Tax Rates and Corporate Decision Making
John R. Graham
Duke University
[email protected]
Michelle Hanlon
Massachusetts Institute of Technology
[email protected]
Terry Shevlin
University of California, Irvine
[email protected]
Nemit Shroff
Massachusetts Institute of Technology
[email protected]
Comments Welcome
Please do not quote without permission
September 2014
ABSTRACT
It has long been suspected that managers use short-cuts (e.g., heuristics) to make many decisions
and that their decisions are affected by behavioral biases such as a tendency to overly rely on
‘salient’ or vivid metrics/information. We document that managers do indeed rely on heuristics
when they incorporate taxes into decision-making, and that tax rate salience affects their
decision-making. Further, we document, for the first time in a corporate setting, that these
behavioral biases lead to suboptimal decisions, and we provide estimates of the economic
magnitude of the loss in firm value as a result of these biases. For example, we find that many
firms employ the average tax rate paid on their income (i.e., the GAAP effective tax rate [ETR])
rather than the marginal tax rate (MTR) to evaluate incremental decisions, and that using the
GAAP ETR for decision-making leads the typical firm to experience a deadweight loss of nearly
$10 million for making the incorrect capital structure decisions. We also find that firms that
employ the GAAP ETR for investment decision-making are less responsive to their growth
opportunities and have smaller acquisition announcement returns than those using the MTR,
leading to a loss in firm value of more than $20 million from suboptimal acquisitions.
We appreciate helpful comments from Mary Barth, Lil Mills (discussant), Richard Sansing, seminar participants at
the INSEAD conference, Stanford Summer Camp, and University of California, Davis. Thanks to the following
people for helpful comments on the development of survey questions: Jennifer Blouin, Merle Erickson, Ken
Klassen, Ed Maydew, Peter Merrill, Lil Mills, Sonja Rego, Richard Sansing, Stephanie Sikes, Joel Slemrod, and
Ryan Wilson. We also appreciate the support of PricewaterhouseCoopers (especially Peter Merrill) and the Tax
Executives Institute (especially Tim McCormally) in asking firms to participate and in reviewing the survey
document. Finally, each author is grateful for the financial support of the Fuqua School of Business, Paton
Accounting Fund at the University of Michigan, MIT Junior Faculty Research Assistance Program, and the Paul
Merage School of Business at the University of California-Irvine. All errors are our own.
1. Introduction
Taxes represent a significant cost for most profitable corporations and, thus, are an
important input into many corporate decisions (see Shackelford and Shevlin (2001), Graham
(2003), and Hanlon and Heitzman (2010) for reviews). According to theory, the marginal tax rate
(MTR), defined as the present value of additional taxes paid on an additional dollar of income
earned today (Scholes, Wolfson, Erickson, Hanlon, Maydew, and Shevlin, 2014), is the
appropriate rate to use to evaluate incremental corporate decisions (MacKie-Mason, 1990;
Graham, 1996a; Graham, 1996b; Brealey, Myers, and Allen, 2014; and Scholes et al., 2014).
Consistent with this theory, prior research finds that the MTR is correlated with firms’ capital
structure decisions (e.g., Graham, 1996a; Heider and Ljungqvist, 2013). However, prior research
also finds that firms often have conservative debt policies given their MTRs (e.g., Graham, 2000;
Strebulaev and Yang, 2013), raising the question of whether managers do indeed base decisions
on the MTR to make incremental leverage decisions or whether the MTR is simply correlated
with the tax rate they use.1 As a result, Shackelford and Shevlin (2001, p. 367) suggest that “a
worthwhile endeavor would be to document (possibly by field study) how firms incorporate their
tax status into their decisions.”
In this paper, we use a survey to directly ask tax executives, for the first time, which tax
rate their firms use when making corporate financing and investment decisions. Survey data are
particularly useful to address this question because the rate employed cannot be directly
observed. Indeed, archival-based tests designed to identify which tax rates managers incorporate
into decision making are necessarily joint tests of what measure of taxes (if any) is used to make
the decision and whether the researchers’ empirical proxy of the rate is correct.2 We combine the
1
Fama and French (1998) caution that cross-sectional studies are vulnerable to endogeneity biases as firms’ tax
status may correlate with omitted variables.
2
Survey data are, of course, subject to many of their own concerns, which we acknowledge and attempt to mitigate
as much as possible. Our goal in this paper is to complement the evidence in prior empirical-archival studies using
survey evidence.
1
survey data with Compustat data to explore the determinants of managers’ tax rate choices and
the economic consequences of deviating from the theoretically appropriate tax rate.
We directly ask tax executives of both public and private firms which tax rates their
company employs in the following decisions: (i) mergers and acquisitions (M&A), (ii) capital
structure, (iii) capital investment, (iv) purchase versus lease, (v) cost of capital computations, (vi)
where to locate new facilities, and (vii) compensation. We find that fewer than 13% of our
sample firms say that they use the MTR in any of these decision making contexts. Rather, survey
responses indicate that most firms use either the U.S. statutory tax rate (STR) or the ‘average’ tax
rate computed in financial statements (the GAAP effective tax rate [GAAP ETR], defined as
income tax expense scaled by pretax book income, both financial accounting numbers) for
evaluating incremental decisions.3 For example, 30% (26%) of the respondents indicate that they
use the GAAP ETR (STR) for making capital structure decisions.
Although these findings are seemingly inconsistent with corporate finance theory, they
are potentially consistent with the theories and evidence in psychology and behavioral
economics, which have focused primarily on consumers/individuals in non-corporate settings
(DellaVigna, 2009). For example, prior research finds that (i) individuals (and managers in some
cases) use simple heuristics in many decision-making contexts rather than more complex (and
fully rational) approaches (e.g., Tversky and Kahneman, 1974; Simon, 1979; Gabaix, Laibson,
Moloche, and Weinberg, 2006) and (ii) individuals are affected by tax rate salience (e.g., de
Bartolome, 1995; Finkelstein, 2009; Chetty, Looney, and Kroft, 2009). Computing the MTR is
complicated due to unique features of the tax code (e.g., the treatment of net operating losses,
alternative minimum tax, etc.) coupled with the need to forecast taxable income many years into
the future. It is precisely in such complicated situations that managers are likely to make
decisions based on heuristics such as the STR, which is well-known to all firms and readily
3
GAAP stands for Generally Accepted Accounting Principles.
2
available. Similarly, for publicly traded companies, the GAAP ETR is easily computed using
financial statement data, and is indeed the focus of much managerial attention (Healy, 1985;
Degeorge, Patel, and Zeckhauser, 1999; Graham, Harvey, and Rajgopal, 2005; Graham, Hanlon,
Shevlin, and Shroff, 2014), which potentially leads to a salience bias. That said, prior research in
psychology argues that profit-maximizing firms are less likely to deviate from fully rational
decision-making because relative to individuals, firms have more experience and are disciplined
by forces such as competition (DellaVigna, 2009; DellaVigna and Pollett, 2013).
We explore each of these issues in detail. We find that the difference between the STR
and the MTR is less than two percentage points for the majority of firms that use the STR as the
tax rate input for decision making.4 This suggests that when a firm uses the STR, in many cases
the STR closely approximates the MTR, consistent with the STR being a simple heuristic
employed by managers. We also find that public firms (relative to private firms) and firms with
high analyst following (relative to firms with low analyst following) are more likely to use the
GAAP ETR as the tax rate in their decisions, consistent with the idea that capital market pressure
increases the salience of the GAAP ETR and thus increases its usage in decision-making.
Finally, we find that larger firms (conditional on being public), high R&D intensity firms, and
high institutional ownership firms are more (less) likely to use the MTR (ETR) for decision
making, consistent with sophisticated firms and firms with greater external monitoring (i.e.,
institutional ownership) more correctly incorporating taxes into their decision making.
Given that many firms use a theoretically incorrect tax rate, we explore the economic
consequences of doing so. To conduct this test we focus on firms that say they use the GAAP
ETR.5 We gather GAAP ETR and estimated MTR measures for these firms over time and
4
Where possible, we use both Graham’s (1996a) MTR estimates and Blouin, Core, and Guay’s (2010) MTR
estimates. The results using both measures are very similar and are discussed in more detail below.
5
We focus on the firms that indicate they use the GAAP ETR and not the group of firms that indicate they use the
STR because as discussed above (and below), in many cases the STR is equivalent to the MTR and thus we would
3
associate the GAAP ETR vs. MTR difference with outcomes of corporate decisions.
Specifically, in this analysis we focus on capital structure, M&A, and capital investment
outcomes. We focus on these three decisions because they are important, often-studied corporate
decisions, and because we have empirical proxies from prior research to evaluate the efficiency
of these decisions. Our prediction is that there is a positive association between the amount of
estimated error in the rate employed (i.e., the magnitude of the difference between the GAAP
ETR and the estimated MTR) and the inefficiency of the outcome.6
We find that firms using GAAP ETRs as the tax rate input for their capital structure
decisions adopt a sub-optimal debt policy when their GAAP ETR differs from their estimated
MTR. To assess the dollar cost of these inefficiencies, we trace firm-specific marginal cost of
debt and marginal benefit of debt curves using the approach in van Binsbergen, Graham, and
Yang (2010). The intersection of these cost and benefit curves provides an estimate of the
optimal leverage ratio; the area between the benefit and cost curves, integrated over the distance
from chosen to optimal leverage, provides an estimate of the cost of being under- or overlevered. We find that a one percentage point increase in the difference between the MTR and
GAAP ETR leads to a 1.75 percentage point increase in the distance between the optimal and
actual leverage ratios. This increase in the distance between the optimal and actual leverage
ratios leads to a deadweight cost of $1.28 million (or 0.034% of assets), on average. The average
not expect to observe significant negative consequences for this group of firms (and we do not in untabulated tests).
In contrast, the GAAP ETR is an “average” tax rate based on financial accounting data and is unlikely to resemble
the appropriate tax rate to use for decision making in many circumstances (see Brealey et al., 2014, p. 449).
6
An alternative research design is to compare, for example, the group of firms that say they use the GAAP ETR
with the group of firms that say they use the MTR. We do not conduct such a test because of (standard) econometric
concerns about omitted correlated variables and endogeneity (e.g., unmeasurable firm characteristics that impact
survey responses and corporate outcomes, selection into the groups, etc.). Rather than attempting econometric fixes
that would be difficult at best, and certainly not well specified (e.g., we do not have a good determinants model), we
employ a research design strategy that effectively uses a firm as its own control by focusing only on the firms that
say they use the GAAP ETR and examining these firms over time. Another benefit of this research design is that
because it exploits time-series differences between the ETR and estimated MTR of the same firm, it holds firms’
survey responses constant. As a result, our design mitigates potential response biases induced via the survey
instrument (e.g., a manager more susceptible to salience might answer the survey in a particular way and also
employ tax rates in a particular way that could, in theory, affect our results).
4
firm in our sample that responded that their company uses the GAAP ETR has a GAAP ETR that
is 7.7 percentage points away from their MTR, suggesting that the average firm has a deadweight
cost of using the incorrect tax rate of $9.86 million or 0.26% of assets.
In terms of investment outcomes, we find that the firms that use the GAAP ETR as the
tax rate input for investment decisions are less responsive to their investment opportunities and
have acquisition announcement returns that decrease in the difference between the firm’s MTR
and GAAP ETR. For example, a one percentage point increase in the difference between the
MTR and GAAP ETR (for firms using ETRs for decision making) reduces the average
acquisition announcement return by 0.08–0.10 percentage points, representing a 2.03–2.64%
lower average announcement return for the acquirer. In dollar terms, our coefficients imply that
the acquirer experiences a $21–28 million drop in market capitalization from using the GAAP
ETR for M&A decisions.
Our primary results are robust to controlling for a number of variables associated with
firms’ debt and investment policy including firm fixed effects. In addition, we conduct a
falsification test where we examine outcome efficiency for firms that state they use the MTR for
decision making. These tests serve to mitigate concerns that our results are spurious, that
measurement error in the MTR (e.g., when firms have foreign income in low-tax jurisdictions
and/or other non-debt tax shields such as employee stock options) is inducing our results, and
other concerns. The intuition is that if firms use the MTR as the tax rate input for their decisions,
then the difference between the MTR and GAAP ETR should be uncorrelated with their capital
structure, investment, and acquisition outcomes. This is exactly what we find.
To summarize, despite the importance of understanding taxes and corporate decision
making, there is little empirical evidence on the extent to which company management actually
use the MTR as the input to capture tax effects when making corporate decisions. We contribute
to the literature by asking managers directly what tax rate they use when making corporate
5
decisions. We then examine potential determinants and investigate whether there are observable
negative outcomes of using the GAAP ETR, a theoretically incorrect rate. Our paper provides
further evidence that there is an association between taxes and corporate decision-making, such
as leverage and investment; associations that have drawn skepticism at times (see Hanlon and
Heitzman (2010) for a review). In addition, our paper goes further to show and quantify the
negative economic effects from managers using the GAAP ETR in their decisions. If our
conjecture is true that the decision to use the GAAP ETR is due to salience, our estimates of such
costs could be interpreted as estimates of the costs of such biases.
Our paper also contributes to the growing body of evidence in psychology and behavioral
economics that agents often deviate from decision rules predicted by standard decision-making
models that assume agents to be fully rational and optimize perfectly. This literature has
predominantly focused on decision-making by individuals in non-corporate settings. DellaVigna
(2009) discusses that the intuition for focusing on individuals rather than firms is that, “firms can
specialize, hire consultants, and obtain feedback from capital markets. Firms are also subject to
competition…therefore, firms are less likely to be affected by biases (except for principle–agent
problems), and we expect them to be close to profit maximization.” (p. 361) We contribute to
this literature by showing that biases that affect individuals also affect managers with respect to
their choice of tax inputs used in important corporate decisions, ultimately leading to suboptimal
decisions and a loss in firm value.7
Finally, our findings shed light on the effect of tax policy on corporate behavior.
Governments use tax policy to provide incentives and disincentives for certain actions (e.g.,
investment) and thus the degree to which taxes actually impact corporate decisions determines
whether such policies are effective. Standard economic models assume that firms respond to
7
Camerer and Malmendier (2007) summarize research documenting that managers are affected by biases other than
those documented here, such as overconfidence and corporate socialism, which also affect corporate decisions.
6
changes in their MTR by making the right connection between their income and tax schedule
(Mirrlees, 1971; Atkinson and Stiglitz, 1976; Diamond, 1998). As a result, the effects of tax
changes predicted by standard economic models are likely to be different when managers base
decisions on an average tax rate such as the GAAP ETR rather than the MTR. Our results
provide fresh insights that potentially help clarify the implications of tax policy changes for
corporate behavior.8
The paper proceeds as follows. Section 2 describes our survey methodology and sample.
Section 3 provides descriptive statistics about our sample firms and discusses tests of nonresponse bias. Section 4 presents the responses to the survey questions. Sections 5 and 6 examine
the determinants and consequences of managers’ survey responses, and Section 7 concludes.
2. Survey methodology and sample9
We developed an initial survey instrument to ask about taxes in the context of key
corporate decisions. We solicited feedback from several academic researchers, Tax Executives
Institute (TEI) and PricewaterhouseCoopers (PwC) on the survey content and design.10 Survey
Sciences Group (SSG), a survey research consulting firm, assisted with the survey formatting
and programmed an online version. We had two executives beta test the survey and we made
revisions based on their suggestions. The final survey contained 64 questions, most with
subparts. The paper version of the survey was 12 pages long. There were many branching
8
Prior literature has documented the use of average rates by individuals. We discuss this literature below.
Our survey has four parts and the data from different parts of this survey are used in Graham, Hanlon, and Shevlin
(2010, 2011), and Graham, Hanlon, Shevlin, and Shroff (2014). As a result, the discussion in this section is similar
to that in those papers. However, we note that the research questions addressed in these papers are very different
than the research question of the current paper. Specifically, Graham et al. (2010) focus on the 2004 American Jobs
Creation Act and repatriation decisions in response to that Act. Graham et al. (2011) explore questions concerning
the location, reinvestment, and repatriation of foreign earnings. In particular, they examine the effect of an
accounting rule, APB 23, on these decisions. Finally, Graham et al. (2014) examine the effects of reputational and
financial accounting concerns on tax planning decisions.
