taxpayer behavior under audit certainty
TRANSCRIPT
Taxpayer Behavior under Audit Certainty
Benjamin C. Ayers University of Georgia
Jeri K. Seidman
McIntire School of Commerce, University of Virginia
Erin M. Towery University of Georgia
September 2015
ABSTRACT
This study uses a confidential dataset of firms assigned to the Internal Revenue Service’s Coordinated Industry Case (CIC) program to examine the effect of audit certainty on taxpayer behavior. We first model the determinants of assignment to the program. Though the ability or incentives to avoid taxes are related to CIC assignment, we find that the IRS targets firms primarily based on size and complexity. We then test whether audit certainty has a significant effect on taxpayers’ initial filing liability (a deterrence effect), total filing liability (combined effect of deterrence and enforcement), or tax reserves (expected future tax payments associated with positions claimed in the current year). Results suggest that audit certainty alters managers’ expectations regarding future tax payments but does not have significant deterrence or enforcement effects. Our paper provides new empirical evidence on the strategic game between the taxpayer and the tax authority and has important implications for tax authorities as they consider the costs and benefits of expensive certain audit programs. Keywords: Tax examination; Internal Revenue Service; Coordinated Industry Case program
JEL Classification: H25; M20; M41 The authors appreciate helpful comments from Christine Cheng, Danielle Higgins, and workshop participants at the College of William & Mary, University of Houston, University of Kansas, University of Virginia McIntire School of Commerce, the 2014 American Accounting Association Annual Meeting, and the 2015 IRS-TPC Research Conference. We also thank John Miller and Barbara Hecimovich for providing information about the IRS CIC program.
The Internal Revenue Service (IRS) provided confidential tax information to Towery pursuant to provisions of the Internal Revenue Code that allow disclosure of information to a contractor to the extent necessary to perform a research contract for the IRS. None of the confidential tax information received from the IRS will be disclosed in this treatise. Statistical aggregates will be used so that a specific taxpayer cannot be identified from information supplied by the IRS. All opinions are those of the authors and do not reflect the views of the IRS.
Contact author: Erin Towery, Physical address: Tull School of Accounting, University of Georgia, 232 Brooks Hall, 310 Herty Drive, Athens, GA, 30602, USA. Telephone number: 706-542-3620. Email address: [email protected].
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I. INTRODUCTION
This study investigates the effect of tax audit certainty on corporate taxpayer behavior.1
Understanding how audit certainty affects corporate taxpayers is important because many of the
largest firms in the U.S. are assigned the Internal Revenue Service’s Coordinated Industry Case
(CIC) program, where the risk of audit examination is 100 percent. We estimate that firms
assigned to the CIC program account for between 65 and 70% of the U.S. market cap in 2011.
Further, the IRS invests a sizeable portion of its resources in these efforts. The mean (median)
CIC audit used approximately 2,500 (1,700) IRS-personnel-hours during our sample period. We
also estimate that less than half of tax returns filed in the year preceding CIC assignment are
audited.2 Thus, the IRS commitment to a program of audit certainty is both an economically
substantial resource allocation decision for the U.S. government and a nontrivial increase in audit
probability for the CIC firms. Studying the effect of audit certainty on taxpayer behavior has
been difficult because corporate taxpayers are not required to publicly disclose whether they face
certain audit. We overcome this data limitation using confidential corporate tax return data and a
confidential dataset of corporate taxpayers in the CIC program.
The effect of audit certainty on taxpayer behavior is not clear ex ante. Over the past
several decades, both theoretical and empirical studies have documented that the risk of tax audit
examination affects taxpayer behavior. These studies generally predict that taxpayers enter fewer
uncertain tax positions to reduce their probability of audit when tax uncertainty increases the
probability of audit selection. However, prior research has not specifically considered whether
1 We define tax audit certainty as a 100 percent likelihood that a tax return is subject to audit. 2 We use confidential IRS audit examination data to estimate hours per CIC audit and the audit rate before CIC assignment. Our audit rate before CIC assignment is overstated to the extent that the CIC team audits pre-CIC tax return years after taxpayers are assigned to the CIC program. See Footnote 9 for further detail.
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this prediction applies to firms subject to audit certainty.3 Indeed, because tax uncertainty does
not affect the probability of tax audit examination for taxpayers facing audit certainty, these
firms likely have different incentives to engage in uncertain tax avoidance.4 On the one hand,
taxpayers facing audit certainty could have less incentive to claim uncertain tax positions if the
increased detection risk lowers the expected benefit of tax uncertainty such that a subset of tax
positions are not value‐creating. This would be consistent with the negative relation between
audit selection risk and tax avoidance documented at lower points on the audit probability
spectrum (Hoopes, Mescall, and Pittman 2012).
On the other hand, because taxpayers facing certain audit have no incentive to reduce
their tax planning to avoid IRS audit selection, audit certainty could result in increased tax
uncertainty. Consistent with this intuition, Slemrod, Blumenthal, and Christian (2001) finds that
high-income individual taxpayers reported lower taxable income amounts when told before filing
their tax return that they will be audited with certainty. The authors conjecture that taxpayers
claim additional tax benefits to create a more aggressive starting point for negotiations, with the
goal of minimizing tax liability and under the assumption that the audit will not detect and
punish all tax avoidance. In sum, how tax audit certainty affects taxpayer behavior is an
empirical question.
Before testing our research question, we first analyze the determinants of assignment to
the CIC program using selection factors outlined in the Internal Revenue Manual (IRM), which
3 One exception is Hanlon, Mills, and Slemrod (2007), a study that examines determinants of IRS proposed audit adjustments. In a supplemental test, the authors find using a levels analysis that CIC firms and non-CIC firms have similar GAAP effective tax rates (computed using current tax expense from the financial statements). In contrast, our study uses both levels and changes analyses to investigate how CIC assignment impacts initial tax return filing positions, initial filing positions adjusted for future settlements, and tax reserves. The first two measures enable us to disentangle deterrence and enforcement effects, and the latter measure is a proxy for managers’ expectations regarding future tax payments. 4 While the probability of audit is 100 percent for firms in the CIC program, the probability of audit for any particular transaction remains less than 100 percent. Thus, taxpayer behavior could continue to affect the audit risk of any particular transaction.
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capture various size and complexity determinants. Though other research cites size and
complexity as determinants of CIC assignment (Mills 1998; Hanlon, Mills, and Slemrod 2007),
these statements are based on the IRM listed factors rather than on empirical tests. Our analysis
tests these statements and serves two purposes: (i) to determine whether CIC program
assignment is mechanical in nature based upon the IRM listed factors or whether factors
associated with tax avoidance (beyond size and complexity) also significantly contribute to
assignment, and (ii) to provide researchers without access to CIC assignment data a model of
audit certainty.
We find that many of the IRM listed factors are positively associated with assignment
into the CIC program, with gross receipts being the most significant size determinant and the
number of geographic segments being the most significant complexity determinant. When we
include factors known to affect firms’ incentives or ability to avoid taxes (such as research and
development expenses, excess stock option deductions, and net operating loss carryforwards) as
potential determinants, we find that a number of the factors are significantly associated with CIC
assignment. However, their inclusion does not dramatically improve the fit of the model. These
results collectively suggest that although inclusion in the CIC program is associated with firms’
incentives or ability to avoid taxes, the CIC assignment decision is primarily based on firm size
and complexity and is largely mechanical.
Next, we study the effect of audit certainty on taxpayers’ initial filing liabilities, total
filing liabilities, and financial statement reserves for uncertain tax positions. The taxpayer’s
initial federal filing liability rate serves as our variable to test whether audit certainty has a
deterrence effect on tax avoidance behavior. The advantage of the taxpayer’s initial federal
liability rate relative to tax avoidance measures used in prior literature is that it captures initial
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tax payments to the tax authority and is not confounded by financial reporting incentives. We
measure the combined effect of deterrence and enforcement using: (i) the taxpayer’s initial
federal liability rate adjusted for settlements, and (ii) the cash effective tax rate (ETR). Financial
statement reserves for uncertain tax positions claimed in the current year proxy for managers’
expectations of future tax payments associated with current tax return positions.
We use both a levels approach and a changes approach to test our research question. We
implement our levels analysis using a pooled sample from 2000 to 2011 of firms assigned to the
CIC program and firms not assigned the CIC program. Using a variety of different scalars and
fixed effects, we find no significant differences in initial federal filing liability rates, adjusted
federal filing liability rates, or cash effective tax rates between firms assigned to the CIC
program and firms not assigned to the program. We do, however, find that firms assigned to the
CIC program report higher reserves for current year tax positions relative to non-CIC firms,
suggesting that CIC firms expect higher future tax payments associated with current tax return
positions than non-CIC firms.
To implement our changes analysis, we identify 405 corporate taxpayers that are first
assigned to the CIC program between 2000 and 2011 (‘newly-assigned firms’). We then
construct two samples of propensity-matched control firms—(i) firms never assigned to the
program between 2000 and 2011 (‘non-assigned firms’), and (ii) firms assigned to the program
both before and after newly-assigned firms enter the CIC program (‘long-assigned firms’). The
matched sample design allows us to not only compare the tax behavior of a firm to itself before
and after the change in its CIC program status, but to also compare its tax behavior with the tax
behavior of a firm that does not experience a change in CIC status.
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We find that, post-assignment, the federal filing liability rates, the adjusted federal filing
liability rates, and the cash effective tax rates of newly-assigned firms are not statistically
different than those of the matched sample of non-assigned firms. Further, post-assignment, none
of the tax payment rates of newly-assigned firms are statistically different than the tax payment
rates of the matched sample of long-assigned firms. Thus, our results suggest that the CIC
program does not have significantly higher deterrence and enforcement effects relative to the
IRS’s standard selection and audit process for large corporations not included in the CIC
program.
Consistent with our levels analysis, we find that newly-assigned firms report higher
financial statement reserves for current year tax positions relative to both non-assigned and long-
assigned firms, suggesting that audit certainty does impact financial reporting for income taxes.
