How Reliably Do Empirical Tests Identify Tax Avoidance?

LISA DE SIMONE, Stanford Graduate School of Business

JORDAN NICKERSON, Boston College

JERI SEIDMAN, University of Virginia – McIntire School of Commerce

BRIDGET STOMBERG, Indiana University

AbstractResearch on the determinants of tax avoidance have relied on tests using GAAP and cash effective tax rates (ETRs) and total and permanent book-tax differences (BTDs). Two new proxies have emerged that overcome documented limitations of these proxies: one, developed by Henry and Sansing (2018), allows for more meaningful interpretation of results estimated in samples that include loss observations. The other, reserves for unrecognized tax benefits (UTB), provides new data on tax uncertainty. We offer empirical evidence on how well tests using these new proxies perform relative to those extensively used in prior research. The paper finds that tests using the proxy developed by Henry and Sansing (2018) have lower power relative to those using other proxies across all samples, including a sample that includes loss observations. In contrast, when firms accrue reserves for uncertain tax avoidance, tests using the current year addition to the UTB have the highest power across all proxies, samples, and levels of reserves. In the absence of reserves, tests using the GAAP ETR best detect uncertain tax avoidance, on average. This study contributes to the literature by using a controlled environment to provide the first large-scale empirical evidence on how the power of tests varies with the use of relatively new proxies, the inclusion of loss observations, and the advent of FIN 48.

Acknowledgements: We thank Linda Bamber, Mary Barth, Shuping Chen, Michelle Hanlon, Amy Hutton, Steve Kachelmeier, William Kinney Jr., David Larcker, Eduardo P. Lotor, Edward Maydew, John McInnis, Lillian Mills, Tom Omer (Editor), Barry Schaudt, Terry Shevlin, two anonymous reviewers, and workshop participants at the University of Texas for valuable comments and suggestions.

Contact author: Jeri Seidman. Physical address: McIntire School of Commerce, University of Virginia, P.O. Box 400173, Charlottesville, VA, 22904. Telephone: 434-294-8976. Fax: 434-924-7074. Email address: jseidman@virginia.edu.

How Reliably Do Empirical Tests Identify Tax Avoidance?

Abstract

Research on the determinants of tax avoidance have relied on tests using GAAP and cash effective tax rates (ETRs) and total and permanent book-tax differences (BTDs). Two new proxies have emerged that overcome documented limitations of these proxies: one, developed by Henry and Sansing (2018), allows for more meaningful interpretation of results estimated in samples that include loss observations. The other, reserves for unrecognized tax benefits (UTB), provides new data on tax uncertainty. We offer empirical evidence on how well tests using these new proxies perform relative to those extensively used in prior research. The paper finds that tests using the proxy developed by Henry and Sansing (2018) have lower power relative to those using other proxies across all samples, including a sample that includes loss observations. In contrast, when firms accrue reserves for uncertain tax avoidance, tests using the current year addition to the UTB have the highest power across all proxies, samples, and levels of reserves. In the absence of reserves, tests using the GAAP ETR best detect uncertain tax avoidance, on average. This study contributes to the literature by using a controlled environment to provide the first large-scale empirical evidence on how the power of tests varies with the use of relatively new proxies, the inclusion of loss observations, and the advent of FIN 48.

JEL: H25, H26, M41Keywords: Uncertain Tax Avoidance, Loss Firms, Tax avoidance Simulated data

1. Introduction

We evaluate the relative power of empirical tests of the determinants of tax avoidance.1 A

vast literature exploring the determinants of tax avoidance exists (Wilde and Wilson 2018). Yet,

researchers and shareholders still must infer tax avoidance from financial statements because

firms typically do not disclose the details of their tax strategies. Researchers have thus relied on

GAAP and cash effective tax rates (ETRs) and total and permanent book-tax differences (BTDs)

as proxies for avoidance. However, limitations of these proxies have been documented, including

the confounding effects of financial reporting rules and earnings management, their inability to

capture the uncertainty associated with tax avoidance, and the difficulty of interpreting them for

loss firms (e.g., Blouin 2014; Donohoe, McGill and Outslay 2014; Drake, Hamilton and Lusch

2019; Edwards, Kubata and Shevlin 2018; Hanlon 2003; Henry and Sansing 2018; Schwab,

Stomberg and Xia 2019). Two new proxies have recently emerged that may address some of

these issues. In particular, one developed by Henry and Sansing (2018) better allows for

inclusion of loss observations in a sample. Similarly, reserves for unrecognized tax benefits

(UTB), accrued under FIN 48, provide new data on tax uncertainty. These proxies may enable

researchers to enhance their understanding of tax avoidance. We aim to offer empirical evidence

on how well tests using these new proxies perform relative to those extensively analyzed in prior

research (e.g., Hanlon and Heitzman 2010). We do this by using a controlled environment to

seed a known quantity of tax avoidance in Compustat data and evaluate the relative power of

statistical tests to detect the tax avoidance that we seed.

1 We follow Hanlon and Heitzman (2010, 137) and define tax avoidance as “all transactions that have any effect on the firm’s explicit tax liability.” We focus our analysis on empirical or statistical tests of the determinants of tax avoidance where the empirical model uses a tax avoidance proxy as the dependent variable and includes controls for known economic determinants of tax avoidance. Our view of empirical or statistical tests encompasses the model specification (i.e., independent and dependent variables), sample selection criteria, and skewness correction method (winsorizing, robust regression, etc.)

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Our methodology resembles those of Dechow, Sloan, and Sweeney (1995) and Dechow,

Hutton, Kim, and Sloan (2012). Those authors seed a known quantity of earnings management

into a random subset of Compustat data and evaluate the relative performance of discretionary

accrual models to detect the earnings management that they seed. We use a similar approach and

evaluate the relative performance of statistical tests to detect the tax avoidance that we seed. The

known quantity of tax avoidance allows testing of how well determinants-of-tax-avoidance

models detect tax avoidance as well as exploration of opportunities to increase the power of

these tests. Using simulated data in a regression framework is necessary to evaluate the relative

power of empirical tests because it allows us to assess a number of different research design

choices (i.e., proxies, samples, etc.), while controlling for known economic determinants of tax

avoidance. Tax avoidance is not easily observable in publicly available data. For example,

because of differences between financial and tax reporting rules, observing an effective tax rate

(ETR) below the statutory tax rate does not necessarily indicate avoidance (e.g., Drake et al.

2019; Schwab et al. 2019).

We separately seed three types of tax avoidance: a permanent tax savings strategy (e.g., a

tax credit), a temporary tax savings strategy (e.g., accelerated tax depreciation), and a hybrid

strategy. Hybrid strategies are those for which the entire cash tax savings is not reflected in some

of the tax expense-based proxies because of financial reporting choices made by the firm. For

example, cross-jurisdictional income shifting is one such strategy because management asserts

that a portion of foreign earnings is permanently reinvested (APB 23). Permanent tax avoidance

strategies that are partially offset by UTBs are another example. We hold median cash taxes

saved as a percentage of pre-tax income constant across all strategies for comparability. For each

specification, we run 10,000 simulated regressions. To assess power—that is, the ability of a test

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to detect tax avoidance when it exists—we calculate the percentage of simulated regressions that

detect the tax avoidance that we seed. A higher rate of correct detection implies greater power.

We first examine the power of tests, using the proxy developed by Henry and Sansing

(2018) as the dependent variable, relative to tests using ETRs and BTDs as proxies. Because this

new proxy aims to facilitate more meaningful interpretation of results estimated in a sample that

includes loss observations, we assess relative power using three distinct samples: (i) one that

includes all observations required to calculate variables; (ii) one that omits loss observations; and

(iii) another that omits both loss observations and negative values of tax expense or taxes paid.

Across all samples, tests using the Henry and Sansing (2018) proxy as the dependent variable are

not the most powerful. The power of these tests resembles that of tests using BTDs, with tests

using the Henry and Sansing (2018) proxy offering only modest power gains over total book-tax

differences. Further, although one of the purported advantages of the proxy developed by Henry

and Sansing (2018) is that it provides a meaningful calculation of tax avoidance even for

unprofitable firms, tests using it have lower power in a sample that includes loss observations,

relative to tests using other proxies in the same sample. Despite the difficulty interpreting

negative ETRs, tests using the cash ETR have significantly more power to detect all three tax

avoidance strategies in the sample that includes loss observations. Simply put, using the Henry

and Sansing (2018) proxy in a sample that includes loss observations does not significantly

improve power to detect tax avoidance. However, Henry and Sansing (2018) acknowledge that

not all tax avoidance studies should include loss observations because loss firms may have

different incentives to pursue tax avoidance.

The notion of uncertain tax avoidance has recently gained increasing attention from

stakeholders and researchers. We therefore also examine the relative power of tests using proxies

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for uncertain tax avoidance as the dependent variable. The dependent variables we test are the

ending balance of the UTB and current year additions to the UTB, two commonly-used proxies

for uncertain tax avoidance (e.g., Drake, Goldman and Lusch 2016; Dyreng, Hanlon and

Maydew 2019; Gallemore, Gipper and Maydew 2019). As expected, tests using the UTB proxies

have low power to detect uncertain tax avoidance if firms do not accrue reserves for tax

uncertainty. Tests using the GAAP ETR have the most power, on average, across the three

samples to detect uncertain, permanent tax avoidance for firms not accruing reserves. However,

when firms do reserve for tax uncertainty, tests using current year additions to the UTB have the

highest power across all three samples. In particular, tests using current year additions to the

UTB are always more powerful than those using the ending balance of the UTB. Further, the

power of tests using current year additions to the UTB increases with higher levels of accrued

reserves for tax uncertainty and with the inclusion of loss observations. Researchers aiming to

identify uncertain tax avoidance may be able to improve the power of their tests by retaining loss

observations and using the current year addition to the reserve in lieu of the ending reserve

balance.

This study makes several contributions. First, it adds to the tax avoidance literature by

using a controlled environment to provide the first large-scale empirical evidence on the relative

power of tests using the proxy developed by Henry and Sansing (2018) and UTB proxies. The

paper also provides new insights into how various research design choices affect the power of

tests using GAAP and cash ETRs as well as total and permanent BTDs to detect tax avoidance.