10
TEI is an association founded in 1944. Its members are executives responsible for the tax affairs of U.S. and
foreign businesses. The member companies are from a wide range of industries.
9
7
questions and, as a result, many firms were directed to answer only a portion of the questions.
The paper version of the survey is available upon request.
An initial email invitation was sent on August 9, 2007 to the highest ranking tax
executive who is a member of Tax Executives Institute (TEI) at 2,794 firms (thus, only one
invitation was sent to each company); three of these were returned as undeliverable. We also sent
a letter via two-day express mail to fifteen companies for which we did not have email addresses.
Thus, a total of 2,806 companies received invitations to complete the survey. SSG sent three
email reminders throughout August and September. For those who had still not responded, we
then sent a paper version of the survey, along with instructions of how to complete the survey
online, in September and October. We closed the online survey on November 9, 2007.
A total of 804 firms accessed the survey. Sixty of these companies entered no more than
two responses and thus we delete them from our sample, leaving 744 usable responses. The
response rate for our survey is 26%, which compares favorably to many prior survey studies.11
We eliminate 11 firms that indicate they are not subject to the U.S. corporate income tax (i.e.,
businesses not taxed at the entity level, such as S corporations and other flow-through entities).
We also eliminate 29 companies that indicate that they did not file a corporate income tax
return—Form 1120 (under the assumption that these companies are also not taxed at the entity
level). We restrict the sample further by eliminating firms that are subsidiaries of foreign parents
since their corporate decisions and tax planning incentives are likely to be affected by the tax
rules in the parent’s home country. Finally, we lose 95 firms that did not respond to the section
concerning tax rates and decision-making, the subject of our study. This leaves 500 remaining
firms on which we conduct our analyses. The sample size varies across the individual decisions
(e.g., M&A, capital structure) because of occasional missing responses.
11
For example, Trahan and Gitman (1995), Slemrod and Blumenthal (1996), Graham and Harvey (2001), Slemrod
and Venkatesh (2002), Brav et al. (2005), and Graham et al. (2005) report response rates between 10.4% and 21.8%.
8
There are caveats and limitations to survey research. First, firms that decide to answer the
survey may systematically differ from those that do not answer the survey. We address this
concern by comparing our survey respondents to the average Compustat firm to get a sense of
the characteristics of our sample firms relative to the typical sample of firms studied in the extant
literature.12 In addition, we also compare firms that responded to the survey with firms that did
not respond. These data are tabulated and discussed below (see Section 3).
Another concern with survey based research is that it is plausible that survey respondents
do not tell the complete truth in their responses. In addition, we may not have asked the
questions clearly, the respondent may not have understood some questions, or perhaps the
respondent just answered questions randomly. There is no way to completely eliminate these
possibilities; however, we attempted to mitigate these concerns by having academics,
practitioners, and a set of beta firms carefully review the survey before it was distributed. We
also employed a professional survey consulting firm to assist in programming the survey online
and to provide advice on how to best ask the questions. Finally, most of our inferences are based
on associations between our survey data and Compustat data, which helps mitigate concerns
related to biases in the survey data.
Finally, an observation worth noting is that our survey participants are corporate tax
executives. It is plausible that tax executives are not directly involved in corporate decision
making and thus are not the ideal survey population for questions concerning investment and/or
financing decisions. This concern is less relevant because our research question is about the
manner in which tax rates are incorporated into decision making. Even if tax executives are not
the final decision-maker in these corporate decisions, they are likely to provide the tax rates and
any associated data that other managers use as inputs.
12
Unlike much research based on surveys, we know the identities of the firms that responded (and that did not
respond), allowing tests for potential non-response bias.
9
3. Descriptive statistics and non-response bias test
We obtain demographic information from both survey data and, for publicly traded firms
additional data from Compustat. Table 1 presents the descriptive statistics of our sample firms.
Survey responses indicate that 78% of our sample firms are publicly listed. The average firm in
our sample has $7.8 billion in assets (Assets), with the average public (private) firm having $9.2
billion ($2.5 billion) in assets (untabulated). Survey responses also indicate that the average firm
in our sample has 19% of its assets in foreign locations (Foreign Assets), filed 6.6 tax returns
(i.e., Forms 1120) annually, and has a GAAP ETR of 31%. Finally, 46.3% of our respondents
have a net operating loss carryforward in the U.S. (US NOL).
Additional data from Compustat indicate that the average public firm in our sample has
$5.8 billion in sales (Sales), a market capitalization (MVE) of $8.5 billion and earns a 5.8%
return on assets (i.e., net income scaled by assets; ROA). Tobin’s Q, sales and asset growth (Sales
Growth; Asset Growth) for the average public firm in our sample are 1.97, 14.1% and 13.6%,
respectively. Approximately 45% of the public firms in our sample invest in R&D (R&D
Intensity) and the average R&D Intensity is 2.4%, where R&D Intensity is R&D expense scaled
by assets. The average public firm in our sample is followed by ten analysts (Analyst Following)
and has 53.6% institutional ownership (Institutional Ownership). In terms of tax rate proxies, we
find that the average public firm in our sample has a marginal tax rate before accounting for the
interest on debt (MTR) of 30.9% based on Graham’s methodology and 33.1% based on Blouin,
Core, and Guay’s (2010; BCG henceforth).13 Similarly, the average after-interest marginal tax
13
The two methodologies differ in their approach for forecasting future taxable income. Graham’s MTR
methodology assumes that the level of future taxable income follows a random walk with drift. In contrast, the BCG
methodology is a nonparametric approach that employs bins of firms grouped by their profitability and size, and
then traces the taxable income of these firms into the future.
10
rates (MTR A.I.) are 21.4% and 31.6%, respectively, and the average GAAP ETR is 30.9%.14
The average unsigned difference between the GAAP ETR and MTR (|MTR – GAAP ETR|) is
13.2% (9.5%) using Graham’s (BCG’s) MTR methodology and that between the GAAP ETR and
MTR A.I. (|MTR A.I. – GAAP ETR|) is 16.7% (10.1%). All variable are defined in the Appendix.
Table 2 compares firms that responded to the survey with other firms. The analysis in this
table is restricted to public firms only because the data on firm characteristics come from
Compustat. We present descriptive statistics of (i) the average Compustat firm, (ii) the average
firm we sent the survey to, (iii) the average firm that responded to the survey, and (iv) the
average firm that did not respond to the survey. Our average surveyed firm is larger than the
average Compustat firm in terms of Assets, MVE, and Sales. Our average surveyed firm has a
smaller cash-to-asset ratio (Cash), and a smaller market-to-book (MB) ratio relative to the
average Compustat firm. Further, the firms we surveyed have, on average, a higher ROA, a
higher investment and acquisition intensity, a lower R&D intensity, a higher GAAP ETR, a
higher MTR, and lower Asset Growth and Sales Growth rates. Thus, the firms we surveyed
appear to differ from Compustat firms. To further examine the source of the differences between
our survey firms and Compustat firms, we match each survey firm with Compustat firms based
on size (i.e., Assets, MVE, and Sales) and re-examine the differences in firm characteristics. We
find that the average survey firm is statistically indistinguishable from the average Compustat
firm along the other dimensions once we control for size (untabulated). Specifically, we find that
the survey firms and size-matched Compustat firms have similar Leverage, MB, ROA, Asset
Growth, Sales Growth, investment intensity, NOL, 3-Yr Cash ETR, and MTR. Therefore, once
14
The before interest MTR means computing taxable income prior to the deduction for interest expense (i.e., before
financing). The after interest MTR means computing taxable income including the interest deduction. See Graham,
Lemmon, and Schallheim (1998) for a discussion of when to use before- and after-interest MTRs.
11
one controls for size (which we do in our analysis below), the survey sample appears
representative of Compustat.
Next, we compare respondents to non-respondents (with Compustat data) to test for nonresponse bias. We find that the average respondent firm is statistically no different than the
average non-respondent firm in terms of size (i.e., Assets, MVE and Sales), Leverage, Cash,
growth (MB, Asset Growth, Sales Growth), investment intensity, acquisition intensity, R&D
intensity, NOL, GAAP ETR, 3-Yr Cash ETR, and MTR. The only statistical difference is that the
respondent firms have, on average, a higher ROA than non-respondent firms. We cannot think of
any biases that arise for our tests because of this difference. Overall, there is little evidence of
non-response bias in our sample, but we recognize that it is always a possibility with survey data.
4. Which tax rates do managers use to incorporate taxes into their decisions?
In theory, managers (should) use the MTR to evaluate incremental decisions because it is
the rate paid on an incremental dollar of income earned today. For example, Graham (1996a, p.
42) states that, “Financial theory is clear that the marginal tax rate is relevant when analyzing
incremental financing [and investing] choices.” In addition, the popular corporate finance text,
Brealey, Myers, and Allen (2014, p. 449), explicitly prescribes that managers should “Always
use the marginal corporate tax rate, not the average rate.” Despite the widely held belief that
managers (should) evaluate incremental corporate decisions using the MTR, there is little direct
evidence that managers indeed do so.
In our survey, we ask the corporate tax executives ‘What is the primary tax rate your
company uses to incorporate taxes into each of the following forecasts or decision making
processes?’ The survey respondent is allowed to choose from the following options (or, write in
an answer if the options given are not sufficient): (i) U.S. statutory tax rate, (ii) GAAP effective
tax rate, (iii) jurisdiction-specific statutory tax rate, (iv) jurisdiction-specific effective tax rate,
12
(v) marginal tax rate, and (vi) other. The question then listed the following decision contexts for
the respondent to indicate the primary tax rate used in that setting: (i) mergers and acquisitions,
(ii) capital structure (debt versus equity), (iii) investment decisions (property, equip., etc.), (iv)
decision to purchase versus lease (property, equip., etc.), (v) weighted average cost of capital,
(vi) where to locate new facilities, and (vii) compensation decisions. The respondent could
choose one tax rate for each decision context. Because we were not certain that tax executives
would hold the same definition of MTRs that exists in academia, we defined in parenthesis the
MTR as “an estimation of the change in the present value of taxes from earning a marginal dollar
of income, where the present value computation takes into account net operating loss carrybacks
and carryforwards.” We acknowledge that defining the MTR and no other rate may induce
response biases. Particularly, it is plausible that by defining MTRs, we heightened respondents’
awareness to MTRs over the other tax rates, thereby increasing the number of executives
choosing the MTR.15 Therefore, our results might possibly represent an upper bound estimate of
the number of firms using the MTR as their tax rate input for decision making.16
Figure 1 and Table 3, Panel A present the survey responses to the above question. The
most popular tax rate managers claim to incorporate into their decision making is the GAAP
ETR and the second most popular is the STR. For example, with respect to capital structure
decisions, survey responses indicate that 30% of firms use the GAAP ETR, 26% use the U.S.
15
It is also plausible that defining the rate made it seem complicated and the executive did not choose it as a result.
This alternative seems unlikely if the respondents truly use MTRs for decision making. Further, given the education,
experience, and expertise of the respondents, it is unlikely that the definition of MTRs would be too complicated.
16
The definition of MTR provided in our survey follows from the Scholes-Wolfson framework (see Scholes et al.,
2014). It is plausible that managers use a tax rate that incorporates the Scholes-Wolfson intuition without explicitly
computing MTRs per that definition (e.g., a company might have separate tax rates for different economic scenarios
– high, medium, and low – and these scenarios might include the effect of NOLs, etc.). If so, the number of firms
saying they use the MTR might be understated. We created a provision for such an outcome in our survey by
allowing respondents to choose the “other” option and by providing space for them to write-in an answer.
Depending on the decision, 11 to 20 firms listed a response in this “other” space. From these we see that one firm
indicated that it uses an “Employees marginal tax rate average” for its compensation decisions, and other firms
stated they use “Business plan ETR,” “A standard 41%,” “Effective cash tax rate,” and “cash tax rate.” No firm
suggested using something that we could interpret as equivalent or similar to the MTR in concept.
13
STR, 15% use the jurisdiction specific STR, 15% use the jurisdiction specific ETR, and 12% use
the MTR. Averaging across all decision contexts, the survey responses suggest the following
pattern of tax rate use: 25.8% use GAAP ETRs, 23.1% use STRs, 19.6% use jurisdiction specific
STRs, 17.0% use jurisdiction specific ETRs, 11.2% use MTRs, and 3.2% use some other rate.
These results may seem surprising because very few firms use the MTR. However, the
STR closely approximates the MTR for firms that generate high taxable income and foresee
themselves continuing to do so. Indeed, in untabulated analyses, we find that over 80% of our
sample firms that indicate they use the STR for capital structure decisions have a simulated MTR
that is within two percentage points of the STR. Thus, for the majority of firms that say they use
the STR, we find that the STR closely approximates the estimated MTR and thus, this choice is
unlikely to induce costly errors for these firms. To the extent the STR and the jurisdiction
specific STR closely approximate the MTR of firms, our survey results suggest that over 50% of
our survey respondents use tax rates that are consistent with the predictions of standard finance
theory (e.g., Graham, 2003; Brealey et al., 2014; Scholes et al., 2014).
Another noteworthy observation is that when making a location specific decision, the
majority of managers answered that they use jurisdiction specific rates (statutory and effective)
as the tax rate input for decision making. For example, over 54% of respondents indicated they
use a jurisdiction specific rate for the location of new facilities. In contrast, only 30% and 26% of
respondents say that they use a jurisdiction specific rate for capital structure decisions and
weighted average cost of capital computations, respectively. This change in responses across the
factors is consistent with expectations and suggests that the respondents carefully considered the
questions and varied their answers when appropriate (i.e., the respondents did not provide the
same response for each factor without thought).
14
The most puzzling finding in the table is likely that many firms claim to use a form of
ETR as the tax rate input in all decision making contexts examined. Specifically, in our sample
of firms and policies examined, approximately 17% to 34% of respondents (or 40% to 47% when
including use of a jurisdiction specific ETR) indicate that their firm uses the GAAP ETR as the
tax rate input for decision making. This result is puzzling because the GAAP ETR is an average
tax rate based on financial accounting data and as such has very little theoretical basis to use as
the tax rate input. An average tax rate (such as the GAAP ETR) is very unlikely to be the rate
that is paid on the company’s marginal transaction but instead is a (GAAP accounting) estimate
of the average tax rate paid on all transactions combined. We discuss potential reasons for this
choice in the next section (Section 5) and test whether this choice is associated with inefficient
decision-making in Section 6.
Table 3, Panel B presents correlations of tax rate choices across the different decision
contexts. As expected, we find that the tax rate choices are highly correlated across the different
decision contexts presented to them. The correlation coefficients range from a low of 0.66 to a
high of 0.93. The highest correlation is between the tax rates used in the purchase vs. lease
decision and capital investment decisions and the lowest correlations are between the tax rates
used in the compensation and the other decisions (correlation ranging from 0.66 to 0.77). Given
the high correlations, for the remainder of our analyses, we focus on firms’ capital structure,
capital investment, and acquisition decisions for the sake of brevity; these are important
decisions, and the literature provides measures to evaluate the outcomes of these decisions.
Further, in our analyses of the determinants of managers’ tax rate choice (in the next section), we
combine firms indicating that they use the jurisdiction specific STR (ETR) for decision making
with those using the U.S. STR (GAAP ETR) in the interest of parsimony.
15
5. Determinants of managers’ survey responses
To better understand the factors affecting tax rate choices, we examine the association
between managers’ survey responses and firm characteristics chosen largely based on research in
psychology and behavioral economics. We posit that managers’ tax rate choices are affected by
the following: (i) the complexity of calculating the tax rate and the availability of simple
heuristics that (are equal to or) approximate the expected tax costs, (ii) the salience of different
tax rates to managers, (iii) the likely availability of tax planning opportunities (proxied by firm
size and R&D), and (iv) the existence of mechanisms to monitor managers and increase their
efficiency (e.g., institutional investors and competitive forces). We recognize that the factors we
examine do not comprise a complete list of all the factors that affect managerial tax rate choices;
we view our analyses in this section as an initial step to understand managers’ tax rate choices.