Our result that the initial tax liability does not change for newly-assigned firms suggests that the
increased reserves do not represent an increase in uncertain tax avoidance. More plausible
explanations include: (i) firms systematically underestimated their likelihood of sustaining a
position prior to CIC assignment and subsequently updated their expectations based on ‘learning’
in the audit process, and/or (ii) firms incorporated audit likelihood into their determination of
reserves prior to CIC assignment (inconsistent with the U.S. GAAP requirement that firms
assume audit certainty with respect to each uncertain tax position).
Our study expands the literature in multiple ways. First, our model of CIC determinants
provides researchers with a better proxy for the audit risk of large, publicly-traded companies.
Prior studies measure CIC participation as firms with at least $250 million in assets (e.g. El
Ghoul, Guedhami, and Pittman 2011; Hoopes et al. 2012). We report that only 19.5 percent of
firms with assets greater than $250M are assigned to the CIC program, suggesting that this
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commonly-used proxy is quite weak in distinguishing large firms subject to certain audit.5
Second, we provide evidence that CIC assignment is mechanical in nature and that factors
associated with a firms’ incentives or ability to avoid taxes are not currently a significant
determinant of this substantial resource allocation decision. Third, to our knowledge, our study is
the first to analyze the deterrence and enforcement effects of audit certainty. In doing so, we
further our understanding of the strategic game between the taxpayer and the tax authority. We
do not view our results in contrast with prior research that documents a negative relation, on
average, between audit probability and tax avoidance (Mills and Sansing 2000; Hoopes et al.
2012). Our study focuses on the top end of the audit probability continuum, where reporting
behavior cannot affect audit selection.
Our findings are also important to tax authorities. Understanding how audit risk affects
taxpayer behavior informs the IRS as it designs and implements new audit approaches. Per a
discussion between one of the authors and the IRS, CIC audits consume a substantial portion of
IRS Large Business and International (LB&I) audit resources. Whether and how firms alter
behavior within the CIC program informs the cost-benefit assessment of the program. Our results
suggest that the CIC program does not have significantly higher deterrence and enforcement
effects than the IRS’s non-CIC audit process for large corporations. Although not anticipated ex
ante, these results are consistent with the IRS’s recent announcement to potentially modify the
CIC program to incorporate an as-yet-unspecified risk-based approach that will likely leave
many large corporate taxpayers uncertain of whether to expect an audit.
5 However, given that we find no significant difference in the initial tax liability, the adjusted tax liability, or the cash effective tax rates for CIC firms relative to non-CIC firms, we caution researchers to consider whether IRS audit risk is expected to have the same behavioral effect at the top end of the continuum as researchers have documented at lower points of the continuum.
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The remainder of the paper is organized as follows. Section II outlines the CIC program
and describes relevant literature. Section III details research design. Section IV presents results,
Section V presents robustness checks and Section VI concludes.
II. BACKGROUND
Coordinated Industry Case (CIC) Program
Many of the largest corporate taxpayers in the United States are subject to certain audit as
part of the Internal Revenue Service’s CIC program. Between 500 and 1,500 taxpayers are
assigned to the CIC program in a given year, though the number varies over time. The IRS
implemented the CIC program, formerly the Coordinated Examination Program, in the 1960s in
response to the growing complexity of U.S. business operations. For CIC firms, a team from the
IRS’s Large Business and International (LB&I) group spends a substantial amount of time in the
taxpayer’s primary place of business throughout the year.6 The IRS team consists of the
examination team manager, field agents, industry specialists, and subject matter experts. CIC
audit teams generally provide more in-depth audits than traditional IRS audits. For example,
subject matter experts in areas such as engineering, excise taxes, and employment are included in
the list of specialists assigned to a CIC audit team.
Firms are assigned to, rather than invited into, the CIC Program.7 Per the Internal
Revenue Manual, this assignment is made based on a point system involving seven main criteria:
(i) gross assets; (ii) gross receipts; (iii) operating entities; (iv) number of industries; (v) total
foreign assets; (vi) related transactions; and (vii) foreign taxes paid (Internal Revenue Manual,
6 Anecdotally, the audit team generally spends enough time at the taxpayer’s place of business to warrant an office within the taxpayer’s place of business permanently designated for the team. 7 This contrasts with the IRS Compliance Assurance Program (CAP), where a corporate taxpayer voluntarily agrees to discuss uncertain tax issues with an IRS team prior to filing its annual tax return. See De Simone, Sansing, and Seidman (2013) and Beck and Lisowsky (2013) for evidence on the effect of voluntary audits on taxpayer behavior.
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4.46.2.5). Each criterion has a point value, and a firm is assigned to the CIC program if its total
point value is greater than or equal to 12. Firms with a point value of less than 12 can also be
assigned to the CIC program if they are sufficiently complex to warrant certain audit.
Appendix A provides a timeline of CIC program assignment. When the IRS assigns a
taxpayer into the CIC program, the taxpayer is informed of the assignment after filing its annual
tax return. The taxpayer is considered to be in the CIC program for the year prior to notification,
although the CIC team often audits tax returns for multiple years prior to CIC assignment.8 Once
assigned to the CIC program, a taxpayer typically remains in the CIC program until the audit no
longer requires a team audit approach (their point value falls), or until the taxpayer ceases to
exist because of either bankruptcy or combination with another operating entity.
Related Literature
The strategic game between the taxpayer and the tax authority has been studied
extensively in the accounting, economics, and finance literatures. Early models (e.g., , Allingham
and Sandmo 1972) characterize taxpayer compliance as a function of tax rates, probability of
detection and punishment, penalties, and taxpayer risk-aversion.9,10 Graetz, Reinganum, and
Wilde (1986) extends these basic models to allow the IRS to condition its audit rules on the
reports it receives from taxpayers. This ‘strategic tax compliance model’ provides different
predictions than earlier models because a taxpayer must now consider how its report affects the
8 For example, a taxpayer may be notified in late 2006 after filing its 2005 tax return that it has been assigned to the CIC program. The taxpayer is coded as a CIC firm for 2005 because the audit of the 2005 tax return will be conducted by a CIC team. However, the CIC team sometimes also audits prior years in which the statute of limitations remains open. 9 Noncompliance can either be entering into fraudulent tax positions or entering into uncertain tax positions, where those with weaker facts are more likely to be overturned if discovered than those with stronger facts. 10 Many models assume perfect detection, meaning that hey assume all uncertain tax positions are detected and only those with strong facts are upheld by the taxpayer if the firm is audited,. This simplifying assumption does not generalize well to the reality of audits of large corporations with several uncertain tax positions. However, detection risk is likely to increase under certain audit both because the probability of the firm being audited increases to one and because the coordinated nature of these programs allows the tax authority to gain greater knowledge of the taxpayer’s business, thus potentially enabling the tax authority to better identify uncertain tax positions.
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probability of audit when it chooses which amount to report; that is a taxpayer with high income
who considers reporting low income not only considers the tax savings and potential penalties if
caught, but also that reporting low income increases the probability of audit.
The most basic strategic tax compliance model predicts a negative association between
audit risk and tax uncertainty, consistent with firms engaging in less tax uncertainty to reduce
their probability of audit because uncertain tax positions increase the probability of audit (Graetz
et al. 1986).
The strategic tax compliance model has been extended in many ways.11 Our paper is most
closely related to models which focus on the probability of detection. For example, Sansing
(1993) introduces certain and verifiable information, which allows the IRS to better focus audits
on taxpayers with unverifiable information. Rhoades (1999) allows the audit decision to be made
at the component level and determines that the IRS conditions its second audit decision on the
results of the first component’s audit. Mills and Sansing (2000) models that the difference
between book and taxable income, both of which the IRS observes, can be informative to the
IRS. Their empirical results show that though the magnitude of book-tax differences is positively
associated with IRS proposed audit adjustments, book-tax differences are not associated with
IRS settlements. This result suggests that corporate taxpayers require strong facts to enter into
uncertain tax positions that will generate a book-tax difference because they appreciate that
book-tax differences convey information to the tax authority.
However, for firms facing certain audit, engaging in less tax uncertainty does not reduce
the probability that the firm is subject to audit. Though taxpayer behavior may reduce the
probability that a particular line item or transaction is subject to audit, Rhoades (1999) suggests
11 For example, Beck and Jung (1989a, 1989b) incorporate taxpayer uncertainty about their tax liability and Erard and Feinstein (1994) allows for some taxpayers to be inherently honest.
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that the probability of a line item claimed by a certain audit firm being audited is greater than or
equal to the probability of a line item claimed by a non-certain audit firm being audited. In other
words, firms facing certain audit generally have less ability to influence the probability that a
particular line item is audited than firms not facing certain audit.
The strategic tax compliance model discussed above provides some insight into taxpayer
behavior in our setting. On the one hand, taxpayers could have less incentive to engage in
uncertain tax avoidance if the increased audit probability decreases the expected benefit of tax
uncertainty such that a subset of tax positions are no longer value-creating. On the other hand,
Mills and Sansing (2000) suggests certain audit could increase the incentive to engage in
uncertain tax avoidance. Specifically, because the IRS will audit the firm regardless of the
signals provided in the financial statements, certain audit firms no longer have incentive to
reduce the difference between book and taxable income.
This intuition is consistent with results presented in Slemrod et al. (2001), which reports
results of a 1995 experiment by the Minnesota Department of Revenue under which a random
sample of individual taxpayers were told that the returns that they were about to file would be
“closely examined.” Relative to the sample not told this, the high-income members of the ‘audit
certain’ sample significantly decreased their reported tax liability. The authors conjecture that
these individual taxpayers claim more tax benefits to create a more aggressive starting point for
negotiations with the goal of minimizing tax liability. Further, the authors postulate that this
effect is observed in high-income taxpayers, but not low- or middle-income taxpayers, because
high-income taxpayers believe that the final outcome of the certain audit is more manipulable
due to their ability to hire professional assistance. This logic likely applies to the corporate
taxpayers we study, as firms assigned to the CIC program tend to be large and/or have complex
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operations, both of which are likely correlated with the likelihood of professional tax assistance.
However, the corporate taxpayers in our sample have financial reporting obligations that
individuals do not, which could cause these two types of taxpayers to have different tax
avoidance preferences. In sum, how tax certainty affects taxpayer behavior is an empirical
question.