Studies that validate tax avoidance proxies rely on aggressive tax avoidance that is either

disclosed to the tax authority (Lisowsky 2010; Lisowsky, Robinson and Schmidt 2013) or results

in legal action (Wilson 2009). We extend this literature by assessing the relative power of

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statistical tests using newer proxies, and a range of avoidance strategies that extends beyond tax

shelters. Our results may assist researchers in understanding the strengths and weaknesses of

tests using these proxies and can highlight research design choices that can improve power to

detect tax avoidance. Additionally, the results have implications for interpreting tax avoidance

studies. For example, research that finds a significant result using only a subset of proxies may

be able to use these results to infer the type of tax avoidance in the sample.

Second, we illuminate how the inclusion of loss observations affects power, thus

informing the ongoing debate over this issue in the literature (e.g., Drake et al. 2018; Dyreng,

Hanlon, Maydew and Thornock 2017). This paper finds that the inclusion of loss observations

generally impairs the power of tests using most tax avoidance proxies—even the proxy

developed by Henry and Sansing (2018). The exception is that the power of tests using current

year additions to the UTB increases by roughly 10 percentage points when loss observations are

included in the sample. Thus, the cost of increased noise when including loss observations seems

to outweigh any benefits arising from increased sample size.

Third, the findings here speak to the impact of FIN 48 on tax avoidance research. For

firms unlikely to accrue reserves, tests using UTB proxies offer almost no power to detect

uncertain tax avoidance. This result is highly intuitive yet underscores the importance of

considering managers’ incentives and corporate governance when using UTB proxies to detect

uncertain avoidance. Depending on the percentage of these firms in the sample, inferences could

be affected. To mitigate these concerns, researchers often use multiple proxies in tandem to

strengthen inferences. Yet doing so could lead to mixed results because, in contrast to tests using

UTB proxies, the power of tests using the GAAP ETR and total and permanent BTDs to detect

tax avoidance monotonically decrease as accrued reserves for tax uncertainty increase.

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Additionally, if firms increased tax reserves in response to FIN 48, as some studies suggest (e.g.,

Audit Analytics 2008; Blouin, Gleason, Mills and Sikes 2007, 2010; Dunbar, Kolbasovsky, and

Phillips 2007; Robinson, Stomberg, and Towery 2016), the accounting standard change could

impact the power of tests using the GAAP ETR and total and permanent BTDs. Researchers

examining a sample period spanning FIN 48 adoption could mitigate this concern by using a

proxy not impacted by changing reserve behavior (e.g., the cash ETR and the Henry and Sansing

(2018) proxy).

2. Related literature

In recent years, several studies exploring the determinants of corporate tax avoidance

have been published. Although their exact definitions of avoidance vary, they share a broad view

of transactions that affect the firm’s explicit tax liability (e.g., Hanlon and Heitzman 2010). Their

notion of tax avoidance includes strategies that generate permanent or temporary tax savings as

well as those that vary in terms of aggressiveness and uncertainty. Permanent strategies create

differences between book and taxable income that never reverse (e.g., tax-exempt municipal

bond interest). In contrast, temporary strategies only defer tax payments from one period to

another (e.g., accelerated tax depreciation). U.S. GAAP rules that govern accounting for income

taxes and managers’ financial reporting discretion can obscure whether tax savings are

permanent or temporary. For example, managers can defer recognition of incremental U.S. tax

on some foreign earnings, thereby recasting a portion of temporary tax savings as permanent.2

Because tax returns are confidential, stakeholders rely on financial statement proxies,

such as ETRs and BTDs, to detect tax avoidance. Studies have addressed the unique strengths

and weaknesses of these proxies (e.g., Guenther 2014; Hanlon 2003; Hanlon and Heitzman 2010;

2 Though the financial reporting incentives to assert indefinite reinvestment of foreign earnings have significantly decreased following recently tax law changes, they have not been eliminated.

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Henry and Sansing 2018). Hanlon and Heitzman, in particular, (2010, 144) state: “we cannot

overemphasize that not all measures are equally appropriate for every research question …”

Studies also suggest when a particular proxy might be suitable to use in a statistical test of tax

avoidance. For example, Hanlon and Shevlin (2002) and Austin (2014) outline how accounting

for the tax benefits of stock options affects some tax avoidance proxies, such that they can either

overstate or understate firm tax avoidance. More recent studies note how profitability can

hamper ETRs as proxies for avoidance. Drake et al. (2018) note that failing to consider firms’

history of loses biases GAAP ETRs downward when valuation allowances are released. Schwab

et al. (2018) document a clustering of poorly performing firms in the tails of the GAAP ETR

distribution in a sample of firms with positive pre-tax income. The authors examine how not only

valuation allowance releases, but also valuation allowance accruals and nondeductible goodwill

impairments—all of which are common among firms with poor historical or current performance

—can distort GAAP ETRs as a proxy for tax avoidance. Edwards et al. (2018) demonstrate that

the cash ETR can change over time because of changes in pre-tax income, regardless of changes

in the actual level of tax planning or avoidance. The authors conclude that time-series studies on

changes in tax avoidance can over-attribute the decline in cash ETRs to tax planning. Finally,

Henry and Sansing (2018) note how little is known about the tax positions of unprofitable firms

because they are often eliminated from avoidance studies. The authors develop a proxy for

avoidance that facilitates the inclusion of loss firms in the sample and find, in contrast to other

studies, that the average firm is tax-disfavored over the last 20 years.

As taxes have become more salient to regulators, tax authorities, the public, and the

media, the uncertainty associated with tax avoidance has become of greater interest. Researchers

have struggled with how to measure it because, for example, firms generally did not publicly

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disclose reserves for tax uncertainty prior to 2007. In 2006, the FASB issued ASC 740-10,

Accounting for Uncertainty in Income Taxes, to increase the comparability of accounting for tax

uncertainty. Since then, researchers have used both the level of and changes in these reserves to

explore the determinants and consequences of uncertain tax avoidance (Drake et al. 2016;

Dyreng et al. 2019; Gallemore et al. 2019).

We extend this literature by providing a large-scale empirical evaluation of the relative

power of statistical tests to detect tax avoidance. We focus on assessing the power of new

proxies for tax avoidance—the Henry and Sansing (2018) proxy as well as the level and current-

year change in reserves for unrecognized tax benefits. Even when it is clear that two proxies are

affected by a particular avoidance strategy (e.g., both the cash and GAAP ETRs detect

permanent tax avoidance), this study speaks to the relative power of tests using these proxies. Its

findings can therefore help address questions about whether and to what extent these new proxies

enhance the ability to detect cross-sectional differences in tax avoidance. Mirroring other studies,

the current study exploits samples that include and exclude loss observations and thus speaks

directly to how this important and much-debated research design choice affects the power of

tests to detect tax avoidance.

3. Research Design

Overview

This study examines the power of empirical tests using the proxy developed by Henry

and Sansing (2018) (hereafter HS), the ending balance of the UTB (UTB End), and current year

additions to the UTB (UTB Add) as dependent variables to capture cross-sectional differences in

tax avoidance, relative to tests using the GAAP ETR (GAAP ETR), the cash ETR (Cash ETR),

totals BTD (BTD), and permanent BTDs (Perm Diff) as the dependent variables. Throughout, we

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use one-year measures for all proxies rather than long-run measures. Thus, all proxies are

calculated annually. HS is taxes paid less 35 percent of pre-tax income, all scaled by beginning

market value of assets.3 UTB End is the ending balance of the reserve for unrecognized tax

benefits scaled by total assets. UTB Add is current year additions to the reserve for unrecognized

tax benefits scaled by total assets. GAAP ETR is total tax expense scaled by pre-tax income.

Cash ETR is taxes paid scaled by pre-tax income. BTD is pre-tax income less estimated taxable

income, all scaled by total assets. We estimate taxable income by grossing up current federal and

current foreign tax expense by the highest statutory U.S. statutory rate during our sample period,

35 percent. Finally, Perm Diff adjusts total book-tax differences (BTD) by removing temporary

book-tax differences. We defined temporary book-tax differences total deferred tax expense

grossed up by 35 percent and scaled by total assets.

The power of a test is its ability to detect a difference in the tax avoidance proxy between

firms avoiding tax and all others, holding constant known economic determinants of avoidance.

Power is a function of multiple factors, including the underlying distribution of the tax avoidance

proxy in the sample and the degree to which control variables included in the model do not

explain variation in the proxy. By evaluating econometric models that include control variables,

a researcher can examine the relative power of regressions designed to account for these other

factors. Power is also affected by the magnitude of tax avoidance (i.e., the dollars of tax saved)

and the proportion of firms avoiding tax in the sample. We use these facts to validate our

methodology, as discussed below.

3 We calculate HS following Henry and Sansing (2018) and compute market value of assets as book value of assets plus market value of equity less stockholders’ equity. Market value of equity is price times shares outstanding, both from Compustat. We reset market value of equity and stockholders’ equity to zero if market value of equity is missing. We calculate BTD and Perm Diff following Frank, Lynch, and Rego (2009), except we scale by contemporaneous, rather than lagged, total assets. We do not reset missing ending balances of the reserve for unrecognized tax benefits or missing values of current year additions to the reserve to zero, following guidance from Lisowsky et al. (2013) and Robinson et al. (2016).

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Simulated tax avoidance

Simulating tax avoidance strategies

The approach here relates closely to those of Dechow et al. (1995) and Dechow et al.

(2012). Their studies simulate earnings management in Compustat data to evaluate the relative

performance of models of discretionary accruals to detect the seeded earnings management.