Heuristics: Since Simon’s (1955) early work, research in behavioral economics
recognizes that managerial decision-making often falls short of the purely rational model (see
Barberis and Thaler (2003), Camerer and Malmendier (2007), and DellaVigna (2009) for
reviews of the literature). Research trying to explain deviations from rational decision-making
mostly focuses on biases and heuristics to explain the deviations (Tversky and Kahneman, 1974),
where biases and heuristics are decision rules, cognitive mechanisms, and subjective opinions
people use to assist in making decisions. Kahneman et al. (1982) indicate that managers rely on
biases and heuristics when the decision requires complex calculations and when there is high
uncertainty. They also indicate that the application of heuristics often yields acceptable solutions
that are not too far from a fully rational solution.
Computing a firm’s MTR requires information about the firm’s taxable income well into
the future, the applicable statutory tax rate, the presence of NOL carryforwards, the number of
years before NOLs will be utilized, and whether the firm will have to pay any alternative
minimum tax. As a result, MTRs are very complicated to calculate for many firms; it is precisely
16
in such a circumstance that managers are likely to rely on heuristics for decision making
(Kahneman et al., 1982; Gabaix et al., 2006). Thus, when viewed through the lens of behavioral
economics theories, it is perhaps not surprising that such few companies use the MTR for
decisions making. Some evidence consistent with the use of a heuristic is found in our data as
discussed above; the majority of our firms that indicate they use the STR have a simulated MTR
that equals the STR and over 80% have simulated MTRs that are within 2% of the STR
(untabulated). To further examine this heuristic hypothesis, we compare the tax rate used by
firms with significant foreign operations with that of firms without significant foreign operations.
Our prediction is that firms with more foreign operations are less likely to use MTRs for decision
making and more likely to use heuristics that approximate firms’ MTRs (e.g., a jurisdictionspecific STR). Our intuition is that foreign operations increase the complexity and uncertainty of
computing MTR estimates (because of differences in the likelihood and timing of foreign
earnings repatriations, and cross-country differences in the tax rates/schedules, transfer pricing
rules, tax policy uncertainty, etc.), which increases the likelihood that managers use a heuristic
(Kahneman et al., 1982).
Salience: Prior research finds evidence that individuals often use average tax rates instead
of marginal tax rates when faced with marginal investment choices. For example, de Bartolome
(1995) investigates which tax rates individuals use when making marginal economic decisions.
He finds that many people use the average tax rate “as if” it were the marginal tax rate. He
investigates the cause via a controlled experiment and concludes that they do this because of
salience; if the tax table is changed to stress the marginal tax rate, the marginal tax rate is used.
Similarly, Finkelstein (2009), Chetty (2009), and Chetty et al. (2009) find evidence that tax
salience affects behavior at the individual level. Most prior research on the effects of tax salience
concerns decisions at the individual level rather than decisions by corporate managers. If
managers are more sophisticated decision-makers than individuals, a salience bias might not
17
affect managerial decision-making (DellaVigna, 2009). However, Faulhaber and Baumol (1988)
find that most companies base their pricing decisions on average costs, not marginal costs (at
least in the 1970s), suggesting that corporate entities are at times no more sophisticated than
individuals. Camerer and Malmendier (2007) discuss some reasons why behavioral biases are
unlikely to be eliminated in a corporate setting or remedied by organization design and
governance choices. Thus, the salience explanation is plausible in our setting because the GAAP
ETR and STR are very salient whereas the MTR is not.
To examine the salience hypothesis, we compare the tax rates used by public and private
firms. Our prediction is that the GAAP ETR is more salient to public firms. Prior research finds
that top management of public firms view GAAP earnings as the most important performance
metric of a firm (Graham et al., 2005) and that tax executives are often incrementally
compensated for lowering GAAP ETRs and often view the GAAP ETR as a more important
metric than cash taxes paid (Armstrong, Blouin, and Larcker, 2012; Graham et al., 2014). The
focus on GAAP earnings and by extension GAAP ETRs is likely to make the ETR significantly
more salient to the decision-makers in public firms. In contrast, prior research finds that private
firms are significantly less focused on GAAP based numbers than public firms (Graham et al.,
2005; Graham et al., 2014), making them less likely to use the GAAP ETR as their tax rate input
for decision making. We also use additional variables such as analyst following as a proxy for
capital market pressure, and a survey question on whether managers are more concerned about
the GAAP ETR or cash taxes paid to examine whether salience is a potential determinant of the
tax rate choice.17
17
Another potential source of salience is that managers are often taught to use the GAAP ETR or STR because those
rates are salient to professors. For example, when forecasting earnings in financial statement analysis classes the
GAAP ETR is (and should be) used and this is perhaps carried over to the executives’ (i.e., former students’)
marginal decision-making in other contexts. Similarly, most managerial accounting textbooks just assume a tax rate
of 35 or 40%. While some corporate finance texts mention the MTR and discuss the general effects of losses, at least
for the capital structure decision, they do not generally go into detail about how to compute the MTR. For example,
18
Tax planning opportunities: We conjecture that firms with greater tax planning
opportunities are more likely to be tax savvy and hire well-trained tax personnel because they
can derive greater benefits from the tax planning. Further, tax savvy firms are more likely to
accurately compute MTR estimates and use the MTR (or STR when appropriate) rather than the
GAAP ETR for decision-making. To test this conjecture, we proxy for a firm’s tax planning
opportunity set using firm size and R&D intensity. Our intuition for these proxies is as follows.
First, larger firms benefit from economies of scale of tax planning, and thus are more likely to
invest in well-trained/sophisticated tax personnel (Rego, 2003). Specifically, large firms (by
definition) engage in more business activities than small firms, which potentially allows them to
avoid taxes through intercompany transactions, tax-advantaged leasing and financing
arrangements, and the use of flow-through entities such as partnerships and real estate
investment trusts among other things. Consistent with this argument, Mills, Erickson, and
Maydew (1998) find that larger firms have lower average costs of tax planning. And second, we
use R&D intensity as a proxy for firms’ tax planning opportunities based on the intuition that
R&D generates intellectual capital that can be transferred to low tax jurisdictions to save taxes
(Grubert and Slemrod, 1998). Further, R&D investments generate tax credits that can be offset
against current and future income. Thus, all else equal, high R&D firms are likely to have more
tax planning opportunities than low R&D firms.
Monitoring and competitive pressures: Prior research finds that external monitoring
mechanisms such as institutional investors and product market competition serve to discipline
managers and curb agency problems, thereby making the firm more efficient. For example, prior
research finds that institutional ownership is associated with more efficient outcomes such as
in Horngren, Datar, Foster, Rajan, and Ittner's (2009) text, taxes are provided for in a discounted cash flow analysis
by applying a 40% tax rate stating “the income tax rate is 40 percent of operating income each year” (p. 744).
Brealey et al., (2014) use a flat 35% in their computation but include a footnote that states “Always use the marginal
corporate tax rate, not the average rate” (p. 449).
19
greater R&D and innovation (Bushee, 1998; Aghion, Van Reenen, and Zingales, 2013), better
acquisitions and post-acquisition performance (Chen, Harford, and Li, 2007), greater pay-forperformance sensitivity of executive compensation (Hartzell and Starks, 2003), more accurate
disclosure (Chung, Firth, and Kim, 2002; Ajinkya, Bhojraj, and Sengupta, 2005; Shroff, Sun,
White, and Zhang, 2014), and greater firm value and overall performance (McConnell and
Servaes, 1990; Cornett, Marcus, Saunders, and Tehranian, 2007). These papers suggest that
institutional investors monitor managers and thus reduce agency problems.
Similarly, Shleifer and Vishny (1997, p. 738) argue that “product market competition is
probably the most powerful force towards economic efficiency in the world.” The intuition is
that competition makes it costlier for firms to forego long-run value since such actions would
increase the likelihood of bankruptcy. As a result, competition increases managerial focus on
firm value and reduces their incentives and ability to slack (or to lead a “quiet life”). A number
of studies find evidence consistent with this intuition in a variety of settings (see e.g., Nickell,
1996; Bernard, Jensen, and Schott, 2006; Giroud and Mueller, 2010; Shroff et al., 2014). Based
on the above, we conjecture that firms with greater institutional ownership and firms operating in
more competitive environments are more likely to use MTRs (or STRs when appropriate) for
decision-making and/or are less likely to use ETRs for decision-making.
We present univariate (Table 4) and multivariate (Table 5) tests that examine the above
conjectures. In the multivariate tests, we present results from nine regressions where the
dependent variable is an indicator variable for the manager’s tax rate choice in each decision
context and the independent variables include all firm characteristics examined in Table 4 as well
as some additional characteristics (not tabulated in Table 4 for brevity). The sample in Panel A
of Tables 4 and 5 is comprised of both public and private firms and thus the partitioning (and
independent) variables are limited to firm characteristics obtained from the survey. The sample
in Panel B in these tables is comprised of just public firms and uses data from both the survey
20
and Compustat. Below, we discuss the results in both tables by grouping them as being
consistent or inconsistent with the conjectures put forward in the discussion above.
Our first conjecture is that firms are more likely to rely on simple heuristics (i.e., STR)
for decision making when the purely rational decision rule (i.e., MTR) is complex and uncertain.
Consistent with conjecture, we find that firms with a large proportion of their assets in foreign
locations are significantly less likely to use the MTR and more likely to use the STR (in this case
often a jurisdiction specific STR) as their tax rate input for decision making (see Tables 4 and 5,
Panels A and B). This result is consistent with the hypothesis in Kahneman et al. (1982).
Consistent with our second conjecture that tax rate salience affects managers’ tax rate
choice, we find that private firms are significantly more likely to use STRs in their decision
making process, whereas public firms are significantly more likely to use the GAAP ETR in their
decision making process (Panel A in Tables 4 and 5). To further explore the salience explanation
for this result, we also partition our sample firms based on a survey question: “Which metric is
more important to top management in your company?” The possible answers to the question are
(i) GAAP ETR, (ii) cash taxes paid, or (iii) both are equally important. Both Panels in Table 4
show that when the top management view the GAAP ETR as more important than cash tax taxes
paid, their firm is more likely to use the GAAP ETR for decision making (however this
association is weaker, i.e., it only holds for capital structure decisions, in the multivariate tests in
Table 5). This result, although somewhat weak, is consistent with a salience based explanation
for why managers use the GAAP ETR as their tax rate input for decision making. Finally, Table
5, Panel B shows that firms with high analyst following are more likely to use the GAAP ETR
and less likely to use the MTR for decision-making. To the extent financial analysts increase
capital market pressure and thus increase managerial focus on financial accounting earnings, this
result is also consistent with a salience explanation leading firms to use the GAAP ETR for
decision-making.
21
Another noteworthy observation in Panel B in Tables 4 and 5 is that when the magnitude
of the differences between the MTR and GAAP ETR (|MTR – GAAP ETR|) is large, firms are
more likely to use the GAAP ETR as their tax rate input. Kahneman et al. (1982) suggest that
managers are more likely to rely on heuristics when the heuristic approximates the “fully rational
solution” and serves as an efficient approach to reach the rational solution. Our result that
managers are more likely to use the GAAP ETR when it is farther away from the MTR suggests
that firms do not treat the GAAP ETR as an easy-to-compute heuristic that is to be used as it
converges with the MTR. Rather, it appears that the decision to use the GAAP ETR is a result of
other factors/biases (such as salience) but not heuristics.
Our third conjecture is that firms with greater tax planning opportunities (proxied by firm
size and R&D intensity) are more likely to be tax savvy and thus are more likely to use the MTR
(or STR when appropriate) and less likely to use the GAAP ETR for decision-making. Tables 4
and 5 show that conditional on being public, larger firms and high R&D intensity firms are more
likely to use the MTR (and/or STR) for decision-making. In addition, larger firms and high R&D
intensity firms are less likely to use the GAAP ETR for decision making.18 We interpret these
results as suggesting that firms with greater tax planning opportunities being more tax savvy and
thus more (less) likely to use MTRs or STRs (GAAP ETRs) for decision making.
Our final prediction is that firms with greater external monitoring (i.e., institutional
ownership and competitive pressure) are more likely to use the MTR and less likely to use the
ETR for decision making. Table 5, Panel B shows that firms with high institutional ownership
are significantly more likely to use the MTR for capital structure and M&A decisions, and less
18
In Table 4, Panel A we find that firm size is unrelated to managers’ tax rate choice. This is perhaps because the
univariate relation between firm size and tax rate choice is confounded by the effect of ownership on tax rate choice
(i.e., public firms are larger than private firms and are more likely to use the GAAP ETR due to salience; but
conditional on being public, larger firms are less likely to use the GAAP ETR because such firms have more tax
planning opportunities and thus are more likely to be tax savvy).
22
likely to use the GAAP ETR for M&A decisions. This finding is consistent with such investors
monitoring managers, thereby providing them additional incentives to maximize firm value. We
find little evidence of competition affecting a firm’s tax rate choice. For example, based on our
primary measure of competition, the Hoberg and Phillips’s (2013) text-based industry
concentration index, we find no statistically significant evidence that competition affects
managers’ tax rate choice. Moreover, in untabulated tests, we use a number of additional
competition proxies (e.g., the Census based concentration measure from Ali, Klasa, and Yeung
(2009), the 10-K disclosure measure from Li, Lundholm, and Minnis (2013), and the traditional
Herfindhal index) and find no association between these proxies and managers’ tax rate choice.
In Table 5, Panel B, we also examine the association between tax rate choices and a
number of additional firm characteristics (these are untabulated in Table 4 for brevity). We find
little evidence that the number of tax returns filed (a proxy for tax complexity), ROA, leverage,
NOLs, and intangible intensity affects a manager’s tax rate choice. The lack of evidence could
either be because of limitations in our empirical proxies or because these factors truly do not
affect tax rate choices.
Overall, the results in Tables 4 and 5 are consistent with (i) managers’ relying on
heuristics, such as the STR, for decision-making when it is likely to approximate the MTR and
when computing the MTR is more complex, (ii) a salience effect leading managers to use the
GAAP ETR for decision-making, and (iii) larger firms, firms with more R&D, and firms with
high institutional investor ownership using (not using) the MTR (GAAP ETR) for decisionmaking. However, we caveat that the findings in this section are somewhat exploratory and
based on fairly rough proxies for our constructs of interest. The discussion above should be
interpreted in light of these factors.
23
6. Economic consequences of tax rate choices
In this section, we examine the degree to which not using the MTR (even though
theoretically appropriate) leads to inefficient corporate decisions. One approach to test for such
inefficiencies (that we do not adopt) is to directly compare the outcomes of corporate decisions
of companies that say they use the GAAP ETR for decision-making with those of companies that
say they use the MTR or STR. However, testing the direct relation between managers’ tax rate
choices and the outcomes of their corporate decisions has two important weaknesses. First, it is
plausible that the manner in which we present or phrase our survey questions (e.g., defining
MTRs) leads to response biases that are systematically correlated with the manner in which
managers make corporate decisions. For example, by defining the MTR in our survey, we made
it more salient to managers and plausibly influenced some managers to respond that they use the
MTR for decision making. It is plausible this bias towards salient factors also influences the
same managers when making corporate decisions. Thus, if managers’ response biases in the
survey are indicative of their biases when making other corporate decisions, we cannot draw
reliable inferences about the consequences of managers’ tax rate choices from observing an
association between survey responses and corporate outcomes.