Our paper is also related to three recent applied papers regarding the tax compliance
game between the IRS and corporations. Hoopes et al. (2012), DeBacker, Heim, Tran, and
Yuskavage (2013), and Lennox, Li, Pittman, and Wang (2015) suggest that increased
enforcement leads to greater tax payments. Using data on IRS audit probability by firm size over
time, Hoopes et al. (2012) provides evidence that firms with greater risk of IRS audit report
higher cash effective tax rates relative to firms with lower risk of IRS audit. However, this result
is not necessarily due to a change in tax avoidance behavior because the increase in cash tax
rates could arise because of firms’ decreasing tax avoidance to avoid audit selection or increased
audit settlements as a result of higher tax enforcement.
DeBacker et al. (2013) finds that tax payments are lower immediately following an audit,
when the taxpayer is assumed to have lower expectations of another audit. As time passes and
the probability of another audit increases, tax payments increase. Thus, similar to Hoopes et al.
(2012), Debacker et al. (2013) concludes that firms engage in less tax avoidance behavior when
audit selection probability is higher. Finally, Lennox et al. (2015) examines the effect of tax audit
examination on Chinese firms. Their results suggest firms report higher GAAP effective tax rates
and lower book-tax differences after being audited. Our paper differs from these three studies
because we test how taxpayers behave when facing audit certainty, an economically important
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setting between taxpayers and the tax authority that is fundamentally different from those
previously examined.
Hypothesis Development
Although the aforementioned literature provides rich insight into taxpayer behavior, no
studies have modeled or empirically examined the case where audit probability equals one.
While we can input parameters to force the probability of audit to one, prior theoretical models
are designed for the tax authority to have an audit decision (to audit or not to audit). Thus, while
our research question is informed by prior models, we develop our hypothesis primarily based on
the following intuition.
A taxpayer’s expected total tax payment equals the tax liability on their originally filed
return plus the expectation of any future settlements:
E[total tax payment] = tax liability as originally filed + prob(audit)*E(settlement) [1]
Prior literature primarily considers situations where the probability of the firm being audited is a
function of the tax liability as originally filed:
Prob(audit) = f (tax liability as originally filed, political environment, IRS budget, complexity and size of taxpayer, etc.) [2]
In Equation [2], when the probability of audit selection increases, the taxpayer can increase the
tax liability on the originally-filed return (for example, by not entering into the most aggressive
tax positions) to lower the probability of audit selection.
In our setting, however, the probability of firm audit selection is not a function of the tax
liability as originally filed because Prob(audit) equals one upon assignment to the program.
Additionally, rational taxpayers should adjust E[total tax payment] upwards upon CIC program
assignment as the continued existence of this program suggests that it generates additional
revenue. If a taxpayer believes that an audit reveals complete truth (i.e., perfect detection risk),
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the taxpayer may choose to accept the expected higher tax payment (increase initial filing
liability), which likely decreases the time associated with the audit and eliminates interest and
penalties associated with expected disallowed tax positions. Alternatively, the taxpayer may not
change tax avoidance behavior or even become more aggressive to begin negotiations from a
more favorable starting point if the taxpayer does not believe in perfect detection risk or that a
higher total tax payment is certain despite a certain audit.12 Thus, taxpayers may increase,
decrease, or not change their avoidance behavior when the probability of firm audit increases to
one.
Hypothesis: Audit certainty does not affect taxpayer behavior.
III. RESEARCH DESIGN
Determinants of CIC Assignment
We first study the determinants of CIC Program assignment following the IRS’s stated
assignment criteria. The Internal Revenue Manual lists seven criteria “to be used in identifying
those cases for the CIC Program.” These criteria encompass various measures of size and
complexity. Following IRM Exhibit 4.42.2-1, we specify the following logistic regression:
CICFirm = α + β*Size + γ*Complexity + ε [3]
where CICFirm equals one if firm is assigned to CIC program and zero otherwise. The various
Size and Complexity measures are defined with a discrete point system similar to that specified in
the Internal Revenue Manual.
Although the IRS uses tax return disclosures to assign points, we use only publicly-
available data to estimate the determinants of CIC prediction in our model specifications so that
12 Imperfect detection could arise for a number of reasons, including noise in the audit process, not all transactions being audited, or if a nonstrategic model better describes taxpayer behavior.
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future researchers can use the model to estimate CIC assignment. Both Total Assets (Compustat
Annual disclosures variable AT) and Net Sales (SALE) proxy for Size. We employ four proxies
for Complexity: the number of geographic segments in the Compustat segmerged dataset, the
number of business segments in the Compustat segmerged dataset, foreign sales (SALEFO) and
foreign tax (TXFO+TXDFO). See Appendix B for more detailed information about the point
system.
We next test whether firm attributes that affect the incentive or ability to avoid taxes
significantly influence the CIC assignment decision. We incorporate five firm attributes into
Equation [3] as follows:
CICFirm = α + β*Size + γ*Complexity + δ*FirmAttributes + ε [4]
Leverage (DLTT/AT) captures a conforming book-tax strategy; consistent with the tax
exhaustion theory outlined in DeAngelo and Masulis (1980), firms with high levels of debt tax
shields should engage in fewer additional tax avoidance strategies and thus should be of less
interest to the IRS. R&D (XRD/SALE) may positively affect CIC assignment as the research and
development (R&D) credit is an area of significant complexity, aggressiveness, and conflict, and
the IRS notes that CIC assignment is based in part on complexity. However, following the tax
exhaustion logic outlined for Leverage, R&D may negatively affect CIC assignment because
firms with significant R&D credits should engage in fewer other tax avoidance strategies. CapInt
(PPENT/AT) proxies for the capital intensity of a firm; similar to R&D, this variable may
positively affect CIC assignment if the IRS is concerned about asset category assignment or
negatively affect CIC assignment under the tax exhaustion hypothesis. ExcessStockBen (one if
TXBCOF > 0, zero otherwise) captures tax-saving stock option deductions in excess of stock
option expenses and should be negatively related to CIC assignment under the tax exhaustion
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theory (Graham, Lang and Shackelford 2004). Finally, NOL (one if TLCF > 0, zero otherwise)
captures whether the firm has a net operating loss (NOL) carryforward; firms with NOL
carryforwards have lower incentives to engage in additional tax avoidance strategies.
Multivariate Regression Specification
We first estimate a pooled analysis to test whether firms assigned to the CIC program
report greater tax liabilities and/or higher estimates of future taxes related to current uncertain tax
positions.
Tax = β0 + β1*CICParticipationInd + Controls + ε [5]
We measure Tax in four ways. Fed_Cash_ETR (Total tax reported on Page 1 Line 31 of
Form 1120/PI) represents initial filing behavior. Adj_Fed_Cash_ETR (Total tax reported on Page
1 Line 31 of Form 1120 + Settlements paid to IRS/PI) and Cash_ETR (TXPD/PI) represent total
payments made to the IRS.13 UTB_CY_ADD (TXTUBPOSINC/PI) represents expected cash
payments for current period taxes.14 CICParticipationInd equals one for tax return-year
observations where the firm is aware that it is assigned to the CIC program and zero otherwise.15
We interpret an insignificant coefficient on CICParticipationInd as consistent with the null
hypothesis that audit certainty does not affect taxpayer behavior.
We next use changes analyses to examine whether tax behavior changes for firms that are
assigned to the CIC program during our sample period 2000 to 2011 (‘newly-assigned firms’).
An important issue in examining whether behavior changes when a firm enter the CIC program
is that we do not observe the counterfactual—what would have happened had the firm not been
13 We measure settlements paid to the IRS using a confidential IRS database. Our use of these data demands an important caveat. Even in the CIC program, proposed audit adjustments often take a number of years to become settlements as the firm applies its right to various methods of appeal and litigation. Thus, firms with missing settlement data may still be appealing the proposed audit adjustment. Incorrectly assuming that the final settlement equals zero for ∆Firms or for non-∆Firms would understate the settlement amount. 14 Our results are unchanged if we scale by domestic pretax income (PIDOM) in place of worldwide pretax income. 15 Specifically, CICParticipationInd equals one for years t+1 through t+4 for newly-assigned firms and one for all years for long-assigned firms. Appendix A presents a timeline of CIC program assignment.
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assigned to the CIC program. Specifically, the trends in tax behavior could be explained by the
nature of the firms that are experiencing a program status change, by industry trends, or by mean
reversion. To address the counterfactual issue, we construct two control samples.
The first matched sample for newly-assigned firms begins with all firms that are not
assigned to the CIC program during our sample period 2000 to 2011 (‘non-assigned firms’).
Non-assigned firms are matched with firms in the year the firm is first assigned to the CIC
program on both year and the propensity score generated in Equation [4].16
The second matched sample for the newly-assigned firms begins with all firms that have
been assigned to the CIC program for at least four years and remain assigned to the CIC program
(‘long-assigned firms’). As with non-assigned firms, long-assigned control firms are matched
with newly-assigned treatment firms on year and propensity score. This matched sample design
allows us to not only compare the tax behavior of the firm to itself before and after the change in
its CIC program status, but to also compare its tax behavior with the tax behavior of a firm that
does not experience a change in CIC status.17
Tax = β0 + β1*POST + β2*∆Firm + β3* POST*∆Firm + Controls + ε [6]
As above, we measure Tax as Fed_Cash_ETR, Adj_Fed_Cash_ETR, Cash_ETR, or
UTB_CY_ADD. POST equals one for both the ∆Firm and its matched non-∆Firm for all years
after the change in CIC program status. Thus, POST represents years under which the ∆Firm is
16 A taxpayer first coded as a CIC firm in year t is notified of the assignment in mid- to late t+1. Because year t financial statement data is available at the time of assignment, we use year t data to calculate the propensity score for matching purposes. 17 To assess the viability of our matched samples, we examine the similarity of the covariate distributions between the newly-assigned firms and our matched samples using a standardized bias measure. For each covariate, we subtract the mean for the matched sample from the mean for the newly-assigned sample and divide by the standard deviation of the covariate. Our average covariate balance for the newly-assigned firms and the non-assigned firms equals 0.15; our average covariate balance for the newly-assigned firms and the long-assigned firms equals 0.13. Both balances fall below the generally accepted cut-off of 0.25 (Ho, Imai, King, and Stewart 2007). Nonetheless, because our matched samples are significantly different from our newly-assigned firms on some dimensions, we include the covariates as control variables in our tests of whether audit certainty affects taxpayer behavior.