Similarly, we use data simulations to study the relative power of tests to identify cross-sectional

differences in corporate tax avoidance. We seed three different strategies in Compustat data: a

permanent one, a temporary one, and a hybrid. We select these three because they differ

regarding whether tax benefits claimed in the current period will reverse in the future as well as

how they interact with financial reporting standards to affect the models we test. To ensure that

relative power is not driven by the magnitude of simulated tax avoidance, we hold cash taxes

saved as a percentage of pre-tax income roughly constant at the median across all three

strategies. After seeding tax avoidance, we winsorize the tax avoidance proxies to control for

outliers. We winsorize HS, BTD, and Perm Diff at 1 percent and 99 percent (e.g., Armstrong,

Blouin, and Larcker 2012; Cheng, Huang, Li, and Stanfield 2012; Higgins, Omer, and Phillips

2015; McGuire, Omer, and Wang 2012). We also winsorize UTB End and UTB Add at 1 percent

and 99 percent, consistent with how most papers address outliers with UTB proxies (e.g.,

Gallemore et al. 2019; Klassen, Lisowsky, and Mescall 2016; Law and Mills 2015; Law and

Mills 2017). We winsorize GAAP ETR and Cash ETR at zero and one (e.g., Armstrong et al.

2012; Badertscher, Katz, and Rego 2013; Brown and Drake 2014; Higgins et al. 2015).

We simulate a permanent tax avoidance strategy by assuming a percentage of selling,

general, and administrative expense is eligible for a credit, rather than a deduction. We expect

models using all proxies to capture this strategy. However, we have no expectation regarding

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which tests will have the most power to detect the effects of permanent tax avoidance because

this is unknowable ex ante. For example, it is not possible to analytically determine whether tests

using GAAP ETR should better detect permanent tax avoidance than those using HS, UTB End,

UTB Add, Cash ETR, BTD, or Perm Diff because: (i) all proxies are affected by the permanent

strategy; and (ii) the power of a statistical test to detect seeded tax avoidance depends on the

distribution of the dependent and independent variables within the sample used.

We simulate a temporary tax avoidance strategy by assuming that a percentage of capital

expenditures is eligible for an accelerated deduction (e.g., bonus depreciation or cost

segregation). For simplicity, we assume 100 percent of the benefits claimed in year t reverse in

t+1.4 We expect tests using HS, Cash ETR, and BTD to have more power to detect the effects of

the temporary tax avoidance strategy than tests using GAAP ETR and Perm Diff, because GAAP

ETR and Perm Diff reflect only permanent tax avoidance. As such, we expect tests using GAAP

ETR and Perm Diff to detect the effects of temporary tax avoidance in approximately 5 percent

of simulations when we set α=0.05, because this is the rate at which these tests should reject the

null when there is no effect. For reasons outlined above, we can have no expectation about the

relative power of tests using HS, Cash ETR, or BTD to detect the effects of the temporary

strategy.

Finally, we simulate a hybrid strategy, the cash tax savings of which are not entirely

reflected in some tax expense-based proxies because of managers’ financial reporting choices.

We choose cross-jurisdictional income shifting as the hybrid strategy and implement it by

reclassifying a percentage of domestic pre-tax income to foreign pre-tax income taxed at 15

percent. We assume the firm intends to permanently reinvest half of the foreign income abroad

4 Assumptions about when tax benefits reverse affect our seeding because they change pre-seeded values in t+1 for observations we seed with tax avoidance in t. In untabulated tests, we alternatively assume benefits recorded in year t reverse ratably from t+1 to t+3 and find that power is not significantly affected.

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such that incremental U.S. tax will never be due. Therefore, only half of the immediate cash tax

savings of cross-jurisdictional income shifting is reflected in tax expense; the other half is offset

by an increase in deferred tax expense because the firm records a deferred tax liability for the

foreign earnings it does plan to repatriate. We assume the deferred tax liability recorded in t

reverses in t+1. We seed the hybrid strategy only in observations with nonmissing and

nonnegative pre-tax domestic income to ensure that this strategy increases tax avoidance.

Although cross-jurisdictional income shifting has unique features, we expect the pattern of

results to generalize to other hybrid strategies. We expect tests using HS, GAAP ETR, Cash ETR,

BTD, and Perm Diff to detect the effects of the hybrid strategy but do not predict which test will

have the greatest power. In the Appendix, we provide specific journal entries for the three

strategies described above.

Simulating uncertain tax avoidance

To incorporate tax uncertainty into our analysis, we also seed the permanent tax

avoidance strategy in three samples of observations with nonmissing and nonzero ending

balances of reserves for unrecognized tax benefits accrued in accordance with ASC 740-10

Accounting for Uncertainty in Income Taxes. We focus on the permanent strategy because: (i)

reserves for temporary strategies do not affect total tax expense (Robinson et al. 2016); and,

therefore, would not affect many of the other proxies we test; and (ii) accruing reserves for the

hybrid strategy would require far more assumptions. We then consider the effects of accrued

reserves for tax uncertainty. We accrue either no reserve, a reserve equal to 50 percent of the

claimed tax savings, or a full reserve that offsets the claimed tax savings.5

5 Firms accrue and release reserves for UTBs each year, with releases offsetting the effects of accruals on UTB End; releases do not affect UTB Add. For parsimony, we do not separately simulate the effect of the timing of reserve releases. Our approach encompasses any scenario in which the net effect of the accruals and releases for the year is positive.

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Seeding the uncertain, permanent strategy without reserves affects GAAP ETR, BTD, and

Perm Diff like a permanent strategy because the full amount of the tax savings are reflected in

these proxies. Seeding the permanent strategy with a full reserve does not change the value of

GAAP ETR, BTD, and Perm Diff because none of the claimed tax savings is reflected. As such,

we expect tests using GAAP ETR, BTD, and Perm Diff to detect fully reserved uncertain tax

avoidance in approximately five percent of simulations when we set α=0.05; this is the rate at

which these tests should reject the null when there is no effect. Finally, seeding the uncertain,

permanent strategy and accruing a nonzero but less-than-full reserve affects GAAP ETR, BTD,

and Perm Diff like a hybrid strategy because the full amount of claimed tax savings is not

reflected in these proxies. Thus, simulating uncertain tax avoidance with a less-than-full reserve

allows an alternative evaluation of the relative power of tests using these proxies to detect the

hybrid strategy. A key advantage of viewing uncertain tax avoidance with partial reserves as a

hybrid strategy is that it holds constant the underlying Compustat accounts (i.e., selling, general

and administrative expense) used to seed the tax avoidance and therefore removes a potential

source of variation when assessing the power of tests using these proxies to detect the hybrid

strategy. The power of tests using both UTB End and UTB Add to detect uncertain tax avoidance

should increase with the amount of reserves. Because neither UTB proxy is affected when

reserves are not recorded, we expect tests using UTB End and UTB Add to detect uncertain tax

avoidance in approximately five percent of simulations when we set α=0.05 and do not accrue

reserves. Finally, the power of tests using HS and Cash ETR to detect uncertain, permanent tax

avoidance is unaffected by the level of reserves. Thus, we expect the power of tests using these

proxies to remain constant, regardless of the level of the reserve we accrue.6 The Appendix

6 We do not expect the power of tests using Cash ETR and HS to approximate alpha. These proxies are affected by permanent tax avoidance, although they are affected similarly whether it is reserved.

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provides specific journal entries for seeding the permanent strategy with a 50 percent reserve for

uncertain tax avoidance.

Benefits and limitations of using simulated data and evaluating econometric tests

The first part of our research design is the use of simulated data. Because firms need not

publicly describe their tax avoidance strategies and generally do not voluntarily disclose them, it

is not feasible to validate models of tax avoidance using actual tax avoidance data. Other studies

have attempted to validate proxies by correlating them with incidences of known or alleged tax

sheltering, but this method is limited to a subset of the most aggressive strategies. By using

simulated data, we control the nature and the amount of avoidance. This allows us to examine the

relative power of tests to detect the effects of a broader range of tax avoidance.

Intuitively, strategies that save few dollars will be more difficult to detect than those that

save many more. Likewise, when too few firms avoid taxes, the estimation error associated with

their average level of avoidance is too large to differentiate this group from other firms in the

sample. Furthermore, when tax avoidance is pervasive, there is not enough precision in the

control group. Thus, tax avoidance is more difficult to detect when either too many or too few

firms in the sample avoid tax. One cannot assess the power of a test simply by examining how

tax avoidance impacts a proxy. For example, even though HS more intuitively reflects tax

avoidance for loss observations than other proxies, it is unclear how well relatively powerful

tests using HS detect avoidance.

We rely on these intuitions about power to validate our methodology. Specifically, we

vary the median cash tax savings as a percentage of PI and compare the effects of these various

magnitudes of seeded tax avoidance on power. As expected, tests of tax avoidance have more

power when the magnitude of tax avoidance is larger. Similarly, we observe that power increases

with pervasiveness. Obtaining expected results helps validate our simulation methodology.

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The second part of our research design is examining empirical tests designed to detect tax

avoidance. Thus, instead of using an analytical framework or using simulated tax avoidance data

to estimate univariate changes in tax avoidance proxies, we evaluate the relative power of

statistical tests employed by empirical researchers to detect tax avoidance. Focusing on statistical

tests provides three key benefits. First, we can assess power while holding constant known

economic determinants of tax avoidance. This feature matters because the power of a statistical

test depends on the distributions of the control variables and the tax avoidance proxy chosen as

the dependent variable as well as the correlation between the controls and the proxy. An

analytical framework could not accommodate the effects of these controls on the proxies in a

closed-form. Second, holding constant other research design choices, such as sample selection

and control variables, our approach allows us to compare power across tests that use different

proxies for tax avoidance as the dependent variable. Third, we can quantify the effect of different

sample selection criteria on the researcher’s ability to detect tax avoidance. For these reasons, it

is critical to evaluate the final econometric model, rather than simply calculate the sensitivity of

each proxy to a certain amount of tax avoidance.

Our method of simulating tax avoidance does have limitations. First, tax avoidance

already exists in Compustat data, and any pre-existing avoidance we do not control for is an

omitted variable. However, because we randomly seed additional tax avoidance, this omitted

variable is orthogonal to our coefficient of interest by definition and will not bias inferences

related to the relative power of the tests. This orthogonality also extends to control variables,

such that the incremental tax avoidance we seed does not disrupt the association between pre-

existing avoidance reflected in the proxies and the control variables we include in our

regressions. Second, we simulate each type of tax avoidance separately, thereby assuming firms

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pursue only one additional strategy at a time. In reality, a firm’s specific incentives may induce

them to try multiple strategies at once or to prioritize one strategy over others. Although our

research design does not explicitly consider these incentives, the fact that we seed each strategy

randomly should not affect results because randomly seeded firms are as likely to have

incentives to pursue any one strategy as unseeded firms.