Second, it also plausible that responses to the survey questions are reflective of certain
firm characteristics that are correlated with corporate decisions. For example, the availability of
tax planning opportunities is likely to affect managers’ survey responses and is also likely to
affect corporate decisions. The existence of such factors that are correlated with both managers’
tax rate choices and their corporate decisions potentially exposes our inferences to a correlated
omitted variable bias. Finally, notwithstanding the above challenges, we note that empirically
identifying inefficient corporate decisions is difficult and the proxies used in prior research to
measure inefficiencies are noisy and prone to measurement error. If measurement error in these
24
proxies is correlated with managers’ survey responses, any inference drawn from observing
associations between these proxies is problematic (if the survey responses are uncorrelated with
measurement error in our proxies, the coefficients are biased toward zero).
We choose a research design to mitigate the above concerns. Specifically, we restrict our
analyses in this section to just those firms indicating that they use the GAAP ETR as the tax rate
input for decision making. We then exploit time series variation in the difference between a
firm’s MTR and its GAAP ETRs to draw our inferences. In other words, rather than comparing
firms that provide different responses to our survey question, we compare the same firm at
different points in time.19 We predict that firms using the GAAP ETR as their tax rate input for
decision making are likely to make better decisions when their GAAP ETR is close to their
MTR. However, as the difference between the MTR and GAAP ETR becomes larger, these
firms’ decisions are likely to become more inefficient. The benefits of our chosen research
design are two-fold: First, and perhaps foremost, inferences based on the above research design
are unaffected by (potential) response biases induced by our survey. Second, because we only
exploit variation in the difference between a firm’s MTR and its GAAP ETR, our inferences are
less likely to be affected by any unaccounted for relation between firms’ tax rate choices and
their characteristics.
We merge survey data for firms that say they use the GAAP ETR as the tax rate input
with Compustat data from 1997 to 2006, thereby creating a ten year panel. We measure GAAP
ETRs for each firm-year as total income-tax expense scaled by pre-tax book income and we
measure MTRs for each firm-year using the approaches developed by (i) Shevlin (1987, 1990)
and Graham (1996a) and (ii) Blouin et al. (2010). We recognize that our MTR proxies have
19
An implicit assumption with this research design is that the rate firms say they use in the survey is the rate they
use in the years we include in the sample (i.e., that this choice is time invariant during our sample period).
25
measurement error and devise a falsification test in Section 6.4 to mitigate this concern as well as
other concerns.
6.1. Capital structure consequences
We begin by examining whether firms that use the GAAP ETR as the tax rate input for
capital structure decisions end up using debt either conservatively or aggressively. If a firm’s
GAAP ETR is higher (lower) than its MTR and the GAAP ETR is used for decision making,
then such a firm is likely to overestimate (underestimate) the tax benefits of debt and have a
relatively aggressive (conservative) debt policy. We use the approaches in Graham (2000) and
van Binsbergen et al. (2010) to measure how aggressively firms use debt and how far firms are
from their optimal leverage ratios. The approach in Graham (2000) uses information in a firm’s
marginal tax benefit of debt function to evaluate the extent to which the firm’s capital structure
policy is aggressive or conservative. Whereas, the approach in van Binsbergen et al. (2010)
simulates both the marginal cost and tax benefit of debt functions to identify a firm’s optimal
capital structure. Although the van Binsbergen et al. (2010) method provides a more
comprehensive estimate of the economic cost of not being at the optimal leverage ratio, the
estimation procedure requires more assumptions than the approach in Graham (2000). Thus, we
conduct both tests below.
The first test approach is based on Graham’s (2000) “debt kink” calculation. Graham
(2000) constructs a firm’s tax benefit function using a series of hypothetical marginal tax rates,20
with each rate corresponding to a specific level of interest deductions. The benefit function is
initially flat for small interest deductions but eventually becomes downward sloping as interest
deductions increase (see Figure 2). This result occurs because interest deductions reduce taxable
20
The MTR measures the tax savings benefit of being able to deduct interest. For example, a firm facing a 35%
MTR will save $0.35 in taxes if it is able to deduct $1 in interest from taxable income.
26
income, which decreases the probability that a firm will be fully taxable in all current and future
states, which in turn reduces the tax benefit from the incremental deductions. Graham (2000)
quantifies how aggressively a firm uses debt with the “kink” in the firm’s tax benefit function
(i.e., the point where marginal benefits begin to decline and thus the function begins to slope
downward) (see Figure 2). Specifically, the “kink” is defined as the ratio of the amount of
interest required to make the tax rate function slope downward to actual interest expense. If kink
is less than one, a firm operates on the downward-sloping part of its tax rate function and is
considered to use debt aggressively because it expects reduced tax benefits on the last portion of
its interest deductions. If kink is greater than one, a firm could increase interest expense and
expect full benefit on these incremental deductions; such a firm can be thought of as using debt
conservatively. Therefore, debt conservatism increases with kink. We compute the tax benefit
function and the “debt kink” using the simulation procedures from both Graham (1996a) and
Blouin et al. (2010).
The second test approach is based on van Binsbergen et al. (2010), the authors of which
estimate company-specific optimal capital structure as occurring at the intersection of the
marginal benefit and marginal cost of debt functions. These authors simulate the marginal
benefit of debt function using the approach described above (Graham, 2000). The authors use
shifts in the benefit function to observe a series of optimal cost/benefit intersection points, and
infer the marginal cost of debt function by statistically “connecting the dots” provided by the
intersection points.21 Once we identify a firm’s ‘optimal’ or ‘equilibrium’ leverage ratio at the
intersection of the marginal benefit and cost curves, we then normalize this equilibrium leverage
ratio using the observed leverage ratio to construct an Equilibrium Factor. An Equilibrium
21
An important assumption in the van Binsbergen et al. (2010) approach is that the cost function remains constant as
the benefit function shifts. The authors accomplish this by including control variables designed to hold the cost
function fixed and by observing exogenous variation in benefit functions induced by tax regime changes.
27
Factor of one implies that the firm is at equilibrium and its optimal leverage ratio is 100% of its
actual. An Equilibrium Factor of 1.2 implies that the firm’s optimal leverage ratio is 20% larger
than its actual leverage ratio (the firm is underlevered). Similarly, an Equilibrium Factor of 0.8,
implies that the firm’s optimal leverage ratio is 20% smaller than its actual leverage ratio (the
firm is overlevered).
To test our prediction that differences between the MTR and GAAP ETR lead to debt
policies that are either too aggressive or too conservative, we estimate the following regression:
,
,
′
,
(1)
where Debt Kink is our measure of debt policy conservatism and Equilibrium Factor is the
optimal capital structure normalized by the actual capital structure such that larger (smaller)
values of Equilibrium Factor imply that the firm is underlevered (overlevered). The signed
difference between firms’ MTR (before interest deductions) and their GAAP ETRs (MTR –
GAAP ETR) is the primary independent variable of interest.
We use before-interest MTRs in this analysis because the dependent variables, Debt Kink
and Equilibrium Factor, are based on the cumulative financial policy of all financing decisions
rather than just the marginal financing decision. Before-interest MTRs remove the effect of past
financing decisions (that are still part of existing capital structure) and are appropriate to use
when the dependent variable is a stock measure of capital structure (like the debt kink and
optimal leverage ratio).22 In the regression above, i indexes firms, t indexes years,
and
are
industry and year fixed effects, X is a vector of control variables that includes a number of
nontax factors that affect debt policy, following Graham (2000) or other factors affecting optimal
capital structure. is predicted to be positive: when the GAAP ETR is greater (less) than the
22
In contrast, after-interest MTRs are based on income after interest is deducted and are the appropriate MTRs in
most other settings, including those in which the dependent variable measures future incremental investing and
financing choices (see Graham, Lemmon, and Schallheim, 1998; Graham and Mills, 2008).
28
MTR, firms will use debt too aggressively (conservatively) and the kink/equilibrium factor will
be lower (higher). We cluster standard errors by firm.
Table 6, Panel A presents the descriptive statistics for the variables used in the Debt Kink
regression and Table 6, Panel B presents the regression results when Debt Kink is the dependent
variable. For brevity, we do not discuss the descriptive statistics. In Panel B, we find that the
coefficient for (MTR – GAAP ETR) is positive and statistically significant at the 5% level using
both MTR proxies, Graham (1996a) and Blouin et al. (2010). These coefficients suggest that as
the distance between a firm’s MTR and GAAP ETR widens such that the firm’s GAAP ETR is
higher (lower) than its MTR, the firm adopts a relatively aggressive (conservative) debt policy,
which is consistent with our prediction.
Table 7, Panel A presents the descriptive statistics for the variables used in the
Equilibrium Factor regression. In addition to presenting the regression results, we tabulate the
mean and median Equilibrium Factor of the firms in each quartile of (MTR – GAAP ETR) (Table
7, Panel B). This panel not only shows us the relation between (MTR – GAAP ETR) and
Equilibrium Factor but also tells us if the mean/median firm across the distribution of (MTR –
GAAP ETR) is under- or over-levered (since an Equilibrium Factor smaller (larger) than one
implies that the firm is overlevered (underlevered)). Table 7, Panel B shows that there is a near
monotonic increase in Equilibrium Factor as we move from the first to the fourth quartile of
(MTR – GAAP ETR). Further, the table shows that the median firm in the 1st quartile of the (MTR
– GAAP ETR) distribution is overlevered but the median firm in the remaining three quartiles is
underlevered. The near monotonic relation between (MTR – GAAP ETR) and Equilibrium Factor
combined with the observation that Equilibrium Factor is greater than one in the top three
quartiles of (MTR – GAAP ETR) tells us that firms using the GAAP ETR for capital structure
29
decisions become more underlevered as MTR becomes larger than the GAAP ETR (consistent
with our prediction).23
Table 7, Panel C presents the results from a regression of Equilibrium Factor on (MTR –
GAAP ETR). Consistent with the univariate results in Panel B, we find that the coefficient for
(MTR – GAAP ETR) is positive and statistically significant at the 5% level. The coefficient
estimate for (MTR – GAAP ETR) in the multivariate regression is 1.75, which tells us that a one
percentage point increase in the difference between the MTR and GAAP ETR leads to a 1.75
percentage point increase in Equilibrium Factor. Consider a hypothetical firm that uses the
GAAP ETR for decision making and is currently at its optimal leverage ratio with its GAAP
ETR equal to its MTR. Let’s say the GAAP ETR for this firm decreases from 35% to 34% while
the MTR is remains constant. In this case, our regression coefficient predicts that the firm’s
actual leverage ratio will move from the optimal to being only 98.25% of the optimal (the firm
would be underlevered as a result).
To get a better sense of the magnitude of the loss in firm value as a result of using the
GAAP ETR instead of MTR, in Panel D we change the dependent variable in the above
regression to the total deadweight cost of being under- or over-levered, where Total Deadweight
Loss is measured as the area between the cost and benefit curves when a firm has more/less debt
than recommended by our model (see Figure 2). We use the absolute value of the difference
between MTRs and ETRs in this regression because the dependent variable combines the loss
from being underlevered for some firms with the loss from being overlevered for other firms.24
23
That most firms are underlevered is also consistent with prior research (van Binsbergen et al., 2010; Korteweg,
2010) and the observation that the cost of being overlevered is asymmetrically higher than the cost of being
underlevered (Leary and Roberts, 2005; van Binsbergen et al., 2010). 24
However, in untabulated analyses, we find that the signed difference between the MTR and ETR is positively
(negatively) associated with the deadweight cost of being overlevered (underlevered), which is consistent with our
expectation. Interestingly, we find that the coefficient for (MTR – ETR) is larger in magnitude when the dependent
30
Total Deadweight Loss is reported as a percentage of book value in perpetuity; for example, a
loss of 5% would occur if the annual loss was 0.5% and the discount rate was 0.10.25 The
average Total Deadweight Loss for our sample firms is 1.1% (from Table 7, Panel A), which is
consistent with these firms being off equilibrium (underlevered), on average.
In Table 7, Panel D we find that the relation between |MTR – GAAP ETR| and Total
Deadweight Loss is positive and significant (as expected). The coefficient for |MTR – GAAP
ETR| suggests that a one percentage point increase in the difference between MTRs and ETRs
leads to a 3.8 percentage point increase in the Total Deadweight Loss. In dollar terms, this result
implies that a one percentage point increase difference between MTRs and ETRs leads to $1.28
million increase in deadweight cost.26 Since the average difference between the MTR and ETR
(for firm that responded that their company uses the GAAP ETR) is 7.7 percentage points, our
results suggest that the average firm experiences a deadweight loss of $9.86 million or 0.26% of
assets for making the incorrect capital structure decision. Collectively, the results in Tables 6 and
7 suggest that using the GAAP ETR for decision-making reduces firm value.
6.2. Capital investment consequences
In this section, we examine whether firms that use the GAAP ETR as the tax rate input
for capital investment decisions make inefficient capital investment decisions. The intuition is
that differences between the MTR and GAAP ETR can lead firms to incorrectly forecast the
after-tax cash flows from their current and potential investments. Incorrect cash flow forecasts
are likely to create biases in firms’ evaluation of their existing investments’ NPV as well as that
variable is the deadweight cost from being overlevered. This result is consistent with the asymmetrically higher loss
of overleverage relative to underleverage (Leary and Roberts, 2005).
25
As indicated in the Appendix, we obtain data on Equilibrium Factor and Total Deadweight Cost from van
Binsbergen et al. (2010). Also note that these authors use the Moody’s average corporate bond yield as the discount
rate for all firms in a given year.
26
The dollar magnitude is computed by multiplying the average the book value of assets ($3.8 billion) and the
change in the Total Deadweight Loss (1.138%-1.10%) from a one percentage point increase in |MTR – GAAP ETR|.
31
of their investment opportunities, leading to too little investment in high NPV projects and/or too
much investment in low (or negative) NPV projects, and thus leading to a weaker relation
between investment and investment opportunities (i.e., inefficient decisions). For example, if a
firm’s GAAP ETR is higher (lower) than its MTR and the GAAP ETR is used for decision
making, then such a firm is likely to underestimate (overestimate) the after-tax cash flows from
investing and thus underinvest (overinvest). To test this prediction, we examine the association
between the difference between the MTR and GAAP ETR and responsiveness of investment to
investment opportunities, which prior research interprets as an outcome of more efficient
investment behavior (Hubbard, 1998; Bekaert, Harvey, Lundblad, and Siegel, 2007; Badertscher,
Shroff, and White, 2013; Asker, Farre-Mensa, and Ljungqvist, 2014). Specifically, we estimate
the following equation using OLS in the tradition of Q-theory of investment (Fazzari et al. 1988):
,
1
3
|
. .
_
,
1
|
|
. .
_
,
2
1
|,
,
1
(2)
where CAPEX is capital expenditures scaled by lagged total assets, INV_OPP is our proxy for
investment opportunities (discussed below), |MTR A.I. – GAAP ETR| is the unsigned difference
between firms’ simulated after-interest MTRs and their GAAP ETRs, CFO measures a firm’s
cash flows from operations scaled by total assets, and
( ) are industry (year) fixed effects.
Investment efficiency is estimated by the coefficient for the INV_OPP variable, β1, which is
predicted to be positive. The coefficient of interest in the above equation is β3, which captures
the incremental sensitivity of investment to investment opportunities for firm-years with larger
differences between the MTR and ETR. As any under- or over-investment represents investment
inefficiency, β3 is predicted to be negative.
We use the absolute value of the difference between MTR A.I.s and GAAP ETRs in this
analysis because both over- and under-investment reduces the sensitivity of a firms’ investment
32
to its investment opportunities. For example, firms that increase investment when their growth
opportunities are declining (overinvestment firms) are likely to have lower investment-growth
opportunity sensitivities, and firms that decrease investment when their growth opportunities are
increasing (underinvestment firms) are also likely to have lower investment-growth opportunity
sensitivities. As a result, we use the absolute value of the difference between MTRs and ETRs in
this analysis. Also note that we use an after-interest estimate of MTR in the above regression
(rather than a before interest estimate as we did for our analyses of capital structure decisions)
because the cash flows from the marginal investment decision are subject to the firm’s marginal
tax rate after deducting interest. In other words, capital investment decisions are incremental
decisions and should be analyzed using the tax rate applicable to incremental decisions, which is
the after-interest MTR (Graham et al., 1998; Graham and Mills, 2008).