17
aware that it faces certain audit (i.e., POST equals one for years t+1 through t+4). ∆Firm equals
one for firms that experience a change in their CIC program assignment status during our sample
period and zero otherwise. In other words, ∆Firm equals zero for either non-assigned firms or
long-assigned firms. When we estimate Equations [5] and [6], we include the proxies for Size,
Complexity, and FirmAttributes from Equation [4] to control for differences between the firms
whose CIC assignment status changes and the matched sample that does not experience a status
change. We interpret an insignificant coefficient on β3 as consistent with our hypothesis.
IV. SAMPLES AND RESULTS
CIC Prediction Model
The sample used to estimate Equations [3] and [4] for the determinants of CIC
assignment begins with all publicly-traded corporate tax return-years with at least $250 million
in total assets. We impose this size restriction to at least partially control for differences in size
between CIC and non-CIC firms.18,19 We exclude observations without Compustat data or with
missing values for the required variables and estimate Equations [3] and [4] on a sample of
23,094 firm-year observations. Panel A of Table 1 details the selection of this sample and Panel
B details the number of observations by year.
[Insert Table 1 around here]
Table 2 provides descriptive statistics on our CIC prediction model sample identified in
Table 1. Panel A presents descriptive statistics for the 4,493 firm-years assigned to the CIC
program, and Panel B presents descriptive statistics for 18,601 firm-years not assigned to the
18 We explore an alternative size requirement, $500 million in total assets, in Section V. 19 We acknowledge the possibility that non-CIC firms can be under audit without participating in the CIC program. However, unlike CIC firms that know they will be audited with certainty before filing their tax return, non-CIC firms are not informed of the audit until after filing their tax return.
18
CIC program. Many prior studies proxy for CIC program assignment with an indicator variable
equal to one for firm-years with at least $250 million in total assets, and zero otherwise. That
only 19.5 percent of our firm-year observations with at least $250 million in total assets are
assigned to the CIC program suggests that this commonly-used proxy is quite noisy in
distinguishing large firms under certain audit.
Panels A and B show that firm-years assigned to the CIC program report greater assets
and higher gross receipts than firm-years not assigned to the CIC program. Additionally, firm-
years assigned to the program report more geographic and business segments, higher foreign
sales, and higher foreign tax than firm-years not assigned to the CIC program. These differences
are consistent with the CIC identification criteria outlined in the Internal Revenue Manual.
In addition, untabulated statistics suggest that CIC program assignment is quite sticky.
The median firm assigned to the program for at least one year is assigned to the program for 83
percent of the years it exists in the database of federal tax return data; at least 25 percent of
sample firms are assigned to the program for all the years they exist in the database.20 More than
75 percent of sample firms are never assigned to the CIC program during our sample period.
[Insert Table 2 around here]
Table 3 presents our CIC prediction model which assumes that, consistent with the
Internal Revenue Manual, Size and Complexity are the primary factors of assignment. Model [1]
estimates Equation [3], which includes only the Size and Complexity proxies outlined in IRM
Exhibit 4.42.2-1; Model [2] includes additional firm attributes in estimating Equation [4]. Panel
20 In a previous version, we attempted to study firms that were released from the CIC program. We identified 289 firms that appeared to be released during our period. However, under deeper inquiry, we discovered that only 60 of those firms were truly released. The remaining 229 firms were temporarily released from the CIC audit for various reasons, such as reporting a loss. Because so few firms were truly released from the CIC program, we have removed this analysis from the paper.
19
A includes all firm-year observations outlined in Table 1. In Panel A,CICFirm equals one for
firm-years in which the firm is assigned to the CIC program and zero otherwise.
Panel B tests determinants of the initial CIC assignment decision using a sample of
observations that were not assigned the CIC program in t-1. A firm assigned to the CIC program
during our sample period is removed from the determinant sample beginning the year after its
assignment. Thus, a firm never assigned to the CIC program remains in the sample for all years,
while a firm assigned to the CIC program for every year between 2000 and 2011 is never
included. In Panel B, CICFirm equals one for observations that have initially been assigned to
the CIC program in t and zero otherwise.
[Insert Table 3 around here]
Consistent with IRM Exhibit 4.42.2-1, both Size and Complexity factors contribute to
CIC assignment. However, when CIC firm-years are excluded after the initial year of CIC
assignment (Panel B), foreign complexity is no longer consistently directly related to CIC
assignment but likely remains indirectly related to CIC assignment because of its effect on the
number of geographic and/or business segments. Together, our results suggest that sales are the
most significant Size determinant for CIC assignment and that the number of geographic
segments is the most significant Complexity determinant for CIC assignment.
We estimate that a number of additional firm attributes are also associated with CIC
assignment. Consistent with R&D expenses contributing to firm complexity (and controversy),
we find that firms with higher R&D expenses are more likely to be assigned to the CIC program.
Consistent with tax exhaustion, we find that firms reporting excess tax benefits from stock
options or an NOL carryforward are less likely to be assigned to the CIC program. Despite the
numerous significant coefficients, however, the inclusion of these firm attributes does not change
20
the area under the ROC curve in a meaningful way. Thus, while CIC assignment is associated
with common factors related to firms’ incentives or ability to avoid taxes, it appears that Size and
Complexity are the primary determinants of CIC assignment.
Finally, in untabulated results, we include only the firm attributes that affect the incentive
or ability to avoid taxes or the recent past initial federal filing liability, Fed_Cash_ETR, as
potential determinants of CIC assignment. We find that the firm attributes alone give an area
under the ROC curve of 56.70 percent while the prior year Fed_Cash_ETR alone generates an
area of 50.55 percent. When compared to the areas under the ROC curve of 94.02 percent and
86.53 percent estimated using Model [1] in Table 3, these results continue to suggest that the Size
and Complexity variables outlined by the IRS are the most significant determinants of CIC
assignment.
Effect of Audit Certainty on Taxpayer Behavior
We use the CIC prediction model sample of 23,094 firm-year observations for our pooled
levels analysis. For our changes analyses, we begin the construction of the non-assigned sample
with the 405 firms first assigned to the IRS CIC program between 2000 and 2011. We calculate
the CIC prediction score for these firms in the year of their CIC assignment. Using the sample of
firms that are never in the CIC program between 2000 and 2011, we construct a sample of
control firms that are matched on both year and CIC prediction score.21 For the 810 test and
control firms, we obtain Compustat data from years t-2 to t+4, where year t represents the year
the firm was assigned to the CIC program and post-assignment is considered to be years t+1
through t+4.22 This results in a final sample of 4,266 tax return-year observations.
21 We explore alternative matching specifications in Section V. 22 We require both the newly-assigned firm and the non-assigned match firm to have sufficient data in years t-1 to t+1 for the firms to remain in the sample. If either firm is not in the database in years t-2 or t+2 to t+4, we omit both
21
Panel A of Table 4 provides descriptive statistics for the newly-assigned and non-
assigned firms in year t, the year of the match. Even though firms are matched on year and then
propensity score, firms still differ on some dimensions. Specifically, newly-assigned firms are
larger in terms of both assets and gross receipts and have more foreign sales, foreign tax,
leverage and R&D than their matched sample. Accordingly, we include these variables in our
subsequent models to control for the effects that these differences may have on taxpayer
behavior.
[Insert Table 4 around here]
As above, we begin the construction of the long-assigned sample with the 405 firms first
assigned to the IRS CIC program between 2000 and 2011. Using the sample of firms that are in
the CIC program for the four years prior to the ∆Firms’ assignment to the program, we construct
a sample of control firms that are matched on both year and CIC prediction score.Panel B of
Table 4 provides descriptive statistics for the newly-assigned and long-assigned firms in the year
of the match. For the 234 test and control firms, we obtain Compustat data from t-2 to t+4,
which results in a sample of 1,344 tax return-year observations. Panel C of Table 4 presents
descriptive statistics for each of our Tax measures in: (i) the pooled sample, (ii) the non-assigned
sample, and (iii) the long-assigned sample.
Tables 5 through 8 present results of testing the null hypothesis that taxpayer behavior
does not change when a firm’s audit probability increases to one. In all four tables, Panel A
presents the pooled test while Panels B and C present the changes analysis using the non-
assigned and long-assigned matched samples, respectively. In Panels B and C, the first column
presents results of estimating a simplified version of Equation [6] on only the newly-assigned
firms in those years so that the match remains one-to-one based on firm-year. We follow the same methodology for the long-assigned matched sample.
22
firms (‘∆Firms’); the second column mirrors the first for the matched control sample (‘non-
∆Firms’); and the third column includes the full specification of Equation [6] with interactions to
test the significance of any difference in tax behavior as well as controls for differences with the
match itself.
[Insert Table 5 around here]
We proxy for Tax using the initial federal filing liability rate (Fed_Cash_ETR) in Table
5. In Panel A, we estimate no significant difference in Fed_Cash_ETR between firms assigned to
the CIC program and firms not assigned to the CIC program. Results presented in Panels B and
C confirm this finding: firms newly-assigned to the CIC program do not experience a significant
change in Fed_Cash_ETR relative to either a sample of firms never assigned to the CIC program
or to a sample of firms already assigned to the CIC program. In untabulated results, we include
industry, year or industry-year fixed effects in Panel A or define the initial federal filing liability
as a function of total assets report in Compustat (AT). In all of these alternative specifications,
we continue to estimate no significant change in Fed_Cash_ETR when firms are assigned to the
CIC program. Thus, results suggest that audit certainty does not have a significant deterrence
effect.