Third, the expected number of times a firm (or year) is seeded with tax avoidance is a

function of the number of times that firm (or year) is present in a given sample. For example, in

samples that require profitable observations, each firm will not be in the sample every year.

Thus, our seeding might not reflect the relative frequency of tax avoidance across firms or years

in reality. To explore whether our randomized seeding impacts the relative power of tests, we

conduct robustness tests in which we hold the unconditional likelihood of seeding constant but

intentionally seed tax avoidance so that seeded observations are clustered more in some years

relative to others or in more profitable observations. Although clustering seeded observations by

year or profitability does not impact inferences, we cannot rule out possible effects of other

forms of tax avoidance clustering. Finally, some strategies generate nontax costs (e.g., the

nondeductibility of interest expense associated with generating tax-exempt income) or implicit

taxes (e.g., expenditures become more costly when they are eligible for tax preferences, such as

credits or accelerated cost recovery), which could make them less attractive tax planning

opportunities. We attempt to mitigate this concern by separately simulating three strategies that

broadly reflect multiple tax planning opportunities.

Evaluation of econometric tests

We simulate tax avoidance in 15 percent of observations and seed median cash tax

savings of 0.25 percent of pre-tax income. We select this magnitude of cash tax savings based on

16

a review of papers examining the determinants of tax avoidance and believe it is an economically

plausible addition to the avoidance already embedded in Compustat data. We provide additional

detail in support of this magnitude in the Appendix.

To assess the relative power of statistical tests to identify simulated tax avoidance, we

estimate the rate at which we can reject the null when we estimate Equation (1).

TaxProxy = α + β1TaxSeeded + β2TaxSeedable + β3Size + β4Profitability + β5Leverage + β6CapEx + β7Intangibles + β8ForeignSales + β9R&D. (1)

TaxProxy is HS, UTB End, UTB Add, GAAP ETR, Cash ETR, BTD, or Perm Diff computed after

seeding additional tax avoidance in Compustat data. For each specification, we run 10,000

simulations and calculate the percentage of tests for which β1 is statistically significant at the five

percent level.

We set TaxSeeded to one if we seed tax avoidance in that observation and zero otherwise.

This represents the alternative hypothesis that the true value of β1 is not zero. A statistically

significant β1 means the test correctly detects a differential level of tax avoidance for the seeded

sample. A perfectly powerful test will reject the null hypothesis that β1 is zero 100 percent of the

time. We compute power as the percentage of the 10,000 simulations for which the null

hypothesis is rejected.

Given pre-existing tax avoidance in the data, Equation (1) also includes control variables

derived from the literature. We include controls for size (natural log of market capitalization),

profitability (pre-tax return on assets in models using ETR proxies and operating cash flows

scaled by assets in models using the remaining proxies), leverage, capital expenditures as a

percentage of total assets, intangible assets as a percentage of total assets, foreign sales from the

Compustat Segments database as a percentage of total sales, and research and development

expenditures as a percentage of total assets. We winsorize these continuous control variables at

17

1st and 99th percentiles in all specifications. Further, because not all firms report foreign income,

we include an indicator variable (TaxSeedable) in tests of the hybrid strategy. TaxSeedable

equals one if the firm-year has nonmissing, nonzero foreign pre-tax income and ensures we do

not seed the hybrid strategy of cross-jurisdictional income shifting in firms with no foreign

operations. We also interact each control variable with TaxSeedable to account for potential

differences in how the control variables affect the tax avoidance proxies between seedable and

nonseedable observations.

4. Sample composition

Sample selection

Table 1 describes our sample selection. We estimate regressions on Compustat data from

1993 through 2016. We select 1993 as the first year of the sample because it is the effective date

of ASC 740. We select 2016 as the last year of the sample to avoid the transitory financial

reporting effects of the 2017 tax law, which can substantially alter tax expense (Nichols et al.

2018; Seidman and Stomberg 2018). We exclude flow-through entities (e.g., partnerships and

trusts), entities not incorporated in the United States, financial firms (SIC 6000–6999), and

utilities (SIC 4900–4999). We also remove observations with zero or missing pre-tax income,

negative asset values, or negative market capitalization. Finally, we remove observations lacking

data required to calculate tax avoidance proxies and control variables. This sample is 92,754

firm-years (Full Sample). Because HS is intended to facilitate interpretation of results estimated

on a sample that includes loss observations, the Full Sample, which retains loss observations

(PI<0), is important to our analysis.

Our second sample retains only those observations from the Full Sample that also have

positive pre-tax Profit, which we define as both positive pre-tax income and positive pre-tax

18

operating cash flow resulting in a sample of 54,208 firm-years (Profit Sample). We test this

sample along with the Full Sample because it allows us to examine how the exclusion of loss

observations—a common sample selection criterion—affects power (e.g., Bauer 2015; Dyreng et

al. 2015; Hoi, Wu and Zhang 2013; Hoopes, Mescall and Pitman 2012). Our third sample

requires observations from the Full Sample to have both positive pre-tax Profit as well as

nonnegative total tax expense and taxes paid (Profit and Tax ≥ 0 Sample), resulting in a sample

of 49,491 firm-years. We include this third sample because studies often require both the

numerator and denominator of ETR proxies to be positive to facilitate interpretation (e.g., Hope,

Ma, and Thomas 2013; Jennings, Weaver, and Mayew 2012; Kubick, Lockhard, Mills, and

Robinson 2017; and Kubick, Lynch, Mayberry, and Omer 2015).

To examine the relative power of tests using UTB End and UTB Add as the dependent

variable, we construct three analogous samples that require nonmissing and positive values of the

UTB ending balance. The UTB sample of profitable and unprofitable observations (UTB > 0

Sample) contains 16,867 firm-years. The UTB sample of profitable observations (UTB > 0 and

Profit Sample) contains 11,914 firm-years. Finally, the UTB sample of profitable observations

with nonnegative tax expense or taxes paid (UTB > 0, Profit, and Tax ≥0 Sample) contains

10,733 firm-years.

Descriptive statistics

Table 2 presents descriptive statistics for the tax avoidance proxies across all samples.

Variables are winsorized but not yet seeded with tax avoidance. GAAP ETR and Cash ETR are

winsorized at zero and one; all other proxies are winsorized at 1st and 99 percentiles of their

distributions. Because we estimate 10,000 runs of each specification, we do not show descriptive

statistics for seeded data. However, because we randomly seed tax avoidance and then winsorize

19

variables, we expect the descriptive statistics in Table 2 to offer an unbiased representation of the

descriptive statistics for our average seeded sample.7

Recall that we compare tests using HS as the dependent variable to tests using ETR and

BTD proxies in the Full Sample of profitable and unprofitable firms, the Profit Sample, and the

Profit and Tax ≥ 0 Sample that ensures ETR proxies are positive. Panel A presents descriptive

statistics for all tax avoidance proxies other than the UTB proxies across the three samples. By

construction, HS is more positive as pre-tax income declines. Consistent with this, HS is positive

at the mean (0.0190) and median (0.0016) in the Full Sample, which includes unprofitable and

profitable firms, and is negative at the mean and median in both samples in Panel A that exclude

loss observations.

Panel B provides descriptive statistics for the UTB samples. Unlike other proxies, UTB

proxies are not significantly impacted by the inclusion of loss observations in the sample. Mean

and median values of UTB End and UTB Add are generally static across samples, suggesting that

even loss firms engage in uncertain tax avoidance. In the UTB > 0 Sample, the mean (median)

UTB End is 0.0144 (0.0067) and mean (median) UTB Add is 0.0015 (0.0005).

Turning to the ETR and BTD proxies in Panels A and B, the mean and median values of

the ETR proxies generally increase across samples as negative values of pre-tax income and tax

expense or taxes paid are eliminated. This pattern is consistent with negative values of the ETR

proxies (due either to negative pre-tax income or negative tax values) drawing down sample

averages. Similarly, we find positive mean and median values of the BTD proxies once we

eliminate loss observations from the sample. In Panel A, the mean (median) GAAP ETR is 23.7

7 We first seed additional tax avoidance in raw Compustat data and then winsorize. We do this to mimic archival tax research in which researchers observe data inclusive of tax avoidance and then winsorize. Results are not sensitive to this design choice: inferences are unchanged if we instead first winsorize and then seed.

20

(29.0) percent and mean (median) Cash ETR is 17.8 (7.7) percent in the Full Sample. The mean

(median) BTD is -0.2877 (-0.0003) and mean (median) Perm Diff is -0.2861 (0.0004).

The takeaway from Table 2 is that the distribution of the tax avoidance proxies changes

with sample selection, such that power will also vary across samples. However, because power is

affected by the distribution of both the dependent and independent variables in the sample, we

cannot infer from Table 2 which tests will be relatively more powerful.

5. Results for Tests Using HS

Main results

Figure 1 presents results for examining the power of tests using HS as the dependent

variable, relative to tests using the ETR and BTD proxies. We compare power across all samples

and tax avoidance strategies. We measure power using the percentage of simulations that

accurately identify, at statistically significant levels, observations where we have seeded tax

avoidance, holding known determinants of tax avoidance constant. In other words, power is the

percentage of the 10,000 simulations for which we reject the null hypothesis that β1, the

coefficient associated with TaxSeeded, is zero.

Across all samples and tax avoidance strategies, tests using HS as the dependent variable

are neither the most nor least powerful, compared to tests using GAAP ETR, Cash ETR, BTD,

and Perm Diff. 8 Tests using GAAP ETR or Cash ETR as the dependent variable have more power

to detect the effects of the permanent strategy across all samples relative to tests using HS (and

relative to tests using BTD and Perm Diff). Tests using Cash ETR have more power to detect the

8 When comparing power estimates across tests, we apply a rule of thumb that values at least two percentage points apart are significantly different. The variable we use to assess power has a Bernoulli distribution. The confidence interval for each power estimate is very small, because we estimate 10,000 regressions. In untabulated analysis, we test a sample of 20 power values from Table 3 that are within three percentage points of each other. We find the power estimates are significantly different in 100 percent of tests when they are at least two percentage points apart. However, the power estimates are not all significantly different when they are less than two percentage points apart. Thus, we believe using a rule of thumb that allows a reader to conclude power estimates are significantly different when the values are at least two percentage points different is appropriate.