We proxy for investment opportunities using Tobin’s Q and Sales Growth following a
long list of prior studies (Wurgler, 2000; Whited, 2006; Bloom, Bond, and Van Reenen, 2007;
Badertscher et al., 2013; Shroff et al., 2014). While these proxies for investment opportunities
are measured with error (as in prior research), our inferences are only affected to the extent the
measurement error in these proxies is correlated with time-series changes in the differences
between the MTRs and ETRs of firms. We have no reason to expect such a correlation, however,
our falsification test described in Section 6.4 mitigates such potential concerns.27
Table 8, Panel A presents the descriptive statistics for the variables used in the regression
described above, and Panel B presents the regression results. Consistent with our prediction, we
find that the coefficient for |MTR A.I. – GAAP ETR| × INV_OPP is negative and statistically
27
A significant limitation of using Tobin’s Q as a proxy for investment opportunities is that the numerator in Q
includes the market value of equity. To the extent investors anticipate and identify firms that use the GAAP ETR as
their tax rate for decision making and incorporate this information into the firm’s stock price, our inferences could
be confounded by an endogeneity bias. However, this concern does not affect the Sales Growth proxy.
33
significant in all four regressions presented in the table. These coefficients suggest that as the gap
between a firm’s MTR and GAAP ETR widens, firms that use the GAAP ETR as the tax rate
input for decision making become less responsive to their growth opportunities. This result can
be interpreted as indicating that a firm using the GAAP ETR as its tax rate input is less efficient
in its investment decision-making when the GAAP ETR differs from its MTR. The table also
shows that the coefficients for INV_OPP and CFO are positive and statistically significant in all
our regressions, consistent with our expectations and prior research.
6.3. M&A consequences
In this section, we examine whether firms that use the GAAP ETR as the tax rate input
for M&A decisions are more prone to making value-decreasing acquisitions. Acquisitions are
among the largest and most readily observable forms of corporate investment and thus an
important corporate decision with a significant effect on firm value. Typically, acquisition
decisions require rigorous due diligence that includes evaluating the target firm’s value as a
stand-alone business and the value of potential synergies gained from combining businesses.
Both processes require firms to forecast after-tax cash flows. To the extent that firms use the
GAAP ETR to measure the tax impact of the acquisition decisions, they are likely to under- or
over-estimate the value of the acquisition. Underestimating the expected value created from an
acquisition is likely to result in the firm simply not engaging in the acquisition or being outbid by
competing bidders. However, we are unable to empirically measure such lost opportunities. On
the other hand, conditional on successfully completing an acquisition, companies using the
GAAP ETR as their tax rate input for decision making are more likely to have either overbid for
the target and/or more likely to have made other errors in forecasting the value of the acquisition.
34
Thus, companies that use the GAAP ETR for decision making are more likely to make value
destroying acquisitions, especially when the GAAP ETR is different from the MTR.
Following a long list of prior studies, we measure whether acquisitions are value
enhancing or decreasing by examining the market response to their acquisition announcements
(see e.g., Harford, 1999; Fuller, Netter, and Stegemoller, 2002; Moeller, Schlingemann, and
Stulz, 2004; Masulis, Wang, and Xie, 2007; Goodman, Neamtiu, Shroff, and White, 2014). We
estimate the following OLS regression to test our prediction:
where
|
. .
|,
′
,
(3)
is the five–day [-2,2] cumulative abnormal return around the acquisition
announcement date. We measure abnormal returns as the firm’s return minus the return of the
CRSP value weighted index. |MTR A.I. – GAAP ETR| is the unsigned difference between an
acquirer’s simulated MTR after interest deductions and its GAAP ETR. X is a vector of control
variables that includes a number of other determinants of acquirer returns following Harford
(1999), Masulis et al. (2007), and Goodman et al. (2014). The regression also includes year and
industry fixed effects, and the standard errors are clustered at the firm-level. The coefficient of
interest,
, captures the incremental acquisition announcement return for M&A deals in firm-
years with larger differences between the MTR and ETR, and is predicted to be negative.
Table 9, Panel A presents the descriptive statistics for the variables used in the above
regression and Table 9, Panel B presents the regression results. Consistent with our prediction,
we find that the coefficient for |MTR A.I. – GAAP ETR| is negative and significant using both
MTR proxies (i.e., Graham/Shevlin and BCG). These coefficients suggest that as the gap
between a firm’s MTR and GAAP ETR widens, firms that use the GAAP ETR as the tax rate
input for decision making complete acquisitions that are (relatively) value decreasing for
35
shareholders. This result can be interpreted as indicating that a firm using the GAAP ETR as its
tax rate input is less efficient in its investment decision-making when the GAAP ETR differs
from its MTR. In terms of economic magnitude, our coefficients imply that a one percentage
point increase in the difference between the MTR and ETR reduces the acquisition
announcement return by 0.08 to 0.10 percentage points (depending on the MTR proxy). Since
the average (unsigned) stock return to an acquisition announcement is 3.90%, our results suggest
that using the GAAP ETR for M&A decisions reduces the average acquisition announcement
return by 2.03 to 2.64% per one percentage point increase in the difference between the MTR
and GAAP ETR. Since the market value of the average acquirer is $27 billion (untabulated), in
dollar terms, our coefficients imply that an acquisition announcement by firms using the GAAP
ETR for decision making lowers acquirer market values by $21–$28 million, on average.28
6.4. Falsification tests
Thus far, we focus on firms that use the GAAP ETR as their tax rate input and examine
whether these firms make inefficient corporate decisions when their GAAP ETRs significantly
differ from their MTRs. Although this analysis offers a number of advantages, potential concerns
about measurement error and correlated omitted variables remain. We devise a falsification test
using firms that say they use the MTR as their tax rate input for decision making. The intuition is
that if firms use the MTR as the tax rate input for their decisions, then the difference between the
MTR and GAAP ETR should be uncorrelated with their capital structure, investment, and
acquisition outcomes. If the difference between the MTR and GAAP ETR captures confounding
factors that have a direct relation with corporate decisions, then we would (spuriously) find that
28
The dollar magnitude of the decrease in acquirer market value is computed by multiplying the coefficient estimate
for |MTR A.I. – GAAP ETR| (i.e., -0.103% or -0.079%) with the average acquirer market value (i.e., 27 billion).
36
this difference is related to the corporate outcomes even for firms that use the MTR for decision
making.
Table 10 presents the results of the falsification analyses. Specifically, Panel B (C, D)
presents the results for our analyses of the Debt Kink (Capital Expenditures, Acquisition
Announcement Returns) and Panel A presents the descriptive statistics for all the variables. The
regressions in Table 10 have the same structure that we used in our earlier analyses but now are
estimated using the firms that say they use the MTR for decision-making. Consistent with our
expectations, we find that the coefficient for MTR – GAAP ETR in Panel B, |MTR A.I. – GAAP
ETR| × INV_OPP in Panel C, and |MTR A.I. – GAAP ETR| in Panel D are all statistically
insignificant. These results indicate that the difference between a firm’s MTR and ETR does not
affect capital structure and investment policy for firms that use the MTR as their tax rate input
for decision-making (consistent with our prediction). These results help mitigate concerns that
the inferences drawn from Tables 6, 8 and 9 are confounded by spurious correlations.
This falsification test also helps mitigate the concern that measurement error in our
simulated MTR proxies could drive our results. Simulated MTRs have measurement error when
firms have foreign income in low tax jurisdictions and non-debt tax shields such as employee
stock options, among other things. However, if measurement error in MTR induces a relation
between (MTR – GAAP ETR) and the corporate outcomes we examine, then we should observe
this relation for both firms that use the GAAP ETR for decision-making as well as firms that use
the MTR for decision-making.
Notwithstanding the above, we also partition our data into two groups based on whether
firms have foreign income. Since there is more measurement error in the MTR proxies for firms
with foreign income, we predict that our results from Tables 6 to 9 will be weaker (stronger) for
37
firms with (without) foreign income. Consistent with this prediction, we find that all our results
continue to hold in the sample of domestic firms (with no foreign income) and the results
become weaker in the sample of firms with foreign income (untabulated). This result is
consistent with our expectations and provides additional evidence that our inferences are unlikely
to be confounded by measurement error in the MTR proxy.
7. Conclusion
In this paper, we survey corporate tax executives to examine the manner in which
companies incorporate taxes into their decision making. We directly ask tax executives what tax
rate their companies use in decision making and we merge their responses with Compustat data.
We find, surprisingly, that many firms use the GAAP effective tax rate (ETR) as their tax rate
input for decision making and few firms use the marginal tax rate (MTR). We provide some
conjectures and descriptive results in an attempt to explain the survey responses. We find that the
difference between the statutory tax rate (STR) and MTR is less than two percentage points for
the majority of the firms using the STR as the tax rate input for decision making. This result is
consistent with managers relying on simple heuristics that approximate theoretically correct
constructs in their decision making process. We also find that companies that are more focused
on external reporting (e.g., public firms and firms with high analyst following) are significantly
more likely to use the GAAP ETR as the tax rate in their decisions. In contrast, firms less
focused on external reporting (e.g., private firms) are significantly more likely to use STRs as the
tax rate in their decisions. These results suggest that salience also affects the tax rate used by
managers. Finally, we find that larger firms (conditional on being public), firms with high R&D
intensity and high institutional ownership are more likely to use the MTR for decision making,
38
suggesting that firms with greater tax planning opportunities and more external monitoring
effectively incorporate taxes into their decision making.
We then go on to examine the magnitude of adverse economic consequences for firms
that use the GAAP ETR as their tax rate input for decision making. We find that firms using
GAAP ETRs as the tax rate input for their capital structure decisions adopt an aggressive
(conservative) debt policy when their GAAP ETR is greater (less) than their MTR. In terms of
investment outcomes, we find that the firms that use the GAAP ETR as the tax rate input for
investment decisions are less responsive to their investment opportunities and have lower
acquisition announcement returns when the difference between their firm’s MTR and GAAP
ETR is large. These results suggest that the tax rate firms use to incorporate taxes into their
business decisions has important economic consequences. Specifically, the use of GAAP ETRs
instead of the theoretically suggested MTR as the tax input for decision making leads to
inefficient corporate decisions. We estimate that the capital structure inefficiency alone costs the
firm $10 million (on average) in forgone value.
Our paper answers a call for research from Shackelford and Shevlin (2001) to find out
what rate managers actually use in decision-making settings. However, there remain many
unanswered questions concerning why firms differ in their choice of tax rate input for decision
making and why firms use the GAAP ETR for decision making. More research is needed to
understand and explain these tax rate choices, particularly because some of our results are
contrary to standard economic theory and suggest that managers behave in an inefficient manner
(e.g., the use of GAAP ETRs), leading to a significant loss in firm value.
39
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45
APPENDIX
Variable Definitions
This table provides a detailed description of the procedure used to compute each variable used in our analyses. Our
data are obtained through (i) survey questions, (ii) Compustat, (iii) I/B/E/S, (iv) Thomson Reuters, (v) John
Graham’s website (JG Website), (vi) the Hoberg-Phillips Data Library (H-P Website) (vii) SDC Platinum, and (viii)
courtesy of Jie Yang. The cross-sectional variables are constructed using data from year 2006. All continuous
variables are winsorized at 1% and 99% of the distribution and all dollar amounts are in millions. The variables are
listed in alphabetical order.
Variable
3-Yr Cash ETR
Acquisition
Announcement
Return
Acquisition
Expenditure
Advertising Intensity
Analyst Following
Assets
Asset Growth
BGY Fin. Constraints
Indicator
Cash Acquisition
Cash ETR
Capital Expenditures
CFO
Credit Rating Dummy
Debt Kink
Diversifying
Acquisition
Dividend Dummy
Equilibrium Factor
Firm Size
Foreign Acquisition
Definition
3-Yr Cash ETR is computed as the ratio of the sum of the Cash ETR numerator for the
preceding three years and sum of Cash ETR denominator for the preceding three years.
Where Cash ETR is the cash effective tax rate defined as the sum of total tax paid (data
TXPD) divided by pretax income (data PI).
Five-day cumulative abnormal return for the acquirer around the announcement date of an
acquisition. Abnormal returns are calculated using the Fama-French three-factor daily
market model. The market-model parameters are estimated using daily returns data from
the year prior to the acquisition. The announcement date is the event date disclosed in SDC.
Acquisition expenditures (data AQC) scaled by lag total assets (data AT). Missing
acquisition expense data are coded as zero.
The ratio of advertising expense (data XAD) scaled by total assets (data AT). Missing
advertising expense data are coded as zero.
The number of analyst following a firm in I/B/E/S. We assume that analyst following is
zero for public firms not covered by I/B/E/S.
Worldwide assets (in millions) and corresponds with Compustat data item AT.
Changes in total assets scaled by lag total assets (data AT).
Indicator variable for firm-years in the bottom two terciles of all of the following variables:
long-term debt issuance (data DLTIS), long-term debt reduction (data DLTR), equity
issuance (data SSTK), and equity reduction (data PRSTKC).
An indicator equal to one if the acquisition was paid for in cash.
Cash ETR is the cash effective tax rate defined as the sum of total tax paid (data TXPD)
divided by pretax income (data PI).
Capital expenditures (data CAPX) scaled by lag total assets (data AT).
Cash flow from operations (data OANCF) scaled by lag total assets (data AT).
Indicator variable equal to one if the firm has an S&P credit rating (non-missing Compustat
data item SPLTICRM).
Ratio of the amount of interest required to make the tax rate function slope downward to
actual interest expense. The tax rate function is computed using the MTRs from Graham
(1996a) or Blouin, Core, and Guay (2010). See Graham (2000) for more details.
An indicator equal to one if the target’s one-digit SIC code differs from that of the
acquirer’s.
Indicator variable equal to one if the firm pays a dividend in year in the fiscal year before
the acquisition.
Equilibrium Factor is defined as the intersection of the marginal benefit of debt and
marginal cost of debt curves. The marginal benefit curve is computed following Graham
(2000) and the marginal cost curve is computed following van Binsbergen, Graham, and
Yang (2010). If Equilibrium Factor = 1, the firm’s equilibrium leverage is 100% of its
actual (i.e., firm is at equilibrium). If Equilibrium Factor = 1.2, the firm should have 120%
of its actual leverage (i.e., firm is underlevered). If Equilibrium Factor = 0.8, the firm
should have 80% of its actual leverage (firm is overlevered). We thank Jie Yang for sharing
this variable with us.
The book value of worldwide assets (data AT).
Indicator variable that takes a value of one if the target is a foreign company.
46
Foreign Assets
Foreign Income
GAAP ETR
GAAP ETR
Importance
HP Fin. Constraints
Indicator
Intangible Intensity
Institutional
Ownership (%)
Leverage
MB
MTR
MTR After Interest
(MTR A.I.)
MVE
NOL
Non-Distress
Indicator
No. of Bidders
No. of Tax Returns
Filed
PPE
Pre-Acquisition
Return Run Up
Public
Public Target
R&D Intensity
Proportion of assets owned in foreign locations.
Foreign pre-tax income (data PIFO) divided by total assets (data AT).
GAAP ETR is the GAAP effective tax rate defined as total tax expense (data TXT) divided
by pretax accounting income (data PI). The GAAP ETR is set to missing of PI is less than or
equal to zero.
This variable is computed using the survey responses to the question “Which metric is
more important to the top management at your company?” The respondents could pick one
of the following answers: (i) GAAP ETR, (ii) Cash Taxes Paid, or (iii) Both are equally
important. GAAP ETR Importance is an indicator variable that takes on the value of one for
firms indicating that the GAAP ETR is the most important metric to top management or
both cash taxes and the GAAP ETR are equally important to top management.
Indicator variable for firms in the top tercile of the yearly size-age index developed by
Hadlock and Pierce (2010). The size-age index is (−0.737 × Size) + (0.043 × Size2) −
(0.040 × Age), where size equals the log of inflation-adjusted book assets, and age is the
number of years the firm is listed with a non-missing stock price on Compustat. In
calculating this index, size is winsorized at $4.5 billion, and age is winsorized at 37 years.
