in reported tax are substituted for their gross sales counterparts produced similar results. These results can be contrasted with Murray (1988) and the State of New York (1993) where few if any taxpayer characteristics were found to be correlated with underreporting and audit assessments. Murray at- tributed his findings to data problems, including selection bias arising from systematic audit selection. The New York study relied on a random sample of historical accounts that had not previously been subject to audit. This selection procedure may impart bias on the resulting sample by eliminating accounts with higher likelihoods of positive assessments. Moreover, the random sample was stratified into small cells, in turn pro- ducing large sampling errors for the estimates. Finally, the New York study used actual cash collections as opposed to the assessment data used here. See Maddala (1983). The model was also estimated using a Tobit model subject to nonrandom selection, which accommodates nonzero correlatron between the first stage, and (Jorntly) the second and third stages. Unfortunately, the likelihood function for this model is ill- behaved, with few alternative specifications of the estimating equations achieving con- vergence. While the results are qualitatively similar to those reported in the text, they appear to be too fragile to place much con- fidence in. The predictions for the selectron models were based on Maddala (1983) and Cat- siapis and Robinson (1982).

REFERENCES

Adams, Virginia N. “Sales Tax Audit Selectron Techniques.” Paper presented to Sales and Use

Tax Seminar, National Tax Association, Nash- ville, November, 1988. Alm, James, Roy Bahl, and Matthew N. Murray. “Audit Selection and Income Tax Un- derreporting in the Tax Compliance Game.” /oumal of Development Economic5 42 (Octo- ber, 1993): l-33. Catsiapis, George and Chris Robinson. “Sample Selection Bias with Multiple Selectron Rules.” Journal of Econometrics 18 (April, 1982): 351-68. Dubin, Jeffrey A., Michael 1. Graetz, and Louis L. Wilde. “The Changing Face of Tax Enforcement, 1978-l 988.” Tax Lawyer 43 (1990a): 893-914. Due, John F. “Evaluation of the Effectiveness of State Sales Tax Administration.” National Tax /ourna/ 27 No. 2 (June, 1975): 197-219. Due, John F. and John L. Mikesell. Sales Taxation. Baltimore: Urban Institute Press, 1994. Graetz, Michael J., Jennifer F. Reinganum, and Louis L. Wilde. “The Tax Compliance Game. Towards an Interactive Theory of Law Enforcement.” /ourna/ of Law, Economics and Organization 2 (Spring, 1986): l-32. Maddala, G.S. Llmitecf-Dependent and Qua/i- tative Values in Econometrics. Cambridge: Cambridge University Press, 1983. Marrelli, Massimo. “On Indirect Tax Evas- tion.” lournal of Public Economics 25 (Novem- ber, 1984): 181-96. Mikesell, John L. “Audits and the Tax Base: Evidence on Induced Sales Tax Noncompli- ance.” Western Tax Review 6 (1985): 86-l 14. Murray, Matthew N. “Sales Tax Compli- ance.” In Proceedings of the Eightieth Annual Conference on Taxation, 174-81. Pittsburgh: National Tax Associatron-Tax lnstrtute of Amer- rca, 1988. Ontario Ministry of Revenue. “Audit Cover- age Survey: Sales and Use Tax.” Oshawa, On- tario, Canada: Ontario Ministry of Revenue, 1991. Rice, Eric M. “The Corporate Tax Gap: EVI- dence on Tax Complrance by Small Corpora- tions.“ In Why People Pay Taxes: Tax Compli- ance and Enforcement, edited by Joel Slemrod, 125-61 Ann Arbor: University of Michigan Press, 1992. Slemrod, Joel and Shlomo Yitzhaki. “The Optimal Size of a Tax Collection Agency.” Scandmavian lournal of Economics 89 (Sep- tember, 1987): 183--92. State of California. State Board of Equali- zation. Annual Report. Sacramento: State Board of Equalrzatron, 1993.

State of New York. Department of Taxa- tion and Finance. Office of Tax Policy Anal- ysis. improving S&s Tax Compliance: Recom- mendations for a Compliance improvement Program. New York: State of New York, 1993.

State of Tennessee. Division of Audit. De- partment of Revenue Folhw-up Report. Nash- ville: State of Tennessee, 1988. Tait, Alan A. Value Added Tax. Washington, D.C.: International Monetary Fund, 1988. Tunali, lnsan F. “A General Structure for Models of Double Selectron and an Applicatron

to a Joint Migration-Earniinys Proce:,s with Re- migration.” Research in Labor Econom/cs 8 (1986): 235-83.

the Census, 1994. Stat Government Finance

http:l/www censusgov: $O/ftp/pub/Govt-Stats/ State-Frnance/ Yaniv, Gideon. “WithholdIng and Non-Withheld Tax Evasioln.” /ourna/ of Public Economics 3’5 (1988): 183-204.

SALES TAX COMPLIANCE AND AUDIT SELECTION - …€¦ · · 2016-08-31SALES TAX COMPLIANCE AND...

Documents

Transcript of SALES TAX COMPLIANCE AND AUDIT SELECTION - …€¦ · · 2016-08-31SALES TAX COMPLIANCE AND...