21

temporary strategy across all samples than tests using HS.9 Test using HS also do not have the

most power to detect the hybrid strategy; tests using Cash ETR have more power to detect the

hybrid strategy across all samples and tests using BTD have more power in samples that require

Profit.10 Thus, in a sample that includes loss observations, tests using Cash ETR outperform tests

using HS in detecting the effects of all three tax avoidance strategies we seed, and tests using

GAAP ETR outperform tests using HS to detect the effects of permanent and hybrid tax

avoidance.11 We conclude from this analysis that tests using HS as the dependent variable are

generally not as powerful as tests using ETR proxies, even in samples that include loss

observations.

We next evaluate how the power of tests using HS varies across samples and tax

avoidance strategies. As we note above, tests using HS are not the most powerful for any strategy

in the sample that includes loss observations. Further, tests using HS experience a dramatic

increase in power to detect all strategies in samples that require pre-tax profit versus the Full

Sample. However, they remain some of the less powerful tests in these samples as well. In other

words, despite being intended to facilitate loss observations in the sample, the noise in a sample

that includes losses outweighs any power gains from including loss observations to increase the

sample size. Examining the power of tests using HS across strategies within each sample, we

find these tests have the greatest power to detect the temporary strategy in samples that exclude

9 As expected, tests using GAAP ETR or Perm Diff detect the temporary strategy at a rate very close to α (five percent), providing further validation for our simulation approach.10 Inferences with respect to seeding the hybrid strategy in Profit samples are unchanged if instead of using a Seedable indicator to identify observations with domestic pre-tax income greater than zero, we restrict the entire sample to include only those observations where domestic pre-tax income is greater than zero such that every observation in the sample could be seeded. 11 Because we randomly seed tax avoidance, there is no correlation between losses and TaxSeeded in the sample that includes loss observations. Therefore, the exclusion of a control for losses does not introduce a correlated omitted variable bias for TaxSeeded. Nonetheless, we confirm results are robust to: (i) winsorizing the ETR proxies at the 1st and 99th percentiles to hold constant the number of observations we winsorize across all proxies; (ii) including an indicator variable equal to one if the firm-year is not profitable and zero otherwise; and (iii) including an indicator variable equal to one if the firm-year has a tax net operating loss carryforward, and zero otherwise.

22

loss observations. Tests using HS have similar power to detect all three strategies in the Full

Sample. Table 3 details the results presented in Figure 1.

We acknowledge that researchers use multiple approaches to address the effect of

outliers, such as robust regression (e.g., Leone, Minutti-Meza and Wasley 2019; Powers,

Robinson and Stomberg 2016) or winsorizing to different values (e.g., winsorizing ETRs at one

percent and 99 percent, instead of at zero and one). We ensure inferences are unchanged if we

implement these alternative approaches. Specifically, in untabulated results, we find the relative

power of tests using HS does not change if we use different methods to winsorize GAAP ETR

and Cash ETR, if we use different scalars to calculate ETR proxies, or if we estimate robust

regressions.12

12 Across all samples, we reset GAAP ETR and Cash ETR values less than zero to zero and values greater than one to one. Inferences from Figure 1 are unchanged if we instead winsorize ETR proxies at one percent and 99 percent (as do Rego and Wilson 2012 and Higgins, Omer, and Phillips 2015) or if we scale tax expense or cash taxes paid by operating cash flows (as do McGuire et al. 2012 and Guenther, Krull, and Williams 2014). Results from robust regressions, which are estimated on raw data, also do not change inferences from Figure 1.

23

Implications for future research

24

Our results provide several implications for future research on the determinants of tax

avoidance. First, removing loss observations from the sample generally increases power across

tests using all proxies we examine. This result suggests the additional noise in samples that

include loss observations outweighs any power gains from an increased sample size. This finding

has important implications for sample selection in future studies, particularly in light of recent

research emphasizing the importance of loss firms in tax avoidance research (e.g., Drake et al.

2018; Henry and Sansing 2018; Hanlon and Heitzman 2010).13 Removing observations with

negative values of Tax, in addition to removing loss observations, has a less significant

incremental effect on power.

Second, if a researcher believes it is appropriate to include loss observations, our findings

indicate tests using GAAP ETR and Cash ETR have more power to detect tax avoidance in the

Full Sample than tests using HS. Because ETR proxies lack meaningful economic interpretation

for loss observations, Henry and Sansing (2018) develop HS to overcome these interpretation

issues and state that HS should be most useful in examining cross-sectional variation in tax

avoidance across the entire population of publicly traded firms. Henry and Sansing (2018) also

caution, however, that not all tax avoidance studies should include loss observations because loss

firms may have different incentives to pursue tax avoidance. If researchers find the inclusion of

loss years important to their study — perhaps because they are interested in tax avoidance across

all publicly-traded firms — our results suggest that tests using ETR proxies are more powerful

than tests using HS despite interpretation concerns.

13 Our tests cannot speak to whether loss firms engage in more or less tax avoidance, on average, than profitable firms or whether the determinants of tax avoidance differ between these two groups of firms. Researchers in this area face a trade-off: excluding loss observations may introduce sample selection bias that limits the generalizability of results to samples of only profitable firms, but including them reduces power to detect effects.

25

Third, tests using GAAP ETR and Cash ETR also generally have more power to detect tax

avoidance than tests using HS in samples that exclude loss observations. This finding is an

important consideration for researchers relying exclusively on tests using HS to detect the effects

of tax avoidance in samples that exclude loss observations. Fourth, across all proxies and

samples, we find tests detect the effects of the hybrid strategy, implemented here as cross-

jurisdictional income shifting with a permanently reinvested earnings assertion, with less power

than they detect the effects of the permanent or temporary strategies. Lower power to detect the

hybrid strategy could arise because some proxies do not reflect the temporary portion of the

hybrid strategy, which lowers the power of tests using these proxies. These results validate the

use of alternative proxies specifically designed to detect cross-jurisdictional income shifting

(e.g., Collins, Kemsley, and Lang 1998; Chen, Hepfer, Quinn, and Wilson 2018; De Simone,

Klassen, and Seidman 2019; De Simone, Mills, and Stomberg 2019).

Finally, because the most powerful test varies by tax avoidance strategy, the results in

Table 3 can potentially guide researchers in selecting the appropriate tax avoidance proxy to use.

For example, tests using BTD have the most power to detect the hybrid strategy in samples that

exclude loss observations. Table 3 results also have implications for interpreting results. Finding

that a variable of interest is significantly associated with one avoidance proxy but not others can

help readers isolate the types of tax planning strategies in place. For example, McGuire et al.

(2012) provide evidence (Table 6) that industry-expert financial statement auditors are associated

with lower GAAP and cash ETRs but not with different levels of BTDs. In light of our results,

this pattern could suggest that, when firms purchase tax services from their industry-expert audit

firm, they are more likely to pursue permanent tax avoidance strategies, such as federal and state

26

R&D credits (high-tech industries), tip credits (restaurant industries), or domestic production

manufacturing activities (manufacturing industries), rather than temporary or hybrid strategies.

6. Results for Tests using UTB End and UTB Add

Main results

Table 4 summarizes our results for examining the power of tests using UTB End and

UTB Add as the dependent variables, relative to tests using HS, GAAP ETR, Cash ETR, BTD, and

Perm Diff across all samples and tax avoidance strategies. In the following discussion, we focus

on the case where we seed the permanent tax avoidance strategy with a 50 percent reserve for

uncertain tax avoidance. We find that tests using UTB Add have the highest power of all tax

avoidance proxies across all three samples, ranging from 72 percent to 84 percent versus just

nine percent to 10 percent for tests using UTB End. Tests using UTB End also have lower power

than tests using Cash ETR in the UTB > 0 Sample. Thus, tests using UTB End have the third-

highest power, tied with tests using GAAP ETR and HS in this sample. Further, tests using UTB

End are dominated by tests using HS and Cash ETR in both samples that exclude loss

observations. UTB End reflects multiple years of uncertain tax avoidance, which adds additional

noise that likely contributes to the relatively lower power of tests using UTB End. We confirm

these inferences are unchanged if we estimate robust regressions.

Importantly, the inclusion of unprofitable firms does not impair the power of tests using

UTB End and UTB Add. In fact, the power of tests using UTB End is unaffected by sample

selection choices, and the power of tests using UTB Add is higher in the UTB > 0 Sample than in

the two samples that exclude loss observations. The reduced power of tests using UTB Add in

these other samples is not simply the result of reduced sample size, because the power of tests

using HS and the other proxies is lowest in the largest sample of profitable and unprofitable

27

firms. Examining our samples, we observe loss observations are less likely to have current year

UTBs. Thus, seeding these observations with tax avoidance that increases the current year UTB

from zero to a positive amount increases the likelihood of identifying tax avoidance. Overall, the

pattern of results presented in Table 4 for HS, GAAP ETR, Cash ETR, BTD, and Perm Diff is

generally consistent with those in panel A of Figure 1, though muted, as expected given reduced

sample size.

Next, we consider the effect of varying levels of tax uncertainty by changing the FIN 48

reserve. We assume the permanent strategy requires a FIN 48 reserve equal to zero percent, 50

percent, or 100 percent of the claimed tax benefits. Table 4 also presents results of this test. We

focus our discussion on the UTB > 0 Sample. We present results for all proxies, even though

seeding financial reporting for tax uncertainty does not affect HS or Cash ETR. This allows us to

compare the relative power of tests using these two proxies to detect uncertain tax avoidance to

tests using proxies that are affected by financial accounting for tax uncertainty.