The ratio of intangible assets (data INTAN) scaled by total assets (data AT). Missing data
are coded as zero.
The percentage of the firm’s equity held by institutional investors in year t. Calculated
from data provided in the Thomson-Reuter’s Institutional Holdings (13F) Database. Set
equal to zero if the data are missing.
Ratio of long-term debt (data DLTT) plus the debt included in current liabilities (data DLC)
to total assets (data AT).
Market-to-book ratio (MVE/data CEQ).
MTR is the simulated marginal tax rate (before financing) obtained from John Graham’s
website (https://faculty.fuqua.duke.edu/~jgraham/taxform.html) or from WRDS for the
Blouin, Core, and Guay (2010) procedure.
MTR A.I. is the simulated marginal tax rate (after financing) obtained from John Graham’s
website (https://faculty.fuqua.duke.edu/~jgraham/taxform.html) or from WRDS for the
Blouin, Core, and Guay (2010) procedure.
MVE is the market value of equity (data PRCC_F multiplied by data CSHO).
An indicator variable that equals one if the firm has a positive tax loss carry-forward
(TLCF) on Compustat.
Indicator variable for firm-years in the top tercile of the z-score distribution (following van
Binsbergen, Graham, and Yang (2010)). Z-score is a bankruptcy score that is computed as
follows: 1.2 × working capital + 1.4 × retained earnings + 3.3 × earnings before interest and
taxes + 1.0 × sales.
Indicator variable that takes a value of one if there is more than one bidder for the target
firm.
The number of IRS form 5471 filed by the company.
Book value of Net Property, Plant, and Equipment (data PPENT) scaled by lag total assets
(data AT).
Abnormal stock returns for the period (-250, -12) prior to the acquisition announcement
date, as in Harford (1999). Abnormal returns are calculated using the Fama-French threefactor daily market model. Market-model parameters are estimated over the period (-370,253).
An indicator variable that takes on the value of one for publicly traded firms.
An indicator equal to one if the target is identified as public company in SDC Platinum.
Ratio of research and development expense (data XRD) scaled by total assets (data AT).
Missing R&D data are coded as zero.
47
Relative Value of
Target
ROA
Sales
Sales Growth
Text-Based HHI
Tobin’s Q
Total Deadweight
Loss
US NOL
Z-Score
The value of the acquisition scaled by the acquirer’s market value of equity.
ROA is return-on-assets defined as net income (data NI) divided by total assets (data AT).
Worldwide net sales and corresponds to Compustat data item SALE.
Changes in sales scaled by lag sales (data SALE).
This is an industry concentration measure computed using the Herfindahl-Hirschmann sum
of squared market shares formulation (HHI). These HHI data are computed by grouping
firms based on their product similarity in their 10-K filings. Specifically, the measure of
product similarity is obtained based on text-based analysis of firm 10-K product
descriptions. See Hoberg and Phillips (2013) for a detailed description of the procedure
used to compute this measure of industry concentration.
The market value of worldwide assets, which is computed as the market value of equity
plus book value of debt scaled by the book value of assets [(data PRCC_F × data CSHO +
data DLTT + data DLC) / (data AT)].
Total Deadweight Loss is the loss in firm value from being either underlevered or
overlevered (van Binsbergen, Graham, and Yang, 2010). It is measured as the area between
the cost and benefit curves when a firm has more/less debt than recommended by our
model. Total Deadweight Loss is reported as a percentage of book value in perpetuity; for
example, a loss of 5% would occur if the annual loss was 0.5% and the discount rate was
0.10. We use the Moody’s average corporate bond yield as the discount rate for all firms in
a given year. Figure 2 provides a graphical description of this variable. We thank Jie Yang
for sharing this variable with us.
US NOL is an indicator variable that equals one if the firm has a US net operating loss
carryforward (from survey responses).
Z Score is the bankruptcy score from Altman (1968) as modified by MacKie-Mason
(1990). In terms of Compustat data items it equals: 1.2 × [ACT - LCT]/AT + 1.4 × RE/AT
+ 3.3 × EBIT/AT + 1.0 × SALE/AT.
48
Figure 1
Survey Responses Describing the Tax Rates used by Firms for Corporate Decision Making Averaged Across all
Decisions Examined in the Survey
Notes: This figure presents the responses to the survey question “What is the primary tax rate your company uses to
incorporate taxes into each of the following forecasts or decision making processes?” The survey respondent is allowed
to choose from the following options (or, write in an answer if the options given are not sufficient): (i) U.S. statutory tax
rate, (ii) GAAP effective tax rate, (iii) jurisdiction-specific statutory tax rate, (iv) jurisdiction-specific effective tax rate,
(v) marginal tax rate, and (vi) other. The question then listed a number of decisions and settings for the respondent to
indicate the primary tax rate used in that decision: (i) mergers and acquisitions, (ii) capital structure (debt versus equity),
(iii) investment decisions (property, equip., etc), (iv) decision to purchase versus lease (property, equip., etc.), (v)
weighted average cost of capital, (vi) where to locate new facilities, and (vii) compensation decisions. The figure
provides the percentage of firms using each tax rate averaged across all seven decision contexts. The results are shown
separately for public and private firms. The corresponding data in table form are in Table 3, Panel A.
49
Figure 2
Measure of Total Deadweight Cost from Being Overlevered and Underlevered
Notes: This figure shows the marginal benefit of debt curve, MB(x), the marginal cost of debt curve, MC(x), and the
equilibrium level of debt, x*, that occurs where the marginal cost and benefit curves intersect. The cost of being
underlevered is depicted by the shaded area between the MC and MB curves from the actual debt, x0, to the equilibrium,
x*, in the case in which the actual level of debt is less than the equilibrium level of debt. The cost of been overlevered is
depicted by the shaded area between the MC and MB curves from the equilibrium, x*, to the observed debt, x1, in the case
in which the actual level of debt is greater than the equilibrium level of debt.
50
Table 1
Descriptive Statistics
Variable
Source
N
Mean
SD
P25
P50
P75
Public
Survey
500
0.778
0.416
1.000
1.000
1.000
Assets (in millions)
Survey
493
7,804
19,639
473
1,313
4,895
MVE (in millions)
Compustat
354
8,518
20,311
703
1,936
6,164
Sales (in millions)
Compustat
356
5,772
12,130
575
1,474
5,260
Tobin's Q
Compustat
354
1.970
1.031
1.271
1.594
2.359
ROA
Compustat
356
0.058
0.072
0.024
0.059
0.096
Foreign Assets (% of assets)
Survey
488
0.187
0.216
0.000
0.100
0.305
No. of Tax Returns Filed
Survey
489
6.609
20.506
1.000
1.000
3.000
Leverage
Compustat
356
0.212
0.196
0.048
0.174
0.320
Sales Growth
Compustat
353
0.152
0.239
0.039
0.098
0.193
Asset Growth
Compustat
354
0.143
0.275
0.003
0.071
0.189
Survey
478
0.462
0.499
0.000
0.000
1.000
R&D Intensity
Compustat
356
0.024
0.044
0.000
0.000
0.032
Intangible Intensity
Compustat
351
0.188
0.182
0.030
0.137
0.307
Analyst Following
I/B/E/S
356
9.798
8.253
3.000
8.000
15.000
Thomson Reuters
357
0.536
0.402
0.000
0.688
0.869
H-P Website
340
0.213
0.232
0.059
0.121
0.262
Survey
398
0.309
0.165
0.273
0.340
0.377
GAAP ETR
Compustat
311
0.313
0.154
0.278
0.336
0.375
MTR
JG Website
203
0.309
0.099
0.350
0.350
0.350
MTR After Interest (MTR A.I.)
JG Website
234
0.214
0.156
0.029
0.347
0.350
MTR
WRDS-Blouin/Core/Guay
334
0.331
0.044
0.337
0.345
0.350
MTR After Interest (MTR A.I.)
WRDS-Blouin/Core/Guay
334
0.316
0.060
0.317
0.341
0.348
|MTR - GAAP ETR|
JG Website & Compustat
187
0.132
0.289
0.024
0.050
0.118
|MTR A.I. - GAAP ETR|
JG Website & Compustat
210
0.167
0.164
0.029
0.096
0.306
|MTR - GAAP ETR|
Blouin/Core/Guay & Comp
291
0.095
0.154
0.023
0.044
0.099
|MTR A.I. - GAAP ETR|
Blouin/Core/Guay & Comp
291
0.101
0.156
0.026
0.048
0.107
Compustat
297
0.287
0.236
0.171
0.254
0.356
US NOL
Institutional Ownership (%)
Text-Based HHI
GAAP ETR (survey response)
3-Yr Cash ETR
Notes: The above data are obtained through (i) survey questions, (ii) Compustat, (iii) I/B/E/S, (iv) Thomson Reuters,
(v) John Graham’s website (JG Website: https://faculty.fuqua.duke.edu/~jgraham/taxform.html) or (iv) the HobergPhillips Data Library (H-P Website: http://alex2.umd.edu/industrydata/industryconcen.htm). All continuous
variables are winsorized at 1% and 99% of the distribution and all dollar amounts are in millions. All variables are
defined in the Appendix.
51
Table 2
Non-Response Bias Test
All Compustat
(1)
Assets
MVE
Sales
Leverage
Cash
MB
ROA
Asset Growth
Sales Growth
Capital Exp.
Acquisition Exp.
R&D Exp.
NOL
GAAP ETR
3-Yr Cash ETR
MTR
N
5,938
5,443
5,911
5,921
5,937
5,443
5,911
5,692
5,499
5,877
5,938
5,938
5,938
4,239
4,052
2,097
Mean
3,580.2
2,369.1
1,719.4
0.19
0.20
4.35
-0.04
0.36
0.25
0.04
0.02
0.05
0.37
0.26
0.27
0.27
All Public Firms we
Contacted with
Available Data
(2)
N
1356
1288
1342
1341
1337
1279
1338
1326
1323
1334
1338
1338
1338
1172
1165
756
Mean
9,075.2
7,206.3
5,049.7
0.22
0.14
3.30
0.05
0.15
0.14
0.05
0.03
0.02
0.42
0.29
0.27
0.31
Survey Responders
(Public Firms) with
Available Data
(3)
N
414
396
400
400
396
387
396
396
395
396
396
396
396
349
354
227
Mean
10,173.8
8,283.1
5,717.8
0.21
0.15
3.32
0.07
0.14
0.14
0.05
0.03
0.02
0.39
0.31
0.27
0.31
Survey Nonresponders
(Public Firms) with
Available Data
(4)
N
942
892
942
941
941
892
942
930
928
938
942
942
942
823
811
529
Mean
8,592.3
6,728.2
4,766.1
0.22
0.13
3.30
0.04
0.15
0.14
0.05
0.03
0.02
0.43
0.29
0.28
0.31
p-Value
1 vs. 2
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.72
0.00
1 vs. 3
0.00
0.00
0.00
0.21
0.00
0.00
0.00
0.00
0.00
0.01
0.02
0.00
0.28
0.00
0.67
0.00
2 vs. 3
0.45
0.33
0.29
0.25
0.30
0.95
0.00
0.55
0.75
0.27
0.46
0.80
0.33
0.18
0.55
0.91
3 vs. 4
0.28
0.15
0.13
0.12
0.16
0.93
0.00
0.42
0.66
0.12
0.32
0.74
0.18
0.09
0.42
0.88
Notes: All dollar amounts are in millions. All Compustat variables are measured in the fiscal year ending in 2006 and are winsorized at 1% and 99% of the
distribution. Column (1) consists of all the firms on Compustat except for firms with a negative book value, firms whose names indicate they are limited
partnerships, and firms incorporated outside the United States. Column (2) includes all the firms that were sent a survey (as described in Section 2 in the paper)
that we could match to and retrieve the data from Compustat. Column (3) includes the survey responders with data available on Compustat. Column (4) consists
of the group of firms that are on Compustat and that we sent a survey to but did not receive a response. All variables are defined in the Appendix.
52
Table 3
Survey Responses Describing the Tax Rates used by Firms for Corporate Decision Making
Panel A: What is the primary tax rate your company uses to incorporate taxes into each of the following forecasts or decision making processes?
Jurisdiction
Jurisdiction
Marginal
U.S. Statutory
Other
N
GAAP ETR
Specific STR Specific ETR
Tax Rate
Tax Rate (STR)
Merger &Acquisition Decisions
21.1%
24.9%
20.3%
20.1%
10.1%
3.4%
497
Capital Structure
25.9%
29.7%
14.5%
15.3%
12.0%
2.7%
491
Investment Decisions
22.9%
24.5%
21.1%
16.0%
12.5%
3.1%
489
Decision to Purchase vs. Lease
23.9%
23.7%
20.3%
16.4%
12.3%
3.5%
489
Weighted Average Cost of Capital
25.4%
34.3%
13.4%
12.6%
11.8%
2.5%
484
Where to Locate New Facilities
17.0%
16.6%
28.8%
25.9%
8.8%
2.9%
487
Compensation
25.7%
27.2%
19.1%
12.9%
10.6%
4.6%
482
Panel B: Pearson Correlation
1
2
3
4
5
6
1
Merger &Acquisition Decisions
2
Capital Structure
0.78
1
3
Investment Decisions
0.80
0.80
1
4
Decision to Purchase vs. Lease
0.76
0.80
0.93
1
5
Weighted Average Cost of Capital
0.72
0.78
0.81
0.82
1
6
Where to Locate New Facilities
0.76
0.74
0.82
0.78
0.71
1
7
Compensation
0.66
0.71
0.77
0.77
0.76
0.70
7
1
1
Notes: Panel A in this table summarizes the responses to our survey question and Panel B presents the correlations among the survey responses in Panel A. All
the correlation coefficients in Panel B are statistically significant at the 1% level.
53
Table 4
Univariate Analyses – Percentage of Firms that Use a Given Tax Rate as an Input into a Given Policy Decision
Panel A: Conditioning based on Survey Data
% of All
Firms
GAAP ETR - Capital Structure
GAAP ETR - Capex
GAAP ETR - M&A
MTR - Capital Structure
MTR - Capex
MTR - M&A
STR - Capital Structure
STR - Capex
STR - M&A
45.0%
40.5%
45.1%
12.0%
12.5%
10.1%
40.3%
44.0%
41.4%
Ownership
Firm Size
US NOLs
Public
Private
Below
Median
Above
Median
Yes
No
47.4%
42.1%
47.9%
11.8%
12.6%
8.8%
38.2%
42.4%
39.7%
37.0%
34.6%
35.2%
13.0%
12.1%
14.8%
47.2%
49.5%
47.2%
49.0%
43.7%
44.1%
11.7%
11.3%
10.6%
37.7%
43.3%
42.9%
41.6%
37.7%
46.1%
11.8%
13.1%
9.0%
43.3%
45.1%
40.8%
44.2%
42.3%
44.1%
11.2%
11.3%
10.5%
40.9%
42.7%
41.8%
44.9%
38.6%
45.9%
13.0%
13.8%
9.8%
40.2%
44.9%
41.2%
Metric Important to
Top Management
GAAP
Cash
ETR
Taxes Paid
47.3%
41.1%
46.7%
11.1%
12.1%
9.1%
39.2%
44.1%
40.8%
37.2%
37.6%
38.6%
15.0%
13.8%
13.2%
45.1%
45.0%
45.6%
Proportion of Assets
in Foreign Locations
Below
Above
Median
Median
45.5%
42.4%
47.3%
15.0%
16.1%
14.0%
37.2%
38.8%
36.0%
45.4%
38.8%
43.2%
8.4%
8.0%
5.7%
43.6%
49.6%
47.6%
Panel B: Conditioning based on Compustat Data % of
Public
Firms
GAAP ETR - Capital Structure
GAAP ETR - Capex
GAAP ETR - M&A
MTR - Capital Structure
MTR - Capex
MTR - M&A
STR - Capital Structure
STR - Capex
STR - M&A
47.4%
42.1%
47.9%
11.8%
12.6%
8.8%
38.2%
42.4%
39.7%
Firm Size
Below Above
Median Median
54.0%
47.4%
50.3%
11.1%
10.5%
8.2%
32.3%
39.5%
37.9%
40.9%
37.0%
45.6%
12.4%
14.6%
9.3%
44.0%
45.3%
41.5%
Metric Important
to Top Mgmt.