We proxy for Tax using Adj_Fed_Cash_ETR in Table 6 and Cash_ETR in Table 7. We
discuss the results presented in these two tables together because both measures are intended to
capture total tax payments. In Panel A, we estimate no significant difference in
Adj_Fed_Cash_ETR or Cash_ETR between firms assigned to the CIC program and firms not
assigned to the CIC program. The changes analysis in Panels B and C also estimate that firms do
not report a significant change in total federal cash payments or cash taxes paid, relative to
income, once they are assigned to the CIC program. As above, inferences are unchanged when
23
we include various fixed effects in Panel A or replace PI with AT as the scalar for Cash_ETR
across all three panels. In additional untabulated analysis, we disaggregate the POST variable
into four separate years to examine whether behavior changes over time. We estimate no
significant difference in Fed_Cash_ETRs, Adj_Fed_Cash_ETRs, or Cash_ETRs between ∆Firms
and non-∆Firms in any year after the change in program status. Overall, we are unable to reject
the null hypothesis that assignment to a certain audit program does not affect tax avoidance
behavior.
[Insert Tables 6 & 7 around here]
Finally, Table 8 estimates that audit certainty does impact current year additions to the
contingent tax reserve. Specifically, the pooled regression presented in Panel A estimates that
firms aware of their CIC program assignment report $0.95 million more current year additions to
the contingent tax reserve than do firms not assigned to the CIC program. Panels B and C offer
similar results, suggesting that relative to either matched sample, firms experiencing a change in
program status report an increase in UTB_CY_ADD. Inferences are unchanged if we include
fixed effects or an alternative scalar as outlined above. Thus, in a variety of specifications, we
consistently find that firms in the CIC program report higher contingent tax reserves. Our result
that the initial tax liability does not change for newly-assigned firms suggests that the increased
reserves do not represent an increase in aggressive tax avoidance. Instead, plausible explanations
include: (i) firms systematically underestimated their likelihood of sustaining a position prior to
CIC assignment and subsequently update their expectations based on learning new information
during the audit process, and/or (ii) firms incorporated audit likelihood in their determination of
reserves prior to CIC assignment (inconsistent with the U.S. GAAP requirement that firms
assume audit certainty with respect to each uncertain position).
24
In terms of economic magnitude, we estimate that firms experiencing a change in
program status report $5.4 million ($7.9 million) more current year additions to the contingent
tax reserve after joining the program relative to their matched non-assigned (long-assigned) firms
in the same period. Untabulated analysis suggests that the increase in current year additions
relative to the long-assigned firms is concentrated in the first two years after assignment is
known, while the increase in current year additions relative to the non-assigned firms occurs over
the full four-year time frame we study. It therefore appears that newly-assigned firms converge
to reporting a similar current year addition to long-assigned firms but that the difference between
newly-assigned and non-assigned firms persists.
[Insert Table 8 around here]
Together, results presented in Tables 5 through 8 suggest that audit certainty does not
have significant deterrence or enforcement effects but does increase the expected future tax
payments related to current period uncertain tax positions. This contrasts with the results
observed at lower points in the audit probability continuum that an increase in audit probability
results in fewer uncertain tax positions. Our results also contrast with the findings in Slemrod et
al. (2001) that individual taxpayers claim more uncertain tax positions when facing audit
certainty. One explanation for the differing results is that the individual taxpayers examined by
Slemrod et al. (2001) face only one year of audit certainty, while the corporate taxpayers in our
study face multiple consecutive years of audit certainty (a repeated game). More broadly, these
results suggest that that the CIC program does not have significantly higher deterrence and
enforcement effects than the IRS’s standard selection and audit process for large corporations not
included in the CIC program .
25
V. ROBUSTNESS TESTS
We outline a number of alternative specifications of the CIC prediction model and/or the
∆Firms/non-∆Firms match. Because Mills and Newberry (2001) suggest $500M in assets as an
alternative size cutoff for the CIC program, we estimate Model [1] for all panels of Table 3 on a
sample of firms with total assets of at least $500M. Consistent with their suggestion,
approximately 25.2 percent of the firms in this sample are assigned to the CIC program as
compared with 19.5 percent of the sample in Table 3. This heightened size requirement decreases
our samples in Panels A and B by 22.95 percent and 27.88 percent, respectively. However, signs
and statistical significance are virtually unchanged and the area of the ROC curve is also
relatively stable in this alternative specification. Specifically, the area under the ROC curve for
Model [1] in Panels A and B is 91.82 percent and 81.85 percent, similar to the statistics
presented in Table 3. Thus, results appear robust to alternative sample specifications regarding
firm size. Further, performing the non-assigned match based on this alternative sample does not
significantly change results of our hypothesis tests.
In Table 4, we create our matched samples using the propensity scores generated in
Model [2] in Panel A of Table 3, which models the assignment decision and includes the year of
assignment as well as additional years of program participation. In untabulated analysis, we
obtain consistent results when we instead create our control samples using the propensity scores
generated in Model [2] from Panels B of Table 3, which models the initial assignment. We also
match on the propensity score generated in Model [1] instead of Model [2] from Panel A and
find both statistically and economically similar results. Thus, results appear robust to these
alternative matching specifications.
26
Finally, as mentioned in Section IV,our inferences are unchanged when we include
industry, year or industry-year fixed effects in our pooled regressions. We also redefine our three
dependent variables to be scaled by Compustat Total Assets rather than Pre-tax Income and
continue to find similar results in both the pooled and changes specifications.
VI. CONCLUSION
This study explores the effect of audit certainty on taxpayer behavior by examining
taxpayers in the IRS Coordinated Industry Case (CIC) Program, a set of taxpayers that consume
a substantial portion of IRS LB&I audit resources. Our results suggest firms report similar initial
tax liabilities and total tax liabilities as a percentage of income after entering the CIC program
relative to propensity-matched control firms. In terms of the strategic tax model, our results
suggest that audit certainty does not have significant deterrence or enforcement effects.
However, we find that newly-assigned CIC taxpayers report higher additions to the contingent
tax reserves relative to propensity-matched control firms, suggesting that audit certainty does
impact financial reporting for income taxes.
Our study makes the following contributions. First, our model of the determinants of CIC
assignment provides researchers a more accurate way to estimate whether a firm faces certain
audit by the IRS. Prior studies generally choose an arbitrary size threshold for determining
whether a firm faces certain audit (e.g., $250 million in assets). We report that less than 20
percent of the population of firm-years with at least $250 million in assets face certain audit,
suggesting our model of CIC likelihood is a significantly more accurate way of operationalizing
audit certainty. Second, our findings suggest that CIC assignment is mechanical in nature and
that factors associated with a firms’ incentives or ability to avoid taxes are not a significant
27
determinant of this substantial resource allocation decision. Third, we further our understanding
of the strategic game between the taxpayer and the tax authority by examining the deterrence and
enforcement effects of audit certainty. Finally, our study provides data useful to tax authorities in
assessing the cost and benefits of the CIC program. Our results suggest that the CIC program
does not have significantly higher deterrence and enforcement effects than the IRS’s standard
process of auditing large corporations not in the CIC program – an important finding as the IRS
considers, designs and implements new audit approaches.
This research is subject to a number of caveats. First, although we attempt to address the
counterfactual issue with a matched sample of control firms, we cannot entirely rule out the
possibility that firms experiencing a change in CIC program status might have reported a similar
change in tax payments even absent a status change. Second, the U.S. allocates substantial
resources to its tax authority. To the extent that other tax authorities allocate fewer or greater
resources for tax enforcement, our results might not generalize to other certain audit programs.
28
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Accounting Review 89(3): 867-901. DeAngelo, H., and R. Masulis. 1980. Optimal Capital Structure under Corporate and Personal
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De Simone, L., R. Sansing, and J. Seidman. 2013. When are Enhanced Relationship Tax
Compliance Programs Mutually Beneficial? The Accounting Review 88(6): 1971-1991. El Ghoul, S., O. Guedhami, and J. Pittman. 2011. The Role of IRS Monitoring in Equity Pricing
in Public Firms. Contemporary Accounting Research 28(2): 643–674. Erard, B. and J. S. Feinstein. 1994. Honesty and Evasion in the Tax Compliance Game. The
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Graham, J., M. Lang, and D. Shackelford. 2004. Employee stock options, corporate taxes, and
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Ho, D., K. Imai, G. King, and E. Stewart. 2007. Matching as Nonparametric Preprocessing for
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Hoopes, J. L., D. Mescall, and J. A. Pittman. 2012. Do IRS Audits Deter Corporate Tax
Avoidance? The Accounting Review 87(5): 1603-1639.
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Lennox, C., W. Li, J. Pittman, and Z. Wang. 2015. The Determinants and Consequences of Tax Audits: Some Evidence from China. Nanyang Technological University, Fuzhou University, Memorial University of Newfoundland, and Shanghai University of Finance and Economics working paper.
Mills, L. F. 1998. Book-Tax Differences and Internal Revenue Service Adjustments. The
Accounting Review 36(2): 343-356. Mills, L. F., and K. J. Newberry. 2001. The Influence of Tax and Nontax Costs on Book-Tax
Reporting Differences: Public and Private Firms. Journal of the American Taxation Association 23(1): 1-19.
Mills, L. F., and R. C. Sansing. 2000. Strategic Tax and Financial Reporting Decisions: Theory
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Audit Strategies. The Accounting Review 74(1): 63-85. Sansing, R. C. 1993. Information Acquisition in a Tax Compliance Game. The Accounting
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30
APPENDIX A Timeline for CIC program assignment
file 10-K file 1120
Year end for year 0 for year 0 Year end ________________|__________|______________________|________|____________ 12/31/X0 03/01/X1 09/15/X1 12/31/X1
The highlighted portion (______) is the typical time period that a firm would be notified regarding CIC assignment for year 0. Thus, both the 10-K and the 1120 for year 0 were filed when the firm was not aware of its CIC assignment but both the 10-K and the 1120 for year t+1 are filed under audit certainty.
The propensity score match is done using year 0 10-K data. POST = 1 for years t+1 and forward, years under which the 10-K and 1120 are filed knowing that the
firm will be subject to CIC audit. Though years 0 and forward will certainly be subject to CIC audit, any previous open years could also
be subject to CIC audit.