Our first takeaway of tests using UTB proxies is that, for firms that undertake uncertain

tax avoidance but do not accrue reserves to preserve the financial reporting benefits of their

avoidance, tests using the UTB proxies are relatively powerless. In fact, tests using all other

proxies more powerfully detect uncertain, permanent tax avoidance in all three samples than tests

using UTB End or UTB Add seeded without reserves. Tests using GAAP ETR are most powerful

on average across the three samples when we do not accrue reserves. Further, the power of tests

using UTB End and UTB Add increases with the percentage of seeded permanent tax avoidance

reserved. Although highly intuitive, these findings have important implications for research

because the UTB proxies are commonly used to detect uncertain tax avoidance. However,

research suggests there is significant variation in reserves across firms, even when holding

28

constant industry, year, and transaction, some of which could be attributable to financial

reporting incentives (De Simone, Robinson, and Stomberg 2014). To mitigate these concerns,

researchers often consider using multiple proxies in tandem to triangulate results and strengthen

inferences. However, this approach could be problematic because the power of tests using GAAP

ETR, BTD, and Perm Diff to detect tax avoidance monotonically decrease, as accrued reserves

for tax uncertainty increase, while power increases monotonically for tests using UTB End and

UTB Add. The second takeaway of our UTB analyses is the effect of reserves on power is

significantly larger for tests using UTB Add than for those using UTB End. Even with only a 50

percent reserve, tests using UTB Add as the dependent variable are more powerful than tests

using all other proxies, including UTB End.

The UTB proxy results also provide an alternative analysis of the power of tests to detect

a hybrid strategy. Recall that, in our first sets of tests, the accounts we alter to seed tax avoidance

vary across strategies to reflect how companies implement a common type of each strategy (e.g.,

we alter selling, general, and administrative expense to implement the permanent strategy,

consistent with an R&D credit). Focusing on the Profit Sample, we find tests using all proxies

detect the hybrid strategy with the lowest power among the strategies the proxies are designed to

detect (e.g., GAAP ETR and Perm Diff do not detect the effects of temporary tax avoidance).

Because we use different underlying accounts, differences in power in Table 3 may be

attributable to how we seed tax avoidance. In our UTB tests, we seed only the permanent tax

strategy and vary the amount reserved. Therefore, we hold constant the underlying account (e.g.,

selling, general, and administrative expense) that we use for seeding tax avoidance. This feature

of our UTB tests allows us to assess the generalizability of the results related to the hybrid

strategy that are reported in Table 3.

29

Recall also that seeding the permanent strategy without reserves affects GAAP ETR,

BTD, and PermDiff like a permanent strategy, whereas seeding the permanent strategy with less

than full reserves impacts GAAP ETR, BTD, and Perm Diff like a hybrid strategy because only

the nonreserved portion of the permanent tax savings is reflected in these proxies. Thus, we can

compare the relative power of GAAP ETR, BTD, and Perm Diff to detect the effects of the

permanent and hybrid strategies across Tables 3 and 4. We observe similar patterns: tests using

all three proxies have greater power to detect the effects of permanent tax avoidance than to

detect the effects of hybrid tax avoidance. For example, as shown in Table 3, tests using Perm

Diff to detect the effects of the permanent strategy in the Profit Sample are roughly 2.5 times

more powerful than tests using Perm Diff to detect the effects of the hybrid strategy. In the

comparable sample in Table 4, tests using Perm Diff to detect the effects of uncertain tax

avoidance without reserves are roughly twice as powerful as tests using Perm Diff to detect the

effects of uncertain tax avoidance with a 50 percent reserve. We conclude that the relatively low

power of tests using these proxies to detect hybrid tax avoidance in Table 3 is not an artifact of

the particular hybrid strategy we seed (i.e., income shifting with a partial assertion of permanent

reinvestment), but rather arises because the effects of some strategies are not fully reflected in

some proxies. In contrast, Cash ETR and HS are not affected by reserves, so the power of tests

using these proxies is constant within a sample across all levels of reserves.

30

Implications for future research

Based on key findings presented in Table 4 and discussed above, tests using UTB Add

most powerfully detect the effects of uncertain tax avoidance. However, because the power of

UTB proxies to detect uncertain tax avoidance depends critically on firms’ accruing tax reserves

and partitioning the sample, based on managers’ overall financial reporting aggressiveness or

incentives to understate reserves, could strengthen tests. Retaining loss observations in the

sample when investigating uncertain tax avoidance could also improve power. Thus, for

researchers using multiple proxies to test their research question, this suggestion may require

them to maintain two separate samples to increase power—a sample that excludes loss

observations in tests using all other proxies and one that includes loss observations in tests using

UTB proxies.

Our UTB proxy results can also be interpreted as highlighting the potential effect of FIN

48 on the ETR and BTD proxies. Interpreting the tests in this way assumes FIN 48 did not itself

impact the level or type of tax avoidance firms undertake, and we acknowledge there is evidence

suggesting that assumption is invalid (e.g., McClure 2019). The interpretation also depends on

one’s beliefs about the financial reporting of firms before FIN 48. If reserves for tax uncertainty

were lower, on average, at that time (Audit Analytics 2008; Blouin et al. 2007, 2010; Dunbar et

al. 2007; Robinson et al. 2016), then comparing the power of tests when the reserves we seed are

lower to the power when the reserves we seed are higher informs the impact of FIN 48 adoption.

Because the power of tests using GAAP ETR, BTD, and Perm Diff to detect tax avoidance

decreases as the reserve percentage increases, tests using these proxies may have less power to

detect tax avoidance on average after FIN 48. One potential way to mitigate this concern is to

validate inferences from time-series tax avoidance tests that span the adoption of FIN 48 with

31

tests using Cash ETR or HS, because these proxies are unaffected by reserves for tax uncertainty.

7. Conclusion

This study simulates tax avoidance in Compustat data to evaluate the relative power of

econometric tests using new proxies for corporate tax avoidance, compared to those more

extensively analyzed in prior research. We simulate a tax avoidance strategy that yields

permanent tax benefits, one that yields temporary benefits, and one that we consider a hybrid,

because the entire cash tax savings is not reflected in some of the tax expense-based proxies, due

to financial reporting choices made by the firm. Because the purported benefits of the newer

proxies we test include the ability to analyze loss observations or to capture tax uncertainty, we

examine how the relative power of these tests varies across samples and levels of reserves for tax

uncertainty.

Although one of the purported advantages of the proxy developed by Henry and Sansing

(2018) is that it provides a more meaningful interpretation of results for samples that include

unprofitable firms, tests using the proxy have lower power in a sample that includes loss

observations, relative to tests using other proxies in the same sample. Tests using ETR proxies

are three to four times more powerful in these samples. When firms reserve for tax uncertainty,

tests using UTB Add have the highest power—significantly more power than tests using UTB

End or any other tax avoidance proxy—and this power increases with higher levels of reserves

for tax uncertainty and with the inclusion of loss observations. When firms do not accrue for

uncertain tax avoidance, tests using GAAP ETR have the greatest power to detect the effects of

uncertain, permanent tax avoidance on average.

Our results contribute to the tax avoidance literature by providing the first large-scale

empirical analysis of the relative power of HS and UTB proxies. Further, we inform the ongoing

32

debate over the appropriateness of including loss observations in large-sample tests of tax

avoidance. Our results suggest that the noise in tax avoidance proxies for loss observations

outweighs any power gains that result from a larger sample. Finally, our results can speak to the

effect of FIN 48 on the power of tests to detect tax avoidance. Our findings suggest the power of

tests using GAAP ETR, BTD, and PermDiff to detect uncertain tax avoidance may have declined

after FIN 48 if firms increased reserves for tax uncertainty on average after adoption. However,

we acknowledge that inferences related to the effect of FIN 48 on tax avoidance assume that FIN

48 itself did not directly impact the level of tax avoidance.

33

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38

Appendix – Additional detail on seeding tax avoidance in Compustat data

This section provides details on our methodology. We begin with a more detailed

discussion of how we simulate tax avoidance in Compustat data, including the specific

adjustments we make to underlying accounts to seed tax avoidance. We then discuss more details

of certain aspects of the method we use to assess power.

Detail of simulating tax avoidance

We seed a level of tax avoidance that generates cash tax savings equal to approximately

0.25 percent of pre-tax income at the median. We select this magnitude of cash tax savings based

on a review of papers examining the determinants of tax avoidance and believe it is an

economically plausible addition to the tax avoidance already embedded in Compustat data.

McGuire, Omer, and Wang (2012) estimate GAAP ETRs of firms with tax expert auditors are

one percent lower than firms without tax expert auditors. Robinson, Sikes, and Weaver (2010)

estimate a 4.3 percentage point difference in ETRs for firms whose tax departments are classified

as profit centers. Brown (2011) estimates that the average tax shelter firm in her sample saved

three percent of pre-tax income from this relatively uncertain type of tax avoidance. We believe

the savings from tax avoidance we simulate should be much lower than these savings for two

reasons. First, Compustat data already includes some level of tax avoidance and the point of our

study is to evaluate the power of statistical tests to detect seeded tax avoidance. The median cash

ETR in unadjusted Compustat data for our primary sample is 25 percent, suggesting tax savings

equal to 14 percent of pre-tax income versus a combined U.S. rate of 39 percent. Seeding an

additional 0.25 percent of pre-tax income increases the tax savings of the firm by 1.8 percent,

which we believe is economically significant. Second, given these studies estimate the effect of

certain firm characteristics on all types of tax avoidance while we seed only one type of tax

39

avoidance at a time, we believe the estimates in these studies represent an upper bound of the

magnitude of additional tax avoidance we should seed.

Below, we outline the journal entries required to seed each tax avoidance strategy,

showing the strategy’s effect on the most granular tax accounts in Compustat. We acknowledge

that all journal entries in this Appendix affect multiple accounts and adjusted those accounts in

our data accordingly. For example, a decrease to current federal tax expense also decreases total

tax expense (TXT), net income (NI) and stockholders’ equity (SEQ).

1) Permanent Strategy

To seed a strategy that generates permanent tax savings, we assume the firm shifts a

percentage of selling, general, and administrative expense (XSGA) from a deduction to a credit.