GAAP Cash
ETR
Taxes
48.6%
42.5%
48.6%
11.5%
12.4%
8.9%
37.1%
41.9%
38.8%
38.2%
37.0%
40.4%
14.5%
14.8%
8.8%
45.5%
46.3%
47.4%
Prop. of Assets
in Fgn Locations
Below Above
Median Median
48.1%
45.0%
50.3%
16.0%
17.5%
14.1%
33.7%
35.4%
33.0%
47.3%
39.6%
46.0%
7.4%
7.5%
3.7%
42.6%
49.2%
46.6%
R&D Intensity
Leverage
|MTR-GAAP ETR|
Below Above
Median Median
Below Above
Median Median
Below Above
Median Median
51.8%
44.9%
54.6%
10.3%
11.2%
8.2%
35.9%
41.8%
34.7%
42.9%
37.3%
40.9%
13.6%
13.1%
9.4%
40.9%
45.8%
44.7%
45.4%
39.1%
47.8%
13.2%
14.4%
11.8%
38.5%
43.1%
36.5%
50.3%
44.0%
49.2%
10.3%
9.7%
5.6%
37.7%
44.0%
41.8%
47.5%
38.2%
48.5%
10.9%
13.7%
10.9%
37.6%
44.1%
37.6%
55.1%
49.0%
56.9%
9.2%
10.2%
5.9%
35.7%
39.8%
35.3%
Notes: This table presents the responses to our survey questions after partitioning firms based on their characteristics. Panel A presents data from our entire
sample of firms (both private and public) and partitions firms using variables constructed from our survey instrument. Panel B presents data from our sample of
public firms and partitions firms using variables constructed from our survey instrument and Compustat. Highlighted fields indicate a statistically significant
difference in the percentages reported in the columns at the 10% level or better. All partitioning variables are defined in the Appendix. As an example of how to
interpret this table, consider the Ownership partition in Panel A. The numbers in that column indicate that 47.4% (37%) of the public (private) firms use the
GAAP ETR for capital structure decisions. The remaining numbers are to be interpreted similarly.
54
Table 5
Multivariate Analyses of the Determinants of Firms’ Responses to Survey Questions
Panel A: Determinants of Public and Private Firms Survey Responses using Survey Data as Conditioning Variables
Dependent Variable:
Public
Log(Assets)
US NOL
GAAP ETR Importance
No. of Tax Returns Filed
Foreign Assets
N
Pseudo R-Squared
S.E. Clustered by Industry
Effective Tax Rate (ETR)
Capital Str.
0.380***
(2.77)
-0.145**
(-2.82)
-0.043
(-0.17)
0.386**
(1.96)
-0.001
(-0.22)
-0.052
(-0.13)
442
1.9%
Yes
Capex
M&A
0.423***
(3.04)
-0.156**
(-3.11)
0.140
(0.72)
0.171
(0.77)
0.002
(0.38)
-0.477
(-1.05)
0.516***
(4.04)
-0.041
(-0.81)
-0.033
(-0.18)
0.200
(1.01)
0.003
(0.59)
-0.097
(-0.24)
443
1.9%
Yes
447
1.1%
Yes
Marginal Tax Rate (MTR)
Capital Str.
0.205
(1.01)
0.032
(0.44)
-0.090
(-0.33)
-0.479*
(-1.65)
0.000
(-0.03)
-1.074
(-1.36)
442
1.2%
Yes
55
Statutory Tax Rate (STR)
Capex
M&A
Capital Str.
Capex
M&A
0.202
(0.96)
0.122*
(1.72)
-0.100
(-0.42)
-0.300
(-1.04)
-0.002
(-0.35)
-1.507**
(-2.09)
-0.349
(-1.48)
0.034
(0.29)
0.201
(0.70)
-0.186
(-0.68)
-0.017
(-1.07)
-2.402**
(-2.41)
-0.468***
(-3.07)
0.115*
(2.41)
0.000
0.00
-0.128
(-0.59)
-0.002
(-0.47)
0.481
(1.24)
-0.436**
(-2.59)
0.051
(1.01)
-0.154
(-0.77)
0.022
(0.09)
-0.003
(-0.88)
0.949**
(2.14)
-0.341**
(-2.40)
-0.008
(-0.17)
-0.088
(-0.42)
-0.097
(-0.44)
-0.002
(-0.70)
0.713*
(1.76)
443
2.1%
Yes
447
3.9%
Yes
442
1.3%
Yes
443
1.3%
Yes
447
0.8%
Yes
Table 5 (continued)
Multivariate Analyses of the Determinants of Firms’ Responses to Survey Questions
Panel B: Determinants of Public Firms’ Survey Responses using both Survey and Compustat Data as Conditioning Variables
Dependent Variable:
Log(Assets)
US NOL
GAAP ETR Importance
No. of Tax Returns Filed
Foreign Assets
R&D Intensity
Leverage
ROA
Intangible Intensity
Analyst Following
Institutional Ownership
Sales Growth
Text-Based HHI
|MTR - GAAP ETR|
|MTR - GAAP ETR| Missing Indicator
N
Pseudo R-Squared
S.E. Clustered by Industry
Effective Tax Rate (ETR)
Capital Str.
-0.371***
(-3.45)
-0.376
(-1.23)
0.540
(1.45)
0.000
(0.03)
0.199
(0.33)
-9.092***
(-2.90)
1.167
(1.30)
-1.569
(-0.67)
0.423
(0.54)
0.057**
(2.59)
-0.379
(-1.01)
-0.003
(-0.01)
-0.345
(-0.59)
1.637*
(1.69)
-0.050
(-0.15)
298
7.6%
Yes
Capex
-0.341***
(-3.16)
-0.033
(-0.11)
0.261
(0.76)
0.004
(0.75)
-0.631
(-1.04)
-8.974***
(-3.22)
0.685
(0.81)
-1.866
(-0.89)
1.134
(1.45)
0.050**
(2.18)
-0.167
(-0.46)
0.621
(1.19)
-0.611
(-1.05)
1.620*
(1.64)
-0.016
(-0.05)
300
7.8%
Yes
Marginal Tax Rate (MTR)
M&A
-0.204*
(-1.95)
-0.173
(-0.61)
0.361
(1.04)
0.005
(0.85)
0.109
(0.17)
-7.902*
(-1.84)
0.252
(0.32)
-2.539
(-1.23)
0.675
(0.94)
0.046**
(2.12)
-0.638*
(-1.89)
0.570
(1.13)
-0.276
(-0.48)
1.953*
(1.88)
-0.185
(-0.62)
Capital Str.
0.301**
(2.19)
0.061
(0.16)
-0.452
(-0.79)
0.011
(1.17)
-2.429**
(-2.04)
9.329**
(2.35)
-0.474
(-0.52)
2.357
(0.73)
0.161
(0.17)
-0.059*
(-1.92)
1.283**
(1.98)
-1.825*
(-1.76)
-0.053
(-0.07)
-2.758
(-1.31)
0.602
(1.30)
303
6.0%
Yes
298
9.9%
Yes
Capex
0.393***
(2.91)
-0.055
(-0.15)
-0.451
(-0.77)
0.005
(0.47)
-2.823**
(-2.43)
8.002**
(2.15)
-1.196
(-1.14)
4.306
(1.42)
0.335
(0.33)
-0.051*
(-1.64)
1.033
(1.53)
-1.109
(-1.19)
0.265
(0.28)
-2.270
(-1.19)
0.256
(0.54)
300
9.3%
Yes
56
M&A
0.558***
(2.77)
0.206
(0.50)
-0.128
(-0.17)
-0.093
(-1.44)
-4.832***
(-3.18)
9.231**
(2.07)
-2.044
(-1.50)
4.908
(1.15)
0.495
(0.43)
-0.092**
(-2.26)
1.810**
(2.16)
-2.374**
(-2.30)
0.585
(0.61)
-4.334
(-1.57)
0.147
(0.29)
303
18.9%
Yes
Statutory Tax Rate (STR)
Capital Str.
0.214**
(2.14)
0.272
(0.95)
-0.317
(-0.79)
-0.003
(-0.41)
0.601
(1.12)
4.344*
(1.71)
-0.720
(-0.89)
1.113
(0.48)
-0.267
(-0.36)
-0.033*
(-1.70)
0.044
(0.13)
0.370
(0.65)
0.389
(0.71)
-0.590
(-1.08)
-0.164
(-0.56)
Capex
0.113
(1.15)
0.147
(0.60)
-0.143
(-0.42)
-0.003
(-0.54)
1.441**
(2.55)
2.387
(0.96)
-0.382
(-0.54)
1.627
(0.83)
-0.887
(-1.24)
-0.038*
(-1.81)
-0.037
(-0.11)
-0.390
(-0.74)
0.431
(0.83)
-0.663
(-1.25)
-0.088
(-0.32)
M&A
-0.004
(-0.04)
0.093
(0.35)
-0.286
(-0.78)
0.001
(0.16)
0.884
(1.51)
4.699
(1.46)
0.301
(0.41)
2.055
(1.06)
-0.705
(-1.00)
-0.026
(-1.23)
0.452
(1.43)
-0.175
(-0.36)
0.174
(0.33)
-0.915
(-1.23)
0.095
(0.34)
298
3.4%
Yes
300
4.3%
Yes
303
3.6%
Yes
Notes: This table presents the results from logistic regressions where the dependent variable is an indicator variable constructed based responses to our survey
questions and the independent variables are firm characteristics. Panel A presents result from our entire sample of (public and private) firms and thus the
independent variables include only those constructed from our survey instrument. Panel B presents data from our sample of public firms and the independent
variables include those constructed from our survey instrument as well as those constructed from Compustat, I/B/E/S, and Thomson Reuters. All the independent
variables are defined in the Appendix. The standard errors in the above regressions are clustered at the industry-level. ***, **, * signifies statistical significance at
the two-tailed 1%, 5%, and 10% levels.
57
Table 6
Analyses of Capital Structure Decisions
Panel A: Descriptive Statistics of the Variables Used in the Regression
Variable
N
Mean
SD
P25
P50
P75
Debt Kink (JG)
450
3.856
3.257
1.200
3.000
6.000
Debt Kink (BCG)
450
2.071
2.343
0.400
1.200
3.000
MTR - GAAP ETR (JG)
450
-0.021
0.106
-0.051
-0.023
0.019
MTR - GAAP ETR (BCG)
450
-0.011
0.084
-0.056
-0.030
0.010
Log(Assets)
450
7.115
1.364
6.088
6.983
7.930
Tobin's Q
450
1.767
1.317
0.912
1.356
2.290
Sales Growth
450
0.136
0.156
0.037
0.108
0.194
CFO
450
0.029
0.016
0.017
0.028
0.039
PPE
450
0.290
0.190
0.143
0.241
0.392
Capital Expenditures
450
0.015
0.010
0.008
0.013
0.021
R&D Expenditures
450
0.003
0.007
0.000
0.000
0.002
Acquisition Expenditures
450
0.009
0.016
0.000
0.001
0.011
Leverage
450
0.226
0.144
0.120
0.229
0.338
Credit Rating Dummy
450
0.456
0.499
0.000
0.000
1.000
Z-Score
450
2.040
0.933
1.367
1.943
2.752
Dividend Dummy
450
0.556
0.497
0.000
1.000
1.000
Foreign Income
450
0.013
0.026
0.000
0.000
0.014
NOL Dummy
450
0.287
0.453
0.000
0.000
1.000
58
Table 6 (continued)
Analyses of Capital Structure Decisions
Panel B: Regression Results
Debt Kink
Dependent Variable:
Graham/Shevlin
Method of Simulating MTRs:
Pr. Sign
MTR - GAAP ETR
+
Coefficient
t -Statistic
Blouin, Core, and Guay (2010)
Coefficient
t -Statistic
1.805 **
1.69
2.818 **
2.20
Log(Assets)
0.855 ***
4.02
0.624 ***
3.36
Tobin's Q
0.289 *
1.82
0.154
1.11
Sales Growth
0.784
1.01
0.426
0.63
CFO
34.345 ***
4.25
12.091 *
1.70
PPE
1.042
0.47
-0.725
-0.37
Capital Expenditures
42.150 **
2.21
38.279 **
2.29
R&D Expenditures
-85.796 **
-2.01
-4.872
-0.13
Acquisition Expenditures
18.510 ***
2.82
2.713
0.47
Leverage
-5.488 ***
-4.07
-4.374 ***
-3.72
Credit Rating Dummy
-0.127
-0.26
-0.358
-0.84
Z-Score
0.029
0.10
0.442 *
1.77
Dividend Dummy
-0.960 **
-2.09
0.103
0.26
Foreign Income
0.687
0.09
16.765 **
2.38
NOL Dummy
-0.392
-1.31
-0.143
-0.55
450
450
70.0%
55.5%
S.E. Clustered by Firm
Yes
Yes
Year & Industry Fixed Effects
Yes
Yes
N
Adjusted R-Squared
Notes: Panel (A) B in this table presents the results from (descriptive statistics of the variables used in) a regression
of the Debt Kink, which measures the conservativeness of a firm’s debt policy, on the difference firm’s MTR and its
GAAP ETR, and control variables. The sample is comprised of firms that say they use their GAAP ETR as the tax
rate input for capital structure decisions. The regressions include both year and industry fixed effects and the
standard errors are clustered at the firm-level. ***, **, * signifies statistical significance at the two-tailed (one-tailed)
1%, 5%, and 10% levels (for the coefficients of interest in the highlighted row). All the variables are defined in the
Appendix.
59
Table 7
Economic Magnitude of Capital Structure Effects
Panel A: Descriptive Statistics of the Variables Used in the Analyses
Variable
N
Mean
SD
P25
P50
P75
Equilibrium Factor
440
1.462
1.344
0.736
1.102
1.704
Total Deadweight Loss
440
0.011
0.015
0.001
0.004
0.013
MTR - GAAP ETR
440
-0.018
0.098
-0.050
-0.025
0.015
|MTR - GAAP ETR|
440
0.077
0.093
0.023
0.040
0.078
HP Fin. Constraints Indicator
440
0.434
0.496
0.000
0.000
1.000
BGY Fin. Constraints Indicator
440
0.216
0.412
0.000
0.000
0.000
Non-Distress Indicator
440
0.584
0.493
0.000
1.000
1.000
Panel B: Univariate Analyses
MTR - GAAP ETR Quartile
Ranks
1
2
3
4
Equilibrium Factor
MTR - GAAP ETR
Mean
-0.11
-0.03
0.02
0.16
Mean
1.10
1.52
1.49
2.10
Median
0.90
1.16
1.21
1.58
Panel C: Regression Results with Equilibrium Leverage as Dependent Variable
Equlibrium Factor
Dependent Variable:
MTR - GAAP ETR
HP Fin. Constraints Indicator
BGY Fin. Constraints Indicator
Non-Distress Indicator
Pr. Sign
+
Coefficient
1.427 **
t -Statistic
1.71
t -Statistic
2.06
-1.00
1.89
0.63
440
440
49.0%
Yes
Yes
50.2%
Yes
Yes
N
Adjusted R-Squared
S.E. Clustered by Firm
Year & Industry Fixed Effects
Coefficient
1.753 **
-0.351
0.309 *
0.142
60
Table 7 (continued)
Economic Magnitude of Capital Structure Effects
Panel D: Regression Results with Deadweight Cost of Being Under- or Over-Levered as Dependent Variable
Total Deadweight Loss
Dependent Variable:
|MTR - GAAP ETR|
HP Fin. Constraints Indicator
BGY Fin. Constraints Indicator
Non-Distress Indicator
Pr. Sign
+
Coefficient
0.037 ***
t -Statistic
3.42
t -Statistic
3.39
-0.27
0.71
0.99
440
440
51.7%
Yes
Yes
51.7%
Yes
Yes
N
Adjusted R-Squared
S.E. Clustered by Firm
Year & Industry Fixed Effects
Coefficient
0.038 ***
-0.001
0.001
0.002
Notes: This table examines the economic cost of being sub-optimally levered from using the GAAP ETR in place of
the MTR. Panel A presents the descriptive statistics of the variables used in analyses in the later panels. Panel B
presents the univariate analyses of the economic magnitudes and Panels C and D present the regression results.