31
APPENDIX B Summary of IRM Exhibit 4.46.2-2
The Internal Revenue manual outlines criteria for identification of CIC program assignment in Exhibit 4.46.2-2. The assignment is based on seven criteria: 1) Total Assets, 2) Gross Receipts, 3) Operating Entities, 4) Multiple Industry Status, 5) Total Foreign Assets, 6) Total Related Transactions and 7) Foreign Tax. We model our variables and point assignment scheme off this document.
Aside from book-tax consolidation differences, we are able to use Compustat data and closely follow the variable definition and point assignment system for criteria 1) Total Assets, 2) Gross Receipts and 7) Foreign Tax.
1. Total Assets (Compustat AT, IRS 4.46.2-2 point system) •1 point up to $500 Million in assets; •2 points for assets in the $500 Million to $1 Billion asset range; •3 points for assets in the $1 Billion to $2 Billion asset range; •4 points for assets in the $2 Billion to $5 Billion asset range; •5 points for assets in the $5 Billion to $8 Billion asset range; •Add 1 point for each additional $3 billion in assets or fraction thereof, not to exceed 12 points.
2. Gross Receipts (SALE, IRS 4.46.2-2 point system) •1 point up to $1 Billion in gross receipts; •2 points for gross receipts in the $1 Billion to $2 Billion range; •3 points for gross receipts in the $2 Billion to $3 Billion range; •4 points for gross receipts in the $3 Billion to $5 Billion range; •5 points for gross receipts in the $5 Billion to $10 Billion range; •Add one point for each additional $3 Billion, or fraction thereof, in excess of $10 Billion in gross receipts, not to exceed 10 points.
We define both geographic segments and business segments as proxies for criteria 3) Operating Entities and 4) Multiple Industry Status.
3. Operating Entities (number of geographic segments from Compustat segmerged database, IRS 4.46.2-2 point system)
•Number of Entities 1 - Number of Points 1; •Number of Entities 2-5 - Number of Points 3; •Number of Entities 6-9 - Number of Points 5; •Number of Entities 10-13 - Number of Points 7; •Number of Entities Over 13 - Number of Points 9.
4. Multiple Industry Status (number of business segments from Compustat segmerged database, modified point system)
•Number of Entities 1 - Number of Points 1; •Number of Entities 2-5 - Number of Points 3; •Number of Entities 6-9 - Number of Points 5; •Number of Entities 10-13 - Number of Points 7; •Number of Entities Over 13 - Number of Points 9.
We substitute Foreign Sales for criteria 5) Foreign Assets. 5. Total Foreign Assets (percentage of foreign sales from segmerged*SALE, modified point system)
•Up to $500 Million – 1 point; •$500 Million to $1 Billion – 2 points; •$1 Billion to $1.5 Billion – 3 points; •$1.5 Billion to $2.5 Billion – 4 points; •$2.5 Billion to $5 Billion – 5 points; •Add 1 point for each additional $1.5 Billion or fraction thereof, not to exceed 8 points.
32
7. Foreign Tax (TXFO, IRS 4.46.2-2 point system) •Up to $7 Million – 1 point; •$7 Million - $100 Million – 2 points; •$100 Million - $200 Million – 3 points; •Add 1 point for each additional $200 million or fraction thereof, not to exceed 8 points. We are unable to create a publicly available proxy for criteria 6) Total Related Transactions.
33
APPENDIX C Variable Definitions
AssetPoints =
GrossReceiptsPoints =
GeoSegPoints =
BusSegPoints =
ForeignSalesPoints =
ForeignTaxPoints =
Leverage =
R&D =
CapInt =
ExcessStockBenInd =
NOLInd =
Fed_Cash_ETR =
Adj_Fed_Cash_ETR =
Cash_ETR =
UTB_CY_ADD =
CICParticipationInd =
∆Firm =
Post =
One if Compustat TXBCOF is greater than 0, and zero otherwise
(Total tax reported on Page 1 Line 31 of Form 1120 plus settlements paid to IRS) divided by pretax income (Compustat PI)
Research and development expenses (Compustat XRD) divided by revenues (Compustat SALE)
Number of points assigned for geographic segments (Compustat segmerged dataset) based on IRM Exhibit 4.46.2-2; See Appendix A for specific point ranges
One for firms that experience a change in their CIC program assignment status during our sample period and zero otherwise
Additions to tax contingency reserves for positions claimed in the current year (Compustat TXTUBPOSINC) divided by pretax income (Compustat PI)
One if a firm is aware of CIC program assignment during the current year, and zero otherwise
Number of points assigned for foreign taxes (Compustat TXFO) based on IRM Exhibit 4.46.2-2; See Appendix A for specific point ranges
Long-term debt (Compustat DLTT) divided by total assets (Compustat AT)
Number of points assigned for total assets (Compustat AT) based on IRM Exhibit 4.46.2-2; See Appendix A for specific point ranges
New property, plant and equipment (Compustat PPENT) divided by total assets (Compustat AT)
Number of points assigned for revenues (Compustat SALE) based on IRM Exhibit 4.46.2-2; See Appendix A for specific point ranges
One if Compustat TLCF is greater than 0, and zero otherwise
Number of points assigned for business segments (Compustat segmerged dataset) based on IRM Exhibit 4.46.2-2; See Appendix A for specific point ranges
Number of points assigned for foreign sales (% of foreign sales from Compustat segmerged * SALE) based on IRM Exhibit 4.46.2-2; See Appendix A for specific point ranges
One for both the CIC firm and its matched firm for all years after the change in CIC program status
Total tax reported on Page 1 Line 31 of Form 1120 divided by pretax income (Compustat PI)
Total cash taxes paid (Compustat TXPD) divided by pretax income (Compustat PI)
34
TABLE 1 Sample derivation
This table presents the sample derivation process. Panel A provides the sample selection and Panel B provides the number of observations by year. TaxReturnAssets equals total assets reported on Page 1 of the Form 1120.
Panel A: Sample selection
Publicly-traded firm-years from 2000 to 2011 with >=$250M in TaxReturnAssets 34,379
Less: observations not matched with Compustat data (2,057)
Less: observations missing dependent or explanatory variables (3,611)
Less: observations missing one year lag and/or one year lead (5,617)
Observations for CIC prediction model 23,094
Firms entering the CIC program during sample period 405
Panel B: Number of observations by year
Year Firm-years % CIC Firm-years % CIC2000 2,850 10.2% 1,297 15.3%2001 2,826 12.4% 1,517 16.7%2002 2,803 15.9% 1,604 22.5%2003 2,781 17.6% 1,643 24.1%2004 2,865 16.9% 1,662 23.9%2005 2,891 17.5% 2,190 19.8%2006 2,922 17.4% 2,179 19.6%2007 2,906 17.3% 2,196 19.6%2008 2,875 17.3% 2,226 19.3%2009 2,876 16.6% 2,216 18.7%2010 2,901 15.6% 2,183 18.3%2011 2,883 13.8% 2,181 16.2%Total 34,379 23,094
CIC PredictionInitial sample
35
TABLE 2 Descriptive statistics for CIC prediction model
This table presents descriptive statistics for variables in the CIC prediction model. Panel A presents descriptive statistics for CIC firm-years and Panel B presents descriptive statistics for Non-CIC firm-years. The Points variables are based on the points allocations outlined in the Internal Revenue Manual. See Appendix C for variable definitions. Asterisks ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively.
Panel A: Descriptive statistics for CIC firm-years
Variable N Median Mean SDAssetPoints 4,493 6.0 7.4 3.4GrossReceiptsPoints 4,493 5.0 5.6 2.7GeoSegPoints 4,493 3.0 2.7 1.5BusSegPoints 4,493 3.0 2.5 1.4ForeignSalesPoints 4,493 2.0 3.4 2.9ForeignTaxPoints 4,493 2.0 2.2 1.7Leverage 4,493 0.244 0.263 0.178R&D 4,493 0.000 0.030 0.062CapInt 4,493 0.205 0.273 0.229ExcessStockBenInd 4,493 0.000 0.280 0.449NOLInd 4,493 0.000 0.387 0.487
Panel B: Descriptive statistics for Non-CIC firm-years
Variable N Median Mean SDAssetPoints 18,601 2.0 *** 2.8 *** 1.8GrossReceiptsPoints 18,601 1.0 *** 1.8 *** 1.4GeoSegPoints 18,601 1.0 *** 2.1 *** 1.3BusSegPoints 18,601 1.0 *** 1.9 *** 1.2ForeignSalesPoints 18,601 1.0 *** 1.3 *** 0.9ForeignTaxPoints 18,601 1.0 *** 1.2 *** 0.5Leverage 18,601 0.205 *** 0.249 *** 0.221R&D 18,601 0.000 *** 0.026 *** 0.069CapInt 18,601 0.146 *** 0.236 *** 0.245ExcessStockBenInd 18,601 0.000 0.287 0.452NOLInd 18,601 0.000 *** 0.360 *** 0.480
36
TABLE 3 CIC prediction model
This table presents the results from estimating a logistic regression of CIC program assignment. Panel A models overall CIC assignment, and Panel B models initial CIC assignment. See Appendix C for variable definitions. Asterisks ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively. Standard errors are reported in parentheses.