For example, a firm could elect to claim an R&D credit instead of deducting R&D or a FICA tip

credit instead of deducting payroll taxes. We therefore seed an additional benefit equal to 65

percent of a portion of XSGA to represent an increase in benefit for the change from one dollar

of deduction to one dollar of credit. We set the portion of XSGA shifted to yield additional cash

tax savings approximately equal to 0.25 percent of pre-tax income (PI) at the median, which

requires us to assume approximately 0.15 percent of XSGA is converted from a deduction to a

credit. We summarize the journal entry as follows:

Taxes paid (TXPD) (0.15% × XSGA) × 65%Current federal tax expense (TXFED) (0.15% × XSGA) × 65%

2) Temporary Strategy

To seed a strategy that generates temporary tax savings, we assume the firm accelerates

deductions related to a percentage of capital expenditures (CAPX). For example, a firm could

elect bonus depreciation or conduct a cost segregation study that accelerates tax depreciation

deductions for certain components of real property. We set the portion of CAPX deducted to

40

yield additional cash tax savings approximately equal to 0.25 percent of PI at the median, which

requires us to assume an additional 1.52 percent of CAPX is deductible in the current year. We

summarize the journal entry as follows:

Taxes paid (TXPD) (1.52% × CAPX) × 35%Current federal tax expense (TXFED) (1.52% × CAPX) × 35%

Deferred federal tax expense (TXDFED) (1.52% × CAPX) × 35%Deferred tax liability (TXNDBL) (1.52% × CAPX*35%)

3) Hybrid Strategy

We model our seeding of a strategy that can yield both permanent and temporary tax

benefits from cross-jurisdictional income shifting. We shift a percentage of pre-tax domestic

income (PIDOM) to pre-tax foreign income (PIFO). We assume an advantageous foreign tax rate

on the shifted income of 15 percent. To achieve median cash tax savings approximately equal to

0.25 percent of PI, we shift 1.88 percent of PIDOM to PIFO. We require positive PIDOM, which

ensures this strategy decreases current federal tax expense and increases current foreign tax

expense.

In many cases, foreign source income is not subject to U.S. taxation until it is repatriated.

In accordance with APB 23, firms can assert that they intend to permanently reinvest U.S. tax-

deferred foreign-source income and avoid accruing the incremental U.S. tax due upon

repatriation. We assume the firm asserts that 50 percent of the income we shift is permanently

reinvested. We summarize the journal entry as follows:

Taxes paid (TXPD) (1.88% × PIDOM) × 20%Current foreign tax expense (TXFO) (1.88% × PIDOM) × 15%

Current federal tax expense (TXFED) (1.88% × PIDOM) × 35%

Deferred federal tax expense (TXDFED) (1.88% × PIDOM) × 10%Deferred tax liability (TXNDBL) (1.88% × PIDOM) × 10%

4) Uncertain tax avoidance

41

To examine the effects of uncertain tax avoidance, we seed the permanent tax avoidance

strategy as above and then layer on the financial reporting effects of accruing reserves for

uncertain tax avoidance. We seed multiple levels of reserves, ranging from zero to 100 percent of

claimed tax benefits. Below we present the incremental adjustment made after adjusting for the

effects of permanent tax avoidance, assuming a 50 percent reserve, which means the other 50

percent of the benefit is reflected in tax expense. We summarize this journal entry as:

Current federal tax expense (TXFED) (0.15% × XSGA) × 65% × 50%FIN 48 reserve (TXTUBPOSINC) (0.15% × XSGA) × 65% ×

50%

Assessing power

We apply the seeding methodology to 10,000 independent draws of 15 percent of

observations for each specification we test. We calculate the percentage of tests for which β1

from the following equation is statistically significant.

TaxProxy = α + β1TaxSeeded +β2TaxSeedable +ΣβiControls (1)

TaxProxy is HS, UTB End, UTB Add, GAAP ETR, Cash ETR, BTD, or Perm Diff

computed after seeding additional tax avoidance in Compustat data. TaxSeeded equals one for all

observations (15 percent of each independent sample) seeded with tax avoidance, and zero

otherwise. This variable represents the alternative hypothesis that the true value of β1 is not zero

(i.e., that the observation has not been seeded with tax avoidance). A statistically significant β1

means the test correctly detects a differential level of tax avoidance for the seeded sample. A

perfectly powerful test will reject the null hypothesis that β1 is zero 100 percent of the time. We

compute power as the percentage of the 10,000 simulations for which the null hypothesis that β1

is zero is rejected.

42

Because we hold the amount of cash taxes saved constant at the median across all three

strategies, one might expect power to be the same across all strategies for tests using dependent

variables that are only affected by changes to TXPD (i.e., Cash ETR and HS). However, because

we seed the strategies using different underlying accounts, the amount of seeded tax savings

varies by strategy for the same observation. If we had chosen to seed all three strategies using the

same underlying account (e.g., if the permanent strategy was income shifting with no indefinite

reinvestment assertion, the hybrid strategy was income shifting with a partial indefinite

reinvestment assertion, and the temporary strategy was income shifting with a full indefinite

reinvestment assertion), then power would be identical across strategies. Results for Cash ETR

and HS in Table 4 confirm this: we see identical power across different levels of tax reserves for

tests using these proxies that are unaffected by reserves for tax uncertainty. We chose our

approach so we could evaluate how well tests detect examples of different strategies.

A well-specified test will reject the null hypothesis that there is no effect of tax avoidance

on the dependent variable when the null is true at a frequency equal to the significance level. In

each simulation, we randomly seed tax avoidance in 15 percent of observations. We then

randomly set TaxSeeded to one for 15 percent of observations independent of the actual seeding.

This represents the null hypothesis that TaxSeeded is uncorrelated with seeded tax avoidance.

Because we randomly assign TaxSeeded, it is also uncorrelated with any pre-existing tax

avoidance in the Compustat data. A statistically significant β1 at a given significance level, α,

reflects a type I error in which the null hypothesis that the coefficient β1 equals zero is rejected in

favor of the alternative when the null is true. In our tests which use a one-tailed five percent

significance level (α = 0.05) (negative β1 for HS, GAAP ETR, Cash ETR and positive for UTB

End, UTB Add, BTD and PermDiff) and assuming a binomial distribution, a well-specified test

43

will indicate a positive and significant estimate of β1 between 4.444 percent and 5.556 percent of

the time. Whether a test is well specified is a function of the underlying distribution of the tax

avoidance proxy and the explanatory variables. Skewness in the dependent variable can cause

misspecification because unless explanatory variables sufficiently explain the skewness, the

resulting error term will also be skewed thereby violating the normality assumption underlying

tests of significance for coefficients in OLS regressions. In contrast, this same skewness does not

violate any assumptions and is less concerning when the skewed variable is an explanatory

variable.

Because we can assess power only in well-specified tests, if a test is misspecified when

estimated using OLS, we report power estimated using the empirical p method following Lyon,

Barber and Tsai (1999). The empirical p method uses Monte Carlo methods to approximate the

distribution of an OLS regression coefficient. That is, rather than attempt to minimize skewness

in the sample (as winsorizing does) the empirical p method generates a distribution of

coefficients using random samples that remain skewed. This distribution of coefficients is then

compared to the actual coefficient to generate an empirical p-value. The empirical p method

eliminates misspecification by design; tests employing empirical p will always be well specified.

It is appropriate to compare the power of a well-specified test using OLS with the power of a test

using the empirical p method. We note in the tables any instances where we assess power using

empirical p.

Our methodology is similar to Dechow et al. (1995), who adopt a two-step approach

whereby proxies for discretionary accruals are the residuals from various prediction models for

total accruals. These proxies then become the dependent variables in a second-stage regression.

We base our methodology on a single-stage regression, consistent with how researchers test for

44

tax avoidance. That is, we include control variables in a multivariate regression in addition to an

indicator of tax avoidance. The two approaches yield identical inferences when standard errors

are adjusted for heteroscedasticity in the two-stage approach.14

14 We confirm this in untabulated analysis by repeating a subset of our simulations using the two-step approach in Dechow et al. (1995). We estimate each tax avoidance proxy as a function of controls and capture the residual. This unexpected tax avoidance becomes Tax Proxy in the following regression: TaxProxy= α + β1TaxSeeded + β2TaxSeedable. Results are perfectly correlated with results generated using our single-stage methodology.

45

TABLE 1Sample selection

Observations from Compustat 1993-2016

289,029 Less:  

Flow-through entities (4,8

26) Entities not incorporated in the U.S. (62,078) Financial firms and utilities (36,335) Observations with zero or missing PI (38,093) Observations where AT<0 or market capitalization <0 (20,074) Observations with missing TXT or TXPD (28,815) Observations with missing lagged assets (5,807) Observations with missing data to calculate control variables (247)

Full Sample   92,

754

UTB > 0 Sample: Observations with TXTUBEND > 0 16,

867    

Full Sample   92,

754

Observations with negative pre-tax income (PI) or pre-tax operating cash flow (OANCF + TXPD) (38,54

6)

Profit Sample: Observations with pre-tax Profit 54,

208

UTB > 0 and Profit Sample: Observations with TXTUBEND > 0 11,

914

   

Profit Sample   54,

208

Observations with negative tax expense (TXT) or taxes paid (TXPD) (4,71

7)Profit and Tax ≥ 0 Sample: Observations with pre-tax Profit and non-negative Tax 49,

491 UTB > 0, Profit, and Tax ≥ 0 Sample: Observations with TXTUBEND > 0 10,

733 Table 1 outlines sample selection criteria. Variables in capitalized letters refer to Compustat variable names. Flow-through entities are those with company names containing “Trust”, “LP”, or “Partners”. Country of incorporation is determined using foreign incorporation code (FIC). Financial firms are firms with SIC codes in 6000-6999 and utilities are firms with SIC codes in 4900-4999. Market capitalization is PRCC_F × CSHO. Observations have pre-tax Profit if both pre-tax income (PI) and pre-tax operating cash flows (OANCF + TXPD) are greater than zero. Observations have nonnegative Tax if both TXPD and TXT are greater than or equal to zero.