Equilibrium Factor is defined as the intersection of the marginal benefit of debt and marginal cost of debt curves. If
Equilibrium Factor = 1, the firm's equilibrium leverage is 100% of its actual (i.e., firm is at equilibrium). If
Equilibrium Factor = 1.2, the firm should have 120% of its actual leverage (i.e., firm is underlevered). If
Equilibrium Factor = 0.8, the firm should have 80% of its actual leverage (firm is overlevered). Total Deadweight
Loss is the loss in firm value from being either underlevered or overlevered (see Figure 2). HP Fin. Constraints
Indicator is indicator variable for firms in the top tercile of the yearly size-age index developed by Hadlock and
Pierce (2010). BGY Fin. Constraints Indicator is an indicator variable for firm-years in the bottom two terciles of all
of the following variables: long-term debt issuance, long-term debt reduction, equity issuance, and equity reduction.
This variable follows from van Binsbergen, Graham, and Yang (2010). Non-Distress Indicator is an indicator
variable for firm-years in the top tercile of the zscore distribution (following van Binsbergen, Graham, and Yang
(2010)). The sample comprises of firms that say they use their GAAP ETR as the tax rate input for capital structure
decisions. The regressions include both year and industry fixed effects and the standard errors are clustered at the
firm-level. ***, **, * signifies statistical significance at the two-tailed (one-tailed) 1%, 5%, and 10% levels (for the
coefficients of interest in the highlighted row). All the variables are defined in the Appendix.
61
Table 8
Analyses of Capital Investment Decisions
Panel A: Descriptive Statistics of the Variables Used in the Regression
Variable
N
Mean
SD
P25
P50
P75
Capital Expenditures
648
0.088
0.092
0.027
0.058
0.111
|MTR A.I. - GAAP ETR| (JG)
461
0.151
0.211
0.028
0.052
0.255
|MTR A.I. - GAAP ETR| (BCG)
648
0.106
0.196
0.032
0.057
0.100
Tobin's Q
648
1.753
1.233
0.916
1.354
2.295
Sales Growth
601
0.190
0.259
0.040
0.147
0.267
CFO
648
0.139
0.112
0.072
0.127
0.200
Panel B: Regression Results
Capital Expenditure
Dependent Variable:
Investment Opportunities (INV_OPP) Proxy:
Method of Simulating MTRs:
INV_OPP
|MTR A.I. - GAAP ETR|
|MTR A.I. - GAAP ETR| × INV_OPP
CFO
N
Tobin's Q
Tobin's Q
Graham/Shevlin
Blouin, Core & Guay (2010)
Pr. Sign Coefficient t -Statistic Coefficient
t -Statistic
0.019 ***
3.63
0.016 ***
3.48
0.025 **
1.97
0.011
1.33
+
-0.019 ***
-3.99
-0.006 ***
-2.54
0.186 ***
2.82
0.156 ***
3.48
Sales Growth
Sales Growth
Graham/Shevlin
Blouin, Core & Guay (2010)
Coefficient t -Statistic Coefficient
t -Statistic
0.062 **
2.55
0.033 *
1.81
0.018
1.58
0.005
0.54
-0.118 **
-1.99
-0.020 *
-1.57
0.244 ***
4.19
0.683 ***
3.63
461
648
435
630
65.4%
71.2%
65.1%
67.2%
S.E. Clustered by Firm
Yes
Yes
Yes
Yes
Year & Industry Fixed Effects
Yes
Yes
Yes
Yes
Adjusted R-Squared
Notes: Panel (A) B in this table presents the results from (descriptive statistics of the variables used in) a regression of investment on investment opportunities,
the difference between a firm’s MTR and its GAAP ETR, an interaction between the two variables, and cash flows. The sample comprises of firms that say they
use their GAAP ETR as the tax rate input for capital expenditure decisions. The regressions include both year and industry fixed effects and the standard errors
are clustered at the firm-level. ***, **, * signifies statistical significance at the two-tailed (one-tailed) 1%, 5%, and 10% levels (for the coefficients of interest in the
highlighted row). All the variables are defined in the Appendix.
62
Table 9
Analyses of M&A Decisions
Panel A: Descriptive Statistics of the Variables Used in the Regression
Variable
N
Mean
SD
P25
P50
P75
Acquisition Announcement Returns
381
0.008
0.050
-0.022
0.006
0.037
|MTR A.I. - GAAP ETR| (JG)
307
0.107
0.137
0.019
0.035
0.128
|MTR A.I. - GAAP ETR| (BCG)
381
0.066
0.075
0.019
0.040
0.080
Pre-Acquisition Return Run Up
381
-0.064
0.411
-0.326
-0.082
0.185
Log(Assets)
381
7.442
1.573
6.239
6.912
8.831
Tobin's Q
381
2.777
2.517
1.227
1.740
2.955
Leverage
381
0.146
0.197
0.002
0.070
0.202
Cash Acquisition
381
0.193
0.206
0.033
0.101
0.254
Diversifying Acquisition
381
0.475
0.500
0.000
0.000
1.000
Public Target
381
0.433
0.496
0.000
0.000
1.000
Foreign Acquisition
381
0.223
0.417
0.000
0.000
0.000
Relative Value of Target
381
0.058
0.072
0.008
0.032
0.074
No. of Bidders
381
1.000
0.000
1.000
1.000
1.000
63
Table 9 (continued)
Analyses of M&A Decisions
Panel B: Regression Results
Acquisition Announcement Returns
Dependent Variable:
Graham/Shevlin
Method of Simulating MTRs:
Pr. Sign
|MTR A.I. - GAAP ETR|
-
Coefficient
t -Statistic
Blouin, Core, and Guay (2010)
Coefficient
t -Statistic
-0.103 **
-2.01
-0.079 ***
-2.51
Pre-Acquisition Return Run Up
-0.003
-0.35
-0.007
-0.83
Log(Assets)
-0.013
-0.92
-0.012 **
-2.07
Tobin's Q
0.003 **
1.96
0.003 *
1.84
Leverage
0.028
0.88
0.033 *
1.72
Cash Acquisition
-0.106
-0.81
-0.044
-0.78
Diversifying Acquisition
-0.009
-1.12
-0.007
-0.86
Public Target
-0.004
-0.40
-0.006
-0.72
Foreign Acquisition
0.007
1.04
0.011
1.52
Relative Value of Target
0.028
0.47
0.010
0.16
No. of Bidders
0.008
0.23
0.002
0.07
307
381
Adjusted R-Squared
0.5%
1.8%
S.E. Clustered by Firm
Yes
Yes
Year & Industry Fixed Effects
Yes
Yes
N
Notes: Panel (A) B in this table presents the results from (descriptive statistics of the variables used in) a regression
of acquisition announcement returns on the difference between a firm’s MTR and its GAAP ETR and control
variables. The sample comprises of firms that say they use their GAAP ETR as the tax rate input for acquisition
decisions. The regressions include both year and industry fixed effects and the standard errors are clustered at the
firm-level. ***, **, * signifies statistical significance at the two-tailed (one-tailed) 1%, 5%, and 10% levels (for the
coefficients of interest in the highlighted row). All the variables are defined in the Appendix.
64
Table 10
Falsification Tests
Panel A: Descriptive Statistics of the Variables Used in the Regressions
Variable
Variable Used in the Kink Analyses
Debt Kink (JG)
Debt Kink (BCG)
MTR - GAAP ETR (JG)
MTR - GAAP ETR (BCG)
Log(Assets)
Tobin's Q
Sales Growth
CFO
PPE
Capital Expenditures
R&D Expenditures
Acquisition Expenditures
Leverage
Credit Rating Dummy
Z-Score
Dividend Dummy
Foreign Income
NOL Dummy
N
Mean
SD
P25
P50
P75
211
211
211
211
211
211
211
211
211
211
211
211
211
211
211
211
211
211
4.016
2.105
-0.015
-0.001
7.683
1.567
0.106
0.029
0.300
0.013
0.007
0.008
0.250
0.668
1.548
0.573
0.017
0.365
3.373
2.447
0.113
0.089
1.526
1.024
0.145
0.016
0.191
0.008
0.010
0.016
0.137
0.472
0.609
0.496
0.031
0.483
1.600
0.400
-0.052
-0.053
6.446
0.867
0.016
0.017
0.148
0.007
0.000
0.000
0.143
0.000
1.052
0.000
0.000
0.000
3.000
1.200
-0.005
-0.014
7.327
1.153
0.072
0.027
0.241
0.011
0.002
0.000
0.247
1.000
1.639
1.000
0.000
0.000
6.000
3.000
0.059
0.055
8.622
1.991
0.149
0.039
0.397
0.017
0.011
0.005
0.348
1.000
2.007
1.000
0.021
1.000
Variable Used in the Investment Sensitivity Analyses
Capital Expenditures
|MTR A.I. - GAAP ETR| (JG)
|MTR A.I. - GAAP ETR| (BCG)
Tobin's Q
Sales Growth
CFO
346
243
346
346
310
346
0.061
0.153
0.096
1.681
0.135
0.127
0.060
0.199
0.176
1.332
0.236
0.092
0.025
0.032
0.027
0.866
0.019
0.070
0.044
0.066
0.054
1.206
0.078
0.116
0.077
0.226
0.098
1.907
0.175
0.174
157
130
157
157
157
157
157
157
157
157
157
157
157
0.007
0.123
0.080
-0.070
7.727
2.336
0.140
0.137
0.510
0.497
0.159
0.074
1.000
0.043
0.122
0.082
0.376
1.737
1.392
0.179
0.141
0.502
0.502
0.367
0.092
0.000
-0.013
0.042
0.030
-0.357
6.256
1.316
0.021
0.026
0.000
0.000
0.000
0.007
1.000
0.003
0.068
0.054
-0.055
7.136
1.738
0.058
0.098
1.000
0.000
0.000
0.031
1.000
0.031
0.114
0.076
0.138
9.693
3.234
0.229
0.198
1.000
1.000
0.000
0.102
1.000
Variable Used in the M&A Analyses
Acquisition Announcement Ret.
|MTR A.I. - GAAP ETR| (JG)
|MTR A.I. - GAAP ETR| (BCG)
Pre-Acquisition Return Run Up
Log(Assets)
Tobin's Q
Leverage
Cash Acquisition
Diversifying Acquisition
Public Target
Foreign Acquisition
Relative Value of Target
No. of Bidders
65
Table 10 (continued)
Falsification Tests
Panel A: Analyses of Capital Structure Decisions for Firms using MTRs for Decision Making
Debt Kink
Dependent Variable:
Graham/Shevlin
Method of Simulating MTRs:
Coefficient
t -Statistic
Blouin, Core & Guay (2010)
Coefficient
t -Statistic
MTR - GAAP ETR
-0.541
-0.39
2.107
1.12
Log(Assets)
-0.130
-0.23
-1.115 **
-1.96
Tobin's Q
0.409 *
1.64
1.110 ***
4.45
Sales Growth
0.069
0.06
-0.043
-0.04
CFO
34.796 **
2.47
13.106
0.93
PPE
-6.355 **
-2.10
-5.470 *
-1.81
Capital Expenditures
10.192
0.39
-5.737
-0.22
R&D Expenditures
19.024
0.30
-45.081
-0.71
Acquisition Expenditures
1.948
0.18
5.945
0.55
Leverage
-5.900 ***
-2.67
-2.383
-1.08
Credit Rating Dummy
-0.709
-0.92
-0.169
-0.22
Z-Score
-0.398
-0.56
1.334 *
1.86
Dividend Dummy
0.225
0.31
-0.286
-0.39
Foreign Income
3.467
0.45
1.777
0.23
NOL Dummy
-0.644 *
-1.35
-0.377
-0.80
211
211
79.9%
62.0%
S.E. Clustered by Firm
Yes
Yes
Year & Industry Fixed Effects
Yes
Yes
N
Adjusted R-Squared
66
Table 10 (continued)
Falsification Tests
Panel B: Analyses of Capital Investment Decisions for Firms using MTRs for Decision Making
Capital Expenditure
Dependent Variable:
Tobin's Q
Tobin's Q
Sales Growth
Sales Growth
Graham/Shevlin
Coefficient t -Statistic
0.013 ***
2.62
-0.016
-1.34
0.003
0.33
0.060
1.23
Blouin, Core & Guay (2010)
Coefficient
t -Statistic
0.009 **
2.34
-0.007
-0.61
0.008
0.83
0.145 **
2.00
Graham/Shevlin
Coefficient t -Statistic
0.013
0.38
-0.011
-0.85
0.089
1.23
-0.022
-0.21
Blouin, Core & Guay (2010)
Coefficient
t -Statistic
0.045
1.37
-0.003
-0.19
-0.030
-0.20
0.155
1.62
243
346
223
311
69.9%
66.5%
36.2%
49.6%
S.E. Clustered by Firm
Yes
Yes
Yes
Yes
Year & Industry Fixed Effects
Yes
Yes
Yes
Yes
Investment Opportunities (INV_OPP) Proxy:
Method of Simulating MTRs:
INV_OPP
|MTR A.I. - GAAP ETR|
|MTR A.I. - GAAP ETR| × INV_OPP
CFO
N
Adjusted R-Squared
67
Table 10 (continued)
Falsification Tests
Panel C: Analyses of M&A Decisions for Firms using MTRs for Decision Making
Acquisition Announcement Returns
Dependent Variable:
Method of Simulating MTRs:
Graham/Shevlin
Coefficient
t -Statistic
Blouin, Core & Guay (2010)
Coefficient
t -Statistic
|MTR A.I. - GAAP ETR|
-0.016
-0.31
0.016
0.54
Pre-Acquisition Return Run Up
-0.016 **
-2.52
-0.002
-0.21
Log Assets
0.001
0.09
-0.006
-1.26
Tobin's Q
0.018 *
1.74
0.012 **
1.98
Leverage
0.090
1.13
0.018
0.37
Cash Acquisition
-0.055
-0.64
-0.023
-0.52
Diversifying Acquisition
-0.010
-1.04
-0.006
-0.54
Public Target
-0.019
-0.97
-0.001
-0.07
Foreign Acquisition
-0.018 *
-1.76
-0.006
-0.62
Relative Value of Target
-0.082 **
-2.36
-0.039
-0.73
No. of Bidders
-0.111 ***
-8.63
-0.118 ***
-8.66
N
130
157
19.9%
13.7%
S.E. Clustered by Firm
Yes
Yes
Year & Industry Fixed Effects
Yes
Yes
Adjusted R-Squared
Notes: The sample in this table is comprised of firms that use the MTR for decision-making. Panel A in this
table presents the descriptive statistics of the variables used in the regressions presented in Panels B, C, and D.
Panel B presents the results from a regression of the Debt Kink, which measures the conservativeness of a
firm’s debt policy, on the difference firm’s MTR and its GAAP ETR, and control variables. Panel C presents
the results from a regression of investment on investment opportunities, the difference between a firm’s MTR
and its GAAP ETR, an interaction between the two variables, and cash flows. Panel D presents the results from
a regression of acquisition announcement returns on the difference between a firm’s MTR and its GAAP ETR
and control variables. The sample in Panel B (C, D) comprises of firms that say they use their MTR as the tax
rate input for capital structure (capital expenditure, acquisition) decisions. All regressions include both year
and industry fixed effects and the standard errors are clustered at the firm-level. ***, **, * signifies statistical
significance at the two-tailed 1%, 5%, and 10% levels. All the variables are defined in the Appendix.
68