Constant -5.822 *** -5.984 *** -6.036 *** -5.794 ***
(0.091) (0.105) (0.152) (0.176)
AssetPoints 0.339 *** 0.338 *** 0.194 *** 0.194 ***
(0.012) (0.012) (0.024) (0.025)
GrossReceiptsPoints 0.502 *** 0.533 *** 0.344 *** 0.360 ***
(0.018) (0.019) (0.031) (0.032)
GeoSegPoints 0.221 *** 0.184 *** 0.188 *** 0.165 ***
(0.021) (0.021) (0.039) (0.041)
BusSegPoints 0.154 *** 0.166 *** 0.111 *** 0.106 ***
(0.019) (0.019) (0.041) (0.041)
ForeignSalesPoints 0.052 ** 0.041 * -0.069 -0.084 *
(0.023) (0.023) (0.043) (0.044)
ForeignTaxPoints 0.224 *** 0.240 *** 0.016 0.033(0.047) (0.048) (0.081) (0.082)
Leverage 0.112 -0.501 *
(0.132) (0.285)
R&D 4.258 *** 2.712 ***
(0.351) (0.756)
CapInt 0.274 ** 0.315(0.114) (0.239)
ExcessStockBen -0.183 *** -1.079 ***
(0.054) (0.156)
NOLInd -0.099 * -0.063(0.052) (0.114)
NPseudo R-squaredArea under ROC curve
19,013 19,013
Panel A: CICFirm = 1 if firm is assigned to CIC program during
current year
Panel B: CICFirm = 1 if firm is initially assigned to CIC program
during current year
23,094 23,094
86.43%94.02%
Model [1] Model [2]Model [1]
18.06%48.20%94.22%
Model [2]
47.57% 16.09%86.53%
37
TABLE 4 Univariate analysis of newly-assigned firms, non-assigned firms and long-assigned firms
This table presents results comparing newly-assigned CIC firms to non-assigned firms and long-assigned firms. Panel A presents univariate differences between newly-assigned CIC firms and non-assigned firms in the year of CIC assignment (year t). Panel B presents univariate differences between newly-assigned CIC firms and long-assigned firms in the year of CIC assignment (year t). Panel C presents descriptive statistics for Fed_Cash_ETR, Adj_Fed_Cash_ETR, Cash_ETR, and UTB_CY_ADD. See Appendix C for variable definitions. Asterisks ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively.
Panel A: Descriptive statistics in assignment year for newly-assigned firms and non-assigned firms
Median Mean SD Median Mean SDAssetPoints 4.0 5.4 2.6 4.0 *** 4.1 *** 2.0GrossReceiptsPoints 4.0 4.1 2.3 3.0 *** 2.9 *** 1.5GeoSegPoints 3.0 2.6 1.4 3.0 2.5 1.3BusSegPoints 3.0 2.4 1.4 3.0 2.4 1.3ForeignSalesPoints 1.0 2.4 2.1 1.0 *** 1.5 *** 1.1ForeignTaxPoints 2.0 1.7 1.0 1.0 *** 1.4 *** 0.6Leverage 0.241 0.254 0.182 0.268 ** 0.290 ** 0.209R&D 0.000 0.032 0.066 0.000 0.057 *** 0.119CapInt 0.222 0.278 0.225 0.205 0.270 0.236ExcessStockBen 0.000 0.131 0.338 0.000 0.121 0.327NOLInd 0.000 0.385 0.487 0.000 0.365 0.482
Panel B: Descriptive statistics in assignment year for newly-assigned firms and long-assigned firms
Median Mean SD Median Mean SDAssetPoints 5.0 5.8 2.8 4.0 * 5.3 2.5GrossReceiptsPoints 4.0 4.6 2.4 4.0 * 4.2 2.2GeoSegPoints 3.0 2.6 1.6 3.0 2.5 1.2BusSegPoints 3.0 2.2 1.3 3.0 2.4 1.3ForeignSalesPoints 1.0 2.6 2.3 1.0 2.4 2.2ForeignTaxPoints 2.0 1.8 1.1 1.0 1.6 1.0Leverage 0.190 0.217 0.162 0.248 * 0.257 * 0.185R&D 0.000 0.025 0.047 0.000 0.032 0.065CapInt 0.172 0.247 0.221 0.202 0.277 0.222ExcessStockBen 0.000 0.171 0.378 0.000 * 0.274 * 0.448NOLInd 0.000 0.410 0.494 0.000 0.427 0.497
Newly-assigned firms (n=405) Non-assigned firms (n=405)
Newly-assigned firms (n=117) Long-assigned firms (n=117)
Panel C: Descriptive statistics for Tax measures
N Median Mean SD N Median Mean SD N Median Mean SDFed_Cash_ETR 23,094 0.081 0.129 0.137 4,266 0.055 0.113 0.129 1,344 0.096 0.126 0.125Adj_Fed_Cash_ETR 23,094 0.088 0.136 0.146 4,266 0.068 0.123 0.140 1,344 0.104 0.136 0.134Cash_ETR 23,094 0.211 0.201 0.226 4,266 0.200 0.195 0.224 1,344 0.235 0.223 0.209UTB_CY_ADD 11,002 0.000 0.008 0.036 940 0.000 0.011 0.043 476 0.000 0.015 0.043
Long-assigned samplePooled sample Non-assigned sample
38
TABLE 5
CIC Participation/Assignment and Initial Federal Filing Liability Rates
This table presents results on the relation between CIC participation/assignment and Fed_Cash_ETR. Panel A presents the results from estimating a pooled multivariate OLS regression and Panel B (C) presents the results from estimating a changes model for newly-assigned firms relative to non-assigned (long-assigned) firms. See Appendix C for variable definitions. Asterisks *, **, *** denote two-tailed statistical significance at the 10%, 5%, and 1%, respectively. t-statistics are reported in parentheses.
Variable
Intercept 0.238 *** 0.195 *** 0.202 *** 0.196 *** 0.219 *** 0.202 *** 0.203 ***
(54.15) (18.36) (15.27) (24.36) (9.93) (11.36) (13.86)
CICParticipationInd -0.005
(-1.06)
Post -0.004 -0.011 ** -0.011 ** -0.001 0.007 0.003
(-0.76) (-2.25) (-2.11) (-0.13) (0.74) (0.37)
∆Firm 0.007 0.012
(1.27) (1.36)
Post*∆Firm 0.006 0.001
(0.86) (0.08)
AssetPoints -0.004 *** -0.003 ** 0.003 ** -0.001 -0.005 ** -0.004 ** -0.004 ***
(-3.95) (-2.41) (1.92) (-0.75) (-2.12) (-2.33) (-2.82)
GrossReceiptsPoints 0.012 *** 0.015 *** 0.018 *** 0.016 *** 0.011 *** 0.012 *** 0.011 ***
(9.29) (9.68) (9.25) (13.06) (3.04) (4.61) (5.42)
GeoSegPoints -0.014 *** -0.007 *** -0.009 *** -0.010 *** -0.007 * -0.007 * -0.008 ***
(-11.99) (-3.30) (-4.22) (-6.27) (-1.80) (-1.59) (-2.65)
BusSegPoints -0.002 0.000 0.001 -0.001 -0.005 0.003 0.000
(-1.44) (-0.19) (0.30) (-0.55) (-1.47) (0.76) (-0.18)
ForeignSalesPoints -0.008 *** -0.014 *** -0.021 *** -0.016 *** -0.008 *** -0.015 *** -0.011 ***
(-5.05) (-7.87) (-7.29) (-10.45) (-2.82) (-4.84) (-5.30)
ForeignTaxPoints -0.010 *** -0.009 *** -0.023 *** -0.011 *** -0.012 ** -0.001 ** -0.007 *
(-4.19) (-2.78) (-4.19) (-4.00) (-2.16) (-0.12) (-1.77)
Leverage -0.108 *** -0.080 *** -0.093 *** -0.086 *** -0.142 *** -0.024 *** -0.084 ***
(-14.45) (-5.36) (-7.64) (-9.25) (-5.02) (-0.78) (-4.04)
R&D -0.345 *** -0.252 *** -0.284 *** -0.276 *** -0.231 *** -0.324 *** -0.268 ***
(-17.91) (-6.37) (-10.18) (-12.77) (-2.78) (-3.82) (-4.52)
CapInt -0.092 *** -0.046 *** -0.091 *** -0.071 *** -0.032 -0.067 -0.055 ***
(-14.77) (-3.83) (-7.81) (-8.54) (-1.44) (-2.87) (-3.47)
ExcessStockBen 0.042 *** 0.053 *** 0.043 *** 0.048 *** 0.037 *** 0.025 *** 0.029 ***
(16.11) (8.02) (6.16) (10.18) (3.76) (2.26) (3.95)
NOLInd -0.048 *** -0.039 *** -0.017 *** -0.028 *** -0.008 -0.049 -0.031 ***
(-16.60) (-7.46) (-3.33) (-7.66) (-0.77) (-5.09) (-4.50)
N
R-squared 23.10% 17.00%
All Firms
Panel C: Changes (Newly-assigned & Long-assigned)
∆FirmsNon-∆Firms All Firms∆Firms
Non-∆Firms All Firms
672 672 1,344
22.40%
4,266
21.01% 27.69% 24.03% 13.30%
23,094 2,133 2,133
Panel A: Pooled
Panel B: Changes (Newly-assigned & Non-assigned)
39
TABLE 6 CIC Participation/Assignment and Adjusted Federal Filing Liability Rates
This table presents results on the relation between CIC participation/assignment and Adj_Fed_Cash_ETR. Panel A presents the results from estimating a pooled multivariate OLS regression and Panel B (C) presents the results from estimating a changes model for newly-assigned firms relative to non-assigned (long-assigned) firms. See Appendix C for variable definitions. Asterisks *, **, *** denote two-tailed statistical significance at the 10%, 5%, and 1%, respectively. t-statistics are reported in parentheses.