46

TABLE 2Descriptive statistics

Panel A: Samples for HS tests   Sample  Full Profit Profit and Tax ≥ 0

Proxy Mean Median Min Max Mean Median Min Max Mean Median Min MaxHS 0.0190 0.0016 -0.0894 0.4264 -0.0077 -0.0048 -0.1058 0.0348 -0.0067 -0.0042 -0.1000 0.0360

GAAP ETR 0.2372 0.2903 0.0000 1.0000 0.3233 0.3546 0.0000 1.0000 0.3423 0.3600 0.0000 1.0000Cash ETR 0.1778 0.0769 0.0000 1.0000 0.2716 0.2534 0.0000 1.0000 0.2856 0.2688 0.0000 1.0000

BTD -0.2877 -0.0003 -11.161 0.2395 0.0283 0.0196 -0.0943 0.2567 0.0261 0.0186 -0.0969 0.2418Perm Diff -0.2861 0.0004 -11.122 0.3280 0.0226 0.0089 -0.0806 0.3513 0.0154 0.0081 -0.0843 0.1975

Panel B: Samples for UTB tests

  Sample  UTB > 0 UTB > 0 and Profit UTB > 0, Profit, and Tax ≥ 0

Proxy Mean Median Min Max Mean Median Min Max Mean Median Min MaxUTB End 0.0144 0.0067 0.0001 0.1574 0.0112 0.0062 0.0001 0.0828 0.0109 0.0061 0.0001 0.0783UTB Add 0.0015 0.0005 0.0000 0.0167 0.0014 0.0005 0.0000 0.0138 0.0014 0.0005 0.0000 0.0131

HS 0.0067 -0.0012 -0.0537 0.1727 -0.0058 -0.0046 -0.0570 0.0240 -0.0049 -0.0042 -0.0475 0.0246GAAP ETR 0.2580 0.2882 0.0000 1.0000 0.3080 0.3239 0.0000 1.0000 0.3301 0.3317 0.0000 1.0000Cash ETR 0.2081 0.1711 0.0000 1.0000 0.2751 0.2523 0.0000 1.0000 0.2865 0.2642 0.0000 1.0000

BTD -0.0293 0.0087 -0.8784 0.1784 0.0260 0.0213 -0.0884 0.1823 0.0245 0.0207 -0.0896 0.1708Perm Diff -0.0275 0.0059 -0.8802 0.3095 0.0235 0.0128 -0.0860 0.3279 0.0158 0.0116 -0.0914 0.1319

Table 2 provides descriptive statistics for tax avoidance proxies. In Panel A, the Full Sample contains Compustat observations from 1993-2016 with non-missing data required to calculate tax avoidance proxies and control variables. We further eliminate flow-through entities, entities not incorporated in the U.S., utilities (SIC 4900-4999), and financial firms (SIC 6000-6999). The two sub-samples eliminate observations 1) with Profit less than or equal to zero, where Profit is pre-tax income (PI) and pre-tax operating cash flow (OANCF + TXPD), and 2) with Tax less than zero, where Tax is tax expense (TXT) and taxes paid (TXPD). In panel B, the samples are analagous to those in panel A but also require a positive ending balance in unrecognized tax benefits (TXTUBEND > 0). HS is (TXPD - .35 × PI)/(lag MVA) where MVA = AT + (MVE - SEQ) and MVE = PRCC_F × CSHO. GAAP ETR is TXT/PI. Cash ETR is TXPD/PI. BTD is (PI - (TXFED + TXFO)/.35)/AT. Perm Diff is (PI - (TXFED + TXFO + TXDI)/0.35)) /AT. UTB End is TXTUBEND/AT. UTB Add is TXTUBPOSINC/AT. We winsorize HS, BTD and Perm Diff, UTB End and UTB Add at one and 99 percent and winsorize GAAP ETR and Cash ETR at zero and one.

47

TABLE 3Summary of power in HS tests across multiple proxies, strategies and samples

  Sample

ProxyFull Profit Profit and Tax ≥ 0

PERM HYBRID TEMP PERM HYBRID TEMP PERM HYBRID TEMPHS 6 7 7 22 22 30 22 23 31

GAAP ETR 24 14 5 77 17 5 81 19 5Cash ETR 26 23 32 47 28 63 46 27 62

BTD 5 10 7 36 37 47 36 38 47Perm Diff 5 6 5 31 14 4 54 22 4

Table 3 shows the percentage of simulations (out of 10,000) that correctly identifies seeded tax avoidance for various strategy-proxy pairs. Journal entries showing the effects of seeded tax avoidance for the three strategies – a permanent strategy (PERM), a temporary strategy (TEMP) and a hybrid strategy (HYBRID) – are in the Appendix. We seed tax avoidance such that median cash tax savings from each strategy equal 0.25 percent of pre-tax income (PI). We seed tax avoidance in 15 percent of observations from each of three samples. The Full Sample contains Compustat observations from 1993-2016 with non-missing data required to calculate tax avoidance proxies and control variables. We further eliminate flow-through entities, entities not incorporated in the U.S., utilities (SIC 4900-4999), and financial firms (SIC 6000-6999). The two sub-samples eliminate observations 1) with Profit less than zero, where Profit is pre-tax income (PI) and pre-tax operating cash flow (OANCF + TXPD), and 2) with Tax less than or equal to zero, where Tax is tax expense (TXT) and taxes paid (TXPD). Tests identify seeded tax avoidance using the following regression: TaxProxy = α + β1TaxSeeded + β2TaxSeedable + ΣβiControls. TaxProxy is either HS (TXPD - 0.35 × PI)/(lag MVA) where MVA = AT + (MVE - SEQ) and MVE = PRCC_F × CSHO, GAAP ETR (TXT/PI), Cash ETR (TXPD/PI), BTD (PI - (TXFED + TXFO)/0.35)/AT), or Perm Diff (PI - (TXFED + TXFO + TXDI)/0.35))/AT). TaxProxy is computed after we seed additional tax avoidance in Compustat data. Controls are: size (natural log of MVE); profitability (pre-tax return on assets (PI/AT) in models using ETR proxies and operating cash flows scaled by assets (CFO/AT) in models using other proxies); leverage (LT/AT); capital expenditures (CAPX/AT); intangible assets (INTAN/AT); foreign sales from the Compustat Segments database as a percentage of total sales (SALE); and research and development expenditures (XRD/AT). These continuous control variables are winsorized at one and 99 percent in all specifications. Further, in tests of the hybrid strategy, we include the interaction of each control variable with TaxSeedable to account for differences between seedable and non-seedable observations. We winsorize HS, BTD and Perm Diff at one and 99 percent and GAAP ETR and Cash ETR at zero and one. All tests in Table 3 are well-specified using OLS.

48

TABLE 4Summary of power in tests of tax uncertainty across proxies, samples, and levels of reserves

  Sample

Proxy

UTB > 0 UTB > 0 and Profit UTB > 0, Profit and Tax ≥ 0

no reserve

50% reserve

full reserve

no reserve

50% reserve

full reserve

no reserve

50% reserve

full reserve

UTB End 5 9 15 5 10 17 5 10 77UTB Add 5 84 100 4 72 100 4 72 100

HS 8 8 8 13 13 13 13 13 13GAAP ETR 12 8 5 22 11 5 24 11 5Cash ETR 14 14 14 19 19 19 19 19 19

BTD 10 7 5 16 9 5 16 9 5Perm Diff 9 7 5 13 7 5 22 11 5

Table 4 shows the percentage of simulations (out of 10,000) that correctly identifies seeded tax avoidance for various strategy-proxy pairs. Journal entries showing the effects of seeded permanent and uncertain tax avoidance are in the Appendix. We seed permanent tax avoidance such that median cash tax savings equals 0.25 percent of pre-tax income (PI). We then create a reserve equal to 0 percent, 50 percent, or 100 percent of the claimed benefits to include the effect of tax uncertainty. We seed permanent tax avoidance in 15 percent of observations from each of three samples. The UTB > 0 Sample contains Compustat observations with a positive ending balance in unrecognized tax benefits (TXTUBEND > 0) and non-missing data to calculate required variables. We further eliminate flow-through entities, entities not incorporated in the U.S., utilities (SIC 4900-4999), and financial firms (SIC 6000-6999). The two sub-samples eliminate observations 1) with Profit less than or equal to zero, where Profit is pre-tax income (PI) and pre-tax operating cash flow (OANCF +TXPD), and 2) with Tax less than zero, where Tax is tax expense (TXT) and taxes paid (TXPD). Tests identify seeded tax avoidance using the following regression: TaxProxy = α+ β1TaxSeeded + β2TaxSeedable + ΣβiControls. TaxProxy is either UTB End (TXTUBEND/AT), UTB Add (TXTUBPOSINC/AT), HS (TXPD - 0.35*PI)/(lag MVA) where MVA = AT + (MVE - SEQ) and MVE = PRCC_F × CSHO, GAAP ETR (TXT/PI), Cash ETR (TXPD/PI), BTD (PI - (TXFED + TXFO)/0.35)/AT), or Perm Diff (PI - (TXFED + TXFO + TXDI)/0.35))/AT). TaxProxy is computed after we seed additional tax avoidance in Compustat data. Controls are: size (natural log of MVE); profitability (pre-tax return on assets (PI/AT) in models using ETR proxies and operating cash flows scaled by assets (CFO/AT) in models using other proxies); leverage (LT/AT); capital expenditures (CAPX/AT); intangible assets (INTAN/AT); foreign sales from the Compustat Segments database as a percentage of total sales (SALE); and research and development expenditures (XRD/AT). These continuous control variables are winsorized at one and 99 percent in all specifications. We winsorize UTB End, UTB Add, HS, BTD and Perm Diff at one and 99 percent and GAAP ETR and Cash ETR at zero and one. Percentages in bold indicate tests that are not well-specified using OLS so we evaluate power using the empirical p method.

49

Figure 1: Relative power of tests using HS across three samples

Panel A: Permanent strategy Panel B: Temporary strategy

0%10%20%30%40%50%60%70%80%90%

100%

Full Profit Profit and Tax ≥ 0

HS GAAP ETR Cash ETR BTD Perm Diff

0%10%20%30%40%50%60%70%80%90%

100%

Full Profit Profit and Tax ≥ 0

HS GAAP ETR Cash ETR BTD Perm Diff

Panel C: Hybrid strategy

0%10%20%30%40%50%60%70%80%90%

100%

Full Profit Profit and Tax ≥ 0

HS GAAP ETR Cash ETR BTD Perm Diff

Figure 1 graphs the results in Table 3. All details and variable definitions are in the notes to Table 3.

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