Variable
Intercept 0.250 *** 0.218 *** 0.223 *** 0.214 *** 0.218 *** 0.241 *** 0.221 ***
(54.28) (18.68) (15.39) (24.26) (11.16) (10.14) (13.87)
CICParticipationInd -0.003(-0.65)
Post -0.008 -0.012 ** -0.011 ** 0.004 -0.005 -0.001
(-1.47) (-2.18) (-2.10) (0.43) (-0.48) (-0.10)
∆Firm 0.013 ** 0.012(2.22) (1.20)
Post*∆Firm 0.003 0.003(0.37) (0.24)
AssetPoints -0.004 *** -0.004 *** 0.002 *** -0.001 -0.005 ** -0.005 ** -0.005 ***
(-4.29) (-2.96) (1.40) (-1.45) (-2.34) (-1.94) (-2.91)
GrossReceiptsPoints 0.013 *** 0.017 *** 0.017 *** 0.016 *** 0.014 *** 0.010 *** 0.012 ***
(9.34) (9.54) (8.28) (12.35) (4.86) (2.63) (5.51)
GeoSegPoints -0.014 *** -0.008 *** -0.011 *** -0.011 *** -0.006 -0.008 -0.007 **
(-11.73) (-3.21) (-4.30) (-6.20) (-1.41) (-1.70) (-2.41)
BusSegPoints -0.001 -0.001 0.001 -0.001 0.001 -0.005 -0.001
(-0.86) (-0.35) (0.29) (-0.56) (0.29) (-1.33) (-0.37)
ForeignSalesPoints -0.008 *** -0.015 *** -0.022 *** -0.016 *** -0.015 *** -0.008 *** -0.011 ***
(-5.22) (-7.37) (-7.00) (-9.8) (-4.30) (-2.58) (-4.86)
ForeignTaxPoints -0.011 *** -0.010 *** -0.024 *** -0.012 *** -0.004 -0.013 -0.009 **
(-4.41) (-2.88) (-3.85) (-3.86) (-0.57) (-2.19) (-2.11)
Leverage -0.112 *** -0.079 *** -0.099 *** -0.090 *** -0.022 -0.154 -0.089 ***
(-14.14) (-4.85) (-7.46) (-8.79) (-0.66) (-5.02) (-3.94)
R&D -0.356 *** -0.259 *** -0.313 *** -0.297 *** -0.350 *** -0.236 *** -0.276 ***
(-17.41) (-5.97) (-10.26) (-12.54) (-3.75) (-2.63) (-4.3)
CapInt -0.093 *** -0.052 *** -0.094 *** -0.076 *** -0.077 *** -0.040 *** -0.061 ***
(-14.00) (-3.97) (-7.33) (-8.29) (-2.97) (-1.67) (-3.58)
ExcessStockBen 0.041 *** 0.053 *** 0.041 *** 0.047 *** 0.023 * 0.037 * 0.028 ***
(15.13) (7.27) (5.39) (9.09) (1.88) (3.50) (3.52)
NOLInd -0.051 *** -0.043 *** -0.019 *** -0.031 *** -0.047 *** -0.010 *** -0.030 ***
(-16.77) (-7.36) (-3.37) (-7.60) (-4.36) (-0.92) (-4.06)
NR-squared
Panel A: Pooled
Panel B: Changes (Newly-assigned & Non-assigned)
Panel C: Changes (Newly-assigned & Long-assigned)
All Firms ∆FirmsNon-∆Firms All Firms ∆Firms
Non-∆Firms All Firms
23,094 2,133 2,133 4,266 672 67221.00% 19.60% 25.67% 22.46% 21.00% 12.40% 15.70%
1,344
40
TABLE 7 CIC Participation/Assignment and Cash Effective Tax Rates
This table presents results on the relation between CIC participation/assignment and Cash_ETR. Panel A presents the results from estimating a pooled multivariate OLS regression and Panel B (C) presents the results from estimating a changes model for newly-assigned firms relative to non-assigned (long-assigned) firms. See Appendix C for variable definitions. Asterisks *, **, *** denote two-tailed statistical significance at the 10%, 5%, and 1%, respectively. t-statistics are reported in parentheses.
Variable
Intercept 0.289 *** 0.235 *** 0.203 *** 0.227 *** 0.249 *** 0.272 *** 0.252 ***
(59.62) (11.96) (7.92) (14.93) (7.8) (7.04) (9.75)
CICParticipationInd 0.003(0.62)
Post 0.005 -0.006 -0.004 0.019 -0.004 0.005
(0.48) (-0.63) (-0.44) (1.17) (-0.23) (0.34)
∆Firm 0.017 0.006(1.64) (0.39)
Post*∆Firm 0.005 0.006(0.35) (0.26)
AssetPoints -0.003 *** -0.010 *** -0.004 *** -0.007 *** -0.007 ** -0.002 ** -0.005 *
(-3.32) (-4.31) (-1.47) (-4.48) (-2.13) (-0.47) (-1.95)
GrossReceiptsPoints 0.009 *** 0.016 *** 0.018 *** 0.016 *** 0.006 0.001 0.006(6.47) (5.30) (4.83) (6.99) (1.41) (0.10) (1.63)
GeoSegPoints -0.004 *** 0.004 -0.004 0.000 -0.003 -0.006 -0.004(-2.95) (1.01) (-0.83) (-0.02) (-0.35) (-0.81) (-0.88)
BusSegPoints 0.005 *** -0.003 0.005 -0.001 -0.002 -0.006 -0.004
(3.88) (-0.89) (1.19) (-0.22) (-0.37) (-1.05) (-0.95)
ForeignSalesPoints -0.014 *** -0.011 *** -0.017 *** -0.014 *** -0.003 -0.001 -0.002(-8.70) (-3.28) (-2.96) (-4.82) (-0.52) (-0.11) (-0.56)
ForeignTaxPoints 0.015 *** 0.024 *** 0.040 *** 0.029 *** 0.026 ** 0.032 ** 0.029 ***
(6.33) (4.07) (3.71) (5.64) (2.37) (3.27) (4.09)
Leverage -0.090 *** -0.020 -0.107 -0.076 *** 0.048 -0.150 -0.058
(-9.13) (-0.71) (-4.58) (-4.31) (0.85) (-3.01) (-1.58)
R&D -0.524 *** -0.472 *** -0.428 *** -0.468 *** -0.577 *** -0.744 *** -0.646 ***
(-19.75) (-6.46) (-7.95) (-11.47) (-3.79) (-5.09) (-6.18)
CapInt 0.041 *** -0.076 *** -0.121 *** -0.099 *** -0.093 ** -0.082 ** -0.088 ***
(5.54) (-3.44) (-5.35) (-6.30) (-2.21) (-2.09) (-3.17)
ExcessStockBen 0.033 *** 0.055 *** 0.065 *** 0.061 *** 0.003 0.058 0.032 **
(11.77) (4.51) (4.86) (6.85) (0.13) (3.33) (2.44)
NOLInd -0.027 *** -0.057 *** -0.036 *** -0.046 *** -0.032 * -0.014 * -0.025 **
(-7.3) (-5.83) (-3.64) (-6.60) (-1.83) (-0.81) (-2.11)
NR-squared 7.40%
1,3449.10% 7.35% 11.90% 10.05% 5.50% 11.10%23,094 2,133 2,133 4,266 672 672
All Firms ∆FirmsNon-∆Firms All Firms ∆Firms
Non-∆Firms All Firms
Panel A: Pooled
Panel B: Changes (Newly-assigned & Non-assigned)
Panel C: Changes (Newly-assigned & Long-assigned)
41
TABLE 8 CIC Participation/Assignment and Management Expectations of Future Tax Outcomes
This table presents results on the relation between CIC participation/assignment and UTB_CY_ADD. Panel A presents the results from estimating a pooled multivariate OLS regression and Panel B (C) presents the results from estimating a changes model for newly-assigned firms relative to non-assigned (long-assigned) firms. See Appendix C for variable definitions. Asterisks *, **, *** denote two-tailed statistical significance at the 10%, 5%, and 1%, respectively. t-statistics are reported in parentheses.
Variable
Intercept -0.001 0.011 0.008 0.010 0.032 * 0.024 * 0.032 ***
(-0.98) (1.02) (0.86) (1.35) (1.83) (1.91) (3.10)
CICParticipationInd 0.004 **
(2.41)
Post 0.011 * -0.003 -0.002 0.008 -0.007 -0.007(1.90) (-0.70) (-0.38) (0.97) (-1.37) (-1.13)
∆Firm -0.005 -0.013 *
(-0.79) (-1.67)
Post*∆Firm 0.013 * 0.016 *
(1.91) (1.68)
AssetPoints 0.000 * -0.002 * 0.000 * -0.001 0.000 -0.001 -0.001(-1.90) (-1.68) (-0.11) (-1.44) (0.09) (-1.08) (-1.05)
GrossReceiptsPoints 0.001 * 0.003 0.000 0.001 -0.001 -0.001 -0.001(1.68) (1.41) (0.39) (1.41) (-0.25) (-0.36) (-0.48)
GeoSegPoints 0.002 *** 0.000 0.004 0.002 -0.005 -0.001 -0.003(3.41) (-0.03) (2.64) (1.31) (-1.29) (-0.25) (-1.38)
BusSegPoints 0.000 0.001 -0.003 -0.001 -0.003 -0.003 -0.002(-0.75) (0.48) (-1.90) (-0.53) (-0.74) (-1.55) (-1.45)
ForeignSalesPoints 0.000 -0.001 0.000 -0.001 0.005 0.003 0.004 ***
(0.04) (-0.85) (-0.06) (-0.63) (1.39) (2.16) (2.77)
ForeignTaxPoints 0.002 ** 0.003 -0.003 0.001 -0.001 0.002 0.000(2.22) (1.25) (-0.74) (0.64) (-0.21) (0.60) (-0.14)
Leverage -0.003 -0.014 -0.013 -0.013 * -0.034 -0.019 -0.016(-1.32) (-1.14) (-1.50) (-1.71) (-1.07) (-1.30) (-1.24)
R&D 0.020 * 0.079 ** 0.025 ** 0.057 ** 0.038 0.002 0.023
(1.78) (2.22) (0.78) (2.42) (0.55) (0.03) (0.51)
CapInt -0.001 -0.015 -0.003 -0.007 -0.004 0.009 0.002(-0.87) (-1.19) (-0.34) (-1.04) (-0.14) (0.79) (0.21)
ExcessStockBen 0.003 *** -0.005 0.001 -0.002 -0.012 0.004 0.000(3.95) (-0.89) (0.28) (-0.71) (-1.14) (0.70) (-0.02)
NOLInd 0.003 *** -0.003 0.005 0.000 0.006 0.007 0.004(2.91) (-0.70) (1.27) (0.08) (0.62) (1.42) (0.98)
NR-squared 4.90%
4762.50% 4.54% 4.37% 4.18% 4.50% 7.40%11,002 470 470 940 178 298
All Firms ∆FirmsNon-∆Firms All Firms ∆Firms
Non-∆Firms All Firms
Panel A: Pooled
Panel B: Changes (Newly-assigned & Non-assigned)
Panel C: Changes (Newly-assigned & Long-assigned)